bluuebunny/crossref_metadata_2025 huggingface dataset:
Self-Referenced Multifrequency Phase-Resolved Luminescence Spectroscopy
Authors: de la Torre Angel , Medina-Rodríguez Santiago , Segura Jose C. , Fernández-Sánchez Jorge F.
Published: September 2020

Phase-resolved luminescence chemical sensors provide the analyte determination based on the estimation of the luminescence lifetime. The lifetime is estimated from an analysis of the amplitudes and/or phases of the excitation and emission signals at one or several modulation frequencies. This requires recording both the excitation signal (used to modulate the light source) and the emission signal (obtained from an optical transducer illuminated by the luminescent sensing phase). The excitation signal is conventionally used as reference, in order to obtain the modulation factor (the ratio between the emission and the excitation amplitudes) and/or the phase shift (the difference between the emission and the excitation phases) at each modulation frequency, which are used to estimate the luminescence lifetime. In this manuscript, we propose a new method providing the luminescence lifetimes (based either on amplitudes or phases) using only the emission signal (i.e., omitting the excitation signal in the procedure). We demonstrate that the luminescence lifetime can be derived from the emission signal when it contains at least two harmonics, because in this case the amplitude and phase of one of the harmonics can be used as reference. We present the theoretical formulation as well as an example of application to an oxygen measuring system. The proposed self-referenced lifetime estimation provides two practical advantages for luminescence chemical sensors. On one hand, it simplifies the instrument architecture, since only one analog-to-digital converter (for the emission signal) is necessary. On the other hand, the self-referenced estimation of the lifetime improves the robustness against degradation of the sensing phase or variations in the optical coupling, which reduces the recalibration requirements when the lifetimes are based on amplitudes.
Smartphone-Based Whole-Cell Biosensor Platform Utilizing an Immobilization Approach on a Filter Membrane Disk for the Monitoring of Water Toxicants
Authors: Ma Junning , Harpaz Dorin , Liu Yang , Eltzov Evgeni
Published: September 2020

Bioluminescent bacteria whole-cell biosensors (WCBs) have been widely used in a range of sensing applications in environmental monitoring and medical diagnostics. However, most of them use planktonic bacteria cells that require complicated signal measurement processes and therefore limit the portability of the biosensor device. In this study, a simple and low-cost immobilization method was examined. The bioluminescent bioreporter bacteria was absorbed on a filter membrane disk. Further optimization of the immobilization process was conducted by comparing different surface materials (polyester and parafilm) or by adding glucose and ampicillin. The filter membrane disks with immobilized bacteria cells were stored at −20 °C for three weeks without a compromise in the stability of its biosensing functionality for water toxicants monitoring. Also, the bacterial immobilized disks were integrated with smartphones-based signal detection. Then, they were exposed to water samples with ethanol, chloroform, and H2O2, as common toxicants. The sensitivity of the smartphone-based WCB for the detection of ethanol, chloroform, and H2O2 was 1% (v/v), 0.02% (v/v), and 0.0006% (v/v), respectively. To conclude, this bacterial immobilization approach demonstrated higher sensitivity, portability, and improved storability than the planktonic counterpart. The developed smartphone-based WCB establishes a model for future applications in the detection of environmental water toxicants.
The Feasibility of Longitudinal Upper Extremity Motor Function Assessment Using EEG
Authors: Zhang Xin , D’Arcy Ryan , Chen Long , Xu Minpeng , Ming Dong , Menon Carlo
Published: September 2020

Motor function assessment is crucial in quantifying motor recovery following stroke. In the rehabilitation field, motor function is usually assessed using questionnaire-based assessments, which are not completely objective and require prior training for the examiners. Some research groups have reported that electroencephalography (EEG) data have the potential to be a good indicator of motor function. However, those motor function scores based on EEG data were not evaluated in a longitudinal paradigm. The ability of the motor function scores from EEG data to track the motor function changes in long-term clinical applications is still unclear. In order to investigate the feasibility of using EEG to score motor function in a longitudinal paradigm, a convolutional neural network (CNN) EEG model and a residual neural network (ResNet) EEG model were previously generated to translate EEG data into motor function scores. To validate applications in monitoring rehabilitation following stroke, the pre-established models were evaluated using an initial small sample of individuals in an active 14-week rehabilitation program. Longitudinal performances of CNN and ResNet were evaluated through comparison with standard Fugl–Meyer Assessment (FMA) scores of upper extremity collected in the assessment sessions. The results showed good accuracy and robustness with both proposed networks (average difference: 1.22 points for CNN, 1.03 points for ResNet), providing preliminary evidence for the proposed method in objective evaluation of motor function of upper extremity in long-term clinical applications.
Object Detection Based on Faster R-CNN Algorithm with Skip Pooling and Fusion of Contextual Information
Authors: Xiao Yi , Wang Xinqing , Zhang Peng , Meng Fanjie , Shao Faming
Published: September 2020

