bluuebunny/crossref_metadata_2025 huggingface dataset:
Solar-Induced Chlorophyll Fluorescence Trends and Mechanisms in Different Ecosystems in Northeastern China
Authors: Guo Meng , Li Jing , Li Jianuo , Zhong Chao , Zhou Fenfen
Published: March 2022

Solar-induced chlorophyll fluorescence (SIF), when used as a proxy for plant photosynthesis, can provide an indication of the photosynthesis rate and has the potential to improve our understanding of carbon exchange mechanisms within an ecosystem. However, the relationships between SIF and vegetation indices (VIs) operating within different ecological contexts and the effect of other environmental factors on SIF remain unclear. This study focused on three ecosystems (cropland, forest, and grassland), with different ecological characteristics, located in Northeast China. These areas provide case studies where numerous relationships can be explored, including the correlations between the Orbiting Carbon Observatory-2 (OCO-2) SIF and MODIS products, meteorological factors, and the differences in the relationships between the three different ecosystems. Some interesting results and conclusions were obtained. First, in different ecosystems, the relationships between SIF and MODIS products show different correlations, whereby the enhanced vegetation index (EVI) has a close relationship with SIF in all the three ecosystems of forest, cropland, and grassland. Second, forest-type ecosystems appear to be sensitive to changes in daily temperature, whereas cropland and grassland areas respond more closely to changes in previous 16-day daily minimum temperature. Compared with forest and cropland areas, grasslands were more sensitive to precipitation (although the R2 value was small). Third, different ecosystems have different mechanisms of photosynthesis. Hence, we suggest that it is better to use SIF in areas exhibiting different ecological characteristics, and different models should be employed while simulating SIF.
Lidar and Radar Signal Simulation: Stability Assessment of the Aerosol–Cloud Interaction Index
Authors: Fajardo-Zambrano Carlos Mario , Bravo-Aranda Juan Antonio , Granados-Muñoz María José , Montilla-Rosero Elena , Casquero-Vera Juan Andrés , Rejano Fernando , Castillo Sonia , Alados-Arboledas Lucas
Published: March 2022

Aerosol–cloud interactions (ACI) are in the spotlight of atmospheric science since the limited knowledge about these processes produces large uncertainties in climate predictions. These interactions can be quantified by the aerosol–cloud interaction index (ACI index), which establishes a relationship between aerosol and cloud microphysics. The experimental determination of the ACI index through a synergistic combination of lidar and cloud radar is still quite challenging due to the difficulties in disentangling the aerosol influence on cloud formation from other processes and in retrieving aerosol-particle and cloud microphysics from remote sensing measurements. For a better understanding of the ACI and to evaluate the optimal experimental conditions for the measurement of these processes, a Lidar and Radar Signal Simulator (LARSS) is presented. LARSS simulate vertically-resolved lidar and cloud-radar signals during the formation process of a convective cloud, from the aerosol hygroscopic enhancement to the condensation droplet growth. Through LARSS simulations, it is observed a dependence of the ACI index with height, associated with the increase in number (ACINd) and effective radius (ACIreff) of the droplets with altitude. Furthermore, ACINd and ACIreff for several aerosol types (such as ammonium sulfate, biomass burning, and dust) are estimated using LARSS, presenting different values as a function of the aerosol model. Minimum ACINd values are obtained when the activation of new droplets stops, while ACIreff reaches its maximum values several meters above. These simulations are carried out considering standard atmospheric conditions, with a relative humidity of 30% at the surface, reaching the supersaturation of the air mass at 3500 m. To assess the stability of the ACI index, a sensitivity study using LARSS is performed. It is obtained that the dry modal aerosol radius presents a strong influence on the ACI index fluctuations of 18% cause an ACI variability of 30% while the updraft velocity within the cloud and the wet modal aerosol radius have a weaker impact. LARSS ACI index uncertainty is obtained through the Monte Carlo technique, obtaining ACIreff uncertainty below 16% for the uncertainty of all LARSS input parameters of 10%. Finally, a new ACI index is introduced in this study, called the remote-sensing ACI index (ACIRs), to simplify the quantification of the ACI processes with remote sensors. This new index presents a linear relationship with the ACIreff, which depends on the Angstrom exponent. The use of ACIRs to derive ACIreff presents the advantage that it is possible to quantify the aerosol–cloud interaction without the need to perform microphysical inversion retrievals, thus reducing the uncertainty sources.
Digital Twin Technology Challenges and Applications: A Comprehensive Review
Authors: Botín-Sanabria Diego M. , Mihaita Adriana-Simona , Peimbert-García Rodrigo E. , Ramírez-Moreno Mauricio A. , Ramírez-Mendoza Ricardo A. , Lozoya-Santos Jorge de J.
Published: March 2022

