bluuebunny/crossref_metadata_2025 huggingface dataset:
Does Gait with an Ankle Foot Orthosis Improve or Compromise Minimum Foot Clearance?
Authors: Fonseca Pedro , Machado Leandro , Sousa Manoela Vieira , Sebastião Ricardo , Sousa Filipa , Figueiredo Joana , Santos Cristina P. , Vilas-Boas João Paulo
Published: December 2021

The purpose of this study was to investigate if the use of an ankle foot orthosis in passive mode (without actuation) could modify minimum foot clearance, and if there are any compensatory mechanisms to enable these changes during treadmill gait at a constant speed. Eight participants walked on an instrumented treadmill without and with an ankle foot orthosis on the dominant limb at speeds of 0.8, 1.2, and 1.6 km/h. For each gait cycle, the minimum foot clearance and some gait linear kinematic parameters were calculated by an inertial motion capture system. Additionally, maximum hip and knee flexion and maximum ankle plantar flexion were calculated. There were no significant differences in the minimum foot clearance between gait conditions and lower limbs. However, differences were found in the swing, stance and step times between gait conditions, as well as between limbs during gait with orthosis (p < 0.05). An increase in hip flexion during gait with orthosis was observed for all speeds, and different ankle ranges of motion were observed according to speed (p < 0.05). Thus, the use of an ankle foot orthosis in passive mode does not significantly hinder minimum foot clearance, but can change gait linear and angular parameters in non-pathological individuals.
Visual Attention and Color Cues for 6D Pose Estimation on Occluded Scenarios Using RGB-D Data
Authors: Vidal Joel , Lin Chyi-Yeu , Martí Robert
Published: December 2021

Recently, 6D pose estimation methods have shown robust performance on highly cluttered scenes and different illumination conditions. However, occlusions are still challenging, with recognition rates decreasing to less than 10% for half-visible objects in some datasets. In this paper, we propose to use top-down visual attention and color cues to boost performance of a state-of-the-art method on occluded scenarios. More specifically, color information is employed to detect potential points in the scene, improve feature-matching, and compute more precise fitting scores. The proposed method is evaluated on the Linemod occluded (LM-O), TUD light (TUD-L), Tejani (IC-MI) and Doumanoglou (IC-BIN) datasets, as part of the SiSo BOP benchmark, which includes challenging highly occluded cases, illumination changing scenarios, and multiple instances. The method is analyzed and discussed for different parameters, color spaces and metrics. The presented results show the validity of the proposed approach and their robustness against illumination changes and multiple instance scenarios, specially boosting the performance on relatively high occluded cases. The proposed solution provides an absolute improvement of up to 30% for levels of occlusion between 40% to 50%, outperforming other approaches with a best overall recall of 71% for the LM-O, 92% for TUD-L, 99.3% for IC-MI and 97.5% for IC-BIN.
Coarse-to-Fine Hand–Object Pose Estimation with Interaction-Aware Graph Convolutional Network
Authors: Zhang Maomao , Li Ao , Liu Honglei , Wang Minghui
Published: December 2021

The analysis of hand–object poses from RGB images is important for understanding and imitating human behavior and acts as a key factor in various applications. In this paper, we propose a novel coarse-to-fine two-stage framework for hand–object pose estimation, which explicitly models hand–object relations in 3D pose refinement rather than in the process of converting 2D poses to 3D poses. Specifically, in the coarse stage, 2D heatmaps of hand and object keypoints are obtained from RGB image and subsequently fed into pose regressor to derive coarse 3D poses. As for the fine stage, an interaction-aware graph convolutional network called InterGCN is introduced to perform pose refinement by fully leveraging the hand–object relations in 3D context. One major challenge in 3D pose refinement lies in the fact that relations between hand and object change dynamically according to different HOI scenarios. In response to this issue, we leverage both general and interaction-specific relation graphs to significantly enhance the capacity of the network to cover variations of HOI scenarios for successful 3D pose refinement. Extensive experiments demonstrate state-of-the-art performance of our approach on benchmark hand–object datasets.
Large-Scale Internet of Things Multi-Sensor Measurement Node for Smart Grid Enhancement
Authors: Petrariu Adrian I. , Coca Eugen , Lavric Alexandru
Published: December 2021

Electric power infrastructure has revolutionized our world and our way of living has completely changed. The necessary amount of energy is increasing faster than we realize. In these conditions, the grid is forced to run against its limitations, resulting in more frequent blackouts. Thus, urgent solutions need to be found to meet this greater and greater energy demand. By using the internet of things infrastructure, we can remotely manage distribution points, receiving data that can predict any future failure points on the grid. In this work, we present the design of a fully reconfigurable wireless sensor node that can sense the smart grid environment. The proposed prototype uses a modular developed hardware platform that can be easily integrated into the smart grid concept in a scalable manner and collects data using the LoRaWAN communication protocol. The designed architecture was tested for a period of 6 months, revealing the feasibility and scalability of the system, and opening new directions in the remote failure prediction of low voltage/medium voltage switchgears on the electric grid.
Environment-Monitoring IoT Devices Powered by a TEG Which Converts Thermal Flux between Air and Near-Surface Soil into Electrical Energy
Authors: Paterova Tereza , Prauzek Michal , Konecny Jaromir , Ozana Stepan , Zmij Petr , Stankus Martin , Weise Dieter , Pierer Alexander
Published: December 2021

