bluuebunny/crossref_metadata_2025 huggingface dataset:
Particle Size Related Effects of Multi-Component Flame-Retardant Systems in poly(butadiene terephthalate)
Authors: Tomiak Florian , Schartel Bernhard , Wolf Michael , Drummer Dietmar
Published: June 2020

Aluminum tris-(diethylphosphinate) (AlPi) is known to have an efficient flame-retardant effect when used in poly(butadiene terephthalates) (PBT). Additionally, better flame-retardant effects can be achieved through the partial substitution of AlPi by boehmite in multi-component systems, which have been shown to be an effective synergist due to cooling effects and residue formation. Although the potential of beneficial effects is generally well known, the influence of particle sizes and behavior in synergistic compositions are still unknown. Within this paper, it is shown that the synergistic effects in flammability measured by limiting oxygen index (LOI) can vary depending on the particle size distribution used in PBT. In conducting thermogravimetric analysis (TGA) measurements, it was observed that smaller boehmite particles result in slightly increased char yields, most probably due to increased reactivity of the metal oxides formed, and they react slightly earlier than larger boehmite particles. This leads to an earlier release of water into the system enhancing the hydrolysis of PBT. Supported by Fourier transformation infrared spectroscopy (FTIR), we propose that the later reactions of the larger boehmite particles decrease the portion of highly flammable tetrahydrofuran in the gas phase within early burning stages. Therefore, the LOI index increased by 4 vol.% when lager boehmite particles were used for the synergistic mixture.
Applications of Microbeams Produced by Tapered Glass Capillary Optics
Authors: Ikeda Tokihiro
Published: June 2020

Production of ion microbeams using tapered glass capillary optics was introduced more than 10 years ago. This technique has drawn attention in terms of both its peculiar transmission features and application to ion beam analysis. The transmission mechanism based on a self-organized charge-up process for keV-energy ions was observed for the first time in an experiment using a multitude of nanometer-sized capillaries in a polymer foil. The same mechanism can be seen for the transmission of keV ions through a single tapered glass capillary. The transmission experiments with keV ions showed a delayed transmission, focusing effects, guiding effects, and formation of microbeams. Experiments using MeV-energy ions always aim at applications of microbeam irradiation for material analysis, surface modification, cell surgery, and so on. In this article, the applications of MeV ion microbeams, including the fabrication method of the glass capillary, are reviewed, as well as the experimental and theoretical studies for the transmission mechanisms of keV/MeV ions.
Pathway to Sustainability in the Mining Industry: A Case Study of Alcoa and Rio Tinto
Authors: Shrivastava Prasanna , Vidhi Rachana
Published: June 2020

Sustainability for the mining companies is a critical requirement for their ongoing operations. Regulations and licenses generally depend on the companies’ sustainability practices, forcing them to focus on environmental impact and social welfare. While the mining industry has historically been associated with poor working conditions and their unsustainable practices, closer review of the industry suggests that the last few decades have seen improved performance and heightened focus on doing the right thing for the environment and the society. This research is focused on reviewing the sustainability strategy and initiatives of Alcoa and Rio Tinto, two Fortune-500 mining companies. It was found that these companies had started focusing on sustainability a few decades ago, but developed a holistic sustainability strategy as part of their core business only over the last few years. While there are many controversies still facing the companies, particularly around environmental pollution, their continued focus on sustainability will be beneficial for their employees as well as the communities they operate in.
Dual-Frequency Airborne SAR for Large Scale Mapping of Tidal Flats
Authors: Pinheiro Muriel , Amao-Oliva Joel , Scheiber Rolf , Jaeger Marc , Horn Ralf , Keller Martin , Fischer Jens , Reigber Andreas
Published: June 2020

Digital elevation models of tidal flats are a most valuable data source for the water management of coastal areas and need frequent updates to account for changes in sedimentation, erosion and identification of damages in building infrastructure. This paper presents the conceptual design, the processing methodology and first results of an airborne SAR campaign conducted in July 2019 at the German North Sea coast, showing the potential for accurate monitoring of height changes at decimeter level in mudflat areas, as well as indication of vegetation cover and water flooded areas.
Landscape Patterns and Building Functions for Urban Land-Use Classification from Remote Sensing Images at the Block Level: A Case Study of Wuchang District, Wuhan, China
Authors: Zhang Ye , Qin Kun , Bi Qi , Cui Weihong , Li Gang
Published: June 2020

