THE IMPACT OF REVENUE DIVERSIFICATION AND ECONOMIC BASE ON REVENUE STABILITY: AN EMPIRICAL ANALYSIS OF COUNTY AND STATE GOVERNMENTS

In recent decades, revenue diversification has become a prevalent practice in state and local government finance. The trend of revenue diversification, according to the portfolio theory, has far-reaching implication for public financial management as it may change revenue stability, which has been an important policy objective for state and local government administrators. This study explores how revenue diversification affects revenue stability from both empirical and theoretical perspectives. Drawing on portfolio theory and regional science literature, this study develops a theoretical framework to explain how the effect of revenue diversification on revenue volatility of sub-national governments varies in terms of its economic base instability. To empirically test the theoretical framework, an econometric model that explores a series of factors that could affect revenue stability is estimated using socioeconomic and fiscal data of 156 Georgia county governments and 47 state governments during the years 1986-2004. The findings indicate that revenue diversification affects revenue stability conditional on the instability of a jurisdiction’s economic base. The county level analysis suggests revenue diversification significantly increases the revenue instability of a county that has a stable economic base and the revenue stabilizing effect of diversification is enhanced as an economic base becomes more unstable. However, the state level analysis shows that revenue diversification significantly reduces revenue volatility for a state that has a stable economic base and the revenue stabilizing effect of diversification decreases when an economic base gets more unstable. An important policy implication of the dissertation is that the degree of revenue diversification should be gauged by the condition of its corresponding economic base in order to achieve the goal of revenue stability

Deep reinforcement transfer learning for active flow control of a 3D square cylinder under state dimension mismatch

This paper focuses on developing a deep reinforcement learning (DRL) control strategy to mitigate aerodynamic forces acting on a three dimensional (3D) square cylinder under high Reynolds number flow conditions. Four jets situated at the corners of the square cylinder are used as actuators and pressure probes on the cylinder surface are employed as feedback observers. The Soft Actor-Critic (SAC) algorithm is deployed to identify an effective control scheme. Additionally, we pre-train the DRL agent using a two dimensional (2D) square cylinder flow field at a low Reynolds number (Re =1000), followed by transferring it to the 3D square cylinder at Re =22000. To address the issue of state dimension mismatch in transfer learning from 2D to 3D case, a state dimension mismatch transfer learning method is developed to enhance the SAC algorithm, named SDTL-SAC. The results demonstrate transfer learning across different state spaces achieves the same control policy as the SAC algorithm, resulting in a significant improvement in training speed with a training cost reduction of 51.1%. Furthermore, the SAC control strategy leads to a notable 52.3% reduction in drag coefficient, accompanied by substantial suppression of lift fluctuations. These outcomes underscore the potential of DRL in active flow control, laying the groundwork for efficient, robust, and practical implementation of this control technique in practical engineering

Dynamic Feature-based Deep Reinforcement Learning for Flow Control of Circular Cylinder with Sparse Surface Pressure Sensing

This study proposes a self-learning algorithm for closed-loop cylinder wake control targeting lower drag and lower lift fluctuations with the additional challenge of sparse sensor information, taking deep reinforcement learning as the starting point. DRL performance is significantly improved by lifting the sensor signals to dynamic features (DF), which predict future flow states. The resulting dynamic feature-based DRL (DF-DRL) automatically learns a feedback control in the plant without a dynamic model. Results show that the drag coefficient of the DF-DRL model is 25% less than the vanilla model based on direct sensor feedback. More importantly, using only one surface pressure sensor, DF-DRL can reduce the drag coefficient to a state-of-the-art performance of about 8% at Re = 100 and significantly mitigate lift coefficient fluctuations. Hence, DF-DRL allows the deployment of sparse sensing of the flow without degrading the control performance. This method also shows good robustness in controlling flow under higher Reynolds numbers, which reduces the drag coefficient by 32.2% and 46.55% at Re = 500 and 1000, respectively, indicating the broad applicability of the method. Since surface pressure information is more straightforward to measure in realistic scenarios than flow velocity information, this study provides a valuable reference for experimentally designing the active flow control of a circular cylinder based on wall pressure signals, which is an essential step toward further developing intelligent control in realistic multi-input multi-output (MIMO) system

Chinese Family Strengths and Resiliency

Chinese family and marriage strengths and challenges are delineated in this article, including equity in marriage, affection, the ability to adapt to changes, mutual trust, compatibility, harmony, and family support. Despite the fact that Chinese households are getting smaller as a result of governmental policy and the broadening of housing markets, families remain crucial support networks, especially in the areas of socialization and intergenerational relationships. Current research on Chinese marriages and families is cited, outlining attitudinal changes regarding mate selection, divorce, and childbirth between genders, between older and younger generations, and between urban and rural residents

Research on UBI Auto Insurance Pricing Model Based on Parameter Adaptive SAPSO Optimal Fuzzy Controller

Aiming at the problem of “dynamic” accurate determination of rates in UBI auto insurance pricing, this paper proposes a UBI auto insurance pricing model based on fuzzy controller and optimizes it with a parameter adaptive SASPO. On the basis of the SASPO algorithm, the movement direction of the particles can be mutated and the direction can be dynamically controlled, the inertia weight value is given by the distance between the particle and the global optimal particle, and the learning factor is calculated according to the change of the fitness value, which realizes the parameter in the running process. Effective self-adjustment. A five-dimensional fuzzy controller is constructed by selecting the monthly driving mileage, the number of violations, and the driving time at night in the UBI auto insurance data. The weights are used to form fuzzy rules, and a variety of algorithms are used to optimize the membership function and fuzzy rules and compare them. The research results show that, compared with other algorithms, the parameter adaptive SAPAO algorithm can calculate more reasonable, accurate and high-quality fuzzy rules and membership functions when processing UBI auto insurance data. The accuracy and robustness of UBI auto insurance rate determination can realize dynamic and accurate determination of UBI auto insurance rates

