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This paper presents the determination of the maximum power point of a bifacial photovoltaic (PV) system using two different cell models. The optimal power point is determined by using genetic algorithm (GA), as an optimisation tool. The purpose of this paper is to find which of the two analysed models gives better results in the determination of the maximum power point of a bifacial PV system for different solar irradiations. The quality of the results gained from both models is analysed based on the value of the objective function.

In this research work, the maximum power point of bifacial PV modules is determined by using two different PV cell models, such as the simplified and two-diode models of PV cells. Based on the input electrical data for the analysed bifacial PV module as well as the mathematical model of the two PV cell presentations, the values for the current and the voltage at the maximum power point for a given solar irradiation and working temperature are determined by the algorithm for each solution in the population and generation.

From the presented results and the performed analysis, it can be concluded that GA is quite appropriate for this purpose and gives adequate results for both models and for all working conditions. The two-diode model was found to be more suitable compared with the simplified model due to its complexity. Therefore, although the power difference for each of the scenarios for the two compared models does not differ significantly among the two models, it is in favour of the two-diode model. Which implicates that the for fast and simple calculation the simplified model can also do the job.

This approach can be very successfully applied in the design process of a PV plant to forecast the output characteristics of the PV system if there is enough information about the weather conditions for a given location. This procedure can be very helpful in the process of selection of right PV module and inverter for a given location.

An optimisation technique using GA as an optimisation tool has been developed and successfully applied in the determination of the maximum power point for a bifacial PV module using to different models of solar cell. The results are compared with the analytically determined values as well as with the values given from the producer and they show good agreement.

Research on solar energy adoption offers a multidimensional scope and warrants exploration from multiple perspectives, including political, economic, management, behavioral, policy and innovation aspects. The aim of this paper is to comprehensively consolidate major research findings on the premise of solar energy adoption and to disclose gaps in the existing literature.

A bibliometric analysis of the vast literature is conducted on 1,009 meticulously shortlisted articles following the semi-systematic literature review methodology. A text analytics tool named BibExcel is used for synthesizing the literature, and the results are visualized using Gephi, Pajek and a spreadsheet application.

This paper reports the evolution of research in the selected domain. It is noted that research in this domain was primarily concentrated on four broad themes, namely, peer effects and spatial patterns, public perceptions, policies and economics and technological evolution. The analysis further reveals the merging of two of these themes as a result of transdisciplinary research and also projects future research trends emphasizing political interventions in technological evolution and diffusion.

Research trends and future research scope are identified and discussed in detail. The information revealed from the analysis, along with the research implications, will assist policymakers in noting the flaws in the current doctrines and practices, entrepreneurs in understanding potential enablers and barriers influencing solar energy adoption and budding scholars in comprehending the current research status and framing promising research objectives to close the existing research gaps.

This study evaluates the aesthetic perception of photovoltaic (PV) systems situated at various locations on an apartment building facade, comparing them with the original facade. It also aims to understand how aesthetic dimensions influence the perception of PV installations in diverse building locations. Moreover, it aims to create a framework that will guide for installing PV installations considering both their functionality and aesthetics.

The study uses a mixed-method approach, including qualitative and quantitative approaches. It includes a literature review and a questionnaire. 418 participants evaluated different PV-embedded facades using a Likert scale across various aesthetic variables.

The findings indicate that aesthetic perceptions of PV vary by the location of installation. It also shows that all aesthetic dimensions affect PV installation aesthetics, with location-specific preferences. For original elevation, compatibility and simplicity are given precedence over blending and coherence for windows, creativity and harmony for facades, functionality and harmony for balconies, and innovation potential and simplicity for roofs.

This study focuses on a single building type; further investigation is required to examine other building types. It also examined one PV technology with common visual properties, but future studies can examine others. Additional research is needed to compare the participating groups and the effect of their sociodemographic factors, using on-site surveys and interviews.

Few studies have investigated how PV systems affect apartment building users' architectural aesthetic perception. The results of this study make a valuable contribution to the field of sustainable architecture by providing practical guidance for architects, engineers, stakeholders, and researchers who are interested in integrating aesthetic, user-centric considerations into renewable energy solutions.

The objective of this study is to examine the relationships among intellectual capital (IC), environmental compliance, corporate innovation and social media usage with respect to their influence on overall business performance.

