Miracle-dz/UniFAR-FTUs huggingface dataset:
A Machine Learning Framework to Infer Time-of-Use of Flexible Loads: Resident Behavior Learning for Demand Response

Load shapes obtained from smart meter data are commonly utilized to understand daily energy use patterns for adaptive operations in applications such as Demand Response (DR). However, they do not provide information on the underlying causes of specific energy use patterns – i.e., inference on appliances’ time-of-use (ToU) as actionable information. In this paper, we investigated a scalable machine learning framework to infer the appliances’ ToU from energy load shapes in a collection of residential buildings. A scalable and generalized inference model obviates the need for model training in each building to facilitate its adoption by relying on training data from a set of previously observed buildings with available appliance-level data. To this end, we demonstrated the feasibility of using load shape segmentation to boost ToU inference in buildings by learning from their nearest matches that share similar energy use patterns. To infer an appliance ToU for a building, classification models are trained for inference on subintervals of load shapes from matched buildings with known ToU. The framework was evaluated using real-world energy data from Pecan Street Dataport. The results for a case study on electric vehicles (EV) and dryers showed promising performance by using 15-min smart meter load shape data with 83% and 71% F-score values, respectively, and without in-situ training.
Periodic Event-Triggered Control for the Stabilization of Robotic Manipulators

This paper proposes a periodic event-triggered control (PETC) design applied to nonlinear robotic systems for joint space stabilization. The update of the control signal is based on a predefined triggering mechanism that periodically considers state measurements. So, continuous state measurements required in the continuous event-triggered control (CETC), are no longer needed in this proposal. The main advantages of the proposal are: 1) to assure the convergence of the closed-loop trajectories to a compact ball centered in the origin of the error space, and 2) the relaxation of continuous measurements for the evaluation of the event condition which implies a more suitable design for real-time implementation purposes. Experimental comparative results of the proposed design with a time-triggered control (TTC) scheme show that the PETC can present suitable performance with a significant reduction of 82.20% of the controller updates mainly given in the steady-state of the closed-loop system. This implies a decrement of the computation resources due to information transfer.
Malignant Melanoma Classification Using Deep Learning: Datasets, Performance Measurements, Challenges and Opportunities

Melanoma remains the most harmful form of skin cancer. Convolutional neural network (CNN) based classifiers have become the best choice for melanoma detection in the recent era. The research has indicated that classifiers based on CNN classify skin cancer images equivalent to dermatologists, which has allowed a quick and life-saving diagnosis. This study provides a systematic literature review of the latest research on melanoma classification using CNN. We restrict our study to the binary classification of melanoma. In particular, this research discusses the CNN classifiers and compares the accuracies of these classifiers when tested on non-published datasets. We conducted a systematic review of existing literature, identifying the literature through a systematic search of the IEEE, Medline, ACM, Springer, Elsevier, and Wiley databases. A total of 5112 studies were identified out of which 55 well-reputed studies were selected. The main objective of this study is to collect state of the art research which identify the recent research trends, challenges and opportunities for melanoma diagnosis and investigate the existing solutions for the diagnosis of melanoma detection using deep learning. Moreover, proposed taxonomy for melanoma detection has been presented that summarizes the broad variety of existing melanoma detection solutions. Lastly, proposed model, challenges and opportunities have been presented which helps the researchers in the domain of melanoma detection.
Microwave Sensing of Brain Water – a Simulation and Experimental Study Using Human Brain Models

This paper introduces a microwave-based approach that aims to non-invasively measure water, particularly cerebrospinal fluid (CSF) dynamics, in the human brain. The microwave measurement technique is well-known in industrial applications. More recently microwave techniques have awakened interest also in biomedical applications. This is the first time it is suggested to be utilized in measurements of brain water, particularly of CSF. Two different head phantoms were built in order to validate the sensitivity of the technique to sense dynamic variations of CSF and water volume inside a human skull. These were comprised of multilayered head phantom, including a real human skull, mimicking the electromagnetic properties of a human head. In addition, the variation of the CSF is evaluated with electromagnetic simulations using a planar layer model and a hemispherical layer model. Moreover, propagation and power flow inside the head model is evaluated using 2D power flow presentations. Reflection sensor principle was selected due to its simplicity and ability to measure relatively thick samples. Importantly, reflection sensor requires only one-port measurement making it very feasible for in vivo brain monitoring. In addition, the measurement setup does not require attachment of the sensor to the head, thus the measurement can be realized also without touching the head. Our experimental study as well as simulation results demonstrated the possibility to non-invasively sense, by microwaves, small dynamic variations in CSF volume in the brain, in particularly in the subarachnoid space.
On circulant Lyapunov operators, two-variable polynomials, and DFT

