Miracle-dz/UniFAR-FTUs huggingface dataset:
Hyperspectral imaging utilizing LCTF and DLP technology for surgical and clinical applications

Two different, already characterized, hyperspectral imaging systems created for visualizing the spatial distribution of tissue oxygenation non-invasively for in vivo clinical use are described. Individual components of both liquid crystal tunable filter (LCTF) and digital light processing (DLP) systems were characterized, calibrated, and found to be well within manufacturer specifications. Coupling LCTF with charge coupled device (CCD) technology and acquiring images at multiple, contiguous wavelengths and at narrow bandwidths are formatted into a hyperspectral data cube consisting of one spectral and two spatial dimensions. DLP® technology has the novel ability to conform light to any desired spectral illumination scheme. Subsequently the collected multispectral data are processed into chemically relevant images that are color encoded at each pixel detector for the relative percentage of oxyhemoglobin. Using spectral illumination methods unique to the DLP hyperspectral imager results in producing chemically relevant images at near video rate; 4 frames per second. As an example, both systems are used to collect spectral data from a 27.22 kg porcine kidney whose renal artery has been occluded for 60 minutes. Both systems return nearly identical spectra collected from the surface of the kidney, with a root mean square deviation between the two spectra of 0.02.
Study of Thermal Stability of Ultra Fine-Grained Commercially Pure Titanium Wire Prepared in Conform Equipment

Titanium and its alloys are frequently used in many sectors, including the health care sector, where they outperform other materials. From the biocompatibility viewpoint, the preferred condition of these materials is ultrafine or nanostructured state. Processes based on severe plastic deformation (SPD) that are capable of producing microstructures with sizes of the order of nanometers are gaining importance these days. Their typical limitation is the small volume of material processed. One of available ways to enhancing the productivity is to combine the CONFORM continuous extrusion process with the ECAP method. This paper describes initial experience with this combined process in the CONFORM 315i machine, which is equipped with a specially-designed forming die chamber. Influence the number of passes through CONFORM machine on thermal stability was study by horizontal dilatometer and heat-flux calorimeter. The impact of deformation on the shift in recrystallization temperature of pure titanium was confirmed. The microstructure evolution and the grain growth behavior were investigated by electron back scattered diffraction (EBSD) technique. The deformed UFG titanium was annealed at a range of temperature (400-600 °C) for up to 6 h. The grain growth kinetics was characterized by calculating the grain growth activation energy Q and the time exponent n based on the experimental results for deformed material. Data for annealing temperatures of 550 and 600 °C allowed the values of the time exponent n = 0.19 and activation energy Q = 248 kJ/mol were calculated.
Long‐term ultra‐low‐dose acyclovir against varicella‐zoster virus reactivation after allogeneic hematopoietic stem cell transplantation

To evaluate the efficacy of long‐term prophylaxis with ultra‐low‐dose acyclovir against varicella‐zoster virus (VZV) reactivation, we analyzed the records of 242 Japanese adult patients who underwent allogeneic hematopoietic stem cell transplantation for the first time from 1995 to 2006 at our hospital. We started long‐term oral acyclovir at 200 mg/day in July 2001. Acyclovir was continued until the end of immunosuppressive therapy and at least 1 year after transplantation. Sixty‐six patients developed VZV reactivation at a median of 248 days after HSCT, with a cumulative incidence of 34.7%. Only one breakthrough reactivation occurred during long‐term acyclovir, which responded well to therapeutic dose of valacyclovir. The use of long‐term acyclovir was the only independent determinant that significantly decreased the overall incidence of VZV reactivation (20% vs. 50%, P < 0.0001). With this prophylaxis, visceral dissemination and serious complications other than post‐herpetic neuralgia was completely eliminated, and thereby need for hospitalization was significantly reduced (21% vs. 71%, P = 0.0034). Fifteen of the 57 patients who discontinued acyclovir developed VZV reactivation, with a cumulative incidence of 32.1%. VZV reactivation following discontinuation tended to occur in patients who were receiving immunosuppressive therapy at the cessation of acyclovir. These findings suggested that long‐term prophylaxis of ultra‐low‐dose acyclovir resulted in a successful prevention of severe VZV‐related symptoms and death, with a significantly decreased overall incidence of VZV reactivation. Prolongation of prophylactic acyclovir on profound immunosuppression might be important for thorough suppression of VZV reactivation. Am. J. Hematol. , 2008. © 2008 Wiley‐Liss, Inc.
Interrupted aging and secondary precipitation in aluminium alloys

