A Novel Acoustomicrofluidic Nebulization Technique Yielding New Crystallization Morphologies

A novel acoustic microfluidic nebulization platform is demonstrated, which, due to its unique ability to access intermediate evaporation rate regimes—significantly faster than that in slow solvent evaporation but considerably below that achieved in spray drying, is capable of producing novel crystal morphologies that have yet to be reported in both model inorganic and organic systems. In addition, the potential for simultaneously encapsulating single crystals within a biodegradable polymeric coating in a single simultaneous step together with the crystallization process as the solvent evaporates during nebulization is briefly shown. The platform not only has the potential to be highly scalable by employing a large number of these low‐cost miniature devices in parallel to achieve industrially relevant particle production rates, but could also be advantageous over conventional spray drying in terms of energy utilization, given the tremendous efficiency associated with the high‐frequency ultrasonic microdevice as well as its ambient temperature operation.     

Ultrafast Acoustofluidic Exfoliation of Stratified Crystals

While the remarkable properties of 2D crystalline materials offer tremendous opportunities for their use in optics, electronics, energy systems, biotechnology, and catalysis, their practical implementation largely depends critically on the ability to exfoliate them from a 3D stratified bulk state. This goal nevertheless remains elusive, particularly in terms of a rapid processing method that facilitates high yield and dimension control. An ultrafast multiscale exfoliation method is reported which exploits the piezoelectricity of stratified materials that are noncentrosymmetric in nature to trigger electrically‐induced mechanical failure across weak grain boundaries associated with their crystal domain planes. In particular, it is demonstrated that microfluidic nebulization using high frequency acoustic waves exposes bulk 3D piezoelectric crystals such as molybdenum disulphide (MoS₂) and tungsten disulphide (WS₂) to a combination of extraordinarily large mechanical acceleration (≈10⁸ m s^?2) and electric field (≈10⁷ V m^?1). This results in the layered bulk material being rapidly cleaved into pristine quasi‐2D‐nanosheets that predominantly comprise single layers, thus constituting a rapid and high throughput chip‐scale method that opens new possibilities for scalable production and spray coating deposition.     

Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection

Malware is a ‘malicious software program that performs multiple cyberattacks on the Internet, involving fraud, scams, nation-state cyberwar, and cybercrime. Such malicious software programs come under different classifications, namely Trojans, viruses, spyware, worms, ransomware, Rootkit, botnet malware, etc. Ransomware is a kind of malware that holds the victim’s data hostage by encrypting the information on the user’s computer to make it inaccessible to users and only decrypting it; then, the user pays a ransom procedure of a sum of money. To prevent detection, various forms of ransomware utilize more than one mechanism in their attack flow in conjunction with Machine Learning (ML) algorithm. This study focuses on designing a Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection (LBAAA-OMLMD) approach in Computer Networks. The presented LBAAA-OMLMD model mainly aims to detect and classify the existence of ransomware and goodware in the network. To accomplish this, the LBAAA-OMLMD model initially derives a Learning-Based Artificial Algae Algorithm based Feature Selection (LBAAA-FS) model to reduce the curse of dimensionality problems. Besides, the Flower Pollination Algorithm (FPA) with Echo State Network (ESN) Classification model is applied. The FPA model helps to appropriately adjust the parameters related to the ESN model to accomplish enhanced classifier results. The experimental validation of the LBAAA-OMLMD model is tested using a benchmark dataset, and the outcomes are inspected in distinct measures. The comprehensive comparative examination demonstrated the betterment of the LBAAA-OMLMD model over recent algorithms.     

Extracting a diagnostic gait signature

This research addresses the question of the existence of prominent diagnostic signatures for human walking extracted from kinematics gait data. The proposed method is based on transforming the joint motion trajectories using wavelets to extract spatio-temporal features which are then fed as input to a vector quantiser; a self-organising map for classification of walking patterns of individuals with and without pathology. We show that our proposed algorithm is successful in extracting features that successfully discriminate between individuals with and without locomotion impairment.     

Using Wiki in teacher education: Impact on knowledge management processes and student satisfaction

The current study reports on the use of Wiki as an online didactic tool to develop knowledge management (KM) processes in higher education. This study integrates social constructivist principles to learning where learners are pro-active and collaborative through higher order cognitive processes. The study was administered in two countries, namely Egypt and Italy, to close a gap in the literature with an aim to introduce KM processes in teacher educational programmes. These processes are seen as necessary for teachers’ professional skills. Such processes are claimed to enable teachers and therefore schools to evolve in a networked information-driven global society, especially as the complexity of subject knowledge is increasing. It is also a learning experience where teachers learn how to provide their students with educational settings where technology is enabled.     

Micro-fabrication of ceramics:Additive manufacturing and conventional technologies

Ceramic materials are increasingly used in micro-electro-mechanical systems(MEMS)as they offer many advantages such as high-temperature resistance,high wear resistance,low density,and favourable mechanical and chemical properties at elevated temperature.However,with the emerging of additive manufacturing,the use of ceramics for functional and structural MEMS raises new opportunities and challenges.This paper provides an extensive review of the manufacturing processes used for ceramic-based MEMS,including additive and conventional manufacturing technologies.The review covers the micro-fabrication techniques of ceramics with the focus on their operating principles,main features,and processed materials.Challenges that need to be addressed in applying additive technologies in MEMS include ceramic printing on wafers,post-processing at the micro-level,resolution,and quality control.The paper also sheds light on the new possibilities of ceramic additive micro-fabrication and their potential applications,which indicates a promising future.     

Enhancing The Mechanical and Dielectric Properties of EPDM Filled with Nanosized Rice Husk Powder

Promising nanocomposites (EPDM-nRHP) were prepared from EPDM/ nanosized rice husk powder (nRHP) in the presence of HRH system as modifying dry bonding agent. HRH system comprised hydrated silica, resorcinol and hexamethylenetetramine. Different concentrations from nRHP were used for preparing such nanocomposites. The effect of nRHP was studied on the mechanical and dielectric properties of EPDM-nRHP nanocomposites whether in the presence or absence of HRH system. The presence of HRH modifying system enhanced the dispersion of nRHP in EPDM and consequently improved the mechanical and dielectric properties for the resulting EPDM-nRHP nanocomposites which may be used as electrical insulator.     

Lemon balm extract causes potent antihyperglycemic and antihyperlipidemic effects in insulin‐resistant obese mice

Over the last decades polyetiological metabolic diseases such as obesity and type 2 diabetes have emerged as a global epidemic. Efficient strategies for prevention and treatment include dietary intervention and the development of validated nutraceuticals. Safe extracts of edible plants provide a resource of structurally diverse molecules that can effectively interfere with multifactorial diseases. In this study, we describe the application of ethanolic lemon balm (Melissa officinalis) leaves extract for the treatment of insulin‐resistance and dyslipidemia in mice. We show that lemon balm extract (LBE) activates the peroxisome proliferator‐activated receptors (PPARs), which have key roles in the regulation of whole body glucose and lipid metabolism. Application of LBE (0.6 mg/mL) to human primary adipocytes resulted in specific peroxisome proliferator‐activated receptor target gene expression. LBE treatment of insulin‐resistant high‐fat diet‐fed C57BL/6 mice (200 mg/kg/day) for 6 weeks considerably reduced hyperglycemia and insulin resistance, plasma triacylglycerol, nonesterified fatty acids and LDL/VLDL cholesterol levels. Taken together, ethanolic lemon balm extract can potentially be used to prevent or concomitantly treat type 2 diabetes and associated disorders such as dyslipidemia and hypercholesterolemia.