Deep learning is currently the mainstream method of object detection. Faster region-based convolutional neural network (Faster R-CNN) has a pivotal position in deep learning. It has impressive detection effects in ordinary scenes. However, under special conditions, there can still be unsatisfactory detection performance, such as the object having problems like occlusion, deformation, or small size. This paper proposes a novel and improved algorithm based on the Faster R-CNN framework combined with the Faster R-CNN algorithm with skip pooling and fusion of contextual information. This algorithm can improve the detection performance under special conditions on the basis of Faster R-CNN. The improvement mainly has three parts: The first part adds a context information feature extraction model after the conv5_3 of the convolutional layer; the second part adds skip pooling so that the former can fully obtain the contextual information of the object, especially for situations where the object is occluded and deformed; and the third part replaces the region proposal network (RPN) with a more efficient guided anchor RPN (GA-RPN), which can maintain the recall rate while improving the detection performance. The latter can obtain more detailed information from different feature layers of the deep neural network algorithm, and is especially aimed at scenes with small objects. Compared with Faster R-CNN, you only look once series (such as: YOLOv3), single shot detector (such as: SSD512), and other object detection algorithms, the algorithm proposed in this paper has an average improvement of 6.857% on the mean average precision (mAP) evaluation index while maintaining a certain recall rate. This strongly proves that the proposed method has higher detection rate and detection efficiency in this case.
Implementing Deep Learning Techniques in 5G IoT Networks for 3D Indoor Positioning: DELTA (DeEp Learning-Based Co-operaTive Architecture)
Authors: El Boudani Brahim , Kanaris Loizos , Kokkinis Akis , Kyriacou Michalis , Chrysoulas Christos , Stavrou Stavros , Dagiuklas Tasos
Published: September 2020

In the near future, the fifth-generation wireless technology is expected to be rolled out, offering low latency, high bandwidth and multiple antennas deployed in a single access point. This ecosystem will help further enhance various location-based scenarios such as assets tracking in smart factories, precise smart management of hydroponic indoor vertical farms and indoor way-finding in smart hospitals. Such a system will also integrate existing technologies like the Internet of Things (IoT), WiFi and other network infrastructures. In this respect, 5G precise indoor localization using heterogeneous IoT technologies (Zigbee, Raspberry Pi, Arduino, BLE, etc.) is a challenging research area. In this work, an experimental 5G testbed has been designed integrating C-RAN and IoT networks. This testbed is used to improve both vertical and horizontal localization (3D Localization) in a 5G IoT environment. To achieve this, we propose the DEep Learning-based co-operaTive Architecture (DELTA) machine learning model implemented on a 3D multi-layered fingerprint radiomap. The DELTA begins by estimating the 2D location. Then, the output is recursively used to predict the 3D location of a mobile station. This approach is going to benefit use cases such as 3D indoor navigation in multi-floor smart factories or in large complex buildings. Finally, we have observed that the proposed model has outperformed traditional algorithms such as Support Vector Machine (SVM) and K-Nearest Neighbor (KNN).
Occupancy Prediction Using Low-Cost and Low-Resolution Heat Sensors for Smart Offices
Authors: Sirmacek Beril , Riveiro Maria
Published: September 2020

Solving the challenge of occupancy prediction is crucial in order to design efficient and sustainable office spaces and automate lighting, heating, and air circulation in these facilities. In office spaces where large areas need to be observed, multiple sensors must be used for full coverage. In these cases, it is normally important to keep the costs low, but also to make sure that the privacy of the people who use such environments are preserved. Low-cost and low-resolution heat (thermal) sensors can be very useful to build solutions that address these concerns. However, they are extremely sensitive to noise artifacts which might be caused by heat prints of the people who left the space or by other objects, which are either using electricity or exposed to sunlight. There are some earlier solutions for occupancy prediction that employ low-resolution heat sensors; however, they have not addressed nor compensated for such heat artifacts. Therefore, in this paper, we presented a low-cost and low-energy consuming smart space implementation to predict the number of people in the environment based on whether their activity is static or dynamic in time. We used a low-resolution (8×8) and non-intrusive heat sensor to collect data from an actual meeting room. We proposed two novel workflows to predict the occupancy; one that is based on computer vision and one based on machine learning. Besides comparing the advantages and disadvantages of these different workflows, we used several state-of-the-art explainability methods in order to provide a detailed analysis of the algorithm parameters and how the image properties influence the resulting performance. Furthermore, we analyzed noise resources that affect the heat sensor data. The experiments show that the feature classification based method gives high accuracy when the data are clean from noise artifacts. However, when there are noise artifacts, the computer vision based method can compensate for those artifacts providing robust results. Because the computer vision based method requires an empty room recording, the feature classification based method should be chosen either when there is no expectancy of seeing noise artifacts in the data or when there is no empty recording available. We hope that our analysis brings light into understanding how to handle very low-resolution heat images in these environments. The presented workflows could be used in various domains and applications other than smart offices, where occupancy prediction is essential, e.g., for elderly care.
FASTEN IIoT: An Open Real-Time Platform for Vertical, Horizontal and End-To-End Integration
Authors: Costa Felipe S. , Nassar Silvia M. , Gusmeroli Sergio , Schultz Ralph , Conceição André G. S. , Xavier Miguel , Hessel Fabiano , Dantas Mario A. R.
Published: September 2020

