bluuebunny/crossref_metadata_2025 huggingface dataset:
The Interplay of Protein Hydrolysis and Ammonia in the Stability of Hevea Rubber Latex during Storage
Authors: Payungwong Narueporn , Sakdapipanich Jitladda , Wu Jinrong , Ho Chee-Cheong
Published: December 2023

Natural rubber (NR) latex derived from Hevea brasiliensis is a complex colloid comprising mainly rubber hydrocarbons (latex particles) and a multitude of minor non-rubber constituents such as non-rubber particles, proteins, lipids, carbohydrates, and soluble organic and inorganic substances. NR latex is susceptible to enzymatic attack after it leaves the trees. It is usually preserved with ammonia and, to a lesser extent, with other preservatives to enhance its colloidal stability during storage. Despite numerous studies in the literature on the influence of rubber proteins on NR latex stability, issues regarding the effect of protein hydrolysis in the presence of ammonia on latex stability during storage are still far from resolved. The present work aims to elucidate the interplay between protein hydrolysis and ammoniation in NR latex stability. Both high- and low-ammonia (with a secondary preservative) NR latexes were used to monitor the changes in their protein compositions during storage. High-ammonia (FNR-A) latex preserved with 0.6% (v/v) ammonia, a low 0.1% ammonia/TMTD/ZnO (FNR-TZ) latex, and a deproteinized NR (PDNR) latex were labeled with fluorescence agents and observed using confocal laser scanning microscopy to determine their protein composition. Protein hydrolysis was confirmed via sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE). The results revealed that protein hydrolysis increased with the storage duration. The change in protein composition accompanying hydrolysis also allows the spatial distribution of allergenic proteins to be estimated in the latex. Concurrently, the latex stability increased with the storage duration, as measured by the latex’s mechanical stability time (MST) and the zeta potential of the latex particles. As monitored by AFM, the surface roughness of the NR latex film increased markedly during extended storage compared with that of the DPNR latex, which remained smooth. These results underscore the pivotal role of ammonia in bolstering NR latex stability brought on by protein hydrolysis, which greatly impacts latex film’s formation behavior. NR latex stability underpins the quality of latex-dipped goods during manufacturing, particularly those for medical gloves.
Shakespeare’s Bookish Rulers: Philosophy and Nature Poetry in the Henry VI Trilogy and The Tempest
Authors: Farkas Aviva
Published: December 2023

Shakespeare’s early Henry VI trilogy and late The Tempest both feature reclusive, bookish rulers who are deposed because their rivals perceive an opportunity in the rulers’ trustingness and lack of interest in political affairs. Furthermore, the deposed rulers also share an interest in Platonic philosophies of the Renaissance; they differ, however, in their respective preferences for particular Platonist authors and writings. Henry VI is devoted to Boethius’s The Consolation of Philosophy. While Prospero, the protagonist of The Tempest, may have focused on Boethius and similar authors when he was in Milan, by the time we meet him on his island, he prefers Neoplatonic magic, bequeathed to the Renaissance by Ficino. While the two stories are not often read together, I argue that doing so yields a fascinating contrast in the modes of existence dictated by different streams of Renaissance philosophical thought. While Henry VI’s credulity and Boethianism lead him to express a preference for a contemplative life and to adopt an attitude of extreme passivity and surrender, Prospero’s suspicion and powerful use of magic associate him with the active life. The ultimate expressions of Henry’s preference for the contemplative life and of Prospero’s association with the active life both involve nature poetry. Henry expresses yearning for the peaceful lifestyle of a shepherd in a pastoral lyric he delivers in 3 Henry VI, while Prospero celebrates human labor and achievement in a georgic masque which he produces.
Robot Learning by Demonstration with Dynamic Parameterization of the Orientation: An Application to Agricultural Activities
Authors: Lauretti Clemente , Tamantini Christian , Tomè Hilario , Zollo Loredana
Published: December 2023

This work proposes a Learning by Demonstration framework based on Dynamic Movement Primitives (DMPs) that could be effectively adopted to plan complex activities in robotics such as the ones to be performed in agricultural domains and avoid orientation discontinuity during motion learning. The approach resorts to Lie theory and integrates into the DMP equations the exponential and logarithmic map, which converts any element of the Lie group SO(3) into an element of the tangent space so(3) and vice versa. Moreover, it includes a dynamic parameterization for the tangent space elements to manage the discontinuity of the logarithmic map. The proposed approach was tested on the Tiago robot during the fulfillment of four agricultural activities, such as digging, seeding, irrigation and harvesting. The obtained results were compared to the one achieved by using the original formulation of the DMPs and demonstrated the high capability of the proposed method to manage orientation discontinuity (the success rate was 100 % for all the tested poses).
Semantic-Layout-Guided Image Synthesis for High-Quality Synthetic-Aperature Radar Detection Sample Generation
Authors: Kuang Yi , Ma Fei , Li Fangfang , Liu Yingbing , Zhang Fan
Published: December 2023

