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Abstract The CP-violating phase $$\phi _s$$ ϕ s , the $$B_{s}$$ B s decay width ( $$\varGamma _s$$ Γ s ), and the $$B_{s}$$ B s decay width difference ( $$\varDelta \varGamma _s$$ Δ Γ s ) are sensitive probes to new physics and can constrain the heavy quark expansion theory. The potential for the measurement at future Z factories is studied in this manuscript. It is found that operating at Tera-Z mode, the expected precision can reach: $$\sigma (\phi _s) = 4.6~\textrm{mrad}$$ σ ( ϕ s ) = 4.6 mrad , $$\sigma (\varDelta \varGamma _s) = 2.4~\mathrm {ns^{-1}}$$ σ ( Δ Γ s ) = 2.4 ns - 1 and $$\sigma (\varGamma _s) = 0.72~\mathrm {ns^{-1}}$$ σ ( Γ s ) = 0.72 ns - 1 . The precision of $$\phi _s $$ ϕ s is 40% larger than the expected precision with the LHCb experiment at HL-LHC. If operating at 10-Tera-Z mode, the precision of $$\phi _s $$ ϕ s can be measured at 45% of the precision obtained from the LHCb experiment at HL-LHC. However, the measurement of $$\varGamma _s$$ Γ s and $$\varDelta \varGamma _s$$ Δ Γ s cannot benefit from the excellent time resolution and tagging power of the future Z-factories. Only operating at 10-Tera-Z mode can the $$\varGamma _s$$ Γ s and $$\varDelta \varGamma _s$$ Δ Γ s reach an 18% larger precision than the precision expected to be obtained from LHCb at HL-LHC. The control of penguin contamination at the future Z-factories is also discussed.

Accurate interpretation of in vivo wide-field fluorescent imaging (WFFI) data requires precise separation of raw fluorescence signals into neural and hemodynamic components. The classical Beer-Lambert law-based approach, which uses concurrent 530-nm illumination to estimate relative changes in cerebral blood volume (CBV), fails to account for the scattering and reflection of 530-nm photons from non-neuronal components leading to biased estimates of CBV changes and subsequent misrepresentation of neural activity. This study introduces a novel linear regression approach designed to overcome this limitation. This correction provides a more reliable representation of CBV changes and neural activity in fluorescence data. Our method is validated across multiple datasets, demonstrating its superiority over the classical approach.

Abstract Accurate jet charge identification is essential for precise electroweak and flavor measurements at the high-energy frontier. We propose a novel method called the Leading Particle Jet Charge method (LPJC) to determine the jet charge based on information of the leading charged particle. Tested on Z → b b ¯ $$ b\overline{b} $$ and Z → c c ¯ $$ c\overline{c} $$ samples at center-of-mass energy of 91.2 GeV, the LPJC achieves an effective tagging power ϵ eff of 20%/9% for c/b jet, respectively. In combination with the Weighted Jet Charge method (WJC), we develop a Heavy Flavor Jet Charge method (HFJC), which achieves an effective tagging power ϵ eff of 39%/20% for c/b jet, respectively. This paper also discusses the dependencies between jet charge identification performance and the fragmentation process of heavy flavor jets, as well as critical detector performances.

Abstract In partial onset epilepsy, seizures arise focally in the brain and often propagate. Patients frequently become refractory to medical management, leaving neurosurgery, which can cause neurologic deficits, as a primary treatment. In the cortex, focal seizures spread through horizontal connections in layers II/III, suggesting that severing these connections can block seizures while preserving function. Focal neocortical epilepsy is induced in mice, sub‐surface cuts are created surrounding the seizure focus using tightly‐focused femtosecond laser pulses, and electrophysiological recordings are acquired at multiple locations for 3–12 months. Cuts reduced seizure frequency in most animals by 87%, and only 5% of remaining seizures propagated to the distant electrodes, compared to 80% in control animals. These cuts produced a modest decrease in cortical blood flow that recovered and left a ≈20‐µm wide scar with minimal collateral damage. When placed over the motor cortex, cuts do not cause notable deficits in a skilled reaching task, suggesting they hold promise as a novel neurosurgical approach for intractable focal cortical epilepsy.

