Aiming at reducing the compressive damage of high-pressure vessels (HPVs) during ocean application, a protective structure based on Roller Arrowhead Wings Honeycomb (RAWH) following with the equivalent method is proposed. Structural parameter correlations are systematically quantified through derived expressions of the thickness-to-width ratio factor n, with parametric influence rules extended from Double Arrowhead Wings Honeycomb (DAWH) unit cells to integrated RAWH configurations. The mechanical properties, energy absorption, structural stability and structural efficiency of RAWH are investigated by numerical simulation compared with Roller Hexagonal Honeycomb (RHH) and Roller Re-entrant Honeycomb (RRH). By integrating experiment results including engineering strain, work energy absorption, and displacement with simulation results, the study elucidates RAWH's mechanical behavior under multi-velocity compression. Notably, at a target Specific Energy Absorption (SEA), RAWH achieves a 64.46 % weight reduction relative to RHH, while demonstrating 87.01 % average equivalent stress reduction and 90.94 % energy absorption mitigation in protected HPVs under three compression velocities. The protection effectiveness for HPV components is dependent on the hub supportability of the protective structure, deformation modes, and force transmission paths. For each honeycomb configuration, task-capability matching criteria are rigorously quantified, and a honeycomb selection strategy is proposed based on damage reduction mechanisms. This work establishes a foundational framework for optimizing marine HPV protection, addressing parts challenges in weight reduction and compressive resilience while supporting the scientific understanding of honeycomb structures in ocean environments.

The practical implementation of membrane distillation (MD) for water purification is often constrained by the limited availability of membranes that combine robust wetting resistance with a scalable fabrication process. Herein, we present a facile integrated approach, combining surface spraying of carbon nanoparticles (CNPs) with hot-pressing, to engineer a durable hierarchical micro/nanostructure on electrospun polyvinylidene fluoride (PVDF) nanofibers (CNP@PVDF). The hot-pressing step permanently anchors CNPs onto the fibers, creating a composite interface that maintains stable superhydrophobicity even under harsh conditions such as prolonged ultrasonication and hydrodynamic flow. The resultant hydrophobic rough structure creates an energy barrier that suspends the droplets atop the nano-textured surface. This not only elevates the hydrophobicity but, more critically, substantially enlarges the effective evaporation area at the interface. Compared to the pristine PVDF membrane, the optimal CNP@PVDF membrane achieves a 33.5% higher water flux and a 25.8% longer wettingresistance time during continuous vacuum membrane distillation (VMD) using a 3.5 wt% NaCl solution as the feed. This work provides a straightforward and scalable strategy for constructing mechanically robust,

The preparation of highly active and sintering resistant catalysts for VOCs oxidation by facile methods still faces great challenges. This paper proposes a one-step sacrificial carbon nanotube (CNT) templating method for preparing mesoporous CoOX/CeO2 catalysts, which are used for the low temperature oxidation of ethyl acetate (EA) and room temperature oxidation of formaldehyde (HCHO). Among the prepared catalysts, the CoCe10C-500 catalyst with 10 wt% CNT template content exhibits the best activity, achieving complete conversion (T100) at 200 degrees C for EA and 30 degrees C for HCHO. The CNT template improves the dispersion of CoOX and enables more Co species to enter the CeO2 lattice. This promotes the formation of a high content of Co-O-Ce structures, weakens the Ce-O bonds, and results in abundant oxygen vacancies. Notably, the T100 of the CoCe10C-800 catalyst for EA and HCHO slightly increases to 220 and 50 degrees C after treatment at 800 degrees C. Nevertheless, the CoCe-800 catalyst without CNT template exhibits high T100 at 310 and 160 degrees C for EA and HCHO. The excellent sintering resistance of the CoCe10C-800 catalyst can be attributed to the sacrificial CNT template, which effectively constructs mesoporous structures and introduce a physical confinement effect into the catalyst. During the calcination process, this structure can inhibit the sintering of CoOX and CeO2 species. In addition, the in situ FTIR spectroscopy and MS results suggest possible oxidation pathways and reaction mechanisms for HCHO and EA on the CoCe10C-500 catalyst. During HCHO oxidation, the adsorbed oxygen (O-, O2- ) species on the catalyst serve as the main active oxygen species. On the other hand, the mobility and activation of lattice oxygen (O2-) are crucial for the conversion of EA.

Correction for ‘Unraveling the role of the Mn–[O x ]–Ce structure in MnO x /CeO 2 catalysts for the catalytic oxidation of VOCs: resistance to sintering, increasing oxygen vacancies and activation of lattice oxygen’ by Yuchuan Ye et al. , J. Mater. Chem. A , 2025, https://doi.org/10.1039/D5TA07303K.

