Academic literature on the topic '120202 Building Science and Techniques'

Origami can turn a sheet of paper into complex three-dimensional shapes, and similar folding techniques can produce structures and mechanisms. To demonstrate the application of these techniques to the fabrication of machines, we developed a crawling robot that folds itself. The robot starts as a flat sheet with embedded electronics, and transforms autonomously into a functional machine. To accomplish this, we developed shape-memory composites that fold themselves along embedded hinges. We used these composites to recreate fundamental folded patterns, derived from computational origami, that can be extrapolated to a wide range of geometries and mechanisms. This origami-inspired robot can fold itself in 4 minutes and walk away without human intervention, demonstrating the potential both for complex self-folding machines and autonomous, self-controlled assembly.

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
Includes bibliographical references (p. 63-65).
by Theresa K. Burianek.
M.Eng.

The traditional power systems are statically rated and sometimes renewable energy sources (RES) are curtailed in order not to exceed this static rating. The RES are curtailed because of their intermittent character and therefore, it is difficult to predict their output at specific time periods throughout the day. Dynamic Line Rating (DLR) technology can overcome this constraint by leveraging the available weather data and technical parameters of the transmission line. The main goal of the thesis is to present prediction models of Dynamic Line Rating (DLR) capacity on two days ahead and on one day ahead. The models are evaluated based on their error rate profiles. DLR provides the capability to up-rate the line(s) according to the environmental conditions and has always a much higher profile than the static rating. By implementing DLR a power utility can increase the efficiency of the power system, decrease RES curtailment and optimize their integration within the grid. DLR is mainly dependent on the weather parameters and specifically, in large wind speeds and low ambient temperature, the DLR can register the highest profile. Additionally, this is especially profitable for the wind energy producers that can both, produce more (until pitch control) and transmit more in high wind speeds periods with the same given line(s), thus increasing the energy efficiency.  The DLR was calculated by employing modern Data Science and Machine Learning tools and techniques and leveraged historical weather and transmission line data provided by SMHI and Vattenfall respectively. An initial phase of Exploratory Data Analysis (EDA) was developed to understand data patterns and relationships between different variables, as well as to determine the most predictive variables for DLR. All the predictive models and data processing routines were built in open source R and are available on GitHub. There were three types of models built: for historical data, for one day-ahead and for two days-ahead time-horizons. The models built for both time-horizons registered a low error rate profile of 9% (for day-ahead) and 11% (for two days-ahead). As expected, the predictive models built on historical data were more accurate with an error as low as 2%-3%.  In conclusion, the implemented models met the requirements set by Vattenfall of maximum error of 20% and they can be applied in the control room for that specific line. Moreover, predictive models can also be built for other lines if the required data is available. Therefore, this Master Thesis project’s findings and outcomes can be reproduced in other power lines and geographic locations in order to achieve a more efficient power system and an increased share of RES in the energy mix

This thesis studies the problem of estimating the interior structure of a collapsed building using embedded Ultra-Wideband (UWB) radios as sensors. The two major sensing problems needed to build the mapping system are determining wall type and wall orientation. We develop sensing algorithms that determine (1) load-bearing wall composition, thickness, and location and (2) wall position within the indoor cavity. We use extensive experimentation and measurement to develop those algorithms.

In order to identify wall types and locations, our research approach uses Received Signal Strength (RSS) measurement between pairs of UWB radios. We create an extensive database of UWB signal propagation data through various wall types and thicknesses. Once the database is built, fingerprinting algorithms are developed which determine the best match between measurement data and database information. For wall mapping, we use measurement of Time of Arrival (ToA) and Angle of Arrival (AoA) between pairs of radios in the same cavity. Using this data and a novel algorithm, we demonstrate how to determine wall material type, thickness, location, and the topology of the wall.

Our research methodology utilizes experimental measurements to create the database of signal propagation through different wall materials. The work also performs measurements to determine wall position in simulated scenarios. We ran the developed algorithms over the measurement data and characterized the error behavior of the solutions.

The experimental test bed uses Time Domain UWB radios with a center frequency of 4.7 GHz and bandwidth of over 3.2 GHz. The software was provided by Time Domain as well, including Performance Analysis Tool, Ranging application, and AoA application. For wall type identification, we use the P200 radio. And for wall mapping, we built a special UWB radio with both angle and distance measurement capability using one P200 radio and one P210 radio.