With the widespread application and functional complexity of deep neural networks (DNNs), the demand for training samples is increasing. This elevated requirement also extends to DNN-based SAR object detection. Most public SAR object detection datasets are oriented to marine targets such as ships, while data sets oriented to land targets are relatively rare, though they are an effective way to improve the land object detection capability of deep models through SAR sample generation. In this paper, a synthesis generation collaborative SAR sample augmentation framework is proposed to achieve flexible and diverse high-quality sample augmentation. First, a semantic-layout-guided image synthesis strategy is proposed to generate diverse detection samples. The issues of object location rationality and object layout diversity are also addressed. Meanwhile, a pix2pixGAN network guided by layout maps is utilized to achieve diverse background augmentation. Second, a progressive training strategy of diffusion models is proposed to achieve semantically controllable SAR sample generation to further improve the diversity of scene clutter. Finally, a sample cleaning method considering distribution migration and network filtering is employed to further improve the quality of detection samples. The experimental results show that this semantic synthesis generation method can outperform existing sample augmentation methods, leading to a comprehensive improvement in the accuracy metrics of classical detection networks.
Training Methods of Multi-Label Prediction Classifiers for Hyperspectral Remote Sensing Images
Authors: Haidar Salma , Oramas José
Published: December 2023

Hyperspectral remote sensing images, with their amalgamation of spectral richness and geometric precision, encapsulate intricate, non-linear information that poses significant challenges to traditional machine learning methodologies. Deep learning techniques, recognised for their superior representation learning capabilities, exhibit enhanced proficiency in managing such intricate data. In this study, we introduce a novel approach in hyperspectral image analysis focusing on multi-label, patch-level classification, as opposed to applications in the literature concentrating predominantly on single-label, pixel-level classification for hyperspectral remote sensing images. The proposed model comprises a two-component deep learning network and employs patches of hyperspectral remote sensing scenes with reduced spatial dimensions yet with a complete spectral depth derived from the original scene. Additionally, this work explores three distinct training schemes for our network: Iterative, Joint, and Cascade. Empirical evidence suggests the Joint approach as the optimal strategy, but it requires an extensive search to ascertain the optimal weight combination of the loss constituents. The Iterative scheme facilitates feature sharing between the network components from the early phases of training and demonstrates superior performance with complex, multi-labelled data. Subsequent analysis reveals that models with varying architectures, when trained on patches derived and annotated per our proposed single-label sampling procedure, exhibit commendable performance.
A Green Fingerprint of Antarctica: Drones, Hyperspectral Imaging, and Machine Learning for Moss and Lichen Classification
Authors: Sandino Juan , Bollard Barbara , Doshi Ashray , Randall Krystal , Barthelemy Johan , Robinson Sharon A. , Gonzalez Felipe
Published: December 2023

Mapping Antarctic Specially Protected Areas (ASPAs) remains a critical yet challenging task, especially in extreme environments like Antarctica. Traditional methods are often cumbersome, expensive, and risky, with limited satellite data further hindering accuracy. This study addresses these challenges by developing a workflow that enables precise mapping and monitoring of vegetation in ASPAs. The processing pipeline of this workflow integrates small unmanned aerial vehicles (UAVs)—or drones—to collect hyperspectral and multispectral imagery (HSI and MSI), global navigation satellite system (GNSS) enhanced with real-time kinematics (RTK) to collect ground control points (GCPs), and supervised machine learning classifiers. This workflow was validated in the field by acquiring ground and aerial data at ASPA 135, Windmill Islands, East Antarctica. The data preparation phase involves a data fusion technique to integrate HSI and MSI data, achieving the collection of georeferenced HSI scans with a resolution of up to 0.3 cm/pixel. From these high-resolution HSI scans, a series of novel spectral indices were proposed to enhance the classification accuracy of the model. Model training was achieved using extreme gradient boosting (XGBoost), with four different combinations tested to identify the best fit for the data. The research results indicate the successful detection and mapping of moss and lichens, with an average accuracy of 95%. Optimised XGBoost models, particularly Model 3 and Model 4, demonstrate the applicability of the custom spectral indices to achieve high accuracy with reduced computing power requirements. The integration of these technologies results in significantly more accurate mapping compared to conventional methods. This workflow serves as a foundational step towards more extensive remote sensing applications in Antarctic and ASPA vegetation mapping, as well as in monitoring the impact of climate change on the Antarctic ecosystem.
Cloud-Type Classification for Southeast China Based on Geostationary Orbit EO Datasets and the LighGBM Model
Authors: Lin Jianan , Bao Yansong , Petropoulos George P. , Mehraban Abouzar , Pang Fang , Liu Wei
Published: December 2023