Leaf color mutants serve as valuable models for studying the regulation of plant photosynthesis, alternations in chloroplast structure and function, and the analysis of associated gene functions. A yellow leaf mutant, ylm, was separated from the wild tomato M82, with its yellowing intensity influenced by low temperature. To assess the low-temperature sensitivity of this mutant, the photosynthetic and chlorophyll fluorescence responses of ylm and M82 were examined under different temperature conditions. In this study, the ylm mutant and its wild type, M82, were exposed to three temperature levels, 16, 25, and 30 °C, for 48 h. The impact of these temperature treatments on leaf color change, chlorophyll content, photosynthetic performance, and chlorophyll fluorescence characteristics of mutant ylm was investigated. The results revealed the following: (1) After exposure to 16 °C, the ylm mutant exhibited significant yellowing, a marked reduction in chlorophyll content, and a notable increase in carotenoid content. At 25 °C, the differences were less pronounced, and at 30 °C, the differences between ylm and M82 were minimal. (2) The photosynthetic rate of the ylm mutant was lower than that of M82 at 16 °C, with the gap narrowing as temperature increased, eventually converging at higher temperatures. (3) The fluorescence transient curve (OJIP) of the ylm mutant differed significantly from that of M82 at 16 °C, with higher fluorescence intensity at the O point and lower intensity at the J, I, and P points. This difference was decreased at 25 °C and nearly disappeared at 30 °C. Additionally, the Fv/Fm, Fv/Fo, PIabs, PItotal, ABS/CSm, TRo/CSm, and ETo/CSm values of ylm were lower than those of M82 at 16 °C, while the ABS/RC and DIo/RC values were higher, with no significant differences observed at 30 °C. These findings suggest that the ylm mutant is highly sensitive to low temperature, with pronounced yellowing, reduced light energy absorption and capture efficiency, and impaired electron transport at lower temperature.

Under the background of carbon neutrality, it is important to construct a large number of high-permeability power grid engineering (HPGE) systems, since these can aid in addressing the security and stability challenges brought about by the high proportion of renewable energy. Construction and engineering frequently involve multiple risk considerations. In this study, we constructed a three-stage comprehensive risk management model of HPGE, which can help to overcome the issues of redundant risk indicators, imprecise risk assessment techniques, and irrational risk warning models in existing studies. First, we use the fuzzy Delphi model to identify the key risk indicators of HPGE. Then, the Bayesian best–worst method (Bayesian BWM) is adopted, as well as the measurement alternatives and ranking according to the compromise solution (MARCOS) approach, to evaluate the comprehensive risks of projects; these methods are proven to have more reliable weighting results and a larger sample separation through comparative analysis. Finally, we established an early warning risk model on the basis of the non-compensation principle, which can help prevent the issue of actual risk warning outcomes from being obscured by some indicators. The results show that the construction of the new power system and clean energy consumption policy are the key risk factors affecting HPGE. It was found that four projects are in an extremely high-risk warning state, five are in a relatively high-risk warning state, and one is in a medium-risk warning state. Therefore, it is necessary to strengthen the risk prevention of HPGE and to develop a reasonable closed-loop risk control mechanism.

Due to the extensive utilization of liquid nature gas (abbreviated as LNG) resources and a multitude of considerations, LNG storage tanks are gradually transitioning towards smaller footprints and heightened safety standards. Consequently, underground LNG storage tanks are being designed and constructed. However, underground LNG storage tanks release a considerable quantity of cold into the ground under both accidental and normal conditions. The influence of cold results in the ground freezing, which further compromises the safety of the structure. Existing research has neglected to consider the effects of this. This oversight could potentially lead to serious safety accidents. In this work, a complete set of experiments using a novel LNG underground storage tank fluid-solid-thermal coupled cryogenic leakage scale model were conducted for the first time to simulate the effect of the tank on the soil temperature field, stress field, and displacement field and to analyze the development of the three fields and the results of the effect. This research helps the related personnel to better design, construct, and evaluate the LNG underground storage tanks to avoid the catastrophic engineering risks associated with cryogenic leakage and helps to improve the design process of LNG underground storage tanks.

