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The bio-corrosion behavior and bio-compatibility of TiNbZrTa and TiNbZrTaMo high entropy alloys (HEAs) are crucial for their efficient maintenance during biological implantation. In this work, TiNbZrTa and TiNbZrTaMo HEAs were successfully prepared via vacuum melting, with a β-type titanium alloy Ti35Nb7Zr5Ta, exhibiting good biocompatibility used as a comparison. Due to the high entropy effect of equal atomic ratios, both TiNbZrTa and TiNbZrTaMo HEAs exhibit body-centered cubic (BCC) structure and lattice distortion compared to the β titanium alloy Ti35Nb7Zr5Ta. Mo elements contribute to the formation of a new BCC phase, resulting in a white matrix rich in Ta and Mo, and a dark second phase rich in Ti, Zr, and Nb. Electrochemical test results at 37 °C shown that TiNbZrTa has a wider and more stable passivation area than TiNbZrTaMo. The XPS results shown that formation of the passive film is related to the main elements added. Ti, Nb, Ta and Mo formed oxides via the solid-liquid interface and migrate inward, while Zr is formed at the passivation film/metal substrate interface and migrates outward, finally forming the layered structure of the passivation film. Additionally, the cytotoxicity test of mouse fiber cells was carried out to evaluate the biocompatibility of the HEAs. The results shown that the cell proliferation rate of TiNbZrTa and TiNbZrTaMo HEAs reached 0 and 1, respectively, with TiNbZrTa exhibiting better biocompatibility, as adding Mo reduced the cell proliferation rate. These findings may provide a method for predicting the bio-corrosion behavior and bio-compatibility of HEAs used for biological implantation.

The semiconductor silicide β-FeSi2has garnered significant attention due to its potential applications across photovoltaics, optoelectronics, and thermoelectric devices. The emergence of ε-FeSi during the synthesis of β-FeSi2is a common phenomenon. The intricacies of the crystal structure and chemical bonds of β-FeSi2and ε-FeSi are imperative for understanding their physical properties, yet these characteristics have not been thoroughly investigated. This study presents an in-depth examination of the temperature-dependent evolution of the crystal structures and chemical bonding of β-FeSi2and ε-FeSi, utilizing high-resolution synchrotron powder X-ray diffraction and density functional theory calculations with Bader’s quantum theory of atoms in molecules. The research spans a temperature range from 117.2 to 771.2 K, during which the crystal structures exhibit significant stability with no evidence of structural collapse or phase transitions. In terms of chemical bonding, the Si–Si bonds in β-FeSi2are typically covalent, while the Fe–Si bonds display a polar covalent character with partial ionicity. ε-FeSi exhibits a similar bonding motif with Fe–Si bonds being either covalent or polar covalent. In addition, large Debye temperatures and a marginal weakening of bond strength with rising temperatures elucidate the strong bonding interactions as an origin of good thermal stability.

Simultaneous localization and mapping (SLAM) has been indispensable for autonomous driving vehicles. Since the visual images are vulnerable to light interference and the light detection and ranging (LiDAR) heavily depends on geometric features of the surrounding scene, only relying on a camera or LiDAR show limitations in challenging environment. This article proposes a Visual-LiDAR-IMU fusion method for high precision and robust vehicle localization. In the front end, the LiDAR point cloud is used to obtain the depth information of visual features with the synchronized IMU measurements are input into the pose estimation module in a loose-coupled manner. In the back end, two critical strategies are proposed to reduce the computation amount of the algorithm. Where the balanced selection strategy is based on keyframe and sliding window algorithms, and the classification optimization strategy is based on feature points and pose estimation assistance. In addition, an improved loop detection algorithm based on Iterative Closest Point (ICP) is proposed to reduce large-scale drift. Experimental results on the real-world scenes show that the average positioning error of our algorithm is 1.10 m, 0.91 m, 1.04 m in x, y, z-direction, the average rotation error is 1.03 deg, 0.81 deg, 0.70 deg for roll, pitch, yaw, and the average resource utilization rate is 32.04% (CPU) and 13.18% (memory), the average consumption time is 24.87 ms. Compared with ORB-SLAM3, LVIO, LVI-SAM, R3LIVE and Fast-LIVO algorithms, the proposed algorithm has a better performance on both accuracy and robustness with best real-time performance.

This brief seeks to introduce an observed-based sliding mode control (SMC) for fuzzy systems with stochastic communication protocol characterized by a more general switching rule. In contrast to prior research, the innovative stochastic switching rule for the stochastic communication protocol is unique in that it incorporates both sojourn probability and sojourn time concurrently, aiming to enhance communication efficiency and mitigate computational complexity. Additionally, by capitalizing on observed state information, a fuzzy sliding surface is devised, and a suitable fuzzy SMC law is suggested to ensure the reachability of the designated sliding function. Ultimately, the efficacy of this proposed control strategy is confirmed through a tunnel diode circuit model.

