Search

In this chapter we focuses on the issues that arise out of the relationship between the developer and the buyer, including claims, user expectations, and the legal ramifications that may follow an unhealthy relationship. The discussion touches on standards, reliability, security, safety, quality of software, quality of service of software products, causes of software failures, developer and buyer protection, and techniques for improving software quality. Causes of software failures or poor performance of a software product are discussed attributing the causes to a variety of reasons but most notably human error, the nature of software itself, and the environment in which software is produced and used. Finally historic examples of software caused accidents are given including the Therac–25, the Space Shuttle Challenger, the Indian Bhopal chemical accident and the Chernobyl Nuclear Power accident. Both consumer protection and techniques for improving software quality are also discussed.

Background Whilst access to cannabis-based medicinal products (CBMPs) has increased globally subject to relaxation of scheduling laws globally, one of the main barriers to appropriate patient access remains a paucity of high-quality evidence surrounding their clinical effects. Discussion Whilst randomised controlled trials (RCTs) remain the gold-standard for clinical evaluation, there are notable barriers to their implementation. Development of CBMPs requires novel approaches of evidence collection to address these challenges. Real world evidence (RWE) presents a solution to not only both provide immediate impact on clinical care, but also inform well-conducted RCTs. RWE is defined as evidence derived from health data sourced from non-interventional studies, registries, electronic health records and insurance data. Currently it is used mostly to monitor post-approval safety requirements allowing for long-term pharmacovigilance. However, RWE has the potential to be used in conjunction or as an extension to RCTs to both broaden and streamline the process of evidence generation. Conclusion Novel approaches of data collection and analysis will be integral to improving clinical evidence on CBMPs. RWE can be used in conjunction or as an extension to RCTs to increase the speed of evidence generation, as well as reduce costs. Currently, there is an abundance of potential data however, whilst a number of platforms now exist to capture real world data it is important the right tools and analysis are utilised to unlock potential insights from these.

Achieving community immunity against the coronavirus disease 2019 (COVID-19) depends on vaccinating the largest number of people within a specific period while taking all precautionary measures. To address this problem, this paper presents a smart parking system that will help the health crisis management committee to vaccinate the largest number of people with the minimum period of time while ensuring that all precautionary measures are followed, through a set of algorithms. These algorithms seek to ensure a uniform distribution of persons in parking. This paper proposes a novel complex system for smart parking and nine algorithms to address the NP-hard problem. The experimental results demonstrate the performance of the proposed algorithms in terms of gap and time. Applying these algorithms to smart cities to ensure precautionary measures against COVID-19 can help fight against this pandemic.

Understanding the long-term benefits and risks of treatments, devices, and vaccines is critically important for individual- and population-level healthcare decision-making. Extension studies, or ‘roll-over studies,’ are studies that allow for patients participating in a parent clinical trial to ‘roll-over’ into a subsequent related study to continue to observe and measure long-term safety, tolerability, and/or effectiveness. These designs are not new and are often used as an approach to satisfy regulatory post-approval safety requirements. However, designs using traditional clinical trial infrastructure can be expensive and burdensome to conduct, particularly, when following patients for many years post trial completion. Given the increasing availability and access of real-world data (RWD) sources, direct-to-patient technologies, and novel real-world study designs, there are more cost-efficient approaches to conducting extension studies while assessing important long-term outcomes. Here, we describe various fit-for-purpose design options for extension studies, discuss related methodological considerations, and provide scientific and operational guidance on practices when planning to conduct an extension study using RWD. This manuscript is endorsed by the International Society for Pharmacoepidemiology (ISPE).

Robotics, once combined with cold atmospheric plasma, represent key elements of the next generation of personalized medicine and contribute to the effective yet immediate response to pandemics. Plasma robots can serve as CAP delivery vehicle to assist in tumor therapeutics and viral disease prevention in addition to the already prevalent utilities of robots in precision surgery, diagnosis, and risk prevention. Plasma robots may develop at either the macro- or the micro- scale, successful navigations at which require joint effort from multiple research domains.