A correction to this paper has been published: https://doi.org/10.1007/JHEP05(2024)210

The seismic response of underground liquefied natural gas (LNG) storage tanks has been a significant focus in both academic and engineering circles. This study utilized Ansys (2021R1) to conduct seismic analyses of large-capacity LNG tanks, considering the fluid–structure–soil coupling interaction (FSSI), and it was solved using the Volume of Fluid model (VOF) and Finite Element Method (FEM). The mechanical properties of both the LNG tank structure and soil were simulated using solid elements, and seismic acceleration loads were applied. An analysis of liquefied natural gas was performed using fluid elements within FLUENT. Initially, a modal analysis of the tank was conducted, which revealed lower frequencies for a full-liquid tank (3.193 Hz) compared to an empty tank (3.714 Hz). Subsequently, the seismic responses of both the aboveground and underground LNG tank structures were separately simulated, comparing the acceleration, stress, and displacement of the tank wall structures. The findings indicate that the peak relative displacement of the aboveground empty tank wall is 122 mm, less than that of a full tank (136 mm), while the opposite holds true for underground tanks. The period and wave height of LNG liquid shaking in underground tanks are lower than those in aboveground tanks, which is more conducive to tank safety. The deformation and acceleration of underground tanks are lower than those of aboveground tanks, but the Mises stress is higher. The results indicate that underground LNG tank structures are safer under earthquake conditions.

In order to achieve efficient recognition of 3D images and reduce the complexity of network parameters, we proposed a novel 3D image recognition method combining deep neural networks with fractional-order Chebyshev moments. Firstly, the fractional-order Chebyshev moment (FrCM) unit, consisting of Chebyshev moments and the three-term recurrence relation method, is calculated separately using successive integrals. Next, moment invariants based on fractional order and Chebyshev moments are utilized to achieve invariants for image scaling, rotation, and translation. This design aims to enhance computational efficiency. Finally, the fused network embedding the FrCM unit (FrCMs-DNNs) extracts depth features to analyze the effectiveness from the aspects of parameter quantity, computing resources, and identification capability. Meanwhile, the Princeton Shape Benchmark dataset and medical images dataset are used for experimental validation. Compared with other deep neural networks, FrCMs-DNNs has the highest accuracy in image recognition and classification. We used two evaluation indices, mean square error (MSE) and peak signal-to-noise ratio (PSNR), to measure the reconstruction quality of FrCMs after 3D image reconstruction. The accuracy of the FrCMs-DNNs model in 3D object recognition was assessed through an ablation experiment, considering the four evaluation indices of accuracy, precision, recall rate, and F1-score.

The combined use of Through Silicon Via (TSV) and metal lines, referred to as TSV-metal lines, is an essential structure in three-dimensional integrated circuits. In-depth research into the electromigration failure mechanism of TSV and the microstructure evolution can serve as theoretical guidance for optimizing three-dimensional stacking. This article conducted electromigration experiments on TSV-metal line structural samples at current densities of 1.0 × 105 A/cm2, 5 × 105 A/cm2, and 1 × 106 A/cm2. Additionally, Electron Back Scattered Diffraction (EBSD) technology was employed to systematically investigate the microstructural evolution of the TSV-metal line structure profiles before and after the application of electrical testing. The results indicate that the current induces a change in the crystal orientation at the TSV-metal interface (TSV/metal interface) and the bottom metal line. This phenomenon notably depends on the initial angle between the grain orientation and the current flow direction. When the angle between the current direction and the grain orientations [001] and [010] is relatively large, the crystals are more likely to deviate in the direction where the angle between the grain orientation and the current is smaller. This is because, at this point, the current direction is precisely perpendicular to the crystal plane family, where the atomic density is lowest, and the energy required for electron transport is minimal. Therefore, the current readily rotates in the direction of this crystal orientation. Before the electromigration tests, areas with a high level of misorientation were primarily concentrated at the TSV/metal interface and the corners of the TSV-metal line. However, these areas were found to be more prone to developing voids after the tests. It is conjectured that the high misorientation level leads to elevated stress gradients, which are the primary cause of cracking failures in the TSV-metal line. As the current density increases from 5 × 105 A/cm2 to 1 × 106 A/cm2, the electromigration failure phenomena in the TSV become even more severe.