Possible mechanism of EA and toluene oxidation on the multiple oxygen vacancies on MnO x /CeO 2 catalysts with sintering resistance.

Artificial intelligence-generated content (AIGC) is an automated method that generates the content according to its knowledge and the intent information. Deploying the AIGC model in edge networks unlocks new possibilities. Unfortunately, realizing AIGC in distributed edge networks faces critical challenges, including the interdependent control decisions, the complex trade-offs between energy consumption and total variation (TV) distance, the extension of TV distance due to insufficient denoising steps, the restriction of AIGC inferencing deadlines, and the uncertainty of AIGC task arrivals. In this paper, targeting AIGC tasks, we design polynomial- time online algorithms to overcome all these challenges. Firstly, we formulate distributed AIGC as a non-linear mixed-integer program for long-term total cost optimization. Subsequently, we propose a novel algorithmic approach that generates candidate inferencing schedules, reformulates the original problem into anew schedule selection problem, and solves this new problem using an online primal-dual-based algorithm. Moreover, we rigorously prove that our approach leads to a constant competitive ratio for the long-term total cost. Through extensive evaluations using real-world data, the superior practical performance of our approach is demonstrated, reducing the total cost by more than 50% compared to various alternative methods.

In this paper, an in-situ reaction system of 6016Al-Na2B4O7-K2ZrF6 was designed. The dual ceramic nanoparticles reinforced 6016Al composite (Al2O3+ ZrB2)np/6016Al was firstly prepared by the DMR(Direct Melt Reaction) method and magnetic field modulation. The effects of magnetic field modulation on the composites' microstructure morphology and mechanical properties were investigated. For microstructure analysis, SEM, TEM, EDS, PC, and XRD were used. To study the mechanical properties, room-temperature tensile and high-temperature creep experiments were carried out. The results show that the in situ dual ceramic nanoparticles ZrB2 and Al2O3 were successfully prepared with sizes of about 102 nm and 67 nm, respectively. After the magnetic field was introduced, the particle agglomeration phenomenon improved. The number of nucleation sites increased. The matrix alpha-Al grain size of the composites was significantly refined to 79.75 mu m. In terms of mechanical properties, the magnetic field-modulated composites exhibited significantly improved tensile strength (263.12 MPa) and elongation (19.05 %), which were 29.4% and 20.4% higher compared to the original matrix alloy, respectively. High-temperature creep properties were tested at 523 K/80 MPa. For magnetic field- modulated composites, the apparent stress index was 17.72, and the apparent activation energy was 219.27 kJ. These values were significantly better than composites with no magnetic field applied (15.65 and 196.71 kJ) and the 6016Al matrix (9.24 and 173.65 kJ). In addition, the creep mechanism of all the samples at elevated temperatures was identified as a dislocation climbing mechanism. Furthermore, under the regulation of a magnetic field, the composite material exhibits the most extended creep life, reaching 40.42 hours. This is 264.8% greater than that of the 6016Al matrix. The above results indicate that the magnetic field treatment significantly improves the composites' creep resistance. This enables the composites to exhibit more stable mechanical properties in high-temperature environments. It further validates the effectiveness of magnetic field modulation in optimizing the internal microstructure of the materials.

The preparation of a catalyst with high activity and sintering resistance remains a great challenge. In this study, a series of MnOx/CeO2 catalysts denoted as 5-M-Y (Mn loading of 5 wt% and calcination temperature of Y) were prepared through the modified impregnation (MI) method for the catalytic oxidation of volatile organic compounds (VOCs). Among the synthesized catalysts, the 5-M-500 catalyst demonstrated superior catalytic performance, achieving complete oxidation of both ethyl acetate (EA) and toluene at 230 degrees C (T100 value is operationally defined as the minimum temperature required to attain 100% conversion of VOCs). More importantly, the 5-M-800 catalyst exhibited excellent sintering resistance, and the T100 value only slightly increased to 250 degrees C. However, the 5-AO-800 and 5-O-800 catalysts prepared by the conventional impregnation on cerium oxides from cerium acetate decomposition and commercial product exhibited high T100 values of 320 degrees C and 400 degrees C, respectively. The exceptionally high activities and sintering resistances of the 5-M-Y catalysts are attributed to the high contents of the Mn-[Ox]-Ce structure. The Mn-[Ox]-Ce structure anchored the Mn species in the CeO2 crystals through their strong interactions. Meanwhile, the robust Mn-[Ox]-Ce structure inhibited the ordered growth of CeO2 crystallites during the calcination process. Thus, the aggregation and sintering of MnOx and CeO2 particles at high temperatures were limited. In addition, the Mn-[Ox]-Ce structure promoted the formation of multisite oxygen vacancies and enhanced the adsorption and activation of VOCs and oxygen. On the other hand, the Mn-[Ox]-Ce structure weakened the Ce-O bond to increase the content of surface lattice oxygen and boost the lattice oxygen migration and activation ability. This study emphasizes the important role of the Mn-[Ox]-Ce structure in the high activities and sintering resistances of the catalysts, and it illustrates the possible reaction mechanism for the simultaneous oxidation of EA and toluene.

