Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward the Development of Flexible Mixed-Initiative Scheduling Tools

In this paper, we discuss work aimed at the development of interactive decision-support tools for complex, large-scale scheduling applications. Our approach is grounded on three basic premises: (1) that system organization and decision-support \services" should directly re ect the inherently reactive nature of decision-making in complex scheduling environments, (2) that diversity in the character and requirements of various user tasks will invariably require di erent specialized scheduling services in di erent decision-making contexts, and (3) that problem scale and complexity will necessitate user interaction at aggregate, task-oriented levels. We describe DITOPS, a transportation scheduling tool which integrates a hierarchical modeling infra-structure and reactive scheduling methodology with graphical schedule visualization and manipulation capabilities to provide a exible interactive environment for construction and management of transportation schedules.
Breast cancer: The translation of big genomic data to cancer precision medicine

Cancer is a complex genetic disease that develops from the accumulation of genomic alterations in which germline variations predispose individuals to cancer and somatic alterations initiate and trigger the progression of cancer. For the past 2 decades, genomic research has advanced remarkably, evolving from single‐gene to whole‐genome screening by using genome‐wide association study and next‐generation sequencing that contributes to big genomic data. International collaborative efforts have contributed to curating these data to identify clinically significant alterations that could be used in clinical settings. Focusing on breast cancer, the present review summarizes the identification of genomic alterations with high‐throughput screening as well as the use of genomic information in clinical trials that match cancer patients to therapies, which further leads to cancer precision medicine. Furthermore, cancer screening and monitoring were enhanced greatly by the use of liquid biopsies. With the growing data complexity and size, there is much anticipation in exploiting deep machine learning and artificial intelligence to curate integrative “−omics” data to refine the current medical practice to be applied in the near future.
Mechanical property and structure of alkali-activated fly ash and slag blends

Mechanical property and structure of alkali-activated fly ash (FA)/ground granulated blast furnace slag (GGBFS) blends were investigated via compressive strength testing, X-ray diffractometry (XRD), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy/energy dispersive spectroscopy. It is shown that the incorporation of slag into solid precursors can improve compressive strength of the geopolymer binders and the optimal slag content ratio which will result in the highest strength is 0.8. GGBFS is relatively more reactive than FA in alkaline activation. The binder is predominantly a class of Al-substituted sodium calcium silicate hydrate (N-C-A-S-H) gel phase, which distributes around the solid particles homogeneously. Combining the results obtained from the glass diffraction maximum of XRD and the wavenumber of T-O-Si bands displayed in FTIR, it suggests that the degree of polymerization of geopolymer binders decreases and increases. This means that the microstructure of the binder is more complex. The microstructure and chemical composition of the binders are between those of aluminosilicate gel formed in silicate-activated FA and those of calcium silicate hydrate gel formed in silicate-activated GGBFS. The major reasons contributing to this structure are the numerous calcium cations which will lead to a depolymerisation influence on the network, and/or the majority of linear chains of Si-O-Si supplied by GGBFS.
Optimal Glycemic Control, Pre-eclampsia, and Gestational Hypertension in Women With Type 1 Diabetes in the Diabetes and Pre-eclampsia Intervention Trial

