The optimization of heat conduction is a critical task with widespread applications, and the approaches are typically categorized into two main categories: thermal conductivity distribution ...optimization (TCDO) and heat source layout optimization (HSLO). While extensive research efforts have been devoted to each of these two categories, standalone TCDO and HSLO limit the design possibilities and may lead to suboptimal solutions. In this work, a collaborative methodology combining TCDO and HSLO is proposed by transforming the collaborative optimization problem into a two-level nested problem. In this approach, TCDO forms the inner subproblem, tackled using the gradient-based method, while HSLO constitutes the outer subproblem addressed through Bayesian optimization (BO). The proposed method is employed to solve two problem cases involving volume-to-point and volume-to-edge boundaries, respectively. The results demonstrate that the present method is capable of achieving collaborative optimization of TCDO and HSLO for both scenarios of continuous and discrete thermal conductivity distributions. Comparing with standalone TCDO and HSLO that reduce the average temperature by ▪ and ▪, respectively, the proposed method achieves a significantly greater reduction of ▪, underscoring its efficacy. We anticipate that the proposed method will serve as a valuable tool for optimizing heat conduction across diverse applications, and its adaptable framework holds promise for addressing broader optimization challenges.
•Thermal conductivity and heat source layout are collaboratively optimized.•A nested optimization framework is proposed for the collaborative optimization.•Collaborative optimization outperforms standalone ones by 38.26 K and 82.55 K.•The proposed method is adaptive and efficient for wider range of problems.
Ideally, 360° imagery could inherit the deep convolutional neural networks (CNNs) already trained with great success on perspective projection images. However, existing methods to transfer CNNs from ...perspective to spherical images introduce significant computational costs and/or degradations in accuracy. We present the Kernel Transformer Network (KTN) to efficiently transfer convolution kernels from perspective images to the equirectangular projection of 360° images. Given a source CNN for perspective images as input, the KTN produces a function parameterized by a polar angle and kernel as output. Given a novel 360° image, that function in turn can compute convolutions for arbitrary layers and kernels as would the source CNN on the corresponding tangent plane projections. Distinct from all existing methods, KTNs allow model transfer: the same model can be applied to different source CNNs with the same base architecture. This enables application to multiple recognition tasks without re-training the KTN. Validating our approach with multiple source CNNs and datasets, we show that KTNs improve the state of the art for spherical convolution. KTNs successfully preserve the source CNN's accuracy, while offering transferability, scalability to typical image resolutions, and, in many cases, a substantially lower memory footprint.
Extracellular vesicles (EVs) are lipid‐bilayer membrane structures secreted by most cell types. EVs act as messengers via the horizontal transfer of lipids, proteins, and nucleic acids, and influence ...various pathophysiological processes in both parent and recipient cells. Compared to EVs obtained from body fluids or cell culture supernatants, EVs isolated directly from tissues possess a number of advantages, including tissue specificity, accurate reflection of tissue microenvironment, etc., thus, attention should be paid to tissue‐derived EVs (Ti‐EVs). Ti‐EVs are present in the interstitium of tissues and play pivotal roles in intercellular communication. Moreover, Ti‐EVs provide an excellent snapshot of interactions among various cell types with a common histological background. Thus, Ti‐EVs may be used to gain insights into the development and progression of diseases. To date, extensive investigations have focused on the role of body fluid‐derived EVs or cell culture‐derived EVs; however, the number of studies on Ti‐EVs remains insufficient. Herein, we summarize the latest advances in Ti‐EVs for cancers and non‐cancer diseases. We propose the future application of Ti‐EVs in basic research and clinical practice. Workflows for Ti‐EV isolation and characterization between cancers and non‐cancer diseases are reviewed and compared. Moreover, we discuss current issues associated with Ti‐EVs and provide potential directions.
Heat conduction optimization with arbitrary boundary conditions is a challenging problem that lacks a universal optimization criterion. In the present work, the concept of generalized entransy ...dissipation (GED) is proposed through transforming heat conduction optimization problems with arbitrary boundaries into their homogeneous counterparts. It is demonstrated that minimizing GED leads to optimal thermal performance of heat conduction problems with arbitrary boundary conditions. In addition, GED-based continuous optimization problems are convex, guaranteeing the uniqueness and global optimality of the solution and benefitting numerical calculations. Two typical problems with complex boundary conditions are studied by applying the minimum principle of GED, and the results are compared with other optimization objectives. The numerical results show that GED achieves better thermal performance than entropy generation- (EG) and entransy dissipation- (ED) based optimizations. For the optimization of boundary average temperature under the given input heat flux of ▪, GED achieves the best result, where the optimized average temperature is ▪ and ▪ lower compared with EG and ED optimizations, respectively. In general, GED offers a reasonable and easy to implement optimization principle for heat conduction processes with arbitrary boundaries and may provide new insights for heat conduction optimization.
