bluuebunny/crossref_metadata_2025 huggingface dataset:
DoSGuard: Mitigating Denial-of-Service Attacks in Software-Defined Networks
Authors: Li Jishuai , Tu Tengfei , Li Yongsheng , Qin Sujuan , Shi Yijie , Wen Qiaoyan
Published: January 2022

Software-defined networking (SDN) is a new networking paradigm that realizes the fast management and optimal configuration of network resources by decoupling control logic and forwarding functions. However, centralized network architecture brings new security problems, and denial-of-service (DoS) attacks are among the most critical threats. Due to the lack of an effective message-verification mechanism in SDN, attackers can easily launch a DoS attack by faking the source address information. This paper presents DoSGuard, an efficient and protocol-independent defense framework for SDN networks to detect and mitigate such attacks. DoSGuard is a lightweight extension module on SDN controllers that mainly consists of three key components: a monitor, a detector, and a mitigator. The monitor maintains the information between the switches and the hosts for anomaly detection. The detector utilizes OpenFlow message and flow features to detect the attack. The mitigator protects networks by filtering malicious packets. We implement a prototype of DoSGuard in the floodlight controller and evaluate its effectiveness in a simulation environment. Experimental results show the DoSGuard achieves 98.72% detecion precision, and the average CPU utilization of the controller is only around 8%. The results demonstrate that DoSGuard can effectively mitigate DoS attacks against SDN with limited overhead.
Experimental Shape Sensing and Load Identification on a Stiffened Panel: A Comparative Study
Authors: Esposito Marco , Mattone Massimiliano , Gherlone Marco
Published: January 2022

The monitoring of loads and displacements during service life is proving to be crucial for developing a modern Structural Health Monitoring framework. The continuous monitoring of these physical quantities can provide fundamental information on the actual health status of the structure and can accurately guide pro-active condition-based maintenance operations, thus reducing the maintenance costs and extending the service life of the monitored structures. Pushed by these needs and by the simultaneous development in the field of strain sensing technologies, several displacement reconstruction and load identification methods have been developed that are based on discrete strain measurements. Among the different formulations, the inverse Finite Element Method (iFEM), the Modal Method (MM) and the 2-step method, the latter being the only one able to also compute the loads together with the displacements, have emerged as the most accurate and reliable ones. In this paper, the formulation of the three methods is summarized in order to set the numerical framework for a comparative study. The three methods are tested on the reconstruction of the external load and of the displacement field of a stiffened aluminium plate starting from experimentally measured strains. A fibre optic sensing system has been used to measure surface strains and an optimization procedure has been performed to provide the best fibre pattern, based on five lines running along the stiffeners’ direction and with a back-to-back measuring scheme. Additional sensors are used to measure the applied force and the plate’s deflection in some locations. The comparison of the results obtained by each method proves the extreme accuracy and reliability of the iFEM in the reconstruction of the deformed shape of the panel. On the other hand, the Modal Method leads to a good reconstruction of the displacements, but also exhibits a sensitivity to the choice of the modes considered for the specific application. Finally, the 2-step approach is able to correctly identify the loads and to reconstruct the displacements with an accuracy that depends on the modeling of the experimental setup.
Multi-Modal Song Mood Detection with Deep Learning
Authors: Pyrovolakis Konstantinos , Tzouveli Paraskevi , Stamou Giorgos
Published: January 2022

The production and consumption of music in the contemporary era results in big data generation and creates new needs for automated and more effective management of these data. Automated music mood detection constitutes an active task in the field of MIR (Music Information Retrieval). The first approach to correlating music and mood was made in 1990 by Gordon Burner who researched the way that musical emotion affects marketing. In 2016, Lidy and Schiner trained a CNN for the task of genre and mood classification based on audio. In 2018, Delbouys et al. developed a multi-modal Deep Learning system combining CNN and LSTM architectures and concluded that multi-modal approaches overcome single channel models. This work will examine and compare single channel and multi-modal approaches for the task of music mood detection applying Deep Learning architectures. Our first approach tries to utilize the audio signal and the lyrics of a musical track separately, while the second approach applies a uniform multi-modal analysis to classify the given data into mood classes. The available data we will use to train and evaluate our models comes from the MoodyLyrics dataset, which includes 2000 song titles with labels from four mood classes, {happy, angry, sad, relaxed}. The result of this work leads to a uniform prediction of the mood that represents a music track and has usage in many applications.
A Method of Short Text Representation Fusion with Weighted Word Embeddings and Extended Topic Information
Authors: Liu Wenfu , Pang Jianmin , Du Qiming , Li Nan , Yang Shudan
Published: January 2022

