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The research aims to investigate the impact of thermo-mechanical aging on SBR under cyclic-loading. By conducting experimental analyses and developing a 3D finite element analysis (FEA) model, it seeks to understand chemical and physical changes during aging processes. This research provides insights into nonlinear mechanical behavior, stress softening and microstructural alterations in SBR compounds, improving material performance and guiding future strategies.

This study combines experimental analyses, including cyclic tensile loading, attenuated total reflection (ATR), spectroscopy and energy-dispersive X-ray spectroscopy (EDS) line scans, to investigate the effects of thermo-mechanical aging (TMA) on carbon-black (CB) reinforced styrene-butadiene rubber (SBR). It employs a 3D FEA model using the Abaqus/Implicit code to comprehend the nonlinear behavior and stress softening response, offering a holistic understanding of aging processes and mechanical behavior under cyclic-loading.

This study reveals significant insights into SBR behavior during thermo-mechanical aging. Findings include surface roughness variations, chemical alterations and microstructural changes. Notably, a partial recovery of stiffness was observed as a function of CB volume fraction. The developed 3D FEA model accurately depicts nonlinear behavior, stress softening and strain fields around CB particles in unstressed states, predicting hysteresis and energy dissipation in aged SBRs.

This research offers novel insights by comprehensively investigating the impact of thermo-mechanical aging on CB-reinforced-SBR. The fusion of experimental techniques with FEA simulations reveals time-dependent mechanical behavior and microstructural changes in SBR materials. The model serves as a valuable tool for predicting material responses under various conditions, advancing the design and engineering of SBR-based products across industries.

This study aims to introduce the results of a research carried out to develop a prototype of a highly energy-efficient modular detached house, called CASA+ CASA means HOUSE in spanish, adapted to the climatic features of central-southern Chile. The project enables a sustainable alternative to facilitate the reconstruction of the residential areas after the impact of the 2010 earthquake.

The methodology is based on an “integrated design process” of a case study that proposes a constructive response that quantitatively and qualitatively improves the initial data of traditional dwellings. The characteristics of the new system have been simulated with specific software to validate the final decisions, considering the cost–benefit ratio.

Simulation tools were used to assess and improve the system’s energy performance with respect to present options and to analyse its economic and construction viability. We obtained several economically competitive housing prototypes that substantially reduced energy consumption and the CO₂ footprint by between 20% and 80%.

The prototype has not been developed, as we are waiting for funding, but all its energy features have been simulated.

Furthermore, this experience also identified similar modifications made to the design of the houses, which revealed general possibilities for improving energy performance.

The origin of this research is a public call for international researchers to improve the quality of the new homes to be built in Chile after the strong earthquake of 2010. The result of the research has been put at the direct service of Chilean society and in other international projects for the construction of low-energy social housing.

These are the result of a long research aimed at establishing a new architectural model that, in addition to improving the architectural quality of the product, obtains significant improvements in energy consumption and CO₂ emissions. The most particular aspect is the practical vocation and its implementation with real construction with the support of construction companies.

This paper aims to investigate how manufacturers can foster insights and improvements from real-time data among shop-floor workers by developing organisational “learning-to-learn” capabilities based on both the lean- and action learning principle of learning through problem-solving. Second, the purpose is to extrapolate findings on how action learning can enable the complementarity between lean and industry 4.0.

An insider action research approach is adopted to investigate how manufacturers can enable their shop-floor workers to foster insights and improvements from real-time data at VELUX.

The findings report that enabling shop-floor workers to use real-time data consist of developing three consecutive organisational building blocks of learning-to-learn, learning-to-learn using real-time data and learning-to-learn generating real-time data − and helping others to learn (to learn).

First, the study contributes to theory and practice by demonstrating that a learning-to-learn capability is a core construct for manufacturers seeking to enable shop-floor workers to use real-time data-capturing systems to drive improvement. Second, the study outlines how lean and industry 4.0 complementarity can be enabled by action learning. Moreover, the study allows us to deduce six necessary conditions for enabling shop-floor workers to foster insights and improvements from real-time data.

