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Coordinate transformation is a fundamental issue in the related studies of measurement. However, existing methodologies often need to pay more attention to the available spatial information, leading to suboptimal results. This paper addresses this issue by incorporating geometric constraints into the symmetric coordinate transformation. We propose the so-called geo-constrained transformation method based on the joint adjustment of the coordinate transformation in conjunction with the geometric constraints over a set of points. By formulating the geometric constraints as the conditional model, we analyze the effects of geometric constraints on the estimated transformation parameters and point locations. By removing such effects during the symmetric transformation algorithm, the results show better statistical performance and satisfy geometric constraints. Two numerical examples are given to demonstrate the expected improvement in the statistical accuracy. It is shown that the improvement of the point determination accuracy can go beyond 50%.

Background Cerebral microbleeds (CMBs) are common and varied in patients receiving extracorporeal membrane oxygenation (ECMO). Here, the authors describe CMB findings in patients receiving ECMO and their association with clinical factors. Methods and Results A total of 138 patients receiving ECMO were enrolled and categorized as venovenous and venoarterial. Blood coagulation profiles during ECMO support and Glasgow Coma Scale (GCS) scores within 7 days were recorded. Patients with CMBs exhibited prolonged activated clotting time (P

Natural gas hydrates (NGH) are considered a very promising source of clean energy due to their widespread distribution, high energy density, and pure combustion products. Currently, there are few studies on NGH reservoir well testing, and the models are often idealistic, lacking practical guidance for field application. In this paper, a well-testing model for partially perforated wells in the NGH reservoir is proposed, which takes into account the dynamic decomposition of hydrates. This model can simulate the performance of the perforated NGH well with a dynamic dissociation interface, which divides the reservoir into decomposed and undecomposed regions. Governing equations in cylindrical coordinates are formulated to depict fluid flow. Moving boundaries and dissociation coefficients are incorporated to describe the solid-to-gas transition within hydrates. Analytical solutions including the pressure transient behaviors of the NGH reservoir and the bottomhole pressure (BHP) of partially perforated wells are derived by utilizing the Laplace transform method of the separation of variables and the Stehfest numerical inversion algorithm. Sensitivity analysis is conducted using the parameters from partially perforated wells and NGH formation properties. We plot the pressure and pressure derivative curves in double logarithmic coordinates to study the pressure transient behaviors. There are seven flow regimes that are typical for partially perforated wells in the NGH reservoir, namely, pure wellbore storage, skin effect, spherical flow, pseudo-radial flow, composite effect, improvement, and radial flow regimes.

Objective In urban rail transit systems, train velocity measuring and positioning technology offers critical information to many applications such as train autonomous driving, collision avoidance, dispatching and directing. The architecture and function of the train control systems are increasingly complex, and the continuous level-up of the operation efficiency and the services requires higher performance for train velocity measuring and positioning, so that the ability of the ATP (automatic train protection) measuring velocity and positioning in different scenarios needs to be improved. Method A train velocity measurement approach based on wheel-axle creep rate detection is proposed. First, the architecture of the train velocity measurement and localization system is introduced and the technical process of train velocity measurement based on wheel-axle creep rate is explained. Then the wheel-axle creep theory is explained and the velocity measurement error of the Doppler radar sensor is modeled to obtain the radar maximum reference velocity, which is used to calculate the wheel-axle creep. Different compensation for the odometer velocity is applied based on different wheel-axle creep status. Finally, the velocity measurement approach proposed is verified using the experimental data sets. Result & Conclusion The experimental results show that most of the time the wheel-axle is in a low creep state during the train braking stage. The velocity obtained by less compensation for the odometer velocity is much lower than the maximum velocity calculated by the existing ATP. This approach remarkably improves the velocity measurement accuracy under the premise of safety when the train minimal velocity is identical.

To improve the performance of hydrocyclones for in-situ natural gas hydrate recovery and desanding, this study analyzed the hydrocyclone’s sensitivity factors, optimized the structural parameters, and investigated the adaptability to the reservoir and operating parameters through numerical simulations and experiments. Results showed that the optimal structural parameters were spiral pitch of 24 mm, vortex finder diameter of 36 mm, spigot diameter of 26 mm, number of spirals equals 5, and cone angle of 6°. Under the optimal parameter configuration, the efficiency of the hydrocyclone is exceeded 90%, and the pressure drop was within 0.5 MPa. When the inlet natural gas hydrate volume concentration, or the inlet sand volume concentration are increased, the separation efficiency and pressure drop changed only slightly. The natural gas hydrate hydrocyclone showed good adaptability to the reservoir and operating parameters. These findings provide theoretical guidance for the design and construction of natural gas hydrate in-situ separators.

To achieve precise control of the symmetrical unmanned surface vehicle (USV) under strong external disturbances, we propose a disturbance estimation and conditional disturbance compensation control (CDCC) scheme. First, the differential flatness method is applied to convert the underactuated model into a fully actuated one, simplifying the controller design. Then, a nonlinear disturbance observer (NDOB) is designed to estimate the lumped disturbance. Subsequently, a continuous disturbance characterization index (CDCI) is proposed, which not only indicates whether the disturbance is beneficial to the system stability but also makes the controller switch smoothly and suppresses the chattering phenomenon greatly. Indicated by the CDCI, the proposed CDCC method can not only utilize the beneficial disturbance but also compensate for the detrimental disturbance, which improves the USV’s control performance under strong external disturbances. Moreover, a trajectory-planning method is designed to generate an obstacle avoidance reference trajectory for the controller. Finally, simulations verify the feasibility of applying the proposed control method to USV.

Mixed micelles (MMs), as a new targeted drug delivery system with high adjustability, have been widely studied in biomedicines. In order to study whether mixtures containing two different molecules have excellent targeted anti-tumor effect, MMs based on D-α-tocopherol polyethylene glycol 1000 succinate (TPGS) and triblock copolymer P84 were prepared, and the characterization, cell endocytosis, and exocytosis of P84/TPGS MMs were observed. Paclitaxel (PTX), a classical anti-tumor drug, was selected as a model compound to explore its anti-tumor effect in vivo. The results showed that P84/TPGS MMs had better stability, suitably distributed particle size, good cell endocytosis, and tumor sphere permeability, which could improve the permeability of PTX in the tumor site and effectively inhibit the growth of H22 tumor in mice. In conclusion, P84/TPGS MMs is a potential micelle carrier system of cancer targeting, needing further investigation.