The cohesive performance and durability of the bonding layer with semiconductor substrates are of paramount importance for realizing the high thermal conductivity capabilities of diamond. Utilizing electron beam evaporation and the room-temperature, low-pressure bonding process, robust adhesion between diamonds and silicon substrates has been achieved through the application of the metal modification layer comprised of Ti/Cu/Ti/Au (5/300/5/50 nm). Characterization with optical microscopy and atomic force microscopy reveals the uniformity and absence of defects on the surface of the deposited layer. Observations through X-ray and scanning acoustic microscopy indicate no discernible bonding defects. Scanning electron microscopy observation and energy-dispersive spectroscopy analysis of the fracture surface show distinct fracture features on the silicon substrate surface, indicating that the bonding strength of the Ti/Cu/Ti/Au metallization layer surpasses that of the base material. Furthermore, the fracture surface exhibits the presence of Cu and trace amounts of Ti, suggesting that the fracture also occurs at the interface between Ti and Cu. Characterization of the metal modification layer using X-ray diffraction reveals significant lattice distortion in the Ti layer, leading to noticeable stress accumulation within the crystalline structure. Thermal–mechanical fatigue simulations of the Ti/Cu/Ti/Au metal modification layer indicate that, owing to the difference in the coefficient of thermal expansion, the stress exerted by the Cu layer on the Ti layer results in the accumulation of fatigue damage within the Ti layer, ultimately leading to a reduction in its strength and eventual failure.

Due to the growing demand for ultra-high-density integrated circuits in the integrated circuit industry, flip-chip bonding (FCB) has become the mainstream solution for chip interconnection. In flip-chip bonding (FCB), however, alloy solder is no longer adequate to meet the high heat dissipation demands of high-power devices with over 100 kW/cm2 in power density due to its low reflow temperature. Nano-silver solder, on the other hand, exhibits superior thermal and electrical conductivity, making it an excellent alternative to traditional solder for FCB. This study explored nano-silver’s thermal reliability and electrical performance as a solder material. The following results were obtained through temperature cycle (with temperatures ranging from −55 to 150 °C) and high-temperature storage experiments (with applied temperatures of over 170 °C). The results indicate that as the duration of the high-temperature storage increased, the grain continued to coarsen, resulting in an average pore size transition from 0.004 to 0.072 μm2. A strong correlation coefficient of 0.9913 was observed between the duration of high-temperature exposure and the porosity within the time range of 0–200 h. Following the reliability test, the shear strength of the nano-silver interconnect samples showed varying degrees of decrease. The bonding effect with the nano-silver layer can be enhanced, and the thermal reliability can be improved by depositing Ni/Ag on the surface of Cu, making it less prone to cracking. Regarding the electrical performance, the square resistance of the nano-silver interconnect structures increased by 35% after the reliability test. This indicates a significant degradation in the electrical reliability of nano-silver interconnects under temperature stress.

In this work, field-scale experiments were carried out on two energy piles to investigate the mechanical behavior of the piles when subjected to thermomechanical loads under a liquefied natural gas tank. The results showed that the super-long and large-diameter energy pile exhibited a better heat transfer performance. After pile heating or cooling, the temperature in the mid-depth of the pile increased or decreased rapidly, and at the two ends, the temperature varied relatively slowly. Regarding the observed axial strain, energy pile 1 exhibited predominately compressive deformation during the coupled heating–loading process, while a certain tensile deformation was found near the pile toe of energy pile 2 during the coupled cooling–loading process. Moreover, for both energy piles, positive shaft resistances appeared predominately under both the pure mechanical and coupled thermomechanical conditions, and occasional and local occurrences of negative resistances could be related to the ground conditions on site. The settlement and bearing capacity values of the two energy piles were not significantly affected by the coupled thermomechanical loads, and thus, the serviceability of the gas tank would not diminish.