Forests are negatively affected from rapid world population increase and industrialization that create intense pressures on natural resources and the possibility of an achieving circular economy. Forests can be considered as essential resources for providing sustainable society and meeting the requirements of future generations and circular economy. Therefore sustainable production tools as part of circular economy can be handled as one of the basic indicators for achieving circular economy. Accordingly the main purpose of this study is developing a novel rough – fuzzy multi-criteria decision-making model (MCDM) for evaluation sustainable production for forestry firms in Eastern Black Sea Region.

For determining 18 criteria weights a novel Rough PIPRECIA (PIvot Pairwise RElative Criteria Importance Assessment) method is developed. Eight decision-makers (DMs) participated in the research, and to obtain group rough decision matrix, rough Dombi weighted geometric averaging (RNDWGA) operator has been applied. For evaluation forestry firms fuzzy MARCOS (Measurement of alternatives and ranking according to COmpromise solution) method was utilized.

After application developed model the fourth alternative was found as the best. Sensitivity analysis and comparison were made to present the applicability of this method.

Development of novel integrated Rough PIPRECIA-Fuzzy MARCOS model with emphasis on developing new Rough PIPRECIA method.

The main objective of the quantitative study is to ascertain the relationship between the circular economy (CE) and carbon emissions. And also, the study examines the threshold beyond which the quality of governance reduces carbon emissions.

The autoregressive distributed lag approach is employed for the econometrics analysis. The study employed quarterly data from 2006Q1 to 2017Q4 on Ghana.

The results indicated that, although the CE had a positive and significant effect on carbon emissions, the moderating term had an adverse and significant effect on carbon emissions. This result suggests that to mitigate carbon emissions, a robust and efficient quality of institutions should be sustained. Finally, the study also identified a quality of governance threshold of 1.155 beyond which a shift to a CE would result in a reduction in carbon emissions.

The study recommends that policymakers should initiate policies that would enhance quality governance.

The main contributions of the study are that the paper ascertained the threshold beyond which quality of governance assists circular economic practices to mitigate carbon emissions. Also, the study revealed that quality of governance is a catalyst to promote circular economic practices in reducing carbon emissions. Finally, the study ascertains the long-run effect of the variables of interest on carbon emissions.

The purpose of this study is to assess the potential benefits of digital technologies (DTs) in bolstering the circular economy (CE) transition in the construction industry, to speed up the attainment of sustainable development objectives.

A detailed literature review was undertaken to identify DTs that could influence CE transition and the benefits of these DTs in the CE transitioning efforts of the construction industry. Based on these, a survey questionnaire was formulated and administered to construction professionals using convenient sampling techniques. With a response rate of 49.42% and data reliability of over 0.800, the gathered data were analysed using frequency and percentage, mean item score, normalisation value, coefficient of variation, Kendall’s coefficient of concordance, analysis of variance and factor analysis.

This study found that the construction experts agreed that building information modelling, blockchain technology, RFID, drone technology and cloud computing are the leading DTs that have the potential to influence and speed up CE transition in construction. Also, six clusters of benefits of DTs in bolstering EC are quicken CE transition, proactive waste management, recycling and zero waste, data management and decision-making, enhance productivity and performance and resource optimisation.

Studies on the integration of DTs in CE transition are scarce and it is even lacking in the Nigerian context. To the best of the authors’ knowledge, this study is the first to assess the role of DTs in CE transitioning in the Nigerian construction industry.

The objective of this research is to put forward a novel closed-loop circular economy (CE) approach to forecast the sustainability of supply chains (SCs). We provide a practical and real-world CE framework to improve and fill the current knowledge gap in evaluating sustainability of SCs. Besides, we aim to propose a real-life managerial forecasting approach to alert the decision-makers on the future unsustainability of SCs.

It is needed to develop an integrated mathematical model to deal with the complexity of sustainability and CE criteria. To address this necessity, for the first time, network data envelopment analysis (NDEA) is incorporated into the dynamic data envelopment analysis (DEA) and artificial neural network (ANN). In general, methodologically, the paper uses a novel hybrid decision-making approach based on a combination of dynamic and network DEA and ANN models to evaluate sustainability of supply chains using environmental, social, and economic criteria based on real life data and experiences of knowledge-based companies so that the study has a good adaptation with the scope of the journal.

