bluuebunny/crossref_metadata_2025 huggingface dataset:
AI-Assisted In-House Power Monitoring for the Detection of Cognitive Impairment in Older Adults
Authors: Nakaoku Yuriko , Ogata Soshiro , Murata Shunsuke , Nishimori Makoto , Ihara Masafumi , Iihara Koji , Takegami Misa , Nishimura Kunihiro
Published: September 2021

In-home monitoring systems have been used to detect cognitive decline in older adults by allowing continuous monitoring of routine activities. In this study, we investigated whether unobtrusive in-house power monitoring technologies could be used to predict cognitive impairment. A total of 94 older adults aged ≥65 years were enrolled in this study. Generalized linear mixed models with subject-specific random intercepts were used to evaluate differences in the usage time of home appliances between people with and without cognitive impairment. Three independent power monitoring parameters representing activity behavior were found to be associated with cognitive impairment. Representative values of mean differences between those with cognitive impairment relative to those without were −13.5 min for induction heating in the spring, −1.80 min for microwave oven in the winter, and −0.82 h for air conditioner in the winter. We developed two prediction models for cognitive impairment, one with power monitoring data and the other without, and found that the former had better predictive ability (accuracy, 0.82; sensitivity, 0.48; specificity, 0.96) compared to the latter (accuracy, 0.76; sensitivity, 0.30; specificity, 0.95). In summary, in-house power monitoring technologies can be used to detect cognitive impairment.
Adaboost-Based Machine Learning Improved the Modeling Robust and Estimation Accuracy of Pear Leaf Nitrogen Concentration by In-Field VIS-NIR Spectroscopy
Authors: Wang Jie , Xue Wei , Shi Xiaojun , Xu Yangchun , Dong Caixia
Published: September 2021

Different cultivars of pear trees are often planted in one orchard to enhance yield for its gametophytic self-incompatibility. Therefore, an accurate and robust modelling method is needed for the non-destructive determination of leaf nitrogen (N) concentration in pear orchards with mixed cultivars. This study proposes a new technique based on in-field visible-near infrared (VIS-NIR) spectroscopy and the Adaboost algorithm initiated with machine learning methods. The performance was evaluated by estimating leaf N concentration for a total of 1285 samples from different cultivars, growth regions, and tree ages and compared with traditional techniques, including vegetation indices, partial least squares regression, singular support vector regression (SVR) and neural networks (NN). The results demonstrated that the leaf reflectance responded to the leaf nitrogen concentration were more sensitive to the types of cultivars than to the different growing regions and tree ages. Moreover, the AdaBoost.RT-BP had the best accuracy in both the training (R2 = 0.96, root mean relative error (RMSE) = 1.03 g kg−1) and the test datasets (R2 = 0.91, RMSE = 1.29 g kg−1), and was the most robust in repeated experiments. This study provides a new insight for monitoring the status of pear trees by the in-field VIS-NIR spectroscopy for better N managements in heterogeneous pear orchards.
A Deep Learning System for Recognizing and Recovering Contaminated Slider Serial Numbers in Hard Disk Manufacturing Processes
Authors: Chousangsuntorn Chousak , Tongloy Teerawat , Chuwongin Santhad , Boonsang Siridech
Published: September 2021

This paper outlines a system for detecting printing errors and misidentifications on hard disk drive sliders, which may contribute to shipping tracking problems and incorrect product delivery to end users. A deep-learning-based technique is proposed for determining the printed identity of a slider serial number from images captured by a digital camera. Our approach starts with image preprocessing methods that deal with differences in lighting and printing positions and then progresses to deep learning character detection based on the You-Only-Look-Once (YOLO) v4 algorithm and finally character classification. For character classification, four convolutional neural networks (CNN) were compared for accuracy and effectiveness: DarkNet-19, EfficientNet-B0, ResNet-50, and DenseNet-201. Experimenting on almost 15,000 photographs yielded accuracy greater than 99% on four CNN networks, proving the feasibility of the proposed technique. The EfficientNet-B0 network outperformed highly qualified human readers with the best recovery rate (98.4%) and fastest inference time (256.91 ms).
Industrial Masonry Chimney Geometry Analysis: A Total Station Based Evaluation of the Unmanned Aerial System Photogrammetry Approach
Authors: Zrinjski Mladen , Tupek Antonio , Barković Đuro , Polović Ante
Published: September 2021