Short text representation is one of the basic and key tasks of NLP. The traditional method is to simply merge the bag-of-words model and the topic model, which may lead to the problem of ambiguity in semantic information, and leave topic information sparse. We propose an unsupervised text representation method that involves fusing word embeddings and extended topic information. Following this, two fusion strategies of weighted word embeddings and extended topic information are designed: static linear fusion and dynamic fusion. This method can highlight important semantic information, flexibly fuse topic information, and improve the capabilities of short text representation. We use classification and prediction tasks to verify the effectiveness of the method. The testing results show that the method is valid.
Improving SDG Classification Precision Using Combinatorial Fusion
Authors: Hsu D. Frank , LaFleur Marcelo T. , Orazbek Ilyas
Published: January 2022

Combinatorial fusion algorithm (CFA) is a machine learning and artificial intelligence (ML/AI) framework for combining multiple scoring systems using the rank-score characteristic (RSC) function and cognitive diversity (CD). When measuring the relevance of a publication or document with respect to the 17 Sustainable Development Goals (SDGs) of the United Nations, a classification scheme is used. However, this classification process is a challenging task due to the overlapping goals and contextual differences of those diverse SDGs. In this paper, we use CFA to combine a topic model classifier (Model A) and a semantic link classifier (Model B) to improve the precision of the classification process. We characterize and analyze each of the individual models using the RSC function and CD between Models A and B. We evaluate the classification results from combining the models using a score combination and a rank combination, when compared to the results obtained from human experts. In summary, we demonstrate that the combination of Models A and B can improve classification precision only if these individual models perform well and are diverse.
Integrating the HFACS Framework and Fuzzy Cognitive Mapping for In-Flight Startle Causality Analysis
Authors: Yusuf Abiodun Brimmo , Kor Ah-Lian , Tawfik Hissam
Published: January 2022

This paper discusses the challenge of modeling in-flight startle causality as a precursor to enabling the development of suitable mitigating flight training paradigms. The article presents an overview of aviation human factors and their depiction in fuzzy cognitive maps (FCMs), based on the Human Factors Analysis and Classification System (HFACS) framework. The approach exemplifies system modeling with agents (causal factors), which showcase the problem space’s characteristics as fuzzy cognitive map elements (concepts). The FCM prototype enables four essential functions: explanatory, predictive, reflective, and strategic. This utility of fuzzy cognitive maps is due to their flexibility, objective representation, and effectiveness at capturing a broad understanding of a highly dynamic construct. Such dynamism is true of in-flight startle causality. On the other hand, FCMs can help to highlight potential distortions and limitations of use case representation to enhance future flight training paradigms.
LT-FS-ID: Log-Transformed Feature Learning and Feature-Scaling-Based Machine Learning Algorithms to Predict the k-Barriers for Intrusion Detection Using Wireless Sensor Network
Authors: Singh Abhilash , Amutha J. , Nagar Jaiprakash , Sharma Sandeep , Lee Cheng-Chi
Published: January 2022

The dramatic increase in the computational facilities integrated with the explainable machine learning algorithms allows us to do fast intrusion detection and prevention at border areas using Wireless Sensor Networks (WSNs). This study proposed a novel approach to accurately predict the number of barriers required for fast intrusion detection and prevention. To do so, we extracted four features through Monte Carlo simulation: area of the Region of Interest (RoI), sensing range of the sensors, transmission range of the sensor, and the number of sensors. We evaluated feature importance and feature sensitivity to measure the relevancy and riskiness of the selected features. We applied log transformation and feature scaling on the feature set and trained the tuned Support Vector Regression (SVR) model (i.e., LT-FS-SVR model). We found that the model accurately predicts the number of barriers with a correlation coefficient (R) = 0.98, Root Mean Square Error (RMSE) = 6.47, and bias = 12.35. For a fair evaluation, we compared the performance of the proposed approach with the benchmark algorithms, namely, Gaussian Process Regression (GPR), Generalised Regression Neural Network (GRNN), Artificial Neural Network (ANN), and Random Forest (RF). We found that the proposed model outperforms all the benchmark algorithms.
A Comprehensive Survey on Signcryption Security Mechanisms in Wireless Body Area Networks
Authors: Hussain Saddam , Ullah Syed Sajid , Uddin Mueen , Iqbal Jawaid , Chen Chin-Ling
Published: January 2022