The advent of Computing Power Network (CPN) has opened up vast opportunities for machine learning inference, yet the challenge of reducing high operational cost due to intensive computations and the sheer volume of inference tasks cannot be overlooked. Scheduling inference tasks for mitigating operational cost involves various challenges, such as migrating tasks under unpredictable CPN status, making time-coupled decisions for resource provisioning, and selecting computing sites based on dynamic electricity prices. To address these issues, we introduce CPN-Inference, a novel and flexible inference framework built upon CPN. Specifically, we formulate a time-varying integer program problem that aims to minimize long-term cost, involving switching cost, operational cost, communication cost, queuing cost, and accuracy loss. We also propose a group of polynomial-time online algorithms for supporting the formulated problem by solving delicately constructed subproblems based on the inputs predicted via online learning. Furthermore, our algorithms are proven for their competitive ratio, showcasing the performance gap between our approach and the offline optimum. A testbed is constructed to evaluate inference performance on real devices. Our comprehensive evaluations, based on datasets from real systems, demonstrate that our algorithms outperform multiple alternatives, by achieving an average cost reduction of 35%.

Ion-selective membranes, crucial for diverse applications such as water purification, brine disposal and resource recovery, rely heavily on the pore architecture and surface charge. Narrowing the pore size distribution (PSD) of the membrane is generally acknowledged to be essential for achieving higher ion selectivity. Here we challenge the conventional emphasis on PSD by introducing an alternative determinant-surface charge homogeneity-drawing inspiration from a counterintuitive relationship between PSD and ion selectivity observed in both commercial and laboratory-made polyamide nanofiltration membranes. By integrating multimodal atomic force microscopy technologies, we visually extracted nanoscale charge maps from three dimensions: surface potential, phase and functional groups. The metrological analysis methodology was originally developed to quantitatively describe the spatial charge distribution. It is demonstrated that nanoscale spatial charge homogeneity plays a crucial role in governing ion selectivity, surpassing the influence of PSD. Based on this perception, we devised the high-selective nanofiltration membranes and modules for the lithium-magnesium mixture separation by using a polyethyleneimine multivariate strategy to program polyamide membranes with stepwise-enhanced homogeneous distribution of electropositive-amine moieties. Our work unveils a unique charge homogeneity-dominated selectivity mechanism and demonstrates the feasibility of developing highly ion-selective membranes by facile nanocharge manipulation, surpassing the need for precise PSD control.

The rotary energy recovery device (RERD) plays an important role in reverse osmosis (RO) desalination; however, few investigations on the formation and influence of lateral force on the RERD rotor have been published. The transient characteristics of lateral force and its relationship with pressure distribution and fluctuation in the clearance were analyzed via computational fluid dynamics (CFD) simulation. The clearance pressure distribution and lateral force were quantified under different working conditions. The eccentricity of the rotor, resistance torque and decrease in the rotary speed due to the lateral force were simulated and they were found to change with flow rate and pressure of high-pressure outlet (PHO). A new rotary speed prediction method including the effect of PHO was developed. With the increasing flow rate or PHO, the stability of RERD declined. A design optimization direction was proposed. The variation trends of rotary speed, pressure in the clearance and its fluctuation were verified through experiment. This research provides an explanation why in practice the rotary speed decreases with increasing pressure. The conclusions obtained herein can be of great significance for future research on improving the stability and lifespan and reducing the maintenance consumption of RERD.

