bluuebunny/crossref_metadata_2025 huggingface dataset:
Human Activity Recognition by Sequences of Skeleton Features
Authors: Ramirez Heilym , Velastin Sergio A. , Aguayo Paulo , Fabregas Ernesto , Farias Gonzalo
Published: May 2022

In recent years, much effort has been devoted to the development of applications capable of detecting different types of human activity. In this field, fall detection is particularly relevant, especially for the elderly. On the one hand, some applications use wearable sensors that are integrated into cell phones, necklaces or smart bracelets to detect sudden movements of the person wearing the device. The main drawback of these types of systems is that these devices must be placed on a person’s body. This is a major drawback because they can be uncomfortable, in addition to the fact that these systems cannot be implemented in open spaces and with unfamiliar people. In contrast, other approaches perform activity recognition from video camera images, which have many advantages over the previous ones since the user is not required to wear the sensors. As a result, these applications can be implemented in open spaces and with unknown people. This paper presents a vision-based algorithm for activity recognition. The main contribution of this work is to use human skeleton pose estimation as a feature extraction method for activity detection in video camera images. The use of this method allows the detection of multiple people’s activities in the same scene. The algorithm is also capable of classifying multi-frame activities, precisely for those that need more than one frame to be detected. The method is evaluated with the public UP-FALL dataset and compared to similar algorithms using the same dataset.
TIMo—A Dataset for Indoor Building Monitoring with a Time-of-Flight Camera
Authors: Schneider Pascal , Anisimov Yuriy , Islam Raisul , Mirbach Bruno , Rambach Jason , Stricker Didier , Grandidier Frédéric
Published: May 2022

We present TIMo (Time-of-flight Indoor Monitoring), a dataset for video-based monitoring of indoor spaces captured using a time-of-flight (ToF) camera. The resulting depth videos feature people performing a set of different predefined actions, for which we provide detailed annotations. Person detection for people counting and anomaly detection are the two targeted applications. Most existing surveillance video datasets provide either grayscale or RGB videos. Depth information, on the other hand, is still a rarity in this class of datasets in spite of being popular and much more common in other research fields within computer vision. Our dataset addresses this gap in the landscape of surveillance video datasets. The recordings took place at two different locations with the ToF camera set up either in a top-down or a tilted perspective on the scene. Moreover, we provide experimental evaluation results from baseline algorithms.
Characterization and Evaluation of Human–Exoskeleton Interaction Dynamics: A Review
Authors: Massardi Stefano , Rodriguez-Cianca David , Pinto-Fernandez David , Moreno Juan C. , Lancini Matteo , Torricelli Diego
Published: May 2022

Exoskeletons and exosuits have witnessed unprecedented growth in recent years, especially in the medical and industrial sectors. In order to be successfully integrated into the current society, these devices must comply with several commercialization rules and safety standards. Due to their intrinsic coupling with human limbs, one of the main challenges is to test and prove the quality of physical interaction with humans. However, the study of physical human–exoskeleton interactions (pHEI) has been poorly addressed in the literature. Understanding and identifying the technological ways to assess pHEI is necessary for the future acceptance and large-scale use of these devices. The harmonization of these evaluation processes represents a key factor in building a still missing accepted framework to inform human–device contact safety. In this review, we identify, analyze, and discuss the metrics, testing procedures, and measurement devices used to assess pHEI in the last ten years. Furthermore, we discuss the role of pHEI in safety contact evaluation. We found a very heterogeneous panorama in terms of sensors and testing methods, which are still far from considering realistic conditions and use-cases. We identified the main gaps and drawbacks of current approaches, pointing towards a number of promising research directions. This review aspires to help the wearable robotics community find agreements on interaction quality and safety assessment testing procedures.
Wearable Blood Pressure Sensing Based on Transmission Coefficient Scattering for Microstrip Patch Antennas
Authors: El Abbasi Mona K. , Madi Mervat , Jelinek Herbert F. , Kabalan Karim Y.
Published: May 2022

