Dynamic Scheme Research Articles

Digital Grading Company: Deep Neural Networks for Predictive Analytics for Alternative Asset Portfolio Management

In this study, we investigate the application of deep neural networks for predictive analytics in the realm of alternative asset portfolio management, focusing on Trading Card Games (TCGs) like Pokemon. Our primary objective is to understand the optimal trading cards for investment and to determine the appropriate timing for divestment and retention of these assets. Drawing on the successful implementation of machine learning predictive analytics in traditional financial markets, we aim to extend these techniques to alternative investments. To achieve this, we employ a two-pronged approach using a comprehensive dataset of historical sales values for Pokemon TCGs spanning five years, comprising over 200 million unlabeled data points. The first approach utilizes a semi-supervised sequence learning method, where a Long Short-Term Memory (LSTM) model is initially trained as a sequence autoencoder. The pre-trained model's weights are then used in a supervised phase to fine-tune the predictions for future trading card values. The second approach involves heuristic pretraining, which leverages market-derived indicators from existing literature to bridge the gap between traditional feature engineering and deep learning methods. Our technology stack is designed to handle end-to-end data collection, analytics generation, and execution of recommended trading strategies. Simulated backtesting results demonstrate that our Digital Grading Company (DGC) portfolio model outperforms traditional indices, achieving a 357% return compared to the 61% return of the S&P 500 Index over the same period. Real-time analysis further indicates that our model consistently identifies profitable trading opportunities in the TCG market using hourly data and proprietary algorithmic trade logic, executing trades 24/7. The DGC Predictive Portfolio, based on ungraded Pokemon TCG trading cards, also shows superior performance when compared to the Nasdaq 100 Index (Invesco QQQ ETF). Our analytics framework not only predicts the direction of price movement but also provides insights into the magnitude of changes, enabling a more dynamic and responsive trading strategy. The findings suggest that the integration of deep learning models with heuristic indicators has the potential to revolutionize portfolio management in alternative investments, paving the way for more accurate and profitable decision-making in niche markets. This research highlights the promising role of machine learning in enhancing alternative asset management strategies, suggesting future applications in other asset classes. We conclude that deep neural networks, when combined with data-driven heuristics, can significantly enhance trading outcomes, making them valuable tools for investors in the evolving landscape of alternative investments.

Dynamic pollution management strategies and coordination in aquatic product supply chain under eco-compensation: Perspective of extreme weather crisis

The ecological compensation mechanism emerges as a solution to reduce pollution for aquatic product supply chain. To comprehend the dynamic pollution management strategies and coordination under the ecological compensation mechanism, this study develops a stochastic differential game between farmers and firms from the perspective of extreme weather crisis. The subjects’ optimal decisions are explored in three cases including cooperative case, noncooperative case, and land transfer-cost sharing contract case. Then we simulate theoretical results and illustrate a case study to demonstrate the applicability of ecological compensation collaboration. The results indicate that the extreme weather crisis changes the evolutionary trend and steady state of pollution accumulation. Moreover, the land transfer-cost sharing contract could realize the Pareto improvement in high-risk environment especially after the crisis. But its coordination relies on the ecological compensation coefficient, which gets larger with the interval of [4.9,6.3] after the crisis, compared to the range of [4,5.4] before the crisis. Notably, the contract and ecological compensation mechanism exhibit a complementary relationship in response to the crisis. The findings could provide new inspirations for aquatic product supply chain to balance production and environmental protection.

Hybrid trajectory tracking control of wheeled mobile robots using predictive kinematic control and dynamic robust control

