bluuebunny/crossref_metadata_2025 huggingface dataset:
Combined Use of Aerial Photogrammetry and Terrestrial Laser Scanning for Detecting Geomorphological Changes in Hornsund, Svalbard
Authors: Błaszczyk Małgorzata , Laska Michał , Sivertsen Agnar , Jawak Shridhar D.
Published: January 2022

The Arctic is a region undergoing continuous and significant changes in land relief due to different glaciological, geomorphological and hydrogeological processes. To study those phenomena, digital elevation models (DEMs) and highly accurate maps with high spatial resolution are of prime importance. In this work, we assess the accuracy of high-resolution photogrammetric DEMs and orthomosaics derived from aerial images captured in 2020 over Hornsund, Svalbard. Further, we demonstrate the accuracy of DEMs generated using point clouds acquired in 2021 with a Riegl VZ®-6000 terrestrial laser scanner (TLS). Aerial and terrestrial data were georeferenced and registered based on very reliable ground control points measured in the field. Both DEMs, however, had some data gaps due to insufficient overlaps in aerial images and limited sensing range of the TLS. Therefore, we compared and integrated the two techniques to create a continuous and gapless DEM for the scientific community in Svalbard. This approach also made it possible to identify geomorphological activity over a one-year period, such as the melting of ice cores at the periglacial zone, changes along the shoreline or snow thickness in gullies. The study highlights the potential for combining other techniques to represent the active processes in this region.
An Experimental Safety Response Mechanism for an Autonomous Moving Robot in a Smart Manufacturing Environment Using Q-Learning Algorithm and Speech Recognition
Authors: Kiangala Kahiomba Sonia , Wang Zenghui
Published: January 2022

The industrial manufacturing sector is undergoing a tremendous revolution moving from traditional production processes to intelligent techniques. Under this revolution, known as Industry 4.0 (I40), a robot is no longer static equipment but an active workforce to the factory production alongside human operators. Safety becomes crucial for humans and robots to ensure a smooth production run in such environments. The loss of operating moving robots in plant evacuation can be avoided with the adequate safety induction for them. Operators are subject to frequent safety inductions to react in emergencies, but very little is done for robots. Our research proposes an experimental safety response mechanism for a small manufacturing plant, through which an autonomous robot learns the obstacle-free trajectory to the closest safety exit in emergencies. We implement a reinforcement learning (RL) algorithm, Q-learning, to enable the path learning abilities of the robot. After obtaining the robot optimal path selection options with Q-learning, we code the outcome as a rule-based system for the safety response. We also program a speech recognition system for operators to react timeously, with a voice command, to an emergency that requires stopping all plant activities even when they are far away from the emergency stops (ESTOPs) button. An ESTOP or a voice command sent directly to the factory central controller can give the factory an emergency signal. We tested this functionality on real hardware from an S7-1200 Siemens programmable logic controller (PLC). We simulate a simple and small manufacturing environment overview to test our safety procedure. Our results show that the safety response mechanism successfully generates paths without obstacles to the closest safety exits from all the factory locations. Our research benefits any manufacturing SME intending to implement the initial and primary use of autonomous moving robots (AMR) in their factories. It also impacts manufacturing SMEs using legacy devices such as traditional PLCs by offering them intelligent strategies to incorporate current state-of-the-art technologies such as speech recognition to improve their performances. Our research empowers SMEs to adopt advanced and innovative technological concepts within their operations.
Improving the Deployment of WSNs by Localized Detection of Covered Redundant Nodes in Industry 4.0 Applications
Authors: Aljubori Marwa Hussein Hamad , Khalilpour Akram Vahid , Challenger Moharram
Published: January 2022

Wireless sensor networks can be used as cost-effective monitoring and automation platforms in smart manufacturing and Industry 4.0. Maximizing the covered area and increasing the network lifetime are two challenging tasks in wireless sensor networks. A feasible solution for maximizing the coverage area and network lifetime is detecting and relocating the covered redundant nodes. A covered redundant node is a node whose covered area is also covered by the other active nodes in the network. After identifying the covered redundant nodes, putting them in sleep mode can increase the network lifetime. In addition, moving the detected redundant nodes to the uncovered locations can improve the overall covered area by the sensor nodes. However, finding the redundant nodes is an NP-complete problem. In this paper, we propose a localized distributed algorithm for identifying the redundant nodes based on the 2-hop local neighborhood information of the nodes. The proposed algorithm uses the existing connections between the neighbors of each sensor node to decide the redundancy of the node. The algorithm is localized and does not need the entire topology of the network or the coordinates of the nodes.
A Systematic Review of Research on Robot-Assisted Therapy for Children with Autism
Authors: Alabdulkareem Amal , Alhakbani Noura , Al-Nafjan Abeer
Published: January 2022