The current COVID-19 pandemic poses numerous challenges for ongoing clinical trials and provides a stress-testing environment for the existing principles and practice of estimands in clinical trials. The pandemic may increase the rate of intercurrent events (ICEs) and missing values, spurring a great deal of discussion on amending protocols and statistical analysis plans to address these issues. In this article we revisit recent research on estimands and handling of missing values, especially the ICH E9 (R1) on Estimands and Sensitivity Analysis in Clinical Trials. Based on an in-depth discussion of the strategies for handling ICEs using a causal inference framework, we suggest some improvements in applying the estimand and estimation framework in ICH E9 (R1). Specifically, we discuss a mix of strategies allowing us to handle ICEs differentially based on reasons for ICEs. We also suggest ICEs should be handled primarily by hypothetical strategies and provide examples of different hypothetical strategies for different types of ICEs as well as a road map for estimation and sensitivity analyses. We conclude that the proposed framework helps streamline translating clinical objectives into targets of statistical inference and automatically resolves many issues with defining estimands and choosing estimation procedures arising from events such as the pandemic., Comment: 5 pages and 1 table

Background Risk-based monitoring (RBM) is a slow uptake in some trial sponsors. There are three main reasons for this. First, there is the fear of making large investments into advanced RBM technology solutions. Second, it is considered that RBM is most suitable for large, complex trials. Third, there is the fear of errors in both critical and non-critical data, appearing as reduced on-site monitoring is being conducted. Methods Our RBM team identified, evaluated, and mitigated trial risks, as well as devised a monitoring strategy. The clinical research associate (CRA) assessed the site risks, and the RBM team conducted central monitoring. We compared all data errors and on-site monitoring time between the partial switching sites [sites that had switched to partial source data verification (SDV) and source data review (SDR)] and the 100% SDV and SDR sites (sites that had implemented 100% SDV and SDR). Results Partial switching sites did not require any critical data correction and had a smaller number of data corrections through on-site monitoring than the 100% SDV and SDR sites. The RBM strategy reduced the on-site monitoring time by 30%. Conclusions The results suggest that RBM can be successfully implemented through the use of site risk assessment and central monitoring with practically no additional investment in technology and still produced similar results in terms of subject safety and data quality, as well as the cost savings that have been reported in global complex studies.

Every medical product requires additional study even after regulatory approval. We highlight several lines of enquiry to advance our understanding of COVID19 vaccines post authorization: identifying key population segments warranting more study, assessment of efficacy, and of safety data, harmonization of data relating to immune response and developing mechanisms for data and knowledge sharing across countries. We show how innovative trial designs and sources from real world data play a critical role in generating evidence.

Firms today face rapidly changing and complex environments that managers and leaders must navigate carefully because confronting these changes is directly connected with success and failure in business. In particular, business leaders are adopting a new paradigm of planning, dynamic adaptive plans, which react adaptively to uncertainties by adjusting plans according to rapid changes in circumstances. However, these dynamic plans have been applied in larger-scale industries such as wastewater management in longer-range time frames. This paper follows the dynamic adaptive plan paradigm but transfers it to the technology management context with shorter-range action plans. Based on this new paradigm of risk management and technology planning, we propose a risk-adaptive technology roadmap (TRM) that can adapt to changing complex environments. First we identify risk by topic modeling based on futuristic data and then by sentiment analysis. Second, for the derived risks, we determine new and alternative plans by co-occurrence of risk-related keywords. Third, we convert an existing TRM to network topology with adaptive plans and construct a conditional probability table for the network. Finally, we estimate posterior probability and infer it by Bayesian network by adjusting plans depending on occurrence of risk events. Based on this posterior probability, we remap the paths in the previous TRM to new maps, and we apply our proposed approach to the field of artificial intelligence to validate its feasibility. Our research contributes to the possibility of using dynamic adaptive planning with technology as well as to increase the sustainability of technology roadmapping.

Improving interpretation of existing guidelines and management of protocol deviation processes could increase process efficiencies and help reduce noise to support rapid identification of important protocol deviations. Towards this end, TransCelerate identified key principles to build upon and clarify the definition of a protocol deviation and developed a holistic approach to protocol deviation management. The approaches are flexible to suit a variety of indications, study designs, and investigational agents while also supporting consistent application within a study, program or organization.

