bluuebunny/crossref_metadata_2025 huggingface dataset:
Would You Trust Driverless Service? Formation of Pedestrian’s Trust and Attitude Using Non-Verbal Social Cues
Authors: Choi Suji , Kim Soyeon , Kwak Mingi , Park Jaewan , Park Subin , Kwak Dongjoon , Lee Hyun Woo , Lee Sangwon
Published: April 2022

Despite the widespread application of Autonomous Vehicles (AV) to various services, there has been relatively little research carried out on pedestrian–AV interaction and trust within the context of service provided by AV. This study explores the communication design strategy promoting a pedestrian’s trust and positive attitude to driverless services within the context of pedestrian–AV interaction using non-verbal social cues. An empirical study was conducted with an experimental VR environment to measure participants’ intimacy, trust, and brand attitude toward AV. Further understanding of their social interaction experiences was explored through semi-structured interviews. As a result of the study, the interaction effect of social cues was found, and it was revealed that brand attitude was formed by the direct effects of intimacy and trust as well as the indirect effects of intimacy through trust’s mediation. Furthermore, ‘Conceptual Definition of Space’ was identified to generate differences in the interplay among intimacy, trust, and brand attitude according to social cues. Quantitative and qualitative results were synthesized to discuss implications considering the service context. Practical implications were also addressed suggesting specific design strategies for utilizing the sociality of AV.
Software Architecture Patterns for Extending Sensing Capabilities and Data Formatting in Mobile Sensing
Authors: Bardram Jakob E. 
Published: April 2022

Mobile sensing—that is, the ability to unobtrusively collect sensor data from built-in phone and attached wearable sensors—have proven to be a powerful approach to understanding the behavior, well-being, and health of people in their everyday life. Different platforms for mobile sensing have been presented and significant knowledge on how to facilitate mobile sensing has been accumulated. However, most existing mobile sensing platforms only support a fixed set of mobile phone and wearable sensors which are `built into’ the platform’s generic `study app’. This creates some fundamental challenges for the creation and approval of application-specific mobile sensing studies, since there is little support for adapting the sensing capabilities to what is needed for a specific study. Moreover, most existing platforms use their own proprietary data formats and there is no standardization in how data are collected and in what formats. This poses some fundamental challenges to realizing the vision of using mobile sensing in health applications, since mobile sensing data collected across different phones and studies cannot be compared, thus hampering generalizability and reproducibility across studies. This paper presents two software architecture patterns enabling (i) dynamic extension of mobile sensing to incorporate new sensing capabilities, such as collecting data from a wearable sensor, and (ii) handling real-time transformation of data into standardized data formats. These software patterns are derived from our work on CARP Mobile Sensing (CAMS), which is a cross-platform (Android/iOS) software architecture providing a reactive and unified programming model that emphasizes extensibility. This paper shows how the framework uses the two software architecture patterns to add sampling support for an electrocardiography (ECG) device and support data transformation into the new Open mHealth (OMH) data format. The paper also presents data from a small study, demonstrating the robustness and feasibility of using CAMS for data collection and transformation in mobile sensing.
Low-Power, Flexible Sensor Arrays with Solderless Board-to-Board Connectors for Monitoring Soil Deformation and Temperature
Authors: Wielandt Stijn , Uhlemann Sebastian , Fiolleau Sylvain , Dafflon Baptiste
Published: April 2022

Landslides are a global and frequent natural hazard, affecting many communities and infrastructure networks. Technological solutions are needed for long-term, large-scale condition monitoring of infrastructure earthworks or natural slopes. However, current instruments for slope stability monitoring are often costly, require a complex installation process and/or data processing schemes, or have poor resolution. Wireless sensor networks comprising low-power, low-cost sensors have been shown to be a crucial part of landslide early warning systems. Here, we present the development of a novel sensing approach that uses linear arrays of three-axis accelerometers for monitoring changes in sensor inclination, and thus the surrounding soil’s deformation. By combining these deformation measurements with depth-resolved temperature measurements, we can link our data to subsurface thermal–hydrological regimes where relevant. In this research, we present a configuration of cascaded I2C sensors that (i) have ultra-low power consumption and (ii) enable an adjustable probe length. From an electromechanical perspective, we developed a novel board-to-board connection method that enables narrow, semi-flexible sensor arrays and a streamlined assembly process. The low-cost connection method relies on a specific FR4 printed circuit board design that allows board-to-board press fitting without using electromechanical components or solder connections. The sensor assembly is placed in a thin, semi-flexible tube (inner diameter 6.35 mm) that is filled with an epoxy compound. The resulting sensor probe is connected to an AA-battery-powered data logger with wireless connectivity. We characterize the system’s electromechanical properties and investigate the accuracy of deformation measurements. Our experiments, performed with probes up to 1.8 m long, demonstrate long-term connector stability, as well as probe mechanical flexibility. Furthermore, our accuracy analysis indicates that deformation measurements can be performed with a 0.390 mm resolution and a 95% confidence interval of ±0.73 mm per meter of probe length. This research shows the suitability of low-cost accelerometer arrays for distributed soil stability monitoring. In comparison with emerging low-cost measurements of surface displacement, our approach provides depth-resolved deformation, which can inform about shallow sliding surfaces.
Two-Dimensional Phononic Crystal Based Sensor for Characterization of Mixtures and Heterogeneous Liquids
Authors: Mukhin Nikolay , Kutia Mykhailo , Aman Alexander , Steinmann Ulrike , Lucklum Ralf
Published: April 2022