The Industry 4.0 paradigm, since its initial conception in Germany in 2011, has extended its scope and adoption to a broader set of technologies. It is being considered as the most vital mechanism in the production systems lifecycle. It is the key element in the digital transformation of manufacturing industry all over the world. This scenario imposes a set of major unprecedented challenges which require to be overcome. In order to enable integration in horizontal, vertical, and end-to-end formats, one of the most critical aspects of this digital transformation process consists of effectively coupling digital integrated service/products business models with additive manufacturing processes. This integration is based upon advanced AI-based tools for decentralized decision-making and for secure and trusted data sharing in the global value. This paper presents the FASTEN IIoT Platform, which targets to provide a flexible, configurable, and open solution. The platform acts as an interface between the shop floor and the industry 4.0 advanced applications and solutions. Examples of these efforts comprise management, forecasting, optimization, and simulation, by harmonizing the heterogeneous characteristics of the data sources involved while meeting real-time requirements.
A Novel Post-Processing Method Based on a Weighted Composite Filter for Enhancing Semantic Segmentation Results
Authors: Cheng Xin , Liu Huashan
Published: September 2020

Image semantic segmentation is one of the key problems in computer vision. Despite the enormous advances in applications, almost all the image semantic segmentation algorithms fail to achieve satisfactory segmentation results due to lack of sensitivity to details, or difficulty in evaluating the global similarity of pixels, or both. Posting-processing enhancement methods, as the outstandingly crucial means to ameliorate the above-mentioned inherent flaws of algorithms, are almost based on conditional random fields (CRFs). Inspired by CRFs, this paper proposes a novel post-processing enhancement framework with theoretical simplicity from the perspective of filtering, and a new weighted composite filter (WCF) is designed to enhance the segmentation masks in a unified framework. First, by adjusting the weight ratio, the WCF is decomposed into a local part and a global part. Secondly, a guided image filter is designed as the local filter, which can restore boundary information to present necessary details. Moreover, a minimum spanning tree (MST)-based filter is designed as the global filter to provide a natural measure of global pixel similarity for image matching. Thirdly, a unified post-processing enhancement framework, including selection and normalization, WCF and argmax, is designed. Finally, the effectiveness and superiority of the proposed method for enhancement, as well as its range of applications, are verified through experiments.
A Task-Learning Strategy for Robotic Assembly Tasks from Human Demonstrations
Authors: Ding Guanwen , Liu Yubin , Zang Xizhe , Zhang Xuehe , Liu Gangfeng , Zhao Jie
Published: September 2020

In manufacturing, traditional task pre-programming methods limit the efficiency of human–robot skill transfer. This paper proposes a novel task-learning strategy, enabling robots to learn skills from human demonstrations flexibly and generalize skills under new task situations. Specifically, we establish a markerless vision capture system to acquire continuous human hand movements and develop a threshold-based heuristic segmentation algorithm to segment the complete movements into different movement primitives (MPs) which encode human hand movements with task-oriented models. For movement primitive learning, we adopt a Gaussian mixture model and Gaussian mixture regression (GMM-GMR) to extract the optimal trajectory encapsulating sufficient human features and utilize dynamical movement primitives (DMPs) to learn for trajectory generalization. In addition, we propose an improved visuo-spatial skill learning (VSL) algorithm to learn goal configurations concerning spatial relationships between task-relevant objects. Only one multioperation demonstration is required for learning, and robots can generalize goal configurations under new task situations following the task execution order from demonstration. A series of peg-in-hole experiments demonstrate that the proposed task-learning strategy can obtain exact pick-and-place points and generate smooth human-like trajectories, verifying the effectiveness of the proposed strategy.
Effects of a Multi-Ingredient Preworkout Supplement Versus Caffeine on Energy Expenditure and Feelings of Fatigue during Low-Intensity Treadmill Exercise in College-Aged Males
Authors: Lutsch Daniel J. , Camic Clayton L. , Jagim Andrew R. , Stefan Riley R. , Cox Brandon J. , Tauber Rachel N. , Henert Shaine E.
Published: September 2020

