A cloud-based healthcare infrastructure for neonatal intensive-care units

Intensive medical attention of preterm babies is crucial to avoid short-term and long- term complications. Within neonatal intensive care units (NICUs), cribs are equipped with electronic devices aimed at: monitoring, administering drugs and supporting clinician in making diagnosis and offer treatments. To manage this huge data flux, a cloud-based healthcare infrastructure that allows data collection from different devices (i.e., patient monitors, bilirubinometers, and transcutaneous bilirubinometers), storage, processing and transferring will be presented. Communication protocols were designed to enable the communication and data transfer between the three different devices and a unique database and an easy to use graphical user interface (GUI) was implemented. The infrastructure is currently used in the “Women’s and Children’s Hospital G.Salesi” in Ancona (Italy), supporting clinicians and health opertators in their daily activities

Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence

The research explores the potential of digital-twin-based methods and approaches aimed at achieving an intelligent optimization and automation system for energy management of a residential district through the use of three-dimensional data model integrated with Internet of Things, artificial intelligence and machine learning. The case study is focused on Rinascimento III in Rome, an area consisting of 16 eight-floor buildings with 216 apartment units powered by 70% of self-renewable energy. The combined use of integrated dynamic analysis algorithms has allowed the evaluation of different scenarios of energy efficiency intervention aimed at achieving a virtuous energy management of the complex, keeping the actual internal comfort and climate conditions. Meanwhile, the objective is also to plan and deploy a cost-effective IT (information technology) infrastructure able to provide reliable data using edge-computing paradigm. Therefore, the developed methodology led to the evaluation of the effectiveness and efficiency of integrative systems for renewable energy production from solar energy necessary to raise the threshold of self-produced energy, meeting the nZEB (near zero energy buildings) requirements

Narrative Review: Food Image Use for Machine Learnings’ Function in Dietary Assessment and Real Time Nutrition Feedback and Education

Technology has played a key role in advancing the health and agriculture sectors to improve obesity rates, diseasecontrol, food waste, and overall health disparities. However, these health and lifestyle determinants continue to plague theUnited States population. While new technologies have been and are currently being developed to address these concerns, they may not be practical for the general population. Utilizing machine learning advancement in food recognition using smartphone technology may be a means to improve the dietary component of nutrition assessments while providing valuable nutrition feedback. This narrative review was conducted to assess the current state of the literature on nutrition technology using image recognition for practical applications, while also proposing theoretical uses for the technology to improve quality of life through dietary feedback

Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing

Designing for patient risk assessment in primary health care: a case study for ergonomic work analysis

In this paper, we study the importance of a consistent description of real work in patient risk assessment in the primary healthcare domain. Through a case study in the context of primary health care, we address the research problem of finding ways to build consistent real work descriptions of the patient risk assessment system in the primary healthcare domain, in order to foster the design of improved work situations and support devices. This is a qualitative field study based on ethnographic observation and semi-structured interviews carried out among professionals involved in the risk assessment process in a primary healthcare facility. The objects of ergonomic work analysis were work places and work situations with focus on human activity, as well as surrounding aspects. The analysis identified elements in the work domain with high cognitive demand and operations that could increase mental workload, providing elements for the earlier stages of the design of work situations and support devices to improve the risk assessment in primary health care. This paper shows the usefulness of real work descriptions in the design for complex situations like the risk assessment in health care, as well the impact of poor descriptions in generating harmful situations for both the patient and healthcare practitioners in the explored domain

