Miracle-dz/UniFAR-FTUs huggingface dataset:
Power-Extraction Circuits for Piezoelectric Energy Harvesters in Miniature and Low-Power Applications

When a piezoelectric energy harvester is connected to a simple load circuit, the damping force which the piezoelectric element is able to generate is often below the optimal value to maximize electrical power generation. Circuits that aim to increase the power output of a piezoelectric energy harvester do so by modifying the voltage onto which the piezoelectric current source drives its charge. This paper presents a systematic analysis and comparison of all the principal types of power extraction circuit that allow this damping force to be increased, under both ideal and realistic constraints. Particular emphasis is placed on low-amplitude operation. A circuit called single-supply prebiasing is shown to harvest more power than previous approaches. Most of the analyzed circuits able to increase the power output do so by synchronously inverting or charging the piezoelectric capacitance through an inductor. For inductor Q factors greater than around only 2, the single-supply prebiasing circuit has the highest power density of the analyzed circuits. The absence of diodes in conduction paths, achievable with a minimum number of synchronous rectifiers, means that the input excitation amplitude is not required to overcome a minimum value before power can be extracted, making it particularly suitable for microscale applications or those with a wide variation in amplitude.
An Analysis of Acceptance Policies For Blockchain Transactions

The standard acceptance policy for a cryptocurrency transaction at most exchanges is to wait until the transaction is placed in the blockchain and followed by a certain number of blocks. However, as noted by Sompolinsky and Zohar [16], the amount of time for blocks to arrive should also be taken into account as it affects the probability of double spending. Specifically, they propose a dynamic policy for transaction acceptance that depends on both the number of confirmations and the amount of time since transaction broadcast. In this work we study the implications of using such a policy compared with the standard option that ignores block timing information. Using an exact expression for the probability of double spend, via numerical results, we analyze time to transaction acceptance (performance) as well as the time and cost to perform a double spend attack (security). We show that while expected time required for transaction acceptance is improved using a dynamic policy, the time and cost to perform a double spend attack for a particular transaction is reduced.
Impact of Threshold Clipping on Bit Error Rate in OFDM-Like Systems

In wireless communications, 3GPP LTE is one of the solutions to meet the greater transmission data rate demand. One issue inherent to this technology is the PAPR (Peak-to-Average Power Ratio) of OFDM (Orthogonal Frequency Division Multiplexing) modulation. This high PAPR affects of the efficiency of power amplifiers. One approach to mitigate this effect is the Crest Factor Reduction (CFR) technique. In this work we simulate the impact of Hard Limited Clipping Crest Factor Reduction technique on BER (Bit Error Rate) in OFDM based Systems. In general, the results showed CFR has more effect on higher digital modulation schemes, as expected. More importantly, we show the worst-case degradation due to CFR on QPSK, 16QAM, and 64QAM signals in a linear system. For example, hard clipping of 9dB results in a 2dB increase in signal to noise energy at a 1% BER for 64-QAM modulation.
Metformin treatment reduces temozolomide resistance of glioblastoma cells

It has been reported that metformin acts synergistically with temozolomide (TMZ) to inhibit proliferation of glioma cells including glioblastoma multiforme (GBM). However, the molecular mechanism underlying how metformin exerts its anti-cancer effects remains elusive. We used a combined experimental and bioinformatics approach to identify genes and complex regulatory/signal transduction networks that are involved in restoring TMZ sensitivity of GBM cells after metformin treatment. First, we established TMZ resistant GBM cell lines and found that the resistant cells regained TMZ sensitivity after metformin treatment. We further identified that metformin down-regulates SOX2 expression in TMZ-resistant glioma cells, reduces neurosphere formation capacity of glioblastoma cells, and inhibits GBM xenograft growth in vivo. Finally, the global gene expression profiling data reveals that multiple pathways are involved in metformin treatment related gene expression changes, including fatty acid metabolism and RNA binding and splicing pathways. Our work provided insight of the mechanisms on potential synergistic effects of TMZ and metformin in the treatment of glioblastoma, which will in turn yield potentially translational value for clinical applications.
BIOINFORMATICS ORIGINAL PAPER

