bluuebunny/crossref_metadata_2025 huggingface dataset:
Analyzing the Effects of Renewable and Nonrenewable Energy Usage and Technological Innovation on Environmental Sustainability: Evidence from QUAD Economies
Authors: Imran Muhammad , Ali Sajid , Shahwan Yousef , Zhang Jijian , Al-Swiety Issa Ahmad
Published: November 2022

The following study examined how energy use and technological advancement impacted environmental sustainability in QUAD (US, Japan, Australia, and India) economies between 1991 and 2021. The study considers the generation of renewable energy, fossil fuel use, and the effects of economic expansion on environmental sustainability. The research used the moment quantile regression technique based on the outcomes of slope heterogeneity, cross-sectional dependence, and the order of the unit-root by the using second-generation method of cross-sectional augmented Im, Pesaran, and Shin tests. The study discovered that renewable energy production and technological innovation enhances environmental sustainability, whereas the use of nonrenewable energy and economic growth worsen it. When implementing policies regarding the environment, energy, and the growth of QUAD economies based on concrete evidence, policy makers and environmentalists in QUAD countries should also take into account the asymmetrical performance of efficiency in energy production, technological innovation, and economic growth.
Buck-Boost-Integrated, Dual-Active Bridge-Based Four-Port Interface for Hybrid Energy Systems
Authors: Vettuparambil Anees , Raveendran Nair Prasannakumari Praveen , Alharbi Walied , Humayd Abdullah S. Bin , Awan Ahmed Bilal
Published: November 2022

A power electronic interface using four ports to interconnect the solar photovoltaic panels, wind generator, battery, and DC microgrid is proposed in this paper. The proposed converter employs a two-winding transformer to interface 380 V of the DC microgrid with all the other three ports, namely, the solar port, wind port, and battery port, which have relatively low nominal voltages. However, the power transfer from solar and wind ports to the battery port bypasses this transformer, thus reducing the potential power losses in the transformer. Furthermore, the maximum and minimum values of the voltage range in which the converter can tap maximum power from solar and wind sources are not constrained by the battery voltage, thus improving the power extraction capability of the multiport converter. The controller for the multiport converter has been developed for extracting the maximum power from renewable sources and to control the charging current of the battery. The simulated response of the converter was studied using the MATLAB/Simulink platform. The simulated response confirms the operation of the converter.
Recycling of Post-Use Bioprocessing Plastic Containers—Mechanical Recycling Technical Feasibility
Authors: Luu Duc-Nam , Barbaroux Magali , Dorez Gaelle , Mignot Katell , Doger Estelle , Laurent Achille , Brossard Jean-Michel , Maier Claus-Jürgen
Published: November 2022

Most of the plastic-based solutions used in bio-manufacturing are today incinerated after use, even the not “bio-contaminated”. Bioprocessing bags used for media and buffer preparation and storage represent the largest amount today. The aim of this work was to technically assess the feasibility of the mechanical recycling of bioprocessing bags. Materials from different sorting and recycling strategies have been characterized, for their suitability of further use. Quantitative physical and mechanical tests and analysis (FTIR, DSC, TGA, density, MFI, color, tensile, flexural, and Charpy choc) were performed. The data show that these recycled plastics could be oriented towards second use requiring physical properties similar to equivalent virgin materials. A comparative life cycle assessment, based on a theoretical framework, shows that mechanical recycling for end of life presents the advantage of keeping material in the loop, without showing a significant statistical difference compared to incineration with regards to the climate change indicator.
Biodegradation of Cyanide by a New Isolated Aerococcus viridans and Optimization of Degradation Conditions by Response Surface Methodology
Authors: Jiang Wenjin , Lu Yang , Feng Zezhong , Yu Haixiao , Ma Ping , Zhu Jinqi , Wang Yingnan , Sun Jinfu
Published: November 2022

Microbial treatment of cyanide pollution is an effective, economical, and environmentally friendly method compared with physical or chemical approaches. A cyanide-degrading bacterium was isolated from electroplating sludge and identified as Aerococcus viridans (termed A. viridans T1) through an analysis of the biochemical reaction and 16 S rDNA gene sequence. A. viridans T1 showed a maximum resistance to 550 mg L−1 CN−. The effect of pH and temperature on cyanide degradation and bacterial growth was evaluated. The highest cyanide removal efficiency and bacterial growth occurred at pH 8 and pH7, respectively. The optimum temperature for cyanide degradation and bacterial growth was 34 ∘C. In addition, the carbon source and nitrogen source for cyanide degradation were optimized. The optimal carbon source and nitrogen source were glycerol and peptone, respectively. The cyanide degradation experiment indicated that A. viridans T1 was able to remove 84.1% of free cyanide at an initial concentration of 200 mg L−1 CN− within 72 h and 86.7% of free cyanide at an initial concentration of 150 mg L−1 CN− within 56 h. To improve the cyanide-degrading efficiency of A. viridans T1, eight process variables were further optimized using a response surface methodology. Three significant variables (soybean meal, corn flour, and L-cysteine) were identified using a Plackett–Burman design, and the variable levels were optimized using a central composite design. The optimal values of soybean meal, corn flour, and L-cysteine were 1.11%, 1.5%, and 1.2%, respectively. Under these optimal conditions, the confirmatory experiments showed that the actual degradation rate was 97.3%, which was similar to the predicted degradation rate of 98.87%. Its strong resistance to cyanide and cyanide-degrading activity may allow A. viridans T1 to be a candidate for the bioremediation of cyanide-contaminated environments.
Evaluation of the Stability Behavior of an Agricultural Unmanned Ground Vehicle
Authors: Rondelli Valda , Capacci Enrico , Franceschetti Bruno
Published: November 2022

