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Abstract Low‐temperature energy harvest, delivery, and utilization pose significant challenges for thermal management in extreme environments owing to heat loss during transport and difficulty in temperature control. Herein, we propose a light‐driven photo‐energy delivery device with a series of photo‐responsive alkoxy‐grafted azobenzene‐based phase‐change materials (a‐g‐Azo PCMs). These a‐g‐Azo PCMs store and release crystallization and isomerization enthalpies, reaching a high energy density of 380.76 J/g even at a low temperature of −63.92 °C. On this basis, we fabricate a novel three‐branch light‐driven microfluidic control device for distributed energy recycling that achieves light absorption, energy storage, controlled movement, and selective release cyclically over a wide range of temperatures. The a‐g‐Azo PCMs move remote‐controllably in the microfluidic device at an average velocity of 0.11–0.53 cm/s owing to the asymmetric thermal expansion effect controlled by the temperature difference. During movement, the optically triggered heat release of a‐g‐Azo PCMs achieves a temperature difference of 6.6 °C even at a low temperature of −40 °C. These results provide a new technology for energy harvest, delivery, and utilization in low‐temperature environments via a remote manipulator.

The temperature field within porous media is considerably affected by different boundary conditions, and effective thermal conductivity varies with spatial structure morphologies. At present, traditional prediction methods for the temperature field are expensive and time consuming, particularly for large structures and dimensions, whereas deep learning surrogate models have limitations related to constant boundary conditions and two-dimensional input slices, lacking the three-dimensional topology and spatial correlations. Herein, a constraint-incorporated model using U-Net architecture as the backbone is proposed to predict the temperature field and effective thermal conductivity of sphere-packed porous media, considering diverse external heat fluxes. A total of 510 original samples of temperature fields are generated through lattice Boltzmann method (LBM) simulations, which are further augmented to 33,150 samples using the self-amplification method for the training. Physical prior knowledge is incorporated into the model to constrain the training direction by adding physical constraint terms as well as adaptive weights to the loss function. Input vectors with different heat fluxes and porosities are embedded into latent features for predicting different boundary conditions. Results indicate that the constraint-incorporated model has a mean relative error ranging between 1.1 % and 5.7 % compared with the LBM results in the testing set. It exhibits weak dependence on the database size and substantially reduces computational time, with a maximum speedup ratio of 7.14 × 106. This study presents a deep learning model with physical constraints for predicting heat conduction in porous media, alleviating the burden of extensive experiments and simulations.

The grain size of sediments is a crucial parameter in sedimentology, with significant implications for submarine engineering and water conservancy projects. In this study, we developed an acoustic reflection measurement system using a self-developed, high-precision, high-frequency shallow stratigraphic profiler. The system's accuracy was validated with standard acrylic samples. Results showed that within the sediment grain size range of 0.3 to 2.5 mm, the acoustic reflection amplitude increased with grain size. However, distinguishing grain sizes between 0.1 and 0.3 mm from those between 1.0 and 1.5 mm based solely on reflection amplitude proved challenging. Notably, the differences in wavefront flare shapes between these grain sizes were readily apparent. Therefore, combining reflection peak amplitude with time-domain waveform analysis enables more precise sediment grain size classification.

Abstract As nonlinear thermal devices, thermal regulators can intelligently respond to temperature and control heat flow through changes in heat transfer capacities, which allows them to reduce energy consumption without external intervention. However, current thermal regulators generally based on high‐quality crystalline‐structure transitions are intrinsically rigid, which may cause structural damage and functional failure under mechanical strain; moreover, they are difficult to integrate into emerging soft electronic platforms. In this study, we develop a flexible, elastic thermal regulator based on the reversible thermally induced deformation of a liquid crystal elastomer/liquid metal (LCE/LM) composite foam. By adjusting the crosslinking densities, the LCE foam exhibits a high actuation strain of 121% with flexibility below the nematic–isotropic phase transition temperature (TNI) and hyperelasticity above TNI. The incorporation of LM results in a high thermal resistance switching ratio of 3.8 over a wide working temperature window of 60°C with good cycling stability. This feature originates from the synergistic effect of fragmentation and recombination of the internal LM network and lengthening and shortening of the bond line thickness. Furthermore, we fabricate a “grid window” utilizing photic‐thermal integrated thermal control, achieving a superior heat supply of 13.7°C at a light intensity of 180 mW/cm2 and a thermal protection of 43.4°C at 1200 mW/cm2. The proposed method meets the mechanical softness requirements of thermal regulator materials with multimode intelligent temperature control.

