bluuebunny/crossref_metadata_2025 huggingface dataset:
Root Endophytism by Pochonia chlamydosporia Affects Defense-Gene Expression in Leaves of Monocot and Dicot Hosts under Multiple Biotic Interactions
Authors: Tolba Shimaa R. T. , Rosso Laura C. , Pentimone Isabella , Colagiero Mariantonietta , Moustafa Mahmoud M. A. , Elshawaf Ibrahim I. S. , Bubici Giovanni , Prigigallo Maria Isabella , Ciancio Aurelio
Published: April 2021

A study was carried out on the effect of the root endophytic fungus Pochonia chlamydosporia on plant systemic signal of defense related genes during fungal or nematode parasitism. Different biotic stress factors were examined, inoculating roots of dicot and monocot hosts with the endophyte, and measuring the expression of defense genes in leaves. A first greenhouse assay was carried out on expression of PAL, PIN II, PR1 and LOX D in leaves of tomato cv Tondino inoculated with Phytophthora infestans (CBS 120920), inoculating or not the roots of infected plants with P. chlamydosporia DSM 26985. In a second assay, plants of banana (Musa acuminata cv Grand Naine) were artificially infected with Fusarium oxysporum f. sp. cubense Tropical race 4 (TR4) and inoculated or not with DSM 26985. In a further experiment, banana plants were inoculated or not with P. chlamydosporia plus juveniles of the root knot nematode (RKN) Meloidogyne incognita. A similar assay was also carried out in vitro with adults and juveniles of the lesion nematode Pratylenchus goodeyi. Differential expression of the defense genes examined was observed for all plant-stress associations, indicative of early, upward systemic signals induced by the endophyte. Changes in expression profiles included a 5-fold down-regulation of PIN II at 2 dai in leaves of tomato plants treated with P. infestans and/or P. chlamydosporia, and the up-regulation of PAL by the endophyte alone, at 2 and 7 dai. In the TR4 assay, PR1 was significantly up-regulated at 7 dai in banana leaves, but only in the P. chlamydosporia treated plants. At 10 dai, PIN II expression was significantly higher in leaves of plants inoculated only with TR4. The banana-RKN assay showed a PR1 expression significantly higher than controls at 4 and 7 dai in plants inoculated with P. chlamydosporia alone, and a down-regulation at 4 dai in leaves of plants also inoculated with RKN, with a PR1 differential up-regulation at 10 dai. Pratylenchus goodeyi down-regulated PIN at 21 dai, with or without the endophyte, as well as PAL but only in presence of P. chlamydosporia. When inoculated alone, the endophyte up-regulated PR1 and LOX. The gene expression patterns observed in leaves suggest specific and time-dependent relationships linking host plants and P. chlamydosporia in presence of biotic stress factors, functional to a systemic, although complex, activation of defense genes.
Green Design of Novel Starch-Based Packaging Materials Sustaining Human and Environmental Health
Authors: Mironescu Monica , Lazea-Stoyanova Andrada , Barbinta-Patrascu Marcela Elisabeta , Virchea Lidia-Ioana , Rexhepi Diana , Mathe Endre , Georgescu Cecilia
Published: April 2021

A critical overview of current approaches to the development of starch-containing packaging, integrating the principles of green chemistry (GC), green technology (GT) and green nanotechnology (GN) with those of green packaging (GP) to produce materials important for both us and the planet is given. First, as a relationship between GP and GC, the benefits of natural bioactive compounds are analyzed and the state-of-the-art is updated in terms of the starch packaging incorporating green chemicals that normally help us to maintain health, are environmentally friendly and are obtained via GC. Newer approaches are identified, such as the incorporation of vitamins or minerals into films and coatings. Second, the relationship between GP and GT is assessed by analyzing the influence on starch films of green physical treatments such as UV, electron beam or gamma irradiation, and plasma; emerging research areas are proposed, such as the use of cold atmospheric plasma for the production of films. Thirdly, the approaches on how GN can be used successfully to improve the mechanical properties and bioactivity of packaging are summarized; current trends are identified, such as a green synthesis of bionanocomposites containing phytosynthesized metal nanoparticles. Last but not least, bioinspiration ideas for the design of the future green packaging containing starch are presented.
The Polymer-Based Technology in the Endovascular Treatment of Abdominal Aortic Aneurysms
Authors: de Donato Gianmarco , Pasqui Edoardo , Panzano Claudia , Brancaccio Brenda , Grottola Gaia , Galzerano Giuseppe , Benevento Domenico , Palasciano Giancarlo
Published: April 2021