Sequential use of alternative treatments for chronic conditions represents a complex intervention pathway; previous treatment and patient characteristics affect both the choice and effectiveness of subsequent treatments. This paper critically explores the methods for quantitative evidence synthesis of the effectiveness of sequential treatment options within a health technology assessment (HTA) or similar process. It covers methods for developing summary estimates of clinical effectiveness or the clinical inputs for the cost-effectiveness assessment and can encompass any disease condition. A comprehensive review of current approaches is presented, which considers meta-analytic methods for assessing the clinical effectiveness of treatment sequences and decision-analytic modelling approaches used to evaluate the effectiveness of treatment sequences. Estimating the effectiveness of a sequence of treatments is not straightforward or trivial and is severely hampered by the limitations of the evidence base. Randomised controlled trials (RCTs) of sequences were often absent or very limited. In the absence of sufficient RCTs of whole sequences, there is no single best way to evaluate treatment sequences; however, some approaches could be re-used or adapted, sharing ideas across different disease conditions. Each has advantages and disadvantages, and is influenced by the evidence available, extent of treatment sequences (number of treatment lines or permutations), and complexity of the decision problem. Due to the scarcity of data, modelling studies applied simplifying assumptions to data on discrete treatments. A taxonomy for all possible assumptions was developed, providing a unique resource to aid the critique of existing decision-analytic models. Electronic supplementary material The online version of this article (10.1007/s40273-020-00980-w) contains supplementary material, which is available to authorized users.

Introduction Understanding the balance between the benefits and risks of vaccination is essential to ensure informed and adequate public health decision making. Quantitative benefit–risk models (qBRm) represent useful tools to help decision makers with supporting benefit–risk assessment throughout the lifecycle of a medical product. However, few initiatives have been launched to harmonise qBRm approaches, specifically for vaccines. Objectives The aim of this paper was to identify publications about qBRm applied to vaccines through a systematic literature review, and to describe their characteristics. Methods Medline, Scopus and Institute for Scientific Information Web of Knowledge databases were searched to identify articles in English, published from database inceptions up to December 2019. The search strategy included the combination of three key concepts: ‘benefit–risk’, ‘modelling’ and ‘vaccines’. Data extracted included the modelling context and the methodological approaches used. Results Of 3172 publications screened, 48 original publications were included. Most of the selected studies were published over the past decade and focused on rotavirus (15), dengue (10) and influenza (6) vaccines. The majority (30) of studies reported analyses related to high-income countries. The methodology of the studies differed, particularly in modelling techniques, benefit–risk measures, and sensitivity analyses. The present work also pointed out a high level of variability in the quality of reporting across studies, with particular regard to input parameters and methodological approaches. Conclusions This review provides an extensive list of qBRm applied to vaccines. Discrepancies across studies were identified during our review. While the number of published qBRm studies is increasing, no reporting guidance for qBRm applied to vaccines is currently available. This may affect decision makers’ confidence in the results and their benefit–risk assessment(s); therefore, the development of such reporting guidance is highly needed. Electronic supplementary material The online version of this article (10.1007/s40264-020-00984-7) contains supplementary material, which is available to authorized users.

Background The European Medicines Agency Good Pharmacovigilance Practices (GVP) guidelines provide a framework for pharmacovigilance (PV) audits, including limited guidance on risk assessment methods. Quality assurance (QA) teams of large and medium sized pharmaceutical companies generally conduct annual risk assessments of the PV system, based on retrospective review of data and pre-defined impact factors to plan for PV audits which require a high volume of manual work and resources. In addition, for companies of this size, auditing the entire “universe” of individual entities on an annual basis is generally prohibitive due to sheer volume. A risk assessment approach that enables efficient, temporal, and targeted PV audits is not currently available. Methods In this project, we developed a statistical model to enable holistic and efficient risk assessment of certain aspects of the PV system. We used findings from a curated data set from Roche operational and quality assurance PV data, covering a span of over 8 years (2011–2019) and we modeled the risk with a logistic regression on quality PV risk indicators defined as data stream statistics over sliding windows. Results We produced a model for each PV impact factor (e.g. 'Compliance to Individual Case Safety Report') for which we had enough features. For PV impact factors where modeling was not feasible, we used descriptive statistics. All the outputs were consolidated and displayed in a QA dashboard built on Spotfire®. Conclusion The model has been deployed as a quality decisioning tool available to Roche Quality professionals. It is used, for example, to inform the decision on which affiliates (i.e. pharmaceutical company commercial entities) undergo audit for PV activities. The model will be continuously monitored and fine-tuned to ensure its reliability.