Background: The overall incidence of acute kidney injury (AKI) in neonates undergoing emergency gastrointestinal surgery is yet to be determined. The study aims are to analyze our experience in emergency gastrointestinal surgery for neonates and to evaluate the incidence of AKI. Methods: We conducted a retrospective study of neonates undergoing emergency gastrointestinal surgery between June 31, 2018 and May 10, 2022 (N = 329). The primary outcome was the overall incidence of AKI. The diagnostic AKI was based on the Modified Kidney Disease: Improving Global Outcomes (KDIGO) definition of neonatal AKI. The secondary outcomes, including the postoperative length of hospital stay (PLOS), 24-h mortality, in-hospital mortality, and total in-hospital cost, were analyzed. The risk factors associated with the development of postoperative AKI were also analyzed. Results: The incidence of postoperative AKI was 9.1% (30/329). No significant differences were detected in the 24-h mortality and in-hospital mortality between the two cohorts. In the final model, patients undergoing mechanical ventilation before surgery, vasopressor support, surgical duration, intraoperative oliguria and preoperative lowest serum creatinine (SCr), were independently associated with AKI. Conclusion: Our study found that patients undergoing mechanical ventilation before surgery, vasopressor support, surgical duration, intraoperative oliguria and preoperative lowest SCr were independently associated with postoperative AKI in neonates who accepted emergency gastrointestinal surgeries.

Visual object tracking is an important technology in camera-based sensor networks, which has a wide range of practicability in auto-drive systems. A transformer is a deep learning model that adopts the mechanism of self-attention, and it differentially weights the significance of each part of the input data. It has been widely applied in the field of visual tracking. Unfortunately, the security of the transformer model is unclear. It causes such transformer-based applications to be exposed to security threats. In this work, the security of the transformer model was investigated with an important component of autonomous driving, i.e., visual tracking. Such deep-learning-based visual tracking is vulnerable to adversarial attacks, and thus, adversarial attacks were implemented as the security threats to conduct the investigation. First, adversarial examples were generated on top of video sequences to degrade the tracking performance, and the frame-by-frame temporal motion was taken into consideration when generating perturbations over the depicted tracking results. Then, the influence of perturbations on performance was sequentially investigated and analyzed. Finally, numerous experiments on OTB100, VOT2018, and GOT-10k data sets demonstrated that the executed adversarial examples were effective on the performance drops of the transformer-based visual tracking. White-box attacks showed the highest effectiveness, where the attack success rates exceeded 90% against transformer-based trackers.

Currently, new clean energy storage technology must be effective, affordable, and ecologically friendly so as to meet the diverse and sustainable needs of the energy supply. In this work, NiCo-LDH containing intercalated EG was successfully prepared within 210 s using an ultrafast microwave radiation technique. Subsequently, a series of characterization and systematic electrochemical tests were conducted to analyze the composition, structure, and energy storage mechanism of the NiCo-LDH material. The Ni:Co ratio of 5:5 results in the highest capacitance value of 2156 F/g at 1 A/g and an outstanding rate performance of 86.8% capacity retention rate at 10 A/g. The results demonstrated that the unique porous structure of NiCo-LDH and large layer spacing were conducive to more electrochemical reactions. Additionally, an electrochemical test was carried out on the NiCo-LDH as a hybrid supercapacitor electrode material, with NiCo-LDH-5:5 serving as the positive electrode and activated carbon as the negative electrode, the asymmetric supercapacitor can achieve a maximum energy density of 82.5 Wh kg−1 and power density of 8000 W kg−1. The NiCo-LDH-5:5//AC hybrid supercapacitors own 81.5% cycle stability and 100% coulombic efficiency after 6000 cycles at 10 A/g.

In real estate valuation using the Hedonic Price Model (HPM) estimated via Ordinary Least Squares (OLS) regression, subjectivity and measurement errors in the independent variables violate the Gauss–Markov theorem assumption of a non-random coefficient matrix, leading to biased parameter estimates and incorrect precision assessments. In this contribution, the Errors-in-Variables model equipped with Total Least Squares (TLS) estimation is proposed to address these issues. It fully considers random errors in both dependent and independent variables. An iterative algorithm is provided, and posterior accuracy estimates are provided to validate its effectiveness. Monte Carlo simulations demonstrate that TLS provides more accurate solutions than OLS, significantly improving the root mean square error by over 70%. Empirical experiments on datasets from Boston and Wuhan further confirm the superior performance of TLS, which consistently yields a higher coefficient of determination and a lower posterior variance factor, which shows its more substantial explanatory power for the data. Moreover, TLS shows comparable or slightly superior performance in terms of prediction accuracy. These results make it a compelling and practical method to enhance the HPM.

Due to the problems such as fuzzy state assessment grading boundaries, the recognition accuracy is low when using traditional fuzzy techniques to grade the switchgear state. To address this problem, this paper proposes a switchgear state assessment and grading method based on deep belief network (DBN) and improved fuzzy C-means clustering (IFCM). Firstly, the switchgear state information data are processed by normalization method; then the feature parameters are extracted from the switchgear state information data by using DBN, and finally the extracted feature parameters are categorised according to the condition of switchgear equipment through clustering using IFCM. The experimental results show that the accuracy of the method in assessing the switchgear state under small sample conditions reaches 94, which exceeds the accuracy of other switchgear state assessment grading methods currently in use.

Introduction and objectives: To investigate the efficacy of combined immunosuppressive regimens of cyclosporine (CsA), tacrolimus (TAC), or cyclophosphamide (CTX) combined with steroids in the treatment of idiopathic membranous nephropathy (IMN). Materials and methods: A total of 150 biopsy-proven IMN patients were divided into three groups: CTX, TAC, and CsA groups (50 cases each). Patients received a selected regimen for 48 weeks. The efficacy (remission rate, 24 h urinary protein, and serum albumin and creatinine) and safety (adverse events) profiles of administered regimens were evaluated at 12, 24 and 48 weeks. Results: At 12 weeks, the response rates for CsA, TAC, and CTX groups were 14%, 50%, and 22%, respectively. This increased to 74%, 84%, and 82%, respectively at 48 weeks. During follow-up, 24 h urinary protein significantly reduced from baseline in all regimens (P 0.05). Safety was comparable in all groups, with lower respiratory tract infection being the most frequent adverse event. Conclusions: The combined regimens (i.e., TAC, CsA, and CTX) are effective in the treatment of patients with IMN at 48 weeks, while TAC and CTX might be more beneficial in terms of shortened time to remission and increased complete response rate. Resumen: Introducción y objetivos: Investigar la eficacia de los regímenes inmunosupresores combinados de ciclosporina (CsA), tacrolimús (TAC) o ciclofosfamida (CTX) combinados con esteroides en el tratamiento de la nefropatía membranosa idiopática (NMI). Materiales y métodos: Un total de 150 pacientes con NMI comprobada por biopsia se dividieron en 3 grupos: grupos CTX, TAC y CsA (50 casos cada uno). Los pacientes recibieron un régimen seleccionado durante 48 semanas. Se evaluaron los perfiles de eficacia (tasa de remisión, proteína en orina de 24 h y albúmina y creatinina séricas) y seguridad (eventos adversos) de los regímenes administrados a las 12, 24 y 48 semanas. Resultados: A las 12 semanas, las tasas de respuesta para los grupos CsA, TAC y CTX fueron del 14, el 50 y el 22%, respectivamente. Esto aumentó al 74, el 84 y el 82%, respectivamente, a las 48 semanas. Durante el seguimiento, la proteína urinaria de 24 h se redujo significativamente desde el inicio en todos los regímenes (p 0.05). La seguridad fue comparable en todos los grupos, siendo la infección del tracto respiratorio inferior el evento adverso más frecuente. Conclusiones: Los regímenes combinados (es decir, TAC, CsA y CTX) son eficaces en el tratamiento de pacientes con NMI a las 48 semanas, mientras que TAC y CTX podrían ser más beneficiosos en términos de reducción del tiempo de remisión y aumento de la tasa de respuesta completa.

