bluuebunny/crossref_metadata_2025 huggingface dataset:
Are the Significant Ionospheric Anomalies Associated with the 2007 Great Deep-Focus Undersea Jakarta–Java Earthquake?
Authors: Tao Dan , Wang Guangxue , Zong Jiayi , Wen Yuanzheng , Cao Jinbin , Battiston Roberto , Zeren Zhima
Published: May 2022

This work is an attempt to critically analyze the correlation between great deep-focus undersea earthquake and possible ionospheric anomalies. The significant TEC (total electron content) temporal and spatial anomalies were detected over the epicenter of 2007 Mw7.5 Jakarta–Java earthquake, and they coincide well with the striking plasma anomalies in the ionosphere in situ observed by the LEO (low Earth orbit) satellite. The localization and synchronization of the disturbances during the earthquake suggest that these ionospheric anomalies are highly related to this large undersea earthquake. In order to identify this correlation, we made efforts to distinguish seismo-associated signals from large electromagnetic noise due to solar and geomagnetic activities, natural non-seismic sources and known artificial signals. Nevertheless, the difficulties of this work should be recognized and approached with caution. This special (deep-focus and undersea) case study here provides us with valuable information on the study of lithosphere–atmosphere–ionosphere (LAI) coupling process and reminds us that deep-focus undersea seismic events need to be treated more carefully and discreetly in the future.
Using Optical Water-Type Classification in Data-Poor Water Quality Assessment: A Case Study in the Torres Strait
Authors: Petus Caroline , Waterhouse Jane , Tracey Dieter , Wolanski Eric , Brodie Jon
Published: May 2022

For many years, local communities have expressed concerns that turbid plume waters from the Fly River in Papua New Guinea may potentially deliver mine-derived contaminants to the Torres Strait, an ecologically and culturally unique area north of the Australian mainland. Information on suspended sediment transport and turbidity patterns are needed in this data-limited region to identify and manage downstream ecosystems that may be at risk of exposure from the Fly River runoff. This study used MODIS satellite time series and a colour-classification approach to map optical water types around the data-poor Gulf of Papua and Torres Strait region. The satellite data were supported by field data, including salinity and suspended sediment measurements, and used together in qualitative water quality assessments to evaluate the habitats that are likely exposed to Fly River discharge and/or derived sediments. It showed that the Fly River influence in the Torres Strait region is largely limited to the north-east corner of the Torres Strait. The drivers of turbidity vary between locations, and it is impossible to fully separate direct riverine plume influence from wave and tidally driven sediment resuspension in the satellite maps. However, results indicate that coastal habitats located as far east as Bramble Cay and west to Boigu Island are located in an area that is most likely exposed to the Fly River discharge within the region, directly or through sediment resuspension. The area that is the most likely exposed is a relatively small proportion of the Torres Strait region, but encompasses habitats of high ecological importance, including coral reefs and seagrass meadows. Satellite data showed that the period of highest risk of exposure was during the south-east trade wind season and complemented recent model simulations in the region over larger spatial and temporal frames. This study did not evaluate transboundary pollution or the ecological impact on local marine resources, but other recent studies suggest it is likely to be limited. However, this study did provide long-term, extensive but qualitative, baseline information needed to inform future ecological risk mapping and to support decision making about management priorities in the region. This is important for ensuring the protection of the Torres Strait ecosystems, given their importance to Torres Strait communities and turtle and dugong populations, and the Torres Strait’s connectivity with the Great Barrier Reef Marine Park.
Strain and Moment Rates from GPS and Seismological Data in Northern Iran: Implications for an Evaluation of Stress Trajectories and Probabilistic Fault Rupture Hazard
Authors: Rashidi Ahmad , Derakhshani Reza
Published: May 2022

This study provides a multi-disciplinary overview of the seismology and geodetic data with tectonics analysis in order to provide an evaluation of stress trajectories, and probabilistic fault rupture hazard assessment. Based on the different scenarios obtained from the comparison of several overviews and their interpretation, we investigated the kinematics and active tectonics of different structural zones. In this study, the magnitudes and directions of seismic and geodetic strain rates (SSR and GSR) were evaluated using reliable earthquake focal mechanisms and all available GPS data (1999–2015) in the structural subzones of northern Iran, where have experienced more than 14 strong instrumental earthquakes (Mw ≥ 6). In addition, a tectonic stress model was inferred from the Focal Mechanism Stress Inversion (FMSI). The new crustal stress map was proposed by the weighted average analysis of the SSR, GSR, and FMSI. N35.5° E and N104° E were estimated for the Alborz and Talesh mountains, respectively. The numerical analysis of stress regimes confirms the slip partitioning mechanism of oblique shortening on the sub-parallel thrusts and strike-slip faults in the area. Four main stress regime categories were defined, including thrust (49.37%), strike-slip (39.24%), thrust with a strike-slip component (2.53%), normal (1.27%), and unknown faulting (7.59%). Seismic and geodetic moment rates (SMR and GMR) and their comparison were also calculated in order to evaluate the function of these parameters in determining the seismicity arrangement. The ratio of the seismic/geodetic moment rates for the area is ~70.7%. This ratio for the Alborz, western Koppeh-Dagh, north part of Central Iran, South Caspian Basin, and Talesh is ~0.9, 0.3, 11.9, 0.3, and 57.3, respectively, which indicates the most elastic energy has been released in the Talesh and the north part of Central Iran. The comparison of geodetic moment rates in the subzones of the area indicates that geodetic deformation is high in the Central Alborz (networks 8, 9, 17) and western Koppeh-Dagh (networks 5, 13).
Machine Learning-Based Diabetic Neuropathy and Previous Foot Ulceration Patients Detection Using Electromyography and Ground Reaction Forces during Gait
Authors: Haque Fahmida , Reaz Mamun Bin Ibne , Chowdhury Muhammad Enamul Hoque , Ezeddin Maymouna , Kiranyaz Serkan , Alhatou Mohammed , Ali Sawal Hamid Md , Bakar Ahmad Ashrif A , Srivastava Geetika
Published: May 2022

