bluuebunny/crossref_metadata_2025 huggingface dataset:
Water Conservation Estimation Based on Time Series NDVI in the Yellow River Basin
Authors: Zhang Yangchengsi , Du Jiaqiang , Guo Long , Sheng Zhilu , Wu Jinhua , Zhang Jing
Published: March 2021

Accurate estimation of the water conservation is of great significance for ecological red line planning. The water conservation of the Yellow River Basin has a vital influence on the development of the environment and the supply of ecological services in China. However, the existing methods used to estimate water conservation have many disadvantages, such as requiring numerous parameters, a complex calculation model, and using data that is often difficult acquire. It is often hard to provide sufficiently precise parameters and data, resulting in a large amount of calculation time and the difficulties in the study of large scale and long time series. In this study, a time series of the Normalized Difference Vegetation Index (NDVI) was applied to estimate water conservation in two aspects using the idea of wholeness and stratification, respectively. The overall fitting results can explain nearly 30% of the water conservation by partial least squares regression and nearly 50% of it by a support vector machine. However, the results of a stratified simulation showed that water conservation and the NDVI have a certain stratified heterogeneity among different ecosystem types. The optimal fitting result was achieved in a water/wetland ecosystem with the highest coefficient of determination (R2P) of 0.768 by the stratified support vector machine (SVM) model, followed by the forest and grassland ecosystem (both R2P of 0.698). The spatial mapping results showed that this method was most suitable for grassland ecosystem, followed by forest ecosystem. According to the results generated using the NDVI time series data, it is feasible to complete a spatial simulation of water conservation. This research can provide a reference for calculating regional or large-scale water conservation and in ecological red line planning.
Combined Rule-Based and Hypothesis-Based Method for Building Model Reconstruction from Photogrammetric Point Clouds
Authors: Xie Linfu , Hu Han , Zhu Qing , Li Xiaoming , Tang Shengjun , Li You , Guo Renzhong , Zhang Yeting , Wang Weixi
Published: March 2021

Three-dimensional (3D) building models play an important role in digital cities and have numerous potential applications in environmental studies. In recent years, the photogrammetric point clouds obtained by aerial oblique images have become a major source of data for 3D building reconstruction. Aiming at reconstructing a 3D building model at Level of Detail (LoD) 2 and even LoD3 with preferred geometry accuracy and affordable computation expense, in this paper, we propose a novel method for the efficient reconstruction of building models from the photogrammetric point clouds which combines the rule-based and the hypothesis-based method using a two-stage topological recovery process. Given the point clouds of a single building, planar primitives and their corresponding boundaries are extracted and regularized to obtain abstracted building counters. In the first stage, we take advantage of the regularity and adjacency of the building counters to recover parts of the topological relationships between different primitives. Three constraints, namely pairwise constraint, triplet constraint, and nearby constraint, are utilized to form an initial reconstruction with candidate faces in ambiguous areas. In the second stage, the topologies in ambiguous areas are removed and reconstructed by solving an integer linear optimization problem based on the initial constraints while considering data fitting degree. Experiments using real datasets reveal that compared with state-of-the-art methods, the proposed method can efficiently reconstruct 3D building models in seconds with the geometry accuracy in decimeter level.
Structural Damage Identification of Composite Rotors Based on Fully Connected Neural Networks and Convolutional Neural Networks
Authors: Scholz Veronika , Winkler Peter , Hornig Andreas , Gude Maik , Filippatos Angelos
Published: March 2021

Damage identification of composite structures is a major ongoing challenge for a secure operational life-cycle due to the complex, gradual damage behaviour of composite materials. Especially for composite rotors in aero-engines and wind-turbines, a cost-intensive maintenance service has to be performed in order to avoid critical failure. A major advantage of composite structures is that they are able to safely operate after damage initiation and under ongoing damage propagation. Therefore, a robust, efficient diagnostic damage identification method would allow monitoring the damage process with intervention occurring only when necessary. This study investigates the structural vibration response of composite rotors by applying machine learning methods and the ability to identify, localise and quantify the present damage. To this end, multiple fully connected neural networks and convolutional neural networks were trained on vibration response spectra from damaged composite rotors with barely visible damage, mostly matrix cracks and local delaminations using dimensionality reduction and data augmentation. A databank containing 720 simulated test cases with different damage states is used as a basis for the generation of multiple data sets. The trained models are tested using k-fold cross validation and they are evaluated based on the sensitivity, specificity and accuracy. Convolutional neural networks perform slightly better providing a performance accuracy of up to 99.3% for the damage localisation and quantification.
NT-FDS—A Noise Tolerant Fall Detection System Using Deep Learning on Wearable Devices
Authors: Waheed Marvi , Afzal Hammad , Mehmood Khawir
Published: March 2021