Precision farming is the newest agricultural approach in countries with highly mechanized field operations, and the role of unmanned ground vehicles (UGVs) in smart farming is becoming increasingly prominent. This work aimed to evaluate the stability of the DEDALO UGV developed by the University of BOLOGNA for precision orchard and vineyard management. The driving part of the machine is somewhat peculiar; it moves autonomously in the field combined with a tank to store water and pesticide mixture for crop protection, with an additional structure to carry agricultural implements. The study aimed to evaluate the stability of the agricultural unladen UGV, and mulcher and sprayer mounted configurations. In the case of the sprayer, the stability behavior was evaluated with an empty and full tank. The machine, in terms of stability, was studied both laterally and longitudinally. A theoretical model was developed based on the upstream side forces measured during experimental tipping tests. The results of the experimental data were compared with the theoretical predicted results to validate the model. In the lateral test, the average value of the limit stability angle was 48 degrees, while in the longitudinal test, it was 49 degrees. The results of the model were statistically correlative (R2 > 95) and denoted that the most stable condition occurred in the case of the UGV fitted with the mulcher in the longitudinal tipping position (56 degrees), while the most unstable condition was the case of the unladen UGV in the longitudinal tipping position (40 degrees). Although the stability problem is not directly connected with the operator, as these machines do not require a driver, the lack of stability can lead to the UGV overturning with consequent risks for the surrounding environment and damage to the UGV body.
Ameliorative Effects of Biochar for Cadmium Stress on Bean (Phaseolus vulgaris L.) Growth
Authors: Dadasoglu Esin , Ekinci Melek , Turan Metin , Yildirim Ertan
Published: November 2022

In order to investigate the changes in the morphological and biochemical characteristics of bean plants in response to biochar treatment under cadmium (Cd) stress, a pot experiment was conducted in a greenhouse. Bean plants were subjected to different amounts of Cd (0, 100, 150 and 200 mg kg−1) and biochar applied at different doses (0, 2.5 and 5%). Under Cd stress, the growth and development of bean seedlings were remarkably inhibited, whereas the biochar treatment could effectively improve the heavy metal tolerance of bean seedlings. Cd stress caused an increase in the hydrogen peroxide (H2O2), malondialdehyde (MDA), proline and sucrose content, catalase (CAT), peroxidase (POD) and superoxide dismutase (SOD) activity of leaves. However, biochar treatments reduced the CAT, POD and SOD activity of bean seedlings. Growing beans on Cd medium led to a significant reduction in plant nutrient element content. However, biochar amendment to the soil elevated the plant nutrient element content compared to untreated soil. Cd content of the bean seedlings increased with increasing Cd doses. There was a sharp decrease in available concentration for Cd with the addition of biochar. In conclusion, biochar incorporation into the soil can alleviate the adverse impacts of Cd stress on the growth of bean seedlings.
How the “Absorption Processes” of Urban Innovation Contribute to Sustainable Development—A Fussy Set Qualitative Comparative Analysis Based on Seventy-Two Cities in China
Authors: Li Fanbo , Zhang Hongfeng
Published: November 2022

The goals and transformation of sustainable urban development require fundamental innovation; however, urban innovation is a cyclical process, the paradigm of innovation itself needs to be dramatically transformed, and all innovation key elements need to be coordinated with each other to achieve sustainable urban outputs. Based on this, this paper uses the absorptive capacity theory to construct a model of the relationships between the acquisition dimension, digestion dimension, conversion dimension, utilization dimension, and sustainable development in the innovation process of smart cities and uses the fuzzy set qualitative comparative analysis (FSQCA) method to investigate the configuration mechanism in depth to show how the innovation process of these cities affects their sustainable development, taking the top 72 cities in China in terms of innovation capacity in 2020 as the research objects. The results of the study form three complex configuration paths that affect sustainable urban development, centered on the transformation of technological achievements, innovation management drive, and smart cities, and reveal that the economic and market foundations as the dimensions of urban innovation acquisition are not the core conditions for sustainable urban development. Based on this, this study develops a configuration classification for innovative cities that can achieve sustainable development, i.e., industrial paths, governance paths, and technology paths, and proposes strategic directions for sustainable urban innovation development.
Climate-Smart Agriculture in Iran: Strategies, Constraints and Drivers
Authors: Memarbashi Payam , Mojarradi Gholamreza , Keshavarz Marzieh
Published: November 2022

