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Two Los Angeles stylists do easy lifestyle jeans rich with history.

Madly mismatched suits star this spring: Smart little jackets mixed and mingled with all manner of relaxed tops and whisper-weight skirts.

Flexible sensors based on laser‐induced graphene (LIG) are widely used in wearable personal devices, with the morphology and lattice arrangement of LIG the key factors affecting their performance in various applications. In this study, femtosecond‐laser‐induced MXene‐composited graphene (LIMG) is used to improve the electrical conductivity of graphene by incorporating MXene, a 2D material with a high concentration of free electrons, into the LIG structure. By combining pump‐probe detection, laser‐induced breakdown spectroscopy (LIBS), and density functional theory (DFT) calculations, the morphogenesis and lattice structuring principles of LIMG is explored, with the results indicating that MXene materials are successfully embedded in the graphene lattice, altering both their morphology and electrical properties. The structural sparsity and electrical conductivity of LIMG composites (up to 3187 S m−1) are significantly enhanced compared to those of LIG. Based on these findings, LIMG has been used in wearable electronics. LIMG electrodes are used to detect uric acid, with a minimum detection limit of 2.48 µM. Additionally, LIMG‐based pressure and bending sensors have been successfully used to monitor human limb movement and pulse. The direct in situ femtosecond laser patterning synthesis of LIMG has significant implications for developing flexible wearable electronic sensors. In this study, femtosecond‐laser‐induced MXene‐composited graphene (LIMG) is successfully synthesized. Transmission electron microscopy （TEM） analysis shows that the lattice structures of MXene and graphene are interspersed with each other. The study also elucidates the mechanism of LIMG enhanced conductivity. LIMG is successfully applied to a high‐performance multifunctional sensing platform.

This review is an account of centrifugal microfluidic systems that use various actuation strategies in addition to intrinsic centrifugal forces to accurately regulate the motion of fluids during rotation. Platforms that integrate active methods of pumping and flow control render centrifugal microfluidics more versatile as they facilitate integration and process automation by enabling (or improving the reliability of) important fluidic functions, such as metering, aliquoting, valving, flow switching, mixing, and inward pumping. Principles and working mechanisms underlying these strategies are described in the context of recent trends in instrument design and development where centrifugal platforms have been equipped with pneumatic, magnetic or electromechanical actuators serving as pumping and valving elements. The potential of these platforms to perform complex bioanalytical assays in an automated fashion is illustrated by several examples, which include on-chip preparation of aliquot libraries, nucleic acid purification, amplification and analysis as well as blood separation.

Glioblastoma (GBM) remains one of the most difficult tumors to treat. The mean overall survival rate of 15 months and the 5-year survival rate of 5% have not significantly changed for almost 2 decades. Despite progress in understanding the pathophysiology of the disease, no new effective treatments to combine with radiation therapy after surgical tumor debulking have become available since the introduction of temozolomide in 1999. One of the main reasons for this is the scarcity of compounds that cross the blood–brain barrier (BBB) and reach the brain tumor tissue in therapeutically effective concentrations. In this review, we focus on the role of the BBB and its importance in developing brain tumor treatments. Moreover, we discuss drug repurposing, a drug discovery approach to identify potential effective candidates with optimal pharmacokinetic profiles for central nervous system (CNS) penetration and that allows rapid implementation in clinical trials. Additionally, we provide an overview of repurposed candidate drug currently being investigated in GBM at the preclinical and clinical levels. Finally, we highlight the importance of phase 0 trials to confirm tumor drug exposure and we discuss emerging drug delivery technologies as an alternative route to maximize therapeutic efficacy of repurposed candidate drug.

It is urgent that the built environment transitions towards renewable energies and adopts ambitious renovations strategies as both are unavoidable steps towards long term sustainability goals. While extensive research has been focused on the integration of buildings in renewable energy production networks as well as on renovation measures such as thermal insulation and installation of heat-pumps, it is still unclear how these actions perform at scale when combined together. To fill this gap, this paper introduces the digital platform Open-data for Building Energy Efficiency, Renovation and Storage (OpenBEERS) that allows simulation of renewable energy deployment scenarios at the urban scale to help municipalities and energy professionals plan transition strategies effectively. The platform combines the state-of-the-art CitySim physics-based solver with the BASOPRA optimization framework allowing us to assess the techno-economic feasibility of distributed energy resources (DERs), including heat pumps and electric vehicles (EVs). Out tool integrates a 3D interface for the visualization of the input and output data. Our approach is validated with three case studies, municipalities in the State of Valais where a detailed analysis of DERs deployment scenarios is conducted.

