Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of nuclear reaction cross-sections and fragment yields in carbon beams using the SHIELD-HIT Monte Carlo code. Comparison with experiments

In light ion therapy, the knowledge of the spectra of both primary and secondary particles in the target volume is needed in order to accurately describe the treatment. The transport of ions in matter is complex and comprises both atomic and nuclear processes involving primary and secondary ions produced in the cascade of events. One of the critical issues in the simulation of ion transport is the modeling of inelastic nuclear reaction processes, in which projectile nuclei interact with target nuclei and give rise to nuclear fragments. In the Monte Carlo code SHIELD-HIT, inelastic nuclear reactions are described by the Many Stage Dynamical Model (MSDM), which includes models for the different stages of the interaction process. In this work, the capability of SHIELD-HIT to simulate the nuclear fragmentation of carbon ions in tissue-like materials was studied. The value of the parameter κ, which determines the so-called freeze-out volume in the Fermi break-up stage of the nuclear interaction process, was adjusted in order to achieve better agreement with experimental data. In this paper, results are shown both with the default value κ = 1 and the modified value κ = 10 which resulted in the best overall agreement. Comparisons with published experimental data were made in terms of total and partial charge-changing cross-sections generated by the MSDM, as well as integral and differential fragment yields simulated by SHIELD-HIT in intermediate and thick water targets irradiated with a beam of 400 MeV u−1 12C ions. Better agreement with the experimental data was in general obtained with the modified parameter value (κ = 10), both on the level of partial charge-changing cross-sections and fragment yields.
A Generic Algorithm for Sleep-Wake Cycle Detection using Unlabeled Actigraphy Data

One key component when analyzing actigraphy data for sleep studies is sleep-wake cycle detection. Most detection algorithms rely on accurate sleep diary labels to generate supervised classifiers, with parameters optimized for a particular dataset. However, once the actigraphy trackers are deployed in the field, labels for training models and validating detection accuracy are often not available. In this paper, we propose a generic, training-free algorithm to detect sleep-wake cycles from minute-by-minute actigraphy. Leveraging a robust nonlinear parametric model, our proposed method refines the detection region by searching for a single change point within bounded regions defined by the parametric model. Challenged by the absence of ground truth labels, we also propose an evaluation metric dedicated to this problem. Tested on week-long actigraphy from 112 children, the results show that the proposed algorithm improves on the baseline model consistently and significantly $(\mathbf{p} < 3\mathbf{e}-15)$. Moreover, focusing on the commonality in human circadian rhythm captured by actigraphy, the proposed method is generic to data collected by various actigraphy trackers, circumventing the laborious label collection step in developing customized classifiers for sleep detection.
Applying multipath routing to a video surveillance system deployed over a wireless mesh network

Large-scale distributed video surveillance systems typically consists of many video sources distributed over a wide area, transmitting live video streams to a central location for processing and monitoring. The target of this paper to bring down the overall system cost, increasing deployability, scalability, and performance is to propose a new architecture for a WVSN, which is based on a wireless mesh network, and to analyze its performance in order to choose some project parameters to achieve the best trade-off between video encoding quality and the generated network traffic
Nonstationary increments, scaling distributions, and variable diffusion processes in financial markets

Fat-tailed distributions have been reported in fluctuations of financial markets for more than a decade. Sliding interval techniques used in these studies implicitly assume that the underlying stochastic process has stationary increments. Through an analysis of intraday increments, we explicitly show that this assumption is invalid for the Euro–Dollar exchange rate. We find several time intervals during the day where the standard deviation of increments exhibits power law behavior in time. Stochastic dynamics during these intervals is shown to be given by diffusion processes with a diffusion coefficient that depends on time and the exchange rate. We introduce methods to evaluate the dynamical scaling index and the scaling function empirically. In general, the scaling index is significantly smaller than previously reported values close to 0.5. We show how the latter as well as apparent fat-tailed distributions can occur only as artifacts of the sliding interval analysis.
The use of syllable phonotactics for word hypothesization

