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To meet the challenges of energy sustainability, the integrated energy system (IES) has become a key component in promoting the development of innovative energy systems. Accurate and reliable multivariate load prediction is a prerequisite for IES optimal scheduling and steady running, but the uncertainty of load fluctuation and many influencing factors increase the difficulty of forecasting. Therefore, this article puts forward a multi-energy load prediction approach of the IES, which combines the fennec fox optimization algorithm (FFA) and hybrid kernel extreme learning machine. Firstly, the comprehensive weight method is used to combine the entropy weight method and Pearson correlation coefficient, fully considering the information content and correlation, selecting the key factors affecting the prediction, and ensuring that the input features can effectively modify the prediction results. Secondly, the coupling relationship between the multi-energy load is learned and predicted using the hybrid kernel extreme learning machine. At the same time, the FFA is used for parameter optimization, which reduces the randomness of parameter setting. Finally, the approach is utilized for the measured data at Arizona State University to verify its effectiveness in multi-energy load forecasting. The results indicate that the mean absolute error (MAE) of the proposed method is 0.0959, 0.3103 and 0.0443, respectively. The root mean square error (RMSE) is 0.1378, 0.3848 and 0.0578, respectively. The weighted mean absolute percentage error (WMAPE) is only 1.915%. Compared to other models, this model has a higher accuracy, with the maximum reductions on MAE, RMSE and WMAPE of 0.3833, 0.491 and 2.8138%, respectively.

Abstract Background Lateral lymph node metastasis (LLNM) is frequent in papillary thyroid carcinoma (PTC) and is associated with a poor prognosis. This study aimed to developed a clinical-ultrasound (Clin-US) nomogram to predict LLNM in patients with PTC. Methods In total, 2612 PTC patients from two hospitals (H1: 1732 patients in the training cohort and 578 patients in the internal testing cohort; H2: 302 patients in the external testing cohort) were retrospectively enrolled. The associations between LLNM and preoperative clinical and sonographic characteristics were evaluated by the univariable and multivariable logistic regression analysis. The Clin-US nomogram was built basing on multivariate logistic regression analysis. The predicting performance of Clin-US nomogram was evaluated by calibration, discrimination and clinical usefulness. Results The age, gender, maximum diameter of tumor (tumor size), tumor position, internal echo, microcalcification, vascularization, mulifocality, and ratio of abutment/perimeter (A/P) > 0.25 were independently associated with LLNM metastatic status. In the multivariate analysis, gender, tumor size, mulifocality, position, microcacification, and A/P > 0.25 were independent correlative factors. Comparing the Clin-US nomogram and US features, Clin-US nomogram had the highest AUC both in the training cohort and testing cohorts. The Clin‑US model revealed good discrimination between PTC with LLNM and without LLNM in the training cohort (AUC = 0.813), internal testing cohort (AUC = 0.815) and external testing cohort (AUC = 0.870). Conclusion Our findings suggest that the ClinUS nomogram we newly developed can effectively predict LLNM in PTC patients and could help clinicians choose appropriate surgical procedures.

A new class of fuzzy constrained multi-objective games with fuzzy payoffs (FCMGFPs) is considered in this paper. First, Berge's maximum theorem for the fuzzy-vector-valued function is obtained. Based on this theorem and the Fan-Glicksberg fixed point theorem, the existence theorem of the fuzzy Pareto-Nash equilibrium for the FCMGFP is established. Second, the abstract rationality function for the FCMGFP is given by using a nonlinear scalarization function of interval vectors. Finally, a series of results, such as structural stability ($ (\gamma, \epsilon) $-stability) and robustness to $ \epsilon $-equilibrium ($ (\gamma, \epsilon) $-robustness), are obtained.

