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Background
This is a systematic review protocol to identify automated features, applied technologies, and algorithms in the electronic early warning/track and triage system (EW/TTS) developed to predict clinical deterioration (CD).
Methodology
This study will be conducted using PubMed, Scopus, and Web of Science databases to evaluate the features of EW/TTS in terms of their automated features, technologies, and algorithms. To this end, we will include any English articles reporting an EW/TTS without time limitation. Retrieved records will be independently screened by two authors and relevant data will be extracted from studies and abstracted for further analysis. The included articles will be evaluated independently using the JBI critical appraisal checklist by two researchers.
Discussion
This study is an effort to address the available automated features in the electronic version of the EW/TTS to shed light on the applied technologies, automated level of systems, and utilized algorithms in order to smooth the road toward the fully automated EW/TTS as one of the potential solutions of prevention CD and its adverse consequences.
Multi-faceted stresses of social, environmental, and economic nature are increasingly challenging the existence and sustainability of our societies. Cities in particular are disproportionately threatened by global issues such as climate change, urbanization, population growth, air pollution, etc. In addition, urban space is often too limited to effectively develop sustainable, nature-based solutions while accommodating growing populations. This research aims to provide new methodologies by proposing lightweight green bridges in inner-city areas as an effective land value capture mechanism. Geometry analysis was performed using geospatial and remote sensing data to provide geometrically feasible locations of green bridges. A multi-criteria decision analysis was applied to identify suitable locations for green bridges investigating Central European urban centers with a focus on German cities as representative examples. A cost-benefit analysis was performed to assess the economic feasibility using a case study. The results of the geometry analysis identified 3249 locations that were geometrically feasible to implement a green bridge in German cities. The sample locations from the geometry analysis were proved to be validated for their implementation potential. Multi-criteria decision analysis was used to select 287 sites that fall under the highest suitable class based on several criteria. The cost-benefit analysis of the case study showed that the market value of the property alone can easily outweigh the capital and maintenance costs of a green bridge, while the indirect (monetary) benefits of the green space continue to increase the overall value of the green bridge property including its neighborhood over time. Hence, we strongly recommend light green bridges as financially sustainable and nature-based solutions in cities worldwide.
Recognizing Human Activity of Daily Living Using a Flexible Wearable for 3D Spine Pose Tracking
(2023)
The World Health Organization recognizes physical activity as an influencing domain on quality of life. Monitoring, evaluating, and supervising it by wearable devices can contribute to the early detection and progress assessment of diseases such as Alzheimer’s, rehabilitation, and exercises in telehealth, as well as abrupt events such as a fall. In this work, we use a non-invasive and non-intrusive flexible wearable device for 3D spine pose measurement to monitor and classify physical activity. We develop a comprehensive protocol that consists of 10 indoor, 4 outdoor, and 8 transition states activities in three categories of static, dynamic, and transition in order to evaluate the applicability of the flexible wearable device in human activity recognition. We implement and compare the performance of three neural networks: long short-term memory (LSTM), convolutional neural network (CNN), and a hybrid model (CNN-LSTM). For ground truth, we use an accelerometer and strips data. LSTM reached an overall classification accuracy of 98% for all activities. The CNN model with accelerometer data delivered better performance in lying down (100%), static (standing = 82%, sitting = 75%), and dynamic (walking = 100%, running = 100%) positions. Data fusion improved the outputs in standing (92%) and sitting (94%), while LSTM with the strips data yielded a better performance in bending-related activities (bending forward = 49%, bending backward = 88%, bending right = 92%, and bending left = 100%), the combination of data fusion and principle components analysis further strengthened the output (bending forward = 100%, bending backward = 89%, bending right = 100%, and bending left = 100%). Moreover, the LSTM model detected the first transition state that is similar to fall with the accuracy of 84%. The results show that the wearable device can be used in a daily routine for activity monitoring, recognition, and exercise supervision, but still needs further improvement for fall detection.
