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This paper aims to explore the numerical study of the steady two-dimensional MHD hybrid Cu-Fe₃O₄/EG nanofluid flows over an inclined porous plate with an inclined magnetic effect. Iron oxide (Fe₃O₄) and copper (Cu) are hybrid nanoparticles, with ethylene glycol as the base fluid. The effects of several physical characteristics, such as the inclination angle, magnetic parameter, thermal radiation, viscous propagation, heat absorption and convective heat transfer, are revealed by this exploration.

Temperature and velocity descriptions, along with the skin friction coefficient and Nusselt number, are studied to see how they change depending on the parameters. Using compatible similarity transformations, the controlling equations, including those describing the momentum and energy descriptions, are turned into a set of non-linear ordinary differential equations. The streamlined mathematical model is then solved numerically by using the shooting approach and the Runge–Kutta method up to the fourth order. The numerical findings of skin friction and Nusselt number are compared and discussed with prior published data by Nur Syahirah Wahid.

The graphical representation of the velocity and temperature profiles within the frontier is exhibited and discussed. The various output values related to skin friction and the Nusselt number are shown in the table.

The new results are compared to past research and discovered to agree significantly with those authors’ published works.

To become a sustainable lean manufacturing (LM) system, an organization must be first distinctly cognizant of the terms “lean” and “sustainability” as they relate to the state of affairs of their particular industry and business. Next, the organization must identify and acquire the necessary qualities it needs to become sustainable in lean philosophy and its practices in the organization. The LM paradigm has been a top priority for many businesses; thus, this article is based on actual research done in Indian small and medium scale organizations to see how widely it is understood and implemented.

A framework was developed based on literature review and academic research. A preliminary analysis of a small number of small and medium-sized enterprises (SMEs) that, conceptually, summarizes and demonstrates the concerted efforts that a company may undertake to increase its leanness. This conceptual model was employed to create a questionnaire that was administered to survey the SMEs of India. The information gathered through this questionnaire was analyzed using the model developed by the researchers. Then fuzzy logic and systems approach were used to find out the effectiveness index (EI) of the organization.

The EI for system leanness at different organizational levels within an organization is determined using fuzzy logic and systems approach for 48 SMEs in different clusters. The average EI of the system was found to be 0.336 on a scale of 0–1 which indicates that the current state of lean implementation and its sustainability is very low and poor in Indian SMEs. This article outlines the key model components and describes how they were applied to analyze the data gathered from an industry study.

The research aims to make lean continuously sustainable by surfacing and eliminating the wastes in the Indian SMEs whenever it appears rather than using it as a cleaning tool. The present study was focused on India’s southern industrial areas and it was difficult to gather the information around the country due to its diverse industrial culture and geography. Hence, more research and the comparative study of the same that takes into account the various regions of the nation’s industrial lean behavior can be conducted.

The generalized sustainable lean framework analyzed using fuzzy logic and systems approach gives the current effectiveness of the leanness in SMEs of south India. This model can be effectively implemented in other areas of the nation to identify the scenario of lean and its sustainability and a final comprehensive model can be developed.

There is a dearth of comprehensive studies on the assessment of sustainability of the lean philosophy in Indian SMEs. With the help of combined fuzzy logic and systems approach, the model developed in this study evaluates the sustainability of the lean methodology using the EI used in SMEs by taking into account both the lean and sustainability factors as well as enablers like customer satisfaction, ethics, innovation and technology.

This study aims to construct a framework to evaluate technology innovation performance (TIP) of manufacturing organizations by adopting a fuzzy-based approach. In very short time, the world’s economic order has been reformed by economic globalization, thereby bringing new challenges as well as opportunities for the manufacturing industries. Policy makers encounter different decisions that require the use of various types of data in their decision-making process. These challenges raised the necessity of measuring innovation capability, which is critical issue for decision makers in today’s competitive world. The research results reveal the complexity of the path to technology innovation evaluation, constituting a novel contribution to the literature.