The study of clouds and their characteristics provides important information for understanding climate change and its impacts as it provides information on weather conditions and forecasting. In this study, Earth observation (EO) data from the FY4A AGRI and Himawari-8 CLP products were used to classify and identify distinct cloud types in southeastern China. To reduce the impact of parallax between geostationary satellites, we proposed adopting a sliding detection method for quality control of cloud-type data. Additionally, the Bayesian optimization method was employed herein to tune the hyperparameters of the LightGBM model. Our study results demonstrated that Bayesian optimization significantly increased model performance, resulting in successful cloud-type classification and identification. The simultaneous use of visible and shortwave infrared channels, and brightness temperature difference channels, enhanced the model’s classification performance. Those channels accounted for 43.79% and 21.84% of the overall features, respectively. Certainly, the model in this study outperformed compared with the traditional thresholding method (TT), support vector machine (SVM), and random forest (RF). Results showed a model prediction accuracy of 97.54%, which was higher than that of TT (51.06%), SVM (96.47%), and RF (97.49%). Additionally, the Kappa coefficient of the model was 0.951, indicating the model’s classification results were consistent with the true values. Notably, this performance also surpassed TT (0.351), SVM (0.929), and RF (0.950).
A First Step towards Meteosat Third Generation Day-2 Precipitation Rate Product: Deep Learning for Precipitation Rate Retrieval from Geostationary Infrared Measurements
Authors: D’Adderio Leo Pio , Casella Daniele , Dietrich Stefano , Panegrossi Giulia , Sanò Paolo
Published: December 2023

The estimate of precipitation from satellite measurements is an indirect estimate if compared to rain gauges or disdrometer measurements, but it has the advantage of complete coverage over oceans, mountainous regions, and sparsely populated areas where other sources of precipitation data (e.g., weather radar) are unavailable or unreliable. Among the satellite-based precipitation estimates, geostationary (GEO) data ensure the highest spatial and temporal resolution. At the same time, the IR/VIS channels deployed on GEO satellites have lower capabilities than microwave (MW) channels in characterizing the cloud structure. Machine learning (ML) techniques can be considered a powerful tool to overcome the limitations related to the physical relationship between IR/VIS channels and precipitation estimation. This study describes the development of a convolutional neural network (U-Net) to retrieve the precipitation rate using IR measurements only from the Meteosat Second Generation (MSG) satellite. Its performances are evaluated through a comparison with H SAF and NASA operational products (e.g., H60B or H03B and IMERG-E, respectively), of which the algorithms are based on different principles. The results highlight a lower error in precipitation rate estimates for the U-Net with respect to the other products but also some issues in correctly estimating the more intense precipitation (>5 mmh−1). On the other hand, the precipitation detection capabilities of the U-Net outperform the H SAF products for lower precipitation rate, while IMERG-E shows the best performance regardless of the precipitation regime. Furthermore, the U-Net is able to account for and correct the parallax displacement that affects the measurement as the satellite viewing angle increases.
Vertically Resolved Global Ocean Light Models Using Machine Learning
Authors: Renosh Pannimpullath Remanan , Zhang Jie , Sauzède Raphaëlle , Claustre Hervé
Published: December 2023

The vertical distribution of light and its spectral composition are critical factors influencing numerous physical, chemical, and biological processes within the oceanic water column. In this study, we present vertically resolved models of downwelling irradiance (ED) at three different wavelengths and photosynthetically available radiation (PAR) on a global scale. These models rely on the SOCA (Satellite Ocean Color merged with Argo data to infer bio-optical properties to depth) methodology, which is based on an artificial neural network (ANN). The new light models are trained with light profiles (ED/PAR) acquired from BioGeoChemical-Argo (BGC-Argo) floats. The model inputs consist of surface ocean color radiometry data (i.e., Rrs, PAR, and kd(490)) derived by satellite and extracted from the GlobColour database, temperature and salinity profiles originating from BGC-Argo, as well as temporal components (day of the year and local time in cyclic transformation). The model outputs correspond to ED profiles at the three wavelengths of the BGC-Argo measurements (i.e., 380, 412, and 490 nm) and PAR profiles. We assessed the retrieval of light profiles by these light models using three different datasets: BGC-Argo profiles that were not used for the training (i.e., 20% of the initial database); data from four independent BGC-Argo floats that were used neither for the training nor for the 20% validation dataset; and the SeaBASS database (in situ data collected from various oceanic cruises). The light models show satisfactory predictions when thus compared with real measurements. From the 20% validation database, the light models retrieve light variables with high accuracies (root mean squared error (RMSE)) of 76.42 μmol quanta m−2 s−1 for PAR and 0.04, 0.08, and 0.09 W m−2 nm−1 for ED380, ED412, and ED490, respectively. This corresponds to a median absolute percent error (MAPE) that ranges from 37% for ED490 and PAR to 39% for ED380 and ED412. The estimated accuracy metrics across these three validation datasets are consistent and demonstrate the robustness and suitability of these light models for diverse global ocean applications.
Comparative Analysis of Resident Space Object (RSO) Detection Methods
Authors: Suthakar Vithurshan , Sanvido Aiden Alexander , Qashoa Randa , Lee Regina S. K.
Published: December 2023