An abdominal aortic aneurysm (AAA) is a dilatation of the abdominal aorta that progressively grows until it ruptures. Treatment is typically recommended when the diameter is more than 5 cm. The EVAR (Endovascular aneurysm repair) is a minimally invasive procedure that involves the placement of an expandable stent graft within the aorta to treat aortic disease without operating directly on the aorta. For years, stent grafts’ essential design was based on metallic stent frames to support the fabric. More recently, a polymer-based technology has been proposed as an alternative method to seal AAA. This review underlines the two platforms that are based on a polymer technology: (1) the polymer-filled endobags, also known as Endovascular Aneurysm Sealing (EVAS) with Nellix stent graft; and (2) the O-ring EVAR polymer-based proximal neck sealing device, also known as an Ovation stent graft. Polymer characteristics for this particular aim, clinical applications, and durability results are hereby summarized and commented critically. The technique of inflating endobags filled with polymer to exclude the aneurysmal sac was not successful due to the lack of an adequate proximal fixation. The platform that used polymer to create a circumferential sealing of the aneurysmal neck has proven safe and effective.
Analytical Synthesis of Non-Linear Control Algorithms of a Chemical Reactor Thermal Mode
Authors: Labutin Alexander Nikolaevich , Vaško Milan , Kuric Ivan , Nevinitsyn Vladimir Yuryevich , Sága Milan , Zagarinskaya Yulia Nikolaevna , Volkova Galina Vitalievna
Published: April 2021

The paper deals with two approaches to the synthesis of a non-linear control system of the thermal regime of a liquid-phase chemical reactor at the realization of a bimolecular exothermic reaction. Synthesis of control algorithms is carried out by the method of analytical design of aggregated regulators (ADAR). The first variant assumes synthesis of temperature controller by classic ADAR method on the basis of a sequential set of invariant manifolds. The second one is based on the cascade control system structure. Computer simulation is used to study and compare the synthesized control systems.
Assessing Stream Thermal Heterogeneity and Cold-Water Patches from UAV-Based Imagery: A Matter of Classification Methods and Metrics
Authors: Kuhn Johannes , Casas-Mulet Roser , Pander Joachim , Geist Juergen
Published: April 2021

Understanding stream thermal heterogeneity patterns is crucial to assess and manage river resilience in light of climate change. The dual acquisition of high-resolution thermal infrared (TIR) and red–green–blue-band (RGB) imagery from unmanned aerial vehicles (UAVs) allows for the identification and characterization of thermally differentiated patches (e.g., cold-water patches—CWPs). However, a lack of harmonized CWP classification metrics (patch size and temperature thresholds) makes comparisons across studies almost impossible. Based on an existing dual UAV imagery dataset (River Ovens, Australia), we present a semi-automatic supervised approach to classify key riverscape habitats and associated thermal properties at a pixel-scale accuracy, based on spectral properties. We selected five morphologically representative reaches to (i) illustrate and test our combined classification and thermal heterogeneity assessment method, (ii) assess the changes in CWP numbers and distribution with different metric definitions, and (iii) model how climatic predictions will affect thermal habitat suitability and connectivity of a cold-adapted fish species. Our method was successfully tested, showing mean thermal differences between shaded and sun-exposed fluvial mesohabitats of up to 0.62 °C. CWP metric definitions substantially changed the number and distance between identified CWPs, and they were strongly dependent on reach morphology. Warmer scenarios illustrated a decrease in suitable fish habitats, but reach-scale morphological complexity helped sustain such habitats. Overall, this study demonstrates the importance of method and metric definitions to enable spatio-temporal comparisons between stream thermal heterogeneity studies.
Impending Hydrological Regime of Lhasa River as Subjected to Hydraulic Interventions—A SWAT Model Manifestation
Authors: Yasir Muhammad , Hu Tiesong , Abdul Hakeem Samreen
Published: April 2021

The damming of rivers has altered their hydrological regimes. The current study evaluated the impacts of major hydrological interventions of the Zhikong and Pangduo hydropower dams on the Lhasa River, which was exposed in the form of break and change points during the double-mass curve analysis. The coefficient of variability (CV) for the hydro-meteorological variables revealed an enhanced climate change phenomena in the Lhasa River Basin (LRB), where the Lhasa River (LR) discharge varied at a stupendous magnitude from 2000 to 2016. The Mann–Kendall trend and Sen’s slope estimator supported aggravated hydro-meteorological changes in LRB, as the rainfall and LR discharge were found to have been significantly decreasing while temperature was increasing from 2000 to 2016. The Sen’s slope had a largest decrease for LR discharge in relation to the rainfall and temperature, revealing that along with climatic phenomena, additional phenomena are controlling the hydrological regime of the LR. Reservoir functioning in the LR is altering the LR discharge. The Soil and Water Assessment Tool (SWAT) modeling of LR discharge under the reservoir’s influence performed well in terms of coefficient of determination (R2), Nash–Sutcliffe coefficient (NSE), and percent bias (PBIAS). Thus, simulation-based LR discharge could substitute observed LR discharge to help with hydrological data scarcity stress in the LRB. The simulated–observed approach was used to predict future LR discharge for the time span of 2017–2025 using a seasonal AutoRegressive Integrated Moving Average (ARIMA) model. The predicted simulation-based and observation-based discharge were closely correlated and found to decrease from 2017 to 2025. This calls for an efficient water resource planning and management policy for the area. The findings of this study can be applied in similar catchments.
Flood Monitoring in Rural Areas of the Pearl River Basin (China) Using Sentinel-1 SAR
Authors: Qiu Junliang , Cao Bowen , Park Edward , Yang Xiankun , Zhang Wenxin , Tarolli Paolo
Published: April 2021