• The successful achievement of a record high doping content of 0.24 in Co-doped β -FeSi 2 through an extended annealing process. • The observation of a detailed transition from hopping to impurity band conduction with increasing doping level in Co-doped β -FeSi 2 thermoelectric materials. • Furthermore, we predict that Ni and Cu may undergo a similar transport transition from hopping conduction to band conduction when heavily doped, as observed in Co-doped β -FeSi 2. Element doping is a widely employed strategy to enhance the thermoelectric (TE) properties of various materials. β -FeSi 2 is a promising low-cost high-temperature TE material with exceptional thermal stability; however, the doping limit of β -FeSi 2 is usually very low, which limits the tunability of electrical and thermal properties. Recently, a high doping content of 0.16 in β -FeSi 2 has been achieved by the introduction of iridium (Ir), leading to the highest reported figure of merit (zT) of 0.6 in β -FeSi 2. Motivated by the successful heavy doping with Ir, this work aims to explore element heavy doping in β -FeSi 2 with cobalt (Co), a cheaper, more readily available dopant with a smaller atomic radius and closer electronegativity to iron (Fe). In this study, we successfully obtained a record-high doping content of 0.24 in Co-doped β -FeSi 2 through a prolonged annealing process. Despite the absence of a substantial enhancement in the zT of Co-doped β -FeSi 2 at high doping levels, with a maximum zT of 0.3 at 900 K in Fe 0.92 Co 0.08 Si 2 , we observed a transition in the carrier transport mechanism as a function of Co doping content, attributed to changes in the band structure. At a low Co doping content (x ≤ 0.12), Fe 1 – x Co x Si 2 demonstrates dominant carrier transport via impurity levels within the band gap, exhibiting hopping conduction. As the Co doping content increases (x > 0.16), the impurity levels overlap and form an impurity band, and the carrier transport turns into the impurity band conduction. The observed band conduction behavior of Fe 1 – x Co x Si 2 (x > 0.16) mirrors that of Ir-doped β -FeSi 2 , but Fe 1 – x Co x Si 2 shows much lower mobility, which can be attributed to the localized feature of the impurity band introduced by the Co doping. Overall, this study provides insights into the heavy Co doping and its influence on the TE properties and carrier conduction mechanisms in β -FeSi 2 , helpful for the further development of this TE system. [ABSTRACT FROM AUTHOR]

To enhance deoxygenation and cracking performance of microalgal biodiesel to produce a jet fuel-range hydrocarbon blend, an efficient Ni-carbon composite was prepared by pyrolyzing Ni-based metal–organic frameworks (Ni-1,3,5-benzenetricarboxylate, Ni-BTC) for catalytic conversion with a substantially reduced catalyst dosage. Coordinated Ni ions in the Ni-BTC precursor were converted into highly active Ni nanoparticles due to catalyst pyrolysis, while an increased specific surface area of the catalyst facilitated mass transfer in microalgal biodiesel conversion. X-ray absorption fine structure analysis confirmed the formation of Ni–Ni active sites, while density functional theory calculations revealed that the C═C bond was the initial site for the cracking reaction of long-chain fatty acids. The selectivity of jet-fuel-range products in methyl palmitate conversion over the Ni@C500(Ni-BTC pyrolyzed at 500 °C) catalyst increased to 71.46% with a substantially reduced catalyst dosage (the mass ratio of catalyst to reactant was 1:200). The Ni@C500catalyst exhibited excellent performance with high selectivity (71.6%) and conversion efficiency (97.46%) in deoxygenation and cracking of microalgal biodiesel for jet fuel-range hydrocarbon blend production.

Stem cell-based therapy is a novel technique used to reconstruct the ocular surface of patients with limbal stem cell deficiency (LSCD). However, the reported stem cell therapies for LSCD are limited to two-dimensional (2D) culture systems, which cannot mimic the native microenvironment in which stem cells reside in vivo. Herein, an injectable photocrosslinkable hydrogel was developed based on porous gelatin methacryloyl (GelMA)/silk fibroin glycidyl methacrylate (SilMA), which encapsulated limbal stem cells (LSCs) to reconstruct ocular surfaces. The porous GelMA/SilMA hydrogels demonstrated excellent transparency and good adhesion. More importantly, this hydrogel maintained viability, supported the adhesion of encapsulated rabbit LSCs and promoted their migration in vitroin three-dimensional (3D) culture environments. Proteomic analysis confirmed that the porous GelMA/SilMA hydrogel 3D cell culture system maintained both the self-renewal and differentiation of the LSCs. Furthermore, histological analyses demonstrated that the porous GelMA/SilMA hydrogel encapsulating LSCs effectively promoted the re-epithelization of LSCD. Porous hydrogels were subjected to simplified serial cell culture operations to construct an ocular surface with a uniform cell distribution. These findings provide valuable insights into stem cell therapies and regenerative medicine for ocular surface reconstruction.