Diabetic neuropathy (DN) is one of the prevalent forms of neuropathy that involves alterations in biomechanical changes in the human gait. Diabetic foot ulceration (DFU) is one of the pervasive types of complications that arise due to DN. In the literature, for the last 50 years, researchers have been trying to observe the biomechanical changes due to DN and DFU by studying muscle electromyography (EMG) and ground reaction forces (GRF). However, the literature is contradictory. In such a scenario, we propose using Machine learning techniques to identify DN and DFU patients by using EMG and GRF data. We collected a dataset from the literature which involves three patient groups: Control (n = 6), DN (n = 6), and previous history of DFU (n = 9) and collected three lower limb muscles EMG (tibialis anterior (TA), vastus lateralis (VL), gastrocnemius lateralis (GL)), and three GRF components (GRFx, GRFy, and GRFz). Raw EMG and GRF signals were preprocessed, and different feature extraction techniques were applied to extract the best features from the signals. The extracted feature list was ranked using four different feature ranking techniques, and highly correlated features were removed. In this study, we considered different combinations of muscles and GRF components to find the best performing feature list for the identification of DN and DFU. We trained eight different conventional ML models: Discriminant analysis classifier (DAC), Ensemble classification model (ECM), Kernel classification model (KCM), k-nearest neighbor model (KNN), Linear classification model (LCM), Naive Bayes classifier (NBC), Support vector machine classifier (SVM), and Binary decision classification tree (BDC), to find the best-performing algorithm and optimized that model. We trained the optimized the ML algorithm for different combinations of muscles and GRF component features, and the performance matrix was evaluated. Our study found the KNN algorithm performed well in identifying DN and DFU, and we optimized it before training. We found the best accuracy of 96.18% for EMG analysis using the top 22 features from the chi-square feature ranking technique for features from GL and VL muscles combined. In the GRF analysis, the model showed 98.68% accuracy using the top 7 features from the Feature selection using neighborhood component analysis for the feature combinations from the GRFx-GRFz signal. In conclusion, our study has shown a potential solution for ML application in DN and DFU patient identification using EMG and GRF parameters. With careful signal preprocessing with strategic feature extraction from the biomechanical parameters, optimization of the ML model can provide a potential solution in the diagnosis and stratification of DN and DFU patients from the EMG and GRF signals.
Ensuring the Reliability of Virtual Sensors Based on Artificial Intelligence within Vehicle Dynamics Control Systems
Authors: Sieberg Philipp Maximilian , Schramm Dieter
Published: May 2022

The use of virtual sensors in vehicles represents a cost-effective alternative to the installation of physical hardware. In addition to physical models resulting from theoretical modeling, artificial intelligence and machine learning approaches are increasingly used, which incorporate experimental modeling. Due to the resulting black-box characteristics, virtual sensors based on artificial intelligence are not fully reliable, which can have fatal consequences in safety-critical applications. Therefore, a hybrid method is presented that safeguards the reliability of artificial intelligence-based estimations. The application example is the state estimation of the vehicle roll angle. The state estimation is coupled with a central predictive vehicle dynamics control. The implementation and validation is performed by a co-simulation between IPG CarMaker and MATLAB/Simulink. By using the hybrid method, unreliable estimations by the artificial intelligence-based model resulting from erroneous input signals are detected and handled. Thus, a valid and reliable state estimate is available throughout.
Modeling and Fault Detection of Brushless Direct Current Motor by Deep Learning Sensor Data Fusion
Authors: Suawa Priscile , Meisel Tenia , Jongmanns Marcel , Huebner Michael , Reichenbach Marc
Published: May 2022