Abstract It is shown that, if elevated temperature aging of aluminium alloys is interrupted with a dwell period at a low temperature (65 °C), age hardening continues due to so called secondary precipitation. If elevated temperature aging is then resumed, significant improvements can be obtained in mechanical properties compared with those available using a conventional T6 temper. Average increases in 0.2% proof stress and tensile strength of 10%, combined with improved fracture toughness, have been achieved in a wide range of alloys. These effects arise primarily because interrupted aging promotes the formation of more finely dispersed precipitates in the final microstructures. The concept of interrupted aging is described in some detail with respect to the model system, Al-4Cu, and examples are then given of the effects of the treatment on the microstructures and mechanical properties of several wrought and cast aluminium alloys.
Neuro Fuzzy Modeling of Control Systems

The analysis of the models is carried out starting from experimental data of a multivariable system MISO (Many Input Single Output). The models’ implementation was made using fuzzy logic. In fuzzy logic, the cluster technique was used to decrease the number of rules to use in the identification. This technique is opposed to the conventional method which requires a considerable number of fuzzy inference rules to approach the model. In the consequence of fuzzy model, different techniques are used to implement Takagi-Sugeno type rules. By other hand, we implemented the Neuro-fuzzy modeling methods, which let represent the non-linear system and at the same time a system with some learning degree using different topologies. By comparison the goodness of each method is obtained.
Developing methods for assessing abundance and distribution of European oysters (Ostrea edulis) using towed video

Due to large-scale habitat losses and increasing pressures, benthic habitats in general, and perhaps oyster beds in particular, are commonly in decline and severely threatened on regional and global scales. Appropriate and cost-efficient methods for mapping and monitoring of the distribution, abundance and quality of remaining oyster populations are fundamental for sustainable management and conservation of these habitats and their associated values. Towed video has emerged as a promising method for surveying benthic communities in a both non-destructive and cost-efficient way. Here we examine its use as a tool for quantification and monitoring of oyster populations by (i) analysing how well abundances can be estimated and how living Ostrea edulis individuals can be distinguished from dead ones, (ii) estimating the variability within and among observers as well as the spatial variability at a number of scales, and finally (iii) evaluating the precision of estimated abundances under different scenarios for monitoring. Overall, the results show that the can be used to quantify abundance and occurrence of Ostrea edulis in heterogeneous environments. There was a strong correlation between abundances determined in the field and abundances estimated by video-analyses (r2 = 0.93), even though video analyses underestimated the total abundance of living oysters by 20%. Additionally, the method was largely repeatable within and among observers and revealed no evident bias in identification of living and dead oysters. We also concluded that the spatial variability was an order of magnitude larger than that due to observer errors. Subsequent modelling of precision showed that the total area sampled was the main determinant of precision and provided general method for determining precision. This study provides a thorough validation of the application of towed video on quantitative estimations of live oysters. The results suggest that the method can indeed be very useful for this purpose and we therefor recommend it for future monitoring of oysters and other threatened habitats and species.
Variable Discretisation for Anomaly Detection using Bayesian Networks

Abstract : Anomaly detection is the process by which low probability events are automatically found against a background of normal activity.By definition there must be many more normal events than anomalous ones. This rare nature of anomalies causes numerical problemsfor probabilistic methods designed to automatically detect them. This report describes an algorithm that introduces new discretisationlevels to support the representation of low probability values in the context of Bayesian network anomaly detection. It is an engineeringsolution to a problem with an extant discretisation tool that represents a data sets fine structure but fails to capture extreme values ornulls between modes in its probability density. It is demonstrated that the limitations of the extant tool can be overcome using examplesof integer and continuous data.
The Axl-Regulating Tumor Suppressor miR-34a Is Increased in ccRCC but Does Not Correlate with Axl mRNA or Axl Protein Levels

Background High expression of the receptor tyrosine kinase Axl is associated with poor prognosis in patients with Renal Cell Carcinoma (RCC), the most common malignancy of the kidney. The miR-34a has been shown to directly regulate Axl in cancer cells. The miR-34a is a mediator of p53-dependent tumor suppression, and low expression of miR-34a has been associated with worse prognosis in several cancers. Our aim was to elucidate whether miR-34a or the other members of the miR-34 family (miR-34b/c) regulate Axl in RCC. Methodology and Results Using western blot, flow cytometry, and RT-qPCR, we showed that Axl mRNA and protein are downregulated in 786-O cells by miR-34a and miR-34c but not by miR-34b. A luciferase reporter assay demonstrated direct interaction between the Axl 3’ UTR and miR-34a and miR-34c. The levels of miR-34a/b/c were measured in tumor tissue in a cohort of 198 RCC patients, and the levels of miR-34a were found to be upregulated in clear cell RCC (ccRCC) tumors, but not associated with patient outcome. Neither of the miR-34 family members correlated with Axl mRNA, soluble Axl protein in serum, nor with immunohistochemistry of Axl in tumor tissue. In addition, we measured mRNA levels of a known miR-34a target, HNF4A, and found the HNF4A levels to be decreased in ccRCC tumors, but unexpectedly correlated positively rather than negatively with miR-34a. Conclusions Although miR-34a and miR-34c can regulate Axl expression in vitro, our data indicates that the miR-34 family members are not the primary regulators of Axl expression in RCC.
Data Clustering and Fuzzy Neural Network for Sales Forecasting in Printed Circuit Board Industry

