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With the successful application of artificial intelligence technology in various fields, deep reinforcement learning (DRL) algorithms have applied in active corrective control in the power system to improve accuracy and efficiency. However, the "black-box" nature of deep reinforcement learning models reduces their reliability in practical applications, making it difficult for operators to comprehend the decision-making mechanism. process of these models, thus undermining their credibility. In this paper, a DRL model is constructed based on the Markov decision process (MDP) to effectively address active corrective control issues in a 36-bus system. Furthermore, a feature importance explainability method is proposed, validating that the proposed feature importance-based explainability method enhances the transparency and reliability of the DRL model for active corrective control. [ABSTRACT FROM AUTHOR]

In the context of the new power system, the widespread access to massive distributed new energy sources has led to the power distribution and consumption tasks characterized by multiple time scales, wide random distribution, and large demand differences, resulting in unpredictable resource peaks in the tasks computing resource demand curve. In view of this situation, a method of forecasting and dynamic balancing of computing resource demand for power distribution and consumption tasks based on state iteration was proposed: firstly, the tasks computing resource demand model was established under the analysis of the attributes and parameter demand of the power distribution and consumption tasks scenario. Secondly, on the basis of the short-term effectiveness prediction of the traditional Markov model, the firstorder difference of the state is used for data training to track the state fluctuation, and the historical state and the predicted state are used for state iteration, so as to avoid the convergence of long-term prediction. Finally, a dynamic balancing model is established according to the time-scale characteristics of cyclical and non-cyclical tasks, and the optimal configuration of load imbalance is achieved through the identification and adjustment of historical data and burst data. The simulation results show that the improved Markov model based on first-order difference and state iteration has the short-term accuracy of the traditional model and the long-term traceability of data fluctuations. The dynamic balancing model can combine the characteristics of historical data and burst data to effectively reduce the imbalance of resource demand, and show good ability to cope with resource imbalance deviation. [ABSTRACT FROM AUTHOR]

The ability of fast restoration reflects power system resilience and safety. This paper proposes a partitioning decision-making method for parallel restoration based on the label propagation algorithm and the cooperative game theory. The topological and physical characteristics of blackout systems are considered as the edge weight and the node importance simultaneously for formulating the partitioning model. By the label propagation algorithm, various labels representing different subsystems mark all buses. To accelerate the speed of convergence and prevent the label oscillation, this paper proposes a game strategy of cooperation between buses and subsystems by evaluating the Shapley value of buses. The partitioning constraints are integrated into each label propagation process to assess the feasibility of the partitioning strategy. Finally, case studies on the IEEE 39- and 118-bus test systems and an actual urban power system in South China verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Non-intrusive load identification can improve the interaction efficiency between the power supply side and the user side of the grid. Applying this technology can alleviate the problem of energy shortage and is a key technique for achieving efficient management on the user side. In response to the cumbersome process of manually selecting load features and the low accuracy of identification in traditional machine learning algorithms for non-intrusive load identification, this paper proposes a method that transforms the one-dimensional reactive electric signal of the load into a two-dimensional image using Gram coding and utilizes the Residual Attention Network (RAN) for load classification and recognition. By transforming the one-dimensional electrical signal into a two-dimensional image as the input to the RAN network, this approach retains the original load information while providing richer information for the RAN network to extract load features. Furthermore, the RAN network effectively addresses the poor performance and gradient vanishing issues of deep learning networks through bottleneck residual blocks. Finally, experiments were conducted on a public dataset to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Continued research into thermal barrier coatings (TBCs) for internal combustion engines has generated insights into the design of TBCs that can increase fuel conversion efficiency. In this work, two low thermal inertia TBCs (a commercially available Gen 1 material, Gadolinium Zirconate, and a proprietary Gen 2 material) were experimentally tested in gasoline compression ignition (GCI) on a light-duty single cylinder research engine. Compared to a metal baseline, a 250-micron thick Gen 1 coated piston produced an efficiency benefit of 0.4 percentage points (pp) at 6 and 10 bar net indicated mean effective pressure (IMEPn) and a 0.2 pp penalty at 15 bar IMEPn. The Gen 1 coating also showed an uHC and smoke penalty. An 80-micron thick Gen 2 coated piston showed an efficiency benefit of 0.9 pp at 6 bar IMEPn and 0.4 pp at 10 bar IMEPn, but an efficiency penalty of 0.5 pp at 15 bar IMEPn. The Gen 2 coating, which has a lower thermal inertia than the Gen 1 coating, displayed durability problems while the Gen 1 coating did not. A third piston was coated with a 120-micron thick Gen 2 coating everywhere except the high failure outer bowl region, which was coated with a 120-micron Gen 1 coating. This hybrid coating displayed good durability, an uHC emission penalty, a smoke benefit, an efficiency benefit of 0.1 pp at 6 bar IMEPn, an efficiency benefit of 0.4 pp at 10 bar IMEPn, and no change in efficiency at 15 bar IMEPn. These results indicate that a durable Gen 1/Gen 2 coating can provide an efficiency benefit to GCI combustion, and that further improvements on piston-spray interaction optimization and surface sealing of the TBC, as well as increasing the coated combustion chamber surface area (e.g. head and valves) can add further improve efficiency. [ABSTRACT FROM AUTHOR]

