Miracle-dz/UniFAR-FTUs huggingface dataset:
Patterns and controls of nitrous oxide emissions from waters draining a subtropical agricultural valley

Although nitrous oxide (N2O) emission from agricultural runoff is thought to constitute a globally important source of this greenhouse gas, N2O flux from polluted aquatic systems is poorly understood and scarcely reported, especially in low‐latitude (0°–30°) regions where rapid agricultural intensification is occurring. We measured N2O emissions, dissolved N2O concentrations, and factors likely to control rates of N2O production in drainage canals receiving agricultural and mixed agricultural/urban inputs from the intensively farmed Yaqui Valley of Sonora, Mexico. Average per‐area N2O flux in both purely agricultural and mixed urban/agricultural drainage systems (16.5 ng N2O‐N cm−2 hr−1) was high compared to other fresh water fluxes, and extreme values ranged up to 244.6 ng N2O‐N cm−2 hr−1. These extremely high N2O fluxes occurred during green algae blooms, when organic carbon, nitrogen, and oxygen concentrations were high, and only in canals receiving pig‐farm and urban inputs, suggesting an important link between land‐use and N2O emissions. N2O concentrations and fluxes correlated significantly with water column concentrations of nitrate, particulate organic carbon and nitrogen, ammonium, and chlorophyll a, and a multiple linear regression model including ammonium, dissolved organic carbon, and particulate organic carbon was the best predictor of [N2O] (r2 = 52%). Despite high per‐area N2O fluxes, our estimate of regional N2O emission from surface drainage (20,869 kg N2O‐N yr−1; 0.046% of N‐fertilizer inputs) was low compared to values predicted by algorithms used in global budgets.
Predictive Factors of Bladder Tumor Recurrence after Nephro-Ureterectomy for Urothelial Carcinoma

Objectives: To evaluate the incidence of bladder cancer after nephro-ureterectomy (NUT) and determine the potential risk factors of bladder recurrence in patients with upper urinary tract urothelial carcinoma (UUT-UC). Materials and Methods: We retrospectively assessed 37 patients with UUT-UC including a significant follow-up after NUT of 34 months (range 12 - 120 months). The median age of the population was 72 years (range 48 - 83 years). Patients with a previous history of bladder cancer, concomitant diagnosis of bladder tumor and UUT-UC; or a metastatic UUT-UC were excluded from the study. Results: Out of these 37 patients, 17 presented a bladder recurrence within an average time of 12 months (range 3 to 31 months) after the NUT. In 94% of the cases, bladder recurrence occurred within the first 24 months following the NUT. Histological distribution was: 13 Ta tumors (76%), 2 pT1 tumors (11.7%); 11 patients had a high stage lesion (76%), whereas 4 patients had a low stage lesion (23.5%). As regards the anatomo-pathological characteristics of the UUT-UC, the supra iliac localization of the tumor is a significant risk factor of bladder recurrence (p = 0.04). Conclusion: Bladder recurrence after NUT occurred frequently and could have possibly been under-estimated. The use of intra-vesical instillation of Mitomycin C after NUT has been recently recommended by the European Association of Urology, but no predictive factors have yet been identified. Early diagnosis of bladder recurrence could certainly have reduced mortality in this patient population.
EASIN-Lit: a geo-database of published alien species records

EASIN-Lit is an initiative of the Joint Research Centre of the European Commission, aiming to facilitate access to spatial data published in the literature through the EASIN (European Alien Species Information Network) portal. Currently, information from 227 publications has been included, covering geo-referenced records for 236 species (1655 single records) and country-level occurrences for 3105 species. Systematic and harmonized access to such kind of data, otherwise dispersed in scientific libraries and archives, can be crucial for alien species distribution modeling and the implementation of early warning systems.
Development of High-Performance Green Concrete using Demolition and Industrial Wastes for Sustainable Construction

Growth of population, increasing urbanization and industrialization contributed to fast consumption of available natural resources and generation of considerable amount of wastes. The recycling of wastes in concrete industry will lead to greener and sustainable concrete, unless they are proven harmful by testing. This study was conducted to investigate the feasibility of recycling air-cooled slag (ACS) as a substitute of natural coarse aggregate on the strength and durability of high performance concrete containing natural aggregates or a blend of natural aggregates and recycled concrete aggregate (RCA) as a new approach to massively recycle these wastes for sustainable construction. The durability of concrete was evaluated by assessing abrasion resistance, water permeability, behavior of concrete after exposure to wetting and drying cycles, resistance to sulfate attack, and alkali-aggregate reaction. The results indicated that it is feasible to produce high performance concrete with satisfactory properties by using recycled aggregates and supplementary cementing material. ACS can be recycled successfully as a coarse aggregate in high performance concrete as it offers an approach to solve the problems arising from its disposal; in the meantime modified properties are added to the concrete. In general, concrete containing ACS had better performance compared to concrete entirely made with natural aggregates. Furthermore, the use of ACS is particularly beneficial for concrete containing RCA as it attenuated the negative impacts of RCA on concrete strength and durability without the need to increase the cement content. (Dina M. Sadek and Mohamed M. El-Attar. Development of High-Performance Green Concrete using Demolition and Industrial Wastes for Sustainable Construction. Journal of American Science 2012; 8(4):120-131). (ISSN: 1545-1003). http://www.americanscience. org. 16
Photonics-Based Microwave Image-Reject Mixer