Painless, cuffless and continuous blood pressure monitoring sensors provide a more dynamic measure of blood pressure for critical diagnosis or continuous monitoring of hypertensive patients compared to current cuff-based options. To this end, a novel flexible, wearable and miniaturized microstrip patch antenna topology is proposed to measure dynamic blood pressure (BP). The methodology was implemented on a simulated five-layer human tissue arm model created and designed in High-Frequency Simulation Software “HFSS”. The electrical properties of the five-layer human tissue were set at the frequency range (2–3) GHz to comply with clinical/engineering standards. The fabricated patch incorporated on a 0.4 mm epoxy substrate achieved consistency between the simulated and measured reflection coefficient results at flat and bent conditions over the frequency range of 2.3–2.6 GHz. Simulations for a 10 g average specific absorption rate (SAR) based on IEEE-Standard for a human arm at different input powers were also carried out. The safest input power was 50 mW with an acceptable SAR value of 3.89 W/Kg < 4W/Kg. This study also explored a novel method to obtain the pulse transit time (PTT) as an option to measure BP. Pulse transmit time is based on obtaining the time difference between the transmission coefficient scattering waveforms measured between the two pairs of metallic sensors underlying the assumption that brachial arterial geometries are dynamic. Consequently, the proposed model is validated by comparing it to the standard nonlinear Moens and Korteweg model over different artery thickness-radius ratios, showing excellent correlation between 0.76 ± 0.03 and 0.81 ± 0.03 with the systolic and diastolic BP results. The absolute risk of arterial blood pressure increased with the increase in brachial artery thickness-radius ratio. The results of both methods successfully demonstrate how the radius estimates, PTT and pulse wave velocity (PWV), along with electromagnetic (EM) antenna transmission propagation characteristics, can be used to estimate continuous BP non-invasively.
Fast Fault Diagnosis in Industrial Embedded Systems Based on Compressed Sensing and Deep Kernel Extreme Learning Machines
Authors: Shan Nanliang , Xu Xinghua , Bao Xianqiang , Qiu Shaohua
Published: May 2022

With the complexity and refinement of industrial systems, fast fault diagnosis is crucial to ensuring the stable operation of industrial equipment. The main limitation of the current fault diagnosis methods is the lack of real-time performance in resource-constrained industrial embedded systems. Rapid online detection can help deal with equipment failures in time to prevent equipment damage. Inspired by the ideas of compressed sensing (CS) and deep extreme learning machines (DELM), a data-driven general method is proposed for fast fault diagnosis. The method contains two modules: data sampling and fast fault diagnosis. The data sampling module non-linearly projects the intensive raw monitoring data into low-dimensional sampling space, which effectively reduces the pressure of transmission, storage and calculation. The fast fault diagnosis module introduces the kernel function into DELM to accommodate sparse signals and then digs into the inner connection between the compressed sampled signal and the fault types to achieve fast fault diagnosis. This work takes full advantage of the sparsity of the signal to enable fast fault diagnosis online. It is a general method in industrial embedded systems under data-driven conditions. The results on the CWRU dataset and real platforms show that our method not only has a significant speed advantage but also maintains a high accuracy, which verifies the practical application value in industrial embedded systems.
Assessment of the Mechanical Support Characteristics of a Light and Wearable Robotic Exoskeleton Prototype Applied to Upper Limb Rehabilitation
Authors: Vélez-Guerrero Manuel Andrés , Callejas-Cuervo Mauro , Álvarez Juan C. , Mazzoleni Stefano
Published: May 2022

Robotic exoskeletons are active devices that assist or counteract the movements of the body limbs in a variety of tasks, including in industrial environments or rehabilitation processes. With the introduction of textile and soft materials in these devices, the effective motion transmission, mechanical support of the limbs, and resistance to physical disturbances are some of the most desirable structural features. This paper proposes an evaluation protocol and assesses the mechanical support properties of a servo-controlled robotic exoskeleton prototype for rehabilitation in upper limbs. Since this prototype was built from soft materials, it is necessary to evaluate the mechanical behavior in the areas that support the arm. Some of the rehabilitation-supporting movements such as elbow flexion and extension, as well as increased muscle tone (spasticity), are emulated. Measurements are taken using the reference supplied to the system’s control stage and then compared with an external high-precision optical tracking system. As a result, it is evidenced that the use of soft materials provides satisfactory outcomes in the motion transfer and support to the limb. In addition, this study lays the groundwork for a future assessment of the prototype in a controlled laboratory environment using human test subjects.
Artificial Neurons on Flexible Substrates: A Fully Printed Approach for Neuromorphic Sensing
Authors: Singaraju Surya A. , Weller Dennis D. , Gspann Thurid S. , Aghassi-Hagmann Jasmin , Tahoori Mehdi B.
Published: May 2022

Printed electronic devices have demonstrated their applicability in complex electronic circuits. There is recent progress in the realization of neuromorphic computing systems (NCSs) to implement basic synaptic functions using solution-processed materials. However, a fully printed neuron is yet to be realised. We demonstrate a fully printed artificial neuromorphic circuit on flexible polyimide (PI) substrate. Characteristic features of individual components of the printed system were guided by the software training of the NCS. The printing process employs graphene ink for passive structures and In2O3 as active material to print a two-input artificial neuron on PI. To ensure a small area footprint, the thickness of graphene film is tuned to target a resistance and to obtain conductors or resistors. The sheet resistance of the graphene film annealed at 300 °C can be adjusted between 200 Ω and 500 kΩ depending on the number of printed layers. The fully printed devices withstand a minimum of 2% tensile strain for at least 200 cycles of applied stress without any crack formation. The area usage of the printed two-input neuron is 16.25 mm2, with a power consumption of 37.7 mW, a propagation delay of 1 s, and a voltage supply of 2 V, which renders the device a promising candidate for future applications in smart wearable sensors.
Assessing the Impact of Wildlife on Vegetation Cover Change, Northeast Namibia, Based on MODIS Satellite Imagery (2002–2021)
Authors: Gbagir Augustine-Moses Gaavwase , Sikopo Colgar Sisamu , Matengu Kenneth Kamwi , Colpaert Alfred
Published: May 2022