Face generation and manipulation techniques based on deep learning have enabled the creation of sophisticated forged facial videos, which are indistinguishable by human eyes. However, the illegal use of deep fake technology will have a serious impact on social stability, personal reputation, and even national security. Therefore, the detection technology of fake facial videos is of great significance to protect national security and maintain social order. Although the existing video-based fake face video detection technology has achieved good detection performance on the public fake face video database, there are still the following problems: (1) the existing technology uses a 2D attention mechanism to obtain local region features from face images and lacks a 3D attention mechanism to obtain local area features from face videos; (2) after obtaining local area features, the existing technology is directly used to classify or only model the inter-regional relationship of images without modeling the temporal relationship between regions of the video. This paper proposes a fake facial video detection framework based on multi-region temporal relationship features, including designing a three-dimensional attention mechanism to extract local features of multiple regions of the face from the video. In order to model the time series relationship between different face areas, a time series graph convolution neural network is also introduced to extract the time series relationship features between multiple areas. In order to model the time sequence relationship between different face regions, the convolution neural network of a time sequence diagram is also introduced to extract the characteristics of the time sequence relationship between multiple regions. Through the change characteristics of the time sequence relationship between face regions, the timing inconsistency of the face video is detected so as to determine whether the face has been deeply forged. Through experiments on multiple datasets, the experimental results of the model accuracy test show that the method proposed in the present invention achieves the highest detection accuracy, and the accuracy in the FaceForensics++ (low definition) dataset is 18.19% higher than that of the benchmark method. The experimental results of the generalization ability test show that the method proposed in the present invention achieves the highest generalization performance, and the detection accuracy in the Celeb-DF dataset is 11.92% higher than that of the benchmark method.

Dear Editor, In our subsequent study after the receipt of the article, it was found that the P84Ms carrier had certain cytotoxicity. As can be seen from the Figure 1 and Figure 2, compared with the cells in the control group, after incubation with different prepare preparations, the single carrier group Micelles-Blank and Mixed Micelles-Blank could inhibit the growth of the cells. However, the cytotoxicity data in the paper did not set Micelles-Blank and Mixed Micelles-Blank groups, which was also an oversight of our design at that time. Hence it is hard to say whether the therapeutic effect of the preparation is due to the toxicity of the carrier or the therapeutic effect of the drug in this article Therefore, the statement in the article that the carrier is safe and effective may be wrong. So the article needs to be withdrawn, and we will further modify the carrier in the future to reduce its toxicity.

In modern information technology, research on block cipher security is imperative. Concerning the ultra lightweight block cipher PICO, there has been only one study focused on recovering its complete master key, with a large search space of 264, and no fault analysis yet. This paper proposes a new fault analysis approach, combining differential fault and algebraic equation techniques. It achieved the recovery of PICO’s entire master key with 40 faults in an average time of 0.57 h. S-box decomposition was utilized to optimize our approach, reducing the time by a remarkable 75.83% under the identical 40-fault condition. Furthermore, PICO’s complete master key could be recovered with 28 faults in an average time of 0.78 h, indicating a significant 237 reduction in its search space compared to the previous study. This marks the first fault analysis on PICO. Compared to conventional fault analysis methods DFA (differential fault analysis) and AFA (algebraic fault analysis), our approach outperforms in recovering PICO’s entire master key, highlighting the cruciality of key expansion complexity in block cipher security. Therefore, our approach could serve to recover master keys of block ciphers with comparably complicated key expansions, and production of more secure block ciphers could result.

Abstract To optimize the optimal nozzle structure and multiple nozzle arrangement for hydrate jet crushing and to promote the development of solid fluidization extraction technology for shallow unconformity hydrate in China’s oceans, the submerged jet flow field of six commonly used nozzle structures in the downhole in-situ jet crushing process was analyzed on the basis of the solid fluidization extraction process in double-layer pipes, and the effect of the jet hole arrangement on the overflow performance of hydrate slurry in the outer annulus of double-layer pipes was also investigated. It was demonstrated that tapered straight nozzles were preferably selected as the nozzle type for hydrate solid fluidization mining process based on jet core stage length, jet energy dissipation rate, and jet fluid axial velocity. In the meantime, the optimum arrangement of the nozzles was preferred on the basis of the annular pressure drop and the flow resistance coefficient, with the number of single circle not higher than 3 and the axial spacing of the nozzles not lower than 50 mm. This study can provide a theoretical basis for nozzle selection and tool design for the solid fluidization jet crushing process of marine unconformable hydrates.

IntroductionSince COVID-19, medical resources have been tight, making it inconvenient to go offline for the sequelae of diseases such as post-stroke depression (PSD) that require long-term follow-up. As a new digital therapy, VRTL began to gain popularity.MethodThe research is divided into two parts: pre-test and post-test. In the pre-test, an evaluation method integrating reality-based interaction (RBI), structural equation model (SEM), analytic hierarchy process (AHP), and entropy weight method is proposed. In the post-test the patients’ physiological indicators (Diastolic blood pressure, systolic blood pressure and heart rate) are measured to verify the effectiveness of RBI-SEM model using T-test method.ResultsIn the pre-test, using SEM, it was confirmed that Pi physical awareness, Bi body awareness, Ei environmental awareness, and Si social awareness were significantly correlated and positively affected VRTL satisfaction (p >> F 0.217; B >> F 0.130; E >> F 0.243; S >> F 0.122). The comprehensive weight ranking based on RBI-SEM considered light environment (0.665), vegetation diversity (0.667), accessible roaming space (0.550) et al. relatively of importance. And T-tset in the post-test experiment considered that the data of the two measurements before and after the VRTL experience, systolic blood pressure (p 0.01).ConclusionThis research validated the effectiveness of RBI theory for VRTL design guidelines, established an RBI-SEM based VRTL evaluation model, and the output VRTL for PSD in the older adults was confirmed to have significant therapeutic benefits. This lays the foundation for designers to decompose design tasks and integrate VRTL into traditional clinical treatment systems.Contribution from the public or patientsFour public health department employees helped to improve the research’s content.