Flood hazards result in enormous casualties and huge economic losses every year in the Pearl River Basin (PRB), China. It is, therefore, crucial to monitor floods in PRB for a better understanding of the flooding patterns and characteristics of the PRB. Previous studies, which utilized hydrological data were not successful in identifying flooding patterns in the rural and remote regions in PRB. Such regions are the key supplier of agricultural products and water resources for the entire PRB. Thus, an analysis of the impacts of floods could provide a useful tool to support mitigation strategies. Using 66 Sentinel-1 images, this study employed Otsu’s method to investigate floods and explore flood patterns across the PRB from 2017 to 2020. The results indicated that floods are mainly located in the central West River Basin (WRB), middle reaches of the North River (NR) and middle reaches of the East River (ER). WRB is more prone to flood hazards. In 2017, 94.0% flood-impacted croplands were located in WRB; 95.0% of inundated croplands (~9480 hectares) were also in WRB. The most vulnerable areas to flooding are sections of the Yijiang, Luoqingjiang, Qianjiang, and Xunjiang tributaries and the lower reaches of Liujiang. Our results highlight the severity of flood hazards in a rural region of the PRB and emphasize the need for policy overhaul to enhance flood control in rural regions in the PRB to ensure food safety.
Seamless 3D Image Mapping and Mosaicing of Valles Marineris on Mars Using Orbital HRSC Stereo and Panchromatic Images
Authors: Tao Yu , Michael Greg , Muller Jan-Peter , Conway Susan J. , Putri Alfiah R. D.
Published: April 2021

A seamless mosaic has been constructed including a 3D terrain model at 50 m grid-spacing and a corresponding terrain-corrected orthoimage at 12.5 m using a novel approach applied to ESA Mars Express High Resolution Stereo Camera orbital (HRSC) images of Mars. This method consists of blending and harmonising 3D models and normalising reflectance to a global albedo map. Eleven HRSC image sets were processed to Digital Terrain Models (DTM) based on an opensource stereo photogrammetric package called CASP-GO and merged with 71 published DTMs from the HRSC team. In order to achieve high quality and complete DTM coverage, a new method was developed to combine data derived from different stereo matching approaches to achieve a uniform outcome. This new approach was developed for high-accuracy data fusion of different DTMs at dissimilar grid-spacing and provenance which employs joint 3D and image co-registration, and B-spline fitting against the global Mars Orbiter Laser Altimeter (MOLA) standard reference. Each HRSC strip is normalised against a global albedo map to ensure that the very different lighting conditions could be corrected and resulting in a tiled set of seamless mosaics. The final 3D terrain model is compared against the MOLA height reference and the results shown of this intercomparison both in altitude and planum. Visualisation and access mechanisms to the final open access products are described.
Responses of Summer Upwelling to Recent Climate Changes in the Taiwan Strait
Authors: Zhang Caiyun
Published: April 2021

The response of a summer upwelling system to recent climate change in the Taiwan Strait has been investigated using a time series of sea surface temperature and wind data over the period 1982–2019. Our results revealed that summer upwelling intensities of the Taiwan Strait decreased with a nonlinear fluctuation over the past four decades. The average upwelling intensity after 2000 was 35% lower than that before 2000. The long-term changes in upwelling intensities show strong correlations with offshore Ekman transport, which experienced a decreasing trend after 2000. Unlike the delay effect of canonical ENSO events on changes in summer upwelling, ENSO Modoki events had a significant negative influence on upwelling intensity. Strong El Niño Modoki events were not favorable for the development of upwelling. This study also suggested that decreased upwelling could not slow down the warming rate of the sea surface temperature and would probably cause the decline of chlorophyll a in the coastal upwelling system of the Taiwan Strait. These results will contribute to a better understanding of the dynamic process of summer upwelling in the Taiwan Strait, and provide a sound scientific basis for evaluating future trends in coastal upwelling and their potential ecological effects.
Fusion of Multispectral Aerial Imagery and Vegetation Indices for Machine Learning-Based Ground Classification
Authors: Zhang Yanchao , Yang Wen , Sun Ying , Chang Christine , Yu Jiya , Zhang Wenbo
Published: April 2021