Energy harvesting has an essential role in the development of reliable devices for environmental wireless sensor networks (EWSN) in the Internet of Things (IoT), without considering the need to replace discharged batteries. Thermoelectric energy is a renewable energy source that can be exploited in order to efficiently charge a battery. The paper presents a simulation of an environment monitoring device powered by a thermoelectric generator (TEG) that harvests energy from the temperature difference between air and soil. The simulation represents a mathematical description of an EWSN, which consists of a sensor model powered by a DC/DC boost converter via a TEG and a load, which simulates data transmission, a control algorithm and data collection. The results section provides a detailed description of the harvested energy parameters and properties and their possibilities for use. The harvested energy allows supplying the load with an average power of 129.04 μW and maximum power of 752.27 μW. The first part of the results section examines the process of temperature differences and the daily amount of harvested energy. The second part of the results section provides a comprehensive analysis of various settings for the EWSN device’s operational period and sleep consumption. The study investigates the device’s number of operational cycles, quantity of energy used, discharge time, failures and overheads.
Enhanced Bounding Box Estimation with Distribution Calibration for Visual Tracking
Authors: Yu Bin , Tang Ming , Zhu Guibo , Wang Jinqiao , Lu Hanqing
Published: December 2021

Bounding box estimation by overlap maximization has improved the state of the art of visual tracking significantly, yet the improvement in robustness and accuracy is restricted by the limited reference information, i.e., the initial target. In this paper, we present DCOM, a novel bounding box estimation method for visual tracking, based on distribution calibration and overlap maximization. We assume every dimension in the modulation vector follows a Gaussian distribution, so that the mean and the variance can borrow from those of similar targets in large-scale training datasets. As such, sufficient and reliable reference information can be obtained from the calibrated distribution, leading to a more robust and accurate target estimation. Additionally, an updating strategy for the modulation vector is proposed to adapt the variation of the target object. Our method can be built on top of off-the-shelf networks without finetuning and extra parameters. It yields state-of-the-art performance on three popular benchmarks, including GOT-10k, LaSOT, and NfS while running at around 40 FPS, confirming its effectiveness and efficiency.
Experimental Detection and Measurement of Crack-Type Damage Features in Composite Thin-Wall Beams Using Modal Analysis
Authors: Pacheco-Chérrez Josué , Cárdenas Diego , Probst Oliver
Published: December 2021

An experimental proof-of-concept for damage detection in composite beams using modal analysis has been conducted. The purpose was to demonstrate that damage features can be detected, located, and measured on the surface of a relatively complex thin-wall beam made from composite material. (1) Background: previous work has been limited to the study of simple geometries and materials. (2) Methods: damage detection in the work is based on the accurate measurement of mode shapes and an appropriate design of the detection mesh. Both a method requiring information about the healthy structure and a baseline-free method have been implemented. (3) Results: short crack-type damage features, both longitudinal and transverse, were detected reliably, and the true length of the crack can be estimated from the damage signal. Simultaneous detection of two cracks on the same sample is also possible. (4) This work demonstrates the feasibility of automated damage detection in composite beams using sensor arrays.
Validating a 5G-Enabled Neutral Host Framework in City-Wide Deployments
Authors: Fernández-Fernández Adriana , Colman-Meixner Carlos , Ochoa-Aday Leonardo , Betzler August , Khalili Hamzeh , Siddiqui Muhammad Shuaib , Carrozzo Gino , Figuerola Sergi , Nejabati Reza , Simeonidou Dimitra
Published: December 2021

Along with the adoption of 5G, the development of neutral host solutions provides a unique opportunity for mobile networks operators to accommodate the needs of emerging use-cases and in the consolidation of new business models. By exploiting the concept of network slicing, as one key enabler in the transition to 5G, infrastructure and service providers can logically split a shared physical network into multiple isolated and customized networks to flexibly address the specific demands of those tenant slices. Motivated by this reality, the H2020 5GCity project proposed a novel 5G-enabled neutral host framework for three European cities: Barcelona (ESP), Bristol (UK), and Lucca (IT). This article revises the main achievements and contributions of the 5GCity project, focusing on the deployment and validation of the proposed framework. The developed neutral host framework encompasses two main parts: the infrastructure and the software platform. A detailed description of the framework implementation, in terms of functional capabilities and practical implications of city-wide deployments, is provided in this article. This work also presents the performance evaluation of the proposed solution during the implementation of real vertical use cases. Obtained results validate the feasibility of the neutral host model and the proposed framework to be deployed in city-wide 5G infrastructures.
A Method for Identification and Assessment of Radioxenon Plumes by Absorption in Polycarbonates
Authors: Pressyanov Dobromir , Stavrev Pavel
Published: December 2021

