La cantidad de madera muerta y sus tasas de descomposición asociadas en reservas forestales y bosques manejados en el noroeste de Turquía

This study describes the state of coarse dead wood (CDW) in the Forest Reserve and the Managed Forest zones of northern conifer-broadleaved mixed forest. The results showed mean total CDW volumes in the ranges 30,05±11,06 m3/ha in the Forest Reserve (6,33±2,98% of the LW volume), and 9,31±2,84 m3/ha in the Managed Forest (1,96±0,84% of the LW volume). The total CDW volume was 3,22 times higher in the Forest Reserve than in the Managed Forest. The CDWlog1 and CDWsnag1 were the most abundant CDW decay classes, whilst CDWlog2 and CDWsnag2 were the lowest. Comparisons of ratios between the Managed Forest and the Forest Reserve with abundant decay classes CDWlog1 and CDWsnag1 indicated large differences. The CDWlog1 volume was 4,09 times higher, and the CDWsnag1 volume was 3,68 times greater in the Forest Reserve than in the Managed Forest. The ratio of different CWD classes in the Managed Forest to CWD classes in the Reserve Forest confirms the pattern. In both Managed and Reserve Forest zones there is balance between total CDWlogs and total CDWsnags, but the differences between total CDWlogs and total CDWsnags was not statistically significant. The total CDW volume was significantly dependent on the forest management system. The system influenced amount and diversity of CDW. In commercially managed forest the abundance and structure of CDW retained is a compromise between the needs of timber production and nature conservation.Este estudio describe el estado de la madera muerta en la zona de reserva forestal y zonas de bosques manejados de coníferas del norte de bosques mixtos de frondosas. Los resultados mostraron que la media total de los volúmenes de madera muerta es igual a 30,05 ± 11,06 m3 / ha en la Reserva Forestal (6,33 ± 2,98% del volumen de madera en pie), y 9,31 ± 2,84 m3 / ha en los bosques manejados (1,96 ± 0,84% del volumen de LW). El volumen total de madera muerta fue de 3,22 veces mayor en la Reserva Forestal de que en el bosque administrado. Las clases de decaimiento de madera muerta más abundantes eran CDWlog1 y CDWsnag1, mientras que CDWlog2 y CDWsnag2 fueron los menos abundantes. Las comparaciones de las proporciones entre el bosque manejado y la Reserva Forestal con las clases de decaimiento más abundantes (CDWlog1 y CDWsnag1) indican grandes diferencias ente las dos zonas. El volumen CDWlog1 fue 4,09 veces mayor, y el volumen CDWsnag1 fue 3,68 veces mayor en la Reserva Forestal de que en el bosque manejado. La relación de las diferentes clases de decaimiento entre los bosques manejados y la Reserva Forestal confirma el patrón. En ambos casos, bosque manejado y zonas de reserva forestal, existe un equilibrio entre CDWlogs total y CDWsnags total, pero las diferencias entre CDWlogs total y CDWsnags total no fue estadísticamente significativa. El volumen total de madera muerta depende significativamente del sistema de gestión forestal. El sistema de manejo influye sobre la cantidad y diversidad de madera muerta. En una gestión comercial de los bosques, la abundancia y estructura de madera muerta presente es un compromiso entre las necesidades de la producción de madera y la conservación de la naturaleza

Chiral metamaterials with negative refractive index based on four "U" split ring resonators

A uniaxial chiral metamaterial is constructed by double-layered four "U" split ring resonators mutually twisted by 90 degrees. It shows a giant optical activity and circular dichroism. The retrieval results reveal that a negative refractive index is realized for circularly polarized waves due to the large chirality. The experimental results are in good agreement with the numerical results.Comment: 4 pages, 4 figures, Published as cover on AP

Transmission enhancement through deep subwavelength apertures using connected split ring resonators