A theoretical model and related hypotheses are offered, all of which are grounded in both the resource-based view and social network theory. The data were collected through a well-structured questionnaire, and 330 responses from manufacturing firms in Vietnam were deemed appropriate for data analysis using partial least squares structural equation modeling (PLS-SEM).

IC and social media usage have significantly positive effects on corporate innovation and business performance. Moreover, corporate innovation substantially enhances business performance. Furthermore, the results demonstrate that corporate innovation plays a partly mediating role in the research model. Meanwhile, IC fully mediates the relationship between environmental compliance and business performance.

This study offers valuable insights into intellectual capital, innovation, environmental compliance and social media usage for governments, practitioners and academics. Managers can incorporate social media usage strategies into their operational practices, enhancing environmental compliance, fostering innovation and ultimately promoting company success. Furthermore, the findings lead to practical recommendations for manufacturers seeking to adopt the CE model as part of taking a green production approach.

Organizational researchers have an ongoing interest in examining the connections among IC, innovation, environmental compliance and social media usage. Nevertheless, few papers have empirically investigated the interconnections among these constructs and their impact on organizational performance. This study examines these connections and provides concrete evidence for them.

A highly selective cyanide phenoxazine-based fluorescence chemosensor POH was created to detect cyanide (CN) ions.

A malonitrile was added to a phenoxazine fluorophore to make this widely available chemosensor. By fluorescence spectroscopy, the sensor POH showed turn-off fluorescence emission for CN with 2:1 binding stoichiometry in CH₃CN/H₂O (90:10 v/v) medium.

The detection limits for CN were 9.8 × 10⁻⁹ M, which were much lower than WHO standards. NMR and FT-IR investigations backed up the suggested sensor POH mechanism.

The detection CN method should be applicable in a number of situations, where the CN anion for fresh water and drinking water has to be quickly and accurately analyzed.

This paper aims to assess the economic, environmental, policy-related and social implications of establishing green hydrogen production in Jordan.

The comprehensive analysis has been investigated, including economic assessments, environmental impact evaluations, policy examinations and social considerations. Furthermore, the research methodology encompasses energy demand, sector, security and supply analysis, as well as an assessment of the availability of renewable energy resources.

The results indicate substantial economic benefits associated with green hydrogen production, including job creation, increased tax revenue and a reduction in energy imports. Additionally, the study identifies positive environmental impacts, such as decreased greenhouse gas emissions and air pollution. Noteworthy, two methods could be used to produce hydrogen, namely: electrolysis and thermochemical water splitting. As a recommendation, the study proposes that Jordan, particularly Aqaba, take proactive measures to foster the development of a green hydrogen industry and collaborate with international partners to exchange best practices and establish the necessary infrastructure.

To the best of the authors’ knowledge, this paper is among the first to provide a comprehensive perspective on the potential of green hydrogen production as a driving force for Jordan’s economy, while also benefiting the environment and society. However, the research recognizes several challenges that must be addressed to materialize green hydrogen production in Jordan, encompassing high renewable energy costs, infrastructure development requirements and community concerns. Despite these obstacles, the study asserts that the potential advantages of green hydrogen production outweigh the associated risks.

This study aims to investigate the daily performance of the proposed microgrid (MG) that comprises photovoltaic, wind turbines and is connected to the main grid. The load demand is a residential area that includes 20 houses.

The daily operational strategy of the proposed MG allows to vend and procure utterly between the main grid and MG. The smart metre of every consumer provides the supplier with the daily consumption pattern which is amended by demand side management (DSM). The daily operational cost (DOC) CO2 emission and other measures are utilized to evaluate the system performance. A grey wolf optimizer was employed to minimize DOC including the cost of procuring energy from the main grid, the emission cost and the revenue of sold energy to the main grid.

The obtained results of winter and summer days revealed that DSM significantly improved the system performance from the economic and environmental perspectives. With DSM, DOC on winter day was −26.93 ($/kWh) and on summer day, DOC was 10.59 ($/kWh). While without considering DSM, DOC on winter day was −25.42 ($/kWh) and on summer day DOC was 14.95 ($/kWh).

As opposed to previous research that predominantly addressed the long-term operation, the value of the proposed research is to investigate the short-term operation (24-hour) of MG that copes with vital contingencies associated with selling and procuring energy with the main grid considering the environmental cost. Outstandingly, the proposed research engaged the consumers by smart meters to apply demand-sideDSM, while the previous studies largely focused on supply side management.

