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In today’s mass production era, the world is making things (products and systems) so quickly and systematically in huge volume. The demand for these products is very high and, at the same time, consumers are still in search for a need for making the production very personalized. Hence, the “one mold fits all” approach may not seem to be enough. The present approach is facing the lack of networking between the automation pyramid levels, that is, especially between enterprise resource planning (ERP) and manufacturing execution system (MES) layers and, in turn, communicating directly with the lower layers is not possible. This missing communication among the process equipment like machineries and field control systems like PLCs at the production shop floors implies that customization at the product layer for the consumer is still in progress in classical manufacturing. Mini-MES is a new concept being introduced here to solve the existing techniques reported in the literature and is followed by industry best practices. The novel mini-MES platform provides an avenue for the technology process level (the most bottom layer) to interplay interconnectivity and interoperability with its higher levels until the above pain points are addressed holistically. The chapter is going to focus mainly on the factory production of digital manufacturing and on describing the 3-Cs implementation plan, the enabling technology, and the achievable outcome ahead.

The seamless integration of sensor technologies with the transportation infrastructure has leveraged the sensing and communication capabilities to achieve a sustainable intelligent transportation system through which safety, traffic control, and infotainment applications can benefit from multiple sensors deployed in different elements of the transportation system. Being one of the most active research fields in both academia and industry, sensor technologies enable various types of vehicular applications, such as autonomous driving, precise fleet management, intelligent traffic and congestion management, etc. A combination of low cost and reliable passive road sensors as well as traffic sensors (e.g., piezoelectric or magnetic micro-electro-mechanical system (MEMS) sensors) along with the IoT and fog computing provide an enormous potential for improving traffic efficiency, driving safety, and ride comfort.

Background: Primary aldosteronism (PA) is the most common treatable cause of secondary hypertension. In addition to increased cardiovascular risk, patients also suffer from impaired quality of life (QoL) and psychological symptoms. We assessed for changes in QoL and depressive symptoms in a cohort of Asian patients with PA, after surgical and medical therapy. Methods: We administered questionnaires to 34 patients with PA, mean age, 51.3 years, 29.4% females, in a prospective observational study from 2017 to 2020. QoL was assessed using RAND-36 and EQ-5D-3L, and depressive symptoms was assessed using Beck Depression Inventory (BDI-II) at baseline, 6 months, and 1 year post-treatment. Results: Significant improvement was observed 1 year after treatment in both physical and mental summative scores of RAND-36 from baseline, +3.65 (p = 0.023) and +3.41 (p = 0.033) respectively, as well as four subscale domains (physical functioning, bodily pain, role emotional and mental health). Significant improvement was also seen in EQ-5D dimension of anxiety/depression at 1 year post-treatment. Patients treated with surgery (N=21) had significant improvement in EQ-5D index score post-treatment, and better EQ-5D outcomes compared to medical group (N=13) at 1 year post-treatment. 37.9%, 41.6% and 60.7% of patients had symptoms in the cognitive, affective and somatic domains of the BDI-II respectively. There was significant improvement in the affective domain of BDI after 1 year of treatment. Conclusion: Appropriate treatment with surgical and medical therapy improves QoL and psychological symptoms in patients with PA, highlighting the importance of early diagnosis and treatment of this common condition.

This paper investigates an adaptive sampling rate control scheme for networked control systems (NCSs) subject to packet disordering. The main objectives of the proposed scheme are (a) to avoid heavy packet disordering existing in communication networks and (b) to stabilize NCSs with packet disordering, transmission delay and packet loss. First, a novel sampling rate control algorithm based on statistical characteristics of disordering entropy is proposed; secondly, an augmented closed-loop NCS that consists of a plant, a sampler and a state-feedback controller is transformed into an uncertain and stochastic system, which facilitates the controller design. Then, a sufficient condition for stochastic stability in terms of Linear Matrix Inequalities (LMIs) is given. Moreover, an adaptive tracking controller is designed such that the sampling period tracks a desired sampling period, which represents a significant contribution. Finally, experimental results are given to illustrate the effectiveness and advantages of the proposed scheme.

Wireless Sensor Networks (WSNs) can be typically used to achieve Continuous Monitoring (CM) or Event-Detection Driven (EDD) inside the supervised area. For both applications, sensors consume energy for three main reasons: sensing, processing and wireless communicating. The wireless communication refers to data transmission and reception. Among these three operations, it is known that the most power consuming task is data transmission. Approximatively 80% of power consumed in each sensor node is used for data transmission. Hence, unnecessary transmissions and/or unnecessary large data packets reduce the system’s lifetime. In this work, we are interested in studying different data transmission schemes that reduce the energy consumption by means of compression, in order to reduce the data packet’s length, or by means of avoiding transmission of redundant information. Continuous-monitoring applications require periodic refreshed data information at the sink nodes. To date, this entails the need of the sensor nodes to transmit continuously in a periodic fashion to the sink nodes, which may lead to excessive energy consumption. In this work, we show that continuous-monitoring does not imply necessarily continuous reporting. Instead, we demonstrate that we can achieve continuous-monitoring using an event-driven reporting approach. For example, consider a continuous-monitoring temperature application, where each sensor node transmits periodically the sensed temperature to the sink node. In such application, it may happen that sensors have very similar reading during long periods of time and it would not be energy-efficient for sensors to continuously send the same value to the sink node. The network lifetime would be greatly increased by programming the sensors to transmit only when they have sensed a change in the temperature compared to the last transmitted information. In doing so, the end user would have a refreshed value of the temperature in the supervised area even if the sensors are not transmitting continuously in a periodic fashion. The final user would have exactly the same information gathered by the WSN as with the classical continuous-monitoring applications, but while the sensors only transmit when there is relevant data. 5