Although climate-smart agriculture can simultaneously decline greenhouse gas emissions, increase the adaptive capacity of farmers and improve food security under climate change, constraints and drivers of scaling up are not entirely addressed in developing countries. This qualitative case study was conducted on both strawberry growers and agricultural experts to explore the perceived causes, evidence and impacts of climate change, adaptation strategies used by farmers, and constraints and drivers of climate-smart agriculture development on the strawberry farms in Kurdistan province, Western Iran. Findings indicated that the causes of climate change could be divided into anthropogenic and natural forces. Decreased precipitation, increased temperature, dust storms, greenhouse gases, forest fires, spring frosts, severe hail, floods and droughts comprised the most notable climate change evidence in the region. Both groups confirmed the impacts of climate change on the reduction in strawberry yield, increasing the perishability of the fruits, poverty, migration and other social problems. Adaptation strategies used by farmers are classified into technical–agricultural, water conservation, farm smartening, and institutional adaptation practices. However, poverty, the shortage of strawberry-processing industries, insufficient financial support, the presence of intermediaries and brokers, traditional cultivation, difficulties in shipping strawberry crops to the market, the lack of storage facilities and equipment and the export terminal along with the mistrust of strawberry growers in the agricultural organization hinder climate-smart agriculture development in the study area. Finally, several drivers were proposed, which were considered the basis for providing practical suggestions for planning and policy making for climate-smart agriculture development in strawberry farms.
Impact Assessment of COVID-19 Severity on Environment, Economy and Society towards Affecting Sustainable Development Goals
Authors: Hannan M. A. , Abd Rahman M. S. , Al-Shetwi Ali Q. , Begum R. A. , Ker Pin Jern , Mansor M. , Mia M. S. , Hossain M. J. , Dong Z. Y. , Mahlia T. M. I.
Published: November 2022

The COVID-19 pandemic has affected every sector in the world, ranging from the education sector to the health sector, administration sector, economic sector and others in different ways. Multiple kinds of research have been performed by research centres, education institutions and research groups to determine the extent of how huge of a threat the COVID-19 pandemic poses to each sector. However, detailed analysis and assessment of its impact on every single target within the 17 Sustainable Development Goals (SDGs) have not been discussed so far. We report an assessment of the impact of COVID-19 effect towards achieving the United Nations SDGs. In assessing the pandemic effects, an expert elicitation model is used to show how the COVID-19 severity affects the positive and negative impact on the 169 targets of 17 SDGs under environment, society and economy groups. We found that the COVID-19 pandemic has a low positive impact in achieving only 34 (20.12%) targets across the available SDGs and a high negative impact of 54 targets (31.95%) in which the most affected group is the economy and society. The environmental group is affected less; rather it helps to achieve a few targets within this group. Our elicitation model indicates that the assessment process effectively measures the mapping of the COVID-19 pandemic impact on achieving the SDGs. This assessment identifies that the COVID-19 pandemic acts mostly as a threat in enabling the targets of the SDGs.
Comparative Study on the Adsorption Characteristics of Heavy Metal Ions by Activated Carbon and Selected Natural Adsorbents
Authors: Li Jiashi , Dong Xiaoqiang , Liu Xiaofeng , Xu Xin , Duan Wei , Park Junboum , Gao Lei , Lu Yisi
Published: November 2022

In this study, adsorption of the heavy metal ions (Pb(II), Cu(II) and Cd(II)) from water by peanut shells (PS), sawdust (S) and commercial activated carbon (AC) were comparatively studied. Thus, the relationship between different adsorption parameters and different heavy metal ion removal rates was investigated. The adsorption capacity of the three adsorbents for heavy metal ions increased with an increase in temperature, pH value, contact time, adsorbent dosage, and heavy metal ion concentration, however, it decreased with an increase of adsorbent particle size. All the adsorption processes are better described by Langmuir isotherm or Freundlich isotherm. Thus, the results show good agreement with pseudo-second-order kinetics and the adsorption processes are spontaneous heat absorption processes. Herein, all adsorbents have higher affinity for Pb(II) ions, and hence possess higher removal rates. In addition, heavy metal ions were desorbed significantly at acidic conditions in the desorption experiments. The results demonstrate that PS can be used as a green adsorbent instead of AC for the adsorption of heavy metal ions from the water.
Cross-Border Innovation: Assessing Concepts, Contexts, and Content
Authors: Rodrigues Ricardo , Sampaio Carlos , Duarte Paulo , Hernández-Mogollón José Manuel
Published: November 2022

This study provides an outlook of the evolution of cross-border innovation literature. A sample of 226 articles addressing cross-border innovation and knowledge transfer published between 1997 and 2022 was screened using bibliometric methods. Data are split over three time slices and a thematic analysis is conducted, suggesting three main themes in literature: research-and-development, mostly linked with mergers and acquisitions and competitiveness, knowledge creation and transfer linked with innovations systems and performance, and innovation linked with networks, firms’ capabilities, and absorptive capacity. A co-citation analysis was also conducted which showed that the background literature of the research stream is based on the internationalization process, knowledge transfer and competitiveness, research-and-development, business performance and innovation systems. Results further introduce a geographic boundary to the research in knowledge and innovation, particularly frontier and regional innovation systems. Based on the results, future research lines are suggested, including the role of organizational learning and knowledge creation, towards the innovation of new products and services of companies seeking internationalization, and how disruptive events in worldwide supply chains affect cross-border innovation.
Assessing National Progress in Achieving the Sustainable Development Goals: A Case Study of Morocco
Authors: Ammari Manal , Chentouf Mohammed , Ammari Mohammed , Ben Allal Laïla
Published: November 2022

