bluuebunny/crossref_metadata_2025 huggingface dataset:
Protein Supplementation Does Not Maximize Adaptations to Low-Volume High-Intensity Interval Training in Sedentary, Healthy Adults: A Placebo-Controlled Double-Blind Randomized Study
Authors: Reljic Dejan , Zieseniss Nilas , Herrmann Hans J. , Neurath Markus F. , Zopf Yurdagül
Published: September 2022

There is ample evidence that specific nutritional strategies can enhance adaptions to resistance and endurance training. However, it is still unclear whether post-session protein supplementation may increase the effects of low-volume high-intensity interval training (LOW-HIIT). We examined the impact of LOW-HIIT combined with protein vs. placebo supplementation on cardiometabolic health indices in sedentary healthy individuals. Forty-seven participants (31.1 ± 8.0 yrs) performed cycle ergometer LOW-HIIT (5–10x1 min at 80–95% maximum heart rate) for eight weeks and randomly received double-blinded 40 g of whey protein (PRO-HIIT, N = 24) or an isocaloric placebo (maltodextrin, PLA-HIIT, N = 23) after each session. The maximum oxygen uptake (VO2max, primary outcome) and several secondary cardiometabolic outcomes were determined pre-/post-intervention. VO2max increased in PRO-HIIT (+2.8 mL/kg/min, p = 0.003) and PLA-HIIT (+3.5 mL/kg/min, p < 0.001). Systolic and diastolic blood pressure decreased in PRO-HIIT (−7/3 mmHg, p < 0.05) and PLA-HIIT (−8/5 mmHg, p < 0.001). Gamma glutamyl transferase (−2 U/L, p = 0.003) decreased in PRO-HIIT and alanine aminotransferase (−3 U/L, p = 0.014) in PLA-HIIT. There were no significant between-group differences in any of the outcome changes. In conclusion, LOW-HIIT improved VO2max and other cardiometabolic markers irrespective of the supplementation condition. Post-session protein supplementation does not seem to provide any additional benefit to LOW-HIIT in improving cardiometabolic health in sedentary healthy individuals.
The Impact of Foods, Nutrients, or Dietary Patterns on Telomere Length in Childhood and Adolescence: A Systematic Review
Authors: Valera-Gran Desirée , Prieto-Botella Daniel , Hurtado-Pomares Miriam , Baladia Eduard , Petermann-Rocha Fanny , Sánchez-Pérez Alicia , Navarrete-Muñoz Eva-María
Published: September 2022

Environmental factors such as diet can affect telomere length (TL) dynamics. However, the role that children’s and adolescents’ diets play in maintaining TL is not well understood. Thus, we conducted a systematic review to examine the association between the intake of nutrients, foods, food groups, and/or dietary patterns and TL in childhood and adolescence. Following the PRISMA guidelines, we searched MEDLINE via PubMed, Embase, and Cochrane databases and additional registers and methods. The five selected studies were cross-sectional and conducted in children and adolescents aged 2 to 18 years. The main results suggest that a higher consumption of fish, nuts and seeds, fruits and vegetables, green leafy and cruciferous vegetables, olives, legumes, polyunsaturated fatty acids, and an antioxidant-rich diet might positively affect TL. On the contrary, a higher intake of dairy products, simple sugar, sugar-sweetened beverages, cereals, especially white bread, and a diet high in glycaemic load were factors associated with TL shortening. To our knowledge, this is the first systematic review examining the impact of dietary intake factors on TL in childhood and adolescence. Although limited, these results are consistent with previous studies in different adult populations. Further research is needed to ascertain potential nutritional determinants of TL in childhood and adolescence.
Hyaluronan Oligosaccharides-Coated Paclitaxel-Casein Nanoparticles with Enhanced Stability and Antitumor Activity
Authors: Wang Man , Zhang Yahui , Fei Zuqi , Xie Dongchao , Zhang Haihua , Du Qizhen , Jin Peng
Published: September 2022

This study aims to develop specific-molecular-weight hyaluronic acid oligosaccharides-coated paclitaxel-loaded casein nanoparticles (HA-PT-Cas NPs) via chemical conjugation to increase the stability and antitumor effects. Optimized HA-PT-Cas NPs (HA/casein of 3:1) were obtained with a mean size of 235.3 nm and entrapment efficiency of 93.1%. HA-PT-Cas exhibited satisfactory stability at 4 °C for 12 days and 37 °C for 3 h; paclitaxel was retained at rates of 81.4% and 64.7%, respectively, significantly higher than those of PT-Cas (only 27.8% at 4 °C after 16 h and 20.3% at 37 °C after 3 h). HA-PT-Cas exhibited high efficiency (61.3%) in inhibiting A375 tumor owing to the enhanced stability of HA oligosaccharides barrier, which was comparable with that of 10 μg/mL cis-platinum (64.9%). Mice experiments showed the 74.6% tumor inhibition of HA-PT-Cas by intravenously administration, significantly higher than that of PT-casein (39.8%). Therefore, this work provides an effective carrier for drug delivery via HA oligomers-coated modification.
Zinc Fortification: Current Trends and Strategies
Authors: Hall Andrew G. , King Janet C.
Published: September 2022

