Split Rate Research Articles

Design and experimental characterization of a propane-based reversible dual source/sink heat pump

The current paper presents the design and energy performance analysis of a propane-based reversible Dual Source/Sink Heat Pump (DSHP). DSHPs offer an alternative to conventional water to water and air to water heat pumps, leveraging the strengths of both technologies in an efficient manner. The developed prototype incorporates an innovative Dual Source/Sink Heat eXchanger (DSHX), enabling the unit operating in various modes, including space heating, space cooling, and domestic hot water production using brine, air or both simultaneously as a source/sink. The DSHX serves as as both a condenser or an evaporator, directly rejecting or absorbing heat from air and/or brine. By eliminating secondary loops and defrost cycles, the DSHX minimizes energy losses. The main novelty of this work lies in the DSHX that integrates external units typically duplicated in DSHPs into a single component, eliminating the need for split refrigerant flow rates, thus avoiding maldistribution, refrigerant charge increase and draining valves. A steady state experimental campaign was conducted in a climatic chamber to characterize the DSHP prototype and validate the DSHX performance models. Heating capacity up to 11.2 kW and COP values up to 4.7 were achieved at nominal compressor speed by supplying hot water at 35 °C with an ambient temperature of 7 °C. Similarly, when producing cold water at 7 °C, cooling capacity and EER reached 9.8 kW and 3.6, respectively, at nominal compressor speed using air as heat sink at 35 °C. The effects of various operating parameters on the overall coefficient of performance and heat duty in both heating and cooling modes, considering air or brine as heat source/sink are analyzed in detail. Results demonstrate enhancements of approximately 15 % in capacity and efficiency compared to earlier work. Moreover, four deterministic models were created in order to predict the behaviour of the DSHX and validated against experimental results, reaching deviation values below 15 %.

Impact of high-speed railway construction on freight capacity on sections of existing railways- A case on the Yangtze River Delta

The construction of high-speed railway (HSR) can effectively release the freight capacity of existing railways. By analyzing the influencing factors of passengers’ travel choices, the Utility Function and Logit model are adopted to calculate the utility and passenger flow split rate, and get the passenger flow of the existing railways after the diversion of the newly-built HSR. The passenger flow allocation model is established based on User Equilibrium, and improved Frank-Wolfe (FW) algorithm is applied to solve it, and obtain the passenger capacity of the sections on existing railways. Based on the deduction coefficient method, the passenger capacity is deducted from total capacity to get the freight capacity of each section on existing railways after release. Finally, take the railway channel in the Yangtze River Delta as a case, the results show that after the construction of the HSR, the freight capacity of sections on existing railways in the Yangtze River Delta is released to varying degrees. The freight capacity of Hefei-Wuhu section will be released the most, which is 19.5 pairs of trains, while that of Shanghai-Jiaxing section is the least, which is only 3.5 pairs, following by Wenzhou-Jinhua and Hangzhou-Ningbo sections, which are 4 pairs. Therefore, the construction of HSR should be accompanied by focusing on sections with less released freight capacity to enhance the overall capacity of the regional railway network.

Controlling the False Split Rate in Tree-Based Aggregation

In many domains, data measurements can naturally be associated with the leaves of a tree, expressing the relationships among these measurements. For example, companies belong to industries, which in turn belong to ever coarser divisions such as sectors; microbes are commonly arranged in a taxonomic hierarchy from species to kingdoms; street blocks belong to neighborhoods, which in turn belong to larger-scale regions. The problem of tree-based aggregation that we consider in this article asks which of these tree-defined subgroups of leaves should really be treated as a single entity and which of these entities should be distinguished from each other. We introduce the false split rate, an error measure that describes the degree to which subgroups have been split when they should not have been. While expressible as the false discovery rate in a special case, we show that these measures can be quite different for the general tree structures common in our setting. We then propose a multiple hypothesis testing algorithm for tree-based aggregation, which we prove controls this error measure. We focus on two main examples of tree-based aggregation, one which involves aggregating means and the other hich involves aggregating regression coefficients. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Climate risk and split ratings: evidence from Korea