We show new approaches to developing acoustic liquid sensors based on phononic crystals. The proposed phononic crystal integrates fluidic elements. A solid block with periodic cylindrical holes contains a defect—a liquid-filled cylindrical cavity. We pay attention to acoustic excitation and the readout of the axisymmetric cylindrical resonator eigenmode of the liquid-filled defect in the middle of the phononic crystal structure. This mode solves the challenge of mechanical energy losses due to liquid viscosity. We also analyze the coupling effects between oscillations of liquid and solid systems and consider coupling issues between piezoelectric transducers and the liquid-filled cavity resonator. The numerical simulation of the propagation of acoustic waves through the phononic crystal sensor was carried out in COMSOL Multiphysics Software. The phononic crystal was made of stainless steel with mechanically drilled holes and was fabricated for experimental verification. We show that a tuning of the solid–liquid vibrational modes coupling is the key to an enhanced level of sensitivity to liquid properties. Besides (homogeneous) water–propanol mixtures, experimental studies were carried out on (disperse) water–fuel emulsions.
A One-Stage Ensemble Framework Based on Convolutional Autoencoder for Remaining Useful Life Estimation
Authors: Park Yong-Keun , Kim Min-Kyung , Um Jumyung
Published: April 2022

As the legislative pressure to reduce energy consumption is increasing, data analysis of power consumption is critical in the production planning of manufacturing facilities. In legacy studies, a machine conducting a single continuous operation has been mainly observed for power estimation. However, the production machine of a modularized line, which conducts complex discrete operations, is more like the actual factory system than an identical simple machine. During the information collection of this kind of production line, it is important to interpret mixed signals from multiple machines to ensure that there is no reduction in the information quality due to noise and signal fusion and discrete events. A data pipeline—from data collection (from different sources) to preprocessing, data conversion, synchronization, and deep learning classification—to estimate the total power use of the future process plan, is proposed herein. The pipeline also establishes an auto-labeled data set of individual operations that contributes to building an power estimation model without manual data preprocessing. The proposed system is applied to a modular factory, connected with machine controllers, using standardized protocols individually and linked to a centralized power monitoring system. Specifically, a robot arm cell was investigated to evaluate the pipeline, with the result of the power profile being synchronized with the robot program.
Biased Pressure: Cyclic Reinforcement Learning Model for Intelligent Traffic Signal Control
Authors: Ibrokhimov Bunyodbek , Kim Young-Joo , Kang Sanggil
Published: April 2022

Existing inefficient traffic signal plans are causing traffic congestions in many urban areas. In recent years, many deep reinforcement learning (RL) methods have been proposed to control traffic signals in real-time by interacting with the environment. However, most of existing state-of-the-art RL methods use complex state definition and reward functions and/or neglect the real-world constraints such as cyclic phase order and minimum/maximum duration for each traffic phase. These issues make existing methods infeasible to implement for real-world applications. In this paper, we propose an RL-based multi-intersection traffic light control model with a simple yet effective combination of state, reward, and action definitions. The proposed model uses a novel pressure method called Biased Pressure (BP). We use a state-of-the-art advantage actor-critic learning mechanism in our model. Due to the decentralized nature of our state, reward, and action definitions, we achieve a scalable model. The performance of the proposed method is compared with related methods using both synthetic and real-world datasets. Experimental results show that our method outperforms the existing cyclic phase control methods with a significant margin in terms of throughput and average travel time. Moreover, we conduct ablation studies to justify the superiority of the BP method over the existing pressure methods.
Agent-Based Medical Health Monitoring System
Authors: Humayun Mamoona , Jhanjhi Noor Z. , Almotilag Abdullah , Almufareh Maram Fahhad
Published: April 2022