Unmanned Aerial Vehicles (UAVs) are emerging and promising platforms for carrying different types of cameras for remote sensing. The application of multispectral vegetation indices for ground cover classification has been widely adopted and has proved its reliability. However, the fusion of spectral bands and vegetation indices for machine learning-based land surface investigation has hardly been studied. In this paper, we studied the fusion of spectral bands information from UAV multispectral images and derived vegetation indices for almond plantation classification using several machine learning methods. We acquired multispectral images over an almond plantation using a UAV. First, a multispectral orthoimage was generated from the acquired multispectral images using SfM (Structure from Motion) photogrammetry methods. Eleven types of vegetation indexes were proposed based on the multispectral orthoimage. Then, 593 data points that contained multispectral bands and vegetation indexes were randomly collected and prepared for this study. After comparing six machine learning algorithms (Support Vector Machine, K-Nearest Neighbor, Linear Discrimination Analysis, Decision Tree, Random Forest, and Gradient Boosting), we selected three (SVM, KNN, and LDA) to study the fusion of multi-spectral bands information and derived vegetation index for classification. With the vegetation indexes increased, the model classification accuracy of all three selected machine learning methods gradually increased, then dropped. Our results revealed that that: (1) spectral information from multispectral images can be used for machine learning-based ground classification, and among all methods, SVM had the best performance; (2) combination of multispectral bands and vegetation indexes can improve the classification accuracy comparing to only spectral bands among all three selected methods; (3) among all VIs, NDEGE, NDVIG, and NDVGE had consistent performance in improving classification accuracies, and others may reduce the accuracy. Machine learning methods (SVM, KNN, and LDA) can be used for classifying almond plantation using multispectral orthoimages, and fusion of multispectral bands with vegetation indexes can improve machine learning-based classification accuracy if the vegetation indexes are properly selected.
Remote Sensing to Study Mangrove Fragmentation and Its Impacts on Leaf Area Index and Gross Primary Productivity in the South of Peninsular Malaysia
Authors: Kanniah Kasturi Devi , Kang Chuen Siang , Sharma Sahadev , Amir A. Aldrie
Published: April 2021

Mangrove is classified as an important ecosystem along the shorelines of tropical and subtropical landmasses, which are being degraded at an alarming rate despite numerous international treaties having been agreed. Iskandar Malaysia (IM) is a fast-growing economic region in southern Peninsular Malaysia, where three Ramsar Sites are located. Since the beginning of the 21st century (2000–2019), a total loss of 2907.29 ha of mangrove area has been estimated based on medium-high resolution remote sensing data. This corresponds to an annual loss rate of 1.12%, which is higher than the world mangrove depletion rate. The causes of mangrove loss were identified as land conversion to urban, plantations, and aquaculture activities, where large mangrove areas were shattered into many smaller patches. Fragmentation analysis over the mangrove area shows a reduction in the mean patch size (from 105 ha to 27 ha) and an increase in the number of mangrove patches (130 to 402), edge, and shape complexity, where smaller and isolated mangrove patches were found to be related to the rapid development of IM region. The Moderate Resolution Imaging Spectro-radiometer (MODIS) Leaf Area Index (LAI) and Gross Primary Productivity (GPP) products were used to inspect the impact of fragmentation on the mangrove ecosystem process. The mean LAI and GPP of mangrove areas that had not undergone any land cover changes over the years showed an increase from 3.03 to 3.55 (LAI) and 5.81 g C m−2 to 6.73 g C m−2 (GPP), highlighting the ability of the mangrove forest to assimilate CO2 when it is not disturbed. Similarly, GPP also increased over the gained areas (from 1.88 g C m−2 to 2.78 g C m−2). Meanwhile, areas that lost mangroves, but replaced them with oil palm, had decreased mean LAI from 2.99 to 2.62. In fragmented mangrove patches an increase in GPP was recorded, and this could be due to the smaller patches (<9 ha) and their edge effects where abundance of solar radiation along the edges of the patches may increase productivity. The impact on GPP due to fragmentation is found to rely on the type of land transformation and patch characteristics (size, edge, and shape complexity). The preservation of mangrove forests in a rapidly developing region such as IM is vital to ensure ecosystem, ecology, environment, and biodiversity conservation, in addition to providing economical revenue and supporting human activities.
Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey
Authors: Ayyad Sarah M. , Shehata Mohamed , Shalaby Ahmed , Abou El-Ghar Mohamed , Ghazal Mohammed , El-Melegy Moumen , Abdel-Hamid Nahla B. , Labib Labib M. , Ali H. Arafat , El-Baz Ayman
Published: April 2021

Prostate cancer is one of the most identified cancers and second most prevalent among cancer-related deaths of men worldwide. Early diagnosis and treatment are substantial to stop or handle the increase and spread of cancer cells in the body. Histopathological image diagnosis is a gold standard for detecting prostate cancer as it has different visual characteristics but interpreting those type of images needs a high level of expertise and takes too much time. One of the ways to accelerate such an analysis is by employing artificial intelligence (AI) through the use of computer-aided diagnosis (CAD) systems. The recent developments in artificial intelligence along with its sub-fields of conventional machine learning and deep learning provide new insights to clinicians and researchers, and an abundance of research is presented specifically for histopathology images tailored for prostate cancer. However, there is a lack of comprehensive surveys that focus on prostate cancer using histopathology images. In this paper, we provide a very comprehensive review of most, if not all, studies that handled the prostate cancer diagnosis using histopathological images. The survey begins with an overview of histopathological image preparation and its challenges. We also briefly review the computing techniques that are commonly applied in image processing, segmentation, feature selection, and classification that can help in detecting prostate malignancies in histopathological images.
3D Face Point Cloud Reconstruction and Recognition Using Depth Sensor
Authors: Wang Cheng-Wei , Peng Chao-Chung
Published: April 2021