Recent developments in photonics-based microwave image-reject mixers (IRMs) are reviewed with an emphasis on the pre-filtering method, which applies an optical or electrical filter to remove the undesired image, and the phase cancellation method, which is realized by introducing an additional phase to the converted image and cancelling it through coherent combination without phase shift. Applications of photonics-based microwave IRM in electronic warfare, radar systems and satellite payloads are described. The inherent challenges of implementing photonics-based microwave IRM to meet specific requirements of the radio frequency (RF) system are discussed. Developmental trends of the photonics-based microwave IRM are also discussed.
An intracranial dermoid tumour with large right parietal infarct- A rare case report

Intracranial dermoid tumours arise from inclusion of ectodermally committed cells at the time of closure of the neural groove between the third & fifth week of embryonic life, and account for 0.04% to 0.25% of the all intracranial tumor. Intracranial tumours may rarely present with stroke, which is mostly related to invasion, compression, or encasement of intracranial vessels by the tumour mass. Although dermoid tumours are known to produce a mass effect and occasionally to narrow intracranial vessels, it is very unusual for them to present with sudden ischaemic symptoms. We report the case of a man presenting with convulsion, later revealing a parietal dermoid tumor. To our knowledge, it is very rare in the literature. We discuss here thoroughly the probable mechanism of association of stroke with such an intracranial tumor. Surgical intervention using right parietal craniotomy was approached and complete removal of the tumor was achieved. The patient recovered thereby and histopathological study proved the tumor’s type. Introduction Intracranial dermoid tumours arise from inclusion of ectodermally committed cells at the time of closure of the neural groove between the third and fifth week of embryonic life, and account for 0.04% to 0.25% of the all intracranial tumors [1]. Intracranial tumours may rarely present with stroke, which is mostly related to invasion, compression, or encasement of intracranial vessels by the tumour mass. Although dermoid tumours are known to produce a mass effect and occasionally to narrow intracranial vessels, it is very unusual for them to present with sudden ischaemic symptoms [2,3]. We describe a case of intracranial dermoid tumour associated with infarction in the right parietal area. Case report A 37-year-old, right-handed man presented with chronic headache with epilepsy persisting for the past 5 years. The patient had a history of stroke 8 years back with left sided hemiparesis. On admission, neurological examination revealed mild bilateral papilloedema with left sided hemiparesis. Magnetic Resonance Imaging showed a hyper intense lesion with a large area of hypo intensity on right parietal region on T1 image with slight homogenous enhancement in contrast image suggestive of intracranial dermoid tumour with large infarct (Figure 1). Right parietal craniotomy was done and complete removal Correspondence to: Sarkar S, Department of Neurosurgery, Shaheed Suhrawardy Medical College, Dhaka Bangladesh, E-mail: saumitra_nsurg@yahoo.com
Speed Estimator Based on Type-1 and Type-2 Fuzzy Logic Controller for the Speed Sensorless DTFC-SVPWM of an Induction Motor Drive

This paper presents model reference adaptive system speed estimators based on Type-1 and Type-2 fuzzy logic controllers for the speed sensorless direct torque and flux control of an induction motor drive (IMD) using space vector pulse width modulation. A Type-1 fuzzy logic controller (T1FLC) based adaptation mechanism scheme is initially presented to achieve high performance sensorless drive in both transient as well as in steady-state conditions. However, the Type-1 fuzzy sets are certain and cannot work effectively when a higher degree of uncertainties occurs in the system, which can be caused by sudden changes in speed or different load disturbances and, process noise. Therefore, a new Type-2 FLC (T2FLC) based adaptation mechanism scheme is proposed to better handle the higher degree of uncertainties, improve the performance, and is also robust to different load torque and sudden changes in speed conditions. The detailed performance of different adaptation mechanism schemes are performed in a MATLAB/Simulink environment with a speed sensor and sensorless modes of operation when an IMD is operates under different operating conditions, such as no-load, load, and sudden changes in speed. To validate the different control approaches, the system is also implemented on a real-time system, and adequate results are reported for its validation.
Artificial Neural Networks Numerical Forecasting of Economic Time Series