The primary purpose of this study was to examine the acute effects of a multi-ingredient (i.e., caffeine, green tea extract, Yohimbe extract, capsicum annum, coleus extract, L-carnitine, beta-alanine, tyrosine) preworkout supplement versus a dose of caffeine (6 mg·kg−1) on energy expenditure during low-intensity exercise. The effects of these treatments on substrate utilization, gas exchange, and psychological factors were also investigated. Twelve males (mean ± SD: age = 22.8 ± 2.4 years) completed three bouts of 60 min of treadmill exercise on separate days after consuming a preworkout supplement, 6 mg·kg−1 of caffeine, or placebo in a randomized fashion. The preworkout and caffeine supplements resulted in significantly greater energy expenditure (p < 0.001, p = 0.006, respectively), V˙O2 (p < 0.001, p = 0.007, respectively), V˙CO2 (p = 0.006, p = 0.049, respectively), and V˙E (p < 0.001, p = 0.007, respectively) compared to placebo (collapsed across condition). There were no differences among conditions, however, for rates of fat or carbohydrate oxidation or respiratory exchange ratio. In addition, the preworkout supplement increased feelings of alertness (p = 0.015) and focus (p = 0.005) 30-min postingestion and decreased feelings of fatigue (p = 0.014) during exercise compared to placebo. Thus, the preworkout supplement increased energy expenditure and measures of gas exchange to the same extent as 6 mg·kg−1 of caffeine with concomitant increased feelings of alertness and focus and decreased feelings of fatigue.
Decarbonization and Circular Economy in the Sustainable Development and Renovation of Buildings and Neighbourhoods
Authors: Mercader-Moyano Pilar , Esquivias Paula M.
Published: September 2020

In recent years, the building sector has been turning towards intervening in the existing city building stock. In fact, it is generally accepted that the refurbishment of buildings and the urban regeneration based on sustainability must form the axis of reformulation of the building sector. Nowadays, achieving sustainable urban development inevitably involves improving existing buildings, thereby preventing the need for city growth, and for the emptying of established neighbourhoods. Furthermore, considering the whole life cycle, it is well known the great amount of greenhouse emissions derived from the construction sector, so in order to reach a decarbonized society it is important to provide eco-efficient construction materials and solutions, adding the principles of circular economy and resource efficiency. The articles of this special issue show different aspects to be considered in order to reach a decarbonized and circular building stock.
Empowering Communications in Vehicular Networks with an Intelligent Blockchain-Based Solution
Authors: Mbarek Bacem , Jabeur Nafaa , Pitner Tomás , Yasar Ansar-Ul-Haque
Published: September 2020

Blockchains have emerged over time as a reliable and secure way to record transactions in an immutable manner in a wide range of application domains. However, current related solutions are not yet capable of appropriately checking the authenticity of data when their volumes are huge. They are not also capable of updating Blockchain data blocks and synchronizing them within reasonable timeframes. This is the case within the specific context of Blockchain vehicular networks, where these solutions are commonly cumbersome when attempting to add new vehicles to the network. In order to address these problems, we propose in this paper a new Blockchain-based solution that intelligently implement selective communication and collaborative endorsement approaches to reduce communications between vehicles. Our solution represents the vehicles of the Blockchain as intelligent software agents with a Belief–Desire–Intention (BDI) architecture. Furthermore, we propose an approach based on multi-endorsement levels to exchange data of varying sensitive categories. This approach, which is based on endorsing scores, is also used to shorten the admission of new vehicles into the Blockchain. We run simulations using the Hyperledger Fabric Blockchain tool. Results show the efficiency of our solution in reducing the processing times of transactions within two different scenarios.
Homothetic Behavior of Betweenness Centralities: A Multiscale Alternative Approach to Relate Cities and Large Regional Structures
Authors: Altafini Diego , Cutini Valerio
Published: September 2020

Regional configuration can reveal important aspects about city sustainability, as local-regional interactions shape the evolution and inner geography of urban settlements. However, modelling these large-scale structures remains a challenge, due to their sheer size as physical objects. Despite recent improvements in processing power and computing methods, extensive time periods are still required for ordinary microprocessors to model network centralities in road-graphs with high element counts, connectivity and topological depth. Generalization is often the chosen option to mitigate time-constraints of regional network complexity. Nevertheless, this can impact visual representation and model precision, especially when multiscale comparisons are desired. Tests using Normalized Angular Choice (NACH), a Space Syntax mathematical derivative of Betweenness Centrality, found recursive visual similitudes in centrality spatial distribution when modelling distinct scaled map sections of the same large regional network structure. Therefore, a sort of homothetic behavior is identified, since statistical analyses demonstrate that centrality values and distributions remain rather consistent throughout scales, even when considering edge effects. This paper summarizes these results and considers homotheties as an alternative to extensive network generalization. Hence, data maps can be constructed sooner and more accurately as “pieces of a puzzle”, since each individual lesser scale graph possesses a faster processing time.
An Urban Regeneration Planning Scheme for the Souq Waqif Heritage Site of Doha
Authors: Furlan Raffaello , AL-Mohannadi Asmaa
Published: September 2020