ABSTRACT This study examines the effects of climate risk on split ratings. Numerous studies have examined the correlation between financial reporting quality and split ratings. However, few studies have explored the effect of business ethics on split ratings. Thus, this study focuses particularly on environmental factors within the broader context of Environmental, Social, and Governance (ESG), that have recently attracted significant attention. We conducted a multiple regression analysis of the impact of climate risk on split ratings using a dataset of companies listed on the Korea Exchange. The analysis indicates that climate risk, which encompasses a firm’s greenhouse gas emissions and energy consumption, contributes to the split ratings of corporate bonds. This observation implies that heightened exposure to climate risk amplifies the likelihood of split ratings owing to diminished accounting transparency resulting from practices such as earnings management and a decline in the quality of disclosures. This study not only expands the understanding of the determinants of split ratings theoretically, but also offers investors empirically supported insights into the relevance of considering climate risk in their investment and credit decisions. Furthermore, this suggests the necessity for government policymakers to establish a monitoring system aimed at alleviating potential information asymmetry issues for firms facing elevated climate risk.

Functional outcomes in robot-assisted partial nephrectomy with three-dimensional images reconstructed from computed tomography: a propensity score-matched comparative analysis.

Our objective was to investigate the long-term functional outcomes of robot-assisted partial nephrectomy (RAPN) combined with three-dimensional (3D) imaging. The 3D images, reconstructed using computed tomography, were introduced in RAPN procedures. The demographic, oncological, functional, and volumetric outcomes of 296 patients who underwent RAPN with and without 3D imaging between 2013 and 2021 were analyzed retrospectively. Propensity score matching (1:1) was performed to adjust for potential baseline confounders. After matching, 71 patients were allocated to each group. In the 3D RAPN (3DRPN) group, functional outcomes significantly improved: the number of patients with over 90% estimated glomerular filtration rate (eGFR) preservation rate (40 vs. 43, P = 0.044), eGFR preservation rate (88.0% vs. 91.6%, P = 0.006), the number of patients with chronic kidney disease (CKD) upstaging (26 vs. 13, P = 0.023), and split renal function preservation rate (operated kidney: 84.9% vs. 88.5%, P = 0.015). The 3DRPN group showed superiority in terms of >90% eGFR preservation (P = 0.010), CKD upstaging-free survival rates (P < 0.001), and volumetric outcomes (excess parenchymal volume: 27.9 vs. 17.7mL, P = 0.030; parenchyma volume preservation rate: 81.6% vs. 88.8%, P = 0.006). Three-dimensional imaging was positively associated with eGFR preservation (P = 0.023, odds ratio: 2.34) and prevention of CKD upstaging (P = 0.013, odds ratio: 2.90). In this study, RAPN combined with 3D imaging underscored the preservation of eGFR > 90% and the prevention of CKD upstaging by improving the preservation rate of renal parenchyma and split renal function.

Split bond ratings: Evidence from Japanese credit rating agencies

This study examines why split bond ratings occur between credit rating agencies with different reputations in Japan. Using a sample of Japanese corporate bonds newly issued during the 2006–2021 period, I find that the ratings assigned by a less reputable Japanese rating agency (JCR) are significantly higher than those by a more reputable Japanese rating agency (R&I) for the same bonds because JCR is likely to rate the creditworthiness of bond issuers more highly than R&I. The disagreement between JCR and R&I over the creditworthiness of issuers causes split bond ratings. Moreover, bonds with multiple split ratings have higher yield spreads.