Facial recognition has attracted more and more attention since the rapid growth of artificial intelligence (AI) techniques in recent years. However, most of the related works about facial reconstruction and recognition are mainly based on big data collection and image deep learning related algorithms. The data driven based AI approaches inevitably increase the computational complexity of CPU and usually highly count on GPU capacity. One of the typical issues of RGB-based facial recognition is its applicability in low light or dark environments. To solve this problem, this paper presents an effective procedure for facial reconstruction as well as facial recognition via using a depth sensor. For each testing candidate, the depth camera acquires a multi-view of its 3D point clouds. The point cloud sets are stitched for 3D model reconstruction by using the iterative closest point (ICP). Then, a segmentation procedure is designed to separate the model set into a body part and head part. Based on the segmented 3D face point clouds, certain facial features are then extracted for recognition scoring. Taking a single shot from the depth sensor, the point cloud data is going to register with other 3D face models to determine which is the best candidate the data belongs to. By using the proposed feature-based 3D facial similarity score algorithm, which composes of normal, curvature, and registration similarities between different point clouds, the person can be labeled correctly even in a dark environment. The proposed method is suitable for smart devices such as smart phones and smart pads with tiny depth camera equipped. Experiments with real-world data show that the proposed method is able to reconstruct denser models and achieve point cloud-based 3D face recognition.
Modelling and Experimental Assessment of Inter-Personal Distancing Based on Shared GNSS Observables
Authors: Minetto Alex , Nardin Andrea , Dovis Fabio
Published: April 2021

In the last few years, all countries worldwide have fought the spread of SARS-CoV-2 (COVID-19) by exploiting Information and Communication Technologies (ICT) to perform contact tracing. In parallel, the pandemic has highlighted the relevance of mobility and social distancing among citizens. The monitoring of such aspects appeared prominent for reactive decision-making and the effective tracking of the infection chain. In parallel to the proximity sensing among people, indeed, the concept of social distancing has captured the attention to signal processing algorithms enabling short-to-medium range distance estimation to provide behavioral models in the emergency. By exploiting the availability of smart devices, the synergy between mobile network connectivity and Global Navigation Satellite Systems (GNSS), cooperative ranging approaches allow computing inter-personal distance measurements in outdoor environments through the exchange of light-weight navigation data among interconnected users. In this paper, a model for Inter-Agent Ranging (IAR) is provided and experimentally assessed to offer a naive collaborative distancing technique that leverages these features. Although the technique provides distance information, it does not imply the disclosure of the user’s locations being intrinsically prone to protect sensitive user data. A statistical error model is presented and validated through synthetic simulations and real, on-field experiments to support implementation in GNSS-equipped mobile devices. Accuracy and precision of IAR measurements are compared to other consolidated GNSS-based techniques showing comparable performance at lower complexity and computational effort.
Recent Trends in the Improvement of the Electrochemical Response of Screen-Printed Electrodes by Their Modification with Shaped Metal Nanoparticles
Authors: Torres-Rivero Karina , Florido Antonio , Bastos-Arrieta Julio
Published: April 2021

Novel sensing technologies proposed must fulfill the demands of wastewater treatment plants, the food industry, and environmental control agencies: simple, fast, inexpensive, and reliable methodologies for onsite screening, monitoring, and analysis. These represent alternatives to conventional analytical methods (ICP-MS and LC-MS) that require expensive and non-portable instrumentation. This needs to be controlled by qualified technicians, resulting moreover in a long delay between sampling and high-cost analysis. Electrochemical analysis based on screen-printed electrodes (SPEs) represents an excellent miniaturized and portable alternative due to their disposable character, good reproducibility, and low-cost commercial availability. SPEs application is widely extended, which makes it important to design functionalization strategies to improve their analytical response. In this sense, different types of nanoparticles (NPs) have been used to enhance the electrochemical features of SPEs. NPs size (1–100 nm) provides them with unique optical, mechanical, electrical, and chemical properties that give the modified SPEs increased electrode surface area, increased mass-transport rate, and faster electron transfer. Recent progress in nanoscale material science has led to the creation of reproducible, customizable, and simple synthetic procedures to obtain a wide variety of shaped NPs. This mini-review attempts to present an overview of the enhancement of the electrochemical response of SPEs when NPs with different morphologies are used for their surface modification
Spatiotemporal Dynamics of Snowline Altitude and Their Responses to Climate Change in the Tienshan Mountains, Central Asia, during 2001–2019
Authors: Deng Gang , Tang Zhiguang , Hu Guojie , Wang Jingwen , Sang Guoqing , Li Jia
Published: April 2021

