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Partial shading conditions (PSCs) are responsible for the root causes of photovoltaic (PV) system performance deprivation such as hotspots (damaged PV cells), mismatch power losses and multiple power maxima. Recently, PV array reconfiguration strategies have proven to be beneficial in improving PV system performance and achieving improved shade dispersion properties. This research analyzes the improved Su-Do-Ku (I-SDK) PV array configuration in order to counteract the shading effect. This approach implements a 6 × 6 size PV array configuration and performance evaluation under different realistic shading scenarios. The performance of the I-SDK configuration is assessed and compared to that of the total-cross-tied (TCT) and Su-Do-Ku (SDK) arrangements. The performance indices such as power loss (PL), power at global maximum power point (GMPP), fill-factor (FF), performance ratio (PR), power enhancement (PE) and execution ratio (ER) are analyzed to show comprehensive comparison. An experimental analysis confirms the MATLAB/Simulink findings, demonstrating that the I-SDK configuration outperforms both the TCT and SDK array setups. The GMPP values of 143.5 W, 141.7 W, 138.1 W and 129.3 W also show the superiority of I-SDK during four shading instances compared to conventional SP, TCT, SDK and SM arrangements. Moreover, under similar PSCs, higher %FF (74.61%, 76.10%, 77.1%, 75.92%) and lower PL (36.7 W, 38.5 W, 42.1 W, 50.9 W) support the adoptability of I-SDK for experimental validation/commercial viability.

Recent years have seen a rise in the frequency and severity of extreme rainstorm events, which have caused widespread damage and death in numerous cities. The manufacture and use of storm drainage materials result in numerous environmental concerns in the construction industry. Green materials for storm drainage networks are environmentally friendly compared to their traditional counterparts. Identifying and assessing sustainability criteria for green materials for storm drain networks has been challenging. This study aims to determine the critical criteria for selecting green materials for storm drainage networks using a stationary analysis approach. To this end, a questionnaire survey was administered to Egyptian storm engineers to assess their importance based on a selection criteria 29 green materials. From the results obtained, “Operation and maintenance cost” and “Use of local material” were seen to be the “stationary materials”. The obtained findings in this research pave the way for the Egyptian storm industry towards becoming environmentally friendly, which will in turn improve the functioning mechanism of sewer networks.

This work intended to explore some motivations that influence people’s eating habits towards sustainability. This was an observational, cross-sectional study, carried out by questionnaire survey on a non-probabilistic sample of 10,067 participants from 13 countries (Argentina, Brazil, Croatia, Greece, Hungary, Latvia, Lithuania, Poland, Portugal, Serbia, Slovenia, Romania and United States). Results indicated that people prefer fresh local foods from the season, being important because it allows transportation and storage to be reduced, which in many cases implies refrigeration systems and consequent energy expenditure. Although people avoid food waste at home, the awareness for the waste at restaurants still needs to be improved. Consumers seem to prefer foods that have been produced and packed in sustainable ways but still give importance to the package—understandable for food products. The results also indicated significant differences in the food choice motivations between groups for all sociodemographic variables tested (age, sex, marital status, education, professional area, living environment and country), but the association was high only for variable country. Additionally, a tree classification analysis allowed to identify the relative importance of the influential variables on the sustainable food choices, with country being the most important, followed by age and sex. Additionally, discriminant function analysis allowed establishing a model for the relation between country and six variables accounting for preservation of biodiversity, respect for life, save natural resources, save energy, reduce industrial pollution and minimal packaging. Although with some limitations, this study brings valuable insight into some aspects linked with sustainable food choices on a number of countries and how people shape their food choices according to some sustainability issues.

