Potato disease management plays a valuable role in the agriculture field as it might cause a significant loss in crops production. Therefore, timely recognition and classification of potato leaves diseases are necessary to minimize the loss, however, it is time taking task and requires human efforts. Thus, an accurate automated technique for timely detection and classification is needed to cope with the aforementioned challenges.There exist techniques grounded on machine learning and deep learning procedures that use the existing dataset i.e., ‘The Plant Village Dataset’ and perform classification into only two classes in potato leaves. Therefore, this article proposes a technique based on an improved deep learning algorithm that uses the potato leaf visual features to classify them into five classes i.e., Potato Late Blight (PLB), Potato Early Blight (PEB), Potato Leaf Roll (PLR), Potato Verticillium_wilt (PVw) and Potato Healthy (PH) class. The propose model is trained on the existing dataset i.e., “The Plant Village” that comprises of images having two ailments such as Early Blight (EB) and Late Blight (LB), and a Healthy class for potato leaves. Additionally, we have gathered the data for classes i.e., Potato Leaf Roll (PLR), Potato Verticillium_wilt (PVw) and Potato Healthy (PH) manually. A pre-trained Efficient DenseNet model has been employed utilizing an extra transition layer in DenseNet-201 to classify the potato leave diseases efficiently. Moreover, the usage of the reweighted cross-entropy loss function makes our proposed algorithm more robust as the training data is highly imbalanced. The dense connections with regularization power help to minimize the overfitting during the training of small training sets of potato leaves samples. The proposed algorithm is a novel and first technique to address and report the successful implementation for the detection and classification of four diseases in potato leaves. The algorithm’s performance was evaluated on the testing set and gave an accuracy of 97.2%. Various experiments have been performed to confirm that our proposed algorithm is more consistent and proficient to detect and classify potato leaves diseases than existing models.

The current article aims to present a numerical analysis of MHD Williamson nanofluid flow maintained to flow through porous medium bounded by a non-linearly stretching flat surface. The second law of thermodynamics was applied to analyze the fluid flow, heat and mass transport as well as the aspects of entropy generation using Buongiorno model. Thermophoresis and Brownian diffusion is considered which appears due to the concentration and random motion of nanoparticles in base fluid, respectively. Uniform magnetic effect is induced but the assumption of tiny magnetic Reynolds number results in zero magnetic induction. The governing equations (PDEs) are transformed into ordinary differential equations (ODEs) using appropriately adjusted transformations. The numerical method is used for solving the so-formulated highly nonlinear problem. The graphical presentation of results highlights that the heat flux receives enhancement for augmented Brownian diffusion. The Bejan number is found to be increasing with a larger Weissenberg number. The tabulated results for skin-friction, Nusselt number and Sherwood number are given. A decent agreement is noted in the results when compared with previously published literature on Williamson nanofluids.

Industry 4.0 is a new era of industrial revolution in which textile and apparel (T&A) companies are adopting and integrating advanced technologies to achieve sustainability and a competitive edge. Previous studies have just focused on the perspective of big data utilization in Industry 4.0 and neglected the role of business intelligence systems (BIS), especially in the T&A industry. The current study is one of the first to investigate the determinants of BIS adoption with an eye towards understanding how BIS can resolve sustainability issues in T&A companies with Industry 4.0 technologies. Methodology: A qualitative research approach is applied with 14 semi-structured in-depth interviews from 12 of the world’s high-end T&A companies. The snowball and purposeful sampling strategy is used to select the participants. The qualitative content analysis technique is used to analyze the interview data. Results: The findings revealed various themes, such as sustainability issues in T&A companies, improved value creation processes with leading BI solutions, and perceived difficulties in the adoption of BIS. Major improvements are perceived in the apparel retail business because apparel companies are more prone to adopt the Industry 4.0 technologies with advanced business intelligence (BI) solutions. The results prove the pivotal role of economic sustainability in the adoption of BIS and Industry 4.0 technologies in T&A companies.

The need for beneficial innovations in filtration expertise has lead to little consideration of cutting-edge materials, such as nanofiber membranes for water distillation. The presence of organic matter and traces of organics accumulation in wastewater poses a major problem and current technologies such as coagulation/flocculation and chlorine technology are unable to yield satisfying results. The extra volume of sludge generated by these technologies needs further processing and disposal. Nanotechnology has outstanding potential for filtration applications due to its capability to create precise structural controlled materials for such requirements. Electrospun nanofibrous membranes (ENMs) are cutting edge membrane technology that offer substantial high flux and high rejection rates compared to conventional membranes. ENMs present a revolution in water and sewage purification by offering a lightweight, cost-effective, and lower energy consumption process compared with conventional membranes. ENMs possess high porosity, generally approximately 80%, while conventional membranes have 5–35% porosity. Nano-engineered membranes have great potential in water treatment due to their exotic properties. In this connection, electrospinning membranes are emerging as a versatile technique with promising features for water treatment. This work highlights the application of ENM in wastewater treatment and surface modification of nanomembranes in order to address fouling issues and wastewater treatment from Tabuk Sewage Treatment Plant, Saudi Arabia.

In the present work, an experimental investigation is performed to assess the thermal and electrical performance of a photovoltaic solar panel cooling with multi-walled carbon nanotube–water/ethylene glycol (50:50) nano-suspension (MWCNT/WEG50). The prepared nanofluid was stabilized using an ultrasonic homogenizer together with the addition of 0.1vol% of nonylphenol ethoxylates at pH = 8.9. To reduce the heat loss and to improve the heat transfer rate between the coolant and the panel, a cooling jacket was designed and attached to the solar panel. It was also filled with multi-walled carbon nanotube–paraffin phase change material (PCM) and the cooling pipes were passed through the PCM. The MWCNT/WEG50 nanofluid was introduced into the pipes, while the nano-PCM was in the cooling jacket. The electrical and thermal power of the system and equivalent electrical–thermal power of the system was assessed at various local times and at different mass fractions of MWCNTs. Results showed that with an increase in the mass concentration of the coolant, the electricity and power production were promoted, while with an increase in the mass concentration of the nanofluid, the pumping power was augmented resulting in the decrease in the thermal–electrical equivalent power. It was identified that a MWCNT/WEG50 nano-suspension at 0.2wt% can represent the highest thermal and electrical performance of 292.1 W/m². It was also identified that at 0.2wt%, ~45% of the electricity and 44% of the thermal power can be produced with a photovoltaic (PV) panel between 1:30 pm to 3:30 pm.

This study is carried out to scrutinize the gyrotactic bioconvection effects on modified second-grade nanofluid with motile microorganisms and Wu’s slip (second-order slip) features. The activation energy and thermal radiation are also incorporated. The suspended nanoparticles in a host fluid are practically utilized in numerous technological and industrial products such as metallic strips, energy enhancement, production processes, automobile engines, laptops, and accessories. Nanoparticles with high thermal characteristics and low volume fraction may improve the thermal performance of the base fluid. By employing the appropriate self-similar transformations, the governing set of partial differential equations (PDEs) are reduced into the ordinary differential equations (ODEs). A zero mass flux boundary condition is proposed for nanoparticle diffusion. Then, the transmuted set of ODEs is solved numerically with the help of the well-known shooting technique. The numerical and graphical illustrations are developed by using a collocation finite difference scheme and three-stage Lobatto III as the built-in function of the bvp4c solver via MATLAB. Behaviors of the different proficient physical parameters on the velocity field, temperature distribution, volumetric nanoparticles concentration profile, and the density of motile microorganism field are deliberated numerically as well as graphically.