High industrial chimney inclination monitoring and analysis is crucial from a stability point of view because, if not maintained, it can pose a great potential hazard for its surroundings. Various modern approaches of chimneys’ geometrical parameters determination have been proposed and are actively in use. However, little research regarding the applicability of the unmanned aerial system (UAS)-based photogrammetric approach of chimney structural monitoring has been conducted and a comprehensive analysis with validated methods is lacking. Therefore, this research is focused on the determination of geometrical structural parameters of a masonry chimney whereby two independent methods have been applied. Reference values of the chimney geometrical parameters have been determined by precise total station (TS) measurements and, in relation to them, the applicability of the UAS-based photogrammetric approach is evaluated. Methodologically, the reference and validation values of the chimney geometrical parameters have been determined based on double modeling of the chimney structure. Firstly, cross-sectional elliptical regression has been applied to determine the geometrical values of the chimney at predefined above-ground levels (AGLs). Secondly, the spatial chimney axis has been derived by polynomial regression to determine the inclination of the full chimney structure. Lastly, the structural stability of the chimney is validated based on its axis inclination whereby permitted deviations are determined according to the European Standard EN 1996-1-1:2005. Experimental results of our research show that consistently better results are gained by TS-based surveys and, although the determination of the chimney’s geometrical values by the UAS-based approach is certainly possible, great attention must be given to the accuracy of the UAS-generated point cloud (PC) if high accuracy results are needed.
An Improved Multioperator-Based Constrained Differential Evolution for Optimal Power Allocation in WSNs
Authors: Li Wei , Gong Wenyin
Published: September 2021

Optimal power allocation (OPA), which can be transformed into an optimization problem with constraints, plays a key role in wireless sensor networks (WSNs). In this paper, inspired by ant colony optimization, an improved multioperator-based constrained adaptive differential evolution (namely, IMO-CADE) is proposed for the OPA. The proposed IMO-CADE can be featured as follows: (i) to adaptively select the proper operator among different operators, the feedback of operators and the status of individuals are considered simultaneously to assign the selection probability; (ii) the constrained reward assignment is used to measure the feedback of operators; (iii) the parameter adaptation is used for the parameters of differential evolution. To extensively evaluate the performance of IMO-CADE, it is used to solve the OPA for both the independent and correlated observations with different numbers of sensor nodes. Compared with other advanced methods, simulation results clearly indicate that IMO-CADE yields the best performance on the whole. Therefore, IMO-CADE can be an efficient alternative for the OPA of WSNs, especially for WSNs with a large number of sensor nodes.
sTetro-Deep Learning Powered Staircase Cleaning and Maintenance Reconfigurable Robot
Authors: Ramalingam Balakrishnan , Elara Mohan Rajesh , Balakrishnan Selvasundari , Elangovan Karthikeyan , Félix Gómez Braulio , Pathmakumar Thejus , Devarassu Manojkumar , Mohan Rayaguru Madan , Baskar Chanthini
Published: September 2021

Staircase cleaning is a crucial and time-consuming task for maintenance of multistory apartments and commercial buildings. There are many commercially available autonomous cleaning robots in the market for building maintenance, but few of them are designed for staircase cleaning. A key challenge for automating staircase cleaning robots involves the design of Environmental Perception Systems (EPS), which assist the robot in determining and navigating staircases. This system also recognizes obstacles and debris for safe navigation and efficient cleaning while climbing the staircase. This work proposes an operational framework leveraging the vision based EPS for the modular re-configurable maintenance robot, called sTetro. The proposed system uses an SSD MobileNet real-time object detection model to recognize staircases, obstacles and debris. Furthermore, the model filters out false detection of staircases by fusion of depth information through the use of a MobileNet and SVM. The system uses a contour detection algorithm to localize the first step of the staircase and depth clustering scheme for obstacle and debris localization. The framework has been deployed on the sTetro robot using the Jetson Nano hardware from NVIDIA and tested with multistory staircases. The experimental results show that the entire framework takes an average of 310 ms to run and achieves an accuracy of 94.32% for staircase recognition tasks and 93.81% accuracy for obstacle and debris detection tasks during real operation of the robot.
The Role of Surface Electromyography in Data Fusion with Inertial Sensors to Enhance Locomotion Recognition and Prediction
Authors: Meng Lin , Pang Jun , Wang Ziyao , Xu Rui , Ming Dong
Published: September 2021

Locomotion recognition and prediction is essential for real-time human–machine interactive control. The integration of electromyography (EMG) with mechanical sensors could improve the performance of locomotion recognition. However, the potential of EMG in motion prediction is rarely discussed. This paper firstly investigated the effect of surface EMG on the prediction of locomotion while integrated with inertial data. We collected EMG signals of lower limb muscle groups and linear acceleration data of lower limb segments from ten healthy participants in seven locomotion activities. Classification models were built based on four machine learning methods—support vector machine (SVM), k-nearest neighbor (KNN), artificial neural network (ANN), and linear discriminant analysis (LDA)—where a major vote strategy and a content constraint rule were utilized for improving the online performance of the classification decision. We compared four classifiers and further investigated the effect of data fusion on the online locomotion classification. The results showed that the SVM model with a sliding window size of 80 ms achieved the best recognition performance. The fusion of EMG signals does not only improve the recognition accuracy of steady-state locomotion activity from 90% (using acceleration data only) to 98% (using data fusion) but also enables the prediction of the next steady locomotion (∼370 ms). The study demonstrates that the employment of EMG in locomotion recognition could enhance online prediction performance.
Can Frailty Be a Predictor of ICD Shock after the Implantation of a Cardioverter Defibrillator in Elderly Patients?
Authors: Mlynarska Agnieszka , Mlynarski Rafal , Uchmanowicz Bartosz , Mikuľáková Wioletta
Published: September 2021