A search technique incorporating the automatic modeling of lexical variability is introduced for medium or large-vocabulary speaker-independent speech recognition. Current state-of-art systems depend on being able to model the entire language based on acoustic features and the constraints of syntax or inter-word probabilities. These methods often fail in the presence of multiple speakers, new vocabulary, noise, and spontaneous speech phenomena. A new approach for word hypothesization is proposed, based on an acoustic-phonetic unit called the pseudo-syllable segment. An algorithm is described for transforming a sequence of syllables into words. Techniques are suggested for controlling the accuracy of the syllabic hypothesis set, and learning the phonotactics of syllables automatically in a statistical framework.
Chinese Calligraphy Font Classification and Transformation

This project explores Chinese character font classiﬁcation and transformation, which are the most important two steps in reconstructing weathered Chinese characters. For font classiﬁcation, SIFT is ﬁrst used to capture font features, and neural network is built in Keras with almost perfect results achieved. For font transformation, a convolutional neural network is implemented in Tensorﬂow , for which diﬀerent models are compared and discussed. Expected results are generated from transformation, and eﬀectiveness of neural network in font classiﬁcation and transformation is veriﬁed.
Counting on count data models : Quantitative policy evaluation can benefit from a rich set of econometric methods for analyzing count data

Often, economic policies are directed toward outcomes that are measured as counts. Examples of economic variables that use a basic counting scale are number of children as an indicator of fertility, number of doctor visits as an indicator of health care demand, and number of days absent from work as an indicator of employee shirking. Several econometric methods are available for analyzing such data, including the Poisson and negative binomial models. They can provide useful insights that cannot be obtained from standard linear regression models. Estimation and interpretation are illustrated in two empirical examples. DOI: https://doi.org/10.15185/izawol.148 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-110642 Published Version Originally published at: Winkelmann, Rainer (2015). Counting on count data models : Quantitative policy evaluation can benefit from a rich set of econometric methods for analyzing count data. IZA world of labor, 148:online. DOI: https://doi.org/10.15185/izawol.148 World of Labor Evidence-based policy making RaineR Winkelmann University of Zurich, Switzerland, and IZA, Germany Counting on count data models. IZA World of Labor 2015: 148 doi: 10.15185/izawol.148 | Rainer Winkelmann © | May 2015 | wol.iza. org 1 eleVaTOR PiTCH Often, economic policies are directed toward outcomes that are measured as counts. Examples of economic variables that use a basic counting scale are number of children as an indicator of fertility, number of doctor visits as an indicator of health care demand, and number of days absent from work as an indicator of employee shirking. Several econometric methods are available for analyzing such data, including the Poisson and negative binomial models. They can provide useful insights that cannot be obtained from standard linear regression models. Estimation and interpretation are illustrated in two empirical examples. aUTHOR’S main meSSaGe Empirical analyses often encounter variables on a 0, 1, 2, etc., scale, such as hours of work or the annual number of doctor visits made by a person. Policymakers may be interested in the distributional effects of a reform on such outcomes, not just the mean effects. For example, does a policy affect heavy users of a service more than occasional users? Poisson and negative binomial models and their extensions can answer such a question, and they are no more complicated than a linear regression model. Hurdle models are useful for predicting the effect of a policy on the probability of a zero count as opposed to a count of one or more. Counting on count data models Quantitative policy evaluation can benefit from a rich set of econometric methods for analyzing count data
Marine Diesel Engine Condition Monitoring by Use of BP Neural Network

network. The engine surface vibration signals are measured and analyzed using time series method. The characteristic parameters of engine vibration signals obtained from time series analysis are used to build a suitable artificial neural network by means of Back Propagation Algorithm in order to detect the engine operating faults and improve the diagnosis accuracy. The diagnoses of variations in valve clearances and engine cylinder loads are discussed. The relationships between vibration characteristics and engine working conditions are presented. Experimental investigations were carried out on a medium speed 6-cylinder marine diesel engine. The cylinder cover vibration signals are measured and analyzed for monitoring the working conditions of valve clearances and engine loads. The results show that it is effective to detect the variations in valve clearance and engine cylinder load from cylinder cover vibration signals.
Topology simplification and channel assignment in multi-radio Wireless Mesh Networks