Zinc, through its structural and cofactor roles, affects a broad range of critical physiological functions, including growth, metabolism, immune and neurological functions. Zinc deficiency is widespread among populations around the world, and it may, therefore, underlie much of the global burden of malnutrition. Current zinc fortification strategies include biofortification and fortification with zinc salts with a primary focus on staple foods, such as wheat or rice and their products. However, zinc fortification presents unique challenges. Due to the influences of phytate and protein on zinc absorption, successful zinc fortification strategies should consider the impact on zinc bioavailability in the whole diet. When zinc is absorbed with food, shifts in plasma zinc concentrations are minor. However, co-absorbing zinc with food may preferentially direct zinc to cellular compartments where zinc-dependent metabolic processes primarily occur. Although the current lack of sensitive biomarkers of zinc nutritional status reduces the capacity to assess the impact of fortifying foods with zinc, new approaches for assessing zinc utilization are increasing. In this article, we review the tools available for assessing bioavailable zinc, approaches for evaluating the zinc nutritional status of populations consuming zinc fortified foods, and recent trends in fortification strategies to increase zinc absorption.
The Role of Group Sharing: An Action Research Study of Psychodrama Group Therapy in a Psychiatric Inpatient Ward
Authors: Ron Yiftach
Published: September 2022

Numerous studies point to the acute distress associated with the experience of coping with severe mental illness and psychiatric hospitalization. Another strand of research notes the therapeutic benefits of psychodrama and its efficacy in increasing empathy and self-awareness, improving interpersonal relationships, reducing stress and anxiety, and in treating particularly vulnerable populations for whom traditional psychotherapy’s usefulness is limited. The goal of this paper is to provide a framework for understanding the potential of group sharing in dealing with the experience of loneliness and distress, and to serve as a space for relatedness and self-expression in psychodrama group therapy. A qualitative action research study following an open inpatients’ psychodrama group in a psychiatric hospital in Israel demonstrates the role of group sharing in creating an accommodating space of self-expression, relatedness, and mutual support, which offers relief for the distress of psychiatric inpatients. Within the inpatients’ group, the participants used sharing to distribute the burden among the group members along with the resources to cope with it. The paper seeks to illuminate the unique contribution of a therapeutic tool rarely dealt with by the existing literature–the group sharing—in dealing with situations of acute mental distress.
Image-Based Classification of Double-Barred Beach States Using a Convolutional Neural Network and Transfer Learning
Authors: Oerlemans Stan C. M. , Nijland Wiebe , Ellenson Ashley N. , Price Timothy D.
Published: September 2022

Nearshore sandbars characterize many sandy coasts, and unravelling their dynamics is crucial to understanding nearshore sediment pathways. Sandbar morphologies exhibit complex patterns that can be classified into distinct states. The tremendous progress in data-driven learning in image recognition has recently led to the first automated classification of single-barred beach states from Argus imagery using a Convolutional Neural Network (CNN). Herein, we extend this method for the classification of beach states in a double-barred system. We used transfer learning to fine-tune the pre-trained network of ResNet50. Our data consisted of labelled single-bar time-averaged images from the beaches of Narrabeen (Australia) and Duck (US), complemented by 9+ years of daily averaged low-tide images of the double-barred beach of the Gold Coast (Australia). We assessed seven different CNNs, of which each model was tested on the test data from the location where its training data came from, the self-tests, and on the test data of alternate, unseen locations, the transfer-tests. When the model trained on the single-barred data of both Duck and Narrabeen was tested on unseen data of the double-barred Gold Coast, we achieved relatively low performances as measured by F1 scores. In contrast, models trained with only the double-barred beach data showed comparable skill in the self-tests with that of the single-barred models. We incrementally added data with labels from the inner or outer bar of the Gold Coast to the training data from both single-barred beaches, and trained models with both single- and double-barred data. The tests with these models showed that which bar the labels used for training the model mattered. The training with the outer bar labels led to overall higher performances, except at the inner bar. Furthermore, only 10% of additional data with the outer bar labels was needed for reasonable transferability, compared to the 20% of additional data needed with the inner bar labels. Additionally, when trained with data from multiple locations, more data from a new location did not always positively affect the model’s performance on other locations. However, the larger diversity of images coming from more locations allowed the transferability of the model to the locations from where new training data were added.
Detection of Ships Cruising in the Azimuth Direction Using Spotlight SAR Images with a Deep Learning Method
Authors: Yoshida Takero , Ouchi Kazuo
Published: September 2022

