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The purpose of the paper is to identify the multiple types of data that can be collected and analyzed by practitioners across the cold chain, the ICT infrastructure required to enable data capture and how to utilize the data for decision making in cold chain logistics.

Content analysis based literature review of 38 selected research articles, published between 2000 and 2016, was used to create an overview of data capture, technologies used for collection and sharing of data, and decision making that can be supported by the data, across the cold chain and for different types of perishable food products.

There is a need to understand how continuous monitoring of conditions such as temperature, humidity, and vibration can be translated to support real-time assessment of quality, determination of actual remaining shelf life of products and use of those for decision making in cold chains. Firms across the cold chain need to adopt appropriate technologies suited to the specific contexts to capture data across the cold chain. Analysis of such data over longer periods can also unearth patterns of product deterioration under different transportation conditions, which can lead to redesigning the transportation network to minimize quality loss or to take precautions to avoid the adverse transportation conditions.

The findings need to be validated through further empirical research and modeling. There are opportunities to identify all relevant parameters to capture product condition as well as transaction data across the cold chain processes for fish, meat and dairy products. Such data can then be used for supply chain (SC) planning and pricing products in the retail stores based on product conditions and traceability information. Addressing some of the above research gaps will call for multi-disciplinary research involving food science and engineering, information technologies, computer science and logistics and SC management scholars.

The findings of this research can be beneficial for multiple players involved in the cold chain like food processing companies, logistics service providers, ports and wholesalers and retailers to understand how data can be effectively used for better decision making in cold chain and to invest in the specific technologies, which will suit the purpose. To ensure adoption of data analytics across the cold chain, it is also important to identify the player in the cold chain, which will drive and coordinate the effort.

This paper is one of the earliest to recognize the need for a comprehensive assessment for adoption and application of data analytics in cold chain management and provides directions for future research.

Many investigations are going on in monitoring, contact tracing, predicting and diagnosing the COVID-19 disease and many virologists are urgently seeking to create a vaccine as early as possible. Even though there is no specific treatment for the pandemic disease, the world is now struggling to control the spread by implementing the lockdown worldwide and giving awareness to the people to wear masks and use sanitizers. The new technologies, including the Internet of things (IoT), are gaining global attention towards the increasing technical support in health-care systems, particularly in predicting, detecting, preventing and monitoring of most of the infectious diseases. Similarly, it also helps in fighting against COVID-19 by monitoring, contract tracing and detecting the COVID-19 pandemic by connection with the IoT-based smart solutions. IoT is the interconnected Web of smart devices, sensors, actuators and data, which are collected in the raw form and transmitted through the internet. The purpose of this paper is to propose the concept to detect and monitor the asymptotic patients using IoT-based sensors.

In recent days, the surge of the COVID-19 contagion has infected all over the world and it has ruined our day-to-day life. The extraordinary eruption of this pandemic virus placed the World Health Organization (WHO) in a hazardous position. The impact of this contagious virus and scarcity among the people has forced the world to get into complete lockdown, as the number of laboratory-confirmed cases is increasing in millions all over the world as per the records of the government.

COVID-19 patients are either symptomatic or asymptotic. Symptomatic patients have symptoms such as fever, cough and difficulty in breathing. But patients are also asymptotic, which is very difficult to detect and monitor by isolating them.

Asymptotic patients are very hazardous because without knowing that they are infected, they might spread the infection to others, also asymptotic patients might be having very serious lung damage. So, earlier prediction and monitoring of asymptotic patients are mandatory to save their life and prevent them from spreading.

The purpose of this paper is to develop a prototype of a wearable medical device in the form of a bandage with a real-time data monitoring platform, which can be used domestically for diabetic patients to identify the possibility of foot ulceration at the early stage.

The prototype can measure blood volumetric change and temperature variation in the forefoot area simultaneously. The waveform extracted using a pulsatile-blood-flow signal was used to assess blood perfusion-related information, and hence, predict ischemic ulcers. The temperature difference between ulcerated and the reference was used to predict neuropathic ulcers. The medical device can be used as a bandage during the application wherein the sensory module is placed inside the hollow pocket of the bandage. A platform was developed through a mobile application where doctors can extract real-time information, and hence, determine the possibility of ulceration.