Introduction: Silicon is a major trace element in humans and a prospective supporting biomaterial to bone regeneration. Submicron silicon pillars, as a representative surface topography of silicon-based biomaterials, can regulate macrophage and osteoblastic cell responses. However, the design of submicron silicon pillars for promoting bone regeneration still needs to be optimized. In this study, we proposed a submicron forest-like (Fore) silicon surface (Fore) based on photoetching. The smooth (Smo) silicon surface and photoetched regular (Regu) silicon pillar surface were used for comparison in the bone regeneration evaluation.Methods: Surface parameters were investigated using a field emission scanning electron microscope, atomic force microscope, and contact angle instrument. The regulatory effect of macrophage polarization and succedent osteogenesis was studied using Raw264.7, MC3T3-E1, and rBMSCs. Finally, a mouse calvarial defect model was used for evaluating the promoting effect of bone regeneration on the three surfaces. Results: The results showed that the Fore surface can increase the expression of M2-polarized markers (CD163 and CD206) and decrease the expression of inflammatory cytokines, including interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-α). Fore surface can promote the osteogenesis in MC3T3-E1 cells and osteoblastic differentiation of rBMSCs. Furthermore, the volume fraction of new bone and the thickness of trabeculae on the Fore surface were significantly increased, and the expression of RANKL was downregulated. In summary, the upregulation of macrophage M2 polarization on the Fore surface contributed to enhanced osteogenesis in vitro and accelerated bone regeneration in vivo.Discussion: This study strengthens our understanding of the topographic design for developing future silicon-based biomaterials.

Air-cooled proton exchange membrane fuel cell (AC-PEMFC) is widely considered as a promising power source for unmanned aerial vehicles (UAVs) due to its merits such as high energy density, short refueling time, and simple auxiliary system. However, the performance of AC-PEMFC is not satisfactory due to the poor membrane hydration caused by the large air supply for heat dissipation demand. This study proposes a staggered tapered slotted flow field (STSF) configuration to address this issue, which has higher contact area between the airflow and the bipolar plate by arranging tapered and slotted sections in the channels along the airflow direction, aiming to enhance the cooling effect and improve the membrane water hydration. Utilizing a three-dimensional (3D) multiphase non-isothermal model verified against experimental data, it was found that the STSF configuration reduces the internal temperature of the cell by about 14.2–28.3 K and increases the water content in the membrane by about 35.1–85.7 % compared with traditional straight channels. In addition, the STSF configuration can enhance mass transfer by inducing cross-flow, reducing concentration losses, which takes more effect for UAVs working at high altitude. Moreover, the slotted sections reduced the volume and weight of the bipolar plates, contributing to an additional power density improvement. Finally, the pressure drop within the flow channels and net power was compared. Due to the increased contact area between the cooling airflow and the bipolar plates, the STSF configuration inevitably results in a higher pressure drop within the channels, but the net power of PEMFC with STSF still increased under severe conditions by 0.080 W.

During the operation of the proton exchange membrane fuel cell (PEMFC), the chemical reaction yields a large amount of heat. Long-term operation may cause local overheating problems which damage the proton exchange membrane structure, pull down the fuel cell performance drastically. Aiming at improving the temperature distribution and cooling capacity of PEMFC, a novel tree-shaped fractal fuel cell bipolar plate cooling flow field is proposed. The polarization curve, current density distribution, maximum temperature, temperature uniformity, cooling hydraulic pressure drop and the water content of proton exchange membrane are investigated for the fractal cooling flow field with different number of dimensions. The results show that the tree-shaped fractal cooling flow field can achieve better distributions of coolant and temperature uniformity than parallel cooling flow field. The maximum temperature reduces from 340.7 K to 337.8 K, and temperature uniformity index reduces from 1.47 to 0.45, respectively. Compared with the serpentine cooling flow field, the tree-shaped fractal cooling flow field effectively solves the problem of excessive cooling pressure drop and reduces the parasitic power loss while ensuring efficient cooling performance. This novel cooling flow field design offers an excellent solution to solve the local overheating of PEMFC.