A practical CE evaluation framework is proposed by incorporating recyclable undesirable outputs into the models and developing a new hybrid “dynamic NDEA” and “ANN” model. Using ANN, the sustainability trend of supply chains for future periods is forecasted, and the benchmarks are proposed. We deal with the undesirable recycling outputs, inputs, desirable outputs and carry-overs simultaneously.

We propose a novel hybrid dynamic NDEA and ANN approach for forecasting the sustainability of SCs. To do so, for the first time, we incorporate a practical CE concept into the NDEA. Applying the hybrid framework provides us a new ranking approach based on the sustainability trend of SCs, so that we can forecast unsustainable supply chains and recommend preventive solutions (benchmarks) to avoid future losses. A practicable case study is given to demonstrate the real-life applications of the proposed method.

Improving productivity and efficiency has always been crucial for industrial companies to remain competitive. In recent years, the topic of environmental impact has become increasingly important. Published research indicates that environmental and economic goals can enforce or rival each other. However, few papers have been published that address the interaction and integration of these two goals.

In this paper, we identify both, synergies and trade-offs based on a systematic review incorporating 66 publications issued between 1992 and 2021. We analyze, quantify and cluster examples of conjunctions of ecological and economic measures and thereby develop a framework for the combined improvement of performance and environmental compatibility.

Our findings indicate an increased significance of a combined consideration of these two dimensions of sustainability. We found that cases where enforcing synergies between economic and ecological effects were identified are by far more frequent than reports on trade-offs. For the individual categories, cost savings are uniformly considered as the most important economic aspect while, energy savings appear to be marginally more relevant than waste reduction in terms of environmental aspects.

No previous literature review provides a comparable graphical treatment of synergies and trade-offs between cost savings and ecological effects. For the first time, identified measures were classified in a 3 × 3 table considering type and principle.

The purpose of this study is to numerically model the rigid pavement resting on two-parameter soil and to examine its modal parameters.

This study is carried out using a one-dimensional beam element with three rotational and three translational degrees of freedom based on the finite element method. MATLAB programming is used to perform the free vibration analysis of the rigid pavement.

Cyclic frequency and their corresponding mode shapes were determined. It has been investigated how cyclic frequency changes as a result of variations in the thickness, span length of pavement, shear modulus, modulus of subgrade, different boundary conditions and element discretization. Thickness of the pavement and span length has greater effect on the cyclic frequency. Maximum increase of 29.7% is found on increasing the thickness, whereas the cyclic frequency decreases by 63.49% on increasing span length of pavement.

The pavement's free vibration is the sole subject of the current investigation. This study limits for the preliminary design phase of rigid pavements, where a complete three-dimensional finite element analysis is unnecessary. The current approach can be extended to future research using a different method, such as finite element grilling technique, mesh-free technique on reinforced concrete pavements or jointed concrete pavements.

The finite element approach adopted in this paper involves six degrees of freedom for each node. Furthermore, to the best of the authors’ knowledge, no prior study has done seven separate parametric investigations on the modal analysis of rigid pavement resting on two-parameter soil.

Appropriate thermal properties of walls can lead to the improvement of the indoor environment of buildings especially in countries with low energy availability such as Burkina Faso. In order to benefit from these advantages, the thermal properties must be properly characterized. This paper investigates the impact of the design of single- and double-layer walls based on compressed Earth blocks (CEB) on the risk of indoor overheating.

First a building has been used as a tool to measure climate data. Then, a software program was used to define an accurate thermal model. Two indices were defined: weighted exceedance hour (WEH) related to the risk of overheating and cyclic thickness (ξ) related to the thermal properties of the walls. The aim is to define the appropriate values of ξ which minimized the WEH. The study also assesses the sensitivity of these thermal properties to occupancy profiles.

The results indicate the arrangements of the thermal properties that can promote comfortable environments. In single-layer wall buildings, ξ = 2.43 and ξ = 3.93 are the most suitable values to minimize WEH for the room occupied during the day and night, respectively. If a double-layer wall is used, ξ = 1.42 and CEB layer inside is the most suitable for the room occupied during the day, while ξ = 2.43 and CEB outside should be preferred in the case of a room with night occupancy profile.

The findings indicate that occupation patterns at room scale should be systematically considered when dealing with wall design in order to improve the thermal comfort.