Spotlight synthetic aperture radar (SAR) achieves a high azimuth resolution with long integration times. Meanwhile, the long integration times also cause defocused and smeared images of moving objects such as cruising ships This is a typical imaging mechanism for moving objects in Spotlight SAR images. Conversely, ships can be classified as stationary or moving from the amount of smearing, and this classification method is, in general, based on manual observation. This paper proposes an automatic method for detecting cruising ships using deep learning known as the “You Only Look Once (YOLO) v5 model”, which is one of the frameworks of the YOLO family. In this study, ALOS-2/PALSAR-2 L-band Spotlight SAR images over the waters around the Miura Peninsula, Japan, were analyzed using the YOLO v5 model with a total of 53 ships’ images and compared with Automatic Identification System (AIS) data. The results showed a precision of approximately 0.85 and a recall rate of approximately 0.89 with an F-measure of 0.87. Thus, sufficiently high values were achieved in the automatic detection of moving ships using the deep learning method with the YOLO v5 model. As for false detections, images of breakwaters were classified as ships cruising in the azimuth direction. Further, range moving ships were found to be difficult to detect. From the present preliminary study, it was found that the YOLO v5 model is limited to ships cruising predominantly in the azimuth direction.
Gap-Filling and Missing Information Recovery for Time Series of MODIS Data Using Deep Learning-Based Methods
Authors: Wang Yidan , Zhou Xuewen , Ao Zurui , Xiao Kun , Yan Chenxi , Xin Qinchuan
Published: September 2022

Sensors onboard satellite platforms with short revisiting periods acquire frequent earth observation data. One limitation to the utility of satellite-based data is missing information in the time series of images due to cloud contamination and sensor malfunction. Most studies on gap-filling and cloud removal process individual images, and existing multi-temporal image restoration methods still have problems in dealing with images that have large areas with frequent cloud contamination. Considering these issues, we proposed a deep learning-based method named content-sequence-texture generation (CSTG) network to generate gap-filled time series of images. The method uses deep neural networks to restore remote sensing images with missing information by accounting for image contents, textures and temporal sequences. We designed a content generation network to preliminarily fill in the missing parts and a sequence-texture generation network to optimize the gap-filling outputs. We used time series of Moderate-resolution Imaging Spectroradiometer (MODIS) data in different regions, which include various surface characteristics in North America, Europe and Asia to train and test the proposed model. Compared to the reference images, the CSTG achieved structural similarity (SSIM) of 0.953 and mean absolute errors (MAE) of 0.016 on average for the restored time series of images in artificial experiments. The developed method could restore time series of images with detailed texture and generally performed better than the other comparative methods, especially with large or overlapped missing areas in time series. Our study provides an available method to gap-fill time series of remote sensing images and highlights the power of the deep learning methods in reconstructing remote sensing images.
Relationship between Topological Structure and Ecosystem Services of Forest Grass Ecospatial Network in China
Authors: Yang Linzhe , Niu Teng , Yu Qiang , Zhang Xiao , Wu Heng
Published: September 2022

Forest and grass ecological space is the key component of the ecosystem and plays a vital role in regulating the carbon, water, and energy cycle. The long-term exploitation of forest and grass ecological space and huge population pressure have gradually degraded the function of China’s ecosystem. Therefore, forest and grass ecological space plays an important role in maintaining the stability of the ecosystem. The relationship between forest and grass ecospatial network structure and ecosystem service has been the focus of research. In this study, the forest and grass ecospatial network is constructed based on the minimum cumulative resistance (MCR) model. Then, the topological indicators (degree, weight clustering coefficient, node weight, unit weight, weight distribution difference, betweenness, PageRank) of the forest and grass ecospatial network were calculated by combining the complex network theory to analyze the relationship between these topological indicators and the three ecosystems (water retention, soil conservation, carbon storage). Based on the ecological significance of topological indicators, we identified ecologically fragile areas and proposed areas and directions for optimizing the ecospatial structure. Results show that the spatial distribution of the three ecosystem services in the southeast region of China is higher than that in the northwest region of China and shows a gradual decrease from the east to the west. The degree, node weight, unit weight, PageRank, and betweenness were highly significant and positively correlated with the three ecosystem services, among which PageRank had the highest correlation with water retention (p < 0.01, R2 = 0.835). Based on the spatial distribution characteristics of the different topological indicators, the quantitative relationship between the structural characteristics of the forest and grass ecospatial network and ecosystem services is clarified, revealing the intrinsic connection between ecological processes and ecosystem services. Through rational optimization of the forest and grass ecospatial network, ecosystem services can be effectively improved and ecosystem stability can be enhanced.
Millimeter-Wave Multi-Channel Backscatter Communication and Ranging with an FMCW Radar
Authors: An Sining , Bu Xiangyuan , Wymeersch Henk , Zirath Herbert , He Zhongxia Simon
Published: September 2022