AbstractTrajectory tracking control of wheeled mobile robots (WMRs) is still a remarkable problem for many applications. In the present paper, a hybrid control is presented based on dynamic and kinematic equations of motion for wheeled mobile robots in the presence of the sum of the external disturbances and parametric uncertainty. The designed control for the WMR utilizes control and guidance to reach the reference path. In many studies, a control strategy is normally employed for WMR. However, in this study, hybrid control was used for the mentioned purpose. Akin to other studies, the kinematic control scheme here was based on the predictive control, and the dynamic control scheme was designed based on the robust control. Therefore, in this article, having introduced the kinematic model, a nonlinear predictive control was proved and designed. In the next step, a finite‐time integral type terminal sliding mode control (FITSMC) was designed based on the nonlinear dynamic model in order to automatically adjust the control gain and eliminate online disturbances and destructive chattering phenomena completely. In particular, a finite‐time disturbance observer was designed to estimate the external disturbances. The proof of the new proposed control scheme was presented using Lyapunov stability theory and numerical results. The mentioned integrated scheme, including predictive control (outer loop) and nonlinear adaptive control (inner loop), ensures the convergence and optimal tracking performance of all signals, as a result of which the tracking errors can arbitrarily converge to the origin in a finite time. In the final step, the simulation results were presented to show the effectiveness of the proposed scheme using MATLAB software, and the introduced control design was compared with a similar controller quantitatively and qualitatively.

An enhanced slime mould algorithm with triple strategy for engineering design optimization

Abstract This paper introduces an enhanced slime mould algorithm (EESMA) designed to address critical challenges in engineering design optimization. The EESMA integrates three novel strategies: the Laplace logistic sine map technique, the adaptive t-distribution elite mutation mechanism, and the ranking-based dynamic learning strategy. These enhancements collectively improve the algorithm’s search efficiency, mitigate convergence to local optima, and bolster robustness in complex optimization tasks. The proposed EESMA demonstrates significant advantages over many conventional optimization algorithms and performs on par with, or even surpasses, several advanced algorithms in benchmark tests. Its superior performance is validated through extensive evaluations on diverse test sets, including IEEE CEC2014, IEEE CEC2020, and IEEE CEC2022, and its successful application in six distinct engineering problems. Notably, EESMA excels in solving economic load dispatch problems, highlighting its capability to tackle challenging optimization scenarios. The results affirm that EESMA is a competitive and effective tool for addressing complex optimization issues, showcasing its potential for widespread application in engineering and beyond.

Comparisons of experimentally and numerically determined statistics for predicting low-occurrence wind speeds around a 1:1:2 block model

Accurate prediction of low-occurrence wind speeds around urban structures is crucial for safe building design. Although Large-eddy simulation (LES) is commonly used as a high-fidelity model as compared with the Reynolds-Averaged Navier–Stokes (RANS) simulations, the present validation process relies on the comparison of fundamental statistics of the mean and standard deviations. The discrepancies in LESs and wind-tunnel experiments (WTEs) are unclear in terms of physical quantities characterizing the unsteadiness of the simulated turbulent flow such as probability density and power spectral densities, and low-occurrence winds speeds. Therefore, this study aims to evaluate the effectiveness of LES in predicting unsteady wind behavior around a 1:1:2 block model. The study identifies prominent differences to improve the accuracy of unsteady numerical simulations especially for the purpose of predicting low-occurrence wind speeds. Various advection schemes in LESs were investigated, including first-order upwind, second-order linear, and dynamic interpolation schemes. The results show significant discrepancies, particularly in higher-order statistics and low-occurrence wind speeds, with WTE consistently exhibiting higher energy levels across all frequencies. These findings highlight the need to refine advection schemes to enhance their predictive accuracy. LESs with minimal numerical errors from discretization schemes can substantially improve urban wind assessments and contribute to the design of safer structures.

Lie-theory-based dynamic model identification of serial robots considering nonlinear friction and optimal excitation trajectory

Abstract Accurate dynamic model is essential for the model-based control of robotic systems. However, on the one hand, the nonlinearity of the friction is seldom treated in robot dynamics. On the other hand, few of the previous studies reasonably balance the calculation time-consuming and the quality for the excitation trajectory optimization. To address these challenges, this article gives a Lie-theory-based dynamic modeling scheme of multi-degree-of-freedom (DoF) serial robots involving nonlinear friction and excitation trajectory optimization. First, we introduce two coefficients to describe the Stribeck characteristics of Coulomb and static friction and consider the dependency of friction on load torque, so as to propose an improved Stribeck friction model. Whereafter, the improved friction model is simplified in a no-load scenario, a novel nonlinear dynamic model is linearized to capture the features of viscous friction across the entire velocity range. Additionally, a new optimization algorithm of excitation trajectories is presented considering the benefits of three different optimization criteria to design the optimal excitation trajectory. On the basis of the above, we retrieve a feasible dynamic parameter set of serial robots through the hybrid least square algorithm. Finally, our research is supported by simulation and experimental analyses of different combinations on the seven-DoF Franka Emika robot. The results show that the proposed friction has better accuracy performance, and the modified optimization algorithm can reduce the overall time required for the optimization process while maintaining the quality of the identification results.