Single-cell RNA sequencing technology offers a unique opportunity to dissect cell heterogeneity of animals. Here, the authors construct a Xenopus cell landscape including larval and adult organs to dissect cell heterogeneity of the amphibian.

In this work, a series of urchin-like Ce(HCOO)3 nanoclusters were synthesized via a facile and scalable microwave-assisted method by varying the irradiation time, and the structure–property relationship was investigated. The optimization of the reaction time was performed based on structural characterizations and electrochemical performances, and the Ce(HCOO)3-210 s sample shows a specific capacitance as high as 132 F g−1 at a current density of 1 A g−1. This is due to the optimal mesoporous hierarchical structure and crystallinity that are beneficial to its conductivity, offering abundant Ce3+/Ce4+ active sites and facilitating the transportation of electrolyte ions. Moreover, an asymmetric supercapacitor based on Ce(HCOO)3//AC was fabricated, which delivers a maximum energy density of 14.78 Wh kg−1 and a considerably high power density of 15,168 W kg−1. After 10,000 continuous charge–discharge cycles at 3 A g−1, the ASC device retains 81.3% of its initial specific capacitance. The excellent comprehensive electrochemical performance of this urchin-like Ce(HCOO)3 offers significant promise for practical supercapacitor applications.

The leakage signals, including electromagnetic, energy, time, and temperature, generated during the operation of password devices contain highly correlated key information, which leads to security vulnerabilities. In traditional encryption algorithms, the length of the key greatly affects the upper limit of its security against cracking. Regarding side-channel attacks on long-key algorithms, traditional template attack methods characterize the energy traces using multivariate Gaussian distribution during the template construction phase. The exhaustive key-guessing process is expected to consume a significant amount of time and computational resources. Therefore, to analyze the effectiveness of obtaining key values from the side information of password devices, we propose an innovative attack method based on a divide-and-conquer logical structure, targeting semi-bytes. We construct a collection of key classification submodules with symmetric correlations. By integrating a differential network model for byte-block sets and an end-to-end direct attack method, we form a holistic symmetric decision framework and propose a key classification structure based on deep transfer learning. This structure consists of three main parts: side information data acquisition, analysis of key-value effectiveness, and determination of attack positions. It employs multiple parallel symmetric subnetworks, effectively improving attack efficiency and reducing the key enumeration range. Experimental results show that the optimal attack accuracy of the network model can reach 91%, with an average attack accuracy of 78%. It overcomes overfitting issues under small sample dataset conditions.

The permeable brick pavement system (PBPs) is one of a widely used low impact development (LID) measures to alleviate runoff volume and pollution caused by urbanization. The performance of PBPs on decreasing runoff volume is decided by its permeability, and it was general described by hydraulic conductivity based on Darcy's law. But there is large error when using hydraulic conductivity to describe the infiltration of PBPs, and which infiltration process is not following Darcy's law, so it is important to find more accurate infiltration models to describe the infiltration of PBPs. The Horton, Philip, Green-Ampt, and Kostiakov infiltration models were selected to find an optimal model to investigate infiltration performance of PBPs via a laboratory-scale experiment, and the maximum absolute error (MAE), Bias, and coefficient of determination (R2) were selected to evaluate the models' errors via fitting with experiment data. The results showed that the fitting accuracy of Kostiakov, Philip, and Green-Ampt models was significantly affected by the monitoring area and hydraulic gradients. Meanwhile, Horton model fitted well (MAE = 0.25–0.32 cm/h, Bias = 0.07–0.11 cm/h, and R2 = 0.98–0.99) with the experiment data, and the parameters of the Horton model often can be achieved by monitoring, such as the maximum infiltration rate and the stable infiltration rate. Therefore, the Horton model is an optimal model to describe the infiltration performance of PBPs, which can also be adopted to evaluate hydrological characterization of PBPs. HIGHLIGHTS The constant and falling head monitoring methods based on Darcy's law have large error to describe the infiltration process of PBPs.; Horton model is an optimal model used to describe the infiltration process of PBPs, and fits well with experiment data.; Horton model is flexible under different compositions of PBPs, and the parameters of the Horton model are determined based on experiment data which can use for mointoring.;

Thermal management serves a crucial role in the stable operation of electronic equipment due to its miniaturization and functionality. Enhancing radiation heat transfer through heat dissipation coating is an effective method to improve the heat dissipation performance of electronic devices in thermal management of narrow space. In this paper, we employed the high emissivity property of multi-walled carbon nanotubes to improve the radiative heat dissipation performance of the coating. The effect of the distribution of multi-walled carbon nanotubes on the emissivity of the coating was studied with the finite-difference time-domain method. The emissivity of the coating with random distribution of multi-walled carbon nanotubes was found to be greater than that of the coating with vertical distribution of multi-walled carbon nanotubes. In order to further investigate the radiative properties of randomly distributed multi-walled carbon nanotube coatings, we experimentally investigated the effect of coating thickness and multi-walled carbon nanotube mass fraction on the spectral radiative properties of the coatings. We obtained the relationships between coating thickness, carbon nanotube mass fraction and coating radiation power. The data can provide some guidance for the regulation of radiation power of heat dissipation coatings and the selection of common coating processes. The maximum average emissivity and radiation power of the multi-walled carbon nanotube coating is 0.97, and 491.6 W m−2, respectively.

An electrode material with high performance, long durability, and low cost for supercapacitors has long been desired in academia and industry. Among all the factors that affect the electrochemical performance and cycling stability of electrode materials, the morphology and intrinsic structure characteristics are the most important. In this study, a novel 3D flower-like Ce(COOH)3 electrode material was designed by taking advantage of the Ce3+ and -COOH groups and fabricated by a one-pot microwave-assisted method. The morphology and structure characteristics of the sample were examined by SEM, EDS, TEM, XRD, FT-IR, XPS, N2 adsorption/desorption techniques, and the electrochemical behaviors were investigated in a three-electrode configuration. The Ce(COOH)3 electrode presents an excellent specific capacitance of 140 F g−1 at 1 A g−1, higher than many other previously reported Ce-based electrodes. In addition, it delivers high rate capability that retains 60% of its initial capacitance when the current density is magnified 20 times. Dramatically, the Ce(COOH)3 electrode exhibits an ultra-high cycling stability with capacitance retention of 107.9% after 60,000 cycles, which is the highest durability among reported Ce–organic compound electrodes to the best of our knowledge. The excellent electrochemical performance is ascribed to its intrinsic crystal structure and unique morphology. This work indicates that the 3D flower-like Ce(COOH)3 has significant potential for supercapacitor applications and the facile and scalable synthesis strategy can be extended to produce other metal–organic composite electrodes.