Deep learning-based measurement of split glomerular filtration rate with 99mTc-diethylenetriamine pentaacetic acid renal scan

PurposeTo develop a deep learning (DL) model for generating automated regions of interest (ROIs) on 99mTc-diethylenetriamine pentaacetic acid (DTPA) renal scans for glomerular filtration rate (GFR) measurement.MethodsManually-drawn ROIs retrieved from a Picture Archiving and Communications System were used as ground-truth (GT) labels. A two-dimensional U-Net convolutional neural network architecture with multichannel input was trained to generate DL ROIs. The agreement between GFR values from GT and DL ROIs was evaluated using Lin’s concordance correlation coefficient (CCC) and slope coefficients for linear regression analyses. Bias and 95% limits of agreement (LOA) were assessed using Bland-Altman plots.ResultsA total of 24,364 scans (12,822 patients) were included. Excellent concordance between GT and DL GFR was found for left (CCC 0.982, 95% confidence interval [CI] 0.981–0.982; slope 1.004, 95% CI 1.003–1.004), right (CCC 0.969, 95% CI 0.968–0.969; slope 0.954, 95% CI 0.953–0.955) and both kidneys (CCC 0.978, 95% CI 0.978–0.979; slope 0.979, 95% CI 0.978–0.979). Bland-Altman analysis revealed minimal bias between GT and DL GFR, with mean differences of − 0.2 (95% LOA − 4.4–4.0), 1.4 (95% LOA − 3.5–6.3) and 1.2 (95% LOA − 6.5–8.8) mL/min/1.73 m² for left, right and both kidneys, respectively. Notably, 19,960 scans (81.9%) showed an absolute difference in GFR of less than 5 mL/min/1.73 m².ConclusionOur DL model exhibited excellent performance in the generation of ROIs on 99mTc-DTPA renal scans. This automated approach could potentially reduce manual effort and enhance the precision of GFR measurement in clinical practice.

High-Temperature Stirring Pretreatment of Waste Rubber Particles Enhances the Interfacial Bonding and Mechanical Properties of Rubberized Concrete

This paper innovatively proposes a method of 180 °C high-temperature stirring pretreatment for waste rubber particles and compares this method with untreated, NaOH-treated, and silane coupling agent KH570-treated waste rubber particles. Fourier-transform infrared spectroscopy, X-ray diffraction analysis, water contact angle measurement, scanning electron microscopy, and energy-dispersive X-ray study are used to investigate the effects and mechanisms of different pretreatment methods on waste rubber particles. The results indicate that compared to NaOH-treated and KH570-treated waste rubber particles, the 180 °C high-temperature-stirred pretreated waste rubber particles show significantly improved cleanliness and form a hard oxide film. The study also investigates the effects of different pretreatment methods on the mechanical properties and interface binding performance of rubber concrete made from pretreated waste rubber particles. The results demonstrate that rubber concrete prepared using 180 °C high-temperature-stirred pretreated waste rubber particles substituting 20% fine aggregate exhibits the best mechanical properties and interface bonding performance. The compressive strength recovery rates after 7 and 28 days are 41.6% and 37.3%, respectively; the split tensile strength recovery rates are 47.3% and 60.6%; the axial compressive strength recovery rates are 34.1% and 18.8%; and the static compression moduli of elasticity recovery rates are 46.8% and 26.3%. High-temperature stirring pretreatment of waste rubber particles is simple to operate and suitable for scaled production. Its pretreatment effect is superior to those of the KH570 and NaOH methods, providing a reference value for the scalable application of waste rubber particles as a substitute for fine aggregate in rubber concrete.