Landscape patterns and building functions are successfully used to provide the social sensing information of urban areas. However, previous studies treated ground objects equally, ignoring their size differences. Considering the different contributions of various types of ground objects in land-use classification, this paper measured nine area-weighted mean landscape-level metrics to describe landscape patterns based on the land-cover map, derived from remote sensing images. Additionally, the same idea was applied for identifying building functions. Impervious surfaces, which occupy the majority of urban areas, have a decisive impact on land-use classes. In terms of this, this paper proposed the impervious surface area-weighted building-based indexes from the building outline data. To better represent the physical structure of urban areas, the entire study was based on the analysis units delineated by the OpenStreetMap road network. Finally, a random forest model combining the landscape-level metrics and building-based indexes was adopted in Wuchang District of Wuhan city, China. The results showed that the proposed method was effective at describing landscape patterns and identifying building functions for accurate urban land-use classification, increasing the precision by 10.67%. In general, the contribution of landscape-level metrics to the urban land-use classification is slightly greater than that of building-based indexes. Moreover, different land-use types of analysis units express different landscape patterns. It is of great significance for improving urban form and guiding future urban design. The paper demonstrates that area-weighted landscape metrics and building-based indexes offer a better understanding of urban land use, which plays a vital role in urban planning, construction, and management.
Effects of Typhoon Paths on Storm Surge and Coastal Inundation in the Pearl River Estuary, China
Authors: Du Mei , Hou Yijun , Hu Po , Wang Kai
Published: June 2020

A coastal inundation simulation system was developed for the coast of the Pearl River estuary (PRE), which consists of an assimilation typhoon model and the coupled ADCIRC (Advanced Circulation) + SWAN (Simulating Waves Nearshore) model. The assimilation typhoon model consists of the Holland model and the analysis products of satellite images. This is the first time an assimilation typhoon model has been implemented and tested for coastal inundation via case studies. The simulation results of the system agree well with the real measurements. Three observed typhoon paths (Hope, Nida, and Hato) were chosen to be the studied paths based on their positions relative to the PRE, China. By comparing the results of experiments with different forcing fields, we determined that the storm surge and the coastal inundation were mainly induced by wind forcing. By simulating coastal inundation for different typhoon center speeds, the Hato3 path most easily causes coastal inundation in the PRE. Moreover, the moving speed of the typhoon’s center significantly affects the coastal inundation in the PRE. The inundation becomes very serious as the movement of the typhoon center was slow down. This study provides a new reference for future predictions of coastal inundations.
Effects of Spring–Neap Tidal Cycle on Spatial and Temporal Variability of Satellite Chlorophyll-A in a Macrotidal Embayment, Ariake Sea, Japan
Authors: Yang Mengmeng , Goes Joaquim I. , Tian Hongzhen , Maúre Elígio de R. , Ishizaka Joji
Published: June 2020

We investigated the spatio-temporal variability of chlorophyll-a (Chl-a) and total suspended matter (TSM) associated with spring–neap tidal cycles in the Ariake Sea, Japan. Our study relied on significantly improved, regionally-tuned datasets derived from the ocean color sensor Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua over a 16-year period (2002–2017). The results revealed that spring–neap tidal variations in Chl-a and TSM within this macrotidal embayment (the Ariake Sea) are clearly different regionally and seasonally. Generally, the spring–neap tidal variability of Chl-a in the inner part of the Ariake Sea was controlled by TSM for seasons other than summer, whereas it was controlled by river discharge for summer. On the other hand, the contribution of TSM to the variability of Chl-a was not large for two areas in the middle of Ariake Sea where TSM was not abundant. This study demonstrates that ocean color satellite observations of Chl-a and TSM in the macrotidal embayment offer strong advantages for understanding the variations during the spring–neap tidal cycle.
Self-Supervised Representation Learning for Remote Sensing Image Change Detection Based on Temporal Prediction
Authors: Dong Huihui , Ma Wenping , Wu Yue , Zhang Jun , Jiao Licheng
Published: June 2020

Traditional change detection (CD) methods operate in the simple image domain or hand-crafted features, which has less robustness to the inconsistencies (e.g., brightness and noise distribution, etc.) between bitemporal satellite images. Recently, deep learning techniques have reported compelling performance on robust feature learning. However, generating accurate semantic supervision that reveals real change information in satellite images still remains challenging, especially for manual annotation. To solve this problem, we propose a novel self-supervised representation learning method based on temporal prediction for remote sensing image CD. The main idea of our algorithm is to transform two satellite images into more consistent feature representations through a self-supervised mechanism without semantic supervision and any additional computations. Based on the transformed feature representations, a better difference image (DI) can be obtained, which reduces the propagated error of DI on the final detection result. In the self-supervised mechanism, the network is asked to identify different sample patches between two temporal images, namely, temporal prediction. By designing the network for the temporal prediction task to imitate the discriminator of generative adversarial networks, the distribution-aware feature representations are automatically captured and the result with powerful robustness can be acquired. Experimental results on real remote sensing data sets show the effectiveness and superiority of our method, improving the detection precision up to 0.94–35.49%.
Polyelectrolytes Assembly: A Powerful Tool for Electrochemical Sensing Application
Authors: Škugor Rončević Ivana , Krivić Denis , Buljac Maša , Vladislavić Nives , Buzuk Marijo
Published: June 2020