Hydroxychloroquine (HCQ) is derivative of the heterocyclic aromatic compound quinoline, which has been used for the treatment of autoimmune diseases. The central purpose of this study was to investigate therapeutic effects and inflammatory immunological molecular mechanism of HCQ in experimental autoimmune hepatitis (AIH). Treatment with HCQ ameliorated hepatic pathologic damage, inflammatory infiltration, while promoted regulatory T cell (Treg) and down-regulated CD8+T cell differentiation in AIH mice induced by S-100 antigen. In vitro, HCQ also suppressed pro-inflammatory cytokine (IFN-γ, TNF-α, and IL-12) secretion, promoted anti-inflammatory cytokine (TGF-β1) secretion. HCQ mainly impaired T cell lipid metabolism but not glycolysis to promote Treg differentiation and function. Mechanistically, HCQ down-regulated GRK2 membrane translocation in T cells, inhibited GRK2-PI3K interaction to reduce the PI3K recruiting to the membrane, followed by suppressing the phosphorylation of PI3K-AKT-mTOR signal. Pretreating T cells with paroxetine, a GRK2 inhibitor, disturbed HCQ effect to T cells. HCQ also reversed the activation of the PI3K-AKT axis by 740 Y-P (PI3K agonist). Meanwhile, HCQ inhibited the PI3K-AKT-mTOR, JAK2-STAT3-SOCS3 and increased the AMPK signals in the liver and T cells of AIH mice. In conclusion, HCQ exhibited specific and potent therapeutic effects on AIH and attendant liver injury, which was attributed to HCQ acted on GRK2 translocation, inhibited metabolism-related PI3K-AKT and inflammation-related JAK2-STAT3 signal in T lymphocytes, thereby modulating lipid metabolism of T cell function to regulate Treg differentiation and function.

The traces used in side-channel analysis are essential to breaking the key of encryption and the signal quality greatly affects the correct rate of key guessing. Therefore, the preprocessing of side-channel traces plays an important role in side-channel analysis. The process of side-channel leakage signal acquisition is usually affected by internal circuit noise, external environmental noise, and other factors, so the collected signal is often mixed with strong noise. In order to extract the feature information of side-channel signals from very low signal-to-noise ratio traces, a hybrid threshold denoising framework using singular value decomposition is proposed for side-channel analysis preprocessing. This framework is based on singular value decomposition and introduces low-rank matrix approximation theory to improve the rank selection methods of singular value decomposition. This paper combines the hard threshold method of truncated singular value decomposition with the soft threshold method of singular value shrinkage damping and proposes a hybrid threshold denoising framework using singular value decomposition for the data preprocessing step of side-channel analysis as a general preprocessing method for non-profiled side-channel analysis. The data used in the experimental evaluation are from the raw traces of the public database of DPA contest V2 and AES_HD. The success rate curve of non-profiled side-channel analysis further confirms the effectiveness of the proposed framework. Moreover, the signal-to-noise ratio of traces is significantly improved after preprocessing, and the correlation with the correct key is also significantly enhanced. Experimental results on DPA v2 and AES_HD show that the proposed noise reduction framework can be effectively applied to the side-channel analysis preprocessing step, and can successfully improve the signal-to-noise ratio of the traces and the attack efficiency.

Metal-organic frameworks—through the use of creative synthetic designs—could produce MOF materials with excellent porosity, stability, particle microstructures, and conductivity, and their inherent characteristics—including their porosity and controllable structure—may result in an immense number of prospects for energy storage. In this paper, a nanosphere-like NiCo-MOF was effectively manufactured via an ultra-fast microwave technique. Additionally, the ideal synthesis conditions of the NiCo-MOF were investigated by adjusting the microwave output power and microwave reaction time. Under the reaction conditions of a 600 W microwave and a 210 s microwave reaction time, the NiCo-MOF exhibited an excellent capacitance of 1348 F/g at a current density of 1 A/g and an 86.1% capacity retention rate at 10 A/g. In addition, self-assembled NiCo-MOF/AC asymmetric capacitors showed a splendid energy density of 46.6 Wh/kg and a power density of 8000 W/kg.

Lightweight block ciphers are normally used in low-power resource-constrained environments, while providing reliable and sufficient security. Therefore, it is important to study the security and reliability of lightweight block ciphers. SKINNY is a new lightweight tweakable block cipher. In this paper, we present an efficient attack scheme for SKINNY-64 based on algebraic fault analysis. The optimal fault injection location is given by analyzing the diffusion of a single-bit fault at different locations during the encryption process. At the same time, by combining the algebraic fault analysis method based on S-box decomposition, the master key can be recovered in an average time of 9 s using one fault. To the best of our knowledge, our proposed attack scheme requires fewer faults, is faster to solve, and has a higher success rate than other existing attack methods.

Abstract Hypopharyngeal squamous cell carcinoma (HPSCC) is one of the most common malignant tumors in otolaryngology head and neck surgery and is one of the worst prognostic malignant tumors. Endogenous circular RNA (circRNA) is more stable than mRNA, microRNA (miRNA), and long non-coding RNA (LncRNA) in exosomes, plasma, and urine, and participates in gene expression regulation to perform different functions. Therefore, circRNA is expected to become a biomarker and therapy target for many tumors. However, the expression and function of circRNA regulated by N6-methyladenosine (m6A) are still unclear in HNSCC. In this study, we demonstrated that a specific circRNA, circCUX1, was upregulated in HPSCC patients who are resistant to radiotherapy and predicts poor survival outcome. We further found that methyltransferase like 3 (METTL3) mediated the m6A methylation of circCUX1 and stabilizes its expression. Knockdown circCUX1 promotes the sensitivity of hypopharyngeal cancer cells to radiotherapy. In addition, circCUX1 binds to Caspase1 and inhibits its expression, resulting in a decrease in the release of inflammatory factors, thereby developing tolerance to radiotherapy. Our findings indicate that circCUX1 is a potential therapeutic target for radiotherapy tolerance in HPSCC patients.