One of the leading healthcare concerns worldwide is the aging population. Aged patients require more significant healthcare resources because they are more likely to have chronic diseases that result in higher healthcare expenses. The design and implementation of e-health solutions, which offer patients mobile services to assist and enhance their treatment based on monitoring specific physiological data, is one of the key achievements in medical information technology. In the last few decades, there have been tremendous advancements in healthcare technology regarding mobility, size, speed, and communication. However, the critical drawback of today’s e-Health monitoring systems is that patients are confined to smart rooms and beds with monitoring gadgets. Such tracking is not widespread due to chronic patients’ mobility, privacy, and flexibility issues. Further, health monitoring devices that are fastened to a patient’s body do not give any analysis or advice. To improve the health monitoring process, a multi-agent-based system for health monitoring is provided in this study, which entails a group of intelligent agents that gather patient data, reason together, and propose actions to patients and medical professionals in a mobile context. A multi-agent-based framework presented in this study is evaluated through a case study. The results show that the proposed system provides an efficient health monitoring system for chronic, aged, and remote patients. Further, the proposed approach outperforms the existing mHealth system, allowing for timely health facilities for remote patients using 5G technology.
Data Twin-Driven Cyber-Physical Factory for Smart Manufacturing
Authors: Jwo Jung-Sing , Lee Cheng-Hsiung , Lin Ching-Sheng
Published: April 2022

Because of the complex production processes and technology-intensive operations that take place in the aerospace and defense industry, introducing Industry 4.0 into the manufacturing processes of aircraft composite materials is inevitable. Digital Twin and Cyber-Physical Systems in Industry 4.0 are key techniques to develop digital manufacturing. Since it is very difficult to create high-fidelity virtual models, the development of digital manufacturing for aircraft manufacturers is challenging. In this study, we provide a view from a data simulation perspective and adopt machine learning approaches to simplify the high-fidelity virtual models in Digital Twin. The novel concept is called Data Twin, and the deployable service to support the simulation is known as the Data Twin Service (DTS). Relying on the DTS, we also propose a microservice software architecture, Cyber-Physical Factory (CPF), to simulate the shop floor environment. Additionally, there are two war rooms in the CPF that can be used to establish a collaborative platform: one is the Physical War Room, used to integrate real data, and the other is the Cyber War Room for handling simulation data and the results of the CPF.
Detection of Human Gait Phases Using Textile Pressure Sensors: A Low Cost and Pervasive Approach
Authors: Milovic Matko , Farías Gonzalo , Fingerhuth Sebastián , Pizarro Francisco , Hermosilla Gabriel , Yunge Daniel
Published: April 2022

Human gait analysis is a standard method used for detecting and diagnosing diseases associated with gait disorders. Wearable technologies, due to their low costs and high portability, are increasingly being used in gait and other medical analyses. This paper evaluates the use of low-cost homemade textile pressure sensors to recognize gait phases. Ten sensors were integrated into stretch pants, achieving an inexpensive and pervasive solution. Nevertheless, such a simple fabrication process leads to significant sensitivity variability among sensors, hindering their adoption in precision-demanding medical applications. To tackle this issue, we evaluated the textile sensors for the classification of gait phases over three machine learning algorithms for time-series signals, namely, random forest (RF), time series forest (TSF), and multi-representation sequence learner (Mr-SEQL). Training and testing signals were generated from participants wearing the sensing pants in a test run under laboratory conditions and from an inertial sensor attached to the same pants for comparison purposes. Moreover, a new annotation method to facilitate the creation of such datasets using an ordinary webcam and a pose detection model is presented, which uses predefined rules for label generation. The results show that textile sensors successfully detect the gait phases with an average precision of 91.2% and 90.5% for RF and TSF, respectively, only 0.8% and 2.3% lower than the same values obtained from the IMU. This situation changes for Mr-SEQL, which achieved a precision of 79% for the textile sensors and 36.8% for the IMU. The overall results show the feasibility of using textile pressure sensors for human gait recognition.
Experimental Study on Bottom-Up Detection of Underwater Targets Based on Polarization Imaging
Authors: Pan Tianfeng , He Xianqiang , Zhang Xuan , Liu Jia , Bai Yan , Gong Fang , Li Teng
Published: April 2022

Previous studies on the polarization imaging of underwater targets mainly focused on top-down detection; however, the capacities of bottom-up detection were poorly known. Based on in situ experiments, the capability of bottom-up detection of underwater targets using polarization imaging was investigated. First, to realize the objective of bottom-up polarization imaging, a SALSA polarization camera was integrated into our Underwater Polarization Imaging System (UPIS), which was integrated with an attitude sensor. At Qiandao Lake, where the water is relatively clear, experiments were conducted to examine the capacity of the UPIS to detect objects from the bottom up. Simultaneously, entropy, clarity, and contrast were adopted to compare the imaging performance with different radiation parameters. The results show that among all the used imaging parameters, the angle of polarization is the optimal parameter for bottom-up detection of underwater targets based on polarization imaging, which may result from the different diffused reflectance of the target surface to the linear polarization components of the Stokes vector.
Electric Vehicle Smart Charging Reservation Algorithm
Authors: Flocea Radu , Hîncu Andrei , Robu Andrei , Senocico Stelian , Traciu Andrei , Remus Baltariu Marian , Răboacă Maria Simona , Filote Constantin
Published: April 2022

