Autogenous bone block osteotomy in the chin using a robotic system: A clinical report.

An autogenous bone block osteotomy in the chin assisted by a robotic system is described. The size of the required bone graft was designed in the robotic system before surgery, and a precise bone block osteotomy was achieved with the assistance of the robotic system in a visualized, safe, and accurate surgical approach.

Integrated analysis of transcriptomics to identify hub genes in primary Sjögren's syndrome.

OBJECTIVE: The treatment of patients with primary Sjögren's syndrome is a clinical challenge. Gene expression profile analysis and comprehensive network methods for complex diseases can provide insight into molecular characteristics in the clinical context. MATERIALS AND METHODS: We downloaded gene expression datasets from the Gene Expression Omnibus (GEO) database. We screened differentially expressed genes (DEG) between the pSS patients and the controls by the robust rank aggregation (RRA) method. We explored DEGs' potential function using gene function annotation and PPI network analysis. RESULTS: GSE23117, GSE40611, GSE80805, and GSE127952 were included, including 38 patients and 30 controls. The RRA integrated analysis determined 294 significant DEGs (241 upregulated and 53 downregulated), and the most significant gene aberrantly expressed in SS was CXCL9 (p = 6.39E-15), followed by CXCL13 (p = 1.53E-13). Immune response (GO:0006955; p = 4.29E-32) was the most significantly enriched biological process in GO (gene ontology) analysis. KEGG pathway enrichment analysis showed that cytokine-cytokine receptor interaction (hsa04060; p = 6.46E-10) and chemokine signaling pathway (hsa04062; p = 9.54E-09) were significantly enriched. We defined PTPRC, CD86, and LCP2 as the hub genes based on the PPI results. CONCLUSION: Our integrated analysis identified gene signatures and helped understand molecular changes in pSS.

Cost-Effective Wearable Indoor Localization and Motion Analysis via the Integration of UWB and IMU.

Wearable indoor localization can now find applications in a wide spectrum of fields, including the care of children and the elderly, sports motion analysis, rehabilitation medicine, robotics navigation, etc. Conventional inertial measurement unit (IMU)-based position estimation and radio signal indoor localization methods based on WiFi, Bluetooth, ultra-wide band (UWB), and radio frequency identification (RFID) all have their limitations regarding cost, accuracy, or usability, and a combination of the techniques has been considered a promising way to improve the accuracy. This investigation aims to provide a cost-effective wearable sensing solution with data fusion algorithms for indoor localization and real-time motion analysis. The main contributions of this investigation are: (1) the design of a wireless, battery-powered, and light-weight wearable sensing device integrating a low-cost UWB module-DWM1000 and micro-electromechanical system (MEMS) IMU-MPU9250 for synchronized measurement; (2) the implementation of a Mahony complementary filter for noise cancellation and attitude calculation, and quaternions for frame rotation to obtain the continuous attitude for displacement estimation; (3) the development of a data fusion model integrating the IMU and UWB data to enhance the measurement accuracy using Kalman-filter-based time-domain iterative compensations; and (4) evaluation of the developed sensor module by comparing it with UWB- and IMU-only solutions. The test results demonstrate that the average error of the integrated module reached 7.58 cm for an arbitrary walking path, which outperformed the IMU- and UWB-only localization solutions. The module could recognize lateral roll rotations during normal walking, which could be potentially used for abnormal gait recognition.

Single-shot 3D shape measurement of discontinuous objects based on a coaxial fringe projection system.

Fringe projection profilometry has been widely used in high-speed three-dimensional (3D) shape measurement. To improve the speed without loss of accuracy, we present a novel single-shot 3D shape measuring system that utilizes a coaxial fringe projection system and a 2CCD camera. The coaxial fringe projection system, comprising a visible light (red, green, and blue) projector and an infrared (IR) light projector, can simultaneously project red, green, blue, and IR fringe patterns. The 2CCD camera, as the name suggests, has two CCD chips that can acquire visible and IR fringe patterns at the same time. Combining the two-step phase-shifting algorithm, Fourier transform profilometry, and the optimum three-frequency selection method, 3D shape measurement of complex surfaces such as large slopes or discontinuous objects can be obtained from single-shot acquisition. A virtual fringe projection measurement system has been established to generate pre-deformed fringe patterns to correct positional deviations of the coaxial fringe projection system. This method has been applied to simulations and experiments on static and dynamic objects with promising results.

Photoelectrochemical hydrogen evolution of tapered silicon nanowires.