DMSCTS: Dynamic measurement scheme for the containers-hybrid-deployment based on trusted subsystem

Hybrid deployment of containers with different kernel types offers a novel solution for cloud service providers. While extensive research has been conducted on shared kernel containers, the security risks associated with diverse kernel types in hybrid deployment scenarios present more complex challenges. Establishing trusted relationships from hardware to containers for hybrid deployment has become a primary concern. Additional challenges include the lack of measurement and communication methods for independent kernel containers and insufficient dynamic measurement capabilities for containers. To address these issues, we propose a novel approach of achieving secure hybrid deployment of containers through the provision of trusted assurance in three layers: container infrastructure, container application environment, and container runtime. We propose the corresponding measurement schemes for each trust layer. Through functional verification and performance evaluation, we demonstrate that our architecture exhibits improved feasibility and effectiveness.

CDP-MVS: Forest Multi-View Reconstruction with Enhanced Confidence-Guided Dynamic Domain Propagation

Using multi-view images of forest plots to reconstruct dense point clouds and extract individual tree parameters enables rapid, high-precision, and cost-effective forest plot surveys. However, images captured at close range face challenges in forest reconstruction, such as unclear canopy reconstruction, prolonged reconstruction times, insufficient accuracy, and issues with tree duplication. To address these challenges, this paper introduces a new image dataset creation process that enhances both the efficiency and quality of image acquisition. Additionally, a block-matching-based multi-view reconstruction algorithm, Forest Multi-View Reconstruction with Enhanced Confidence-Guided Dynamic Domain Propagation (CDP-MVS), is proposed. The CDP-MVS algorithm addresses the issue of canopy and sky mixing in reconstructed point clouds by segmenting the sky in the depth maps and setting its depth value to zero. Furthermore, the algorithm introduces a confidence calculation method that comprehensively evaluates multiple aspects. Moreover, CDP-MVS employs a decentralized dynamic domain propagation sampling strategy, guiding the propagation of the dynamic domain through newly defined confidence measures. Finally, this paper compares the reconstruction results and individual tree parameters of the CDP-MVS, ACMMP, and PatchMatchNet algorithms using self-collected data. Visualization results show that, compared to the other two algorithms, CDP-MVS produces the least sky noise in tree reconstructions, with the clearest and most detailed canopy branches and trunk sections. In terms of parameter metrics, CDP-MVS achieved 100% accuracy in reconstructing tree quantities across the four plots, effectively avoiding tree duplication. The accuracy of breast diameter extraction values of point clouds reconstructed by CDPMVS reached 96.27%, 90%, 90.64%, and 93.62%, respectively, in the four sample plots. The positional deviation of reconstructed trees, compared to ACMMP, was reduced by 0.37 m, 0.07 m, 0.18 m and 0.33 m, with the average distance deviation across the four plots converging within 0.25 m. In terms of reconstruction efficiency, CDP-MVS completed the reconstruction of the four plots in 1.8 to 3.1 h, reducing the average reconstruction time per plot by six minutes compared to ACMMP and by two to three times compared to PatchMatchNet. Finally, the differences in tree height accuracy among the point clouds reconstructed by the different algorithms were minimal. The experimental results demonstrate that CDP-MVS, as a multi-view reconstruction algorithm tailored for forest reconstruction, shows promising application potential and can provide valuable support for forestry surveys.