Introduction: The aim of the study was to assess the prevalence of frailty among elderly patients who had an implanted cardioverter defibrillator, as well as the influence of frailty on the main endpoints during the follow-up. Methods: The study included 103 patients > 60 years of age (85M, aged 71.56–8.17 years). All of the patients had an implanted single or dual-chamber cardioverter-defibrillator. In the research, there was a 12-month follow-up. The occurrence of frailty syndrome was assessed using the Tilburg Frailty Indicator scale (TFI). Results: Frailty syndrome was diagnosed in 75.73% of the patients that were included in the study. The mean values of the TFI were 6.55 ± 2.67, in the physical domain 4.06 ± 1.79, in the psychological domain 2.06 ± 1.10, and in the social domain 0.44 ± 0.55. During the follow-up period, 27.2% of patients had a defibrillator cardioverter electric shock, which occurred statistically more often in patients with diagnosed frailty syndrome (34.6%) compared to the robust patients (4%); p = 0.0062. In the logistic regression, frailty (OR: 1.203, 95% CI:1.0126–1.4298; p < 0.030) was an independent predictor of a defibrillator cardioverter electric shock. Similarly, in the logistic regression, frailty (OR: 1.3623, 95% CI:1.0290–1.8035; p = 0.019) was also an independent predictor for inadequate electric shocks. Conclusion: About three-quarters of the elderly patients that had qualified for ICD implantation were affected by frailty syndrome. In the frailty subgroup, adequate and inadequate shocks occurred more often compared to the robust patients.
EEG Mental Stress Assessment Using Hybrid Multi-Domain Feature Sets of Functional Connectivity Network and Time-Frequency Features
Authors: Hag Ala , Handayani Dini , Pillai Thulasyammal , Mantoro Teddy , Kit Mun Hou , Al-Shargie Fares
Published: September 2021

Exposure to mental stress for long period leads to serious accidents and health problems. To avoid negative consequences on health and safety, it is very important to detect mental stress at its early stages, i.e., when it is still limited to acute or episodic stress. In this study, we developed an experimental protocol to induce two different levels of stress by utilizing a mental arithmetic task with time pressure and negative feedback as the stressors. We assessed the levels of stress on 22 healthy subjects using frontal electroencephalogram (EEG) signals, salivary alpha-amylase level (AAL), and multiple machine learning (ML) classifiers. The EEG signals were analyzed using a fusion of functional connectivity networks estimated by the Phase Locking Value (PLV) and temporal and spectral domain features. A total of 210 different features were extracted from all domains. Only the optimum multi-domain features were used for classification. We then quantified stress levels using statistical analysis and seven ML classifiers. Our result showed that the AAL level was significantly increased (p < 0.01) under stress condition in all subjects. Likewise, the functional connectivity network demonstrated a significant decrease under stress, p < 0.05. Moreover, we achieved the highest stress classification accuracy of 93.2% using the Support Vector Machine (SVM) classifier. Other classifiers produced relatively similar results.
VIAE-Net: An End-to-End Altitude Estimation through Monocular Vision and Inertial Feature Fusion Neural Networks for UAV Autonomous Landing
Authors: Zhang Xupei , He Zhanzhuang , Ma Zhong , Jun Peng , Yang Kun
Published: September 2021

Altitude estimation is one of the fundamental tasks of unmanned aerial vehicle (UAV) automatic navigation, where it aims to accurately and robustly estimate the relative altitude between the UAV and specific areas. However, most methods rely on auxiliary signal reception or expensive equipment, which are not always available, or applicable owing to signal interference, cost or power-consuming limitations in real application scenarios. In addition, fixed-wing UAVs have more complex kinematic models than vertical take-off and landing UAVs. Therefore, an altitude estimation method which can be robustly applied in a GPS denied environment for fixed-wing UAVs must be considered. In this paper, we present a method for high-precision altitude estimation that combines the vision information from a monocular camera and poses information from the inertial measurement unit (IMU) through a novel end-to-end deep neural network architecture. Our method has numerous advantages over existing approaches. First, we utilize the visual-inertial information and physics-based reasoning to build an ideal altitude model that provides general applicability and data efficiency for neural network learning. A further advantage is that we have designed a novel feature fusion module to simplify the tedious manual calibration and synchronization of the camera and IMU, which are required for the standard visual or visual-inertial methods to obtain the data association for altitude estimation modeling. Finally, the proposed method was evaluated, and validated using real flight data obtained during a fixed-wing UAV landing phase. The results show the average estimation error of our method is less than 3% of the actual altitude, which vastly improves the altitude estimation accuracy compared to other visual and visual-inertial based methods.
System Implementation Trade-Offs for Low-Speed Rotational Variable Reluctance Energy Harvesters
Authors: Xu Ye , Bader Sebastian , Magno Michele , Mayer Philipp , Oelmann Bengt
Published: September 2021

