Personalized Data Research Articles

Network modeling predicts personalized gene expression and drug responses in valve myofibroblasts cultured with patient sera

Aortic valve stenosis (AVS) patients experience pathogenic valve leaflet stiffening due to excessive extracellular matrix (ECM) remodeling. Numerous microenvironmental cues influence pathogenic expression of ECM remodeling genes in tissue-resident valvular myofibroblasts, and the regulation of complex myofibroblast signaling networks depends on patient-specific extracellular factors. Here, we combined a manually curated myofibroblast signaling network with a data-driven transcription factor network to predict patient-specific myofibroblast gene expression signatures and drug responses. Using transcriptomic data from myofibroblasts cultured with AVS patient sera, we produced a large-scale, logic-gated differential equation model in which 11 biochemical and biomechanical signals were transduced via a network of 334 signaling and transcription reactions to accurately predict the expression of 27 fibrosis-related genes. Correlations were found between personalized model-predicted gene expression and AVS patient echocardiography data, suggesting links between fibrosis-related signaling and patient-specific AVS severity. Further, global network perturbation analyses revealed signaling molecules with the most influence over network-wide activity, including endothelin 1 (ET1), interleukin 6 (IL6), and transforming growth factor β (TGFβ), along with downstream mediators c-Jun N-terminal kinase (JNK), signal transducer and activator of transcription (STAT), and reactive oxygen species (ROS). Lastly, we performed virtual drug screening to identify patient-specific drug responses, which were experimentally validated via fibrotic gene expression measurements in valvular interstitial cells cultured with AVS patient sera and treated with or without bosentan-a clinically approved ET1 receptor inhibitor. In sum, our work advances the ability of computational approaches to provide a mechanistic basis for clinical decisions including patient stratification and personalized drug screening.

Decision-making of IoT device operation based on intelligent-task offloading for improving environmental optimization

Computation offloading enables intensive computational tasks to be separated into multiple computing resources for overcoming hardware limitations. Leveraging cloud computing, edge computing can be enabled to apply not only large-scale and personalized data but also intelligent algorithms based on offloading the intelligent models to high-performance servers for working with huge volumes of data in the cloud. In this paper, we propose a getaway-centric Internet of Things (IoT) system to enable the intelligent and autonomous operation of IoT devices in edge computing. In the proposed edge computing, IoT devices are operated by a decision-making model that selects an optimal control factor from multiple intelligent services and applies it to the device. The intelligent services are provided based on offloading multiple intelligent and optimization approaches to the intelligent service engine in the cloud. Therefore, the decision-making model in the gateway is enabled to select the best solution from the candidates. Also, the proposed IoT system provides monitoring and visualization to users through device management based on resource virtualization using the gateway. Furthermore, the gateway interprets scenario profiles to interact with intelligent services dynamically and apply the optimal control factor to the actual device through the virtual resource. For implementing the improved energy optimization using the proposed IoT system, we propose two intelligent models to learn parameters of a user’s residential environment using deep learning and derive the inference models to deploy in the intelligent service engine. The inference models are used for predicting a heater energy consumption that is applied to the heater. The heater updates the environment parameters to reach the user-desired values. Moreover, based on two energy consumption values, the decision-making model brings a smaller value to operate the heater to enable reducing the energy consumption as well as providing a user-desired environment.

User Control of Personal mHealth Data Using a Mobile Blockchain App: Design Science Perspective.