Only with new sensor concepts in a network, which go far beyond what the current state-of-the-art can offer, can current and future requirements for flexibility, safety, and security be met. The combination of data from many sensors allows a richer representation of the observed phenomenon, e.g., system degradation, which can facilitate analysis and decision-making processes. This work addresses the topic of predictive maintenance by exploiting sensor data fusion and artificial intelligence-based analysis. With a dataset such as vibration and sound from sensors, we focus on studying paradigms that orchestrate the most optimal combination of sensors with deep learning sensor fusion algorithms to enable predictive maintenance. In our experimental setup, we used raw data obtained from two sensors, a microphone, and an accelerometer installed on a brushless direct current (BLDC) motor. The data from each sensor were processed individually and, in a second step, merged to create a solid base for analysis. To diagnose BLDC motor faults, this work proposes to use data-level sensor fusion with deep learning methods such as deep convolutional neural networks (DCNNs) for their ability to automatically extract relevant information from the input data, the long short-term memory method (LSTM), and convolutional long short-term memory (CNN-LSTM), a combination of the two previous methods. The results show that in our setup, sound signals outperform vibrations when used individually for training. However, without any feature selection/extraction step, the accuracy of the models improves with data fusion and reaches 98.8%, 93.5%, and 73.6% for the DCNN, CNN-LSTM, and LSTM methods, respectively, 98.8% being a performance that, according to our reading, has never been reached in the analysis of the faults of a BLDC motor without first going through the extraction of the characteristics and their fusion by traditional methods. These results show that it is possible to work with raw data from multiple sensors and achieve good results using deep learning methods without spending time and resources on selecting appropriate features to extract and methods to use for feature extraction and data fusion.
Design and Implementation of a Multi-Hop Real-Time LoRa Protocol for Dynamic LoRa Networks
Authors: Tran Huu Phi , Jung Woo-Sung , Yoo Dae-Seung , Oh Hoon
Published: May 2022

Recently, LoRa (Long Range) technology has been drawing attention in various applications due to its long communication range and high link reliability. However, in industrial environments, these advantages are often compromised by factors such as node mobility, signal attenuation due to various obstacles, and link instability due to external signal interference. In this paper, we propose a new multi-hop LoRa protocol that can provide high reliability for data transmission by overcoming those factors in dynamic LoRa networks. This study extends the previously proposed two-hop real-time LoRa (Two-Hop RT-LoRa) protocol to address technical aspects of dynamic multi-hop networks, such as automatic configuration of multi-hop LoRa networks, dynamic topology management, and updating of real-time slot schedules. It is shown by simulation that the proposed protocol achieves high reliability of over 97% for mobile nodes and generates low control overhead in topology management and schedule updates. The protocol was also evaluated in various campus deployment scenarios. According to experiments, it could achieve high packet delivery rates of over 97% and 95%, respectively, for 1-hop nodes and 2-hop nodes against node mobility.
Cyber-Security Threats and Side-Channel Attacks for Digital Agriculture
Authors: Alahmadi Adel N. , Rehman Saeed Ur , Alhazmi Husain S. , Glynn David G. , Shoaib Hatoon , Solé Patrick
Published: May 2022

The invention of smart low-power devices and ubiquitous Internet connectivity have facilitated the shift of many labour-intensive jobs into the digital domain. The shortage of skilled workforce and the growing food demand have led the agriculture sector to adapt to the digital transformation. Smart sensors and systems are used to monitor crops, plants, the environment, water, soil moisture, and diseases. The transformation to digital agriculture would improve the quality and quantity of food for the ever-increasing human population. This paper discusses the security threats and vulnerabilities to digital agriculture, which are overlooked in other published articles. It also provides a comprehensive review of the side-channel attacks (SCA) specific to digital agriculture, which have not been explored previously. The paper also discusses the open research challenges and future directions.
A Perspective on Passive Human Sensing with Bluetooth
Authors: Iannizzotto Giancarlo , Milici Miryam , Nucita Andrea , Lo Bello Lucia
Published: May 2022

Passive human sensing approaches based on the analysis of the radio signals emitted by the most common wireless communication technologies have been steadily gaining momentum during the last decade. In this context, the Bluetooth technology, despite its widespread adoption in mobile and IoT applications, so far has not received all the attention it deserves. However, the introduction of the Bluetooth direction finding feature and the application of Artificial Intelligence techniques to the processing and analysis of the wireless signal for passive human sensing pave the way for novel Bluetooth-based passive human sensing applications, which will leverage Bluetooth Low Energy features, such as low power consumption, noise resilience, wide diffusion, and relatively low deployment cost. This paper provides a reasoned analysis of the data preprocessing and classification techniques proposed in the literature on Bluetooth-based remote passive human sensing, which is supported by a comparison of the reported accuracy results. Building on such results, the paper also identifies and discusses the multiple factors and operating conditions that explain the different accuracy values achieved by the considered techniques, and it draws the main research directions for the near future.
Cost-Effective, Single-Frequency GPS Network as a Tool for Landslide Monitoring
Authors: Zuliani David , Tunini Lavinia , Di Traglia Federico , Chersich Massimiliano , Curone Davide
Published: May 2022