Over the past decade, transit-oriented development (TOD) has been advocated as an applicable urban regeneration planning model to promote the sustainability of cities along with city dwellers’ standards of urban living. On a regional scale, under the directives of the Qatar National Vision 2030 (QNV-2030), the Qatar National Development Framework (QNDF-2032), and the strategies for planned mega events, such as the FIFA World Cup 2022, the State of Qatar launched the construction of the Doha Metro, which consists of four lines. This transport system, linking the center of Doha to several transit villages around approximately 100 metro stations, aims at reducing the number of vehicles on the road networks while providing an integrated transportation and land use strategy through the urban regeneration of transit-oriented developments (TODs), providing both social and environmental economic benefits. Among the most significant transit sites within the Doha Metro lines is the Souq Waqif station. This station is a historical–heritage spot that represents a potential socio-cultural site for the creation of a distinctive urban environment. This research study investigates an approach suitable for an urban regeneration planning scheme for the Souq Waqif TOD, aiming at (i) preserving and consolidating the deeply rooted cultural heritage of the historical site and (ii) enhancing the city dwellers’ and/or the community’s standards of urban living. This study aims to explore the applicability of a TOD planning scheme for the new metro station through urban regeneration and land infill in the existing built environment of the Souq. This study contends that the efficient integration of land use with transport systems contributes to shaping an environment with enhanced standards of living for users while supporting social, economic, and environmental factors. The present research design comprises qualitative data based on theoretical studies and site-based analysis to assess (i) the principles of TODs and (ii) the extent to which their application can be employed for the Souq Waqif to become a sustainable TOD.
Removal of Cd(II) from Water by HPEI Modified Humin
Authors: Li Sanmei , Wu Mingda , Lu Linghong , Zhu Jiabao
Published: September 2020

Humin is the waste residue from the process of preparing humic acid, which accounts for a large proportion of the raw material (weathered coal humic acid). Its Cd(II) adsorption performance is far inferior to that of humic acid. How to regenerate humin is of great significance to the low-cost treatment of Cd(II) pollution in wastewater. In this study, humin was modified by hyperbranched polyethyleneimine to enhance the adsorption capacity for Cd(II). Fourier transform infrared spectroscopy and the X-ray photoelectron spectrometer showed that hyperbranched polyethyleneimine was grafted to the surface of humin. Flame atomic absorption spectroscopy showed that the saturated Cd(II) adsorption capacity of the modified humin was increased to 11.975 mg/g, which is about 5 times than that of humin and is also higher than that of humic acid. The adsorption kinetics, adsorption isotherm, and thermodynamic properties of humic acid, humin, and modified humin were also studied. This study may provide a foundation for research utilizing natural resources to reduce heavy metal pollution in the environment.
Effect of Fattening Period Length on Intramuscular and Subcutaneous Fatty Acid Profiles in Iberian Pigs Finished in the Montanera Sustainable System
Authors: Ayuso Dolores , González Ana , Peña Francisco , Hernández-García Francisco I. , Izquierdo Mercedes
Published: September 2020

Twenty-four extensively reared Iberian pigs were used to study the influence of fattening period length (30, 60 or 90 days) on the fatty acid profiles of intramuscular and subcutaneous fat and the relationships between both profiles. Regarding fatty acid (FA) percentage, PUFA was greater in backfat and MUFA was greater in intramuscular fat (IMF), regardless of fattening period length. The longer fattening period increased MUFA content in backfat (which had a more marked change in oleic acid) and decreased PUFA content in backfat and IMF, but it did not affect SFA content. Within the three-layer subcutaneous backfat, SFA content was greater in the inner layer, MUFA was greater in the outer layer and PUFA was greater in both of these layers. The few differences in FA composition between both adipose tissues suggest that the changes due to the feeding regime are slow and, therefore, although the length of the fattening phase was increased, the fatty acid profile did not change substantially. The strong relationship between the FA profiles of IMF and backfat might be used to predict one profile from the other one when the latter is more readily available for sampling or analytical reasons.
Development of a High-Performance Electric Pressure Regulator Applied for Compressed-Natural-Gas-Fueled Vehicles
Authors: Hung Nguyen Ba , Lim Ocktaeck
Published: September 2020

A model-based study is carried out based on a combination of mathematical and Maxwell models to develop a high-performance electric pressure regulator utilized for compressed-natural-gas-fueled vehicles. To reduce computational cost, a symmetric two-direction model of the electric pressure regulator is established in Maxwell software, in which its material properties and dimension parameters are obtained on the base of specifications of a real electric pressure regulator. The output of simulating in Maxwell is the electromagnetic force, which is significantly improved when changing core shape in the various dimensions ∆1, ∆2, and ∆3. The optimal electromagnetic force is utilized for the mathematical models as an input variable to simulate the operational characteristics of the electric pressure regulator such as displacement and response time of plunger. The operational characteristics of the electric pressure regulator are examined under the influences of key parameters, including inlet gas pressure, diameter of orifice, and spring stiffness. By optimizing these key parameters, the simulated results in this study show that an electric pressure regulator with high performance can be obtained.
The Foster Family Process to Maintain the Will to Remain in Foster Care—Implications for a Sustainable Programme
Authors: Diogo Elisete , Branco Francisco
Published: September 2020