Recent studies have shown that children with autism may be interested in playing with an interactive robot. Moreover, the robot can engage these children in ways that demonstrate essential aspects of human interaction, guiding them in therapeutic sessions to practice more complex forms of interaction found in social human-to-human interactions. We review published articles on robot-assisted autism therapy (RAAT) to understand the trends in research on this type of therapy for children with autism and to provide practitioners and researchers with insights and possible future directions in the field. Specifically, we analyze 38 articles, all of which are refereed journal articles, that were indexed on Web of Science from 2009 onward, and discuss the distribution of the articles by publication year, article type, database and journal, research field, robot type, participant age range, and target behaviors. Overall, the results show considerable growth in the number of journal publications on RAAT, reflecting increased interest in the use of robot technology in autism therapy as a salient and legitimate research area. Factors, such as new advances in artificial intelligence techniques and machine learning, have spurred this growth.
Strain Modal Testing with Fiber Bragg Gratings for Automotive Applications
Authors: Falcetelli Francesco , Martini Alberto , Di Sante Raffaella , Troncossi Marco
Published: January 2022

Strain Modal Testing (SMT), based on strain sensors signal processing, is an unconventional approach to perform Experimental Modal Analysis which is typically based on data measured by accelerometers. SMT is still mainly restricted to academia and requires additional investigation for a successful transition towards industry. This paper critically reviews why the automotive sector can benefit from this relatively new approach for a variety of reasons. Moreover, a case study representative of the automotive field is analyzed and discussed. Specifically, an SMT methodology is applied to evaluate the modal properties of a reinforced composite roof belonging to a racing solar powered vehicle. In the experimental activity, signals from Fiber Bragg Grating (FBG) sensors, strain gauges, and accelerometers were simultaneously acquired and further processed. The advantages of using optical fibers were discussed, together with their weaknesses and ongoing challenges. The FBG results were compared with the conventional analysis performed with the accelerometers, emphasizing the main similarities and discrepancies.
Development of a Real-Time Pectic Oligosaccharide-Detecting Biosensor Using the Rapid and Flexible Computational Identification of Non-Disruptive Conjugation Sites (CINC) Biosensor Design Platform
Authors: Smith Dustin D. , King Joshua P. , Abbott D. Wade , Wieden Hans-Joachim
Published: January 2022

Fluorescently labeled, solute-binding proteins that change their fluorescent output in response to ligand binding are frequently used as biosensors for a wide range of applications. We have previously developed a “Computational Identification of Non-disruptive Conjugation sites” (CINC) approach, an in silico pipeline utilizing molecular dynamics simulations for the rapid design and construction of novel protein–fluorophore conjugate-type biosensors. Here, we report an improved in silico scoring algorithm for use in CINC and its use in the construction of an oligogalacturonide-detecting biosensor set. Using both 4,5-unsaturated and saturated oligogalacturonides, we demonstrate that signal transmission from the ligand-binding pocket of the starting protein scaffold to the CINC-selected reporter positions is effective for multiple different ligands. The utility of an oligogalacturonide-detecting biosensor is shown in Carbohydrate Active Enzyme (CAZyme) activity assays, where the biosensor is used to follow product release upon polygalacturonic acid (PGA) depolymerization in real time. The oligogalacturonide-detecting biosensor set represents a novel enabling tool integral to our rapidly expanding platform for biosensor-based carbohydrate detection, and moving forward, the CINC pipeline will continue to enable the rational design of biomolecular tools to detect additional chemically distinct oligosaccharides and other solutes.
Linear Interval Approximation for Smart Sensors and IoT Devices
Authors: Marinov Marin B. , Nikolov Nikolay , Dimitrov Slav , Todorov Todor , Stoyanova Yana , Nikolov Georgi T.
Published: January 2022

In this work, we introduce and use an innovative approach for adaptive piecewise linear interval approximation of sensor characteristics, which are differentiable functions. The aim is to obtain a discreet type of inverse sensor characteristic, with a predefined maximum approximation error, with minimization of the number of points defining the characteristic, which in turn is related to the possibilities for using microcontrollers with limited energy and memory resources. In this context, the results from the study indicate that to overcome the problems arising from the resource constraints of smart devices, appropriate “lightweight” algorithms are needed that allow efficient connectivity and intelligent management of the measurement processes. The method has two benefits: first, low-cost microcontrollers could be used for hardware implementation of the industrial sensor devices; second, the optimal subdivision of the measurement range reduces the space in the memory of the microcontroller necessary for storage of the parameters of the linearized characteristic. Although the discussed computational examples are aimed at building adaptive approximations for temperature sensors, the algorithm can easily be extended to many other sensor types and can improve the performance of resource-constrained devices. For prescribed maximum approximation error, the inverse sensor characteristic is found directly in the linearized form. Further advantages of the proposed approach are: (i) the maximum error under linearization of the inverse sensor characteristic at all intervals, except in the general case of the last one, is the same; (ii) the approach allows non-uniform distribution of maximum approximation error, i.e., different maximum approximation errors could be assigned to particular intervals; (iii) the approach allows the application to the general type of differentiable sensor characteristics with piecewise concave/convex properties.
Secure Telemedicine System Design for COVID-19 Patients Treatment Using Service Oriented Architecture
Authors: Shaikh Asadullah , Al Reshan Mana Saleh , Sulaiman Adel , Alshahrani Hani , Asiri Yousef
Published: January 2022