PurposeRecent neuroimaging reports have shown the microstructural changes in coronary artery disease (CAD) and its correlation with cognitive dysfunction while little is known about the functional characteristics of CAD. We hypothesize that functional characteristics may give clues to underlying pathology in CAD and its link with cognitive dysfunction. Degree centrality (DC), a graph-based assessment of network organization was performed to explore the neural connectivity changes in CAD patients compared with healthy controls and their correlation with cognitive measures.MethodsThirty CAD patients and 36 healthy controls were included in our study. All participants underwent functional magnetic resonance imaging (fMRI) of the brain. We performed DC analysis to identify voxels that showed changes in whole-brain functional connectivity with other voxels. DC was measured by the fMRI graph method and comparisons between the two groups were done. All participants underwent neuropsychological assessment (Montreal Cognitive Assessment, MoCA and Mini-Mental State Examination, MMSE).ResultsOur data analysis included 30 CAD patients (59.90 ± 7.53 years) and 36 HCs (61.61 ± 6.19 years). CAD patients showed a greater prevalence of white matter lesions using the Fazekas score than healthy controls (P < 0.001). Importantly, CAD patients showed significantly lower (P < 0.001) MoCA and MMSE scores compared with healthy controls. CAD patients showed significantly decreased DC value (P < 0.001) in the right hippocampus (hippocampus_R), right lingual gyrus (lingual_R), and significantly increased DC value (P < 0.001) in the left middle frontal gyrus (Frontal_Mid_L) when compared with healthy controls respectively. DC value in the hippocampus_R significantly correlated (P < 0.00) with MMSE and MoCA scores in CAD patients. Fazekas scores in CAD patients showed a significant correlation (P < 0.001) with the DC value in the hippocampus_R.ConclusionThese findings suggest that reduced cerebral neural connectivity in CAD may contribute to their cognitive impairment and white matter microstructural damage.

Study Region: ChinaStudy Focus: In sponge city construction in China, rainfall data is the key to determining the rainwater source control scale. However, changes in rainfall characteristics can reduce the representativeness of the data. To obtain stable volume capture ratios (VCRs) of the annual rainfall through as little rainfall data as possible, this paper compares daily rainfall data from 159 cities in China in recent decades and finds the minimum number of years required to maintain stable VCRs under different design rainfalls. Finally, it gives the recommended observation periods (OPs) for different regions. New Hydrological Insights for the Region: For the last 35 years, the variation of daily rainfall in 75% of cities in China has been less than 0.66 mm, and the variation of skewness is less than 0.62. In general, the longer the observation period, the smaller the calculated VCR anomaly value and variation gradient will be. About 10% of cities cannot achieve stable VCRs by increasing the number of years of data. The denser urban distributed area needs larger OPs to determine the stable VCR; with the increase in design rainfall, the required OPs will also be lowered; in most cities, the VCR calculation does not require 30 years of rainfall data.

For a given source-destination pair in multi-hop underwater acoustic sensor networks (UASNs), an optimal route is the one with the lowest energy consumptions that usually consists of the same relay nodes even under different transmission tasks. However, this will lead to the unbalanced payload of the relay nodes in the multi-hop UASNs and accelerate the loss of the working ability for the entire system. In this paper, we propose a node payload balanced ant colony optimal cooperative routing (PB-ACR) protocol for multi-hop UASNs, through combining the ant colony algorithm and cooperative transmission. The proposed PB-ACR protocol is a relay node energy consumption balanced scheme, which considers both data priority and residual energy of each relay node, aiming to reduce the occurrence of energy holes and thereby prolong the lifetime of the entire UASNs. We compare the proposed PB-ACR protocol with the existing ant colony algorithm routing (ACAR) protocol to verify its performances in multi-hop UASNs, in terms of network throughput, energy consumption, and algorithm complexity. The simulation results show that the proposed PB-ACR protocol can effectively balance the energy consumption of underwater sensor nodes and hence prolong the network lifetime.

Lacking of forest hardening makes low ductility in steels long challenge particularly at high yield strength. Here, we report to make good use of hetero-structuring for superior strength-ductility synergy in high manganese steel. The point is to retain original deformed structure of large quantity, along with recrystallized ultrafine-grains and fine grains, jointly for most effective heterogeneous deformation-induced (HDI) hardening especially at high strength. The residual plastic strain of larger than 0.2% and large proportion of HDI stress over 60% indicate the crucial role in ductility by HDI hardening. This renders a significantly upgraded strength-ductility combination within high strength scope.

Background: Extracorporeal membrane oxygenation (ECMO) is used as temporary cardiorespiratory support in patients with critical ailments, but very little is known about the functional cerebral changes in ECMO survivors. Degree centrality (DC), a graph-based assessment of network organization, was performed to explore the neural connectivity changes in ECMO survivors compared to controls and their correlation with cognitive and neurological measures. Methods: This exploratory observational study was conducted from August 2020 to May 2022. ECMO survivors and controls underwent functional magnetic resonance imaging (fMRI) of the brain. We performed DC analysis to identify voxels that showed changes in whole-brain functional connectivity with other voxels. DC was measured by the fMRI graph method and comparisons between the two groups were performed. All participants underwent neuropsychological assessment (Montreal Cognitive Assessment, MoCA). Blood serum neuron-specific enolase and the Glasgow Coma Scale (GCS) were assessed in ECMO survivors. Results: DC values in the right insula and right precuneus gyrus were lower in ECMO survivors and higher in the right medial superior frontal gyrus compared to controls (all p < 0.001). Decreased connectivity in the right insular and right precuneus gyrus correlated with total MoCA scores, delayed recollection, and calculation (all p < 0.05). Increased serum NSE levels, GCS score, and GCS–motor response correlated with decreased connectivity in the right insular and right precuneus gyrus and increased connectivity in the right medial superior frontal gyrus (all p < 0.05). Conclusions: We showed that both functional impairment and adaptation were observed in survivors of ECMO, suggesting that neural connectivity changes may provide insights into the mechanisms that may potentially link ECMO survivors to neurological and cognitive disorders.

The microstructure evolution and mechanical properties of air-hardening steel subjected to different austenitizing annealing treatments were investigated in this study and, especially, the precipitation behavior of the steel was analyzed, as well as the strengthening mechanism of the steel was elucidated on the basis of systematic microstructural characterization. Results reveal that a ferrite + martensite dual-phase structure with about 700 MPa tensile strength and 20% elongation can be obtained by austenitizing the experimental steel in the range of 750∼800 °C; while austenitizing between 850 °C and 950 °C results in granular bainite + lath bainite with about 950 MPa tensile strength and 12% elongation. The experimental steel has the highest strength after austenitizing at 900 °C with lots of nano-scale (Ti, Mo, V)C particles distributed in its matrix. Quantitative calculation results illustrate that the main strengthening factors are grain refinement strengthening, dislocation strengthening and precipitation strengthening. In addition, due to the potential interaction effect between different strengthening factors, a modified strengthening model is proposed to describe the strengthening behavior of the air-hardening steel when it is heat-treated in the two-phase region.