OBJECTIVE To assess the relationship between glycemic control, pre-eclampsia, and gestational hypertension in women with type 1 diabetes. RESEARCH DESIGN AND METHODS Pregnancy outcome (pre-eclampsia or gestational hypertension) was assessed prospectively in 749 women from the randomized controlled Diabetes and Pre-eclampsia Intervention Trial (DAPIT). HbA1c (A1C) values were available up to 6 months before pregnancy (n = 542), at the first antenatal visit (median 9 weeks) (n = 721), at 26 weeks’ gestation (n = 592), and at 34 weeks’ gestation (n = 519) and were categorized as optimal (<6.1%: referent), good (6.1–6.9%), moderate (7.0–7.9%), and poor (≥8.0%) glycemic control, respectively. RESULTS Pre-eclampsia and gestational hypertension developed in 17 and 11% of pregnancies, respectively. Women who developed pre-eclampsia had significantly higher A1C values before and during pregnancy compared with women who did not develop pre-eclampsia (P < 0.05, respectively). In early pregnancy, A1C ≥8.0% was associated with a significantly increased risk of pre-eclampsia (odds ratio 3.68 [95% CI 1.17–11.6]) compared with optimal control. At 26 weeks’ gestation, A1C values ≥6.1% (good: 2.09 [1.03–4.21]; moderate: 3.20 [1.47–7.00]; and poor: 3.81 [1.30–11.1]) and at 34 weeks’ gestation A1C values ≥7.0% (moderate: 3.27 [1.31–8.20] and poor: 8.01 [2.04–31.5]) significantly increased the risk of pre-eclampsia compared with optimal control. The adjusted odds ratios for pre-eclampsia for each 1% decrement in A1C before pregnancy, at the first antenatal visit, at 26 weeks’ gestation, and at 34 weeks’ gestation were 0.88 (0.75–1.03), 0.75 (0.64–0.88), 0.57 (0.42–0.78), and 0.47 (0.31–0.70), respectively. Glycemic control was not significantly associated with gestational hypertension. CONCLUSIONS Women who developed pre-eclampsia had significantly higher A1C values before and during pregnancy. These data suggest that optimal glycemic control both early and throughout pregnancy may reduce the risk of pre-eclampsia in women with type 1 diabetes.
Global Population Genetic Analysis of Aspergillus fumigatus Eta

Aspergillus fumigatus is a ubiquitous opportunistic fungal pathogen capable of causing invasive aspergillosis, a globally distributed disease with a mortality rate of up to 90% in high-risk populations. Effective control and prevention of this disease require a thorough understanding of its epidemiology. However, despite significant efforts, the global molecular epidemiology of A. fumigatus remains poorly understood. In this study, we analyzed 2,026 A. fumigatus isolates from 13 countries in four continents using nine highly polymorphic microsatellite markers. Genetic cluster analyses suggest that our global sample of A. fumigatus isolates belonged to eight genetic clusters, with seven of the eight clusters showing broad geographic distributions. We found common signatures of sexual recombination within individual genetic clusters and clear evidence of hybridization between several clusters. Limited but statistically significant genetic differentiations were found among geographic and ecological populations. However, there was abundant evidence for gene flow at the local, regional, and global scales. Interestingly, the triazole-susceptible and triazole-resistant populations showed different population structures, consistent with antifungal drug pressure playing a significant role in local adaptation. Our results suggest that global populations of A. fumigatus are shaped by historical differentiation, contemporary gene flow, sexual reproduction, and the localized antifungal drug selection that is driving clonal expansion of genotypes resistant to multiple
Learning robust latent subspace for discriminative regression

In this paper, we present a generic effective formulation, dubbed discriminative latent linear regression (DLL-R), for multi-category classification. We formulate the DLLR optimization problem as a joint learning framework of discriminative latent feature selection and robust linear regression. Specifically, instead of directly projecting the original high-dimensional features onto a target space, DLLR learns discriminative latent representation by concurrently suppressing the redundant information from original features and constructing a robust latent subspace. To improve the effectiveness of the regression task, a capped lp-norm regression model is formulated for robust linear regression. Furthermore, DLLR incorporates learning latent representation and building regressing prediction into one framework for reducing the classification error of the regression model. An efficient optimization algorithm is developed to solve the resulting optimization problem. Extensive experimental results conducted on diverse databases validate the effectiveness of the proposed DLLR method in comparison with state-of-the-art regression methods.
LAND USE/LAND COVER CHANGES IN SEMI-ARID MOUNTAIN LANDSCAPE IN SOUTHERN INDIA: A GEOINFORMATICS BASED MARKOV CHAIN APPROACH