WBANs (Wireless Body Area Networks) are frequently depicted as a paradigm shift in healthcare from traditional to modern E-Healthcare. The vitals of the patient signs by the sensors are highly sensitive, secret, and vulnerable to numerous adversarial attacks. Since WBANs is a real-world application of the healthcare system, it’s vital to ensure that the data acquired by the WBANs sensors is secure and not accessible to unauthorized parties or security hazards. As a result, effective signcryption security solutions are required for the WBANs’ success and widespread use. Over the last two decades, researchers have proposed a slew of signcryption security solutions to achieve this goal. The lack of a clear and unified study in terms of signcryption solutions can offer a bird’s eye view of WBANs. Based on the most recent signcryption papers, we analyzed WBAN’s communication architecture, security requirements, and the primary problems in WBANs to meet the aforementioned objectives. This survey also includes the most up to date signcryption security techniques in WBANs environments. By identifying and comparing all available signcryption techniques in the WBANs sector, the study will aid the academic community in understanding security problems and causes. The goal of this survey is to provide a comparative review of the existing signcryption security solutions and to analyze the previously indicated solution given for WBANs. A multi-criteria decision-making approach is used for a comparative examination of the existing signcryption solutions. Furthermore, the survey also highlights some of the public research issues that researchers must face to develop the security features of WBANs.
Development of Intelligent Fault Diagnosis Technique of Rotary Machine Element Bearing: A Machine Learning Approach
Authors: Saha Dip Kumar , Hoque Md. Emdadul , Badihi Hamed
Published: January 2022

The bearing is an essential component of a rotating machine. Sudden failure of the bearing may cause an unwanted breakdown of the manufacturing plant. In this paper, an intelligent fault diagnosis technique was developed to diagnose various faults that occur in a deep groove ball bearing. An experimental setup was designed and developed to generate faulty data in various conditions, such as inner race fault, outer race fault, and cage fault, along with the healthy condition. The time waveform of raw vibration data generated from the system was transformed into a frequency spectrum using the fast Fourier transform (FFT) method. These FFT signals were analyzed to detect the defective bearing. Another significant contribution of this paper is the application of a machine learning (ML) algorithm to diagnose bearing faults. The support vector machine (SVM) was used as the primary algorithm. As the efficiency of SVM heavily depends on hyperparameter tuning and optimum feature selection, the particle swarm optimization (PSO) technique was used to improve the model performance. The classification accuracy obtained using SVM with a traditional grid search cross-validation (CV) optimizer was 92%, whereas the improved accuracy using the PSO-based SVM was found to be 93.9%. The developed model was also compared with other traditional ML techniques such as k-nearest neighbor (KNN), decision tree (DT), and linear discriminant analysis (LDA). In every case, the proposed model outperformed the existing algorithms.
Secure Smart Wearable Computing through Artificial Intelligence-Enabled Internet of Things and Cyber-Physical Systems for Health Monitoring
Authors: Ramasamy Lakshmana Kumar , Khan Firoz , Shah Mohammad , Prasad Balusupati Veera Venkata Siva , Iwendi Celestine , Biamba Cresantus
Published: January 2022

The functionality of the Internet is continually changing from the Internet of Computers (IoC) to the “Internet of Things (IoT)”. Most connected systems, called Cyber-Physical Systems (CPS), are formed from the integration of numerous features such as humans and the physical environment, smart objects, and embedded devices and infrastructure. There are a few critical problems, such as security risks and ethical issues that could affect the IoT and CPS. When every piece of data and device is connected and obtainable on the network, hackers can obtain it and utilise it for different scams. In medical healthcare IoT-CPS, everyday medical and physical data of a patient may be gathered through wearable sensors. This paper proposes an AI-enabled IoT-CPS which doctors can utilise to discover diseases in patients based on AI. AI was created to find a few disorders such as Diabetes, Heart disease and Gait disturbances. Each disease has various symptoms among patients or elderly. Dataset is retrieved from the Kaggle repository to execute AI-enabled IoT-CPS technology. For the classification, AI-enabled IoT-CPS Algorithm is used to discover diseases. The experimental results demonstrate that compared with existing algorithms, the proposed AI-enabled IoT-CPS algorithm detects patient diseases and fall events in elderly more efficiently in terms of Accuracy, Precision, Recall and F-measure.
A High-Performance Gamma Spectrometer for Unmanned Systems Based on Off-the-Shelf Components
Authors: Chierici Andrea , Malizia Andrea , Di Giovanni Daniele , Ciolini Riccardo , d’Errico Francesco
Published: January 2022

Since the Fukushima Daiichi Nuclear Power Plant accident in March 2011, the technology available for unmanned aerial vehicles (UAVs) for radiation monitoring has improved greatly. Remote access to radiation-contaminated areas not only eliminates unnecessary exposure of civilians or military personnel, but also allows workers to explore inaccessible places. Hazardous levels of radioactive contamination can be expected as a result of accidents in the nuclear power industry or as a result of the intentional release of radioactive materials for terrorist purposes (dirty bombs, building contamination, etc.). The possibility to detect, identify, and characterize radiation and nuclear material using mobile and remote sensing platforms is a common requirement in the radiation sensing community. The technology has applications in homeland security and law enforcement, customs and border protection, nuclear power plant safety and security, nuclear waste monitoring, environmental recovery, and the military. In this work, the authors have developed, implemented, and characterized a gamma-ray detection and spectroscopy system capable of operating on a UAV. The system was mainly developed using open-source software and affordable hardware components to reduce development and maintenance costs and provide satisfactory performance as a detection instrument. The designed platform can be used to perform mapping or localization tasks to improve the risk assessment process for first responders during the management of radiological and nuclear incidents. First, the design process of the system is described; the result of the characterization of the platform is then presented together with the use of the prototype installed on a UAV in an exercise simulating a radiological and nuclear contamination scenario.
Study of Ionization Charge Density-Induced Gain Suppression in LGADs
Authors: Jiménez-Ramos M. Carmen , García López Javier , García Osuna Adrián , Vila Iván , Currás Esteban , Jaramillo Richard , Hidalgo Salvador , Pellegrini Giulio
Published: January 2022