Generation of an external guide sequence library for a reverse genetic screen in Caenorhabditis elegans

<p>Abstract</p> <p>Background</p> <p>A method for inhibiting the expression of particular genes using external guide sequences (EGSs) has been developed in bacteria, mammalian cells and maize cells.</p> <p>Results</p> <p>To examine whether EGS technology can be used to down-regulate gene expression in <it>Caenorhabditis elegans </it>(<it>C. elegans</it>), we generated EGS-Ngfp-lacZ and EGS-Mtgfp that are targeted against <it>Ngfp-lacZ </it>and <it>Mtgfp </it>mRNA, respectively. These EGSs were introduced, both separately and together, into the <it>C. elegans </it>strain PD4251, which contains <it>Ngfp-lacZ </it>and <it>Mtgfp</it>. Consequently, the expression levels of <it>Ngfp-lacZ </it>and <it>Mtgfp </it>were affected by EGS-Ngfp-lacZ and EGS-Mtgfp, respectively. We further generated an EGS library that contains a randomized antisense domain of tRNA-derived EGS ("3/4 EGS"). Examination of the composition of the EGS library showed that there was no obvious bias in the cloning of certain EGSs. A subset of EGSs was randomly chosen for screening in the <it>C. elegans </it>strain N2. About 6% of these EGSs induced abnormal phenotypes such as P0 slow postembryonic growth, P0 larval arrest, P0 larval lethality and P0 sterility. Of these, EGS-35 and EGS-83 caused the greatest phenotype changes, and their target mRNAs were identified as ZK858.7 mRNA and <it>Lin-13 </it>mRNA, respectively.</p> <p>Conclusion</p> <p>EGS technology can be used to down-regulate gene expression in <it>C. elegans</it>. The EGS library is a research tool for reverse genetic screening in <it>C. elegans</it>. These observations are potentially of great importance to further our understanding and use of <it>C. elegans </it>genomics.</p

Phase Reshaping via All-Pass Filters for Robust LCL-Filter Active Damping

Active damping is a common way to stabilize the current control of LCL-filtered converters. In this paper, the stable region of-180° phase crossing is first identified within a predefined range of grid impedance and LCL parameter variations. Once the phase of the current control loop is in the identified region, a stabilization control can be attained. Subsequently, digital filters can be adopted to achieve active damping by reshaping the open-loop phase. Various digital filters are selected and benchmarked in this paper. It is confirmed that the all-pass filter has a unity gain and adjustable lagging phase before the Nyquist frequency, thereby being a promising solution to the phase reshaping. Therefore, the all-pass filter is employed to move the phase of the open-loop control (i.e.,-180° phase crossing) into the targeted region for active damping. Notably, the current controller and the all-pass filter-based active damping can be separately designed, indicating the easy implementation of the active damping. Experimental tests demonstrate that the proposed method can ensure the system stability over a wide range of parameter variations (e.g., grid impedance changes and LCL-filter parameter drifts) while maintaining fast dynamics with the grid-side current control

LncRNA Expression Profiling of Ischemic Stroke During the Transition From the Acute to Subacute Stage

Ischemic stroke induces profound effects on the peripheral immune system, which may participate the infectious complications. However, the exact function and mechanism of immune reaction in stroke development are not well-elucidated. Recently, several long non-coding RNAs (LncRNAs) are reported to affect ischemic stroke process, especially the immunological response after stroke. In the present study, we investigated the profile of LncRNAs in human ischemic stroke during the transition from the acute to subacute stage, when the state of the peripheral immune system changes from activation to systemic immunosuppression. In this study, we analyzed the RNA-sequencing (RNA-seq) datasets obtained at two time points (24 h and 7 days) from the peripheral blood mononuclear cells of ischemic patients. Vascular risk factor-matched healthy adults were enrolled as controls. A total of 3,009 LncRNAs and 3,982 mRNAs were identified as differentially expressed 24 h after stroke. Furthermore, 2,034 LncRNAs and 1,641 mRNAs were detected to be differentially expressed on day 7. Bioinformatics analyses, including GO, KEGG pathway enrichment analysis, and network analysis, were performed for the identified dysregulated genes. Our study reveals that ischemic stroke can influence the expression of LncRNAs and mRNAs in the peripheral blood at both the acute and subacute stages; the level of LncRNAs in the antigen processing and presentation pathway was clearly upregulated at 24 h and had recovered to normal levels on day 7 after stroke. Moreover, inflammatory mediator regulation of TRP channels and GABAergic synapses were two specifically downregulated pathways on day 7 after stroke. Our findings provide a valuable resource for further study of the role of LncRNAs in peripheral immune system changes following ischemic stroke

Managing Risk and Growth of Nonprofit Revenue

Managers of nonprofit organizations are challenged to manage revenue growth and risk (i.e., volatility) in order to sustain current and future financial operations. Although the negative repercussions of revenue risk are generally perceived as undesirable, not all risk is bad. If higher levels of revenue risk are compensated with a greater amount of revenue growth, then organizations may rationally pursue volatile revenues that produce growth. This article examines the extent to which a reliance on major revenue sources by nonprofit organizations affects the magnitude of total revenue volatility as well as the pace of total revenue growth. A monitoring application is introduced that can be used to compare the effectiveness of revenue management among similar nonprofit organizations. It can also be used to guide nonprofit managers striving to achieve sustainable financial growth for their organizations