Abstract. Nowadays land use/ land cover in mountain landscape is in critical condition; it leads to high risky and uncertain environments. These areas are facing multiple stresses including degradation of land resources; vagaries of climate and depletion of water resources continuously affect land use practices and livelihoods. To understand the Land use/Land cover (Lu/Lc) changes in a semi-arid mountain landscape, Kallar watershed of Bhavani basin, in southern India has been chosen. Most of the hilly part in the study area covers with forest, plantation, orchards and vegetables and which are highly affected by severe soil erosion, landslide, frequent rainfall failures and associated drought. The foothill regions are mainly utilized for agriculture practices; due to water scarcity and meagre income, the productive agriculture lands are converted into settlement plots and wasteland. Hence, land use/land cover change deduction; a stochastic processed based method is indispensable for future prediction. For identification of land use/land cover, and vegetation changes, Landsat TM, ETM (1995, 2005) and IRS P6- LISS IV (2015) images were used. Through CAMarkov chain analysis, Lu/Lc changes in past three decades (1995, 2005, and 2015) were identified and projected for (2020 and 2025); Normalized Difference Vegetation Index (NDVI) were used to find the vegetation changes. The result shows that, maximum changes occur in the plantation and slight changes found in forest cover in the hilly terrain. In foothill areas, agriculture lands were decreased while wastelands and settlement plots were increased. The outcome of the results helps to farmer and policy makers to draw optimal lands use planning and better management strategies for sustainable development of natural resources.
A review of the outcome expectancy construct in physical activity research

Background: Outcome expectancy is a central construct in social cognitive models of health behavior widely used as frameworks for physical activity research.Purpose: This article provides a review of the outcome expectancy construct and its application to research on physical activity.Methods: Theoretical articles describing definitions and placement of outcome expectancy within social cognitive models, as well as empirical research on outcome expectancy and physical activity, were reviewed.Results: Self-efficacy theory, the transtheoretical model, the theory of planned behavior, and protection motivation theory differ in their labeling and conceptualization of outcome expectancy but unanimously include expected outcomes of behavior. Preliminary empirical investigation of the role of outcome expectancy in understanding physical activity has yielded mixed results. Positive outcome expectancy appears to be more predictive of physical activity in older adults than in young to middle-aged adults, and personal barriers appear to be the most predictive subtype of negative outcome expectancy. In addition, a small number of studies indicate relations between outcome expectancy and other theoretical variables, including behavioral intention, stage of change, and self-efficacy.Conclusions: Further research on the role of outcome expectancy is necessary to design effective physical activity interventions. New directions in outcome expectancy research could involve (a) expanding the conceptualization of outcome expectancy to include expected outcomes of sedentary behavior and affective responses to physical activity, (b) further examination of potential moderators of the relation between outcome expectancy and physical activity (such as outcome value and outcome proximity), (c) distinguishing between the role of outcome expectancy in behavior onset versus behavior maintenance, (d) examining outcome expectancy as a mechanism of change in environmental intervention approaches, and (e) further analysis of interrelations between outcome expectancy and other social cognitive variables. (Ann Behav Med 2005,29(1):70-79)
Carbon black/ polyurethane nanocomposite-coated fabric for microwave attenuation in X & Ku-band (8–18 GHz) frequency range

In the present study, coating formulations based on carbon nanoparticle (125–150 nm) and two component polyurethane resin [Polyester polyol-8 and hexamethylene diisocyanate [C8H12N2O2 (NCO-(CH2)6–NCO)] have been prepared in concentration of 5,6,7,8, 9 and 10 g of carbon black in 100 ml of polyurethane solution and was applied on cotton fabric adopting knife over roller coating technique. The coated fabrics were evaluated for surface resistivity and relevant microwave properties i.e. permittivity, scattering parameters, reflection, transmission, absorption, reflection loss and electromagnetic interference shielding in HVS free-space microwave measurement system. It is observed that increasing the carbon content increases the permittivity values and decreases the impedance. It is seen that with increase of carbon content up to 7 g, absorption increases due to increase of permittivity while with further increase of carbon content from 8 to 10 g, absorption decreases due to increase of conductivity and thereby increased reflection. The coated fabric at 6.55 wt% carbon and 93.45 wt% PU in thickness of 1.3 mm has shown about 46% absorption, 29.49% transmission and 24.5% reflectance in X (8.2–12.4 GHz) and Ku (12–18 GHz) frequency band. When electrical thickness of coated fabric equals λ/4, a peak of reflection loss is observed due to Salisbury absorption mechanism. Highest reflection loss of –31.39 dB at 17 GHz, more than 20 dB in bandwidth of 16–18 GHz and more than 10 dB in entire Ku- band is achieved. Coated fabric may be used as apparel for protecting human being from hazardous microwave and for radar absorbing structural materials for defence applications.
Optimizing routine collection efficiency in IoT based garbage collection monitoring systems