Gain suppression induced by excess carriers in Low Gain Avalanche Detectors (LGADs) has been investigated using 3 MeV protons in a nuclear microprobe. In order to modify the ionization density inside the detector, Ion Beam Induced Current (IBIC) measurements were performed at different proton beam incidence angles between 0° and 85°. The experimental results have been analyzed as a function of the ionization density projected on the multiplication layer, finding that the increase of ionization density leads to greater gain suppression. For bias voltages close to the gain onset value, this decrease in gain results into a significant distortion of the transient current waveforms measured by the Time-Resolved IBIC (TRIBIC) technique due to a deficit in the secondary holes component. For angles of incidence such that the Bragg peak falls within the sensitive volume of the detector, the formation of microplasmas modifies the behavior of the gain curves, producing an abrupt decrease in gain as the angle increases.
Analysis of Volatiles in Senecio anteuphorbium Essential Oil with a Focus on Its Allelopathic Effect by Means of Gas Chromatography
Authors: Ouhaddou Soukaina , Aghraz Abdellah , Ben Bakrim Widad , Sissi Saida , Larhsini Mustapha , Markouk Mohamed , Bekkouche Khalid , Arrigo Sara , Cicero Nicola , Costa Rosaria , Vadalà Rossella
Published: January 2022

The present study aimed to investigate Senecio anteuphorbium, an endemic plant growing in West Morocco and widely used in local folk medicine. The essential oil (EO) extracted from the aerial parts was analyzed by gas chromatography and tested for allelopathic activity. The quantitation of the volatiles was carried out by means of GC-FID with response factors, which were validated through reliable calibration procedures, based on external and internal standardization. This analytical approach allowed to define the real concentration of each constituent (weight%, g/100 g) alongside the conventional relative percent. On the other hand, the identification process was supported by a dual matching based on both mass spectra and retention indices. The essential oil resulted in being rich in sesquiterpenes, with the predominant constituents being bicyclogermacrene (22.75 g/100 g), spathulenol (25.26 g/100 g), epi-γ-eudesmol (6.8 g/100 g), and selina-4,11-diene (5.08 g/100 g). The allelopathic effect was evaluated by studying the inhibition of the germination and growth of Lactuca sativa seeds. A potent allelopathic effect was recorded by the essential oil at a dose of 0.281 mg/mL, with almost a total inhibition of germination.
Acknowledgment to Reviewers of Smart Cities in 2021
Authors:   Smart Cities Editorial Office
Published: January 2022

Rigorous peer-reviews are the basis of high-quality academic publishing [...]
Co-Designing with Migrants’ Easier Access to Public Services: A Technological Perspective
Authors: Concilio Grazia , Costa Giuliana , Karimi Maryam , Vitaller del Olmo Maria , Kehagia Olga
Published: January 2022

The integration of migrants, refugees, and asylum seekers remains a challenge for both public authorities and local communities at the national, regional, and local levels. For migrants, the lack of information and inability to use technology due to insufficient access skills and proficiency in the language of the host country may lead to limited interaction with the new community and consequently to social exclusion. One of the issues of the hyper-complexity in migrant integration can be related to a problem of communication that not only involves the migrants also the local administrators and service providers. This problem requires systemic solutions rather than simple “ad hoc” solutions. This article describes the case of the easyRights project, an ongoing Horizon 2020 project aimed at improving migrants’ access to public services to ease exercising their rights. It presents the application of a co-design learning framework for the development of hackathons that aim to improve existing services. It also promotes the collaborative design of solutions that use ICTs to overcome the bureaucratic and idiomatic barriers migrants experience in their interactions with formal procedures. The example of the four ecosystems generated in the four pilot cities of the project shows the relevance of interconnecting stakeholders in the co-design of services and the potential of exploiting the multifaceted attributes of ICTs to solve the complexity in the access to such services. The results invite reflection on the necessity of exploring the capability of technology application through user-centric approaches to offer substantial solutions to the imperative—yet intricate—challenge of migrants’ inclusion in society.
Becoming a Migrant Mother: An Intersectional Approach to the Narratives of Cape Verdean Women in Portugal
Authors: Neves Júlia
Published: January 2022