This numerical study focuses on assessing the key design parameters of interest for use of an active prechamber igniter as an ignition assistance device to enable mixing-controlled combustion (MCC) of ethanol (E100) in a heavy-duty Caterpillar C9.3B engine. Computational fluid dynamic (CFD) simulations of a baseline diesel and prechamber retrofitted C9.3B at a gross indicated mean effective pressure (IMEPg) of 5 bar and 1800 rpm are carried out using CONVERGE. In particular, the sizing of the prechamber volume, sizing of total orifice cross sectional area (orifice diameter), and location of the prechamber relative to a centrally mounted common rail direct injector are varied to discern the appropriate operational and design characteristics to achieve robust ignition assistance at the selected conditions. Simulation results indicate that use of an active prechamber igniter with E100 as a standalone fuel source can replicate ignition delays and thermal efficiencies similar to diesel combustion at the same engine boundary conditions, thus not requiring any changes to the engine's air handling system. Igniter mounting location, orifice sizing, and jet targeting were found to have the strongest influence on ignition assistance with preference toward larger orifice diameters that appropriately located heating contributions in near vicinity to the direct injector. [ABSTRACT FROM AUTHOR]

In order to obtain films with better mechanical properties and fracture toughness, the (TiAlCrZrNb)- Six-N (x=0, 4%, 8%, 12% and 16%) high entropy ceramic nanocomposite films with different Si contents were deposited on Si substrate by magnetron sputtering technology. The effects of Si doping on the microstructure, mechanical properties! and fracture toughness of the films were investigated by X-ray diffractometry, scanning electron microscopy, high-resolution transmission electron microscopy and Nano indentation apparatus. The results show that the mechanical properties and fracture toughness of the films increase first and then decrease with the addition of Si element, which can be attributed to the formation of nanocomposite structures. When Si content is 4%, (TiAlCrZrNb)-Six-N film has the best comprehensive mechanical properties, and Us maximum hardness and elastic modulus are 22.7 GPa and 192.0 GPa, respectively. In this case, the fracture toughness reaches the best, and the radial crack length C as 6.760 µm, The K1C was 1.77 MPa • m1/2. [ABSTRACT FROM AUTHOR]