The development of sensing coatings, as important sensor elements that integrate functionality, simplicity, chemical stability, and physical stability, has been shown to play a major role in electrochemical sensing system development trends. Simple and versatile assembling procedures and scalability make polyelectrolytes highly convenient for use in electrochemical sensing applications. Polyelectrolytes are mainly used in electrochemical sensor architectures for entrapping (incorporation, immobilization, etc.) various materials into sensing layers. These materials can often increase sensitivity, selectivity, and electronic communications with the electrode substrate, and they can mediate electron transfer between an analyte and transducer. Analytical performance can be significantly improved by the synergistic effect of materials (sensing material, transducer, and mediator) present in these composites. As most reported methods for the preparation of polyelectrolyte-based sensing layers are layer-by-layer and casting/coating methods, this review focuses on the use of the latter methods in the development of electrochemical sensors within the last decade. In contrast to many reviews related to electrochemical sensors that feature polyelectrolytes, this review is focused on architectures of sensing layers and the role of polyelectrolytes in the development of sensing systems. Additionally, the role of polyelectrolytes in the preparation and modification of various nanoparticles, nanoprobes, reporter probes, nanobeads, etc. that are used in electrochemical sensing systems is also reviewed.
Predicting Wins, Losses and Attributes’ Sensitivities in the Soccer World Cup 2018 Using Neural Network Analysis
Authors: Hassan Amr , Akl Abdel-Rahman , Hassan Ibrahim , Sunderland Caroline
Published: June 2020

Predicting the results of soccer competitions and the contributions of match attributes, in particular, has gained popularity in recent years. Big data processing obtained from different sensors, cameras and analysis systems needs modern tools that can provide a deep understanding of the relationship between this huge amount of data produced by sensors and cameras, both linear and non-linear data. Using data mining tools does not appear sufficient to provide a deep understanding of the relationship between the match attributes and results and how to predict or optimize the results based upon performance variables. This study aimed to suggest a different approach to predict wins, losses and attributes’ sensitivities which enables the prediction of match results based on the most sensitive attributes that affect it as a second step. A radial basis function neural network model has successfully weighted the effectiveness of all match attributes and classified the team results into the target groups as a win or loss. The neural network model’s output demonstrated a correct percentage of win and loss of 83.3% and 72.7% respectively, with a low Root Mean Square training error of 2.9% and testing error of 0.37%. Out of 75 match attributes, 19 were identified as powerful predictors of success. The most powerful respectively were: the Total Team Medium Pass Attempted (MBA) 100%; the Distance Covered Team Average in zone 3 (15–20 km/h; Zone3_TA) 99%; the Team Average ball delivery into the attacking third of the field (TA_DAT) 80.9%; the Total Team Covered Distance without Ball Possession (Not in_Poss_TT) 76.8%; and the Average Distance Covered by Team (Game TA) 75.1%. Therefore, the novel radial based function neural network model can be employed by sports scientists to adapt training, tactics and opposition analysis to improve performance.
Ambiguity of Residual Constraint-Based Precise Point Positioning with Partial Ambiguity Resolution under No Real-Time Network Corrections Using Real Global Positioning System (GPS) Data
Authors: Qin Honglei , Liu Peng , Cong Li , Xue Xia
Published: June 2020

Although precise point positioning (PPP) is a well-established and promising technique with the use of precise satellite orbit and clock products, it costs a long convergence time to reach a centimeter-level positioning accuracy. The PPP with ambiguity resolution (PPP-AR) technique can improve convergence performance by resolving ambiguities after separating the fractional cycle bias (FCB). Now the FCB estimation is mainly realized by the regional or global operating reference station network. However, it does not work well in the areas where network resources are scarce. The contribution of this paper is to realize an ambiguity residual constraint-based PPP with partial ambiguity resolution (PPP-PARC) under no real-time network corrections to speed up the convergence, especially when the performance of the float solution is poor. More specifically, the update strategy of FCB estimation in a stand-alone receiver is proposed to realize the PPP-PAR. Thereafter, the solving process of FCB in a stand-alone receiver is summarized. Meanwhile, the influencing factors of the ambiguity success rate in the PPP-PAR without network corrections are analyzed. Meanwhile, the ambiguity residual constraint is added to adapt the particularity of the partial ambiguity-fixing without network corrections. Moreover, the positioning experiments with raw observation data at the Global Positioning System (GPS) globally distributed reference stations are conducted to determine the ambiguity residual threshold for post-processing and real-time scenarios. Finally, the positioning performance was verified by 22 GPS reference stations. The results show that convergence time is reduced by 15.8% and 26.4% in post-processing and real-time scenarios, respectively, when the float solution is unstable, compared with PPP using a float solution. However, if the float solution is stable, the PPP-PARC method has performance similar to the float solution. The method shows the significance of the PPP-PARC for future PPP applications in areas where network resource is deficient.
NLP-Based Approach for Predicting HMI State Sequences Towards Monitoring Operator Situational Awareness
Authors: V. P. Singh Harsh , Mahmoud Qusay H.
Published: June 2020

