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To study the law of gas transportation in mining areas, Fluent numerical simulation software was applied to examine the influence of different pseudo-slope lengths (PSL) on gas concentration in a U-ventilated working area under no-extraction conditions. Based on this, numerical simulation experiments were conducted on the buried pipe extraction arrangement parameters. The simulation found that when there was no extraction, the PSL had an impact on the airflow in the extraction area, which caused the airflow in the extraction area to be disordered, causing gas to accumulate locally at the working area. When the buried pipe depths (BPDs) and PSLs of the working area worked together, the gas concentration of the working area was lower when the inlet air influence zone and the extraction influence zone were through; otherwise, gas concentration accumulation occurred at the working area. The research results showed that when the PSL was at 25 m and BPD was at 20 m, the gas concentration at the working area was not abnormal, and the gas concentration in the upper corner was lower. By adjusting the PSL and BPD of the test working area, the maximum gas concentration in the upper corner was reduced to 0.46% and the maximum gas concentration in the return air outlet was reduced to 0.41%. The experimental and practical results provide important reference values for coal and gas co-mining.

BACKGROUND Encouraging office workers to break up prolonged sedentary behavior (SB) at work with regular microbreaks can be beneficial yet challenging. The Internet of Things (IoT) offers great promise for delivering more subtle and hence acceptable behavior change interventions in the workplace. We previously developed an IoT-enabled SB intervention, called WorkMyWay, by applying a combination of theory-informed and human-centered design approaches. According to the Medical Research Council’s framework for developing and evaluating complex interventions such as WorkMyWay, process evaluation in the feasibility phase can help establish the viability of novel modes of delivery and identify facilitators and barriers to successful delivery. OBJECTIVE This study aims to evaluate the feasibility and acceptability of the WorkMyWay intervention and its technological delivery system. METHODS A mixed methods approach was adopted. A sample of 15 office workers were recruited to use WorkMyWay during work hours for 6 weeks. Questionnaires were administered before and after the intervention period to assess self-report occupational sitting and physical activity (OSPA) and psychosocial variables theoretically aligned with prolonged occupational SB (eg, intention, perceived behavioral control, prospective memory and retrospective memory of breaks, and automaticity of regular break behaviors). Behavioral and interactional data were obtained through the system database to determine adherence, quality of delivery, compliance, and objective OSPA. Semistructured interviews were conducted at the end of the study, and a thematic analysis was performed on interview transcripts. RESULTS All 15 participants completed the study (attrition=0%) and on average used the system for 25 tracking days (out of a possible 30 days; adherence=83%). Although no significant change was observed in either objective or self-report OSPA, postintervention improvements were significant in the automaticity of regular break behaviors (t14=2.606; P=.02), retrospective memory of breaks (t14=7.926; Pt14=–2.661; P=.02). The qualitative analysis identified 6 themes, which lent support to the high acceptability of WorkMyWay, though delivery was compromised by issues concerning Bluetooth connectivity and factors related to user behaviors. Fixing technical issues, tailoring to individual differences, soliciting organizational supports, and harnessing interpersonal influences could facilitate delivery and enhance acceptance. CONCLUSIONS It is acceptable and feasible to deliver an SB intervention with an IoT system that involves a wearable activity tracking device, an app, and a digitally augmented everyday object (eg, cup). More industrial design and technological development work on WorkMyWay is warranted to improve delivery. Future research should seek to establish the broad acceptability of similar IoT-enabled interventions while expanding the range of digitally augmented objects as the modes of delivery to meet diverse needs.