There were 7032 children in out-of-home care in 2018 in Portugal. Of these, only 2.8% were in foster care, despite this being the recommended response according to legal regulations. It is critical that more families be encouraged to become foster carers and also that experienced carers stay in the system to create a sustainable programme. How is the will to foster a child maintained? What can we learn from foster families’ experiences to improve childcare and the child protection system? The methodology of this study was based on interviews with foster carers. The analysis was inspired by grounded theory. We found three types of foster families, classified according to their will to leave or remain in foster care—unconditional, hesitant, or retired. The results suggest that the key elements for foster carers to remain in the foster care system are (i) their level of satisfaction with how the previous placement concluded, (ii) keeping in touch with the ex-foster child, (iii) the feeling of acknowledgement by all the stakeholders, and (iv) the quality of social services as well as the support of the professional teams.
The Influence of Economic Barriers and Drivers on Energy Efficiency Investments in Maritime Shipping, from the Perspective of the Principal-Agent Problem
Authors: Longarela-Ares Ángeles , Calvo-Silvosa Anxo , Pérez-López José-Benito
Published: September 2020

Maritime transport stands out as a strategic sector; the increasing trend in maritime traffic makes it essential to reduce energy consumption and emissions through investment in energy efficiency. However, investments can be hindered by barriers, and drivers are necessary to reduce or overcome them and promote investment. Consequently, the purpose of this study is to analyze what factors influence investment decisions—and how they do so—when there are principal-agent problems in the shipowner–charterer relationship. The methodology is based on the following process: model and hypotheses formulation, variable definition, the creation of a study sample and statistical treatment through a descriptive analysis of variables and a binomial logistic regression model, all based on a state-of-the-art application. The results corroborate the hypotheses and indicate that principal-agent problems and split incentives, especially in time charter contracts, and a lack of verified information make the shipowners less likely to invest. Moreover, energy efficiency measures are less likely to be implemented in older vessels, possibly due to the difficulty associated with recovering the investment; they are more likely in larger and newer vessels, and regulation encourage their adoption. Furthermore, investment is more likely in vessels with verified information and high levels of both activity and harmful emissions. Improved knowledge in this field could help businesses and governments to act in a more sustainable manner, without detriment to an innovative and competitive sector.
How Tillage and Fertilization Influence Soil N2O Emissions after Forestland Conversion to Cropland
Authors: Ren Xiao , Zhu Bo , Bah Hamidou , Raza Syed Turab
Published: September 2020

Soil nitrous oxide (N2O) emissions are influenced by land use adjustment and management practices. To meet the increasing socioeconomic development and sustainable demands for food supply, forestland conversion to cropland occurs around the world. However, the effects of forestland conversion to cropland as well as of tillage and fertilization practices on soil N2O emissions are still not well understood, especially in subtropical regions. Therefore, field experiments were carried out to continuously monitor soil N2O emissions after the conversion of forestland to cropland in a subtropical region in Southwest China. One forestland site and four cropland sites were selected: forestland (CK), short-term croplands (tillage with and without fertilization, NC-TF and NC-T), and long-term croplands (tillage with and without fertilization, LC-TF and LC-T). The annual cumulative N2O flux was 0.21 kg N ha−1 yr−1 in forestland. After forestland conversion to cropland, the annual cumulative N2O flux significantly increased by 76‒491%. In the short-term and long-term croplands, tillage with fertilization induced cumulative soil N2O emissions that were 94% and 235% higher than those from tillage without fertilization. Fertilization contributed 63% and 84% to increased N2O emissions in the short-term and long-term croplands, respectively. A stepwise regression analysis showed that soil N2O emissions from croplands were mainly influenced by soil NO3− and NH4+ availability and WFPS (water-filled pore space). Fertilization led to higher soil NH4+ and NO3− concentrations, which thus resulted in larger N2O fluxes. Thus, to reduce soil N2O emissions and promote the sustainable development of the eco-environment, we recommend limiting the conversion of forestland to cropland, and meanwhile intensifying the shift from grain to green or applying advanced agricultural management practices as much as possible.
Coping of Chinese Citizens Living in Spain during the COVID-19 Pandemic: Lessons for Personal Well-Being and Social Cohesion
Authors: Guo Mengna , Joanpere Mar , Pulido Cristina , Padrós Cuxart Maria
Published: September 2020

Chinese citizens in China were the first affected by the COVID-19 outbreak. Nevertheless, the disease rapidly spread around the world, leading to the worst pandemic experienced in modern societies. Spain has become one of the countries more severely affected by it, while having a large Chinese community. This study aims to explore the perception of Chinese citizens living in Spain regarding the outbreak in their host country. Communicative interviews were conducted with ten Chinese men and women who had been living in Spain for at least five years. Results show cultural differences in the coping strategies of the Chinese as a response to the pandemic, which differ from those observed by the participants in their fellow Spanish citizens. These findings unveil the potential of integrating the cultural knowledge and coping strategies of migrant groups in the host communities in managing a pandemic, as well as the negative outcomes for social cohesion and well-being from new racism emerging in the context of COVID-19.
Using the Social Influence of Electronic Word-of-Mouth for Predicting Product Sales: The Moderating Effect of Review or Reviewer Helpfulness and Product Type
Authors: Lee Sangjae , Choeh Joon Yeon
Published: September 2020