Δ1-Pyrroline-5-carboxylate synthetase (P5CS) acts as the rate-limiting enzyme in the biosynthesis of proline in plants. Although P5CS plays an essential role in plant responses to environmental stresses, its biological functions remain largely unclear in pear (Pyrus betulifolia). In the present study, 11 putative pear P5CSs (PbP5CSs) were identified by comprehensive bioinformatics analysis and classified into five subfamilies. Segmental and tandem duplications contributed to the expansion and evolution of the PbP5CS gene family. Various cis-acting elements associated with plant development, hormone responses, and/or stress responses were identified in the promoters of PbP5CS genes. To investigate the regulatory roles of PbP5CS genes in response to abiotic and biotic stresses, gene expression patterns in publicly available data were explored. The tissue-specific expressional dynamics of PbP5CS genes indicate potentially important roles in pear growth and development. Their spatiotemporal expression patterns suggest key functions in multiple environmental stress responses. Transcriptome and real-time quantitative PCR analyses revealed that most PbP5CS genes exhibited distinct expression patterns in response to drought, waterlogging, salinity-alkalinity, heat, cold, and infection by Alternaria alternate and Gymnosporangium haraeanum. The results provide insight into the versatile functions of the PbP5CS gene family in stress responses. The findings may assist further exploration of the physiological functions of PbP5CS genes for the development and enhancement of stress tolerance in pear and other fruits.

Medicinal chemistry investigators have isolated and synthesized benzoflavone analogues with diverse bioactivities including enzyme inhibitory activity, central nervous system (CNS) activity, anti-inflammatory activity, anti-microorganism activity, hypoglycemic activity, and receptor modulating potential. SAR (Structure-Activity Relationship) studies have been conducted extensively and plenty of benzoflavone analogues have been prepared for potential targets. Herein, we review the pharmacology properties and SAR for benzoflavone analogues, and provide a brief summarization of synthetic strategies, wishing to give perspective on the research and development of benzoflavone derivatives.

Unambiguously identifying an epileptic focus with high spatial resolution is a challenge, especially when no anatomic abnormality can be detected. Neurovascular coupling (NVC)-based brain mapping techniques are often applied in the clinic despite a poor understanding of ictal NVC mechanisms, derived primarily from recordings in anesthetized animals with limited spatial sampling of the ictal core. In this study, we used simultaneous wide-field mesoscopic imaging of GCamp6f and intrinsic optical signals (IOS) to record the neuronal and hemodynamic changes during acute ictal events in awake, behaving mice. Similar signals in isoflurane-anesthetized mice were compared to highlight the unique characteristics of the awake condition. In awake animals, seizures were more focal at the onset but more likely to propagate to the contralateral hemisphere. The HbT signal, derived from an increase in cerebral blood volume (CBV), was more intense in awake mice. As a result, the “epileptic dip” in hemoglobin oxygenation became inconsistent and unreliable as a mapping signal. Our data indicate that CBV-based imaging techniques should be more accurate than blood oxygen level dependent (BOLD)-based imaging techniques for seizure mapping in awake behaving animals.