Decision makers increasingly require cost-effectiveness evidence to inform resource allocation and the need for systematic reviews of economic evaluations (SREEs) has grown accordingly. The objective of this article is to describe current practice and identify unique challenges in conducting and reporting SREEs. Current guideline documents for SREEs were consulted and summarised. A rapid review of English-language SREEs, using MEDLINE and EMBASE, published in 2017/2018, containing at least 20 studies was undertaken to describe current practice. Information on data extraction methods, quality assessment (QA) tools and reporting methods were narratively summarised. Lessons learned from a recently conducted SREE of weight loss interventions for severely obese adults were discussed. Sixty-three publications were included in the rapid review. Substantial heterogeneity in review methods, reporting standards and QA approaches was evident. Our recently conducted SREE on weight loss interventions identified scope to improve process efficiency, opportunity for more transparent and succinct reporting, and potential to improve consistency of QA. Practical solutions may include (1) using pre-piloted data extraction forms linked explicitly to results tables; (2) consistently reporting on key assumptions and sensitivity analyses that drive results; and (3) using checklists that include topic-specific items where relevant and allow reviewers to distinguish between reporting, justification and QA. The lack of a mutually agreed, standardised set of best practice guidelines has led to substantial heterogeneity in the conduct and reporting of SREEs. Future work is required to standardise the approach to conducting SREEs so that they can generate efficient, timely and relevant evidence to support decision-making. We suggest only data extracting information that will be reported, focusing discussion around the key drivers of cost-effectiveness, and improving consistency in QA by distinguishing between what is reported, justified by authors and deemed appropriate by the reviewer. Electronic supplementary material The online version of this article (10.1007/s40273-019-00878-2) contains supplementary material, which is available to authorized users.

The medical device industry, physicians, and global regulatory authorities collaborate in the introduction of medical devices that are safe and effective for their intended uses. Knowledge regarding regulatory processes is required to successfully navigate the available pathways to market for innovative pediatric orthopedic medical devices and provide timely access to potentially life-enhancing and lifesaving orthopedic devices. Medical device technologies directed toward care of pediatric patients with disorders affecting the spine and thorax represent a critical unmet need in the United States. Specific challenges encountered in the development of pediatric spinal devices relate to economic factors, target population considerations, liability concerns, available regulatory pathways, and clinical trial design considerations. Processes for regulation of medical devices worldwide are variable and ultimately remain subject to control by the regulatory body of the specific country. International medical device regulatory harmonization and convergence offer potential to improve regulatory processes for pediatric spinal devices.

To help promote ecologically sustainable development (ESD), industrial ecology (IE) has emerged as a framework for proactive management of human impacts on the natural environment. IE focuses on borrowing natural ecological principles and applying them to the design and management of commercial systems, and the infrastructure required by them. IE principles can be relevant to the workings of both the private and public sectors, and have the potential to be applied to internal processes within a single enterprise and the interactions between a network of enterprises. As a concept, IE is finding favor at a time when traditional depollution approaches which rely on end-of-pipe solutions are increasingly being regarded as inefficient (Erkman, 1997), and has been defined as: “…An emerging framework for environmental management, seeking transformation of the industrial system in order to match its inputs and outputs to planetary and local carrying capacity. A central IE goal is to move from a linear to a closed-loop system in all realms of human production and consumption.” (Lowe and Evans, 1995)

The ECLS circuit is composed of a variety of specialized devices that need to function separately and in concert to support the patient. As each case and patient will demand different solutions, circuits vary in their features. This chapter provides a fund of knowledge regarding these components: what function they provide, the general principles involved, and how to identify device compromise or failure. It therefore serves as a guide for building a circuit for each patient; balancing the various needs at hand requires an understanding of such matters. Also discussed are priming of the circuit and patient–circuit interactions such as drug adsorption and inflammation due to blood–surface interaction.

