Stockformer: A Price-Volume Factor Stock Selection Model Based on Wavelet Transform and Multi-Task Self-Attention Networks

As the Chinese stock market continues to evolve and its market structure grows increasingly complex, traditional quantitative trading methods are facing escalating challenges. Particularly, due to policy uncertainty and the frequent market fluctuations triggered by sudden economic events, existing models often struggle to accurately predict market dynamics. To address these challenges, this paper introduces Stockformer, a price-volume factor stock selection model that integrates wavelet transformation and a multitask self-attention network, aimed at enhancing responsiveness and predictive accuracy regarding market instabilities. Through discrete wavelet transform, Stockformer decomposes stock returns into high and low frequencies, meticulously capturing long-term market trends and short-term fluctuations, including abrupt events. Moreover, the model incorporates a Dual-Frequency Spatiotemporal Encoder and graph embedding techniques to effectively capture complex temporal and spatial relationships among stocks. Employing a multitask learning strategy, it simultaneously predicts stock returns and directional trends. Experimental results show that Stockformer outperforms existing advanced methods on multiple real stock market datasets. In strategy backtesting, Stockformer consistently demonstrates exceptional stability and reliability across market conditions-whether rising, falling, or fluctuating-particularly maintaining high performance during downturns or volatile periods, indicating a high adaptability to market fluctuations. To foster innovation and collaboration in the financial analysis sector, the Stockformer model's code has been open-sourced and is available on the GitHub repository: https://github.com/Eric991005/Multitask-Stockformer.Comment: Currently under consideration for publication in the Expert Systems With Application

Evaluating Learning-to-Rank Models for Prioritizing Code Review Requests using Process Simulation

In large-scale, active software projects, one of the main challenges with code review is prioritizing the many Code Review Requests (CRRs) these projects receive. Prior studies have developed many Learning-to-Rank (LtR) models in support of prioritizing CRRs and adopted rich evaluation metrics to compare their performances. However, the evaluation was performed before observing the complex interactions between CRRs and reviewers, activities and activities in real-world code reviews. Such a pre-review evaluation provides few indications about how effective LtR models contribute to code reviews. This study aims to perform a post-review evaluation on LtR models for prioritizing CRRs. To establish the evaluation environment, we employ Discrete-Event Simulation (DES) paradigm-based Software Process Simulation Modeling (SPSM) to simulate real-world code review processes, together with three customized evaluation metrics. We develop seven LtR models and use the historical review orders of CRRs as baselines for evaluation. The results indicate that employing LtR can effectively help to accelerate the completion of reviewing CRRs and the delivery of qualified code changes. Among the seven LtR models, LambdaMART and AdaRank are particularly beneficial for accelerating completion and delivery, respectively. This study empirically demonstrates the effectiveness of using DES-based SPSM for simulating code review processes, the benefits of using LtR for prioritizing CRRs, and the specific advantages of several LtR models. This study provides new ideas for software organizations that seek to evaluate LtR models and other artificial intelligence-powered software techniques

Subsynchronous Oscillation Source Location in Power System with High Penetration of Wind Power Using Multivariate Variational Mode Decomposition

Accurately and promptly extracting subsynchronous oscillation (SSO) components from measurements and locating SSO sources are crucial for SSO suppression. Existing transient energy flow (TEF) based SSO location methods suffer from low location accuracy and poor robustness. To cope with the shortcoming of the traditional TEF in SSO source location, this paper proposes a multivariate variational mode decomposition (MVMD) based SSO source location method to locate the SSO source from the measurements. Firstly, the multi-channel measurement matrix of each generator, including voltage and current measurements, is formed. Then, the multi-channel intrinsic mode functions (IMFs) are simultaneously decomposed from the formed multi-channel measurement matrix by using the MVMD approach, enabling the simultaneous decomposition of SSO components from measurements. Furthermore, the IMFs associated with the SSO mode are identified according to the Hilbert transform (HT). Using the identified IMFs, the MVMD-based TEF is calculated and the SSO source is located. Finally, the performance of the proposed method is evaluated using the simulation data of the modified 4-machine 11-bus test system and the field measurements from the Guyuan SSO event in the North China region. The results validate the accuracy and effectiveness of the proposed method in the SSO source location

Highly dispersed and ultrafine Co3O4@N-doped carbon catalyst derived from metal-organic framework for efficient oxygen reduction reaction

932-937Electrocatalysts are composed of transition metal/metal oxide and N-doped carbon can overcome the sluggish kinetics of oxygen reduction reactio. Herein, the Co3O4/ketjen black (KB)@MOF-derived with uniformly dispersed and ultrafine Co3O4 nanoparticles (1-5 nm) are synthesized by a facile in-situ method and subsequent mild pyrolysis process. It exhibits enhanced activity with onset potential of 0.96 V (vs. RHE) and a half-wave potential of 0.86 V (vs. RHE) in 0.1 M KOH solution, the excellent durability with E1/2 a small negative shift of 10 mV after 5000 continuous cycles and good methanol-tolerance property. The ultrahigh catalytic performance of Co3O4/KB@MOF-derived can be ascribed to the small particle size range of 1-5 nm of Co3O4, as well as the strong interaction between the in-situ formed N-Co3O4 active sites and substrate under the mild calcination temperature. Above all, these indicate that the as-prepared Co3O4/KB@MOF-derived may be a good alternative to commercial Pt-based catalysts

