Friction and Wetting Transitions of Magnetic Droplets on Micropillared Superhydrophobic Surfaces

Reliable characterization of wetting properties is essential for the development and optimization of superhydrophobic surfaces. Here, the dynamics of superhydrophobicity is studied including droplet friction and wetting transitions by using droplet oscillations on micropillared surfaces. Analyzing droplet oscillations by high‐speed camera makes it possible to obtain energy dissipation parameters such as contact angle hysteresis force and viscous damping coefficients, which indicate pinning and viscous losses, respectively. It is shown that the dissipative forces increase with increasing solid fraction and magnetic force. For 10 µm diameter pillars, the solid fraction range within which droplet oscillations are possible is between 0.97% and 2.18%. Beyond the upper limit, the oscillations become heavily damped due to high friction force. Below the lower limit, the droplet is no longer supported by the pillar tops and undergoes a Cassie–Wenzel transition. This transition is found to occur at lower pressure for a moving droplet than for a static droplet. The findings can help to optimize micropillared surfaces for low‐friction droplet transport.     

Detecting,classifying, and tracing non-functional software requirements

In this paper, we describe a novel unsupervised approach for detecting, classifying, and tracing non-functional software requirements (NFRs). The proposed approach exploits the textual semantics of software functional requirements (FRs) to infer potential quality constraints enforced in the system. In particular, we conduct a systematic analysis of a series of word similarity methods and clustering techniques to generate semantically cohesive clusters of FR words. These clusters are classified into various categories of NFRs based on their semantic similarity to basic NFR labels. Discovered NFRs are then traced to their implementation in the solution space based on their textual semantic similarity to source code artifacts. Three software systems are used to conduct the experimental analysis in this paper. The results show that methods that exploit massive sources of textual human knowledge are more accurate in capturing and modeling the notion of similarity between FR words in a software system. Results also show that hierarchical clustering algorithms are more capable of generating thematic word clusters than partitioning clustering techniques. In terms of performance, our analysis indicates that the proposed approach can discover, classify, and trace NFRs with accuracy levels that can be adequate for practical applications.     

Transmission of vibration through glove materials: effects of contact force

This study investigated effects of applied force on the apparent mass of the hand, the dynamic stiffness of glove materials and the transmission of vibration through gloves to the hand. For 10 subjects, 3 glove materials and 3 contact forces, apparent masses and glove transmissibilities were measured at the palm and at a finger at frequencies in the range 5–300 Hz. The dynamic stiffnesses of the materials were also measured. With increasing force, the dynamic stiffnesses of the materials increased, the apparent mass at the palm increased at frequencies greater than the resonance and the apparent mass at the finger increased at low frequencies. The effects of force on transmissibilities therefore differed between materials and depended on vibration frequency, but changes in apparent mass and dynamic stiffness had predictable effects on material transmissibility. Depending on the glove material, the transmission of vibration through a glove can be increased or decreased when increasing the applied force.

Practitioner summary: Increasing the contact force (i.e. push force or grip force) can increase or decrease the transmission of vibration through a glove. The vibration transmissibilities of gloves should be assessed with a range of contact forces to understand their likely influence on the exposure of the hand and fingers to vibration.     

An economic model predictive control approach to integrated production management and process operation

Managing production schedules and tracking time‐varying demand of certain products while optimizing process economics are subjects of central importance in industrial applications. We investigate the use of economic model predictive control (EMPC) in tracking a production schedule. Specifically, given that only a small subset of the total process state vector is typically required to track certain scheduled values, we design a novel EMPC scheme, through proper construction of the objective function and constraints, that forces specific process states to meet the production schedule and varies the rest of the process states in a way that optimizes process economic performance. Conditions under which feasibility and closed‐loop stability of a nonlinear process under such an EMPC for schedule management can be guaranteed are developed. The proposed EMPC scheme is demonstrated through a chemical process example in which the product concentration is requested to follow a certain production schedule. © 2016 American Institute of Chemical Engineers AIChE J, 63: 1892–1906, 2017     

Prediction of COVID-19 Pandemic Spread in Kingdom of Saudi Arabia

A significant increase in the number of coronavirus cases can easily be noticed in most of the countries around the world. Inspite of the consistent preventive initiatives being taken to contain the spread of this virus, the unabated increase in the cases is both alarming and intriguing. The role of mathematical models in predicting and estimating the spread of the virus, and identifying various preventive factors dependencies has been found important and effective in most of the previous pandemics like Severe Acute Respiratory Syndrome (SARS) 2003. In this research work, authors have proposed the Susceptible-Infectected-Removed (SIR) model variation in order to forecast the pattern of coronavirus disease (COVID-19) spread for the upcoming eight weeks in perspective of Saudi Arabia. The study has been performed by using SIR model with a proposed simplification using average progression for further estimation of β and γ values for better curve fittings ratios. The predictive results of this study clearly show that under the current public health interventions, there will be an increase in the COVID-19 cases in Saudi Arabia in the next four weeks. Hence, a set of strong health primitives and precautionary measures are recommended in order to avoid and prevent the further spread of COVID-19 in Saudi Arabia.     