A novel approach presented herein transforms the Human Machine Interface (HMI) states, as a pattern of visual feedback states that encompass both operator actions and process states, from a multi-variate time-series to a natural language processing (NLP) modeling domain. The goal of this approach is to predict operator response patterns for n − a h e a d time-step window given k − l a g g e d past HMI state patterns. The NLP approach offers the possibility of encoding (semantic) contextual relations within HMI state patterns. Towards which, a technique for framing raw HMI data for supervised training using sequence-to-sequence (seq2seq) deep-learning machine translation algorithms is presented. In addition, a custom Seq2Seq convolutional neural network (CNN) NLP model based on current state-of-the-art design elements such as attention, is compared against a standard recurrent neural network (RNN) based NLP model. Results demonstrate comparable effectiveness of both the designs of NLP models evaluated for modeling HMI states. RNN NLP models showed higher ( ≈ 26 % ) forecast accuracy, in general for both in-sample and out-of-sample test datasets. However, custom CNN NLP model showed higher ( ≈ 53 % ) validation accuracy indicative of less over-fitting with the same amount of available training data. The real-world application of the proposed NLP modeling of industrial HMIs, such as in power generating stations control rooms, aviation (cockpits), and so forth, is towards the realization of a non-intrusive operator situational awareness monitoring framework through prediction of HMI states.
Investigation on Stray-Capacitance Influences of Coaxial Cables in Capacitive Transducers for a Space Inertial Sensor
Authors: Yu Jianbo , Wang Chengrui , Wang Ying , Bai Yanzheng , Hu Ming , Li Ke , Li Zhuxi , Qu Shaobo , Wu Shuchao , Zhou Zebing
Published: June 2020

Ultra-sensitive inertial sensors are one of the key components in satellite Earth’s gravity field recovery missions and space gravitational wave detection missions. Low-noise capacitive position transducers are crucial to these missions to achieve the scientific goal. However, in actual engineering applications, the sensor head and electronics unit usually place separately in the satellite platform where a connecting cable is needed. In this paper, we focus on the stray-capacitance influences of coaxial cables which are used to connect the mechanical core and the electronics. Specially, for the capacitive transducer with a differential transformer bridge structure usually used in high-precision space inertial sensors, a connecting method of a coaxial cable between the transformer’s secondary winding and front-end circuit’s preamplifier is proposed to transmit the AC modulated analog voltage signal. The measurement and noise models including the stray-capacitance of the coaxial cable under this configuration is analyzed. A prototype system is set up to investigate the influences of the cables experimentally. Three different types and lengths of coaxial cables are chosen in our experiments to compare their performances. The analysis shows that the stray-capacitance will alter the circuit’s resonant frequency which could be adjusted by additional tuning capacitance, then under the optimal resonant condition, the output voltage noises of the preamplifier are measured and the sensitivity coefficients are also calibrated. Meanwhile, the stray-capacitance of the cables is estimated. Finally, the experimental results show that the noise level of this circuit with the selected cables could all achieve 1–2 × 10−7 pF/Hz1/2 at 0.1 Hz.
Synthesis and Evaluation of the (S)-BINAM Derivatives as Fluorescent Enantioselective Detectors
Authors: Shaferov Alexander V. , Malysheva Anna S. , Averin Alexei D. , Maloshitskaya Olga A. , Beletskaya Irina P.
Published: June 2020

Pd(0)-catalyzed amination was employed for the synthesis of a new family of (S)-1,1′-bianaphthalene-2,2′-diamine derivatives possessing additional chiral and fluorophore substituents. The Compounds thus obtained were tested as potential detectors of seven amino alcohols, and some of them were found to be able to recognize individual enantiomers of certain amino alcohols by specific changes of their emission spectra in the presence of these analytes. A pronounced dependence of the detecting abilities on the nature of the substituents in the (S)-BINAM derivatives was observed.
A Mobile Application for Smart Computer-Aided Self-Administered Testing of Cognition, Speech, and Motor Impairment
Authors: Lauraitis Andrius , Maskeliūnas Rytis , Damaševičius Robertas , Krilavičius Tomas
Published: June 2020