Ubiquitous objects are getting “smarter” and more “connected”, every day. With this ever-growing Internet of Things, every object can now be uniquely identified and made to communicate with each other. This approach has been applied to dustbins too, to monitor garbage collection, throwing light on numerous valuable insights. Our project too employs a similar approach, to not only monitor garbage collection but also optimize it, using machine learning. The method of unsupervised learning we utilize is K Means Clustering, widely used in data mining and analytics. Our physical device uses an ultrasonic sensor to be aware of a dustbin's current content level. If the level reaches or exceeds a threshold percentage of the total capacity of the dustbin, it informs our servers, via an online application programming interface (API) developed for this purpose. The API also stores related data – fill time, cleanup time, and location, to name a few. This dynamic dataset generated is analyzed by our algorithm, to determine the times of the day, when a regular cleanup should be performed, such that the dustbins are clean, for the maximum possible portion of the day. The algorithm also shows the locations, where another dustbin should be installed, for further optimization. This is found out by inspecting each cluster individually and scanning out – items which are the furthest away from its closest centroid; and multiple items related to the same dustbin. In either case, a new dustbin installation is advised at such locations. Data henceforth generated revealed that the installation has had a positive effect on the optimization.
Evaluation of marginal gap of lithium disilicate glass ceramic crowns with optical coherence tomography

Abstract. Marginal gap (MG) was the most important factor to evaluate the success of crowns. The study was to assess the MG of lithium disilicate glass ceramic crowns with spectral domain optical coherence tomography (OCT) and stereomicroscopy in vitro, and to provide evidence to measure the MG with OCT. Consistency was observed between OCT and stereomicroscopy to measure the MG after cementation. There was no significant difference between the MG of lithium disilicate glass ceramic crowns using OCT and stereomicroscopy (OCT 59.55 ± 7.22 μm, stereomicroscope 59.48 ± 6.53 μm, P = 0.736) after cementation. OCT was a noninvasive diagnostic technique to measure the MG of lithium disilicate glass ceramic crowns.
Fight, fatigue and flight: narrowing of attention to a threat compensates for decreased anti-predator vigilance

ABSTRACT Fighting carries a predation risk because animals have limited attention, constraining their ability to simultaneously engage in aggression and anti-predator vigilance. However, the influence of interspecific aggression and fatigue on the predation cost of fighting is seldom examined, although both are unignorable aspects of fighting. Here, I incorporated both factors in a series of field experiments on the cichlid Lamprologus ocellatus. If territorial males respond more strongly to conspecific territorial intruders than to heterospecific intruders, then they should delay escape more frequently during intraspecific fighting than during interspecific fighting. Consequently, although I predicted that vigilance would be decreased as fighting progresses in both cases, intraspecific aggression should decrease vigilance more than interspecific aggression. Males were also exposed to a predator approaching at different (slow or fast) speeds during these fighting bouts. Delays in predator detection and flight initiation were quantified and these predictions were tested. As predicted, males responded more strongly to intraspecific intruders, resulting in slower predator detection and fleeing times than when encountering interspecific intruders. Furthermore, flight latency decreased with increasing fight duration, suggesting that fatigue negatively influenced escape responses. However, contrary to the prediction, the vigilance decrement rate was faster in response to slow predators than in response to rapid predators, and was not influenced by intruder identity. This suggests that fighting males reserve their attention for information critical to their survival and are less vigilant toward a less-threatening (slowly approaching) predator. This cognitive allocation may be an adaptive compensation for fatigue-related low vigilance during fighting. Summary: Male fish gradually reduce anti-predator vigilance with increased fight duration, but can maintain greater vigilance for high danger threats than for low danger threats.
Personalized Gaussian Processes for Forecasting of Alzheimer’s Disease Assessment Scale-Cognition Sub-Scale (ADAS-Cog13)