In our experimental design for wall identification, we varied wall type and distance between the radios, while fixing the number of radios, transmit power and the number of antennas per radio. For wall mapping, we varied the locations of reference node sensors and receiver sensors on adjoining and opposite walls, while fixing cavity size, transmit power, and the number of antennas per radio.

As we present in following chapters, our algorithms have very small estimation errors and can precisely identify wall types and wall positions.

Les réglementations concernant l'industrie de la construction deviennent de plus en plus complexes et touchent plus d'un domaine à la fois. Elles portent sur les produits, les composants et l'exécution des projets. Elles jouent aussi un rôle important pour garantir la qualité d'un bâtiment, ses caractéristiques et minimiser son impact environnemental. Depuis 30 ans, le CSTB prouve son savoir-faire en la matière au travers du développement du REEF, l'encyclopédie complète des textes techniques et réglementaires de la construction. Dans le cadre d'une collaboration entre le CSTB et le laboratoire I3S, nous avons travaillé à la formalisation et au traitement automatisé des informations technico-réglementaires contenues dans le REEF. Nous avons mis en œuvre notre approche pour aider à la création de nouveaux Avis Techniques. Il s'agit de préciser comment ils sont rédigés et comment standardiser leur structure grâce à la mise en œuvre de services sémantiques adaptés. Nous avons réussi à identifier et à comprendre les problèmes liés à la rédaction d'avis techniques et nous nous sommes focalisés sur le renseignement des dossiers techniques par les industriels. Nos contributions sont les suivantes : Nous avons construit manuellement une ontologie du domaine, qui définit les principaux concepts impliqués dans l'élaboration des Avis Technique. Cette ontologie appelée "OntoDT" est couplée avec le thésaurus du projet REEF. Nous l'avons définie à partir de l'étude des dossiers techniques existants, du thesaurus REEF et en interviewant les instructeurs du CSTB. Nous utilisons conjointement les standards SBVR et SPARQL pour reformuler, à la fois dans un langage contrôlé et dans un langage formel, les contraintes réglementaires présentes dans les Guides pratiques. SBVR représente une assurance de la qualité du texte des contraintes réglementaires présentées à l'utilisateur et SPARQL permet l'automatisation de la vérification de ces contraintes. Ces deux représentations reposent sur l'ontologie de domaine que nous avons développée. Nous intégrons des connaissances expertes sur le processus même de vérification des dossiers techniques. Nous avons organisé en différents processus les requêtes SPARQL représentant des contraintes réglementaires. A chaque composant intervenant dans un dossier technique correspond un processus de vérification de sa conformité à la réglementation en vigueur. Les processus sont représentés de manière déclarative en RDF et un moteur de processus interprète ces descriptions RDF pour ordonner et déclencher l'exécution des requêtes nécessaires à la vérification d'un dossier technique particulier. Enfin, nous représentons de façon déclarative en RDF l'association des représentations SBVR et SPARQL des réglementations et nous utilisons ces annotations pour produire à l'utilisateur un rapport de conformité en langue naturelle pour l'assister dans la rédaction d'un avis technique.

The residential sector is responsible for approximately 21% of the total energy use in the U.S. As a result, there have been various programs and studies aiming to reduce energy consumption and utility burden on individual households. Among various energy efficiency strategies, behavior-based approaches have received considerable attention because they significantly affect operational energy consumption without requiring building upgrades. For example, up to 30% of heating and cooling energy savings can be achieved by having an efficient temperature setpoint schedule. Such approaches can be particularly beneficial for multi-family residential buildings because 88% of their residents are renters paying their own utility bills without being allowed to upgrade their housing unit.

In this context, eco-feedback has emerged as an approach to motivate residents to reduce energy use by providing information (feedback) on human behavior and environmental impact. This research has gained significant attention with the development of new smart home technology such as smart thermostats and home energy management systems. Research on the design of effective eco-feedback focuses on how to motivate residents to change their behavior by identifying and notifying implementable actions in a timely manner via energy analytics such as energy prediction models, energy disaggregation, etc.