In recent years, there has been a significant increase in satellite launches, resulting in a proliferation of satellites in our near-Earth space environment. This surge has led to a multitude of resident space objects (RSOs). Thus, detecting RSOs is a crucial element of monitoring these objects and plays an important role in preventing collisions between them. Optical images captured from spacecraft and with ground-based telescopes provide valuable information for RSO detection and identification, thereby enhancing space situational awareness (SSA). However, datasets are not publicly available due to their sensitive nature. This scarcity of data has hindered the development of detection algorithms. In this paper, we present annotated RSO images, which constitute an internally curated dataset obtained from a low-resolution wide-field-of-view imager on a stratospheric balloon. In addition, we examine several frame differencing techniques, namely, adjacent frame differencing, median frame differencing, proximity filtering and tracking, and a streak detection method. These algorithms were applied to annotated images to detect RSOs. The proposed algorithms achieved a competitive degree of success with precision scores of 73%, 95%, 95%, and 100% and F1 scores of 68%, 77%, 82%, and 79%.
Effects of Drainage on Carbon Stock in Hemiboreal Forests: Insights from a 54-Year Study
Authors: Dubra Stefānija , Samariks Valters , Līcīte Ieva , Butlers Aldis , Purviņa Dana , Lupiķis Ainārs , Jansons Āris
Published: December 2023

In the Northern Hemisphere, forests play an important role in carbon storage. During the past few decades in the eastern Baltic and Nordic regions, forest drainage has been a common occurrence, which also has an effect on carbon stock. Most of the studies on this issue were carried out in boreal zones and were focused on short-term effects. Thus, our aim was to evaluate the long-term (after 54 years) effect of drainage on carbon stock (CS) changes in organic soil (Fibric histosols) in hemiboreal forests. Three forest types were selected in drained (Myrtillosa turf. mel (Mmel)) and undrained (Caricoso–phragmitosa (CP) and Sphagnosa (Sph)) parts of the same area. Surface level changes, soil penetration resistance, and soil and tree biomass carbon stock were assessed to evaluate the drainage effect. Drainage caused an average surface level drop of 25 cm, but did not deplete the soil carbon pool, resulting in significantly and substantially higher (2 to 6 times) tree biomass carbon stock. The drainage of organic soils in managed wet forests leads to an increased long-term contribution to climate change mitigation, thus such areas should be established or maintained in conjunction with areas that maximize other ecosystem services to ensure the sustainability of forest landscapes.
Ecological Impact of Hydraulic Dredging from an Alpine Reservoir on the Downstream River
Authors: Quadroni Silvia , Crosa Giuseppe , Gentili Gaetano , Doretto Alberto , Talluto Niccolò , Servanzi Livia , Espa Paolo
Published: December 2023

The evacuation of impounded sediments is one of the most critical aspects associated with reservoirs, with possible drawbacks on the water quality, biodiversity, and ecosystem integrity of downstream river reaches. In this study, the impacts of hydraulic dredging at the Ambiesta Reservoir (Eastern Italian Alps) on the physical habitat and the biological communities (i.e., benthic macroinvertebrates and fish) of the downstream river were assessed by comparing the pre-dredging conditions with data collected on three post-dredging occasions. The dredging operation lasted 68 days and removed an overall sediment volume of 30,600 m3. During this operation, suspended sediment concentration (SSC) was monitored by turbidimeters and, on average, it was considerably lower than the SSC limit of 1.5 g/L, which exceeded approximately 15% of the overall operation time. Additionally, the dredging operation resulted in negligible deposition of fine sediment on/into the riverbed (0.24–0.7 kg/m2). Results for fish and benthic macroinvertebrate communities indicated weak differences in the density (~20% reduction) and diversity of these organisms between pre- and post-dredging sampling occasions. Moreover, the results on the biomonitoring indices based on macroinvertebrates showed a recovery during the last two sampling occasions. Compliance with the SSC limit and avoidance of high SSC peaks, along with limited fine sediment deposition, allowed to successfully mitigate the ecological impacts of this relatively long operation of sediment removal.
Autonomous Vehicles as Viewed by Future Users in Poland
Authors: Bęczkowska Sylwia , Czerepicki Andrzej , Grabarek Iwona , Kozłowski Maciej , Zysk Zuzanna
Published: December 2023

The article discusses the issue of how potential users perceive automated and autonomous vehicle (AV) technology, emphasizing that its sophistication and reliability are crucial for the safety of driving vehicles with a level of automation starting from the third degree. Safety plays a significant role in determining public acceptance of autonomous vehicles. Without the acceptance of potential users and their willingness to use smart electric vehicles, it is challenging to talk about the rapid implementation of autonomous vehicles, despite their undeniable benefits. The article presents an analysis of survey results regarding public opinion in Poland on this matter. The survey utilized various methods, including CAWI surveys, Focus Group Interviews (FGI), and Individual In-depth Interviews (IDI). The CAWI survey involved 1067 respondents in Poland, with a 95% confidence level and a maximum margin of error of 3%, representing a representative sample of the Polish population aged 16 and above. The article examines issues that illustrate respondents’ acceptance levels concerning perceived security and trust in AVs. The obtained results are compared with opinions from surveys conducted in other countries. Additionally, the article attempts to identify reasons for the low assessment of the technologies among respondents and proposes measures to boost confidence in automated and autonomous vehicles.
Box–Behnken Design to Optimize Standardized Mangiferin-Rich Mango Peel Extract from Agro-Industrial Waste Product
Authors: Sumpavapol Punnanee , Waehayee Aenna , Suklim Paranee , Rachpirom Mingkwan , Puttarak Panupong
Published: December 2023