Background: Pancreatic enzyme elevation has been reported in patients with COVID-19 during the pandemic. However, with the shortage of medical resources and information, several challenges are faced in the examination and treatment of this condition in COVID-19 patients. There is little information on whether such condition is caused by pancreatic injury, and if this is a warning sign of life threatening complications like multiple organ failure in patients. The objective of this study is to explore the relationship between elevated pancreatic enzymes and the underlying risk factors during the management of COVID-19 patients.Method: A total of 55 COVID-19 patients admitted to the intensive care unit (ICU) of Wuhan Jinyintan hospital from January 1 to March 30, 2020 were enrolled in this study. All participants underwent transabdominal ultrasound imaging to assess their pancreas.Results: Out of the 55 patients, three patients had pancreatitis, 29 (52.7%) with elevated pancreatic enzymes, and 23 (41.8%) without. The most common symptoms of patients with COVID-19 were fever and cough. There was no statistical difference in most baseline characteristics except myalgia on admission. Compared with those having normal enzyme levels, patients with elevated pancreatic enzymes had higher rates of mortality (79.3 vs. 52.2%; P = 0.038), and lower rates of discharge (20.7 vs. 47.8%; P = 0.038). Patients with elevated enzymes had higher incidence of mechanical ventilation (P = 0.004) and kidney injury (P = 0.042) than patients without elevated pancreatic enzymes. The results of multivariable logistic analysis showed that the odds ratio were 10.202 (P = 0.002) for mechanical ventilation and 7.673 (P = 0.014) for kidney injury with the elevated enzymes vs. the normal conditions.Conclusions: The findings show that the incidences of pancreatic enzymes elevation are not low in critical COVID-19 patients and only a few of them progressed to acute pancreatitis (AP). Increased pancreatic enzymes levels is associated with poor prognosis in COVID-19 patients. In addition, the kidney injury and oxygenation degradation are associated with the pancreatic enzymes elevation in COVID-19 patients.

The improved particle filter (PF) based on the geometric center and likelihood estimation is proposed in order to solve the problem of particle dilution and degradation. In the resampling stage, the geometric center is used to resample the particles. The particles are filtered according to the distance between the particles and the geometric center, and then the particles are resampled. The resampled particles are composed of newborn particles and non-resampled particles. The former can help alleviate the degradation problem, while the latter can keep the diversity of the particle set. In order to ensure effectiveness of the PF, the positioning error threshold of the particle filter is introduced. In the phase of particle weighting calculation, in view of the problem of low accuracy and divergence of PF state estimation caused by non-stationary and non-Gaussian noise, it adopts non-Gaussian noise parameter estimation based on likelihood to approximately estimate the measurement noise instead of the Gaussian density function. The proposed model is applied to particle weight calculation to avoid particle degradation caused by Gaussian density function approximation. The simulation results show that, after the improved algorithm, the root-mean-square error is reduced to 0.085, the variance is reduced to 0.014, and the running time is shortened by 14.8% compared with the polynomial resampling algorithm, which can effectively alleviate particle degradation and dilution in the traditional PF algorithm, and the positioning accuracy is also improved.

Abstract Sentiment analysis is recognized as one of the most important sub-areas in Natural Language Processing (NLP) research, where understanding implicit or explicit sentiments expressed in social media contents is valuable to customers, business owners, and other stakeholders. Researchers have recognized that the generic sentiments extracted from the textual contents are inadequate, thus, Aspect Based Sentiment Analysis (ABSA) was coined to capture aspect sentiments expressed toward specific review aspects. Existing ABSA methods not only treat the analytical problem as single-label classification that requires a fairly large amount of labelled data for model training purposes, but also underestimate the entity aspects that are independent of certain sentiments. In this study, we propose a transfer learning based approach tackling the aforementioned shortcomings of existing ABSA methods. Firstly, the proposed approach extends the ABSA methods with multi-label classification capabilities. Secondly, we propose an advanced sentiment analysis method, namely Aspect Enhanced Sentiment Analysis (AESA) to classify text into sentiment classes with consideration of the entity aspects. Thirdly, we extend two state-of-the-art transfer learning models as the analytical vehicles of multi-label ABSA and AESA tasks. We design an experiment that includes data from different domains to extensively evaluate the proposed approach. The empirical results undoubtedly exhibit that the proposed approach outperform all the baseline approaches.

Abstract Thermal stratification in reservoirs is a critical process that regulates downstream riverine energy and biogeochemical cycling. Current stratification models either simplify vertical energy process, reservoir geometry or neglecting the effects of reservoir operation. Here we present a new multilayer reservoir stratification model that can be applied for reservoir and stream temperature simulation at regional or global scale. With a multilayer vertical discretization, we introduce a newly developed storage‐area‐depth dataset to improve parameterization of advection processes in and out of the reservoir. The new model better represents vertical temperature gradient and subsequently temperature of water released to downstream. The stratification model is applied to 1,400 reservoirs over the contiguous United States and validated against observed surface, profile, and outflow temperature data over 130 reservoirs subjected to various levels of regulation. The Nash‐Sutcliffe values are higher than 0.5 for about 77% of the validated reservoirs using surface temperature while the average values of root mean square error and bias are 3.6 °C and −1.1 °C, respectively. Using the new reservoir storage‐area‐depth dataset improves the simulation of surface temperature at over 69% of the validated reservoirs compared to using simplified reservoir geometry. The reservoir stratification model contributes to improving predictive understanding of anthropogenic impact on terrestrial hydrological, ecological, and biogeochemical cycles.

Abstract Background There is growing literature suggesting a link between vitamin D and asthma lung function, but the results from systematic reviews are conflicting. We conducted this meta-analysis to investigate the relation between serum vitamin D and lung function in asthma patients. Methods Major databases, including OVID, MEDLINE, Web of Science and PUBMED, were searched until 10th October 2018. All published observational studies related to vitamin D and asthma were extracted. All meta-analyses were performed using Review Manager 5.3.5. Results This quantitative synthesis found that asthma patients with low vitamin D levels had lower forced expiratory volume In 1 s (FEV1) (mean difference (MD) = − 0.1, 95% CI = − 0.11 to − 0.08,p