However, unit-level energy analytics pose significant challenges in multi-family residential buildings tasks due to the inter-unit heat transfer, unobserved variables (e.g., infiltration, human body heat gain, etc.), and limited data availability from the existing infrastructure (i.e., smart thermostats and smart meters). Furthermore, real-time model inference can facilitate up-to-date eco-feedback without a whole year of data to train models. To tackle the aforementioned challenges, three new modeling approaches for energy analytics have been proposed in this Thesis is developed based on the data collected from WiFi-enabled smart thermostats and power meters in a multi-family residential building in IN, U.S.

First, this Thesis presents a unit-level data-driven modeling approach to normalize heating and cooling (HC) energy usage in multi-family residential buildings. The proposed modeling approach provides normalized groups of units that have similar building characteristics to provide the relative evaluation of energy-related behaviors. The physics-informed approach begins from a heat balance equation to derive a linear regression model, and a Bayesian mixture model is used to identify normalized groups in consideration of the inter-unit heat transfer and unobserved variables. The probabilistic approach incorporates unit- and season-specific prior information and sequential Bayesian updating of model parameters when new data is available. The model finds distinct normalized HC energy use groups in different seasons and provides more accurate rankings compared to the case without normalization.

Second, this Thesis presents a real-time modeling approach to predict the HC energy consumption of individual units in a multi-family residential building. The model has a state-space structure to capture the building thermal dynamics, includes the setpoint schedule as an input, and incorporates real-time state filtering and parameter learning to consider uncertainties from unobserved boundary conditions (e.g., temperatures of adjacent spaces) and unobserved disturbances (i.e., window opening, infiltration, etc.). Through this real-time form, the model does not need to be re-trained for different seasons. The results show that the median power prediction of the model deviates less than 3.1% from measurements while the model learns seasonal parameters such as the cooling efficiency coefficient through sequential Bayesian update.

Finally, this Thesis presents a scalable and practical HC energy disaggregation model that is designed to be developed using data from smart meters and smart thermostats available in current advanced metering infrastructure (AMI) in typical residential houses without additional sensors. The model incorporates sequential Bayesian update whenever a new operation type is observed to learn seasonal parameters without long-term data for training. Also, it allows modeling the skewed characteristics of HC and non-HC power data. The results show that the model successfully predicts disaggregated HC power from 15-min interval data, and it shows less than 12% of error in weekly HC energy consumption. Finally, the model is able to learn seasonal parameters via sequential Bayesian update and gives good prediction results in different seasons.

abstract: In supervised learning, machine learning techniques can be applied to learn a model on a small set of labeled documents which can be used to classify a larger set of unknown documents. Machine learning techniques can be used to analyze a political scenario in a given society. A lot of research has been going on in this field to understand the interactions of various people in the society in response to actions taken by their organizations. This paper talks about understanding the Russian influence on people in Latvia. This is done by building an eeffective model learnt on initial set of documents containing a combination of official party web-pages, important political leaders' social networking sites. Since twitter is a micro-blogging site which allows people to post their opinions on any topic, the model built is used for estimating the tweets sup- porting the Russian and Latvian political organizations in Latvia. All the documents collected for analysis are in Latvian and Russian languages which are rich in vocabulary resulting into huge number of features. Hence, feature selection techniques can be used to reduce the vocabulary set relevant to the classification model. This thesis provides a comparative analysis of traditional feature selection techniques and implementation of a new iterative feature selection method using EM and cross-domain training along with supportive visualization tool. This method out performed other feature selection methods by reducing the number of features up-to 50% along with good model accuracy. The results from the classification are used to interpret user behavior and their political influence patterns across organizations in Latvia using interactive dashboard with combination of powerful widgets.
Dissertation/Thesis
Masters Thesis Computer Science 2016

Commercial buildings not only have significant impacts on occupants’ well-being, but also contribute to more than 19% of the total energy consumption in the United States. Along with improvements in building equipment efficiency and utilization of renewable energy, there has been significant focus on the development of advanced heating, ventilation, and air conditioning (HVAC) system controllers that incorporate predictions (e.g., occupancy patterns, weather forecasts) and current state information to execute optimization-based strategies. For example, model predictive control (MPC) provides a systematic implementation option using a system model and an optimization algorithm to adjust the control setpoints dynamically. This approach automatically satisfies component and operation constraints related to building dynamics, HVAC equipment, etc. However, the wide adaptation of advanced controls still faces several practical challenges: such approaches involve significant engineering effort and require site-specific solutions for complex problems that need to consider uncertain weather forecast and engaging the building occupants. This thesis explores smart building operation strategies to resolve such issues from the following three aspects.