Temperature swing thermal barrier coatings (TBCs) have the potential to improve efficiency and performance through thermal management of the combustion chamber. However, the effects of combustion chamber deposits (CCDs) on the surface of the TBCs are unclear. Therefore, this paper analyzes the impact of CCD formation on a temperature swing TBC. A piston and a heat flux probe coated with a novel material are installed in a single-cylinder research engine at a low-load condition for 62.5 h to promote CCD growth. Every 12.5 h, the performance at a knock limited condition is assessed and thermophysical property measurements on the heat flux probe are performed. Net thermal efficiency increased by 0.4% absolute after 12.5 h relative to the baseline condition, but further CCD growth caused a dithering of efficiency between the 12.5 h and baseline points. The KLSA retarded consistently throughout this period. External property measurements with the coated heat flux probe showed an improvement in the thermophysical properties of the TBC/CCD layer. [ABSTRACT FROM AUTHOR]

Application of thermal barrier coatings in spark ignition engines have historically been avoided due to the knock penalty associated with higher surface temperatures induced by the ceramic layer. However, advances in low thermal inertia coatings (i.e. temperature swing coatings) that combine low thermal conductivity with low volumetric heat capacity can prevent excessively high surface temperatures during the intake stroke and reduce or avoid knock while improving performance and efficiency. In this study, a novel coating material was tested in a low compression ratio spark ignition engine to evaluate the performance of an advanced low thermal inertia coating during steady and cold-start conditions. A total of four pistons with this novel material was tested, with an additional piston coated with gadolinium zirconate. Spark timing sweeps demonstrated a maximum 0.8% relative thermal efficiency gain with a thin coating of the novel material. The novel material coated above 200-microns showed a deterioration in performance and efficiency due to charge heating increasing knock propensity. Cold-start tests demonstrated that charge heating is beneficial for reducing unburned hydrocarbons and particle matter emissions. [ABSTRACT FROM AUTHOR]

The application of thermal barrier coatings (TBCs) for internal combustion engines has drawn more research attention in recent years due to the improved coating technology and advanced material properties. This paper presents the combination of thermal barrier coatings with an advanced combustion concept: gasoline compression ignition (GCI). This study aims to evaluate the coating performance with GCI combustion on Aramco's light-duty GCI engine. The thermodynamic engine model has been established and collaborated with CFD results to investigate thermal barrier coatings for GCI with a PPCI-diffusion combustion strategy primarily at a load of 23.5 bar IMEPg. Two real-world coating materials were pre-selected as Gen.1 and Gen.2 candidates. The investigation consists of three main topics: the effect of coating thickness on GCI, the effect of intake boost on the performance of traditional coatings and temperature swing coatings, and the effect of combustion chamber coverage on engine performance. The result shows that the TBC thickness became the key parameter to optimize for TBC with GCI combustion mode, and in the end, 250–500 microns is recommended based on the temperature limit of ceramic material. It was also found that the high level of intake boosting could significantly alleviate the charge heating penalty. The coverage of piston, engine head, and both intake and exhaust valves should be applied to achieve the highest efficiency gains. It was found that the gain ratio from each chamber component can be drastically different at different loads. [ABSTRACT FROM AUTHOR]

Methanol is a fuel that can be produced using renewable electricity, green hydrogen, and a carbon-neutral carbon source. As a low-carbon liquid fuel, methanol is a leading candidate as a drop-in replacement fuel for rapid decarbonization of the transportation sector. In this work, methanol is tested experimentally in mixing-controlled compression ignition using a single-cylinder research engine. Specifically, the impact of a double injection strategy and high exhaust gas recirculation (EGR) dilution on performance and emissions parameters are studied. As a single-carbon, oxygenated fuel, methanol has a near-zero sooting potential, meaning there is no soot-NOx tradeoff as EGR dilution is increased. EGR dilution effectively reduces NOx emissions without a significant combustion efficiency or thermal efficiency penalty until the global equivalence ratio exceeds 0.9, at which point, a significant combustion efficiency penalty is incurred in the form of increased CO emissions. If stoichiometric operation with high EGR dilution is considered, engine-out NOx emissions are low, even at very high loads, and NOx aftertreatment is simplified through the use of a three-way catalyst. However, a relative decrease in the net fuel conversion efficiency of up to 8.5% was observed, demonstrating an emissions-efficiency tradeoff. Under lean and stoichiometric conditions, no appreciable agglomeration of particulate matter was observed. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid data-driven method for fast solutions of preventive security-constrained optimal power flow (SCOPF) of power systems. The proposed method formulates the SCOPF problem as constraints-satisfying training of a deep reinforcement learning (DRL) agent, where the action-value function of DRL is augmented by contingency security constraints. In the training process, the proposed method hybridizes the primal-dual deep deterministic policy gradient (PD-DDPG) and the classic SCOPF model. Instead of building reward critic networks and cost critic networks via interacting with the environment (i.e., power flow equations), the actor gradients are approximated by solving KKT conditions of the Lagrangian. Finally, with the formulated sparse Jacobians of constraints and sparse Hessians of Lagrangians, the interior point method is incorporated in PD-DDPG to derive the parameters updating rule of the DRL agent. Numerical tests are carried out on a modified IEEE 57-bus system and a modified IEEE 300-bus system for critical contingencies. The results show that the well-trained DRL agent can rapidly (real-time) obtain high-quality SCOPF solutions that satisfy the security constraints. [ABSTRACT FROM AUTHOR]