The coronavirus pandemic, also known as the COVID-19 pandemic, is an ongoing virus. It was first identified on December 2019 in Wuhan, China, and later spread to 192 countries. As of now, 251,266,207 people have been affected, and 5,070,244 deaths are reported. Due to the growing number of COVID-19 patients, the demand for COVID wards is increasing. Telemedicine applications are increasing drastically because of convenient treatment options. The healthcare sector is rapidly adopting telemedicine applications for the treatment of COVID-19 patients. Most telemedicine applications are developed for heterogeneous environments and due to their diverse nature, data transmission between similar and dissimilar telemedicine applications is a difficult task. In this paper, we propose a Tele-COVID system architecture design along with its security aspects to provide the treatment for COVID-19 patients from distance. Tele-COVID secure system architecture is designed to resolve the problem of data interchange between two different telemedicine applications, interoperability, and vendor lock-in. Tele-COVID is a web-based and Android telemedicine application that provides suitable treatment to COVID-19 patients. With the help of Tele-COVID, the treatment of patients at a distance is possible without the need for them to visit hospitals; in case of emergency, necessary services can also be provided. The application is tested on COVID-19 patients in the county hospital and shows the initial results.
Ransomware: Analysing the Impact on Windows Active Directory Domain Services
Authors: McDonald Grant , Papadopoulos Pavlos , Pitropakis Nikolaos , Ahmad Jawad , Buchanan William J.
Published: January 2022

Ransomware has become an increasingly popular type of malware across the past decade and continues to rise in popularity due to its high profitability. Organisations and enterprises have become prime targets for ransomware as they are more likely to succumb to ransom demands as part of operating expenses to counter the cost incurred from downtime. Despite the prevalence of ransomware as a threat towards organisations, there is very little information outlining how ransomware affects Windows Server environments, and particularly its proprietary domain services such as Active Directory. Hence, we aim to increase the cyber situational awareness of organisations and corporations that utilise these environments. Dynamic analysis was performed using three ransomware variants to uncover how crypto-ransomware affects Windows Server-specific services and processes. Our work outlines the practical investigation undertaken as WannaCry, TeslaCrypt, and Jigsaw were acquired and tested against several domain services. The findings showed that none of the three variants stopped the processes and decidedly left all domain services untouched. However, although the services remained operational, they became uniquely dysfunctional as ransomware encrypted the files pertaining to those services.
A Setup for Camera-Based Detection of Simulated Pathological States Using a Neonatal Phantom
Authors: Voss Florian , Lyra Simon , Blase Daniel , Leonhardt Steffen , Lüken Markus
Published: January 2022

Premature infants are among the most vulnerable patients in a hospital. Due to numerous complications associated with immaturity, a continuous monitoring of vital signs with a high sensitivity and accuracy is required. Today, wired sensors are attached to the patient’s skin. However, adhesive electrodes can be potentially harmful as they can damage the very thin immature skin. Although unobtrusive monitoring systems using cameras show the potential to replace cable-based techniques, advanced image processing algorithms are data-driven and, therefore, need much data to be trained. Due to the low availability of public neonatal image data, a patient phantom could help to implement algorithms for the robust extraction of vital signs from video recordings. In this work, a camera-based system is presented and validated using a neonatal phantom, which enabled a simulation of common neonatal pathologies such as hypo-/hyperthermia and brady-/tachycardia. The implemented algorithm was able to continuously measure and analyze the heart rate via photoplethysmography imaging with a mean absolute error of 0.91 bpm, as well as the distribution of a neonate’s skin temperature with a mean absolute error of less than 0.55 °C. For accurate measurements, a temperature gain offset correction on the registered image from two infrared thermography cameras was performed. A deep learning-based keypoint detector was applied for temperature mapping and guidance for the feature extraction. The presented setup successfully detected several levels of hypo- and hyperthermia, an increased central-peripheral temperature difference, tachycardia and bradycardia.
A Method of Optimizing Characteristic Impedance Compensation Using Cut-Outs in High-Density PCB Designs
Authors: Barzdenas Vaidotas , Vasjanov Aleksandr
Published: January 2022