Parvalbumin-expressing, fast spiking interneurons have high-energy demands, which make them particularly susceptible to energy impairment. Recent evidence suggests a link between mitochondrial dysfunction in fast spiking cortical interneurons and neuropsychiatric disorders. However, the effect of mitochondrial dysfunction restricted to parvalbumin interneurons has not been directly addressed in vivo. To investigate the consequences of mitochondrial dysfunction in parvalbumin interneurons in vivo, we generated conditional knockout mice with a progressive decline in oxidative phosphorylation by deleting cox10 gene selectively in parvalbumin neurons (PV-Cox10 CKO). Cox10 ablation results in defective assembly of cytochrome oxidase, the terminal enzyme of the electron transfer chain, and leads to mitochondrial bioenergetic dysfunction. PV-Cox10 CKO mice showed a progressive loss of cytochrome oxidase in cortical parvalbumin interneurons. Cytochrome oxidase protein levels were significantly reduced starting at postnatal day 60, and this was not associated with a change in parvalbumin interneuron density. Analyses of intrinsic electrophysiological properties in layer 5 primary somatosensory cortex revealed that parvalbumin interneurons could not sustain their typical high frequency firing, and their overall excitability was enhanced. An increase in both excitatory and inhibitory input onto parvalbumin interneurons was observed in PV-Cox10 CKO mice, resulting in a disinhibited network with an imbalance of excitation/inhibition. Investigation of network oscillations in PV-Cox10 CKO mice, using local field potential recordings in anesthetized mice, revealed significantly increased gamma and theta frequency oscillation power in both medial prefrontal cortex and hippocampus. PV-Cox10 CKO mice did not exhibit muscle strength or gross motor activity deficits in the time frame of the experiments, but displayed impaired sensory gating and sociability. Taken together, these data reveal that mitochondrial dysfunction in parvalbumin interneurons can alter their intrinsic physiology and network connectivity, resulting in behavioral alterations similar to those observed in neuropsychiatric disorders, such as schizophrenia and autism.

This paper presents an experimental investigation of the role of pipe-roofs in the improvement of the stability of rectangular excavations constructed using pipe-roof technology. This technology is suitable for the construction of underground passages in crowded areas subjected to high requirements concerning soil settlement and stability during excavation construction. The design of a rectangular pipe-roof excavation required an understanding of the interaction between the soil, the pipe-roof and the excavation. This interaction is complex and plays an important role in the features of the pipe roof excavation. This paper presents a series of 1g physical experimental tests conducted in dry sand soil with an advanced monitoring system, which allows tracking of the soil settlement, the pipe deformation and the soil pressure. Analysis of these tests shows the effective role of the pipe-roof in reducing both the soil settlement and the soil pressure on the excavation. It also shows the influence of pipes on the deformation mechanism of the soil and its evolution from low deformation to the instability phase.

The application of Light Detection And Ranging (LiDAR) technology has become increasingly extensive in tunnel structure monitoring. The proposed processing method aims to carry out non-contact monitoring for circular stormwater sewage tunnels and provides an efficient workflow. This allows the automatic processing of raw point data and the acquisition of visualization results to analyze the health state of a tunnel within a short period of time. The proposed processing method employs a series of algorithms to extract the point cloud of a single tunnel segment without obvious noise by main three steps: axis acquisition, segment extraction, and denoising. The tunnel axis is extracted by fitting boundaries of the tunnel point cloud projection in the plane. With the guidance of the axis, the entire preprocessed tunnel point cloud is segmented by equal division to get a section of the tunnel point cloud which corresponds to a single tunnel segment. Then, the noise in every single point cloud segment is removed by clustering the algorithm twice, based on the distance and intensity. Finally, clean point clouds of tunnel segments are processed by an effective deformation extraction processor to determine the ovality and to get a three-dimensional visual deformation nephogram. The proposed method can significantly improve the efficiency of LiDAR data processing and extend the application of LiDAR technology in circular stormwater sewage tunnel monitoring.

In this paper, considering real time wind power uncertainties, the strategic behaviors of wind power producers adopting two different bidding modes in day-ahead electricity market is modeled and experimentally compared. These two different bidding modes only provide a wind power output plan and a bidding curve consisting of bidding price and power output, respectively. On the one hand, to significantly improve wind power accommodation, a robust market clearing model is employed for day-ahead market clearing implemented by an independent system operator. On the other hand, since the Least Squares Continuous Actor-Critic algorithm is demonstrated as an effective method in dealing with Markov decision-making problems with continuous state and action sets, we propose the Least Squares Continuous Actor-Critic-based approaches to model and simulate the dynamic bidding interaction processes of many wind power producers adopting two different bidding modes in the day-head electricity market under robust market clearing conditions, respectively. Simulations are implemented on the IEEE 30-bus test system with five strategic wind power producers, which verify the rationality of our proposed approaches. Moreover, the quantitative analysis and comparisons conducted in our simulations put forward some suggestions about leading wind power producers to reasonably bid in market and bidding mode selections.

