Novel method to transform ZnO nanorods into interlaced tattered nanosheets by changing the target inclination angle

ZnO nanoparticles were synthesized by liquid-phase pulse laser ablation of a Zn foil target immersed in deionized water. Nanosecond Q-switched Nd:YAG laser pulses of 532 nm were applied to the Zn foil target at a perpendicular and inclined (θ = 45°) angles. X-ray diffraction analysis revealed that both cases feature a ZnO nanostructure with a hexagonal wurtzite structure and that the particle size increases with the inclined target angle. Field emission scanning electron microscopy results of a colloidal drop cast on a glass substrate showed the ZnO has a nanorod structure in the case of a perpendicular target angle and an interlaced tattered nanosheet structure in the case of an inclined target angle. Photoluminescence spectra showed emission peaks in the UV, violet, blue, and green spectral regions, which correspond to excitonic and various defects resulting in an enhancement of emissions at inclined target angle.     

Empirical study on multiclass classification‐based network intrusion detection

Early and effective network intrusion detection is deemed to be a critical basis for cybersecurity domain. In the past decade, although a significant amount of work has focused on network intrusion detection, it is still a challenge to establish an intrusion detection system with a high detection rate and a relatively low false alarm rate. In this paper, we have performed a comprehensive empirical study on network intrusion detection as a multiclass classification task, not just to detect a suspicious connection but also to assign the correct type as well. To surpass the previous studies, we have utilized four deep learning models, namely, deep neural networks, long short‐term memory recurrent neural networks, gated recurrent unit recurrent neural networks, and deep belief networks. Our approach relies on the pretraining of the models by exploiting a particle swarm optimization–based algorithm for their hyperparameters selection. In order to investigate the performance differences, we also included two well‐known shallow learning methods, namely, decision forest and decision jungle. Furthermore, we used in our experiments four datasets, which are dedicated to intrusion detection systems to explore various environments. These datasets are KDD CUP 99, NSL‐KDD, CIDDS, and CICIDS2017. Moreover, 22 evaluation metrics are used to assess the model's performance in each of the datasets. Finally, intensive quantitative, Friedman test, and ranking methods analyses of our results are provided at the end of this paper. The results show a significant improvement in the detection of network attacks with our recommended approach.     

Particle/cell separation on microfluidic platforms based on centrifugation effect: a review

Particle/cell separation in heterogeneous mixtures including biological samples is a standard sample preparation step for various biomedical assays. A wide range of microfluidic-based methods have been proposed for particle/cell sorting and isolation. Two promising microfluidic platforms for this task are microfluidic chips and centrifugal microfluidic disks. In this review, we focus on particle/cell isolation methods that are based on liquid centrifugation phenomena. Under this category, we reviewed particle/cell sorting methods which have been performed on centrifugal microfluidic platforms, and inertial microfluidic platforms that contain spiral channels and multi-orifice channels. All of these platforms implement a form of centrifuge-based particle/cell separation: either physical platform centrifugation in the case of centrifugal microfluidic platforms or liquid centrifugation due to Dean drag force in the case of inertial microfluidics. Centrifugal microfluidic platforms are suitable for cases where the preparation step of a raw sample is required to be integrated on the same platform. However, the limited available space on the platform is the main disadvantage, especially when high sample volume is required. On the other hand, inertial microfluidics (spiral and multi-orifice) showed various advantages such as simple design and fabrication, the ability to process large sample volume, high throughput, high recovery rate, and the ability for multiplexing for improved performance. However, the utilization of syringe pump can reduce the portability options of the platform. In conclusion, the requirement of each application should be carefully considered prior to platform selection.     

The coding of roughness.

This review examines the way information about textures is captured, encoded, and processed by the somatosensory system to produce sensations of roughness/smoothness. Textures with spatial periods exceeding about 200 μm are encoded spatially, so roughness is nearly independent of the speed and direction of their movement across the skin. The information consists of spatial variations in activity among slowly adapting (SA1) mechanoreceptors, and appears to be extracted by specialized cortical neurons. Perception of the roughness of finer surfaces is mediated by detection, primarily by Pacinian afferents, of cutaneous vibrations generated when textures move across the skin. Movement is necessary to the perception of these textures, and vibrotactile adaptation interferes with it. The code is an intensitive one (i.e., the amount of activity in Pacinian afferents). (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Extensive malignant schwannoma of the sciatic nerve. Contribution of imaging techniques

The authors reports a case of malignant schwannoma of sciatic nerve. Clinically, this tumor was manifested by a progressive tumefaction of the leg, and moderate pain, without functional signs. The origin of the sciatic nerve has been suspected by computed tomography, because its location, the vascular contacts, and the lesion's extension. Biopsy leads to confirm the nerve tumor, and precised the histologic type: malignant schwannoma. Local, regional and general extension did not premise treatment.     

The integration of grain-size distribution and plasticity parameters for characterizing and classifying unconsolidated fine-grained sediments

Bulletin of Engineering Geology and the Environment - Grain-size distribution (GSD) characteristics are often used to classify different types of sediment and to understand how variations in...     

Production TiO2 Nanoparticles Using Laser Ablation in Ethanol

Silicon - Pulsed laser ablation method was used to fabricate TiO2 nanoparticles using titanium target (purity of 99.99%) immersed in ethanol and ablated with Nd: YAG laser with wavelength 532 nm...     

Patient mood flux on‐ and off‐hemodialysis

Mood in hemodialysis patients is most often evaluated off‐dialysis, possibly underestimating mood during dialysis. We compared mood in patients on‐ and off‐dialysis using the Positive and Negative Affect Schedule for 6 consecutive days. Initially, scores are normal, but subsequently positive affect falls below, and negative affect increases above, off‐dialysis values, suggesting increasing depression and anxiety, particularly in women. Quality‐of‐life questions confirm the effects of the dialysis session on mood. Prevalence and severity of depression evaluated off‐dialysis, or once only, may be underestimated, especially in women, because hemodialysis patients undergo mood swings centering on the dialysis session. Focusing insight on the dialysis session could improve coping among patients and caretakers.     

Integrating physical and virtual environments to conserve energy in buildings

Fundamental understandings of how energy is consumed, monitored, and controlled are key prerequisites for an energy conservation process. Users who know exactly when energy consumption occurs and where it takes place are able to make more informed decisions about how to lower their energy consumption. Energy conservation projects in the U.S. have reported a 2-11% savings when users are presented with information on energy consumption. Engineers are increasingly turning to virtual environments to improve system design and development and optimize performance and increase efficiency during operation. A bottom-up approach to real-time energy monitoring by integrating virtual and physical domains to increase user awareness on where, when, how and why aspect of energy to make inform decisions regarding energy consumption, optimization and conservation is proposed. A virtual 3-D environment is developed to display actual space/zone/building real-time power consumption information and to allow users to easily locate equipment/loads that are in standby/inefficient and causing energy waste in the real/physical environment. The proposed system is demonstrated via a prototype board that is virtually integrated with an actual Zero Net Energy Test House (ZNETH) as part of future Net-Zero Energy buildings research. The results establish a promising tool in this filed.