The constant monitoring of active landslides, particularly those located in the proximity of populated areas or touristic places, is crucial for early warning and risk-management purposes. The commonly used techniques deploy expensive instrumentation that can be hardly afforded, especially by small mountain communities in which landslide events often occur repeatedly. In recent years, the scientific community, as well as the private sector, have devoted growing effort to reducing the costs of monitoring systems. In this work, we present a monitoring network based on single-frequency Global Positioning System (GPS) sensors that have been activated to monitor an active landslide in the Carnic Alps, North-Eastern Italy. The system, which was composed of 12 single-frequency GPS stations, one seismometric station coupled with a single-frequency GPS instrument for real-time monitoring, and one permanent dual-frequency GPS station located in a stable area, provided daily reports of the landslide motion to the local authorities and administration. We show that this system is a valuable, flexible, and cost-effective tool for quick landslide characterization, and has high potential to be used as a landslide early warning system in case of emergency situations.
PDCP: A Set of Tools for Extracting, Transforming, and Loading Radiotherapy Data from the Orthanc Research PACS
Authors: Haidar Ali , Aly Farhannah , Holloway Lois
Published: May 2022

The Orthanc server is a light-weight open-source picture imaging and archiving system (PACS) used to store digital imaging and communications in medicine (DICOM) data. It is widely used in research environments as it is free, open-source and scalable. To enable the use of Orthanc stored radiotherapy (RT) data in data mining and machine learning tasks, the records need to be extracted, validated, linked, and presented in a usable format. This paper reports patient data collection and processing (PDCP), a set of tools created using python for extracting, transforming, and loading RT data from Orthanc PACs. PDCP enables querying, retrieving, and validating patient imaging summaries; analysing associations between patient DICOM data; retrieving patient imaging data into a local directory; preparing the records for use in various research questions; tracking the patient’s data collection process and identifying reasons behind excluding patient’s data. PDCP targeted simplifying the data preparation process in such applications, and it was made expandable to facilitate additional data preparation tasks.
Biofillers Improved Compression Modulus of Extruded PLA Foams
Authors: Mort Rebecca , Peters Erin , Curtzwiler Greg , Jiang Shan , Vorst Keith
Published: May 2022

Foams produced with biobased materials, such as poly(lactic acid) (PLA), cellulose, starch, and plant oil-based polyurethanes, have become more and more important in the circular economy. However, there are still significant challenges, including inferior performance and higher cost. The use of low-cost filler material has the potential to reduce the cost and alter the composite properties of biobased foams. By selecting biofillers derived from plant material, we can reduce the cost without sacrificing the compostability. This study explored the impact of landfill-diverted biofiller material, ground coffee chaff and rice hulls on the physical properties of biobased foams. Both biofillers were extrusion compounded with PLA, then extruded into rigid foams using a physical blowing agent. A filler concentration up to 10 weight % rice hull or 5 weight % coffee chaff could be incorporated without a significant increase in density, in comparison to the regular PLA foam. The thermal conductivity was similarly unaffected by biofiller loading, with values ranging between 71.5 and 76.2 mW/m-K. Surprisingly, the filler composite foams possessed impressive mechanical properties with all compressive moduli above 300 MPa. Only 5 weight % loading resulted in the doubling of compressive modulus, compared to the regular PLA foam. These results indicate that landfill-diverted fillers can strengthen foam mechanical properties without impacting thermal insulation performance, by forming reinforcing networks within the cell walls.
A Sustainable Everyday Life for Counterurbanising Swedish Families
Authors: Hoppstadius Fredrik , Åkerlund Ulrika
Published: May 2022

Even though urbanisation is the prevailing trend in modern societies, the net migration balance of Sweden’s largest cities has been negative for the past few years, and overrepresented among these migrants are families with young children. The stories of counterurbanisation have often relied on rather stereotypical representations of unsustainable city life versus sustainable rural life, thus strengthening the much criticised rural–urban binary. The aim of this article is to explore how the counterurbanising families’ ideas of “a sustainable everyday life” developed during and after the migration event. We uncover the needs, ideological foundations, practices, capacities, social atmosphere, temporality, and place-based understanding of one’s own role and responsibilities in society by studying what the families do in their everyday lives, what they are striving to achieve, and how they understand sustainability. Counterurbanising families represent a driven group that are not primarily guided by economic wants—as many of their active choices are lifestyle-driven. Our theoretical foundation highlights the structures and dimensions of social sustainability, relational place, and learning, contrasted with the subjectivity of everyday life in the urban–rural transition. Forty-five in-depth interviews (1–2 h) were conducted via video conference software, and the material was analysed using thematic analysis. The findings indicate that the views and understandings of social sustainability among counterurbanising young families highlight place-based needs and conditions, with implications for sustainability and mobility research, individuals, and contemporary society as a whole in navigating the somewhat diminishing rural–urban dichotomy.
A Study for Development of Digital Contents Management Systems Based on Smart Home
Authors: Ahn Byeongtae
Published: May 2022

