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The aging and deterioration of engineering building structures present significant risks to both life and property. Fiber Bragg grating (FBG) sensors, acclaimed for their outstanding reusability, compact form factor, lightweight construction, heightened sensitivity, immunity to electromagnetic interference and exceptional precision, are increasingly being adopted for structural health monitoring in engineering buildings. This research paper aims to evaluate the current challenges faced by FBG sensors in the engineering building industry. It also anticipates future advancements and trends in their development within this field.

This study centers on five pivotal sectors within the field of structural engineering: bridges, tunnels, pipelines, highways and housing construction. The research delves into the challenges encountered and synthesizes the prospective advancements in each of these areas.

The exceptional performance of FBG sensors provides an ideal solution for comprehensive monitoring of potential structural damages, deformations and settlements in engineering buildings. However, FBG sensors are challenged by issues such as limited monitoring accuracy, underdeveloped packaging techniques, intricate and time-intensive embedding processes, low survival rates and an indeterminate lifespan.

This introduces an entirely novel perspective. Addressing the current limitations of FBG sensors, this paper envisions their future evolution. FBG sensors are anticipated to advance into sophisticated multi-layer fiber optic sensing networks, each layer encompassing numerous channels. Data integration technologies will consolidate the acquired information, while big data analytics will identify intricate correlations within the datasets. Concurrently, the combination of finite element modeling and neural networks will enable a comprehensive simulation of the adaptability and longevity of FBG sensors in their operational environments.

The purpose of this paper is to achieve intelligent superstore site selection. Yonghui Superstores partnered with Cardinal Operations to incorporate a tremendous amount of site-related information (e.g. points of interest, population density and features, distribution of competitors, transportation, commercial ecosystem, existing own-store network) into its store site optimization.

This paper showcases the integration of regression, optimization and machine learning approaches in site selection, which has proven practical and effective.

The result was the development of the “Yonghui Intelligent Site Selection System” that includes three modules: business district scoring, intelligent site engine and precision sales forecasting. The application of this system helps to significantly reduce the labor force required to visit and investigate all potential sites, circumvent the pitfalls associated with possibly biased experience or intuition-based decision making and achieve the same population coverage as competitors while needing only half the number of stores as its competitors.

To our knowledge, this project is among the first to integrate regression, optimization and machine learning approaches in site selection. There is innovation in optimization techniques.

This paper integrates condition-based maintenance (CBM) with production planning in a single-stage production system that deteriorates with usage during a specified finite planning horizon. The purpose of this paper is to develop an integrated production and maintenance model to minimize the expected total cost over the horizon.

A joint production planning and CBM model is proposed. In the model, a set of products must be produced in lots. The system degradation is a stationary gamma process and the degradation level is detected by inspection between production lots. Maintenance actions including imperfect preventive maintenance (PM) should be taken when the failure risk exceeds the maintenance threshold. A fix-iterative heuristic algorithm is proposed to address the joint model.

The proactive policy expressed as a prognosis maintenance threshold is introduced to integrate CBM with batch production perfectly. Experiments are carried out to conduct sensitivity analysis, which provides some insights to facilitate industrial manufacturing. The superiority of the proposed joint model compared with a separate decision method is demonstrated. The results show an advantage in cost saving.

Few studies have been made to integrate production planning and CBM decisions, especially for a multi-product system. Their maintenance decisions are usually based on a periodic review policy, which is not appropriate for batch production system. A prognosis maintenance threshold based on system condition and production quantity is suitable for the integrated decisions. Moreover, the imperfect PM is taken into consideration in this paper. A fix-iterative algorithm is developed to solve the joint model. This work forms a proactive maintenance for batch production.

The purpose of this review was to highlight the association between ID and obesity in toddlers and preschool children.

This review aimed to review and evaluate literature of the published research discussing the relationship between ID and overweight and obesity in children under the age of 5 years. Conflicting results of iron status in overweight and obese children under the age of 5 years had been found. However, most articles concluded that ID is associated significantly with overweight and obesity in children because of the systemic inflammatory reaction which is considered the major cause of ID; hepcidin with its resultant effect in decreasing duodenal absorption of iron; in addition to other causes including dietary and genetic factors.

Conflicting results of iron status in overweight and obese children under the age of 5 years had been found, but most articles concluded that ID is associated significantly with overweight and obesity in children, with systemic inflammatory reaction being the major cause through hepcidin with its resultant effect in decreased duodenal absorption of iron, in addition to other causes including dietary and genetic factors.

Many nutrients have been associated with weight gain and ID development. Unbalanced diet either in excess or shortage may affect weight status and serum iron profile. Future research is needed to study more in depth the association between ID and obesity in toddlers and preschool children and to further explore the various factors involved in pathogenesis of ID.

There is growing evidence of a knowledge gap in the association of maintenance with production activities in bottling plants. Indeed, insights into how to jointly optimise these activities are not clear. In this paper, two optimisation models, Taguchi schemes and response surface methodology are proposed.

Borrowing from the “hard” total quality management elements in optimisation and prioritisation literature, two new models were developed based on factor, level and orthogonal array selection, signal-to-noise ratio, analysis of variance and optimal parametric settings as Taguchi–ABC and Taguchi–Pareto. An additional model of response surface methodology was created with analysis on regression, main effects, residual plots and surface plots.

The Taguchi S/N ratio table ranked planned maintenance as the highest. The Taguchi–Pareto shows the optimal parametric setting as A4B4C1 (28 h of production, 30.56 shifts and 37 h of planned maintenance). Taguchi ABC reveals that the planned maintenance and number of shifts will influence the outcome of production greatly. The surface regression table reveals that the production hours worked decrease at a value of planned maintenance with a decrease in the number of shifts.

This is the first time that joint optimisation for bottling plant will be approached using Taguchi–ABC and Taguchi–Pareto. It is also the first time that response surface will be applied to optimise a unique platform of the bottling process plant.

The traditional practice for maintenance, quality control and production scheduling is to plan independently irrespective of an interrelationship exist between them. The purpose of this paper is to develop an approach for integrating maintenance, quality control and production scheduling. The objective is to investigate the benefits of the integrated effect in terms of the expected total cost of system operation of the three functions.

The proposed approach is based on the conditional reliability of the components. Cost model for integrating selective maintenance, quality control using sampling-based procedure and production scheduling is developed using the conditional reliability. An integrated approach is such that, first an optimal schedule for the batches to be processed is obtained independently while the maintenance and quality control decisions are optimized considering the optimal schedule on the machine. The expected total cost of conventional approach, i.e. “No integration” is calculated to compare the effectiveness of integrated approach.

The integrated approach have shown a higher cost saving as compared to the independent planning approach. The approach is practical to implement as the results are obtained in a reasonable computational time.

The approach presented in this paper is generic and can be applied at planned as well as unplanned opportunities. The proposed integrated approach is dynamic in nature, as during maintenance opportunities, it is possible to optimize the decision on maintenance, quality control and production scheduling considering the current age of components and production requirement.

The originality of the paper is in the approach for integration of the three elements of shop floor operations that are usually treated separately and rarely touched upon by researchers in the literature.