As the price of hardware decreases, Wireless Mesh Networks (WMNs) will become practical commodity in a few years. By equipping each node with multiple network interface cards, multiple channels can be applied to improve the performance of WMNs. With the advantages of both wireless LAN and Ad Hoc, WMN also has challenges. Connectivity of the communication network and the interferences between the links of the network are two most important points to be considered carefully. This paper presents some efficient technologies to handle the two points. It firstly simplifies the topology of the communication network and gets a bi-connected network; then for the resulted network, it greedily assigns channels such that the interference of the network is as small as possible. The simulation results show that, by applying the technologies presented in this paper, a bi-connected network with minimum interference is created.
Master-Slave Synchronization of General Lur'e Systems with Time-Varying Delay and Parameter Uncertainty

This paper deals with the problem of master-slave synchronization for uncertain Lur'e systems via time-varying delay feedback control. The parametric uncertainty is assumed to be norm bounded. Several new and sufficient conditions are presented such that the uncertain Lur'e master and slave systems are synchronous for all admissible uncertainties. These synchronization criteria are dependent on the size of time delay, which can be expressed by means of matrix inequalities. The adopted method is based on defining a new Lyapunov–Krasovskii function and using some inequalities techniques. Our results obtained here extend and improve some previously related results. Finally, two numerical examples are provided to demonstrate the applications of our proposed results.
The relationship of self-disclosure, interpersonal dependency, and life changes to loneliness in young adults.

Using a sample of 209 volunteer students between the ages of 18 and 25 from an urban university, the relationships between self-disclosure, interpersonal dependency, life changes, and loneliness were studied. Respondents completed the Jourard 40-Item Self-Disclosure Questionnaire, the Interpersonal Dependency Inventory, the Recent Life Change Questionnaire, and the revised UCLA Loneliness Scale. Pearson product-moment correlation coefficients and multiple regression analysis were used to test the hypotheses. Hypotheses were supported predicting an inverse relationship between self-disclosure and loneliness (p less than .005) and a positive relationship between interpersonal dependency and loneliness (p less than .005). However, a positive relationship between life changes and loneliness was not supported. Data also supported the hypothesis that self-disclosure, interpersonal dependency, and life changes would account for greater variance in loneliness than any single variable alone (F(3,204 = 14.433), p less than . 01). From a stepwise multiple regression analysis, self-disclosure and interpersonal dependency together accounted for 17.4 percent of the variance in loneliness. Implications for nursing practice are discussed with particular emphasis on potential strategies for preventing loneliness.
Pulmonary function and oxidative stress in workers exposed to styrene in plastic factory

Styrene is a volatile organic compound used in factories for synthesis of plastic products. The pneumotoxicity of styrene in experimental animals is known. The aim of the present study was to study the effect of styrene on lung function and oxidative stress in occupationally exposed workers in plastic factory. Thirty-four male workers, between 18 and 40 years of age, exposed to styrene for atleast 8 hours a day for more than a year were studied, while 30 age- and sex-matched healthy subjects not exposed to styrene served as controls. Assessment of lung functions showed a statistically significant reduction (p < 0.05) in most of the lung volumes, capacities (FVC, FEV1, VC, ERV, IRV, and IC) and flow rates (PEFR, MEF75%, and MVV) in the study group (workers) as compared to controls. Malondialdehyde (MDA) was observed to be significantly high (p < 0.05) while ferric-reducing ability of plasma (FRAP) was significantly low (p < 0.05) in styrene-exposed subjects. Reduced glutathione (GSH) level was significantly depleted in exposed subjects as compared to control group. The mean value of serum cytochrome c in styrene-exposed subjects was found to be 1.1 ng/ml (0.89–1.89) while in control its levels were under detection limit (0.05 ng/ml). It shows that styrene inhalation by workers leads to increased level of oxidative stress, which is supposed to be the cause of lung damage.
Prevalence and Correlates of Prenatal Vitamin A Deficiency in Rural Sidama, Southern Ethiopia