Optimising miniaturised hydrocyclones for enhanced separation of microplastics

Microplastics, particularly those in the 5–20µm range, pose substantial risks in aquatic environments, yet effective removal techniques are limited. This study assesses two critical geometric parameters of a 10 mm mini-hydrocyclone: inlet radius and insertion angle. Along with the inlet velocity, they are optimised collectively to enhance the MPs’ removal. Computational Fluid Dynamics (CFD) simulations using the Mixture model and Reynolds stress model were employed to track the particle–water–air interactions, revealing the flow dynamics and absence of an air core. The Response Surface Methodology (RSM) using a Box-Behnken design of experiments was adopted to assess the impact of the geometric and operational parameters and determine the optimal design. Recovery efficiency, concentration efficiency, and split rate were recorded during experiments and compared with CFD results, showing a good agreement between experiments and CFD simulations. The resulting novel design achieved an 88.53% MPs recovery and a concentration efficiency of 1.83, which represents an obvious improvement over the commercial design’s MPs recovery efficiency of 79.97% and concentration efficiency of 1.68. Simulations reveal that the novel design shortens the path for MPs through the forced turbulence region and modifies the velocity and turbulence fields, enhancing the concentration gradient of MPs within the mini-hydrocyclone’s chamber. Moreover, the novel design exhibits a more robust response to inlet velocity variations, a crucial advantage for its application in arrays. Experiments further confirm that a matrix composed of 10 novel mini-hydrocyclones achieves 10 percentage points higher recovery efficiency than the matrix with 10 commercial mini-hydrocyclones. These findings mark a significant step towards effective MP removal, addressing a critical environmental issue.

Water Structure in the First Layers on TiO2: A Key Factor for Boosting Solar-Driven Water-Splitting Performances.

The water hydrogen-bonded network is strongly perturbed in the first layers in contact with the semiconductor surface. Even though this aspect influences the outer-sphere electron transfer, it was not recognized that it is a crucial factor impacting the solar-driven water-splitting performances. To fill this gap, we have selected two TiO2 anatase samples (with and without B-doping), and by extensive experimental and computational investigations, we have demonstrated that the remarkable 5-fold increase in water-splitting photoactivity of the B-doped sample cannot be ascribed to effects typically associated to enhanced photocatalytic properties, such as band gap, heterojunctions, crystal facets, and other aspects. Studying these samples by combining FTIR measurements under controlled humidity with first-principles simulations sheds light on the role and nature of the first-layer water structure in contact with the photocatalyst surfaces. It turns out that the doping hampers the percolation of tetrahedrally coordinated water molecules while enhancing the population of topological H-bond defects forming approximately linear H-bonded chains. This work unveils how doping the semiconductor surface affects the local electric field, determining the water splitting rate by influencing the H-bond topologies in the first water layers. This evidence opens new prospects for designing efficient photocatalysts for water splitting.

Effect of Mineral Fertilization and Seed Inoculation with Microbial Preparation on Seed and Protein Yield of Pea (Pisum sativum L.)

The aim of this study was to determine the effects of different NPK rates and N application methods and seed inoculation with a microbial preparation on selected elements of plant growth and the productivity parameters seed yield, protein content in seeds and the yield of protein. The research hypothesis suggested that seed inoculation and a split rate of N application with an optimal supply of plants with PK could improve the nutritional status and increase the efficiency of nutrient use in peas. The studies included two factors: the application of NPK at doses of N0P0K0 (control), N15P15K15 (pre-sowing), N15P30K30 + N15 (pre-sowing + N15 at BBCH 22–23), N30P30K30 (pre-sowing), N30P45K45 + N15 (pre-sowing + N15 at BBCH 22–23) and N45P45K45 (pre-sowing), and seed inoculation with the microbial preparation Rhizogumin. The results of the study showed significant effects of seed inoculation and mineral fertilization on pea plant growth and the productivity parameters seed yield, protein content and protein yield. It was concluded that among the studied combinations, seed inoculation and the application of mineral fertilizers with fractional nitrogen fertilization with N30P45K45 + N15 were the most effective. This combination significantly increased seed yield, protein content and protein yield compared to the control treatment (by 26.2%, 11.1% and 43,5%, respectively).

