Advanced Technologies for Oral Controlled Release: Cyclodextrins for oral controlled release

Cyclodextrins (CDs) are used in oral pharmaceutical formulations, by means of inclusion complexes formation, with the following advantages for the drugs: (1) solubility, dissolution rate, stability and bioavailability enhancement; (2) to modify the drug release site and/or time profile; and (3) to reduce or prevent gastrointestinal side effects and unpleasant smell or taste, to prevent drug-drug or drug-additive interactions, or even to convert oil and liquid drugs into microcrystalline or amorphous powders. A more recent trend focuses on the use of CDs as nanocarriers, a strategy that aims to design versatile delivery systems that can encapsulate drugs with better physicochemical properties for oral delivery. Thus, the aim of this work was to review the applications of the CDs and their hydrophilic derivatives on the solubility enhancement of poorly water soluble drugs in order to increase their dissolution rate and get immediate release, as well as their ability to control (to prolong or to delay) the release of drugs from solid dosage forms, either as complexes with the hydrophilic (e.g. as osmotic pumps) and/ or hydrophobic CDs. New controlled delivery systems based on nanotechonology carriers (nanoparticles and conjugates) have also been reviewed

Managing changes initiated by industrial big data technologies : a technochange management model

With the adoption of Internet of Things and advanced data analytical technologies in manufacturing firms, the industrial sector has launched an evolutionary journey toward the 4th industrial revolution, or so called Industry 4.0. Industrial big data is a core component to realize the vision of Industry 4.0. However, the implementation and usage of industrial big data tools in manufacturing firms will not merely be a technical endeavor, but can also lead to a thorough management reform. By means of a comprehensive review of literature related to Industry 4.0, smart manufacturing, industrial big data, information systems (IS) and technochange management, this paper aims to analyze potential changes triggered by the application of industrial big data in manufacturing firms, from technological, individual and organizational perspectives. Furthermore, in order to drive these changes more effectively and eliminate potential resistance, a conceptual technochange management model was developed and proposed. Drawn upon theories reported in literature of IS technochange management, this model proposed four types of interventions that can be used to copy with changes initiated by industrial big data technologies, including human process intervention, techno-structural intervention, human resources management intervention and strategic intervention. This model will be of interests and value to practitioners and researchers concerned with business reforms triggered by Industry 4.0 in general and by industrial big data technologies in particular

Scenario-Driven Supply Chain Charaterization Using a Multi-Dimensional Approach

Extreme disruptive events, such as the volcano eruption in Iceland, the Japanese tsunami, and the COVID-19 pandemic, as well as constant changes in customers’ needs and expectations, have forced supply chains to continuously adapt to new environments. Consequently, it is paramount to understand the supply chain characteristics for possible future scenarios, in order to know how to respond to threats and take advantage of the opportunities that the next years will bring. This chapter focuses on describing the characteristics of the supply chain in each of the six macro-scenarios presented in Sardesai et al. (2020b), as final stage of the scenario building methodology. Supply chains for each scenario are characterized in eight dimensions: Products and Services, Supply Chain Paradigm, Sourcing and Distribution, Technology Level, Supply Chain Configuration, Manufacturing Systems, Sales Channel, and Sustainability

A cloud-based, knowledge-enriched framework for increasing machining efficiency based on machine tool monitoring

The ever-increasing complexity in manufacturing systems caused by the fluctuating customer demands has highly affected the contemporary shop-floors. The selection of the appropriate cutting parameters is becoming more and more challenging due to the increasing complexity of products. Until now, the knowledge of the machine operators concerning the modification of the machining parameters and the monitoring information is not sufficiently exploited by the optimization systems. Web and Cloud technologies together with wireless sensor networks are required to capture the shop-floor data and enable the ubiquitous access from multiple IT tools. For addressing these challenges, this research work proposes a Cloud-based, knowledge-enriched framework for machining efficiency based on machine tool monitoring. More precisely, it focuses on the optimization of the machining parameters and moves through an event-driven optimization algorithm, utilizing the existing machining knowledge captured by the monitoring system. Based on the features of a new part, a similarity mechanism retrieves the cutting parameters of successfully executed past parts that have been machined. Afterwards, the optimization module, using event-driven function blocks, adapts these parameters to efficiently optimize the moves and the cutting parameters. The monitoring system uses a wireless sensor network and a human operator input via mobile devices. A case study from the mould-making industry is used for validating the proposed framework

Integrated Production and Maintenance Scheduling Through Machine Monitoring and Augmented Reality: An Industry 4.0 Approach

Part 5: Sustainable Human Integration in Cyber-Physical Systems: The Operator 4.0International audienceMaintenance tasks are a frequent part of shop floor machines’ schedule, varying in complexity, and as a result in required time and effort, from simple cutting tool replacement to time consuming procedures. Nowadays, these procedures are usually called by the machine operator or shop floor technicians, based on their expertise or machine failures, commonly without flagging the shop floor scheduling. Newer approaches promote mobile devices and wearables as a mean of communication among the shop floor operators and other departments, to quickly notify for similar incidents. Shop floor scheduling is frequently highly influenced by maintenance tasks, thus the need to include them into the machine schedule has arisen. Moreover, production is highly disturbed by unexpected failures. As a result, the last few years through the industry 4.0 paradigm, production line machinery is more and more equipped with monitoring software, so as to flag the technicians before a maintenance task is required. Towards that end, an integrated system is developed, under the Industry 4.0 concept, consisted of a machine tool monitoring tool and an augmented reality mobile application, which are interfaced with a shop-floor scheduling tool. The mobile application allows the operator to monitor the status of the machine based on the data from the monitoring tool and decide on immediately calling AR remote maintenance or scheduling maintenance tasks for later. The application retrieves the machine schedule, providing the available windows for maintenance planning and also notifies the schedule for the added task. The application is tested on a CNC milling machine

Cyclodextrin-based systems for photoinduced hydrogen evolution

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