Motivation: It is far from trivial to select the most effective clustering method and its parameterization, for a particular set of gene expression data, because there are a very large number of possibilities. Although many researchers still prefer to use hierarchical clustering in one form or another, this is often sub-optimal. Cluster ensemble research solves this problem by automatically combining multiple data partitions from different clusterings to improve both the robustness and quality of the clustering result. However, many existing ensemble techniques use an association matrix to summarize sample-cluster co-occurrence statistics, and relations within an ensemble are encapsulated only at coarse level, while those existing among clusters are completely neglected. Discovering these missing associations may greatly extend the capability of the ensemble methodology for microarray data clustering. Results: The link-based cluster ensemble (LCE) method, presented here, implements these ideas and demonstrates outstanding performance. Experiment results on real gene expression and synthetic datasets indicate that LCE: (i) usually outperforms the existing cluster ensemble algorithms in individual tests and, overall, is clearly class-leading; (ii) generates excellent, robust performance across different types of data, especially with the presence of noise and imbalanced data clusters; (iii) provides a high-level data matrix that is applicable to many numerical clustering techniques; and (iv) is computationally efﬁcient for large datasets and gene clustering. Availability: Online supplementary and implementation are available at:
Evaluation of Analysis by Cross-Validation. Part I: Using Verification Metrics

We examine how passive and active observations are useful to evaluate an air quality analysis. By leaving out observations from the analysis, we form passive observations, and the observations used in the analysis are called active observations. We evaluated the surface air quality analysis of O3 and PM2.5 against passive and active observations using standard model verification metrics such as bias, fractional bias, fraction of correct within a factor of 2, correlation and variance. The results show that verification of analyses against active observations always give an overestimation of the correlation and an underestimation of the variance. Evaluation against passive or any independent observations display a minimum of variance and maximum of correlation as we vary the observation weight, thus providing a mean to obtain the optimal observation weight. For the time and dates considered, the correlation between (independent) observations and the model is 0.55 for O3 and 0.3 for PM2.5 and for the analysis, with optimal observation weight, increases to 0.74 for O3 and 0.54 for PM2.5. We show that bias can be a misleading measure of evaluation and recommend the use of a fractional bias such as the modified normalized mean bias (MNMB). An evaluation of the model bias and variance as a function of model values also show a clear linear dependence with the model values for both O3 and PM2.5.
Effect of Temperature on Synthesis and Morphology of Nickel Doped Cobalt Ferrite in Presence of Htab by Co-precipitation Route

Nanoparticles of nickel substituted cobalt ferrite have been synthesized in presence hexa-decyl tri- methyl ammonium bromide (HTAB) as a surfactant by co-precipitation route. The particle size was estimated by the full width half maximum (FWHM) of the strongest X-ray diffraction (XRD) peak. The average particle size varied in the range of 22-38 nm; the particle size was controlled via controlling calcination temperature which was in the range of 600 to 900°C. Their morphology structure have been determined by scanning electron microscopy (SEM) and energy dispersive X-ray (EDX) analysis confirms the presence of Co, Ni, Fe and oxygen as well as the desired phases in the prepared nanoparticles. The results of SEM show that the surfactant played an important role in morphology of nanoparticles and a well crystalline single cubic structure of nickel doped CoFe O phase was formed through precipitation precursors at pH value of 11. 24
DNA barcoding species inventory of Microgastrinae wasps (Hymenoptera, Braconidae) from a Mexican tropical dry forest