BackgroundIntegrating pervasive computing with blockchain’s ability to store privacy-protected mobile health (mHealth) data while providing Health Insurance Portability and Accountability Act (HIPAA) compliance is a challenge. Patients use a multitude of devices, apps, and services to collect and store mHealth data. We present the design of an internet of things (IoT)–based configurable blockchain with different mHealth apps on iOS and Android, which collect the same user’s data. We discuss the advantages of using such a blockchain architecture and demonstrate 2 things: the ease with which users can retain full control of their pervasive mHealth data and the ease with which HIPAA compliance can be accomplished by providers who choose to access user data.ObjectiveThe purpose of this paper is to design, evaluate, and test IoT-based mHealth data using wearable devices and an efficient, configurable blockchain, which has been designed and implemented from the first principles to store such data. The purpose of this paper is also to demonstrate the privacy-preserving and HIPAA-compliant nature of pervasive computing-based personalized health care systems that provide users with total control of their own data.MethodsThis paper followed the methodical design science approach adapted in information systems, wherein we evaluated prior designs, proposed enhancements with a blockchain design pattern published by the same authors, and used the design to support IoT transactions. We prototyped both the blockchain and IoT-based mHealth apps in different devices and tested all use cases that formed the design goals for such a system. Specifically, we validated the design goals for our system using the HIPAA checklist for businesses and proved the compliance of our architecture for mHealth data on pervasive computing devices.ResultsBlockchain-based personalized health care systems provide several advantages over traditional systems. They provide and support extreme privacy protection, provide the ability to share personalized data and delete data upon request, and support the ability to analyze such data.ConclusionsWe conclude that blockchains, specifically the consensus, hasher, storer, miner architecture presented in this paper, with configurable modules and software as a service model, provide many advantages for patients using pervasive devices that store mHealth data on the blockchain. Among them is the ability to store, retrieve, and modify ones generated health care data with a single private key across devices. These data are transparent, stored perennially, and provide patients with privacy and pseudoanonymity, in addition to very strong encryption for data access. Firms and device manufacturers would benefit from such an approach wherein they relinquish user data control while giving users the ability to select and offer their own mHealth data on data marketplaces. We show that such an architecture complies with the stringent requirements of HIPAA for patient data access.

A gamified app for supporting undergraduate students' mental health: A feasibility and usability study.

Resilience, a person's mental ability to deal with challenging situations adaptively, is an important life skill. Supporting students in building psychological resilience and coping during crises (with the COVID-19 pandemic being a prime example) is crucial. Very few mobile applications (apps) for mental health explicitly report behavioral change techniques. Moreover, only a handful of the apps that support resilience are gamified, or use smartphone sensors readily available in modern smartphones for health self-management, or were designed for use by a nonclinical population. This study describes the design of a prototype for a gamified, theory-based mobile app that utilizes the Internet of Things to provide personalized data and enhance undergraduate students’ resilience. A total of 74 participants evaluated the prototype and completed an online questionnaire during the COVID-19 lockdowns. The questionnaire included questions examining the design's feasibility for supporting resilience and questions on the System Usability Scale evaluating its usability. Regarding the evaluation of the prototype on improving psychological resilience, positive responses (M = 3.76 out of 5, SD = 0.82) were received for all functions (goal setting for studying, socializing and physical exercise, progress monitoring using sensors or self-reporting, reflection, motivational badges). The System Usability Scale returned an evaluation score of 72.9, indicating a satisfactory degree of usability. The resilience app is a promising proof of concept. Combining Internet of Things capabilities with active user interaction while incorporating behavior change techniques in a gamified environment was well accepted by students. Implications for the design of gamified environments for well-being are drawn. Future research will empirically validate its design using quasi-experimental methods.