At the end of this chapter, you should be able to: Understand the multiple goals of strategic compensation and benefits Describe the dominant approaches to strategic compensation and benefits Analyse the relationship between of strategic compensation and benefits and performance Identify the theoretical bases for determining strategic compensation and benefits

Until relatively recently, the only way that most Child Protective Services (CPS) systems could respond to allegations of child abuse and neglect was through an investigation. Concerns with certain elements of this forensic approach to child protection led to the development of a CPS reform known as Differential Response. This chapter will describe the key elements of a Differential Response approach to child protective services and highlight how it differs from a traditional investigative approach. Although a core set of practice elements have been defined that characterize Differential Response systems, wide variations in practice exist between systems, several of which will be described. As with most child welfare interventions, rigorous research on the effectiveness of Differential Response systems is limited, but quickly growing. A summary and critical review of the current evidence is provided, as well as suggestions for next steps.

The growing number of emerging medical technologies and sophistication of modern medical devices (MDs) that improve both survival and quality of life indexes are often challenged by alarming cases of vigilance data cover-up and lack of sufficient pre- and post-authorization controls. Combining Quality with Risk Management processes and implementing them as early as possible in the design of MDs has proven to be an effective strategy to minimize residual risk. This article aims to discuss how the design of MDs interacts with their safety profile and how this dipole of intended performance and safety may be supported by Human Factors Engineering (HFE) throughout the Total Product Life-Cycle (TPLC) of an MD in order to capitalize on medical technologies without exposing users and patients to unnecessary risks.

The technology can be considered a systematic, knowledge-based action, applicable in industrial processes in order to transform resources into products requested by the customer. Technology uses scientific resources but is, at the same time, itself a science of applying knowledge for practical purposes. Technology is ultimately the measure of the efficiency of industrial management. The management, as a science, takes the information regarding the management processes and submits them to some analysis procedures in order to perfect the existing management methods and to complete them with new ones. The applicative character of the management results from the fact that the experiences regarding the systems management are materialized in the design of new systems, methods, techniques, and procedures that constitute the tools made available to the managers for achieving the company's objectives. The manager must prove the ability to understand economic and social phenomena, the laws that govern them, objectively identifying the risks and opportunities, in order to plan the company's activities. An equidistant, efficient and coherent policy established by the management guarantees the success and ensures the agreement of the company's existence with the market evolution.

The direct medical costs for falls only—what patients and insurance companies pay—was $34 billion [Center for Disease Control and Prevention, 2013]. The necessity for continuous monitoring and assisting of persons in risk, and the increasing prevalence of chronic conditions among our aging population present great challenges to our healthcare system. These costs are also expected to surpass $54 billion by 2020 (http://www.cdc.gov/ncipc/factsheets/fallcost.htm, in 2007). In addition, the larger healthcare providers offer low quality services. Thus, making a robotic machine “lifting-up” elderly persons or persons with disabilities from a sitting position and transferring them to the bathroom is a very challenging scenario that urgently require innovated solutions. Thus, advanced technological achievements based on robotic nurses and wearable health monitoring devices may represent the up-coming hopeful and desirable solutions. Thus, the challenges here are based on the importance of IT-Engineering solutions to healthcare needs. In addition these challenges offer a paradigm on the already growing personalized/person-centered healthcare and the role of ‘smart homes’ and ‘smart beds’ for independent living and self-management of chronic conditions. While the importance of such care is recognized, not many efforts have been pursued to develop low-cost (and inconspicuous) technologies. Thus, the chapter presents issues for the development of a novel synergistic interactive intelligent framework-model between intelligent wearable health-monitoring devices (WS) and intelligent robotic wheelchairs (IRC) for assisting people (HS) in risk at smart homes, improving safety and quality of life, and reducing healthcare cost. In particular, it will focus on two challenging scenarios (lifting-transferring) reported above providing concepts but at the same time maintaining the generality of this synergistic model. The challenges are related with a new generation of intelligent synergistic models of inexpensive, unobtrusive intelligent wearable sensors, monitoring devices, intelligent robotic assistants, and surveillance methodologies. In addition, the healthcare advanced research technologies and educational training is expected to transform the way physicians and nurses provide care and the way of people at risk will safely stay at their homes. Concurrently, they will offer a ground for new research directions for engineers to contribute in healthcare applications. In addition, commercialization of these devices will certainly transform our healthcare monitoring systems for a large population, and transform the living conditions of people at need.