Wet ethanol is a biofuel that can be rapidly integrated into the existing transportation sector infrastructure and have an immediate impact on decarbonization. Compared to conventional hydrocarbon fuels, wet ethanol has unique fuel properties (e.g., short carbon chain, oxygenated, high heat of vaporization, no cool-flame reactivity), which can actually improve the efficiency and engine-out emissions of internal combustion engines while decarbonizing. In this work, wet ethanol 80 (80% ethanol, 20% water by mass) was experimentally studied at high loads under boosted conditions in compression ignition to study the tradeoffs in efficiency and emissions based on boosting and injection strategies. Specifically, this work explores the potential of adding a third, mixing-controlled injection at high loads. The results indicate that adding a third, mixing-controlled injection results in combustion stabilization at high loads, where the peak pressure limit of the engine is a constraint that requires combustion phasing to retard. However, since the heat of vaporization of wet ethanol 80 is ~6% of its lower heating value, evaporation of fuel injected near top dead center imposes a thermodynamic efficiency penalty by absorbing heat from the working fluid at a time in the cycle when adding heat produces net work out. Additionally, the mixing-controlled injection increases NOx emissions. Therefore, the amount of fuel injected in the mixing-controlled injection should be limited to only what is necessary to stabilize combustion. Ultimately, by using wet ethanol 80 in a triple injection strategy, a load of 22 bar IMEPn is achieved with a net fuel conversion efficiency of 42.2%, an engine-out indicated specific emissions of NOx of 1.3 g/kWh, and no measurable particulate matter, while maintaining a peak cylinder pressure below 150 bar. [ABSTRACT FROM AUTHOR]

Ethanol, n -propanol, isopropanol, n -butanol, isobutanol, and sec-butanol are six potential carbon-neutral fuels of the future. One application of these carbon-neutral fuels is in high-dilution spark ignition. To understand the potential of these fuels in high-dilution spark ignition, this work experimentally determines the spark ignition lean flammability limit of each fuel with no external, cooled exhaust gas recirculation (EGR), and with 20% external, EGR. The spark ignition lean flammability limit in this work is defined as the excess-air ratio that results in a coefficient of variance of gross indicated mean effective pressure greater than 5%. This is done on an engine with a compression ratio of 12.5, using an intake pressure of 1 bar and an intake temperature of 320 K. It was found that ethanol had the leanest lean limit, due to its high flame speed, followed by n -propanol, isopropanol, and sec-butanol, which all had similar lean limits. n -Butanol and isobutanol had the richest lean limits due their high knock propensity and low flame speed, respectively. The lean limit of each fuel decreased with external, cooled EGR addition, with ethanol as the least sensitive and isopropanol as the most sensitive to EGR addition. Overall, using a high dilution strategy increased the cycle efficiency for each fuel. Ethanol, n -propanol, isopropanol, and sec-butanol all showed promising performance and are great candidates to be combined with an advanced high-dilution SI strategy to enable high-dilution SI with a carbon-neutral fuel. [ABSTRACT FROM AUTHOR]