A digital twin is a virtual representation of a physical object or process capable of collecting information from the real environment to represent, validate and simulate the physical twin’s present and future behavior. It is a key enabler of data-driven decision making, complex systems monitoring, product validation and simulation and object lifecycle management. As an emergent technology, its widespread implementation is increasing in several domains such as industrial, automotive, medicine, smart cities, etc. The objective of this systematic literature review is to present a comprehensive view on the DT technology and its implementation challenges and limits in the most relevant domains and applications in engineering and beyond.
Beached and Floating Litter Surveys by Unmanned Aerial Vehicles: Operational Analogies and Differences
Authors: Andriolo Umberto , Garcia-Garin Odei , Vighi Morgana , Borrell Asunción , Gonçalves Gil
Published: March 2022

The abundance of litter pollution in the marine environment has been increasing globally. Remote sensing techniques are valuable tools to advance knowledge on litter abundance, distribution and dynamics. Images collected by Unmanned Aerial Vehicles (UAV, aka drones) are highly efficient to map and monitor local beached (BL) and floating (FL) marine litter items. In this work, the operational insights to carry out both BL and FL surveys using UAVs are detailly described. In particular, flight planning and deployment, along with image products processing and analysis, are reported and compared. Furthermore, analogies and differences between UAV-based BL and FL mapping are discussed, with focus on the challenges related to BL and FL item detection and recognition. Given the efficiency of UAV to map BL and FL, this remote sensing technique can replace traditional methods for litter monitoring, further improving the knowledge of marine litter dynamics in the marine environment. This communication aims at helping researchers in planning and performing optimized drone-based BL and FL surveys.
Online Anomaly Detection for Smartphone-Based Multivariate Behavioral Time Series Data
Authors: Liu Gang , Onnela Jukka-Pekka
Published: March 2022

Smartphones can be used to collect granular behavioral data unobtrusively, over long time periods, in real-world settings. To detect aberrant behaviors in large volumes of passively collected smartphone data, we propose an online anomaly detection method using Hotelling’s T-squared test. The test statistic in our method was a weighted average, with more weight on the between-individual component when the amount of data available for the individual was limited and more weight on the within-individual component when the data were adequate. The algorithm took only an O(1) runtime in each update, and the required memory usage was fixed after a pre-specified number of updates. The performance of the proposed method, in terms of accuracy, sensitivity, and specificity, was consistently better than or equal to the offline method that it was built upon, depending on the sample size of the individual data. Future applications of our method include early detection of surgical complications during recovery and the possible prevention of the relapse of patients with serious mental illness.
Mid-Infrared Sensor Based on Dirac Semimetal Coupling Structure
Authors: Zou Yuxiao , Liu Ying , Song Guofeng
Published: March 2022

A multilayer structure based on Dirac semimetals is investigated, where long-range surface plasmon resonance (LRSPR) of a dielectric layer/Dirac semimetal/dielectric layer are coupled with surface plasmon polaritons (SPPs) on graphene to substantially improve the Goos–Hänchen (GH) shift of Dirac semimetals in the mid-infrared band. This has important implications for the study of mid-infrared sensors. We studied the reflection coefficient and phase of this multilayer structure using a generalized transport matrix. We established that subtle changes in the refractive index of the sensing medium and the Fermi energy of the Dirac semimetal significantly affected the GH shift. Our numerical simulations show that the sensitivity of the coupling structure is more than 2.7×107 λ/RIU, which can be used as a potential new sensor application. The novelty of this work is the design of a tunable, highly sensitive, and simple structured mid-infrared sensor that takes advantage of the excellent properties of Dirac semimetals.
Internal Parameters Calibration of Vision Sensor and Application of High Precision Integrated Detection in Intelligent Welding Based on Plane Fitting
Authors: Zhu Chuanhui , Zhu Zhiming , Ke Zhijie , Zhang Tianyi
Published: March 2022

Vision sensing is a key technology to realize on-line detection of welding groove sizes and welding torch relative position and posture parameters during the arc welding process of intelligent production. For the specially designed vision sensor based on combined laser structured lights, an integrated calibration method for its internal parameters is proposed firstly, which improves the efficiency, accuracy and comprehensiveness of internal parameter calibration for a line structured light vision sensor and provides a good foundation for industrial application of the vision sensor. Then, the high precision integrated detection algorithms are derived for the V-groove size parameters and the spatial position and posture (SPP) parameters of the welding torch relative to the welding groove based on a single modulated laser lines image. The algorithms make full use of the data in a single modulated laser lines image, adopting data segmentation and plane fitting to realize the 3D reconstruction of V-groove surfaces and its adjacent workpiece surfaces of planar workpiece, so solving the parameters with high precision. In the verification tests, the relative detection error of V-groove size parameters of planar workpiece is less than 1%, and the relative detection error of SPP parameters of welding torch relative to the welding groove is less than 5%, which separately shows the effectiveness and accuracy of the calibration method and the detection algorithms. This research work provides a good technical support for the practical application of the specially designed vision sensor in the intelligent welding production.
Application and Advances in Radiographic and Novel Technologies Used for Non-Intrusive Object Inspection
Authors: Mamchur Dmytro , Peksa Janis , Le Clainche Soledad , Vinuesa Ricardo
Published: March 2022