First, the thesis explores a stochastic model predictive control (SMPC) method for the optimal utilization of solar energy in buildings with integrated solar systems. This approach considers the uncertainty in solar irradiance forecast over a prediction horizon, using a new probabilistic time series autoregressive model, calibrated on the sky-cover forecast from a weather service provider. In the optimal control formulation, we model the effect of solar irradiance as non-Gaussian stochastic disturbance affecting the cost and constraints, and the nonconvex cost function is an expectation over the stochastic process. To solve this optimization problem, we introduce a new approximate dynamic programming methodology that represents the optimal cost-to-go functions using Gaussian process, and achieves good solution quality. We use an emulator to evaluate the closed-loop operation of a building-integrated system with a solar-assisted heat pump coupled with radiant floor heating. For the system and climate considered, the SMPC saves up to 44% of the electricity consumption for heating in a winter month, compared to a well-tuned rule-based controller, and it is robust, imposing less uncertainty on thermal comfort violation.

Second, this thesis explores user-interactive thermal environment control systems that aim to increase energy efficiency and occupant satisfaction in office buildings. Towards this goal, we present a new modeling approach of occupant interactions with a temperature control and energy use interface based on utility theory that reveals causal effects in the human decision-making process. The model is a utility function that quantifies occupants’ preference over temperature setpoints incorporating their comfort and energy use considerations. We demonstrate our approach by implementing the user-interactive system in actual office spaces with an energy efficient model predictive HVAC controller. The results show that with the developed interactive system occupants achieved the same level of overall satisfaction with selected setpoints that are closer to temperatures determined by the MPC strategy to reduce energy use. Also, occupants often accept the default MPC setpoints when a significant improvement in the thermal environment conditions is not needed to satisfy their preference. Our results show that the occupants’ overrides can contribute up to 55% of the HVAC energy consumption on average with MPC. The prototype user-interactive system recovered 36% of this additional energy consumption while achieving the same overall occupant satisfaction level. Based on these findings, we propose that the utility model can become a generalized approach to evaluate the design of similar user-interactive systems for different office layouts and building operation scenarios.

Finally, this thesis presents an approach based on meta-reinforcement learning (Meta-RL) that enables autonomous optimal building controls with minimum engineering effort. In reinforcement learning (RL), the controller acts as an agent that executes control actions in response to the real-time building system status and exogenous disturbances according to a policy. The agent has the ability to update the policy towards improving the energy efficiency and occupant satisfaction based on the previously achieved control performance. In order to ensure satisfactory performance upon deployment to a target building, the agent is trained using the Meta-RL algorithm beforehand with a model universe obtained from available building information, which is a probability measure over the possible building dynamical models. Starting from what is learned in the training process, the agent then fine-tunes the policy to adapt to the target building based on-site observations. The control performance and adaptability of the Meta-RL agent is evaluated using an emulator of a private office space over 3 summer months. For the system and climate under consideration, the Meta-RL agent can successfully maintain the indoor air temperature within the first week, and result in only 16% higher energy consumption in the 3^rd month than MPC, which serves as the theoretical upper performance bound. It also significantly outperforms the agents trained with conventional RL approach.

Current air conditioning systems generally operate with a relatively fixed moisture removal capacity, and indoor humidity conditions are usually not actively controlled in most buildings. If we focus only on sensible heat removal, an air conditioning system could operate with a fairly high evaporating temperature, and consequently a high coefficient of performance (COP). However, to provide an acceptable level of dehumidification, air conditioners typically operate with a much lower evaporating temperature (and lower COP) to ensure that the air is cooled below its dew point to achieve dehumidification. The latent (moisture related) loads in a space typically only represent around 20-30% of the total load in many environments; however, the air conditioning system operates 100% of the time at a low COP to address this small fraction of the load. To address issues associated with inadequate dehumidification and high energy consumption of conventional air conditioning systems, the use of a separate sensible and latent cooling (SSLC) system can dramatically increase system COP and provide active humidity control. Most current SSLC approaches that are reported in the literature require the installation of multiple components or systems in addition to a conventional air conditioner to separately address the sensible and latent loads. This approach increases the overall system installation and maintenance costs and complicates the controller design.