Smart Cities is a new torrent and its concept is evolved to benefit people in different ways by providing solutions to many problems. We are racing to achieve the goal of Smart Cities and completely ignoring the perilous consequences. Out of many, one of the gigantic problems is E-waste. E-waste is growing day by day jeopardizing the environment and in turn Sustainable Development. E-waste contains many elements like mercury, lead which are toxic to humans, water, air and soil which lead to another set of serious problems. There is an urgent need to look into this matter and resolve it as expeditiously as possible. To achieve Sustainable Development, we need to get rid of E-waste in the most efficient way possible. In this chapter, we will discuss various problems caused by e-waste and approaches to manage E-waste by applying Smart Technologies. We have also proposed a model for handling e-waste.

Decision-making and control in healthcare applications are essential activities, which imply a large number of medical and technical aspects, and the nonlinearity of systems specific to these applications makes them challenging. In addition, these activities involve humans: on the one hand, the patient, who has a medical problem and who requires the best solution; on the other hand, the physician, who should be able to provide, in any circumstances, a decision or a prediction regarding the current and the future medical status of a patient. The technology, in general, and particularly the artificial intelligence tools could help both of them, and it is assisted by appropriate theory regarding modeling tools. Diagnosis, modeling, prediction and control represent the mechanisms that support theoretically the healthcare applications as far as decision-making is involved. Two of the most powerful intelligent paradigms that are successfully used in this field are artificial neural networks and fuzzy logic, with their corresponding models. This chapter presents several applications developed by the Process Control Group of the Politehnica University Timisoara, Romania and emphasizes that these techniques, which produce intelligent models, even if they are artificial, are able to make decisions and to control various processes, being useful tools for prevention, early detection and personalized healthcare.

Together with their undeniable advantages, the new technologies of the Fintech Revolution bring new risks. Some of these risks are already known but have taken on a new form; some are entirely new. Among the latter, one of the most relevant concerns the opacity of artificial intelligence (AI).

Although the virtues of corporate compliance are widely acknowledged, co-regulation and compliance management schemes constantly meet with failure, as emerges from the empirical records. This chapter is about the structural limits of corporate compliance and the factors that make it inevitable that in some cases the programs to prevent corporate wrongdoings will fail. The failure of existing compliance models should therefore not come as a surprise, since these models suffer from three structural limits. The understanding of these limits is crucial to identify what corporate compliance can realistically achieve and to isolate those deviant behaviors that require different and more effective measures. First, compliance is an imperfect science when it comes to rulemaking, resulting in the fact that no compliance program can perfectly avert failures of all sorts. Second, from a criminological point of view, compliance presents a second structural weakness in its inability to prevent the individuals in control of a company from acting illegally. Finally, the third limit stems from the economic framework in which compliance models are applied and the fact that these models, designed for large public companies, are frequently unfit for small- and medium-sized entities (SMEs).

In this contribution, a mathematical model will be shown in order to determine the optimal frequency of maintenance considering costs, risk, and performance, where the application of tools based on IoT and cloud may provide useful advantages. With this purpose, it starts introducing the topic and indicating the usual decisions related to asset management. Then, the main variables (reliability and risk, operational costs, asset life extension) for the optimization of maintenance frequency are depicted, in order to define after that some mathematical models for the proactive replacement. Finally, an example applied to a steam turbine installed in a petrochemical plant is discussed, together with some conclusions.

Why was the world not ready for COVID-19, in spite of many warnings over the past 20 years of the high likelihood of a global pandemic? This chapter argues that the economic goal of efficiency, focused on short-term optimization, has distracted us from resilience, which is focused on long-term optimization. Computing also seems to have generally emphasized efficiency at the expense of resilience. But computing has discovered that resilience is enabled by redundancy and distributivity. These principles should be adopted by society in the “after-COVID” era.