Intervertebral disc degeneration (IVDD) can be caused by aging, injury, and genetic factors. The pathological changes associated with IVDD include the excessive accumulation of reactive oxygen species (ROS), cellular pyroptosis, and extracellular matrix (ECM) degradation. There are currently no approved specific molecular therapies for IVDD. In this study, we developed a multifunctional and microenvironment-responsive metal-phenolic network release platform, termed TMP@Alg-PBA/PVA, which could treat (IL-1β)-induced IVDD. The metal-phenolic network (TA-Mn-PVP, TMP) released from this platform targeted mitochondria to efficiently scavenge ROS and reduce ECM degradation. Pyroptosis was suppressed through the inhibition of the IL-17/ERK signaling pathway. These findings demonstrate the versatility of the platform. And in a rat model of IVDD, TMP@Alg-PBA/PVA exhibited excellent therapeutic effects by reducing the progression of the disease. TMP@Alg-PBA/PVA, therefore, presents clinical potential for the treatment of IVDD.

Introduction: Frequency of imaging markers (FIM) has been described as a novel predictor for hematoma expansion after intracerebral hemorrhage (ICH). A revised definition of hematoma expansion that incorporates intraventricular hemorrhage (IVH) growth, that is, revised hematoma expansion (RHE), has also been proposed. Nevertheless, the associations between FIM and IVH growth or RHE remains unexplored. The objective of this study was to assess the influence and performance of the FIM on two types.Materials and Methods: Patient selection and variables were based on our published protocol. FIM was defined as the ratio of the number of imaging markers to the onset-to-neuroimaging time. The association between FIM and two definitions was tested by multivariate analysis. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the FIM on two definitions were also evaluated.Results: There were 303 (20.36%) and 583 (39.18%) subjects in the IVH growth and RHE, respectively. Multivariate analysis demonstrated that FIM was associated with both IVH growth and RHE (odds ratio [OR] = 1.96, 95% confidence interval [CI] = 1.60–2.39; OR = 15.01, 95% CI = 10.51–21.43, respectively). The optimal cutoff points for FIM to predict IVH growth and RHE were 0.63 and 0.62, with AUC, sensitivity, specificity, PPV, and NPV of 0.66, 0.50, 0.78, 0.36, and 0.86 versus 0.80, 0.60, 0.93, 0.84, and 0.78, respectively.Discussion and Conclusion: FIM was not only a predictor of IVH growth, but also of RHE. These findings may have important clinical implications for decision-making based on risk stratification of patients with ICH.

•EXT1 and EXT2 were highly expressed in HNSC.•EXT2 was significantly negatively correlated to the prognosis of patients with HNSC.•EXT2 was considerably negatively connected with immune infiltrating levels of HNSC.

Due to the increased concern about energy and environmental issues, significant attention has been paid to the development of large-scale energy storage devices to facilitate the utilization of clean energy sources. The redox flow battery (RFB) is one of the most promising systems. Recently, the high cost of transition-metal complex-based RFB has promoted the development of aqueous RFBs with redox-active organic molecules. To expand the working voltage, computational chemistry has been applied to search for organic molecules with lower or higher redox potentials. However, redox potential computation based on implicit solvation models would be challenging due to difficulty in parametrization when considering the complex solvation of supporting electrolytes. Besides, although ab initio molecular dynamics (AIMD) describes the supporting electrolytes with the same level of electronic structure theory as the redox couple, the application is impeded by the high computation costs. Recently, machine learning molecular dynamics (MLMD) has been illustrated to accelerate AIMD by several orders of magnitude without sacrificing the accuracy. It has been established that redox potentials can be computed by MLMD with two separated machine learning potentials (MLPs) for reactant and product states, which is redundant and inefficient. In this work, an automated workflow is developed to construct a universal MLP for both states, which can compute the redox potentials or acidity constants of redox-active organic molecules more efficiently. Furthermore, the predicted redox potentials can be evaluated at the hybrid functional level with much lower costs, which would facilitate the design of aqueous organic RFBs.