Integrating Evolutionary Game-Theoretical Methods and Deep Reinforcement Learning for Adaptive Strategy Optimization in User-Side Electricity Markets: A Comprehensive Review

With the rapid development of smart grids, the strategic behavior evolution in user-side electricity market transactions has become increasingly complex. To explore the dynamic evolution mechanisms in this area, this paper systematically reviews the application of evolutionary game theory in user-side electricity markets, focusing on its unique advantages in modeling multi-agent interactions and dynamic strategy optimization. While evolutionary game theory excels in explaining the formation of long-term stable strategies, it faces limitations when dealing with real-time dynamic changes and high-dimensional state spaces. Thus, this paper further investigates the integration of deep reinforcement learning, particularly the deep Q-learning network (DQN), with evolutionary game theory, aiming to enhance its adaptability in electricity market applications. The introduction of the DQN enables market participants to perform adaptive strategy optimization in rapidly changing environments, thereby more effectively responding to supply–demand fluctuations in electricity markets. Through simulations based on a multi-agent model, this study reveals the dynamic characteristics of strategy evolution under different market conditions, highlighting the changing interaction patterns among participants in complex market environments. In summary, this comprehensive review not only demonstrates the broad applicability of evolutionary game theory in user-side electricity markets but also extends its potential in real-time decision making through the integration of modern algorithms, providing new theoretical foundations and practical insights for future market optimization and policy formulation.

A dynamic approach for MR T2-weighted pelvic imaging

T2-weighted 2D fast spin echo sequence serves as the standard sequence in
clinical pelvic MR imaging protocols. However, motion artifacts and blurring caused
by peristalsis present significant challenges. Patient preparation such as administering
antiperistaltic agents is often required before examination to reduce artifacts, which
discomfort the patients. This work introduce a novel dynamic approach for T2
weighted pelvic imaging to address peristalsis-induced motion issue without any patient
preparation.
Approach: A rapid dynamic data acquisition strategy with complementary sampling
trajectory is designed to enable highly undersampled motion-resistant data sampling,
and an unrolling method based on deep equilibrium model is leveraged to reconstruct
images from the dynamic sampled k-space data. Moreover, the fix-point convergence of
the equilibrium model ensures the stability of the reconstruction. The high acceleration
factor in each temporal phase, which is much higher than that in traditional static
imaging, has the potential to effectively freeze pelvic motion, thereby transforming
the imaging problem from conventional motion prevention or removal to motion
reconstruction.
Main results: Experiments on both retrospective and prospective data have
demonstrated the superior performance of the proposed dynamic approach in reducing
motion artifacts and accurately depicting structural details compared to standard static
imaging.
Significance: The proposed dynamic approach effectively captures motion states
through dynamic data acquisition and deep learning-based reconstruction, addressing
motion-related challenges in pelvic imaging.

LRSDet: lightweight remote sensing target detection network with local-global information awareness and rapid sample assignment

Abstract With the rapid development of aerospace and unmanned aerial vehicles, using neural networks for object detection in optical remote sensing images (O-RSI) has encountered heightened challenges. The optical remote sensing images have the characteristics of complex geometric scenes, dense groups of objects, and significant multi-scale variations of objects; researchers need to use more complex models to achieve higher accuracy. However, this complexity also brings challenges to the application of lightweight scenes. Therefore, to cope with the trade-off challenge between model complexity and detection accuracy, we propose a lightweight network model LRSDet in this study. The model integrates local and global information processing mechanisms and introduces a fast positive sample assignment strategy to adapt to resource-constrained embedded and mobile platforms. By constructing a lightweight feature extraction network and a lightweight path aggregation network and incorporating the ESM-Attention module, the feature extraction capability of the model in complex remote sensing scenarios is significantly improved. In addition, the application of a dynamic soft threshold strategy further optimizes the positive sample selection process and improves the detection efficiency of the model. Experimental on the O-RSI datasets DIOR, NWPU VHR-10, and RSOD, while analyzing model real-time performance on aerial video and embedded devices, outperforming other state-of-the-art methods.