Given the high prevalence and detrimental effects of unintentional falls in the elderly, fall detection has become a pertinent public concern. A Fall Detection System (FDS) gathers information from sensors to distinguish falls from routine activities in order to provide immediate medical assistance. Hence, the integrity of collected data becomes imperative. Presence of missing values in data, caused by unreliable data delivery, lossy sensors, local interference and synchronization disturbances and so forth, greatly hamper the credibility and usefulness of data making it unfit for reliable fall detection. This paper presents a noise tolerant FDS performing in presence of missing values in data. The work focuses on Deep Learning (DL) particularly Recurrent Neural Networks (RNNs) with an underlying Bidirectional Long Short-Term Memory (BiLSTM) stack to implement FDS based on wearable sensors. The proposed technique is evaluated on two publicly available datasets—SisFall and UP-Fall Detection. Our system produces an accuracy of 97.21% and 97.41%, sensitivity of 96.97% and 99.77% and specificity of 93.18% and 91.45% on SisFall and UP-Fall Detection respectively, thus outperforming the existing state of the art on these benchmark datasets. The resultant outcomes suggest that the ability of BiLSTM to retain long term dependencies from past and future make it an appropriate model choice to handle missing values for wearable fall detection systems.
Deep PUF: A Highly Reliable DRAM PUF-Based Authentication for IoT Networks Using Deep Convolutional Neural Networks
Authors: Najafi Fatemeh , Kaveh Masoud , Martín Diego , Reza Mosavi Mohammad
Published: March 2021

Traditional authentication techniques, such as cryptographic solutions, are vulnerable to various attacks occurring on session keys and data. Physical unclonable functions (PUFs) such as dynamic random access memory (DRAM)-based PUFs are introduced as promising security blocks to enable cryptography and authentication services. However, PUFs are often sensitive to internal and external noises, which cause reliability issues. The requirement of additional robustness and reliability leads to the involvement of error-reduction methods such as error correction codes (ECCs) and pre-selection schemes that cause considerable extra overheads. In this paper, we propose deep PUF: a deep convolutional neural network (CNN)-based scheme using the latency-based DRAM PUFs without the need for any additional error correction technique. The proposed framework provides a higher number of challenge-response pairs (CRPs) by eliminating the pre-selection and filtering mechanisms. The entire complexity of device identification is moved to the server side that enables the authentication of resource-constrained nodes. The experimental results from a 1Gb DDR3 show that the responses under varying conditions can be classified with at least a 94.9% accuracy rate by using CNN. After applying the proposed authentication steps to the classification results, we show that the probability of identification error can be drastically reduced, which leads to a highly reliable authentication.
Measuring Nanoscale Distances by Structured Illumination Microscopy and Image Cross-Correlation Spectroscopy (SIM-ICCS)
Authors: Cainero Isotta , Cerutti Elena , Faretta Mario , Dellino Gaetano Ivan , Pelicci Pier Giuseppe , Diaspro Alberto , Lanzanò Luca
Published: March 2021

Since the introduction of super-resolution microscopy, there has been growing interest in quantifying the nanoscale spatial distributions of fluorescent probes to better understand cellular processes and their interactions. One way to check if distributions are correlated or not is to perform colocalization analysis of multi-color acquisitions. Among all the possible methods available to study and quantify the colocalization between multicolor images, there is image cross-correlation spectroscopy (ICCS). The main advantage of ICCS, in comparison with other co-localization techniques, is that it does not require pre-segmentation of the sample into single objects. Here we show that the combination of structured illumination microscopy (SIM) with ICCS (SIM-ICCS) is a simple approach to quantify colocalization and measure nanoscale distances from multi-color SIM images. We validate the SIM-ICCS analysis on SIM images of optical nanorulers, DNA-origami-based model samples containing fluorophores of different colors at a distance of 80 nm. The SIM-ICCS analysis is compared with an object-based analysis performed on the same samples. Finally, we show that SIM-ICCS can be used to quantify the nanoscale spatial distribution of functional nuclear sites in fixed cells.
Invasive Devices and Sensors for Remote Care of Heart Failure Patients
Authors: Radhoe Sumant P. , Veenis Jesse F. , Brugts Jasper J.
Published: March 2021

The large and growing burden of chronic heart failure (CHF) on healthcare systems and economies is mainly caused by a high hospital admission rate for acute decompensated heart failure (HF). Several remote monitoring techniques have been developed for early detection of worsening disease, potentially limiting the number of hospitalizations. Over the last years, the scope has been shifting towards the relatively novel invasive sensors capable of measuring intracardiac filling pressures, because it is believed that hemodynamic congestion precedes clinical congestion. Monitoring intracardiac pressures may therefore enable clinicians to intervene and avert hospitalizations in a pre-symptomatic phase. Several techniques have been discussed in this review, and thus far, remote monitoring of pulmonary artery pressures (PAP) by the CardioMEMS (CardioMicroelectromechanical system) HF System is the only technique with proven safety as well as efficacy with regard to the prevention of HF-related hospital admissions. Efforts are currently aimed to further develop existing techniques and new sensors capable of measuring left atrial pressures (LAP). With the growing body of evidence and need for remote care, it is expected that remote monitoring by invasive sensors will play a larger role in HF care in the near future.
A Multivariate Time-Series Based Approach for Quality Modeling in Wireless Networks
Authors: Aguayo Leonardo , Fortes Sergio , Baena Carlos , Baena Eduardo , Barco Raquel
Published: March 2021