In this paper we deal with a special class of Lyapunov equations which we call circulant Lyapunov equations. These are Lyapunov equations arising from system matrices that are circulant. Here we bring out new theoretical insights into such Lyapunov operators. We show that, under a suitable projection map, two-variable polynomials are related to circulant Lyapunov operators. We also give necessary and sufficient conditions for the solvability of a circulant Lyapunov equation using two dimensional Fourier transform operation (2D-DFT) performed on a specially constructed matrix. Using these links among circulant Lyapunov operators, two-variable polynomials and 2D-DFT, an algorithm to solve circulant Lyapunov equations using 2D-DFT is developed in this paper.
Structural insight into precursor ribosomal RNA processing by ribonuclease MRP

Adapted to the task at hand RNA-based catalysts perform fundamental tasks in cellular RNA metabolism, especially in eukaryotes, where RNAs are cut by specialized ribonucleoproteins (RNPs) as part of ribosome assembly or messenger RNA regulation or splicing. Both RNA and protein components play a role in shaping how these large catalytic complexes interact with their RNA substrates. Lan et al. determined the cryo–electron microscopy structures of a yeast RNP called ribonuclease MRP both alone and bound to a small RNA substrate. Comparison with the related ribonuclease P revealed differences in both protein and RNA components that enable ribonuclease MRP to recognize substrates with a specific sequence motif, rather than purely recognizing RNA structure as ribonuclease P does. These structures aid in considering how RNPs evolved and why they remain central to eukaryotic RNA processing. Science, this issue p. 656 Structures of the RNA-cleaving ribonuclease MRP reveal the basis for substrate recognition by sequence. Ribonuclease (RNase) MRP is a conserved eukaryotic ribonucleoprotein complex that plays essential roles in precursor ribosomal RNA (pre-rRNA) processing and cell cycle regulation. In contrast to RNase P, which selectively cleaves transfer RNA–like substrates, it has remained a mystery how RNase MRP recognizes its diverse substrates. To address this question, we determined cryo–electron microscopy structures of Saccharomyces cerevisiae RNase MRP alone and in complex with a fragment of pre-rRNA. These structures and the results of biochemical studies reveal that coevolution of both protein and RNA subunits has transformed RNase MRP into a distinct ribonuclease that processes single-stranded RNAs by recognizing a short, loosely defined consensus sequence. This broad substrate specificity suggests that RNase MRP may have myriad yet unrecognized substrates that could play important roles in various cellular contexts.
The Effectiveness of Educational Interventions on Traditional Bullying and Cyberbullying Among Adolescents: A Systematic Review and Meta-Analysis

In consideration of the adverse societal, physical, and psychological impacts of bullying on a child’s development and future, many studies have developed anti-bullying programs and educational interventions to curb bullying occurrences. Therefore, this systematic review aimed to examine the effectiveness of such educational interventions at reducing the frequencies of traditional bullying or cyberbullying and cybervictimization among adolescents. A comprehensive search was conducted using PubMed, Embase, PsycINFO, Cumulative Index to Nursing and Allied Health Literature, Google Scholar, and ProQuest Dissertations and Theses. Only quantitative studies that reported the effects of educational interventions on reducing the frequencies of traditional bullying or cyberbullying victimization and perpetration were included. Seventeen studies (Ntotal = 35,694 adolescents, Rangechild age = 10–18 years) were finalized, and meta-analyses were conducted using a random effect model. Overall, the existing educational interventions had very small to small effect sizes on traditional bullying and cyberbullying perpetration (traditional: standardized mean differences [SMD] = −.30 and cyber: SMD = −.16) and victimization (traditional: SMD = −18 and cyber: SMD = −.13) among adolescents. Type of intervention (i.e., whole school–based or classroom-based), program duration, and presence of parental involvement did not moderate program effectiveness, but cyberbullying programs were more effective when delivered by technology-savvy content experts compared to teachers. Since existing educational interventions were marginally effective in reducing bullying frequencies, further research is needed to identify key moderators that enhance educational programs or develop alternative forms of anti-bullying interventions.
Systematics and the Origin of Species from the Viewpoint of a Zoologist

Foreword by Theodosius Dobzhansky Preface Introduction, 1999 Introduction by Niles Eldredge I. The Methods and Principles of Systematics II. Taxonomic Characters and Their Variation III. Phenomena of Geographic Variation IV. Some Aspects of Geographic Variation V. The Systematic Categories and the New Species Concept VI. The Polytypic Species, In Nature and in Systematics VII. The Species in Evolution VIII. Nongeographic Speciation IX. The Biology of Speciation X. The Higher Categories and Evolution Literature Index
Decoy Meta–Clustering Through Rough Graded Possibilistic C-Medoids