Data-Driven Carbohydrate Counting Accuracy Monitoring: A Personalized Approach

Accurate carbohydrate counting is crucial for type 1 diabetes mellitus patients on intensive insulin therapy to get on-target blood glucose values. So, it is fundamental to assess their ability to estimate meals’ carbohydrate content and, if needed, recommend carbohydrate counting training. In this context, we propose a personalized data-driven approach to monitor the patients’ ability to estimate the carbohydrate content of meals.The proposed approach uses personalized data to compute a safe range for the carbohydrate counting error according to the characteristics of each patient and adjust this interval to the patient's daily routines and food habits. Initially, the proposed method uses the insulin-to-carbohydrate ratio, the insulin sensitivity factor, the blood glucose limits, and the blood glucose target to compute a safe interval for the carbohydrate counting error, so the patient could train to reach this goal. Then, the app uses collected daily life data (i.e., blood glucose, meals carbohydrates content, and insulin bolus) to adjust the initial safe interval for the carbohydrate counting error according to the patient's needs.Preliminary assessment using the FDA-approved University of Virginia (UVA)/Padova Type 1 Diabetes Simulator shows the potential of the proposed approach to help type 1 diabetes patients being aware of their needs for carbohydrate counting education and how accurate they should be to achieve suitable blood glucose levels. Therefore, this tool has the potential to be a great asset to healthcare professionals and patients, improving the carbohydrate counting learning outcomes and leading to better glycemic control.

Mass customization and testing of braces using additive manufacturing

Current methods to fabricate customized braces typically require a mold of the foot and often require casts to be pre-manufactured or milled from a solid block. This is time consuming and expensive. However, additive manufacturing opens opportunities for mass customization. Selected regions of customized protection or varying properties for the joint range of motion for braces is also possible with 3D printing solutions. This study explored the use of additive manufacturing for the mass customization of braces, using the ankle brace as an illustration. This paper showcases the methodology of using a glass scanner to obtain individual biomechanics data; incorporating personalized data in a pre-designed brace template; designing mass customized braces using Rhinoceros 6 Grasshopper; and manufacturing of the ankle brace using fused deposition modelling. Initial tests on the dynamometer showed that maximum ankle inversion angle was reduced by almost a third, as compared to barefoot conditions. This potentially will significantly reduce ankle lateral sprain injury risks at ankle inversion positions especially beyond functional range of motion and successfully demonstrated the possibility of using 3D printing solutions for similar mass customized applications.

Radiogenomics influence on the future of prostate cancer risk stratification

In an era of powerful computing tools, radiogenomics provides a personalized, precise approach to the detection and diagnosis in patients with prostate cancer (PCa). Radiomics data are obtained through artificial intelligence (AI) and neural networks that analyze imaging, usually MRI, to assess statistical, geometrical, and textural features of images to provide quantitative data of shape, heterogeneity, and intensity of tumors. Genomics involves assessing the genomic markers that are present from tumor biopsies. In this article, we separately investigate the current landscape of radiomics and genomics within the realm of PCa and discuss the integration and validity of both into radiogenomics using the data from three papers on the topic. We also conducted a clinical trials search using the NIH’s database, where we found two relevant actively recruiting studies. Although there is more research needed to be done on radiogenomics to fully adopt it as a viable diagnosis tool, its potential by providing personalized data regarding each tumor cannot be overlooked as it may be the future of PCa risk-stratification techniques.

Expert-analytical approach to assessing activity of vitamin D based on determination of vitamin D associated links by indicators of water-electrolyte metabolism

The authors used the proposed method of mathematical and statistical processing of laboratory data (indicators of electrolyte metabolism and osteomarkers) of archived data of 82 patients with various bone diseases aged 9.90 ± 0.55 years compared the structural changes in the panel of ratios of individual electrolytes and the influence of individual indicators on them in personalized data, on the basis of which a conclusion was made about a single mechanism of coinciding influences in the exchange of bone tissue. At the same time, it was found that the complex of associated links detected by the vitamin influence on the panel of electrolyte ratios may differ signifcantly in some cases from each other. These differences consisted in highly pronounced differences in the activity of vitamin D in relation to various components of osteosynthesis and osteolysis, which are described in the modern literature. On this basis, the authors conclude that the used method (analytical system) allows to identify the functional connections of the dynamics of the indicator of vitamin D in individual cases with the dynamics of other indicators of bone, which signifcantly expands the informativeness of the results of laboratory examination of the patient in determining the leading systems the implementation of functional activity of the vitamin. The presented results justify the possibility of creating and describing different images of vitamin D-related changes in the plasma electrolyte composition, followed by their use in the identifcation of certain disorders of calcium metabolism and/or evaluating the effectiveness of the therapy used in each individual case.