Reliable prediction of sales can improve the quality of business strategy. This research develops a hybrid model by integrating K-mean cluster and fuzzy back propagation network (KFBPN) to forecast the future sales of a printed circuit board factory. Based on the K-mean clustering technique, the historic data can be classified into different clusters, thus the noise of the original data can be reduced and a more homogeneous region can be established for a more accurate prediction. Numerical data of various affecting factors and actual demand of the past 5 years of the printed circuit board (PCB) factory are collected and input into the hybrid model for future monthly sales forecasting. Experimental results show the effectiveness of the hybrid model when compared with other approaches
A genetic Algorithm-Based feature selection

This article details the exploration and application of Genetic Algorithm (GA) for feature selection. Particularly a binary GA was used for dimensionality reduction to enhance the performance of the concerned classifiers. In this work, hundred (100) features were extracted from set of images found in the Flavia dataset (a publicly available dataset). The extracted features are Zernike Moments (ZM), Fourier Descriptors (FD), Lengendre Moments (LM), Hu 7 Moments (Hu7M), Texture Properties (TP) and Geometrical Properties (GP). The main contributions of this article are (1) detailed documentation of the GA Toolbox in MATLAB and (2) the development of a GA-based feature selector using a novel fitness function (kNN-based classification error) which enabled the GA to obtain a combinatorial set of feature giving rise to optimal accuracy. The results obtained were compared with various feature selectors from WEKA software and obtained better results in many ways than WEKA feature selectors in terms of classification accuracy.
Explaining the dependences of electron and hole mobilities in Si MOSFET's inversion layer

In this paper we report a detailed study of the surface roughness (SR) limited mobility and, for the first time, a physical explanation of the different dependences of the electron and hole mobilities. Based on full-band Monte Carlo simulations, we show that the differences between hole and electron experimental mobilities can be accounted for by using a steeper tail of the SR power spectrum at high wave-vector values. The new power spectral density allows to fit all the experimental data at room and cryogenic temperature, with the same parameter values for both electrons and holes. The spatial dependence of the new SR spectrum is discussed and compared to the features of the SR described by exponential and Gaussian auto-covariance functions.
Super Bowl Ads

We explore the effects of television advertising in the setting of the NFL's Super Bowl telecast. The Super Bowl is the largest advertising event of the year and is well suited for measurement. The event has the potential to create significant increases in "brand capital" because ratings average over 40 percent of households and ads are a focal point of the broadcast. Furthermore, variation in exposures is exogenous because a brand cannot choose how many impressions it receives in each market. Viewership is determined based on local preferences for watching the two competing teams. With this significant and exogenous variation in Super Bowl advertising exposures we test whether advertisers' sales are affected accordingly. We run our analysis using Nielsen ratings and store level sales data in the beer and soda categories. We find that Super Bowl ads can generate significant increases in revenue per household. However, when two major soda brands both advertise, much of this gain is lost. Exploring the mechanism behind the ad effects, we find that Super Bowl ads build an association between the brand and viewership of sports more broadly.
Towards Dynamic Walking in Musculoskeletal Simulation

An important next step is to increase the capabilities of musculo-skeletal simulations to accurately predict robust stability of whole-body dynamic locomotion. Accomplishing this step is expected to positively enable biomechanical simulation, prosthetics, rehabilitation, and robotics research. Greater stability and robustness of musculo-skeletal simulations enables vertical advancement of the field. The longterm outcomes of this work will enable improved understanding and rehabilitation of gait dysfunction and enhanced tools to analyze stability and robustness of gait.
Channel and Receiver State Feedback for Frequency-Selective Block Fading Channels