The modern era of technology contains a myriad of high-speed standards and proprietary serial digital protocols, which evolve alongside the microwave and RF realm. The increasing data rate push the requirements for hardware design, including modern printed circuit boards (PCB). One of these requirements for modern high-speed PCB interfaces are a homogenous track impedance all the way from the source to the load. Even though some high-speed interfaces don’t require any external components embedded into the interconnects, there are others which require either passive or active components—or both. Usually, component package land-pads are of fixed size, thus, if not addressed, they create discontinuities and degrade the transmitted signal. To solve this problem, impedance compensation techniques such as reference plane cut-out are employed for multiple case studies covering this topic. This paper presents an original method of finding the optimal cut-out size for the maximum characteristic impedance compensation in high-density multilayer PCB designs, which has been verified via theoretical estimation, computer simulation, and practical measurement results. Track-to-discontinuity ratios of 1:1.75, 1:2.5, and 1:5.0 were selected in order to resemble most practical design scenarios on a 6-layer standard thickness PCB. The measurements and simulations revealed that the compensated impedance saturation occurs at (150–250%) cut-out widths for a 50 Ω microstrip.
Study of Generalized Phase Spectrum Time Delay Estimation Method for Source Positioning in Small Room Acoustic Environment
Authors: Faerman Vladimir , Avramchuk Valeriy , Voevodin Kirill , Sidorov Ivan , Kostyuchenko Evgeny
Published: January 2022

This paper considers the application of signal processing methods to passive indoor positioning with acoustics microphones. The key aspect of this problem is time-delay estimation (TDE) that is used to get the time difference of arrival of the source’s signal between the pair of distributed microphones. This paper studies the approach based on generalized phase spectrum (GPS) TDE methods. These methods use frequency-domain information about the received signals that make them different from widely applied generalized cross-correlation (GCC) methods. Despite the more challenging implementation, GPS TDE methods can be less demanding on computational resources and memory than conventional GCC ones. We propose an algorithmic implementation of a GPS estimator and study the various frequency weighting options in applications to TDE in a small room acoustic environment. The study shows that the GPS method is a reliable option for small acoustically dead rooms and could be effectively applied in presence of moderate in-band noises. However, GPS estimators are far less efficient in less acoustically dead environments, where other TDE options should be considered. The distinguishing feature of the proposed solution is the ability to get the time delay using a limited number of the adjusted bins. The solution could be useful for passively locating moving emitters of narrow-band continual noises using computationally simple frequency detection algorithms.
Towards Semantic Photogrammetry: Generating Semantically Rich Point Clouds from Architectural Close-Range Photogrammetry
Authors: Murtiyoso Arnadi , Pellis Eugenio , Grussenmeyer Pierre , Landes Tania , Masiero Andrea
Published: January 2022

Developments in the field of artificial intelligence have made great strides in the field of automatic semantic segmentation, both in the 2D (image) and 3D spaces. Within the context of 3D recording technology it has also seen application in several areas, most notably in creating semantically rich point clouds which is usually performed manually. In this paper, we propose the introduction of deep learning-based semantic image segmentation into the photogrammetric 3D reconstruction and classification workflow. The main objective is to be able to introduce semantic classification at the beginning of the classical photogrammetric workflow in order to automatically create classified dense point clouds by the end of the said workflow. In this regard, automatic image masking depending on pre-determined classes were performed using a previously trained neural network. The image masks were then employed during dense image matching in order to constraint the process into the respective classes, thus automatically creating semantically classified point clouds as the final output. Results show that the developed method is promising, with automation of the whole process feasible from input (images) to output (labelled point clouds). Quantitative assessment gave good results for specific classes e.g., building facades and windows, with IoU scores of 0.79 and 0.77 respectively.
Lie to Me: Shield Your Emotions from Prying Software
Authors: Baia Alina Elena , Biondi Giulio , Franzoni Valentina , Milani Alfredo , Poggioni Valentina
Published: January 2022

Deep learning approaches for facial Emotion Recognition (ER) obtain high accuracy on basic models, e.g., Ekman’s models, in the specific domain of facial emotional expressions. Thus, facial tracking of users’ emotions could be easily used against the right to privacy or for manipulative purposes. As recent studies have shown that deep learning models are susceptible to adversarial examples (images intentionally modified to fool a machine learning classifier) we propose to use them to preserve users’ privacy against ER. In this paper, we present a technique for generating Emotion Adversarial Attacks (EAAs). EAAs are performed applying well-known image filters inspired from Instagram, and a multi-objective evolutionary algorithm is used to determine the per-image best filters attacking combination. Experimental results on the well-known AffectNet dataset of facial expressions show that our approach successfully attacks emotion classifiers to protect user privacy. On the other hand, the quality of the images from the human perception point of view is maintained. Several experiments with different sequences of filters are run and show that the Attack Success Rate is very high, above 90% for every test.
Evaluation of a Binary Classification Approach to Detect Herbage Scarcity Based on Behavioral Responses of Grazing Dairy Cows
Authors: Hart Leonie , Dickhoefer Uta , Paulenz Esther , Umstaetter Christina
Published: January 2022