AbstractBackground:Tuberculosis (TB) infection prevention and control (IPC) in healthcare facilities is key to reducing transmission risk. A framework for systematically improving TB IPC through training and mentorship was implemented in 9 healthcare facilities in China from 2017 to 2019.Methods:Facilities conducted standardized TB IPC assessments at baseline and quarterly thereafter for 18 months. Facility-based performance was assessed using quantifiable indicators for IPC core components and administrative, environmental, and respiratory protection controls, and as a composite of all control types We calculated the percentage changes in scores over time and differences by IPC control type and facility characteristics.Results:Scores for IPC core components increased by 72% during follow-up when averaged across facilities. The percentage changes for administrative, environmental, and respiratory protection controls were 39%, 46%, and 30%, respectively. Composite scores were 45% higher after the intervention. Overall, scores increased most during the first 6 months. There was no association between IPC implementation and provincial economic development or volume of TB services.Conclusions:TB IPC policies and practices showed most improvement early during implementation and did not differ consistently by facility characteristics. The training component of the project helped increase the capacity of healthcare professionals to manage TB transmission risks. Lessons learned here will inform national TB IPC guidance.

Four new [FeFe]-hydrogenase-mimic-containing CPs and a supramolecule, {(CO)6[Fe2S2–Ag](2-cpmt)2(SbF6)·0.5(CH2Cl2)}n(1), {(CO)6[Fe2S2–Ag](2-cpmt)2(ClO4)}n(2), {(CO)6[Fe2S2](2-cpmt)2Ag(p-xylene)(CF3SO3)}n(3), and {(CO)5(PPh3)[Fe2S2–Ag](2-cpmt)2(ClO4)·1.5(toluene)}n(4), were obtained from two new chelating ligands [FeFe](2-cpmt)2(CO)6(L1) and [FeFe](2-cpmt)2(CO)5(PPh3) (L2) by direct synthesis. All new compounds are characterized by single-crystal X-ray diffraction, FTIR, solid-state UV–vis spectra, and CV. It was noteworthy that L1and L2serve as multidentate ligands with two terminal −CN groups, the μ2-S atoms, and the CC≡Oas coordination sites, and the coordination modes between Ag+and multidentate chelating ligands are completely different from each other in four complexes. The results of photochemical H2generation experiments indicated that L2has greatly improved catalytic performance over L1due to the PPh3substituent. The amount of hydrogen produced from four Ag complexes 1–4is different due to the coordination environment difference around catalytic active sites. This work enriched the coordination chemistry of [FeFe]-hydrogenase mimics and improved the H2production performance of heterogeneous [FeFe]-hydrogenase catalysts.

In order to promote electron transfer rates in microbial electrolysis cell (MEC) methanogenesis reactions, an iron-doped zeolite imidazolate framework-67 (Fe/Co-ZIF) was synthesized and added to the solution of MEC to serve as an electron shuttle, improving the conversion of CO2to CH4. Fe/Co-ZIF effectively promoted the expression of heme protein-related genes to accelerate direct electron transfer by cytochrome C while also stimulating the secretion of extracellular polymeric substances (EPS) and redox-active flavin to increase indirect electron transfer. Fe/Co-ZIF increased the expression of heme protein-related genes by 29.1% and the relative content of flavin by 53.2% at most. With the addition of Fe/Co-ZIF, methanoculleus became the dominant archaeal genera both on electrodes and in solution, while hydrogen-producing bacterial genera such as Sulfurospirillum also increased, benefiting interspecies coculture. Therefore, Fe/Co-ZIF increased the specific capacitance of the MEC system and enhanced its charge storing capability, which promoted CH4yields by 1.62-fold. This study provides comprehensive insights into the role of Fe/Co-ZIF as an electron shuttle, facilitating extracellular direct and indirect electron transfer in microbial electrochemical systems.

To improve flue gas CO2fixation of Euglena gracilisunder a photoautotrophic environment, mixed red and blue wavelengths were used to promote photosynthetic growth and cell division with 15% CO2. The biomass dry weight increased by 385% to 1.31 g/L with a peak ribulose-1,5-bisphosphate carboxylase/oxygenase enzyme activity and oxygen release, when the ratio of red to blue wavelength decreased to 2:3. E. gracilismainly engaged in photochemical reactions with accumulated photosynthetic pigment to focus on light conversion by optimizing the carotenoid/chlorophyll ratio and chlorophyll a/bratio. The energy capture efficiency of open photosystem II (PSII) reaction centers and quantum efficiency of charge separation in PSII reaction centers were improved to maintain active photosynthesis. Blue light facilitated transition from G1 to S phase, while red light promoted transition from S to G2 phase. The malondialdehyde content decreased to a valley of 1.36 nmol/mg protein to keep efficient photosynthetic apparatus with carbon fixation.