This paper studies the vulnerability of deep reinforcement learning (DRL) models for power systems topology optimization under data perturbations and cyber-attack. DRL has recently solved many complex power system optimization problems. However, it has been practically proven that small perturbations of input data can lead to drastically different control decisions and induce danger. To evaluate and mitigate the security risks of DRL models in power systems, we propose a vulnerability assessment method for such DRL models under noisy data and cyber-attack. In specific, we assess the vulnerability of a DRL model in a way that perturbations are constructed to minimize the model’s performance. Besides, several vulnerability indices are proposed to identify the characteristics of perturbations that may cause malfunction of DRL. Simulations on the 14-bus system and the IEEE 118-bus system for topology optimization are carried out to validate the effectiveness of the proposed vulnerability assessment method. The results show that the performance of DRL models for power systems can be significantly degraded under cyber-attack and data perturbations, especially when a proposed vulnerability index has abnormal values. [ABSTRACT FROM AUTHOR]

The number of electric vehicles (EVs) is expected to grow significantly, which calls for effective planning of charging infrastructures. While the planning of the charging infrastructure relies on an accurate charging demands, the behaviours of EVs charging are not always predictable and can be sensitive to many uncertain future environmental factors. Considering such uncertainties, this study aims to robustly and optimally determine the chargers and main switch board (MSB) capacities without violating queuing time constraints and load flow constraints. The non-parametric estimations of charging demands are derived with data-driven charging behaviour analysis considering diverse social factors, including travelling patterns, queuing, and changes of charging facilities. Then, the impacts of the EV integration are modeled by a stochastic load flow program. The samples of the stochastic load flow stipulate the conditional value-at-risk constraints for the planning of chargers and MSBs, which consider the probabilities and scenarios in a box of ambiguity with bounds. Afterwards, by limiting the frequency and severity of constraints violation, the total investment cost is minimized with a distributionally robust optimisation program. Simulation based on a real-world residential community in Singapore is carried out to testify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A battery energy storage system (BESS) is an effective solution to mitigate real-time power imbalance by participating in power system frequency control. However, battery aging resulted from intensive charge–discharge cycles will inevitably lead to lifetime degradation, which eventually incurs high-operating costs. This study proposes a deep reinforcement learning-based data-driven approach for optimal control of BESS for frequency support considering the battery lifetime degradation. A cost model considering battery cycle aging cost, unscheduled interchange price, and generation cost is proposed to estimate the total operational cost of BESS for power system frequency support, and an actor–critic model is designed for optimising the BESS controller performance. The effectiveness of the proposed optimal BESS control method is verified in a three-area power system. [ABSTRACT FROM AUTHOR]

This paper proposes a data-driven cooperative method for load frequency control (LFC) of the multi-area power system based on multi-agent deep reinforcement learning (MA-DRL) in continuous action domain. The proposed method can nonlinearly and adaptively derive the optimal coordinated control strategies for multiple LFC controllers through centralized learning and decentralized implementation. The centralized learning is achieved by MA-DRL based on a global action-value function to quantify overall LFC performance of the power system. To solve the MA-DRL problem, multi-agent deep deterministic policy gradient (DDPG) is derived to adjust control agents’ parameters considering the nonlinear generator behaviors. For implementation, each individual controller only needs local information in its control area to deliver optimal control signals. Numerical simulations on a three-area power system and the fully-modeled New-England 39-bus system demonstrate that the proposed method can effectively minimize control errors against stochastic frequency variations caused by load and renewable power fluctuations. [ABSTRACT FROM AUTHOR]