The light mango or “Ma-Muang Bao” (Mangifere indica L. var.) is a native mango species originating from Malaysia and southern Thailand. The whole Ma-Muang Bao fruit, except peels, is popular as both a raw and ripe fruit for consumption, as well as being used in various processed food products. This study aims to transform the peel of a specific mango variety, which is a byproduct of the agro-industrial sweet, pickled mangoes industry, into a valuable bioactive ingredient for healthcare products. This is achieved through the establishment of a standardized mangiferin-rich mango peel extract (SMPE). Employing the Box–Behnken design (BBD) within the framework of response surface methodology (RSM), an optimal microwave-assisted extraction procedure was developed. A total of 27 experiments, each with four independent variables, including solvent ratio, extraction power, extraction time, and ethanol (EtOH) ratio, were conducted to optimize the extraction method in terms of mangiferin content and extraction yield. The optimized extraction conditions encompassed a solvent ratio of 120 mL EtOH/100 g sample, an extraction power of 450 W, an extraction time of approximately 4.3 min, and an EtOH ratio of 69.44% (EtOH in water). Small-scale extractions were carried out using the following specified parameters: solvent ratio of 120 mL, extraction power of 450 W, extraction time of 4 min, and EtOH ratio of 70% EtOH. These extractions yielded an extract with a mangiferin content of 27.24 ± 2.05 mg/g and an extraction yield of 3.71 ± 0.17% w/w. Notably, these outcomes were better from the mangiferin content of 19.62 mg/g and a yield of fresh peel of 5.61% estimated through BBD analysis. Furthermore, a pilot-scale extraction was performed using 7 kg of fresh mango peel and 70% EtOH (8.4 L) for 4 min, resulting in an extract with a mangiferin content of 51.85 ± 0.35 mg/g and a fresh peel yield of 4.35% w/w. This method emerges as the most suitable for mango peel extraction and forms the basis of the SMPE. The results from biological activities highlight the potential use of SMPE as the active ingredient for cosmeceutical or healthcare products for wound-healing and skin-brightening agents. Additionally, the knowledge from this study presents an alternative approach to various plant sources and sustainable extraction methods for the herbal extract industry.
Trends and Challenges in Railway Sustainability: The State of the Art regarding Measures, Strategies, and Assessment Tools
Authors: Giunta Marinella
Published: December 2023

Rail is expected to become the backbone of future mobility in the world as the cleanest and greenest high-volume transport. Rail generates the lowest CO2 emissions and energy consumption when in operation, with respect to the other transportation modes, but during construction and maintenance phases, its environmental impacts are significant and need to be carefully assessed and properly mitigated. This paper, through an extensive analysis of the recent literature, aims to provide a comprehensive framework of trends and challenges in railway sustainability, with particular attention paid to track and related materials and components, maintenance strategies, and methods of assessment of sustainability. The followed approach takes into consideration the lifespan of the track and the related main stages. The results show that: (i) several innovative sustainable materials have been introduced with significant environmental performances and limitations, mainly due to the lack of knowledge of long-term mechanical behavior; (ii) appropriate strategies of maintenance, supported by effective monitoring of the track conditions, can reduce negative effects on the environment and society and contribute to making this transportation mode greener; (iii) many devices for the automated detection of the track defects allow increasingly widespread and effective monitoring of the track and are essential means in overcoming the challenge of “smart rails”; and (iv) life cycle assessment (LCA) and circularity metrics are effective and indispensable tools in the decision-making process, since they help to quantify the potential environmental enhancement of different materials and solutions.
Methane Exchange Flux Monitoring between Potential Source Sewage Inspection Wells and the Atmosphere Based on Laser Spectroscopy Method
Authors: Wang Yihao , Zhao Xiande , Dong Daming , Zhao Chunjiang , Bao Feng , Guo Rui , Zhu Fangxu , Jiao Leizi
Published: December 2023

