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Owing to highly volatile and chaotic external events, predicting future movements of cryptocurrencies is a challenging task. This paper advances a granular hybrid predictive modeling framework for predicting the future figures of Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Stellar (XLM) and Tether (USDT) during normal and pandemic regimes.

Initially, the major temporal characteristics of the price series are examined. In the second stage, ensemble empirical mode decomposition (EEMD) and maximal overlap discrete wavelet transformation (MODWT) are used to decompose the original time series into two distinct sets of granular subseries. In the third stage, long- and short-term memory network (LSTM) and extreme gradient boosting (XGB) are applied to the decomposed subseries to estimate the initial forecasts. Lastly, sequential quadratic programming (SQP) is used to fetch the forecast by combining the initial forecasts.

Rigorous performance assessment and the outcome of the Diebold-Mariano’s pairwise statistical test demonstrate the efficacy of the suggested predictive framework. The framework yields commendable predictive performance during the COVID-19 pandemic timeline explicitly as well. Future trends of BTC and ETH are found to be relatively easier to predict, while USDT is relatively difficult to predict.

The robustness of the proposed framework can be leveraged for practical trading and managing investment in crypto market. Empirical properties of the temporal dynamics of chosen cryptocurrencies provide deeper insights.

This study aims to analyze and compare the relationship between international trade in global value chains (GVC) and greenhouse gas (GHG) emissions for Brazil and China from 2000 to 2016.

The input-output method apply to multiregional tables from Eora-26 to decompose the GHG emissions of the Brazilian and Chinese productive structure.

The data reveals that Chinese production and consumption emissions are associated with power generation and energy-intensive industries, a significant concern among national and international policymakers. For Brazil, the largest territorial emissions captured by the metrics come from services and traditional industry, which reveals room for improving energy efficiency. The analysis sought to emphasize how the productive structure and dynamics of international trade have repercussions on the environmental dimension, to promote arguments that guide the execution of a more sustainable, productive and commercial development strategy and offer inputs to advance discussions on the attribution of climate responsibility.

The metrics did not capture emissions related to land use and deforestation, which are representative of Brazilian emissions.

Comparative analysis of emissions embodied in traditional sectoral trade flows and GVC, on backward and forward sides, for developing countries with the main economic regions of the world.

Non-profit organizations (NPOs) are exposed to a highly competitive environment in which they are forced to grow their commercial activity to acquire additional financial resources. This study aims to create an understanding of how NPOs involved in textile reuse as a revenue-generating programme manage their reverse supply chains (RSC).

The research involves an embedded single-case study of NPOs in Finland involved in post-use textile collection. The main data sources are semi-structured interviews and participant observations.

This study is inspired by the microfoundations movement and identifies the underlying microfoundations of the NPOs’ capabilities for managing RSC for textile reuse. The study contributes to the literature by demonstrating NPOs’ lower-level, granular practices and their adaptations for achieving quality outcomes in textile reuse.

The findings have context sensitivity and apply to the NPOs which operate in a context similar to Finland, such as in other Nordic countries.

This study continues the discussion on the adoption of “business-like” practices in the NPOs’ pursuit of additional revenue streams to finance humanitarian work. The findings of this study can also be transferred to the growing area of domestic textile circularity.

Using the case of NPOs in textile reuse, the study illustrates how RSC management can serve a social, non-profit cause and transform unwanted textile products into a source of fundraising for humanitarian work.

This enriches the understanding of NPOs’ practices within the scope of revenue-generating programmes by examining one of them – textile reuse through charity shops from an RSC perspective.

The study aims to identify the possible risk factors for electricity grids operational disruptions and to determine the most critical and influential risk indicators.

A multi-criteria decision-making best-worst method (BWM) is employed to quantitatively identify the most critical risk factors. The grey causal modeling (GCM) technique is employed to identify the causal and consequence factors and to effectively quantify them. The data used in this study consisted of two types – quantitative periodical data of critical factors taken from their respective government departments (e.g. Indian Meteorological Department, The Central Water Commission etc.) and the expert responses collected from professionals working in the Indian electric power sector.

The results of analysis for a case application in the Indian context shows that temperature dominates as the critical risk factor for electrical power grids, followed by humidity and crop production.

The study helps to understand the contribution of factors in electricity grids operational disruptions. Considering the cause consequences from the GCM causal analysis, rainfall, temperature and dam water levels are identified as the causal factors, while the crop production, stock prices, commodity prices are classified as the consequence factors. In practice, these causal factors can be controlled to reduce the overall effects.