Predict Anterior Cruciate Ligament Injury In Elite Male Soccer Players? Focus On The Five Factors Maximum Model

Background: For years, the rupture of the anterior cruciate ligament (ACL) has been a feared injury in young soccer players since it causes physiological and psychological changes while incurring significant economic losses.Methods: Through clinical research, understanding the mechanisms and identifying risk factors has enabled sports and performance professionals to establish robust models to predict and reduce the risk of ACL injury. Developments in medical devices also allow a more precise, more objective assessment and improve the traceability of the various quantitative and qualitative parameters necessary to detect the risk of ACL injury.Results: While general preventive protocols have shown evidence for an overall reduction in injury, there are still limitations regarding ACL injury. Therefore, we can collect personalized data from these models and the variables involved, predict, prevent, and program performance throughout the season. Additionally, the multi-professional team supervising the athlete must establish a close relationship centered on feeling and listening to the player who remains in control of his performance.Conclusion: Let the players decide on their career and not an injury.

COVID-19 ResNet: Residual neural network for COVID-19 classification with bayesian data augmentation

COVID-19 is an infectious disease caused by a novel coronavirus called SARS-CoV-2. The first case appeared in December 2019, and until now it still represents a significant challenge to many countries in the world. Accurately detecting positive COVID-19 patients is a crucial step to reduce the spread of the disease, which is characterize by a strong transmission capacity. In this work we implement a Residual Convolutional Neural Network (ResNet) for an automated COVID-19 diagnosis. The implemented ResNet can classify a patient´s Chest-Xray image into COVID-19 positive, pneumonia caused from another virus or bacteria, and healthy. Moreover, to increase the accuracy of the model and overcome the data scarcity of COVID-19 images, a personalized data augmentation strategy using a three-step Bayesian hyperparameter optimization approach is applied to enrich the dataset during the training process. The proposed COVID-19 ResNet achieves a 94% accuracy, 95% recall, and 95% F1-score in test set. Furthermore, we also provide an insight into which data augmentation operations are successful in increasing a CNNs performance when doing medical image classification with COVID-19 CXR.

FIMED: Flexible management of biomedical data

Background and objectives: In the last decade, clinical trial management systems have become an essential support tool for data management and analysis in clinical research. However, these clinical tools have design limitations, since they are currently not able to cover the needs of adaptation to the continuous changes in the practice of the trials due to the heterogeneous and dynamic nature of the clinical research data. These systems are usually proprietary solutions provided by vendors for specific tasks. In this work, we propose FIMED, a software solution for the flexible management of clinical data from multiple trials, moving towards personalized medicine, which can contribute positively by improving clinical researchers quality and ease in clinical trials.Methods: This tool allows a dynamic and incremental design of patients’ profiles in the context of clinical trials, providing a flexible user interface that hides the complexity of using databases. Clinical researchers will be able to define personalized data schemas according to their needs and clinical study specifications. Thus, FIMED allows the incorporation of separate clinical data analysis from multiple trials.Results: The efficiency of the software has been demonstrated by a real-world use case for a clinical assay in Melanoma disease, which has been indeed anonymized to provide a user demonstration. FIMED currently provides three data analysis and visualization components, guaranteeing a clinical exploration for gene expression data: heatmap visualization, clusterheatmap visualization, as well as gene regulatory network inference and visualization. An instance of this tool is freely available on the web at https://khaos.uma.es/fimed. It can be accessed with a demo user account, “researcher”, using the password “demo”.Conclusion: This paper shows FIMED as a flexible and user-friendly way of managing multidimensional clinical research data. Hence, without loss of generality, FIMED is flexible enough to be used in the context of any other disease where clinical data and assays are involved.