Increase in trading and travelling flows has resulted in the need for non-intrusive object inspection and identification methods. Traditional techniques proved to be effective for decades; however, with the latest advances in technology, the intruder can implement more sophisticated methods to bypass inspection points control techniques. The present study provides an overview of the existing and developing techniques for non-intrusive inspection control, current research trends, and future challenges in the field. Both traditional and developing methods, techniques, and technologies were analyzed with the use of traditional and novel sensor types. Finally, it was concluded that the improvement of non-intrusive inspection experience could be gained with the additional use of novel types of sensors (such as biosensors) combined with traditional techniques (X-ray inspection).
Applying Sensor-Based Information Systems to Identify Unplanned Downtime in Mining Machinery Operation
Authors: Brodny Jarosław , Tutak Magdalena
Published: March 2022

Underground mining belongs to immensely complex processes and depends on many natural, technical and organizational factors. The main factor that hinders this process is the environmental conditions in which it is carried out. One of the problems associated with the use of increasingly modern machines in such conditions is the issue of unplanned downtime during their operation. This paper presents the developed methodology and IT system for recording breaks in the operation of mining machines and identifies their causes. The basis of this methodology is a sensor-based information system used to register mining machinery parameters, based on which interruptions in their operation can be determined. In order to register these parameters, an industrial automation system (together with a SCADA system supervising the process) was used, which is practically independent from the operator and enables continuous registration of these parameters. In order to identify the reasons for the recorded breaks, an IT tool was developed in the form of an application in the module of the integrated mining enterprise management support system (ERP system). This application enables (with a continuously updated database) the identification of the causes in question. Thus, the developed solution enables the objective registration of machine downtime and, for most cases, the identification of causes. The acquired knowledge, so far largely undisclosed, has created opportunities to improve the utilization level of machinery exploited in the mining production process. The paper discusses the methodology, together with the IT system, for identifying the causes of machine downtime and presents an example of its application for a shearer loader, which is the basic machine of a mechanized longwall system. The results indicate great potential for the application of the developed system to improve the efficiency of machinery utilization and the whole process of mining production.
Conceptual Framework for Future WSN-MAC Protocol to Achieve Energy Consumption Enhancement
Authors: Sadeq Abdulrahman Sameer , Hassan Rosilah , Sallehudin Hasimi , Aman Azana Hafizah Mohd , Ibrahim Anwar Hassan
Published: March 2022

Nowadays, the rapid deployment of Wireless Sensor Networks (WSNs) and the integration of Internet of Things (IoT) technology has enabled their application to grow in various industrial fields in our country. Various factors influence the success of WSN development, particularly improvements in Medium Access Control (MAC) protocols, for which WSNs-IoT are deemed vital. Several aspects should be considered, such as energy consumption reduction, performance, scalability for a large deployment of nodes, and clustering intelligence. However, many protocols address this aspect in a constrained view of handling the medium access. This work presents a state-of-the-art review of recently proposed WSN MAC protocols. Different methods and approaches are proposed to enhance the main performance factors. Various performance issue factors are considered to be the main attribute that the MAC protocol should support. A comparison table is given to provide further details about using these approaches and algorithms to improve performance issues, such as network throughput, end-to-end delay, and packet drop, translated into energy consumption.
Development of an Accurate and Robust Air-Coupled Ultrasonic Time-of-Flight Measurement Technique
Authors: Bühling Benjamin , Küttenbaum Stefan , Maack Stefan , Strangfeld Christoph
Published: March 2022

Ultrasonic time-of-flight (ToF) measurements enable the non-destructive characterization of material parameters as well as the reconstruction of scatterers inside a specimen. The time-consuming and potentially damaging procedure of applying a liquid couplant between specimen and transducer can be avoided by using air-coupled ultrasound. However, to obtain accurate ToF results, the waveform and travel time of the acoustic signal through the air, which are influenced by the ambient conditions, need to be considered. The placement of microphones as signal receivers is restricted to locations where they do not affect the sound field. This study presents a novel method for in-air ranging and ToF determination that is non-invasive and robust to changing ambient conditions or waveform variations. The in-air travel time was determined by utilizing the azimuthal directivity of a laser Doppler vibrometer operated in refracto-vibrometry (RV) mode. The time of entry of the acoustic signal was determined using the autocorrelation of the RV signal. The same signal was further used as a reference for determining the ToF through the specimen in transmission mode via cross-correlation. The derived signal processing procedure was verified in experiments on a polyamide specimen. Here, a ranging accuracy of <0.1 mm and a transmission ToF accuracy of 0.3μs were achieved. Thus, the proposed method enables fast and accurate non-invasive ToF measurements that do not require knowledge about transducer characteristics or ambient conditions.
The Geography of Jobs: How Proximity to a Prestige Labor Market Shapes Opportunity for Computer Science Degree Holders
Authors: Chow Tiffany
Published: March 2022