The current global market is driven by many factors, e.g. by the facts that we live in the information age and that information is distributed in short times, large amounts and by many data channels. It is practically impossible to analyse all kinds of incoming information flows and transform them to data by classical methods. New requirements call for new methods. Artificial neural networks once trained on patterns can be used for forecasting and they are able to work with extremely big datasets in reasonable time. Traditionally, this is solved by means of a statistical analysis first a time-series model is constructed and then statistical prediction algorithms are applied to it in order to obtain future values. The common point for both methods is the learning process from samples of past data, or learning from the past. From many of the uncommon points the input conditions for the model creation and the length of the time series pattern set could be pointed out. On one hand, very sophisticated statistical methods exist that have strictly defined input conditions for datasets; on the other hand, practically open input conditions of artificial neural networks can be used. Regarding the length of the time series, the main problem of the Czech Republic, short and middle term predictions are valuable datasets. The lengths of selected economic values are not huge enough for quality of prediction or forecasting. Hand-in-hand with typical problems of real datasets (noisiness and/or missing data), there is the issue of the quality of the numerical forecasting. In addition, the strong nonlinearity of the models leads to an unsolvable usage of classical methods or construction of models that are not representing the reality. These are only few of the difficulties related to economic and financial modelling and prediction. Possible problems of numerous types of the artificial neural networks with n-setups make the issue even more complicated. The aim of this chapter is to compare different types of artificial neural networks using short and middle terms predictions of a real-world economic index. A number of papers dealing with artificial neural networks used for particular problems and often for the test do not use real-world economic indexes. The chapter is divided into four sections. The first simply presents the introduction to the research domain. The second section describes state-of-the-art artificial intelligence approaches to both prediction and forecasting of economic indexes. In the third section, neural network types and learning algorithms dealing with the prediction of time series and learning optimization are presented. In detail, the third section also includes methods of verification and validation of artificial neural networks and description of real-world economic indexes
Explaining Cross-Country Differences in Productivity: Is it Efficiency or Factor Endowments?

In this paper we develop a two-sector growth model of optimizing agents and apply this model to the data for the purpose of addressing the two interrelated questions that preoccupy the literature on development and growth accounting, namely: (1) What determines sustained growth and (2) What explains the vast cross-country differences in labor productivity. Concerning the first questions our findings support the view that to some extend the growth in effective human capital is a by-product of learning-by-doing. On the second question we find that differences in factors of production explain twice as much of the difference in labor productivity between developed and developing countries than differences in efficiency.
ASPEN: A Scalable In-SRAM Architecture for Pushdown Automata

Many applications process some form of tree-structured or recursively-nested data, such as parsing XML or JSON web content as well as various data mining tasks. Typical CPU processing solutions are hindered by branch misprediction penalties while attempting to reconstruct nested structures and also by irregular memory access patterns. Recent work has demonstrated improved performance for many data processing applications through memory-centric automata processing engines. Unfortunately, these architectures do not support a computational model rich enough for tasks such as XML parsing. In this paper, we present ASPEN, a general-purpose, scalable, and reconfigurable memory-centric architecture for processing of tree-like data. We take inspiration from previous automata processing architectures, but support the richer deterministic pushdown automata computational model. We propose a custom datapath capable of performing the state matching, stack manipulation, and transition routing operations of pushdown automata, all efficiently stored and computed in memory arrays. Further, we present compilation algorithms for transforming large classes of existing grammars to pushdown automata executable on ASPEN, and demonstrate their effectiveness on four different languages: Cool (object oriented programming), DOT (graph visualization), JSON, and XML. Finally, we present an empirical evaluation of two application scenarios for ASPEN: XML parsing, and frequent subtree mining. The proposed architecture achieves an average 704.5 ns per KB parsing XML compared to 9983 ns per KB in a state-of-the-art XML parser across 23 benchmarks. We also demonstrate a 37.2x and 6x better end-to-end speedup over CPU and GPU implementations of subtree mining.
Mapping the potential distribution of Rhododendron arboreum Sm. ssp. nilagiricum (Zenker) Tagg (Ericaceae), an endemic plant using ecological niche modelling

Rhododendron arboreum Sm. ssp. nilagiricum (Zenker) Tagg, a tree species endemic to the southern Western Ghats of peninsular India, is restricted to a narrow topographic and bioclimatic niche. Since information on ecological and geographic extent of the species is lacking, we used Genetic Algorithm for Rule-set Prediction (GARP), an ecological niche modelling (ENM) method to model its spatial distribution. The model is developed based on its occurrence in the Western Ghats, in relation to topographic and environmental variables. Prediction of R. arboreum ssp. nilagiricum in the Western Ghats is strongly related to local climatic conditions in terms of annual temperature, precipitation and diurnal temperature ranges and not so much dependent on altitudinal variations. The numerous bioclimatic, topographic and anthropogenic factors have led to the formation of islands of species distribution, restricted to the Nilgiri, Anamalai, Palni and Meghamalai hills of the Western Ghats. Predictions allow identification of key areas for in situ conservation of the species and prioritize areas falling in the ecological extent of the species for conservation.
Effekt av tilpassede tiltak for helsestasjonstjenesten