Snow cover is an important water resource in arid and semi-arid regions of Central Asia, and is related to agricultural and livestock production, ecosystems, and socio-economic development. The snowline altitude (SLA) is a significant indicator for monitoring the changes in snow cover in mountainous regions under the changing climate. Here, we investigate the spatiotemporal variation of SLA in the Tienshan Mountains (TS) during 2001–2019 using Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products on a grid-by-grid basis. The potential influence of topographic factors (slope gradient and aspect) on SLA and the correlation between SLA, temperature, precipitation, and solar radiation are also investigated. The results are as follows: (1) The annual cycle of SLA shows strong seasonal fluctuations (from about 2000 m in late December to 4100 m in early August). The SLA over the TS exhibits a large spatiotemporal heterogeneity. (2) SLA increases with a steeper slope gradient. The SLA of the northerly aspect is generally less than the southerly. (3) The SLA over the TS generally shows an increasing trend in the recent years (2001–2019). The change trend of SLA varies in different months. Except for a slight decrease in June, the SLA increased in almost all months, especially at the start of the melt season (March and April) and the end of melting season (July and August). (4) The SLA increases with increased temperature/radiation in the TS, and decreases with increased precipitation. Solar radiation is the dominant climatic factor affecting the changes of SLA in the TS. Compared with precipitation, temperature is more correlated to SLA dynamics.
The Gouy-Stodola Theorem—From Irreversibility to Sustainability—The Thermodynamic Human Development Index
Authors: Lucia Umberto , Grisolia Giulia
Published: April 2021

Today, very complex economic relationships exist between finance, technology, social needs, and so forth, which represent the requirement of sustainability. Sustainable consumption of resources, production and energy policies are the keys for a sustainable development. Moreover, a growing request in bio-based industrial raw materials requires a reorganization of the chains of the energy and industrial sectors. This is based on new technological choices, with the need of sustainable measurements of their impacts on the environment, society and economy. In this way, social and economic requirements must be taken into account by the decision-makers. So, sustainable policies require new indicators. These indicators must link economics, technologies and social well-being, together. In this paper, an irreversible thermodynamic approach is developed in order to improve the Human Development Index, HDI, with the Thermodynamic Human Development Index, THDI, an indicator based on the thermodynamic optimisation approach, and linked to socio-economic and ecological evaluations. To do so, the entropy production rate is introduced into the HDI, in relation to the CO2 emission flows due to the anthropic activities. In this way, the HDI modified, named Thermodynamic Human Development Index THDI, results as an indicator that considers both the socio-economic needs, equity and the environmental conditions. Examples of the use of the indicator are presented. In particular, it is possible to highlight that, if environmental actions are introduced in order to reduce the CO2 emission, HDI remains constant, while THDI changes its value, pointing out its usefulness for decision makers to evaluate a priori the effectiveness of their decisions.
Prioritising CAP Intervention Needs: An Improved Cumulative Voting Approach
Authors: Cagliero Roberto , Bellini Francesco , Marcatto Francesco , Novelli Silvia , Monteleone Alessandro , Mazzocchi Giampiero
Published: April 2021

The process to define the 2023–2027 Common Agricultural Policy (CAP) is underway. The implementation model governing the process requires each EU Member State to design a National Strategic Plan to deliver operational actions exploiting the synergies under the two pillars of the policy. Each Plan must be built from an evidence-based needs assessment that undergoes rigorous prioritisation and planning to create comprehensive, integrated, and achievable interventions. In Italy, the success of this planning process requires all interested stakeholders to generate options for the regional authorities who plan, manage, and legislate agricultural activities. This research proposes a decision-making technique, based on the cumulative voting approach, that can be used effectively when multiple persons from different backgrounds and perspectives are engaged in problem-solving and needs prioritisation. The results indicate that the model can be applied both theoretically and practically to prioritise Strategic Plan needs that involve national and regional authorities. Validation of the model allows it to be used in the next consultative processes and for expansion to socioeconomic stakeholders.
Machine Learning Based Vehicle to Grid Strategy for Improving the Energy Performance of Public Buildings
Authors: Scott Connor , Ahsan Mominul , Albarbar Alhussein
Published: April 2021

Carbon neutral buildings are dependent on effective energy management systems and harvesting energy from unpredictable renewable sources. One strategy is to utilise the capacity from electric vehicles, while renewables are not available according to demand. Vehicle to grid (V2G) technology can only be expanded if there is funding and realisation that it works, so investment must be in place first, with charging stations and with the electric vehicles to begin with. The installer of the charging stations will achieve the financial benefit or have an incentive and vice versa for the owners of the electric vehicles. The paper presents an effective V2G strategy that was developed and implemented for an operational university campus. A machine learning algorithm has also been derived to predict energy consumption and energy costs for the investigated building. The accuracy of the developed algorithm in predicting energy consumption was found to be between 94% and 96%, with an average of less than 5% error in costs predictions. The achieved results show that energy consumption savings are in the range of 35%, with the potentials to achieve about 65% if the strategy was applied at all times. This has demonstrated the effectiveness of the machine learning algorithm in carbon print reductions.
Gender Inequality in Safety and Security Perceptions in Railway Stations
Authors: Coppola Pierluigi , Silvestri Fulvio
Published: April 2021