Sleep disorders can impact daily life, affecting physical, emotional, and cognitive well-being. Due to the time-consuming, highly obtrusive, and expensive nature of using the standard approaches such as polysomnography, it is of great interest to develop a noninvasive and unobtrusive in-home sleep monitoring system that can reliably and accurately measure cardiorespiratory parameters while causing minimal discomfort to the user’s sleep. We developed a low-cost Out of Center Sleep Testing (OCST) system with low complexity to measure cardiorespiratory parameters. We tested and validated two force-sensitive resistor strip sensors under the bed mattress covering the thoracic and abdominal regions. Twenty subjects were recruited, including 12 males and 8 females. The ballistocardiogram signal was processed using the 4th smooth level of the discrete wavelet transform and the 2nd order of the Butterworth bandpass filter to measure the heart rate and respiration rate, respectively. We reached a total error (concerning the reference sensors) of 3.24 beats per minute and 2.32 rates for heart rate and respiration rate, respectively. For males and females, heart rate errors were 3.47 and 2.68, and respiration rate errors were 2.32 and 2.33, respectively. We developed and verified the reliability and applicability of the system. It showed a minor dependency on sleeping positions, one of the major cumbersome sleep measurements. We identified the sensor under the thoracic region as the optimal configuration for cardiorespiratory measurement. Although testing the system with healthy subjects and regular patterns of cardiorespiratory parameters showed promising results, further investigation is required with the bandwidth frequency and validation of the system with larger groups of subjects, including patients.
Linear and nonlinear response functions (RF) are extracted for the climate system and the carbon cycle represented by the MPI-ESM and cGENIE models, respectively. Appropriately designed simulations are run for this purpose. Joining these RFs, we have a climate emulator with carbon emissions as the forcing and any desired observable quantity (provided the data is saved), such as the surface air temperature or precipitation, as the predictand. Like e.g. for atmospheric CO2 concentration, we also have RFs for the solar constant as a forcing — mimicking solar radiation management (SRM) geoengineering. We consider two application cases. 1. One is based on the Paris 2015 agreement, determining the necessary least amount of SRM geoengineering needed to keep the global mean surface air temperature below a certain threshold, e.g. 1.5 or 2 [oC], given a certain amount of carbon emission abatement (ABA) and carbon dioxide removal (CDR) geoengineering. 2. The other application considers the conservation of the Greenland ice sheet (GrIS). Using a zero-dimensional simplification of a complex ice sheet model, we determine (a) if we need SRM given some ABA and CDR, and, if possible, (b) the required least amount of SRM to avoid the collapse of the GrIS. Keeping temperatures below 2 [oC] even is hardly possible without sustained SRM (1.); however, the collapse of the GrIS can be avoided applying SRM even for moderate levels of CDR and ABA, an overshoot being affordable (2.).
Insecurity Refactoring is a change to the internal structure of software to inject a vulnerability without changing the observable behavior in a normal use case scenario. An implementation of Insecurity Refactoring is formally explained to inject vulnerabilities in source code projects by using static code analysis. It creates learning examples with source code patterns from known vulnerabilities.
Insecurity Refactoring is achieved by creating an Adversary Controlled Input Dataflow tree based on a Code Property Graph. The tree is used to find possible injection paths. Transformation of the possible injection paths allows to inject vulnerabilities. Insertion of data flow patterns introduces different code patterns from related Common Vulnerabilities and Exposures (CVE) reports. The approach is evaluated on 307 open source projects. Additionally, insecurity-refactored projects are deployed in virtual machines to be used as learning examples. Different static code analysis tools, dynamic tools and manual inspections are used with modified projects to confirm the presence of vulnerabilities.
The results show that in 8.1% of the open source projects it is possible to inject vulnerabilities. Different inspected code patterns from CVE reports can be inserted using corresponding data flow patterns. Furthermore the results reveal that the injected vulnerabilities are useful for a small sample size of attendees (n=16). Insecurity Refactoring is useful to automatically generate learning examples to improve software security training. It uses real projects as base whereas the injected vulnerabilities stem from real CVE reports. This makes the injected vulnerabilities unique and realistic.