The height of the peaks in the pulsatile-blood-flow signal measured from the subject with foot ischemic ulcers is significantly less than that of the subject without ischemic ulcers. In the presence of ischemic ulcers, the captured waveform flattens. Therefore, the blood perfusion from arteries to the tissue of the forefoot is considerably low for the subject with ischemic ulcers. According to the temperature difference data measured over 25 consecutive days, the temperature difference of the subject with neuropathic ulcers occasionally exceeded the 4 °F range but mostly had higher values closer to the 4 °F range. However, the temperature difference of the subject who had no complications of neuropathic ulcers did not exceed the 4 °F range, and the majority of the measurements occupy a narrow range from −2°F to 2 °F.

The proposed prototype of wearable medical apparatus can monitor both temperature variation and pulsatile-blood-flow signal on the forefoot simultaneously and thereby predict both ischemic and neuropathic diabetes using a single device. Most importantly, the wearable medical device can be used domestically without clinical assistance with a real-time data monitoring platform to predict the possibility of ulceration and the course of action thereof.

The friction between brake pair causes an intense temperature rise on interface during braking, which affects the braking performance seriously. Therefore, building an accurate testing method for frictional temperature rise (FTR) is a prerequisite.

Facing braking conditions of automobiles, an experimental system for testing of FTR based on preset thermometry method (PTM) was established. The FTR was collected by the PTM and the variation laws as well as the cause of errors were analyzed by experiments. The deviations between tested and real temperature were corrected based on tribology and heat theories. Finally, an online prediction method for FTR was pointed out.

After correction, the temperature curve accords well with the laws of tribology and thermal theories. The corrected FTR at braking end point is approximately equal to the authentic temperature test by hand thermometer.

This study eliminated the hysteresis phenomenon of temperature rise sequence and lays a foundation for online accurate monitoring and warning of brake friction temperature rise. It has important theoretical and practical value for expanding the monitoring and improvement of brake performance.

The usage of additive manufacturing (AM) technology in industries has reached up to 50 per cent as prototype or end-product. However, for AM products to be directly used as final products, AM product should be produced through advanced quality control process, which has a capability to be able to prove and reach their desire repeatability, reproducibility, reliability and preciseness. Therefore, there is a need to review quality-related research in terms of AM technology and guide AM industry in the future direction of AM development.

This paper overviews research progress regarding the QC in AM technology. The focus of the study is on manufacturing quality issues and needs that are to be developed and optimized, and further suggests ideas and directions toward the quality improvement for future AM technology. This paper is organized as follows. Section 2 starts by conducting a comprehensive review of the literature studies on progress of quality control, issues and challenges regarding quality improvement in seven different AM techniques. Next, Section 3 provides classification of the research findings, and lastly, Section 4 discusses the challenges and future trends.

This paper presents a review on quality control in seven different techniques in AM technology and provides detailed discussions in each quality process stage. Most of the AM techniques have a trend using in-situ sensors and cameras to acquire process data for real-time monitoring and quality analysis. Procedures such as extrusion-based processes (EBP) have further advanced in data analytics and predictive algorithms-based research regarding mechanical properties and optimal printing parameters. Moreover, compared to others, the material jetting progresses technique has advanced in a system integrated with closed-feedback loop, machine vision and image processing to minimize quality issues during printing process.

This paper is limited to reviewing of only seven techniques of AM technology, which includes photopolymer vat processes, material jetting processes, binder jetting processes, extrusion-based processes, powder bed fusion processes, directed energy deposition processes and sheet lamination processes. This paper would impact on the improvement of quality control in AM industries such as industrial, automotive, medical, aerospace and military production.

Additive manufacturing technology, in terms of quality control has yet to be reviewed.

This work analyzes a pharmaceutical supply chain (PSC) in terms of supply chain visibility (SCV). The current good distribution practice (GDP) guideline demands increased visibility from firms. The purpose of this paper is to propose a solution for SCV enhancements based on automatic identification (Auto-ID) technologies.

The authors qualitatively analyze data from ten case studies of actors in a PSC. A review of Auto-ID technologies supports the derivation of solutions to enhance SCV.

This work shows that the functionalities of Auto-ID technologies offered by current practical monitoring solutions and challenges created by the GDP guideline necessitate further SCV enhancements. To enhance SCV, the authors propose three solutions: securPharm with passive radio frequency identification tags, transport containers with sensor nodes, and an SCV dashboard.

This study is limited to a PSC in Germany and is therefore not intended to be exhaustive. Thus, the results serve as a foundation for further analyses.

This study provides an overview of the functionality of Auto-ID technologies. In juxtaposition with the influence of the GDP guideline, the use of our Auto-ID-based solutions can help to enhance SCV.