Low-power energy harvesting has been demonstrated as a feasible alternative for the power supply of next-generation smart sensors and IoT end devices. In many cases, the output of kinetic energy harvesters is an alternating current (AC) requiring rectification in order to supply the electronic load. The rectifier design and selection can have a considerable influence on the energy harvesting system performance in terms of extracted output power and conversion losses. This paper presents a quantitative comparison of three passive rectifiers in a low-power, low-voltage electromagnetic energy harvesting sub-system, namely the full-wave bridge rectifier (FWR), the voltage doubler (VD), and the negative voltage converter rectifier (NVC). Based on a variable reluctance energy harvesting system, we investigate each of the rectifiers with respect to their performance and their effect on the overall energy extraction. We conduct experiments under the conditions of a low-speed rotational energy harvesting application with rotational speeds of 5 rpm to 20 rpm, and verify the experiments in an end-to-end energy harvesting evaluation. Two performance metrics—power conversion efficiency (PCE) and power extraction efficiency (PEE)—are obtained from the measurements to evaluate the performance of the system implementation adopting each of the rectifiers. The results show that the FWR with PEEs of 20% at 5 rpm to 40% at 20 rpm has a low performance in comparison to the VD (40–60%) and NVC (20–70%) rectifiers. The VD-based interface circuit demonstrates the best performance under low rotational speeds, whereas the NVC outperforms the VD at higher speeds (>18 rpm). Finally, the end-to-end system evaluation is conducted with a self-powered rpm sensing system, which demonstrates an improved performance with the VD rectifier implementation reaching the system’s maximum sampling rate (40 Hz) at a rotational speed of approximately 15.5 rpm.
Classification and Automated Interpretation of Spinal Posture Data Using a Pathology-Independent Classifier and Explainable Artificial Intelligence (XAI)
Authors: Dindorf Carlo , Konradi Jürgen , Wolf Claudia , Taetz Bertram , Bleser Gabriele , Huthwelker Janine , Werthmann Friederike , Bartaguiz Eva , Kniepert Johanna , Drees Philipp , Betz Ulrich , Fröhlich Michael
Published: September 2021

Clinical classification models are mostly pathology-dependent and, thus, are only able to detect pathologies they have been trained for. Research is needed regarding pathology-independent classifiers and their interpretation. Hence, our aim is to develop a pathology-independent classifier that provides prediction probabilities and explanations of the classification decisions. Spinal posture data of healthy subjects and various pathologies (back pain, spinal fusion, osteoarthritis), as well as synthetic data, were used for modeling. A one-class support vector machine was used as a pathology-independent classifier. The outputs were transformed into a probability distribution according to Platt’s method. Interpretation was performed using the explainable artificial intelligence tool Local Interpretable Model-Agnostic Explanations. The results were compared with those obtained by commonly used binary classification approaches. The best classification results were obtained for subjects with a spinal fusion. Subjects with back pain were especially challenging to distinguish from the healthy reference group. The proposed method proved useful for the interpretation of the predictions. No clear inferiority of the proposed approach compared to commonly used binary classifiers was demonstrated. The application of dynamic spinal data seems important for future works. The proposed approach could be useful to provide an objective orientation and to individually adapt and monitor therapy measures pre- and post-operatively.
Thermal Characterization of New 3D-Printed Bendable, Coplanar Capacitive Sensors
Authors: Ragolia Mattia Alessandro , Lanzolla Anna M. L. , Percoco Gianluca , Stano Gianni , Di Nisio Attilio
Published: September 2021

In this paper a new low-cost stretchable coplanar capacitive sensor for liquid level sensing is presented. It has been 3D-printed by employing commercial thermoplastic polyurethane (TPU) and conductive materials and using a fused filament fabrication (FFF) process for monolithic fabrication. The sensor presents high linearity and good repeatability when measuring sunflower oil level. Experiments were performed to analyse the behaviour of the developed sensor when applying bending stimuli, in order to verify its flexibility, and a thermal characterization was performed in the temperature range from 10 °C to 40 °C to evaluate its effect on sunflower oil level measurement. The experimental results showed negligible sensitivity of the sensor to bending stimuli, whereas the thermal characterization produced a model describing the relationship between capacitance, temperature, and oil level, allowing temperature compensation in oil level measurement. The different temperature cycles allowed to quantify the main sources of uncertainty, and their effect on level measurement was evaluated.
Remaining Useful Life Estimation for Engineered Systems Operating under Uncertainty with Causal GraphNets
Authors: Mylonas Charilaos , Chatzi Eleni
Published: September 2021