Energy storage systems based on off-grid fluctuated wind power offer an attractive approach through the electrocatalytic reduction of CO2to methanol. Potential fluctuations derived from wind power cause various reduction products and decrease electrocatalytic efficiency in the CO2-to-methanol system. A periodic rotation control method of individual electrolyzers in electrolysis cells array with rated power (100%), fluctuating (0–100%), and standby (0%) status is proposed to solve potential fluctuation problems. O2volume is creatively used as an intermediate bridge to link potential and power so that a curve is fitted as a function of power–methanol Faradaic efficiency. The rotation control method improves the methanol Faradaic efficiency by ∼18.5% compared to the conventional average and cumulation control method, while it reduces the coefficient of variation by 67.9% compared to the cumulation control method. This demonstration of the rotation control method provides a promising approach for the efficient utilization of wind energy with energy storage systems.

In order to reduce the deformation resistance, the rolling process of metastable β titanium alloys is generally carried out in the β single-phase state, which causes the problem of non-uniform grain size during the subsequent annealing process, thus affecting the alloy properties. Here we first solution-treated the as-cast Ti–15Mo–3Al-2.7Nb-0.2Si alloy at 740 °C to obtain α + β phase, then cold rolled it with a reduction of 60 %, and finally annealed it at 710–810 °C for 2–240min. Characterization of the annealed metastable β alloy revealed that α phase was involved in the rolling deformation at the same time and recrystallized on the β matrix during the subsequent annealing process, known as equiaxial dispersion, which impeded the recrystallization of the β grains, and ultimately an ultrafine crystalline microstructure of α + β phases with an average grain size of less than 2 μm was obtained.

As the classic cathode material, lithium cobalt oxide (LiCoO2, LCO) suffers from severe structural and interfacial degradation at voltage >4.5 V, which induces fast capacity decay of the cells. Herein, we adopt a simple and effective method, doping aluminum (Al) cations in precursors, to improve the structural stability of LCO and systematically investigate the effect of Al doping on the electrochemical performances. Doping in precursors rather than bulk particles is beneficial to realize uniform Al ions distribution. Even at 4.5 V charging voltage, the LCO/graphite pouch cells with high Al doping levels (8500 ppm) deliver initial and reversible discharge capacities of 386 and 369 mAh after 500 cycles, respectively. The capacity retention is as high as 95.5%. When the cutoff voltage reaches 4.55 V, the pouch cell maintains 79.0% of the first-cycle discharge capacity after 500 cycles. With optimized electrolyte, the pouch cell realizes 87.3% of the initial discharge capacity after 500 cycles at 45 °C. Moreover, the thermal safety performance of the pouch cells with Al doping is promising. Our work displays an excellent inspiration for developing high-voltage, long-cycle, and safe LCO cathode for commercial lithium-ion batteries.

Macular corneal dystrophy (MCD) is a progressive, bilateral stromal dystrophic disease that arises from mutations in carbohydrate sulfotransferase 6 (CHST6). Corneal transplantation is the ultimate therapeutic solution for MCD patients. Unfortunately, postoperative recurrence remains a significant challenge. We conducted a retrospective review of a clinical cohort comprising 102 MCD patients with 124 eyes that underwent either penetrating keratoplasty (PKP) or deep anterior lamellar keratoplasty (DALK). Our results revealed that the recurrence rate was nearly three times higher in the DALK group (39.13%, 9/23 eyes) compared with the PKP group (10.89%, 11/101 eyes), suggesting that surgical replacement of the corneal endothelium for treating MCD is advisable to prevent postoperative recurrence. Our experimental data confirmed the robust mRNA and protein expression of CHST6 in human corneal endothelium and the rodent homolog CHST5 in mouse endothelium. Selective knockdown of wild-type Chst5in mouse corneal endothelium (ACsiChst5), but not in the corneal stroma, induced experimental MCD with similar extracellular matrix synthesis impairments and corneal thinning as observed in MCD patients. Mice carrying Chst5point mutation also recapitulated clinical phenotypes of MCD, along with corneal endothelial abnormalities. Intracameral injection of wild-type Chst5rescued the corneal impairments in ACsiChst5mice and retarded the disease progression in Chst5mutant mice. Overall, our study provides new mechanistic insights and therapeutic approaches for MCD treatment by high-lighting the role of corneal endothelium in MCD development.

In this study, homogeneously distributed PdxPt1–xdual-atom-site catalysts (DASC) were successfully synthesized over the whole composition range. In the three-way catalytic performance tests, Pd0.5Pt0.5and Pd0.9Pt0.1DASC exhibited greatly enhanced C3H6oxidation activities over those of pure Pd and Pt nanoparticles. During in situFourier transform infrared measurements, the direct C3H6dissociations on Pd–Pt dual-atom sites were discovered, which were mainly responsible for the superior C3H6oxidation activities of Pd0.5Pt0.5and Pd0.9Pt0.1DASC. In addition, a linear correlation between activity curves and Pt–CO wavenumbers was found, indicating that the IR wavenumber is a descriptor for activation energy.