Given a frequency-selective fading channel, feedback can be used to conveyChannel State Information (CSI)and Receiver State Information (RSI) to the transmitter. RSI refers to information about the receiver’s estimate of the message, which can be used to improve reliability, e.g., through retransmissions. This paper studies the trade-off between these two types of feedback under total feedback constraint, assuming multi-carrier transmission through a doubly-selective Rayleigh fading channel. The objective is to maximize the error probability exponent. The CSI feedback scheme specifies particular groups of sub-channels over which uniform power is allocated, and the RSI feedback determines retransmissions of the codeword. The optimal tradeoff exhibits phase transitions which depends critically on the coherence time and the total amount of feedback. Specifically, the first few feedback bits should be allocated to CSI up to a critical amount. Additional feedback bits, if available, should first be allocated to RSI only, and then allocated to both CSI and RSI. This is because as the feedback rate increases beyond a certain amount, additional RSI feedback is not beneficial unless CSI feedback increases simultaneously.
Lyapunov-function based control approach with cascaded PR controllers for single-phase grid-tied LCL-filtered quasi-Z-source inverters

This paper presents a Lyapunov-function based control approach with cascaded proportional-resonant (PR) controller for single-phase grid-tied LCL-filtered quasi-Z-source inverter (qZSI). The proposed control approach guarantees the global stability of the closed-loop system and zero steady-state error in the grid current. The reference values for the inverter current and capacitor voltage are generated by using cascaded connected PR controllers. As a consequence of using PR controllers, the need for performing derivative operations and estimating grid side inductance and capacitance is eliminated which, in turn, achieves zero steady-state error in the grid current. The shoot through control of the qZSI is achieved by the simple boost control technique. Computer simulations are conducted to show that the proposed control approach provides a good steady-state and dynamic performance in achieving the required control objectives.
Territorial Defense, Territory Size, and Population Regulation

The carrying capacity of an environment is determined partly by how individuals compete over the available resources. To territorial animals, space is an important resource, leading to conflict over its use. We build a model where the carrying capacity for an organism in a given environment results from the evolution of territorial defense effort and the consequent space use. The same evolutionary process can yield two completely different modes of population regulation. Density dependence arises through expanding and shrinking territories if fecundity is low, breeding success increases gradually with territory size, and/or defense is cheap. By contrast, when fecundity is high, breeding success sharply saturates with territory size, and/or defense is costly, we predict fixed territory sizes and regulation by floaters. These “surplus” individuals form a buffer against population fluctuations. Yet floaters can also harm breeder performance, and by comparing population growth of a territorial population to a nonterritorial (and individually suboptimal) alternative, we can quantify the harmful effect of evolutionary conflict on population performance. Territoriality has often been found to increase population stability, but this may come at a cost of reduced equilibrium densities.
Probabilistic Soft Logic for Trust Analysis in Social Networks

Trust plays a key role in social interactions. Explicitly modeling trust is therefore an important aspect of social network analysis in settings such as reputation management systems, recommendation systems, and viral marketing. Within the social sciences, trust is known to depend on network structure, context, individual actors’ attributes, and group memberships and affiliations. Furthermore, trust is often measured quantitatively, according to degrees of trust, rather than as a binary indicator. In this paper, we propose trust modeling as a rich challenge for statistical relational learning (SRL). Additionally, we show that probabilistic soft logic (PSL) is particularly well-suited for this problem. PSL, like many SRL languages, provides an intuitive framework for capturing the relational aspects of trust modeling, while its soft truth values easily accommodate varying strengths of trust. We model various sociological theories of trust in PSL and experimentally compare the resulting PSL programs to existing trust prediction methods, demonstrating the ease of model development and showing that these interpretable first-order logic models produce results of competitive quality.
Rate analysis of distributed multiuser MIMO protocols for the 802.11ac

The 802.11ac is a significant landmark in wireless communications, as it pushes towards new rate limits of more than one Gigabit per second, utilizing downlink multiuser multiple-input multiple-output (MIMO) beamforming to transmit data to various locations simultaneously. However, successful beamforming relies on intelligent user selection which requires extensive overhead of channel calibration between the AP and each of the candidate users, which hinders the utilization of multiuser MIMO. Thus, the subtle relation between the achievable rate of a scheduling algorithm and the overhead it requires is significant for the 802.11ac performance analysis. The significance of this paper is in the rigorous analysis of distributed scheduling algorithms for the 802.11ac, which holds even when the number of users is moderate. In particular, we accommodate common scheduling methods for the 802.11ac protocol and analyze their expected achievable rate. In terms of throughput, both analysis and extensive simulations depict the superiority of simple threshold-based methods.
Precise-Spike-Driven Synaptic Plasticity: Learning Hetero-Association of Spatiotemporal Spike Patterns

A new learning rule (Precise-Spike-Driven (PSD) Synaptic Plasticity) is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe.