bluuebunny/crossref_metadata_2025 huggingface dataset:
The Impact of Financial Culture on the Operation of Hungarian SMEs before and during COVID-19
Authors: Toth Robert , Kasa Richard , Lentner Csaba
Published: July 2022

The main aim of this study is to explore the conceptual framework of corporate financial culture and its practical relevance in an emerging Central European market economy, at the level of the Hungarian SME, with a special emphasis on the Hungarian SME sector. In our study, we highlight each dimension of corporate financial culture, focusing on the established corporate financial culture index, and within it, we examine the significance of the financial management elements sub-index and the risk and insurance sub-index separately. In addition, we look for logical, causal, and statistically verifiable relationships between corporate financial literacy and the outcome of corporate financial decisions and corporate risk taking. The relationships were broken down over two years in the analysis. Approximately 2167 responses were included in the 2019 sample and 3281 in the 2021 sample. These representative samples were taken from the Hungarian SME sector and multiple linear regression models were built to find a significant moderation effect of financial literacy between perceived risks and the insurance activity of companies. We conducted our research in two different periods, the unique feature of which is that we conducted a survey before and during the coronavirus crisis, so we could make a comparative analysis. The method used in this research study is a literature review analysis of reference manuscripts, discussing topics related to financial literacy, corporate risk management, and corporate financial management, published in the last 10 years. Our results show that there are positive and significant relationships between company management, corporate risk management, and corporate financial literacy. The results of our study draw the attention of company leaders to the practical significance of financial culture—efficiency, profitability, and stability.
Online Computation of Time-Optimization-Based, Smooth and Path-Consistent Stop Trajectories for Robots
Authors: Rojas Rafael A. , Giusti Andrea , Vidoni Renato
Published: July 2022

Enforcing the cessation of motion is a common action in robotic systems to avoid the damage that the robot can exert on itself, its environment or, in shared environments, people. This procedure raises two main concerns, which are addressed in this paper. On the one hand, the stopping procedure should respect the collision free path computed by the motion planner. On the other hand, a sudden stop may produce large current peaks and challenge the limits of the motor’s control capabilities, as well as degrading the mechanical performance of the system, i.e., increased wear. To address these concerns, we propose a novel method to enforce a mechanically feasible, smooth and path-consistent stop of the robot based on a time-minimization algorithm. We present a numerical implementation of the method, as well as a numerical study of its complexity and convergence. Finally, an experimental comparison with an off-the-shelf stopping scheme is presented, showing the effectiveness of the proposed method.
Comparison of Deep Learning Methods for Detecting and Counting Sorghum Heads in UAV Imagery
Authors: Li He , Wang Peng , Huang Chong
Published: July 2022

With the rapid development of remote sensing with small, lightweight unmanned aerial vehicles (UAV), efficient and accurate crop spike counting, and yield estimation methods based on deep learning (DL) methods have begun to emerge, greatly reducing labor costs and enabling fast and accurate counting of sorghum spikes. However, there has not been a systematic, comprehensive evaluation of their applicability in cereal crop spike identification in UAV images, especially in sorghum head counting. To this end, this paper conducts a comparative study of the performance of three common DL algorithms, EfficientDet, Single Shot MultiBox Detector (SSD), and You Only Look Once (YOLOv4), for sorghum head detection based on lightweight UAV remote sensing data. The paper explores the effects of overlap ratio, confidence, and intersection over union (IoU) parameters, using the evaluation metrics of precision P, recall R, average precision AP, F1 score, computational efficiency, and the number of detected positive/negative samples (Objects detected consistent/inconsistent with real samples). The experiment results show the following. (1) The detection results of the three methods under dense coverage conditions were better than those under medium and sparse conditions. YOLOv4 had the most accurate detection under different coverage conditions; on the contrary, EfficientDet was the worst. While SSD obtained better detection results under dense conditions, the number of over-detections was larger. (2) It was concluded that although EfficientDet had a good positive sample detection rate, it detected the fewest samples, had the smallest R and F1, and its actual precision was poor, while its training time, although medium, had the lowest detection efficiency, and the detection time per image was 2.82-times that of SSD. SSD had medium values for P, AP, and the number of detected samples, but had the highest training and detection efficiency. YOLOv4 detected the largest number of positive samples, and its values for R, AP, and F1 were the highest among the three methods. Although the training time was the slowest, the detection efficiency was better than EfficientDet. (3) With an increase in the overlap ratios, both positive and negative samples tended to increase, and when the threshold value was 0.3, all three methods had better detection results. With an increase in the confidence value, the number of positive and negative samples significantly decreased, and when the threshold value was 0.3, it balanced the numbers for sample detection and detection accuracy. An increase in IoU was accompanied by a gradual decrease in the number of positive samples and a gradual increase in the number of negative samples. When the threshold value was 0.3, better detection was achieved. The research findings can provide a methodological basis for accurately detecting and counting sorghum heads using UAV.
Using Remote Sensing to Identify Urban Fringe Areas and Their Spatial Pattern of Educational Resources: A Case Study of the Chengdu-Chongqing Economic Circle
Authors: Lu Wei , Li Yuechen , Zhao Rongkun , Wang Yue
Published: July 2022