In precision grazing, pasture allocation decisions are made continuously to ensure demand-based feed allowance and efficient grassland utilization. The aim of this study was to evaluate existing prediction models that determine feed scarcity based on changes in dairy cow behavior. During a practice-oriented experiment, two groups of 10 cows each grazed separate paddocks in half-days in six six-day grazing cycles. The allocated grazing areas provided 20% less feed than the total dry matter requirement of the animals for each entire grazing cycle. All cows were equipped with noseband sensors and pedometers to record their head, jaw, and leg activity. Eight behavioral variables were used to classify herbage sufficiency or scarcity using a generalized linear model and a random forest model. Both predictions were compared to two individual-animal and day-specific reference indicators for feed scarcity: reduced milk yields and rumen fill scores that undercut normal variation. The predictive performance of the models was low. The two behavioral variables “daily rumination chews” and “bite frequency” were confirmed as suitable predictors, the latter being particularly sensitive when new feed allocation is present in the grazing set-up within 24 h. Important aspects were identified to be considered if the modeling approach is to be followed up.
Permanent Magnet Tracking Method Resistant to Background Magnetic Field for Assessing Jaw Movement in Wearable Devices
Authors: Jucevičius Mantas , Ožiūnas Rimantas , Narvydas Gintautas , Jegelevičius Darius
Published: January 2022

There is a large gap between primitive bruxism detectors and sophisticated clinical machines for jaw kinematics evaluation. Large, expensive clinical appliances can precisely record jaw motion, but completely restrain the patient for the duration of the test. Wearable bruxism detectors allow continuously counting and recording bites, but provide no information about jaw movement trajectories. Previously, we developed a permanent magnet and three-axis magnetometer-based method for wearable, intra-oral continuous jaw position registration. In this work, we present an effective solution of the two main drawbacks of the method. Firstly, a two-adjacent-magnetometer approach is able to compensate for background magnetic fields with no reference sensor outside of the system’s magnetic field. Secondly, jaw rotational angles were included in the position calculations, by applying trigonometric equations that link the translation of the jaw to its rotation. This way, we were able to use a three-degree-of-freedom (3-DOF) magnetic position determination method to track the positions of the 5-DOF human masticatory system. To validate the method, finite element modeling and a 6-DOF robotic arm (0.01 mm, 0.01°) were used, which showed a 37% decrease in error in the average RMSE = 0.17 mm. The method’s potentially can be utilized in small-scale, low-power, wearable intra-oral devices for continuous jaw motion recording.
Wearable Motion Sensors in Older Adults: On the Cutting Edge of Health and Mobility Research
Authors: Jansen Carl-Philipp , Gordt-Oesterwind Katharina , Schwenk Michael
Published: January 2022

Wearable motion sensors have been gaining ground for quite some time now; a large proportion of research projects in the field of physical activity, health, and mobility are being carried out using an electronic form of outcome assessment, and there are good reasons for this [...]
The Importance of Global Value Chains in Developing Countries’ Agricultural Trade Development
Authors: Białowąs Tomasz , Budzyńska Anna
Published: January 2022

The role of global value-added chains is growing in international trade. The development gap between highly developed economies and developing countries means that the only products with which less developed economies can compete on the international arena are agricultural products. Moreover, integration into global value-added chains is an important factor in achieving comparative advantages in trade and improving export competitiveness. The main aim of this article is to assess the impact of integration within global value chains on changes in the comparative advantage of developing countries in the global agricultural trade. The degree of participation of developing countries in global value chains (GVC) was assessed using data from the Trade in Value-added OECD–WTO database (November 2021 edition) by calculating domestic (DVA), foreign value-added (FVA), and revealed comparative advantage (RCA) indexes. The analysis covers the period 1995–2018 and 28 developing economies. The research showed that the foreign value added in the gross exports of agricultural commodities of developing countries was an important factor in strengthening their export position and the importance of integration within global value chains rises with increasing product complexity. In agricultural production, FVA in most developing countries does not have a significant impact on the shaping of the comparative advantage.
High-Speed Railway Opening and High-Quality Development of Cities in China: Does Environmental Regulation Enhance the Effects?
Authors: Jiang Yuxian , Xiao Xiang , Li Xiaoyue , Ge Ge
Published: January 2022