•GED-criterion is proposed for optimizing heat conduction with arbitrary boundaries.•Minimizing GED leads to optimal thermal performance of complex boundary conditions.•Convexity of GED-based continuous optimization guarantees global optimality.•GED outperforms EGMP and EDEP, lowering average temperature by 48.8 and 27.5 K.•GED offers a universal and easy-to-use principle for heat conduction processes.
Drawing on social exchange theory, this study examines the relationship between internal corporate social responsibility (CSR) and hospitality employees' customer-oriented organizational citizenship ...behavior (CO-OCB). The role of three distinct aspects (work, family, and life aspects) function as parallel mediating mechanisms, with organizational tenure as a moderator. The sample consisted of 156 flight attendants working in airlines based in Asian countries. The findings show that internal CSR (i.e., legal employment, training, internal dissemination, compensation, health and safety) promotes CO-OCB. Job satisfaction and work-family facilitation (WFF) partially mediate the positive relationship between internal CSR and CO-OCB, whereas life satisfaction does not serve the same function. The multiple mediation findings indicate that in stimulating flight attendants' CO-OCB, WFF accounts for more effects of internal CSR than job satisfaction does. Findings also suggest that the impact of internal CSR on CO-OCB is more pronounced among long-tenured than short-tenured employees. This study broadens the tourism literature by advancing our comprehension of not only the multiple mediating mechanisms involved but also the boundary conditions that influence the impact of internal CSR on CO-OCB.
A representative condensation of acrylonitrile and aryl acetonitrile has been reported for the synthesis of α-amino-β-cyano cyclohexene. The reaction was carried out mildly in an open environment at ...room temperature. The scope and versatility of the method have been demonstrated with 20 examples, containing highly active ethynyl groups. Further applications for 4-aminopyrimidine compounds were performed. A mechanism was proposed, involving Michael additions between acrylonitrile and aryl acetonitriles as well as intramolecular condensation.
A condensation reaction between acrylonitrile and benzyl cyanide for the synthesis of α-amino-β-cyano cyclohexene was reported. The reaction could be carried out mildly with high atomic efficiency to build the cyclohexene skeleton.
A delay-locked loop (DLL) circuit is indispensable for clock synchronization in a chip incorporating several heterogeneous dice. It has been shown previously that a fault and soft-error-tolerant DLL ...can be achieved by triple-module redundancy (TMR) enhanced with a timing correction scheme. However, the prior work still has a severe limitation-it does not consider the latency of the clock tree, and this limitation will make it infeasible in realistic situations. We demonstrate in this article that this limitation can be overcome by a new "clock-latency-aware" architecture, thereby making a fault and soft-error-tolerant DLL truly realistic.
Studies have revealed that the failure rates of storage devices can often be as high as fourteen percent. To make matters worse, there are frequently no warning signs for precaution before ...catastrophic failure of storage devices occurs. A real-time predictive maintenance system that provides an automatic means for predicting when maintenance should be performed to ultimately eliminate unexpected breakdowns needs to be developed. Unlike traditional regression predictive modeling, the failure detection of storage devices is a problem of time series prediction, which adds the complexity of a sequence dependence among the input variables. The proposed LSTM (Long Short-Term Memory) network is a branch of RNN (Recurrent Neural Network) used in deep learning, which presents a very large architecture that can be successfully trained. LSTM is good at extracting patterns in input feature space, where the input data spans over long sequences. With the gated architecture of LSTM, it is capable of learning the context required to make predictions in time series forecasting. It is ideal for generating responses that depend on a time-evolving state; for example detecting the condition of storage devices over time. This paper describes our development of an LSTM (Long short-term memory), a special kind of RNN (Recurrent Neural Network)—based real-time predictive maintenance system (RPMS) built on top of Apache Spark for detecting storage device failure. By streaming real-time data into a RPMS directly from the device itself, the issues can be revealed and addressed early before they cause costly downtime.
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