Rapid urbanization has already caused many impacts, such as environmental degradation and imbalanced resource allocation. As the frontiers of urbanization, urban fringe areas (UFAs) present both urban and rural characteristics and undergo complex socio-economic structural changes. Accurately identifying the spatial extent of UFAs is highly significant because it contributes to understanding the pattern of urban spatial expansion and guides future urban planning. However, existing methods are strongly affected by subjective factors. To solve this problem, this study presents a new approach to identifying UFAs, with the Chengdu-Chongqing economic circle as the study area. The new method achieved an identification accuracy of 74.2%, effectively eliminated some noise points, and reduced the influence of subjective factors. From an applied perspective, this study employed the Geo-information Tupu and density-field-based hotspot detector to analyze the spatial pattern of educational resources. Overall, the results showed that hotspots of educational resources are concentrated in places with good transportation or near urban areas; and the generalized symmetric structure Tupu of hotspots is diverse. In addition, the results can reveal the hotspot formation mechanism and provide a reference for resource allocation.
Dual Receiver EGNOS+SDCM Positioning with C1C and C1W Pseudo-Range Measurements
Authors: Bakuła Mieczysław , Krasuski Kamil , Dawidowicz Karol
Published: July 2022

The paper presents an approach to the simultaneous use of SDCM and EGNOS corrections for two GNSS receivers placed at a constant distance. The SDCM and EGNOS corrections were applied for two GPS code measurements on L1 frequency: C1C and C1W. The approach is based mainly on the constrained least squares adjustment, but for the horizontal and vertical coordinates, the Kalman Filter was applied in order to reduce pseudo-range noises. It allows for obtaining a higher autonomous accuracy of GPS/(SDCM+EGNOS) positioning than when using only the GPS/EGNOS or GPS/SDCM system. The final dual-redundant solution, in which two SBAS systems were used (EGNOS+SDCM) and two GPS pseudo-ranges (C1C+C1W) were present, yielded RMS errors of 0.11 m for the horizontal coordinates and 0.25 m for the vertical coordinates. Moreover, the accuracy analysis in the developed mathematical model for the determined 3D coordinates with simultaneous use of EGNOS and SDCM systems proved to be much more reliable than using only a single EGNOS or SDCM system. The presented approach can be used not only for precise navigation, but also for some geoscience applications and remote sensing where the reliable accuracy of autonomous GPS positioning is required.
A Novel Multi-Task Learning Model with PSAE Network for Simultaneous Estimation of Surface Quality and Tool Wear in Milling of Nickel-Based Superalloy Haynes 230
Authors: Cheng Minghui , Jiao Li , Yan Pei , Gu Huiqing , Sun Jie , Qiu Tianyang , Wang Xibin
Published: July 2022

For data-driven intelligent manufacturing, many important in-process parameters should be estimated simultaneously to control the machining precision of the parts. However, as two of the most important in-process parameters, there is a lack of multi-task learning (MTL) model for simultaneous estimation of surface roughness and tool wear. To address the problem, a new MTL model with shared layers and two task-specific layers was proposed. A novel parallel-stacked auto-encoder (PSAE) network based on stacked denoising auto-encoder (SDAE) and stacked contractive auto-encoder (SCAE) was designed as the shared layers to learn deep features from cutting force signals. To enhance the performance of the MTL model, the scaled exponential linear unit (SELU) was introduced as the activation function of SDAE. Moreover, a dynamic weight averaging (DWA) strategy was implemented to dynamically adjust the learning rate of different tasks. Then, the time-domain features were extracted from raw cutting signals and low-frequency reconstructed wavelet packet coefficients. Frequency-domain features were extracted from the power spectrum obtained by the Fourier transform. After that, all features were combined as the input vectors of the proposed MTL model. Finally, surface roughness and tool wear were simultaneously predicted by the trained MTL model. To verify the superiority and effectiveness of the proposed MTL model, nickel-based superalloy Haynes 230 was machined under different cutting parameter combinations and tool wear levels. Some other intelligent algorithms were also implemented to predict surface roughness and tool wear. The results showed that compared with the support vector regression (SVR), kernel extreme learning machine (KELM), MTL with SDAE (MTL_SDAE), MTL with SCAE (MTL_SCAE), and single-task learning with PSAE (STL_PSAE), the estimation accuracy of surface roughness was improved by 30.82%, 16.67%, 14.06%, 26.17%, and 16.67%, respectively. Meanwhile, the prediction accuracy of tool wear was improved by 46.74%, 39.57%, 41.51%, 38.68%, and 39.57%, respectively. For practical engineering application, the dimensional deviation and surface quality of the machined parts can be controlled through the established MTL model.
A Device to Register Temperature in Boreholes in Northwest Spain for Geothermal Research
Authors: Marcos-Robredo Germán , Rey-Ronco Miguel Ángel , Castro-García María Pilar , Alonso-Sánchez Teresa
Published: July 2022