The continuous improvement of transportation infrastructure is an important support for achieving high-quality development, while the high-quality characteristics of development will inevitably promote the process of economic and social sustainability. From the dual perspectives of economic transformation and people’s social livelihoods, we regard the opening of a high-speed railway (HSR) in China’s cities as a quasi-natural experiment, and utilize the difference-in-difference (DID) method to examine the impact of improved transportation infrastructure on the high-quality development of prefecture-level cities and its action mechanism. This study is the first to incorporate HSR openings and environmental regulation into the same framework, focusing on the high-quality development problem of cities. The empirical results reveal that: (1) the opening of an HSR can advance industrial structure upgrading and increase social employment level, thereby promoting the high-quality development of cities; (2) the intensity of environmental regulation is an important action mechanism that affects such relationships, but it presents two different influences on an HSR’s industrial effect and employment effect, that is, inhibition and reinforcement, respectively; and (3) urban heterogeneity tests illustrate that an HSR opening plays a more significant role in promoting the high-quality development of China’s eastern region cities and non-resource-based cities. Our findings are beneficial to improve the effectiveness and accuracy of decision-makers’ investment in transportation infrastructure as well as to facilitate the benign interaction between the national HSR policy and local environmental regulation strategies, thereby achieving the high-quality and sustainable development of urban economy and society.
Pedagogical Ecology for an Alternative Sustainability: With Insights from Francis of Assisi and Contemporary Life Sciences
Authors: Buffon Giuseppe , Colagè Ivan
Published: January 2022

Sustainability is a widely discussed issue nowadays. The “human factor” appears to be the key to a suitable theory of sustainable development and, even more, to understanding the real scope of the issue at stake. We begin by highlighting that the issue of sustainability and the related ecological crisis ultimately stem from the fundamental view of the human–environment relationships. We tackle such a fundamental view from two apparently distant but converging perspectives: the one of Francis of Assisi (the patron saint of ecologists) and the one of contemporary advancements in evolutionary biology known as the “extended evolutionary theory” (EES). This will allow us to highlight how current life sciences ground a strong form of organism–environment complementarity—a core point for any allegedly comprehensive approach to sustainability and ecology. After that, we focus on recent developments in cultural evolution studies that see culture both as the driving force of (recent) human evolution and as the general context where the human–environment relationships take place and develop. Therefore, we argue that the environment exerts a powerful pedagogical influence on the human being and on humanity as a whole. We conclude by proposing a pedagogical criterion for ecology and sustainable development, according to which the modifications caused by the human being to the environment must be assessed (also) for their pedagogical import.
Olive Mill Waste-Based Anaerobic Digestion as a Source of Local Renewable Energy and Nutrients
Authors: Aboelfetoh Mohamed , Hassanein Amro , Ragab Mohamed , El-kassas Mohamed , Marzouk Ezzat R.
Published: January 2022

This study focused on what combination of anaerobic digestion (AD) temperature (ambient, mesophilic, and thermophilic) and olive mill waste (OMW) to dairy manure (DM) ratio mixture delivers the desired renewable energy and digestate qualities when using AD as olive mill waste treatment. OMW is widespread in the local environment in the North Sinai region, Egypt, which causes many environmental hazards if left without proper treatment. Three different mixtures consisting of OMW, dairy manure (DM), and inoculum (IN) were incubated under ambient, mesophilic, and thermophilic conditions for 45 days. The results showed that mixture B (2:1:2, OMW:DM:IN) at 55 °C produced more methane than at 35 °C and ambient temperature by 40% and 252%, respectively. Another aim of this study was to investigate the effects of the different concentrations of the digestate taken from each mixture on faba bean growth. The results showed that the maximum fresh weight values of the shoot system were observed at 10% and 15% for mixture B at ambient temperature. The best concentration value for the highest root elongation rate is a 5% addition of digestate mixture A at 55 °C, compared with other treatments.
The Role of Blockchain Technologies in the Sustainable Development of Students’ Learning Process
Authors: (Popa) Chivu Raluca-Giorgiana , Popa Ionuț-Claudiu , Orzan Mihai-Cristian , Marinescu Cristian , Florescu Margareta Stela , Orzan Anca-Olguța
Published: January 2022

Technological evolution has contributed to considerable changes in the learning methods used today. If a few years ago, before the technological boom, students relied only on their memory capacity, on notes on sheets of paper and written materials (in the form of course materials, books, or textbooks), today the process of learning is significantly facilitated by the means and techniques involved in the use of technology, such as artificial intelligence, smartphones, and tablets, which are rapidly replacing bulky desktop computers and outdated textbooks used in the learning process. Moreover, blockchain technology can also be used as a motivational factor for developing learning abilities by implementing a system that can bring students credit points convertible into cryptocurrencies or online badges. Studies conducted in the field show that students’ motivation for learning and creativity is significantly improved with the help of this reward system. In this article, two specialized studies were conducted (in Romania), one on the perception of teachers in the university environment towards the use of new technologies in the learning process (artificial intelligence, smartphones, and tablets), and the other on the perception of students on the technological integration of blockchain (by implementing a system that can bring students credit points convertible into cryptocurrencies or online badges to certify their knowledge) in the learning process and measuring their satisfaction.
Using OpenGovB Transparency Indicator to Evaluate National Open Government Data
Authors: Milić Petar , Veljković Nataša , Stoimenov Leonid
Published: January 2022