A computer science degree is seen as a good investment, given the lucrative calling of Silicon Valley and the ever-growing demand for software engineers. Yet, it is unclear whether all computer science degree holders fare equally well on the job market. This study explores how the routing of computer science (CS) students to specific educational geographies plays a major role in determining their career trajectories post-graduation. Using a new survey for recent computer science graduates of three public universities in Texas, I measure labor market outcomes for CS degree holders along three metrics: salary, job location, and job title. Results from 157 respondents show that alumni from universities near a major tech hub are more likely to earn higher wages in desirable job markets compared with graduates from a university located far from a dense tech hub. Although most previous studies have focused on gender and racial disparities within the high-tech industry, I provide a new lens to understand how inequality manifests through geographic segregation and leaves even high-skilled job seekers vulnerable to spatial mismatch between their place of residence and proximity to desirable, elite jobs.
Disaster Resilience Differs between Survivors and Victims’ Families: A Semantic Network Analysis
Authors: Lee Jae-Eun , Kwon Seol-A , Song Eugene , Ryu Sang Il
Published: March 2022

The main purpose of this study is to clarify the difference in disaster resilience between survivors and victims’ families by analyzing the language used in popular literature on disaster cases. The results showed that there were differences in emotions, behaviors, attitudes, role perceptions, etc., between survivors and victims’ families in dealing with a disaster. In particular, survivors remember and think about the situation that occurred at the time of the disaster, which creates resilience to the incident, while victims’ families attempt to establish resilience to the incident by investigating the facts and government countermeasures. While survivors were focused on building their own resilience, victims’ families were more focused on improving government countermeasures to prevent such accidents from recurring. This can be considered as social or national resilience. Based on this comparative analysis, it is necessary to prepare various theoretical foundations for disaster preparedness and resilience, while further elaborating the theory.
Mobile Learning Acceptance Post Pandemic: A Behavioural Shift among Engineering Undergraduates
Authors: Kumar Jeya Amantha , Osman Sharifah , Sanmugam Mageswaran , Rasappan Rasammal
Published: March 2022

Mobile learning has become an essential telematic tool to facilitate and compliment online teaching and learning during the pandemic. This study investigates the change of behaviour and acceptance of using mobile learning specifically for engineering undergraduates due to this shift. The data collected pre-Covid19 (n = 326) and post-pandemic (n = 349) indicated an inclination for utilizing laptops than smartphones, while Telegram prevails as a popular tool for communicating and sharing information within the learning community. Next, while video conferencing tools and online learning management systems utilization increased, educational games and reading behaviour via mobile devices declined. Concurrently, behavioural intention post-pandemic were found to reduce marginally as importance were also given towards establishing learning communities via social influence compared to perceived usefulness. The outcome of this study contributes to the limited body of literature on engineering education mobile learning acceptance, and recommendations are provided for further investigation to ensure continuous sustainable use.
The Phenomenon and Development of K-Pop: The Relationship between Success Factors of K-Pop and the National Image, Social Network Service Citizenship Behavior, and Tourist Behavioral Intention
Authors: Kim Joon-ho , Kim Kwang-jin , Park Bum-tae , Choi Hyun-ju
Published: March 2022

This study aims to understand the impact of six success factors of K-pop on the national image of Korea perceived by global viewers and SNS citizenship behavior. In addition, this study seeks to validate the impact of the national image of Korea/SNS citizenship behavior, as defined in the foregoing, on tourist behavioral intention. Our analysis was conducted within the theoretical frameworks of the SERVQUAL, Image Theory, and the Theory of Planned Behavior. To that end, 1247 global viewers (eight countries) who have listened, watched and searched for information on K-pop are surveyed. Four statistical programs (SPSS/SmartPLS/GSCA Pro/JASP) are used for regression analysis and structural equation modeling. The results indicate the following. (1) Four K-pop success factors (producers, casting, producing/promotion, and contents) demonstrate a statistically significant positive influence on national image. (2) Two K-pop success factors (casting and producing/promotion) demonstrate a statistically significant positive influence on SNS citizenship behavior. In addition, K-pop contents has a statistically significant positive influence on SNS citizenship behavior (only in SPSS). (3) National image has a statistically significant positive influence on SNS citizenship behavior; and national image and SNS citizenship behavior have a statistically significant positive influence on tourist behavioral intention. As an exemplary cultural product, K-pop is creating economic added value. It is necessary to establish the measures to integrate K-pop in product planning and PR for sustainable marketing for Hallyu tourism.
Synchromodal Transportation Analysis of the One-Belt-One-Road Initiative Based on a Bi-Objective Mathematical Model
Authors: Zahid Taiba , Gillani Fouzia , Ghafoor Usman , Bhutta Muhammad Raheel
Published: March 2022