Postharvest Treatment Effects on ‘Somerset Seedless’ Cold-Hardy Table Grapes

ABSTRACT Limited amount of information is available for cold-hardy table grape postharvest storage and strategies to extend the storage time. ‘Somerset Seedless’ is a cold-hardy table grape with a potential market for the Upper Midwest and Northern Great Plains. Postharvest treatments were assessed as a possible route to increase cold-hardy table grape shelf-life. In this study, a 1.4% chitosan postharvest treatment was tested on ‘Somerset Seedless’ grapes through 7 weeks of storage with temperature 1–4°C, humidity ≥90%. The effects were compared to two controls: one was diH2O, and the other one was acetic acid. The assessment included grape appearance traits (rachis, decay, mold, scattering, and splitting), physiochemical properties (TSS, pH and total acidity), antioxidant activity, as well as total phenolics and flavonoid content. In general, 1.4% chitosan, 1% acetic acid, and diH2O had a large impact on grape appearance during storage with limited impact on chemistry. Throughout 7 weeks of storage, no significant differences were observed in grape physiochemical and phytochemical changes. Chitosan performed the best for ‘Somerset Seedless’ postharvest storage in regard to the low rate of visible damage. Although acetic acid had similarly positive effects on mold control as chitosan treatment, it caused the highest split rate after 1 week and the highest shatter rate after 5 weeks of storage. Chitosan treated ‘Somerset Seedless’ still met the USDA standard of table grapes after 5 weeks. This study suggests chitosan postharvest treatments may have applications for enhancing the shelf-life of cold-hardy table grapes.

Seismic monitoring of EGS fracture stimulations at Utah FORGE (Part 1): Time-lapse variations of b-values and Shear-wave Splitting Rates of induced microearthquakes

Characterizing geologic formations near geothermal wells is crucial for efficient extraction of subsurface geothermal energy resources. To monitor effective fracture stimulation of enhanced geothermal systems (EGS), we analyze a catalog of induced microearthquakes to characterize seismicity induced by Phase 2C fracture stimulations of the Utah FORGE project and derive the b-values for 424 induced microearthquakes in three stages of fracture stimulation. We find the time-lapse variations of these b-values. We apply eigenvector rotation and waveform correlation to 3C geophone data to separate fast S1 and slow S2 waves, determine their directions, measure their lag time, and calculate the S-wave splitting rate (SSR) for six induced microearthquakes in Stage 1 and 21 in Stage 3. Both SSR values and b-values increase with time. The increase in these values may be related to heavily fractured zones created by fracture stimulations during Stage 2 injections. Large and highly variable SSR values in Stage 3 may reflect heterogeneities in the fracture zones. Time-lapse variations of SSR values and b-values may be one of the most useful approaches to monitoring and characterizing the effectiveness and efficiency of various types of fracture stimulations.

Integration of physical solution and ionic liquid toward efficient phase splitting for energy-saving CO2 capture

Carbon dioxide (CO2) capture from flue gas is crucial for achieving industrial carbon neutrality. Using a biphasic solvent can significantly reduce the volume of regenerated solvent for energy-saving carbon capture. In this study, a novel strategy for biphasic solvent preparation was proposed by combining amine blends (3,3′-Diaminodipropylamine, DADPA; N-Methyldiethanolamine, MDEA) and composite agents (1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide, BN; sulfolane, TMS). The screened proportion of DADPA:MDEA:TMS:BN:H2O was 2:6:2:1:2 (labelled DMTB). Experimental evaluation revealed that the DMTB solvent exhibits a high CO2 capacity of 4.59 mol/L (in the 50 vol% rich phase), an efficient phase splitting rate (within 3 min) and a fast CO2 desorption rate. The dynamic phase-splitting capacity was evaluated by a quasi-two-dimensional separator, under various operational condition. The 13C NMR results indicated the formation of DADPACOO- in the CO2-saturated phase in the presence of water rather than bicarbonate while observing dissociative behaviour in MDEA and TMS. Therefore, the phase separation analysis revealed that the products of protonated amines and carbamic acid species were highly polar and preferred to dissolve in the water phase. This study provides an optional biphasic solvent with phase splitting ability for CO2 capture from flue gas.

Ultra-thin double-layered hexagonal CuI: strain tunable properties and robust semiconducting behavior.