A method for the retrospective evaluation of the integrated activity concentration of 133Xe during radioxenon plumes and the moment of the plume’s center is proposed and explored by computer modeling. The concept is to use a specimen of polycarbonate material (a stack of Makrofol N foils of thickness 120 µm and 40 µm in 1 L non-hermetic Marinelly beaker) that is placed in the environment or in a controlled nuclear or radiopharmaceutical facility. On a regular basis or incidentally, the specimen may be retrieved and gamma spectrometry in two consecutive time intervals with durations of 8 h and 16 h is performed. To assess the performance of the method, 133Xe plumes of various integrated activity concentrations and with a duration of up to 10 h are simulated and analyzed, assuming that the measurement starts with a delay of up to one day after the moment of the plume center. It is found that the deviation between the estimates by the method and their true values are within a few percent. Depending on the delay, events of integrated 133Xe activity concentration 250–1000 Bq h m−3 might be qualitatively identified. At levels >10,000 Bq h m−3, the uncertainty of the quantitative estimates might be ≤10%.
An Intelligence Method for Recognizing Multiple Defects in Rail
Authors: Deng Fei , Li Shu-Qing , Zhang Xi-Ran , Zhao Lin , Huang Ji-Bing , Zhou Cheng
Published: December 2021

Ultrasonic guided waves are sensitive to many different types of defects and have been studied for defect recognition in rail. However, most fault recognition algorithms need to extract features from the time domain, frequency domain, or time-frequency domain based on experience or professional knowledge. This paper proposes a new method for identifying many different types of rail defects. The segment principal components analysis (S-PCA) is developed to extract characteristics from signals collected by sensors located at different positions. Then, the Support Vector Machine (SVM) model is used to identify different defects depending on the features extracted. Combining simulations and experiments of the rails with different kinds of defects are established to verify the effectiveness of the proposed defect identification techniques, such as crack, corrosion, and transverse crack under the shelling. There are nine channels of the excitation-reception to acquire guided wave detection signals. The results show that the defect classification accuracy rates are 96.29% and 96.15% for combining multiple signals, such as the method of single-point excitation and multi-point reception, or the method of multi-point excitation and reception at a single point.
Engineering Approaches for Programming Agent-Based IoT Objects Using the Resource Management Architecture
Authors: Brandão Fabian Cesar , Lima Maria Alice Trinta , Pantoja Carlos Eduardo , Zahn Jean , Viterbo José
Published: December 2021

The Internet of Things (IoT) allows the sharing of information among devices in a network. Hardware evolutions have enabled the employment of cognitive agents on top of such devices, which could help to adopt pro-active and autonomous IoT systems. Agents are autonomous entities from Artificial Intelligence capable of sensing (perceiving) the environment where they are situated. Then, with these captured perceptions, they can reason and act pro-actively. However, some agent approaches are created for a specific domain or application when dealing with embedded systems and hardware interfacing. In addition, the agent architecture can compromise the system’s performance because of the number of perceptions that agents can access. This paper presents three engineering approaches for creating IoT Objects using Embedded Multi-agent systems (MAS)—as cognitive systems at the edge of an IoT network—connecting, acting, and sharing information with a re-engineered IoT architecture based on the Sensor as a Service model. These engineering approaches use Belief-Desire-Intention (BDI) agents and the JaCaMo framework. In addition, it is expected to diversify the designers’ choice in applying embedded MAS in IoT systems. We also present a case study to validate the whole re-engineered architecture and the approaches. Moreover, some performance tests and comparisons are also presented. The study case shows that each approach is more or less suitable depending on the domain tackled. The performance tests show that the re-engineered IoT architecture is scalable and that there are some trade-offs in adopting one or another approach. The contributions of this paper are an architecture for sharing resources in an IoT network, the use of embedded MAS on top IoT Objects, and three engineering approaches considering agent and artifacts dimensions.
Metamaterial-Inspired Flat Beamsteering Antenna for 5G Base Stations at 3.6 GHz
Authors: Reis João Ricardo , Vala Mário , Oliveira Tiago Emanuel , Fernandes Telmo Rui , Caldeirinha Rafael Ferreira Silva
Published: December 2021

In this paper, a metamaterial-inspired flat beamsteering antenna for 5G applications is presented. The antenna, designed to operate in the 3.6 GHz at 5G frequency bands, presents an unique flat form factor which allows easy deployment and low visual impact in 5G dense scenarios. The antenna presents a multi-layer structure where a metamaterial inspired transmitarray enables the two-dimensional (2D) beamsteering, and an array of microstrip patch antennas is used as RF source. The use of metamaterials in antenna beamsteering allows the reduction of costly and complex phase-shifter networks by using discrete capacitor diodes to control the transmission phase-shifting and subsequently, the direction of the steering. According to simulations, the proposed antenna presents steering range up to ±20∘, achievable in both elevation and azimuth planes, independently. To prove the concept, a prototype of the antenna has been built and experimentally characterised inside an anechoic chamber. Although constructed in a different substrate (FR4 substrate) as initially designed, beamsteering ranges up to 8∘ in azimuth and 13∘ in elevation, limited to the proposed case-studies, are reported with the prototype, validating the antenna and the usefulness of the proposed design.
Internet of Things in Space: A Review of Opportunities and Challenges from Satellite-Aided Computing to Digitally-Enhanced Space Living
Authors: Kua Jonathan , Loke Seng W. , Arora Chetan , Fernando Niroshinie , Ranaweera Chathurika
Published: December 2021