In 2019, 229 million cases of malaria were recorded worldwide. For epidemiologic surveillance and proper treatment of persons infected with Plasmodium spp., rapid detection of infections by Plasmodium spp. is critical. Thus, Plasmodium spp. diagnosis is one of the indispensable measures for malaria control. Although microscopy is the gold standard for diagnosis, it has restrictions related mainly to the lack of qualified human resources, which is a problem in many regions. Thus, this review presents major innovations in diagnostic methods as alternatives to or complementary to microscopy. Detection platforms in lateral flow systems, electrochemical immunosensors, molecular biology and, more recently, those integrated with smartphones, are highlighted, among others. The advanced improvement of these tests aims to provide techniques that are sensitive and specific, but also quick, easy to handle and free from the laboratory environment. In this way, the tracking of malaria cases can become increasingly effective and contribute to controlling the disease.

In the presented article, the authors investigate the issue of the role and significance in the application of technologies for processing large data arrays using Big Data in the development of the pharmaceutical industry. Taking into account all the complexity and knowledge-intensiveness of the process of developing and launching a new drug on the market, the introduction of new approaches to the collection of additional information can significantly facilitate and simplify this procedure. It is also important that the use of Big Data technologies in pharmaceuticals significantly reduces the cost of the development process and minimizes possible risks associated with human health. In this regard, the relevance of the application of Big Data technology in the pharmaceutical business today is extremely relevant, since both manufacturing companies and consumers benefit. Within the framework of this work, the authors set the goal to analyze the main areas of the pharmaceutical business, the use of Big Data technology in which today is the most in demand.

Gene therapy has made tremendous progress over recent years to the point of becoming a first-line therapy for specific genetic diseases. Various technologies can now be applied to detect and correct genetic deficiencies with a high degree of specificity and safety for the patients. As gene therapy technologies develop, inevitably the risk of misuse of such technologies outside of clinical settings is becoming a reality. Several examples of uncontrolled, homemade genetic experiments on animals or humans have now been made public. Sport has always been an area where athletes are pushing human boundaries and rapidly integrating a high level of innovations. Anti-doping authorities, like the World Anti-Doping Agency (WADA), have identified many years ago the risk of misuse of gene therapy in sport for the purpose of enhancing performance, named ’gene doping’, as a significant threat for the future and developed a range of strategies preventing such technologies from becoming the next generation of uncontrolled doping agents. This chapter will review various uses and applications of gene therapy and the development of deterrence strategies to detect the risk of gene therapy in sport.

The paper deals with the digital technologies implementation in the companies’ activity, including transport enterprises. The authors pay attention to the fact that the processes of digitalization and digital transformation are necessary, and they contribute to achieving competitive advantages, reducing operating costs, improving business processes, developing customer services, and meeting customer needs at a higher level. The digital technologies implementation contributes to developing innovative business models and improving the company’s efficiency. The digital transformation of the transport industry leads to improving the service quality (safety, reliability, timing, and availability) and has a positive impact on the economic efficiency as a whole. The authors developed a modified method for measuring the service quality of passenger transport and defined an integral indicator of the quality based on economic and mathematical modeling methods. The approach makes it possible to ensure consistency and complexity of the digitalization and digital transformation processes. The authors distinguished the positive effects of the digital transformation both for manufacturing enterprises and consumers.

This chapter develops a model of resilient action in situations where established rules or behavioural routines are either not available or are misleading, thus exposing actors to high means-end ambiguity. The model suggests that an ‘action space’ must be created by stabilizing the action system and expanding options for action. It is based on our qualitative research in the Austrian Military (high degree of publicness) on cases of resilient field action, especially as regards ‘bouncing back’ incidents. We contend that different types of drill combined with the acquisition of background knowledge are essential for organizational resilience, the management of unexpected situations and the explanation of success, leading to controlled reproducibility solutions of typical problems. As such, the model intends to explain exploitation types of learning. However, as an antecedent for installing the action space, we explore so-called the ‘exaptation’ of drilled procedures, pertaining to the transfer of procedures to serve novel requirements, thus located in the exploration domain. This phenomenon leads to properties that contribute to recovery from shock in critical situations, through innovation. In short, the chapter provides novel empirical evidence that applying rules does not lead to resilient action in the case of unknown or unexpected situations. Instead, we show robust evidence that a corrective understanding and reflective use of rules and routines is causally related to the ability to deal with surprise and fostering resilience.