A cross-sectional study was conducted to assess the prevalence and correlates of prenatal vitamin A deficiency (VAD) in rural Sidama, Southern Ethiopia. Seven hundred randomly-selected pregnant women took part in the study. Serum retinol concentration was determined using high-performance liquid chromatography. Data were analyzed by logistic and linear regression. Interpretation of data was made using adjusted odds ratio (AOR) and adjusted linear regression coefficient. The prevalence of VAD (serum retinol <0.7 µmol/L) was 37.9%. Advanced gestational age and elevated C-reactive protein (CRP ≥5 mg/dL) were negatively associated with retinol concentration (p<0.05). The odds of VAD was significantly higher among the women with no education and those devoid of self-income. Women aged 35-49 years had 2.23 (95% CI 1.31-3.81) times higher odds compared to those aged 15-24 years. The lower the dietary diversity score in the preceding day of the survey, the higher were the odds of VAD. With reference to nulliparas, grand multiparas had 1.92 (95% CI 1.02-3.64) times increased odds of VAD. VAD and zinc deficiency (serum zinc <8.6 µmol/L during the first trimester, or <7.6 µmol/L during the second or third trimester) were significantly associated with AOR of 1.80 (95% CI 1.28-2.53). VAD has major public-health significance in the area. Accordingly, it should be combated through enhancement of diet diversity, birth control, and socioeconomic empowerment of women.
Experimental manipulation of selfish genetic elements links genes to microbial community function

Microbial communities underpin earth’s biological and geochemical processes, but their complexity hampers understanding. Here we draw upon predictions from the theory concerning selfish genetic elements (SGEs), combined with approaches from experimental evolution, comparative metagenomics and biochemistry, and show how naturally occurring SGEs can be used to manipulate genes that underpin community function. Communities comprising hundreds of bacterial genera established from garden compost were propagated with bi-weekly transfer for one year in nitrogen-limited minimal medium with cellulose (paper) as sole carbon source. At each transfer, SGEs from one set of independent communities were collected, pooled and redistributed among this same set of communities (horizontal treatment). A control was included in which SGEs never moved beyond the community in which they were originally present (vertical treatment). SGEs along with genes of ecological relevance were rapidly amplified across horizontal communities and their dynamics tracked. Enrichment of genes implicated in nitrogen metabolism, and particularly ammonification, prompted biochemical assays that showed a significant functional difference between communities subject to horizontal versus vertical treatments. This simple experimental strategy offers a powerful new approach for unravelling dynamical processes underpinning microbial community function.
Non-linear bandwidth reduction schemes for transmission of multivariate Gaussian sources over noisy channels

In this paper we investigate the design of bandwidth reduction non-linear schemes for the transmission of multivariate Gaussian sources over noisy channels. The non-linear mappings are designed utilizing an algorithm called “Power Constrained Channel Optimized Vector Quantization”(PCCOVQ). Practical examples for 2:1, 3:1 and 4:2 systems are provided, and simulation results are compared with the respective Optimal Performance Theoretically Attainable (OPTA).
DREAMing: a simple and ultrasensitive method for assessing intratumor epigenetic heterogeneity directly from liquid biopsies