The mechanical LiDAR sensor is crucial in autonomous vehicles. After projecting a 3D point cloud onto a 2D plane and employing a deep learning model for computation, accurate environmental perception information can be supplied to autonomous vehicles. Nevertheless, the vertical angular resolution of inexpensive multi-beam LiDAR is limited, constraining the perceptual and mobility range of mobile entities. To address this problem, we propose a point cloud super-resolution model in this paper. This model enhances the density of sparse point clouds acquired by LiDAR, consequently offering more precise environmental information for autonomous vehicles. Firstly, we collect two datasets for point cloud super-resolution, encompassing CARLA32-128in simulated environments and Ruby32-128 in real-world scenarios. Secondly, we propose a novel temporal and spatial feature-enhanced point cloud super-resolution model. This model leverages temporal feature attention aggregation modules and spatial feature enhancement modules to fully exploit point cloud features from adjacent timestamps, enhancing super-resolution accuracy. Ultimately, we validate the effectiveness of the proposed method through comparison experiments, ablation studies, and qualitative visualization experiments conducted on the CARLA32-128 and Ruby32-128 datasets. Notably, our method achieves a PSNR of 27.52 on CARLA32-128 and a PSNR of 24.82 on Ruby32-128, both of which are better than previous methods.

Based on our previous research, this paper further discusses the multi-objective bi-matrix game with fuzzy payoffs (MBGFP), which is a special case of the fuzzy constrained multi-objective game with fuzzy payoffs. We first prove that any bi-matrix game with interval payoffs (BGIP) has at least one Pareto–Nash equilibrium. Then, with the help of BGIP, we obtain the necessary and sufficient conditions for the existence of fuzzy Pareto–Nash equilibrium of MBGFP. Secondly, based on the bilinear programming method for calculating Nash equilibrium in crisp bi-matrix games, we established a bilinear programming method with parameters for calculating fuzzy Pareto–Nash equilibrium. By considering the importance of each objective to the players, MBGFP is transformed into a bi-matrix game with fuzzy payoffs (BGFP). Furthermore, we obtained the necessary and sufficient conditions for the existence of fuzzy weighted Pareto–Nash equilibrium and its calculation method. Finally, a practical example is used to illustrate the effectiveness of our proposed calculation method.

Knee osteoarthritis (Knee OA) is a degenerative disease that often perplexes the elderly and the whole society, and its timely recognition receives interest worldwide. However, traditional imaging examinations cannot reflect dynamic function nor implement long-term monitoring. To address this issue, this article suggests a piezoresistive-based gait monitoring method to recognize patients with Knee OA by assessing the plantar pressure signals during the subjects’ walking, which is mobile, wearable, low-cost, and convenient. Eighteen subjects diagnosed with Knee OA and twenty-two control subjects participated in the experiment. Considering the asymmetric pressure distribution in feet and the landing habits of Knee OA patients, the plantar surface was split into eight areas, calculating the contact time and maximum force of each area in a gait cycle. Using these characteristics to train, the support vector machine (SVM) reached an accuracy of 93.15%, a precision of 92.39%, and a recall of 92.79%. Furthermore, a prediction model was proposed for the application that aggregates all the results in one test and gives a more accurate result, and the classification accuracy for individuals in the ensemble model is 90.90%. Our technique fills the vacancy of the recognition of patients with Knee OA based on wearable instruments. It provides ideas for intelligent healthcare, which benefits potential Knee OA patients’ early diagnosis and treatment.

In the first half of the 20th century, 5 classic articles were written by 3 outstanding scholars, namely, Wiener (1894 to 1964), the father of cybernetics, Schrödinger (1887 to 1961), the father of quantum mechanics, and Turing (1912 to 1954), the father of artificial intelligence. The articles discuss the concepts such as computability, life, machine, control, and artificial intelligence, establishing a solid foundation for the intelligence of machines (how can machines recognize as humans do?) and its future development.

Abstract Consciousness is one of the unique features of creatures, and is also the root of biological intelligence. Up to now, all machines and robots haven’t had consciousness. Then, will the artificial intelligence (AI) be conscious? Will robots have real intelligence without consciousness? The most primitive consciousness is the perception and expression of self‐existence. In order to perceive the existence of the concept of ‘I’, a creature must first have a perceivable boundary such as skin to separate ‘I’ from ‘non‐I’. For robots, to have the self‐awareness, they also need to be wrapped by a similar sensory membrane. Nowadays, as intelligent tools, AI systems should also be regarded as the external extension of human intelligence. These tools are unconscious. The development of AI shows that intelligence can exist without consciousness. When human beings enter into the era of life intelligence from AI, it is not the AI became conscious, but that conscious lives will have strong AI. Therefore, it becomes more necessary to be careful on applying AI to living creatures, even to those lower‐level animals with only consciousness. The subversive revolution of such application may produce more careful thinking.