Human–wildlife conflict in the Zambezi region of northeast Namibia is well documented, but the impact of wildlife (e.g., elephants) on vegetation cover change has not been adequately addressed. Here, we assessed human–wildlife interaction and impact on vegetation cover change. We analyzed the 250 m MODIS and ERA5 0.25° × 0.25° drone and GPS-collar datasets. We used Time Series Segmented Residual Trends (TSS-RESTREND), Mann–Kendall Test Statistics, Sen’s Slope, ensemble, Kernel Density Estimation (KDE), and Pearson correlation methods. Our results revealed (i) widespread vegetation browning along elephant migration routes and within National Parks, (ii) Pearson correlation (p-value = 5.5 × 10−8) showed that vegetation browning areas do not sustain high population densities of elephants. Currently, the Zambezi has about 12,008 elephants while these numbers were 1468, 7950, and 5242 in 1989, 1994, and 2005, respectively, (iii) settlements and artificial barriers have a negative impact on wildlife movement, driving vegetation browning, and (iv) vegetation greening was found mostly within communal areas where intensive farming and cattle grazing is a common practice. The findings of this study will serve as a reference for policy and decision makers. Future studies should consider integrating higher resolution multi-platform datasets for detailed micro analysis and mapping of vegetation cover change.
Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning
Authors: Varandas Rui , Lima Rodrigo , Bermúdez I Badia Sergi , Silva Hugo , Gamboa Hugo
Published: May 2022

Wearable sensors have increasingly been applied in healthcare to generate data and monitor patients unobtrusively. Their application for Brain–Computer Interfaces (BCI) allows for unobtrusively monitoring one’s cognitive state over time. A particular state relevant in multiple domains is cognitive fatigue, which may impact performance and attention, among other capabilities. The monitoring of this state will be applied in real learning settings to detect and advise on effective break periods. In this study, two functional near-infrared spectroscopy (fNIRS) wearable devices were employed to build a BCI to automatically detect the state of cognitive fatigue using machine learning algorithms. An experimental procedure was developed to effectively induce cognitive fatigue that included a close-to-real digital lesson and two standard cognitive tasks: Corsi-Block task and a concentration task. Machine learning models were user-tuned to account for the individual dynamics of each participant, reaching classification accuracy scores of around 70.91 ± 13.67 %. We concluded that, although effective for some subjects, the methodology needs to be individually validated before being applied. Moreover, time on task was not a particularly determining factor for classification, i.e., to induce cognitive fatigue. Further research will include other physiological signals and human–computer interaction variables.
Asian Australians’ Experiences of Online Racism during the COVID-19 Pandemic
Authors: Kamp Alanna , Denson Nida , Sharples Rachel , Atie Rosalie
Published: May 2022

Between 13 November 2020 and 11 February 2021, an online national survey of 2003 Asian Australians was conducted to measure the type and frequency of self-identified Asian Australians’ experiences of racism during the COVID-19 pandemic. The survey also aimed to gauge the relationships between racist experiences and targets’ mental health, wellbeing and sense of belonging. In this paper, we report findings on the type and frequency of online racist experiences and their associations with mental health, wellbeing and belonging. The survey found that 40 per cent of participants experienced racism during the COVID-19 pandemic. Within that group, 66 per cent experienced racism online. The demographic pattern of those most likely to experience online racism were younger age groups, males, those born in Australia, English speakers at home, non-Christians, and migrants who have been in Australia less than 20 years. Analysis also found a strong correlation between Asian Australians’ experiences of online racism and poor mental health, wellbeing and belonging. The relationship between experiencing racism, non-belonging and morbidity were more pronounced for those who experienced online racism compared to those who experienced racism in other offline contexts. This points to the corrosive nature of online racism on social cohesion, health and belonging.
Tough Love—Impactful, Caring Coaching in Psychologically Unsafe Environments
Authors: Taylor Jamie , Ashford Michael , Collins Dave
Published: May 2022