Transparency evaluation in open government is a process of measuring the extent of transparency against a predefined set of indicators. In this paper, we address the existing initiatives regarding data and government transparency evaluation as two separate indicators and present the analysis of their advantages and drawbacks. Based on that analysis, we extend a part of the OpenGovB benchmark related to transparency in open government. What is unique about this benchmark is that it utilizes metadata of data published on the open government data portals to calculate the majority of indicators related to data transparency indicators. For the government transparency indicator evaluation, the benchmark utilizes some of the well-known transparency indicators. The article shows concrete results obtained from the application of the defined transparency evaluation model on 22 open data portals, thus demonstrating the possibilities of its application as well as the gains regarding generated results. The proposed model bridges the gap between available methodologies for evaluating transparency based on collaboration and participation and methodologies for evaluating transparency based on open data.
Do Academic Stress, Burnout and Problematic Internet Use Affect Perceived Learning? Evidence from India during the COVID-19 Pandemic
Authors: Basri Savitha , Hawaldar Iqbal Thonse , Nayak Raveendranath , Rahiman Habeeb Ur
Published: January 2022

Distress in online classrooms and problematic internet use are two issues that have caused student burnout and affect perceived learning during the COVID-19 pandemic. Given the high pressure placed on students throughout COVID-19, it is critical to understand the influence of problematic internet use (PIU), psychological stress, academic burnout, and resilience on perceived learning (PL). A cross-sectional analytical study was chosen to collect data from 350 learners pursuing undergraduate and postgraduate business/management degrees in Karnataka, India. The data were analyzed using SPSS (Statistical Package for Social Science) and Smart PLS 3. The present study reports a non-significant negative total effect of stress on PL, while there was a significant positive direct effect but a significant indirect negative effect of multiple mediators, namely PIU, burnout, and resilience. In the relationship between stress and PL, burnout has full competitive mediation, and the suppressive effect of burnout and resilience wipes out the beneficial benefit of stress on PL, resulting in reduced PL. As a societal problem, a change in educational policy and prevention strategies for students and organizations (reducing the number of courses, number of exams, and handling parental expectations) would be effective. Emotional intelligence to improve resilience, which assists students in sailing through a current challenging situation and using IT for reducing negative and unexpected emotional outbursts should be encouraged.
Circular Economy, International Cooperation, and Solid Waste Management: A Development Project in La Paz (Bolivia)
Authors: Ferronato Navarro , Pasinetti Rodolfo , Valencia Vargas Daysi , Calle Mendoza Iris Jabneel , Guisbert Lizarazu Edith Gabriela , Gorritty Portillo Marcelo Antonio , Conti Fabio , Torretta Vincenzo
Published: January 2022

This paper introduces the preliminary results of a development project focused on waste recycling and recovery in La Paz (Bolivia). The aim is to share best practices and to present real-world challenges when implementing appropriate waste management systems in developing countries. Environmental pollution, social inequality, lack of resources, and economic discrepancies are challenges still present in the 21st century, and a global call-for-action is needed to support sustainable development. The project “LaPazRecicla”, financed by the Italian Agency for Development Cooperation, provides perspectives that are potentially useful for policy-makers, waste management practitioners, and circular economy visionaries. The article aims to present the effective contribution of the practical actions to the local municipality, and to introduce the reason for why theoretical methods were employed to support the project. The outcomes provided two main indications: on one hand, cooperation among interdisciplinary actors and financial support can give the chance of improvement, suggesting international donors should continue in this direction; on the other, political instability, lack of local technical knowledge, and the absence of planning for a long-term period makes these actions unsuitable for tangible change. Global reflections are required in order to measure the potential benefits of small-scale projects, evaluating the time needed to move towards a sustainable future in low-income countries.
Construction of the Evaluation Model of Open Government Data Platform: From the Perspective of Citizens’ Sustainable Use
Authors: Zhang Wenli , Jiang Hongbo , Shao Qigan , Shao Ting
Published: January 2022

