Branched‐alkyl‐substituted poly(thieno3,4‐cpyrrole‐4,6‐dione‐alt‐3,4‐difluorothiophene) (PTPD2FT) can be used as a polymer acceptor in bulk heterojunction (BHJ) solar cells with a low‐band‐gap ...polymer donor (PCE10) commonly used with fullerenes. The “all‐polymer” BHJ devices made with PTPD2FT achieve efficiencies of up to 4.4 %. While, to date, most efficient polymer acceptors are based on perylenediimide or naphthalenediimide motifs, our study of PTPD2FT polymers shows that linear, all‐thiophene systems with adequately substituted main chains can also be conducive to efficient BHJ solar cells with polymer donors.
Better performance through polymers: Branched‐alkyl‐substituted poly(thieno3,4‐cpyrrole‐4,6‐dione‐alt‐3,4‐difluorothiophenes) (PTPD2FT) can be used as polymer acceptors in bulk heterojunction (BHJ) solar cells, achieving some of the best VOC figures (ca. 1.1 V) reported to date for BHJ devices, and power conversion efficiencies of up to 4.4 %, with a low‐band‐gap polymer donor (PCE10) commonly used with fullerenes. 2DT=2‐decyltetradecyl, 2OD=2‐octyldodecyl, 2HD=2‐hexyldecyl.
Data annotation is a time-consuming process posing major limitations to the development of Human Activity Recognition (HAR) systems. The availability of a large amount of labeled data is required for ...supervised Machine Learning (ML) approaches, especially in the case of online and personalized approaches requiring user specific datasets to be labeled. The availability of such datasets has the potential to help address common problems of smartphone-based HAR, such as inter-person variability. In this work, we present (i) an automatic labeling method facilitating the collection of labeled datasets in free-living conditions using the smartphone, and (ii) we investigate the robustness of common supervised classification approaches under instances of noisy data. We evaluated the results with a dataset consisting of 38 days of manually labeled data collected in free living. The comparison between the manually and the automatically labeled ground truth demonstrated that it was possible to obtain labels automatically with an 80⁻85% average precision rate. Results obtained also show how a supervised approach trained using automatically generated labels achieved an 84% f-score (using Neural Networks and Random Forests); however, results also demonstrated how the presence of label noise could lower the f-score up to 64⁻74% depending on the classification approach (Nearest Centroid and Multi-Class Support Vector Machine).
Background
In 2020 Globocan reported nearly 1.4 million new cases of gynaecology cancer worldwide. Cancer related fatigue has been identified as a symptom that can be present for gynaecology cancer ...patients many years after treatment. The current evidence around the management of this symptom suggests that exercise has the most positive outcome. However, some ambiguity remains around the evidence and whether it can address all areas of fatigue effectively. More recently, other interventions such as mindfulness have begun to show a favourable response to the management of symptoms for cancer patients. To date there has been little research that explores the feasibility of using both these interventions together in a gynaecology cancer population. This study aims to explore the feasibility of delivering an intervention that involves mindfulness and mindfulness and exercise and will explore the effect of this on fatigue, sleep, mood and quality of life.
Methods/Design
This randomised control trial will assess the interventions outcomes using a pre and post design and will also include a qualitative process evaluation. Participants will be randomised into one of 2 groups. One group will undertake mindfulness only and the other group will complete exercise and mindfulness. Both groups will use a mobile application to complete these interventions over 8 weeks. The mobile app will be tailored to reflect the group the participants have drawn during randomisation. Self-reported questionnaire data will be assessed at baseline prior to commencing intervention and at post intervention. Feasibility will be assessed through recruitment, adherence, retention and attrition. Acceptability and participant perspective of participation (process evaluation), will be explored using focus groups.
Discussion
This trial will hope to evidence and demonstrate that combination of two interventions such as mindfulness and exercise will further improve outcomes of fatigue and wellbeing in gynaecology cancer. The results of this study will be used to assess (i) the feasibility to deliver this type of intervention to this population of cancer patients using a digital platform; (ii) assist this group of women diagnosed with cancer to manage fatigue and other symptoms of sleep, mood and impact their quality of life.
Trial registration
NCT05561413
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The desire to remain living in one's own home rather than a care home by those in need of 24/7 care is one that requires a level of understanding for the actions of an environment's inhabitants. This ...can potentially be accomplished with the ability to recognise Activities of Daily Living (ADLs); however, this research focuses first on producing an unobtrusive solution for pose recognition where the preservation of privacy is a primary aim. With an accurate manner of predicting an inhabitant's poses, their interactions with objects within the environment and, therefore, the activities they are performing, can begin to be understood. This research implements a Convolutional Neural Network (CNN), which has been designed with an original architecture derived from the popular AlexNet, to predict poses from thermal imagery that have been captured using thermopile infrared sensors (TISs). Five TISs have been deployed within the smart kitchen in Ulster University where each provides input to a corresponding trained CNN. The approach is evaluated using an original dataset and an F1-score of 0.9920 was achieved with all five TISs. The limitations of utilising a ceiling-based TIS are investigated and each possible permutation of corner-based TISs is evaluated to satisfy a trade-off between the number of TISs, the total sensor cost and the performances. These tests are also promising as F1-scores of 0.9266, 0.9149 and 0.8468 were achieved with the isolated use of four, three, and two corner TISs, respectively.