Greenhouse gas emissions are changing the climate and affecting human activities. In cities, the anaerobic environment in sewage inspection wells produces CH4, which is exchanged with the atmosphere and causes pollution. Moreover, although the number of inspection wells has increased with the development of cities, people have not paid enough attention to this potential source of CH4 and ignored it in the statistics of greenhouse gas inventories. Conventional gas monitoring methods like gas chromatography are complex and expensive. Based on the portable TDLAS CH4 sensor developed by our team, combined with a gas velocity analyser, we realised in situ continuous flux monitoring. We corrected the effect of temperature on the results according to the theory of infrared thermometry. We showed that the measurement results of the sensor fluctuate within the range of ±0.1 ppm of the measured standard gas concentration. We also verified its repeatability and ensured its reliability in field applications by comparing its results with the results of gas chromatography analysis. In addition to flux monitoring, based on the monitoring data of 56 inspection wells in the study area, the average concentration was estimated using the Bootstrap method, and combined with the average value of gas velocity, the average flux was estimated to be 2.19 × 10−6 mol/s, and the daily exchange mass was 3.03 g CH4 d−1. Combined with information such as the length of sewage pipes, we estimate that the annual CH4 exchange mass in the city is about 5.49 × 105 kg CH4 yr−1. This monitoring method will help us to understand climate change and improve greenhouse gas inventories.
Efficiency of Low-Carbon Technologies Implementation at Non-Ferrous Metallurgy Enterprises under the Conditions of Carbon-Regulation Development in Russia
Authors: Cherepovitsyn Alexey , Stroykov Gennady , Nevolin Alexander
Published: December 2023

Carbon markets are highly relevant to the need to regulate man-made greenhouse gas emissions. As the world faces the dangerous effects of global warming, reducing greenhouse gas emissions has become a critical priority for governments, corporations and individuals around the world. Carbon markets offer a way to incentivize and encourage emissions reductions and facilitate a smooth transition to a low-carbon economy. Low-carbon development is possible by building transparent and understandable organizational and economic conditions for the operation of carbon regulation. This article considers the feasibility of introducing energy-efficient technologies at Polymetal International PLC enterprises located in Khabarovsk region, engaged in the mining and processing of non-ferrous and precious metals (gold, silver and copper) in light of the country’s growing attention to the regulation of carbon dioxide emissions. The objective of this study is to model the organizational and economic conditions of the carbon market and estimate the economic and climate outcomes from the implementation of energy-efficient technologies at Polymetal International PLC. This study analyzes the current energy-consumption structure of non-ferrous metallurgy enterprises in Russia and identifies potential areas for the use of energy-efficient technologies. An important aspect is the assessment of government policies and incentives that could facilitate the introduction of low-carbon technologies. It is important to assess the impact of prospective carbon-management plans in Russia on the economic performance of steel companies. The results of this study suggest that the introduction of low-carbon technologies has the potential to significantly reduce energy consumption, lower operating costs and reduce carbon dioxide emissions from non-ferrous metal companies in Russia. However, the successful implementation of these technologies will require significant investment, stakeholder cooperation and political support from the Russian government.
Unveiling the Influence: Exploring the Impact of Interrelationships among E-Commerce Supply Chain Members on Supply Chain Sustainability
Authors: Sindakis Stavros , Showkat Saloome , Su Jiafu
Published: December 2023

This study explores the critical role of interrelationships among e-commerce supply chain members in shaping sustainability outcomes. It adopts a qualitative approach, drawing from Sustainable Supply Chain Management (SSCM) and Resource Dependency Theory (RDT) to gain a deeper understanding of sustainability within e-commerce supply chains. In a comprehensive investigation involving 35 key stakeholders from prominent e-commerce companies in China, such as Amazon, Alibaba, Suning.com, Shein, and Wayfair, this research examines how robust interrelationships, characterized by collaboration, knowledge exchange, trust-building, and joint problem-solving, facilitate efficient resource utilization, innovation, waste reduction, and enhanced social and environmental responsibility throughout the supply chain. The findings underline the practical implications for supply chain managers and practitioners, emphasizing the need to foster these robust interrelationships through effective communication, trust-building, collaboration, and information sharing as tangible strategies to elevate sustainability performance and gain a competitive edge in the dynamic e-commerce landscape. The insights are based on structured, in-depth interviews conducted in English with participants familiar with the language, lasting approximately 35 to 55 min.
The Impact of Higher Education on Health Behavior during the COVID-19 Pandemic: Evidence from China
Authors: Liu Shanshan , Zhang Yi , Liu Haijing , Yan Cheng
Published: December 2023

The COVID-19 pandemic and its associated illness significantly damage people’s physical and mental health. Previous studies focus on the role of strong government public-health management to improve people’s health conditions. The objective of this study is to explore the specific impact of higher education on health behavior and pandemic policy, which remains relatively unexplored. Using data from the 2021 Chinese Social Survey, we employ an OLS regression to examine the effect of education on the change in health behaviors. Then, we use China’s higher education expansion as an instrumental variable to mitigate the potential endogeneity. The results reveal a positive relationship between higher education and health behaviors, both due to improved socioeconomic conditions and enhanced psychological wellbeing. Additionally, we find that the health return of higher education differs significantly between rural and urban areas, suggesting the presence of education inequality. These findings underscore the importance of higher education and individual health behavior in controlling the spread of major pandemics, emphasizing the need for public policy to consider the role of higher education in pandemic management.
Exploring the Impact of the Sustainable Development Goals on Sustainability Trends
Authors: Ordonez-Ponce Eduardo
Published: December 2023