AN OVERVIEW OF THE NOTTINGHAM RESEARCH PROGRAMME ON PRIMARY BREAST CANCER IN OLDER WOMEN

The incidence of breast cancer increases with age. Despite this, most research in this field is aimed at the younger patient. Preliminary studies have shown that older women with breast cancer have distinct biological features compared to their younger counterparts. In addition, the focus of treatment of older women may not simply be curative but may be a trade-off between risks of treatment and impact on independence and quality of life. The Nottingham research programme is a unique programme dedicated to primary breast cancer in older women. There are two arms of the research programme 1) determining the biological differences of breast cancer in this cohort 2) exploring the use of geriatric assessment to understand the complex needs and factors contributing to treatment decision making in this group of patients. The overall aim of the research programme is to optimise both the biological and geriatric assessment of older women with primary breast cancer, to provide personalised data at diagnosis, on likely survival and quality of life outcomes following breast cancer treatment. This article will outline why this research programme is important, what it has achieved so far and future aspirations.

Blockchain-Secured Recommender System for Special Need Patients Using Deep Learning.

Recommender systems offer several advantages to hospital data management units and patients with special needs. These systems are more dependent on the extreme subtle hospital-patient data. Thus, disregarding the confidentiality of patients with special needs is not an option. In recent times, several proposed techniques failed to cryptographically guarantee the data privacy of the patients with special needs in the diet recommender systems (RSs) deployment. In order to tackle this pitfall, this paper incorporates a blockchain privacy system (BPS) into deep learning for a diet recommendation system for patients with special needs. Our proposed technique allows patients to get notifications about recommended treatments and medications based on their personalized data without revealing their confidential information. Additionally, the paper implemented machine and deep learning algorithms such as RNN, Logistic Regression, MLP, etc., on an Internet of Medical Things (IoMT) dataset acquired via the internet and hospitals that comprises the data of 50 patients with 13 features of various diseases and 1,000 products. The product section has a set of eight features. The IoMT data features were analyzed with BPS and further encoded prior to the application of deep and machine learning-based frameworks. The performance of the different machine and deep learning methods were carried out and the results verify that the long short-term memory (LSTM) technique is more effective than other schemes regarding prediction accuracy, precision, F1-measures, and recall in a secured blockchain privacy system. Results showed that 97.74% accuracy utilizing the LSTM deep learning model was attained. The precision of 98%, recall, and F1-measure of 99% each for the allowed class was also attained. For the disallowed class, the scores were 89, 73, and 80% for precision, recall, and F1-measure, respectively. The performance of our proposed BPS is subdivided into two categories: the secured communication channel of the recommendation system and an enhanced deep learning approach using health base medical dataset that spontaneously identifies what food a patient with special needs should have based on their disease and certain features including gender, weight, age, etc. The proposed system is outstanding as none of the earlier revised works of literature described a recommender system of this kind.

Informing Patients With Esophagogastric Cancer About Treatment Outcomes by Using a Web-Based Tool and Training: Development and Evaluation Study