Thermally stratified compression ignition is a new advanced, low-temperature combustion mode that aims to control the heat release process in a lean, premixed, compression ignition combustion mode by controlling the level of thermal stratification in the cylinder. Specifically, this work uses a mixture of 80% ethanol and 20% water by mass, referred to as "wet ethanol" herein, to increase thermal stratification via evaporative cooling of areas targeted by an injection event during the compression stroke. The experiments conducted aim to both fundamentally understand the effect that a late cycle injection of wet ethanol has on the heat release process, and to use that effect to explore the high-load limit of thermally stratified compression ignition with wet ethanol. At an equivalence ratio of 0.5, injecting just 8% of the fuel during the compression stroke was shown to reduce the peak heat release rate by a factor of 2, subsequently avoiding excessive pressure rise rates. Under pure homogeneous charge compression ignition using wet ethanol as the fuel, the load range was found to be 2.5–3.9 bar gross indicated mean effective pressure. Using a split injection of wet ethanol, the high-load limit was extended to 7.0 bar gross indicated mean effective pressure under naturally aspirated conditions. Finally, intake boost was used to achieve high-load operation with low NOx (oxides of nitrogen (NO or NO2)) emissions and was shown to further increase the high-load limit to 7.6 bar gross indicated mean effective pressure at an intake pressure of 1.43 bar. These results show the ability of a split injection of wet ethanol to successfully control the heat release process and expand the operable load range in low-temperature combustion. [ABSTRACT FROM AUTHOR]

High-level penetration of intermittent renewable energy sources has introduced significant uncertainties and variabilities into modern power systems. In order to rapidly and economically respond to the changes in power system operating state, this letter proposes a real-time optimal power flow (RT-OPF) approach using Lagrangian-based deep reinforcement learning (DRL) in continuous action domain. A DRL agent to determine RT-OPF decisions is constructed and optimized using the deep deterministic policy gradient. The DRL action-value function is designed to simultaneously model RT-OPF objective and constraints. Instead of using the critic network, the deterministic gradient is derived analytically. The proposed method is tested on the IEEE 118-bus system. Compared with the state-of-the-art methods, the proposed method can achieve a high solution optimality and constraint compliance in real-time. [ABSTRACT FROM AUTHOR]

This letter proposes a data-driven, model-free method for load frequency control (LFC) against renewable energy uncertainties based on deep reinforcement learning (DRL) in continuous action domain. The proposed method can nonlinearly derive control strategies to minimize frequency deviation with faster response speed and stronger adaptability for unmolded system dynamics. It consists of offline optimization of LFC strategies with DRL and continuous action search, and online control with policy network where features are extracted by stacked denoising auto-encoders. Numerical simulations verify the effectiveness and advantages of proposed method over existing approaches. [ABSTRACT FROM AUTHOR]

In this work, the autoignition properties of methanol, ethanol, n-propanol, isopropanol, n-butanol, isobutanol, and sec-butanol are studied in HCCI and compared. Experiments were performed, characterizing the sensitivity of these fuels to equivalence ratio, intake temperature, residual fraction, intake pressure, and engine speed. Under all of the low boost pressure operating conditions considered (1.15 bar), the order of reactivity of each fuel from low to high is: isopropanol, sec-butanol, ethanol ≈ n-propanol ≈isobutanol, methanol, n-butanol. n-butanol was less sensitive to changes in intake temperature than the other six fuels at low boost conditions. The required intake temperature to maintain a constant combustion phasing decreased significantly for n-butanol with intake pressure due to emergent intermediate temperature heat release (ITHR). At an intake pressure of 1.95 bar, sec-butanol showed some amount of ITHR which decreased its sensitivity to intake temperature. However, sec-butanol still maintained its high autoignition resistance, unlike n-butanol. Isobutanol did not show any ITHR at this intake temperature, though did show a decrease in intake temperature sensitivity. With the exception of n-butanol, the behavior and sensitivity of each alcohol fuel to the wide array of operating conditions considered were similar, with small differences in autoignition resistance. This means that these six fuels could potentially be blended and used interchangeably with little change to their autoignition characteristics. [ABSTRACT FROM AUTHOR]

