Miracle-dz/UniFAR-FTUs huggingface dataset:
Rayleigh Time-varying Channel Complex Gains Estimation and ICI Cancellation in OFDM Systems

In this paper, we consider an orthogonal-frequency-division-multiple xing (OFDM) mobile communication system operating in downlink mode in a time-varying multipath Rayleigh channe l scenario. We present a Mean Square Error theoretical analysis for a multipath channel complex gains estimation algorithm with inter-sub-carrier-interference (ICI) reduction using a comb-type p ilot. Assuming the presence of delayrelated information, the time average of the multipath complex gains, over the effective duration of each OFDM symbol, are estimated using LS criterion. After that, the time-variation o f the multipath complex gains within one OFDM symbol are obtained by interpolating the time-average d symbol values using lowpass interpolation. Hence, the channel matrix, which contains the channe l frequency response and the coefficients of ICI, can be computed and the ICI can be reduced by us ing successive interference suppression (SIS) in data symbol detection. The algorithm’s performance is further enhanced by an iterative procedure, performing channel estimation and ICI suppression at each iteration. T heoretical analysis and simulation results show a significant performance improvement for high normaliz ed Doppler spread (especially after the first iteration) in comparison to conventional methods.
Using Paleolandscape Modeling to Investigate the Impact of Native American–Set Fires on Pre-Columbian Forests in the Southern Sierra Nevada, California, USA

Ethnographic accounts document widespread use of low-intensity surface fires by California's Native Americans to manage terrestrial resources, yet the effects of such practices on forest composition and structure remain largely unknown. Although numerous paleoenvironmental studies debate whether proxy interpretations indicate climatic or anthropogenic drivers of landscape change, available data sources (e.g., pollen, charcoal) are generally insufficient to resolve anthropogenic impacts and do not allow for hypothesis testing. We use a modeling approach with LANDIS-II, a spatially explicit forest succession and disturbance model, to test whether the addition of Native American–set surface fires was necessary to approximate vegetation change as reconstructed from fossil pollen. We use an existing 1,600-year pollen and charcoal record from Holey Meadow, Sequoia National Forest, California, as the empirical data set to which we compared modeled results of climatic and anthropogenic fire regimes. We found that the addition of anthropogenic burning best approximated fossil pollen–reconstructed vegetation change, particularly during periods of prolonged cooler, wetter periods coinciding with greater regional Native American activity (1550–1050 and 750–100 cal yr BP). For lightning-caused wildfires to statistically approximate the pollen record required at least twenty times more ignitions and 870 percent more area burned annually during the Little Ice Age (750–100 cal yr BP) than observed during the modern period (AD 1985–2006), a level of natural fire increase we consider highly improbable. These results demonstrate that (1) anthropogenic burning was likely an important cause of pre-Columbian forest structure at the site and (2) dynamic landscape models provide a valuable method for testing hypotheses of paleoenvironmental change.
Cassini/MIMI Observations on the Dungey Cycle Reconnection and Kelvin‐Helmholtz Instability in Saturn's Magnetosphere

A new composite analysis of ∼13 years of Cassini's Charge Energy Mass Spectrometer measurements, presented by Allen et al. (2018, https://doi.org/10.1029/2018JA025262), suggests that the asymmetrically distributed suprathermal He++ in Saturn's magnetosphere is due to Dungey cycle related reconnection and Kelvin‐Helmholtz instabilities that provide paths to the solar wind plasma to reach distances even deep in the inner magnetosphere and accumulate in the ring current region, together with the internally sourced plasma. The results provide a significant addition to the ongoing discussions concerning the contribution of the solar wind input to the dynamics of an otherwise rotationally driven system, such as Saturn's magnetosphere.
Reconsidering Zipf’s law for regional development: The case of settlements and cities in Croatia