BackgroundDue to the increasing use of shared decision-making, patients with esophagogastric cancer play an increasingly important role in the decision-making process. To be able to make well-informed decisions, patients need to be adequately informed about treatment options and their outcomes, namely survival, side effects or complications, and health-related quality of life. Web-based tools and training programs can aid physicians in this complex task. However, to date, none of these instruments are available for use in informing patients with esophagogastric cancer about treatment outcomes.ObjectiveThis study aims to develop and evaluate the feasibility of using a web-based prediction tool and supporting communication skills training to improve how physicians inform patients with esophagogastric cancer about treatment outcomes. By improving the provision of treatment outcome information, we aim to stimulate the use of information that is evidence-based, precise, and personalized to patient and tumor characteristics and is communicated in a way that is tailored to individual information needs.MethodsWe designed a web-based, physician-assisted prediction tool—Source—to be used during consultations by using an iterative, user-centered approach. The accompanying communication skills training was developed based on specific learning objectives, literature, and expert opinions. The Source tool was tested in several rounds—a face-to-face focus group with 6 patients and survivors, semistructured interviews with 5 patients, think-aloud sessions with 3 medical oncologists, and interviews with 6 field experts. In a final pilot study, the Source tool and training were tested as a combined intervention by 5 medical oncology fellows and 3 esophagogastric outpatients.ResultsThe Source tool contains personalized prediction models and data from meta-analyses regarding survival, treatment side effects and complications, and health-related quality of life. The treatment outcomes were visualized in a patient-friendly manner by using pictographs and bar and line graphs. The communication skills training consisted of blended learning for clinicians comprising e-learning and 2 face-to-face sessions. Adjustments to improve both training and the Source tool were made according to feedback from all testing rounds.ConclusionsThe Source tool and training could play an important role in informing patients with esophagogastric cancer about treatment outcomes in an evidence-based, precise, personalized, and tailored manner. The preliminary evaluation results are promising and provide valuable input for the further development and testing of both elements. However, the remaining uncertainty about treatment outcomes in patients and established habits in doctors, in addition to the varying trust in the prediction models, might influence the effectiveness of the tool and training in daily practice. We are currently conducting a multicenter clinical trial to investigate the impact that the combined tool and training have on the provision of information in the context of treatment decision-making.

Research on a Visual Comfort Model Based on Individual Preference in China through Machine Learning Algorithm

Recently, decreasing energy consumption under the premise of building comfort has become a popular topic, especially visual comfort. Existing research on visual comfort lacks a standard of how to select indicators. Moreover, studies on individual visual preference considering the interaction between internal and external environment are few. In this paper, we ranked common visual indicators by the cloud model combined with the failure mode and effect analysis (FMEA) and hierarchical technique for order of preference by similarity to ideal solution (TOPSIS). Unsatisfied vertical illuminance, daylight glare index, luminance ratio, and shadow position are the top four indicators. Based on these indicators, we also built the individual visual comfort model through five categories of personalized data obtained from the experiment, which was trained by four machine learning algorithms. The results show that random forest has the best prediction performance and support vector machine is second. Gaussian mixed model and classification tree have the worst performance of stability and accuracy. In addition, this study also programmed a BIM plug-in integrating environmental data and personal preference data to predict appropriate vertical illuminance for a specific occupant. Thus, managers can adjust the intensity of artificial light in the office by increasing or decreasing the height of table lamps, saving energy and improving occupant comfort. This novel model will serve as a paradigm for selecting visual indicators and make indoor space be tailored to meet individual visual preferences.

PNAS: A privacy preserving framework for neural architecture search services

The success of deep neural networks has contributed to many fields, such as finance, medic and speech recognition. Machine learning models adopted in these fields are always trained with a massive amount of distributed and highly personalized data harvested directly from users. Concerns for data privacy and the demand for better data exploitation have prompted the design of several secure schemes that allow an untrusted server to train ML models for one or multiple parties. However, these existing schemes only focus on network parameter, and hardly extend their optimization range to model architecture scope. Sine the performance of a neural network is closely related to both parameter and its architecture, service providers are difficult to deliver customized and flexible neural networks to each client. To this end, in this paper we propose PNAS, a novel MLaaS framework that enables a server to jointly optimize network parameter and architecture while ensuring the privacy of training sets. A double-encryption scheme is derived to prevent privacy leakage from sample itself, as well as intermediate feature maps during training. Specifically, we adopt functional encryption and feature transformation to secure forward and back propagation. Extensive experiments have demonstrated the superiority of our proposal.

A Blockchain-Based Healthcare Platform for Secure Personalised Data Sharing.