The SDGs have made a significant contribution to the sustainability movement, being used by many organisations from across sectors all over the world as their sustainability framework. However, have they impacted the previous trend of sustainability challenges just because of their existence? This article aims to contribute to answering this question by statistically comparing the trends in the sustainability performance of the SDGs before and after they were launched in 2015. Data were collected for every SDG and their trends were quantitatively assessed using non-parametric tests, finding that most of the SDGs have not significantly improved and that most of the sustainability indicators are still performing poorly in developing countries. While this research is exploratory and does not assess the direct impact of the SDGs on sustainability, it suggests that for the most part, the SDGs have not significantly changed sustainability trends since they were launched in 2015, which is a concerning finding. This article should serve as a wake-up call to design more suitable sustainability frameworks as the SDGs expire in 2030, and for those using them to be more critical of their reach rather than being satisfied with a framework that although helping will not achieve its main goal.
Sustainable Consumer Behavior: The Driving Force of Innovation in Retail
Authors: Šálková Daniela , Hes Aleš , Kučera Petr
Published: December 2023

The coronavirus COVID-19 pandemic was an unprecedented shock to the world economy. Consumer purchase of a country’s brand and the products manufactured in their own country plays a vital role in that country’s GDP, and helps revive the country’s economy. This study focuses on understanding the factors that influence consumers’ buying behavior, and on modeling these factors to understand the causal relationship, using partial least squares structural equation modeling. The data analysis is based on the online survey carried out between 30 March 2020 and 18 April 2020. A total of 367 responses were collected during this period. The study’s findings indicate that there are changes in consumer buying behavior, and the factors that are decisive for purchasing decisions in times of social crisis are changing. Therefore, the purpose of the study is to understand the impact of the COVID-19 pandemic on consumers’ lives and the changes in their behavior during and after the crisis. Consumers’ attitudes towards traditionalism and retail innovation are essential for the sustainable development of consumer behavior. Under the influence of change and shopping constraints, unplanned- and impulse-shopping behavior has changed into a deliberate search for more sustainable options, with women shopping for sustainable food more often than men. Women are more health-conscious, and consider sustainable food healthier than conventional food. On the retail side, trusted certification of origin supports the rate of sustainable-food purchases, and, from the viewpoint of innovation, the use of digital communication with consumers across different communication channels and platforms is essential.
Model for Determining Insurance Premiums Taking into Account the Rate of Economic Growth and Cross-Subsidies in Providing Natural Disaster Management Funds in Indonesia
Authors: Kalfin  , Sukono  , Supian Sudradjat , Mamat Mustafa
Published: December 2023

Natural disasters are increasing due to climate change, which is causing economic losses for countries affected by them. Disaster management funds need to be provided, including through purchasing insurance. Determining natural disaster insurance premiums needs to involve consideration of the geographical conditions of the country. The aim of this research was to develop a model for determining natural disaster insurance premiums using the jumping processes method and a cross-subsidy system. The model takes into account the level of economic growth and the natural disaster potential index. The data analyzed relate to cases of natural disasters and losses that occurred in each province in Indonesia. From the results of the analysis, it was found that through a cross-subsidy system, the principle of mutual cooperation can be applied in managing natural disasters. Regions with a high level of economic growth and a low natural disaster potential index need to provide subsidies to regions with a low economic growth rate and a high natural disaster potential index. It was also found that the cost of insurance premiums was influenced by the size of losses and the frequency of natural disasters in the province. The greater the potential for disasters and economic losses experienced by a province due to disasters, the greater the premium burden that must be borne, and vice versa. Based on these conditions, insurance premiums vary in each province in Indonesia. It is hoped that the results of this research can provide a reference for the government in determining policies for providing funds for natural disaster management using a cross-subsidy system. In addition, this research can provide a reference for insurance companies in determining natural disaster insurance premiums in Indonesia.
Symmetry Properties of Models for Reversible and Irreversible Thermodynamic Processes
Authors: Lurie S. A. , Belov P. A. , Matevossian H. A.
Published: December 2023