This paper presents a device used to measure and register temperature for long-term subsoil measurements in boreholes. The borehole of this study is located in Gijón (Asturias, Spain). The measurements were made through two fixed sets of sensors coupled to the geothermal pipe, constituting two independent installations: (a) a commercial device called “Hobo”, which uses TMCx-HD-specific sensors based on resistors with variable resistance; and (b) a device built by this research group, which uses DS12B20 Maxim sensors, a bus 1-wire, and a recording device based on a conventional Arduino board. Temperature was registered every 5 min across several years. These measurements were used to thermally characterize the subsoil, determining the apparent thermal diffusivity, and to study the thermo-hydrogeology of the Lower Jurassic Gijón’s formation made of Liassic limestones and dolomites. This work is part of the Q-Thermie group’s research called “Shallow Thermal Energy”.
Flexible Machine Learning Algorithms for Clinical Gait Assessment Tools
Authors: Greve Christian , Tam Hobey , Grabherr Manfred , Ramesh Aditya , Scheerder Bart , Hijmans Juha M.
Published: July 2022

The current gold standard of gait diagnostics is dependent on large, expensive motion-capture laboratories and highly trained clinical and technical staff. Wearable sensor systems combined with machine learning may help to improve the accessibility of objective gait assessments in a broad clinical context. However, current algorithms lack flexibility and require large training datasets with tedious manual labelling of data. The current study tests the validity of a novel machine learning algorithm for automated gait partitioning of laboratory-based and sensor-based gait data. The developed artificial intelligence tool was used in patients with a central neurological lesion and severe gait impairments. To build the novel algorithm, 2% and 3% of the entire dataset (567 and 368 steps in total, respectively) were required for assessments with laboratory equipment and inertial measurement units. The mean errors of machine learning-based gait partitions were 0.021 s for the laboratory-based datasets and 0.034 s for the sensor-based datasets. Combining reinforcement learning with a deep neural network allows significant reduction in the size of the training datasets to <5%. The low number of required training data provides end-users with a high degree of flexibility. Non-experts can easily adjust the developed algorithm and modify the training library depending on the measurement system and clinical population.
On the Automatic Detection and Classification of Skin Cancer Using Deep Transfer Learning
Authors: Fraiwan Mohammad , Faouri Esraa
Published: July 2022

Skin cancer (melanoma and non-melanoma) is one of the most common cancer types and leads to hundreds of thousands of yearly deaths worldwide. It manifests itself through abnormal growth of skin cells. Early diagnosis drastically increases the chances of recovery. Moreover, it may render surgical, radiographic, or chemical therapies unnecessary or lessen their overall usage. Thus, healthcare costs can be reduced. The process of diagnosing skin cancer starts with dermoscopy, which inspects the general shape, size, and color characteristics of skin lesions, and suspected lesions undergo further sampling and lab tests for confirmation. Image-based diagnosis has undergone great advances recently due to the rise of deep learning artificial intelligence. The work in this paper examines the applicability of raw deep transfer learning in classifying images of skin lesions into seven possible categories. Using the HAM1000 dataset of dermoscopy images, a system that accepts these images as input without explicit feature extraction or preprocessing was developed using 13 deep transfer learning models. Extensive evaluation revealed the advantages and shortcomings of such a method. Although some cancer types were correctly classified with high accuracy, the imbalance of the dataset, the small number of images in some categories, and the large number of classes reduced the best overall accuracy to 82.9%.
Detection of Partially Structural Collapse Using Long-Term Small Displacement Data from Satellite Images
Authors: Entezami Alireza , De Michele Carlo , Arslan Ali Nadir , Behkamal Bahareh
Published: July 2022

The development of satellite sensors and interferometry synthetic aperture radar (InSAR) technology has enabled the exploitation of their benefits for long-term structural health monitoring (SHM). However, some restrictions cause this process to provide a small number of images leading to the problem of small data for SAR-based SHM. Conversely, the major challenge of the long-term monitoring of civil structures pertains to variations in their inherent properties by environmental and/or operational variability. This article aims to propose new hybrid unsupervised learning methods for addressing these challenges. The methods in this work contain three main parts: (i) data augmentation by the Markov Chain Monte Carlo algorithm, (ii) feature normalization, and (iii) decision making via Mahalanobis-squared distance. The first method presented in this work develops an artificial neural network-based feature normalization by proposing an iterative hyperparameter selection of hidden neurons of the network. The second method is a novel unsupervised teacher–student learning by combining an undercomplete deep neural network and an overcomplete single-layer neural network. A small set of long-term displacement samples extracted from a few SAR images of TerraSAR-X is applied to validate the proposed methods. The results show that the methods can effectively deal with the major challenges in the SAR-based SHM applications.
Smart Systems: The Role of Advanced Technologies in Improving Business Quality, Performance and Supply Chain Integration
Authors: Schmidt Ivett , Morris Wyn , Thomas Andrew , Manning Louise
Published: July 2022