(1) Background: The interpersonal dimensions of coaching in high performance sport have been subject to increasing scrutiny but with limited evidence to guide practice. Similarly, there is increasing practical interest in the concept of psychological safety, often portrayed as an implicitly desirable characteristic of all sporting environments but, as yet, still to receive research attention in high performance. As a first step to addressing these deficiencies, the present study addressed two research aims: (a) to examine the extent to which matched groups of international and released professional rugby union players perceived psychological safety to be an adaptive feature of their developmental experience and (b) to understand what elements of the player’s coaching experience were perceived to be enabling or disenabling of future progress. (2) Methods: Seven rugby union players who had ‘made it’ and eight players who had been released from their professional contracts took part in a semi-structured interview exploring their developmental experiences. Data were subsequently analysed using Reflexive Thematic Analysis. (3) Results: Both groups of players found each of their talent development and high performance environments to be psychologically unsafe. Furthermore, players perceived coaches who were the most impactful in their development as offering ‘tough love’. This included a range of ‘harder’ and ‘softer’ interpersonal approaches that presented the player with clear direction, role clarity and a sense of care. It appeared that this interpersonal approach helped the player to navigate, and benefit from, the psychologically unsafe high performance milieu. (4) Conclusions: There appear to be a number of balances for the coach in the high performance setting to navigate and a need for more nuance in applying constructs such as psychological safety.
Editorial: Nano- and Micro-Contaminants and Their Effect on the Humans and Environment
Authors: Silva Luis F. O. , Niu Hongya
Published: May 2022

Nano- and ultra-fine materials have a significant influence on construction building materials, the environment, and human health [...]
Sustainability Assessment of Family Agricultural Properties: The Importance of Homeopathy
Authors: Cordoba Correoso Claudio , Agostinho Feni , Smaniotto James Rodrigo , Boff Mari Carissimi , Boff Pedro
Published: May 2022

Family farming is a lifestyle and agricultural base that has ensured food for the world’s growing population in addition to the family’s own subsistence. However, the intensification of production processes to primarily generate exportable surpluses is based on the constant input of industrial inputs of low local socioeconomic viability. This study aims to evaluate the sustainability of family farms and their impact on the adoption of homeopathy instead of traditional/conventional intensification techniques. The study was conducted at six family farms located in the Serrana Mesoregion, Santa Catarina, Brazil, and include farms were classified according to the management and representativeness of their agricultural activity, i.e., conventional grains-cattle, milk-grains, grains and diversified, and according to their ecological basis, i.e., agroecological and organic. To discuss the sustainability of the family farms that were evaluated, the of metrics emergy synthesis, ecotoxicity potential and socioeconomic indicators are used. The results indicate that conventional diversified property has the best overall performance with regard to sustainability, including emergy yield ratio (EYR 1.88), emergy investment ratio (EIR 1.13), return on assets (1.22), hourly income of work (36.6 BRL/h) and income sufficiency (3.3). Agroecological and organic properties have better performance in renewability (76% and 75%), environmental load (ELR = 0.32 and 0.34), sustainability (ESI = 4.78 and 3.5) and potential ecotoxicity (1.736 and 1.579 kg 1.4 DCB-eq/ha). The contribution of homeopathy in an alternative scenario results in a 19% reduction in nonrenewable flows in conventional management properties and a decrease of up to 91% in ecotoxicity in grain + cattle properties. Using homeopathy, the return on assets and profit margin can be increased by up to 43% and income per hour of work and income sufficiency can be increased by 20% and 16%, respectively. This study contributes to discussions about the importance of using homeopathic therapies as a viable strategy that can be used in strategic public policy plans to improve the sustainability of family farms.
Risk Network Evaluation of Prefabricated Building Projects in Underdeveloped Areas: A Case Study in Qinghai
Authors: Wang Qiuyu , Gong Zhiqi , Liu Chengkui
Published: May 2022

Prefabricated building projects (PBPs) face more risks than traditional construction projects, especially in underdeveloped areas. This study takes Qinghai Province as a study case. Social network analysis (SNA) is adopted to develop a risk network of PBPs, and nine core risk factors and five key risk relationships are identified. Risk effect detection reveals the effectiveness of risk response strategies. The research shows that PBPs in underdeveloped areas are still in the early stage of development, and developers generally lack a leading role. There are prominent problems in the design stage of PBPs, so the stakeholders pay special attention to them. In underdeveloped regions, the development of PBPs must rely on the strong promotion of the government. Limited by natural and economic conditions, the market mechanism of PBPs in underdeveloped areas is not perfect, and policy regulation greatly affects the spread of the risk network. Therefore, local governments need to actively introduce corresponding supportive policies and mobilize the enthusiasm of stakeholders. This is the first study to consider the risk within the life cycle of PBPs in underdeveloped plateau areas. This study expands the research system of risk management of PBPs and provides valuable risk response strategies for the stakeholders.
Extreme Spillover between Green Bonds and Clean Energy Markets
Authors: Jiang Dongming , Jia Fang
Published: May 2022