In this paper, we introduce the use of a personalized Gaussian Process pGP model to predict per-patient changes in ADAS-Cog13-a significant predictor of Alzheimer’s Disease (AD) in the cognitive domain – using data from each patient’s previous visits, and testing on future (held-out) data. We start by learning a population-level model using multi- modal data from previously seen patients using a base Gaussian Process (GP) regression. The pGP is then formed by adapting the base GP sequentially over time to a new (target) patient using domain adaptive GPs [1]. We extend this personalized approach to predict the values of ADAS-Cog13 over the future 6, 12, 18, and 24 months. We compare this approach to a GP model trained only on past data of the target patients tGP, as well as to a new approach that combines pGP with tGP. We find that this new approach (pGP+tGP) leads to significant improvements in accurately forecasting future ADAS-Cog13 scores.
Neuropeptide S Counteracts Paradoxical Sleep Deprivation-Induced Anxiety-Like Behavior and Sleep Disturbances

Disturbed sleep is a common subjective complaint among individuals with anxiety disorders. Sleep deprivation increases general and specific anxiety symptoms among healthy individuals. The amygdala is critical for regulating anxiety and also involved in mediating the effects of emotions on sleep. Neuropeptide S (NPS) and NPS receptors (NPSR) are reported as a novel endogenous arousal and anxiolytic system, but it is unclear yet whether this system is involved in anxiety-like behavior and sleep caused by sleep deprivation, and how it plays anxiolytic effect underlying the comorbid condition. In the present study, we demonstrate that paradoxical sleep deprivation (PSD) induced by modified multiple platform method (MMPM) for 24 h caused anxiety-like behavior, a prolonged sleep latency and subsequent paradoxical sleep (PS) rebound accompanied by an increase in electroencephalogram (EEG) theta (4.5–8.5 Hz) activities across light and dark phase in rats. The increase of PS after PSD was due to an increase of episode number during light phase and both episode number and duration during dark phase. Central action of NPS (1 nmol) attenuated PSD-induced anxiety-like behavior, and altered PSD-induced sleep-wake disturbances through increasing wakefulness, and suppressing PS and EEG theta activities. The reduction in PS time following NPS administration during light phase was because of a decreased episode number. Furthermore, sleep amount in 24 h in PSD rats given NPS was lesser than that given saline. PSD significantly enhanced NPSR mRNA expression level in the amygdala. NPS remarkably increased the number of Fos-ir neurons in the basolateral amygdala (BLA), the central amygdala (CeA) and medial amygdala (MeA). The majority of Fos-ir neurons induced by NPS also expressed NPSR. These results suggest that NPSR upregulation in the amygdala is presumably related to the PSD-induced anxiety-like behavior and sleep disturbances, and that NPS counteracts PSD-induced anxiety-like behavior and sleep disturbances possibly through activating the neurons bearing NPSR in the amygdala. In addition, the little sleep increase in PSD rats treated with NPS suggests that NPS can function as an anxiolytic without causing a subsequent sleep rebound.
Multi-wall carbon nanostructured paper: characterization and potential applications definition

In the present work, a free-standing paper-like sample made of a three-dimensional web of multiwall carbon nanotubes that exhibit cross-linking and wall sharing is characterized with respect to its electrical, electrochemical, surface and wetting properties. All these parameters are studied simultaneously to assess the potential of the sample for demanding applications with multiple material requirements. It is observed that many characteristics of the one-dimensional counterpart are retained by the three-dimensional web. In addition, the properties of this nanostructured material are compared against zero-, one- two- and three-dimensional carbon-based materials, while potential applications that require high electrical conductivity and energy storage capabilities and related properties, are discussed.