Mycobacterium avium subspecies paratuberculosis (MAP) infection, and its impact on gut microbiome of individuals with multiple sclerosis

The microbial ecology of Mycobacterium avium subspecies paratuberculosis infections (MAP) within the context of Multiple Sclerosis (MS) is largely an unexplored topic in the literature. Thus, we have characterized the compositional and predicted functional differences of the gut microbiome between MS patients with MAP (MAP+) and without (MAP−) infection. This was done in the context of exposome differences (through self-reported filled questionnaires), principally in anthropometric and sociodemographic patterns to gain an understanding of the gut microbiome dynamics. 16S rRNA microbiome profiling of faecal samples (n = 69) was performed for four groups, which differed by disease and MAP infection: healthy cohort (HC) MAP−; HC MAP+ ; MS MAP−; and MS MAP+ . Using a dynamic strategy, with MAP infection and time of sampling as occupancy models, we have recovered the core microbiome for both HC and MS individuals. Additional application of neutral modeling suggests key genera that are under selection pressure by the hosts. These include members of the phyla Actinobacteriota, Bacteroidota, and Firmicutes. As several subjects provided multiple samples, a Quasi Conditional Association Test that incorporates paired-nature of samples found major differences in Archaea. To consolidate treatment groups, confounders, microbiome, and the disease outcome parameters, a mediation analysis is performed for MS cohort. This highlighted certain genera i.e., Sutterella, Akkermansia, Bacteriodes, Gastranaerophilales, Alistipes, Balutia, Faecalibacterium, Lachnospiraceae, Anaerostipes, Ruminococcaceae, Eggerthellaceae and Clostridia-UCG-014 having mediatory effect using disease duration as an outcome and MAP infection as a treatment group. Our analyses indicate that the gut microbiome may be an important target for dietary and lifestyle intervention in MS patients with and without MAP infection.

MisORFPred: A Novel Method to Mine Translatable sORFs in Plant Pri-miRNAs Using Enhanced Scalable k-mer and Dynamic Ensemble Voting Strategy.

The primary microRNAs (pri-miRNAs) have been observed to contain translatable small open reading frames (sORFs) that can encode peptides as an independent element. Relevant studies have proven that those of sORFs are of significance in regulating the expression of biological traits. The existing methods for predicting the coding potential of sORFs frequently overlook this data or categorize them as negative samples, impeding the identification of additional translatable sORFs in pri-miRNAs. In light of this, a novel method named misORFPred has been proposed. Specifically, an enhanced scalable k-mer (ESKmer) that simultaneously integrates the composition information within a sequence and distance information between sequences is designed to extract the nucleotide sequence features. After feature selection, the optimal features and several machine learning classifiers are combined to construct the ensemble model, where a newly devised dynamic ensemble voting strategy (DEVS) is proposed to dynamically adjust the weights of base classifiers and adaptively select the optimal base classifiers for each unlabeled sample. Cross-validation results suggest that ESKmer and DEVS are essential for this classification task and could boost model performance. Independent testing results indicate that misORFPred outperforms the state-of-the-art methods. Furthermore, we execute misORFPerd on the genomes of various plant species and perform a thorough analysis of the predicted outcomes. Taken together, misORFPred is a powerful tool for identifying the translatable sORFs in plant pri-miRNAs and can provide highly trusted candidates for subsequent biological experiments.

An investigation on energy-saving scheduling algorithm of wireless monitoring sensors in oil and gas pipeline networks

With the rapid development of the oil and gas industry, monitoring the safety and efficiency of pipeline networks has become particularly important. In this context, Wireless Sensor Networks (WSNs) are widely used for monitoring oil and gas pipelines due to their flexible deployment and cost-effectiveness. However, since sensor nodes typically rely on limited battery power, extending the network’s lifecycle and improving energy utilization efficiency have become focal points of research. Therefore, this paper proposes an energy-saving scheduling algorithm based on transformer networks, aimed at optimizing energy consumption and data transmission efficiency of wireless monitoring sensors in oil and gas pipelines. Firstly, this study designs a deep learning-based Transformer model that learns from historical data on energy consumption patterns and environmental variables to predict the energy and data transmission needs of each sensor node. Secondly, based on the prediction results, this algorithm employs a dynamic scheduling strategy that automatically adjusts the sensor’s operational mode and communication frequency according to the node’s energy status and task urgency. Additionally, we have validated the effectiveness of the proposed algorithm through field tests and simulation experiments. According to the experimental results, our model has higher efficiency in energy saving. Compared with Convolutional Neural Networks, Recurrent Neural Networks and Graph Neural Networks, the total energy consumption of sensor networks under the model scheduling in this paper was reduced by 6.7%, 33.4% and 26.3%, respectively. Our algorithms improve the energy efficiency and stability of the monitoring system and provide important technical support for future intelligent pipeline monitoring systems. We hope this paper will inspire future scientific research in this field.