Abstract Zipf’s law is a striking regularity in the field of urban economics that states that the sizes of cities should follow the rank-size distribution. Rank-size distribution, or the rank-size rule, is a commonly observed statistical relationship between the population size and population rank of a nations’ cities. The goal of this paper is to test Zipf’s law as applied to data for settlements and cities in Croatia using the Census of Population Survey for the year 2011. The results of the analysis have shown that Zipf’s law for settlements in Croatia holds true for the majority of the settlement sizes. However, the rank-size distribution does not hold true for extremely small and extremely large settlement sizes. When city proper and urban agglomeration of 127 Croatian cities were examined, Zipf’s law was found to hold true only for urban agglomerations. The results of the study are discussed in terms of regional development.
Visualizing the Protein Sequence Universe

Modern biology is experiencing a rapid increase in data volumes that challenges our analytical skills and existing cyberinfrastructure. Exponential expansion of the Protein Sequence Universe (PSU), the protein sequence space, together with the costs and complexities of manual curation creates a major bottleneck in life sciences research. Existing resources lack scalable visualization tools that are instrumental for functional annotation. Here, we describe a new visualization tool using multi-dimensional scaling (MDS) to create a 3D embedding of the protein space. The advantages of the proposed PSU method include the ability to scale to large numbers of sequences, integrate different similarity measures with other functional and experimental data, and facilitate protein annotation. We applied the method to visualize the prokaryotic PSU using sequence alignment scores. As an annotation example, we used the interpolation approach to map the set of annotated archaeal proteins into the prokaryotic PSU. Transdisciplinary approaches akin to the one described in this paper are urgently needed to quickly and efficiently translate the influx of new data into tangible innovations and groundbreaking discoveries.
Spatial prediction of soil erosion susceptibility using a fuzzy analytical network process: Application of the fuzzy decision making trial and evaluation laboratory approach

Soil erosion is a worldwide threat that results in soil degradation, agriculture abandonment, and crop yield reduction. There is a need to find methods to survey soil erosion rates in order to improve and develop sustainable land planning. The present study utilizes new approaches based on the fuzzy set both in designing the problem (through the fuzzy decision making trial and evaluation laboratory) and in prioritizing the effective factors to mitigate soil erosion (using a fuzzy analytical network process, FANP). This study is first to apply these methods to soil erosion. A set of geo‐environmental factors influencing soil erosion was characterized to evaluate the potential risk of soil erosion in the Nor‐Rood watershed in Iran. The layers of information were developed using expert knowledge, and a network structure was designed by the fuzzy decision making trial and evaluation laboratory method. Then, the weights of layers were calculated by the FANP method by considering the internal and external interaction between factors. The erosion susceptibility map was produced by combining layers based on their weights in a geographic information system platform and was validated using erosion occurrences recorded in field surveys. Results revealed that FANP model accuracy is high (83.4% accuracy) for the study area. We found that vegetation, drainage density, land use, and soil erodibility are the key parameters to explain the soil erosion rates. The soil erosion risk map developed by the FANP method provides useful information for sustainable planning and risk mitigation and can be used in a data‐poor environment.
Effectiveness of vegetative buffer strips at reducing runoff, soil erosion, and nitrate transport during degraded hillslope restoration in northern Iran

Soil and water conservation in natural and cultivated areas is a major concern for humankind. However, there are severe problems with degraded hillslopes due to bare soils in northern Iran, which are one of the most important factors driving land degradation processes. Subsequently, soil erosion, pollutant transport, and/or nutrient impoverishment are affecting large territories; therefore, rapid and inexpensive soil conservation measures need to be implemented. The use of vegetative buffer strips could be an effective strategy to reduce pollutant transport as well as soil erosion. Thus, this research aimed to investigate the possible efficiency of two different vegetative buffer strips composed of vetiver‐grass (Chrysopogon zizanioides) and native turf‐grass (Festuca arundinacea) at reducing runoff and soil losses as well as nitrate transport on a representative degraded hillslope with bare soils in Mazandaran, Iran. Twelve 10 m2 experimental plots were tested over 1 year using a runoff simulator that produced overland flow that corresponded to the 25‐ and 100‐year return period rainfall events. The plots with bare soils had the highest runoff volumes (30.5 and 55.4 L m−2), sediment concentrations (101.2 and 430.6 g L−1), and nitrate concentrations (10.4 and 37.6 mg L−1). Vetiver was the most useful tool to reduce runoff, soil loss, and nitrate concentration, with values of 13.4 and 28.6 L m−2, 13.4 and 90.9 g L−1, and 2.9 and 16.4 mg L−1, respectively. Of the treatments investigated, vetiver provided the most rapid cover and was the most efficient at preventing soil erosion and nitrate transport directly after plantation.
Modeling extra-deep EM logs using a deep neural network