This paper examines green bonds (GB), which have received much attention for providing funding for clean energy (CE) market reforms. We investigate the extreme spillover effects between GB and CE markets by using both MVMQ-CAViaR and Granger causality in risk methods over the period from 5 July 2011 to 24 February 2020. Since there are usually extreme asymmetric spillovers between financial markets, we examined whether this phenomenon exists between GB and CE markets. Our empirical analysis results find the significant extreme spillovers from GB to CE markets. In addition, we find that the upside and downside risk spillovers between GB and CE markets are asymmetric. The upside spillover is greater than downside spillover from GB to CE markets and the impact of GB on CE markets is greater. However, the extreme spillover from CE to GB markets is not significant by either the Granger causality in risk or the MVMQ-CAViaR model. Our findings have important implications for investors, policy makers and researchers.
A Deep Learning-Based Model for Date Fruit Classification
Authors: Albarrak Khalied , Gulzar Yonis , Hamid Yasir , Mehmood Abid , Soomro Arjumand Bano
Published: May 2022

A total of 8.46 million tons of date fruit are produced annually around the world. The date fruit is considered a high-valued confectionery and fruit crop. The hot arid zones of Southwest Asia, North Africa, and the Middle East are the major producers of date fruit. The production of dates in 1961 was 1.8 million tons, which increased to 2.8 million tons in 1985. In 2001, the production of dates was recorded at 5.4 million tons, whereas recently it has reached 8.46 million tons. A common problem found in the industry is the absence of an autonomous system for the classification of date fruit, resulting in reliance on only the manual expertise, often involving hard work, expense, and bias. Recently, Machine Learning (ML) techniques have been employed in such areas of agriculture and fruit farming and have brought great convenience to human life. An automated system based on ML can carry out the fruit classification and sorting tasks that were previously handled by human experts. In various fields, CNNs (convolutional neural networks) have achieved impressive results in image classification. Considering the success of CNNs and transfer learning in other image classification problems, this research also employs a similar approach and proposes an efficient date classification model. In this research, a dataset of eight different classes of date fruit has been created to train the proposed model. Different preprocessing techniques have been applied in the proposed model, such as image augmentation, decayed learning rate, model checkpointing, and hybrid weight adjustment to increase the accuracy rate. The results show that the proposed model based on MobileNetV2 architecture has achieved 99% accuracy. The proposed model has also been compared with other existing models such as AlexNet, VGG16, InceptionV3, ResNet, and MobileNetV2. The results prove that the proposed model performs better than all other models in terms of accuracy.
The Effect of Value Innovation in the Superior Performance and Sustainable Growth of Telecommunications Sector: Mediation Effect of Customer Satisfaction and Loyalty
Authors: Hajar Mohammed A. , Alkahtani Ammar Ahmed , Ibrahim Daing Nasir , Al-Sharafi Mohammed A. , Alkawsi Gamal , Iahad Noorminshah A. , Darun Mohd Ridzuan , Tiong Sieh Kiong
Published: May 2022

The telecommunications industry has recognized innovation as the key to growth and survival. Globalization, liberalization, and privatization, the terms most commonly associated with this sector, have resulted in fierce competition, making it more difficult for telecommunications firms to increase their market share and, as a result, their customer base, sales volume, and, ultimately, profits. The new success strategy is differentiation through innovation, with the aim of breaking out of the competition and creating an uncontested market. This can be accomplished by providing high-value, innovative services that result in customer satisfaction and promote customer loyalty. The primary goal of this study was to create and validate a conceptual model of value innovation and its impact on firm performance and long-term growth by examining the mediation effect of customer satisfaction and loyalty. The empirical analysis results were based on 304 respondents who completed a paper-based survey provided to employees of Yemeni mobile service providers using a convenience non-probability sampling technique. SmartPLS 3 was used to test the hypothesized relationships using partial least squares structural equation modeling (PLS-PM). As a result, the findings empirically validated the theoretical research model, confirming the importance of the value innovation approach to achieving company performance and long-term growth by promoting customer satisfaction and loyalty. Finally, we have provided a discussion of the study’s theoretical contributions, managerial implications, and future research directions.
Teaching Two-Eyed Seeing in Education for Sustainable Development: Inspirations from the Science|Environment|Health Pedagogy in Pandemic Times
Authors: Zeyer Albert
Published: May 2022

This conceptual paper starts by outlining six important concerns of Science|Environment|Health (S|E|H), a new pedagogy of science that has been developed during the last decade by a Special Interest Group of the ESERA community. The paper points out that the importance of these six concerns even increased during the SARS-CoV-2 pandemic. They play an essential role in preparing future citizens not only for coping with the pandemic but in general with other great challenges that lie ahead of our world. In this way S|E|H is naturally connected to the UN Sustainable Development Goals, and the paper discusses how S|E|H work in recent years may inspire education for sustainable development. The six concerns are: (1) the question of curricular change, (2) the role of knowledge in S|E|H contexts, (3) the danger of scientism and the tension between individual and political responsibility, (4) decision-making in S|E|H contexts, (5) the challenge of coping with uncertainty, and (6) the question of scientific holism. Structured by these concerns, the paper reviews recent research of the S|E|H community. These findings are reframed by the Two-Eyed Seeing approach that has recently found growing interest in the S|E|H community. This new approach distinguishes between the scientific image and the life-world image on an ontological basis, which helps to disentangle the six concerns and to provide a framework for tackling them in teacher education and educational research—in S|E|H contexts and also in education for sustainable development.
Practice of Sustainability Leadership: A Multi-Stakeholder Inclusive Framework
Authors: Jayashree Payyazhi , El Barachi May , Hamza Feras
Published: May 2022