With the development of high-speed wireless LAN, the use of multimedia-type digital contents is increasing significantly even in the smart home environment. Therefore, it is very important to efficiently process and manage the use of digital content in the form of multimedia. In order to effectively process multimedia digital contents in the wireless LAN environment of a smart home, a server higher than the PC level is required. There is also a need for a support system that can exchange multimedia digital contents between clients of home appliances. Therefore, in this paper, we propose a Smart Home Digital Contents Management and Operation System (SDCMOS) that effectively processes and supports multimedia digital contents. This system is designed to effectively process real-time multimedia processing in home service in a wireless LAN environment. In addition, it was made possible to efficiently search multimedia information directly from the home server. SDCMOS is designed to be usable in fields such as biomedical image search, history museums, art exhibition halls, tourism information, geographic information, and e-commerce.
Performance Analysis of a Solar-Powered Multi-Purpose Supply Container
Authors: Peter Stephan , Schirmer Matthias , Lathan Philippe , Stimpfl Georg , Ibrahim Bashar
Published: May 2022

In this article, the performance of a solar-powered multi-purpose supply container used as a service module for first-aid, showering, freezing, refrigeration and water generation purposes in areas of social emergency is analyzed. The average daily energy production of the solar panel is compared to the average daily energy demands of the above-mentioned types of service modules. The comparison refers to five different locations based on the Köppen–Geiger classification of climatic zones with the data for energy demand being taken from another publication. It is shown that in locations up to mid-latitudes, the supply container is not only able to power all types of modules all year round but also to provide up to 15 m3 of desalinated water per day for drinking, domestic use and irrigation purposes. This proves and quantifies the possibility of combining basic supply with efficient transport and self-sufficiency by using suitably equipped shipping containers. Thus, flexible solutions are provided to some of the most challenging problems humans will face in the future, such as natural disasters, water scarcity, starvation and homelessness.
An Empirical Analysis of the Benefits of Opening a Highway in Terms of Changes in Housing Prices
Authors: Kim Wonchul , Hong Sung Hyo
Published: May 2022

This study empirically analyzes the social benefits of opening a highway by assessing the increase in housing prices in two surrounding regions: one defined as the treatment group and the other defined as the control group. Although the two regions are geographically adjacent, they belong to different administrative districts and are physically separated by natural topographical features such as mountain ranges. Both aspects make it so that the interaction or influence between the two regions is limited, which raises the probability that the control group will show a trend similar to that of the treatment group under the influence of economic factors but will not be affected by the opening of the highway. With this in mind, the benefits of accessibility improvement due to the opening of the highway are estimated by using the difference-in-differences framework, i.e., the relative change in housing prices in the treatment group compared to the control group. In addition, the corresponding highway route is analyzed by dividing it into three sections according to their opening times and locations. The findings suggest that the estimated benefits are not fictional but robust. The increase in housing prices due to the opening of a highway is estimated to be, on average, 586 to 3075 dollars per apartment (equivalently, USD 10 to 53 per square meter). These benefits are worthy of being reflected upon, complementary to a traditional cost-benefit analysis.
Preservation and Recovery of Metal-Tolerant Fungi from Industrial Soil and Their Application to Improve Germination and Growth of Wheat
Authors: Akbar Mahnoor , El-Sabrout Ahmed M. , Shokralla Shadi , Mahmoud Eman A. , Elansary Hosam O. , Akbar Fizza , Din Burhan ud , Haroon Urooj , Ali Musrat , Saleem Hira , Anar Maryam , Kamal Asif , Tahir Kinza , Ahmed Junaid , Zubair Mohammad Sameer , Javed Chaudhary Hassan , Munis Muhammad Farooq Hussain
Published: May 2022

Heavy metals contaminate soil and adversely affect plant growth. These soils contain different fungi and bacteria which exhibit metal tolerance and work as bioremediation agents to detoxify polluted soils. In the present study, polluted soil samples were collected to estimate the contamination of copper (Cu) and cadmium (Cd). From this contaminated soil, metal tolerant fungi were isolated and characterized. Copper and cadmium were found in a range of 190.2–300.4 mg/kg and 46.8–56.1 mg/kg, respectively. For the isolation of metal tolerant fungi, soil dilutions were made in water and inoculated on potato dextrose agar (PDA) media. Fungal growth was observed on PDA and successive screening resulted in the isolation of four multi-metal tolerant fungal species, including Penicillim oxalicum, Fusarium solani, Aspergillus niger and Trichoderma harzianum. Sequencing of 18S rRNA genes of isolated fungi also efficiently identified them. To reveal minimum inhibitory concentrations (MIC), these fungi were exposed to increasing concentrations of cadmium and copper chlorides (100 to 1000 ppm) and a variable MIC range of 400 ppm to 1000 ppm was estimated. Based on tolerance index analysis, F. solani was found to be resistant at 1 mM copper, while P. oxalicum was the most tolerant species under cadmium stress. F. solani and P. oxalicum demonstrated the highest biosorption capacity of Cu and Cd, respectively. Both metals negatively affected wheat seedlings in a pot experiment, while the treatment of both F. solani and P. oxalicum positively influenced the germination and growth of wheat. Based on these observations, it could be inferred that F. solani and P. oxalicum can be used for the myco-remediation of Cu and Cd, respectively.
Validation Method for a Multimodal Freight Transport Model Exploiting Floating Car Data
Authors: Ballarano Dario , Petrelli Marco , Renna Alessandra
Published: May 2022

