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This work explores the properties of four‐noded quadrilateral elements for which integration is carried out using low‐order integration rules, based on one‐point integration over subelements making up the quadrilaterals. The purpose is to identify those rules which lead to elements having the desirable properties of high coarse‐mesh accuracy, and stability in the incompressible limit. A two‐point rule is investigated in detail, as is its counterpart for problems of incompressible media, in which the volumetric term is integrated using a one‐point rule. Numerical results indicate that the new elements perform well in general when compared with existing enhanced strain or equivalent elements, and appear to be particularly efficient in cases in which meshes are severely distorted.

Supply chains (SCs) have been growingly virtualized in response to the market challenges and opportunities that are presented by new and cost-effective internet-based technologies today. This paper designed a virtual closed-loop supply chain (VCLSC) network based on multiperiod, multiproduct and by using the Internet of Things (IoT). The purpose of the paper is the optimization of the VCLSC network.

The proposed model considers the maximization of profit. For this purpose, costs related to virtualization such as security, energy consumption, recall and IoT facilities along with the usual costs of the SC are considered in the model. Due to real-world demand fluctuations, in this model, demand is considered fuzzy. Finally, the problem is solved using the Grey Wolf algorithm and Firefly algorithm. A numerical example and sensitivity analysis on the main parameters of the model are used to describe the importance and applicability of the developed model.

The findings showed that the Firefly algorithm performed better and identified more profit for the SC in each period. Also, the results of the sensitivity analysis using the IoT in a VCLSC showed that the profit of the virtual supply chain (VSC) is higher compared to not using IoT due to tracking defective parts and identifying reversible products. In proposed model, chain members can help improve chain operations by tracking raw materials and products, delivering products faster and with higher quality to customers, bringing a new level of SC efficiency to industries. As a result, VSCs can be controlled, programmed and optimized remotely over the Internet based on virtual objects rather than direct observation.

There are limited researches on designing and optimizing the VCLSC network. This study is one of the first studies that optimize the VSC networks considering minimization of virtual costs and maximization of profits. In most researches, the theory of VSC and its advantages have been described, while in this research, mathematical optimization and modeling of the VSC have been done, and it has been tried to apply SC virtualization using the IoT. Considering virtual costs in VSC optimization is another originality of this research. Also, considering the uncertainty in the SC brings the issue closer to the real world. In this study, virtualization costs including security, recall and energy consumption in SC optimization are considered.

1. Investigates the role of IoT for virtual supply chain profit optimization and mathematical optimization of virtual closed-loop supply chain (VCLSC) based on multiperiod, multiproduct with emphasis on using the IoT under uncertainty.

2. Considering the most important costs of virtualization of supply chain include: cost of IoT information security, cost of IoT energy consumption, cost of recall the production department, cost of IoT facilities.

3. Selection of the optimal suppliers in each period and determination of the price of each returned product in virtual supply chain.

4. Solving and validating the proposed model with two meta-heuristic algorithms (the Grey Wolf algorithm and Firefly algorithm).

Investigates the role of IoT for virtual supply chain profit optimization and mathematical optimization of virtual closed-loop supply chain (VCLSC) based on multiperiod, multiproduct with emphasis on using the IoT under uncertainty.

Considering the most important costs of virtualization of supply chain include: cost of IoT information security, cost of IoT energy consumption, cost of recall the production department, cost of IoT facilities.

Selection of the optimal suppliers in each period and determination of the price of each returned product in virtual supply chain.

Solving and validating the proposed model with two meta-heuristic algorithms (the Grey Wolf algorithm and Firefly algorithm).

This paper aims to investigate hypothesized relationships between the Internet of things (IoT) and big data analytics (BDA) with supply chain visibility (SCV) and operational performance (OP) in the pharmaceutical manufacturing sector in Jordan. The paper also aims to test the conceptual model related to the indirect effects of SCV.

To achieve the objectives of this paper, a conceptual model was developed through a review of the current literature. Data analysis was performed by covariance-based structural equation modelling using Amos 25 software, and the convergent validity, discriminant validity, reliability and confirmatory factor analysis were verified. Then the hypotheses were tested.

