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We study the effects of particle production on the evolution of the inflaton field in an axion monodromy model with the goal of discovering in which situations the resulting dynamics will be consistent with the swampland constraints. In the presence of a modulated potential the evolving background field (solution of the inflaton homogeneous in space) induces the production of long wavelength inflaton fluctuation modes. However, this either has a negligible effect on the inflaton dynamics (if the field spacing between local minima of the modulated potential is large), or else it traps the inflaton in a local minimum and leads to a graceful exit problem. On the other hand, the production of moduli fields at enhanced symmetry points can lead to a realization of trapped inflation consistent with the swampland constraints, as long as the coupling between the inflaton and the moduli fields is sufficiently large.

The tracking of a particular pedestrian is an important issue in computer vision to guarantee societal safety. Due to the limited computing performances of unmanned aerial vehicle (UAV) systems, the Correlation Filter (CF) algorithm has been widely used to perform the task of tracking. However, it has a fixed template size and cannot effectively solve the occlusion problem. Thus, a tracking-by-detection framework was designed in the current research. A lightweight YOLOv3-based (You Only Look Once version 3) mode which had Efficient Channel Attention (ECA) was integrated into the CF algorithm to provide deep features. In addition, a lightweight Siamese CNN with Cross Stage Partial (CSP) provided the representations of features learned from massive face images, allowing the target similarity in data association to be guaranteed. As a result, a Deep Feature Kernelized Correlation Filters method coupled with Siamese-CSP(Siam-DFKCF) was established to increase the tracking robustness. From the experimental results, it can be concluded that the anti-occlusion and re-tracking performance of the proposed method was increased. The tracking accuracy Distance Precision (DP) and Overlap Precision (OP) had been increased to 0.934 and 0.909 respectively in our test data.

Desert grassland ecosystems on China’s Loess Plateau are characterized by diverse land use types and varying human disturbances. We aimed to evaluate how land use influences soil microbial communities and functional genes related to carbon (C) and phosphorus (P) cycling. To do this, we selected five representative land use types: natural grassland, 20-year abandoned farmland, 12-year alfalfa grassland, 5-year Lanzhou lily farmland, and 17-year Platycladus orientalis forest. High-throughput metagenomic sequencing and soil physicochemical analyses were conducted. Proteobacteria dominated the nutrient-rich lily soil, while Actinobacteria were more abundant in the other soils. Available phosphorus (AP) had the strongest influence on microbial community structure and gene composition ( p  < 0.01). The relative abundance of p pdK, rpiB, glpX , and epi (C fixation genes), and purS (purine metabolism) was significantly higher in forest soil than in abandoned farmland ( p  < 0.05). Similarly, forest soil showed elevated levels of mttB and acs (methanogenesis), sdhA (TCA cycle), pstS (P transport), and pps (pyruvate metabolism) compared to alfalfa soil. Lily soil exhibited significantly higher abundance of acr genes (involved in the hydroxypropionate–hydroxybutylate cycle) and phnE (an ATP-binding cassette transporter) than natural grassland and alfalfa soils ( p  < 0.05). Microbial networks involved in C and P cycling were simpler but more functionally specialized in forest soil. Positive microbial interactions related to C and P cycling were strongest in lily soil. These findings provide important insights into soil microbial functional adaptation and offer a foundation for sustainable land use management on the Loess Plateau.

Ultra-High-Performance Concrete (UHPC) is increasingly used in various engineering projects due to its exceptional mechanical properties. This work conducts a literature review of research on the shear performance of UHPC beams in recent decades, with a focus on summarizing the formulas for calculating shear capacity and the main factors influencing shear performance. Firstly, this work reviews the calculation methods for the shear capacity of UHPC beams in different countries, along with their respective advantages and limitations. Subsequently, it provides a detailed analysis of various factors influencing the shear performance of UHPC beams, including longitudinal and stirrup reinforcement, steel fiber content, aggregates, admixtures, the shear-span ratio, shear keys, bolts, shear-reinforcement techniques, and environmental impacts. Through horizontal comparisons, the performance of UHPC beams and ordinary concrete beams under similar experimental conditions is examined to reveal the optimal shear working conditions for UHPC beams. Additionally, it is found that UHPC performs exceptionally well in composite beams, being compatible with numerous materials and significantly enhancing the shear strength of these beams. Lastly, the paper proposes suggestions for maximizing the shear performance of UHPC beams within a safe and reliable operating range and outlines future research directions.