The implementation of valid freight transport simulation models requires an extensive and detailed validation phase for understanding the feasibility of the outputs and the capacity of the structure of the proposed models in representing the real-world data. Traditional methods involve the use of surveys in order to describe the behaviour of stakeholders and to gather some aspects of the modal choices. Recent studies integrate this approach with Big Data as Floating Car Data to obtain better statistical information of the details at different levels. The current research involves the unexplored field of the validation of freight transport simulation models using a data-driven approach based on a large database of over 292 million Floating Car Data (FCD) signals generated by 29,298 commercial vehicles during the month of October 2019. The paper proposes an FCD processing methodology to identify freight vehicles using Ro-Ro/Ro-Pax services, and presents the results of an in-depth tracking analysis for combined transport and road transport. The validation phase permits the evaluation of the simulation tool results with real choices of heavy vehicles, referring also to the statistical information on travel times and the achievement of additional information through an in-depth analysis of tracking single vehicles.
Highway Planning Trends: A Bibliometric Analysis
Authors: Castañeda Karen , Sánchez Omar , Herrera Rodrigo F. , Mejía Guillermo
Published: May 2022

An efficient highway infrastructure network is a determining factor in promoting the socio-economic development of countries and regions. Highway planning activities are highly important because their results ensure that projects are delivered within budget, schedule, and scope. Therefore, academics and professionals have promoted various methodological and technological advances focused on improving highway planning processes, evidenced by many scientific documents that address the topic. Despite progress, few studies have focused on analyzing the state of the knowledge structure and the technological and methodological trends of the highway planning topic. Considering this gap, this study presents trends in highway planning and its knowledge structure based on a bibliometric analysis from January 2015 to September 2021. The research method is based on a bibliometric analysis composed of five main stages: (1) scope definition, (2) selection of bibliometric analysis techniques, (3) data collection, (4) bibliometric analysis execution, and (5) evidence analysis and synthesis. Information from 1703 journal papers was collected and analyzed. The findings show that the main trends of highway planning focus on life cycle analysis, computational tools, smart cities, sustainability issues, construction processes, new equipment and materials, and multi-objective optimization, among others. Thus, the findings of this study allow the reader to identify the methodological and technological trends in highway planning and their knowledge gaps to guide future studies in the field.
Development and Comparison of Prediction Models for Sanitary Sewer Pipes Condition Assessment Using Multinomial Logistic Regression and Artificial Neural Network
Authors: Atambo Daniel Ogaro , Najafi Mohammad , Kaushal Vinayak
Published: May 2022

Sanitary sewer pipes infrastructure system being in good condition is essential for providing safe conveyance of the wastewater from homes, businesses, and industries to the wastewater treatment plants. For sanitary sewer pipes to deliver the wastewater to the treatment plants, they must be in good condition. Most of the water utilities have aged sanitary sewer pipes. Water utilities inspect sewer pipes to decide which segments of the sanitary sewer pipes need rehabilitation or replacement. The process of inspecting the sewer pipes is described as condition assessment. This condition assessment process is costly and necessitates developing a model that predicts the condition rating of sanitary sewer pipes. The objective of this study is to develop Multinomial Logistic Regression (MLR) and Artificial Neural Network (ANN) models to predict sanitary sewer pipes condition rating using inspection and condition assessment data. MLR and ANN models are developed from the City of Dallas’s data. The MLR model is built using 80% of randomly selected data and validated using the remaining 20% of data. The ANN model is trained, validated, and tested. The significant physical factors influencing sanitary pipes condition rating include diameter, age, pipe material, and length. Soil type is the environmental factor that influences sanitary sewer pipes condition rating. The accuracy of the performance of the MLR and ANN is found to be 75% and 85%, respectively. This study contributes to the body of knowledge by developing models to predict sanitary sewer pipes condition rating that enables policymakers and sanitary sewer utilities managers to prioritize the sanitary sewer pipes to be rehabilitated and/or replaced.
Distributionally Robust Model and Metaheuristic Frame for Liner Ships Fleet Deployment
Authors: Bukljaš Mihaela , Rogić Kristijan , Jerebić Vladimir
Published: May 2022

The container shipping industry market is very dynamic and demanding, economically, politically, legally, and financially. Considering the high cost of core assets, ever rising operating costs, and the volatility of demand and supply of cargo space, the result is an industry under enormous pressure to remain profitable and competitive. To maximize profits while maintaining service levels and ensuring the smooth flow of cargo, it is essential to make strategic decisions in a timely and optimal manner. Fleet deployment selection, which includes the profile of vessel hire, as well as their capacity and port rotation, is one of the most important strategic and tactical decisions container shipping operators must make. Bearing in mind that maritime business is inherently stochastic and uncertain, the key aims of this paper are to address the problem of fleet deployment under uncertain operating conditions, and to provide an integrated and optimized tool in the form of a mathematical model, metaheuristic algorithm, and computer program. Furthermore, this paper will show that the properties of the provided solutions exceed those offered in the literature so far. Such a solution will provide the shipping operator with a decision tool to best deploy its fleet in a way that responds more closely to real life situations and to meet the maximum demand for cargo space with minimal expense. The final goal is to minimize the operating costs while managing cargo flows and reducing the risks of unfulfilled customer demands.
Investigation on New Metaheuristic Algorithms for Solving Dynamic Combined Economic Environmental Dispatch Problems
Authors: Larouci Benyekhlef , Ayad Ahmed Nour El Islam , Alharbi Hisham , Alharbi Turki E. A. , Boudjella Houari , Tayeb Abdelkader Si , Ghoneim Sherif S. M. , Abdelwahab Saad A. Mohamed
Published: May 2022