Abstract Nanochannel‐based ion transport is an important field of study in various disciplines, including physics, chemistry, energy, materials science, biology, and earth science. The unique features of natural nanochannels have inspired numerous innovative designs that seek to achieve high ion permeability, selectivity, and rectification. A notable example is osmotic energy conversion, which harvests sustainable salinity‐gradient energy to generate electricity without moving parts, noise, or carbon emissions and thus serves as a promising alternative to traditional fossil fuel utilization. This review focuses on the fundamental principles, regulatory methods, and practical applications of nanochannel‐based ion transport for osmotic energy conversion. The physical mechanisms of ion transport and the intriguing phenomena of ion behaviors in nanoconfined spaces are discussed first, followed by a thorough examination of the overall process of osmotic power generation from mathematical, numerical, parametric, first‐principles, molecular simulation, and modeling perspectives. Strategies for enhancing the osmotic performance are then discussed to overcome the trade‐off between ion selectivity and ion flux, including the theoretical design of nanochannel geometry and electrification, experimental optimization of nanoporous membranes, and electrolyte thermal enhancement. The existing challenges and opportunities for the future development of nanochannel‐based ion transport and osmotic power generation are addressed at the end.

As a typical maneuver model, the second-order Markov model, which is based on acceleration periodic autocorrelation, is an effective way used for tracking near space hypersonic vehicle(NSHV) target tracking. It is found that, however, the model responds slowly to the target maneuvering and has weak maneuverability. To solve this problem, the adaptive non-zero mean damped oscillation model (ANM-DO), which based on the idea of mean compensation, is proposed. Then the difference of the mean compensation method between the first order Markov model and the two order Markov model is analyzed, and the physical essence of adaptive nonzero mean is discussed from time domain and frequency domain. Furthermore, to further investigate the performance of the ANM-DO model, we deduced the systematic dynamic errors of ANM-DO taking Kalman Filter (KF) as filtering algorithm. On this basis, the superior performance of ANM-DO model is verified in terms of maneuverability. Finally, simulation experiments in different scenarios show that the ANM-DO model shows lower filtering errors tracking near space hypersonic jump gliding targets, and verified the adaptability of the model proposed in this paper.

Abstract Lung adenocarcinoma (LUAD) is the main subtype of non-small cell lung cancer with a poor survival prognosis. In our study, gene expression, DNA methylation, and clinicopathological data of primary LUAD were utilized to identify potential prognostic markers for LUAD, which were recruited from The Cancer Genome Atlas (TCGA) database. Univariate regression analysis showed that there were 21 methylation-associated DEGs related to overall survival (OS), including 9 down- and 12 up-regulated genes. The 12 up-regulated genes with hypomethylation may be risky genes, whereas the other 9 down-regulated genes with hypermethylation might be protective genes. By using the Step-wise multivariate Cox analysis, a methylation-associated 6-gene (consisting of CCL20, F2, GNPNAT1, NT5E, B3GALT2, and VSIG2) prognostic signature was constructed and the risk score based on this gene signature classified patients into high- or low-risk groups. Patients of the high-risk group had shorter OS than those of the low-risk group in both the training and validation cohort. Multivariate Cox analysis and the stratified analysis revealed that the risk score was an independent prognostic factor for LUAD patients. The methylation-associated gene signature may serve as a prognostic factor for LUAD patients and the represent hypermethylated or hypomethylated genes might be potential targets for LUAD therapy.