The problem of formulating variational models for irreversible processes of media deformation is considered in this paper. For reversible processes, the introduction of variational models actually comes down to defining functionals with a given list of arguments of various tensor dimensions. For irreversible processes, an algorithm based on the principle of stationarity of the functional is incorrect. In this paper, to formulate a variational model of irreversible deformation processes with an expanded range of coupled effects, an approach is developed based on the idea of the introduction of the non-integrable variational forms that clearly separate dissipative processes from reversible deformation processes. The fundamental nature of the properties of symmetry and anti-symmetry of tensors of physical properties in relation to multi-indices characterizing independent arguments of bilinear forms in the variational formulation of models of thermomechanical processes has been established. For reversible processes, physical property tensors must necessarily be symmetric with respect to multi-indices. On the contrary, for irreversible thermomechanical processes, the tensors of physical properties that determine non-integrable variational forms must be antisymmetric with respect to the permutation of multi-indices. As a result, an algorithm for obtaining variational models of dissipative irreversible processes is proposed. This algorithm is based on determining the required number of dissipative channels and adding them to the known model of a reversible process. Dissipation channels are introduced as non-integrable variational forms that are linear in the variations of the arguments. The hydrodynamic models of Darcy, Navier–Stokes, and Brinkman are considered, each of which is determined by a different set of dissipation channels. As another example, a variational model of heat transfer processes is presented. The equations of heat conduction laws are obtained as compatibility equations by excluding the introduced thermal potential from the constitutive equations for temperature and heat flux. The Fourier and Maxwell–Cattaneo equations and the generalized heat conduction laws of Gaer–Krumhansl and Jeffrey are formulated.
Evaluation of Lean Manufacturing Tools and Digital Technologies Effectiveness for Increasing Labour Productivity in Construction
Authors: Kulakov Kirill Y. , Orlov Alexandr K. , Kankhva Vadim S.
Published: December 2023

Multiple studies are devoted to problems of construction labour productivity and methods of increasing it. These studies contain systematized factors and the main measures that can be applied to influence them. However, the issues of reducingdowntime in design and construction by integrating Lean manufacturing tools and innovative digital technologies to increase construction labour productivity have not yet been actively studied. This paper examines the quantitative assessment of the impact of tools for Lean construction and the digitalization of business processes on labour productivity when implementing investment projects in development and changes in the effectiveness of projects. The conducted study contains an extensive review of the literature, identifies time losses as an important labour productivity factor, proposes a practical approach to the implementation of Lean 4.0 technology in the activities of a development company, and provides practical calculations of labour productivity for the existing project. Expert and calculated evidence of the positive impact of Lean 4.0 on labour productivity and performance parameters of construction projects are presented here. The effects of the introduction of tools and principles of Lean-digital technologies for construction project participants, as well as recommendations for the implementation of the proposed approach in construction practice, are discussed.
IFITM1 and IFITM3 Proteins Inhibit the Infectivity of Progeny HIV-1 without Disrupting Envelope Glycoprotein Clusters
Authors: Verma Smita , Chen Yen-Cheng , Marin Mariana , Gillespie Scott E. , Melikyan Gregory B.
Published: December 2023

Human interferon-induced transmembrane (IFITM) proteins inhibit the fusion of a broad spectrum of enveloped viruses, both when expressed in target cells and when present in infected cells. Upon expression in infected cells, IFITMs incorporate into progeny virions and reduce their infectivity by a poorly understood mechanism. Since only a few envelope glycoproteins (Envs) are present on HIV-1 particles, and Env clustering has been proposed to be essential for optimal infectivity, we asked if IFITM protein incorporation modulates HIV-1 Env clustering. The incorporation of two members of the IFITM family, IFITM1 and IFITM3, into HIV-1 pseudoviruses correlated with a marked reduction of infectivity. Super-resolution imaging of Env distribution on single HIV-1 pseudoviruses did not reveal significant effects of IFITMs on Env clustering. However, IFITM3 reduced the Env processing and incorporation into virions relative to the control and IFITM1-containing viruses. These results show that, in addition to interfering with the Env function, IFITM3 restricts HIV-1 Env cleavage and incorporation into virions. The lack of notable effect of IFITMs on Env clustering supports alternative restriction mechanisms, such as modification of the properties of the viral membrane.
Snapshot of the Phylogenetic Relationships among Avian Poxviruses Circulating in Portugal between 2017 and 2023
Authors: Santos Daniela , Fagulha Teresa , Duarte Margarida Dias , Duarte Ana , Ramos Fernanda , Barros Sílvia Carla , Luís Tiago , Henriques Ana Margarida
Published: December 2023

Avipoxvirus (APV), a linear dsDNA virus belonging to the subfamily Chordopoxvirinae of the family Poxviridae, infects more than 278 species of domestic and wild birds. It is responsible for causing avian pox disease, characterized by its cutaneous and diphtheric forms. With a high transmission capacity, it can cause high economic losses and damage to the ecosystem. Several diagnostic methods are available, and bird vaccination can be an effective preventive measure. Ten APV-positive samples were analyzed to update the molecular characterization and phylogenetic analysis of viruses isolated in Portugal between 2017 and 2023. A P4b gene fragment was amplified using a PCR, and the nucleotide sequence of the amplicons was determined using Sanger sequencing. The sequences obtained were aligned using ClustalW, and a maximum likelihood phylogenetic tree was constructed. With this study, it was possible to verify that the analyzed sequences are distributed in subclades A1, A2, B1, and B3. Since some of them are quite similar to others from different countries and obtained in different years, it is possible to conclude that there have been several viral introductions in Portugal. Finally, it was possible to successfully update the data on Avipoxviruses in Portugal.