Technologies such as the Internet of Things (IoT), cloud computing, and Smart Systems (SSs) have become an important focus for industry, especially in the manufacturing and retail sectors. The aim of this paper is to analyse the importance of SSs and their related technologies in improving business performance. Through an initial systematic review of sixty-one papers, the authors identify six key determinants that lead to the effective application of SSs in business systems, namely, the application of effective managerial skills, supply chain integration, financial analysis, business performance, strategic and operational capabilities, and technologies. The work then goes on to develop an industry case study that informs thinking on the capabilities of smart technologies in collaborative working environments and then onto the development of a practice-focused framework for future research. This study identifies that the implementation of SSs within organisations not only improves business performance but also their supply chains through the effective integration of business activities and systems, and through the degree to which communication and decision-making is facilitated between humans and devices. This leads to the improvement of quality, speed of information, and information sharing. This study also finds that there is a lack of systems standards that currently govern IoT and SSs integration and data security within businesses.
Contribution of Conceptual-Drawing Methods to Raise Awareness on Landscape Connectivity: Socio-Environmental Analysis in the Regional Context of Trentino (Italy)
Authors: Nikologianni Anastasia , Betta Alessandro , Gretter Alessandro
Published: July 2022

This paper deals with landscape understanding and connectivity from an ecological as well as a human perspective. It is based on a broader research study known as EIT Climate-KIC SATURN (System and sustainable Approach to virTuous interaction of Urban and Rural LaNdscapes) co-funded by the European Institute of Innovation & Technology (EIT), Climate-KIC and the institutions participating in this project. The aim of the study was to explore how landscape connectivity is perceived, on urban and regional scales, by decision-makers and key stakeholders as well as the impact this can have on building sustainable cities. The paper used a series of drawing and visualization workshops, community engagement methods, and participatory tools to identify the connection communities and decision-makers have with their landscape surroundings as well as the impact landscape connectivity has on our health and wellbeing. Through a series of specifically designed workshops following a landscape visioning approach, the paper explored how drawings and visualizations can support decision-makers to create a vision that addresses landscape connectivity, considering the socio-ecological factors in their area and creating a holistic regional approach between urban and rural landscapes. The study concludes that landscape connectivity is of major importance when creating visions for a sustainable future; however, a better connection between ecological and human elements needs to be established to improve landscape design.
The Sustainability and Development Strategy of a Cattle Feed Bank: A Case Study
Authors: Mashur Mashur , Bilad Muhammad Roil , Kholik Kholik , Munawaroh Muhammad , Cheok Quentin , Huda Nurul , Kobun Rovina
Published: July 2022

One of the Indonesian government’s policies to achieve national beef self-sufficiency is the 1000 beef village program. The program was piloted in many cattle-farming centers involving the operation of a feed bank to supply animal feed to tackle the challenge of feed limitation during the dry season. This study evaluates the sustainability status of an ongoing feed bank program and its development strategy based on the current progress of a feed bank used to serve five groups of farmers. Ninety sustainability attributes were derived based on six dimensions. The attributes were compiled from the primary data collected using a questionnaire. Expert opinions from practitioners were also considered in evaluating the attributes. The feed bank’s sustainability status and development strategy were determined using the multi-dimensional scaling method with the rapid appraisal approach. It was found that the overall sustainability status of the feed bank was less sustainable, with an overall score of 49.55. The individual dimensions of (A) policy and government support, (B) raw material, (C) facilities and infrastructure, (D) feed bank management, (D) human resource management, (E) price, production, and (F) distribution systems posed sustainability scores of 48.48, 60.33, 48.57, 47.89, 48.76, and 44.64, respectively. Among the 90 predefined attributes, 21 were identified as highly sensitive through both the root mean square and expert opinion. Those attributes led to five main recommended development strategies: (1) strengthening the institution, (2) intensifying training, (3) increasing human resources (4) partnership developments, and (5) increasing the role of multi-stakeholders.
Second Language Teaching with a Focus on Different Learner Cultures for Sustainable Learner Development: The Case of Sino-Korean Vocabulary
Authors: Li Yishu , Wei Huiping , Li Yongjian
Published: July 2022