We present a model for digital neural impairment screening and self-assessment, which can evaluate cognitive and motor deficits for patients with symptoms of central nervous system (CNS) disorders, such as mild cognitive impairment (MCI), Parkinson’s disease (PD), Huntington’s disease (HD), or dementia. The data was collected with an Android mobile application that can track cognitive, hand tremor, energy expenditure, and speech features of subjects. We extracted 238 features as the model inputs using 16 tasks, 12 of them were based on a self-administered cognitive testing (SAGE) methodology and others used finger tapping and voice features acquired from the sensors of a smart mobile device (smartphone or tablet). Fifteen subjects were involved in the investigation: 7 patients with neurological disorders (1 with Parkinson’s disease, 3 with Huntington’s disease, 1 with early dementia, 1 with cerebral palsy, 1 post-stroke) and 8 healthy subjects. The finger tapping, SAGE, energy expenditure, and speech analysis features were used for neural impairment evaluations. The best results were achieved using a fusion of 13 classifiers for combined finger tapping and SAGE features (96.12% accuracy), and using bidirectional long short-term memory (BiLSTM) (94.29% accuracy) for speech analysis features.
Bayesian Sigmoid-Type Time Series Forecasting with Missing Data for Greenhouse Crops
Authors: Kocian Alexander , Carmassi Giulia , Cela Fatjon , Incrocci Luca , Milazzo Paolo , Chessa Stefano
Published: June 2020

This paper follows an integrated approach of Internet of Things based sensing and machine learning for crop growth prediction in agriculture. A Dynamic Bayesian Network (DBN) relates crop growth associated measurement data to environmental control data via hidden states. The measurement data, having (non-linear) sigmoid-type dynamics, are instances of the two classes observed and missing, respectively. Considering that the time series of the logistic sigmoid function is the solution to a reciprocal linear dynamic model, the exact expectation-maximization algorithm can be applied to infer the hidden states and to learn the parameters of the model. At iterative convergence, the parameter estimates are then used to derive a predictor of the measurement data several days ahead. To evaluate the performance of the proposed DBN, we followed three cultivation cycles of micro-tomatoes (MicroTom) in a mini-greenhouse. The environmental parameters were temperature, converted into Growing Degree Days (GDD), and the solar irradiance, both at a daily granularity. The measurement data were Leaf Area Index (LAI) and Evapotranspiration (ET). Although measurement data were only available scarcely, it turned out that high quality measurement data predictions were possible up to three weeks ahead.
Fifteen Years of Wireless Sensors for Balance Assessment in Neurological Disorders
Authors: Zampogna Alessandro , Mileti Ilaria , Palermo Eduardo , Celletti Claudia , Paoloni Marco , Manoni Alessandro , Mazzetta Ivan , Dalla Costa Gloria , Pérez-López Carlos , Camerota Filippo , Leocani Letizia , Cabestany Joan , Irrera Fernanda , Suppa Antonio
Published: June 2020

Balance impairment is a major mechanism behind falling along with environmental hazards. Under physiological conditions, ageing leads to a progressive decline in balance control per se. Moreover, various neurological disorders further increase the risk of falls by deteriorating specific nervous system functions contributing to balance. Over the last 15 years, significant advancements in technology have provided wearable solutions for balance evaluation and the management of postural instability in patients with neurological disorders. This narrative review aims to address the topic of balance and wireless sensors in several neurological disorders, including Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, stroke, and other neurodegenerative and acute clinical syndromes. The review discusses the physiological and pathophysiological bases of balance in neurological disorders as well as the traditional and innovative instruments currently available for balance assessment. The technical and clinical perspectives of wearable technologies, as well as current challenges in the field of teleneurology, are also examined.
Numerical and Experimental Verification of a Multiple-Variable Spatiotemporal Regression Model for Grout Defect Identification in a Precast Structure
Authors: Zhang Xuan , Tang Hesheng , Zhou Deyuan , Chen Shanshan , Zhao Taotao , Xue Songtao
Published: June 2020

Due to the increased service life, environmental corrosion, unreasonable construction, and other issues, local defects inevitably exist in civil structures, which affect the structural performance and can lead to structural failure. However, research on grout defect identification of precast reinforced concrete frame structures with rebars spliced by sleeves faces great challenges owing to the complexity of the problem. This study presents a multiple-variable spatiotemporal regression model algorithm to identify local defects based on structural vibration responses collected using a sensor network. First, numerical simulations were carried out on precast beam–column connection models by comparing the identification results based on a single-variable regression model, two-variable spatial regression model, and two-variable spatiotemporal regression model; furthermore, a multiple-variable spatiotemporal regression model was proposed and robustness analysis of the damage indicator was carried out. Then, to explore the validity of the proposed method, a nondestructive vibration experiment was considered on a half-scaled, two-floor, precast concrete frame structure with column rebars spliced by defective grout sleeves. The results show that local defects were successfully identified based on a multiple-variable spatiotemporal regression model.
A Median-Ratio Scene-Based Non-Uniformity Correction Method for Airborne Infrared Point Target Detection System
Authors: Ding Shuai , Wang Dejiang , Zhang Tao
Published: June 2020