In this work, a novel approach, termed GNN-tCNN, is presented for the construction and training of Remaining Useful Life (RUL) models. The method exploits Graph Neural Networks (GNNs) and deals with the problem of efficiently learning from time series with non-equidistant observations, which may span multiple temporal scales. The efficacy of the method is demonstrated on a simulated stochastic degradation dataset and on a real-world accelerated life testing dataset for ball-bearings. The proposed method learns a model that describes the evolution of the system implicitly rather than at the raw observation level and is based on message-passing neural networks, which encode the irregularly sampled causal structure. The proposed approach is compared to a recurrent network with a temporal convolutional feature extractor head (LSTM-tCNN), which forms a viable alternative for the problem considered. Finally, by taking advantage of recent advances in the computation of reparametrization gradients for learning probability distributions, a simple, yet efficient, technique is employed for representing prediction uncertainty as a gamma distribution over RUL predictions.
Bus Crash Severity in Hanoi, Vietnam
Authors: Nguyen Thanh Chuong , Nguyen Minh Hieu , Armoogum Jimmy , Ha Thanh Tung
Published: September 2021

Recently, there has been an increasing interest in targeting the safety of bus operations worldwide; however, little is known about the determinants of the bus crash severity in developing countries. By estimating an ordered logit model using the bus-involved collision data in Hanoi (Vietnam), spanning the period from 2015 to 2019, this study investigates various factors associated with the crash severity. The results reveal that the severity risk increases for (1) large buses, (2) raining conditions, (3) evening or night, (4) sparse traffic, (5) non-urban areas, (6) roads with at least three lanes, (7) curved roads, (8) two-way roads without a physical barrier, (9) head-on collision, and (10) pedestrian-related crashes. Aside from confirming the crucial roles of a wide range of factors, this research has examined the effects of two determinants (traffic density and crash area) that have not been considered for the cases of developing countries previously. Based on the findings on the impacts of factors, a series of policy recommendations regarding improving road conditions in non-urban areas, promoting walking infrastructure, reminders of high-risk situations for drivers, safety notes when improving bus service quality, and recording bus-related crashes are proposed.
Optimal Planning of Electrical Appliance of Residential Units in a Smart Home Network Using Cloud Services
Authors: Azimi Nasab Morteza , Zand Mohammad , Eskandari Mohsen , Sanjeevikumar Padmanaban , Siano Pierluigi
Published: September 2021

One of the important aspects of realizing smart cities is developing smart homes/buildings and, from the energy perspective, designing and implementing an efficient smart home area energy management system (HAEMS) is vital. To be effective, the HAEMS should include various electrical appliances as well as local distributed/renewable energy resources and energy storage systems, with the whole system as a microgrid. However, the collecting and processing of the data associated with these appliances/resources are challenging in terms of the required sensors/communication infrastructure and computational burden. Thanks to the internet-of-things and cloud computing technologies, the physical requirements for handling the data have been provided; however, they demand suitable optimization/management schemes. In this article, a HAEMS is developed using cloud services to increase the accuracy and speed of the data processing. A management protocol is proposed that provides an optimal schedule for a day-ahead operation of the electrical equipment of smart residential homes under welfare indicators. The proposed system comprises three layers: (1) sensors associated with the home appliances and generation/storage units, (2) local fog nodes, and (3) a cloud where the information is processed bilaterally with HAEMS and the hourly optimal operation of appliances/generation/storage units is planned. The neural network and genetic algorithm (GA) are used as part of the HAEMS program. The neural network is used to predict the amount of workload corresponding to users’ requests. Improving the load factor and the economic efficiency are considered as the objective function that is optimized using GA. Numerical studies are performed in the MATLAB platform and the results are compared with a conventional method.
Intra-Rater and Inter-Rater Reliability of Pressure Pain Algometry of the Sural and Tibial Nerves in Asymptomatic Elite Youth Footballers
Authors: Richards Daniel , Jones Simon , Jeffery Josh , Lowe Matthew , Godwin Mark , Willett Matthew
Published: September 2021

Ankle injuries are highly prevalent in elite youth footballers and increase the mechanosensitivity of the local neural tissue, which may predispose athletes to re-injury and prolong rehabilitation periods. Increased neural mechanosensitivity presents clinically as altered pain pressure thresholds (PPTs) which are measured with pressure algometry. The purpose of this study was to determine the intra-rater and inter-rater reliability of PPTs of the ankle neural tissue in asymptomatic elite youth football players. Three raters utilised a digital algometer to evaluate the PPTs of the Sural and Tibial nervous tissue at the ankle of elite youth male footballers. Intraclass correlation coefficients (ICCs) with 95% confidence intervals (CI) were calculated to assess intra-rater and inter-rater reliability and Bland–Altman figures were plotted to enable visual evaluation of measurement error with a significance level of p < 0.05. Thirty-four players (16–18 years old) were assessed. Excellent intra-rater (Tibial ICC 0.88 (0.76–0.94); Sural ICC 0.89 (0.79–0.95)) and good inter-rater reliability (Tibial ICC 0.66 (0.40–0.82); Sural 0.71 (0.50–0.85)) was demonstrated. Bland–Altman plots demonstrated low levels of measurement error. Pressure algometry can be utilised clinically to accurately evaluate the PPTs of the Tibial and Sural nervous tissue at the ankle in asymptomatic elite male youth footballers.
German Marathon Runners’ Opinions on and Willingness to Pay for Environmental Sustainability
Authors: Könecke Thomas , Schunk Holger , Schappel Tabea , Hugaerts Ine , Wagner Fabio , Malchrowicz-Mośko Ewa
Published: September 2021