The results of the study indicate that there is a positive and statistically significant relationship between the IoT and BDA on SCV and OP. The relationship was positive and statistically significant between SCV and OP. In addition, support for the mediation hypothesis that SCV mediates the relationship among IoT, BDA and OP was confirmed.

This paper provides new theoretical and managerial contributions that have not been covered in previous studies, and it is considered the first study that uses this conceptual model with this mechanism in terms of the theoretical lens and empirical application. This paper contributes to understanding the dynamic mechanisms of the IoT and BDA in enhancing OP, which contributes to creating a supply chain capable of facing various environmental fluctuations and pressures. This study presents new implications that can be used in the supply chain literature.

Blockchain technology offers numerous venues for supply chain applications and research. However, the connections between specific blockchain features and future applications have been unclear to date in its evolution. The purpose of this study is to fill this void.

The authors advance the understanding of blockchain in supply chain management by providing a new research framework built on unique blockchain features as applied across core supply chain functions.

This study’s framework is a feature-function matrix that integrates four overarching supply chain functions (i.e. supplier management, logistics, production processes and customer management) with nine blockchain features (i.e. traceability/provenance, accessibility, visibility, immutability, distributed/shared ledger, validity, peer-to-peer transacting, pseudonymity and programmability). This study’s feature-function framework is supported by a structured, systematic review of reviews using PRISMA methods. The authors use the framework to present a future blockchain research agenda in supply chain management.

The authors provide a new blockchain feature/supply chain function framework and provide a structured path for future research.

Over the past decades, changes in climate have been commonly observed in many parts of the world. It is apparent that changes in temperature and rainfall and resulting increases in frequency and intensity of flood and drought events have affected ecological and social systems on the earth. According to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, warming atmosphere of the climate system is now unequivocal (IPCC, 2007). Climate change poses significant risks to the livelihoods, culture, and health of millions of people (Barnett, 2003). Ecological and climatic disasters – hurricanes, tornadoes, draught, flooding, landslides – are becoming more frequent, resulting in devastation to family and communities, especially the poor living in precarious environments (Ogata & Sen, 2003). Related to this, the number of climate-led disaster events and affected populations has been increasing during the last decades according to the data from EM-DAT (2010).

Currently, digital technology has been proposed as a new archetype for developing an effective traceability system in the perishable food supply chain (FSC). Implementation of such a system needs significant investment and the burden lies with the members of the supply chain. The purpose of this paper is to examine the impact on the profit of the supply chain members due to the implementation of an effective traceability system with such a large investment. The study also tries to explore the impact of the implementation of such a system by coordination among the members through a cost-sharing mechanism.

A two-level supply chain that comprises a supplier and retailer is analyzed using a game-theoretic approach. The mathematical models are developed considering the scenario for an individual, centralized and both members invest using a cost-sharing mechanism. For each of the models, the impact of product selling price, information sensing price and quality improvement level on profit is analyzed through numerical analysis.

The study reveals that consumer involvement can be a strong motivation for the supply chain members to initiate investment in the traceability system. Further, from an investment perspective cost-sharing model is beneficial compared to the individual investment-bearing model. This mechanism can coordinate as well as benefit the FSC members. However, the model is less beneficial to the centralized model from profit and quality improvement levels.

Food wastage can be less from supplier and retailer perspectives. Moreover, consumers can purchase food items only after verifying their shipping conditions. Consequently the food safety scandals can be reduced remarkably.

Digital technology adoption in the perishable FSC is still considered emerging. The present study helps organizations to implement a traceability system in the perishable FSC through consumer involvement and a cost-sharing mechanism.

Abundant studies of outpatient visits apply traditional recurrent neural network (RNN) approaches; more recent methods, such as the deep long short-term memory (DLSTM) model, have yet to be implemented in efforts to forecast key hospital data. Therefore, the current study aims to reports on an application of the DLSTM model to forecast multiple streams of healthcare data.

As the most advanced machine learning (ML) method, static and dynamic DLSTM models aim to forecast time-series data, such as daily patient visits. With a comparative analysis conducted in a high-level, urban Chinese hospital, this study tests the proposed DLSTM model against several widely used time-series analyses as reference models.

