Explore the vast range of titles available.

The application of multiphoton microscopy in the field of biomedical research and advanced diagnostics promises unique insights into the pathophysiology of inflammatory skin diseases. In the present study, we combined multiphoton-based intravital tomography (MPT) and fluorescence lifetime imaging (MPT-FLIM) within the scope of a clinical trial of atopic dermatitis with the aim of providing personalised data on the aetiopathology of inflammation in a non-invasive manner at patients’ bedsides. These ‘optical biopsies’ generated via MPT were morphologically analysed and aligned with classical skin histology. Because of its subcellular resolution, MPT provided evidence of a redistribution of mitochondria in keratinocytes, indicating an altered cellular metabolism. Two independent morphometric algorithms reliably showed an even distribution in healthy skin and a perinuclear accumulation in inflamed skin. Moreover, using MPT-FLIM, detection of the onset and progression of inflammatory processes could be achieved. In conclusion, the change in the distribution of mitochondria upon inflammation and the verification of an altered cellular metabolism facilitate a better understanding of inflammatory skin diseases and may permit early diagnosis and therapy.

Background It has been demonstrated that von Willebrand factor (VWF) mediated platelet-endothelium and platelet-platelet interactions are shear dependent. The VWF’s mobility under dynamic conditions (e.g. flow) is pivotal to platelet adhesion and VWF-mediated aggregate formation in the cascade of VWF-platelet interactions in haemostasis. Results Combining microfluidic tools with fluorescence and reflection interference contrast microscopy (RICM), here we show, that specific deletions in the A-domains of the biopolymer VWF affect both, adhesion and aggregation properties independently. Intuitively, the deletion of the A1-domain led to a significant decrease in both adhesion and aggregate formation of platelets. Nevertheless, the deletion of the A2-domain revealed a completely different picture, with a significant increase in formation of rolling aggregates (gain of function). We predict that the A2-domain effectively ‘masks’ the potential between the platelet glycoprotein (GP) Ib and the VWF A1-domain. Furthermore, the deletion of the A3-domain led to no significant variation in either of the two functional characteristics. Conclusions These data demonstrate that the macroscopic functional properties i.e. adhesion and aggregate formation cannot simply be assigned to the properties of one particular domain, but have to be explained by cooperative phenomena. The absence or presence of molecular entities likewise affects the properties (thermodynamic phenomenology) of its neighbours, therefore altering the macromolecular function.

Blood clotting is a process by which a haemostatic plug is assembled at the site of injury. The formation of such a plug, which is essentially a (bio)polymer–colloid composite, is believed to be driven by shear flow in its initial phase, and contrary to our intuition, its assembly is enhanced under stronger flowing conditions. Here, inspired by blood clotting, we show that polymer–colloid composite assembly in shear flow is a universal process that can be tailored to obtain different types of aggregates including loose and dense aggregates, as well as hydrodynamically induced ‘log’-type aggregates. The process is highly controllable and reversible, depending mostly on the shear rate and the strength of the polymer–colloidbinding potential. Our results have important implications for the assembly of polymer–colloid composites, an important challenge of immense technological relevance. Furthermore, flow-driven reversible composite formation represents a new paradigm in non-equilibrium self-assembly. Blood clotting is caused by biopolymer-mediated aggregation of platelets and is enhanced by fast shear flows. Chen et al . find a similar process that arises during the self-assembly of polymer–colloid composites—a process that can be controlled and even reversed by flow rate and interparticle interaction.

Background It has been demonstrated that von Willebrand factor (VWF) mediated platelet-endothelium and platelet-platelet interactions are shear dependent. The VWF's mobility under dynamic conditions (e.g. flow) is pivotal to platelet adhesion and VWF-mediated aggregate formation in the cascade of VWF-platelet interactions in haemostasis. Results Combining microfluidic tools with fluorescence and reflection interference contrast microscopy (RICM), here we show, that specific deletions in the A-domains of the biopolymer VWF affect both, adhesion and aggregation properties independently. Intuitively, the deletion of the A1-domain led to a significant decrease in both adhesion and aggregate formation of platelets. Nevertheless, the deletion of the A2-domain revealed a completely different picture, with a significant increase in formation of rolling aggregates (gain of function). We predict that the A2-domain effectively ‘masks’ the potential between the platelet glycoprotein (GP) Ib and the VWF A1-domain. Furthermore, the deletion of the A3-domain led to no significant variation in either of the two functional characteristics. Conclusions These data demonstrate that the macroscopic functional properties i.e. adhesion and aggregate formation cannot simply be assigned to the properties of one particular domain, but have to be explained by cooperative phenomena. The absence or presence of molecular entities likewise affects the properties (thermodynamic phenomenology) of its neighbours, therefore altering the macromolecular function.

