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The paper aims to evaluate drying performance of earth mortar by solar drying for more durability, minimize pathologies in traditional construction and determine the influence of temperature and humidity on the microstructure of earth mortar using static gravimetric method.

A convective solar dryer was used for the pretreatment of building and solid materials for construction.

The humidity influences the mortar sorption – surface water sorption of earth mortar increased with increasing temperature.

The study used a novel method for pretreatment building materials by using solar dryer.

This study aims to provide an alternative adoption to overcome the energy crisis and environmental effluence by comparative theoretical and trial testing analysis of an innovative combined condenser unit over traditional individual condenser unit water heating systems.

The presented innovative new unit of the combined condenser heat pipe works efficiently through its improved idea and unique design, providing uniform heating to improve the heat transfer and, finally, the temperature of water increases without enhancing the cost. In this design, all these five evaporator units were connected with a single combined condenser unit in such a manner that after the condensation of heat transfer fluid vapour, it goes equally into the evaporator pipe.

The maximum temperature of hot water obtained from the combined condenser heating system was 60.6, 55.5 and 50.3°C at a water flow rate of 0.001, 0.002 and 0.003 kg/s, respectively. The first and second law thermodynamic efficiency of the combined condenser heating system were 55.4%, 60.5% and 89.0% and 2.6%, 3.7% and 4.1% at 0.001, 0.002 and 0.003 kg/s of water flow rates, respectively. The combined condenser heat pipe solar evacuated tube heating system promoting progressive performance is considered efficient and environment-friendly compared to the traditional condenser unit water heating system.

Innovative combined condenser heat pipe evacuated tube collector assembly was designed and developed for the study. A comparative theoretical and experimental energy-exergy performance analysis was performed of innovated collective condenser and traditional individual condenser heat pipe water heating system at various mass flow rate.

This paper aims to develop a framework for the bioaccounting measurement of environmental assets based on natural wealth sustainability. Specifically, this paper proposes a theoretical structure for qualitative and quantitative organization-level assessments of the existence and circulation of water, air, wildlife, flora, soil and subsoil resources.

This research used an inductive method with a qualitative and quantitative approach. The authors postulate a systemic and comprehensive bioaccounting measurement of environmental assets, including heterogeneous and homogeneous methods and quantitative and qualitative valuations of the resources that comprise environmental assets.

The authors describe a theoretical structure for the bioaccounting measurement of environmental assets based on the sustainability of natural wealth through heterogeneous and homogeneous measurement methods and show how to integrate these assets through an homogeneous method.

The development of this general theoretical structure will require the integration of theoretical, conceptual and technical developments from multiple disciplines. The authors hope that the scientific community will evaluate and study this proposal for faster progress towards its practical implementation in organizations.

The authors structured the bioaccounting measurements, which are presented individually for each class of environmental assets. Each of these assets requires subcategories (accounts, subaccounts and resources) and recognition/measurement units. Environmental value units (EVUs) are used to standardize the plurality of measurement units.

This study aims to introduce a novel machine learning feature vector (MLFV) method to bring machine learning to overcome the time-consuming computational fluid dynamics (CFD) simulations for rapidly predicting turbulent flow characteristics with acceptable accuracy.

In this method, CFD snapshots are encoded in a tensor as the input training data. Then, the MLFV learns the relationship between data with a rod filter, which is named feature vector, to learn features by defining functions on it. To demonstrate the accuracy of the MLFV, this method is used to predict the velocity, temperature and turbulent kinetic energy fields of turbulent flow passing over an innovative nature-inspired Dolphin turbulator based on only ten CFD data.

The results indicate that MLFV and CFD contours alongside scatter plots have a good agreement between predicted and solved data with R² ≃ 1. Also, the error percentage contours and histograms reveal the high precisions of predictions with MAPE = 7.90E-02, 1.45E-02, 7.32E-02 and NRMSE = 1.30E-04, 1.61E-03, 4.54E-05 for prediction velocity, temperature, turbulent kinetic energy fields at Re = 20,000, respectively.

The method can have state-of-the-art applications in a wide range of CFD simulations with the ability to train based on small data, which is practical and logical regarding the number of required tests.

The paper introduces a novel, innovative and super-fast method named MLFV to address the time-consuming challenges associated with the traditional CFD approach to predict the physics of turbulent heat and fluid flow in real time with the superiority of training based on small data with acceptable accuracy.