Second language teaching (SLT) focuses on the cultural backgrounds of different learners. This not only facilitates the construction of knowledge using the learner’s identity, but also helps learners acquire skills for cross-cultural communication, both of which are needed for learners to develop sustainably. Sino-Korean vocabulary is an important element in the representation of Korean culture and in teaching the Chinese language to Korean speakers. In addition, it is a means of promoting the Teaching Chinese as a Second language (TCSL) to achieve sustainable development for Korean learners. Much of the research on Sino-Korean vocabulary has focused on the similarities between Chinese and Korean cultures that facilitate Korean learners’ understanding and use of Sino-Korean vocabulary, but little attention has been paid to the role played by cultural Sino-Korean vocabulary that represent the distinctive Korean culture. We collected a news corpus on six major themes from the NAVER website in Korea from between 2010 and 2020. The keywords were extracted using the TF–IDF algorithm and then transcribed and culled to form a distinctive cultural Sino-Korean vocabulary corpus. We examined the Korean Version of Experience Chinese: Living in China (Ti yan Han yu Sheng huo pian) and suggested that a collection of Sino-Korean vocabulary that are culturally distinctive to the learner’s home country can provide a more pedagogically meaningful addition to the selection of words for TCSL textbooks. These words, based on similarities, further enable Korean learners to acquire a sense of familiarity with, and belonging to, their identity, and thus acquire the Chinese language, and also facilitate expression in intercultural communication. By extracting the distinctive Korean cultural Sino-Korean vocabulary and discovering their value in terms of TCSL educational material, we can promote the sustainable development of Korean learners.
Research on Green Innovation Performance of Manufacturing Industry and Its Improvement Path in China
Authors: Yang Haochang , Zhu Xuan
Published: July 2022

Green innovation, which combines “innovation-driven” and “green development,” is one of the most powerful ways to overcome resource and environmental constraints and enhance manufacturing industry sustainability. Based on the innovation value chain perspective, the green innovation process of manufacturing industry is decomposed into two stages: green scientific and technological R&D and achievement transformation. Then, using the three-stage DEA and Malmquist index model to measure the green innovation performance of China’s manufacturing industry, and compare its regional heterogeneity from the dual perspectives of static efficiency and dynamic productivity. In addition, this paper further discusses the improvement path of green innovation performance of China’s manufacturing industry. The findings are as follows: (1) The green innovation efficiency of manufacturing industry in China is at a comparatively low degree and has great potential for improvement. Moreover, it shows apparent regional heterogeneity: The green innovation efficiency in the eastern region is higher than that in the western region, and both are higher than that in the center region, confirming the phenomenon of “central collapse”. (2) The green innovation productivity of China’s manufacturing industry shows a “W-type” dynamic evolution tendency, with green technological progress as the key driving factor, while the green technical efficiency does not clearly exhibit a “catch-up effect”. Additionally, it shows significant regional heterogeneity: green innovation productivity in the western region is higher than that in the central and eastern regions, indicating a potential “backwardness advantage”. (3) The eastern region of China is located in combination IV, which indicates that it has a high rate of green innovation efficiency but a low rate of green innovation productivity; the central region is located in combination III, which indicates that it has a low rate of both green innovation efficiency and productivity; and the western region is located in combination II, which indicates that it has a low rate of green innovation efficiency but a high rate of green innovation productivity. Last but not least, this paper puts forward three kinds of paths for the improvement of the green innovation performance of China’s manufacturing industry: unilateral breakthrough, step-by-step and stimulating jumping type.
Multi-Objective Optimization for Cooling and Interior Natural Lighting in Buildings for Sustainable Renovation
Authors: Ruggiero Silvia , Iannantuono Marco , Fotopoulou Anastasia , Papadaki Dimitra , Assimakopoulos Margarita Niki , De Masi Rosa Francesca , Vanoli Giuseppe Peter , Ferrante Annarita
Published: July 2022

In order to achieve the ‘nearly zero-energy’ target and a comfortable indoor environment, an important aspect is related to the correct design of the transparent elements of the building envelope. For improving indoor daylight penetration, architectural solutions such as light shelves are nowadays commercially available. These are defined as horizontal or inclined surfaces, fixed or mobile, placed on the inner and/or the outer side of windows, with surface features such to reflect the sunlight to the interior. Given the fact that these elements can influence different domains (i.e., energy need, daylighting, thermal comfort, etc.), the aim of this paper is to apply a multi-objective optimization method within the design of this kind of technology. The case study is a student house in the University of Athens Campus, subject to a deep energy renovation towards nZEB, under the frame of H2020 European project Pro-GET-onE (G.A No 723747). Starting from the numerical model of the building, developed in EnergyPlus, the multi-objective optimization based on a genetic algorithm is implemented. The variables used are various light shelves configurations by differing materials and geometry, as well as different window types and interior context scenarios. Finally, illuminance studies of the pre- and post-retrofit building are also provided through Revit illuminance rendering.
The Driver of Workplace Alienation or the Cost of Effective Stewardship? The Consequences of Wage Gap for Corporate Performance
Authors: Osiichuk Dmytro
Published: July 2022