It is difficult for decision makers to make appropriate and effective decisions without knowing the innovation capability of companies in a particular sector or a region. As a result, not only on a macro but also on a micro level, an integrated and complete technique of measuring, estimating and even projecting innovation performance is necessary. In light of above mentioned facts, the present study proposes a technology innovation performance evaluation system of manufacturing organizations using fuzzy logic.

A model has been developed to be beneficial for any kind of organization where TIP evaluation is important consideration for enhancing manufacturing performance. Fuzzy control is used to determine the overall performance index by combining results of the TIP in selected criteria, which will certainly ensure suitability of the concerned organizations during performance rating calculations.

This study elaborates a fuzzy model to predict TIP with fuzzy rules. This is the first time a TIP evaluation model is developed using fuzzy approach that will be applicable for any kind of manufacturing industry where TIP evaluation is considered to be significantly important for cooperation’s to sustain competitiveness in the dynamic landscape.

A rapid increase in traditional industries is creating social and environmental problems through extensive usage of natural resources and polluting the environment. A circular economy provides curative and renewing lines of action about these problems. Therefore, this study aims to examine the factors that lead toward sustainable performance in a circular economy context and empirically test the relationships between green technology adoption (GTA), circular economy principles (CEP), sustainable supply chain practices (SSCM) and sustainable performance (SP).

Using the well-developed governmental databases, data from 435 small and medium enterprises (SMEs) in the textile sector of Pakistan were collected and tested through AMOS using a structural equation model.

The results disclosed that GTA, CEP and SSCM have significant and positive direct relationships and facilitate improving SMEs’ SP. Circular economy entrepreneurship (CEE) and customer pressure (CP) were found to have a significant and positive influence on the relationships of GTA and CEP with SSCM.

The role of GTA in circular economy and the moderating effect of CEE and CP is an addition to the literature. SMEs’ GTA allows them to reuse, reduce and recycle natural resources rather than obtain new ones from the ecosystem.

The purpose of this paper is to investigate the vehicle-based sensor effect and pavement temperature on road condition assessment, as well as to compute a threshold value for the classification of pavement conditions.

Four sensors were placed on the vehicle’s control arms and one inside the vehicle to collect vibration acceleration data for analysis. The Analysis of Variance (ANOVA) tests were performed to diagnose the effect of the vehicle-based sensors’ placement in the field. To classify road conditions and identify pavement distress (point of interest), the probability distribution was applied based on the magnitude values of vibration data.

Results from ANOVA indicate that pavement sensing patterns from the sensors placed on the front control arms were statistically significant, and there is no difference between the sensors placed on the same side of the vehicle (e.g., left or right side). A reference threshold (i.e., 1.7 g) was computed from the distribution fitting method to classify road conditions and identify the road distress based on the magnitude values that combine all acceleration along three axes. In addition, the pavement temperature was found to be highly correlated with the sensing patterns, which is noteworthy for future projects.

The paper investigates the effect of pavement sensors’ placement in assessing road conditions, emphasizing the implications for future road condition assessment projects. A threshold value for classifying road conditions was proposed and applied in class assignments (I-17 highway projects).

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.

This study aims to investigate the feasibility of proposed microgrid (MG) that comprises photovoltaic, wind turbines, battery energy storage and diesel generator to supply a residential building in Grindelwald which is chosen as the test location.

Three operational configurations were used to run the proposed MG. In the first configuration, the electric energy can be vended and procured utterly between the main-grid and MG. In the second configuration, the energy trade was performed within 15 kWh as the maximum allowable limit of energy to purchase and sell. In the third configuration, the system performance in the stand-alone operation mode was investigated. A whale optimization technique is used to determine the optimal size of MG in all proposed configurations. The cost of energy (COE) and other measures are used to evaluate the system performance.

The obtained results revealed that the first configuration is the most beneficial with COE of 0.253$/KWh and reliable 100%. Furthermore, the whale optimization algorithm is sufficiently feasible as compared to other techniques to apply in the applications of MG.

The value of the proposed research is to investigate to what extend the integration between MG and main-grid is beneficial economically and technically. As opposed to previous research studies that have focused predominantly only on the optimal size of MG.