The achievement of the Sustainable Development Goals (SDGs) for any nation has become more of an urgent priority in the global agenda than at any time before, especially under the light of recent disrupting events such as the COVID-19 pandemic, the alarming food–water–energy trilemma, and the geopolitical upheavals impacting the supply chain of vital commodities. Assessing the sustainable progress of a country over time can help policy makers establish and develop robust strategies by identifying their strengths and weaknesses, allocating adequate resources, and understanding how far the country is from achieving the SDGs. No prior research has evaluated the SDGs performance composite index in Morocco. Hence, this study evaluates the national level of SDG progress by analyzing 13 out of 17 SDGs, including 46 available economic, social, and environmental indicators from 2001 to 2018, to provide factual data that can be used as a decision-making basis. The selection of indicators is established based on the framework adopted by the UN General Assembly. A composite index was created and quantified using the min–max normalization technique, the geometric product aggregation, and the overall scores of Morocco’s SDG performance and the different dimensions were calculated. The results show that economic performance was the worst among the other dimensions. Morocco is progressing to achieve the environmental and social targets, but more effort is required to absorb the needs linked with population growth and improvement of living standards. The developed framework could be of great interest for scientists and researchers to assess the national SDG progress of other countries.
Optimization of a Textile Effluent Treatment System and Evaluation of the Feasibility to Be Reused as Influents in Textile Dyeing Processes
Authors: López-Casaperalta Patricia , Molina-Rodríguez Fredy Nicolás , Fernandez-F Fernando , Díaz-Quintanilla Jeanette Fabiola , Barreda-Del-Carpio Jaime E. , Bernabe-Ortiz Julio Cesar , Aguilar-Pineda Jorge Alberto
Published: November 2022

Textile effluents derived from azo-reactive dyeing processes represent a severe problem for aquatic ecosystems and human health. The large amounts of water used in this process and the poor quality of the discharges urge the need to develop treatment systems that involve reusing treated water. In this research, we present the optimization of a feasible, simple, and efficient treatment system that improves the quality of the effluents from the cotton fabric dyeing process. Through the characterization of the influents and effluents, we have identified seven parameters that have allowed the optimization of the treatment. Analytical techniques, such as nephelometry, EDTA, gravimetry, and BOD5, among others, and specialized equipment, such as the spectrophotometer, have been used for these purposes. The results showed that using combustion gases in the neutralization stage and new flocculant-coagulant reagents improved parameters, such as pH, total solids, hardness, and conductivity. The quality of the effluents thus obtained allowed their reuse only in the stages before the dyeing bath without affecting the final quality of the cotton fabrics in dark colors. This effort implies savings in water and supplies, and opens the door to future research on the treatment of textile effluents that help improve the environmental conditions of our region.
Solute Geochemistry and Water Quality Assessment of Groundwater in an Arid Endorheic Watershed on Tibetan Plateau
Authors: Wang Fenglin , Yang Hongjie , Zhang Yuqing , Wang Shengbin , Liu Kui , Qi Zexue , Chai Xiaoran , Wang Liwei , Wang Wanping , Banadkooki Fatemeh Barzegari , Senapthi Venkatramanan , Xiao Yong
Published: November 2022

Understanding groundwater geochemistry is crucial for water supply in arid regions. The present research was conducted in the arid Mo river watershed on the Tibetan plateau to gain insights into the geochemical characteristics, governing processes and quality of groundwater in arid endorheic watersheds. A total of 28 groundwater samples were collected from the phreatic and confined aquifers for hydrochemical analysis. The results showed that the groundwater was slightly alkaline in all aquifers of the watershed. The phreatic groundwater samples (PGs) and confined groundwater samples (CGs) had the TDS value in the ranges of 609.19–56,715.34 mg/L and 811.86–2509.51 mg/L, respectively. PGs were salter than CGs, especially in the lower reaches. Both the PGs and CGs were dominated by the Cl-Na type, followed by the mixed Cl-Mg·Ca type. The toxic elements of NO2− (0.00–0.20 mg/L for PGs and 0.00–0.60 mg/L for CGs), NH4+ (0.00–0.02 mg/L for PGs and 0.00–0.02 mg/L for CGs) and F− (0.00–4.00 mg/L for PGs and 1.00–1.60 mg/L for CGs) exceeded the permissible limits of the Chinese guidelines at some sporadic sites. Water–rock interactions, including silicates weathering, mineral dissolution (halite and sulfates) and ion exchange, were the main contributions to the groundwater chemistry of all aquifers. The geochemistry of PGs in the lower reach was also greatly influenced by evaporation. Agricultural sulfate fertilizer input was responsible for the nitrogen pollutants and salinity of PGs. All CGs and 73.91% of PGs were within the Entropy-weighted water quality index (EWQI) of below 100 and were suitable for direct drinking purposes. Precisely 8.70 and 17.39% of PGs were within the EWQI value in the range of 100–150 (medium quality and suitable for domestic usage) and beyond 200 (extremely poor quality and not suitable for domestic usage), respectively. The electrical conductivity, sodium adsorption ratio, sodium percentage and permeability index indicated that groundwater in most parts of the watershed was suitable for irrigation, and only a small portion might cause salinity, sodium or permeability hazards. Groundwater with poor quality was mainly distributed in the lower reaches. CGs and PGs in the middle-upper reaches could be considered as the primary water resources for water supply. Agricultural pollution should be paid more attention to safeguard the quality of groundwater.
The New Prediction Methodology for CO2 Emission to Ensure Energy Sustainability with the Hybrid Artificial Neural Network Approach
Authors: Aksu İnayet Özge , Demirdelen Tuğçe
Published: November 2022