Tanshinone IIA (Tan 2A) is a lipid‑soluble compound extracted from the Chinese herb Danshen (

Infectious diseases are a long-standing and severe global public health problem. The rapid diagnosis of infectious diseases is an urgent need to solve this problem. MicroRNA (miRNA) plays an important role in the intervention of some infectious diseases and is expected to become a potential biomarker for the diagnosis and prognosis of infectious diseases. It is of great significance to develop rapid and sensitive methods for detecting miRNA for effective control of infectious diseases. In this study, a simple and highly sensitive homogeneous electrochemical method for microRNAs using enzyme-driven cascaded signal amplification has been developed. In the presence of target miRNA, the reaction system produced plenty of MB-labeled single-nucleotide fragments (MB-MF) containing a few negative charges, which can diffuse to the negative surface of the ITO electrode easily, so an obvious electrochemical signal enhancement was obtained. Without the target, MB-HP contains a relatively large amount of negative charges due to the phosphates on the DNA chain, which cannot be digested by the enzyme and cannot diffuse freely to the negatively charged ITO electrode, so only a small signal was detected. The enhanced electrochemical response has a linear relationship with the logarithm of miRNA concentration in the range of 10 fM to 10 nM and the limit of detection as low as 3.0 fM. Furthermore, the proposed strategy showed the capability of discriminating single-base mismatch and performed eligibly in the analysis of miRNA in cell lysates, exhibiting great potential for disease diagnosis and biomedical research. Graphical abstract.

In the past few decades, mathematical models based on dynamical systems theory have provided new insight into diverse biological systems. In this review, we ask whether the recent success of machine learning techniques for large-scale biological data analysis provides a complementary or competing approach to more traditional modeling approaches. Recent applications of machine learning to the problem of learning biological dynamics in diverse systems range from neuroscience to animal behavior. We compare the underlying mechanisms and limitations of traditional dynamical models with those of machine learning models. We highlight the unique role that traditional modeling has played in providing predictive insights into biological systems, and we propose several avenues for bridging traditional dynamical systems theory with large-scale analysis enabled by machine learning.

Vascular smooth muscle cells (VSMCs) in the normal arterial media continually express contractile phenotypic markers which are reduced dramatically in response to injury. Tripartite motif-containing proteins are a family of scaffold proteins shown to regulate gene silencing, cell growth, and differentiation. We here investigated the biological role of tripartite motif-containing 28 (TRIM28) and tripartite motif-containing 27 (TRIM27) in VSMCs. We observed that siRNA-mediated knockdown of TRIM28 and TRIM27 inhibited platelet-derived growth factor (PDGF)-induced migration in human VSMCs. Both TRIM28 and TRIM27 can regulate serum response element activity and were required for maintaining the contractile gene expression in human VSMCs. At the same time, TRIM28 and TRIM27 knockdown reduced the expression of PDGF receptor-β (PDGFRβ) and the phosphorylation of its downstream signaling components. Immunoprecipitation showed that TRIM28 formed complexes with TRIM27 through its N-terminal RING-B boxes-Coiled-Coil domain. Furthermore, TRIM28 and TRIM27 were shown to be upregulated and mediate the VSMC contractile marker gene and PDGFRβ expression in differentiating human bone marrow mesenchymal stem cells. In conclusion, we identified that TRIM28 and TRIM27 cooperatively maintain the endogenous expression of PDGFRβ and contractile phenotype of human VSMCs.

The virus SARS-CoV2, which causes coronavirus disease (COVID-19) has become a pandemic and has spread to every inhabited continent Given the increasing caseload, there is an urgent need to augment clinical skills in order to identify from among the many mild cases the few that will progress to critical illness We present a first step towards building an artificial intelligence (AI) framework, with predictive analytics (PA) capabilities applied to real patient data, to provide rapid clinical decision-making support COVID-19 has presented a pressing need as a) clinicians are still developing clinical acumen to this novel disease and b) resource limitations in a surging pandemic require difficult resource allocation decisions The objectives of this research are: (1) to algorithmically identify the combinations of clinical characteristics of COVID-19 that predict outcomes, and (2) to develop a tool with AI capabilities that will predict patients at risk for more severe illness on initial presentation The predictive models learn from historical data to help predict who will develop acute respiratory distress syndrome (ARDS), a severe outcome in COVID-19 Our results, based on data from two hospitals in Wenzhou, Zhejiang, China, identified features on initial presentation with COVID-19 that were most predictive of later development of ARDS A mildly elevated alanine aminotransferase (ALT) (a liver enzyme), the presence of myalgias (body aches), and an elevated hemoglobin (red blood cells), in this order, are the clinical features, on presentation, that are the most predictive The predictive models that learned from historical data of patients from these two hospitals achieved 70% to 80% accuracy in predicting severe cases