A sequential SSLC system is proposed and described in this work takes full advantage of readily available variable speed technology and utilizes independent speed control of both the compressor and evaporator fan, so that a single direct expansion (DX) air-conditioning (A/C) system can be operated in such a way to separately address the sensible and latent loads in a highly efficient manner. In this work, a numerical model of DX A/C system is developed and validated through experiential testing to predict the performance under varied equipment speeds and then used to investigate the energy saving potential with the implementation of the proposed sequential SSLC system. To realize the sequential SSLC system approach, various corresponding control strategies are proposed and explained in this work that minimizes energy consumption while provides active control over both space temperature and relative humidity. At the end of this document, the benefits of applying the SSLC system in a prototype residential building under different typical climate characteristics are demonstrated.

Cancer cells must rewrite their ‘‘internal code’’ to satisfy the demand for growth and proliferation. Such changes are driven by a combination of genetic (e.g., genes’ mutations) and non-genetic factors (e.g., tumour microenvironment) that result in an alteration of cellular metabolism. For this reason, understanding the metabolic and genomic changes of a cancer cell can provide useful insight on cancer progression and survival outcomes. In our work, we present a computational framework that uses patient-specific data to investigate cancer metabolism and provide personalised survival predictions and cancer development outcomes. The proposed model integrates patient-specific multi-omics data (i.e., genomic, metabolomic and clinical data) into a metabolic model of cancer to produce a list of metabolic reactions affecting cancer progression. Quantitative and predictive analysis, through survival analysis and machine learning techniques, is then performed on the list of selected reactions. Since our model performs an analysis of patient-specific data, the outcome of our pipeline provides a personalised prediction of survival outcome and cancer development based on a subset of identified multi-omics features (genomic, metabolomic and clinical data). In particular, our work aims to develop a computational pipeline for clinicians that relates the omic profile of each patient to their survival probability, based on a combination of machine learning and metabolic modelling techniques. The model provides patient-specific predictions on cancer development and survival outcomes towards the development of personalised medicine.

Data Mining (DM) is a very crucial issue in knowledge discovery processes. The basic facilities to create data mining models were implemented successfully on Oracle 9i as the extension of the database server. DM tools enable developers to create Business Intelligence (BI) applications. As a result Data Mining models can be used as support of knowledge-based management. The main goal of the paper is to present new features of the Oracle platform in building and testing DM models. Authors characterize methods of building and testing Data Mining models available on the Oracle 9i platform, stressing the critical steps of the whole process and presenting examples of practical usage of DM models. Verification techniques of the generated knowledge bases are discussed in the mentioned environment.

We describe a method for the manufacturing of metallic lattices with tunable properties through the reversible assembly of building block elements, which we call discrete metal lattice assembly (DMLA). These structures can have sub-millimeter scale features on millimeter scale parts used to assemble structures spanning tens of centimeters, comparable to those currently made with Direct Metal Laser Sintering (DMLS). However, unlike traditional additive manufacturing (AM) methods, the use of discrete assembly affords a number of benefits, such as extensible, incremental construction and being repairable and reconfigurable. We show this method results in large scale (tens of centimeters), ultralight (<10 kg/m3 effective density) lattices which are currently not possible with state of the art additive manufacturing techniques. The lattice geometry used here is a combination of two geometries with quadratic property scaling, resulting in a novel lattice with sub-quadratic scaling.

The energy consumption of network infrastructures is increasing; therefore, research efforts designed to diminish this growing carbon footprint are necessary. Building on prior work, which determined a difference in the energy consumption of network hardware based on their forwarding configurations and developed a real-time network energy monitoring tool, this research proposes a novel technique to incorporate individual device energy efficiency into network routing decisions. A new routing metric and algorithm are presented to select the lowest-power, least-congested paths between destinations, known as Green Power Forwarding (GPF). In addition, a network dial is developed to enhance GPF by allowing network administrators to tune the network to optimally operate between energy savings and network performance. To ensure the scope of this research for industry adoption, implementation details for different generations of networking infrastructure (past, present, and future) are also discussed. The experiment results indicate that significant energy and, in turn, cost savings can be achieved by employing the proposed GPF technique without a reduction in network performance. The future directions for this research include developing dynamically-tuning network dial modes and extending the principles to inter-domain routing.

