Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical fiber sensors and their application in monitoring stress build-up in dental resin cements

The field of optical fiber sensing is highly diverse and this diversity is perceived as a great advantage over more conventional sensors in that an optical sensor can be tailored to measure any of a myriad of physical parameters. In this paper we present a niche application for optical fiber sensors in the domain of biophotonics, namely the monitoring of stress build-up during the curing process of dental resin cements. We discuss the origin of this stress build-up and the problems it can cause when treating patients. Optical fiber sensors aim at excelling in two kind of applications: firstly to perform quality control on batch produced dental cements and measure their total material shrinkage, secondly to monitor the hardening of the cement during in-vivo measurements resulting in the dynamic measurement of the shrinkage and to control the stress in a facing based restoration. We therefore investigated two types of optical fiber sensors as alternatives to conventional measurement techniques; namely polarimetric optical fiber sensors and fiber Bragg gratings written in polarization maintaining fibers. After discussing the results obtained with both optical fiber sensors, we will conclude with a critical assessment of the suitability of the two proposed sensing configurations for multi-parameter stress monitoring.
The effect of the mindfulness-based transition to motherhood program in pregnant women with preterm premature rupture of membranes

ABSTRACT The purpose of the researchers is to determine the effect of a mindfulness program on readiness for motherhood, the level of maternal attachment, and on postpartum self-evaluation. We used a quasiexperimental design. Researchers applied the mindfulness-based Transition to Motherhood program to the treatment group for 7 days. Data were collected between December 2012 and June 2014 in Turkey. At the end of the study, the treatment group showed improvement in measures of acceptance of pregnancy, level of readiness to give birth, level of maternal attachment, and level of competence in the role of motherhood.
Invitation to a dialogue between researchers and clinicians about evidence-based behavioral medicine

Background: Evidence-based behavioral medicine (EBBM) aims to improve the process through which best scientific research evidence can be obtained and translated into best clinical decisions regarding behavioral treatments to improve health.Purpose: The objective was to examine some legitimate concerns raised by both clinicians and researchers about the evidence-based movement.Methods: This article begins with a discussion of clinicians’ fears that EBBM devalues clinical judgment and the therapist-patient relationship, will be used to restrict practice, is unnecessary, and is based on research that is irrelevant to clinical decision making. Next we consider researchers’ worries that EBBM neglects evidence not based on randomized controlled trials and ignores causal mechanisms.Results: We find that these fears, although understandable, largely reflect misinterpretations of the evidence-based movement. Further, it is suggested that behavioral medicine is in a unique position to enhance the evidence-based movement by encouraging increased attention to treatment mechanisms and to knowledge translation.Conclusions: Clinicians, researchers, and, importantly, the public will benefit from the evidence-based movement by having a health care system that is built on solid grounds of evidence in determining which treatments should constitute the standard of care. A full partnership between clinicians and researchers is called for to generate the practical, rigorous evidence base needed to take behavioral health treatments to the next level of scientific support and implementation.
Investment Reluctance in Supply Chains: An Agent-Based Real Options Approach

This paper shows how agent-based stochastic approaches can provide a complementary and more flexible approach to study investment incentives and price dynamics in a real options framework. We particularly study the case of two-stage production chains in which one sector produces an intermediate product and the other the final product, and the intermediate product is traded on the spot market. An agent-based competitive model using a genetic algorithm allows us to explicitly model the behaviors and interactions of the firms competing in each subsector and trading the intermediate product with each other on a spot market, and optimal investment strategies can be identified.
Practical behavioral trials to advance evidence-based behavioral medicine

There is a well-documented gap between research and practice in many areas of behavioral medicine. This gap is due in part to limitations in the capacity of the research database to address questions that are of central concern to clinicians, administrative decision makers, and policymakers. Thus, there has been a call for “practical clinical trials” that compare clinically viable alternative interventions and assess multiple outcomes important for clinical and policy decisions in diverse patient populations and settings. Such trials offer great potential, and they raise interesting challenges regarding optimal research design and source of funding. We discuss issues related to practical clinical trials in behavioral medicine, propose a need for practical behavioral trials (PBTs), and describe design features that will facilitate clinical and policy decision making. This type of PBT can help to close the gap between research and practice and advance the field of evidence-based behavioral medicine. We discuss potential challenges and objections to PBTs and conclude by providing recommendations for the design, conduct, reporting, and review of practical trials.
The role of the cooccurrence matrix based on complex extended microstructures in discovering the cirrhosis severity grades within US images