A multi-channel backscatter communication and radar sensing system is proposed and demonstrated in this paper. Frequency modulated continuous wave (FMCW) radar ranging is integrated with simultaneous uplink data transmission from a self-packaged active radio frequency (RF) tag. A novel package solution is proposed for the RF tag. With the proposed package, the RF tag can transmit a 32-QAM signal up to 2.5 Gbps and QPSK signal up to 8 Gbps. For a multi-tag scenario, we proposed using spread spectrum code to separate the data from each tag. In this case, tags can be placed at arbitrary locations without adjacent channel interference. Proof-of-concept simulations and measurements are demonstrated. A 625 Mbps data rate is achieved in a dual-tag scenario for two tags.
An Improved FAST Algorithm Based on Image Edges for Complex Environment
Authors: Lu Cunzhe , Qi Xiaogang , Ding Kai , Yu Baoguo
Published: September 2022

In complex environments such as those with low textures or obvious brightness changes, point features extracted from a traditional FAST algorithm cannot perform well in pose estimation. Simultaneously, the number of point features extracted from FAST is too large, which increases the complexity of the build map. To solve these problems, we propose an L-FAST algorithm based on FAST, in order to reduce the number of extracted points and increase their quality. L-FAST pays more attention to the intersection of line elements in the image, which can be extracted directly from the related edge image. Hence, we improved the Canny edge extraction algorithm, including denoising, gradient calculation and adaptive threshold. These improvements aimed to enhance the sharpness of image edges and effectively extract the edges of strong light or dark areas in the images as brightness changed. Experiments on digital standard images showed that our improved Canny algorithm was smoother and more continuous for the edges extracted from images with brightness changes. Experiments on KITTI datasets showed that L-FAST extracted fewer point features and increased the robustness of SLAM.
Morphological and Seed Germination Behavior of Three Herba Swertiae Species from Hulunbuir, Inner Mongolia: Temperature and Substrate Effects
Authors: Ta Na , A Lisha , Bai Siriguleng , Mu Xiyele , Bai Li , A Rure , Feng Lan , Fu Minghai
Published: September 2022

The Mongolian medicinal herb Herba Swertiae is a group of critically endangered medicinal herbs that are difficult to obtain from the wild. This is due to their low percentage of seed germination and difficult domestication. The aim of this study was to investigate the suitable germination conditions for enabling large-scale cultivation and improving the breeding efficiency of these herbs. We used the seeds of Lomatogonium rotatum (L.) Fries ex Nym, Gentianopsis barbata (Froel.) Ma., and Parnassis palustris L. to study their morphological characteristics and the required germination temperature and substrate. Results showed that there were aages outside the seed coats of all three Herba Swertiae collected from Hulunbuir, which had a binding effect on the primary root breaking through the seed coat to form seedlings. In addition, the seeds showed a significantly higher germination rate at 25 °C, and the percentage of seeds germinating in the mixed medium of soil and vermiculite at a 1:1 ratio was significantly higher than on filter paper. We conclude that the morphological characteristics of the seeds have great significance in promoting seed germination. Germination substrate, as an important medium, and temperature, as an important environmental condition, play crucial roles in plant seed germination.
Renewable Energy in Smart Cities: Challenges and Opportunities by the Case Study of Russia
Authors: Vukovic Natalia Anatolievna , Nekhorosheva Daria Evgenievna
Published: September 2022

This study analyzes the development of renewable energy sources and factors affecting the choice of energy source. Also, we focus on the renewable energy programs implemented in the smart cities of the world and apply the prospects of its development to a Russian case study, using the method of cluster analysis. The results of the study showed that the share of renewable energy in Russian cities might reach 50% by 2050. However, the analysis demonstrates that currently, the average value is less than 26%. The results of the study show that the overall level of development of renewable sources in a particular country significantly affects the level of development of renewable energy in smart cities. Finally, the results reveal that Russian smart cities should aim at a much more intensive green energy policy to implement the fundamental principles of the global strategy of Net Zero (IEA).
Will Biochar Suppress or Stimulate Greenhouse Gas Emissions in Agricultural Fields? Unveiling the Dice Game through Data Syntheses
Authors: Li Simeng , Chan Celeste Y.
Published: September 2022

With the increasing popularity of biochar as a soil amendment worldwide in recent years, a question of concern arises as to whether the application of biochar would suppress or stimulate greenhouse gas (GHG) emissions. In this study, published data extracted from independent individual studies were systematically selected, statistically processed, graphically presented and critically analyzed to understand biochar’s influences on the emissions of CO2, CH4 and N2O—the three major GHGs emitted in agricultural fields. The results revealed not only the significant importance of biochar’s pyrolysis temperature for its impacts on GHG emissions, but also the dissimilar influences on the generations of different GHGs. The application of biochar, in general, stimulated the emissions of CO2 and CH4 to various extents. With biochar pyrolyzed under relatively lower temperatures (e.g., <500 °C), higher application rates generally resulted in more stimulated CO2 and CH4 emissions; whereas those pyrolyzed under relatively higher temperatures (e.g., >550 °C) became less stimulative (and sometimes even suppressive) for CO2 and CH4 emissions, especially when applied at higher rates. Nevertheless, the response of N2O emission to biochar application contrasted with those of CO2 and CH4. The results may contribute to better regulations for biochar application in combating GHG emissions in agriculture.
Correction: Spyridonidis et al. Effect of Zeolite on the Methane Production from Chicken Manure Leachate. Sustainability 2022, 14, 2207
Authors: Spyridonidis Apostolos , Vasiliadou Ioanna A. , Stamatelatou Κaterina
Published: September 2022