Becoming a mother in the context of migration configures itself as a dynamic process of identity constitution that raises questions about citizenship, belonging, and migration policies. Furthermore, it is a process that involves a new set of maternal positions and practices that are composed through possibly conflicting cultural values. Based on the occurrence of unplanned pregnancies, this study sought to analyze the dimensions crossed by this event through an intersectional perspective of the narratives of Cape Verdean immigrant women who had children in Portugal, focusing on gender, migration, race/ethnicity. In-depth interviews were conducted with nine women, born in Cape Verde and residing in Greater Lisbon, through the Biographic-Narrative Interpretative Method (BNIM), which enabled an approach to the different dimensions that intersect in life stories of becoming an immigrant mother. Thematic analysis was chosen as an analytical tool, and the framework provided by intersectionality allows us to see the multiple identities that shape the processes of becoming a migrant mother. The results brought to light themes and issues that overlap and make motherhood more complex in the context of migration, highlighting and reinforcing the conditions of inequality.
Tourism Workers and Entrepreneurs during the 1950s and 1960s in Sant Antoni de Portmany (Ibiza, Spain)
Authors: Ramón-Cardona José , Sánchez-Fernández María Dolores
Published: January 2022

Ibiza is very dependent on the tourism sector and is a highly recognized international brand. However, before the tourist boom, the situation was different, since it was an economically underdeveloped and almost unknown island. In the 1950s, an eminently endogenous tourism development began thanks to local entrepreneurship, usually workers and modest traders who dared to invest in the new sector. The development was not homogeneous throughout the island, with Sant Antoni de Portmany being the pioneer town in the post-Spanish Civil War recovery (1939–1959), the example from which the rest of the island learned and the starting point of the current image of Ibiza. This article seeks to delve into the social context of Sant Antoni in its tourist beginnings, especially in the mentality and way of being of the workers and businessmen of that time. It is an economic history paper that analyzes bibliography and testimonies of that time to provide a vision of what society, tourism and pioneering entrepreneurs were like. The peculiarities of the society, workers and entrepreneurs from the beginnings of the tourism sector have generated many of the positive and negative elements of current society and tourism in Ibiza, highlighting the survival of a mentality, way of doing and vital approach based on networking, irregularities and improvisation.
Response of Wheat Genotypes to Drought Stress Stimulated by PEG
Authors: Sharma Vandana , Kumar Ashwini , Chaudhary Astha , Mishra Anurag , Rawat Suniti , B. Basavaraj Y. , Shami Vaishali , Kaushik Prashant
Published: January 2022

Wheat is a cereal grain crop that is commonly cultivated and is a good source of nutrients that are beneficial to human health. In recent years, the productivity of wheat has been steadily declining, with abiotic pressures accounting for almost half of all yield losses. Drought stress is a significant limiting factor for plant development and production around the planet. The influence of polyethylene glycol (PEG) (at concentrations of 5, 10, and 15%)-induced drought stress on the morphological, physiological, and biochemical characteristics of fifteen wheat genotypes was investigated in this work. Overall, it was discovered that morphological and physiological indicators such as germination % and shoot-root lengths during the seedling stage had reduced significantly. The proline content, on the other hand, was shown to be positively correlated with the concentration of PEG treatments. There was a significant difference between the genotypes HD2733, HD2888, and RAJ3765 regarding tolerance to abiotic stress caused by drought. A further finding was that under stressful settings, the first three main components explained 56.65 percent, 65.06 percent, and 72.47 percent of the total variability in PEG treatment levels of five, ten, and fifteen percent, respectively. These collective morphological and physiological parameters, and analyses of their diverse responses, could be used for screening of drought tolerance among the 15 wheat genotypes to select for significant drought tolerance and diverse molecular responses during breeding of stress resistant forms.
Acknowledgment to Reviewers of Stresses in 2021
Authors:   Stresses Editorial Office
Published: January 2022

Rigorous peer-reviews are the basis of high-quality academic publishing [...]
Social Sustainability and School Segregation in the Region of Murcia (Spain)
Authors: Antón Hurtado Fina , Sánchez Vera Fulgencio , Martínez Guirao Javier Eloy , Tellez Infantes Anastasia
Published: January 2022

In this article we show an anthropological analysis of the processes of distribution and segregation of students in the Autonomous Community of the Region of Murcia (Spain). This region has experienced an intense migratory flow in the last three decades from Africa, America and Europe. In a short time, the region has become a heterogeneous society, affecting social cohesion. The objective of the research was to analyze the degree and causes of school segregation by sex, socioeconomic reasons and origin of the students. In order to know the degree of segregation, we have carried out a quantitative analysis of the enrollment data from last five years through the databases of the Regional Center for Statistics of the Region of Murcia, and of the Ministry of Education and Professional Training, EDUCAbase. To understand the causes underlying this distribution, an ethnographic fieldwork was carried out visiting nine educational centers (four public, three subsidized and two private) that present a high segregation of students. Members of the management team of the centers, teachers and parents were interviewed, compiling their interpretations about the social reality of the center and the environment, as well as their point of view on education and the inclusion of diversity in the classroom. The information obtained sufficiently clarifies the reality and the causes of the distribution between the public and private network of immigrant students, and even makes visible important differences between the different groups according to their origin.
Sustainable Tourism in Cities—Nature Reserves as a ‘New’ City Space for Nature-Based Tourism
Authors: Gonia Alicja , Jezierska-Thöle Aleksandra
Published: January 2022