Energy is one of the most fundamental elements of today’s economy. It is becoming more important day by day with technological developments. In order to plan the energy policies of the countries and to prevent the climate change crisis, CO2 emissions must be under control. For this reason, the estimation of CO2 emissions has become an important factor for researchers and scientists. In this study, a new hybrid method was developed using optimization methods. The Shuffled Frog-Leaping Algorithm (SFLA) algorithm has recently become the preferred method for solving many optimization problems. SFLA, a swarm-based heuristic method, was developed in this study using the Levy flight method. Thus, the speed of reaching the optimum result of the algorithm has been improved. This method, which was developed later, was used in a hybrid structure of the Firefly Algorithm (FA). In the next step, a new Artificial Neural Network (ANN)-based estimation method is proposed using the hybrid optimization method. The method was used to estimate the amount of CO2 emissions in Türkiye. The proposed hybrid model had the RMSE error 5.1107 and the R2 0.9904 for a testing dataset, respectively. In the last stage, Türkiye’s future CO2 emission estimation is examined in three different scenarios. The obtained results show that the proposed estimation method can be successfully applied in areas requiring future estimation.
Predicting Effluent Quality in Full-Scale Wastewater Treatment Plants Using Shallow and Deep Artificial Neural Networks
Authors: Jafar Raed , Awad Adel , Jafar Kamel , Shahrour Isam
Published: November 2022

This research focuses on applying artificial neural networks with nonlinear transformation (ANNs) models to predict the performance of wastewater treatment plant (WWTP) processes. The paper presents a novel machine learning (ML)-based approach for predicting effluent quality in WWTPs through explaining the relationships between the multiple influent and effluent pollution variables of an existing WWTP. We developed AI models such as feed-forward neural network (FFNN) and random forest (RF) as well as deep learning methods such as convolutional neural network (CNN), recurrent neural network (RNN), and pre-train stacked auto-encoder (SAE) in order to avoid various shortcomings of conventional mechanistic models. The developed models focus on providing an adaptive, functional, and alternative methodology for modeling the performance of the WWTP. They are based on pollution data collected over three years. It includes chemical oxygen demand (COD), biochemical oxygen demand (BOD5), phosphates (PO₄−3), and nitrates (NO₃−), as well as auxiliary indicators including the temperature (T), degree of acidity or alkalinity (pH), electric conductivity (EC), and the total dissolved solids (TDS). The paper presents the results of using SNN- and DNN-based models to predict the effluent concentrations. Our results show that SNN can predict plant performance with a correlation coefficient (R) up to 88%, 90%, 93%, and 96% for the single models COD, BOD5, NO₃−, and PO₄−3, respectively, and up to 88%, 96%, and 93% for the ensemble models (BOD5 and COD), (PO₄−3 and NO₃−), and (COD, BOD5, NO₃−, PO₄−3), respectively. The results also show that the two-hidden-layers model outperforms the one-hidden-layer model (SNN). Moreover, increasing the input parameters improves the performance of models with one and two hidden layers. We applied DNN (CNN, RNN, SAE) with three, four, and five hidden layers for WWTP modeling, but due to the small datasets, it gave a low performance and accuracy. In sum, this paper shows that SNN (one and two hidden layers) and the random forest (RF) machine learning technique provide effective modeling of the WWTP process and could be used in the WWTP management.
Environmental Impact of ICT on Disaggregated Energy Consumption in China: A Threshold Regression Analysis
Authors: Batool Zakia , Ali Sajjad , Rehman Abdul
Published: November 2022

Due to resource scarcity, high energy demand, and environmental degradation, China’s rapid economic growth over the past three decades has been accompanied by certain serious issues that require quick attention. The excessive use of fossil fuels worsens the ecosystem and raises the level of carbon in the atmosphere. However, the use of ICT has affected the behavior of energy use in various sectors differently. Although ICT-induced activities, on one hand, may affect the environment positively by reducing energy consumption, on the other hand, they may affect the environment adversely by causing an energy rebound effect. Therefore, this study aims to investigate the nonlinear impact of ICT on the environmental effects of energy consumption in the residential, transport, and industrial sectors in China. The study used threshold regression for empirical analysis by employing data for the period from 1990 to 2021. ICT is used as a threshold variable, while energy consumption in the residential, industrial, and transport sectors is used as a regime-dependent variable. Based on the findings, we deduce that the use of ICT asymmetrically affects sectoral energy consumption and the empirical result varies across sectors. Based on the results, we recommend that the possibility of rebound effects should be given more attention in the development of policies regarding the digitalization of the sectors.
Decoherence Effects in a Three-Level System under Gaussian Process
Authors: Zangi Sultan M. , ur Rahman Atta , Ji Zhao-Xo , Ali Hazrat , Zhang Huan-Guo
Published: November 2022