Fine-tuning pre-trained models with task-specific data can produce customized models effective for downstream tasks. However, operating large-scale such fine-tuning tasks in real time in the data center faces non-trivial challenges, including unpredictable task arrival and system environment dynamics, complex deadline-driven fine-tuning scheduling, and intertwined task pricing and cost management. In this paper, targeting the popular Low-Rank Adaptation (LoRA) fine-tuning technique, we present the design and study of a novel auction-based mechanism to jointly schedule and price LoRA tasks in an online manner. We first model the social welfare maximization problem as an integer program for the fine-tuning service provider, capturing all the aforementioned challenges. Then, to solve this NP-hard problem online, we equivalently reformulate this original problem into a schedule selection problem, where each schedule corresponds to a concrete pre-specified operation plan over time for a task. We can thus design a polynomial-time online approximation algorithm via the online primal-dual method to determine the schedule, and with the dual variables, also determine the pricing for each admitted task. We rigorously prove the competitiveness of our online approach against the offline optimum, and prove the economic properties of truthfulness and individual rationality regarding pricing. Finally, we conduct extensive experiments and have validated the substantial advantages of our approach compared to existing methods.

Aiming at reducing damage between ship berthing and high-pressure vessels, the protective honeycomb filled with Roller Arrowhead Wings Honeycomb (RAWH) was constructed based on equivalent method. A novel Double Arrowhead Wings Honeycomb (DAWH), inspired by Tilia Platyphylloswas, was proposed for arrangement in RAWH. The analytical models were derived with size factor n for honeycomb selection and design. The mechanical properties, energy absorption, compression response, structure efficiency, and deformation mode of RAWH were investigated by Finite Element Analysis (FEA) and Experiment (EXP), comparing with Roller Hexagonal Honeycomb (RHH), Roller Re-entrant Honeycomb (RRH), and Roller Solid. With particular calculation methods, the work energy absorption, engineering strain, and engineering stress were obtained within acceptable error compared to finite element analysis. The contact force of RAWH exceeded RHH, RRH and Solid about 1.7, 5 , and 0.32 times, with a maximum of 15000 N. Further, the deformation modes of RAWH were discussed and recorded with “V” mode and “U” mode appearing special characteristics of enhancement and resilience. The equivalent Poisson’s ratios for protective honeycombs were studied and described in the Morgan-Mercer-Flodin (MMF) growth model with the phenomena of “Step Fluctuation”, “Inter-layer Progression” and “Step Stability”. These results, methods, and discussions might bring significance for reducing damage from collisions in ocean engineering.

While ensemble methods can tackle concept drifts, obtaining pretrained models and conducting ensemble learning upon streamed data impose fundamental challenges, including the dynamic balance between system overhead and inference accuracy in uncertain system environments, and the interlacement between desired economic properties and long-term participation. In this paper, we propose the joint optimization which enables service providers to obtain models via repetitive auctions from the model providers and conduct ensemble methods online in a cost-efficient manner. We design polynomial-time online algorithms to solve the underlying non-linear mixed-integer social cost minimization problem, involving bid selection, payment allocation, model hosting, and ensemble model-weight adaption. We further rigorously prove the performance guarantees with our approach, such as the sub-linear dynamic regret for the bidding cost, the sub-linear dynamic fit for the long-term participation constraint, the truthfulness and the individual rationality for the auctions, the upper bound for ensemble inference loss, and the parameterized-constant competitive ratio for the long-term social cost. Through extensive trace-driven evaluations under real-world settings, we have validated the significant advantages of our approach over multiple baselines and state-of-the-art algorithms.

In the present investigation, the production of composites based on 7075Al is involved, reinforced with particles of high-entropy alloy (AlCoCrFeNi), using the friction stir processing (FSP) technique. The primary objective is to examine how varying the rotational speed during processing affects the uniformity of the composite microstructure, the strength of the bonding between different materials, and the mechanical properties of the composite. In these findings, it is indicated that higher processing rotational speeds lead to enhanced homogeneity of the composite material and promote strong bonding with the matrix. The Al13Co4 phase is generated at the interface before the formation of the Al5Co2 phase. The microhardness of the composites exhibits an increase in hardness of 78%, 84%, 86%, and 83% compared to the hardness of the 7075Al. Similarly, the tensile strength is enhanced by 26%, 36.7%, 49%, and 40%, respectively. The broken surface shows an even spread of particles with many small depressions, which is a clear sign of a common type of fracture that can stretch without breaking. The primary processes that enhance the strength of the FeCoNiCrAl/7075Al composite manufactured by FSP include the load-transfer effect, dispersion strengthening, grain refinement strengthening, and thermal mismatch strengthening.