The widespread adoption of electromobility constitutes one of the measures designed to reduce air pollution caused by traditional fossil fuels. However, several factors are currently impeding this process, ranging from insufficient charging infrastructure, battery capacity, and long queueing and charging times, to psychological factors. On top of range anxiety, the frustration of the EV drivers is further fuelled by the uncertainty of finding an available charging point on their route. To address this issue, we propose a solution that bypasses the limitations of the “reserve now” function of the OCPP standard, enabling drivers to make charging reservations for the upcoming days, especially when planning a longer trip. We created an algorithm that generates reservation intervals based on the charging station’s reservation and transaction history. Subsequently, we ran a series of test cases that yielded promising results, with no overlapping reservations and the occupation of several stations without queues, assuring, thus, a proper distribution of the available energy resources, while increasing end-user satisfaction. Our solution is independent from the OCPP reservation method; therefore, the authentication and reservation processes performed by the proposed algorithm run only through the central system, authorizing only the creator of the reservation to start the charging transaction.
MEDEP: Maintenance Event Detection for Multivariate Time Series Based on the PELT Approach
Authors: Gashi Milot , Gursch Heimo , Hinterbichler Hannes , Pichler Stefan , Lindstaedt Stefanie , Thalmann Stefan
Published: April 2022

Predictive Maintenance (PdM) is one of the most important applications of advanced data science in Industry 4.0, aiming to facilitate manufacturing processes. To build PdM models, sufficient data, such as condition monitoring and maintenance data of the industrial application, are required. However, collecting maintenance data is complex and challenging as it requires human involvement and expertise. Due to time constraints, motivating workers to provide comprehensive labeled data is very challenging, and thus maintenance data are mostly incomplete or even completely missing. In addition to these aspects, a lot of condition monitoring data-sets exist, but only very few labeled small maintenance data-sets can be found. Hence, our proposed solution can provide additional labels and offer new research possibilities for these data-sets. To address this challenge, we introduce MEDEP, a novel maintenance event detection framework based on the Pruned Exact Linear Time (PELT) approach, promising a low false-positive (FP) rate and high accuracy results in general. MEDEP could help to automatically detect performed maintenance events from the deviations in the condition monitoring data. A heuristic method is proposed as an extension to the PELT approach consisting of the following two steps: (1) mean threshold for multivariate time series and (2) distribution threshold analysis based on the complexity-invariant metric. We validate and compare MEDEP on the Microsoft Azure Predictive Maintenance data-set and data from a real-world use case in the welding industry. The experimental outcomes of the proposed approach resulted in a superior performance with an FP rate of around 10% on average and high sensitivity and accuracy results.
Microseismic Time Delay Estimation Method Based on Continuous Wavelet
Authors: Du Cunpeng , Yu Shengwen , Yin Haitao , Sun Zhen
Published: April 2022

The microseismic signal is easily affected by observation noise and the inaccurate estimation of traditional methods will seriously reduce the location accuracy of the microseismic event. Therefore, based on the continuous wavelet spectrum and the similarity coefficient, a fast and efficient microseismic time delay estimation method is proposed. Firstly, the original signals are denoised by continuous wavelet transform. Subsequently, the time-frequency transform of the original signal by continuous wavelet transform, time-frequency signal extraction is the process of band-pass filtering, which can further reduce the influence of noise interference on the time delay estimation. Finally, we calculated the similarity between the time-frequency signals via the time domain and frequency domain integration. The similarity function is based on correlation and proposed according to the time-frequency transformation provided by the phase spectrum to evaluate the similarity between two noisy signals. The time delay estimation is determined by searching for the similarity function peak. The experimental results show the precision and accuracy of the method over the cross-correlation method and generalized cross-correlation phase transformation method, especially when the signal-to-noise ratio is low. Therefore, a new time delay estimation method for non-stationary random signals is presented in this paper.
High-Resolution Optical Fiber Temperature Sensor Based on Draw Tower Grating Array
Authors: Liu Hanjie , Zhou Ciming , Pang Yandong , Chen Xi , Xu Ye , Fan Dian
Published: April 2022