The two-stroke pre-mixed dual-fuel marine engine is prone to knocking at full load in gas mode, which affects the overall dynamic and economic performance of the engine. In this paper, the 7X82DF engine produced by Winterthur Gas & Diesel Ltd. (WinGD) was selected as the research object, aiming to investigate the effect of different parameters on engine power and knocking. Multi-objective optimizations were carried out. First, we used the one-dimensional simulation software AVL-BOOST to build the gas mode model of 7X82DF. Second, the pilot fuel start of combustion timing (SOC), the gas injection pressure, and the mass of diesel were taken as independent variables. The response surface methodology analysis of the independent variables was completed using the Design-Expert software and corresponding prediction model equations were generated. Finally, we took ringing intensity (RI) as the knock intensity evaluation index, combined with multi-objective particle swarm optimization (MOPSO) to optimize multiple-parameters to improve the overall performance and reduce combustion roughness of the engine. The optimization results showed that when the SOC was −8.36 °CA ATDC, the gas injection pressure was 20.00 bar, the mass of diesel was 14.96 g, the corresponding power was 22,668 kW, which increased by 0.68%, the brake-specific fuel consumption was 156.256 g/kWh, which was reduced by 3.58%, the RI was 4.4326 MW/m2, and the knock intensity decreased by 6.49%.

Both the current upgrades to accelerator-based HEP detectors (e.g. ATLAS, CMS) and also future projects (e.g. CEPC, FCC) feature large-area silicon-based tracking detectors. We are investigating the feasibility of using CMOS foundries to fabricate silicon radiation detectors, both for pixels and for large-area strip sensors. A successful proof of concept would open the market potential of CMOS foundries to the HEP community, which would be most beneficial in terms of availability, throughput and cost. In addition, the availability of multi-layer routing of signals will provide the freedom to optimize the sensor geometry and the performance, with biasing structures implemented in poly-silicon layers and MIM-capacitors allowing for AC coupling. A prototyping production of strip test structures and RD53A compatible pixel sensors was recently completed at LFoundry in a 150nm CMOS process. This presentation will focus on the characterization of pixel modules, studying the performance in terms of charge collection, position resolution and hit efficiency with measurements performed in the laboratory and with beam tests. We will report on the investigation of RD53A modules with 25x100 μm 2 cell geometry.

We study the effects of particle production on the evolution of the inflaton field in an axion monodromy model with the goal of discovering in which situations the resulting dynamics will be consistent with the {\\it swampland constraints}. In the presence of a modulated potential the evolving background field (solution of the inflaton homogeneous in space) induces the production of long wavelength inflaton fluctuation modes. However, this either has a negligible effect on the inflaton dynamics (if the field spacing between local minima of the modulated potential is large), or else it traps the inflaton in a local minimum and leads to a graceful exit problem. On the other hand, the production of moduli fields at enhanced symmetry points can lead to a realization of {\\it trapped inflation} consistent with the swampland constraints, as long as the coupling between the inflaton and the moduli fields is sufficiently large.

Reconfigurable intelligent surfaces (RIS) can improve signal propagation environments by adjusting the phase of the incident signal. However, optimizing the phase shifts jointly with the beamforming vector at the access point is challenging due to the non-convex objective function and constraints. In this study, we propose an algorithm based on weighted minimum mean square error optimization and power iteration to maximize the weighted sum rate (WSR) of a RIS-assisted downlink multi-user multiple-input single-output system. To further improve performance, a model-driven deep learning (DL) approach is designed, where trainable variables and graph neural networks are introduced to accelerate the convergence of the proposed algorithm. We also extend the proposed method to include beamforming with imperfect channel state information and derive a two-timescale stochastic optimization algorithm. Simulation results show that the proposed algorithm outperforms state-of-the-art algorithms in terms of complexity and WSR. Specifically, the model-driven DL approach has a runtime that is approximately 3% of the state-of-the-art algorithm to achieve the same performance. Additionally, the proposed algorithm with 2-bit phase shifters outperforms the compared algorithm with continuous phase shift.

Both the current upgrades to accelerator-based HEP detectors (e.g. ATLAS, CMS) and also future projects (e.g. CEPC, FCC) feature large-area silicon-based tracking detectors. We are investigating the feasibility of using CMOS foundries to fabricate silicon radiation detectors, both for pixels and for large-area strip sensors. A successful proof of concept would open the market potential of CMOS foundries to the HEP community, which would be most beneficial in terms of availability, throughput and cost. In addition, the availability of multi-layer routing of signals will provide the freedom to optimize the sensor geometry and the performance, with biasing structures implemented in poly-silicon layers and MIM-capacitors allowing for AC coupling. A prototyping production of strip test structures and RD53A compatible pixel sensors was recently completed at LFoundry in a 150nm CMOS process. This presentation will focus on the characterization of pixel modules, studying the performance in terms of charge collection, position resolution and hit efficiency with measurements performed in the laboratory and with beam tests. We will report on the investigation of RD53A modules with 25x100 mu^2 cell geometry.

Channel estimation in wideband millimeter-wave (mmWave) systems is very challenging due to the beam squint effect. To solve the problem, we propose a learnable iterative shrinkage thresholding algorithm-based channel estimator (LISTA-CE) based on deep learning. The proposed channel estimator can learn to transform the beam-frequency mmWave channel into the domain with sparse features through training data. The transform domain enables us to adopt a simple denoiser with few trainable parameters. We further enhance the adaptivity of the estimator by introducing hypernetwork to automatically generate learnable parameters for LISTA-CE online. Simulation results show that the proposed approach can significantly outperform the state-of-the-art deep learning-based algorithms with lower complexity and fewer parameters and adapt to new scenarios rapidly.