Cirrhosis is an important disease, as it can precede liver cancer, and also can lead to death by itself. Detecting the severity grades of cirrhosis is a major issue in this context. The best nowadays standard for this purpose is the biopsy, however this procedure is invasive, dangerous for the patient. Also, there is no objective study in order to establish which the cirrhosis grades are. Our research purpose is to discover the cirrhosis grades using computerized methods and to perform non-invasive, computer assisted and automatic diagnosis of the disease evolution phases. Concerning the employed features, we adopted the texture-based methods, able to emphasize those characteristics of the tissue that cannot be detected by the eye of the medical expert. In this paper, we emphasized the role of the CETMCM Matrix concerning the detection of the cirrhosis severity grades. The method was validated by supervised classification, providing a recognition rate above 95%.
Impact of a Pediatric Residential Burn Camp Experience on Burn Survivors’ Perceptions of Self and Attitudes Regarding the Camp Community

Summer camp is reported to be a positive and valuable experience for burn and nonburned children. Objective studies comparing the effectiveness, similarities, and differences of rehabilitative vs recreational camps are limited. The aim of this study, year 1, was to assess the effect of burn camp on self-esteem and integration as reported by burn children via the Rosenberg Self-Esteem Scale and a community integration survey. During year 2, burn campers completed these measures again and their self-esteem and integration scores were compared with a group of nonburn campers. The first year results showed significant improvement in burn camper’s self-esteem from the beginning to end of camp and successful integration into the camp community. During year 2, burn surviving children reported significantly lower self-esteem before camp than the comparison group, but after camp, burn children’s self-esteem was comparable with that of nonburn campers. White non-Hispanic campers reported more sustained improvement in self-esteem than other ethnic groups. Burn campers reported a high level of integration into the camp across sex, years since burn, years at camp, or ethnicity, scoring slightly higher than the comparison group. Age was positively correlated with integration among the burn, but not the nonburn campers. Helping children deal with their burn injuries through a rehabilitative program such as burn camp appears to provide benefit for child burn survivors.
LLCNN: A convolutional neural network for low-light image enhancement

In this paper, we propose a CNN based method to perform low-light image enhancement. We design a special module to utilize multiscale feature maps, which can avoid gradient vanishing problem as well. In order to preserve image textures as much as possible, we use SSIM loss to train our model. The contrast of low-light images can be adaptively enhanced using our method. Results demonstrate that our CNN based method outperforms other contrast enhancement methods.
Enriching iTunes App Store Categories via Topic Modeling

Mobile application development is an emerging lucrative and fast growing market. With the steady growth of the number of apps in the repositories the providers will inevitably face the need to fine-grain the existing hierarchy of categories used to organize the apps. In this paper we present a method to bootstrap the categorization process via topic modeling. We apply Latent Dirichlet Allocation (LDA) to the textual descriptions of iTunes apps in order to identify recurrent topics in the collection. We evaluate and discuss the results obtained from training the model on a set of almost 600,000 English-language app descriptions. Our results demonstrate that automated categorization via LDA-based topic modeling is a promising approach, that can help to structure, analyze and manage the content of app repositories. The topics produced complement the original iTunes categories, concretize and extend them by providing insights into the underlying category content.
A Compact, Low-Profile Wire Antenna Applied to Wide-Angle Scanning Phased Array

A linearly polarized wire antenna has been proposed. This antenna has wide-beam radiation patterns. The half-power beamwidth (HPBW) is 150° in the E-plane and 130° in the H-plane. The proposed antenna has a simple structure with compact size of 0.5 λ<sub>g</sub> × 0.1 λ<sub>g</sub> and low profile of 0.05 λ<sub>g </sub> (λ<sub>g</sub> is the waveguide wavelength at the operating frequency). The compact structure makes it easy to be integrated with RF circuits. A nine-element E-plane linear-phased array is constructed to demonstrate its potential application in wide-angle scanning phased array. The array can scan its main beam from –75° to 75° with a low gain fluctuation and has been experimentally verified. Measured results show a good agreement with the simulation.
INBAR: A new interest-based routing framework in vehicular social networks

In recent years, socially aware networking (SAN) has given rise to vehicular social networks (VSNs). By exploiting social properties, SAN provides a promising paradigm for data routing and forwarding. However, routing in such VSNs is challenging since it needs to handle a series of problems such as the dynamic topology, network partitioning and long delays. Recently, interest-based approaches, which try to exploit the interest information of VSN nodes to make better forwarding decisions, have attract tremendous attention in VSN routing design. In this paper, we propose a new INterest-BAsed Routing (INBAR) framework for VSNs. Inspired by general laws in molecular chemistry, we develop a new metric community energy to indicate nodes' social proximity to make forwarding decisions. Community energy is generated when two nodes encounter and it is shared by encountering nodes. The proposed INBAR framework considers community energy of encountering nodes and selects forwarders with higher community energy to the destination node or the destination community. Extensive simulations have been conducted to demonstrate that INBAR outperforms the existing algorithms including the BEEINFO, Epidemic and PRoPHET.
Epistatic Interactions are Critical to Gene-Association Studies: PAI-1 and Risk for Mortality After Burn Injury