Visible trends in city tourism related to the development of sustainable tourism clearly imply an increase in the significance of green areas and the development of nature-based tourism. Natural areas in cities that ideally fit the assumptions of sustainable tourism are nature reserves—areas where protection of the valuable environment is a priority. This paper aims to highlight that nature reserves are green spots in cities that can be excellent sites for sustainable tourism. The choice of big cities was dictated by the fact that they have the highest requirement for recreational green spaces due to a high concentration of tourist traffic in historic city centres and a clearer need for sustainable forms of tourism. Sixteen nature reserves in five big Polish cities with a population of more than 100,000: Gdańsk, Łódź, Poznań, Toruń, and Warsaw were selected for the study. Field surveys were carried out in nature reserves to see whether basic tourism facilities providing information about nature, supporting education, and conservation of the natural environment existed in the publicly available areas of such reserves. The attractiveness of nature reserves to tourists and their suitability for developing sustainable tourism was evaluated through score-based valuation of the reserves. The valorisation index was used for a synthetic rating and classification of the reserves in terms of attractiveness. The results of surveys imply a possibility of using selected nature reserves in cities for developing sustainable forms of active tourism—in particular hiking and cycling, educational tourism and daily recreational activities of city residents. The sites in question can be regarded as attractive ecotourism products, and as such hold the potential to become a popular destination among tourists and eco-tourists in particular.
Heterogeneous Effects of Urban Sprawl on Economic Development: Empirical Evidence from China
Authors: Zhang Mingdou , Li Yue , Guo Rui , Yan Yurui
Published: January 2022

Identifying the effects of urban sprawl on urban development is of strategic importance. This study takes 285 prefecture-level and above cities in China as research samples and empirically analyzes the heterogeneous impact of urban sprawl on economic development from 2009 to 2018. Results indicate the threshold effect of urban sprawl on economic development. That is, moderate urban sprawl has a significantly positive influence on economic development, whereas excessive urban sprawl has a significantly negative impact on economic development. The empirical analysis also identifies heterogeneities in the effects of urban sprawl on economic development. Compared with the sprawls of small- and medium-sized cities, those of large cities have a greater negative impact on economic development. Compared with the sprawls of cities dominated by the tertiary industry, those of cities dominated by the secondary industry have a greater negative impact on economic development. Findings of this study have important policy implications for scientific urban expansion, reasonable urban spatial layout, and sustainable urban economic development.
A Deep Learning Model Development to Predict Safety Accidents for Sustainable Construction: A Case Study of Fall Accidents in South Korea
Authors: Kim Ji-Myong , Lim Kwang-Kyun , Yum Sang-Guk , Son Seunghyun
Published: January 2022

So far, studies for predicting construction safety accidents have mostly been conducted by statistical analysis methods that assume linear models, such as regression and time series analysis. However, it is difficult for this statistical analysis method to reflect the nonlinear characteristics of construction safety accidents determined by complex influencing factors. In general, deep learning techniques are used to analyze the nonlinear characteristics of complex influencing factors. Therefore, the purpose of this study is to propose a framework for developing a deep learning model for predicting safety accidents for sustainable construction. For this study, 1766 cases of actual accidents were collected by the Korea Occupational Safety Authority (KOSHA) over the 10-year period from 2010 to 2019. Eight factors influencing accident prediction such as medical day, progress rate, and construction scale were selected. Subsequently, the predictive power between deep learning models and conventional multi-regression models was compared using actual accident data at construction sites. As a result, a deep neural network (DNN) improved predictive power by 9.3% in mean absolute error (MAE) and 10.6% in root mean square error (RMSE) compared to a conventional multi-regression model. The results of this study provide guidelines for the introduction of deep learning technology to construction safety management.
Multicriteria Model of Support for the Selection of Pear Varieties in Raising Orchards in the Semberija Region (Bosnia and Herzegovina)
Authors: Nedeljković Miroslav , Puška Adis , Suzić Radmila , Maksimović Aleksandar
Published: January 2022