The cosmopolitan Microgastrinae is probably the most diverse braconid subfamily of parasitoid wasps, yet its species diversity is far from being known. As part of a global initiative for DNA barcoding Microgastrinae species, here we show the results of a study that assessed the species richness of this subfamily in a Mexican tropical dry forest located in the Chamela region, near the Pacific coast of Jalisco. Barcoding sequences of a total of 551 microgastrine specimens were generated, corresponding to 238 haplotypes. Performance of two species delineation approaches, a 2% corrected pairwise distance criterion and the general mixed Yule‐coalescent (GMYC) method, yielded 100 and 112 putative species, respectively, which belong to 13 genera. The species delimited by the above two approaches were mostly congruent with our morphospecies identification. Ten molecular operational taxonomic units (MOTUs) were split into twenty‐two species by the GMYC approach. We found morphological differences between the GMYC species corresponding to three of these MOTUs. Thus, a total of 103 microgastrine species were confirmed for the region of study. Thirty‐three species were only represented by males, and therefore, their generic assignment is only tentatively proposed. Fornicia, Dolichogenoidea, Distatrix, Glyptapanteles and Pholetesor represent new generic records for the Mexican territory. A new record for the country is also provided for the Diolcogaster‐basimacula species group. Based on a comparison of nearly 20 000 barcoding sequences released for Microgastrinae from 75 countries, only five microgastrine species from Chamela were found to occur in other countries, four in Costa Rica and one in Canada and the United States.
Expression of ALS/FTD-linked mutant CCNF in zebrafish leads to increased cell death in the spinal cord and an aberrant motor phenotype

Amyotrophic lateral sclerosis (ALS) is a rapidly progressive, fatal neurodegenerative disease characterised by the death of upper and lower motor neurons. Approximately 10% of cases have a known family history of ALS and disease-linked mutations in multiple genes have been identified. ALS-linked mutations in CCNF were recently reported, however the pathogenic mechanisms associated with these mutations are yet to be established. To investigate possible disease mechanisms, we developed in vitro and in vivo models based on an ALS-linked missense mutation in CCNF. Proteomic analysis of the in vitro models identified the disruption of several cellular pathways in the mutant model, including caspase-3 mediated cell death. Transient overexpression of human CCNF in zebrafish embryos supported this finding, with fish expressing the mutant protein found to have increased levels of cleaved (activated) caspase-3 and increased cell death in the spinal cord. The mutant CCNF fish also developed a motor neuron axonopathy consisting of shortened primary motor axons and increased frequency of aberrant axonal branching. Importantly, we demonstrated a significant correlation between the severity of the CCNF-induced axonopathy and a reduced motor response to a light stimulus (photomotor response). This is the first report of an ALS-linked CCNF mutation in vivo and taken together with the in vitro model identifies the disruption of cell death pathways as a significant consequence of this mutation. Additionally, this study presents a valuable new tool for use in ongoing studies investigating the pathobiology of ALS-linked CCNF mutations.
Post-transcriptional regulation across human tissues

Transcriptional and post-transcriptional regulation shape tissue-type-specific proteomes, but their relative contributions remain contested. Estimates of the factors determining protein levels in human tissues do not distinguish between (i) the factors determining the variability between the abundances of different proteins, i.e., mean-level-variability and, (ii) the factors determining the physiological variability of the same protein across different tissue types, i.e., across-tissues variability. We sought to estimate the contribution of transcript levels to these two orthogonal sources of variability, and found that scaled mRNA levels can account for most of the mean-level-variability but not necessarily for across-tissues variability. The reliable quantification of the latter estimate is limited by substantial measurement noise. However, protein-to-mRNA ratios exhibit substantial across-tissues variability that is functionally concerted and reproducible across different datasets, suggesting extensive post-transcriptional regulation. These results caution against estimating protein fold-changes from mRNA fold-changes between different cell-types, and highlight the contribution of post-transcriptional regulation to shaping tissue-type-specific proteomes.
Wonderland: A Novel Abstraction-Based Out-Of-Core Graph Processing System