To address the challenges associated with winter heating in high-latitude regions of China, solar-assisted biogas heating systems have emerged as the predominant focus of research due to their cost-effectiveness, accessibility, and environmentally friendly attributes. However, traditional solar-assisted biogas heating systems encounter issues of low efficiency and limited practicality resulting from unstable solar radiation and extreme ambient temperatures during the heating season. To improve the economy and stability of the system, this study proposed a novel operational control method for a small-scale energy station system in rural regions of North China, named Solar-Biomass-Air Source Heat Pump Hybrid Heating System (SBHP-HHS). The integration of solar energy, biogas energy, and air-source heat pump (ASHP) systems in this proposed work has shown to create effective complementarity and enhances the production efficiency of the existing system. Test and simulation studies have been carried out for this system. The layout of buildings and equipment within a university campus in Beijing is reconfigured and redesigned, incorporating an ASHP into the existing heat source configuration. To begin with, a mathematical model is established for the complementary heating system that incorporates solar energy, a biogas digester, and an ASHP. Subsequently, a dynamic simulation model is developed using the TRNSYS platform, and a corresponding operational control strategy for the multi-energy complementary heating system is proposed. Dynamic simulation and analysis of the newly implemented system are performed using the TRNSYS platform, focusing on energy flow and thermodynamic performance. Throughout the heating season, the solar-biogas integrated system achieves a remarkable assurance rate of up to 79%. Additionally, ASHP maintains a relatively high heating efficiency, coefficient of performance (COP) reaches 3.02. Finally, an economic evaluation of the multi-energy complementary system was conducted based on the annual cost method. This was compared with the clean approach of using only an ASHP unit. The results indicate that the SBHP-HHS is more economical when the anticipated useful life is 6 years or longer. The results indicate that the proposed can achieve significant energy-saving and carbon-reduction benefits in rural areas, catering to their heating needs.

Based on the effect of damped shear deformation on energy dissipation, a new constrained damping base for a polymer injection platform deck is proposed to reduce the excessive vibrations caused when multiple plunger pumps are jointly operated. A model for analyzing the vibration response of an I-beam-constrained damping base for a polymer injection platform with multiple plunger pumps was established using Abaqus 6.14 software and compared with rigid base and traditional rubber vibration isolators in terms of its vibration isolation performance. Furthermore, the effects of the damping material’s loss factor, the thickness of the damping layer, and the number of expansion layers on the vibration isolation characteristics of the constrained damping base were explored. This study shows that, with an increase in the damping material’s loss factor, the thickness of the damping layer and the number of extended layers, the vibration isolation performance of the constrained damping base is gradually enhanced. When the damping material’s loss factor is 1.0, the thickness of the damping layer is 20 mm, and the number of extended layers is 3, the constrained damping base’s vibration damping effect is optimized, and its vibration isolation rate becomes as high as 46.63%, which can significantly reduce the vibration response of the polymer injection platform.

Mixed flow pumps driven by hydraulic motors have been widely used in drainage in recent years, especially in emergency pump trucks. Limited by the power of the truck engine, its operating efficiency is one of the key factors affecting the rescue task. In this study, an automated optimization platform was developed to improve the operating efficiency of the mixed flow pump. A three-dimensional hydraulic design, meshing, and computational fluid dynamics (CFD) were executed repeatedly by the main program. The objective function is to maximize hydraulic efficiency under design conditions. Both meridional shape and blade profiles of the impeller and diffuser were optimized at the same time. Based on the CFD results obtained by Optimal Latin Hypercube (OLH) sampling, surrogate models of the head and hydraulic efficiency were built using the Radial Basis Function (RBF) neural network. Finally, the optimal solution was obtained by the Multi- Island Genetic Algorithm (MIGA). The local energy loss was further compared with the baseline scheme using the entropy generation method. Through the regression analysis, it was found that the blade angles have the most significant influence on pump efficiency. The CFD results show that the hydraulic efficiency under design conditions increased by 5.1%. After optimization, the incidence loss and flow separation inside the pump are obviously improved. Additionally, the overall turbulent eddy dissipation and entropy generation were significantly reduced. The experimental results validate that the maximum pump efficiency increased by 4.3%. The optimization platform proposed in this study will facilitate the development of intelligent optimization of pumps.