Thermal batteries are high-temperature lithium metal batteries. The thermal stability and the specific capacity of cathode materials are key factors to its application. In this case, NiO with high decomposition temperature and theoretical capacity was constructed as a thermal battery cathode. At 500℃, the specific capacity of NiO cathode was about 642 mAh g−1 with a large current density of 0.1 A cm−2 and a cut-off voltage of 1.0 V. This capacity reached 89.4% of the theoretical value at those harsh discharge conditions. As the discharge temperature increased to 700℃, an obvious discharge voltage platform of 1.5 V and a specific capacity of 243 mAh g−1 were still observed for NiO cathodes. While thermal batteries using other common cathode materials cannot discharge efficiently at 700 °C. The XRD and TEM indicated that the high-temperature reaction of the NiO cathode was a one-step reaction: NiO + 2Li++2e-→ Ni + Li2O. This ultra-high temperature discharge ability suggests the NiO cathode can be very promising in high-capacity and long-life thermal battery applications.

The type, model, quantity, and location of sensors installed on the intelligent vehicle test platform are different, resulting in different sensor information processing modules. The driving map used in intelligent vehicle test platform has no uniform standard, which leads to different granularity of driving map information. The sensor information processing module is directly associated with the driving map information and decision-making module, which leads to the interface of intelligent driving system software module has no uniform standard. Based on the software and hardware architecture of intelligent vehicle, the sensor information and driving map information are processed by using the formal language of driving cognition to form a driving situation graph cluster and output to a decision-making module, and the output result of the decision-making module is shown as a cognitive arrow cluster, so that the whole process of intelligent driving from perception to decision-making is completed. The formalization of driving cognition reduces the influence of sensor type, model, quantity, and location on the whole software architecture, which makes the software architecture portable on different intelligent driving hardware platforms. Keywords: Driving brain, Intelligent driving, Hardware platform framework

Acetaminophen (APAP) overdose causes acute liver injury and leads to fatal liver damage. However, the therapies are quite limited. Shikonin is a natural product with antioxidant and anti-inflammatory activities. In the present study, the hepatoprotective effects and the underlying mechanisms of shikonin in APAP-induced hepatotoxicity in vivo and in vitro were investigated. APAP-induced acute liver injury and shikonin pretreatment models were established in vivo and in vitro, as evidenced by serum hepatic enzymes, histological changes, oxidative stress indicators and proinflammatory cytokines. The results revealed that shikonin pretreatment prevented the elevation of serum alanine transaminase (ALT), aspartate transaminase (AST) and lactate dehydrogenase (LDH) levels and markedly reduced APAP-induced histological alterations in liver tissues. Additionally, shikonin restored superoxide dismutase (SOD) expression and glutathione (GSH) content in line with the blockade of oxidative stress. The changes in gene expression involved in oxidative stress including methionine sulfoxide reductase (such as MsrA and MsrB1), heme oxygenase-1 (HO-1), SOD2 and cytochrome P450 2E1 (CYP2E1), were markedly reversed after shikonin therapy. Furthermore, shikonin markedly attenuated the APAP-induced production of tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and interleukin-1β (IL-1β) and suppressed the expression of genes related to inflammation. In AML-12 cells, shikonin pretreatment decreased APAP-induced cytotoxicity as measured by CCK-8 assay and LDH release. The changes in gene expression involved in oxidative stress and the inflammatory response were consistent with those in mouse livers. This study indicated that shikonin attenuated APAP-induced acute liver injury via inhibiting oxidative stress and inflammatory responses in vivo and in vitro. These findings offer new insights into the potential therapy for APAP hepatotoxicity.