Multi-region hierarchical surrogate-assisted quantum-behaved particle swarm optimization for expensive optimization problems

Surrogate-assisted evolutionary algorithms (SAEAs) have been successfully applied to solve computationally expensive optimization problems. However, most SAEAs struggle to achieve good results in solving complex multimodal problems, especially high-dimensional ones. Moreover, for problems with complex landscapes, SAEAs typically require constructing complex global surrogates to model the landscape and performing many iterations to identify the surrogate’s optimum, thereby reducing the efficiency of SAEAs. To deal with these issues, this paper proposes a multi-region hierarchical surrogate-assisted quantum-behaved particle swarm optimization (MHS-QPSO) algorithm for expensive optimization problems. To better balance exploration and exploitation, a search behavior selection strategy is proposed, enabling MHS-QPSO to appropriately switch between global and local searches. For the global search, the search space is divided into multiple regions that can adaptively adjust the size of the areas. A surrogate is constructed in each region, requiring only a small number of QPSO iterations to find the optimum of each surrogate. Furthermore, a novel reliability-based criterion is proposed to screen candidate solutions in different regions for exact evaluations, which can save the number of exact function evaluations and can rapidly improve the fitting accuracy of the surrogates in regions with superior fitness. During local searches, a dynamic boundary adjustment strategy is introduced to guide the QPSO to faster approach the potential optimal region. Experimental results on seven benchmark functions with dimensions from 10 to 100, and on a complex real application, demonstrate that MHS-QPSO significantly outperforms several state-of-the-art algorithms within a limited computational budget. Code for MHS-QPSO is available at https://github.com/quanshuzhang/MHS-QPSO.git.

Improving the Quality of Education at Madrasah Tsanawiyah Negeri Fakfak through Leadership Strategies implemented by the Head of Madrasah

The goal of this study is to assess how the Head of the MTsN Fakfak carries out their main roles and functions, identify the factors that assist or hinder the head of the Fakfak State Tsanawiyah Madrasah in improving the quality of education, and investigate the solutions implemented by the Madrasah Head to overcome obstacles to carrying out these primary tasks. The qualitative study draws on pedagogical, management, and theological approaches. Primary data was gathered from the madrasah head, teachers, administrative staff, and relevant individuals within the madrasah, while secondary data was collected from books, the internet, and other sources related to the research problem. Data collection methods included observation, interviews, and documentation studies. The study found that the head of MTsN Fakfak utilizes a democratic leadership style in fulfilling their main responsibilities, as evidenced by the active, dynamic, and purposeful strategies, actions, and programs that involve all stakeholders in advancing the institution. The leadership of the madrasah head has had a positive impact on the quality of education, but there are areas for improvement that require a more effective strategy for the madrasah head to fulfill their duties. The study suggests that as a leader, the head of the madrasah must continuously and optimally carry out their primary responsibilities to enhance the quality of education within the madrasah. Consequently,madrasah heads must adeptly mobilize resources in program planning and evaluation, curriculum development, learning, personnel management, learning facilities and resources, finance, student and community services, and fostering a conducive madrasah environment.

Dynamic Micelle-Hydrogels for 3D-Architected Transition Metal Sulfides.

Additive manufacturing of transition metal sulfides (TMS) enables the creation of complex 3D structures, significantly expanding their applications. However, preparing 3D-structured TMS remains challenging due to difficulties in developing suitable inks. In this study, a supramolecular micelle hydrogel as the ink to fabricate 3D-structured TMS is utilized. Initially, the hydrogels are printed and infused with metal salt solutions to stabilize the structures, which are then calcined to convert into miniaturized 3D-TMS replicas. The micellar hydrogels crosslink via hydrophobic interactions, with sodium dodecyl sulfonate (SDS) micelles providing both a hydrophobic environment and sulfur sources. During calcination, the decomposed sulfur precursors coordinate with metal ions to form various TMS, including FeS2, Cu2S, Ni3S2, and Co9S8, along with several metal sulfides like PbS and SnS. Additionally, the method also allows for the preparation of transition metal dichalcogenides such as MoS2 and WS2. The formation mechanism is demonstrated using Ni3S2 as an example which exhibits notable catalytic activity in oxygen evolution reactions (OER) and hydrogen evolution reactions (HER). Given its simplicity and versatility, this dynamic micellar hydrogel-derived strategy offers a promising pathway for creating advanced TMS materials.