In this paper, the dynamic combined economic environmental dispatch problems (DCEED) with variable real transmission losses are tackled using four metaheuristics techniques. Due to the consideration of the valve-point loading effects (VPE), DCEED have become a non-smooth and more complex optimization problem. The seagull optimization algorithm (SOA), crow search algorithm (CSA), tunicate swarm algorithm (TSA), and firefly algorithm (FFA), as both nature and biologic phenomena-based algorithms, are investigated to solve DCEED problems. Our proposed algorithms, SOA, TSA, and FFA, were evaluated and applied on the IEEE five-unit test system, and the effectiveness of the proposed CSA approach was applied on two-unit, five-unit, and ten-unit systems by considering VPE. We defined CSA for different objective functions, such as cost of production, emission, and CEED, by considering VPE. The obtained results reveal the efficiency and robustness of the CSA compared to SOA, TSA, FFA, and to other optimization algorithms reported recently in the literature. In addition, Matlab simulation results show the advantages of the proposed approaches for solving DCEED problems.
Public Research and Development Funding for Renewable Energy Technologies in Europe: A Cross-Country Analysis
Authors: Gasser Maximilian , Pezzutto Simon , Sparber Wolfram , Wilczynski Eric
Published: May 2022

The European Union recognizes the vital role that innovation in renewable energy technologies plays in accelerating the energy transition. In the largest ever transnational research and innovation program, Horizon Europe (2021–2027), the EU allocated 35% of the total budget of €95.5 billion to green technology research. This paper examines public research and development (R&D) funding for renewable energy technologies in 17 European countries from 2000 to 2020 by analyzing its extent, composition, and effectiveness. While large economies lead in the size of total available public R&D support, Nordic countries lead when it comes to available public R&D funding relative to the sizes of their respective economies. Thereby, the share of the European Union’s funding within the total public R&D support available in each country is vastly heterogeneous across countries, ranging from 13% to 63%. Furthermore, based on detailed and recent data, the effectiveness of public R&D funding is estimated through a Negative Binomial Regression model with fixed effects. Overall, public R&D funding is confirmed as an effective driver of green innovation. Like in previous studies, limitations stem from restricted data availability and temporal uncertainty of innovation. These limitations are addressed, which shall incentivize future research and policymaking.
Investigation of Dual–Pass Inclined Oscillating Bed Solar Dryer for Drying of Non-Parboiled Paddy Grains
Authors: Arul Ganapathy Ponnambalam , Thulasi Selvam , Kumar Pitchaipillai , Arunprasad Veeranan , Shaik Saboor , Abbas Mohamed , Rajendran Parvathy , Khan Sher Afghan , Saleel C. Ahamed
Published: May 2022

This Paper determines an experimental study of dual-pass solar dryer with a bed tilt of 0.5° and varying oscillating frequency of drying chamber namely 1.25, 1.75 and 2.25 Hz for drying an agricultural produce namely non–parboiled paddy grains. The oscillations and bed tilt are provided to move the grains from entry to exit of the top bed and moving down to bottom bed and finally exits from the dryer. The new technology has been used in the solar dryer for drying of agricultural produce such as non–parboiled paddy grains to increase the quality and to decrease the loss of the dried produce. The present dryer model was used for drying 45 kg of non–parboiled paddy grains from 19% (w.b) to the approved range of 12–14% of moisture content obtained in a single experimental day. The dried paddy grains obtained an average moisture content are 13.03, 13.22 and 13.51% at the frequency of oscillation of 1.25, 1.75, 2.25 Hz, respectively. The maximum thermal and pick-up efficiency of the model were obtained at 1.00 p.m. in all cases. For the frequency of oscillation of 1.25, 1.75 and 2.25 Hz, the maximum dryer thermal efficiency was 44.47, 43.39 and 41.39%, respectively, and the maximum pick–up efficiency was 80.41, 79.19 and 76.21%, respectively. The optimum drying performance was obtained at the oscillating frequency of 1.75 Hz with the bed tilt of 0.5°.
Participatory Design of Nature-Based Solutions: Usability of Tools for Water Professionals
Authors: Bogatinoska Borjana , Lansu Angelique , Hugé Jean , Dekker Stefan C.
Published: May 2022