Synchromodality is the key to finding sustainable solutions for logistics, especially across larger networks. The era of the COVID-19 pandemic has brought special attention to the disruptions in demand and supply across the world and has accentuated the need for sustainable transportation networks to handle such anomalies in supply chains. The proposed research develops a mathematical model for an intermodal transportation network and investigates the model on one of the largest and most widely discussed supply chain projects of the One-Belt-One-Road (OBOR) initiative. The proposed bi-objective model focuses on time and cost functions with rail, roads, and ships as modes of transportation. A detailed analysis was performed on various mode alternatives and links to evaluate their performance. The study provides an insightful understanding of the network with several suggestions. In contrast to roads and trains, container ships depict a fourfold increase in fuel consumption for an average ship weighing 4500 TEUs with the increase in shipping speed. It was concluded that increasing port capacity and reducing custom clearance time can have a major impact on lead times, and this is directly influenced by a country’s ease of doing business. Moreover, with its several branches, the OBOR initiative can provide a robust supply chain with increased logistical capacity.
Understanding the Survival Ability of Franchise Industries during the COVID-19 Crisis in Malaysia
Authors: Abd Aziz Nurul Ashykin , Hizam-Hanafiah Mohd , Hasbollah Hasif Rafidee , Aziz Zuraimi Abdul , Hussin Nik Syuhailah Nik
Published: March 2022

Since the world was hit by the COVID-19 pandemic crisis that began in December 2019, many industries have been affected, including the franchise industry in Malaysia. Thus, the COVID-19 pandemic has had a great impact on business survival. Direct effects can be seen in reduced income, job losses, changes in customer preferences, and business relationships between franchisors and franchisees. Some franchises have had to close their operations, and others still struggled to survive during the pandemic crisis. In addressing this situation, the role of government is crucial in supporting the resilience of these franchisor entrepreneurs in an increasingly worrisome situation around the world. However, the existing literature that focuses on the role of government in developing countries such as Malaysia is still poorly understood. In addition, a study of the Malaysian franchising industry during the pandemic crisis is still inadequate, especially concerning the government’s role in the survival of local franchises during the pandemic era. Therefore, understanding the role of the government in advocating the survival of local Malaysian franchises is worth studying. A qualitative research approach was applied through multiple cases involving twelve (12) franchise business owners and four (4) franchise-related agencies in Malaysia. In-depth interviews were conducted in exploring this topic. Thematic analysis has been used by applying “Atlas.ti” in analysing the data. Hence, the findings have indicated four themes from the grounded data. There are: (i) financial assistance; (ii) virtual franchise exhibition; (iii) training and support; and (iv) business development grants. This study is expected to highlight the role of government as well as agencies involved with the franchising industry in improving policies, strategies, and programs to ensure the viability of the franchise industry during periods of pandemic outbreaks.
Technological Sustainability and Artificial Intelligence Algor-ethics
Authors: Mantini Alessandro
Published: March 2022

Since 2018, a new terminology has been developed, called Algor-ethics, indicating the necessity for a dedicated study concerning the evaluation of an ethics applied to technology, to Algorithms and to Artificial Intelligence (AI). At the same time, since 1987, when the concept of sustainability was introduced, the discussion on this issue has become increasingly lively and has now spread to every area of life. In this paper, we would like to propose an application of the concept of sustainability to technological processes and in particular to the elaboration of AI systems. To reach this goal we will first try to build an ethical framework, here called Dynamical Techno-Algor-Ethical Composition, to define the interaction between the most important ethical ingredients involving the human person in relation to technology, taking a person-centered approach. Out of this will emerge a possible structure and definition of Technological Sustainability. The second step will consist of evaluating the process for the elaboration of an AI algorithm as a concrete application of the previously analyzed framework, to set ethical contents composing what we might call a good and sustainable algorithm.
The Importance of Food Attributes and Motivational Factors for Purchasing Local Food Products: Segmentation of Young Local Food Consumers in Hungary
Authors: Kovács Ildikó , Balázsné Lendvai Marietta , Beke Judit
Published: March 2022

Future trends in sustainable food consumption include the emergence of short food supply chains and growing interest in local food products. Among the drivers are the more urgent sustainability expectations, the emphasis on environmental and social responsibility, and the changing consumer needs, of which the desire for healthy and quality products, curiosity, uniqueness, and experience are the most prominent drivers. Today’s customers are becoming more aware and open to culinary discoveries and exotic delights. In this study, we investigated the importance of product attributes related to local products, and the motivational factors that determine purchase intentions. The significance of our work lies in the fact that we have studied young consumers’ intrinsic and extrinsic motivational factors. A quantitative consumer survey was conducted using a hybrid data collection method on a sample of 1756 respondents aged between 18 and 45 years. The questionnaire focused on product features, intrinsic and extrinsic motivational elements, and demographic characteristics. The results of our study provide strong evidence that the features associated with local products include but are not limited to the following: freshness, high quality, nutritional value, reliability, safety, evoking domestic flavours, naturalness, being healthy, environmentally friendly, etc. The motivational elements were grouped into four factors: hedonism, curiosity, nutritional value, and tradition. The main reasons for young respondents purchasing local food were taste and curiosity. Based on the results of the cluster analysis, we formed three groups having different features and different motivations for purchasing local products. Moreover, we had the opportunity to understand the attitudes and perceptions of young people towards buying local products. A key result of our study is that the “value-creators” segment considers local products to be healthy and nutritious.
Proposal of a New Green Process for Waste Valorization and Cascade Utilization of Essential Oil Plants
Authors: Jensch Christoph , Strube Jochen
Published: March 2022