Recent scientific and technological advancements driven by the Internet of Things (IoT), Machine Learning (ML) and Artificial Intelligence (AI), distributed computing and data communication technologies have opened up a vast range of opportunities in many scientific fields—spanning from fast, reliable and efficient data communication to large-scale cloud/edge computing and intelligent big data analytics. Technological innovations and developments in these areas have also enabled many opportunities in the space industry. The successful Mars landing of NASA’s Perseverance rover on 18 February 2021 represents another giant leap for humankind in space exploration. Emerging research and developments of connectivity and computing technologies in IoT for space/non-terrestrial environments is expected to yield significant benefits in the near future. This survey paper presents a broad overview of the area and provides a look-ahead of the opportunities made possible by IoT and space-based technologies. We first survey the current developments of IoT and space industry, and identify key challenges and opportunities in these areas. We then review the state-of-the-art and discuss future opportunities for IoT developments, deployment and integration to support future endeavors in space exploration.
SPETS: Secure and Privacy-Preserving Energy Trading System in Microgrid
Authors: Samy Ahmed , Yu Haining , Zhang Hongli , Zhang Guangyao
Published: December 2021

Recently, the development of distributed renewable energy resources, smart devices, and smart grids empowers the emergence of peer-to-peer energy trading via local energy markets. However, due to security and privacy concerns in energy trading, sensitive information of energy traders could be leaked to an adversary. In addition, malicious users could perform attacks against the energy market, such as collusion, double spending, and repudiation attacks. Moreover, network attacks could be executed by external attackers against energy networks, such as eavesdropping, data spoofing, and tampering attacks. To tackle the abovementioned attacks, we propose a secure and privacy-preserving energy trading system (SPETS). First, a permissioned energy blockchain is presented to perform secure energy transactions between energy sellers and buyers. Second, a discrete-time double auction is proposed for energy allocation and pricing. Third, the concept of reputation scores is adopted to guarantee market reliability and trust. The proposed energy system is implemented using Hyperledger Fabric (HF) where the chaincode is utilized to control the energy market. Theoretical analysis proves that SPETS is resilient to several security attacks. Simulation results demonstrate the increase in sellers’ and buyers’ welfare by approximately 76.5% and 26%, respectively. The proposed system ensures trustfulness and guarantees efficient energy allocation. The benchmark analysis proves that SPETS consumes few resources in terms of memory and disk usage, CPU, and network utilization.
Magnetoelastic Ribbons as Vibration Sensors for Real-Time Health Monitoring of Rotating Metal Beams
Authors: Samourgkanidis Georgios , Kouzoudis Dimitris
Published: December 2021

In the current work, magnetoelastic material ribbons are used as vibration sensors to monitor, in real time and non-destructively, the mechanical health state of rotating beam blades. The magnetoelastic material has the form of a thin ribbon and is composed of Metglas alloy 2826 MB. The study was conducted in two stages. In the first stage, an experiment was performed to test the ability of the ribbon to detect and transmit the vibration behavior of four rotating blades, while the second stage was the same as the first but with minor damages introduced to the blades. As far as the first stage is concerned, the results show that the sensor can detect and transmit with great accuracy the vibratory behavior of the rotating blades, through which important information about the mechanical health state of the blade can be extracted. Specifically, the fast Fourier transform (FFT) spectrum of the recorded signal revealed five dominant peaks in the frequency range 0–3 kHz, corresponding to the first five bending modes of the blades. The identification process was accomplished using ANSYS modal analysis, and the comparison results showed deviation values of less than 1% between ANSYS and the experimental values. In the second stage, two types of damages were introduced to the rotating blades, an edge cut and a hole. The damages were scaled in number from one blade to another, with the first blade having only one side cut while the last blade had two side cuts and two holes. The results, as was expected, show a measurable shifting on the frequency values of the bending modes, thus proving the ability of the proposed magnetoelastic sensors to detect and transmit changes of the mechanical state of rotating blades in real time.
Vital Signs Prediction for COVID-19 Patients in ICU
Authors: Youssef Ali Amer Ahmed , Wouters Femke , Vranken Julie , Dreesen Pauline , de Korte-de Boer Dianne , van Rosmalen Frank , van Bussel Bas C. T. , Smit-Fun Valérie , Duflot Patrick , Guiot Julien , van der Horst Iwan C. C. , Mesotten Dieter , Vandervoort Pieter , Aerts Jean-Marie , Vanrumste Bart
Published: December 2021

This study introduces machine learning predictive models to predict the future values of the monitored vital signs of COVID-19 ICU patients. The main vital sign predictors include heart rate, respiration rate, and oxygen saturation. We investigated the performances of the developed predictive models by considering different approaches. The first predictive model was developed by considering the following vital signs: heart rate, blood pressure (systolic, diastolic and mean arterial, pulse pressure), respiration rate, and oxygen saturation. Similar to the first approach, the second model was developed using the same vital signs, but it was trained and tested based on a leave-one-subject-out approach. The third predictive model was developed by considering three vital signs: heart rate (HR), respiration rate (RR), and oxygen saturation (SpO2). The fourth model was a leave-one-subject-out model for the three vital signs. Finally, the fifth predictive model was developed based on the same three vital signs, but with a five-minute observation rate, in contrast with the aforementioned four models, where the observation rate was hourly to bi-hourly. For the five models, the predicted measurements were those of the three upcoming observations (on average, three hours ahead). Based on the obtained results, we observed that by limiting the number of vital sign predictors (i.e., three vital signs), the prediction performance was still acceptable, with the average mean absolute percentage error (MAPE) being 12%,5%, and 21.4% for heart rate, oxygen saturation, and respiration rate, respectively. Moreover, increasing the observation rate could enhance the prediction performance to be, on average, 8%,4.8%, and 17.8% for heart rate, oxygen saturation, and respiration rate, respectively. It is envisioned that such models could be integrated with monitoring systems that could, using a limited number of vital signs, predict the health conditions of COVID-19 ICU patients in real-time.
An Efficient Group-Based Replica Placement Policy for Large-Scale Geospatial 3D Raster Data on Hadoop
Authors: Liu Zhipeng , Hua Weihua , Liu Xiuguo , Liang Dong , Zhao Yabo , Shi Manxing
Published: December 2021

Geospatial three-dimensional (3D) raster data have been widely used for simple representations and analysis, such as geological models, spatio-temporal satellite data, hyperspectral images, and climate data. With the increasing requirements of resolution and accuracy, the amount of geospatial 3D raster data has grown exponentially. In recent years, the processing of large raster data using Hadoop has gained popularity. However, data uploaded to Hadoop are randomly distributed onto datanodes without consideration of the spatial characteristics. As a result, the direct processing of geospatial 3D raster data produces a massive network data exchange among the datanodes and degrades the performance of the cluster. To address this problem, we propose an efficient group-based replica placement policy for large-scale geospatial 3D raster data, aiming to optimize the locations of the replicas in the cluster to reduce the network overhead. An overlapped group scheme was designed for three replicas of each file. The data in each group were placed in the same datanode, and different colocation patterns for three replicas were implemented to further reduce the communication between groups. The experimental results show that our approach significantly reduces the network overhead during data acquisition for 3D raster data in the Hadoop cluster, and maintains the Hadoop replica placement requirements.
Fast γ Photon Imaging for Inner Surface Defects Detecting
Authors: Yao Min , Luo Guangdong , Zhao Min , Guo Ruipeng , Liu Jian
Published: December 2021

Only a few effective methods can detect internal defects and monitor the internal state of complex structural parts. On the basis of the principle of PET (positron emission computed tomography), a new measurement method, using γ photon to detect defects of an inner surface, is proposed. This method has the characteristics of strong penetration, anti-corrosion and anti-interference. With the aim of improving detection accuracy and imaging speed, this study also proposes image reconstruction algorithms, combining the classic FBP (filtered back projection) with MLEM (maximum likelihood expectation Maximization) algorithm. The proposed scheme can reduce the number of iterations required, when imaging, to achieve the same image quality. According to the operational demands of FPGAs (field-programmable gate array), a BPML (back projection maximum likelihood) algorithm is adapted to the structural characteristics of an FPGA, which makes it feasible to test the proposed algorithms therein. Furthermore, edge detection and defect recognition are conducted after reconstructing the inner image. The effectiveness and superiority of the algorithm are verified, and the performance of the FPGA is evaluated by the experiments.
Pocketable Labs for Everyone: Synchronized Multi-Sensor Data Streaming and Recording on Smartphones with the Lab Streaming Layer
Authors: Blum Sarah , Hölle Daniel , Bleichner Martin Georg , Debener Stefan
Published: December 2021

The streaming and recording of smartphone sensor signals is desirable for mHealth, telemedicine, environmental monitoring and other applications. Time series data gathered in these fields typically benefit from the time-synchronized integration of different sensor signals. However, solutions required for this synchronization are mostly available for stationary setups. We hope to contribute to the important emerging field of portable data acquisition by presenting open-source Android applications both for the synchronized streaming (Send-a) and recording (Record-a) of multiple sensor data streams. We validate the applications in terms of functionality, flexibility and precision in fully mobile setups and in hybrid setups combining mobile and desktop hardware. Our results show that the fully mobile solution is equivalent to well-established desktop versions. With the streaming application Send-a and the recording application Record-a, purely smartphone-based setups for mobile research and personal health settings can be realized on off-the-shelf Android devices.
Creating Innovation in Achieving Sustainability: Halal-Friendly Sustainable Port
Authors: Jaafar Harlina Suzana , Abd Aziz Mona Leza , Ahmad Muhammad Razif , Faisol Nasruddin
Published: December 2021

