From Practice to Policy to Practice

Recent molecular dynamics simulation results have increased conceptual understanding of the grazing and the ploughing friction at elevated temperatures, particularly near the substrate's melting point. In this commentary we address a major constraint concerning its experimental verification

Advances in the Management of Gastrointestinal Cancers—An Upcoming Role of Immune Checkpoint Blockade

Gastrointestinal cancers are a group of highly aggressive malignancies, and novel therapeutic strategies with higher clinical efficacy are being actively sought. \u27Immunotherapy\u27 is now emerging as one such promising strategy for the treatment of these tumors. This article briefly reviews the recent advances that utilize targeting of immune checkpoint pathways, in the management of gastrointestinal malignancies

Load Balancer using Whale-Earthworm Optimization for Efficient Resource Scheduling in the IoT-Fog-Cloud Framework

Cloud-Fog environment is useful in offering optimized services to customers in their daily routine tasks. With the exponential usage of IoT devices, a huge scale of data is generated. Different service providers use optimization scheduling approaches to optimally allocate the scarce resources in the Fog computing environment to meet job deadlines. This study introduces the Whale-EarthWorm Optimization method (WEOA), a powerful hybrid optimization method for improving resource management in the Cloud-Fog environment. Striking a balance between exploration and exploitation of these approaches is difficult, if only Earthworm or Whale optimization methods are used. Earthworm technique can result in inefficiency due to its investigations and additional overhead, whereas Whale algorithm, may leave scope for improvement in finding the optimal solutions using its exploitation.  This research introduces an efficient task allocation method as a novel load balancer. It leverages an enhanced exploration phase inspired by the Earthworm algorithm and an improved exploitation phase inspired by the Whale algorithm to manage the optimization process. It shows a notable performance enhancement, with a 6% reduction in response time, a 2% decrease in cost, and a 2% improvement in makespan over EEOA. Furthermore, when compared to other approaches like h-DEWOA, CSDEO, CSPSO, and BLEMO, the proposed method achieves remarkable results, with response time reductions of up to 82%, cost reductions of up to 75%, and makespan improvements of up to 80%

Role of Modern Immunotherapy in Gastrointestinal Malignancies: A Review of Current Clinical Progress

Gastrointestinal (GI) cancers are a group of highly aggressive malignancies with a huge disease burden worldwide. There is clearly a significant unmet need for new drugs and therapies to further improve the treatment outcomes of GI malignancies. Immunotherapy is a novel treatment strategy that is emerging as an effective and promising treatment option against several types of cancers. CTLA-4 and PD-1 are critical immune checkpoint molecules that negatively regulate T cell activation via distinct mechanisms. Immune checkpoint blockade with antibodies directed against these pathways has already shown clinical efficacy that has led to their FDA approval in the treatment of several solid tumors including melanoma, non-small cell lung cancer, renal cell carcinoma, urothelial carcinoma, and head and neck cancer. This review will summarize the current clinical progress of modern immunotherapy in the field of GI tumors, with a special focus on immune checkpoint blockade

Effects of lengthscales and attractions on the collapse of hydrophobic polymers in water

We present results from extensive molecular dynamics simulations of collapse transitions of hydrophobic polymers in explicit water focused on understanding effects of lengthscale of the hydrophobic surface and of attractive interactions on folding. Hydrophobic polymers display parabolic, protein-like, temperature-dependent free energy of unfolding. Folded states of small attractive polymers are marginally stable at 300 K, and can be unfolded by heating or cooling. Increasing the lengthscale or decreasing the polymer-water attractions stabilizes folded states significantly, the former dominated by the hydration contribution. That hydration contribution can be described by the surface tension model, $\Delta G=\gamma (T)\Delta A$, where the surface tension, $\gamma$, is lengthscale dependent and decreases monotonically with temperature. The resulting variation of the hydration entropy with polymer lengthscale is consistent with theoretical predictions of Huang and Chandler (Proc. Natl. Acad. Sci.,97, 8324-8327, 2000) that explain the blurring of entropy convergence observed in protein folding thermodynamics. Analysis of water structure shows that the polymer-water hydrophobic interface is soft and weakly dewetted, and is characterized by enhanced interfacial density fluctuations. Formation of this interface, which induces polymer folding, is strongly opposed by enthalpy and favored by entropy, similar to the vapor-liquid interface.Comment: 24 pages, 5 figure

Computing the Probability Vectors for Random Walks on Graphs with Bounded Arboricity

The problem of detecting dense subgraphs (\emph{communities}) in large sparse graphs is inherent to many real world domains like social networking. A popular approach of detecting these communities involves first computing the \emph{probability~vectors} for \emph{random~walks} on the graph for a fixed number of steps, and then using these probability vectors to detect the communities. Such an approach has been discussed by Latapy and Pons in \cite{latapypons}. They compute the probability vectors using simple matrix multiplication and define a measure of the structural similarity between vertices which they call \emph{distance}. Based on the probability vectors, they compute the distances between vertices and then based on these distances group the vertices into communities. Their algorithm takes $O(n^2\log n)$ time where $n$ is the number of vertices in the graph. We focus on the first part of the approach i.e. computation of the probability vectors for the random walks, and propose a more efficient algorithm (than matrix multiplication) for computing these vectors in time complexity that is linear in the size of the output

Tuning density profiles and mobility of inhomogeneous fluids

Density profiles are the most common measure of inhomogeneous structure in confined fluids, but their connection to transport coefficients is poorly understood. We explore via simulation how tuning particle-wall interactions to flatten or enhance the particle layering of a model confined fluid impacts its self-diffusivity, viscosity, and entropy. Interestingly, interactions that eliminate particle layering significantly reduce confined fluid mobility, whereas those that enhance layering can have the opposite effect. Excess entropy helps to understand and predict these trends.Comment: 5 pages, 3 figure

Emergence of machine learning in the development of high entropy alloy and their prospects in advanced engineering applications

The high entropy alloys have become the most intensely researched materials in recent times. They offer the flexibility to choose a large array of metallic elements in the periodic table, a combination of which produces distinctive desirable properties that are not possible to be obtained by the pristine metals. Over the past decade, a myriad of publications has inundated the aspects of materials synthesis concerning HEA. Hitherto, the practice of HEA development has largely relied on a trial-and-error basis, and the hassles associate with this effort can be reduced by adopting a machine learning approach. This way, the “right first time” approach can be adopted to deterministically predict the right combination and composition of metallic elements to obtain the desired functional properties. This article reviews the latest advances in adopting machine learning approaches to predict and develop newer compositions of high entropy alloys. The review concludes by highlighting the newer applications areas that this accelerated development has enabled such that the HEA coatings can now potentially be used in several areas ranging from catalytic materials, electromagnetic shield protection and many other structural applications