In this study, different unit operations for distillation, extraction, capturing and separation of essential oil components from essential oil plants are evaluated, on the example of limonene and carvone from caraway fruits. Hydrodistillation is the standard process for obtaining essential oils and leaves the distillation water (hydrolate) after phase separation of the essential oil and the distilled plant material (pomace) as waste streams. In this study, a process for the valorization of these waste streams, using different techniques, such as traps and pressurized hot water extraction, is developed, reaching high yields, with a plus of 35%, compared to established methods, and reducing the overall global warming potential of the whole process, as well as reducing the cost of goods by around 30%.
Supervisor Leadership and Subordinates’ Innovative Work Behaviors: Creating a Relational Context for Organizational Sustainability
Authors: Kim Kyungmin
Published: March 2022

Under turbulent environmental changes during the pandemic, organizational sustainability requires employees of all levels to perform innovative work behaviors in their daily jobs. Since virtual work environments could deteriorate the quality of collaborative interactions and interpersonal bonds among employees, organizations need to create more relation-focused contexts to trigger innovative behaviors from people. This study aims to explore the influence of supervisors’ relational leadership on the contexts in which subordinates are drawn to innovative work behaviors. Particularly, this study explains the process of this relationship by considering the psychological contract construct as the mediating variable, assuming that the social exchange between employees and the organization influences their decision to perform innovative behaviors. A total of 237 newcomers from a Korean conglomerate participated in the study, and surveys were conducted at two time points for the same participant to detect changes over time. The results showed that the perception of supervisors’ relational leadership was positively related to employees’ performance in innovative work behaviors over time. This relationship was partially mediated by an individual’s perception of employee promises, implying that a sense of obligation towards the organization could be an essential condition for innovative work behaviors. The theoretical and practical implications are discussed.
Impact of Living Conditions on Online Education: Evidence from China
Authors: Gu Jiafeng
Published: March 2022

Students need to maintain certain living conditions in order to pursue online learning at home. However, there is a lack of a scientific explanation for the extent to which students’ performance in online at-home education is influenced by living conditions. Students from 2002 low-income households in China were surveyed, and a multivariate logistic regression analysis was conducted in order to explore the impact of living conditions on online education. The results showed that, rather than computers and smartphones, broadband Internet at home can affect students’ performance in online learning. The larger the residence area, the better the children’s performance in at-home e-learning. Moreover, children living in dilapidated houses are unable to satisfactorily perform in an e-learning environment. Contrarily, children who live in families with separate rooms and tap water show better performances. Additionally, the performance will be worse in the case of unattended students. Furthermore, children from low-income and -status families in the community are often at a disadvantage in an at-home e-learning environment. Cognition regarding the connection between living conditions and online education can be crucial for the improvement of the living conditions of low-income families in order to achieve online education equity.
Slow Pyrolysis of Ulva lactuca (Chlorophyta) for Sustainable Production of Bio-Oil and Biochar
Authors: Amrullah Apip , Farobie Obie , Bayu Asep , Syaftika Novi , Hartulistiyoso Edy , Moheimani Navid R. , Karnjanakom Surachai , Matsumura Yukihiko
Published: March 2022

Ulva Lactuca is a fast-growing algae that can be utilized as a bioenergy source. However, the direct utilization of U. lactuca for energy applications still remains challenging due to its high moisture and inorganics content. Therefore, thermochemical processing such as slow pyrolysis to produce valuable added products, namely bio-oil and biochar, is needed. This study aims to conduct a thorough investigation of bio-oil and biochar production from U. lactuca to provide valuable data for its further valorization. A slow pyrolysis of U. lactuca was conducted in a batch-type reactor at a temperature range of 400–600 °C and times of 10–50 min. The results showed that significant compounds obtained in U. lactuca’s bio-oil are carboxylic acids (22.63–35.28%), phenolics (9.73–31.89%), amines/amides (15.33–23.31%), and N-aromatic compounds (14.04–15.68%). The ultimate analysis revealed that biochar’s H/C and O/C atomic ratios were lower than feedstock, confirming that dehydration and decarboxylation reactions occurred throughout the pyrolysis. Additionally, biochar exhibited calorific values in the range of 19.94–21.61 MJ kg−1, which is potential to be used as a solid renewable fuel. The surface morphological analysis by scanning electron microscope (SEM) showed a larger surface area in U. lactuca’s biochar than in the algal feedstock. Overall, this finding provides insight on the valorization of U. lactuca for value-added chemicals, i.e., biofuels and biochar, which can be further utilized for other applications.
Achieving Competitive Sustainable Advantages (CSAs) by Applying a Heuristic-Collaborative Risk Model
Authors: Nunes Marco , Bagnjuk Jelena , Abreu António , Saraiva Célia , Nunes Edgar , Viana Helena
Published: March 2022