Increasing demand for sustainable, resilient, and low-carbon construction materials has highlighted the potential of Compacted Mineral Mixtures (CMMs), which are formulated from various soil types (sand, silt, clay) and recycled mineral waste. This paper presents a comprehensive inter- and transdisciplinary research concept that aims to industrialise and scale up the adoption of CMM-based construction materials and methods, thereby accelerating the construction industry’s systemic transition towards carbon neutrality. By drawing upon the latest advances in soil mechanics, rheology, and automation, we propose the development of a robust material properties database to inform the design and application of CMM-based materials, taking into account their complex, time-dependent behaviour. Advanced soil mechanical tests would be utilised to ensure optimal performance under various loading and ageing conditions. This research has also recognised the importance of context-specific strategies for CMM adoption. We have explored the implications and limitations of implementing the proposed framework in developing countries, particularly where resources may be constrained. We aim to shed light on socio-economic and regulatory aspects that could influence the adoption of these sustainable construction methods. The proposed concept explores how the automated production of CMM-based wall elements can become a fast, competitive, emission-free, and recyclable alternative to traditional masonry and concrete construction techniques. We advocate for the integration of open-source digital platform technologies to enhance data accessibility, processing, and knowledge acquisition; to boost confidence in CMM-based technologies; and to catalyse their widespread adoption. We believe that the transformative potential of this research necessitates a blend of basic and applied investigation using a comprehensive, holistic, and transfer-oriented methodology. Thus, this paper serves to highlight the viability and multiple benefits of CMMs in construction, emphasising their pivotal role in advancing sustainable development and resilience in the built environment.
Digital federated platforms and data cooperatives for secure, trusted and sovereign data exchange will play a central role in the construction industry of the future. With the help of platforms, cooperatives and their novel value creation, the digital transformation and the degree of organization of the construction value chain can be taken to a new level of collaboration. The goal of this research project was to develop an experimental prototype for a federated innovation data platform along with a suitable exemplary use case. The prototype is to serve the construction industry as a demonstrator for further developments and form the basis for an innovation platform. It exemplifies how an overall concept is concretely implemented along one or more use cases that address high-priority industry pain points. This concept will create a blueprint and a framework for further developments, which will then be further established in the market. The research project illuminates the perspective of various governance innovations to increase industry collaboration, productivity and capital project performance and transparency as well as the overall potential of possible platform business models. However, a comprehensive expert survey revealed that there are considerable obstacles to trust-based data exchange between the key stakeholders in the industry value network. The obstacles to cooperation are predominantly not of a technical nature but rather of a competitive, predominantly trust-related nature. To overcome these obstacles and create a pre-competitive space of trust, the authors therefore propose the governance structure of a data cooperative model, which is discussed in detail in this paper.
The scoring of sleep stages is one of the essential tasks in sleep analysis. Since a manual procedure requires considerable human and financial resources, and incorporates some subjectivity, an automated approach could result in several advantages. There have been many developments in this area, and in order to provide a comprehensive overview, it is essential to review relevant recent works and summarise the characteristics of the approaches, which is the main aim of this article. To achieve it, we examined articles published between 2018 and 2022 that dealt with the automated scoring of sleep stages. In the final selection for in-depth analysis, 125 articles were included after reviewing a total of 515 publications. The results revealed that automatic scoring demonstrates good quality (with Cohen's kappa up to over 0.80 and accuracy up to over 90%) in analysing EEG/EEG + EOG + EMG signals. At the same time, it should be noted that there has been no breakthrough in the quality of results using these signals in recent years. Systems involving other signals that could potentially be acquired more conveniently for the user (e.g. respiratory, cardiac or movement signals) remain more challenging in the implementation with a high level of reliability but have considerable innovation capability. In general, automatic sleep stage scoring has excellent potential to assist medical professionals while providing an objective assessment.