Ocean temperature monitoring is of great significance to marine fishing, aquaculture, and marine operations. Traditional electric sensors lack the potential to multiplex several sensors, and may suffer from electromagnetic interference. Meanwhile, fiber Bragg grating-based sensors have the advantages of high sensitivity, possibility for large-scale multiplexing, and immunity to electromagnetic interference. In this paper, we propose a Fabry–Pérot (FP) interferometer based on the draw tower grating array and combine it with the phase measurement method for demonstration and testing. In the sensor system, two adjacent fiber Bragg gratings (FBGs) are used as mirrors and an optical fiber connects them, forming a sensor unit. The signal was detected through the compensation of the optical path difference via two-arm path differences in an unbalanced interferometer. The sensor is calibrated in the range of 36.00–36.50 °C, and back to 36.00 °C, in steps of 0.10 °C. A thermocouple (DW1222) is used as a reference. Experimental testing demonstrates that under the thermal loop, the temperature and phase can be approximated as a linear relationship, the Pearson square correlation coefficient is 0.9996, and the temperature sensitivity is −9846 rad/°C. To prove that our experimental device can achieve a higher temperature resolution, we measured the background noise of the system. The experimental results indicate that the order of magnitude of our system temperature resolution can reach 10−5 °C. Thus, we believe that the sensor system is promising for the application of ocean temperature detection, and owing to the ultraweak reflection characteristics of the FBG, this method provides the possibility for large-scale multiplexing of the system.
Control Systems with Tomographic Sensors—A Review
Authors: Hlava Jaroslav , Abouelazayem Shereen
Published: April 2022

Industrial process tomography offers two key advantages over conventional sensing systems. Firstly, process tomography systems provide information about 2D or 3D distributions of the variables of interest. Secondly, tomography looks inside the processes without penetrating them physically, i.e., sensing is possible despite harsh process conditions, and the operation of the process is not disturbed by intrusive sensors. These advantages open new perspectives for the field of process control, and the potential of closed-loop control applications is one of the main driving forces behind the development of industrial tomography. Despite these advantages and decades of development, closed-loop control applications of tomography are still not really common. This article provides an overview of the current state-of-the-art in the field of control systems with tomographic sensors. An attempt is made to classify the different control approaches, critically assess their strengths and weak points, and outline which directions may lead to increased future utilization of industrial tomography in the closed-loop feedback control.
Dynamic QoS Management for a Flexible 5G/6G Network Core: A Step toward a Higher Programmability
Authors: Bojović Petar D. , Malbašić Teodor , Vujošević Dušan , Martić Goran , Bojović Živko
Published: April 2022

The academic and professional community has recently started to develop the concept of 6G networks. The scientists have defined key performance indicators and pursued large-scale automation, ambient sensing intelligence, and pervasive artificial intelligence. They put great efforts into implementing new network access and edge computing solutions. However, further progress depends on developing a more flexible core infrastructure according to more complex QoS requirements. Our research aims to provide 5G/6G core flexibility by customizing and optimizing network slices and introducing a higher level of programmability. We bind similar services in a group, manage them as a single slice, and enable a higher level of programmability as a prerequisite for dynamic QoS. The current 5G solutions primarily use predefined queues, so we have developed highly flexible, dynamic queue management software and moved it entirely to the application layer (reducing dependence on the physical network infrastructure). Further, we have emulated a testbed environment as realistically as possible to verify the proposed model capabilities. Obtained results confirm the validity of the proposed dynamic QoS management model for configuring queues’ parameters according to the service management requirements. Moreover, the proposed solution can also be applied efficiently to 5G core networks to resolve complex service requirements.
Enhanced Antibacterial Activity of Brevibacillus sp. SPR19 by Atmospheric and Room Temperature Plasma Mutagenesis (ARTP)
Authors: Songnaka Nuttapon , Nisoa Mudtorlep , Atipairin Apichart , Wanganuttara Thamonwan , Chinnawong Thapanee
Published: April 2022

Antibiotic resistance is a major health concern worldwide. In our previous study, some bacterial isolates exhibited antibacterial activity against Staphylococcus aureus and methicillin-resistant S. aureus (MRSA). However, the production of antibacterial substances by native microorganisms is limited by biosynthetic genes. This study aimed to improve the antibacterial activity of SPR19 using atmospheric and room temperature plasma mutagenesis (ARTP). The results showed that SPR19 belonged to the Brevibacillus genus. The growth curves and production kinetics of antibacterial substances were investigated. Argon-based ARTP was applied to SPR19, and the 469 mutants were preliminarily screened using agar overlay method. The remaining 25 mutants were confirmed by agar well diffusion assay against S. aureus TISTR 517 and MRSA isolates 142, 1096, and 2468. M285 exhibited the highest activity compared to the wild-type strain (10.34–13.59%) and this mutant was stable to produce the active substances throughout 15 generations consistently. The antibacterial substances from M285 were tolerant to various conditions (heat, enzyme, surfactant, and pH) while retaining more than 90% of their activities. Therefore, Brevibacillus sp. SPR19 is a potential source of antibacterial substances. ARTP mutagenesis is a powerful method for strain improvement that can be utilized to treat MRSA infection in the future.
Kinesiology, Physical Activity, Physical Education, and Sports through an Equity/Equality, Diversity, and Inclusion (EDI) Lens: A Scoping Review
Authors: Arora Khushi , Wolbring Gregor
Published: April 2022