Since the good application of quantum mechanism in the field of communication, quantum secure communication has become a research hotspot. The existing quantum secure communication protocols usually assume that the quantum channel is noise-free. But the inevitable quantum noise in quantum channel will greatly interferes the transmission of quantum bits or quantum states, seriously damaging the security and reliability of the quantum system. This paper analyzes and discusses the performance of a χ state based steganography protocol under four main quantum noises, i.e., Amplitude Damping (AD), Phase damping (Phs), Bit Flip (BF) and Depolarizing (D). The results show that the protocol is least affected by amplitude damping noise when only the sender's first transmission in quantum channel is affected by quantum noise. Then, we analyze the performance of the protocol when both the sender's two transmissions are affected by quantum noise, and find that the specific combination of different noises will increase the performance of the protocol in quantum noisy channel. This means that an extra quantum noise can be intentionally added to quantum channel according to the noise intensity, so that the protocol can improve performance under the influence of quantum noises. Finally, the detailed mathematical analysis proves the conclusions.

Based on quantum identity authentication, a novel continuous variable quantum steganography protocol is proposed in this paper. It can effectively transmit deterministic secret information in the public quantum channel by taking full advantage of entanglement properties of continuous variable GHZ state. Compared with the existing quantum steganography results, this protocol has the advantages of good imperceptibility and easy implementation. Finally, the detailed performance analysis proves that the proposed protocol has not only these advantages, but also good security and information transmission efficiency, even under eavesdropping attacks, especially to the spectroscopic noise attack.

The traditional sound absorption problem has not been completely resolved over the last 200 years. At every stage, its research has changed depending on practical requirements and current technologies. Phononic crystals (PCs) and acoustic metamaterials (AMs) have gained attention because of their extensive investigation and development over the past 30 years. Especially, the use of these materials brings new vitality into the traditional sound absorption problem to figure out broad working band and low-frequency absorption. This review highlights recent progress in sound absorption—from airborne to waterborne absorption—and gradient-index AMs. Progress in gradient-index AMs is singled out because of their favorable impedance matching, good viscous and thermal dissipation, and lengthened propagation paths compared with those of other materials. The progress in sound absorption of PCs and AMs is promising to serve as the next-generation sound absorbing materials, trap and reuse acoustic energy, and attenuate earthquake/tsunami wave in the future.

Quantum image steganography is one of the important research branches of quantum secure communications. In this paper, a large payload quantum image steganography protocol based on quantum image expansion and the Grover search algorithm is proposed. The new algorithm adopts quantum log-polar image (QUALPI) representation to prepare the quantum image before introducing a quantum expansion technique to form the superposition of multiple image copies with the same size angle difference as the carrier. Then, it embeds a secret message into one quantum image copy with a specific rotation angle encoded. In order to accurately extract the secret message embedded, the Grover search algorithm is used to locate the correct quantum image copy. Based on the quantum uncertainty and quantum non-cloning theorems, the new algorithm can not only achieve good imperceptibility and security but also large payload due to the algorithm's good coding scalability. The experimental results by performing MATLAB simulation prove the conclusions.

Embedding secret information into quantum carrier image for covert communication is one of significant research fields of quantum secure communication. Using good imperceptibility and high embedding efficiency of matrix coding, this paper proposes a novel matrix coding-based quantum steganography algorithm for quantum color images. In order to better apply matrix coding in actual demand, two different embedding methods are proposed. One embedding method is single pixel-embedded (1, 3, 2) coding, called SPE (1, 3, 2) coding for short. This method embeds two qubits of secret information into three least significant qubits (LSQbs) of a single pixel of quantum carrier image, and at most only one LSQb would be changed. The other embedding method is the multiple pixels-embedded (1, 3, 2) coding, called MPsE (1, 3, 2) coding, in which three LSQbs of multiple carrier pixels are utilized to embed two secret qubits. On account of experimental simulation obtained in the MATLAB environment, it shows that the new algorithm has good performance in the imperceptibility, the security, the embedding efficiency, and the embedding capacity.