This work presents a method for estimating key quality indicators (KQIs) from measurements gathered at the nodes of a wireless network. The procedure employs multivariate adaptive filtering and a clustering algorithm to produce a KQI time-series suitable for post-processing by the network management system. The framework design, aimed to be applied to 5G and 6G systems, can cope with a nonstationary environment, allow fast and online training, and provide flexibility for its implementation. The concept’s feasibility was evaluated using measurements collected from a live heterogeneous network, and initial results were compared to other linear regression techniques. Suggestions for modifications in the algorithms are also described, as well as directions for future research.
IT-SVO: Improved Semi-Direct Monocular Visual Odometry Combined with JS Divergence in Restricted Mobile Devices
Authors: Liu Chang , Zhao Jin , Sun Nianyi , Yang Qingrong , Wang Leilei
Published: March 2021

Simultaneous localization and mapping (SLAM) has a wide range for applications in mobile robotics. Lightweight and inexpensive vision sensors have been widely used for localization in GPS-denied or weak GPS environments. Mobile robots not only estimate their pose, but also correct their position according to the environment, so a proper mathematical model is required to obtain the state of robots in their circumstances. Usually, filter-based SLAM/VO regards the model as a Gaussian distribution in the mapping thread, which deals with the complicated relationship between mean and covariance. The covariance in SLAM or VO represents the uncertainty of map points. Therefore, the methods, such as probability theory and information theory play a significant role in estimating the uncertainty. In this paper, we combine information theory with classical visual odometry (SVO) and take Jensen-Shannon divergence (JS divergence) instead of Kullback-Leibler divergence (KL divergence) to estimate the uncertainty of depth. A more suitable methodology for SVO is that explores to improve the accuracy and robustness of mobile devices in unknown environments. Meanwhile, this paper aims to efficiently utilize small portability for location and provide a priori knowledge of the latter application scenario. Therefore, combined with SVO, JS divergence is implemented, which has been realized. It not only has the property of accurate distinction of outliers, but also converges the inliers quickly. Simultaneously, the results show, under the same computational simulation, that SVO combined with JS divergence can more accurately locate its state in the environment than the combination with KL divergence.
The Extensive Usage of the Facial Image Threshing Machine for Facial Emotion Recognition Performance
Authors: Kim Jung Hwan , Poulose Alwin , Han Dong Seog
Published: March 2021

Facial emotion recognition (FER) systems play a significant role in identifying driver emotions. Accurate facial emotion recognition of drivers in autonomous vehicles reduces road rage. However, training even the advanced FER model without proper datasets causes poor performance in real-time testing. FER system performance is heavily affected by the quality of datasets than the quality of the algorithms. To improve FER system performance for autonomous vehicles, we propose a facial image threshing (FIT) machine that uses advanced features of pre-trained facial recognition and training from the Xception algorithm. The FIT machine involved removing irrelevant facial images, collecting facial images, correcting misplacing face data, and merging original datasets on a massive scale, in addition to the data-augmentation technique. The final FER results of the proposed method improved the validation accuracy by 16.95% over the conventional approach with the FER 2013 dataset. The confusion matrix evaluation based on the unseen private dataset shows a 5% improvement over the original approach with the FER 2013 dataset to confirm the real-time testing.
A Reinforcement Learning Approach to View Planning for Automated Inspection Tasks
Authors: Landgraf Christian , Meese Bernd , Pabst Michael , Martius Georg , Huber Marco F.
Published: March 2021

Manual inspection of workpieces in highly flexible production facilities with small lot sizes is costly and less reliable compared to automated inspection systems. Reinforcement Learning (RL) offers promising, intelligent solutions for robotic inspection and manufacturing tasks. This paper presents an RL-based approach to determine a high-quality set of sensor view poses for arbitrary workpieces based on their 3D computer-aided design (CAD). The framework extends available open-source libraries and provides an interface to the Robot Operating System (ROS) for deploying any supported robot and sensor. The integration into commonly used OpenAI Gym and Baselines leads to an expandable and comparable benchmark for RL algorithms. We give a comprehensive overview of related work in the field of view planning and RL. A comparison of different RL algorithms provides a proof of concept for the framework’s functionality in experimental scenarios. The obtained results exhibit a coverage ratio of up to 0.8 illustrating its potential impact and expandability. The project will be made publicly available along with this article.
Validity of a Novel Research-Grade Physical Activity and Sleep Monitor for Continuous Remote Patient Monitoring
Authors: McDevitt Bríd , Moore Lisa , Akhtar Nishat , Connolly James , Doherty Rónán , Scott William
Published: March 2021

