A digital twin is a virtual representation of a physical object or process capable of collecting information from the real environment to represent, validate and simulate the physical twin’s present ...and future behavior. It is a key enabler of data-driven decision making, complex systems monitoring, product validation and simulation and object lifecycle management. As an emergent technology, its widespread implementation is increasing in several domains such as industrial, automotive, medicine, smart cities, etc. The objective of this systematic literature review is to present a comprehensive view on the DT technology and its implementation challenges and limits in the most relevant domains and applications in engineering and beyond.
This study centers on creating a real-time algorithm to estimate brain-to-brain synchronization during social interactions, specifically in collaborative and competitive scenarios. This type of ...algorithm can provide useful information in the educational context, for instance, during teacher-student or student-student interactions. Positioned within the context of neuroeducation and hyperscanning, this research addresses the need for biomarkers as metrics for feedback, a missing element in current teaching methods. Implementing the bispectrum technique with multiprocessing functions in Python, the algorithm effectively processes electroencephalography signals and estimates brain-to-brain synchronization between pairs of subjects during (competitive and collaborative) activities that imply specific cognitive processes. Noteworthy differences, such as higher bispectrum values in collaborative tasks compared to competitive ones, emerge with reliability, showing a total of 33.75% of significant results validated through a statistical test. While acknowledging progress, this study identifies areas of opportunity, including embedded operations, wider testing, and improved result visualization. Beyond academia, the algorithm's utility extends to classrooms, industries, and any setting involving human interactions. Moreover, the presented algorithm is shared openly, to facilitate implementations by other researchers, and is easily adjustable to other electroencephalography devices. This research not only bridges a technological gap but also contributes insights into the importance of interactions in educational contexts.
Service Robots: Trends and Technology Gonzalez-Aguirre, Juan Angel; Osorio-Oliveros, Ricardo; Rodríguez-Hernández, Karen L. ...
Applied sciences,
11/2021, Letnik:
11, Številka:
22
Journal Article
Recenzirano
Odprti dostop
The 2021 sales volume in the market of service robots is attractive. Expert reports from the International Federation of Robotics confirm 27 billion USD in total market share. Moreover, the number of ...new startups with the denomination of service robots nowadays constitutes 29% of the total amount of robotic companies recorded in the United States. Those data, among other similar figures, remark the need for formal development in the service robots area, including knowledge transfer and literature reviews. Furthermore, the COVID-19 spread accelerated business units and some research groups to invest time and effort into the field of service robotics. Therefore, this research work intends to contribute to the formalization of service robots as an area of robotics, presenting a systematic review of scientific literature. First, a definition of service robots according to fundamental ontology is provided, followed by a detailed review covering technological applications; state-of-the-art, commercial technology; and application cases indexed on the consulted databases.
Sensors for Sustainable Smart Cities: A Review Ramírez-Moreno, Mauricio A.; Keshtkar, Sajjad; Padilla-Reyes, Diego A. ...
Applied sciences,
09/2021, Letnik:
11, Številka:
17
Journal Article
Recenzirano
Odprti dostop
Experts confirm that 85% of the world’s population is expected to live in cities by 2050. Therefore, cities should be prepared to satisfy the needs of their citizens and provide the best services. ...The idea of a city of the future is commonly represented by the smart city, which is a more efficient system that optimizes its resources and services, through the use of monitoring and communication technology. Thus, one of the steps towards sustainability for cities around the world is to make a transition into smart cities. Here, sensors play an important role in the system, as they gather relevant information from the city, citizens, and the corresponding communication networks that transfer the information in real-time. Although the use of these sensors is diverse, their application can be categorized in six different groups: energy, health, mobility, security, water, and waste management. Based on these groups, this review presents an analysis of different sensors that are typically used in efforts toward creating smart cities. Insights about different applications and communication systems are provided, as well as the main opportunities and challenges faced when making a transition to a smart city. Ultimately, this process is not only about smart urban infrastructure, but more importantly about how these new sensing capabilities and digitization developments improve quality of life. Smarter communities are those that socialize, adapt, and invest through transparent and inclusive community engagement in these technologies based on local and regional societal needs and values. Cyber security disruptions and privacy remain chief vulnerabilities.