Research on sustainability and/in sport and, specifically, on the ecological aspects of participatory sporting events is still very scarce despite the recognition these topics have received by actors like, for instance, the European Commission and the United Nations. Against this backdrop, this paper sheds light on a field that is virtually uncharted in academic research, which is the environmental attitudes and willingness to pay for environmental sustainability of participants in participatory sporting events in Europe. In collaboration with the organizer of the Frankfurt Marathon, a study was conducted with a specific focus on German (speaking) marathon runners. In total 1764 data sets were collected with a German (1455 respondents) and an English (309 respondents) online questionnaire. Very insightful descriptive analyses showed that the awareness for the issue of sustainability and sport is still low and that ecological initiatives only have a medium influence on the evaluation of a marathon event. Nevertheless, particularly the German-speaking respondents indicated a high willingness to pay for environmental sustainability in general and for specific measures that can enhance the environmental friendliness of a marathon event, in particular. As will be discussed in the paper, these insights have important managerial implications and are a valuable basis for further research in this evolving field.
To Act or Not to Act: Are Natural Landscapes a Key Force in the Resilience of Historic Urban Landscapes?
Authors: Gao Wei , Chen Gengyu , Jiang Fanying , Shen Jiake , Wang Yuncai
Published: September 2021

Ignoring the function of natural landscapes in the rapid development of urbanization, and especially in the conservation of historic urban landscapes, is still obvious today, and this has caused a large decrease in natural space, loss of habitats, and an increase in disasters. The resilience of a whole city and parts of it, such as historic urban areas where the historical process of man and nature have been recorded, as well as the interaction between nature, economy, and culture, is not strong enough to maintain the stability of urban ecosystems. It is misleading to think that the resilience can be built in a historic urban area without a natural landscape. We question whether this is true. Using a semantic differential analysis method from a historical perspective, this paper aims to answer this question through research on the correlation between resilience and man and nature through a case study of Yudai Trench historic urban landscape in Guangzhou, a historic urban area with 1000 years of history. A total of 212 pieces of evidence were extracted from 59 historical sources. The results showed that the cultural and economic conditions were in the same step and cycles as nature, which were influenced strongly by climate change, and that the natural landscape has a correlation on and is a dominant force in the resilience of historic urban landscapes.
A Study on the Coupling and Coordination between Logistics Industry and Economy in the Background of High-Quality Development
Authors: Yan Bo-Rui , Dong Qian-Li , Li Qian , Amin Fahim UI , Wu Jia-Ni
Published: September 2021

China’s economy has stepped up from the high-speed growth stage to the high-quality development stage. With the high-quality development of the economy, the logistics industry is bound to move towards high-quality development. By establishing the index system of high-quality economic development and logistics, this work has measured their high-quality development level and analyzed their coupling coordination degree by using the coupling coordination model. The results show that there are great regional differences in high-quality economic development, high-quality logistics development and their coupling coordination degree in China. In the areas with high-quality economic development, the logistics industry is not necessarily high quality, and the coupling coordination degree between the logistics industry and high-quality economic development is generally not high. The main reason for this is that the coordination between the logistics industry and high-quality economic development is poor. While vigorously improving the high-quality level of the logistics industry, it should strengthen the high-quality connection between the logistics industry and the economy, so as to form a situation of mutual promotion and common development.
Sustainable Approach and Safe Use of Biochar and Its Possible Consequences
Authors: Jatav Hanuman Singh , Rajput Vishnu D. , Minkina Tatiana , Singh Satish Kumar , Chejara Sukirtee , Gorovtsov Andrey , Barakhov Anatoly , Bauer Tatiana , Sushkova Svetlana , Mandzhieva Saglara , Burachevskaya Marina , Kalinitchenko Valery P.
Published: September 2021

Biochar is considered as a potential substitute for soil organic matter (SOM). Considering the importance of biochar, the present review is based on the different benefits and potential risks of the application of biochar to the soil. Biochar addition to low organic carbon soils can act as a feasible solution to keep soil biologically active for the cycling of different nutrients. The application of biochar could improve soil fertility, increase crop yield, enhance plant growth and microbial abundance, and immobilize different contaminants in the soil. It could also be helpful in carbon sequestration and the return of carbon stock back to the soil in partially combusted form. Due to the large surface area of biochar, which generally depends upon the types of feedstock and pyrolysis conditions, it helps to reduce the leaching of fertilizers from the soil and supplies additional nutrients to growing crops. However, biochar may have some adverse effects due to emissions during the pyrolysis process, but it exerts a positive priming effect (a phenomenon in which subjection to one stimulus positively influences subsequent stimulus) on SOM decomposition, depletion of nutrients (macro- and micro-) via strong adsorption, and impact on soil physicochemical properties. In view of the above importance and limitations, all possible issues related to biochar application should be considered. The review presents extensive detailed information on the sustainable approach for the environmental use of biochar and its limitations.
Special Issue “Audit and Financial Control Tools Aimed at Ensuring the Sustainable Performance of Organizations”
Authors: Bostan Ionel
Published: September 2021