Increasing disruption and turmoil continuously challenges organizations regarding the achievement of short- and long-term objectives. Such a hostile environment results from both the natural evolution of the business landscape complexity and the emergence of unpredictable disruptive evets such as the COVID-19 pandemic. More than ever, organizations should continuously develop business strategies that help them to become more agile, adaptative, sustainable, and effectively respond to the countless business risks (threats and opportunities). Innovation, such as the development and implementation of new technology, new ways of thinking and executing work, are just some of the major factors that can help organizations to increase their likelihood of success. In this work, is proposed the incorporation of a heuristic risk model into a typical organizational business intelligence architecture, to identify collaborative critical success factors across the different phases of a project life cycle which can be used to guide, monitor, and increase the success outcome likelihood of ongoing and upcoming projects. Some benefits of the incorporation include: a higher speed regarding the collection and treatment process of project collaborative data, the output of more accurate results with residual bias associated, a timely and efficient 360° view regarding the identification of project collaborative risks, and the impact (positive or/and negative) of these on a project’s outputs and outcomes. Finally, the model capabilities of performing descriptive, predictive, and prescriptive analysis, enables the generation of unique and actionable project’s lessons learned which can be used to make more data-informed decisions, and thus enhances the achievement of sustainable competitive advantages. The development and implementation of the proposed incorporation is illustrated with a with a real case study.
Production of β-Cyclocitral and Its Precursor β-Carotene in Microcystis aeruginosa: Variation at Population and Single-Cell Levels
Authors: Wang Xuejian , Zhu Yinjie , Hou Delin , Teng Fei , Cai Zhonghua , Tao Yi
Published: March 2022

Bloom-forming cyanobacteria produce and release odorous compounds and pose threats to the biodiversity of aquatic ecosystem and to the drinking water supply. In this study, the concentrations of β-cyclocitral in different bacterial growth phases were investigated using GC–MS to determine the growth stage of Microcystis aeruginosa at high risk for β-cyclocitral production. Moreover, the synchronicity of the production of β-cyclocitral and its precursor β-carotene at both population and single-cell levels was assessed. The results indicated that β-cyclocitral was the main odorous compound produced by M. aeruginosa cells. The intracellular concentration of β-cyclocitral (Cβ-cc) as well as its cellular quota (Qβ-cc) increased synchronously in the log phase, along with the increase of cell density. However, they reached the maximum values of 415 μg/L and 10.7 fg/cell in the late stationary phase and early stationary phase, respectively. The early stage of the stationary phase is more important for β-cyclocitral monitoring, and the sharp increase in Qβ-cc is valuable for anticipating the subsequent increase in Cβ-cc. The molar concentrations of β-cyclocitral and β-carotene showed a linear relationship, with an R2 value of 0.92, suggesting that the production of β-cyclocitral was linearly dependent on that of β-carotene, especially during the log phase. However, the increase in Qβ-cc was slower than that in β-carotene during the stationary phase, suggesting that β-cyclocitral production turned to be carotene oxygenase-limited when the growth rate decreased. These results demonstrate that variations of β-cyclocitral production on a single-cell level during different bacterial growth phases should be given serious consideration when monitoring and controlling the production of odorous compounds by M. aeruginosa blooms.
CovDif, a Tool to Visualize the Conservation between SARS-CoV-2 Genomes and Variants
Authors: Cedeño-Pérez Luis F. , Gómez-Romero Laura
Published: March 2022

The spread of the newly emerged severe acute respiratory syndrome (SARS-CoV-2) virus has led to more than 430 million confirmed cases, including more than 5.9 million deaths, reported worldwide as of 24 February 2022. Conservation of viral genomes is important for pathogen identification and diagnosis, therapeutics development and epidemiological surveillance to detect the emergence of new viral variants. An intense surveillance of virus variants has led to the identification of Variants of Interest and Variants of Concern. Although these classifications dynamically change as the pandemic evolves, they have been useful to guide public health efforts on containment and mitigation. In this work, we present CovDif, a tool to detect conserved regions between groups of viral genomes. CovDif creates a conservation landscape for each group of genomes of interest and a differential landscape able to highlight differences in the conservation level between groups. CovDif is able to identify loss in conservation due to point mutations, deletions, inversions and chromosomal rearrangements. In this work, we applied CovDif to SARS-CoV-2 clades (G, GH, GR, GV, L, O, S and G) and variants. We identified all regions for any defining SNPs. We also applied CovDif to a group of population genomes and evaluated the conservation of primer regions for current SARS-CoV-2 detection and diagnostic protocols. We found that some of these protocols should be applied with caution as few of the primer-template regions are no longer conserved in some SARS-CoV-2 variants. We conclude that CovDif is a tool that could be widely applied to study the conservation of any group of viral genomes as long as whole genomes exist.
Conserved Targets to Prevent Emerging Coronaviruses
Authors: Gonzalez Lomeli Fernanda , Elmaraghy Nicole , Castro Anthony , Osuna Guerrero Claudia V. , Newcomb Laura L.
Published: March 2022