The empirical results show that the static DLSTM approach outperforms seasonal autoregressive integrated moving averages (SARIMA), single and multiple RNN, deep gated recurrent units (DGRU), traditional long short-term memory (LSTM) and dynamic DLSTM, with smaller mean absolute, root mean square, mean absolute percentage and root mean square percentage errors (RMSPE). In particular, static DLSTM outperforms all other models for predicting daily patient visits, the number of daily medical examinations and prescriptions.

With these results, hospitals can achieve more precise predictions of outpatient visits, medical examinations and prescriptions, which can inform hospitals' construction plans and increase the efficiency with which the hospitals manage relevant information.

To address a persistent gap in smart hospital and ML literature, this study offers evidence of the best forecasting models with a comparative analysis. The study extends predictive methods for forecasting patient visits, medical examinations and prescriptions and advances insights into smart hospitals by testing a state-of-the-art, deep learning neural network method.

The purpose of this paper is to summarize the scientific information of various qualities of bael fruit juice used in traditional system of medicine for variety of purposes. Utilization of bael fruit juice in day-to-day life has great nutritional, therapeutic, and commercial importance. Bael fruit contains nutrients like vitamins (riboflavin), minerals, trace elements, energy and phytochemicals, including flavonoids, polyphenols and antioxidants, that have been shown to have varied health benefits. In past few decades, bael has been extensively studied for its medicinal properties by advanced scientific techniques, and a variety of bioactive compounds like marmelosin, tannins, alkaloids, coumarins, steroids, rutacine, y-sitosterol, psoralin, xanthotoxin, scopolotein, aegelemine, aegeline, marmeline, fragrine, dictamine, cinnamide and different derivatives of cinnamide have been isolated from its fruit juice.

The medicinal value of bael fruit is very high when the harvests just begin to ripen. As a result, it has a high demand as alternative medicine for curing the diseases like diabetes, high cholesterol, peptic ulcer, inflammation, diarrhea and dysentery, constipation, respiratory infection. Furthermore, the bael fruit juice has anticancer, cardio protective, antibacterial, antifungal, radio protective, antipyretic, analgesic, antioxidant, antiviral, anthelmintic and anti-inflammatory, hepatoprotective, wound healing properties. The ripe fruit juice is aromatic, has cooling and laxative effects, and arrests secretion or bleeding.

The unripe or half-ripe fruit juice is good for digestion, useful in preventing or curing scurvy, and it strengthens the stomach action. It helps in the healing of ulcerated intestinal surfaces and has appreciable activity against intestinal pathogenic organisms. The present review summarizes the scientific information of various qualities of bael fruit juice used in traditional system of medicine for a variety of purposes.

It is quite evident from this review that bael is an important medicinal herb and extensively used in Ayurveda, Siddha and other medicinal systems. Bael fruit juice is an excellent source of water and natural sugar and is important principally for containing vitamins, minerals, phytochemicals, antioxidants, pigments, energy, organic acids, dietary fiber and other food components, which are the key factors in the medicinal value of this plant. Moreover, mechanisms of action of a few bioactive compounds have been identified so far.

Through the use of the Markov Decision Model (MDM) approach, this study uncovers significant variations in the availability of machines in both faulty and ideal situations in small and medium-sized enterprises (SMEs). The first-order differential equations are used to construct the mathematical equations from the transition-state diagrams of the separate subsystems in the critical part manufacturing plant.

To obtain the lowest investment cost, one of the non-traditional optimization strategies is employed in maintenance operations in SMEs in this research. It will use the particle swarm optimization (PSO) algorithm to optimize machine maintenance parameters and find the best solutions, thereby introducing the best decision-making process for optimal maintenance and service operations.

The major goal of this study is to identify critical subsystems in manufacturing plants and to use an optimal decision-making process to adopt the best maintenance management system in the industry. The optimal findings of this proposed method demonstrate that in problematic conditions, the availability of SME machines can be enhanced by up to 73.25%, while in an ideal situation, the system's availability can be increased by up to 76.17%.

The proposed new optimal decision-support system for this preventive maintenance management in SMEs is based on these findings, and it aims to achieve maximum productivity with the least amount of expenditure in maintenance and service through an optimal planning and scheduling process.