The authors would like to make the following correction to a typographical error in the published paper [...]
Developing Creative Leadership in the Use of Digital Communication Tools: A Psychological Perspective
Authors: Wang Yinan , Wang Yingchong
Published: September 2022

Creative organizations have shifted their daily communication to the virtual environment in response to COVID-19. This study examines how media use affects the communication effectiveness between creative leaders and followers from a psychological perspective by reviewing the comprehensive literature on creative leadership, media use, and psychological distance. A mixed-method methodology is employed to investigate ten common media configurations. The findings indicate that configured digital communication tools create certain levels of psychological distance between leaders and followers. Thus, it is crucial for creative leaders to match the psychological distance of different media and construal orientations of creative tasks to cultivate followers’ creativity. Using reflective interviews with creative professionals, we determined the impact of psychological distance on creative leadership from three aspects: task performance, member feelings perception and technology selection. We also identified several situations that might hinder creativity. To conclude, this study proposes a “Cognitive Model of Media Use Strategy in Creative Leadership”, which aims to bridge the gap between creative leadership theory and media use theory from the organizational psychology perspective. This study confirms that psychological distance exists in the digital environment, thus expanding the application scenarios of “Construal Level Theory” as well as providing practical implications for media selection in creative leading processes.
A Proposed Performance-Measurement System for Enabling Supply-Chain Strategies
Authors: Varadejsatitwong Paitoon , Banomyong Ruth , Julagasigorn Puthipong
Published: September 2022

Existing performance-measurement systems in the supply-chain literature have been designed for specific supply chains. Without a business-appropriate performance-measurement system, practitioners devise strategies that are neither scientific nor supported by data. The objective of this paper was to propose a performance-measurement system to support the enabling of supply-chain strategies. The proposed performance-measurement system (PMS), combining the Plan–Do–Check–Act cycle (PDCA) with the evidence-based management (EBM) concept, served as the basis for developing a procedural framework. The PMS was validated using the case logistics service providers (LSPs). The PDCA methodology was used to develop a structural framework for fourth-party logistics (4PL). In the Plan step, the research team identified the business problems of fourth-party logistics (4PL). In the Do step, the LSP literature was reviewed, to identify five performance dimensions (‘Service Quality’; ‘Social and Environmental’; ‘Inter-organizational Relationship’; ‘Financial’; and ‘Efficiency and Effectiveness’), and key performance indicators (KPIs). The 4PL management team participated in the finalization of the five performance dimensions and the 53 KPIs, which were used to propose a preliminary version of the structural framework for the 4PL. In the Check step, the data collected from 478 subcontractors of the 4PL were analyzed, using confirmatory factor analysis and structural equation modeling, and were used to validate the proposed structural framework for the 4PL. The validated structural framework was further presented at an academic conference, and to the 4PL for feedback, and was used to formulate supply-chain strategies through brainstorming. The findings include a validated structural framework containing five performance dimensions and 32 KPIs. The research revealed that input performance (‘Service Quality’, ‘Social and Environmental’, and ‘Inter-organizational Relationship’) positively affected output performance (‘Financial’ and ‘Efficiency and Effectiveness’). Supply-chain strategies were further suggested to the 4PL. The validating case in this study demonstrated that the employed procedural framework (PDCA and EBM) was applicable, and could be used to derive a structural framework and supply-chain strategies for the 4PL. This study contributes to the literature, by proposing a PMS for supply-chain strategy development. The paper’s illustrative case provides a practical application of how to develop a performance-measurement system.
Exploring the Potential of Tomato Processing Byproduct as a Natural Antioxidant in Reformulated Nitrite-Free Sausages
Authors: Cadariu Andreea I. , Cocan Ileana , Negrea Monica , Alexa Ersilia , Obistioiu Diana , Hotea Ionela , Radulov Isidora , Poiana Mariana-Atena
Published: September 2022