In Situ Generatable and Recyclable Oxygen Vacancy-Modified Fe2O3-Decorated WO3 Nanowires with Super Stability for ppb-Level H2S Sensing.

Detecting hydrogen sulfide (H2S) odor gas in the environment at parts-per-billion-level concentrations is crucial. However, a significant challenge is the rapid deactivation caused by SO42- deposition. To address this issue, we developed a sensing material comprising Fe2O3-decorated WO3 nanowires (FWO) with strong interfacial interaction. During the H2S sensing process, important oxygen vacancies (OVs) are generated in situ and are recyclable on the surface of the Fe2O3 cluster. This sensor achieves a response of 140 (Ra/Rg) toward 50 ppm of H2S at 250 °C, with an experimentally measured detection limit of 1 ppb. It also exhibits remarkable stability, with no significant change observed over a long period of 150 days. Based on a combination of in situ DRIFT and DFT calculations, we have identified that the overactivation of O2 is the key step in the formation of SO42-. This overactivation can be partially modulated by the synergistic effect of Fe2O3 decoration and the in situ generated OVs, regulating the oxidation product to SO2 rather than the toxic SO42-. Furthermore, the continuous generation of OVs compensates for the loss of active sites pertaining to SO42- deposition, thereby contributing to the excellent stability of the sensor. This study underscores the beneficial impact of in situ OV generation in FWO for H2S sensing, offering a dynamic strategy to enhance sensor performance, particularly in terms of stability.

Herwig 7.3 release note

A new release of the Monte Carlo event generator Herwig (version 7.3) has been launched. This iteration encompasses several enhancements over its predecessor, version 7.2. Noteworthy upgrades include: the implementation of a process-independent electroweak angular-ordered parton shower integrated with QCD and QED radiation; a new recoil scheme for initial-state radiation improving the behaviour of the angular-ordered parton shower; the incorporation of the heavy quark effective theory to refine the hadronization and decay of excited heavy mesons and heavy baryons; a dynamic strategy to regulate the kinematic threshold of cluster splittings within the cluster hadronization model; several improvements to the structure of the cluster hadronization model allowing for refined models; the possibility to extract event-by-event hadronization corrections in a well-defined way; the possibility of using the string model, with a dedicated tune. Additionally, a new tuning of the parton shower and hadronization parameters has been executed. This article discusses the novel features introduced in version 7.3.0.

Embracing market dynamics in the post-COVID era: A data-driven analysis of investor sentiment and behavioral characteristics in stock index futures returns

This paper aims to enhance the understanding and prediction of stock market behavior during unexpected events like the COVID-19 pandemic, with a specific focus on the role of market attention, social media sentiment indicators, and the development and evolution of unexpected events. We highlight that the common trading and technical indicators used in forecasting the stock index futures prices often overlook investor sentiment and pandemic-related data, which can be instrumental in predicting stock market behavior during significant emergencies. In response, we propose a multi-faceted approach that incorporates these overlooked factors. First, we enhance the predictive index system by integrating investor sentiment, derived from stock message board commentary, and investor behavior influenced by the development and evolution of the pandemic. This innovative approach refines our model's predictive capabilities and is validated through comparative analysis. Second, we introduce a hybrid framework for predicting stock index futures closing prices. By decomposing the closing price series into long-term trends, cyclical variations, and random fluctuations, we create a more nuanced forecast. Each component is predicted separately using appropriate time-series algorithms, improving the overall predictive accuracy and offering generalizability and scalability. Third, we devise a dynamic trading strategy that recognizes pandemic-related data, evolving over time, as a pivotal factor. This strategy is adaptable to evolving market conditions, and our experimental evidence demonstrates its effectiveness in yielding higher returns and reducing associated risks. Our findings underline the importance of incorporating investor sentiment and pandemic-related data into stock market predictions, thus offering a more comprehensive and accurate approach to market forecasting and risk management.