The expansion of liberalized trade has forced companies to consider the global market demand to stay competitive. Hence, ports have started to embrace sustainability practices in their activities throughout port operations. Various research has suggested that there is more innovation when sustainability is adopted as an integral part of their business activities. This study established a halal-friendly sustainable port concept and its implementation in meeting the objectives of sustainability practices to boost innovation. To embed sustainability within port organizations, it is vital to create an organizational culture that supports innovation and integrative thinking. Based on the qualitative data obtained from 38 port stakeholders in the southern of Malaysia, the respondents supported a halal-friendly sustainable port as a potential innovation in meeting the objectives of the sustainable practices. Four components that were found crucial for the proposed framework reflect the novelty of the research and its successful implementation.
Empirical Analysis of the Effects of Ownership Model (Public vs. Private) on the Efficiency of Urban Rail Firms
Authors: Costa Álvaro , Cruz Carlos Oliveira , Sarmento Joaquim Miranda , Sousa Vitor Faria
Published: December 2021

The discussion over public vs. private management in the operation of public transport has been on the research agenda for the past decade. Several studies have analyzed the benefits of private management; however, no study has analyzed the effects of the management model while controlling for other external factors such as economic crises and political factors. This study intends to focus on the impact of the ownership model (public vs. private) of urban rail firms on their efficiency, while expanding the existing literature by controlling for economic and political factors. The methodology consisted of the calculation of DEA scores and subsequent use of regression analysis to identify the main determinants. We used a data set of four Portuguese rail firms during the period 2009–2018 along with five distinct efficiency scores. The results show that privately managed firms tend to be more efficient, but with distinct behavior depending on the economic cycle. In periods of growing GDP, private firms lose their potential superiority over public firms. The results also show that election years and unemployment rate also play a role in understanding the efficiency scores of these firms.
Different Shades of Nudges: Moderating Effects of Individual Characteristics and States on the Effectiveness of Nudges during a Fast-Food Order
Authors: Dolgopolova Irina , Toscano Alessia , Roosen Jutta
Published: December 2021

Nudges, or subtle changes to a choice environment, are increasingly used in online food ordering platforms to improve dietary choices and reduce calorie intake. We report the results of an experiment aimed at nudging young adults to reduce calories in a fast-food order (N = 994). The nudging interventions used were: an order assistant, a color-coded system, and a combination of the order assistant and color-coded system. We hypothesized that participants’ characteristics (sex, BMI, education) and states (positive affect, negative affect, hunger) moderate the effectiveness of nudges. Our analysis shows that the effect of nudges is slightly increasing at higher BMI levels. In the combined treatment, hunger and negative affect significantly moderate the effect of nudges. We do not observe the moderating effects of participants’ sex, educational level, and positive affect in any of the treatments.
Why Do We Harm the Environment or Our Personal Health despite Better Knowledge? The Knowledge Action Gap in Healthy and Climate-Friendly Behavior
Authors: Frick Melanie , Neu Leonie , Liebhaber Nina , Sperner-Unterweger Barbara , Stötter Johann , Keller Lars , Hüfner Katharina
Published: December 2021

Non-communicable diseases, such as hypertension, diabetes, or depression, result from an interplay of physiological, genetic, behavioral, and environmental aspects. Together with climate change, they are arguably among the most significant challenges mankind faces in the 21st century. Additionally, the bidirectional influences of climate change and health on each other are undisputed. Behavioral changes could curb both climate change and the spread of non-communicable diseases. Much effort has been put into information campaigns in both fields, but success has been limited. In the following, the knowledge action gap is compared and analyzed in healthy and climate-friendly behavior from a practical point of view and the supporting theoretical models are highlighted. The analysis shows that self-efficacy plays an essential role in both areas of research for effecting behavioral changes. The models of ‘Planned Behavior’ and ‘Stages of Change’ seems helpful and can be applied and adapted to explain behavioral changes in health and climate changes settings. We compared two previously unrelated research fields to uncover new avenues for further study and stimulate fruitful transdisciplinary discussion. Future directions on how behavioral medicine and climate change research can learn from each other are discussed.
Inspection and Assessment of Masonry Arch Bridges: Ivanjica Case Study
Authors: Sokolović Neda M. , Petrović Milica , Kontić Ana , Koprivica Suzana , Šekularac Nenad
Published: December 2021