The paper draws on a didactic experiment conducted in a secondary school mathematics classroom in Greece which aimed to explore a) ways in which students develop problem representations, reasoning and problem-solving, making decisions and receiving feedback about their ideas and strategies in a DGS-supported environment b) ways in which students develop rigourous proof through building linking visual active representations and c) ways to develop students’ van Hiele level. The mathematical problem the students engaged with - either in the Geometer’s Sketchpad dynamic geometry enviroment (Jackiw, 1988) or in the static environment - generated potentially insightful data on the issues focused on the comparison between the experimental and control groups. Initially, three pairs from the experimental group explored the treasure problem within a dynamic geometry environment. The discussions and results of the discussion were videotaped. The problem was then reformulated by the researcher taking into account the research group’s retroaction, and re-explored by both the control and experimental groups in a paper-pencil test. The researcher then (semi) pre-designed multiple-page sketches detailing the sequential phases of the solution to the problem using rigorous proof, and in so doing transferring her classroom reaching style into the software design, drawing on the chain questioning method of Socrates, which aim to stimulate interaction. For this reason, she linked all the software func-tions/actions using the interaction techniques supported /facilitated by the Geometer’s Sketchpad v4 (DGS) environment (Jackiw, 1988) to better allow students to discover solution paths and to reason by rigorous proof. This mode of design and the results of the experimental use of the software with students led to the need to define two new concepts: the meanings of Linking Visual Active Representations (LVAR) and Reflective Visual Reaction (RVR). The researcher observed the students’ actions and thinking processes during the research process and offers a description and analysis of these processes. An analysis of the results of the experimental procedure revealed

Residential and commercial buildings account for more than 40% of U.S. energy consumption, most of which is related to heating, ventilation and air conditioning (HVAC). Consequently, energy conservation is important to building owners and to the economy generally. In this paper we describe a process under development to continuously evaluate a building’s heating and cooling energy performance in near real-time with a procedure we call Continuous Monitoring, Modeling, and Evaluation (CMME). The concept of CMME is to model the expected operation of a building energy system with actual weather and internal load data and then compare modeled energy consumption with actual energy consumption. For this paper we modeled two buildings on the Georgia Institute of Technology campus. After creating our building models, internal lighting loads and equipment plug-loads were collected through electrical sub-metering, while the building occupancy load was recorded using doorway mounted people counters. We also collected on site weather and solar radiation data. All internal loads were input into the models and simulated with the actual weather data. We evaluated the building’s overall performance by comparing the modeled heating and cooling energy consumption with the building’s actual heating and cooling energy consumption. Our results demonstrated generally acceptable energy performance for both buildings; nevertheless, certain specific energy inefficiencies were discovered and corrective actions are being taken. This experience shows that CMME is a practical procedure for improving the performance of actual well performing buildings. With improved techniques, we believe the CMME procedure could be fully automated and notify building owners in real-time of sub-optimal building performance.

Artificial Intelligence (AI) is one of the oldest fields of computer science used in building structures that look like human beings in terms of thinking, learning, solving problems, and decision making (Jovanovic et al., 2021). AI technologies and techniques have been in application in various aspects to aid in solving problems and performing tasks more reliably, efficiently, and effectively than what would happen without their use. These technologies have also been reshaping the health sector's field, particularly digital tools and medical robotics (Dantas & Nogaroli, 2021). The new reality has been feasible since there has been exponential growth in the patient health data collected globally. The different technological approaches are revolutionizing medical sciences into dataintensive sciences (Dantas & Nogaroli, 2021). Notably, with digitizing medical records supported the increasing cloud storage, the health sector created a vast and potentially immeasurable volume of biomedical data necessary for implementing robotics and AI. Despite the notable use of AI in healthcare sectors such as dermatology and radiology, its use in psychological healthcare has neem models. Considering the increased mortality and morbidity levels among patients with psychiatric illnesses and the debilitating shortage of psychological healthcare workers, there is a vital requirement for AI and robotics to help in identifying high-risk persons and providing measures that avert and treat mental disorders (Lee et al., 2021). This discussion is focused on understanding how AI and robotics could be employed in improving mental health in the human community. The continued success of this technology in other healthcare fields demonstrates that it could also be used in redefining mental sicknesses objectively, identifying them at a prodromal phase, personalizing the treatments, and empowering patients in their care programs.