Gender Discrimination towards Borrowers in Online P2PLending

Online peer-to-peer (P2P) lending has developed fast around the world in recent years; however, studies regarding gender discrimination and its rationality for developing countries are limited. Gender discrimination towards borrowers and its rationality in P2P lending in China are studied in this paper. Using data collected from PPdai.com, one of the largest P2P lending platforms in China, we found that, female borrowers are less likely to be funded than male borrowers, but their default rates are lower. Such results suggested that there is significant gender discrimination in P2P lending market in China, but such discrimination is out of prejudice rather than from rational reasoning. Eliminating such gender discrimination is not only beneficial to female borrowers, but also helpful for improving returns of lenders

Toward an Understanding of Online Lending Intentions: Evidence from a Survey in China

The online peer-to-peer lending marketplace has experienced rapid growth since its inception in 2005. It has played a significant role in helping small and micro-enterprises resolve financing problems. However, this marketplace is still in its infant stage. To better understand the lending activities associated with peer-to-peer lending, we need theoretically grounded empirical research. In this study, we investigate the perceptual drivers of online lending from the perspective of lenders. We empirically test our research model with survey data collected from 217 lenders in a major online peer-to-peer lending website in China. Our results reveal that trust was the most critical determinant of willingness to lend. Perceived information quality was important in mitigating perceived risk and enhancing trust, and perceived social capital impacted trust in borrowers. Furthermore, perceived risk did not significantly influence lending willingness, but had a negative impact on trust. These findings indicate that transaction behaviors in the peer-to-peer market may not be the same as that in the purchase-oriented e-commerce settings. We conclude by discussing the study’s implications for research and practice along with the its limitations

Optimized Non-Primary Channel Access Design in IEEE 802.11bn

The IEEE 802.11 standards, culminating in IEEE 802.11be (Wi-Fi 7), have significantly expanded bandwidth capacities from 20 MHz to 320 MHz, marking a crucial evolution in wireless access technology. Despite these advancements, the full potential of these capacities remains largely untapped due to inefficiencies in channel management, in particular, the underutilization of secondary (non-primary) channels when the primary channel is occupied. This paper delves into the Non-Primary Channel Access (NPCA) protocol, initially proposed by the IEEE 802.11 Ultra-High Reliability (UHR) group, aimed at addressing these inefficiencies. Our research not only proposes an analytical model to assess the throughput of NPCA in terms of average throughput but also crucially identifies that the overhead associated with the NPCA protocol is significant and cannot be ignored. This overhead often undermines the effectiveness of the NPCA, challenging the assumption that it is invariably superior to traditional models. Based on these findings, we have developed and simulated a new hybrid model that dynamically integrates the strengths of both legacy and NPCA models. This model overall outperforms the existing models under all channel occupancy conditions, offering a robust solution to enhance throughput efficiency.Comment: This work has been submitted to the IEEE for possible publication. 6 pages, 5 figure

Learning for Semantic Knowledge Base-Guided Online Feature Transmission in Dynamic Channels

With the proliferation of edge computing, efficient AI inference on edge devices has become essential for intelligent applications such as autonomous vehicles and VR/AR. In this context, we address the problem of efficient remote object recognition by optimizing feature transmission between mobile devices and edge servers. We propose an online optimization framework to address the challenge of dynamic channel conditions and device mobility in an end-to-end communication system. Our approach builds upon existing methods by leveraging a semantic knowledge base to drive multi-level feature transmission, accounting for temporal factors and dynamic elements throughout the transmission process. To solve the online optimization problem, we design a novel soft actor-critic-based deep reinforcement learning system with a carefully designed reward function for real-time decision-making, overcoming the optimization difficulty of the NP-hard problem and achieving the minimization of semantic loss while respecting latency constraints. Numerical results showcase the superiority of our approach compared to traditional greedy methods under various system setups.Comment: 6 page

Non-Primary Channel Access in IEEE 802.11 UHR: Comprehensive Analysis and Evaluation

The evolution of the IEEE 802.11 standards marks a significant throughput advancement in wireless access technologies, progressively increasing bandwidth capacities from 20 MHz in the IEEE 802.11a to up to 320 MHz in the latest IEEE 802.11be (Wi-Fi 7). However, the increased bandwidth capacities may not be well exploited due to inefficient bandwidth utilization on multiple channels. This issue typically occurs when the primary channel is busy, secondary channels (also known as non-primary channels) are prevented from being utilized even if they are idle, thereby wasting the available bandwidth. This paper investigates the fundamentals of the Non-Primary Channel Access (NPCA) protocol that was defined in IEEE 802.11 Ultra-High Reliability (UHR) group to cope with the above issue. We develop a novel analytical model to assess NPCA protocol performance in terms of the average throughput and delay. Via simulation, we verify that the NPCA network outperforms the legacy network by increasing at least 50% average throughput while reducing at least 40% average delay.Comment: This work has been submitted to the IEEE for possible publication. 6 pages, 7 figure

Abnormal alterations in the Ca2+/CaV1.2/calmodulin/caMKII signaling pathway in a tremor rat model and in cultured hippocampal neurons exposed to Mg2+-free solution

Voltage-dependent calcium channels (VDCCs) are key elements in epileptogenesis. There are several binding-sites linked to calmodulin (CaM) and several potential CaM-dependent protein kinase II (CaMKII)-mediated phosphorylation sites in CaV1.2. The tremor rat model (TRM) exhibits absence-like seizures from 8 weeks of age. The present study was performed to detect changes in the Ca(2+)/CaV1.2/CaM/CaMKII pathway in TRMs and in cultured hippocampal neurons exposed to Mg(2+)-free solution. The expression levels of CaV1.2, CaM and phosphorylated CaMKII (p-CaMKII; Thr-286) in these two models were examined using immunofluorescence and western blotting. Compared with Wistar rats, the expression levels of CaV1.2 and CaM were increased, and the expression of p-CaMKII was decreased in the TRM hippocampus. However, the expression of the targeted proteins was reversed in the TRM temporal cortex. A significant increase in the expression of CaM and decrease in the expression of CaV1.2 were observed in the TRM cerebellum. In the cultured neuron model, p-CaMKII and CaV1.2 were markedly decreased. In addition, neurons exhibiting co-localized expression of CaV1.2 and CaM immunoreactivities were detected. Furthermore, intracellular calcium concentrations were increased in these two models. For the first time, o the best of our knowledge, the data of the present study suggested that abnormal alterations in the Ca(2+)/CaV1.2/CaM/CaMKII pathway may be involved in epileptogenesis and in the phenotypes of TRMs and cultured hippocampal neurons exposed to Mg(2+)-free solution