Many important graph applications are iterative algorithms that repeatedly process the input graph until convergence. For such algorithms, graph abstraction is an important technique: although much smaller than the original graph, it can bootstrap an initial result that can significantly accelerate the final convergence speed, leading to a better overall performance. However, existing graph abstraction techniques typically assume either fully in-memory or distributed environment, which leads to many obstacles preventing the application to an out-of-core graph processing system. In this paper, we propose Wonderland, a novel out-of-core graph processing system based on abstraction. Wonderland has three unique features: 1) A simple method applicable to out-of-core systems allowing users to extract effective abstractions from the original graph with acceptable cost and a specific memory limit; 2) Abstraction-enabled information propagation, where an abstraction can be used as a bridge over the disjoint on-disk graph partitions; 3) Abstraction guided priority scheduling, where an abstraction can infer the better priority-based order in processing on-disk graph partitions. Wonderland is a significant advance over the state-of-the-art because it not only makes graph abstraction feasible to out-of-core systems, but also broadens the applications of the concept in important ways. Evaluation results of Wonderland reveal that Wonderland achieves a drastic speedup over the other state-of-the-art systems, up to two orders of magnitude for certain cases.
Improved Acquisition of Underdrawings in Oil-Paintings Using IR-Reflectography

Forms of infrared analysis have been widely used to evaluate underdrawings in works of art. Although infrared analysis offers an easy yet nondestructive means of removing the overlying paint from the underdrawings without any damage to the painting, rarely has an underdrawing been fully exposed by any method of infrared analysis. This limited success is due to several factors, including paint layers or other artifacts that absorb and scatter most of the infrared radiation and underdrawings that do not reflect infrared radiation. This thesis proposes additional steps be taken in the infrared reflectography method to help solve one of these problems. It is possible to expose a larger portion of the underdrawing by using approximations of the Kubelka-Munk theory to evaluate the infrared and visible radiation reflected by a painting. Table of
Diagnostic sensitivity and specificity of dark adaptometry for detection of age-related macular degeneration.

PURPOSE Difficulty with night vision is a common complaint of patients with age-related macular degeneration (AMD). Consistent with this complaint, dark adaptation (DA) is substantially impaired in these patients. Because of the severity of the deficit, measurement of DA has been suggested as a means for the diagnosis of AMD. Previous methods for measurement of DA were time intensive (>30 minutes), which made them unsuitable for clinical use. This study evaluated a rapid DA test (≤ 6.5 minutes) for the detection of AMD. METHODS Dark adaptation was measured by using the AdaptDx dark adaptometer in two groups: subjects with normal retinal health and subjects with AMD. Subjects were assigned to their group by clinical examination and grading of fundus photographs. Subjects were classified as having DA consistent with normal retinal health (rod intercept ≤ 6.5 minutes) or having dark adaptation consistent with AMD (rod intercept > 6.5 minutes). RESULTS The eligible sample for analysis included 21 normal adults and 127 AMD patients. The rapid test was found to have a diagnostic sensitivity of 90.6% (P < 0.001) and specificity of 90.5% (P < 0.027). Thus, abnormal DA was detected in 115 of 127 AMD patients, and normal DA was found in 19 of 21 normal adults. CONCLUSIONS The high diagnostic sensitivity and specificity compared favorably to long-duration research methods for the measurement of DA, and slit lamp biomicroscopy performed by a retina specialist. These results suggest that a rapid DA test is useful for the detection of AMD.
Performance of Drought Indices for Ecological, Agricultural, and Hydrological Applications

AbstractIn this study, the authors provide a global assessment of the performance of different drought indices for monitoring drought impacts on several hydrological, agricultural, and ecological response variables. For this purpose, they compare the performance of several drought indices [the standardized precipitation index (SPI); four versions of the Palmer drought severity index (PDSI); and the standardized precipitation evapotranspiration index (SPEI)] to predict changes in streamflow, soil moisture, forest growth, and crop yield. The authors found a superior capability of the SPEI and the SPI drought indices, which are calculated on different time scales than the Palmer indices to capture the drought impacts on the aforementioned hydrological, agricultural, and ecological variables. They detected small differences in the comparative performance of the SPI and the SPEI indices, but the SPEI was the drought index that best captured the responses of the assessed variables to drought in summer, the seas.
Improving the Performance of an Auditory Brain-Computer Interface Using Virtual Sound Sources by Shortening Stimulus Onset Asynchrony