Nearly half of the world's urban population depends on aquifers for drinking water. These are increasingly vulnerable to pollution and overexploitation. Besides anthropogenic sources, pollutants such ...as arsenic (As) are also geogenic and their concentrations have, in some cases, been increased by groundwater pumping. Almost 40 % of Mexico's population relies on groundwater for drinking water purposes; much the aquifers in semi-arid and arid central and northern Mexico is contaminated by As. These are agricultural regions where irrigation water is primarily provided from intenstive pumping of the aquifers leading to long-standing declines in the water table. The focus of this study is the main aquifer within the Comarca Lagunera region in Northern Mexico. Although the scientific evidence demonstrates that health effects are associated with long-term exposure to elevated As concentrations, this knowledge has not yielded effective groundwater development and public health policy. A multidisciplinary approach – including the evaluation of geochemistry, human health risk and development and public health policy - was used to provide a current account of these links. The dissolved As concentrations measured exceeded the corresponding World Health Organization guideline for drinking water in 90 % of the sampled wells; for the population drinking this water, the estimated probability of presenting non-carcinogenic health effects was >90 %, and the lifetime risk of developing cancer ranged from 0.5 to 61 cases in 10,000 children and 0.2 to 33 cases in 10,000 adults. The results suggest that insufficient policy responses are due to a complex and dysfunctional groundwater governance framework that compromises the economic, social and environmental sustainability of this region. These findings may valuable to other regions with similar settings that need to design and enact better informed, science-based policies that recognize the value of a more sustainable use of groundwater resources and a healthier population.
Display omitted
•Systematical analysis of As geochemical processes, health impacts and public policy•As pollution in the aquifer system caused by overextraction of groundwater mainly for irrigation•Unacceptable As levels in >90 % of the study area and exposure of 2–5 times of safety levels•Insufficient policy responses due to legal and normative dysfunctional framework evidenced•Design of orchestrated and coherent collective actions proposed putting health in the center.
When the sensory–motor integration system is malfunctioning provokes a wide variety of neurological disorders, which in many cases cannot be treated with conventional medication, or via existing ...therapeutic technology. A brain–computer interface (BCI) is a tool that permits to reintegrate the sensory–motor loop, accessing directly to brain information. A potential, promising and quite investigated application of BCI has been in the motor rehabilitation field. It is well-known that motor deficits are the major disability wherewith the worldwide population lives. Therefore, this paper aims to specify the foundation of motor rehabilitation BCIs, as well as to review the recent research conducted so far (specifically, from 2007 to date), in order to evaluate the suitability and reliability of this technology. Although BCI for post-stroke rehabilitation is still in its infancy, the tendency is towards the development of implantable devices that encompass a BCI module plus a stimulation system.
•BCIs permit to reintegrate the sensory–motor loop by accessing to brain information.•Motor imagery based BCIs seem to be an effective system for an early rehabilitation.•This technology does not need remaining motor activity and promotes neuroplasticity.•BCI for rehabilitation tends towards implantable devices plus stimulation systems.
Environmental pollution is a critical issue that requires proper measures to maintain environmental health in a sustainable and effective manner. The growing persistence of several active ...pharmaceutical residues, such as antibiotics like tetracycline, and anti-inflammatory drugs like diclofenac in water matrices is considered an issue of global concern. Numerous sewage/drain waste lines from the domestic and pharmaceutical sector contain an array of toxic compounds, so-called “emerging pollutants” and possess adverse effects on entire living ecosystem and damage its biodiversity. Therefore, effective solution and preventive measures are urgently required to sustainably mitigate and/or remediate pharmaceutically active emerging pollutants from environmental matrices. In this context, herein, the entry pathways of the pharmaceutical waste into the environment are presented, through the entire lifecycle of a pharmaceutical product. There is no detailed review available on carbon-dots (CDs) as robust materials with multifunctional features that support sustainable mitigation of emerging pollutants from water matrices. Thus, CDs-based photocatalysts are emerging as an efficient alternative for decontamination by pharmaceutical pollutants. The addition of CDs on photocatalytic systems has an important role in their performance, mainly because of their up-conversion property, transfer photoinduced electron capacities, and efficient separation of electrons and holes. In this review, we analyze the strategies followed by different researchers to optimize the photodegradation of various pharmaceutical pollutants. In this manner, the effect of different parameters such as pH, the dosage of photocatalyst, amount of carbon dots, and initial pollutant concentration, among others are discussed. Finally, current challenges are presented from a pollution prevention perspective and from CDs-based photocatalytic remediation perspective, with the aim to suggest possible research directions.
Display omitted
•Pollution is a critical issue that requires significant efforts to green the environment.•The trends in carbon dots-based photocatalysts from the past decade are scrutinized.•Sustainable mitigation strategies for highly hazardous toxins are given.•Several active pharmaceutical residues in water are pollutants of concern.