Modern geosteering is heavily dependent on real-time interpretation of deep electromagnetic (EM) measurements. This work presents a deep neural network (DNN) model trained to reproduce the full set of extra-deep real-time EM logs consisting of 22 measurements per logging position. The model is trained in a 1D layered environment and has sensitivity for up to seven layers with different resistivity values. A commercial simulator provided by a tool vendor is utilized to generate a training dataset. The impossibility of parallel execution of the simulator effectively limits the permissible dataset size. Therefore, the geological rules and geosteering specifics supported by the forward model are embraced when designing the dataset. It is then used to produce a fully parallel EM simulator based on a DNN without access to the proprietary information about the EM tool configuration or the original simulator source code. Despite a relatively small training set size, the resulting DNN forward model is quite accurate for synthetic geosteering cases, yet independent of the logging instrument vendor. The observed average evaluation time of 0.15 milliseconds per logging position makes it also suitable for future use as part of evaluation-hungry statistical and/or Monte-Carlo inversion algorithms.
pro-VTLN in the MFCC Domain: Band-Limited versus Local Interpolation

We propose a new easy-to-implement method to compute a Linear Transform (LT) to perform Vocal Tract Length Normalization (VTLN) on truncated Mel Frequency Cepstral Coefﬁcients (MFCCs) normally used in distributed speech recognition. The method is based on a Local Interpolation which is independent of the Mel ﬁlter design. Local Interpolation (LILT) VTLN is theoretically and experimentally compared to a global scheme based on band-limited interpolation (BLI-VTLN) and the conventional frequency warping scheme (FFT-VTLN). Investigating the interoperability of these methods shows that the performance of LILT-VTLN is on par with FFT-VTLN and BLI-VTLN. The statistical signiﬁcance test also shows that there are no signiﬁcant differences between FFT-VTLN, LILT-VTLN, and BLI-VTLN, even if the models and front ends do not match.
Assessment of soil properties from catchment areas of Ravi and Beas rivers: a review

ABSTRACT Soil is a substantive environmental medium that is subjected to various physiochemical challenges derived by natural, as well as human activities. The present review attempts to summarize the pollution status of soil from the catchments areas of Beas and Ravi Rivers in Punjab, India, as reported by different workers. Principal component analysis (PCA) showed that anthropogenic activities and lithogenic factors are the major sources of metals. The average values of heavy metals of Beas River for heavy metals, Cr, Cu, Co, and Cd, were lower than the values suggested by Awasthi, European Union, and Ewers. The average values of C (0.29%), P (0.05 mg/g), and N (0.13 mg/g) were found for the Beas River, whereas for Ravi River the average values recorded were C (0.32%), P (0.01 mg/g), and N (0.16 mg/g). The results of contamination factor (CF) indicate that soil of Beas River is less contaminated by the metals. The results of ecological risk index indicate that metals showed low ecological risk in the soils of Beas River.
Cross-population Meta-analysis of eQTLs: Fine Mapping and Functional Study

Mapping expression quantitative trait loci (eQTLs) has been shown as a powerful tool to uncover the genetic underpinnings of many complex traits at the molecular level. In this paper, we present an integrative analysis approach that leverages eQTL data collected from multiple population groups. In particular, our approach eﬀectively identiﬁes multiple independent cis -eQTL signals that are consistently presented across populations, accounting for population heterogeneity in allele frequencies and linkage disequilibrium patterns. Furthermore, by integrating genomic annotations, our analysis framework enables high-resolution functional analysis of eQTLs. We applied our statistical approach to analyze the GEUVADIS data consisting of samples from ﬁve population groups. From this analysis, we concluded that i) jointly analysis across population groups greatly improves the power of eQTL discovery and the resolution of ﬁne mapping of causal eQTL. ii) many genes harbor multiple independent eQTLs in their cis regions iii) genetic variants that disrupt transcription factor binding are signiﬁcantly enriched in eQTLs ( p -value = 4 . 93 × 10 − 22 ).
A RAINBOW BLOW-UP LEMMA FOR ALMOST OPTIMALLY