Small-molecule (SM) donors that can be solution-processed with fullerene acceptors (e.g., PC61/71BM), or their “nonfullerene” counterparts, are proving particularly promising for the realization of ...high-efficiency bulk-heterojunction (BHJ) solar cells. In several recent studies, solvent vapor annealing (SVA) protocols have been found to yield significant BHJ device efficiency improvements via structural changes in the active layer morphologies. However, the mechanisms by which active layer morphologies evolve when subjected to SVA treatments, and the structural factors impacting charge generation, carrier transport, recombination, and extraction in BHJ solar cells with SM donors and fullerene acceptors, remain important aspects to be elucidated. In this report, we show thatin BHJ solar cells with SM donors and fullerene acceptorsselective crystallization promoted by SVA mediates the development of optimized morphologies across the active layers, setting domain sizes and boundaries. Examining BHJ solar cells subjected to various SVA exposure times, with BDT2FQdC as the SM donor and PC71BM as the acceptor, we connect those morphological changes to specific carrier effects, showing that crystal growth effectively directs charge generation and recombination. We find that the SM donor-pure domains growing at the expense of a mixed donor–acceptor phase play a determining role, establishing optimum networks with 10–20 nm sized domains during the SVA treatment. Longer SVA times result in highly textured active layers with crystalline domains that can exceed the length scale of exciton diffusion, while inducing detrimental vertical morphologies and deep carrier traps. Last, we emphasize the field-dependence charge generation occurring upon SVA-mediated crystallization and link this carrier effect to the mixed phase depletion across the BHJ active layer.
Computer networks form much of the infrastructure supporting day-to-day life in this digital age. Computer networks, however, are prone to attack and therefore require intrusion detection systems. ...Intrusion detection systems provide a mechanism to detect network attacks at an early stage and generate alerts. These systems, however, are far from a panacea. Rather, they tend to overwhelm their operators with alerts, which in more than 90% of cases can be false positives. As such, the problem of false positives in intrusion detection systems is a costly issue. This paper presents research to design a hierarchical network intrusion detector, using deep learning, which protects against raising vast numbers of false positives through the design and implementation of a hierarchical NIDS. This paper presents a valuable advancement in performance by reducing the occurrence of false alarms by 87.52%. The research contained in this paper presents three contributions to knowledge. The first of these is the comparison between hierarchical systems and non-hierarchical systems to understand which would yield fewer false alarms. The second contribution is the formulation of a hierarchical approach, which was able to reduce false alarms by 87.52%. Lastly, the proposed hierarchical model was deployed in a live IoT environment, exposed to genuine threats, and the performance in this environment was analysed.
Smartphone-based approaches for Human Activity Recognition have become prevalent in recent years. Despite the amount of research undertaken in the field, issues such as cross-subject variability are ...still posing an obstacle to the deployment of solutions in large scale, free-living settings. Personalized methods ( i.e. aiming to adapt a generic classifier to a specific target user) attempt to solve this problem. The lack of labeled data for training purposes, however, represents a major barrier. This is especially the case when taking into consideration that personalization generally requires labeled data to be user-specific. This paper presents a novel personalization method combining a semi-population based approach with user adaptation. Personalization is achieved through the following. Firstly, the proposed method identifies a subset of users from the available population as best candidates for initializing the classifier to the target user. Subsequently, a semi-population Neural Network classifier is trained using data from this subset of users. The classifier's network weights are then updated using a small amount of labeled data from the target user subsequently implementing personalization. This approach was validated on a large publicly available dataset collected in a free-living scenario. The personalized approach using the proposed method has shown to improve the overall F-score to 74.4% compared to 70.9% when using a generic non-personalized approach. Results obtained, with statistical significance being confirmed on a set of 57 users, indicate that model initialization using the semi-population approach can reduce the amount of labeled data required for personalization. As such, the proposed method for model initialization could facilitate the real-world deployment of systems implementing personalization by reducing the amount of data needed for personalization.
Activity Recognition is an active research topic focused on detecting human actions and behaviours in smart environments. In most cases, the use of data-driven models aim to relate data from sensors ...to an activity through a model developed by a supervised approach. In this work, we focus on the goal of domain adaptation between smart environments, which has required a novel approach to relate the concepts of domain adaptation using binary sensor and learning from daily imbalanced data. In this work, the sensor activation from a given context is translated to a different one, based on the temporal alignment from human activities. The domain adaptation of binary sensor is accomplished through a three step procedure: i) clustering of sensor activation, ii) activity based alignment of sensor data between the two environments, iii) an ensemble of classifiers used to mine a mapping function, translating sensor data between the two environments. The proposed method was evaluated over a publicly available dataset, and obtained preliminary results which were encouraging with an F1-Score of 87%.
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