This paper presents an integrated monitoring system for the driver and the vehicle in a single case of study easy to configure and replicate. On-board vehicle sensors and remote sensors are combined ...to model algorithms for estimating polluting emissions, fuel consumption, driving style and driver's health. The main contribution of this paper is the analysis of interactions among the above monitored features highlighting the influence of the driver in the vehicle performance and vice versa. This analysis was carried out experimentally using one vehicle with different drivers and routes and implemented on a mobile application. Compared to commercial driver and vehicle monitoring systems, this approach is not customized, uses classical sensor measurements, and is based on simple algorithms that have been already proven but not in an interactive environment with other algorithms. In the procedure design of this global vehicle and driver monitoring system, a principal component analysis was carried out to reduce the variables used in the training/testing algorithms with objective to decrease the transfer data via Bluetooth between the used devices: a biometric wristband, a smartphone and the vehicle's central computer. Experimental results show that the proposed vehicle and driver monitoring system predicts correctly the fuel consumption index in 84%, the polluting emissions 89%, and the driving style 89%. Indeed, interesting correlation results between the driver's heart condition and vehicular traffic have been found in this analysis.
Application of time-series complexity measures (also known as entropies) has been quite useful in many Biomedical Engineering fields as in Brain–Computer Interfaces (BCI). Nevertheless, ...recently-proposed-entropy parameters referred to as dynamic complexity measures have not concretely been tested in BCI environment. The novelty of our research is evaluating the performance of Dynamic Bivariate Multiscale Entropy parameters (DBMSE) as alternative to typical classification algorithms in BCI, as common spatial patterns (CSP). After developing BCI training, the statistical results showed that CSP performed better than our initial DBMSE (DBMSE-I). This entropy uses a constant threshold r. However, after tunably including that quantity r, DBMSE-I improved to be almost as good as CSP. This updated entropy parameter was referred to as DBMSE-II. Since the limitations of these results were attributed to the shortness of the EEG records, a short-term-DBMSE (named here DBMSE-III) was developed. The findings revealed that DBMS-III had a slightly better performance than CSP outcomes, thus offering a new classifier alternative in BCI. Besides, we present entropy paths that reveal high/low complexity brain activity. This will be useful to identify EEG electrodes to move a robot within a preliminary assistance protocol in progress. Summing up: Besides knowing that DBMSE parameters are a good choice to show brain dynamics, DBMSE-III proved to be an alternative as classifier for CSP in our study, allowing interaction with a robot. In fact, we offer in this document a complete development from theory to execution of software and hardware. This experience will help support people with disabilities and their carers.
Prognosis patients with COVID-19 using deep learning Guadiana-Alvarez, José Luis; Hussain, Fida; Morales-Menendez, Ruben ...
BMC medical informatics and decision making,
03/2022, Letnik:
22, Številka:
1
Journal Article
Recenzirano
Odprti dostop
The coronavirus (COVID-19) is a novel pandemic and recently we do not have enough knowledge about the virus behaviour and key performance indicators (KPIs) to assess the mortality risk forecast. ...However, using a lot of complex and expensive biomarkers could be impossible for many low budget hospitals. Timely identification of the risk of mortality of COVID-19 patients (RMCPs) is essential to improve hospitals' management systems and resource allocation standards.
For the mortality risk prediction, this research work proposes a COVID-19 mortality risk calculator based on a deep learning (DL) model and based on a dataset provided by the HM Hospitals Madrid, Spain. A pre-processing strategy for unbalanced classes and feature selection is proposed. To evaluate the proposed methods, an over-sampling Synthetic Minority TEchnique (SMOTE) and data imputation approaches are introduced which is based on the K-nearest neighbour.
A total of 1,503 seriously ill COVID-19 patients having a median age of 70 years old are comprised in the research work, with 927 (61.7%) males and 576 (38.3%) females. A total of 48 features are considered to evaluate the proposed method, and the following results are achieved. It includes the following values i.e., area under the curve (AUC) 0.93, F2 score 0.93, recall 1.00, accuracy, 0.95, precision 0.91, specificity 0.9279 and maximum probability of correct decision (MPCD) 0.93.
The results show that the proposed method is significantly best for the mortality risk prediction of patients with COVID-19 infection. The MPCD score shows that the proposed DL outperforms on every dataset when evaluating even with an over-sampling technique. The benefits of the data imputation algorithm for unavailable biomarker data are also evaluated. Based on the results, the proposed scheme could be an appropriate tool for critically ill Covid-19 patients to assess the risk of mortality and prognosis.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
PDF