From the results of the analysis, managers can use these outputs and compare the risk factors in electrical power grids for prioritization and subsequent considerations. It can assist the managers in efficient allocation of funds and manpower for building safeguards and creating risk management protocols based on the severity of the critical factor.

The research comprehensively analyses the risk factors of electrical power grids in India. Moreover, the study apprehends the cause-consequence pair of factors, which are having the maximum effect. Previous studies have been focused on identification of risk factors and preliminary analysis of their criticality using autoregression. This research paper takes it forward by using decision-making methods and causal analysis of the risk factors with blend of quantitative and expert response based data analysis to focus on the determination of the criticality of the risk factors for the Indian electric power grid.

The purpose of this study is to investigate the structural, surface roughness and corrosion properties of the zirconium oxide thin films deposited onto SS304 substrates using the direct current (DC) magnetron sputtering technique.

DC sputtering at different powers – 80, 100 and 120 W – was used to deposit ZrO₂ thin films onto different substrates (Si/SS304) without annealing of the substrate. Atomic force microscope (AFM), energy-dispersive X-ray spectroscopy (EDS), Tafel extrapolation and contact angle techniques were applied to investigate the surface roughness, chemical compositions, corrosion behavior and hydrophobicity of these films.

Results showed that the thickness of the deposited film increased with power increase, while the corrosion current decreased with power increase. AFM images indicated that the surface roughness decreased with an increase in DC power. EDS analysis showed that the thin film has a stoichiometric ZrO₂ (Zr:O 1:2) composition with basic uniformity. Water contact angle measurements indicated that the hydrophobicity of the synthesized films decreased with an increase in surface roughness.

DC magnetron sputtering technique is infrequently used to deposition thin films. The obtained thin films showed good hydrophobic and anticorrosion properties. Finally, results are compared with other deposition techniques.

This paper aims to examine dissimilar joints for various applications in chemical, petrochemical, oil, gas, shipbuilding, defense, rail and nuclear industry.

This study examined the effects of cold metal transfer welding on stainless steel welds for 316L austenitic and 430 ferritic dissimilar welds with ER316L, ER309L and without (autogenous) fillers. The microstructural observation was done with an optical microscope. The mechanical test was done to reveal the strength, hardness and toughness of the joint. The electrochemical polarization tests were done to reveal intergranular and pitting corrosion in the dissimilar joints.

This microstructural study shows the presence of austenitic and ferritic phases with vermicular ferrite for ER309L filler weld, and for ER316L filler weld specimen shows predominately martensitic phase in the weld region, whereas the autogenous weld shows lathy ferrite mixed with martensitic phase. Mechanical test results indicated that filler welded specimen (ER316L and ER309L) has relatively higher strength and hardness than the autogenous weld, whereas ER316L filler weld exhibited the highest impact toughness than ER309L filler weld and lowest in autogenous weld. The electrochemical corrosion results displayed the highest degree of sensitization (DOS) in without filler welded specimen (45.62%) and lower in case of filler welded specimen ER309L (4.95%) and least in case of ER316L filler welded specimen (3.51%). The high DOS in non-filler welded specimen is correlated with the chromium carbide formation. The non-filler welded specimen shows the highest pitting corrosion attack as compared to the ER316L filler weld specimen and relatively better in ER309L filler welded specimen. The highest pitting corrosion resistance is related with the high chromium content in ER309L composition.

This experimental study is original and conducted with 316L and 430 stainless steel with ER316L, ER309 and without fillers, which will help the oil, shipbuilding and chemical industries.

Nowadays, thermal comfort plays a prominent role in contemporary construction practices. Appropriate thermal insulation not only offers energy efficiency benefits in buildings but also enhances occupant well-being, comfort, and productivity. Therefore, a comprehensive understanding of the thermal properties of building materials is essential. This research aims to prepare and investigate a lightweight gypsum-based composite incorporating nano montmorillonite with advanced thermal insulation properties, considering both quality and cost-effectiveness while ensuring environmental compatibility.

This study adopts a laboratory experimental approach. A gypsum sample (without additives) and seven samples of gypsum combined with varying percentages of sodium and calcium montmorillonite nanoclays undergo extensive testing and analysis. Subsequently, the properties of these samples are compared.

The results indicate that adding montmorillonite nanoclays to gypsum composites reduces the density of the tested samples and increases their porosity. Moreover, the thermal conductivity coefficient decreases in these samples, significantly improving the thermal insulation properties of the lightweight gypsum plaster. This improvement is more pronounced in samples containing sodium montmorillonite nanoclay compared to calcium-based samples. Additionally, the investigations reveal that compressive strength decreases with the addition of montmorillonite to the samples.