The performance and durability of proton exchange fuel cells (PEMFCs) are greatly affected by the bipolar plate (BP). In this paper, the thermal and electrical conductivities and mechanical property of graphite filled with resin composite BPs were collectively enhanced through the effectively coupled manipulations of molding pressure and impregnation pressure. The microstructures show that the resin tends to distribute at the top region of the rib under high impregnation pressure. The thermal and electrical conductivities of the pure expanded graphite BP is well reserved in the composite BPs under high molding pressure, which can facilitate the heat transfer and electron conduction in the PEMFCs. The relative density and compressive strength of composite BPs were greatly enhanced by the impregnation of resin compared to the expanded graphite under high molding pressure without the impregnation of resin (HU-BP). The maximum thermal conductivity, compressive strength, and minimum interfacial contact resistance (ICR) are collectively achieved in the HL-BP. The enhanced thermal-electrical and mechanical properties could be mainly attributed to the well-reserved continuous networks of graphite in the composite BPs. The findings in this paper are expected to synergetically improve the thermal, electrical, and mechanical properties of composite BPs through coupled manipulations of the molding and impregnation pressures, which in turn enhances the power density and durability of PEMFCs.

This paper proposed a compact design of the subsea cobalt-rich crust mining vehicle with a general purpose support vessel for subsea resource exploration, sample collection, and research. The necessary functions were considered in the concept design, including walk, crushing/mining, sample collection, cutter head adaptation, vehicle orientation, crust texture measurement, awareness, positioning, and navigation. The prototype was tested in both tank and subsea environment. The sea trials were carried out with the support of a general purpose support vessel. The track design worked well in both the tank and subsea environment and the mining vehicle walked smoothly in the sea trial. The crust was crushed to the size of 2 mm and 10 mm with different cutting parameters and successfully collected by the jet pump, 6 kg in total. The crust texture was measured by the onboard sonar successfully and can be used for cutting parameter selection. The cameras captured the images of the subsea environment, but the actions of crushing and sample collection produced plumes, which blocked the camera vision. In the situation, the front image sonar can be used to keep the vehicle away from big rocks. The mining vehicle is not limited to the mining and sampling of subsea cobalt-rich crust. Most of the subsea solid resources on the seabed can be considered to use the compact mining vehicle for sampling and related research. The only issues to be considered are the crushing ability and sample size required.

The traditional image authentication technique generally determines the image attribution by extracting specific features and combining the similarity calculation algorithm. Because of the selected features dimensions, characterization and other factors, the accuracy and speed of image authentication have been restricted. In this paper, Recog-Net, an end-to-end image authentication model based on convolution neural network has been proposed. Deep residual network is chosen as the features extractor. Mahalanobis distance and threshold method are used to complete the image authentication. Experiments show that the performance of the extractor's features, compared with the traditional features and the features of other convolution neural network architectures, is more excellent, with a high degree of generality, recognition rate and robustness, still having these advantages even after a substantial compression. The Recog-Net for image authentication is able to accurately authenticate the images tampered with certain range.

Abstract Hepatitis B virus (HBV) infection is a serious public health problem, which can be transmitted through various routes (e.g., blood donation) and cause hepatitis, liver cirrhosis and liver cancer. Hence, it is necessary to do diagnostic screening for high-risk HBV patients in these transmission routes. Nowadays, protein-based technologies have been used for HBV testing, which however involve the issues of large sample volume, antibody instability and poor specificity. Nucleic acid hybridization-based lateral flow assay (LFA) holds great potential to address these limitations due to its low-cost, rapid, and simple features, but the poor analytical sensitivity of LFA restricts its application. In this study, we developed a low-cost, simple and easy-to-use method to improve analytical sensitivity by integrating sponge shunt into LFA to decrease the fluid flow rate. The thickness, length and hydrophobicity of the sponge shunt were sequentially optimized, and achieved 10-fold signal enhancement in nucleic acid testing of HBV as compared to the unmodified LFA. The enhancement was further confirmed by using HBV clinical samples, where we achieved the detection limit of 103 copies/ml as compared to 104 copies/ml in unmodified LFA. The improved LFA holds great potential for diseases diagnostics, food safety control and environment monitoring at point-of-care.

With an increase of service users’ demands on high quality of services (QoS), more and more efficient service computing models are proposed. The development of cloud computing, fog computing, and edge computing brings a number of challenges, e.g., QoS optimization and energy saving. We do a comprehensive survey on QoS optimization and energy saving in cloud computing, fog computing, edge computing, and IoT environments. We summarize the main challenges and analyze corresponding solutions proposed by existing works. This survey aims to help readers have a deeper understanding on the concepts of different computing models and study the techniques of QoS optimization and energy saving in these models.

