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PurposeThe present research investigates the effects of adopting Industry 4.0 technologies (ADT) on the Pakistani food sector’s sustainable performance (SP). Specifically, it focuses on the roles of green supply chain collaboration (GSCC), circular economy practices (CEP) and technological readiness (TR) as mediators and environmental dynamism (EDY) as a moderator.Design/methodology/approachThis study is based on the natural resource-based view (NRBV) theory to determine the 14.0 adoption toward sustainable performance with mediators and moderators. The authors conducted an online questionnaire from 318 employees of the food manufacturing industries in Lahore, Pakistan, and applied the PLS-SEM approach to test the relationships of variables.FindingsThe findings of this study reveal that adopting Industry 4.0 technologies significantly influences sustainable performance through GSCC, CEP and TR in the Pakistani food sector. Moreover, the relation between industry 4.0 technologies, GSCC, CEP and TR is positively moderated by environmental dynamism.Research limitations/implicationsThe findings have marked implications for the literature on adopting I4.0 on sustainable performance in the Pakistani food sector. This research is based on data collected from a single country, and industry is the limitation of this study.Originality/valueThe present study provides conclusive evidence of the influence of the adoption of Industry 4.0 on sustainable performance through GSCC, CEP and TR in the Pakistani food sector. This study is the first to investigate the moderating role of environmental dynamism among industry 4.0 technologies, GSCC, CEP and TR.

Floods are among the most devastating and recurring natural hazards and have caused extensive economic losses to human lives and infrastructures around the world. Swat valley in northern Pakistan is prone to frequent floods and was severely affected by the Flood2010 in the recent past. Flood hazard assessment is a non-structural strategy for flood mitigation in addition to the structure measure. In this study, 60 km long reach of the River Swat (Khwazakhela Bridge–Chakdara Bridge) was modeled using the HEC-RAS 2D model and high-resolution 12-m WorldDEM. The model was calibrated and validated for only historical maximum flood event, i.e., Flood2010 using Manning’s ‘n’ values, flood stage at the Chakdara Bridge and satellite imagery-based Flood2010-observed extent. In addition, flood model sensitivity to the DEM was carried out and simulated maximum depth was 12, 13, 14, and 25 m for the 12-m WorldDEM, 30-m SRTM, 30-m ALOS and 30-m ASTER DEMs, respectively. Designed hydrographs were prepared for 2, 5, 10, 25, 50, and 100-year return periods based on the Flood2010-observed hydrograph. Finally, the model was simulated for 2, 5, 10, 25, 50, and 100-year return periods with full momentum equation as the calculation method. Simulated extents based on the 12-m WorldDEM were used for the preparation of flood hazard maps. Landcover exposure to the designed flood events shows that agriculture including orchards is the major potential affected class with affected areas up to 55 Km2. The developed flood hazard maps will enable the policy makers to mainstream flood hazard assessment in the planning and development process for mitigating flood hazard in Swat Valley.

The concept of digital twins is proposed as a new technology-led advancement to support the processes of the design, construction, and operation of built assets. Commonalities between the emerging definitions of digital twins describe them as digital or cyber environments that are bidirectionally-linked to their physical or real-life replica to enable simulation and data-centric decision making. Studies have started to investigate their role in the digitalization of asset delivery, including the management of built assets at different levels within the building and infrastructure sectors. However, questions persist regarding their actual applications and implementation challenges, including their integration with other digital technologies (i.e., building information modeling, virtual and augmented reality, Internet of Things, artificial intelligence, and cloud computing). Within the built environment context, this study seeks to analyze the definitions and characteristics of a digital twin, its interactions with other digital technologies used in built asset delivery and operation, and its applications and challenges. To achieve this aim, the research utilizes a thorough literature review and semi-structured interviews with ten industry experts. The literature review explores the merits and the relevance of digital twins relative to existing digital technologies and highlights potential applications and challenges for their implementation. The data from the semi-structured interviews are classified into five themes: definitions and enablers of digital twins, applications and benefits, implementation challenges, existing practical applications, and future development. The findings provide a point of departure for future research aimed at clarifying the relationship between digital twins and other digital technologies and their key implementation challenges.