The importance of masonry arch bridges as a traffic network element calls for a thorough analysis focused on both structural stability and loading capacity of these historical structures, considering the usage of these bridges in contemporary traffic conditions. This paper focuses on the analysis of longitudinal cracks in a single span masonry arch bridge to evaluate its influence on structural behaviour of the system. As longitudinal cracks do not necessarily present an inevitable collapse mechanism, analysis of the causes is crucial for evaluating the serviceability and functionality of the bridge investigated. The methodology is based on the following: literature review, observation of the stone bridge in Ivanjica, geological testing of the site, geophysical testing of the bridge, laboratory testing of mechanical characteristics of stone used for the bridge construction and biological analysis of the samples of implemented materials on the bridge. Finite element analysis of the bridge was conducted to define the causes of the longitudinal cracks. The 3D simulation model was based on the data collected through observation and experimental analysis. This paper provides extensive research on a single span masonry bridge, examining how different deterioration mechanisms, in conjunction, can lead to the appearance of cracks in masonry arch bridges and provide remedial measures accordingly.
Green Space and Physical Activity in China: A Systematic Review
Authors: Shen Jing , Cui Jian , Li Mengfei , Clarke Caitlin Vitosky , Gao Yuanyuan , An Ruopeng
Published: December 2021

Green space may play an essential role in residents’ physical activity (PA), but evidence remains scattered in China. This study systematically reviewed scientific evidence regarding the influence of green space on PA among residents in China. Keyword and reference searches were conducted in PubMed, Web of Science, Scopus, EBSCO, and CNKI from the inception of an electronic bibliographic database to May 2021. Eligibility criteria included the following: study designs—observational (e.g., longitudinal or cross-sectional studies) and experimental studies; study subjects—people of all ages; exposures—green space (e.g., parks, vegetation areas, open green fields); outcomes—leisure-time and work/school-related PA (e.g., active commuting); and country—China. All but two studies identified at least one measure of green space to be associated with PA. Street greenness was associated with increased odds of active commuting (e.g., cycling) and walking, and a reduced risk of physical inactivity. Access to green space was associated with increased PA levels and green space usage. Distance to green space was inversely associated with the odds of PA. By contrast, evidence linking overall greenness exposure to PA remains limited. Future studies adopting experimental study design are warranted to establish more robust scientific evidence of causality between green space and PA in China. Future studies are also warranted to examine the underlining mechanisms and the differential impacts of green space on population subgroups in China.
Physical Activity Dimensions Differentially Predict Physical and Mental Components of Health-Related Quality of Life: Evidence from a Sport for All Study
Authors: Ihle Andreas , Gouveia Bruna R. , Gouveia Élvio R. , Cheval Boris , Nascimento Marcelo de Maio , Conceição Lúcia , Marconcin Priscila , Peralta Miguel , Ferrari Gerson , Oliveira Duarte , Kliegel Matthias
Published: December 2021

The present cross-sectional study aimed to investigate the relationship between different dimensions of physical activity (PA) (i.e., work, sport, leisure) and health-related quality of life (HRQoL) (i.e., overall, physical component, mental component) in an adult lifespan sample of 381 active individuals (age range: 18–88 years; 38.8% men), while controlling for important covariates in terms of sex, age, education, and health profile regarding medical history. HRQoL was assessed using the 12-item Short-Form Health Survey (SF-12). Usual (i.e., previous 12 months) PA was assessed during face-to-face interviews using the Baecke questionnaire. Hierarchical regression analyses showed in Step 1 that the three PA dimensions work, sport, and leisure (entered simultaneously) together predicted 8%, 10%, and 4% of the variance in SF-12 total score, SF-12 physical component, and SF-12 mental component, respectively. In the final model, adjusting for sex, age, education, and health profile regarding medical history, sport emerged as the only PA dimension predicting SF-12 total score and the SF-12 physical component. In conclusion, health-policy targets at the community level should include the promotion of lifelong engagement in PA, especially sport, to allow the sustainability of HRQoL across the lifespan of our society.
Landscape Works. Balancing Nature and Culture in the Pantelleria National Park
Authors: D’Ascanio Romina , Barbieri Lorenzo , De Pasquale Giorgia , Filpa Andrea , Palazzo Anna Laura
Published: December 2021

Traditional agricultural practice and peculiar geographical features in the Mediterranean basin have not only moulded cultural and heritage values, but also created the conditions for the development of habitats to be protected. Therefore, Landscape proves a suitable concept both for the enhancement of cultural features and for nature conservation. The aim of this work is to apply the landscape approach to the Pantelleria National Park, providing the opportunity to reflect upon and discuss whether and how to encompass rural landscape planning and management within the broader context of natural values, offering a frame of reference for the zoning of the future Park Plan. Specifically, the research aims to define zoning categories, typical to protected areas planning, using criteria related to landscape features and patterns, environmental quality, traditional agriculture, architectural heritage. Established in 2016, the Pantelleria National Park is the most recent Italian National Park and the first one in Sicily. The Park covers 79% of the island, encompassing two sites of Community Importance as well as one Special Protection Area belonging to the Natura 2000 network. Pantelleria is a microcosm gathering a great variety of natural and human-made landscapes characterized by high levels of complexity embodying the antagonism of two protected ‘noble interests’: Nature and the environment on the one hand, Culture framed as traditional rural practices on the other. The main challenge of the new-founded National Park is to combine quality and values relating to the domain of Nature, which is expanding, with those expressed by Culture, represented by a wide array of historical rural values at risk due to ongoing abandonment of most remote areas.
Digital Health and Care Study on Elderly Monitoring
Authors: Gaiduk Maksym , Seepold Ralf , Martínez Madrid Natividad , Ortega Juan
Published: December 2021