The social engagement of eWOM (electronic word-of-mouth) can reduce the threat of adverse selection in e-commerce. As studies that examine the social influence of eWOM are rare, the present work suggests the moderating effect of review or reviewer helpfulness and product type (experience or search goods) on the relationship between eWOM and product sales. The volume of eWOM, which is defined as the multiplication of the average length by the number of reviews, is shown to be moderated by review and reviewer helpfulness and search goods to affect product sales. Review ratings are moderated by reviewer helpfulness, and review extremity is positively (negatively) moderated by search (experience) goods and review helpfulness to affect product sales. As previous studies of differentiated sampling strategies that consider review helpfulness for predicting product sales using eWOM are lacking, this study compares the prediction power of business intelligence methods for different subsamples of products created according to high or low review and reviewer helpfulness levels. The subsample with high review or reviewer helpfulness demonstrates greater prediction performance than the subsample with low review or reviewer helpfulness when eWOM variables are used as predictors of product sales. Hence, preliminary filtering data preprocessing should consider review or reviewer helpfulness as a crucial criterion of the data quality. This will contribute to the sampling or preprocessing strategy used to predict product sales using eWOM.
Towards Greater Citizen Participation in Financing Public Cultural Institutions—Legal Barriers and Proposed Solutions
Authors: Modzelewska Anna , Skuza Sebastian , Szeluga-Romańska Marta , Materska-Samek Marta
Published: September 2020

The paper contains a conceptual proposal that aims at indicating new models of participatory financing of cultural institutions as well as actions towards supporting culture. It presents results of a qualitative study in the form of action research, on identifying legal barriers of financing of cultural institutions in Poland. Additionally, it presents practical suggestions to the encountered problems that were worked out after discussions with cultural managers, taxpayers, and government administration decision-makers. The unique value of the study is a multidimensional and complex analysis of participatory financing of culture, citing varied sources and bearing a structured research procedure. We suggest a new discussion on establishing legal solutions for increasing of the funding or making the financing of cultural institutions more flexible, which appears to be particularly important in crisis times. All is presented as an original concept of participatory citizen-enterprise fiscal mechanism of supporting cultural institutions. We present a pragmatic solution to a problem of additional public support of culture, which can be put into practice parallel to state mechanisms, considering culture as a part of sustainable development.
Spatial and Behavioral Thermal Adaptation in Net Zero Energy Buildings: An Exploratory Investigation
Authors: Attia Shady
Published: September 2020

Climate responsive design can amplify the positive environmental effects necessary for human habitation and constructively engage and reduce the energy use of existing buildings. This paper aims to assess the role of the thermal adaptation design strategy on thermal comfort perception, occupant behavior, and building energy use in twelve high-performance Belgian households. Thermal adaptation involves thermal zoning and behavioral adaptation to achieve thermal comfort and reduce energy use in homes. Based on quantitative and qualitative fieldwork and in-depth interviews conducted in Brussels, the paper provides insights on the impact of using mechanical systems in twelve newly renovated nearly- and net-zero energy households. The article calls for embracing thermal adaptation as a crucial design principle in future energy efficiency standards and codes. Results confirm the rebound effect in nearly zero energy buildings and the limitation of the current building energy efficiency standards. The paper offers a fresh perspective to the field of building energy efficiency that will appeal to researchers and architects, as well as policymakers.
The Impact of Energy Consumption and Economic Growth on Carbon Dioxide Emissions
Authors: Osobajo Oluyomi A. , Otitoju Afolabi , Otitoju Martha Ajibola , Oke Adekunle
Published: September 2020

This study explored the effect of energy consumption and economic growth on CO2 emissions. The relationship between energy consumption, economic growth and CO2 emissions was assessed using regression analysis (the pooled OLS regression and fixed effects methods), Granger causality and panel cointegration tests. Data from 70 countries between 1994–2013 were analysed. The result of the Granger causality tests revealed that the study variables (population, capital stock and economic growth) have a bi-directional causal relationship with CO2 emissions, while energy consumption has a uni-directional relationship. Likewise, the outcome of the cointegration tests established that a long-run relationship exists among the study variables (energy consumption and economic growth) with CO2 emissions. However, the pooled OLS and fixed methods both showed that energy consumption and economic growth have a significant positive impact on CO2 emissions. Hence, this study supports the need for a global transition to a low carbon economy primarily through climate finance, which refers to local, national, or transnational financing, that may be drawn from public, private and alternative sources of financing. This will help foster large-scale investments in clean energy, that are required to significantly reduce CO2 emissions.
Supersymmetry of Relativistic Hamiltonians for Arbitrary Spin
Authors: Junker Georg
Published: September 2020