Human activities in recent years with the excessive emission of greenhouse gases have had a negative impact on increasing global temperatures. In this regard, one of the best ways to control it is to move toward sustainability with more use of renewable energy. Therefore, this study aims to assess the indicators of sustainable energy, explore benefits of sustainable energy and evaluate strategies to enhance energy sustainability in line with Sustainable Development Goal (SDG) 7.

Quantitative research strategy was adopted. Questionnaires were developed and administered through convenience and snowball sampling techniques to professionals in the energy sector. Data collected was validated by using Cronbach’s alpha coefficient and discriminant validity, whereas objectives were analyzed by using the relative importance index, mean score ranking and Kruskal–Wallis test.

From the findings, the significant indicators of sustainable energy were as follows: reduction in greenhouse gas emissions from energy production, use of renewable energies and policies on proper utilization of energy resources. Furthermore, a reduction in greenhouse gas emissions, less harm caused to the environment and an increase in the economic and social development process were the major benefits of sustainable energy. Finally, the findings of the study revealed that a strong and accounted policy program, adopting sustainable energy indicators and strategic communication are the significant strategies needed to be put in place to enhance energy sustainability.

The study serves as a reminder to policymakers of the crucial role they have to play in enhancing energy sustainability by putting in place suitable policy programs and methods.

The originality of this study is that it is arguably a pioneering study in Ghana and contributes to the body of knowledge on energy sustainability.

Multiple encapsulated phase change materials (PCMs) are used in a wide range of applications, including convective drying, electronic cooling, waste heat recovery and air conditioning. Therefore, it is important to understand the performance of multiple PCMs in channels with flow separation and develop methods to increase their effectiveness. The aim of the study is to analyze the phase transition dynamics of multiple encapsulated PCMs mounted in a U-shaped tube under inclined magnetic field by using ternary nanofluid.

The PCMs used in the upper horizontal channel, vertical channel and lower horizontal channel are denoted by M1, M2 and M3. Magnetic field is uniform and inclined while finite element method is used as the solution technique. Triple encapsulated-PCM system study is carried out taking into account different values of Reynolds number (Re, ranges from 300 to 1,000), Hartmann number (Ha ranges from 0 and 60), magnetic field inclination (between 0 and 90) and solid volume fraction of ternary nanofluid (between 0 and 0.03). The dynamic response of the liquid fraction is estimated for each PCM with varying Re, Ha and t using an artificial neural network.

It is observed that for PCMs M2 and M3, the influence of Re on the phase transition is more effective. For M2 and M3, entire transition time (t-F) lowers by approximately 47% and 47.5% when Re is increased to its maximum value, whereas it only falls by 10% for M1. The dynamic characteristics of the phase transition are impacted by imposing MGF and varying its strength and inclination. When Ha is raised from Ha = 0 to Ha = 50, the t-F for PCM-M2 (PCM-M3) falls (increases) by around 30% (29%). For PCMs M1, M2 and M3, the phase transition process accelerates by around 20%, 30% and 28% when the solid volume fraction is increased to its maximum value.

Outcomes of this research is useful for understanding the phase change behavior of multiple PCMs in separated flow and using various methods such as nano-enhanced magnetic field to improve their effectiveness. Research outputs are beneficial for initial design and optimization of using multiple PCMs in diverse energy system technologies, including solar power, waste heat recovery, air conditioning, thermal management and drying.

This study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem.

This study involves cooling, commodity damage and carbon emissions and establishes the site selection model of low-carbon cold chain logistics distribution center aiming at minimizing total cost, and grey wolf optimization algorithm is used to improve the artificial fish swarm algorithm to solve a cold chain logistics distribution center problem.

The optimization results and stability of the improved algorithm are significantly improved and compared with other intelligent algorithms. The result is confirmed to use the Beijing-Tianjin-Hebei region site selection. This study reduces composite cost of cold chain logistics and reduces damage to environment to provide a new idea for developing cold chain logistics.

This study contributes to propose an optimization model of low-carbon cold chain logistics site by considering various factors affecting cold chain products and converting carbon emissions into costs. Prior studies are lacking to take carbon emissions into account in the logistics process. The main trend of current economic development is low-carbon and the logistics distribution is an energy consumption and high carbon emissions.

Sustainable production (SP) is an efficient and influential approach of production for Indian manufacturing industries as it preserves the social, environmental and economic aspects of production activities altogether. The objective of this research work is to investigate the implementation status of SP practices in Indian manufacturing industries by utilizing empirical survey methodology.