Through this editorial, the author aims to present the main landmarks related to the scientific contributions that can be found in the special issue dedicated to the topic “Audit and Financial Control Tools Aimed at Ensuring the Sustainable Performance of Organizations” [...]
Industry 4.0 Contribution to Asset Management in the Electrical Industry
Authors: Biard Gabrielle , Nour Georges Abdul
Published: September 2021

Industry 4.0 has revolutionized paradigms by leading to major technological developments in several sectors, including the energy sector. Aging equipment fleets and changing demand are challenges facing electricity companies. Forced to limit resources, these organizations must question their method and the current model of asset management (AM). The objective of this article is to detail how industry 4.0 can improve the AM of electrical networks from a global point of view. To do so, the industry 4.0 tools will be presented, as well as a review of the literature on their application and benefits in this area. From the literature review conducted, we observe that once properly structured and managed, big data forms the basis for the implementation of advanced tools and technologies in electrical networks. The data generated by smart grids and data compiled for several years in electrical networks have the characteristics of big data. Therefore, it leaves room for a multitude of possibilities for comprehensive analysis and highly relevant information. Several tools and technologies, such as modeling, simulation as well as the use of algorithms and IoT, combined with big data analysis, leads to innovations that serve a common goal. They facilitate the control of reliability-related risks, maximize the performance of assets, and optimize the intervention frequency. Consequently, they minimize the use of resources by helping decision-making processes.
Comparison between Variable and Constant Refrigerant Flow Air Conditioning Systems in Arid Climate: Life Cycle Cost Analysis and Energy Savings
Authors: Atallah Georges , Tarlochan Faris
Published: September 2021

All over the world, there is a call to encourage sustainable energy thinking and implementation. There is an urgent need to consider sustainable solutions in any design projects that are able to reduce energy consumption. In the heating, ventilation, and air conditioning field, the rise of the variable refrigerant flow systems has made big progress. This study presents a life cycle cost analysis to evaluate the economic feasibility of constant refrigerant flow (CRF), and in particular, the conventional ducted unit air conditioning system and the variable refrigerant flow (VRF) system by using detailed cooling load profiles, as well as initial, operating, and maintenance costs. Two operating hours scenarios are utilized and the present worth value technique for life cycle cost analysis is applied to an existing office building located in Qatar, which can be conditioned by CRF and VRF systems. The results indicate that, although the initial cost of the VRF system is higher than that of the CRF system by 23%, the present worth cost of the VRF system is much lower than that of the CRF system at the end of the lifetime due to lower operating costs. There is also a significant energy saving of 27% by using VRF compared to the CRF. The implementation of these results on a national scale will promote the use of sustainable energy technologies such as the VRF system.
A Quantitative GIS and AHP Based Analysis for Geodiversity Assessment and Mapping
Authors: Ferrando Andrea , Faccini Francesco , Paliaga Guido , Coratza Paola
Published: September 2021

In recent times, the issues of geodiversity assessment and mapping have been subject of great attention, and many evaluation methodologies, either quantitative or qualitative, have been developed. In this research, a first assessment of geodiversity in the Liguria region has been carried out, according to a quantitative method based on spatial analysis techniques implemented in a GIS environment. This method considers four diversity indices obtained by grid analysis, relevant to the four main aspects of geodiversity: geology, geomorphology, hydrogeology and pedology. The geodiversity index was calculated two times, first with a non-weighted sum, then with a weighted sum of the four diversity indices. In the second case, the weights have been assigned according to a semi-quantitative analytical hierarchy process method (AHP) based on experts’ judgment. The results show that the Liguria region is characterized by many areas with high geodiversity, most of them internationally known by geoscientists and tourists for their valuable geoheritage and for their stunning landscapes. The correspondence between these areas and the protected areas of the european Natura 2000 network suggests a link between geodiversity and biodiversity.
Chinese Universities’ Cross-Border Research Collaboration in the Social Sciences and Its Impact
Authors: Liu Yang , Ma Jinyuan , Song Huanyu , Qian Ziniu , Lin Xiao
Published: September 2021

This paper examined the coauthorship patterns in Chinese researchers’ cross-border research collaboration in the social sciences based on articles and reviews indexed in the Scopus database (2010–2019). We explored the evolution of coauthorship patterns by proportion of collaboration, year, research field, country/region, and research institution; additionally, the quality/impact of the coauthored publications was examined using four levels of paper quality (Q1–4), citations per paper, and FWCI. We found that collaboration between Chinese and international scholars is very common, and more than 40% of all papers published by Chinese scholars from 2010 to 2019 involved cross-border collaboration. The growth in collaboration was very steady over the past 10 years, increasing by an average of 20% per year. United States scholars are the most common research collaboration partners for Chinese scholars in the social sciences, followed by those in Hong Kong, the United Kingdom, Australia, and Canada. The field of psychology seeks the most collaboration, followed by economics and finance, business and management, and social issues. The percentage of Q1 papers increased from 36% in 2010 to 66% in 2019. Thus, in the past 10 years, Chinese scholars’ cross-border collaboration has grown extensively in terms of both quantity and impact.
Timber Based Integrated Techniques to Improve Energy Efficiency and Seismic Behaviour of Existing Masonry Buildings
Authors: Busselli Matteo , Cassol Davide , Prada Alessandro , Giongo Ivan
Published: September 2021