Infrared detectors suffer from severe non-uniform noise which highly reduces image resolution and point target signal-to-noise ratio. This is the restriction for airborne point target detection systems in reaching the background limit. The existing methods are either not accurate enough, or too complex to be applied to engineering. To improve the precision and reduce the algorithm complexity of scene-based Non-Uniformity Correction (NUC) for an airborne point target detection system, a Median-Ratio Scene-based NUC (MRSBNUC) method is proposed. The method is based on the assumption that the median value of neighboring pixels is approximately constant. The NUC coefficients are calculated recursively by selecting the median ratio of adjacent pixels. Several experiments were designed and conducted. For both the clear sky scene and scene with clouds, the non-uniformity is effectively reduced. Furthermore, targets were detected in outfield experiments. For Target 1 48.36 km away and Target 2 50.53 km away, employing MRSBNUC the SNR of the target increased 2.09 and 1.73 times respectively compared to Two-Point NUC. It was concluded that the MRSBNUC method can reduce the non-uniformity of the detector effectively which leads to a longer detection distance and fewer false alarms of the airborne point target detection system.
Smart and Sustainable? Positioning Adaptation to Climate Change in the European Smart City
Authors: García Fernández Cristina , Peek Daniël
Published: June 2020

This article reports on the elements of adaptation to climate change in European Smart City initiatives in order to understand to what extent Smart Cities can be the answer in the fight against climate change. On the grounds of innovative efforts implemented in Barcelona, Rotterdam, and Vienna, we examine the opportunities and obstacles to both Smart Environment (defined as an axis of the Smart City) and adaptation to climate change, linking them together. As it is difficult to estimate the benefits of climate action in the short term due to often costly solutions, Smart City proposals could provide the economic incentive to create adaptive, energy-efficient, and sustainable societies. As the need for adaptive and resilient cities in the global context of climate change rises, the concept of Smart City might need to evolve into that of a Smart Sustainable City, positioning the environment at the core of its development. Results from this analysis suggest that the interaction between technology and nature can be enhanced when a Smart City approach promotes the integration of climate strategies and encourages the participation of citizens, something that is crucial since early adaptation efforts can safeguard smart infrastructure from climate impacts.
Agent-Based Analysis of Urban Spaces Using Space Syntax and Spatial Cognition Approaches: A Case Study in Bari, Italy
Authors: Esposito Dario , Santoro Stefania , Camarda Domenico
Published: June 2020

The present study provides a reflection on the agent-based intelligence of urban spatial environments through the comparison of a formal quantitative approach, i.e., space syntax, and a qualitative experimentation based on the spatial cognition approach. Until recently, space syntax was adopted by urban planners and designers to support urban design and planning decisions, based on an analysis of the urban physical environment. Researchers in the cognitive science field have increased their attempts to address space syntax techniques to better understand the relationships of cognitive spatial agents with the spatial features of urban environments. In this context, the experimental approach focuses on the qualities of the environment as interacted, perceived and interpreted by cognitive agents and reflects on the role which it plays in affecting spatial decisions and route choices. The present paper aimed to explore the extent to which possible integration between the different approaches can provide insights on agent-based decisions in actions and behavioural processes in space for useful perspectives in urban analysis and planning. Findings suggest relevant correlations between the experimentation results and space syntax predictions when a correspondence of some aspects can be found. Conversely, interesting qualitative insights from the spatial cognition approach are pointed out to enrich the configurational analysis. The potential and constraints of each approach and the ways of combining these are presented. Evidence supports the suitability of the proposal outlined in the present paper within the framework of urban planning practice.
Optimal Operation Strategy of a Community Integrated Energy System Constrained by the Seasonal Balance of Ground Source Heat Pumps
Authors: Liu Jianan , Yu Hao , Ji Haoran , Zhao Kunpeng , Lv Chaoxian , Li Peng
Published: June 2020

Ground source heat pumps (GSHPs) are now widely used in community integrated energy systems (CIES) because of their high efficiency in energy conversion. However, the coefficient of performance (COP) of GSHPs is unstable if the soil temperature changes with seasonal imbalanced cooling and heating loads, thus downgrading the overall performance of CIES. In this paper, an annual optimization model for CIES that considers the seasonal balance of GSHPs is established. Then, a day-ahead operation strategy based on the pre-allocated load of the GSHP in the yearly balance is proposed while considering the uncertainties in daily conditions. The proposed strategy is validated on a practical CIES in China and assessed on a year-round time scale. The results show that the operation of CIES can be stable, economical and sustainable while ensuring the seasonal balance of the GSHP.
An Integrated Model of Park-And-Ride Facilities for Sustainable Urban Mobility
Authors: Ortega Jairo , Tóth János , Péter Tamás , Moslem Sarbast
Published: June 2020