Sustainable technologies are being increasingly used in various areas of human life. While they have a multitude of benefits, they are especially useful in health monitoring, especially for certain groups of people, such as the elderly. However, there are still several issues that need to be addressed before its use becomes widespread. This work aims to clarify the aspects that are of great importance for increasing the acceptance of the use of this type of technology in the elderly. In addition, we aim to clarify whether the technologies that are already available are able to ensure acceptable accuracy and whether they could replace some of the manual approaches that are currently being used. A two-week study with people 65 years of age and over was conducted to address the questions posed here, and the results were evaluated. It was demonstrated that simplicity of use and automatic functioning play a crucial role. It was also concluded that technology cannot yet completely replace traditional methods such as questionnaires in some areas. Although the technologies that were tested were classified as being “easy to use”, the elderly population in the current study indicated that they were not sure that they would use these technologies regularly in the long term because the added value is not always clear, among other issues. Therefore, awareness-raising must take place in parallel with the development of technologies and services.
Analysis and Prediction of Land Resources’ Carrying Capacity in 31 Provinces of China from 2008 to 2016
Authors: Han Chuqiao , Lu Binbin , Zheng Jianghua
Published: December 2021

The rapid development of urbanization, population growth, and unreasonable use of land resources have made the contradiction between human beings and land increasingly prominent, and the carrying capacity of land resources has become an important factor affecting the sustainable development of a city or even a country. Based on the carrying capacity of agricultural land, construction land, and ecological land in 31 provinces of China, this paper analyzed the spatiotemporal characteristics of the carrying capacity of land resources in 31 provinces of China from 2008 to 2016 by using single-factor carrying capacity evaluation and comprehensive carrying capacity evaluation methods. The evaluation results were divided into six levels (No pressure, Lower pressure, Low pressure, Medium pressure, High pressure, Higher pressure), and the ArcGIS10.2 software was used for visualization, and the carrying capacity prediction model was constructed to predict the development trend of the comprehensive carrying capacity of land resources in 2020 and 2025. The results showed that China’s land carrying capacity is extremely unbalanced. Whether from the evaluation results of single-factor carrying capacity or comprehensive carrying capacity, we showed the characteristics of high pressure in the east and low pressure in the west, and that the regional land resources have a deteriorating trend. Through the prediction of the future land resource carrying capacity index, it was found that the comprehensive carrying capacity index of land resources in 31 provinces of China will still show an upward trend in the future.
A Novel Stacked Long Short-Term Memory Approach of Deep Learning for Streamflow Simulation
Authors: Mirzaei Majid , Yu Haoxuan , Dehghani Adnan , Galavi Hadi , Shokri Vahid , Mohsenzadeh Karimi Sahar , Sookhak Mehdi
Published: December 2021

Rainfall-Runoff simulation is the backbone of all hydrological and climate change studies. This study proposes a novel stochastic model for daily rainfall-runoff simulation called Stacked Long Short-Term Memory (SLSTM) relying on machine learning technology. The SLSTM model utilizes only the rainfall-runoff data in its modelling approach and the hydrology system is deemed a blackbox. Conversely, the distributed and physically-based hydrological models, e.g., SWAT (Soil and Water Assessment Tool) preserve the physical aspect of hydrological variables and their inter-relations while taking a wide range of data. The two model types provide specific applications that interest modelers, who can apply them according to their project specification and objectives. However, sparse distribution of point-data may hinder physical models’ performance, which may not be the case in data-driven models. This study proposes a specific SLSTM model and investigates the SLSTM and SWAT models’ data dependency in terms of their spatial distribution. The study was conducted in the two distinct river basins of Samarahan and Trusan, Malaysia, with over 20 years of hydro-climate data. The Trusan basin’s rain gauges are scattered downstream of the basin outlet and Samarahan’s are located around the basin, with one station within each basin’s limits. The SWAT was developed and calibrated following its general modelling approach, however, the SLSTM performance was also tested using data preprocessing with principal component analysis (PCA). Results showed that the SWAT performance for daily streamflow simulation at Samarahan has been superior to that of Trusan. Both the SLSTM and PCA-SLSTM models, however, showed better performance at Trusan with PCA-SLSTM outperforming the SLSTM. This demonstrates that the SWAT model is greatly affected by the spatial distribution of its input data, while data-driven models, irrespective of the spatial distribution of their entry data, can perform well if the data adequacy condition is met. However, considering the structural difference between the two models, each has its specific application in a water resources context. The study of catchments’ response to changes in the hydrology cycle requires a physically-based model like SWAT with proper spatial and temporal distribution of its entry data. However, the study of a specific phenomenon without considering the underlying processes can be done using data-driven models like SLSTM, where improper spatial distribution of data cannot be a restricting factor.