In the midst of the COVID-19 pandemic, Remote Patient Monitoring technologies are highly important for clinicians and researchers. These connected-health technologies enable monitoring of patients and facilitate remote clinical trial research while reducing the potential for the spread of the novel coronavirus. There is a growing requirement for monitoring of the full 24 h spectrum of behaviours with a single research-grade sensor. This research describes a free-living and supervised protocol comparison study of the Verisense inertial measurement unit to assess physical activity and sleep parameters and compares it with the Actiwatch 2 actigraph. Fifteen adults (11 males, 23.4 ± 3.4 years and 4 females, 29 ± 12.6 years) wore both monitors for 2 consecutive days and nights in the free-living study while twelve adults (11 males, 23.4 ± 3.4 years and 1 female, 22 ± 0 years) wore both monitors for the duration of a gym-based supervised protocol study. Agreement of physical activity epoch-by-epoch data with activity classification of sedentary, light and moderate-to-vigorous activity and sleep metrics were evaluated using Spearman’s rank-order correlation coefficients and Bland–Altman plots. For all activity, Verisense showed high agreement for both free-living and supervised protocol of r = 0.85 and r = 0.78, respectively. For physical activity classification, Verisense showed high agreement of sedentary activity of r = 0.72 for free-living but low agreement of r = 0.36 for supervised protocol; low agreement of light activity of r = 0.42 for free-living and negligible agreement of r = −0.04 for supervised protocol; and moderate agreement of moderate-to-vigorous activity of r = 0.52 for free-living with low agreement of r = 0.49 for supervised protocol. For sleep metrics, Verisense showed moderate agreement for sleep time and total sleep time of r = 0.66 and 0.54, respectively, but demonstrated high agreement for determination of wake time of r = 0.83. Overall, our results showed moderate-high agreement of Verisense with Actiwatch 2 for assessing epoch-by-epoch physical activity and sleep, but a lack of agreement for activity classifications. Future validation work of Verisense for activity cut-point potentially holds promise for 24 h continuous remote patient monitoring.
CMUT-Based Sensor for Acoustic Emission Application: Experimental and Theoretical Contributions to Sensitivity Optimization
Authors: Boubenia Redha , Le Moal Patrice , Bourbon Gilles , Ramasso Emmanuel , Joseph Eric
Published: March 2021

The paper deals with a capacitive micromachined ultrasonic transducer (CMUT)-based sensor dedicated to the detection of acoustic emissions from damaged structures. This work aims to explore different ways to improve the signal-to-noise ratio and the sensitivity of such sensors focusing on the design and packaging of the sensor, electrical connections, signal processing, coupling conditions, design of the elementary cells and operating conditions. In the first part, the CMUT-R100 sensor prototype is presented and electromechanically characterized. It is mainly composed of a CMUT-chip manufactured using the MUMPS process, including 40 circular 100 µm radius cells and covering a frequency band from 310 kHz to 420 kHz, and work on the packaging, electrical connections and signal processing allowed the signal-to-noise ratio to be increased from 17 dB to 37 dB. In the second part, the sensitivity of the sensor is studied by considering two contributions: the acoustic-mechanical one is dependent on the coupling conditions of the layered sensor structure and the mechanical-electrical one is dependent on the conversion of the mechanical vibration to electrical charges. The acoustic-mechanical sensitivity is experimentally and numerically addressed highlighting the care to be taken in implementation of the silicon chip in the brass housing. Insertion losses of about 50% are experimentally observed on an acoustic test between unpackaged and packaged silicon chip configurations. The mechanical-electrical sensitivity is analytically described leading to a closed-form amplitude of the detected signal under dynamic excitation. Thus, the influence of geometrical parameters, material properties and operating conditions on sensitivity enhancement is clearly established: such as smaller electrostatic air gap, and larger thickness, Young’s modulus and DC bias voltage.
Detection of Important Scenes in Baseball Videos via a Time-Lag-Aware Multimodal Variational Autoencoder
Authors: Hirasawa Kaito , Maeda Keisuke , Ogawa Takahiro , Haseyama Miki
Published: March 2021

A new method for the detection of important scenes in baseball videos via a time-lag-aware multimodal variational autoencoder (Tl-MVAE) is presented in this paper. Tl-MVAE estimates latent features calculated from tweet, video, and audio features extracted from tweets and videos. Then, important scenes are detected by estimating the probability of the scene being important from estimated latent features. It should be noted that there exist time-lags between tweets posted by users and videos. To consider the time-lags between tweet features and other features calculated from corresponding multiple previous events, the feature transformation based on feature correlation considering such time-lags is newly introduced to the encoder in MVAE in the proposed method. This is the biggest contribution of the Tl-MVAE. Experimental results obtained from actual baseball videos and their corresponding tweets show the effectiveness of the proposed method.
Greening the City: How to Get Rid of Garden Pavement! The ‘Steenbreek’ Program as a Dutch Example
Authors: Stobbelaar Derk Jan , van der Knaap Wim , Spijker Joop
Published: March 2021