Radar is an important sensor to realise intelligent driving environment perception, enabling the detection of static obstacles and dynamic obstacles, and the tracking of a dynamic obstacle. The models, quantities, and installing location of the platform radar sensors as well as the information processing modules differ from each other on different intelligent driving testing platforms, resulting in different quantities and interfaces on the intelligent driving system. Here, the authors build the software architecture of intelligent driving vehicle based on driving brain which is used to adapt to different types of radar sensors and use the variable granularity road ownership radar for radar information fusion. Under the condition of complete driving information, increasing or reducing the number of radar sensors and changing the radar sensor model or installing location will not affect the intelligent driving decision directly. Therefore, the authors meet the demands of multi-radar sensor adapting to different intelligent driving hardware testing platforms.

The camera is one of the important sensors to realise the intelligent driving environment. It can realise lane detection and tracking, obstacle detection, traffic sign detection, identification and discrimination and visual simultaneous localisation and mapping. The visual sensor model, quantity and installation location are different on different intelligent driving hardware experimental platform as well as the visual sensor information processing module, thus a number of intelligent driving system software modules and interfaces are different. In this study, the software architecture of the autonomous vehicle based on the driving brain is used to adapt to different types of visual sensors. The target segment is extracted by the image segmentation algorithm, and then the segmentation of the region of interest is carried out. According to the input feature calculation results, the obstacle search is done in the second segmentation region, the output of the accessible road area. As driving information is complete, the authors will increase or reduce one or more visual sensors, change the visual sensor model or installation location, which will no longer directly affect the intelligent driving decision, they make the multi-vision sensors adapted to the requirements of different intelligent driving hardware test platforms.

Silver thin films were prepared by direct current (DC) magnetron sputtering technique in Ar/N2 atmosphere with various N2 volumetric ratios on Si substrates. Silver thin films prepared in pure Ar atmosphere show highly (111) preferred orientation. However, as the N2 content increases, Ag (111) preferred orientation evolves into (100) preferred orientation gradually. When N2 content is more than 12.5 vol.%, silver thin films exhibit highly (100) preferred orientation. Moreover, the average grain size decreases with increasing N2 content. Silver thin films with low relative density are prepared at high N2 content, which results in higher resistivity of films. By analyzing the resistivity and microstructures of silver thin films, the optimum range of N2 content to get compact silver thin films is found to be not more than 33.3 vol.%. Finally, the mechanism of N2 addition on microstructure evolution of silver thin films was proposed.

Silver-coated molybdenum is an optimum material selection to replace pure silver as solar cell interconnector. However, the low adhesive strength between Ag films and Mo substrate hinders the application of the interconnector, because it is difficult to form metallurgical bonding or compound in the film/substrate interface using conventional deposition. In order to improve the adhesion, some Ag particles were implanted into the surface of Mo substrate by ion beam-assisted deposition (IBAD) before the Ag films were deposited by magnetron sputtering deposition (MD). The objective of this work was to investigate the effect of different assisted ion beam energy on the film/substrate adhesive properties. In addition, the fundamental adhesion mechanism was illustrated. The results revealed that the adhesion between Ag films and Mo substrate could be greatly enhanced by IBAD. With the increase of the assisting ion beam energy, the adhesive strength first increased and then decreased, with the optimum adhesion being able to rise to 25.29 MPa when the energy of the assisting ion beam was 30 keV. It could be inferred that the combination of “intermixing layer” and “implanted layer” formed by the high-energy ion bombardment was the key to enhancing the adhesion between Ag films and Mo substrate effectively.

Reduced graphene oxide (RGO) composite copper matrix powders were fabricated successfully by using a modified molecular-level mixing (MLM) method. Divalent copper ions (Cu2+) were adsorbed in oxygen functional groups of graphene oxide (GO) as a precursor, then were reduced simultaneously by one step chemical reduction. RGO showed a distribution converting from a random to a three-dimensional network in the copper matrix when its content increased to above 1.0 wt.% The tribological tests indicated that the friction coefficient of the composite with 1.0 wt.% RGO decreased markedly from 0.6 to 0.07 at an applied load of 10 N, and the wear rate was about one-third of pure copper. The excellent tribological properties were attributed to a three-dimensional and uniform distribution, which contributes to improving toughness and adhesion strength.