Recent studies have shown that gender is the personal aspect that mostly affects mobility patterns and travel behaviors. It has been observed, for instance, that female perception of unsafety and insecurity when traveling using public transport forces them to make unwanted travel choices, such as avoiding traveling at certain times of day and to specific destinations. In order to improve the attractiveness of public transport services, this gender gap must not be overlooked. This paper aims at contributing to research in gendered mobility by investigating differences in safety and security perceptions in railway stations, and by identifying which policies could be effective in bridging any existing gap. The methodology includes the collection of disaggregate data through a mixed Revealed Preference/Stated Preference survey, and the estimation of fixed and random parameters behavioral models. Results from a medium-sized Italian railway station show that female travelers feel safer in the presence of other people; they prefer intermodal infrastructures close to the entrance of the station and commercial activities in the internal premises. Moreover, unlike male travelers, they do not appreciate the presence of hedges and greenery outside stations.
Evaluating Operation Performance in Higher Education: The Case of Vietnam Public Universities
Authors: Wang Tien-Chin , Phan Binh Ngoc , Nguyen Thuy Thi Thu
Published: April 2021

The system of public universities plays an important role in the socioeconomic development of each country. In Vietnam, public universities perform a leading role in the national higher education system’s operation and development. Therefore, public universities are assigned funds, assets, and facilities to implement goals and prioritize investment policies in the country’s education and training. However, to appropriately allocate funding, the state must reconsider the performance of the public education system. This paper presents a methodology to evaluate the operating performance of public higher education in Vietnam. The research design model includes cluster analysis and ANOVA, and Duncan post hoc tests have been used to provide an overview of public universities’ current state in Vietnam and to identify each of the strengths and weaknesses in cluster-specific groups. Based on this study’s results, educational administrators can develop a reasonable financial budget allocation plan for each university cluster.
Categorizing Bicycling Environment Quality Based on Mobile Sensor Data and Bicycle Flow Data
Authors: Bian Yang , Li Ling , Zhang Huan , Xu Dandan , Rong Jian , Wang Jiachuan
Published: April 2021

The bicycle is a healthy and sustainable transport mode due to its emission-free characteristics. To increase bicycle use, it is fundamental to provide bicycle-friendly environments. To better monitor bicycle environments, this study proposed the concept of bicycling environment quality (BEQ), which was defined by perceived satisfaction and conflict level. Data collection was conducted at 19 road segments in five sites located in Beijing, China. Then, speed-related and acceleration-related bicycling behavior indicators (BBIs) were extracted from data collected using sensors on mobile phones, while bicycling environment indicators (BEIs), such as bicycle flow, were extracted from recorded data. Taking the BBIs and BEIs as input attributes, a two-level BEQ classification assessment model based on a random forest (RF) algorithm was constructed. The proposed RF-based classification assessment model was able to produce approximately 77.35% overall correct classification. The results demonstrate the feasibility of using GPS data in evaluating BEQ. In addition, a novel dockless bicycle-sharing system (DBS)-based framework for bicycle traffic monitoring is discussed, which is of great significance in the sustainable development of bicycles. This study provides a theoretical method for objective BEQ assessment. It can further be used by planners and road administrators to monitor and improve BEQ and by individual cyclists for optimal route choice.
Principles of Sustainable Agriculture: Defining Standardized Reference Points
Authors: Trigo Ana , Marta-Costa Ana , Fragoso Rui
Published: April 2021

No question remains regarding our need to change toward sustainable agriculture. When ranking the industries that have more prevalent environmental impacts, agriculture holds a considerable share of responsibility. However, as sustainability is an ambiguous concept surrounded by controversy and debate, rather than attempt to describe its meaning through a single universal definition, we instead stressed the need to delineate a set of fundamental principles. With the goal of putting the sustainable-agriculture concept into practice, an inductive qualitative content analysis was employed based on multivariate methods on hundreds of different definitions, theories, notions and sustainability indicators gathered through a deep-structured literature review. Through this novel approach, we were able to identify four fundamental principles for sustainable agriculture (integrated management, dynamic balance, regenerative design, and social development), and concluded that in order to shift our current agricultural systems into more efficient and sustainable ones, we need to start making better use of natural and human resources. This work provides guidelines for reference that can be used by anyone whenever they make a decision regarding sustainable agriculture or apply a methodology to assess a particular behavior, process or situation.
Rhetoric, Accounting and Accountability: COVID-19 and the Case of Italy
Authors: Gelmini Lorenzo , Minutiello Valentina , Tettamanzi Patrizia , Comoli Maurizio
Published: April 2021