Summary: Millions of wearable-device users record their heart rate (HR) and activity. We introduce a statistical method to extract and track six key physiological parameters from these data, including an underlying circadian rhythm in HR (CRHR), the direct effects of activity, and the effects of meals, posture, and stress through hormones like cortisol. We test our method on over 130,000 days of real-world data from medical interns on rotating shifts, showing that CRHR dynamics are distinct from those of sleep-wake or physical activity patterns and vary greatly among individuals. Our method also estimates a personalized phase-response curve of CRHR to activity for each individual, representing a passive and personalized determination of how human circadian timekeeping continually changes due to real-world stimuli. We implement our method in the “Social Rhythms” iPhone and Android app, which anonymously collects data from wearable-device users and provides analysis based on our method. Motivation: The exploding popularity of wearable devices, now a multi-billion dollar industry, provides a new opportunity for real-world data collection. Here, we propose a statistical method for analysis of ambulatory wearable-device data that can estimate circadian rhythms. Accounting for circadian rhythms in HR will allow more accurate measurement of other physiological parameters, e.g., basal HR, how activity increases HR, and changes in HR due to infection.

BACKGROUND Cyberbullying has become a growing public health concern that threatens the physical and mental health of young people worldwide. Despite a rising call for more research on victims of cyberbullying, there is still limited understanding of the psychological and behavioral mechanism underlying cyberbullying victimization (CV), especially among the Chinese population. However, such information is crucial for identifying potential victims and planning targeted educational and protective interventions. OBJECTIVE This study aimed to explore the relationship between attachment anxiety (AA), social media self-disclosure (SMSD) and CV among Chinese college students and to examine the moderating role of gender. METHODS A web-based questionnaire was constructed to assess AA, SMSD, and CV with validated scales. Mediation analysis, t-tests, moderation analysis, and moderated mediation analysis were performed and bootstrap method was used to estimate bias-corrected confidence interval. RESULTS The sample consisted of 845 Chinese college students who used social media (female=635, mean age =18.7 years old). AA had a weak but statistically significant positive effect on CV (β=.06, t=2.652, p=.008), partially mediated by SMSD (β=.008, p CONCLUSIONS High AA is associated with high CV in Chinese college students and hence should be attended to in interventions targeting cyberbullying victims. In females, after accounting for SMSD, the AA-CV relationship is no longer significant, which means educating females on risks of excessive and indiscreet SMSD may suffice to mitigate the increased risk of CV brought about by high AA. However, the mechanism underlying males’ AA-CV relationship warrants more research in the future. The model resulting from this research should be a starting point for developing more sophisticated tools for surveilling and protecting potential victims of cyberbullying.

Background Lipogenesis is required for the optimal growth of many types of cancer cells, it is shown to control the biosynthesis of the lipid bilayer membrane during rapid proliferation and metastasis, provides cancer cells with signaling lipid molecules to support cancer development and make cancer cells more resistant to oxidative stress-induced cell death. Though multiple lipogenic enzymes have been identified to mediate this metabolic change, how the expression of these lipogenic enzymes are transcriptionally regulated remains unclear. Methods Gain- and loss-of-function experiments were conducted to assess the role of transcriptional repressor, nuclear receptor sub-family 6, group A, member 1 (NR6A1) in HepG2 cells. RT-qPCR method was performed to investigate target gene of NR6A1. Western blot was employed to determine the mechanisms by which NR6A1 regulates lipid accumulation in HepG2 cells. Results We provide evidence that NR6A1 is a novel regulator of lipid metabolism in HepG2 cells. NR6A1 knockdown can increase lipid accumulation as well as insulin-induced proliferation and migration of HepG2 cells. The lipogenic effect correlated well with the expression of lipogenic genes, including fatty acid synthase (FAS), diglyceride acyltransferase-2 (DGAT2), malic enzyme 1 (ME1), microsomal triglyceride transfer protein (MTTP) and phosphoenolpyruvate carboxykinase (PEPCK). NR6A1 knockdown also increased the expression of carnitine palmitoyltransferase 1A (CPT1a), the rate-limiting enzyme in fatty acid oxidation. Furthermore, NR6A1 knockdown induced lipid accumulation through mammalian target of rapamycin complex 1 (mTORC1), but not mTORC2. Moreover, siRNA-mediated knockdown of NR6A1 increased expression of insulin receptor (INSR) and potentitated insulin-induced phosphorylation of mTOR and AKT partly via miR-205-5p in HepG2 cells. Conclusions These findings provide important new insights into the role of NR6A1 in the lipogenesis in HepG2 cells. Graphical abstract . Electronic supplementary material The online version of this article (10.1186/s12964-019-0389-4) contains supplementary material, which is available to authorized users.