The research aims to prioritize the solutions to overcome the challenges of Lean Six Sigma 4.0 (LSS 4.0). It is an integrated approach with lean, six sigma, and Industry 4.0 attributes. This integrated approach helps to achieve organizational excellence and sustainable development goals. Fuzzy stepwise weight assessment ratio analysis (fuzzy-SWARA) was used to estimate the weights of LSS 4.0 challenges. Furthermore, fuzzy-weighted aggregated sum product assessment (fuzzy-WASPAS) was used to prioritize the LSS 4.0 solutions. In this study, 23 challenges and 23 solutions of LSS 4.0 implementation were identified with the help of an extensive literature review and discussion with the area experts having vast experience. Management participation in LSS 4.0 implementation and planning for long-term vision were found to be the topmost solutions to overcome LSS 4.0 challenges. To the best of our knowledge, to date, the prioritization of solutions to overcome the challenges of LSS 4.0 have not yet been investigated in the developing economic context.

Salt mines, a significant category of local, regional, national, and/or European underground heritage, are becoming attractive tourism destinations. This paper examines three cases of salt mining in different European countries, namely Wieliczka in Poland, Campina de Cima in Portugal, and Turda in Romania. They are analyzed in the context of history, typical attributes of their attractiveness, and new uses after the salt extraction was or is going to be stopped, in order to detect their unique values as important assets for both Underground Built Heritage (UBH) and Salt Heritage Tourism (SHT). The results of their comparison show that despite a positive impact related to their protection as cultural and industrial heritage, there are also some negative aspects related to increasing costs of their maintenance and adaptation of salt mines to new functions and to meet the tourism needs. By putting in place measures to enhance the awareness of their values and for activating the local community, the three mines are showcases for the economic outputs for their sites and regions, as well as for increasing knowledge regarding UBH.

Solid-state transformers (SSTs) have emerged as a superior alternative to conventional transformers and are regarded as the building block of the future smart grid. They incorporate power electronics circuitry and high-frequency operation, which allows high controllability and enables bi-directional power flow, overcoming the limitations of conventional transformers. This paper presents a detailed analysis of the solid-state transformer, expounding the fundamentals, converter topologies, applications, and future challenges of the SST in a systematic manner. The paper discusses the necessity of improved replacement of the low-frequency transformers (LFTs) and presents the configuration of SST. It presents SST fundamentals in individual stages and explores its origin and evolution. The basic topologies, their specifications, and control strategies are also described. The applications of SST as a replacement of LFTs are discussed along with recent applications. The future challenges for real-time implementation of SSTs are explored, and research directions are proposed.

Air pollution is a major issue all over the world because of its impacts on the environment and human beings. The present review discussed the sources and impacts of pollutants on environmental and human health and the current research status on environmental pollution forecasting techniques in detail; this study presents a detailed discussion of the Artificial Intelligence methodologies and Machine learning (ML) algorithms used in environmental pollution forecasting and early-warning systems; moreover, the present work emphasizes more on Artificial Intelligence techniques (particularly Hybrid models) used for forecasting various major pollutants (e.g., PM2.5, PM10, O3, CO, SO2, NO2, CO2) in detail; moreover, focus is given to AI and ML techniques in predicting chronic airway diseases and the prediction of climate changes and heat waves. The hybrid model has better performance than single AI models and it has greater accuracy in prediction and warning systems. The performance evaluation error indexes like R2, RMSE, MAE and MAPE were highlighted in this study based on the performance of various AI models.

In the production system, the production of a perfect item is essential for existing competitive market situations. To produce a perfect finished product, the quality of a raw material is a crucial issue of a production system. This paper has examined this issue with an insightful production-inventory model of the manufacturer of a deteriorating item selling goods to multiple markets with different selling seasons. We have provided an answer strategy to track down the optimal production plan for raw materials and the ideal creation plan for completed items. Transportation cost was incorporated for transporting the raw material. Marketing of the finished product is another crucial factor for selling products and earning revenues. The main objective of the present study is to adopt a production model in inventory for inferring request capacities for multi-item, multi-outlet circumstances. As of late, much accentuation has been given to the consideration of the control and support of creation inventories of disintegrating things. The demand rate is persistent and holding cost is a direct function of time. This paper has followed an analytical approach to diminish the entire inventory cost. Finally, a sensitivity analysis was performed to study the effect of changes of different parameters of the model on the optimal policy. Moreover, in order to approve the determined models, we have clarified mathematical models and examined affectability.