The Steenbreek program is a private Dutch program which aims to involve citizens, municipalities and other stakeholders in replacing pavement with vegetation in private gardens. The Dutch approach is characterized by minimal governmental incentives or policy, which leaves a niche for private initiatives like Steenbreek, that mainly work on behavioural change. The aim of this paper is to build a model based on theory that can be used to improve and better evaluate depaving actions that are based on behavioural change. We tested this garden greening behaviour model in the Steenbreek program. The main result is that the model provides an understanding of the ‘how and why’ of the Steenbreek initiatives. Based on this we are able to provide recommendations for the improvement of future initiatives. Steenbreek covers a wide range of projects that together, in very different ways, take into account elements of the theoretical framework; either more on information factors, or on supporting factors, sometimes taking all elements together in a single action. This focus is sometimes understandable when just one element is needed (e.g., support), sometimes more elements could be taken into account to be more effective. If a certain element of the framework is lacking, the change of behaviour will not (or will only partly) take place. The model also gives insight into a more specific approach aimed at the people most susceptible to changing their behaviour, which would make actions more effective.
Comparison of Smart City Standards, Implementation and Cluster Models of Cities in North America and Europe
Authors: Kubina Milan , Šulyová Dominika , Vodák Josef
Published: March 2021

Sustainability in Smart Cities is a current and trendy topic in a global sense. The primary impetus for writing this article was to create a general implementation model for the smart governance of European Smart Cities based on the American best practice. The ambition is to be able to modify the generally created model to meet the local conditions of all countries. The aim of the article is to point out the essential elements and differences between the implementation standards, models and clusters in the cities of North America and Europe, including their benefits and limitations. This article compared standards, implementation and cluster models for Smart Cities in North America and Europe through a secondary analysis from Arcadis and IDC consultants, standards agencies, and relevant sources. In addition, comparisons and summaries of the results were used. The results of this article point out the fundamental differences between the American and European approaches to building Smart Cities. American models are more centrist-oriented to people and complex in their simplicity, thus achieving a higher degree of reputation. Europeans are less consistent and top-down oriented. The new model will make European Smart Cities more focused on the needs and expectations of all stakeholders. The main results of this article are the answers to the research questions and the general implementation model, the verification of which will take place in practice in the future.
Evaluating the Implementation of the “Build-Back-Better” Concept for Critical Infrastructure Systems: Lessons from Saint-Martin’s Island Following Hurricane Irma
Authors: Der Sarkissian Rita , Dabaj Anas , Diab Youssef , Vuillet Marc
Published: March 2021

A limited number of studies in the scientific literature discuss the “Build-Back-Better” (BBB) critical infrastructure (CI) concept. Investigations of its operational aspects and its efficient implementation are even rarer. The term “Better” in BBB is often confusing to practitioners and leads to unclear and non-uniform objectives for guiding accurate decision-making. In an attempt to fill these gaps, this study offers a conceptual analysis of BBB’s operational aspects by examining the term “Better”. In its methodological approach, this study evaluates the state of Saint-Martin’s CI before and after Hurricane Irma and, accordingly, reveals the indicators to assess during reconstruction projects. The proposed methods offer practitioners a guidance tool for planning efficient BBB CI projects or for evaluating ongoing programs through the established BBB evaluation grid. Key findings of the study offer insights and a new conceptual equation of the BBB CI by revealing the holistic and interdisciplinary connotations behind the term “Better” CI: “Build-Back-resilient”, “Build-Back-sustainable”, and “Build-Back-accessible to all and upgraded CI”. The proposed explanations can facilitate the efficient application of BBB for CI by operators, stakeholders, and practitioners and can help them to contextualize the term “Better” with respect to their area and its CI systems.
Governance for Sustainability: Patterns of Regulation and Self-Regulation in the German Wine Industry
Authors: Spraul Katharina , Höfert Annegret
Published: March 2021

To foster sustainability pursuits, regulation by state-imposed legislation is often crucial, but self-regulation by corporations, associations, and other non-state actors increasingly exerts pressures and provides incentives for sustainable practices. In order to shed more light on the complex interplay among sustainability regulations and self-regulation, this study focused on a highly regulated field: the German wine industry. Using a social network analysis, this study identified the most central actors (e.g., associations, regulatory institutions) that need to be addressed in order to ensure the enforcement of sustainability. By analyzing 15 semi-structured interviews with the key actors, we outlined their understanding of sustainability, and classified three distinctive governance patterns. These mixed methods and in-depth analyses revealed that self-regulation by associations plays a crucial role in terms of enhancing sustainability, but regulation remains an important trigger in this context. This article concludes with some lessons for regulation and self-regulation policies that can ensure sustainability within an organizational field.
The Impact of Land Use Change on Disaster Risk from the Perspective of Efficiency
Authors: Su Qingmu , Chen Kaida , Liao Lingyun
Published: March 2021