Relying on cross-country panel data, the paper investigates the possible repercussions of salary gap for employee productivity and corporate financial performance. Our empirical findings corroborate the presence of a negative tail effect of wage gap on productivity and employee morale. While worsening employee turnover and productivity, and increasing the chances of workplace controversies, high salary gap is found to be associated with a more efficient cost structure and higher profitability. Our evidence suggests that extreme salary gap may be curbed by targeted internal policies favoring internal promotion and career development, unionization, employee and managerial training. The composition of the board’s remuneration committee appears to play but a minor role in shaping the scale of salary gap. The results of the study are in line with equity aversion theory and suggest that extreme wage inequality may impede firms’ growth with spillover effects observable at the macro-level. Targeted policies may be necessary to counter the negative repercussions of high compensation disparities as within-firm mechanisms appear insufficient to mitigate them.
The Effect of Access to the Public Debt Market on Corporate Financing Decisions: The Case of REITs
Authors: Shen Jianfu , Chau Kwong Wing
Published: July 2022

We examined the effects of access to public debt on the corporate financing decisions in real estate investment trusts (REITs) using a difference-in-differences approach and a propensity score approach. The introduction of credit ratings by S&P and Moody’s has allowed REITs to access the public debt market. To investigate the impacts of the introduction of credit ratings, we compared the financing policies in REITs with initial credit ratings before and after the introduction of credit ratings with REITs that had not obtained a credit rating between 1980 and 2016. After obtaining credit ratings, REITs have significantly increased the corporate leverage ratios and the use of long term debt, which suggest that REITs were constrained from debt financing, in particular long term debt financing, in the past until they could gain access to the public debt market after the introduction of credit ratings. Access to the public debt market has also significantly reduced both equity issuances and cash holdings. Our empirical results suggest that the introduction of credit ratings can reduce information asymmetry, and affect REITs’ capital structure decisions and the level of cash holdings.
Hydroponic Cultivation of Vine Leaves with Reduced Carbon Footprint in a Mediterranean Greenhouse
Authors: Chatzigeorgiou Ioanna , Liantas Georgios , Spanos Petros , Gkriniari Vasiliki , Maloupa Eleni , Ntinas Georgios K.
Published: July 2022

Vine leaves are considered a delicacy food however they are only produced as a byproduct for a short harvest period due to grape cultivation practices and numerous chemical applications. In this work, vine plants were cultivated hydroponically in a greenhouse, to extend the cultivation period and along with high plant density, maximize fresh leaves yield. Four different substrates were tested—Perlite, Perlite-Attapulgite, Perlite-Zeolite, 1.7Perlite-higher density planting—with soil treatment as a control, and the experimental cultivation lasted a total of about seven months in the year 2021. Quantitative and qualitative characteristics such as leaves number and weight, color of leaves, nitrates, photosynthetic parameters, total phenols, and plant nutrient concentrations were assessed, while the product’s environmental impact was calculated. The 1.7Perlite treatment produced a 1.6–2.0 times higher number of leaves per hectare than the other hydroponic treatments and 8.7 times higher than the soil treatment, while no statistically significant differences were found regarding qualitative characteristics. Consequently, the 1.7P treatment resulted in a 1.4 to 7.6 times lower product carbon footprint compared to the other treatments. In future research, substrates water and nutrient retention will be further studied along with year-round production in a heated greenhouse with full climate control so that plants are kept evergreen.
The Effect of Customer Involvement on Green Innovation and the Intermediary Role of Boundary Spanning Capability
Authors: Ma Xiang , Qing Lingli , Ock Young-Seok , Wu Jiao , Zhou Yaying
Published: July 2022

With the rapid development of industrialization and the economy, the side effects of ecological problems have become more and more serious, and the importance of the sustainable development paradigm has begun to be valued. This study conducts an empirical analysis of green enterprises and explores the influence of customer involvement and boundary spanning capability on green innovation. The results of the analysis show that there is a positive correlation between customer involvement and boundary spanning capability (including three subordinate factors). Secondly, the results of analyzing the influence relationship between customer involvement and green innovation show that customer involvement has a positive impact on green innovation. In addition, the analysis of the relationship between boundary expansion force and environmental protection innovation shows that there is a positive correlation between the two variables, and the analysis of the mediation effect of boundary spanning capability shows that there is a partial mediation effect between customer involvement and green innovation. However, this study has various limitations in the context of environmental protection, as it is an exploratory study in which the boundary expansion capabilities of firms are manipulated in the context of environmental protection and empirically analyzed through a questionnaire method. For clearer research results, it is necessary to re-validate this research model with objective data in the future.
ICT Uses, Constraints, and Challenges in Flash Flood Risk Management: A Case Study in North-Eastern Haor Areas of Bangladesh
Authors: Abedin Md. Anwarul , Parvin Gulsan Ara , Habiba Umma , Kibria Mohammad Golam , Ahsan Reazul , Onitsuka Kenichiro , Rahman Md. Munsur , Kobeasy Mohamed I. , Gaber Ahmed
Published: July 2022