Sustainability leadership aims at balancing short-term economic goals with long-term sustainable development goals by considering the interests of all stakeholders instead of just shareholders and focusing on a triple bottom line: people, planet, and profit. The existing research on sustainability leadership has mainly focused on the role of individual competencies without considering other meso and macro level factors that can impact the enactment of sustainable leadership. The studies that have considered these micro, meso, and macro levels have conceptualized these levels as stratified and discrete, assuming a hierarchical relationship between them. Such a conceptualization constitutes an impediment to the dynamic communication and engagement that is necessary to the achievement of sustainability goals. Drawing on stakeholder theory, this study investigates the key factors impacting the practice of sustainability leadership in a contextually relevant manner. More specially, we propose a multi-level, multi-stakeholder framework for sustainability leadership that is data driven and supported by evidence. This framework is meant to portray a holistic model that is dynamic and reciprocal in the manner in which micro, meso and macro factors impact each other. Qualitative research methods and purposive sampling were used for four stages of data collection, from 39 individuals with diverse profiles across the sustainable-engineering sector. The data collected were analyzed thematically, and the findings formed the basis of the dynamic inclusive business model for sustainability proposed in this paper, which challenges the traditional hierarchical business models. The data-driven, multi-level, multi-stakeholder framework proposed in this work extends the literature by providing insights on the key factors that impact the practice of sustainability leadership in the context of SMEs, operating in an emerging market. This framework demonstrates that the effective practice of sustainability leadership by SMEs is influenced by the interplay of factors at micro, meso and macro levels, as represented by individuals, organizations/firms, and governments.
Assessment of the Sustainability of Fibre-Reinforced Concrete by Considering Both Environmental and Mechanical Properties
Authors: Soltanzadeh Fatemeh , Behbahani Ali E. , Hosseinmostofi Kasra , Teixeira Carlos A.
Published: May 2022

The environmental consequences of human activities, e.g., the depletion of non-renewable fuel resources, consumption of natural raw materials, and release of huge amounts of CO2 into the atmosphere, resulted in new challenges in materials engineering. Based on these challenges, building materials must fulfil not only mechanical performance criteria, but also produce the least environmental impact accompanied by their production. In the present study, the possibility of employing scrap tire recycled steel fibres (RSF) as a substitution to industrial steel fibres (ISF) for developing more sustainable fibre-reinforced concretes was explored by adopting a life-cycle approach, integrated both environmental and mechanical properties. Four different fibre-reinforced self-compacting concretes–FRSCCs–were tailored by means of replacing the ISFs partially/totally (i.e., 0%, 50%, 67%, 100% by mass of) with the recycled ones. The effect of applying various dosages of RSFs on mechanical behavior of FRSCC–namely compressive, flexural, and splitting tensile responses–were evaluated experimentally. The environmental impacts associated with the production of each FRSCC were also assessed through life-cycle analysis. The potentiality of the RSFs to be used as concrete reinforcement with a comparable mechanical performance to that of ISF-reinforced concrete and lower environmental footprint was evaluated through a consolidated environmental and mechanical index (EM). In this study, using RSFs instead of industrial fibres for developing FRSCC has provided up to 37% higher EM index. The results confirmed the promising prospects for the application of RSFs in developing more eco-efficient and sustainable reinforced concrete.
Impact of COVID-19 Pandemic on Vertical Price Transmission in the U.S. Fresh Banana Market
Authors: Odiase Solomon , Saghaian Sayed H.
Published: May 2022

The U.S. imports about two billion dollars of fresh bananas, accounting for over 99 percent of domestic banana consumption annually. The COVID-19 pandemic disrupted the fresh banana supply chain and caused unexpected price movements along the marketing channel. This research investigated the impact of the COVID-19 pandemic on price adjustments in the U.S. fresh banana market. A Vector Error Correction (VEC) model was employed to evaluate the speeds of price adjustments along the U.S. banana marketing channel at the import and retail levels, and historical decomposition graphs were used to investigate the magnitude of price adjustments caused by the COVID-19 shock. The results show that the deviation from the long-run equilibrium caused by the shock was corrected faster for the import prices than retail prices. Hence, the speeds of price adjustments were asymmetric in the period of the COVID-19 shock. Additionally, the magnitudes of price changes caused by the pandemic shock were different, leading to increased price margins. These results point to the inefficiency of the banana marketing channel with welfare, policy, and agribusiness implications.
The Impact of Farmers’ Perception on Their Cultivated Land Quality Protection Behavior: A Case Study of Ningbo, China
Authors: Wang Xiaoying , Hu Hangang , Ning Aifeng , Li Guan , Wang Xueqi
Published: May 2022