Cataloged from PDF version of article.We report astonishingly high transmission enhancement factors through a subwavelength aperture at microwave frequencies by placing connected split ring resonators in the vicinity of the aperture. We carried out numerical simulations that are consistent with our experimental conclusions. We experimentally show higher than 70,000-fold extraordinary transmission through a deep subwavelength aperture with an electrical size of lambda/31x lambda/12 (width x length), in terms of the operational wavelength. We discuss the physical origins of the phenomenon. Our numerical results predict that even more improvements of the enhancement factors are attainable. Theoretically, the approach opens up the possibility for achieving very large enhancement factors by overcoming the physical limitations and thereby minimizes the dependence on the aperture geometries. (C) 2010 Optical Society of Americ

Frequency dependent steering with backward leaky waves via photonic crystal interface layer

Cataloged from PDF version of article.A Photonic Crystal (PC) with a surface defect layer (made of dimers) is studied in the microwave regime. The dispersion diagram is obtained with the Plane Wave Expansion Method. The dispersion diagram reveals that the dimer-layer supports a surface mode with negative slope. Two facts are noted: First, a guided (bounded) wave is present, propagating along the surface of the dimer-layer. Second, above the light line, the fast traveling mode couple to the propagating spectra and as a result a directive (narrow beam) radiation with backward characteristics is observed and measured. In this leaky mode regime, symmetrical radiation patterns with respect to the normal to the PC surface are attained. Beam steering is observed and measured in a 70 degrees angular range when frequency ranges in the 11.88-13.69GHz interval. Thus, a PC based surface wave structure that acts as a frequency dependent leaky wave antenna is presented. Angular radiation pattern measurements are in agreement with those obtained via numerical simulations that employ the Finite Difference Time Domain Method (FDTD). Finally, the backward radiation characteristics that in turn suggest the existence of a backward leaky mode in the dimer-layer are experimentally verified using a halved dimer-layer structure. (C) 2009 Optical Society of Americ

Peptide Cross-Linked Poly(2-oxazoline) as a Sensor Material for the Detection of Proteases with a Quartz Crystal Microbalance

Inflammatory conditions are frequently accompanied by increased levels of active proteases, and there is rising interest in methods for their detection to monitor inflammation in a point of care setting. In this work, new sensor materials for disposable single-step protease biosensors based on poly(2-oxazoline) hydrogels cross-linked with a protease-specific cleavable peptide are described. The performance of the sensor material was assessed targeting the detection of matrix metalloproteinase-9 (MMP-9), a protease that has been shown to be an indicator of inflammation in multiple sclerosis and other inflammatory conditions. Films of the hydrogel were formed on gold-coated quartz crystals using thiol–ene click chemistry, and the cross-link density was optimized. The degradation rate of the hydrogel was monitored using a quartz crystal microbalance (QCM) and showed a strong dependence on the MMP-9 concentration. A concentration range of 0–160 nM of MMP-9 was investigated, and a lower limit of detection of 10 nM MMP-9 was determined

Correlation Between K-value, Density Index and Bifilm Index in Determination of Liquid Al Cleanliness

Aluminum alloys are widely used in the industry thanks to its many advantages such as light weight and high strength. The use of this material in the market is increasing day by day with the developing technology. Due to the high energy inputs in the primary production, the use of secondary ingots by recycling from scrap material are more advantageous. However, the liquid metal quality is quite important in the use of secondary aluminum. It is believed that the quality of recycled aluminum is low, for this purpose, many liquid metal cleaning methods and test methods are used in the industry to assess the melt cleanliness level. In this study, it is aimed to examine the liquid metal quality in castings with varying temperature using K mold. A206 alloy was used, and the test parameters were selected as: (i) at 725 °C, 750 °C and 775 °C casting temperatures, (ii) different hydrogen levels. The hydrogen level was adjusted as low, medium and high with degassing, as-cast, and upgassing of the melt, respectively. The liquid metal quality of the cast samples was examined by the K mold technique. When the results were examined, it was determined that metal K values and the number of inclusions were high at the as-cast and up-gas liquid with increasing casting temperatures. It has been understood that the K mold technique is a practical method for the determination of liquid metal quality, if there is no reduced pressure test machine available at the foundry floor