A subgraph of an edge-coloured graph is called rainbow if all its edges have different colours. We prove a rainbow version of the blow-up lemma of Komlós, Sárközy and Szemerédi that applies for almost optimally bounded edge-colourings. A corollary of this is that there exists a rainbow copy of any bounded-degree spanning subgraph H in a quasirandom host graph G, assuming that the edge-colouring of G fulfills a boundedness condition that can be seen to be almost best possible. This has many interesting applications beyond rainbow colourings, for example to graph decompositions. There are several well-known conjectures in graph theory concerning tree decompositions, such as Kotzig’s conjecture and Ringel’s conjecture. We adapt these conjectures to general bounded-degree subgraphs, and provide asymptotic solutions using our result on rainbow embeddings.
Assessing the Skill of Medium-Range Ensemble Precipitation and Streamflow Forecasts from the Hydrologic Ensemble Forecast Service (HEFS) for the Upper Trinity River Basin in North Texas

To issue early warnings for the public to act, for emergency managers to take preventive actions, and for water managers to operate their systems cost-effectively, it is necessary to maximize the time horizon over which streamflow forecasts are skillful. In this work, we assess the value of medium-range ensemble precipitation forecasts generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) in increasing the lead time and skill of streamflow forecasts for five headwater basins in the upper Trinity River basin in north-central Texas. The HEFS uses ensemble mean precipitation forecasts from the Global Ensemble Forecast System (GEFS) of the National Centers for Environment Prediction (NCEP). For comparative evaluation, we verify ensemble streamflow forecasts generated with the HEFS forced by the GEFS forecast with those forced by the short-range quantitative precipitation forecasts (QPFs) from the NWS West Gulf River Forecast Center (WGRFC) based on guidance from the NCEP’s Weather Prediction Center. We also assess the benefits of postprocessing the raw ensemble streamflow forecasts and evaluate the impact of selected parameters within the HEFS on forecast quality. The results show that the use of medium-range precipitation forecasts from the GEFS with the HEFS extends the time horizon for skillful forecasting of mean daily streamflow by 1–3 days for significant events when compared with using only the 72-h River Forecast Center (RFC) QPF with the HEFS. The HEFS forced by the GEFS also improves the skill of two-week-ahead biweekly streamflow forecast by about 20% over climatological forecast for the largest 1% of the observed biweekly flow.
Unsupervised Topic Modelling for Multi-Party Spoken Discourse BLIND

We present a method for unsupervised topic modelling which adapts methods used in document classification (Blei et al., 2003; Griffiths and Steyvers, 2004) to unsegmented multi-party discourse transcripts. We show how Bayesian inference in this generative model can be used to simultaneously address the problems of topic segmentation and topic identification; automatically segmenting multiparty meetings into topically coherent segments with performance which compares well with previous unsupervised segmentation-only methods (Galley et al., 2003); while simultaneously extracting topics which rate highly when assessed for coherence by human judges. We also show that this method appears robust in the face of off-topic dialogue and speech recognition errors.
The Economics of Open-Access Journals

A new business model for scholarly journals, open access, has gained wide attention recently. An open-access journal’s articles are available over the Internet free of charge to all readers; revenue to cover publication costs comes from authors’ fees. In this paper, we present a model of the journals market. Drawing upon the emerging literature on two-sided markets, we highlight the features distinguishing journals from examples economists have previously studied (telephony, credit cards, etc.). We analyze the efficiency of equilibrium author and reader fee schedules for various industry structures and for various assumptions about journals’ objective functions. We ask whether open-access journals are viable in these various economic environments.
The Effect Of Music Listening On Work Performance: A Case Study Of Sri Lanka