Frost deposits on the outdoor heat exchanger of an air source heat pump (ASHP) air conditioner and reduces its capacity during winter operation. However, the prevailing reverse-cycle defrosting (RCD) turns the indoor heat exchanger into an evaporator and ceases heat supply to the living space. Consequently, the thermal comfort for indoor occupants is deteriorated. This article proposes a heater-assisted ASHP to tackle this problem. With an 800 W electromagnetic heater equipped upstream of the outdoor heat exchanger to provide refrigerant with additional heat, the ASHP retarded frost under original throttling control and compressor speed during the heating cycle (frostless mode), and even removed frost with uninterrupted heat supply to indoor space under little throttling and reduced compressor speed (anti-frost mode). Compared with the original operation of the ASHP when the heater was off (baseline mode), frostless and anti-frost modes extended heating duration by 17.9% and 99.7%, respectively, with comparative time-averaged supply-air temperature. Moreover, COP for baseline and anti-frost modes was similar by average, about 3% higher than for the frostless mode. Further optimizations will be done on the co-adjustment of throttling control and compressor speed to better fulfill the potential of the heater-assisted ASHP.

Minimizing platinum (Pt) loading while reserving high reaction efficiency in the catalyst layer (CL) has been confirmed as one of the key issues in improving the performance and application of proton exchange membrane fuel cells (PEMFCs). To enhance the reaction efficiency of Pt catalyst in CL, the interfacial interactions in the three-phase interface, i.e., carbon, Pt, and ionomer should be first clarified. In this study, a molecular model containing carbon, Pt, and ionomer compositions is built and the radial distribution functions (RDFs), diffusion coefficient, water cluster morphology, and thermal conductivity are investigated after the equilibrium molecular dynamics (MD) and nonequilibrium MD simulations. The results indicate that increasing water content improves water aggregation and cluster interconnection, both of which benefit the transport of oxygen and proton in the CL. The growing amount of ionomer promotes proton transport but generates additional resistance to oxygen. Both the increase of water and ionomer improve the thermal conductivity of the C. The above-mentioned findings are expected to help design catalyst layers with optimized Pt content and enhanced reaction efficiency, and further improve the performance of PEMFCs.

Effective thermal conductivity is an important thermophysical property in the design of metal-organic framework-5 (MOF-5)-based hydrogen storage tanks. A modified thermal conductivity model is built by coupling a theoretical model with the grand canonical Monte Carlo simulation (GCMC) to predict the effect of the H2 adsorption process on the effective thermal conductivity of a MOF-5 powder bed at pressures ranging from 0.01 MPa to 50 MPa and temperatures ranging from 273.15 K to 368.15 K. Results show that the mean pore diameter of the MOF-5 crystal decreases with an increase in pressure and increases with an increase in temperature. The thermal conductivity of the adsorbed H2 increases with an increased amount of H2 adsorption. The effective thermal conductivity of the MOF-5 crystal is significantly enhanced by the H2 adsorption at high pressure and low temperature. The effective thermal conductivity of the MOF-5 powder bed increases with an increase in pressure and remains nearly unchanged with an increase in temperature. The thermal conductivity of the MOF-5 powder bed increases linearly with the decreased porosity and increased thermal conductivity of the skeleton of the MOF-5 crystal. The variation in the effective thermal conductivities of the MOF-5 crystals and bed mainly results from the thermal conductivities of the gaseous and adsorption phases.

Sensor network–based application has gained increasing attention where data streams gathered from distributed sensors need to be processed and analyzed with timely responses. Distributed complex event processing is an effective technology to handle these data streams by matching of incoming events to persistent pattern queries. Therefore, a well-managed parallel processing scheme is required to improve both system performance and the quality-of-service guarantees of the system. However, the specific properties of pattern operators increase the difficulties of implementing parallel processing. To address this issue, a new parallelization model and three parallel processing strategies are proposed for distributed complex event processing systems. The effects of temporal constraints, for example, sliding windows, are included in the new parallelization model to enable the processing load for the overlap between windows of a batch induced by each input event to be shared by the downstream machines to avoid events that may result in wrong decisions. The proposed parallel strategies can keep the complex event processing system working stably and continuously during the elapsed time. Finally, the application of our work is demonstrated using experiments on the StreamBase system regardless of the increased input rate of the stream or the increased time window size of the operator.