ulike familiers forutsetninger og behov. Rapporten er en oversikt over systematiske oversikter om effekt av tilpassede tiltak til familier med lav sosioøkonomisk status, etniske minoritetsfamilier eller tenåringsmødre, og tiltak for å fremme samspill med barnet for mødre med fødselsdepresjon. Vi inkluderte ti systematiske oversikter av høy metodisk kvalitet. Resultatene av oppsummeringen viser at: • Veiledning, opplæring og informasjon om amming fra helsearbeidere tilpasset mødre med lav sosioøkonomisk status fører trolig til at fl ere starter å amme og fortsatt ammer etter 3-6 måneder. Slike tiltak øker muligens også andelen som starter å fullamme. • Hjemmebesøksprogram til familier med lav sosioøkonomisk status reduserer trolig antall barneulykker noe. Det er usikkert om tiltaket fører til at fl ere fullfører anbefalte barnevaksinasjoner. • Foreldretreningsprogram tilpasset familier som er utsatt for store belastninger kan muligens forebygge utagerende atferd hos barnet. • Støtte eller Effekt av tilpassede tiltak for helsestasjonstjenesten
Synthesis of Two Degrees-of-Freedom Haptic Device

Haptic devices are force-feedback devices that mediate communication between the user and the computer. Such devices allow users to touch, feel and manipulate threedimensional objects in virtual environments and teleoperated systems. In this paper, one such device is synthesized for the purpose of training medical students and professionals, specially those requiring force-feedback from the virtual needle inserted into the body. The device is designed to deliver high force. Kinematic analysis of a suitable mechanism is performed and singularities in its workspace were identified to form constraints for an optimization. Performance index based kinematic optimization of the mechanism was performed over the whole workspace. The performance was then checked and limitations were analyzed by means of the so called force manipulability ellipsoid. We found that the performance in terms of kinematic singularity was greatly improved for the optimized mechanism.
Depressive Symptom Endorsement among Alzheimer's Disease, Vascular Dementia and Mild Cognitive Impairment *

Background: The Geriatric Depression Scale (GDS) is widely used to assess depressive symptoms in clinical and research settings. This study utilized a 4 factor solution for the 30-item GDS to explore differences in the presentation of depressive symptoms in various types of cognitive impairment. Method: Retrospective chart review was conducted on 254 consecutive cases of community dwelling elderly newly diagnosed with mild Alzheimer’s Dementia (AD) n = 122, mild Vascular Dementia (VaD) n = 71 or Amnestic Mild Cognitive Impairment (aMCI) n = 32 and Non-Amnestic MCI (nMCI) n = 29. Results: Analysis revealed no significant differences (p 05). No statistically significant differences were found between VaD and nMCI or between the MCI groups. Conclusions: Support is provided for the use of GDS subscales in a wide range of cognitively impaired elderly. This study suggests in mild dementia the number and type of depressive symptoms vary significantly between AD and VaD. There are indications that aMCI patients are similar in their symptom endorsement to AD and nMCI are similar to VaD which is consistent with some of the notions regarding likely trajectories of the respective MCI groups.
Response to strain rate in a discrete dynamical system model of the high-wavenumber Navier–Stokes equations

The ‘poor man's Navier–Stokes’ subgrid-scale model for use in large-eddy simulation is discussed with focus on its relationship to physics via its derivation from the Navier–Stokes equations. This discrete dynamical system is computationally inexpensive permitting thorough numerical study before its implementation as a subgrid-scale model. Previously we have demonstrated ranges of independent parameter values suitable for use in large-eddy simulations while maintaining a strict coupling of the equations through equating selected strain-rate tensor components. Here we will remove this restriction and again investigate the suitable ranges of bifurcation parameters through their two-dimensional bifurcation diagrams. Additionally we will comment on the possible impacts of our findings with regard to implementation of the subgrid-scale model in large-eddy simulations.
ESTIMATION OF THE EFFECTIVENESS OF CLUSTER ON ATTRIBUTE AND STRUCTURAL SIMILARITIES

The goal of this study is to evaluate the qualities of various clusters and their improvements using some techniques. A comparative analysis is carried to identify the quality cluster. Distance measures are presented for measuring the distance functions. The selection of centroids, influence function, density function, pseudo inverse and other related concepts are mathematically explained for utilizing in this work. Density and entropy expressions are provided to exhibit the quality clusters. For the purpose of discussing the quality of various clusters the densities, entropies and weights relating to S, SA and W clusters are computed. The improvement of cluster quality based on above concepts have been analysed. A comparison is made among S, SA and W clusters and pointed out the quality clusters