The reaction behavior between CaO–Al2O3–La2O3-based slags and La-bearing FeCrAl melt was quantitatively characterized, which was further compared with the reaction behavior of CaO–SiO2-based slags. Based on this, the new type of mold flux for La-bearing FeCrAl alloy continuous casting was designed and its basic properties were evaluated. The results showed that the order of reaction degree of fluxing agents in CaO–Al2O3–La2O3-based slags is (Na2O) > (B2O3) > (Li2O), and the percentages of mass change of fluxing agents were 85.8, 54.29 and 42.35 wt.%, respectively. Moreover, the addition of (Li2O) and (Na2O) promoted the reaction between (CaO) and [Al], and the reaction degree of the former was weaker than that of the latter, which was due to the greater effect of (Na2O) on the activity of (CaO) and (Al2O3) than (Li2O). Compared with the reactivity of CaO–SiO2-based slags, the percentages of mass change of Al and La caused by slag–steel reaction decreased by 10.63–14.36 and 39.78–50.49 wt.%, respectively. The percentages of mass change of (Al2O3), (La2O3) and (CaO) in slags highest increased by 17.71, 17.98, and 7.81 wt.%, respectively. The reactivity of CaO–Al2O3–La2O3-based slags was significantly weakened. Ultimately, the new type of mold flux was designed and the composition range was determined. The fundamental properties of new mold flux basically meet the theoretical requirements for La-bearing FeCrAl alloy continuous casting.

Six-dimensional (6D) pose estimation is an important branch in the field of robotics focused on enhancing the ability of robots to manipulate and grasp objects. The latest research trend in 6D pose estimation is to directly predict the positions of two-dimensional (2D) keypoints from a single red, green, and blue (RGB) image through convolutional neural networks (CNNs) and establish a corresponding relationship with the three-dimensional (3D) keypoints of the model. Then, the perspective-n-point (PnP) algorithm is used to recover the 6D pose parameters. Currently, two challenges are encountered in pose estimation based on an RGB image. On the one hand, an RGB image lacks depth information, and it is thus difficult to directly obtain the corresponding geometric object information. On the other hand, when depth information is available, it is difficult to efficiently fuse the features of the RGB image with the features of the corresponding depth image. In this paper, we propose a bidirectional depth residual fusion network with a depth prediction (DP) network to estimate the 6D poses of objects (BDR6D). The BDR6D network predicts the depth information of objects using an RGB image, converts the depth information into point cloud information, and performs feature extraction and representation together with the RGB information during the feature extraction and representation stages. Specifically, the RGB image is fed into the BDR6D network, the DP network predicts the depth information of the objects in the image, and the depth map and RGB image are input into a point cloud network (PCN) and CNN, respectively, for feature extraction and representation. We build the bidirectional depth residual (BDR) structure so that the CNN and PCN can share information during feature extraction and representation. This approach allows the two networks to use each other’s local and global information to improve feature extraction and representation. For the keypoint selection stage, we propose an effective 2D keypoint selection method that considers the appearance and geometric information of the object of interest. We evaluate the proposed method with three benchmark datasets and compare it with other 6D pose estimation algorithms. The experimental results show that our method outperforms the state-of-the-art approach. Finally, we deploy our proposed method in conjunction with the Universal Robots 5 manipulator (UR5) robot to grasp and manipulate objects. Note to Practitioners—The purpose of this paper is to solve the problem of 6D pose estimation for robot grasping. The existing RGB image-based pose estimation approach faces two challenges. On the one hand, a single RGB image lacks depth information, so that it is difficult to directly obtain the corresponding geometric object information. On the other hand, when depth information is available, it is difficult to efficiently fuse the features of the RGB image with the features of the corresponding depth image. To solve the above problems, a novel network that can predict the depth information of objects from an RGB image and fuse the depth information with the RGB information to estimate the 6D pose of objects is proposed. Furthermore, we propose an effective 2D keypoint selection method that considers the appearance and geometric information of objects of interest. We evaluate the proposed approach based on three benchmark datasets and the UR5 robot platform and verify that our method is effective.