This work analyzes a PSC in Germany, with consideration given to the influence of current legislation. Based on a multiple-case-study design, the authors derive three Auto-ID-based solutions for enhancing SCV.

This paper proposes new methods of fault detection for fuel systems in order to improve system availability. Novel fault systems are required for environmentally friendly lean burn combustion, but can carry high risk failure modes particularly through their control valves. The purpose of the developed technology is the rapid detection of these failure modes, such as valve sticking or impending sticking, and therefore to reduce this risk. However, sensing valve state is challenging due to hot environmental temperatures, which results in a low reliability for conventional position sensing.

Starting with the business needs elicited from stakeholders, a quality functional deployment process is performed to derive sensing system requirements. The process acknowledges the difference between test-bed and in-service aerospace needs through weightings on requirements and maps these customer requirements to systems performance metrics. The design of the system must therefore optimise the sensor suite, on- and off-board signal processing and acquisition strategy.

Against this systems engineering process, two sensing strategies are outlined which illustrate the span of solutions, from conventional gas path sensing with advanced signal processing to novel non-invasive sensing concepts. While conventional sensing may be feasible within a test cell, the constraints of aerospace in-service operation may necessitate more novel alternatives. Acoustic emission (detecting very high frequency surface vibration waves) sensing technology is evaluated to provide a non-invasive, remote and high temperature tolerant solution. Through this comparison, the considerations for the end-to-end system design are highlighted to be critical to sensor deployment success in-service.

The paper provides insight into different means of addressing the important problem of monitoring faults in combustor systems in gas turbines. By casting of the complex design problem within a systems engineering framework, the outline of a toolset for solution evaluation is provided.

The paper provides three areas of significant contributions: a diversity of methods to diagnosing fuel system malfunctions by measuring changes fuel flow distributions, through novel means, and the combustor exit temperature profiles (cause and effect); the use of analytical methods to support the selection (types and quantities) and placement of sensors to ensure adequate state awareness while minimising their impact on the engine system cost and weight; and an end-to-end data processing approach to provide optimised information for the engine maintainers allowing informed decision-making.

To examine the range of fibre‐optic sensors available for monitoring the integrity of buildings and civil engineering structures.

Explains the need for structural monitoring and reviews the types of fibre‐optic sensors. Concentrating on elongation and temperature sensing, shows how each technology works, which companies supply the products, and gives an overview of their technical specifications.

Fibre optics are able to provide integrated, single‐point and distributed sensor systems. The fibre is a communication channel as well as a sensor, and in some systems, carries highly multiplexed data over considerable distances to a central monitoring station. The take‐up of this technology in structural sensing is helped by appropriate packaging that assists attachment to buildings. Durability and measurement stability give a continuity of measurement that was not previously possible.

An introduction to the range of sensors applicable to structural monitoring, of general interest to scientists, but particularly to civil and constructional engineers.

Wireless sensor network (WSN) technologies are now available to implement real time temperature monitoring systems in food supply chains. The aim of this paper is to examine different types of methods and criteria to establish alerts in decision support systems in perishable food supply chains.

Logistic and temperature mapping was performed in cod supply chains to obtain data to establish criteria for temperature alerts. Data were collected for both ambient temperature and temperature of products packed in expanded polystyrene boxes.

Alerts based on single criterion for ambient temperature resulted in false alerts when compared to criteria for product temperature. More complex methods that took into account both temperature abuse and the severity of the abuse resulted in more relevant alerts for the chilled cod supply chain.

The research is based on mapping of cod supply chains with a limited number of iterations.

The scope of the research is the application of WSN in an actual supply chain of chilled cod transported from Iceland to Europe, which has relevance in assisting management decision making in the supply chain to prevent losses of quality and minimize waste.

Failure to maintain a low temperature occurs frequently at handover points where alert systems are usually not in place. The theoretical implication of this paper is the development of a conceptual framework for setting up temperature criteria for real time decision support systems in food supply chains.

Since the introduction of the fibre optic distributed temperature system by York in 1986, the system has been successfully applied in a number of areas. The monitoring of hot‐spots in large power transformers and electricity cables to the analysis of the curing process in large concrete structures such as dams are some examples of where the technology is being used and is providing temperature data. Other more imaginative but nevertheless potentially feasible applications range from detecting black ice on roads to the monitoring of volcanic eruptions for the purpose of disaster prevention.