Replication of statistically significant associations between single nucleotide polymorphisms (SNPs) and disease phenotypes has been problematic. One reason for conflicting observations may be failure to consider confounding factors, including gene–gene (epistatic) interactions. Our experience with the insertion/deletion polymorphism at -688 in the promoter region of plasminogen activator inhibitor (PAI-1) seems to support this contention and may foreshadow problems for genome-wide association scans, which tend to use unadjusted analytical methodologies. One hundred forty-nine patients with ≥15% total body surface area (TBSA) burns, without significant nonburn-related trauma (injury severity score ≤16), traumatic or anoxic brain injury or spinal cord injury who survived >48 hours postadmission were enrolled under a protocol approved by the UT Southwestern and Parkland Hospital IRBs. Clinical data were collected prospectively and candidate polymorphisms in PAI-1 (-688), toll-like receptor 4 (+896), CD14 (-159), tumor necrosis factor-&agr; (-308), and interleukin-6 (-174) were genotyped. The PAI-1 SNP was significantly associated (P-value for trend = 0.036) with risk for death when evaluated in isolation by unadjusted analysis. However, after adjustment for potential confounders using multiple logistic regression, only age, full-thickness burn size, and CD14 genotype (as previously reported) were associated with increased mortality. Genetic association analyses should be adjusted for interactions between multiple SNPs, injury or disease characteristics, and demographic variables. Increasingly sophisticated analytical methods will be required as gene-mapping studies transition from a candidate-gene based approach to genome-wide association scans.
Noise estimation for multiuser multi-carrier PUCCH in LTE-A

In Long Term Evolution (LTE) system, PUCCH — Physical Uplink Control Channel-is responsible for transmission of the control information from the user equipment (UE) to the base station (eNodeB). PUCCH introduced a new format — format 3 — to accommodate the increase of Hybrid Automatic Repeat request (HARQ) bits resulting from Multi-carrier transmission. This format suffers from poor noise estimation accuracy problem that affects the detection probability and degrades the whole system throughput. Two independent algorithms for noise estimation are introduced in this paper for CDMA multiuser multicarrier PUCCH format 3. The first algorithm is based on noise detection from data symbols, while the second algorithm is based on noise estimation from pilot symbols. Simulation results showed the accuracy of noise estimation in both algorithms in the presence of multipath fading channels.
Blocked Elements in 1-D and 2-D Arrays—Part I: Detection and Basic Compensation on Simulated and In Vivo Targets

During a transcostal ultrasound scan, ribs and other highly attenuating and/or reflective tissue structures can block parts of the array. Blocked elements tend to limit the acoustic window and impede visualization of structures of interest. Here, we demonstrate a method to detect blocked elements and we measure the loss of image quality they introduce in simulation and in vivo. We utilize a fullwave simulation tool and a clinical ultrasound scanner to obtain element signals from fully sampled matrix arrays during simulated and in vivo transcostal liver scans, respectively. The elements that were blocked by a rib showed lower average signal amplitude and lower average nearest-neighbor cross correlation than the elements in the remainder of the 2-D aperture. The growing receive-aperture B-mode images created from the element data indicate that the signals on blocked elements are dominated by noise and that turning them OFF has a potential to improve visibility of liver vasculature. Adding blocked elements to the growing receive apertures for five in vivo transcostal acquisitions resulted in average decrease in vessel contrast and contrast to noise ratio of 19% and 10%, respectively.
Effective Glottal Instant Detection and Electroglottographic Parameter Extraction for Automated Voice Pathology Assessment

Accurate determination of glottal instants and electroglottographic (EGG) parameters is most important in voice pathology analysis including multiple voice disorders: neurological, functional, and laryngeal diseases. In this paper, we present a new effective method for reliable detection of glottal instants and EGG parameters from an EGG signal composed of voiced and nonvoice segments. In the first stage, we present an adaptive variational mode decomposition based algorithm for suppressing low-frequency artifacts and additive high-frequency noises. Based upon mode center frequency criterion, the proposed method first constructs a candidate EGG feature signal for determination of glottal closure and opening instants. In the second stage, the candidate glottal instants are determined by detecting the positive and negative zerocrossings in normalized candidate EGG feature signal, respectively. Finally, an autocorrelation features based postprocessing algorithm is presented to reject nonglottal instants from the nonspeech production segments. The accuracy and robustness of the method is tested using noise-free and noisy EGG signals. Evaluation results show that the proposed method achieves an average overall accuracy of 95.06%, identification rate of 95.34%, missed rate of 3.60%, and false alarm rate of 0.06% with average absolute identification error of 0.71 ± 0.66 ms for an SNR of 15 dB. Results demonstrate that the proposed method significantly outperforms the other existing methods under both noise-free and noisy EGG signals.
Analyzing Tongue Images Using a Conceptual Alignment Deep Autoencoder