The flexible job-shop co-scheduling problem (FJCSP) for processing machines and automated guided vehicles (AGVs) in a flexible manufacturing system (FMS) has attracted more attention with the aim of improving production efficiency. In FMS, AGVs in charge of transporting jobs realize the flexible linkage of operations between different processing machines. The added interdependence between transporting and processing tasks brings more difficulties than the traditional flexible job-shop scheduling problem (FJSP). In this paper, the mathematical model of FJCSP is formulated to minimize the makespan. Considering the feature similarity of FJCSP with FJSP and AGV-routing problem in different cases, a multi-view modeling-based hybrid algorithm consisting of an estimation of distribution algorithm (EDA) and an ant colony optimization (ACO) is proposed. In EDA, a probability model abstracts the information in superior solutions about the operation sequencing and the rule selection for scheduling machines and AGVs. In ACO, a job-path pheromone model and an AGV-path pheromone model are designed to jointly select the job-machine-AGV combination with shorter processing time and transportation time. In the proposed hybrid algorithm, EDA and ACO generate solutions independently and achieve cooperation by sharing elites. An adaptive parameter is designed to regulate the use of the two methods to adapt to the varying demands of multi-view modeling in different cases and search stages. Furthermore, a local search with a three-layer operator based on the critical path method is proposed to balance exploration and exploitation in solution space. Finally, computational experiments involving a case study verified the advantage of the multi-view modeling-based hybrid algorithm in comparison with the state-of-the-art approaches. Note to Practitioners—This paper was motivated by the optimization problem of scheduling machines and automated guided vehicles (AGVs) in flexible manufacturing system (FMS). In FMS with AGVs, the transportation stages for jobs by AGVs significantly impact the overall production efficiency of the FMS and cannot be overlooked. This paper suggested a hybrid evolutionary algorithm using an estimation of distribution algorithm (EDA), an ant colony optimization (ACO) and a local search algorithm based on the critical path method. In the proposed hybrid algorithm, an adaptive parameter is introduced to regulate the utilization of EDA and ACO in generating a new population. This paper presents a mathematical characterization of the scheduling problem and subsequently outlines the step-by-step design of the hybrid algorithm. Computational experiments, including a case study, demonstrate that the hybrid algorithm exhibits adaptability to various instances and outperforms state-of-the-art approaches.

This article investigates the resilient proportional-integral observer (PIO) problem for Markov switching memristive neural networks (MSMNNs) with randomly occurring sensor saturation within a finite-time interval. The Markov switching of memristive neural networks is regulated by a higher level deterministic switching signal, whose transition probabilities are piecewise time-varying and can be depicted by the average dwell-time strategy. Meanwhile, a Bernoulli stochastic process associated with an uncertain packet arriving rate is adopted to describe the randomly occurring sensor saturation. The aim is to design a resilient PIO such that the augmented dynamic has the property of stochastic finite-time boundedness while meeting the desired $\mathcal {H}_{\infty }$ performance index. By applying the Lyapunov method and the average dwell-time scheme, sufficient criteria are established for MSMNNs, and a unified design method is presented for the existence of the PIO. Lastly, the attained theoretical results are validated via a numerical simulation.

The double-factored decision theory for Markov decision processes with multiple scenarios of the parameters is proposed in this article. We introduce scenario belief to describe the probability distribution of scenarios in the system, and scenario expectation to formulate the expected total discounted reward of a policy. We establish a new framework named as double-factored Markov decision process (DFMDP), in which the physical state and scenario belief are shown to be the double factors serving as the sufficient statistics for the history of the decision process. Four classes of policies for the finite horizon DFMDPs are studied and it is shown that there exists a double-factored Markovian deterministic policy which is optimal among all policies. We also formulate the infinite horizon DFMDPs and present its optimality equation in this paper. An exact solution method named as double-factored backward induction for the finite horizon DFMDPs is proposed. It is utilized to find the optimal policies for the numeric examples and then compared with policies derived from other methods from the related literatures.

This study is concerned with protocol-based asynchronous filtering for interval type-2 (IT2) fuzzy systems subject to time-varying saturation functions. Two mutually independent Markov processes are proposed to characterize the random manners of intermittently failure and transmission sequence of sensor nodes, and a new joint Markov process is formulated by adopting the merging technique. Aiming at curbing the data collision and improving network utilization in the restricted network, an acrlong ETRP is implemented to govern whether to orchestrate the packets and which one to be launched simultaneously. A novel asynchronous filter is formulated under the constraint of saturation with the hope to improve filtering performance. This construction involves the dynamic adaptation of the saturation level in tandem with the estimation error. Additionally, the mismatched modes between the newly joined Markov process and filter are characterized by a hidden Markov model. Eventually, two examples are applied to verify the availability of the theoretical results.