Background: Equity, equality, diversity, and inclusion are terms covered in the academic literature focusing on sports, kinesiology, physical education, and physical activity, including in conjunction with marginalized groups. Universities in many countries use various EDI policy frameworks and work under the EDI headers “equality, diversity and inclusion”, “equity, diversity and inclusion”, “diversity, equity and inclusion”, and similar phrases (all referred to as EDI) to rectify problems students, non-academic staff, and academic staff from marginalized groups, such as women, Indigenous peoples, visible/racialized minorities, disabled people, and Lesbian, Gay, Bisexual, Transgender, Queer or Questioning, and Two-Spirit (LGBTQ2S+) experience. Which EDI data, if any, are generated influences EDI efforts in universities (research, education, and general workplace climate) of all programs. Method: Our study used a scoping review approach and employed SCOPUS and the 70 databases of EBSCO-Host, which includes SportDiscus, as sources aimed to analyze the extent (and how) the academic literature focusing on sports, kinesiology, physical education, and physical activity engages with EDI. Results: We found only 18 relevant sources and a low to no coverage of marginalized groups linked to EDI, namely racialized minorities (12), women (6), LGBTQ2S+ (5), disabled people (2), and Indigenous peoples (0). Conclusions: Our findings suggest a gap in the academic inquiry and huge opportunities.
Risk Management as a Tool for Sustainability
Authors: Shrestha Asheem , Thaheem Muhammad Jamaluddin
Published: April 2022

Risk management is an ever-growing domain of research that is constantly evolving with the development of new and advanced technologies and processes, along with today’s increasing concern for sustainability [...]
Is “Attending Nearby School” Near? An Analysis of Travel-to-School Distances of Primary Students in Beijing Using Smart Card Data
Authors: Liao Cong , Dai Teqi
Published: April 2022

The distance between home and school is crucial for children’s mobility and education equity. Compared with choice-based enrollment systems, much less attention has been given to the commuting distance to school in proximity-based systems, as if the institutional arrangement of assigning children to nearby schools can avoid the problem of long commuting distances. Using student-type smart card data, this study explored the spatial characteristics of the commuting distance to primary schools by public transport and the residence-school spatial pattern under the proximity-based system in Beijing. The relationships between long school commutes and house price/age were investigated under the context of school gentrification. For the identified primary student users, fewer than 35% of the students travelled fewer than 3 km to school, while more than 80% of students travelled long distances greater than 5 km, which indicated that the policy of “attending nearby school” did not guarantee a shorter commuting distance to school. Long distances to school greater than 5 km correlate negatively with a lower average house price/building age and fewer students. This finding verified the assumptions from China’s school gentrification that people might buy older school-district houses but live far from the school district for a new house. These findings provide a complementary view of previous survey studies and reveal the actual commuting distance by public transport for a group of primary students in a proximity-based enrollment system.
Stakeholder Management in Government-Led Urban Regeneration: A Case Study of the Eastern Suburbs in Chengdu, China
Authors: Wang Jue , Yang Yi , Huang Huan , Wang Fan
Published: April 2022

There are debates on choices between the bottom-up and top-down urban regeneration approaches, and the former is often considered to be better quality since it includes mass stakeholders. This research aims to extend the understanding of the government-led top-down urban regeneration approach with a case study of the Eastern Suburbs in Chengdu, China. A qualitative interview-based approach was adopted. The results show that the top-down approach is efficient and brings high-quality results for large-scale post-industrial transformation with inclusive stakeholder management. There are several possible ways to involve stakeholders: actively or passively, participating in the whole process or some stages, and engaging fully or partially, according to the demand on site.
The Future of Wood Construction: Opportunities and Barriers Based on Surveys in Europe and Chile
Authors: Leszczyszyn Ewa , Heräjärvi Henrik , Verkasalo Erkki , Garcia-Jaca Javier , Araya-Letelier Gerardo , Lanvin Jean-Denis , Bidzińska Gabriela , Augustyniak-Wysocka Dobrochna , Kies Uwe , Calvillo Alex , Wahlströmh Margareta , Kouyoumji Jean-Luc
Published: April 2022