The increasing demand of humankind has caused a large number of land use changes, which pose a direct or indirect threat to the environment while promoting economic growth. The lack of risk-oriented land use changes may increase the disaster risk in the region. Therefore, how to study the relationship between land use change and disaster risk deserves attention. In this study, a research framework with quantitative relationship between land use change and disaster risk was constructed from the perspective of efficiency. The framework integrated land use change, disaster losses and environment variable (runoff increment) into a three-stage data envelopment analysis (DEA) assessment model to dynamically evaluate the impact of land use changes on disasters. The main conclusions include: (I) after the influence of runoff increment and random error was excluded, the overall risk score of counties and cities in Taiwan is 0.643, which represents a relatively high level, indicating that land use changes have caused high disaster risk; and (II) the vulnerability of land development in each county and city can be obtained through the comprehensive score of disaster risk the amount of unused input. The results of this study can help government agencies to rank various types of land development and then determine the acceptable risk level and incorporate disaster risk into land development.
Sustainability Practices in Hospitality: Case Study of a Luxury Hotel in Arrábida Natural Park
Authors: Pereira Vitor , Silva Graça Miranda , Dias Álvaro
Published: March 2021

The agenda of researchers and practitioners in the tourism industry has focused on the demand for sustainable practices. Luxury hotels have also followed this trend, although studies are still limited. The aim of this study is to understand what environmental and social sustainability practices luxury hotels are adopting and how their management perceive the benefits and results from their adoption. This empirical research is based on a case study that triangulates data from semi-structured interviews, document analysis, and observation. The results show that luxury hotels have implemented environmental practices that address energy efficiency measures that reduce water consumption, waste, and carbon emissions and that control, protect, and maintain fauna and flora to promote of unique natural landscapes. They have focused on social practices that address the relations with charitable institutions, provide consumers high-quality services, and create a safe and healthy work environment. Luxury hotels have successfully implemented these practices to reduce operational costs. The study’s findings provide practical guidance for hotel managers who seek to implement sustainability practices.
Sustainability through Operational Excellence: An Emerging Country Perspective
Authors: Henríquez-Machado Rafael , Muñoz-Villamizar Andrés , Santos Javier
Published: March 2021

Operational excellence is directly related to improving performance and efficiency in every dimension of sustainability (i.e., economic, environmental and social). Consequently, companies are implementing this initiative to remain competitive in their industries. Previous works measured operational excellence in first-world companies. However, it is expected that the productivity level in many companies from developing or emerging countries is far below the average level of developed companies. In this context, the research objectives of this study are to (1) evaluate the level of implementation of operational excellence, impacting the three dimensions of sustainability simultaneously in some companies from the central-savanna region of Colombia; (2) compare their implementation status with similar studies carried out in companies of developed countries. In order to solve it, we first present an empirical assessment of operational excellence in that region using semistructured interviews in 79 different companies. Then, we compare the obtained results with the assessments available in the literature. Our results show that operational excellence performance in Colombian companies is lower than in developed companies in terms of economic performance by operations standards and continuous improvement schemes, environmental sustainability and social sustainability.
Mobility Need-Adaptive Housing Platforms: The Benefit of a Commute Time Search Feature
Authors: Lembcke Tim-Benjamin , Willnat Mathias , Lechte Henrik , Greve Maike , Heinsohn Julia , Brendel Alfred Benedikt
Published: March 2021

The growing influx of people to urban areas has resulted in a tense housing market in many places, making the search for a suitable residence an increased challenge. Dedicated online platforms facilitate this process and offer two distinct approaches to find suitable accommodations concerning its location. Traditionally, users can search for a general area like a city to narrow down the results displayed. Additionally, some platforms offer searches based on the maximum commute time between apartments and points of interest. This paper investigates the benefit such approaches yield concerning technology acceptance and the fit of the task and information representation. Thus, a prototypically implemented online platform with and without a commute time search feature was evaluated in an online experiment. The treatment specification achieved significantly better results in terms of information quality and technology acceptance, implicating that such a design should be preferred for websites that facilitate the search for apartments. These insights can contribute to an enhanced understanding of visual system design to reduce the negative sustainability impacts of traffic induced by a divergence of residential and workplaces.
Quantifying Road-Network Robustness toward Flood-Resilient Transportation Systems
Authors: Tachaudomdach Suchat , Upayokin Auttawit , Kronprasert Nopadon , Arunotayanun Kriangkrai
Published: March 2021