When subjected to a classical fluctuating field characterized by a Gaussian process, we examine the purity and coherence protection in a three-level quantum system. This symmetry of the three-level system is examined when the local random field is investigated further in the noiseless and noisy regimes. In particular, we consider fractional Gaussian, Gaussian, Ornstein–Uhlenbeck, and power law noisy regimes. We show that the destructive nature of the Ornstein–Uhlenbeck noise toward the symmetry of the qutrit to preserve encoded purity and coherence remains large. Our findings suggest that properly adjusting the noisy parameters to specifically provided values can facilitate optimal extended purity and coherence survival. Non-vanishing terms appear in the final density matrix of the single qutrit system, indicating that it is in a strong coherence regime. Because of all of the Gaussian noises, monotonic decay with no revivals has been observed in the single qutrit system. In terms of coherence and information preservation, we find that the current qutrit system outperforms systems with multiple qubits or qutrits using purity and von Neumann entropy. A comparison of noisy and noiseless situations shows that the fluctuating nature of the local random fields is ultimately lost when influenced using the classical Gaussian noises.
A Secured Half-Duplex Bidirectional Quantum Key Distribution Protocol against Collective Attacks
Authors: Khawasik Manal , El-Sayed Wagdy Gomaa , Rashad M. Z. , Younes Ahmed
Published: November 2022

Quantum Key Distribution is a secure method that implements cryptographic protocols. The applications of quantum key distribution technology have an important role: to enhance the security in communication systems. It is originally inspired by the physical concepts associated with quantum mechanics. It aims to enable a secure exchange of cryptographic keys between two parties through an unsecured quantum communication channel. This work proposes a secure half-duplex bidirectional quantum key distribution protocol. The security of the proposed protocol is proved against collective attacks by estimating the interception of any eavesdropper with high probability in both directions under the control of the two parties. A two-qubit state encodes two pieces of information; the first qubit represents the transmitted bit and the second qubit represents the basis used for measurement. The partial diffusion operator is used to encrypt the transmitted qubit state as an extra layer of security. The predefined symmetry transformations induced by unitary in conjunction with the asymmetrical two-qubit teleportation scheme retain the protocol’s secrecy. Compared to the previous protocols, the proposed protocol has better performance on qubit efficiency.
SARSPLEX: Multiplex Serological ELISA with a Holistic Approach
Authors: Garg Kunal , Campolonghi Sara , Schwarzbach Armin , Alonso Maria Luisa Garcia , Villavicencio-Aguilar Fausto M. , Fajardo-Yamamoto Liria M. , Gilbert Leona
Published: November 2022

Currently, there are over 602 million severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases and 6.4 million COVID-19 disease-related deaths worldwide. With ambitious vaccine strategies, reliable and accurate serological testing is needed to monitor the dynamics of the novel coronavirus pandemic and community immunity. We set out to improve serological testing of the immune response against SARS-CoV-2. We hypothesize that by multiplexing the serological diagnostic test kit (SARSPLEX) and screening for three antibodies, an even more robust diagnostic can be developed. A total of 293 sera were analyzed for IgM, IgG, or IgA immune reactions to the subunit 1 spike glycoprotein and the nucleocapsid protein in a standardized ELISA platform. Testing IgM, IgG, and IgA demonstrated high positive and negative agreements compared to RT-PCR and serology reference tests. Comparison with the pre-2019-CoV (n = 102) samples highlighted the specificity of this test kit and indicated that no unspecific binding, even with the summer flu patients (n = 44), was detected. In addition, SARSPLEX demonstrated to be a valuable occupational surveillance tool used in a functional medicine facility. With increased and broader testing, SARSPLEX will be a valuable tool in monitoring immunity and aid in prioritizing access to the SARS-CoV-2 vaccine for high-risk patients.
HIV and SARS-CoV-2 Pathogenesis and Vaccine Development
Authors: Fleury Herve
Published: November 2022

Although both HIV and SARS-CoV-2 are associated with pandemics, they are transmitted differently [...]
A Comprehensive Report of German Nationwide Inpatient Data on the Post-COVID-19 Syndrome Including Annual Direct Healthcare Costs
Authors: Walter Nike , Rupp Markus , Lang Siegmund , Leinberger Beate , Alt Volker , Hinterberger Thilo , Loew Thomas
Published: November 2022