Current ab initio methods for structure–prediction of proteins explore multiple simulated conformations, called decoys, to generate families of folds, one of which is the closest to the native one. To limit the exploration of the conformational space, clustering algorithms are routinely applied to group similar decoys and then finding the most plausible cluster centroid, based on the hypothesis that there are more low–energy conformations surrounding the native fold than the others; nevertheless different clustering algorithms, or different parameters, are likely to output different partitions of the input data and choosing only one of the possible solutions can be too restrictive and unreliable. meta–clustering algorithms allow to reconcile multiple clustering solutions by grouping them into meta-clusters (i.e. clusters of clusterings), so that similar partitions are grouped in the same meta–cluster. In this paper the use of meta–clustering is proposed for the selection of lowest energy decoys, testing the Rough Graded Possibilistic c-medoids clustering algorithm for both baseline clustering and meta–clustering. Preliminary tests on real data suggest that meta–clustering is effective in reducing the sensitivity to parameters of the clustering algorithm and to expand the explored space.
Variation and Evolution in Plants

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Effect of Crop Type and Production Systems (Conventional and Organic Agriculture) on the Density of Rove Beetles (Staphylinidae: Coleoptera) in the Punjab, Pakistan

Abstract The density of staphylinid species is affected by crop type and production system. A study was made to compare the effect of conventional system with organic agricultural system on rove beetle fauna regarding the crop type. The study was conducted for a period of two years at eight different localities of Punjab province of Pakistan and five different collection methods i.e., pitfall trap, light trap, flight intercept trap, Berlese funnel trap and sweep net were used for collecting the rove beetles. The population (abundance) and diversity of rove beetles were taken as dependent variables in separate generalized linear mixed models (GLMM). In the GLMM, time (yr) was found non-significant random effect but locality as random effect was significant. In GLMM, with locality as random effect, the fixed effects crop type and soil moisture showed significant effect with population (abundance) as response and fixed effect system was also significant in the model with diversity as response variable. Generally, the farm yard manure had more positive effect on the density of different species than inorganic fertilizers. Hence, the density of all species was more prominent in the organic management system than in the conventional one.
Plant Architecture and Leaf Damage in Bear Oak. I: Physiological Responses

Abstract Bear Oak (Quercus ilicifolia Wang.) presents a unique architecture in which there is a distinct outer set of leaves on the perimeter of the bush and another set of leaves on the inside. In this paper we examine the relationship between plant architecture, leaf-level physiology, and leaf damage patterns by insects. We measured net photosynthesis, stomatal conductance, and leaf water potential in response to different levels of herbivory or leaf-rolling in both outer- and inner-canopy leaves. Leaf-rolling occurred naturally and was mimicked by hand-rolling leaves to different degrees. Net photosynthesis, stomatal conductance, and leaf water potential varied depending upon a combination of leaf position in the crown and degree of rolling, but not herbivory. Outer-canopy leaves had significantly higher rates of net photosynthesis and stomatal conductance, but lower leaf water potentials and leaf areas than inner-canopy leaves. Leaf rolling had an effect upon photosynthesis and stomatal conductance, which varied depending on whether projected areas of rolled leaves or projected areas of the leaves after they had been unrolled were considered. In both canopy layers, using unrolled projected leaf areas, stomatal conductance and net photosynthesis decreased with increasing leaf rolling. In the outer canopy, using projected areas of rolled leaves, photosynthesis decreased while stomatal conductance increased with increasing leaf rolling. In the inner canopy, photosynthesis increased while stomatal conductance was unchanged with increasing leaf rolling. In all cases, leaf water potential increased with increasing leaf rolling. These results suggest (in order of likelihood) that either rolling creates an area of closed stomata (stomatal patchiness) proportional to the amount of rolling, or leaf specific hydraulic conductance increases, or stomata are responding to changes in the boundary layer of the leaf.
WINTER SURVIVAL OF LESSER SCAUP IN EAST-CENTRAL FLORIDA

Abstract The North American continental population of lesser scaup (Aythya affinis) has been declining since the mid-1980s. Seasonal survival estimates may provide insights about the ecological basis for this decline, but such data are not available. We estimated post-harvest winter survival of lesser scaup in east-central Florida, USA, where 62% of the Atlantic Flyway population winters. The Kaplan-Meier survival estimate from 11 January to 14 March 2002 was 0.95 ± 0.04 (SE) for females and 0.90 ± 0.09 for males. These estimates were not different (P = 0.64), and pooled survival was 0.93 ± 0.04. Temporary emigration (movement out of and return to the study area) was exhibited by 25% of the birds during survey periods, but absences were short and were believed to have had little effect on precision of survival estimates. Our findings suggested that natural mortality at Merritt Island National Wildlife Refuge (MINWR) and surrounding estuarine areas was relatively low. Our results also indicate that habitat quality in this portion of east-central Florida was sufficient to meet overwintering requirements and likely contributed to the reported survival rates. Estimating survival during other stages of the annual cycle, as well as an overall winter estimate reflecting harvest mortality, is necessary to determine whether low survival rates are responsible for continental population declines.
Genetic mapping and inheritance of Russian wheat aphid resistance gene in accession IG 100695