The origin of the photocurrent enhancement and the overpotential reduction in solar water splitting employing nanostructured silicon is still a matter of debate. A set of tapered Si nanowires (SiNWs) has been designed for clarifying the impact of nanostructured Si on the hydrogen evolution reaction (HER) while precisely tailoring several interference factors such as surface area, light absorption and surface defect density. We find that defect passivation by KOH achieved by tapering is much more beneficial than the optical gain. Surfactant-mediated modification of SiNWs is capable of engineering the band structure. As a result, we suggest a guideline for nanostructured Si photoelectrodes optimized for the HER.

Wood-Derived Freestanding Carbon-Based Electrode with Hierarchical Structure for Industrial-Level Hydrogen Production.

The sustainable and scalable fabrication of low-cost, efficient, and durable electrocatalysts that operate well at industrial-level current density is urgently needed for large-scale implementation of the water splitting to produce hydrogen. In this work, an integrated carbon electrode is constructed by encapsulating Ni nanoparticles within N-doped carbonized wood framework (Ni@NCW). Such integrated electrode with hierarchically porous structure facilitates mass transfer process for hydrogen evolution reaction (HER). Ni@NCW electrode can be employed directly as a robust electrocatalyst for HER, which affords the industrial-level current density of 1000 mA cm-2 at low overpotential of 401 mV. The freestanding binder-free electrode exhibits extraordinary stability for 100 h. An anion exchange membrane water electrolysis (AEMWE) electrolyzer assembled with such freestanding carbon electrode requires only a lower cell voltage of 2.43 V to achieve ampere-level current of 4.0 A for hydrogen production without significant performance degradation. These advantages reveal the great potential of this strategy in designing cost-effective freestanding electrode with monometallic, bimetallic, or trimetallic species based on abundant natural wood resources for water splitting.

Accuracy of dental implant placement using different dynamic navigation and robotic systems: an in vitro study.

Computer-aided implant surgery has undergone continuous development in recent years. In this study, active and passive systems of dynamic navigation were divided into active dynamic navigation system group and passive dynamic navigation system group (ADG and PDG), respectively. Active, passive and semi-active implant robots were divided into active robot group, passive robot group and semi-active robot group (ARG, PRG and SRG), respectively. Each group placed two implants (FDI tooth positions 31 and 36) in a model 12 times. The accuracy of 216 implants in 108 models were analysed. The coronal deviations of ADG, PDG, ARG, PRG and SRG were 0.85 ± 0.17 mm, 1.05 ± 0.42 mm, 0.29 ± 0.15 mm, 0.40 ± 0.16 mm and 0.33 ± 0.14 mm, respectively. The apical deviations of the five groups were 1.11 ± 0.23 mm, 1.07 ± 0.38 mm, 0.29 ± 0.15 mm, 0.50 ± 0.19 mm and 0.36 ± 0.16 mm, respectively. The axial deviations of the five groups were 1.78 ± 0.73°, 1.99 ± 1.20°, 0.61 ± 0.25°, 1.04 ± 0.37° and 0.42 ± 0.18°, respectively. The coronal, apical and axial deviations of ADG were higher than those of ARG, PRG and SRG (all P < 0.001). Similarly, the coronal, apical and axial deviations of PDG were higher than those of ARG, PRG, and SRG (all P < 0.001). Dynamic and robotic computer-aided implant surgery may show good implant accuracy in vitro. However, the accuracy and stability of implant robots are higher than those of dynamic navigation systems.

Correlation between peri-implant bone mineral density and primary implant stability based on artificial intelligence classification.

Currently, the classification of bone mineral density (BMD) in many research studies remains rather broad, often neglecting localized changes in BMD. This study aims to explore the correlation between peri-implant BMD and primary implant stability using a new artificial intelligence (AI)-based BMD grading system. 49 patients who received dental implant treatment at the Affiliated Hospital of Stomatology of Fujian Medical University were included. Recorded the implant stability quotient (ISQ) after implantation and the insertion torque value (ITV). A new AI-based BMD grading system was used to obtain the distribution of BMD in implant site, and the bone mineral density coefficients (BMDC) of the coronal, middle, apical, and total of the 1 mm site outside the implant were calculated by model overlap and image overlap technology. Our objective was to investigate the relationship between primary implant stability and BMDC values obtained from the new AI-based BMD grading system. There was a significant positive correlation between BMDC and ISQ value in the coronal, middle, and total of the implant (P < 0.05). However, there was no significant correlation between BMDC and ISQ values in the apical (P > 0.05). Furthermore, BMDC was notably higher at implant sites with greater ITV (P < 0.05). BMDC calculated from the new AI-based BMD grading system could more accurately present the BMD distribution in the intended implant site, thereby providing a dependable benchmark for predicting primary implant stability.

3D palmprint and hand imaging system based on full-field composite color sinusoidal fringe projection technique.