Convolutional neural networks (CNNs) are widely used in vision-based autonomous driving, i.e., detecting and localizing objects captured in live video streams. Although CNNs demonstrate the state-of-the-art detection accuracy, processing multiple video streams using such models in real-time imposes a serious challenge to the on-car computing systems. The lack of optimized system support, for example, could lead to a significant frame loss due to the high processing latency, which is unacceptable for safety-critical applications. To alleviate this problem, several optimization strategies such as batching, GPU parallelism, and data transfer modes between CPU/GPU have been proposed, in addition to a variety of deep learning frameworks and GPUs. It is, however, unclear how these techniques interact with each other, which particular combination performs better, and under what settings. In this paper, we set out to answer these questions. We design and develop a Multi-Tenant Parallel CNN Inference Framework, MPInfer, to carefully evaluate the performance of various parallel execution modes with different data transfer modes between CPU/GPU and GPU platforms. We find that on more powerful GPUs such as GTX 1660, it achieves the best performance when we adopt parallelism across CUDA contexts enhanced by NVIDIA Multi-Process Service (MPS), with 147.06 FPS throughput and 14.50 ms latency. Meanwhile, on embedded GPUs such as Jetson AGX Xavier, pipelining is a better choice, with 46.63 FPS throughput and 35.09 ms latency.

Study ObjectivesA critical barrier to successful treatment of circadian misalignment in shift workers is determining circadian phase in a clinical or field setting. Light and movement data collected passively from wrist actigraphy can generate predictions of circadian phase via mathematical models; however, these models have largely been tested in non-shift working adults. This study tested the feasibility and accuracy of actigraphy in predicting dim light melatonin onset (DLMO) in fixed night shift workers.MethodsA sample of 45 night shift workers wore wrist actigraphs before completing DLMO in the laboratory (17.0 days ± 10.3 SD). DLMO was assessed via 24 hourly saliva samples in dim light (ResultsModel predictions of DLMO showed good concordance with in-lab DLMO, with Lin’s concordance coefficient of 0.70, which was twice as high as agreement using average sleep timing as a proxy of DLMO. The absolute mean error of the predictions was 2.88 h, with 76% and 91% of the predictions falling with 2 and 4 h, respectively.ConclusionThis study is the first to demonstrate the use of wrist actigraphy-based estimates of circadian phase as a clinically useful and valid alternative to in-lab measurement of DLMO in fixed night shift workers. Future research should explore how additional predictors may impact accuracy.

We explore how a combination of manipulations and transitions can extend Substitutional Reality to create a highly personal Virtual Reality experience. Our design aimed to meet two challenges faced by museums: the limitations of object handling and the desire for visitors to create their own interpretations. Using a Research-through-Design methodology, we built a performance-led Mixed Reality (MR) experience that lets museum visitors physically handle 3D prints or scans of museum objects to share personal stories about them. The stories are recorded and donated to the participating museum. We reflect on the complex design and the findings gained from a two-day in-the-wild deployment to explore engagement and disruption through manipulations of physicality, visuals, and scale; the transitions between spaces; and a trajectory of storytelling performance. We chart a wide scope for Performative Substitutional Reality and draw implications for VR, MR, and performance-led research in any context.