The Russian wheat aphid (RWA), Diuraphis noxia (Kurdjumov), is an important pest of small-grain cereals, particularly wheat, worldwide. The most efficient strategy against the RWA is to identify sources of resistance and to introduce them into susceptible wheat genotypes. This study was conducted to determine the mode of inheritance of the RWA resistance found in ICARDA accession IG 100695, to identify wheat microsatellite markers closely linked to the gene and to map the chromosomal location of the gene. Simple sequence repeat (SSR) marker scores were identified in a mapping population of 190 F2 individuals and compared, while phenotypic screening for resistance was performed in F2 : 3 families derived from a cross between ‘Basribey’ (susceptible) and IG 100695 (resistant). Phenotypic segregation of leaf chlorosis and rolling displayed the effect of a single dominant gene, temporarily denoted Dn100695, in IG 100695. Dn100695 was mapped on the short arm of chromosome 7D with four linked SSR markers, Xgwm44, Xcfd14, Xcfd46 and Xbarc126. Dn100695 and linked SSR markers may be useful for improving resistance for RWA in wheat breeding.
Enhancing Host Immunity to Avian Influenza Virus using Toll-like Receptor Agonists in Chickens

Toll-like receptors (TLRs) are evolutionarily conserved pattern recognition receptors that mediate host-responses to pathogens. In mammals, TLR ligands promote cellular activation and the production of cytokines. Several TLR ligands have been employed prophylactically for the control of bacterial or viral diseases in the mouse model. However, the TLR-mediated responses in chickens have not been well described. Importantly, the utility of TLR agonists for the control of viral pathogens, such as avian influenza virus (AIV), has not been fully explored in chickens. To this end, the studies described in this thesis characterized the kinetics of in vivo responses in chickens to the TLR4 ligand lipopolysaccharide (LPS) and the TLR21 ligand CpG ODN. It was demonstrated that both of these ligands induced the up-regulation of several immune system genes in the spleen, including those associated with pro-inflammatory and antiviral responses, as well antigen presentation. By harnessing the immunostimulatory properties of TLR ligands, it was also demonstrated that the prophylactic administration of either poly I:C (a TLR3 ligand), LPS or CpG ODN may confer immunity to a low pathogenic avian influenza virus, as determined by a reduction in both oropharyngeal and cloacal virus shedding in infected birds. Furthermore, transcriptional analysis of genes in the spleen and lungs identified interleukin (IL)-8, interferon (IFN)-α and IFN-γ as correlates of immunity. In conclusion, TLR ligands may modulate several aspects of the chicken immune system to induce an anti-viral state, thereby conferring immunity to AIV.
FISHES AS INTEGRATORS OF BENTHIC AND PELAGIC FOOD WEBS IN LAKES

Studies of lake ecosystems generally focus on pelagic food chains and processes. Recently, there has been an emerging recognition of the importance of benthic production and processes to whole-lake ecosystems. To examine the extent to which zoobenthos contribute to higher trophic level production in lakes, we synthesized diet data from 470 fish populations (15 species) and stable isotope data from 90 fish populations (11 species), all of which are common inhabitants of north-temperate lakes. Across all species considered, zoobenthos averaged 50% of total prey consumption. Indirect consumption of zoobenthos (i.e., feeding on zoobenthos-supported fishes) contributed another 15%, for a total of 65% reliance on benthic secondary production. Stable isotopes provided estimates of mean zoobenthivory ranging from 43% to 59%. For most fish species, consumption of zoobenthos was highly variable among populations. The overwhelming concern of ecologists with pelagic food chains and processes contrasts sharply with our finding that benthic secondary production plays a central role in supporting higher trophic level production. This extensive zoobenthivory can subsidize fish populations, leading to apparent competition and otherwise altering trophic dynamics and ecosystem processes in the pelagic zone. We argue for a more integrated view of lake ecosystems that recognizes the duality of benthic and pelagic production pathways. Food web models that explicitly consider energy flow from pelagic and benthic sources will provide a more realistic energy flow template for understanding the regulation of lake ecosystem functioning.