Amidst sudden and unprecedented increases in the severity and frequency of climate-change-induced natural disasters, building critical infrastructure resilience has become a prominent policy issue globally for reducing disaster risks. Sustainable measures and procedures to strengthen preparedness, response, and recovery of infrastructures are urgently needed, but the standard for measuring such resilient elements has yet to be consensually developed. This study was undertaken with an aim to quantitatively measure transportation infrastructure robustness, a proactive dimension of resilience capacities and capabilities to withstand disasters; in this case, floods. A four-stage analytical framework was empirically implemented: (1) specifying the system and disturbance (i.e., road network and flood risks in Chiang Mai, Thailand), (2) illustrating the system response using the damaged area as a function of floodwater levels and protection measures, (3) determining recovery thresholds based on land use and system functionality, and (4) quantifying robustness through the application of edge- and node-betweenness centrality models. Various quantifiable indicators of transportation robustness can be revealed; not only flood-damaged areas commonly considered in flood-risk management and spatial planning, but also the numbers of affected traffic links, nodes, and cars are highly valuable for transportation planning in achieving sustainable flood-resilient transportation systems.
Revisiting the Happy-Productive Worker Thesis from a Eudaimonic Perspective: A Systematic Review
Authors: Peiró José M. , Montesa David , Soriano Aida , Kozusznik Malgorzata W. , Villajos Esther , Magdaleno Jorge , Djourova Nia Plamenova , Ayala Yarid
Published: March 2021

The happy-productive worker thesis (HPWT) is considered the Holy Grail of management research, and it proposes caeteris paribus, happy workers show higher performance than their unhappy counterparts. However, eudaimonic well-being in the relationship between happiness and performance has been understudied. This paper provides a systematized review of empirical evidence in order to make a theoretical contribution to the happy-productive worker thesis from a eudaimonic perspective. Our review covers 105 quantitative studies and 188 relationships between eudaimonic well-being and performance. Results reveal that analyzing the eudaimonic facet of well-being provides general support for the HPWT and a much more comprehensive understanding of how it has been studied. However, some gaps and nuances are identified and discussed, opening up challenging avenues for future empirical research to clarify important questions about the relationship between happiness and performance in organizations.
The Crossovers and Connectivity between Systems Engineering and the Sustainable Development Goals: A Scoping Study
Authors: Yang Lan , Cormican Kathryn
Published: March 2021

The United Nation’s sustainable development goals (SDGs) are interconnected and indivisible and need to be addressed in a systematic and holistic way. However, a lack of stakeholder perspective, fragmented responses, and a dearth of integration across sectors have long been perceived as the SDGs’ main pitfalls. In recent years, scholars are calling to address these issues by adopting a systems engineering perspective, as this approach espouses a stakeholder-focused position, embraces a holistic and dynamic mindset, and provides a variety of technical and managerial toolkits, which can help to untangle the complexity and interactions inherent in global sustainability. Nevertheless, little has been done to map the existing literature, comprehensively review, and synthesize research evidence in this field. Therefore, this paper aims to conduct a scoping study that analyzes the extant evidence to uncover the contributions of systems engineering in advancing the SDGs. A three-phase methodology integrating natural language processing and systematic literature review is used to investigate this space. We conclude that systems engineering has been an active catalyst promoting the SDGs, and that systems engineering has the potential to support more transdisciplinary research to achieve long-term transformational and sustainable change across sectors and disciplines.
A Thermal Analysis-Based Approach to Identify Different Waste Macroplastics in Beach Litter: The Case Study of Aquatina di Frigole NATURA 2000 Site (IT9150003, Italy)
Authors: Frigione Mariaenrica , Marini Gabriele , Pinna Maurizio
Published: March 2021

The dispersion of commercial plastics in the marine environments is a major threat to biodiversity and ecosystem services of the last decades. The lower density of the plastics with respect to marine water density determines their floating, transferring, and accumulation in sandy beaches. Sandy beaches represent a natural sink ecosystem for marine plastics, where the latter are fragmented and photo-degraded in relation to the kind of polymer. Here, we propose an accurate and cost-effective method, the differential scanning calorimetry (DSC), to identify different polymers from plastic samples collected on the Aquatina di Frigole beach (Apulia Region, Southeast of Italy), included in the NATURA 2000 Site coded as IT9150003. Our results reveal the exclusive presence of thermoplastic polymers in the beach plastic samples, mostly belonging to the polyolefin family. They appear to be remnants of larger plastic fragments, which could impact biodiversity and ecosystem services such as beach recreation activities and tourism.
A Mean Extragradient Method for Solving Variational Inequalities
Authors: Buakird Apichit , Nimana Nimit , Petrot Narin
Published: March 2021

We propose a modified extragradient method for solving the variational inequality problem in a Hilbert space. The method is a combination of the well-known subgradient extragradient with the Mann’s mean value method in which the updated iterate is picked in the convex hull of all previous iterates. We show weak convergence of the mean value iterate to a solution of the variational inequality problem, provided that a condition on the corresponding averaging matrix is fulfilled. Some numerical experiments are given to show the effectiveness of the obtained theoretical result.
Rat Tumour Histopathology Associated with Experimental Chronic Dietary Exposure to Ochratoxin A in Prediction of the Mycotoxin’s Risk for Human Cancers
Authors: Herman Diana , Mantle Peter
Published: March 2021