Palmprint and hand shape, as two kinds of important biometric characteristics, have been widely studied and applied to human identity recognition. The existing research is based mainly on 2D images, which lose the third-dimensional information. The biological features extracted from 2D images are distorted by pressure and rolling, so the subsequent feature matching and recognition are inaccurate. This paper presents a method to acquire accurate 3D shapes of palmprint and hand by projecting full-field composite color sinusoidal fringe patterns and the corresponding color texture information. A 3D imaging system is designed to capture and process the full-field composite color fringe patterns on hand surface. Composite color fringe patterns having the optimum three fringe numbers are generated by software and projected onto the surface of human hand by a digital light processing projector. From another viewpoint, a color CCD camera captures the deformed fringe patterns and saves them for postprocessing. After compensating for the cross talk and chromatic aberration between color channels, three fringe patterns are extracted from three color channels of a captured composite color image. Wrapped phase information can be calculated from the sinusoidal fringe patterns with high precision. At the same time, the absolute phase of each pixel is determined by the optimum three-fringe selection method. After building up the relationship between absolute phase map and 3D shape data, the 3D palmprint and hand are obtained. Color texture information can be directly captured or demodulated from the captured composite fringe pattern images. Experimental results show that the proposed method and system can yield accurate 3D shape and color texture information of the palmprint and hand shape.

Accuracy and efficiency of robotic dental implant surgery with different human-robot interactions: An in vitro study.

OBJECTIVES: This study aims to compare the surgical efficiency (preparation and operation time) and accuracy of implant placement between robots with different human-robot interactions. METHODS: The implant robots were divided into three groups: semi-active robot (SR), active robot (AR) and passive robot (PR). Each robot placed two implants (#31 and #36) on a phantom, practising 10 times. The surgical efficiency and accuracy of implant placement were then evaluated. RESULTS: Sixty implants were placed in 30 phantoms. The mean preparation times for the AR, PR and SR groups were 3.85 ± 0.17 min, 2.14 ± 0.06 mins and 1.65 ± 0.19 mins, respectively. The mean operation time of the PR group (3.76 ± 0.59 min) was shorter that of than the AR (4.89 ± 0.70 mins) and SR (4.59 ± 0.56 min) groups (all P < 0.001). The operation time of the AR group in the anterior region (4.47 ± 0.31 min) was longer than that of the SR group (4.07 ± 0.10 min) (P = 0.007). The mean coronal, apical and axial deviations of the PR group (0.40 ± 0.12 mm, 0.49 ± 0.13 mm, 0.96 ± 0.22°) were higher than those of the AR (0.23 ± 0.11 mm, 0.24 ± 0.11 mm, 0.54 ± 0.20 °) (all P < 0.001) and SR (0.31 ± 0.10 mm, 0.36 ± 0.12 mm, 0.43 ± 0.14 °) groups (P = 0.044, P = 0.002, and P < 0.001, respectively). CONCLUSIONS: Human-robot interactions affect the efficiency of implant surgery. Active and semi-active robots show comparable implant accuracy. However, the implants placed by the passive robot show higher deviations. CLINICAL SIGNIFICANCE: This in vitro study preliminarily demonstrates that implant placement is accurate when using implant robots with different human-robot interactions. However, different human-robot interactions have variable surgical efficiencies.

Optimized design of battery pole control system based on dual-chip architecture.

At present, the global demand for lithium batteries is still in a high growth state, and the traditional lithium battery pole mill control system is still dominated by ARM (Artificial Intelligence Enhanced Computing), DSP (Digital Signal Processing), and other single-chip control methods. There are problems such as poor anti-interference ability and insufficient real-time online analysis of production data. This paper adopts the dual-chip control system architecture based on "ARM+DSP", starting from the mechanical characteristics and operating signal features of the pole mill. The hardware system adopts a three-unit joint control hardware structure, which separates the control unit from the data processing unit and improves the operation of the system. The software system adopts fuzzy PID algorithm to realize deflection control and tension control, and verifies that the Fuzzy PID (Proportion Integration Differentiation) control algorithm can effectively improve the anti-interference ability of the deflection system and tension system. The results show that the data loss rate is low with the SPI communication between DSP and ARM. The tension error of the "ARM+DSP" control system does not exceed 5%, and the deviation of the correction band is within ±4mm. The dedicated dual-chip hardware architecture effectively improves the robustness and operation efficiency of the pole mill, solves the problem of low tension control accuracy, and provides a theoretical basis for the application of the dual-roll mill.

Research on Control Method of Waste Heat Utilization System Based on Multi-parameter Coupling.