The factors such as an increase in population, unplanned urbanization, and increasing level of comfort expected from the built environments increase the consumption of energy and cause environmental problems; therefore, the sustainability concept has gained importance among the researches. While increase of costs and decrease of natural resources remain the need for sustainable structures in the built environment, Building Information Modeling (BIM) technologies have radically changed the way of architecture, engineering, and construction (AEC) industry as an emerging technological innovation by leading the transformation of the construction industry. Another driving force for the construction industry has become lean approaches with the philosophy of minimizing wastes and increasing efficiency. The interest in the synergy between these three emerging concepts has increased to reduce the social, environmental, and economic impacts of the built environment. The purpose of this paper is to present a bibliographic literature review about the BIM-Lean-Sustainability synergy. Scopus database is relied on to search articles published between 2011 and 2020 to address the relationships among the concepts changing the way of constructing by determining the most used keywords and the most productive, and the most cited authors. The paper aims to propose useful insights for future research by identifying the challenges in existing studies to create a dynamic platform for the trinity between BIM, Lean, and Sustainability.

Disorders caused by poor lighting, inappropriate thermal environments, noise and manual handling constitute a daily challenge for workers, since these factors influence worker productivity and can trigger long-term health problems. It is essential to ensure that workers carry out their tasks in suitable conditions, without compromising technicians’ physical and psychological wellbeing, as well as their hygiene and safety. In order to address these issues, the support provided by equations such as NIOSH, noise assessment through spectral analysis or exposure time, as well as assessment of lighting and the thermal environment, enable an evaluation of workers’ conditions and the application of measures to guarantee worker safety. The software tool Safe Work Space (SWS) was tested at a furniture manufacturing company, and was developed to facilitate the daily work undertaken by hygiene and safety technicians, reducing the time spent on data treatment. Based on the parameters of load movement, the results of the NIOSH equation [recommended weight limit] and the lifting index are obtainable in fractions of a second. The SWS tool also assesses lighting and thermal environment values which, according to the references of the workplace location, provide results regarding lighting and room temperature. Finally, a third feature evaluates noise by assessing the need for the use of PPE (personal protection equipment) and, if necessary, it provides information as to which equipment is available on the market. This tool is extremely useful for professionals as it was specifically developed for the analysis of noise, lighting, thermal environment and lifting loads, all in one.

Objective: This study describes a simplified computational tool to provide a simple initial diagnostic of occupational hazards and risks and help companies to implement innovative management practices in terms of Occupational Safety and Health (OSH) to prevent work accidents. Background: The enterprises of the non-financial business sector in the EU-28 are mostly micro, small and medium-sized enterprises (MSMEs). Portugal follows this trend, where the largest industry sector is the agri-food. Generally, the companies of this sector have reduced financial, technical and human resources, thus jeopardizing the quality and accuracy of risks evaluation and the implementation of protective measures. Method: This study explains a simplified approach for occupational safety and health risk assessment through a user-friendly and expedite computational tool and its in-field application. Data from sixty OSH audits executed in dairy, meat processing, bakery and horticultural food processing companies in Portugal was analysed. Results: The spreadsheet software, Excel, usually used by the managers of this sector was used to implement the computational tool. It is divided into three “Sheets”: Risk assessment, Results summary and Report with solutions. Conclusion: Thus, it is important to develop OSH simplified risk assessment tools that helps Agri-food MSME to improve their OSH management and ultimately contribute to the productivity and competitiveness of this sector.

The article discusses the application of the mechanism for selecting alternatives from the semantic network of conditional statements when managing product quality parameters at the stages of the life cycle. This will make it possible to implement the timely improvement of quality management processes for aviation products based on the use of information technology to support the product life cycle in real time.