A questionnaire survey methodology was adapted, and the questionnaire was prepared by intense literature survey along with by opinions from experts in the field of SP. This questionnaire was sent to 753 different organizations at different locations across India. This study collected responses from manufacturing industries as per 2021 directory of Confederation of Indian Industries for the duration of 7 week. Top level managers were the target respondents. The study propagated with 242 responses which were observed complete in all respects.

The study identified that though the majority of the organizations are claiming to follow SP practices since long time, they actually are lagging in proper understanding SP practices. Majority of them are implementing it in specific departments in their organization. They are coming across multiple barriers in the implementation of SP practices among which unrecognized financial benefits and lack of proper government policies are prime. The study suggests that the Indian organizations needs feasible framework with adaptable guidelines.

This work is centered towards manufacturing organizations and targets only the leading industrial sectors in India. Thus, the outcomes of this study may not be generalized for all the sectors of Indian industries. Additionally, it can also be assumed that higher number of responses would have contributed to more clear visualization of implementation status of SP practices among Indian industries.

Sustainable approaches in production activities are very lucrative for industries worldwide, due to their advantages. Numerous researchers are also putting their efforts to explore more about various aspects of sustainability. Mostly they are focusing on single or few aspects of SP and its implementation in particular region or country. Very few research works are dedicated to knowing the implementation status of SP in Indian manufacturing industries and they are limited in various aspects. This study presents a dedicated approach to investigate the implementation status of SP practices in Indian manufacturing industries.

Milk is a perishable food product, one of the primary sources of nutrition. Reports worldwide indicate numerous food frauds and foodborne diseases associated with adulterated milk products. These safety concerns highlight the importance of a visible milk supply chain, which can be achieved by cutting-edge technologies. However, these technologies come with high costs. So, this study aims to propose a framework that integrates blockchain, Internet of Things (IoT) and cloud to enhance visibility with reduced cost in an Australian milk supply chain (AMSC).

A design science research methodology is used, where a proof of concept is also developed at the retailer end to show how blockchain, IoT and cloud can improve visibility with reduced cost in an AMSC.

According to cost and visibility analysis, blockchain implementation in AMSC would generate a high return on investment (ROI). For the given case, ROI becomes positive for all stakeholders after 750 cycles. Integrating IoT, cloud and blockchain is more profitable than just using blockchain. Additionally, technology implementation may not benefit all stakeholders equally. For example, the retailer needs 10 cycles to benefit, but the transporter needs 50 in the given case.

The findings of this study assist milk industries in decision-making regarding technology implementation in their supply chain and motivate them to implement these technologies, resulting in improved trust and coordination among entities and consumers.

A cost and visibility analysis are performed to evaluate the impact of technology implementation on cost and visibility in an AMSC. A SOAR (Strength Opportunities Aspiration Results) analysis is also performed for the strategic planning framework.

This paper bridges the gap between the theory and practice by developing a life cycle sustainability tracker (LCST). The study is seeking to proffer solutions to an observed shortcoming of conventional life cycle sustainability assessment (LCSA) communication platforms. Notably, the static nature of the information provided on such platforms has made it difficult for them to be used for real-time decision-making and predictions. The main aim of this paper is to develop a LCST that facilitates a dynamic visualisation of life cycle sustainability results and allows for an integrated benchmark across the dimensions of sustainability.

The study leverages the model development capabilities of the design science research strategy in accomplishing a dynamic and novel communication platform. A life cycle thinking methodology and appropriate multicriteria decision approach (MCDA) is applied to accomplish a comprehensive, streamlined and replicable approach in mapping and tracking the progress of sustainable development goals (SDGs) in the National Infrastructure Pipeline (NIP) projects in India.

It was found that: (1) The use of the LCST tracker provides a dynamic and holistic insight into the key LCSA indicators with clearly defined benchmarks to assess the impact on the SDG 11, (2) The NIP projects achieve an upward trend across all the regions, and the percentage of opportunities ranges from 11 to 24%, with the South experiencing the highest growth and the North having the minimal increase in percentage and (3) The assessment score (52–58%) provides performance metrics that align well with the LCST – which ranges between “Fair” and “Average” for all the regions in India.

The novelty of this research is that the LCST provides a transparent and harmonised approach to reporting on the LCSA results. The LCST utilises heat maps and radial mapping to achieve an intuitive display of large amounts of highly heterogeneous data, thus allowing the synthesis of large sets of information compactly and with coherence. Progress towards the SDGs change on a yearly basis; hence, a dynamic LCSA tool provides a timely and the valuable context to map and track performance across different regions and contexts.