Decoders play significant roles in recovering scene depths. However, the decoders used in previous works ignore the propagation of multilevel lossless fine-grained information, cannot adaptively capture local and global information in parallel, and cannot perform sufficient global statistical analyses on the final output disparities. In addition, the process of mapping from a low-resolution (LR) feature space to a high-resolution (HR) feature space is a one-to-many problem that may have multiple solutions. Therefore, the quality of the recovered depth map is low. To this end, we propose a high-quality decoder (HQDec), with which multilevel near-lossless fine-grained information, obtained by the proposed adaptive axial-normalized position-embedded channel attention sampling module (AdaAxialNPCAS), can be adaptively incorporated into a LR feature map with high-level semantics utilizing the proposed adaptive information exchange scheme. In the HQDec, we leverage the proposed adaptive refinement module (AdaRM) to model the local and global dependencies between pixels in parallel and utilize the proposed disparity attention module to model the distribution characteristics of disparity values from a global perspective. To recover fine-grained HR features with maximal accuracy, we adaptively fuse the high-frequency information obtained by constraining the upsampled solution space utilizing the local and global dependencies between pixels into the HR feature map generated from the nonlearning method. Extensive experiments demonstrate that each proposed component improves the quality of the depth estimation results over the baseline results, and the developed approach achieves state-of-the-art results on the KITTI and DDAD datasets. The code and models will be publicly available at HQDec.

This paper delves into the problem of asynchronous fault detection filter (AFDF) for Takagi–Sugeno (T–S) fuzzy singular semi-Markovian jump systems (FSSJSs) with multi-cyber-attacks via dynamic event-triggering mechanism (ETM) and double-quantized schemes (DQS). First, a new design structure is devised by integrating the ETM and DQS into a unified framework to effectively alleviate the communication pressure. In particular, the event-triggered threshold (ETT) is dynamically regulated in line with system information, and not only the data from the plant to the filter but also that from the filter to the plant, are both quantized using different logarithmic quantizers. Second, the hybrid cyber-attacks platform is adopted, of which including the random deception attacks (DAs), random replay attacks (RAs) and DoS attack. Third, leveraging the principles of Lyapunov theory, some sufficient conditions are derived to guarantee the exponential admissibility (EA) of the resulting error dynamics and their adherence to a prescribed H∞performance index. Through the utilization of several matrix inequalities, the desired filtering gains are given. Finally, the effectiveness of the achieved results is demonstrated by two examples.

The microstructure evolution of AlCoCrFeNiTi0.5alloy and AlCoCrFeNiCu alloy during heat treatment was systematically studied, to reveal the influence rules of chemical activity of adding element on the microstructure evolution of AlCoCrFeNi system. Owing to the negative mixing enthalpy with the constituent elements, Ti element was mainly dissolved in the Al–Ni-rich phases, and aggravated the lattice distortion of B2 phase. The structure variation of BCC phase by adding Ti inhibited the formation of FCC phase and enhanced the precipitation of σphase during heat treatment. Owing to the positive mixing enthalpy with constituent elements, Cu element tended to be repelled to the ID region and formed metastable Cu-rich FCC1 phase which would transform into Cu–Al–Ni-rich FCC2 phase with increasing temperature. The addition of Cu inhibited the precipitation of σphase during heat treatment. Adding Ti maintained the stable dendritic morphology, while adding Cu reduced the thermal stability of microstructure. Two dramatic morphology changes occurred at 1000 °C and 1100 °C in the AlCoCrFeNiCu alloy. The lattice distortion of phase in AlCoCrFeNiTi0.5alloy was aggravated with increasing temperature up to 800 °C, then relaxed together with the dissolution of σphase when temperature was above 900 °C. The variation in lattice distortion dominated the hardness of AlCoCrFeNiTi0.5alloy. With increasing heating temperature, the increasing volume fraction of region with FCC structure due to the transformation between FCC phases, and the pronounced coarsening in microstructure due to the reduced thermal stability, resulted in the mainly decreasing trend in the hardness of AlCoCrFeNiCu alloy.

Under the framework of multiagent systems (MASs), this article is focused on the leaderless consensus of multiple Euler–Lagrange (EL) systems subject to denial-of-service (DoS) attacks. First, a new joint control protocol, which combines the distributed sampled-data (SD) control and adaptive distributed control (DC), is designed. The distributed SD control is equipped with logic processors, which can capture key information of DoS attacks, and the adaptive DC is conducive to relaxing some generally required constraint conditions. Second, a new property of a non-negative differentiable function is proposed, which is very helpful in solving the distributed SD control issue of multiple EL systems. Third, by setting up a $\textbf {W}$ -dependent Lyapunov–Krasovskii functional (LKF) and utilizing the property of the non-negative differentiable function, new leaderless consensus results are derived for multiple EL systems with DoS attacks. The obtained leaderless consensus results are in the shape of linear matrix inequalities (LMIs) and can efficiently counter the influence of the DoS attacks. Ultimately, the effectiveness of the derived results is inspected by an MAS with multiple two-linked robot manipulators.