Hamiltonians describing the relativistic quantum dynamics of a particle with an arbitrary but fixed spin are shown to exhibit a supersymmetric structure when the even and odd elements of the Hamiltonian commute. Here, the supercharges transform between energy eigenstates of positive and negative energy. For such supersymmetric Hamiltonians, an exact Foldy–Wouthuysen transformation exists which brings it into a block-diagonal form separating the positive and negative energy subspaces. The relativistic dynamics of a charged particle in a magnetic field are considered for the case of a scalar (spin-zero) boson obeying the Klein–Gordon equation, a Dirac (spin one-half) fermion and a vector (spin-one) boson characterised by the Proca equation. In the latter case, supersymmetry implies for the Landé g-factor g=2.
Telemedicine Acceptance during the COVID-19 Pandemic: An Empirical Example of Robust Consistent Partial Least Squares Path Modeling
Authors: Ramírez-Correa Patricio , Ramírez-Rivas Catalina , Alfaro-Pérez Jorge , Melo-Mariano Ari
Published: September 2020

The explanation of behaviors concerning telemedicine acceptance is an evolving area of study. This topic is currently more critical than ever, given that the COVID-19 pandemic is making resources scarcer within the health industry. The objective of this study is to determine which model, the Theory of Planned Behavior or the Technology Acceptance Model, provides greater explanatory power for the adoption of telemedicine addressing outlier-associated bias. We carried out an online survey of patients. The data obtained through the survey were analyzed using both consistent partial least squares path modeling (PLSc) and robust PLSc. The latter used a robust estimator designed for elliptically symmetric unimodal distribution. Both estimation techniques led to similar results, without inconsistencies in interpretation. In short, the results indicate that the Theory of Planned Behavior Model provides a significant explanatory power. Furthermore, the findings show that attitude has the most substantial direct effect on behavioral intention to use telemedicine systems.
Violation of the Time-Reversal and Particle-Hole Symmetries in Strongly Correlated Fermi Systems: A Review
Authors: Shaginyan V. R. , Msezane A. Z. , Japaridze G. S. , Stephanovich V. A.
Published: September 2020

In this review, we consider the time reversal T and particle-antiparticle C symmetries that, being most fundamental, can be violated at microscopic level by a weak interaction. The notable example here is from condensed matter, where strongly correlated Fermi systems like heavy-fermion metals and high Tc superconductors exhibit C and T symmetries violation due to so-called non-Fermi liquid (NFL) behavior. In these systems, tunneling differential conductivity (or resistivity) is a very sensitive tool to experimentally test the above symmetry break. When a strongly correlated Fermi system turns out to be near the topological fermion condensation quantum phase transition (FCQPT), it exhibits the NFL properties, so that the C symmetry breaks down, making the differential tunneling conductivity to be an asymmetric function of the bias voltage V. This asymmetry does not take place in normal metals, where Landau Fermi liquid (LFL) theory holds. Under the application of magnetic field, a heavy fermion metal transits to the LFL state, and σ(V) becomes symmetric function of V. These findings are in good agreement with experimental observations. We suggest that the same topological FCQPT underlies the baryon asymmetry in the Universe. We demonstrate that the most fundamental features of the nature are defined by its topological and symmetry properties.
Clar Covers of Overlapping Benzenoids: Case of Two Identically-Oriented Parallelograms
Authors: Witek Henryk , Langner Johanna
Published: September 2020

We present a complete set of closed-form formulas for the ZZ polynomials of five classes of composite Kekuléan benzenoids that can be obtained by overlapping two parallelograms: generalized ribbons Rb, parallelograms M, vertically overlapping parallelograms MvM, horizontally overlapping parallelograms MhM, and intersecting parallelograms MxM. All formulas have the form of multiple sums over binomial coefficients. Three of the formulas are given with a proof based on the interface theory of benzenoids, while the remaining two formulas are presented as conjectures verified via extensive numerical tests. Both of the conjectured formulas have the form of a 2×2 determinant bearing close structural resemblance to analogous formulas for the number of Kekulé structures derived from the John-Sachs theory of Kekulé structures.
An Ensemble Machine Learning Technique for Functional Requirement Classification
Authors: Rahimi Nouf , Eassa Fathy , Elrefaei Lamiaa
Published: September 2020

In Requirement Engineering, software requirements are classified into two main categories: Functional Requirement (FR) and Non-Functional Requirement (NFR). FR describes user and system goals. NFR includes all constraints on services and functions. Deeper classification of those two categories facilitates the software development process. There are many techniques for classifying FR; some of them are Machine Learning (ML) techniques, and others are traditional. To date, the classification accuracy has not been satisfactory. In this paper, we introduce a new ensemble ML technique for classifying FR statements to improve their accuracy and availability. This technique combines different ML models and uses enhanced accuracy as a weight in the weighted ensemble voting approach. The five combined models are Naïve Bayes, Support Vector Machine (SVM), Decision Tree, Logistic Regression, and Support Vector Classification (SVC). The technique was implemented, trained, and tested using a collected dataset. The accuracy of classifying FR was 99.45%, and the required time was 0.7 s.