Artificial intelligence can learn some concepts by analyzing sensory data similarly to humans. This paper explores how artificial neural networks (ANNs) can learn abstract concepts by analyzing tongue images based on concepts from traditional Chinese medicine (TCM), which is a discipline that relies heavily on practitioner experience. A computer-aided method will be investigated that analyzes sensory data for TCM practitioners. This paper proposes capitalizing on deep learning techniques. A method called the conceptual alignment deep autoencoder (CADAE) is proposed to analyze tongue images that represent different body constitution (BC) types, which are the underlying concepts in TCM. In the first step, CADAE encodes the images to a representation space; in the second step, it decodes the patterns. The experiments demonstrate that CADAE can learn effective representations of abstract concepts aligned with BC types by encoding the tongue images. Furthermore, the representation space of the hidden conceptual neurons can be visualized by a decoder network. The experiments also demonstrate that ANNs acquire different data perspectives when different loss functions are used for training. Numerous representation spaces of ANNs remain to be explored. To some extent, our exploration demonstrates that artificial intelligence (AI) has the ability to learn some concepts in a manner similarly to human beings. Based on this ability, AI shows promise in helping humans form new effective concepts that can facilitate medical development and alleviate the burdens of medical practitioners.
A New Low Complexity Angle of Arrival Algorithm for 1D and 2D Direction Estimation in MIMO Smart Antenna Systems

This paper proposes a new low complexity angle of arrival (AOA) method for signal direction estimation in multi-element smart wireless communication systems. The new method estimates the AOAs of the received signals directly from the received signals with significantly reduced complexity since it does not need to construct the correlation matrix, invert the matrix or apply eigen-decomposition, which are computationally expensive. A mathematical model of the proposed method is illustrated and then verified using extensive computer simulations. Both linear and circular sensors arrays are studied using various numerical examples. The method is systematically compared with other common and recently introduced AOA methods over a wide range of scenarios. The simulated results show that the new method has several advantages in terms of reduced complexity and improved accuracy under the assumptions of correlated signals and limited numbers of snapshots.
Single molecule identification of homology-dependent interactions between long ssRNA and dsDNA

Long non-coding RNAs (lncRNAs) are prominently associated with chromosomes in an ever-increasing diversity of roles. To provide further insight into the potential nature of these associations, we have explored, for the first time, the interaction of long single-stranded (ss) RNAs with cognate homologous double-stranded (ds) DNA in vitro. Using magnetic tweezers, we measured the effects of ssRNA on force extension curves for dsDNA. We observe that the presence of ssRNA impedes the extension of dsDNA, specifically at low forces, dependent on homology between the RNA and DNA species, and dependent on ssRNA lengths (≥1 kb). The observed effect also depends on the concentration of ssRNA and is abolished by overstretching of the dsDNA. These findings show that significant homologous contacts can occur between long ssRNA and dsDNA in the absence of protein and that these contacts alter the mechanical properties of the dsDNA. We propose that long ssRNA interacts paranemically with long dsDNA via periodic short homologous interactions, e.g. mediated by RNA/DNA triplex-formation, and that dsDNA extension is impeded by formation of RNA secondary structure in the intervening unbound regions. Analogous interactions in vivo would permit lncRNAs to mediate the juxtaposition of two or more DNA regions on the same or different chromosomes.
Gender classification of full body images based on the convolutional neural network

Gender classification is one of the most interesting and challenging problems in computer vision and has been widely studied based on facial images. However, the images of human we taken from the real-world surveillance are mostly full body and relatively blurry, which is much more difficult to classify due to different poses and backgrounds in unconstrained scenarios. In this paper, we propose a new network structure based on a convolutional neural network (CNN), which is less complicated and has a small number of layers. Moreover, it can achieve a high accuracy with even trained with limited data. We evaluate our method on the dataset collected from real-world video surveillance and compare various learning algorithms including Alex Net and Google Net. The experimental results showed that the proposed model achieved better results than the tested state-of-the-art network structures.