Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Network Based Central Heating System Load Prediction and Constrained Control

A neural network (NN) based heating system load prediction and control scheme are proposed. Different from traditional physical principle based load calculation method, a multilayer NN is incorporated with selected input features and trained to predict the heating load as well as the desired supply water temperature in heating supply loop. In this manner, a complicated load calculation model can be replaced by simple but efficient data-driven scheme and the response time to outdoor temperature variation can be enhanced. Moreover, in order to handle the input and output constraints in valve opening degree control task to achieve desired supply water temperature, Barrier Lyapunov candidate function and axillary system technique are involved. An additional NN is employed to approximate the system transfer function with reliable accuracy. The stability of the system is guaranteed through rigorous mathematical analysis. The excellent performance of the novelly proposed control over traditional PID is demonstrated via extensive simulation study. A quantitative case study is also conducted to verify the flexibility and validity of proposed load prediction strategy.
Monitoring and Measurement of an Artificial Landslide Dam Using UAV Images and Image-Based Modeling

Because of heavy rains or earthquakes, landslide and debris flows may flow into rivers and block river channel so to become a landslide dam. Due to the relatively loose nature and the absence of proper engineering spillway, landslide dams are often vulnerable to catastrophic failure by overtopping and breaching, and cause severe flooding in downstream area possibly with serious casualties, such as life loss and property damage. Therefore, the technology development of time-effective monitoring landslide dams becomes an important issue in disaster mitigation. However, the monitoring and assessment of landslide is often difficult due to the timeliness and effectiveness over an unreachable location. Since the timing of dam failure and the magnitude of the resulting flooding downstream are highly affected by the landslide dam size and geometry, the three-dimensional measurement of landslide dam is an essential task in the mitigation act following the occurrence of a landslide lake. Taking responsiveness and efficiency into consideration, this research proposed a process for the monitoring and assessment of landslide dam by employing unmanned aerial vehicles (UAVs) for aerial surveying and Image based modeling (IBM) for aerial surveying and a 3D model reconstruction of an artificial landslide dam built in a real Landou stream within Hui-Sun experimental forest in Nantou, Taiwan, as a full-scale on-site experiment. The result shows that a 3D terrain model can be successfully constructed based on the UAV images through IBM process. Especially, the volume and stability of landslide dam is the most urgent information needed to be acquired for mitigation strategy making so that the failure of landslide dams due to dam breach and erosion by the overflow stream can be predicted and prepared.
The relationships between clinical, socio-demographic and self-management: factors and complications in Thai peritoneal dialysis patients

(90.7%); PD-related complications — dyslipidaemia (63.8%); and end-stage renal disease-related complications — mineral and bone disorder (90.7%), anaemia (89.3%), and malnutrition (81%). In multiple regression analysis, comorbidity, PD selfmanagement, level of activity in daily living, and age were independently associated with the number of complications (R 2 = .231, p<.001) (F (4,358) = 26.816, p<. 001). Comorbidity was the strongest factor in predicting complications. Conclusion: There was a high prevalence of PD complications occurring in one year period before recruiting to the study in this sample. Patients with higher comorbidity, lower self-management, lower level of activity in daily living, and younger age were more likely to have higher a number of complications. These factors should be specified for PD suitability. Promoting selfmanagement will help them to undertake PD safely.
A Survey on Human Behavior Recognition Using Channel State Information

Recently, device-free human behavior recognition has become a hot research topic and has achieved significant progress in the field of ubiquitous computing. Among various implementation, behavior recognition based on WiFi CSI (channel state information) has drawn wide attention due to its major advantages. This paper investigates more than 100 latest CSI based behavior recognition applications within the last 6 years and presents a comprehensive survey from every aspect of human behavior recognition. Firstly, this paper reviews general behavior recognition applications using the WiFi signal and presents the basic concept of CSI and the fundamental principle of CSI-based behavior recognition. This paper analyzes the key components and core characteristics of the system architecture of human behavior recognition using CSI. Afterward, we divide the sensing procedures into many steps and summarize the typical studies from these steps, including base signal selection, signal preprocessing, and identification approaches. Next, based on the recognition technique, we classify the applications into three groups, including pattern-based, model-based, and deep learning-based approach. In every group, we categorize the state-of-the-art applications into three groups, including coarse-grained specific behavior recognition, fine-grained specific behavior recognition, and activity inference. It elaborates the typical behavior recognition applications from five aspects, including experimental equipment, experimental scenario, behavior, classifier, and system performance. Then, this paper presents comprehensive discussions about representative applications from the implementation view and outlines the major consideration when developing a recognition system. Finally, this article concludes by analyzing the open issues of CSI-based behavior recognition applications and pointing out future research directions.
A consumer designed smartphone app for young people with asthma: pilot of engagement and acceptability

Abstract Objective: This pilot aimed to test the engagement, acceptability, and usefulness of a goal-setting smartphone app, Kiss myAsthma, in young people with asthma. Methods: Young people aged 15–24 years old were invited to trial a smartphone app for asthma management. A mixed-methods approach combined quantitative analysis of self-report questionnaires and app usage log data with qualitative thematic analysis of open-ended questions at baseline and six weeks after downloading the app. App log data (pages visited, frequency of use and content of participants interaction, e.g.goals set, symptoms recorded) were analyzed. Results: Nine of 12 participants completed both baseline and six-week questionnaires. Participants reported high satisfaction with app content and usability (median score 5 out of 6 [range 4–6]) and rated the app highly on “feeling confident in my ability to manage my asthma. ” At six weeks there was a clinically significant change in asthma quality of life (e.g. Emotional Function domain score baseline: 4.7 [2.7–6.3], follow-up: 5.7 [4.7–6.7]; p = 0.043). Participants logged information about asthma severity, flare-ups and mood and tracked their symptoms with the app’s History functionality. Five participants (42%) nominated goals and strategies and 3 participants (25%) entered data in the Inspiration section, a tool to support intrinsic motivation to manage asthma. Qualitative data aligned with quantitative results. Conclusions: This six-week pilot of the Kiss myAsthma app showed its potential to support self-management, quality of life and health behavior change in young people with asthma.
The Reliability and Accuracy of Palpation, Radiography, and Sonography for the Detection of Keel Bone Damage

Simple Summary Due to the recent increase in reports of the prevalence of keel bone damage in laying hens, this has become a topic of welfare concern. Keel bone damage is often in the form of a fracture, and therefore may compromise the hen’s welfare. Detecting keel bone damage in live hens has been problematic, as the bone must either be palpated, which is a measure poor in accuracy, or dissected, in which case the hen must be sacrificed. However, use of imaging technology is increasing in experimental studies. We set out to determine if training with feedback on accuracy could improve palpation accuracy, as well as to determine the accuracy of portable radiography and sonography to detect keel bone damage. Even with feedback, palpation remained an inaccurate method, while both radiography and sonography showed high accuracy for detecting fractures. These two techniques show promise in detecting keel bone fractures in live birds. Abstract Palpation is the most popular method of measuring keel bone damage on live birds, although it has been criticized for being subjective and inaccurate. The goals of this study were to examine intra- and inter-rater reliability when trained with feedback of accuracy, as well as determine the accuracy of portable radiography and sonography. Four evaluators palpated 50 103-week old Lohmann LSL-lite hens immediately following euthanasia. Of those birds, 34 were then radiographed, sonographed, and all 50 were re-palpated. Lastly, the keels were dissected and scored. The presence of deviations (DEV), fractures (FR), and tip fractures (TFR) was scored for each method. Reliability of palpation was analyzed using Cronbach’s Alpha (intra) and Fleiss’ Kappa (inter) tests. Radiography and Sonography scores were further compared with dissection scores to determine sensitivity and specificity. Initial inter-observer reliability was 0.39 DEV, 0.53 FR, and 0.12 TFR, with similar scores for the second round of palpation. Scores for intra-observer reliability ranged from 0.58–0.79 DEV, 0.66–0.90 FR, and 0.37–0.87 TFR. A high prevalence of TFR, but low assessor agreement, warrants the development of specialized training for the palpation of this area. Both radiography and sonography showed relatively high sensitivity for FR and TFR, but low for DEV. On the other hand, specificity was generally high across all damage types. Even with feedback, palpation reliability was poor. However, portable radiography and sonography show promise for detecting keel fractures.
Identifying Multiple Influential Users Based on the Overlapping Influence in Multiplex Networks

Online social networks (OSNs) are interaction platforms that can promote knowledge spreading, rumor propagation, and virus diffusion. Identifying influential users in OSNs is of great significance for accelerating the information propagation especially when information is able to travel across multiple channels. However, most previous studies are limited to a single network or select multiple influential users based on the centrality ranking result of each user, not addressing the overlapping influence (OI) among users. In practice, the collective influence of multiple users is not equal to the total sum of these users’ influences. In this paper, we propose a novel OI-based method for identifying multiple influential users in multiplex social networks. We first define the effective spreading shortest path (ESSP) by utilizing the concept of spreading rate in order to denote the relative location of users. Then, the collective influence is quantified by taking the topological factor and the location distribution of users into account. The identified users based on our proposed method are central and relatively scattered with a low overlapping influence. With the Susceptible-Infected-Recovered (SIR) model, we estimate our proposed method with other benchmark algorithms. Experimental results in both synthetic and real-world networks verify that our proposed method has a better performance in terms of the spreading efficiency.
Application on the Neural Network Information Fusion Technology

Aims at the characteristics of the chemical agents signals, the pattern recognition method based on the wavelet neural network(WNN) is put forward. Firstly, the model design thought of the WNN pattern recognition technology for chemical agents are analyzed, which shows that the wavelet analysis has good time-frequency features due to its capability of localizing and differentiating the high and low frequencies of a signal and keeping the time domain features of the original signal, as a result, the wavelet transform can effectively extract the feature of the chemical agents signals; Secondly, the model and learning algorithm for the WNN are constructed, which implements that the pattern recognition method for the chemical agents combines with the advantage of the wavelet and NN, the wavelet transform method as a fore processing medium is used to extract the feature which reflects the information of the chemical agents, and the features are fed into the NN as the input patterns for training and classifying, to achieve the intelligence distinguishing; Lastly, the results of the examples and simulated test show that: this method is workable, and of the high identification accuracy, remarkable generalization capability, good stability, and high speed and high reliability. The wavelet transform is a kind of the signal time-frequency analysis methods, of the characteristics of multi-recognition-ratios analysis, and furthermore, of the ability of giving expression to local features at both time and frequency domains. The wavelet transform is of the self-adaptability of the signal, and it is very fit for detecting momentary abnormal phenomenon hidden in the normal signal and displaying its components. Using the continuous wavelet transform to process identification and detection for the dynamic system of chemical agent is of nicer results. For implementing effectively detection and rapid exact identification for the chemical agents in sea-battlefield, this paper studies the application about pattern recognition(PR) technology in Naval Ships Chemical Detection (NSCD) field. The PR model is built. At the same time, connecting the wavelet analysis with the neural networks(NN) organically, and based on the wavelet transfer and the NN, the model of the speedy features extraction and identification for chemical agents is founded.
Analysis of the PI 3 K pathway in breast cancer

4 Zusammenfassung: . 5 Introduction . 7 1.1 Study background: . 8 1.2.1 Phosphatidylinositol 3-kinase (PI3K): . 9 . 10 1.2.2 PI3K-pathway alterations in breast cancer: . 10 1.2.3 Point mutations in the PIK3CA gene: . 10 1.2.4 Frequency of PIK3-Kinase Mutations in breast cancer: . 13 1.2.5 Relevance of PIK3CA mutations for prognosis and therapy: . 13 1.3. 1 PI3K/Akt/mTOR pathway: . 14 1.3. 2 Mammalian target of rapamycin (mTOR) complex formation: . 16 1.3. 3 Downstream targets of mTORC1: . 17 1.4. 1 Breast cancer subclasses: . 19 1.4. 2 Estimated risks of breast cancer subgroups: . 21 1.5. 1 Hormone receptor-positive breast cancer therapy: . 22 1.5. 2 Tamoxifen treatment-limits of protective effects: . 24 Material & Methods . 26 2.1 Study population: . 27 2.2 DNA extraction: . 27 2.3. 1 Polymerase Chain Reaction (PCR) . 30 2.3. 2 Primers . 30 2.3. 3 Reagents and Solutions . 31 2.3. 4 Procedure . 32 2.4. 1 Agarose Gel Electrophoresis . 33 2.4. 2. Reagents and Solutions . 34 2.4. 3 Procedure: . 35 2.5. 1 Detection of somatic mutations by Pyrosequencing®: . 35 2.5. 2 Background: . 36 2.5. 3 Technical facilities . 37 2.5. 4 Substrates and Solutions . 38 2.5. 5 Pyrosequencing® assays and primers: . 39
SWIELD: An In Situ Approach for Adaptive Low Power and Error-Resilient Operation

With the rapid CMOS technology downscaling, two requirements for digital integrated circuits (ICs)design are becoming fundamental: low power consumption and error resilience. Many proposed solutions are effective either in reducing the power consumption or improving the error resilience, but a single approach that addresses both aspects seems to be missing. The reason behind this might be the often opposing relationship between the two requirements. However, employing adaptive techniques that adjust the systems' behaviour according to the current needs might be promising. We propose SWIELD - a programmable in-situ monitor (ISM), i.e. a monitor placed inside the actual circuit, able to operate in three modes: Normal, AVS and TMR. The architecture and the operation modes of the SWIELD are described in detail. A specially designed SWIELD operation control unit is also introduced. The proposed ISM design is synthesized using the IHP 130nm technology library. Furthermore, a time-based power consumption analysis is performed. The obtained values are then compared to a similar ISM design. At the expense of small area and complexity overhead, the SWIELD design shows satisfactory results in terms of power saving and adaptivity.
Landmark-based Emulation for Models with Misaligned Functional Response

Many computer models output functional data, and in some cases, these functional data have similar, but misaligned, shape characteristics. We introduce a general approach for building emulators for computer models that output misaligned functional data when key values in the functional response (landmarks) can be easily identified. This approach has two main parts: modeling the aligned (using the landmarks) functional data, and modeling the functions that map the misaligned data to the aligned space (warping functions). As the warping functions are required to be monotonic, we give special attention to modeling monotonic functional response data. We discuss how our approach can be used for a variety of typical emulators, such as Gaussian processes, Bayesian multivariate adaptive regression splines, and Bayesian additive regression trees, and how sensitivity analysis can be performed. We demonstrate these approaches to build emulators for a high-energy-density physics computer model used to simulate inertial confinement fusion ignition experiments.
WHY DO FORAGERS FIGHT FOR FOOD ? A UNIFYING EVOLUTIONARY PERSPECTIVE

In many species, the intake rate of foraging individuals is negatively related to forager density due to agonistic interactions among foragers. Recently a variety of game theory models has been developed to address the question how such interference behaviour is shaped by natural selection. These modelling approaches have not yet led to a comprehensive understanding of interference competition; models that appear to be very similar yield strikingly different predictions regarding the evolutionary stability of various interference strategies. Here we attempt to unify approaches. To avoid model inconsistencies, we plead for a systematic, event-based description of the foraging process, the explicit account of feedback effects and the systematic derivation of a payoff function. To analyze the resulting evolutionary game, we use techniques from Adaptive Dynamics theory, since classical ESS techniques can be highly misleading when applied to the payoff functions resulting from interference competition. By means of this unified approach, we show that foraging animals can generally be expected to make their aggressive behaviour dependent on the role they play in interactions, that alternative evolutionarily stable interference strategies may evolve at the same ecological conditions, and that interference effects on intake rate cannot be taken for granted as the logical outcome of evolution. By critically discussing the setup, the assumptions and the way of analysis of some evolutionary models of interference competition, we identify crucial assumptions and potential pitfalls in modelling the evolution of interference behaviour, and we demonstrate that the discrepancies between earlier model predictions often reflect seemingly subtle differences in the assumptions on behavioural flexibility.
F INANCIAL INCENTIVE APPROACHES FOR REDUCING PEAK ELECTRICITY DEMAND , EXPERIENCE FROM PILOT TRIALS WITH A UK

Whilst tariff-based approaches to load-shifting are common in the residential sector, incentivebased approaches are rare. This is so, even though providing customers incentives to shape their power consumption patterns has substantial potential. This paper presents findings from an exploratory UK pilot study that trials financial payments and detailed energy feedback to incentivise load-shifting of residential electricity consumption. An intervention study was implemented measuring actual energy use by individual households as well as conducting surveys and interviews. From the trials it was found that the approaches resulted in reductions in peak time energy use. Evidence from the study found that the incentives-based approaches were able to overcome some of the barriers to response experienced in Time-of-Use studies, though less good on others. Interestingly, the height of the barriers varied by the electricity-using practice and the incentivising approach applied. The height of the barriers also varied by participant.
Comparing the Impact of an Implicit Learning Approach With Standard Care on Recovery of Mobility Following Stroke: Protocol for a Pilot Cluster Randomized Controlled Trial

Background Although implicit and explicit learning approaches have been well investigated in healthy populations, there is less evidence regarding the relative benefits of each approach in clinical practice. Studies in stroke typically investigate single elements of an implicit learning approach (ILA; eg, reduced quantity feedback or an external focus of attention) within controlled environments. These studies predominantly evaluate performance, with few measuring this over time (ie, learning). The relevance and transferability of current research evidence into stroke rehabilitation is therefore limited. Objective The objective of this study was to compare the ILA with standard care in the acute phase following stroke, to generate data and insights to inform the design of a definitive trial, and to understand patient and therapist perceptions of the ILA. Methods This is a multicenter, assessor-blind, cluster randomized controlled pilot trial with nested qualitative evaluation. Stroke units (clusters) will be randomized to either ILA (intervention) or standard care (control) arms. Therapy teams at the intervention sites will be trained in the ILA and provided with an intervention manual. Those at the control sites will have minimal input from the research team, other than for data collection. Consent will be provided at the individual participant level. Once enrolled, participants will receive rehabilitation that focuses on lower limb recovery, using the designated approach. Measures will be taken at baseline, every 2 weeks until the point of discharge from hospital, and at 3 months post stroke onset. Measures include the Fugl Meyer Assessment (motor leg subsection), modified Rivermead Mobility Index, Swedish Postural Adjustment in Stroke Scale, and achievement of mobility milestones. Fidelity of the treatment approach will be monitored using observational video analysis. Focus groups and interviews will be used to gain insight into the perceptions of trial participants and clinical teams. Results The first site opened to recruitment in February 2019. The opening of a further 5 sites will be staggered throughout 2019. Results are expected in early 2021. Conclusions The findings from this mixed methods pilot study will be used to inform the design of a definitive study, comparing the ILA with standard care in acute stroke rehabilitation. Trial Registration ClinicalTrials.gov NCT03792126; https://clinicaltrials.gov/ct2/show/NCT03792126 International Registered Report Identifier (IRRID) DERR1-10.2196/14222