In this research, laboratory experiments were conducted to investigate the physical and mechanical properties of gypsum plaster with varying percentages of sodium and calcium montmorillonite nanoclays. The studied properties include density, porosity, thermal conductivity coefficient, and compressive strength. Additionally, stress-strain diagrams, elastic modulus, and initial and secondary critical stresses were analyzed for each specimen.

The purpose of this paper is to propose a casting process for the production of double-chamber soft fingers, which avoids the problems of air leakage and fracture caused by multistep casting. This proposed method facilitates the simultaneous casting of the inflation chamber and the jamming chamber.

An integrated molding technology based on the lost wax casting method is proposed for the manufacture of double-chamber soft fingers. The solid wax core is assembled with the mold, and then liquid silicone rubber is injected into it. After cooling and solidification, the mold is stripped off and heated in boiling water, so that the solid wax core melts and precipitates, and the integrated soft finger is obtained.

The performance and fatigue tests of the soft fingers produced by the proposed method have been carried out. The results show that the manufacturing method can significantly improve the fatigue resistance and stability of the soft fingers, while also avoiding the problems such as air leakage and cracking.

The improvement of the previous multistep casting method of soft fingers is proposed, and the integrated molding manufacturing method is proposed to avoid the problems caused by secondary bonding.

Sustainability has become an integral part of the real estate industry, alongside advancing globalization and demographic development. Due to real estate's influence on greenhouse gas emissions throughout its life cycle, both the regulatory and legal requirements concerning the sustainability of real estate are growing and, as a result of social responsibility, the interest of tenants and investors in sustainable real estate. However, criteria for measuring the ecological sustainability of a real estate investment in the purchase process in order to reduce the risk of including “stranded assets” in the portfolio are missing. This paper aims to address the need to integrate the issue of carbon stranding into existing sustainability rating tools.

Existing tools are examined based on defined criteria to determine whether they are suitable for purchasing a property before suitable tools for purchase are compared. Strengths and weaknesses are identified, which are to be remedied with the scoring tool. Taxonomy regulation is integrated into the existing valuation basis as a legal regulation.

The result is a scoring tool that enables real estate companies to measure and evaluate the ecological sustainability performance of a property during the acquisition process, taking into account the three aspects of sustainability and considering them when determining an appropriate purchase price in line with market conditions. Moreover, the developed tool helps to minimize the risk of acquiring a stranding asset.

The environmental, social and governance (ESG) framework employed in this study does not incorporate governance considerations. While the analysis extensively evaluates the building's environmental and social aspects, it does not extend to examining the governance practices of the companies involved. Thus, the assessment is confined solely to the physical attributes of the property without accounting for broader corporate governance factors.

The developed scoring tool represents a valuable tool for the real estate industry, offering insights into sustainability performance during property acquisitions and providing a structured framework for decision-making. By addressing both certification and taxonomy regulation requirements, the tool contributes to the industry's evolution toward more sustainable and environmentally responsible real estate practices.

In response to the growing importance of sustainability in the real estate industry, this paper introduces a novel scoring tool for evaluating the sustainability of real estate investments during the acquisition process.

The world is increasingly threatened by climate change. As the dimensions of this danger grow, it becomes essential to develop the most effective policies to mitigate its impacts and adapt to these new conditions. Technology is one of the most crucial components of this process, and this study focuses on examining climate change adaptation technologies. The aim of the study is to investigate the entire spectrum of technology actors and to concentrate on the technology citation network established from the past to the present, aiming to identify the core actors within this structure and provide a more comprehensive outlook.

The study explores patent citation relationships using social network analysis. It utilizes patent data published between 2000 and 2023 and registered by the US Patent and Trademark Office.

Study findings reveal that technologies related to greenhouse technologies in agriculture, technologies for combatting vector-borne diseases in the health sector, rainwater harvesting technologies for water management, and urban green infrastructure technologies for infrastructure systems emerge as the most suitable technologies for adaptation. For instance, greenhouse technologies hold significant potential for sustainable agricultural production and coping with the adverse effects of climate change. Additionally, ICTs establish intensive connections with nearly all other technologies, thus supporting our efforts in climate change adaptation. These technologies facilitate data collection, analysis, and management, contributing to a better understanding of the impacts of climate change.

Existing patent analysis methods often fall short in detailing the unique contributions of each technology within a technological network. This study addresses this deficiency by comprehensively examining and evaluating each technology within the network, thereby enabling us to better understand how these technologies interact with each other and contribute to the overall technological landscape.