Background: The aim of this study was to provide a comprehensive overview of German nationwide data including (i) the number of hospitalized Post-COVID Syndrome (PCS) cases including in-hospital mortality rates and intensive care unit treatments, (ii) the main common concomitant diagnoses associated with PCS, (iii) the most frequently performed treatment procedures, and (iv) the annual direct healthcare costs. Methods: The incidence was calculated based on annual ICD-10 diagnosis codes “U09.9!, Post-COVID-19 condition”. Data on concomitant diagnoses, treatment procedures, treatment in an intensive care unit (ICU), in-hospital mortality, the proportion of G-DRGs, and cumulative costs were assessed based on the Institute for the Hospital Remuneration System (InEK) data for 2019. Results: A total of 29,808 PCS inpatients could be identified yielding a prevalence of 5.5%. In total, 1330 (4.5%) in-hospital deaths were recorded, and 5140 (17.2%) patients required ICU treatment. The majority of patients (18.6%) were aged 65–74 years. The most common concomitant diagnoses included pneumonia, critical illness polyneuropathy, dyspnea, chronic fatigue syndrome, and pulmonary embolisms. The most frequently performed procedures were computed tomography of the thorax with contrast medium, whole-body plethysmography, and the monitoring of respiration, heart, and circulation. The cost per case of the G-DRG codes that were analyzed ranged from € 620 ± 377 (E64D, Respiratory insufficiency, one day of occupancy) to € 113,801 ± 27,939 (A06B, Ventilation > 1799 h with complex OR procedure). Total cumulative direct healthcare costs of € 136,608,719 were calculated, resulting in mean costs of € 4583 per case. Conclusion: Post-COVID Syndrome is of major public health importance with substantial financial implications. The present article can support stakeholders in health care systems to foresee future needs and adapt their resource management. Consensus diagnostic criteria and rehabilitation guidelines are highly warranted.
Emergence of High Pathogenicity Avian Influenza Virus H5N1 Clade 2.3.4.4b in Wild Birds and Poultry in Botswana
Authors: Letsholo Samantha L. , James Joe , Meyer Stephanie M. , Byrne Alexander M. P. , Reid Scott M. , Settypalli Tirumala B. K. , Datta Sneha , Oarabile Letlhogile , Kemolatlhe Obakeng , Pebe Kgakgamatso T. , Mafonko Bruce R. , Kgotlele Tebogo J. , Kumile Kago , Modise Boitumelo , Thanda Carter , Nyange John F. C. , Marobela-Raborokgwe Chandapiwa , Cattoli Giovanni , Lamien Charles E. , Brown Ian H. , Dundon William G. , Banyard Ashley C.
Published: November 2022

Numerous outbreaks of high-pathogenicity avian influenza (HPAI) were reported during 2020–2021. In Africa, H5Nx has been detected in Benin, Burkina Faso, Nigeria, Senegal, Lesotho, Namibia and South Africa in both wild birds and poultry. Botswana reported its first outbreak of HPAI to the World Organisation for Animal Health (WOAH) in 2021. An H5N1 virus was detected in a fish eagle, doves, and chickens. Full genome sequence analysis revealed that the virus belonged to clade 2.3.4.4b and showed high identity within haemagglutinin (HA) and neuraminidase proteins (NA) for viruses identified across a geographically broad range of locations. The detection of H5N1 in Botswana has important implications for disease management, wild bird conservation, tourism, public health, economic empowerment of vulnerable communities and food security in the region.
Comparative Proteomic Analyses of Susceptible and Resistant Maize Inbred Lines at the Stage of Enations Forming following Infection by Rice Black-Streaked Dwarf Virus
Authors: Wang Rong , Du Kaitong , Jiang Tong , Di Dianping , Fan Zaifeng , Zhou Tao
Published: November 2022

Rice black-streaked dwarf virus (RBSDV) is the main pathogen causing maize rough dwarf disease (MRDD) in China. Typical enation symptoms along the abaxial leaf veins prevail in RBSDV-infected maize inbred line B73 (susceptible to RBSDV), but not in X178 (resistant to RBSDV). Observation of the microstructures of epidermal cells and cross section of enations from RBSDV-infected maize leaves found that the increase of epidermal cell and phloem cell numbers is associated with enation formation. To identify proteins associated with enation formation and candidate proteins against RBSDV infection, comparative proteomics between B73 and X178 plants were conducted using isobaric tags for relative and absolute quantitation (iTRAQ) with leaf samples at the enation forming stage. The proteomics data showed that 260 and 316 differentially expressed proteins (DEPs) were identified in B73 and X178, respectively. We found that the majority of DEPs are located in the chloroplast and cytoplasm. Moreover, RBSDV infection resulted in dramatic changes of DEPs enriched by the metabolic process, response to stress and the biosynthetic process. Strikingly, a cell number regulator 10 was significantly down-regulated in RBSDV-infected B73 plants. Altogether, these data will provide value information for future studies to analyze molecular events during both enation formation and resistance mechanism to RBSDV infection.
Emerging Role of Interferon-Induced Noncoding RNA in Innate Antiviral Immunity
Authors: Min Jie , Liu Wenjun , Li Jing
Published: November 2022

Thousands of unique noncoding RNAs (ncRNAs) exist within the genomes of higher eukaryotes. Upon virus infection, the host generates interferons (IFNs), which initiate the expression of hundreds of interferon-stimulated genes (ISGs) through IFN receptors on the cell surface, establishing a barrier as the host’s antiviral innate immunity. With the development of novel RNA-sequencing technology, many IFN-induced ncRNAs have been identified, and increasing attention has been given to their functions as regulators involved in the antiviral innate immune response. IFN-induced ncRNAs regulate the expression of viral proteins, IFNs, and ISGs, as well as host genes that are critical for viral replication, cytokine and chemokine production, and signaling pathway activation. This review summarizes the complex regulatory role of IFN-induced ncRNAs in antiviral innate immunity from the above aspects, aiming to improve understanding of ncRNAs and provide reference for the basic research of antiviral innate immunity.
Multivariate Statistical Analysis of the Spatial Variability of Hydrochemical Evolution during Riverbank Infiltration
Authors: Bo Yingjie , Chen Yaoxuan , Che Qiaohui , Shi Yakun , Zhang Yiwu
Published: November 2022