This study evaluated the potential of two dried processing by products, obtained from large and cherry tomatoes (LT and CT) after juice extraction to improve the oxidative stability of pork sausages during refrigerated storage for 20 days. For this purpose, reformulated nitrite-free sausages were manufactured by supplementation of raw sausage samples with dried large and cherry tomato processing byproducts (DLTB and DCTB) at a dose that provides a level of polyphenolic compounds equally with 50, 90, 180 and 270 mg gallic acid equivalents (GAE)/kg of processed meat. The developed sausage formulas were subjected to heat treatment, such as smoking and drying and, smoking and scalding, respectively. The reformulated nitrite-free sausages were compared with control samples of sausages with or without the addition of sodium nitrite. Large and cherry tomatoes and their raw and dried processing byproducts were investigated for total and individual polyphenols content as well as lycopene content. The sausage formulas were evaluated in terms of proximate composition. Additionally, the progress of lipid oxidation developed in sausage formulas was assessed by specific indices, such as peroxide value (PV), p-anisidine value (p-AV), TOTOX value and thiobarbituric acid reactive substances (TBA), after 1, 10 and 20 days of storage at 4 °C. Based on the values of PV, p-AV, TOTOX and TBA, it can be stated that the dried tomato processing byproducts applied at doses that ensure a level of polyphenolic compounds of at least 180 mg GAE/kg of processed meat for DCTB and 270 mg GAE/kg of processed meat for DLTB, showed promising potential to replace sodium nitrite in meat products for both dried and scalded sausage formulas. For the same dose of tomato processing byproducts, it was noted a stronger inhibitory effect against lipid oxidation in the case of smoked and scalded sausages compared to smoked and dried ones.
From Waste to Luxury Fashion at Elvis & Kresse: A Business Model for Sustainable and Social Innovation in the Circular Economy
Authors: Dominguez Maria Fernanda Ortiz , Bhatti Yasser Ahmad
Published: September 2022

Managing landfill waste is essential to sustainable resource utilization. With a focus on the luxury fashion industry, this paper highlights the role that sustainable and social innovations can play in reducing environmental waste and improving social outcomes. Elvis & Kresse is a purpose-driven case study, because it was founded to eradicate a problem that had thus far received little attention, namely the problem of disposing of end-of-life fire-hoses. From a business model and circular economy perspective, this article explains how the company’s rescue–transform–donate model has helped to build a sustainable and socially oriented yet profitable luxury brand. The analysis of the case suggests that for scholars, the typical business model canvas merits some revision beyond the current business focus on the financial bottom line to account for the social and ethical dimensions. For practitioners, this case demonstrates how the circular economy can be compatible with luxury fashion by turning waste into durable, fashionable products.
Systems Thinking for Sustainability Education in Building and Business Administration and Management Degrees
Authors: Marcos-Sánchez Rafael , Ferrández Daniel , Morón Carlos
Published: September 2022

In 2015, the United Nations set the 2030 Agenda, which established 17 Sustainable Development Goals (SDGs) including different areas—among them, Education—which determine the reality and the future of new generations. The data of SDG24 2021 establish that, by 2030, the reference values will be below those expressed in SDG 4. Education for sustainable development poses a series of competencies to achieve it; the challenge for teachers is to develop strategies, tools and methodologies that enable meaningful learning for the required complexity. Managing and solving sustainability problems requires people with systemic competences who are able to see the whole in the individuality, presenting a breadth of vision for a complex world. It is important that training takes place in universities so that students are equipped with a holistic understanding of systems. The aim of this research is to propose a didactic proposal for students of the double degree in Building and Business Administration and Management to acquire the competences of systems thinking, interdisciplinary work, personal and inter-collaborative relationships and tolerance to ambiguity and uncertainty using project-based learning and systems simulation. To achieve this, a classroom intervention proposal has been designed for the subjects Strategic Management and Sustainable Building in the fourth year of the double degree using the project-based learning and systems thinking. This practical proposal allows participants to develop a holistic view of the problems, enhancing the capacity for systemic and sustainable resolution in the design of more sustainable building systems, as well as promoting training in strategic decision making in environmental management in the medium and long term.
Were the Higher Education Institutions Prepared for the Challenge of Online Learning? Students’ Satisfaction Survey in the Aftermath of the COVID-19 Pandemic Outbreak
Authors: Ochnio Luiza , Rokicki Tomasz , Czech Katarzyna , Koszela Grzegorz , Hamulczuk Mariusz , Perkowska Aleksandra
Published: September 2022

The main purpose of the paper is to evaluate the online teaching process at universities, and detect shortages and gaps in online learning in the aftermath of the COVID-19 pandemic outbreak. The research results are the initial steps leading to the development of a guide and online open access tools supporting academic teachers in the efficient use of an online didactic process, which will be used broadly. The specific objectives are to identify differences in the assessment of online learning among students from different EU countries, to identify differences between face-to-face and online learning among students from the countries surveyed, to assess the attractiveness of online teaching to students in selected EU countries, and to determine the assessment of the preparation of university teachers for online teaching in the countries surveyed. Using a purposive selection method, five EU countries, i.e., Bulgaria, Greece, Italy, Poland, and Sweden, were selected for the study. The study is based on questionnaires. The survey was conducted among 809 student respondents representing the five countries. The study was carried out in 2021 and the research period was 2020–2021 (before and after pandemic restrictions). It was found that students evaluate online courses much better in countries/universities with a long tradition in online teaching and usage of interactive platforms, than in those without them. Students generally evaluated in-person learning better than online learning, and their evaluation depended on their previous experience of online learning. The degree of preparation for the online courses is strongly correlated with the overall evaluation of these courses. The attractiveness of the online courses is strongly conditioned by the content of engaging, involving and stimulating elements. There is the need for the development of interactive tools and training for teachers, which will increase the attractiveness of online classes for students.
A Review of Contemporary Governance Challenges in Oman: Can Blockchain Technology Be Part of Sustainable Solutions?
Authors: Goldsmith Leon , Shaikh Abdul Khalique , Tan Hacer Yildiz , Raahemifar Kaamran
Published: September 2022