In this study, the freestanding form of ultra-thin CuI crystals, which have recently been synthesized experimentally, and their strain-dependent properties are investigated by means of density functional theory calculations. Structural optimizations show that CuI crystallizes in a double-layered hexagonal crystal (DLHC) structure. While phonon calculations predict that DLHC CuI crystals are dynamically stable, subsequent vibrational spectrum analyzes reveal that this structure has four unique Raman-active modes, allowing it to be easily distinguished from similar ultra-thin two-dimensional materials. Electronically, DLHC CuI is found to be a semiconductor with a direct band gap of 3.24 eV which is larger than that of its wurtzite and zincblende phases. Furthermore, it is found that in both armchair (AC) and zigzag (ZZ) orientations the elastic instabilities occur over the high strain strengths indicating the soft nature of CuI layer. In addition, the stress-strain curve along the AC direction reveal that DLHC CuI undergoes a structural phase transition between the 4% and 5% tensile uniaxial strains as indicated by a sudden drop of the stress in the lattice. Moreover, the phonon band dispersions show that the phononic instability occurs at much smaller strain along the ZZ direction than that of along the AC direction. Furthermore, the external strain direction can be deduced from the predicted Raman spectra through the splitting rates of the doubly degenerate in-plane vibrations. The mobility of the hole carriers display highly anisotropic characteristic as the applied strain reaches 5% along the AC direction. Due to its anomalous strain-dependent electronic features and elastically soft nature, DLHC of CuI is a potential candidate for future electro-mechanical applications.

Designing and development of stable asymmetric bipolar membrane for improved water splitting and product recovery by electrodialysis

The stability and the performance of the bipolar membrane (BPM) are the critical issues to be resolved. The layer thickness plays an important role in improving the performance of bipolar membrane. Here, an efficient transparent asymmetric bipolar membrane has been fabricated by a layer-by-layer solution casting technique comprising cation and anion exchange layers. The thickness of the anion exchange layer was reduced to improve the performance in term of product purity and recovery by minimizing the co-ion leakage. The interface of BPM acts as a catalytic junction for dissociating the water into H+ and OH− ions. A clear membrane interface was confirmed by the scanning electron microscopy (SEM). All three BPMs show the adequate physicochemical and electrochemical properties in term of ionic conductivity and water uptake. Effect of applied potential was studied on the membrane performance through BMED for the dissociation of NaCl. We also performed the bipolar membrane electrodialysis (BMED) for the hydrolysis of different organic and inorganic salts into corresponding acid and alkali. Among the bipolar membranes, BPM with 40 % less AEL thickness (BPM-60 membrane) shows the better performance. BPM-60 shows the highest water splitting rate and alkali recovery of 0.491 g h−1 and 95.5 %, respectively with lowest energy consumption of 1.11 kW h kg−1 and high current efficiency of 90.32 %. Conversion of NaCl in to NaOH and HCl gives the highest product recovery as compared to other salts, due to higher dissociation of NaCl. BPM-60 with higher water splitting rate, lower energy consumption and higher current efficiency for conversion of salt into corresponding acid and alkali made asymmetric bipolar membrane as industrially applicable with high performance.

A Real Time Face Recognition and Tracking Framework Using Lightweight Convolutional Neural Network

Human face recognition and tracking (FRT) plays a vital role in various fields, including security, authentication, and human-computer interaction. The main modules of the FRT system are detection, feature extraction, and FRT. Using a database, these units recognize faces as well as their location, movement, and visible features. The framework aims to process large visual data in real-time, enabling accurate and fast FRT. The paper develops a real-time FRT framework using a lightweight CNN convolutional neural network to accurately match images of faces and environments with different illumination and expression differences to improve performance. This paper focused on the development of real-time facial recognition and tracking systems. The model used to achieve this is based on deep learning (DL) using a lightweight convolutional neural network (CNN) and post-feature extraction using linear discriminant analysis (LDA). Histogram of Oriented Gradients (HOG) experiments demonstrate that DL with lightweight CNN models provides a good solution for FRT tasks, even in challenging situations including changes in position, expression, illumination, and occlusion. The results of CNN-based DL were compared with several experiments. The model was also compared with many modern methods and achieved better results. The lightweight CNN model for DL outperformed it 100% of the time. When the split rate is 70:30 and the learning rate is 0.001, the epoch is 100. This demonstrates the dominance of DL over other techniques and shows how well it handles FRT tasks using lightweight and even real-time CNN methods.