The development of the unmanned aircraft systems is envisioned to greatly reduce the energy consumption of sensor nodes in data gathering process using unmanned aircraft systems as mobile sinks. In traditional sensor networks, compressive sensing and clustering are two key energy-efficient techniques for data gathering. However, how to integrate two techniques into the data gathering for unmanned aircraft system–aided wireless sensor networks effectively is still an open problem. Moreover, most clustering schemes focus on the cluster head selection strategy and simplified the problem of cluster member selection, and most compressive sensing schemes are not integrated with the clustering strategy. To this end, this article studies the problem of integrating compressive sensing with clustering for data gathering in unmanned aircraft system–aided networks. We first give a theoretical formulation of this problem. Considering the non-deterministic polynomial-time hard complexity of the problem, we present two algorithms by jointly considering the compressive ratio variation factor and the distance factor to find near-optimal solutions heuristically. Evaluations based on real data traces show that the proposed algorithms greatly reduced the energy consumption of sensor nodes efficiency.

Modeling is a powerful tool for the design and development of proton exchange membrane fuel cells (PEMFCs). This study presents a one-dimensional, two-phase mathematical model of PEMFC to investigate the two-phase transport process, gas species transport flow and water crossover fluxes. The model reduces the computational time for PEMFC design with guaranteed accuracy. Analysis results show that the concentration and activation overpotentials of the cell decrease with the increase of operation pressure, which result in enhanced cell performance. Proper oxygen stoichiometry ratio in the cathode decreases the cell activation overpotential and is favorable for performance improvement. The cell ohmic resistance correspondingly increases with the increase of catalyst layer thickness, which leads to a deteriorated cell performance. The improvement on cell performance could be facilitated by decreasing the membrane thickness. Predicted results show that the present model is a useful tool for the design optimization of practical PEMFCs.

Numerical simulation with the Forchheimer flow model and local thermal non-equilibrium model for porous region is performed on forced convective heat transfer in a tube partially filled with metallic foams. Flow and heat transfer of fluid in the hollow region and those of fluid in the porous region are conjugated together via the coupling conditions at porous-fluid interface. A heat flow model is proposed with special numerical treatments employed for non-equilibrium conjugated heat transfer in foam-fluid system. Velocity and temperature profiles in the flow direction are obtained and validated with analytical results. Effects of porosity, pore density, dimensionless interfacial radius and fluid-to-solid thermal conductivity ratio on flow characteristics and thermal performance are examined. Accordingly, the entrance effect is analyzed through the numerical simulation in terms of both flow and heat transfer. The present tube exhibits more excellent heat transfer performance at the expense of moderate pressure drop compared with the tube without porous material. The numerical work is not only developed for forced convection in metal-foam partially filled tube, but can also be extended to similar problem with porous-fluid interface for other porous media with significant thermal non-equilibrium effect.

The remarkable advancement of quantum steganography offers enhanced security for quantum communications. However, there is a significant concern regarding the potential misuse of this technology. Moreover, the current research on identifying malicious quantum steganography is insufficient. To address this gap in steganalysis research, this paper proposes a specialized quantum steganalysis algorithm. This algorithm utilizes quantum machine learning techniques to detect steganography in general quantum secure communication schemes that are based on pure states. The algorithm presented in this paper consists of two main steps: data preprocessing and automatic discrimination. The data preprocessing step involves extracting and amplifying abnormal signals, followed by the automatic detection of suspicious quantum carriers through training on steganographic and non-steganographic data. The numerical results demonstrate that a larger disparity between the probability distributions of steganographic and non-steganographic data leads to a higher steganographic detection indicator, making the presence of steganography easier to detect. By selecting an appropriate threshold value, the steganography detection rate can exceed 90%. [ABSTRACT FROM AUTHOR]