Mitophagy, the selective removal of damaged mitochondria, plays a critical role in kidney diseases, but its involvement in hypertensive nephropathy (HTN) is not well understood. To address this gap, we investigated mitophagy-related genes in HTN, identifying potential biomarkers for diagnosis and treatment. Transcriptome datasets from the Gene Expression Omnibus database were analyzed, resulting in the identification of seven mitophagy related differentially expressed genes (MR-DEGs), namely PINK1, ULK1, SQSTM1, ATG5, ATG12, MFN2, and UBA52. Further, we explored the correlation between MR-DEGs, immune cells, and inflammatory factors. The identified genes demonstrated a strong correlation with Mast cells, T-cells, TGFβ3, IL13, and CSF3. Machine learning techniques were employed to screen important genes, construct diagnostic models, and evaluate their accuracy. Consensus clustering divided the HTN patients into two mitophagy subgroups, with Subgroup 2 showing higher levels of immune cell infiltration and inflammatory factors. The functions of their proteins primarily involve complement, coagulation, lipids, and vascular smooth muscle contraction. Single-cell RNA sequencing revealed that mitophagy was most significant in proximal tubule cells (PTC) in HTN patients. Pseudotime analysis of PTC confirmed the expression changes observed in the transcriptome. Intercellular communication analysis suggested that mitophagy might regulate PTC's participation in intercellular crosstalk. Notably, specific transcription factors such as HNF4A, PPARA, and STAT3 showed strong correlations with mitophagy-related genes in PTC, indicating their potential role in modulating PTC function and influencing the onset and progression of HTN. This study offers a comprehensive analysis of mitophagy in HTN, enhancing our understanding of the pathogenesis, diagnosis, and treatment of HTN.Communicated by Ramaswamy H. Sarma.

Objectives: Exploring the anisotropic mechanical behavior of cancellous bone is crucial for in-vivo bone biomechanical analysis. However, it is challenging to characterize anisotropic mechanical behaviors under low-resolution (LR) clinical CT images due to a lack of microstructural information. The data-driven method proposed in this article accurately characterizes the anisotropic mechanical properties of cancellous bone from LR clinical CT images., Methods: The trabecular bone cubes of sheep are used to obtain a high-resolution (HR) micro-CT and an LR clinical CT image dataset. First, an auto-encoder model is trained using HR image data. Microstructural features are extracted by the encoder. A fast super-resolution (FSR) model is trained to map LR bone cubes to the features extracted from corresponding HR samples. The pretrained FSR model is used to convert LR clinical CT images to encoded microstructural features. The features are later used to predict target histomorphological parameters, anisotropic elastic tensors, and fabric tensors based on a fully connected neural network., Results: The data-driven model accurately predicts the elastic tensor and fabric tensor of trabecular bones with LR CT images with 0.6 mm/pixel spatial resolution. It was verified that LR clinical CT images could generate microstructural information using a generative deep-learning model and an up-sampling operation., Significance: This study proves that clinical medical images of cancellous bone can be used for analysis of complex mechanical properties using a data-driven method, which is useful for real-time bone defect diagnosis and personalized bone prosthesis design in clinical application.

Hypoxia in most solid tumors is a major challenge for photodynamic therapy (PDT), and the combination of hypoxia-activated chemotherapy and PDT is a promising approach for enhanced anticancer activity. Herein, we designed hypoxia-responsive polymeric nanoprodrug PNPs to co-deliver photosensitizer 5,10,5,20-tetrakis(4-aminophenyl)-porphine (TAPP) and chlorambucil (CB) to improve the overall therapeutic efficacy. Upon laser irradiation, the central TAPP converted oxygen to produce single oxygen ( 1 O 2 ) for PDT and induced PDT-reduced hypoxia environment, which accelerated the release of activated CB for synergetic cancer cell killing. Consequently, these hypoxia-responsive polymeric nanoprodrugs with a considerable drug-loading content and synergistic therapeutic effect of PDT-CT had great potential for tumor therapy., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)