Background Sedentary behavior (SB) is associated with various adverse health outcomes. The prevalence of prolonged sitting at work among office workers makes a case for SB interventions to target this setting and population. Everyday mundane objects with embedded microelectronics and ubiquitous computing represent a novel mode of delivering health behavior change interventions enabled by internet of things (IoTs). However, little is known about how to develop interventions involving IoT technologies. Objective This paper reports the design and development of an IoT-enabled SB intervention targeting office workers. Methods The process was guided by the behavior change wheel (BCW), a systematic framework for theory-informed and evidence-based development of behavior change interventions, complemented by the human-centered design (HCD) approach. Intervention design was shaped by findings from a diary-probed interview study (n=20), a stakeholder design workshop (n=8), and a series of theoretical mapping and collaborative technical design activities. Results The resulting intervention named WorkMyWay targets a reduction in office workers’ prolonged stationary behaviors at work and an increase in regular breaks by modifying behavioral determinants in 11 theoretical domains with 17 behavior change techniques. The delivery technology consists of a wearable activity tracker, a light-emitting diode reminder device attached to a vessel (ie, water bottle or cup), and a companion Android app connected to both devices over Bluetooth. The delivery plan consists of a 2-week baseline assessment, a 30-min face-to-face action planning session, and 6-week self-directed use of the delivery technology. Conclusions This is the first study to demonstrate that it is possible to develop a complex IoT-enabled intervention by applying a combination of the BCW and HCD approaches. The next step is to assess the feasibility of WorkMyWay prior to testing intervention efficacy in a full-scale trial. The intervention mapping table that links individual intervention components with hypothesized mechanisms of action can serve as the basis for testing and clarifying theory-based mechanisms of action in future studies on WorkMyWay.

This paper focuses on the resilience of a nature-inspired class of algorithms. The issues related to resilience fall under a very wide umbrella. The uncertainties that we face in the world require the need of resilient systems in all domains. Software resilience is certainly of critical importance, due to the presence of software applications which are embedded in numerous operational and strategic systems. For Ant Colony Optimization (ACO), one of the most successful heuristic methods inspired by the communication processes in entomology, performance and convergence issues have been intensively studied by the scientific community. Our approach addresses the resilience of MAX&ndash, MIN Ant System (MMAS), one of the most efficient ACO algorithms, when studied in relation with Traveling Salesman Problem (TSP). We introduce a set of parameters that allow the management of real-life situations, such as imprecise or missing data and disturbances in the regular computing process. Several metrics are involved, and a statistical analysis is performed. The resilience of the adapted MMAS is analyzed and discussed. A broad outline on future research directions is given in connection with new trends concerning the design of resilient systems.

Sprouting angiogenesis is a highly coordinately process controlled by vascular endothelial growth factor receptor (VEGFR)-Notch signaling. Here we investigated whether Tripartite motif-containing 28 (TRIM28), which is an epigenetic modifier implicated in gene transcription and cell differentiation, is essential to mediate sprouting angiogenesis. We observed that knockdown of TRIM28 ortholog in zebrafish resulted in developmental vascular defect with disorganized and reduced vasculatures. Consistently, TRIM28 knockdown inhibited angiogenic sprouting of cultured endothelial cells (ECs), which exhibited increased mRNA levels of VEGFR1, Delta-like (DLL) 3, and Notch2 but reduced levels of VEGFR2, DLL1, DLL4, Notch1, Notch3, and Notch4.The regulative effects of TRIM28 on these angiogenic factors were partially mediated by hypoxia-inducible factor 1 α (HIF-1α) and recombination signal-binding protein for immunoglobulin kappa J region (RBPJκ). In vitro DNA-binding assay showed that TRIM28 knockdown increased the association of RBPJκ with DNA sequences containing HIF-1α-binding sites. Moreover, the phosphorylation of TRIM28 was controlled by VEGF and Notch1 through a mechanism involving RBPJκ-dual-specificity phosphatase (DUSP)-p38 MAPK, indicating a negative feedback mechanism. These findings established TRIM28 as a crucial regulator of VEGFR-Notch signaling circuit through HIF-1α and RBPJκ in EC sprouting angiogenesis.