Recently, a brain-computer interface (BCI) using virtual sound sources has been proposed for estimating user intention via electroencephalogram (EEG) in an oddball task. However, its performance is still insufficient for practical use. In this study, we examine the impact that shortening the stimulus onset asynchrony (SOA) has on this auditory BCI. While very short SOA might improve its performance, sound perception and task performance become difficult, and event-related potentials (ERPs) may not be induced if the SOA is too short. Therefore, we carried out behavioral and EEG experiments to determine the optimal SOA. In the experiments, participants were instructed to direct attention to one of six virtual sounds (target direction). We used eight different SOA conditions: 200, 300, 400, 500, 600, 700, 800, and 1,100 ms. In the behavioral experiment, we recorded participant behavioral responses to target direction and evaluated recognition performance of the stimuli. In all SOA conditions, recognition accuracy was over 85%, indicating that participants could recognize the target stimuli correctly. Next, using a silent counting task in the EEG experiment, we found significant differences between target and non-target sound directions in all but the 200-ms SOA condition. When we calculated an identification accuracy using Fisher discriminant analysis (FDA), the SOA could be shortened by 400 ms without decreasing the identification accuracies. Thus, improvements in performance (evaluated by BCI utility) could be achieved. On average, higher BCI utilities were obtained in the 400 and 500-ms SOA conditions. Thus, auditory BCI performance can be optimized for both behavioral and neurophysiological responses by shortening the SOA.
Edge-Based Recognition of Novel Objects for Robotic Grasping

In this paper, we investigate the problem of grasping previously unseen objects in unstructured environments which are cluttered with multiple objects. Object geometry, reachability, and force-closure analysis are considered to address this problem. A framework is proposed for grasping unknown objects by localizing contact regions on the contours formed by a set of depth edges generated from a single-view 2D depth image. Specifically, contact regions are determined based on edge geometric features derived from analysis of the depth map data. Finally, the performance of the approach is successfully validated by applying it to scenes with both single and multiple objects, in both simulation and experiments. Using sequential processing in MATLAB running on a 4th-generation Intel Core Desktop, simulation results with the benchmark Object Segmentation Database show that the algorithm takes 281 ms on average to generate the 6D robot pose needed to attach with a pair of viable grasping edges that satisfy reachability and force-closure conditions. Experimental results in the Assistive Robotics Laboratory at UCF using a Kinect One sensor and a Baxter manipulator outfitted with a standard parallel gripper showcase the feasibility of the approach in grasping previously unseen objects from uncontrived multi-object settings.
IoT for ITS: A Dynamic Traffic Lights Control based on the Kerner Three Phase Traffic Theory

light is a device for the regulation of traffic between road users, vehicles and pedestrians. The setting and synchronization of traffic lights of an axis or an area are very complex, and sometimes unsatisfactory for all or part of them. In addition, according to several studies, traffic lights would be responsible for half of the traffic jams and thus half the pollution, and poorly regulated lights can cause the tripling of the fuel consumption, therefore CO2 emissions and other pollutants emissions, when traffic is cluttered or too sparse. In our solution, we will try to apply the Kerner three-phase traffic theory to realize a synchronized system by establishing an Intelligent Transport System that will provide automatic management of traffic lights, while establishing a communication mode based on the concept of the Internet of Things for various traffic lights controllers to enable them to collaborate. In order to resolve the traffic jam issues, so the reduce of CO2 emissions and also the mobility metrics like the travel time. This paper is part of a project entitled 'V2IoT' which aims to use the techniques and concepts of the Internet of Things to improve ITS and the vehicular communications, namely, the V2V and the V2I systems within smart cities.