An important goal of China’s electric power system reform is to create a double-side day-ahead wholesale electricity market in the future, where the suppliers (represented by GenCOs) and demanders (represented by DisCOs) compete simultaneously with each other in one market. Therefore, modeling and simulating the dynamic bidding process and the equilibrium in the double-side day-ahead electricity market scientifically is not only important to some developed countries, but also to China to provide a bidding decision-making tool to help GenCOs and DisCOs obtain more profits in market competition. Meanwhile, it can also provide an economic analysis tool to help government officials design the proper market mechanisms and policies. The traditional dynamic game model and table-based reinforcement learning algorithm have already been employed in the day-ahead electricity market modeling. However, those models are based on some assumptions, such as taking the probability distribution function of market clearing price (MCP) and each rival’s bidding strategy as common knowledge (in dynamic game market models), and assuming the discrete state and action sets of every agent (in table-based reinforcement learning market models), which are no longer applicable in a realistic situation. In this paper, a modified reinforcement learning method, called gradient descent continuous Actor-Critic (GDCAC) algorithm was employed in the double-side day-ahead electricity market modeling and simulation. This algorithm can not only get rid of the abovementioned unrealistic assumptions, but also cope with the Markov decision-making process with continuous state and action sets just like the real electricity market. Meanwhile, the time complexity of our proposed model is only O(n). The simulation result of employing the proposed model in the double-side day-ahead electricity market shows the superiority of our approach in terms of participant’s profit or social welfare compared with traditional reinforcement learning methods.

Immune checkpoint regulation is a negative feedback regulatory mechanism in the body, and sequential death receptor-1 (PD-1) and programmed death receptor ligand-1 (PD-L1) are known as immune checkpoints. The PD-1/PD-L1 pathway inhibits the activity of effector T cells through a negative regulatory mechanism to avoid excessive response-induced body damage. PD-L1 is highly expressed in many tumor tissues, and high PD-L1 expression can ultimately lead to tumor immune escape. Therefore, immune checkpoint blockade with inhibition of negative immune regulation therapy has become a cutting-edge hot spot for antitumor therapy, with the main target molecules being PD-1 and PD-L1. Recent years have seen promising progress in the study of traditional Chinese medicines and their effects on gastric and colon cancers, particularly in relation to the PD-1/PD-L1 pathway mechanisms. This review specifically examines the modulation of the PD-L1 pathway by certain traditional Chinese medicines in gastric and colon cancers, aiming to provide insights for the development of innovative drugs for these types of digestive cancers. [ABSTRACT FROM AUTHOR]

Using the improved prospect theory with the linear transformations of rewarding good and punishing bad (RGPBIT), a new investment ranking model for power grid construction projects (PGCPs) is proposed. Given the uncertainty of each index value under the market environment, fuzzy numbers are used to describe qualitative indicators and interval numbers are used to describe quantitative ones. Taking into account decision-maker's subjective risk attitudes, a multi-criteria decision-making (MCDM) method based on improved prospect theory is proposed. First, the [-1, 1]RGPBIToperator is proposed to normalize the original data, to obtain the best andworst schemes of PGCPs. Furthermore, the correlation coefficients between interval/fuzzy numbers and the best/worst schemes are defined and introduced to the prospect theory to improve its value function and loss function, and the positive and negative prospect value matrices of the project are obtained. Then, the optimization model with the maximum comprehensive prospect value is constructed, the optimal attribute weight is determined, and the PGCPs are ranked accordingly. Taking four PGCPs of the IEEERTS-79 node system as examples, an illustration of the feasibility and effectiveness of the proposed method is provided. [ABSTRACT FROM AUTHOR]