Many cancers comprise heterogeneous populations of cells at primary and metastatic sites throughout the body. The presence or emergence of distinct subclones with drug-resistant genetic and epigenetic phenotypes within these populations can greatly complicate therapeutic intervention. Liquid biopsies of peripheral blood from cancer patients have been suggested as an ideal means of sampling intratumor genetic and epigenetic heterogeneity for diagnostics, monitoring and therapeutic guidance. However, current molecular diagnostic and sequencing methods are not well suited to the routine assessment of epigenetic heterogeneity in difficult samples such as liquid biopsies that contain intrinsically low fractional concentrations of circulating tumor DNA (ctDNA) and rare epigenetic subclonal populations. Here we report an alternative approach, deemed DREAMing (Discrimination of Rare EpiAlleles by Melt), which uses semi-limiting dilution and precise melt curve analysis to distinguish and enumerate individual copies of epiallelic species at single-CpG-site resolution in fractions as low as 0.005%, providing facile and inexpensive ultrasensitive assessment of locus-specific epigenetic heterogeneity directly from liquid biopsies. The technique is demonstrated here for the evaluation of epigenetic heterogeneity at p14ARF and BRCA1 gene-promoter loci in liquid biopsies obtained from patients in association with non-small cell lung cancer (NSCLC) and myelodysplastic/myeloproliferative neoplasms (MDS/MPN), respectively.
A network-based data-mining approach to investigate indole-related microbiota-host co-metabolism

Motivation Indoles have been shown to play a significant role in cardiometabolic disorders. While some individual bacterial species are known to produce indole-adducts, to our best knowledge no studies have made use of publicly available genome data to identify prokaryotes, specifically those associated with the human gut microbiota, contributing to the indole metabolic network. Results Here, we propose a computational strategy, comprising the integration of KEGG and BLAST, to identify prokaryote-specific metabolic reactions relevant for the production of indoles, as well as to predict new members of the human gut microbiota potentially involved in these reactions. By identifying relevant prokaryotic species for further validation studies in vitro, this strategy represents a useful approach for those interrogating the metabolism of other gut-derived microbial metabolites relevant to human health. Availability All R scripts and files (gut microbial dataset, FASTA protein sequences, BLASTP output files) are available from https://github.com/AndreaRMICL/Microbial_networks. Contact ARM: andrea.rodriguez-martinez13@imperial.ac. uk; LH: lesley.hoyles@ntu.ac. uk.
An Agent-Based Modeling and Simulation Approach to Commodity Markets

The present work provides an open-source, agent-based behavioral model primarily addressed to the dynamics associated with the commodities spot price formation, as well as variations and contrast patterns belonging to the exchanged quantity. The framework can flexibly be used for several commodities, but it has to be integrated and qualified with the modelization of producers’ and buyers’ behavior when used for the analysis of a specific commodity, stock, or group of merchandise. Due to the fact that various commodities or consumer artifacts are perceived and treated differently in several diverse contexts, the intended tasks or functions are mainly entrusted to those experts in the specific fields, who would find the software tool we designed a good starting point for their investigations. Toward achieving this aim, open-source code is provided along with detailed documentation on how to use, further develop, and enhance the present features of the proposed agent-based modeling and simulation software. This is publicly available at https://github.com/gfgprojects/cms
Low-Cost High Gain Planar Antenna Array for 60-GHz Band Applications

An effective development of a class of low-cost planar antenna arrays having a high reproducibility is presented for 60-GHz band system applications. The proposed antenna arrays, based on the substrate integrated waveguide (SIW) scheme, consists of one compact SIW 12-way power divider and 12 radiating SIWs each supporting 12 radiating slots. A 50-¿ conductor-backed coplanar waveguide (CBCPW) integrated with CBCPW-to-SIW transition is directly used as the input of the antenna array, thus allowing to accommodate other circuits or MMICs at a minimum cost. An antenna array prototype was implemented on Rogers RT/Duroid 6002 substrate with thickness of 20 mils by our standard PCB process. Measured gain is about 22 dBi with a side lobe suppression of 25 dB in the H-plane and 15 dB in the E-Plane while the bandwidth for the 10-dB return loss is 2.5 GHz.