Abstract Background Lymphedema is the most common complication after breast cancer treatment, but management of lymphedema remains a clinical challenge. Several studies have reported the beneficial effect of acupuncture for treating breast cancer-related lymphedema (BCRL). Our objective is to verify the effectiveness of warm acupuncture on BCRL and compare the effectiveness of a local distribution acupoint combination with a local-distal acupoint combination for BCRL. Methods This is a study protocol for a multicenter, three-arm parallel, assessor blinded, randomized controlled trial. A total of 108 participants diagnosed as BCRL will be randomly allocated in equal proportions to a local distribution acupoint (LA) group, a local-distal acupoint (LDA) group, or a waiting-list (WL) group. The LA and LDA groups will receive 20 acupuncture treatment over 8 weeks with local distribution acupoint combination and local-distal acupoint combination, respectively. The WL group will receive acupuncture treatment after the study is concluded. The primary outcome is the mean change in inter-limb circumference difference from baseline to week 8. The secondary outcomes include volume measurement, skin hardness, common terminology criteria for adverse events 4.03 (edema limbs criteria), stages of lymphedema from the International Society of Lymphology, Disabilities of the Arm, Shoulder and Hand questionnaire, and the Medical Outcome Study 36-item Short-form Health Survey. Discussion This study aims to provide data on warm acupuncture as an effective treatment for BCRL and at the same time compare the effectiveness of different acupoint combinations. Trial registration ClinicalTrials.gov: Identifier NCT03373474. Registered on 14th December 2017.

Abstract Like how useful weather forecasting is, the capability of forecasting or predicting cyber threats can never be overestimated. Previous investigations show that cyber attack data exhibits interesting phenomena, such as long-range dependence and high nonlinearity, which impose a particular challenge on modeling and predicting cyber attack rates. Deviating from the statistical approach that is utilized in the literature, in this paper we develop a deep learning framework by utilizing the bi-directional recurrent neural networks with long short-term memory, dubbed BRNN-LSTM. Empirical study shows that BRNN-LSTM achieves a significantly higher prediction accuracy when compared with the statistical approach.

Algebraic persistent fault analysis (APFA), which combines algebraic analysis with persistent fault attacks, brings new challenges to the security of lightweight block ciphers and has received widespread attention since its introduction. Threshold Implementation (TI) is one of the most widely used countermeasures for side channel attacks. Inspired by this method, the SKINNY block cipher adopts the S_box decomposition to reduce the number of variables in the set of algebraic equations and the number of Conjunctive Normal Form (CNF) equations in this paper, thus speeding up the algebraic persistent fault analysis and reducing the number of fault ciphertexts. In our study, we firstly establish algebraic equations for full-round faulty encryption, and then analyze the relationship between the number of fault ciphertexts required and the solving time in different scenarios (decomposed S_boxes and original S_box). By comparing the two sets of experimental results, the success rate and the efficiency of the attack are greatly improved by using S_box decomposition. In this paper, We can recover the master key in a minimum of 2000s using 11 pairs of plaintext and fault ciphertext, while the key recovery cannot be done in effective time using the original S_box expression equations. At the same time, we apply S_box decomposition to another kind of algebraic persistent fault analysis, and the experimental results show that using S_box decomposition can effectively reduce the solving time and solving success rate under the same conditions.

Snowmelt contributions to streamflow in mid-latitude mountain basins typically dominate other runoff sources on annual and seasonal timescales. Future increases in temperature and changes in precipitation will affect both snow accumulation and seasonal runoff timing and magnitude, but the underlying and fundamental roles of mountain basin geometry and hypsometry on snowmelt sensitivity have received little attention. To investigate the role of basin geometry in snowmelt sensitivity, a linear snow accumulation model and the Cold Regions Hydrological Modeling (CRHM) platform driven are used to estimate how hypsometry affects basin-wide snow volumes and snowmelt runoff. Area-elevation distributions for fifty basins in western Canada were extracted, normalized according to their elevation statistics, and classified into three clusters that represent top-heavy, middle, and bottom-heavy basins. Prescribed changes in air temperature alter both the snow accumulation gradient and the total snowmelt energy, leading to snowpack volume reductions (10–40%), earlier melt onsets (1–4 weeks) and end of melt season (3 weeks), increases in early spring melt rates and reductions in seasonal areal melt rates (up to 50%). Basin hypsometry controls the magnitude of the basin response. The most sensitive basins are bottom-heavy, and have a greater proportion of their area at low elevations. The least sensitive basins are top-heavy, and have a greater proportion of their area at high elevations. Basins with similar proportional areas at high and low elevations fall in between the others in terms of sensitivity and other metrics. This work provides context for anticipating the impacts of ongoing hydrological change due to climate change, and provides guidance for both monitoring networks and distributed modeling efforts.

Although side-channel attacks based on deep learning are widely used in AES encryption algorithms, there is little research on lightweight algorithms. Lightweight algorithms have fewer nonlinear operations, so it is more difficult to attack successfully. Taking SPECK, a typical lightweight encryption algorithm, as an example, directly selecting the initial key as the label can only crack the first 16-bit key. In this regard, we evaluate the leakage of SPECK’s operations (modular addition, XOR, shift), and finally select the result of XOR operation as the label, and successfully recover the last 48-bit key. Usually, the divide and conquer method often used in side-channel attacks not only needs to train multiple models, but also the different bytes of the key are regarded as unrelated individuals. Through the visualization method, we found that different key bytes overlap in the position of the complete electromagnetic leakage signal. That is, when SPECK generates a round key, there is a connection between different bytes of the key. In this regard, we propose a transfer learning method for different byte keys. This method can take advantage of the similarity of key bytes, improve the performance starting-point of the model, and reduce the convergence time of the model by 50%.

Laryngeal squamous cell carcinoma (LSCC) arises from the squamous epithelium of the larynx and is associated with a high incidence of cervical lymph node metastasis. MicroRNAs (miRNAs) play a crucial role in the epigenetic regulation of cellular biological processes, including cancer metastasis. However, the molecular mechanisms of specific miRNAs responsible for LSCC metastasis and their clinical significance have yet to be fully elucidated. In this study, LSCC cohort datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were downloaded and examined by comprehensive bioinformatics analysis, which revealed that upregulation of mRNA SERPINE1 and downregulation of miR-181c-5p were associated with unfavorable overall survival. Our analysis showed that SERPINE1 expression negatively correlated with the expression level of miR-181c-5p in our LSCC patient samples. Silencing of miR-181c-5p expression promoted cell migration and invasion in cell lines, whereas the overexpression of miR-181c-5p suppressed cell migration and epithelial-to-mesenchymal transition (EMT) through the downregulation of SERPINE1. Further analysis showed that the enhancement effect on EMT and metastasis induced by silencing miR-181c-5p could be rescued through knockdown of SERPINE1 expression in vitro. Collectively, our findings indicated that miR-181c-5p acted as an EMT suppressor miRNA by downregulation of SERPINE1 in LSCC and offers novel strategies for the prevention of metastasis in LSCC.