To facilitate personalised healthcare provision across Europe, we envision solutions that enable the secure integration and sharing of medical health records. These solutions should address privacy concerns, such as granular access control to personal data, establishing what should be accessible when and by whom, whilst complying with collective regulatory frameworks such as the European General Data Protection Regulation (GDPR) and adhering to international standards on how to manage information security. The proposed healthcare system design integrates technologies such as blockchain and scalable data lakes with adequate system routines to guarantee the secure access of confidential data. In this paper, we present the essential architectural components for the secure integration of medical records in a blockchain-based platform. We present a patient-centric data retrieval approach which incorporates a structured format to compose access rules.

Identity-based outsider anonymous cloud data outsourcing with simultaneous individual transmission for IoT environment

The integration of the Internet of Things (IoT) and cloud computing has become an attractive cloud-oriented big data processing paradigm, which is playing an important role in efficiency and productivity for digitalization of numerous IoT enabled industries. However, cloud-assisted IoT is also becoming an increasingly attractive target for various cyber-attacks, including the authenticity of outsourcing data, untrustworthiness of third parties, and data security and privacy. As a potential and promising solution to securely outsource data, we present the first construction for an efficient cloud data outsourcing system with simultaneous individual transmission preserving outsider anonymity of the subscribed consumer set. In our system, a data owner generates personalized data for each of the consumers in a group and transmits a common encrypted data for the group in such a way that the subscribed consumer set is completely hidden from the outsiders. Personalized data can be recovered only by an authorized consumer, while the common data can be decrypted by all the authorized consumer in that group. The communication bandwidth is compact in our construction, and the decryption algorithm requires significantly less computation cost. We design our scheme using asymmetric bilinear map over the prime order group to prevent fault attacks on symmetric bilinear map. Our construction is built in identity-based setting without any non-standard q-type security assumption and does not use random oracles. Our scheme enjoys adaptive security against an indistinguishable chosen-plaintext attack under the hardness of the standard decisional bilinear Diffie–Hellman exponent problem. Furthermore, our design supports an exponential number of consumers as the size of the valid identity set grows exponentially with the security parameter, whereas it is only polynomial in the security parameter for the existing cloud data outsourcing systems. In particular, the implementation and performance analysis explicates the advantages of our design for resource-constrained IoT enabled frameworks.

REFLECTO – A SMART MIRROR STRUCTURE WITH A CONTEXTUAL ASSOCIATION OF INTERNET OF THINGS

Two of the most valued qualities in the modern-day society are punctuality and presentation. The mission helps to develop the above-mentioned personalities. We have developed an artificial intelligence-based smart mirror that helps in reminding the user about their day to day schedule while they spend their time in front of the mirror. This way the user is ensured of comfort in accessing the basic services with a minimum hinderance. The basic function that our prototype is assumed to carry out is to display user their personalised information like date, time, weather forecast, upcoming holidays or saved events and news bulletin. Face recognition feature is also added in order to identify the user using the camera placed behind the mirror. Because of the face recognition feature the mirror will be able to display personalised data that the identified user wants to know. Reflecto can be easily built by using a two-way mirror, concealed LED screen, microphone and camera to make it interactive. The mirror’s functionalities have been extended by integrating it with Google assistant to access IoT devices that have been enabled with home automation.

Implementing Data Management Workflows in Research Groups Through Integrated Library Consultancy

Comprehensive research data management is fundamental to ensuring reproducible, open scientific research. However, sufficient research data assistance is often not offered at universities, and when offered, typically only provides basic services that are viewed as optional. Integrating information specialists into research groups provides a potentially promising means of improving data management by providing personalized data management workflows. Workflows are comprehensive, executable guides that require planning, implementation, feedback, and adaptation. Comprehensive data management workflows should include a file organization scheme, the creation of data management roles for members, a data storage/sharing guide, and training and evaluation. Librarians, who regularly interact with faculty and students and are familiar with data management tools, are uniquely situated to assist with the creation and assessment of these workflows.