Oman is considering adopting the latest e-governance technology, including Blockchain-based. While much research was conducted into the benefits and risks of Blockchain-based in information systems and finance fields, fewer researchers investigated the opportunities and risks associated with adopting Blockchain-based frameworks for governance and public administration, especially in highly bureaucratic, centralized rentier states, such as Oman. As the first phase of an exploratory sequential mixed-methods study, our purpose was to identify key governance problems in contemporary Oman and analyze each problem against evidence drawn from the relevant parts of the Blockchain-based and e-governance literature to evaluate the potential utility, risks and limitations associated with adopting block-chained e-governance solutions in the Sultanate. Our initial results indicate that there are advantages for states, such as Oman, from being an early mover into block-chained e-governance systems, including greater cost efficiency, drastically improved accuracy and reliability of information systems, transparency and accountability of public services, and an upgrade in the overall level of legitimacy and public trust in the institutions of governance. However, more research into the risks related to reconciling block-chained systems with the dynamics of labor, tax reforms and centralized authority in a rentier social contract is required.
Explainable Risk Assessment of Rockbolts’ Failure in Underground Coal Mines Based on Categorical Gradient Boosting and SHapley Additive exPlanations (SHAP)
Authors: Ibrahim Bemah , Ahenkorah Isaac , Ewusi Anthony
Published: September 2022

The occurrence of premature rockbolt failure in underground mines has remained one of the most serious challenges facing the industry over the years. Considering the complex mechanism of rockbolts’ failure and the large number of influencing factors, the prediction of rockbolts’ failure from laboratory testing may often be unreliable. It is therefore essential to develop new models capable of predicting rockbolts’ failure with high accuracy. Beyond the predictive accuracy, there is also the need to understand the decisions made by these models in order to convey trust and ensure safety, reliability, and accountability. In this regard, this study proposes an explainable risk assessment of rockbolts’ failure in an underground coal mine using the categorical gradient boosting (Catboost) algorithm and SHapley Additive exPlanations (SHAP). A dataset (including geotechnical and environmental features) from a complex underground mining environment was used. The outcomes of this study indicated that the proposed Catboost algorithm gave an excellent prediction of the risk of rockbolts’ failure. Additionally, the SHAP interpretation revealed that the “length of roadway” was the main contributing factor to rockbolts’ failure. However, conditions influencing rockbolts’ failure varied at different locations in the mine. Overall, this study provides insights into the complex relationship between rockbolts’ failure and the influence of geotechnical and environmental variables. The transparency and explainability of the proposed approach have the potential to facilitate the adoption of explainable machine learning for rockbolt risk assessment in underground mines.
Exploring Transport Consumption-Based Emissions: Spatial Patterns, Social Factors, Well-Being, and Policy Implications
Authors: Kilian Lena , Owen Anne , Newing Andy , Ivanova Diana
Published: September 2022

Recent years have seen an increased interest in demand-side mitigation of greenhouse gas emissions. Despite the oftentimes spatial nature of emissions research, links to social factors and infrastructure are often not analysed geographically. To reach substantial and lasting emission reductions without further disadvantaging vulnerable populations, the design of effective mitigation policies on the local level requires considerations of spatial and social inequalities as well as the context of well-being. Consequently, we explore spatial variations in the links between consumption-based transport emissions with infrastructural factors, such as workplace distance and public transport density, and with risk-factors of transport poverty, including income, age, ethnicity, mobility constraints in London. We find that linear models report significant spatial autocorrelation at p ≤ 0.01 in their model residuals, indicating spatial dependency. Using geographically weighted regression models improves model fits by an adjusted R2 value of 9–70% compared to linear models. Here, modelling flight emissions generally sees the lowest improvements, while those models modelling emissions from cars and vans see the highest improvements in model fit. We conclude that using geographically weighted regression to assess the links between social factors and emissions offers insights which global, linear models overlook. Moreover, this type of analysis enables an assessment of where, spatially, different types of policy interventions may be most effective in reducing not only emissions, but transport poverty risks. Patterns of spatial heterogeneity and policy implications of this research are discussed.
Hybrid Feature Selection of Breast Cancer Gene Expression Microarray Data Based on Metaheuristic Methods: A Comprehensive Review
Authors: Mohd Ali Nursabillilah , Besar Rosli , Ab. Aziz Nor Azlina
Published: September 2022