The Park and Ride (P&R) System is part of a set of parking policies provided by Sustainable Urban Mobility Plans (SUMPs) that have been used in developing efficient, safe and environmentally friendly solutions to reduce the undesirable effects of private vehicles in Central Business District (CBD). In fact, the P&Rs are allocated near public transportation stations to ease transfer from a private vehicle to a public transportation mode. Therefore, the P&R system is considered as an alternative transport mode in which the location and potential demand of each facility are fundamental components to be evaluated within sustainable urban planning. The paper proposes an integrated model of P&R facilities based on estimate the potential demand through a mathematical model of the seven park-and-ride (P&R) facilities (designated A to G) in Cuenca city, Ecuador. The developed integrated model includes two cost functions: one is the P&R mode, and the second is the private car mode. Additionally, a SUMP is integrated into the model as a data collection source in order to find the required parameters for the cost functions and origin–destination (O-D) matrix of private vehicles. The results showed that three out of the seven P&R facilities (P&R C, P&R G, and P&R A) had the highest demand (70% of the overall demand). Consequently, these three P&R facilities were studied separately using the same developed model, and the demand proved to be the highest for P&R facility “C” (39% out of 70%). In conclusion, SUMPs, as a methodology for data collection and a mathematical model, proved to be an effective integrated method for evaluating the most attractive P&R location based on the potential demand.
Construction of a Composite Vulnerability Index to Map Peripheralization Risk in Urban and Metropolitan Areas
Authors: Gerundo Roberto , Marra Alessandra , De Salvatore Viviana
Published: June 2020

As cities and poverty continue to grow worldwide, both spatial and a-spatial peripheralization processes expose entire urban and metropolitan areas at risk of degradation, not just traditional peripheries. The main aim of this paper is to propose a methodology for peripheralization risk assessment, according to the general theory of territorial risk, in order to identify priority areas where mitigation actions should be envisaged through urban and territorial planning. Such an approach constitutes the novelty of the work. So, peripheralization risk is defined for the first time, depending on aggregated vulnerability and exposure. Based on a literature review, a set of vulnerability indicators structured in three dimensions is defined in order to construct the composite vulnerability index in the Italian geographical context. Due to the absence of well-established threshold values, an aggregation method based on fuzzy logic is used. The methodology was applied to a conurbation of 16 municipalities in Campania Region (Italy). Obtained results showed that areas most at risk can be both peripheral and central neighborhoods, but also entire municipalities, demonstrating how mitigation actions are needed at different planning levels. Since the necessary input data are ordinarily available in planning processes, the proposed methodology can be transferred to other geographical contexts.
Financial Analyses and Corporate Evaluation on Sustainable Ability to Generate Excess Profit
Authors: Ji Hyunmi
Published: June 2020

This study empirically examined financial analyses and a market assessment on goodwill. Goodwill is not an individually identifiable asset but is recognized as an intangible asset because it is viewed as having future economic benefits from a business combination. The verification period for this study was from 2011 to 2019. The sample companies were 13,522 firms-years satisfying the selection criteria among listed companies in the Korean stock market. As a result of empirical analysis, it was found that goodwill is related to stock prices. Goodwill was shown to serve as useful accounting information by reflecting the economic realities of intangible assets called creating excess profitability and sustainable profit. For analysis, regression analysis was conducted by separating the companies listed on the KOSPI stock market and those listed on the KOSDAQ stock market. The results of the analysis were as follows. In the case of listed companies in the KOSPI stock market, goodwill was found to have a positive (+) stock price relationship as useful accounting information. These results suggested that goodwill is an asset that represents the ability to generate excess profit as a sustainable profit. The contributions of this study are as follows. First, this study verified that goodwill is related to stock prices even after the adoption of International Financial Reporting Standards (IFRSs). Second, it will be possible to induce rational decision-making regarding goodwill to accounting standards setters, supervisors, and users of financial information. Third, it recognized that the value of the financial market can be recognized only by providing reliable accounting information to the managers who prepare financial statements. This can lead managers to provide capital markets with more useful information.
Prioritization of Renewable Energy Alternatives for China by Using a Hybrid FMCDM Methodology with Uncertain Information
Authors: Niu Dongxiao , Zhen Hao , Yu Min , Wang Keke , Sun Lijie , Xu Xiaomin
Published: June 2020

With the deteriorating ecological environment and increasing energy consumption, developing clean and renewable energy sources has become a key measure to solve environmental problems and energy shortages. The multicriteria decision-making (MCDM) technique is widely used in the assessment of renewable energy alternatives (REA) to determine the most sustainable and appropriate option for a country or region. Classic REA ranking is conducted in a deterministic environment through MCDM techniques. However, with the increasing complexity of environmental and energy issues, the REA ranking method is unsuitable for use in today’s China. Therefore, in this paper, a fuzzy MCDM technique based on the interval-valued hesitant fuzzy elimination and choice expressing reality (IVHF-ELECTRE II) method, taking into account the uncertainty and ambiguity of the information, is proposed for REA ranking. A case study in China is conducted to elaborate on the rationality and feasibility of the proposed framework. According to the ranking results, hydro is determined as the best REA in China, followed by wind energy, solar photovoltaic, geothermal, biomass energy, and solar thermal. This research provides a feasible method and insightful reference for national decision-makers to utilize when evaluating the REA and establishing a macroplanning policy for renewable energy under an uncertain environment.
Social and Ecological High Influential Factors in Community Gardens Innovation: An Empirical Survey in Italy
Authors: Rusciano Vincenzo , Civero Gennaro , Scarpato Debora
Published: June 2020