Mammalian animal toxicity of ochratoxin A (OTA) has focused largely in the past half-century on pigs because of initial recognition of it as a principal cause of intermittent growth suppression and renal disease caused by mouldy feed. Subsequent classical toxicology has used laboratory rodents because renal pathology in pigs raised questions concerning possible involvement in the human idiopathic bilateral renal atrophy of Balkan endemic nephropathy for which OTA was a focus of attention for human nephropathy through 1980s and into 2000s. Emphasis on human nephropathy has more recently concerned the plant metabolite aristolochic acid. Recognition that agricultural management can often minimise food and feed-stuff spoilage by OTA-producing Aspergilli and Penicillia has moderated some of the risks for animals. Legislation for human food safety combined with sophisticated analysis generally provides safety in the developed world. Chronic experimental exposure of male rats, in the absence of clinical dis-ease, specifically causes renal cancer. The possibility of this as a unique model for the human has generated considerable experimental evidence which may be more directly relevant for carcinogenesis in the complex kidney than that obtained from biochemical toxicities in vitro. Nevertheless, there does not appear to be any case of human renal or urinary tract cancer for which there is verified etiological proof for causation by OTA, contrary to much claim in the literature. To contribute to such debate, histopathology review of OTA/rat renal cancers, augmented where appropriate by immune profiles, has been completed for all remaining tumours in our research archive. Overall consistency of positivity for vimentin, is matched with occasional positives either for CD10 or the cytokeratin MNF 116. The current situation is discussed. Suggestion that OTA could cause human testicular cancer has also been challenged as unsupported by any experimental findings in rats, where the Leydig cell tumour immune profile does not match that of human germ cell neoplasms.
Botulinum Toxin A and Osteosarcopenia in Experimental Animals: A Scoping Review
Authors: Tang Min Jia , Graham H. Kerr , Davidson Kelsey E.
Published: March 2021

We conducted a scoping review to investigate the effects of intramuscular injection of Botulinum Toxin A (BoNT-A) on bone morphology. We investigated if the muscle atrophy associated with Injection of BoNT-A had effects on the neighboring bone. We used the search terms: osteopenia, bone atrophy, Botulinum Toxin A, Micro-CT, mice or rat. The following databases were searched: Medline, Embase, PubMed and the Cochrane Library, between 1990 and 2020. After removal of duplicates, 228 abstracts were identified of which 49 studies satisfied our inclusion and exclusion criteria. The majority of studies (41/49) reported a quantitative reduction in at least one measure of bone architecture based on Micro-CT. The reduction in the ratio of bone volume to tissue volume varied from 11% to 81% (mean 43%) according to the experimental set up and study time points. While longer term studies showed muscle recovery, no study showed complete recovery of all bone properties at the termination of the study. In experimental animals, intramuscular injection of BoNT-A resulted in acute muscle atrophy and acute degradation of the neighboring bone segment. These findings may have implications for clinical protocols in the use of Botulinum Toxin in children with cerebral palsy, with restraint recommended in injection protocols and consideration for monitoring bone density. Clinical studies in children with cerebral palsy receiving injections of Botulinum are indicated.
Exploring the Human Cytomegalovirus Core Nuclear Egress Complex as a Novel Antiviral Target: A New Type of Small Molecule Inhibitors
Authors: Alkhashrom Sewar , Kicuntod Jintawee , Häge Sigrun , Schweininger Johannes , Muller Yves A. , Lischka Peter , Marschall Manfred , Eichler Jutta
Published: March 2021

Nuclear egress is an essential process in the replication of human cytomegalovirus (HCMV), as it enables the migration of newly formed viral capsids from the nucleus into the cytoplasm. Inhibition of the HCMV core nuclear egress complex (core NEC), composed of viral proteins pUL50 and pUL53, has been proposed as a potential new target for the treatment of HCMV infection and disease. Here, we present a new type of small molecule inhibitors of HCMV core NEC formation, which inhibit the pUL50-pUL53 interaction at nanomolar concentrations. These inhibitors, i.e., verteporfin and merbromin, were identified through the screening of the Prestwick Chemical Library® of approved drug compounds. The inhibitory effect of merbromin is both compound- and target-specific, as no inhibition was seen for other mercury-organic compounds. Furthermore, merbromin does not inhibit an unrelated protein–protein interaction either. More importantly, merbromin was found to inhibit HCMV infection of cells in three different assays, as well as to disrupt HCMV NEC nuclear rim formation. Thus, while not being an ideal drug candidate by itself, merbromin may serve as a blueprint for small molecules with high HCMV core NEC inhibitory potential, as candidates for novel anti-herpesviral drugs.