BACKGROUND Sedentary behavior (SB) is associated with various adverse health outcomes. The prevalence of prolonged sitting at work among office workers makes a case for SB interventions to target this setting and population. Everyday mundane objects with embedded microelectronics and ubiquitous computing represent a novel mode of delivering health behavior change interventions enabled by internet of things (IoTs). However, little is known about how to develop interventions involving IoT technologies. OBJECTIVE This paper reports the design and development of an IoT-enabled SB intervention targeting office workers. METHODS The process was guided by the behavior change wheel (BCW), a systematic framework for theory-informed and evidence-based development of behavior change interventions, complemented by the human-centered design (HCD) approach. Intervention design was shaped by findings from a diary-probed interview study (n=20), a stakeholder design workshop (n=8), and a series of theoretical mapping and collaborative technical design activities. RESULTS The resulting intervention named WorkMyWay targets a reduction in office workers’ prolonged stationary behaviors at work and an increase in regular breaks by modifying behavioral determinants in 11 theoretical domains with 17 behavior change techniques. The delivery technology consists of a wearable activity tracker, a light-emitting diode reminder device attached to a vessel (ie, water bottle or cup), and a companion Android app connected to both devices over Bluetooth. The delivery plan consists of a 2-week baseline assessment, a 30-min face-to-face action planning session, and 6-week self-directed use of the delivery technology. CONCLUSIONS This is the first study to demonstrate that it is possible to develop a complex IoT-enabled intervention by applying a combination of the BCW and HCD approaches. The next step is to assess the feasibility of WorkMyWay prior to testing intervention efficacy in a full-scale trial. The intervention mapping table that links individual intervention components with hypothesized mechanisms of action can serve as the basis for testing and clarifying theory-based mechanisms of action in future studies on WorkMyWay.

From smart work scheduling to optimal drug timing, there is enormous potential in translating circadian rhythms research results for precision medicine in the real world. However, the pursuit of such effort requires the ability to accurately estimate circadian phase outside of the laboratory. One approach is to predict circadian phase noninvasively using light and activity measurements and mathematical models of the human circadian clock. Most mathematical models take light as an input and predict the effect of light on the human circadian system. However, consumer-grade wearables that are already owned by millions of individuals record activity instead of light, which prompts an evaluation of the accuracy of predicting circadian phase using motion alone. Here, we evaluate the ability of four different models of the human circadian clock to estimate circadian phase from data acquired by wrist-worn wearable devices. Multiple datasets across populations with varying degrees of circadian disruption were used for generalizability. Though the models we test yield similar predictions, analysis of data from 27 shift workers with high levels of circadian disruption shows that activity, which is recorded in almost every wearable device, is better at predicting circadian phase than measured light levels from wrist-worn devices when processed by mathematical models. In those living under normal living conditions, circadian phase can typically be predicted to within 1 h, even with data from a widely available commercial device (the Apple Watch). These results show that circadian phase can be predicted using existing data passively collected by millions of individuals with comparable accuracy to much more invasive and expensive methods.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of coronavirus disease 2019, it binds to angiotensin-converting enzyme 2 (ACE2) to enter into human cells. The expression level of ACE2 potentially determine the susceptibility and severity of COVID-19, it is thus of importance to understand the regulatory mechanism of ACE2 expression. Tripartite motif containing 28 (TRIM28) is known to be involved in multiple processes including antiviral restriction, endogenous retrovirus latency and immune response, it is recently reported to be co-expressed with SARS-CoV-2 receptor in type II pneumocytes; however, the roles of TRIM28 in ACE2 expression and SARS-CoV-2 cell entry remain unclear. This study showed that knockdown of TRIM28 induces ACE2 expression and increases pseudotyped SARS-CoV-2 cell entry of A549 cells and primary pulmonary alveolar epithelial cells (PAEpiCs). In a co-culture model of NK cells and lung epithelial cells, our results demonstrated that NK cells inhibit TRIM28 and promote ACE2 expression in lung epithelial cells, which was partially reversed by depletion of interleukin-2 and blocking of granzyme B in the co-culture medium. Furthermore, TRIM28 knockdown enhanced interferon-γ (IFN-γ)- induced ACE2 expression through a mechanism involving upregulating IFN-γ receptor 2 (IFNGR2) in both A549 and PAEpiCs. The upregulated ACE2 induced by TRIM28 knockdown and co-culture of NK cells was partially reversed by dexamethasone in A549 cells. Our study identified TRIM28 as a novel regulator of ACE2 expression and SARS-CoV-2 cell entry., Graphical abstract Unlabelled Image