To mitigate Distributed Denial-of-Service (DDoS) attacks towards enterprise networks, we study the problem of scheduling DDoS traffic through on-premises scrubbing at the local edge and on-demand scrubbing in the remote clouds. We model this problem as a nonlinear mixed- integer program, which is characterized by the inputs of arbitrary dynamics and the trade-offs between staying at suboptimal scrubbing locations and using different best locations with switching overhead. We first design a prediction-oblivious online algorithm which consists of a carefully-designed fractional algorithm to pursue the long-term total cost minimization but avoid excessive switching overhead over time, and a randomized rounding algorithm to derive the flow-based, integral decisions. We next design a prediction-aware online algorithm which leverages the predicted inputs and can make even better scheduling decisions through invoking our prediction-oblivious online algorithm and improving its solutions via re-solving the original problem slice over each prediction window. We further extend our study to prioritize local scrubbing, and adapt our algorithms to this case correspondingly. Then, we rigorously prove the worst-case, constant competitive performance guarantees of our online algorithms. Finally, we conduct extensive evaluations and validate the superiority of our approach over multiple existing alternatives approaches.

Rotary energy recovery device (RERD) that reduces energy consumption from the pressurizing seawater plays an important role in the seawater reverse osmosis desalination system. The rotating progress of the rotor, which is the key part of the RERD, is affected by the lateral unbalance force generated by the non-uniform flow state of the liquid in the circular clearance. In this paper, the flowing characteristics and the cause of lateral force in the clearance were analyzed by CFD. Thus, the pressure distribution curve around the outer wall of the rotor and the character of pressure fluctuation was obtained. Moreover, the influence of the operating pressure was calculated and analyzed. The results showed that the pressure distribution around the outer wall of the rotor presents sinusoidal distribution and it is related to the unbalanced force. Meanwhile, the uniformity and stability of the pressure distribution change with the operating pressure. This research provides a method to predict the stability of the RERD, which will be of great significance for improving the unstable problem of the device in the design stage.

The rotary energy recovery device (RERD) is used in the seawater desalination process. The rotor of the RERD is completely covered by the outer shell. So, the RERD is similar to the “black box”. In order to solve the problem that the operative state is difficult to detect directly, a detection method of operative state based on vibration signal is proposed. It is found that the main vibration frequency corresponded well to the 8 times rotational frequency by analyzing the vibration frequency of the outer shell and the rotational frequency of the rotor. A relationship between the main frequency and the rotational frequency is established. On this basis, the method of obtaining the rotational frequency information from vibration signals is studied, including eliminating baseline drift by wavelet transform, demodulation of vibration signals to better reflect the rotational frequency, and estimating the frequency range to avoid frequency interference. The main vibration frequency is obtained to calculate the rotational speed. The results show that the maximum error rate of rotational speed is 2.55% under the steady state and the maximum error rate is 4.58% under an unsteady state. It can meet the requirements of real-time, accurate, and online detection of the RERD operative state.

Herein, a hierarchically ordered porous superstructure of N-doped carbon embedded with readily accessible Fe-Ni diatomic sites (FeNi DASs/HOPSNC) has been synthesized for highly efficient CO2 electroreduction. By integrating additional secondary mesopores into the ordered macroporous skeleton, this distinctive superstructure exhibits greatly enhanced accessibility and mass transfer. Benefiting from the unique structure merits including the synergistic effect in Fe-Ni atomic pair sites and the multi-level porosity, such diatomic site catalyst affords an outstanding electrocatalytic performance with excellent activity and selectivity for CO2-to-CO conversion and remarkable stability. Furthermore, systematic characterizations and density functional theory calculations unveiled that the electronic interaction within the diatomic pairs leads to the optimized electronic state and decreased reaction energy barrier for generating COOH* intermediate and weakening the binding strength of CO*, thereby improving the intrinsic catalytic activity and selectivity. This work may inspire further development of high-performance diatomic site catalysts for CO2 electroreduction and other electrosynthesis.

Transition metal selenides have attracted particular interest in the energy storage field due to their high con-ductivity, but their low specific capacitances limit their development. Herein, we design the Co0.85Se with an orderly aligned nanowire-arrayed (NWA) structure, which has great micromorphological stability and fast electronic/ionic transport rate, achieving a high specific capacitance of 1800 F g-1 at 1 mV s-1. The asymmetric supercapacitors (ASCs) are built to widen the voltage window to 0- 1.6 V and then increase the energy density, which reaches 71.3 Wh kg-1 at a power density of 800 W kg-1 in the liquid-state electrolyte, and 3.03 mWh cm-3 at the power density of 8.0 W cm-3 in the solid-state electrolyte. It also shows excellent cycling stability of 86 % retention after 10,000 charge/discharge cycles, fast rate capability of 79 % retention at the 15-fold increased current density and little performance degradation even under severe bent deformation. As shown, the Co0.85Se NWAs-based ASCs own high energy and power densities, long cycle life, fast rate capability and stable flexi-bilities simultaneously, which have significant potential in high-performance wearable energy storage systems.