The demand for wood has increased in recent years due to new technical possibilities and environmental concerns. This paper provides an analysis of the factors that affect the use of wood in the construction sector, and an assessment of their importance in individual countries and for groups of stakeholders. The study covers the technical, societal, political, economic, and gender aspects of wood construction, with the aim of increasing global understanding regarding national differences, the current situation, and the potential for further development. The subject was investigated using a survey, and the most important opportunities for and barriers to growth in the use of wood in the construction sector were selected, following a statistical analysis. The results indicate strong regional and cultural differences regarding the acceptance of some of the opportunities and barriers related to the development of wood construction. The findings indicate that there is a need to promote wood construction based on its technical and economic benefits rather than its societal ones. On the other hand, the current societal barriers should be addressed as a priority, together with the establishment of common and harmonized policies. The results of this study, therefore, will contribute to the generation of regional-sensitive information that can be useful for policymakers when updating the building codes in their individual countries.
Learning from Habitat Reconstruction Initiatives—New Approach for Reducing Vulnerability of Rural Housing in India
Authors: Anand Mona Chhabra , Sinsupan Thitiphon , Madurapperuma Sisira , Chariar Vijayaraghavan M.
Published: April 2022

Post-disaster reconstruction offers an opportunity to address some of the fundamental causes of vulnerability that are an inherent part of mainstream housing processes located at the intersectionality of sectoral interdependencies. Well-designed initiatives in the aftermath of a disaster can help displaced populations enter a positive cycle of resilience-building using new approaches. This paper draws from a recent field study to examine the reasons for the poor performance of existing housing stock in the face of disasters and presents a chain of graded causal factors that contribute to their vulnerability. Specifically, in the context of rural housing, the paper looks at three case studies of innovative habitat reconstruction initiatives undertaken in the aftermath of major disasters in India and analyses them for their impact on building the resilience of displaced communities. The study highlights that in order to address the various causes of vulnerability of rural housing, it is important to leverage the existing connections between different dimensions of habitat development, including access to finance, choice of appropriate materials, skill-building, and safe construction methods. This is at the core of the ethos of “building back better.”
European Forest Governance: Status Quo and Optimising Options with Regard to the Paris Climate Target
Authors: Stubenrauch Jessica , Garske Beatrice , Ekardt Felix , Hagemann Katharina
Published: April 2022

This article assesses and develops policy instruments for forest governance in the EU. Methodologically, it examines opportunities and limits for negative emissions by means of a literature review. On this basis, it conducts a qualitative governance analysis of the most important instruments of EU forest policy and presents optimizing policy options, measured against the binding climate and biodiversity targets under international law. Our analysis shows that the potential benefits of afforestation and reforestation for climate mitigation are overestimated, and are often presented as the new saviours to assist in reaching climate neutrality, inter alia, since only biodiverse and thus resilient forests can function as a carbon sink in the long term. Furthermore, we demonstrate that the existing EU law fails to comply with climate and biodiversity targets. Quantity governance systems for livestock farming, fossil fuels and similar drivers of deforestation represent a more promising approach to forest governance than the dominant regulatory and subsidy-based governance. They are most effective when not directly addressing forests due to their heterogeneity but central damaging factors such as fossil fuels and livestock farming. Selected aspects of regulatory and subsidy law can supplement these quantity governance systems when focusing on certain easily attainable and thus controllable subjects. These include, e.g., the regulatory protection of old-growth forests with almost no exceptions and a complete conversion of all agricultural and forest subsidies to “public money for public services” to promote nature conservation and afforestation.
Double-Faceted Environmental Civil Liability and the Separate-Regulatory Paradigm: An Inspiration for China
Authors: Zhai Tiantian
Published: April 2022

The recognition of the intrinsic value of the environment and natural resources contributed to the establishment of liability for damage to the environment per se, which, coupled with the traditional environmental tort liability, constitute the double facets of environmental civil liability. Although the two facets share some common characteristics, their distinct focuses indicate that they cannot be fully covered by tort law or environmental law systems alone. As a result, an international trend toward the regulatory approach to environmental civil liability, which is referred to in this article as the “separate-regulatory paradigm”, is emerging. In such a pattern, as followed by the United States (US) and the European Union (EU), environmental tort liability and liability for environmental damage are mainly regulated by tort law and environmental statutes, respectively. However, China, relying substantially on its civil law system to address liability for environmental harm, seems to deviate from this paradigm. This article analyzes the significance of the separate-regulatory paradigm and argues that it has profound implications for China. This article suggests a separate statutory liability scheme that moves beyond the existing Chinese civil law framework to achieve the full recovery of environmental damage.
Host Population Well-Being through Community-Based Tourism and Local Control: Issues and Ways Forward
Authors: Giampiccoli Andrea , Dłużewska Anna , Mnguni Erasmus Mzobanzi
Published: April 2022