Participatory processes provide opportunities for water professionals such as scientists and policymakers and other stakeholders such as the local communities and farmers to meet, exchange information, deliberate, and share values. There is a diversity of rapidly evolving participatory methods, here defined, as tools for supporting the process of designing nature-based solutions (NbS) together with the stakeholders. However, this requires a systematic and informed selection to facilitate the adequate choice of tools, aligned to the requirements and context of the water professionals and the stakeholders for the design and deployment of NbS. Despite this, there is still little progress and knowledge accumulation on how to select the most context-appropriate tool(s). Consequently, in this research, we propose a stepwise framework for the use of participatory tools, which we categorize as: (i) tools used for defining the hydro-meteorological hazards (HMH) and its impact on stakeholders—knowledge tools (ii) tools used for co-designing NbS with stakeholders—co-creation tools and (iii) tools used for co-implementing the transition towards NbS—transition tools. We then apply and test this stepwise framework on the participatory processes used in eight brook catchments distributed in four countries: the Netherlands, Belgium, France, and the United Kingdom. The framework is designed in steps that would lead to respectively: selecting, classifying, mapping, and grading the participatory tools leading to an informed and systematic decision of a tool or suite of tools for the design and deployment of NbS with stakeholders. With the application of this framework, we see that among the water professionals: (1) knowledge tools are central in the problem definition stage, particularly with non-technical stakeholders; (2) most anticipated co-creation tools are e-Tools/Virtual tools and workshops; (3) transition tools favor visual tools as a way of enabling the transition towards management practices.
Could Social Bots’ Sentiment Engagement Shape Humans’ Sentiment on COVID-19 Vaccine Discussion on Twitter?
Authors: Zhang Menghan , Chen Ze , Qi Xue , Liu Jun
Published: May 2022

During the COVID-19 pandemic, social media has become an emerging platform for the public to find information, share opinions, and seek coping strategies. Vaccination, one of the most effective public health interventions to control the COVID-19 pandemic, has become the focus of public online discussions. Several studies have demonstrated that social bots actively involved in topic discussions on social media and expressed their sentiments and emotions, which affected human users. However, it is unclear whether social bots’ sentiments affect human users’ sentiments of COVID-19 vaccines. This study seeks to scrutinize whether the sentiments of social bots affect human users’ sentiments of COVID-19 vaccines. The work identified social bots and built an innovative computational framework, i.e., the BERT-CNN sentiment analysis framework, to classify tweet sentiments at the three most discussed stages of COVID-19 vaccines on Twitter from December 2020 to August 2021, thus exploring the impacts of social bots on online vaccine sentiments of humans. Then, the Granger causality test was used to analyze whether there was a time-series causality between the sentiments of social bots and humans. The findings revealed that social bots can influence human sentiments about COVID-19 vaccines. Their ability to transmit the sentiments on social media, whether in the spread of positive or negative tweets, will have a corresponding impact on human sentiments.
Game Analysis of the Open-Source Innovation Benefits of Two Enterprises from the Perspective of Product Homogenization and the Enterprise Strength Gap
Authors: Tao Aiping , Qi Qi , Li Yi , Da Dan , Boamah Valentina , Tang Decai
Published: May 2022

Revenue is one of the hottest topics in the field of open-source innovation. Can open-source innovation really bring more revenue to firms? What affects the revenue from open-source innovation? Based on the perspective of product homogenization and the enterprise-strength gap, these questions are answered in this study using theoretical analyses and the construction of a game model to explore the influence of product homogeneity and the strength gap between firms regarding the revenue from open-source innovation. The results show that enterprise homogeneity and the revenue from open-source innovation are not linearly related. High homogeneity does harm the revenue from open-source innovation, while the revenue is relatively high when the homogeneity is moderate. Additionally, it was also identified that the strength gap between firms has a negative influence on the revenue from open-source innovation. The wider the strength gap is, the greater the revenue loss of the weaker firms and, thus, the lower the total revenue of the two firms will be. This paper provides a reference for research on enterprise revenue from open-source innovation and the selection of participants in open-source activities.
Climate-Resilient Microbial Biotechnology: A Perspective on Sustainable Agriculture
Authors: Tan Chengjia , Kalhoro Mohammad Talib , Faqir Yahya , Ma Jiahua , Osei Matthew Duah , Khaliq Ghulam
Published: May 2022

We designed this review to describe a compilation of studies to enlighten the concepts of plant–microbe interactions, adopted protocols in smart crop farming, and biodiversity to reaffirm sustainable agriculture. The ever-increasing use of agrochemicals to boost crop production has created health hazards to humans and the environment. Microbes can bring up the hidden strength of plants, augmenting disease resistance and yield, hereafter, crops could be grown without chemicals by harnessing microbes that live in/on plants and soil. This review summarizes an understanding of the functions and importance of indigenous microbial communities; host–microbial and microbial–microbial interactions; simplified experimentally controlled synthetic flora used to perform targeted operations; maintaining the molecular mechanisms; and microbial agent application technology. It also analyzes existing problems and forecasts prospects. The real advancement of microbiome engineering requires a large number of cycles to obtain the necessary ecological principles, precise manipulation of the microbiome, and predictable results. To advance this approach, interdisciplinary collaboration in the areas of experimentation, computation, automation, and applications is required. The road to microbiome engineering seems to be long; however, research and biotechnology provide a promising approach for proceeding with microbial engineering and address persistent social and environmental issues.