I2ECR: Integrated and Intelligent Environment for Clinical Research

Clinical trials are designed to produce new knowledge about a certain disease, drug or treatment. During these studies, a huge amount of data is collected about participants, therapies, clinical procedures, outcomes, adverse events and so on. A multicenter, randomized, phase III clinical trial in Hematology enrolls up to hundreds of subjects and evaluates post-treatment outcomes on stratified sub- groups of subjects for a period of many years. Therefore, data collection in clinical trials is becoming complex, with huge amount of clinical and biological variables. Outside the medical field, data warehouses (DWs) are widely employed. A Data Ware-house is a “collection of integrated, subject-oriented databases designed to support the decision-making process”. To verify whether DWs might be useful for data quality and association analysis, a team of biomedical engineers, clinicians, biologists and statisticians developed the “I2ECR” project. I2ECR is an Integrated and Intelligent Environment for Clinical Research where clinical and omics data stand together for clinical use (reporting) and for generation of new clinical knowledge. I2ECR has been built from the “MCL0208” phase III, prospective, clinical trial, sponsored by the Fondazione Italiana Linfomi (FIL); this is actually a translational study, accounting for many clinical data, along with several clinical prognostic indexes (e.g. MIPI - Mantle International Prognostic Index), pathological information, treatment and outcome data, biological assessments of disease (MRD - Minimal Residue Disease), as well as many biological, ancillary studies, such as Mutational Analysis, Gene Expression Profiling (GEP) and Pharmacogenomics. In this trial forty-eight Italian medical centers were actively involved, for a total of 300 enrolled subjects. Therefore, I2ECR main objectives are: • to propose an integration project on clinical and molecular data quality concepts. The application of a clear row-data analysis as well as clinical trial monitoring strategies to implement a digital platform where clinical, biological and “omics” data are imported from different sources and well-integrated in a data- ware-house • to be a dynamic repository of data congruency quality rules. I2ECR allows to monitor, in a semi-automatic manner, the quality of data, in relation to the clinical data imported from eCRFs (electronic Case Report Forms) and from biologic and mutational datasets internally edited by local laboratories. Therefore, I2ECR will be able to detect missing data and mistakes derived from non-conventional data- entry activities by centers. • to provide to clinical stake-holders a platform from where they can easily design statistical and data mining analysis. The term Data Mining (DM) identifies a set of tools to searching for hidden patterns of interest in large and multivariate datasets. The applications of DM techniques in the medical field range from outcome prediction and patient classification to genomic medicine and molecular biology. I2ECR allows to clinical stake-holders to propose innovative methods of supervised and unsupervised feature extraction, data classification and statistical analysis on heterogeneous datasets associated to the MCL0208 clinical trial. Although MCL0208 study is the first example of data-population of I2ECR, the environment will be able to import data from clinical studies designed for other onco-hematologic diseases, too

The Semantics of History. Interdisciplinary Categories and Methods for Digital Historical Research

This paper aims at introducing and discussing the data modelling and labelling methods for interdisciplinary and digital research in History developed and used by the authors. Our approach suggests the development of a conceptual framework for interdisciplinary research in history as a much-needed strategy to ensure that historians use all vestiges from the past regardless of their origin or support for the construction of historical discourse. By labelling Units of Topography and Actors in a wide range of historical sources and exploiting the obtained data, we use the Monastery of Sant Genís de Rocafort (Martorell, Spain) as a lab example of our method. This should lead researchers to the development of an integrated historical discourse maximizing the potential of interdisciplinary and fair research and minimizing the risks of bias

The Impact Of Risa Oral Interactions On The Acquisition Of Scientific Classification Language For Slife

This research explored the effects of using structured oral interactions as a scaffolding technique on the use and development of scientific classification language and the ability to engage in cognitively more complex academic tasks for students with limited or interrupted formal education. The study took place during a vertebrates unit in an EL newcomers science classroom. Students engaged in an intervention of two cycles of routinized, content-integrated, structured academic (RISA) oral interactions. Students were tasked to classify animals and to record video samples from the prompt, “What kind of animal is this? How do you know?” before and after the intervention. Responses were mapped conceptually and analyzed through a transitivity process analysis lens from systemic functional linguistics. Elements studied include occurrences of generic referent, intensive and possessive relational processes and uses of classification key lexis. Student responses were also measured for word speed fluency. The results found improvements between pre-intervention and post-intervention samples in the linguistic complexity and accuracy of student responses. Improvements included increased use of generic referent, relational processes and key vocabulary. Additionally, most students moved from descriptions in the pre-assessment to classifications in the post-assessment, demonstrating an increased ability to conceptualize in English. Fluency of samples declined as students transitioned from summation-based responses to classifying responses. The implications from the data suggested a direct relationship between RISA oral interactions and improvements in student responses