Farmers’ protection behavior largely depends on their perceived value of cultivated land quality protection. However, existing research shows that the impact path of these perceived factors on farmers’ cultivated land protection behavior is not clear. Based on the survey data of 288 farmers in Ningbo City, this study empirically analyzed the impact of farmers’ perception on their cultivated land quality protection behavior through structural equation modeling (SEM). The results showed that farmers’ cultivated land quality protection behavior largely depended on perceived value, and they followed the logic paradigm of “perceived tradeoff→perceived value→behavioral intention→behavioral response”. Among them, farmers’ perceived value comes from farmers’ comprehensive tradeoff of benefits and risks in the process of cultivated land quality protection. In other words, improving farmers’ perceived benefits and reducing perceived risks is conducive to improving farmers’ perceived value of cultivated land quality protection. The above findings are helpful to improve farmers’ behavior of farmland land quality protection and provide new ideas and empirical basis for the design and improvement of cultivated land quality protection policies.
Barriers and Enablers of Circular Economy Implementation for Electric-Vehicle Batteries: From Systematic Literature Review to Conceptual Framework
Authors: Sopha Bertha Maya , Purnamasari Dwi Megah , Ma’mun Sholeh
Published: May 2022

With the burgeoning transition toward electrified automobile fleets, electric-vehicle batteries (EVBs) have become one of the critical aspects to be considered to avoid resources issues while achieving necessary climate goals. This paper compiles and syntheses reported barriers, enablers, involved stakeholders, and business models of Circular Economy (CE) implementation of the EVBs based on a systematic literature review (SLR). Findings indicate that inefficient and inadequate government policy, lack of safety standards, and high recycling costs are the three most reported barriers. The barriers have interconnections with each other, implying the necessity for simultaneous strategies. Based on the barriers-enablers analysis, the key strategies establishing the CE for the EVBs are innovative business models, economic incentives, EVB standards, legal environmental responsibilities, and certification, whereas the optimized supply-chain operations can be realized through eco-design of the EVBs, battery modularization, proper technology for checking, diagnosing, tracking, information sharing, extensive collaboration, alignment of supply-chain stakeholders, innovative business model, and certification. A conceptual framework presenting the required strategies for both establishing the CE and optimizing the circular supply chain system of the EVBs was then proposed. Potential future research directions are also discussed.
Automation and Remote Control of an Aquatic Harvester Electric Vehicle
Authors: Tudor Emil , Matache Mihai-Gabriel , Vasile Ionuț , Sburlan Ion-Cătălin , Stefan Vasilica
Published: May 2022

Electric boats are evolving, following the trend of imposing electric powered vehicles in all transportation solutions. For a research project, a reed and aquatic weed harvester, the author’s goal is to develop an experimental electrical vehicle aimed at solving several particular problems such as: small speed, big throttle, high maneuverability, big load capacity, small draught and affordable cost. The solution comprises of one electric motor powered by a converter supplied from Li-Ion batteries, which drives a hydraulic pump for simultaneous operation of two lateral-placed paddle wheels and one complex mechanism of cutter and conveyor. The control system of this vehicle consists of one remote controller, with bidirectional radio communication to three on-board controllers used for the management of the electro-hydraulic actuators, the electric motor and the battery storage system. The hardware and the software architectures are presented, underlining the automated operations designed to increase the safety, the maneuverability and the predictability of the vehicle. The advantages of the use of control electronics is the increasing operability of the vehicle by supervising the available stored energy and the predicted consumption of energy, the fast and remote assistance in case of operational failure using online diagnose and the operation optimization by selecting the best load profile for the cutter and for the paddles. The results of this research are the validation of the proposed hardware and software architectures used for the control of an electro-hydraulic vehicle and the feasibility of using radio communication and remote diagnose for vehicle control.
Interrelations of Ancestral Textile Handicraft Weaving and Tangible Vernacular Karkhanehs (Workspaces) in the Historic Destination of Yazd, Iran
Authors: Seddighikhavidak Somaye , Jamal Tazim
Published: May 2022

This research studies the traces of an ancestral textile produced in karkhanehs (workspaces) located in the historical city of Yazd, Iran. The case study undertaken here demonstrates how an intangible heritage of textile weaving through generations of families in Yazd, Iran, interrelates with tangible vernacular architecture and tourism at three different scales: (i) in everyday life in karkhanehs at home, (ii) in the neighborhoods, and (iii) the UNESCO World Heritage city of Yazd. The three scales related to the enactment of this vernacular handicraft are examined using architectural methods to examine structures ranging from 90 to 600 years in age. This was complemented by discussions with local weavers and residents, as well as direct observation in domiciles, neighborhoods, and the city. Actor-Network Theory helped to trace the networks of actors and relationships between the tangible built architectural heritage and intangible cultural practices of weaving, showing how different genders, ages, worldviews (beliefs) and practices came together to produce this heritage textile. Actor-Network Theory also helped to study the relationships between economy, culture, society and tourism, with respect to the evolution and transformations of the historic urban dwellings, vernacular architecture and vernacular weaving handicrafts through the three scales examined. Implications for sustaining and conserving this ancestral tradition of textile weaving and managing tourism’s positive as well as disruptive influences on cultural heritage conservation are discussed.
Leaf Carbon, Nitrogen and Phosphorus Stoichiometry in a Pinus yunnanensis Forest in Southwest China
Authors: Huang Xiaobo , Lang Xuedong , Li Shuaifeng , Liu Wande , Su Jianrong
Published: May 2022