We experimentally and numerically investigate the flow of a Newtonian fluid through a constricted geometry for Reynolds numbers in the range \\(0.1 - 100\\). The major aim is to study non-linear inertia effects at larger Reynolds numbers (>10) on the shear stress evolution in the fluid. This is of particular importance for blood flow as some biophysical processes in blood are sensitive to shear stresses, e.g., the initialization of blood clotting. We employ the lattice Boltzmann method for the simulations. The conclusion of the predictions is that the peak value of shear stress in the constriction grows disproportionally fast with the Reynolds number which leads to a non-linear shear stress accumulation. As a consequence, the combination of constricted blood vessel geometries and large Reynolds numbers may increase the risk of undesired blood clotting.

Corporate Social Responsibility (CSR) has been an articulated practice for over 7 decades. Still, most corporations lack an integrated framework to develop a strategic, balanced, and effective approach to achieving excellence in CSR. Considering the world’s critical situation during the COVID-19 pandemic, such a framework is even more crucial now. We suggest subsuming CRS categories under Sustainable Development Goals (SDGs) be used and that they subsume CSR categories since SDGs are a comprehensive agenda designed for the whole planet. This study presents a new CSR drivers model and a novel comprehensive CSR model. Then, it highlights the advantages of integrating CSR and SDGs in a new framework. The proposed framework benefits from both CSR and SDGs, addresses current and future needs, and offers a better roadmap with more measurable outcomes.

This study investigates the application of physics-informed neural networks (PINN) for bending and free vibration analysis of three-dimensional functionally graded (TDFG) porous beams. The beam material properties are assumed to vary continuously in three dimensions according to an arbitrary function. The governing equations of motion are obtained using Hamilton's principle and solved by a PINN computational approach. The beam deflection is approximated with a deep feedforward neural network which its input is the spatial coordinate. The network parameters are trained by minimizing a loss function comprised of the governing differential equation and the boundary conditions. The beam natural frequency is considered as an unknown parameter in the governing equation; thus, it has to be obtained by solving an inverse problem. This procedure makes it possible to find higher modes’ natural frequencies, which is impossible according to the previous PINN methods. A systematic procedure for tuning the network's hyperparameters is done based on the Taguchi design of the experiment and the grey relational analysis. The PINN results are validated with analytical and numerical reference solutions. Effects of material distribution, elastic foundation and porosity factor, and porosity distribution type on the bending behavior and natural frequencies of TDFG beams are investigated.

Energy management systems are designed to monitor, optimize, and control the smart grid energy market. Demand-side management, considered as an essential part of the energy management system, can enable utility market operators to make better management decisions for energy trading between consumers and the operator. In this system, a priori knowledge about the energy load pattern can help reshape the load and cut the energy demand curve, thus allowing a better management and distribution of the energy in smart grid energy systems. Designing a computationally intelligent load forecasting (ILF) system is often a primary goal of energy demand management. This study explores the state of the art of computationally intelligent (i.e., machine learning) methods that are applied in load forecasting in terms of their classification and evaluation for sustainable operation of the overall energy management system. More than 50 research papers related to the subject identified in existing literature are classified into two categories: namely the single and the hybrid computational intelligence (CI)-based load forecasting technique. The advantages and disadvantages of each individual techniques also discussed to encapsulate them into the perspective into the energy management research. The identified methods have been further investigated by a qualitative analysis based on the accuracy of the prediction, which confirms the dominance of hybrid forecasting methods, which are often applied as metaheurstic algorithms considering the different optimization techniques over single model approaches. Based on extensive surveys, the review paper predicts a continuous future expansion of such literature on different CI approaches and their optimizations with both heuristic and metaheuristic methods used for energy load forecasting and their potential utilization in real-time smart energy management grids to address future challenges in energy demand management.

The primary focus of autonomous driving research is to improve driving accuracy and reliability. While great progress has been made, state-of-the-art algorithms still fail at times and some of these failures are due to the faults in sensors. Such failures may have fatal consequences. It therefore is important that automated cars foresee problems ahead as early as possible. By using real-world data and artificial injection of different types of sensor faults to the healthy signals, data models can be trained using machine learning techniques. This paper proposes a novel fault detection, isolation, identification and prediction (based on detection) architecture for multi-fault in multi-sensor systems, such as autonomous vehicles.Our detection, identification and isolation platform uses two distinct and efficient deep neural network architectures and obtained very impressive performance. Utilizing the sensor fault detection system’s output, we then introduce our health index measure and use it to train the health index forecasting network.

In this paper, a multi-objective sustainable biomass supply chain network under uncertainty is designed by neutrosophic programming method. In this method, for each objective function of the problem, three functions of truth membership, non-determination and falsehood are considered. Neutrosophic programming method in this paper simultaneously seeks to optimize the total costs of the supply chain network, the amount of greenhouse gas emissions, the number of potential people hired and the time of product transfer along the supply chain network. To achieve the stated objective functions, strategic decisions such as locating potential facilities and tactical decisions such as optimal product flow allocation and vehicle routing must be made. The results of the implementation of neutrosophic programming method show the high efficiency of this method in achieving the optimal values of each objective function. Also, by examining the rate of uncertainty, it was observed that with increasing this rate, the total costs of supply chain network design, greenhouse gas emissions and product transfer times have increased, and in contrast, the potential employment rate of individuals has decreased.