Under the background of big data, citizens can freely access and use open data to create value through the open government data platform (OGDP). The sustainable use of OGDP can meet the needs of citizens. The value created by citizens can also improve quality of life, which is of great significance to the sustainable development of society. From the citizens’ perspective, we constructed an evaluation model of citizens’ sustainable use of OGDP, including 12 indicators in four dimensions: Data, platform, outcome, and citizen. We have built the complete evaluation system with the DANP (Decision-Making Trial and Evaluation Laboratory-Based Analytic Network Process) method. It explores the main influencing factors and mutual influence of citizens’ sustainable use of OGDP. Empirical research is done on four provincial OGDPs in China’s Shanghai, Zhejiang, Guizhou, and Fujian provinces. The TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method was used to rank the OGDPs in four pilot areas in empirical research. The results demonstrate that the improvement priorities of dimensions have the following order: Citizen, outcome, data, and platform, in which data and platform are cause dimensions, and outcome and citizen are result dimensions. The satisfaction indicator has the highest weight among all evaluation indicators, followed by the quality and quantity of outcomes. The one with the lowest weight is non-discrimination. The empirical results show that the OGDP in Zhejiang ranks the highest overall, followed by the OGDPs of Shanghai, Fujian, and Guizhou provinces. In the outcome and citizen dimensions, Zhejiang provincial OGDP does the best. Fujian provincial OGDP does the best in the platform dimension. The citizens’ sustainable use of OGDP can be promoted by timely opening of data that citizens need urgently, perfecting the policy of privacy protection and user guide of OGDP, holding open data innovation competition, providing data visualization function, providing various download formats of data sets, and simplifying the download procedures for citizens.
Is the Ecological Footprint Enough Science for Algerian Fisheries Management?
Authors: Akrour Sonia , Grimes Samir
Published: January 2022

There is an urgent need to understand the fishing sector, given the uncertainties and the importance it has gained due to the potential of Algerian waters. The main objective of this paper is to establish a diagnosis of the demand for fishery resources at Algiers fishing ports and provide additional information regarding energy use at a reduced scale. According to the estimated ecological footprint (EF) of fishery production (EFf), Algiers fishing is in the status of ecological surplus, as the demand for fisheries production averages 15,338.49 gha compared to more than 108 thousand gha of biologically productive fishing area. In addition to this overall output, this metric indicates each species’ primary production requirement (PPR), which could help to restructure the fishing effort based on the stock’s condition. For the energy component, the fuel consumption and greenhouse gas (GHG) emissions expressed in metric tons of CO2 were used to estimate the required area for carbon sequestration, valued roughly at 500 gha. The latter is a reference state and remains approximate because it was derived from unofficial data provided by the fishers. This study demonstrated that EF is informative and could be used for resources management. However, it must be built on precise data and supported by further analysis.
Crop Switching and Farm Sustainability: Empirical Evidence from Multinomial Treatment-Effect Modeling
Authors: Luh Yir-Hueih , Chang Yun-Cih , Ho Shuay-Tsyr
Published: January 2022

Crop switching has been examined in the literature addressing the production effects of irrigation or as viable strategy in the adaptation to climate change, which is closely related to agricultural resilience. Attention to the identification of the direct linkage between crop switching and farm profitability, and, thus, farm sustainability, however, has been quite limited. This study attempts to provide a significant complement to the extant research by identifying the treatment effect of crop switching on the net returns of crop growers in Taiwan. A multinomial endogenous treatment effects model with the latent-factor structure is used to take self-selection into account. The result suggests that farm households’ economic resilience is closely related to their choice of crops, which constitute the major source of farm income. Specifically, among the six cash crop categories, fruit crops and other crops are found to be most remunerating and, thus, suggests possible improvements in farm households’ economic resilience through crop switching. A further analysis of the distributional implications of crop switching through quantile regression confirms the persistent and stronger effects of crop choice on net returns when moving from the bottom to the top quartiles along the net-return distribution. This result suggests a close association of crop choices with farm income inequality among the crop farm households in Taiwan, which in turn implies possible distributional effects of crop switching.
A Comparative Life Cycle Investment Analysis for Biopower Diffusion in Rural Nigeria
Authors: Levi-Oguike June , Sandoval Diego , Ntagwirumugara Etienne
Published: January 2022

This paper adopts the Life Cycle Investment (LCI) approach proposed by Farinha et al. to assess project viability based on the maintenance and operational efficiency of a proposed biopower plant over its useful economic life. The adoption of ISO 55000:2014, its guidance on management and maintenance policies for physical assets, and its contribution to the achievement of sustainable development goals on clean and affordable energy (SDG7) remain relevant for investment decisions regarding waste-to-energy technology systems. Using the parameters defined in a previous biopower feasibility study for Nigeria, the LCI approach is applied to show the change in project profitability over the estimated useful life of the plant, where availability is altered, based on maintenance downtime and overall operational efficiency. The results show positive movement in operational efficiency between 85–91%, which correlates with increased profitability in the same period. The project’s profitability and return on investment is revised downward from 29% to 8% based on the initial availability adjustment, and the changes in derived profit based on plant availability support the argument in favor of operational efficiency and structured maintenance policies as key performance and investment viability indicators, which ultimately impact the total cost of ownership. The results are also interpreted using Pareto Principles for emphasis. The ultimate goal is to encourage due attention and diligence in relation to latent factors which often erode the perceived benefits of viable projects after completion and potentially hamper future investment, specifically in the broader sub-Saharan African waste management context.