Wearable, multisensor, consumer devices that estimate sleep are now commonplace, but the algorithms used by these devices to score sleep are not open source, and the raw sensor data is rarely accessible for external use. As a result, these devices are limited in their usefulness for clinical and research applications, despite holding much promise. We used a mobile application of our own creation to collect raw acceleration data and heart rate from the Apple Watch worn by participants undergoing polysomnography, as well as during the ambulatory period preceding in lab testing. Using this data, we compared the contributions of multiple features (motion, local standard deviation in heart rate, and “clock proxy”) to performance across several classifiers. Best performance was achieved using neural nets, though the differences across classifiers were generally small. For sleep-wake classification, our method scored 90% of epochs correctly, with 59.6% of true wake epochs (specificity) and 93% of true sleep epochs (sensitivity) scored correctly. Accuracy for differentiating wake, NREM sleep, and REM sleep was approximately 72% when all features were used. We generalized our results by testing the models trained on Apple Watch data using data from the Multi-ethnic Study of Atherosclerosis (MESA), and found that we were able to predict sleep with performance comparable to testing on our own dataset. This study demonstrates, for the first time, the ability to analyze raw acceleration and heart rate data from a ubiquitous wearable device with accepted, disclosed mathematical methods to improve accuracy of sleep and sleep stage prediction.

Naturally ventilated offices enable users to control their environment through the opening of windows. Whilst this level of control is welcomed by users, it creates risk in terms of energy performance, especially during the heating season. In older office buildings, facilities managers usually obtain energy information at the building level. They are often unaware or unable to respond to non-ideal facade interaction by users often as a result of poor environmental control provision. In the summer months, this may mean poor use of free cooling opportunities, whereas in the winter space heating may be wasteful. This paper describes a low-cost, camera-based system to diagnose automatically the status of each window (open or closed) in a facade. The system is shown to achieve a window status prediction accuracy level of 90–97% across both winter and summer test periods in a case study building. A number of limitations are discussed including winter daylight hours, the impact of rain, and the use of fixed camera locations and how these may be addressed. Options to use this window-opening information to engage with office users are explored.

The market value of real estate is difficult to predict with simple regression model due to the diversity and complexity of the real data. In this paper, with the latest real estate data of three counties in Los Angeles, California, United States, both linear and non-linear machine learning methods are employed to predict the log error of the home value. The motivation is to improve the accuracy in home value prediction with advanced methods. The main contribution is that it finds that traditional linear models are not predictive for complex home value data sets, while tree based non-linear models are most accurate with the lowest mean square errors.

Mimicking the efficient facilitated transport features of carrier proteins in cytomembranes, constructing facilitated transport pathways in synthetic membranes to achieve the desired performance for task-specific molecules has become attractive. Herein, heterostructured laminates (UGO) were created through in-situ growth of amine-functionalized zirconium-based metal-organic frameworks (UiO) on graphene oxide (GO) nanosheets, and subsequently used as supports to immobilize Ag+. The as-prepared Ag+@UGO were incorporated into poly (ether-block-amide) (Pebax) matrix to fabricate facilitated transport membranes for pervaporative desulfurization. Densely packed UiO on GO nanosheets increase the inter-particles connectivity of the molecular transport channels and reduce the mass transport resistance, intensifying the permeability. Meanwhile, the uniform immobilization of Ag+ can reversibly interact with thiophene via π-complexation reaction and form continuous facilitated transport pathways, significantly enhancing the selectivity. Accordingly, the resultant membrane acquires separation performance with an optimum permeation flux of 23.40 kg/(m2 h) and enrichment factor of 7.12, achieving 52% and 57% higher than the pristine membrane. This study extends strategies to design high-performance hybrid membranes with facilitated transport mechanism through the well-designed heterostructures as supports to immobilize carriers for molecular separations.