Influence of a simple cyst on kidney function.

The aim of the study was to estimate the influence of a simple kidney cyst on kidney function and to determine indications for surgical treatment. In this prospective cohort study, we analyze data on 109 patients who sought counseling with a simple kidney cyst. Patients with solitary cyst of the right or left kidney, grade I-IIF according to the Bosniak classification, were included. Split glomerular filtration rate (sGFR) was calculated. The maximum size of the cyst, single kidney volumes (SKV) and the volume of the lost (atrophied) parenchyma were estimated with computed tomography (CT) scan of the urinary tract with contrast. The average difference between the sGFR of a healthy and affected kidney cyst was 11 [8.70; 13.44] ml/min, which is a statistically significant value (p = 0.001). Correlation analysis revealed a statistically significant relationship between the proportion of lost parenchyma and the maximum cyst size: p = 0.37 with 95% CI [0.20; 0.52] (p = 0). A multivariate logistic regression model showed that a statistically significant factor influencing the likelihood of a significant decrease in sGFR is the percentage of lost kidney parenchyma (OR = 1.13; p = 0). The growth of kidney cyst causes atrophy of the renal parenchyma and a decrease in the sGFR of the affected kidney. An increase in the volume of the atrophied parenchyma leads to a decrease in the sGFR of the affected kidney.

Microscopic mechanism of plasmon-mediated photocatalytic H2 splitting on Ag–Au alloy chain

Alloy nanostructures supporting localized surface plasmon resonances has been widely used as efficient photocatalysts, but the microscopic mechanism of alloy compositions enhancing the catalytic efficiency is still unclear. By using time-dependent density functional theory (TDDFT), we analyze the real-time reaction processes of plasmon-mediated H2 splitting on linear Ag–Au alloy chains when exposed to femtosecond laser pulses. It is found that H2 splitting rate depends on the position and proportion of Au atoms in alloy chains, which indicates that specially designed Ag–Au alloy is more likely to induce the reaction than pure Ag chain. Especially, more electrons directly transfer from the alloy chain to the anti-bonding state of H2, thereby accelerating the H2 splitting reaction. These results establish a theoretical foundation for comprehending the microscopic mechanism of plasmon-induced chemical reaction on the alloy nanostructures.

Regional route guidance with realistic compliance patterns: Application of deep reinforcement learning and MPC

Solving link-based route guidance problems for large-scale networks is computationally challenging and faces practical issues, such as spatial–temporal data coverage. Thus, regional route guidance has emerged as a promising strategy, which utilizes regional approaches (i.e., network-level macroscopic traffic models) to capture traffic dynamics. Existing regional route guidance models have mainly focused on macroscopic flows and aggregated splitting rates, employing uniformly sampled vehicles as controllable targets. These models, however, overlook the inherent nature of individual drivers’ compliance. It may deteriorate the guidance performance as existing route guidance models cannot effectively generate customized route plans for compliant vehicles. This paper aims to introduce a regional route guidance framework with the utilization of different solution approaches, i.e. model-based and data-driven, considering the compliance pattern. In particular, MPC-based and deep reinforcement learning-based schemes are proposed for the information service provider to send customized route plans to compliant individuals. Within each solution approach, different route guidance strategies are developed for specific purposes and evaluated through numerical experiments under low- and high-congestion scenarios. Besides, the trade-off between total travel time and average trip length is studied, wherein the balance reward in a deep reinforcement learning-based approach enables controllable agents to reduce trip costs while compromising partial system utility. The findings indicate the effectiveness of the proposed strategies in alleviating network congestion. Additionally, multi-agent approaches outperform single-agent ones, highlighting the benefits of cooperative decision-making in traffic management.