Pinus yunnanensis forest is a unique forest type in southwest China and one of the main forest types in Yunnan Province, which also has great ecological, economic and social significance. Understanding the changes in the stoichiometric characteristics is a key to study the nutrient cycling, limiting factors and stability mechanisms of the forest ecosystem. However, the stoichiometric characteristics, stability of the ecosystem of P. yunnanensis natural forests and whether they are limited by nutrients are still poorly understood. Based on a K-S test, ANOVA analysis and OLS regression analysis, we analyzed the concentrations of leaf C, N and P in 48 woody species of natural P. yunnanensis forests from 122 plots to explore the pattern of leaf C:N:P stoichiometry. Our results showed that the mean values of leaf C, N and P plus C:N, C:P and N:P for the 48 woody species were 451.12, 11.05 and 1.11 mg/g and 45.03, 496.98 and 11.27, respectively. The coefficients of variation of leaf C, N and P plus C:N, C:P and N:P were 5.29%, 36.75%, 51.53%, 29.63%, 43.46% and 41.68%, respectively. The geometric mean values of leaf N, P and N:P were 10.49 and 1.00 mg/g and 10.51, respectively. Leaf C and N, and C and P relationships showed significant negative correlations, but a significant positive correlation was observed between leaf N and P. There were significant differences in leaf N and C:N across functional groups. There were significant differences in leaf C and P between evergreen and deciduous, conifer and broadleaf trees. Significant differences in leaf C:P were only observed between evergreen and deciduous trees, and significant differences in leaf N:P were observed between conifer and broadleaf trees. The relatively low N:P in all sampled trees indicated that N was a limiting factor in the distribution of natural P. yunnanensis forests. However, the higher leaf C:N and C:P ratios indicated that the P. yunnanensis natural forest ecosystem was in a relatively stable state.
A Thermodynamics-Oriented and Neural Network-Based Hybrid Model for Military Turbofan Engines
Authors: Ren Likun , Qin Haiqin , Xie Zhenbo , Xie Jing , Li Bianjiang
Published: May 2022

Traditional thermodynamic models for military turbofans suffer from non-convergence and inaccuracy due to inaccuracy of the component maps and the instability of the iterative process. To address these problems, a thermodynamically oriented and neural network-based hybrid model for military turbofans is proposed. Different from iteration-based thermodynamic models, the proposed hybrid model transforms the iteration process into a multi-objective optimization and training process for a component-level neural network in order to improve convergence and modeling accuracy. The experiment shows that the accuracy of the proposed hybrid model can reach about 7%, 5% better than the map-fitting-based thermodynamic model and 8% better than the purely data-driven method, with a similar number of network neutrons, verifying its effectiveness. The contributions of this work mainly lie in the following aspects: a new component-level neural network structure is proposed to improve convergence and computational efficiency; a multi-objective loss function based on component co-working is proposed to direct the model to converge toward the physical thermodynamic process; a fusion training method of multiple data sources is established to train the model with good convergence and high computational accuracy.
The Land Use and Individual Correlates of Pedestrian Commuting: Who Walks to Their Work or Place of Study in the Large Cities of the MENA Region?
Authors: Masoumi Houshmand , Sierpiński Grzegorz
Published: May 2022

The body of research on the determinants of pedestrian commuting and the characteristics of on-foot commuters at the international level and especially in the Global South is inconsistent; hence, this study focuses on this topic with the case of megacities in the Middle East and North Africa. The study is based on 8284 face-to-face interviews with respondents in the three cities, 4543 of whom worked, and of those 4543, 658 individuals walked to their work or place of study. By using binary logistic regression, the determinants of walking to work were identified. Age, household car ownership, last relocation time, intersection density, number of accessed facilities from home, average walking accessibility to neighborhood amenities, and commuting distance are the significant determinants of on-foot commuting. The study identifies younger commuters with fewer cars or no driving license living in neighborhoods with connected street networks as the walkers. The results of the Mann–Whitney U Test show that there is a significant difference between the values of these determinants for walking commuters and those who commute by other modes. Based on these findings, this paper encourages urban planners and decision-makers of the MENA region to use urban land use, including street networks and access to local facilities, to motivate commuters to commute on foot, especially those who work within a walkable distance to their work or place of study.