The purpose of this study is to establish a grey-entropy-catastrophe progression method (CPM) model to assess the photovoltaic (PV) industry chain resilience of Jiangsu Province in China.

First, we designed the resilience evaluation index system of such a chain from two aspects: the external environment and internal conditions. We then constructed a PV industry chain resilience evaluation model based on the grey-entropy-CPM. Finally, the feasibility and applicability of the proposed model were verified via an empirical case study analysis of Jiangsu Province in China.

As of the end of 2022, the resilience level of its PV industry chain is medium-high resilience, which indicates a high degree of adaptability to the current unpredictable and competitive market, and can respond to the uncertain impact of changes in conditions effectively and in a timely manner.

The construction of this model can provide reference ideas for related enterprises in the PV industry to analyze the resilience level of the industrial chain and solve the problem of industrial chain resilience.

Firstly, an analysis of the entire industrial chain structure of the PV industry, combined with its unique characteristics is needed to design a PV industry chain resilience evaluation index system. Second, grey relational analysis (GRA) and the entropy method were adopted to improve the importance of ranking the indicators in the evaluation of the CPM, and a resilience evaluation model based on grey-entropy-CPM was constructed.

Exposure to indoor air pollutants poses a significant health risk, contributing to various ailments such as respiratory and cardiovascular diseases. These unhealthy consequences are specifically alarming for athletes during exercise due to their higher respiratory rate. Therefore, studying, predicting and curtailing exposure to indoor air contaminants during athletic activities is essential for fitness facilities. The objective of this study is to develop a neural network model designed for predicting optimal (in terms of health) occupancy intervals using monitored indoor air quality (IAQ) data.

This research study presents an innovative approach employing a long short-term memory (LSTM) recurrent neural network (RNN) to determine optimal occupancy intervals for ensuring the safety and well-being of occupants. The dataset was collected over a 3-month monitoring campaign, encompassing 15 meteorological and indoor environmental parameters monitored. All the parameters were monitored in 5-min intervals, resulting in a total of 77,520 data points. The dataset collection parameters included the building’s ventilation methods as well as the level of occupancy. Initial preprocessing involved computing the correlation matrix and identifying highly correlated variables to serve as inputs for the LSTM network model.

The findings underscore the efficacy of the proposed artificial intelligence model in forecasting indoor conditions, yielding highly specific predicted time slots. Using the training dataset and established threshold values, the model effectively identifies benign periods for occupancy. Validation of the predicted time slots is conducted utilizing features chosen from the correlation matrix and their corresponding standard ranges. Essentially, this process determines the ratio of recommended to non-recommended timing intervals.

Humans do not have the capacity to process this data and make such a relevant decision, though the complexity of the parameters of IAQ imposes significant barriers to human decision-making, artificial intelligence and machine learning systems, which are different. Present research utilizing multilayer perceptron (MLP) and LSTM algorithms for evaluating indoor air pollution levels lacks the capability to predict specific time slots. This study aims to fill this gap in evaluation methodologies. Therefore, the utilized LSTM-RNN model can provide a day-ahead prediction of indoor air pollutants, making its competency far beyond the human being’s and regular sensors' capacities.

The purpose of this study is to explore the determinants influencing industrial adoption of green hydrogen amidst the global transition towards sustainability. Recognizing green hydrogen as a pivotal clean energy alternative for industrial applications is critical for understanding its potential integration into sustainable practices.

This research examines the impact of factors such as innovativeness, perceived ease of use, user comfort, optimism and governmental policies on the industrial intention towards green hydrogen usage. Using responses from 227 Indian industry professionals and conducting analysis via the SmartPLS software, the study reveals a discernible discomfort among industrial workers pertaining to the daily application of green hydrogen.

The research presents an array of policy recommendations for stakeholders. Emphasized strategies include the introduction of green hydrogen certificates, sustainable public procurement mechanisms, tax incentives, green labelling protocols and the establishment of a dedicated hydrogen skill development council, all of which can significantly influence the trajectory of green hydrogen adoption within the industrial sector.

This research synthesizes various elements, from industry perception and challenges to policy implications, presenting a holistic view of green hydrogen’s potential role in industry decarbonization and SDG realization. In essence, this study deepens not only the empirical understanding but also pioneers fresh theoretical frameworks, setting a precedent for subsequent academic endeavours.