Riverbank filtration (RBF) is increasingly being used as a relatively cheap and sustainable means to improve the quality of surface water. Due to the obvious differences in physical, chemical, and biological characteristics between river water and groundwater, there are strong and complex physical, chemical, and biogeochemical effects in the process of bank filtration. In this paper, multivariate statistical analysis was used to identify the spatial variation of hydrogeochemical groundwater in the process of bank filtration. Firstly, the evolution process of groundwater hydrochemistry during the filtration process was identified through factor analysis. According to the results, the evolution of groundwater hydrochemistry in this area is attributable to four main types of reactions: (1) Leaching; (2) Regional groundwater influence; (3) Aerobic respiration and denitrification; and (4) Mn (IV)/Fe (III)/SO42− reduction. According to the similarity of the geochemistry, the flow path could be divided into four different hydrochemical zones through cluster analysis, revealing the evolution law of groundwater hydrochemistry and its main influencing factors during riverbank infiltration. Large hydraulic gradient in The Zone Strongly Influenced by River Water (The first group) resulted in a weak effect of leaching on groundwater chemistry. Reoxygenation and microorganism respiration occurred in The Zone Moderately Influenced by River Water (The second group), The Zone Weakly Influenced by River Water (The third group), and The Zone Strongly Influenced by Regional Groundwater (The fourth group), resulting in fluctuations in Eh and pH values of groundwater. As a result, sulfate reduction and Mn (IV) and Fe (III) reduction alternated along the flow path. The Zone Strongly Influenced by Regional Groundwater (The fourth group) groundwater chemistry was mainly affected by regional groundwater.
Harmonizing and Extending Fragmented 100 Year Flood Hazard Maps in Canada’s Capital Region Using Random Forest Classification
Authors: Bhuiyan Shelina A. , Bataille Clement P. , McGrath Heather
Published: November 2022

With the record breaking flood experienced in Canada’s capital region in 2017 and 2019, there is an urgent need to update and harmonize existing flood hazard maps and fill in the spatial gaps between them to improve flood mitigation strategies. To achieve this goal, we aim to develop a novel approach using machine learning classification (i.e., random forest). We used existing fragmented flood hazard maps along the Ottawa River to train a random forest classification model using a range of flood conditioning factors. We then applied this classification across the Capital Region to fill in the spatial gaps between existing flood hazard maps and generate a harmonized high-resolution (1 m) 100 year flood susceptibility map. When validated against recently produced 100 year flood hazard maps across the capital region, we find that this random forest classification approach yields a highly accurate flood susceptibility map. We argue that the machine learning classification approach is a promising technique to fill in the spatial gaps between existing flood hazard maps and create harmonized high-resolution flood susceptibility maps across flood-vulnerable areas. However, caution must be taken in selecting suitable flood conditioning factors and extrapolating classification to areas with similar characteristics to the training sites. The resulted harmonized and spatially continuous flood susceptibility map has wide-reaching relevance for flood mitigation planning in the capital region. The machine learning approach and flood classification optimization method developed in this study is also a first step toward Natural Resources Canada’s aim of creating a spatially continuous flood susceptibility map across the Ottawa River watershed. Our modeling approach is transferable to harmonize flood maps and fill in spatial gaps in other regions of the world and will help mitigate flood disasters by providing accurate flood data for urban planning.
Kinetics of Arab Light Crude Oil Degradation by Pseudomonas and Bacillus Strains
Authors: Costa Carlos , Santos Anais , Vega Milena A.
Published: November 2022

The biodegradation of crude oil is a consequence of the presence of a specific enzymatic system in the microorganisms selected: the alkane hydroxylase (AlkH). The enzymatic biodegradation has been described since 1994, when the enzyme was first isolated from P. putida (formerly P. oleovorans), but the kinetics of microbial degradation has been weakly considered. We studied and described in this work the kinetics of Arab Light biodegradation, a light crude oil used for gasoline production (46.4% C7–C12 n-alkanes), using two oleophilic strains (Bacillus licheniformis and Pseudomonas putida). Alkanes were extracted from aqueous solutions in the bioreactors by dichloromethane, with a high ratio aqueous:organic volumes (1:0.2 mL) for the amplification of the GC n-alkane signals, and GC spectra were monitored in time over 40 days. Petroleum emulsions were visualized using optical microscopy as a result of biosurfactant segregation, which is necessary for the enzymatic biodegradation of oil by microorganisms. Kinetic analysis in biodegradation of Arab Light (total petroleum hydrocarbons, TPH) exhibits first-order kinetics with 0.098 d−1 and 0.082 d−1 as kinetic coefficients for 8.6 g/L initial crude oil concentration (30 °C), which results in degradation rates of 843 mg/Ld and 705 mg/Ld in B. licheniformis and P. putida, respectively. These results can be applied for oil spill bioremediation, using these microorganisms with the objective of removing contamination by petroleum alkanes.