This study intends to explore the extent of the use of information and communication technology (ICT) in flash-flood-prone haor (wetland ecosystem) areas of north-eastern Bangladesh. Addressing the situation of severe flash floods in 2017 at the community level, this study aims to examine the constraints and challenges in using ICT for flash flood risk management. An empirical study was conducted in two upazilas (administrative sub-districts) within the Sunamganj, the most affected district. A total of 120 household heads were interviewed using a semi-structured questionnaire. Focused group discussions (FGD) and key informant interviews (KII) were also used. Results reveal that people in these areas do not use computers or Internet. However, the use of mobile phones is common for communication during disasters. Illiteracy, poor quality of life, poor social capital, poor infrastructure, and poor communication with institutions are highlighted as the main constraints and challenges in the effective application of ICT in flash flood management. The results showed that the local community people of the study area receive early warning and hazards preparedness-related information through mobile phones, television, radio, and Facebook. However, the results also highlighted the constraints and challenges in applying ICT in DRR at the community level. By addressing a remote region and a relatively less focused disaster (flash flood), this study contributes to boosting the application of ICT in DRR, especially in remote, disadvantaged communities.
Metal Contents and Pollution Indices Assessment of Surface Water, Soil, and Sediment from the Arieș River Basin Mining Area, Romania
Authors: Moldovan Ana , Török Anamaria Iulia , Kovacs Eniko , Cadar Oana , Mirea Ionuț Cornel , Micle Valer
Published: July 2022

The current study was conducted to assess the level and spatial distribution of metal pollution in surface water, soil, and sediment samples from the Arieș River basin, located in central Romania, an area impacted by various mining and industrial operations. Several pollution indices, spatial distributions, cluster analyses, principal component analyses, and heat maps were applied for evaluating the contamination level with Ni, Cu, Zn, Cd, Pb, Mn, As, and Hg in the area. Based on the results of the Heavy-Metal Pollution Index and of the Heavy-Metal Evaluation Index of the surface-water samples, the middle part of the Arieș River basin, near and downstream of the gold mine impoundment, was characterized by high pollution levels. The metal concentration was higher near the tailing impoundment, with increased levels of Cu, Ni, Zn, and Pb in the soil samples and As, Cd, Pb, Na, K, Ca, Mn, and Al in the sediment samples. Ca (23.7–219 mg/L), Mg (2.55–18.30 mg/L), K (0.64–14.70 mg/L), Al (0.06–22.80 mg/L), and Mn (0.03–22.40 mg/L) had the most remarkable spatial variation among the surface-water samples, while various metal contents fluctuated strongly among the sampling locations. Al varied from 743 to 19.8 mg/kg, Fe from 529 to 11.4 mg/kg, Ca from 2316 to 11.8 mg/kg, and Mg from 967 to 2547 mg/kg in the soil samples, and Al varied from 3106 to 8022 mg/kg, Fe from 314 to 5982 mg/kg, Ca from 1367 to 8308 mg/kg, and Mg from 412 to 1913 mg/kg in the sediment samples. The Potential Ecological Risk Index values for soil and sediments were in the orders Cu > Ni > Pb > Hg > Cr > As > Mn > Zn > Cd and As > Cu > Cr > Cd > Pb > Ni > Hg > Mn > Zn, respectively, and the highest values were found around the gold mine impoundment.
Parameterization in the Analysis of Changes in the Rural Landscape on the Example of Agritourism Farms in Kłodzko District (Poland)
Authors: Bocheńska-Skałecka Anna , Ostrowska-Dudys Maria , Hutnik Edward , Jakubowski Wojciech
Published: July 2022

The European Landscape Convention (2006) indicates that landscape conservation is as important as the protection of the overall environment. Although the boundaries between urban and rural areas in many countries are blurring, the rural landscape is still perceived as a valuable landscape artefact. Traditional rural landscapes have undergone significant transformations over the past few decades. The authors attempt to analyze factors causing apparent changes in the rural landscape, based on the example of agritourism farms in Kłodzko District, Lower Silesia. The changes taking place in Poland after 1989 resulted in reduced profitability of agricultural production. This was why small farms stopped using land for agricultural production. Agritourism has become one of the forms of business activity. Therefore, it became necessary to adapt farms to a new function. The 37 agritourism farms registered in rural and rural-urban municipalities of Kłodzko District have been randomly selected for the survey. The research has shown the extent of changes related to the transformation of agricultural farms into agritourism ones. Six areas (categories) where changes took place have been identified based on the analysis of collected data. The authors have included the collected data in the parameterization of surveyed agritourism farms, taking into account: the condition of the agricultural farm before introducing its new role (0) and the present condition, with an agritourism function (1). The complete linkage clustering (the maximum distance) known as cluster analysis was used to examine the variables in terms of farm change. The aim was to select outstanding units from the research sample for further research as case studies.
How Does Retailer-Oriented Remanufacturing Affect the OEM’s Quality Choice?
Authors: Zou Zongbao , Wang Cong , Zhong Qinjia
Published: July 2022