Bosnia and Herzegovina (abbreviated BiH) has great potential for fruit production. BiH has over 1.5 million hectares of agricultural land. In addition, there are excellent climatic conditions for growing fruit. However, although there is a long tradition of fruit production in BiH, this production must be improved. This paper provides guidance on making decisions in fruit growing when there are multiple criteria. All criteria are divided into two groups: economic and technical criteria. The economic criteria are further divided into three subcriteria, namely: marketing costs, orchard construction costs and processing and transport costs. Technical criteria are divided into four subcriteria, namely: fruit, variety resistance, production characteristics and processing and transport. According to these, a multicriteria decision-making model based on linguistic values was created. In order to take advantage of these values, a fuzzy approach was applied. Using this approach, decision-making process is easier because decision making is tailored to human thinking. For the example of raising a new orchard in the area of Semberija, an evaluation of seven different varieties of pears was performed. This problem is solved by applying the method of multicriteria analysis (MCDA). To solve this research problem, the MABAC (Multi-attributive border approximation area comparison) method was used. Using the fuzzy MABAC method, the obtained results show that the Šampionka variety has the best indicators among observed varieties. In addition, the Konferans variety achieved good results, and these two varieties are the first choice for raising a new orchard of pears. The paper validates the results and performs sensitivity analysis. The contribution of this research is to develop a new model of decision making by using a new methodology that facilitates decision making on variety selection. This model and methodology provide a flexible way of making decisions in fruit growing.
ICT Supported Urban Sustainability by Example of Silesian Metropolis
Authors: Pańkowska Małgorzata , Sołtysik-Piorunkiewicz Anna
Published: January 2022

The implementation of sustainability in urban spaces has been studied for many years in different countries. Sustainability means maintaining a certain ecological status enabling longevity and durability. However, in the case of smart cities, sustainability is supported by information communication technology (ICT) adoption and implementation. In this paper, authors focus on metropolis architecture modeling and recommend positive experiences gathered by municipalities in different countries. The collected experiences and good practices concern ICT implementation for sustainable metropolis management. Authors formulated an original Model of Metropolis Ecosystem Architecture in the ArchiMate language. This model preparation required literature review and studies on ICT implementation opportunities for urban governance. The Metropolis Ecosystem Architecture Model (MEAM) is to support strategic planning and ICT eco-innovation management in the Silesian Metropolis. This significant model emphasized the metropolis stakeholders’ interests combined with ICT solutions.
Length Based Stock Assessment of Five Fish Species from the Marine Water of Pakistan
Authors: Raza Hasnain , Liu Qun , Alam Mohammed Shahidul , Han Yanan
Published: January 2022

The marine fisheries resources of Pakistan have been drastically affected in the past few decades. Considering the limitations of previous studies and the data poor condition of the marine fisheries of Pakistan, this study employed the length-based Bayesian biomass (LBB) estimation method for analyzing the fisheries’ representative length-frequency data of five exploited marine fish stocks (Nemipterus japonicus, Nemipterus randalli, Parascolopsis aspinosa, Saurida tumbil, and Lepturacanthus savala). The estimates of relative fishing mortality (F/M) are higher than unity in four stocks except for S. tumbil, indicating overfishing. However, the current values relative to unexploited biomass (B/B0) are below 0.4, which indicates that the stock biomass is deficient in delivering maximum sustainable yield. Overfishing and the mass exclusion of small and older fish from stocks threaten to deplete the biomass of all species. Therefore, this study recommended that increasing the mesh size in commercial fisheries would increase both the catch and biomass of these species. The existing number of boats should be reduced to reduce fishing mortality and bring it back to the ratio of relative fishing mortality (F/M) equal or less than unity, for a sustainable level.
Enhancing Healthcare Project Definition with Lean-Led Design
Authors: Chbaly Hafsa , Brunet Maude
Published: January 2022

Decisions regarding project definition have a significant impact on client value generation. However, although this phase is of utmost importance, traditional management practices are inadequate, as the focus is rather on budget and technical aspects leaving aside the functional ones. Neglecting the functional aspects could have serious consequences on the operation and thus quality of workspace, especially in complex projects including hospitals that involve multiple clients and with a high degree of uncertainty of change. The Lean-led Design approach provides a participative solution which involves the main project clients, namely the users (doctors, patients, etc.), project managers, and the government, with the intention of delivering facilities with a better fit for purpose and use. The main objective of the paper is to develop a framework that summarizes the steps leading to the implementation of such an approach during the project definition of a new hospital. The methodology chosen is a case study and the main contribution is to develop theoretical knowledge regarding its implementation. This may support managers in their decisions when coordinating project definitions.
Sustainability and Employability in the Time of COVID-19. Youth, Education and Entrepreneurship in EU Countries
Authors: Gavriluță Nicu , Grecu Silviu-Petru , Chiriac Horia Costin
Published: January 2022