The current dramatic context of COVID-19 has urged academics and practitioners to tackle the topic of the pandemic not only regarding its medical side but from the perspective of social sciences, accounting and accountability as well. In this sense, our paper moves from the pivotal work of Higgins and Walker (2012) and Merkl-Davies and Brennan (2017) and tries to trace the use and the extent of accounting communication by companies during the peculiar context of the pandemic. Considering the nature of the elements to be evaluated, we applied a manual content analysis, a more suitable technique than software to capture subjective and emotional elements. Among the main preliminary results of the paper, the volume and the importance of emotional content come to the surface, such as self-assessment and emotional tone. The paper confirms the important role of rhetorical analysis in understanding the quality and the meaning of the information provided by companies and contributes to the stream of Critical Discourse Analysis (CDA) studies on corporate reporting.
BIM and Mechanical Engineering—A Cross-Disciplinary Analysis
Authors: Adanič Luka , de Oliveira Sara Guerra , Tibaut Andrej
Published: April 2021

Mechanical Engineering (ME) includes the design, manufacturing, assembly, and maintenance of mechanical subsystems for Architecture, Engineering, Construction, and Owner-Operator (AECO) projects. The intense adoption of information and communication technology in the AECO started with building product modelling, which was originally pioneered in the ME domain (i.e., automotive industry). The complexity and limited openness of product models paved the way for Building Information Modelling (BIM). Today, BIM workflows require an exchange of interoperable architecture, structure, and MEP/HVAC models and their seamless integration into a shared BIM model. Many specialized ME systems exist (i.e., medical gases and vacuum) for which BIM is not mature enough and where the role of BIM has not yet been studied. Therefore, a comprehensive cross-disciplinary analysis on the mutual influence of the BIM and the ME domain is needed for researchers and professionals. It identifies research fields and trends at the intersection of BIM and ME and analyzes their scope, limitations, and requirements for future extensions of BIM for better integration with ME. The analysis is based on an extensive literature search considering the interdisciplinary nature of ME. The initial collection of papers has undergone a rigorous bibliometric analysis that used a text mining approach for validation. Results show the field “Industry 4.0” as the most prosperous BIM influencing research field, followed by “Energy optimisation” and “Environmental Product Declaration”, while identifying “Geometric optimisation” and “Reinforced material” as the trendiest. Finally, conclusions on the impact of BIM on ME were drawn and 11 research opportunities were identified. This paper provides directions for studies where research is focused on the integration of ME systems in BIM workflows and on the extension of BIM capability to model future ME systems.
Multidimensional Conditions of the First Wave of the COVID-19 Epidemic in the Trans-Industrial Region. An Example of the Silesian Voivodeship in Poland
Authors: Krzysztofik Robert , Kantor-Pietraga Iwona , Spórna Tomasz
Published: April 2021

The successive phases of the global COVID-19 pandemic show some differences from the first wave in 2020. The most important of these is some experience in responding to its spread and in applying vaccines. However, new, more aggressive variants of COVID-19 mean that the pandemic is often taking on the nature of the one experienced by societies a year ago. So, the knowledge about the first wave of the COVID-19 pandemic is still up-to-date—significantly where the essential determinants of its spread have not changed. The article presents the Silesian Voivodeship case in Southern Poland, distinguished by different geographical conditions compared to the entire country. The authors showed the relationship between the spread of the COVID-19 epidemic and the characteristic attributes of the analysed area (post-)mining functions or urban shrinkage. The article conducted a dependence study using the Pearson correlation coefficient and the signs table method. In turn, the authors used thematic cartography to present the results of the analysis. It turned out that two attributes, namely (post-)mining and urban shrinkage, are essential in spreading the epidemic with the region analysed. This conclusion may be essential in implementing national and regional policies related to reducing the COVID-19 pandemic. However, a limitation in the scope of the presented applications is the fact that mining regions, such as the Silesian (Śląskie) Voivodeship, are currently less numerous in the world than, for example, those that develop based on services.
Strategies for a Circular Economy in the Construction and Demolition Sector: Identifying the Factors Affecting the Recommendation of Recycled Concrete
Authors: Superti Valeria , Houmani Cynthia , Hansmann Ralph , Baur Ivo , Binder Claudia R.
Published: April 2021

With increasing urbanisation, new approaches such as the Circular Economy (CE) are needed to reduce resource consumption. In Switzerland, Construction & Demolition (C&D) waste accounts for the largest portion of waste (84%). Beyond limiting the depletion of primary resources, implementing recycling strategies for C&D waste (such as using recycled aggregates to produce recycled concrete (RC)), can also decrease the amount of landfilled C&D waste. The use of RC still faces adoption barriers. In this research, we examined the factors driving the adoption of recycled products for a CE in the C&D sector by focusing on RC for structural applications. We developed a behavioural framework to understand the determinants of architects’ decisions to recommend RC. We collected and analysed survey data from 727 respondents. The analyses focused on architects’ a priori beliefs about RC, behavioural factors affecting their recommendations of RC, and project-specific contextual factors that might play a role in the recommendation of RC. Our results show that the factors that mainly facilitate the recommendation of RC by architects are: a senior position, a high level of RC knowledge and of the Minergie label, beliefs about the reduced environmental impact of RC, as well as favourable prescriptive social norms expressed by clients and other architects. We emphasise the importance of a holistic theoretical framework in approaching decision-making processes related to the adoption of innovation, and the importance of the agency of each involved actor for a transition towards a circular construction sector.