We consider that a supply chain consists of an original equipment manufacturer (OEM) and a retailer. We analyze how the retailer’s remanufacturing decision affects the decision of the new products’ qualities for the OEM. We use a game theory approach to investigate the interactions between the OEM and the retailer with or without remanufacturing operation. We find the retailer’s motivation to develop the remanufacturing industry depends on the profitability of remanufactured products and the OEM’s deterring strategy. When the remanufacturing operation profit margin is low, the retailer voluntarily gives up remanufacturing; when this profit margin is moderate, the OEM induces the retailer to give up remanufacturing by improving the quality of the new product; when this profit margin is sufficiently high, the OEM cannot prevent the retailer from developing remanufacturing. The retailer developing remanufacturing as well as their threat of developing decrease the OEM’s profit because the OEM improves the quality of the new products to induce the retailer to give up remanufacturing, at the expense of its own profit. We explicitly characterize the process of the OEM from preventing to accepting the retailer developing the remanufacturing industry, as well as the changes in supply-chain members’ operating decisions and profits in the process. Finally, using MATLAB numerical analysis, we also found that the retailer’s development of the remanufacturing industry always benefits the whole supply chain, consumer surplus, and social welfare. Furthermore, the retailer’s development of the remanufacturing industry is not always good for the environment. This development is only good for the environment when the new products’ environmental impacts in the EOL stage are larger than the total environmental impacts of the remanufactured products in every stage of their life cycle.
Experimental Evaluation of Functional and Energy Performance of Pneumatic Oenological Presses for High Quality White Wines
Authors: Perone Claudio , Bianchi Biagio , Catalano Filippo , Orsino Michela
Published: July 2022

In this article, experimental tests on two different kinds of pneumatic presses have been carried out in two Apulian wineries to evaluate energy consumption related to yield performance. The presses are employed to process Bombino Nero variety grapes, crushed and transformed in rosé wine though a pomace less process. The pneumatic pressured press realized a 2 h:48 min long process, with a maximum pressure on the product of 1.4 bar and a must moulding of 69% of extracted must. In regard to the vacuum press, the process duration is 3 h:18 min, with a maximum pressure of 0.9 bar and a must moulding of 58%. During the pressing operation, mean values of must flow rate are comparable: 2.1 L min−1 m−2 for the pressured press and 2.9 L min−1 m−2 for the vacuum press. However, a more detailed analysis gives more insight on the behaviour of the two presses. In regard to the pressured press, a compression phase characterized by few steps and at lowest pressure values has proven particularly effective, especially in the first phase (must moulding of 41% with a must flow rate of 4.6 L min−1 m−2), at the lowest pressure values. On the other hand, by analysing vacuum press process, the phases sequence is much more gradual, must moulding decreases as the extraction proceeds (from 28% to 6%), and in the last three phases a very low amount of must is extracted, with must moulding smaller than 10%. The energy consumption of the pressured press during compression is mainly related to the engine power absorption (one of the two in operation), and it is below 4 kW except during start-up, due to the starting currents. The highest power is reached in the last phase in which the maximum pressure is applied and a great amount of must is extracted. On the other hand, energy consumption in the vacuum press gradually decreases. A similar trend was not observed for the specific energy for either press: while it increases along the whole process for the vacuum press, it reaches a maximum value in the second phase of the pressured one. Results show the need to pursue new studies on single component design and on pressing cycles, especially in high-capacity pneumatic vacuum presses. Several advantages on wine production costs could be achieved, even retaining high quality wines.
Transfer Characteristics of Embodied Carbon Emissions in Export Trade—Evidence from China
Authors: Zhao Hehua , Chen Hongwen , Fang Ying , Song Apei
Published: July 2022

The export trade of China, the factory of the world, promotes economic growth while increasing carbon emissions. This study integrates China’s multi-regional input–output table and the world input–output table to explore the international transfer-in effect and foreign spillover effect of carbon emissions caused by China’s export trade. A structural decomposition analysis model is also used to identify the influencing factors of carbon emissions caused by China’s export trade of intermediate and final products. Results show that: (1) 45.13–58.87% of the transfer-in carbon emissions resulting from China’s export trade are caused by developed countries and 41.13–54.87% by developing countries; (2) the foreign spillover effect caused by China’s export trade is primarily associated with developing countries, accounting for 63.79–69.61%; (3) carbon emissions caused by the export of intermediate products (final products) in China are primarily caused by the scale effect (industrial linkage). China should adjust the structure of its export trade in accordance with the characteristics of embodied carbon emissions in export trade to achieve low-carbon development.