Solar flare prediction using advanced feature extraction, machine learning and feature selection

YesNovel machine-learning and feature-selection algorithms have been developed to study: (i) the flare prediction capability of magnetic feature (MF) properties generated by the recently developed Solar Monitor Active Region Tracker (SMART); (ii) SMART's MF properties that are most significantly related to flare occurrence. Spatio-temporal association algorithms are developed to associate MFs with flares from April 1996 to December 2010 in order to differentiate flaring and non-flaring MFs and enable the application of machine learning and feature selection algorithms. A machine-learning algorithm is applied to the associated datasets to determine the flare prediction capability of all 21 SMART MF properties. The prediction performance is assessed using standard forecast verification measures and compared with the prediction measures of one of the industry's standard technologies for flare prediction that is also based on machine learning - Automated Solar Activity Prediction (ASAP). The comparison shows that the combination of SMART MFs with machine learning has the potential to achieve more accurate flare prediction than ASAP. Feature selection algorithms are then applied to determine the MF properties that are most related to flare occurrence. It is found that a reduced set of 6 MF properties can achieve a similar degree of prediction accuracy as the full set of 21 SMART MF properties

Automated Prediction of CMEs Using Machine Learning of CME – Flare Associations

YesIn this work, machine learning algorithms are applied to explore the relation between significant flares and their associated CMEs. The NGDC flares catalogue and the SOHO/LASCO CMEs catalogue are processed to associate X and M-class flares with CMEs based on timing information. Automated systems are created to process and associate years of flares and CMEs data, which are later arranged in numerical training vectors and fed to machine learning algorithms to extract the embedded knowledge and provide learning rules that can be used for the automated prediction of CMEs. Different properties are extracted from all the associated (A) and not-associated (NA) flares representing the intensity, flare duration, duration of decline and duration of growth. Cascade Correlation Neural Networks (CCNN) are used in our work. The flare properties are converted to numerical formats that are suitable for CCNN. The CCNN will predict if a certain flare is likely to initiate a CME after input of its properties. Intensive experiments using the Jack-knife techniques are carried out and it is concluded that our system provides an accurate prediction rate of 65.3%. The prediction performance is analysed and recommendation for enhancing the performance are provided

Simulation of grid/standalone solar energy supplied reduced switch converter with optimal fuzzy logic controller using golden BallAlgorithm

This article presents the utilization of a shunt active power filter (SHAPF) in combination with an Energy Storage System (ESS) and a Solar Energy System (SES). Voltage source converters (VSC) are connected in parallel to a direct current (DC) bus. The membership function (MSF) of fuzzy logic controller (FLC) for the shunt control system is optimally adjusted using the golden balloptimization algorithm (GBOA). The present effort aims to achieve the following primary objectives: 1) Quick implementation to stabilize the voltage of the DC Link capacitor (DCLCV); 2) Mitigation of harmonics and improvement of power factor (PF); 3) Satisfactory performance under load as well as solar power varying conditions. The effectiveness of the optimally designed controller is evaluated by studying four test scenarios with grid and standalone conditions. The results are then compared to the existing sliding mode (SMC) and fuzzy logic controllers (FLC)

Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays

Artificial intelligence (AI)-generated clinical advice is becoming more prevalent in healthcare. However, the impact of AI-generated advice on physicians’ decision-making is underexplored. In this study, physicians received X-rays with correct diagnostic advice and were asked to make a diagnosis, rate the advice’s quality, and judge their own confidence. We manipulated whether the advice came with or without a visual annotation on the X-rays, and whether it was labeled as coming from an AI or a human radiologist. Overall, receiving annotated advice from an AI resulted in the highest diagnostic accuracy. Physicians rated the quality of AI advice higher than human advice. We did not find a strong effect of either manipulation on participants’ confidence. The magnitude of the effects varied between task experts and non-task experts, with the latter benefiting considerably from correct explainable AI advice. These findings raise important considerations for the deployment of diagnostic advice in healthcare