This study delves into the interconnections among corporate social responsibility, green intellectual capital, green ambidextrous innovation, and sustainable performance, particularly in the context of Industry 4.0 and sustainability. A questionnaire-based survey was conducted, and a sample of 317 small and medium enterprises was collected. Using Partial Least Squares Structural Equation Modeling in Smart-PLS v4, the findings reveal a significant relationship between corporate social responsibility and sustainable performance, with green intellectual capital and green ambidextrous innovation serving as mediating factors. Moreover, the study highlights the moderating role of Industry 4.0 among green intellectual capital and green ambidextrous innovation with sustainable performance. These findings may guide the managers in designing and implementing CSR strategies beyond compliance and contributing to competitive advantage through green intellectual capital and green ambidextrous innovation for business success in the era of Industry 4.0.

The hybrid power system is a combination of renewable energy power plants and conventional energy power plants. This integration causes power quality issues including poor settling times and higher transient contents. The main issue of such interconnection is the frequency variations caused in the hybrid power system. Load Frequency Controller (LFC) design ensures the reliable and efficient operation of the power system. The main function of LFC is to maintain the system frequency within safe limits, hence keeping power at a specific range. An LFC should be supported with modern and intelligent control structures for providing the adequate power to the system. This paper presents a comprehensive review of several LFC structures in a diverse configuration of a power system. First of all, an overview of a renewable energy-based power system is provided with a need for the development of LFC. The basic operation was studied in single-area, multi-area and multi-stage power system configurations. Types of controllers developed on different techniques studied with an overview of different control techniques were utilized. The comparative analysis of various controllers and strategies was performed graphically. The future scope of work provided lists the potential areas for conducting further research. Finally, the paper concludes by emphasizing the need for better LFC design in complex power system environments.

Over the last decade, neural networks have reached almost every field of science and become a crucial part of various real world applications. Due to the increasing spread, confidence in neural network predictions has become more and more important. However, basic neural networks do not deliver certainty estimates or suffer from over- or under-confidence, i.e. are badly calibrated. To overcome this, many researchers have been working on understanding and quantifying uncertainty in a neural network’s prediction. As a result, different types and sources of uncertainty have been identified and various approaches to measure and quantify uncertainty in neural networks have been proposed. This work gives a comprehensive overview of uncertainty estimation in neural networks, reviews recent advances in the field, highlights current challenges, and identifies potential research opportunities. It is intended to give anyone interested in uncertainty estimation in neural networks a broad overview and introduction, without presupposing prior knowledge in this field. For that, a comprehensive introduction to the most crucial sources of uncertainty is given and their separation into reducible model uncertainty and irreducible data uncertainty is presented. The modeling of these uncertainties based on deterministic neural networks, Bayesian neural networks (BNNs), ensemble of neural networks, and test-time data augmentation approaches is introduced and different branches of these fields as well as the latest developments are discussed. For a practical application, we discuss different measures of uncertainty, approaches for calibrating neural networks, and give an overview of existing baselines and available implementations. Different examples from the wide spectrum of challenges in the fields of medical image analysis, robotics, and earth observation give an idea of the needs and challenges regarding uncertainties in the practical applications of neural networks. Additionally, the practical limitations of uncertainty quantification methods in neural networks for mission- and safety-critical real world applications are discussed and an outlook on the next steps towards a broader usage of such methods is given.

Owing to extraordinary healing power, Terminalia species have been used in traditional medicine systems to treat various diseases. Many folklore uses of Terminalia neotaliala (Madagascar's almond) included treating arterial hypertension, diabetes, diarrhea, dysentery, colic, oral and digestive candidiasis, intestinal parasitic infections, inflammatory skin conditions, postpartum care, and mycotic infections but nevertheless scientifically explored for its medicinal and pharmacological importance. Therefore, the current study intended to prepare methanolic extract and its fractionation with hexane, chloroform, and butanol followed by evaluation of their polyphenolic content, biological activities, and LCMS analysis. The biological study included antioxidant activity and enzyme inhibition assay i.e., α-glucosidase and urease. The insight study of biologically active secondary metabolites of butanol fraction (BUAE) was performed through LCMS. The total phenolic content (TPC) and total flavonoid content (TFC) of hydroalcoholic and its fractions were estimated using the Folin-Ciocalteu and aluminum chloride method. The total tannin content (TTC) was determined using the Folin-Denis spectrophotometric method. Similarly, the antioxidant potential of HAAE, HEAE, CFAE, and BUAE was determined using four methods as DPPH (1,1-diphenyl-2-picrylhydrazyl), 2,2-azinobis(3-ethylbenothiazoline)-6-sulfonic acid, cupric reducing antioxidant capacity (CUPRAC), and ferric reducing antioxidant power (FRAP). The sample extracts were also evaluated against two clinically important enzymes i.e., α-glucosidase and urease. The BUAE (butanol aerial fraction) showed the highest TPC (234.79 ± 0.12 mg.GAE.g-1 DE), TFC (320.75 ± 12.50 mg.QE.g-1 DE), and TTC (143.36 ± 4.32 mg.TA.Eq.g-1 DE). The BUAE also showed the highest scavenging potential determined by DPPH (642.65 ± 1.11 mg.TEq.g-1 DE) and ABTS (543.17 ± 1.11 mg.TEq.g-1 DE), and the metal-reducing capacity determined by CUPRAC (1510.41 ± 4.45 mg.TEq.g-1 DE) and FRAP (739.81 ± 19.32 mg.TEq.g-1 DE). The LCMS of BUAE identified 18 different biologically active phytoconstituents validating a rich source of hydrolyzable tannins including ellagitannins and gallitannins. The present study concluded that T. neotaliala is a rich source of polyphenols capable of neutralizing the damage caused by free radical accumulation in the cells and tissues. The significant antioxidant results and identification of high molecular weight hydrolyzable tannins enlightened the medicinal importance of T. neotaliala.