This paper aims to identify several changes in the labor market structure in COVID-19 pandemic times. The context of the research is represented by the influence of the COVID-19 pandemic in the economic field, especially at the labor market level. This difficult situation could generate a negative impact in the sphere of traditional jobs and economic sectors. The main challenge for sustainable development in this new global situation is represented by human sustainability. Related to human sustainability, we emphasized the role played by the labor market and employability in mantling an optimal function at the social and economic level. For measuring the impact of the COVID-19 pandemic in the economic sphere, we used a quantitative design based on descriptive and inferential statistics. The research variables are represented by unemployment rates in the EU-28, employability rates, educational levels, gender, economic growth, labor mobility, material deprivation, economic freedom, and human development indicators. Empirical findings present the situation of a deep economic crisis generated by economic degrowth and by high levels of unemployment rates in the EU-28. Moreover, we have observed several predictors of employability in the new pandemic context as: material deprivation by age (in the field of young people), employment rate by education (tertiary education), and economic freedom. Another important finding is related to the gender perspective. Statistical correlations estimate a positive linear correlation between gender (women) and low rates of employability in the EU-28. All these empirical results could prove valuable for scholars interested in the relations between employability and sustainability and for political decision makers involved in the effort of reducing the negative effects of the COVID-19 pandemic within national and trans-national economic systems.
Can Carbon Offset Policies Be Effectively Implemented in All Regions of China? An Evolutionary Game Analysis of Decision-Making Dynamics of Local Governments
Authors: Peng Qiao , Xu Wanting , Xiao Yao
Published: January 2022

Carbon offset policies are an effective means of coordinating regional ecological conservation, promoting environmentally friendly economic development, and achieving carbon neutrality; they are being gradually implemented in all regions of China. This study analyzed the decision-making behavior of local governments before and after the introduction of incentive and restraint mechanisms. To this end, it constructed a dynamic evolutionary game model for local governments with a carbon surplus and those with a carbon deficit. The results indicate that it is difficult to implement carbon offset policies between regions without the intervention of the central government. They also show that the effects of different incentive and restraint mechanisms vary significantly. Specifically, a mechanism that targets both carbon surplus and carbon deficit local governments and a unilateral mechanism that targets only carbon deficit local governments are shown to be effective. Finally, the results indicate that the implementation costs of incentive and restraint mechanisms differ, and their implementation intensity affects the time required for carbon offset policies to be rolled out in all regions. Based on these findings, we propose policy recommendations for promoting the implementation of carbon offset policies in all regions of China and alleviating carbon emission pressure.
Worming the Circular Economy for Biowaste and Plastics: Hermetia illucens, Tenebrio molitor, and Zophobas morio
Authors: Kuan Zhi-Jue , Chan Barnabas Kuan-Nang , Gan Samuel Ken-En
Published: January 2022

The negative impact of the modern-day lifestyle on the environment was aggravated during the COVID-19 pandemic through the increased use of single-use plastics from food take-aways to medical supplies. Similarly, the closure of food outlets and disrupted supply chains have also resulted in significant food wastage. As the pandemic rages on, the aggravation of increased waste becomes an increasingly urgent problem that threatens the biodiversity, ecosystems, and human health worldwide through pollution. While there are existing methods to deal with organic and plastic waste, many of the solutions cause additional problems. Increasingly proposed as a natural solution to man-made problems, there are insect solutions for dealing with the artificial and organic waste products and moving towards a circular economy, making the use of natural insect solutions commercially sustainable. This review discusses the findings on how some of these insects, particularly Hermetia illucens, Tenebrio molitor, and Zophobas morio, can play an increasingly important role in food and plastics, with a focus on the latter.
The Private Sector as a Partner for SDG 6-Related Issues in Megacities: Opportunities and Challenges in Rio de Janeiro, Brazil
Authors: Ferreira Maria Inês Paes , Oliveira Vicente de Paulo Santos de , Sakaki Graham , Shaw Pamela
Published: January 2022

This article reviews recent studies that address water sustainable management opportunities and challenges in megacities around the world, with an emphasis on the case of Rio de Janeiro Metropolitan Region, one of the two megacities in Brazil. With reference to recent debates on water, megacities, and the climate crisis, as well as UN Water and Global Report Initiative documents, we focused on the implementation of the 2030 Agenda Sustainable Development Goal 6: Clean Water and Sanitation for All. The new Brazilian sanitation legal framework regulates public–private partnerships. In this context, the manuscript discusses the main question concerning water, sanitation, and hygiene that arises in the Brazilian case study: is universality possible in profit-oriented models? Through the current technical and academic literature consulted, the paper compares initiatives involving multiple stakeholder governance models that depend on private resources to implement universal access to drinking water, sanitation, and water-related extreme event controls, pointing out alternatives that can help to achieve the targets of SDG. Validation by key informants supports the synthesis of the reviewed documents, and the findings illustrate that concerted public efforts together with market mechanisms can help to overcome challenges and surpass the profit-oriented logics of private companies to achieve access to healthy and safe water, adequate sanitation, and improved hygiene, especially for vulnerable populations. This finding has transferability to other megacities in emerging countries that are facing public–private partnership debates on the provision of clean water and sanitation for all.