In 2015, The United Nations adopted an agenda for sustainable development in order to obtain “a shared blueprint for peace and prosperity for people and for the world now and in the future (United Nations). The United Nations has defined 17 main goals, such as ending poverty, improving health, preserving the ocean, and tackling the climate change, in order to achieve worldwide sustainable development. Sustainable development is a crucial worldwide topic that encompasses three dimensions: economic, social and environmental. Nowadays, social ecological innovation has envisaged a new prominent business model focusing on social and environmental goals to achieve sustainable development. The intent of this paper is to propose the community garden framework as a social and ecological innovation tool in order to boost sustainable development in urban areas as well as rural areas. For this purpose, an empirical analysis based on a structured interview was conducted in the area of Naples on a sample of 150 gardeners. The results of the interviews have been aggregated by using a variance and correlation analysis in order to explore to what extent the social and environmental dimensions are linked to the community gardens and to identify a pattern between community gardens and social ecological innovation. Two attributes of community gardens, that is, urbanization effects mitigation and wellness and community, were identified as having the ability to influence other community garden attributes. Thus, the paper suggests using these highly influential factors to define a social and ecological innovation strategy based on a community gardens framework.
How Risk Managers’ Psychological and Social Capital Promotes the Development of Risk-Management Capabilities
Authors: Cui Ge-Qi , Jin Chang-Hyun , Lee Jung-Yong
Published: June 2020

The purpose of this study is to investigate the roles of risk managers’ psychological and social capital in risk-management capabilities (technical, business processes, and external resources) and the relationship between risk-management capabilities and management performance. This study treated organizational culture as a moderating variable. The research subjects (n = 1000) included top managers, general managers, and managers in various industries. This study found that risk managers’ positive psychological and social capital play important roles in corporate risk-management capabilities. The investigation of the moderating effect of organizational culture also provides important guidelines for future studies. This study found that components of psychological capital (e.g., hope, resilience, optimism, and self-efficacy) had a positive effect on substitute-risk-management capabilities (e.g., technology, business, and external management). Moreover, components of social capital (e.g., managerial tie utilization, trust, and solidarity) had a positive effect on substitute-risk-management capabilities (e.g., technology, business, and external management). The social and psychological capital of a company’s top management and managers are essential elements for strengthening the company’s capabilities for business and risk management. This study also found that the strength of the moderating effects on the relationship between psychological and social capital and risk-management capabilities differs according to the organizational culture. The implications of the study and limitations are discussed.
Comparative Life Cycle Assessment of Multiple Liquid Laundry Detergent Packaging Formats
Authors: Kim Saewhan , Park Jonghun
Published: June 2020

The emerging packaging industry trend of focusing on packaging sustainability is also occurring in the laundry detergent industry. This study presents a cradle-to-grave comparative life cycle assessment (LCA) of three different packaging systems for liquid laundry detergent: the conventional pourable bottle, polyethylene terephthalate (PET) container with pods, and flexible pouch with pods. The scope of this study included material production, intermediate processes, transportation, and end-of-life phases of each packaging system. The results showed that the conventional pourable bottle made of high-density polyethylene (HDPE) has less environmental impact than the other two packaging systems in all impact categories, except ecotoxicity, due to the higher amount of packaging material required to produce the pods. The rigid PET container with pods impacted the environment in all categories more than the multi-layered flexible pouch containing pods, due primarily to the amount of material production, heavier weight, and intermediate processing using injection molding.
Socio-Ecological Controversies in the News as Trigger of a Model-Based Inquiry Instructional Sequence about the Effect of Global Warming on the Great Barrier Reef
Authors: Jiménez-Liso M. Rut , González-Herrera Manuela , Banos-González Isabel
Published: June 2020

The use of socio-ecological controversies, such as global warming, in classrooms has been suggested to increase students’ awareness about complex issues, although detailed analysis of their implementation in classrooms are still scarce. This research shows a model-based inquiry approach (MBI) instructional sequence, using scientific news as a trigger, aimed at addressing a global problem on a real socio-ecological system: the effect of global warming on the Great Barrier Reef. Its implementation in a lower secondary school classroom allowed the assessment of the effectiveness of the instructional sequence designed, based on students’ perception of what secondary school students have learned and felt. Results show that the MBI instructional sequence seems to have favored the mobilization of students’ alternative conceptions about global warming, coral reefs, and symbiotic relationships. In addition, it contributed to increasing the students’ awareness of the problem of global warming and its effects on an essential socio-ecological system, such as coral reefs.