For effective monitoring and control of the fermentation process, an accurate real-time measurement of important variables is necessary. These variables are very hard to measure in real-time due to constraints such as the time-varying, nonlinearity, strong coupling, and complex mechanism of the fermentation process. Constructing soft sensors with outstanding performance and robustness has become a core issue in industrial procedures. In this paper, a comprehensive review of existing data pre-processing approaches, variable selection methods, data-driven (black-box) soft-sensing modeling methods and optimization techniques was carried out. The data-driven methods used for the soft-sensing modeling such as support vector machine, multiple least square support vector machine, neural network, deep learning, fuzzy logic, probabilistic latent variable models are reviewed in detail. The optimization techniques used for the estimation of model parameters such as particle swarm optimization algorithm, ant colony optimization, artificial bee colony, cuckoo search algorithm, and genetic algorithm, are also discussed. A comprehensive analysis of various soft-sensing models is presented in tabular form which highlights the important methods used in the field of fermentation. More than 70 research publications on soft-sensing modeling methods for the estimation of variables have been examined and listed for quick reference. This review paper may be regarded as a useful source as a reference point for researchers to explore the opportunities for further enhancement in the field of soft-sensing modeling.

The aim of current study was to develop the transdermal transfersomes of glucosamine for better drug delivery. Stretch ability and plasticity of transfersomes membranes mitigate the risk of vesicle rupture in the skin and allows the drug carrying transfersome to pass through the epidermis following the natural water gradient. Transdermal delivery of Glucosamine has an advantage over oral route, having greater local concentration and fewer systemic effects. Thin Film Rotary method was use to prepare transfersomes, and characterization was carried out physio-chemically using electron microscopic studies, zeta potential evaluation, entrapment efficiency studies. To add on in the stability, development of a secondary topical vehicle using Carbopol 940 was carried out to enhance the shelf life of transfersomes. Furthermore, in vivo studies on rabbits were also carried out using the papain induced arthritis model to support the effectiveness of treatment. The radiology studies of knee joint of rabbits proved the effectiveness of glucosamine loaded transfersomes in healing the osteoarthritis with the blood plasma analysis remain unaltered. In vitro characterization showed the successful development of nano-deformable entities with good entrapment efficiency but with little stability, therefore modified into a gel. In a nut shell this modified new dosage from can be best alternative to other conventional options that owe lot of demerits.

Polymers play a significant role in enhanced oil recovery (EOR) due to their viscoelastic properties and macromolecular structure. Herein, the mechanisms of the application of polymeric materials for enhanced oil recovery are elucidated. Subsequently, the polymer types used for EOR, namely synthetic polymers and natural polymers (biopolymers), and their properties are discussed. Moreover, the numerous applications for EOR such as polymer flooding, polymer foam flooding, alkali–polymer flooding, surfactant–polymer flooding, alkali–surfactant–polymer flooding, and polymeric nanofluid flooding are appraised and evaluated. Most of the polymers exhibit pseudoplastic behavior in the presence of shear forces. The biopolymers exhibit better salt tolerance and thermal stability but are susceptible to plugging and biodegradation. As for associative synthetic polyacrylamide, several complexities are involved in unlocking its full potential. Hence, hydrolyzed polyacrylamide remains the most coveted polymer for field application of polymer floods. Finally, alkali–surfactant–polymer flooding shows good efficiency at pilot and field scales, while a recently devised polymeric nanofluid shows good potential for field application of polymer flooding for EOR.