Isotopic assessment of CO<Subscript>2</Subscript> production through soil organic matter decomposition in the tropics

Values of ¹³C and ¹⁵N of soil organic matter (SOM) under different land cover in Pasir Mayang, Jambi Province, Sumatra Island, Indonesia were examined to apply them as indicators of SOM dynamics and related CO₂ production. The ¹³C and ¹⁵N values of SOM increased with depth in the 0–30 cm layer in the preserved forest, reflecting ¹³C and ¹⁵N richment in SOM through mineralization and immobilization. The degree of vertical enrichment, difference between 0–5 cm and 10–15 cm SOM, was more pronounced in ¹⁵N than in ¹³C at all sites in Pasir Mayang. The ¹³C -SOM profiles fluctuated through clear-cutting the forest and subsequent burning, which was due to input of biomass with higher C/N molar ratio and lower ¹³C value than the original SOM. However, the ¹⁵N-SOM profiles before and after such a drastic event did not change appreciably. The ¹⁵N-SOM became higher as the C/N ratio decreased and as soil sugar content decreased. These observations suggest that ¹⁵N-SOM is a variable that changes with the amount of easily decomposable organic matter (EDOM) in soil. Soil incubation experiments demonstrated a correlation between CO₂ production rate and degree of vertical ¹⁵N-enrichment in SOM, which was applied to field data to estimate CO₂ production through SOM decomposition. A similar analysis was performed with the soils collected at 27 locations in other districts in Jambi Province than Pasir Mayang. In five locations covered by oil palm plantation, CO₂ production through SOM decomposition controlled 70 of variation in CO₂ emission among the locations. In the remaining 22 locations, however, the CO₂ emission was neither related to CO₂ production from SOM nor to ground litter amount. This observation indicated that mechanisms other than dead organic matter decomposition such as root respiration were dominant sources for CO₂ emission in these sites.     

Unraveling the Binding Mechanism of Alzheimer’s Drugs with Irisin: Spectroscopic,Calorimetric, and Computational Approaches

The prevalence of Alzheimer’s disease (AD) has been a major health concern for a long time. Despite recent progress, there is still a strong need to develop effective disease-modifying therapies. Several drugs have already been approved to retard the progression of AD-related symptoms; however, there is a need to develop an effective carrier system for the delivery of drugs to combat such diseases. In recent years, various biological macromolecules, including proteins, have been used as carriers for drug delivery. Irisin is a beneficial hormone in such diseases, including AD and related pathologies. Herein, the interaction mechanism of irisin with AD drugs such as memantine, galantamine, and fluoxetine is investigated. Fluorescence studies revealed that the above drugs bind to irisin with significant affinity, with fluoxetine having the highest binding affinity. Isothermal titration calorimetry (ITC) complemented the spontaneous binding of these drugs with irisin, delineating various associated thermodynamic and binding parameters. Molecular docking further validated the fluorescence and ITC results and unfolded the mechanism that hydrogen bonding governs the binding of fluoxetine to irisin with a significant binding score, i.e., −6.3 kcal/mol. We believe that these findings provide a promising solution to fight against AD as well as a platform for further research to utilize irisin in the drug-delivery system for an effective therapeutic strategy.     

An experimental and numerical analysis of the influence of the inlet temperature,equivalence ratio and compression ratio on the HCCI auto-ignition process of Primary Reference Fuels in an engine

In order to understand better the auto-ignition process in an HCCI engine, the influence of some important parameters on the auto-ignition is investigated. The inlet temperature, the equivalence ratio and the compression ratio were varied and their influence on the pressure, the heat release and the ignition delays were measured. The inlet temperature was changed from 25 to 70 °C and the equivalence ratio from 0.18 to 0.41, while the compression ratio varied from 6 to 13.5. The fuels that were investigated were PRF40 and n-heptane. These three parameters appeared to decrease the ignition delays, with the inlet temperature having the least influence and the compression ratio the most. A previously experimentally validated reduced surrogate mechanism, for mixtures of n-heptane, iso-octane and toluene, has been used to explain observations of the auto-ignition process. The same kinetic mechanism is used to better understand the underlying chemical and physical phenomena that make the influence of a certain parameter change according to the operating conditions. This can be useful for the control of the auto-ignition process in an HCCI engine.