This paper aims to explore three operations and supply chain management (OSCM) approaches for meeting the 2 °C targets to counteract climate change: adaptation (adjusting to climatic impacts); mitigation (innovating towards low-carbon practices); and carbon-removing negative emissions technologies (NETs). We suggest that adaptation nor mitigation may be enough to meet the current climate targets, thus calling for NETs, resulting in the following question: How can operations and supply chains be reconceptualized for NETs?

We draw on the sustainable supply chain and transitions discourses along with interview data involving 125 experts gathered from a broad research project focused on geoengineering and NETs. We analyze three case studies of emerging NETs (biochar, direct air carbon capture and storage and ocean alkalinity enhancement), leading to propositions on the link between OSCM and NETs.

Although some NETs are promising, there remains considerable variance and uncertainty over supply chain configurations, efficacy, social acceptability and potential risks of unintended detrimental consequences. We introduce the concept of transformative OSCM, which encompasses policy interventions to foster the emergence of new technologies in industry sectors driven by social mandates but lack clear commercial incentives.

To the best of the authors’ knowledge, this paper is among the first that studies NETs from an OSCM perspective. It suggests a pathway toward new industry structures and policy support to effectively tackle climate change through carbon removal.

As of July 30, 2020, more than 17 million novel coronavirus disease 2019 (COVID-19) cases were registered including 671,500 deaths. Yet, there is no immediate medicine or vaccination for control this dangerous pandemic and researchers are trying to implement mathematical or time series epidemic models to predict the disease severity with national wide data.

In this study, the authors considered COVID-19 daily infection data four most COVID-19 affected nations (such as the USA, Brazil, India and Russia) to conduct 60-day forecasting of total infections. To do that, the authors adopted a machine learning (ML) model called Fb-Prophet and the results confirmed that the total number of confirmed cases in four countries till the end of July were collected and projections were made by employing Prophet logistic growth model.

Results highlighted that by late September, the estimated outbreak can reach 7.56, 4.65, 3.01 and 1.22 million cases in the USA, Brazil, India and Russia, respectively. The authors found some underestimation and overestimation of daily cases, and the linear model of actual vs predicted cases found a p-value (<2.2e-16) lower than the R2 value of 0.995.

In this paper, the authors adopted the Fb-Prophet ML model because it can predict the epidemic trend and derive an epidemic curve.

This study begins by outlining the core principles of how flat plate solar collectors (FPSC) operate and the underlying mechanisms of heat transfer. It underscores the pivotal role of fin patterns in enhancing convective heat transfer.

The primary objective is to examine the impact of diverse fin patterns on the thermal performance of FPSCs. The study involves the development of a 3D-CFD model for FPSC using the computational fluid dynamics (CFD) software ANSYS FLUENT. The analysis is conducted for variable mass flow rates (m_f) ranging from 0.01 to 0.05 kg/s with an interval of 0.02 kg/s. Each flow rate is assessed for three distinct fin patterns: straight plate fins, V-shaped fins and wavy fins. Recognizing the variation in solar radiation intensity throughout the day, the analysis is executed at six different time points ranging 10:00 a.m. to 03.00 p.m. with a time interval of 1.00 h.

It is observed that the wavy fin pattern with a mass flow rate of 0.01 kg/s exhibited the highest outlet temperature, showing a significant temperature difference of 12.4 K at noon. Conversely, the V-shaped fin pattern with a mf of 0.05 kg/s has the lowest temperature difference value, measuring only 3 K. The analysis included the calculation of thermal efficiency for each case, revealing that the V-shaped fin pattern at a mf of 0.05 kg/s and a sun radiation intensity of 885.42 W/m² achieved the maximum instantaneous thermal efficiency (η_th) of 34.7%. The study records the outlet air temperature for all of these combinations and presents the data graphically in the paper.

The findings of the simulations indicate that the thermal performance that is η_th and maximum temperature of outlet air of FPSCs can be improved through the utilization of variable fin patterns.