Ultrafast laser-writing of liquid crystal waveguides

With the development of conformable photonic platforms, particularly those that could be interfaced with the human body or integrated into wearable technology, there is an ever increasing need for mechanically flexible optical photonic elements in soft materials. Here, we realize mechanically flexible liquid crystal (LC) waveguides using a combination ultrafast direct laser writing and ultraviolet (UV) photo-polymerization. Results are presented that demonstrate that these laser-written waveguides can be either electrically switchable (by omitting the bulk UV polymerization step) or mechanically flexible. Characteristics of the waveguide are investigated for different fabrication conditions and geometrical configurations, including the dimensions of the waveguide and laser writing power. Our findings reveal that smaller waveguide geometries result in reduced intensity attenuation. Specifically, for a 10 μm wide laser written channel in a 14 μm-thick LC layer, a loss factor of -1.8 dB/mm at λ = 650 nm was observed. Following the UV polymerization step and subsequent delamination of the glass substrates, we demonstrate a free-standing flexible LC waveguide, which retains waveguide functionality even when bent, making it potentially suitable for on-skin sensors and other photonic devices that could interface with the human body. For the flexible LC waveguides fabricated in this study, the loss in a straight waveguide with a cross-sectional area of 20 μm × 20 μm was recorded to be -0.2 dB/mm. These results highlight the promising potential of electrically-responsive and mechanically moldable optical waveguides using laser writing and UV-assisted polymer network formation

Mesenchymal Stem Cell-Derived Exosomes Reduce A1 Astrocytes via Downregulation of Phosphorylated NFκB P65 Subunit in Spinal Cord Injury

Background/Aims: Neurotoxic A1 astrocytes are induced by inflammation after spinal cord injury (SCI), and the inflammation-related Nuclear Factor Kappa B (NFκB) pathway may be related to A1-astrocyte activation. Mesenchymal stem cell (MSC) transplantation is a promising therapy for SCI, where transplanted MSCs exhibit anti-inflammatory effects by downregulating proinflammatory factors, such as Tumor Necrosis Factor (TNF)-α and NFκB. MSC-exosomes (MSC-exo) reportedly mimic the beneficial effects of MSCs. Therefore, in this study, we investigated whether MSCs and MSC-exo exert inhibitory effects on A1 astrocytes and are beneficial for recovery after SCI. Methods: The effects of MSC and MSC-exo on SCIinduced A1 astrocytes, and the potential mechanisms were investigated in vitro and in vivo using immunofluorescence and western blot. In addition, we assessed the histopathology, levels of proinflammatory cytokines and locomotor function to verify the effects of MSC and MSC-exo on SCI rats. Results: MSC or MSC-exo co-culture reduced the proportion of SCIinduced A1 astrocytes. Intravenously-injected MSC or MSC-exo after SCI significantly reduced the proportion of A1 astrocytes, the percentage of p65 positive nuclei in astrocytes, and the percentage of TUNEL-positive cells in the ventral horn. Additionally, we observed decreased lesion area and expression of TNFα, Interleukin (IL)-1α and IL-1β, elevated expression of Myelin Basic Protein (MBP), Synaptophysin (Syn) and Neuronal Nuclei (NeuN), and improved Basso, Beattie & Bresnahan (BBB) scores and inclined-plane-test angle. In vitro assay showed that MSC and MSC-exo reduced SCI-induced A1 astrocytes, probably via inhibiting the nuclear translocation of the NFκB p65. Conclusion: MSC and MSC-exo reduce SCI-induced A1 astrocytes, probably via inhibiting nuclear translocation of NFκB p65, and exert antiinflammatory and neuroprotective effects following SCI, with the therapeutic effect of MSCexo comparable with that of MSCs when applied intravenously

Mechano-Graded Contact-Electrification Interfaces Based Artificial Mechanoreceptors for Robotic Adaptive Reception.

Triboelectrification-based artificial mechanoreceptors (TBAMs) is able to convert mechanical stimuli directly into electrical signals, realizing self-adaptive protection and human-machine interactions of robots. However, traditional contact-electrification interfaces are prone to reaching their deformation limits under large pressures, resulting in a relatively narrow linear range. In this work, we fabricated mechano-graded microstructures to modulate the strain behavior of contact-electrification interfaces, simultaneously endowing the TBAMs with a high sensitivity and a wide linear detection range. The presence of step regions within the mechanically graded microstructures helps contact-electrification interfaces resist fast compressive deformation and provides a large effective area. The highly sensitive linear region of TBAM with 1.18 V/kPa can be effectively extended to four times of that for the devices with traditional interfaces. In addition, the device is able to maintain a high sensitivity of 0.44 V/kPa even under a large pressure from 40 to 600 kPa. TBAM has been successfully used as an electronic skin to realize self-adaptive protection and grip strength perception for a commercial robot arm. Finally, a high angle resolution of 2° and an excellent linearity of 99.78% for joint bending detection were also achieved. With the aid of a convolutional neural network algorithm, a data glove based on TBAMs realizes a high accuracy rate of 95.5% for gesture recognition in a dark environment