Effective utilization of human resource is the main focus of human resource management (HRM) of organizations irrespective of their nature. Creating a working environment that ease the mind of worker reducing their work related stresses is one the strategy in HRM. Playing background music is take in to consideration in by most of the organizations specially service organizations with the expectation that it will reduce the work place stresses and improves the performance. However, the effect of music on employees' performance is least attended by the researchers particularly in manufacturing organizations. This study was aimed at examine the effect of background music on performance of the employees of garment manufacturing firms in Sri Lanka. The hypothesis has been developed to fulfill the major objective of identifying effect of music listening to the work performance. The sample size of this study was 50 machine operators out of 64 in the selected garment and data was collected by distributing well- structured questionnaire for same sample of employees in two times as before & after music listening. So that sample listened to the relaxation music for two weeks. Mean comparison test and regression analysis was used for the analysis of the data. It was found that background music has a profound effect on the work performance of the respondents (Correlation before music listening work performance: 0.796, after music listening work performance: 0.590 & Mean before music listening work performance: 34.945 after music listening work performance: 28.742). Final recommendations for the study are to use of fast rhythm songs, first discuss with employees, use more music, consider preference of employees and change the music based on the situation. This study proved the fact that employee performance can be improved further by creating a worker friendly environment by using of back ground music.
Alternatives to potentially inappropriate medications for use in e-prescribing software: triggers and treatment algorithms

Objective: To describe the development of evidence-based electronic prescribing (e-prescribing) triggers and treatment algorithms for potentially inappropriate medications (PIMs) for older adults. Design: Literature review, expert panel and focus group. Setting: Primary care with access to e-prescribing systems. Participants: Primary care physicians using e-prescribing systems receiving medication history. Interventions: Standardised treatment algorithms for clinicians attempting to prescribe PIMs for older patients. Main outcome measure: Development of 15 treatment algorithms suggesting alternative therapies. Results: Evidence-based treatment algorithms were well received by primary care physicians. Providing alternatives to PIMs would make it easier for physicians to change decisions at the point of prescribing. Conclusion: Prospectively identifying older persons receiving PIMs or with adherence issues and providing feasible interventions may prevent adverse drug events.
Article (refereed) - postprint

The spatial and seasonal dynamics of surface water fluxes of the greenhouse gases (GHG) CO 2 , CH 4 , and N 2 O were quantified in the Tay estuary, Scotland, on seven sampling occasions every 3 months during 2009/2010. This estuary is a relatively pristine river-dominated macrotidal estuary system of a type that is sparsely represented in global GHG flux studies. Significant spatial and temporal variability in GHG fluxes were measured, with similar spatial dynamics to that of other European estuaries. Greatest temporal and spatial variability in gas saturations were found for CH 4 , which was higher in the summer, with peaks in saturation occurring in the freshwater upper estuary and sharply decreasing in the mid-estuary mixing zone. Concentrations of CO 2 and N 2 O were also generally higher in the upper to middle estuary in summer, although seasonality was less pronounced. Estimated air-sea fluxes also displayed significant spatial and temporal variability. Total annual CO 2 emissions were greatest in the middle estuary zone (13.8 x 10 6 kg C yr -1 ), and lowest in the upper estuary (1.52 x 10 6 kg C yr -1 ). Seasonally, the highest CO 2 emissions integrated across the estuary were in spring and autumn, with the lowest in winter. Total annual CH 4 emissions were also highest in the middle estuary (0.05 x 10 6 kg C yr -1 ) and lowest in the upper estuary (0.01 x 10 6 kg C yr -1 ), whereas total N 2 O emissions, whilst highest in the middle estuary (2344 kg N yr -1 ), were lowest in the outer estuary (-435 kg N yr -1 ). Emissions of CH 4 and N 2 O were substantially higher in the summer than any other season and lowest emissions were found in winter. The estimated annual exchange of both CO 2 and N 2 O is substantially lower than those reported in other European macrotidal estuaries.