Novel coronaviruses emerged as zoonotic outbreaks in humans in 2003 (SARS), 2012 (MERS), and notably in 2019 (SARS2), which resulted in the COVID-19 pandemic, causing worldwide health and economic disaster. Vaccines provide the best protection against disease but cannot be developed and engineered quickly enough to prevent emerging viruses, zoonotic outbreaks, and pandemics. Antivirals are the best first line of therapeutic defense against novel emerging viruses. Coronaviruses are plus sense, single stranded, RNA genome viruses that undergo frequent genetic mutation and recombination, allowing for the emergence of novel coronavirus strains and variants. The molecular life cycle of the coronavirus family offers many conserved activities to be exploited as targets for antivirals. Here, we review the molecular life cycle of coronaviruses and consider antiviral therapies, approved and under development, that target the conserved activities of coronaviruses. To identify additional targets to inhibit emerging coronaviruses, we carried out in silico sequence and structure analysis of coronavirus proteins isolated from bat and human hosts. We highlight conserved and accessible viral protein domains and residues as possible targets for the development of viral inhibitors. Devising multiple antiviral therapies that target conserved viral features to be used in combination is the best first line of therapeutic defense to prevent emerging viruses from developing into outbreaks and pandemics.
Dengue Virus Replication Is Associated with Catecholamine Biosynthesis and Metabolism in Hepatocytes
Authors: Mpekoulis George , Tsopela Vassilina , Chalari Anna , Kalliampakou Katerina I. , Panos Georgios , Frakolaki Efseveia , Milona Raphaela S. , Sideris Diamantis C. , Vassilacopoulou Dido , Vassilaki Niki
Published: March 2022

Previously, the association between the catecholamine biosynthetic enzyme L-Dopa decarboxylase (DDC) and Dengue virus (DV) replication was demonstrated in liver cells and was found to be mediated at least by the interaction between DDC and phosphoinositide 3-kinase (PI3K). Here, we show that biogenic amines production and uptake impede DV replication in hepatocytes and monocytes, while the virus reduces catecholamine biosynthesis, metabolism, and transport. To examine how catecholamine biosynthesis/metabolism influences DV, first, we verified the role of DDC by altering DDC expression. DDC silencing enhanced virus replication, but not translation, attenuated the negative effect of DDC substrates on the virus and reduced the infection related cell death. Then, the role of the downstream steps of the catecholamine biosynthesis/metabolism was analyzed by chemical inhibition of the respective enzymes, application of their substrates and/or their products; moreover, reserpine, the inhibitor of the vesicular monoamine transporter 2 (VMAT2), was used to examine the role of uptake/storage of catecholamines on DV. Apart from the role of each enzyme/transporter, these studies revealed that the dopamine uptake, and not the dopamine-signaling, is responsible for the negative effect on DV. Accordingly, all treatments expected to enhance the accumulation of catecholamines in the cell cytosol suppressed DV replication. This was verified by the use of chemical inducers of catecholamine biosynthesis. Last, the cellular redox alterations due to catecholamine oxidation were not related with the inhibition of DV replication. In turn, DV apart from its negative impact on DDC, inhibits tyrosine hydroxylase, dopamine beta-hydroxylase, monoamine oxidase, and VMAT2 expression.
Global Transcriptomic Response of Staphylococcus aureus to Virulent Bacteriophage Infection
Authors: Kuptsov Nikita , Kornienko Maria , Bespiatykh Dmitry , Gorodnichev Roman , Klimina Ksenia , Veselovsky Vladimir , Shitikov Egor
Published: March 2022

In light of the ever-increasing number of multidrug-resistant bacteria worldwide, bacteriophages are becoming a valid alternative to antibiotics; therefore, their interactions with host bacteria must be thoroughly investigated. Here, we report genome-wide transcriptional changes in a clinical Staphylococcus aureus SA515 strain for three time points after infection with the vB_SauM-515A1 kayvirus. Using an RNA sequencing approach, we identify 263 genes that were differentially expressed (DEGs) between phage-infected and uninfected host samples. Most of the DEGs were identified at an early stage of phage infection and were mainly involved in nucleotide and amino acid metabolism, as well as in cell death prevention. At the subsequent infection stages, the vast majority of DEGs were upregulated. Interestingly, 39 upregulated DEGs were common between the 15th and 30th minutes post-infection, and a substantial number of them belonged to the prophages. Furthermore, some virulence factors were overexpressed at the late infection stage, which necessitates more stringent host strain selection requirements for further use of bacteriophages for therapeutic purposes. Thus, this work allows us to better understand the influence of kayviruses on the metabolic systems of S. aureus and contributes to a better comprehension of phage therapy.