The retrofit solutions studied herein aim to improve the seismic and energetic behaviours of existing masonry buildings to guarantee safety and the preservation of the building heritage. The retrofit consists of timber-based products (panels and strong-backs) fixed to the masonry walls using mechanical point-to-point connections; the durability and the hygrothermal performance of the solutions are guaranteed by insulation layers and membranes. The thermophysical properties of the retrofitted walls were evaluated by means of analytical and numerical analyses, considering the heat transmission in both steady and unsteady state conditions and the thermal bridge in correspondence with the corner of the wall. The in-plane seismic behaviour of the retrofitted walls was numerically investigated through nonlinear analyses. The influence of various parameters (such as masonry and insulation properties) on the performance of the retrofit solutions was analysed via parametric simulations.
Knowledge Ambidexterity within a Business Context: Taking Stock and Moving Forward
Authors: Serrano-Bedia Ana María , Pérez-Pérez Marta
Published: September 2021

Despite the increasing and recent interest in researching knowledge ambidexterity, there has been no attempt from scholarly research to map and structure extant research on this topic. The aim of this study is to fill this gap, thereby contributing to previous literature by increasing our understanding of the research on knowledge ambidexterity. This paper employs a systematic literature review analysis on a sample of 20 academic papers extracted from the Web of Science database in June 2021. After illustrating the size, growth trajectory, geographic distribution, and key publishing journals in the sample, the paper analyses the intellectual structure and main foci of the research domain. The results show that this topic emerged in the late 2000s with a boom in research in the last 5 years and a clear predominance of quantitative studies. Moreover, content analysis reveals that both a clear definition of knowledge ambidexterity and its main components, knowledge exploration and exploitation, and a valid and reliable scale for measuring this concept are still lacking. The review has also revealed that the research domain on knowledge ambidexterity can be grouped into three different research lines, each of which relates to a different level of analysis—teams, intraorganisational processes within individual firms, and interorganisational collaborations—and concludes by identifying potential areas for future research on this topic that may help to advance in the consolidation of this particularly vibrant field.
The Role of Socially Assistive Robots in the Care of Older People: To Assist in Cognitive Training, to Remind or to Accompany?
Authors: Łukasik Sylwia , Tobis Sławomir , Suwalska Julia , Łojko Dorota , Napierała Maria , Proch Marek , Neumann-Podczaska Agnieszka , Suwalska Aleksandra
Published: September 2021

The rapid development of new technologies has caused interest in the use of socially assistive robots in the care of older people. These devices can be used not only to monitor states of health and assist in everyday activities but also to counteract the deterioration of cognitive functioning. The aim of the study was to investigate the attitudes and preferences of Polish respondents towards interventions aimed at the preservation/improvement of cognitive functions delivered by a socially assistive robot. A total of 166 individuals entered the study. Respondents completed the User’s Needs, Requirements and Attitudes Questionnaire; items connected to cognitive and physical activity and social interventions were analyzed. Perceptions and attitudes were compared by gender and age groups (older adults ≥ 60 years old and younger adults 20–59). Women showed a more positive attitude towards robots than men and had a significantly higher perception of the role of the robots in reminding about medications (p = 0.033) as well as meal times and drinks (p = 0.018). There were no significant differences between age groups. Respondents highly valued both the traditional role of the robot—a reminding function—as well as the cognitive interventions and guided physical exercises provided by it. Our findings point to the acceptance of the use of socially assistive robots in the prevention of cognitive deterioration in older people.
Analyzing Spatiotemporal Development of Organic Farming in Poland
Authors: Antczak Elżbieta
Published: September 2021

Organic farming is one of the most widely known sustainable models of agricultural production. Success in eco-agriculture also depends greatly on agri-environmental, territorial, economic, social, institutional and spatial conditions. Polish eco-farming is very regionally dispersed and diversified. Regarding the important contribution of organic farming, a better understanding of how this phenomenon develops and which factors affect its spatial distribution can be influential for policymakers in planning strategies that pursue sustainable development objectives in rural areas. This paper assesses the development and analyses the spatial distribution of organic farming in Polish LAU-2. The country’s eco-agriculture was mapped and defined using a synthetic measure, described by 27 sub-measurements of ecological crop cultivation, animal maintenance and eco-production. The local spatial patterns (direction, scale, and range) of organic farming were detected by spatial autocorrelation measurements. The analysis was conducted for the period 2014–2020. Possible external and internal determinants of this spatial dispersion were also defined. The results indicate that the distribution and spread of organic farming in Poland are related to public support, institutional regulations, social considerations, environmental concerns, the local job market and spatial dependencies.