Breast cancer (BC) remains the most dominant cancer among women worldwide. Numerous BC gene expression microarray-based studies have been employed in cancer classification and prognosis. The availability of gene expression microarray data together with advanced classification methods has enabled accurate and precise classification. Nevertheless, the microarray datasets suffer from a large number of gene expression levels, limited sample size, and irrelevant features. Additionally, datasets are often asymmetrical, where the number of samples from different classes is not balanced. These limitations make it difficult to determine the actual features that contribute to the existence of cancer classification in the gene expression profiles. Various accurate feature selection methods exist, and they are being widely applied. The objective of feature selection is to search for a relevant, discriminant feature subset from the basic feature space. In this review, we aim to compile and review the latest hybrid feature selection methods based on bio-inspired metaheuristic methods and wrapper methods for the classification of BC and other types of cancer.
Initial Work on the Development of a Hardware-Based Gradient Descent Trained Expert System
Authors: Ahmed Fateen , Straub Jeremy
Published: September 2022

Prior work has introduced a form of explainable artificial intelligence that is able to precisely explain, in a human-understandable form, why it makes decisions. It is also able to learn to make better decisions without potentially learning illegal or invalid considerations. This defensible system is based on fractional value rule-fact expert systems and the use of gradient descent training to optimize rule weightings. This software system has demonstrated efficacy for many applications; however, it utilizes iterative processing and thus does not have a deterministic completion time. It also requires comparatively expensive general-purpose computing hardware to run on. This paper builds on prior work in the development of hardware-based expert systems and presents and assesses the efficacy of a hardware implementation of this system. It characterizes its performance and discusses its utility and trade-offs for several application domains.
Critical Consideration of Tuberculosis Management of Papua New Guinea Nationals and Cross-Border Health Issues in the Remote Torres Strait Islands, Australia
Authors: Foster J’Belle , Mendez Diana , Marais Ben J. , Denholm Justin T. , Peniyamina Dunstan , McBryde Emma S.
Published: September 2022

The international border between Australia and Papua New Guinea (PNG) serves as a gateway for the delivery of primary and tertiary healthcare for PNG patients presenting to Australian health facilities with presumptive tuberculosis (TB). An audit of all PNG nationals with presumptive TB who presented to clinics in the Torres Strait between 2016 and 2019 was conducted to evaluate outcomes for PNG patients and to consider the consistency and equity of decision-making regarding aeromedical evacuation. We also reviewed the current aeromedical retrieval policy and the outcomes of patients referred back to Daru General Hospital in PNG. During the study period, 213 PNG nationals presented with presumptive TB to primary health centres (PHC) in the Torres Strait. In total, 44 (21%) patients were medically evacuated to Australian hospitals; 26 met the evacuation criteria of whom 3 died, and 18 did not meet the criteria of whom 1 died. A further 22 patients who met the medical evacuation criteria into Australia were referred to Daru General Hospital of whom 2 died and 10 were lost to follow-up. The cross-border movement of people from PNG into Australia is associated with an emergent duty of care. Ongoing monitoring and evaluation of patient outcomes are necessary for transparency and justice.
Compact Lie Groups, Generalised Euler Angles, and Applications
Authors: Cacciatori Sergio Luigi , Scotti Antonio
Published: September 2022

This is mainly a review of an intense 15-year long collaboration between the authors on explicit realisations of compact Lie groups and their applications. Starting with an elementary example, we will illustrate the main idea at the foundation of the generalisation of the Euler parametrisation of SU(2) to any compact Lie group. Based on this, we will provide a very detailed reconstruction of the possible Euler parametrisation associated with the so-called symmetric embedding. Then, we will recall how such constructions are related to the Dyson integrals, providing a geometrical interpretation of the latter, at least in certain cases. This includes a short review on the main properties of simple Lie groups, algebras, and their representations. Finally, we will conclude with some applications to nuclear physics and to measure theory in infinite dimensions and discuss some open questions.
Zika Virus Exploits Lipid Rafts to Infect Host Cells
Authors: Peruzzu Daniela , Amendola Antonello , Venturi Giulietta , de Turris Valeria , Marsili Giulia , Fortuna Claudia , Fecchi Katia , Gagliardi Maria Cristina
Published: September 2022

Several flaviviruses such as Hepatitis C virus, West Nile virus, Dengue virus and Japanese Encephalitis virus exploit the raft platform to enter host cells whereas the involvement of lipid rafts in Zika virus–host cell interaction has not yet been demonstrated. Zika virus disease is caused by a flavivirus transmitted by Aedes spp. Mosquitoes, although other mechanisms such as blood transfusion, sexual and maternal–fetal transmission have been demonstrated. Symptoms are generally mild, such as fever, rash, joint pain and conjunctivitis, but neurological complications, including Guillain-Barré syndrome, have been associated to this viral infection. During pregnancy, it can cause microcephaly and other congenital abnormalities in the fetus, as well as pregnancy complications, representing a serious health threat. In this study, we show for the first time that Zika virus employs cell membrane lipid rafts as a portal of entry into Vero cells. We previously demonstrated that the antifungal drug Amphotericin B (AmphB) hampers a microbe–host cell interaction through the disruption of lipid raft architecture. Here, we found that Amphotericin B by the same mechanism of action inhibits both Zika virus cell entry and replication. These data encourage further studies on the off-label use of Amphotericin B in Zika virus infections as a new and alternate antiviral therapy.