The importance and role of tourism around the world is not new; it is enough to mention that tourism represents one of the fastest-growing and most profitable global economic sectors. However, tourism has negative impacts in destinations, such as the displacement and relocation of communities and disruption of economic systems, socio-political processes and organizations. It must be recognized that new strategies are required, because growth itself is not sufficient to fight poverty and inequality. Local people, especially the disadvantaged sections of the communities, need to be protagonists and able to control the tourism sector and benefit from it. The aim of this paper is to contribute to wellbeing, sustainability, and tourism research by proposing issues and ways forward related to enhancing well-being through community-based tourism (CBT). The paper is divided into three sections. The first focuses on CBT approaches. The second concerns the conceptual framework of wellbeing, with special emphasis on wellbeing in tourism, including host/guest relations; we present different evaluations of wellbeing, e.g., (socio-cultural, psychological, economic, and environmental). The third section discusses how wellbeing is presented in CBT and proposes ways forward for research. The paper is theoretical, and is based on previous literature and institutional and organizational documents.
Analytical Model for the Development Strategy of a Low-Density Territory: The Montesinho Natural Park
Authors: Matos Silva Fátima , Sousa Cristina , Albuquerque Helena
Published: April 2022

Montesinho Natural Park is one of the largest Portuguese natural protected areas, presenting good biodiversity and a cultural heritage with a strong connection to the territory and its people. It constitutes a low-density territory, characterized by a human and social landscape based on community practices, such as joint aid and the community use of goods and means of agricultural production, which have contributed to the construction of the “transmontana” identity and to the richness of the habitats. The promotion of the sustainable development of this low-density rural region demands the understanding of its specificities and an appropriate approach to grasp its challenges and develop effective management tools, allowing to preserve and exploit the region’s potential from various perspectives. The purpose of this article is to develop an analytical model using a literature review and a survey of the region’s specificities. This analytical model intends to provide the basis for designing and assessing sustainable development solutions, increasing local entrepreneurship and community empowerment through regional dynamism, with a focus on environment and heritage preservation, universal tourism accessibility, collective memory and endogenous product development. The suggested model adopts an interdisciplinary perspective and stresses that, in order to ensure that the new initiatives will contribute to the territory’s sustainable development, they should be scrutinized by asking four main questions: Is the initiative promoting the rural development of the territory through the creation of synergies between agroforestry and tourism activities? Is the initiative promoting an inclusive and sustainable tourism that is based on the territory’s resources? Are heritage and collective memory being preserved and valued through the initiative? Is the initiative promoting the empowerment of local communities?
The Role of Live-Streaming E-Commerce on Consumers’ Purchasing Intention regarding Green Agricultural Products
Authors: Dong Xiaoxu , Zhao Huawei , Li Tiancai
Published: April 2022

Live-streaming e-commerce has boosted the marketing vitality and possibilities of green agricultural products. However, academic research on this emerging marketing method remains insufficient. To fill this literature gap, this paper examines whether live-streaming e-commerce has gained consumers’ trust and strengthened their intention to purchase green agricultural products. On the basis of a literature review, in this paper, we establish an evaluation system for live-streaming e-commerce which includes information quality, system quality, service quality, telepresence, and social presence and assumes that high-quality live-streaming e-commerce will increase consumers’ green trust and, thus, strengthen green purchase intention. Altogether, 726 valid questionnaires were collected, and structural equation modeling (SEM) and stepwise regression were used to analyze the data. The results demonstrate that the five aforementioned dimensions of live-streaming e-commerce quality that were used as criteria positively impact green trust. The findings provide suggestions for green-product companies on how to improve their live-streaming quality to enhance consumers’ purchase intention to realize economic and social value.
Road Asset Value Calculation Based on Asset Performance, Community Benefits and Technical Condition
Authors: Mikolaj Ján , Remek Ľuboš , Kozel Matúš
Published: April 2022

The article presents a comprehensive asset management method. Here presented method aims to bridge the economic approach to asset management with the technical approach to road infrastructure life cycle, namely its resilience and performance. The presented asset value calculation methods are based both on socio-economic viewpoints on community benefits of an asset, as well as the technical aspect of the technical condition and residual life calculations of a road infrastructure. In contrast to common road asset management methods, asset value is not arbitrarily annually depreciated, instead, it is exactly calculated based on pavement performance models, pavement construction fatigue and paving material properties. Road asset value calculation is based on the asset performance and the technical condition of a pavement structure and other objects. Road asset performance is defined in terms of society and road user demands put on road category and its qualitative standard. Road asset technical condition is evaluated by the procurement cost calculation and condition deterioration. Value of condition deterioration is defined by residual life expectancy based on fatigue and construction reliability of the road infrastructure. The cross-asset allocation method is used for the creation of programs for claim and allocation of funding. The aim was to increase the credibility of the road administrators with the public as they present their decisions based on road asset management, and to increase the level of acceptance for practitioners.