The recovery of low-quality waste heat is a major problem in energy utilization. In order to solve this problem and improve energy utilization, the research group designed a low-quality waste heat power generation device with Roots power machine as the core. However, the device has poor ability to adjust the rotation speed and it is difficult to generate electricity stably. The fundamental reason is that the system has many variables and strong coupling. According to the actual working conditions, the power of the device is 10 kW, and the fluctuation range should be within ± 7%. On the one hand, it can be improved by hardware, on the other hand, the design of software is also very critical. At present, through the investigation of domestic and foreign researches on the control system, it is found that the stability of the system is gradually improved, but the problem of strong coupling between variables has not been effectively solved. Therefore, the research group modeled the variables in the system and obtained a coupled model. Based on the couple model, the research group introduced nonlinear multi-model adaptive closed-loop decoupling control and designed a control system. The simulation results show that the maximum overshoot of the control system is 3.9%, the adjustment time is also reduced, and it is stable in low quality waste heat recovery device. Experimental results show that under the control of the system, the rotational speed of roots motor can keep stable, the maximum deviation is not more than 21.4 r/min, and the fluctuation range is within ± 7%, which meets the requirements of the index. This has laid the foundation for the follow-up research of grid-connected power generation.

Construction of a new automatic grading system for jaw bone mineral density level based on deep learning using cone beam computed tomography.

To develop and verify an automatic classification method using artificial intelligence deep learning to determine the bone mineral density level of the implant site in oral implant surgery from radiographic data obtained from cone beam computed tomography (CBCT) images. Seventy patients with mandibular dentition defects were scanned using CBCT. These Digital Imaging and Communications in Medicine data were cut into 605 training sets, and then the data were processed with data standardization, and the Hounsfiled Unit (HU) value level was determined as follows: Type 1, 1000-2000; type 2, 700-1000; type 3, 400-700; type 4, 100-400; and type 5, - 200-100. Four trained dental implant physicians manually identified and classified the area of the jaw bone density level in the image using the software LabelMe. Then, with the assistance of the HU value generated by LabelMe, a physician with 20 years of clinical experience confirmed the labeling level. Finally, the HU mean values of various categories marked by dental implant physicians were compared to the mean values detected by the artificial intelligence model to assess the accuracy of artificial intelligence classification. After the model was trained on 605 training sets, the statistical results of the HU mean values of various categories in the dataset detected by the model were almost the same as the HU grading interval on the data annotation. This new classification provides a more detailed solution to guide surgeons to adjust the drilling rate and tool selection during preoperative decision-making and intraoperative hole preparation for oral implant surgery.

Research on rapier loom fault system based on cloud-side collaboration.

The electrical control system of rapier weaving machines is susceptible to various disturbances during operation and is prone to failures. This will seriously affect the production and a fault diagnosis system is needed to reduce this effect. However, the existing popular fault diagnosis systems and methods need to be improved due to the limitations of rapier weaving machine process and electrical characteristics. Based on this, this paper presents an in-depth study of rapier loom fault diagnosis system and proposes a rapier loom fault diagnosis method combining edge expert system and cloud-based rough set and Bayesian network. By analyzing the process and fault characteristics of rapier loom, the electrical faults of rapier loom are classified into common faults and other faults according to the frequency of occurrence. An expert system is built in the field for edge computing based on knowledge fault diagnosis experience to diagnose common loom faults and reduce the computing pressure in the cloud. Collect loom fault data in the cloud, train loom fault diagnosis algorithms to diagnose other faults, and handle other faults diagnosed by the expert system. The effectiveness of loom fault diagnosis is verified by on-site operation and remote monitoring of the loom human-machine interaction system. Technical examples are provided for the research of loom fault diagnosis system.

Application of embedded soft PLC in the control system of rapier loom.

At present, the rapier loom has gradually become the mainstream equipment in the manufacturing industry. In order to make the rapier loom realize automated production and further improve the production efficiency of the rapier loom, improve the programmability of the system, and reduce the cost of system maintenance. The thesis developed a rapier loom control system based on embedded soft PLC, and carried out experiments and applications in the field. The contribution and innovation of this paper is to develop a complete low-cost control system, and through a genetic algorithm optimized PID algorithm to complete the more effective control of the loom tension system. The embedded soft PLC system proposed in this paper reduces the overall maintenance cost of the system and improves the programmability of the system. This text carries on the systematic scheme design to the embedded soft PLC from the hardware system and the software system respectively. First, according to the actual requirements, this article designs the overall scheme of the embedded software PLC hardware system with STM32F407ZGT6 as the core. Then this article is based on the embedded soft PLC hardware platform, according to the international standard of industrial control programming, writes the embedded soft PLC low-level driver software. Secondly, this article analyzes the factors that affect the warp tension during the operation of the rapier loom, and proposes the use of genetic algorithm to optimize the warp tension control method of the traditional PID algorithm. Finally, we conducted verification tests and on-site application debugging for the entire set of rapier loom embedded soft PLC control system. We controlled the warp tension as the main experimental object. The results show that this control system effectively improves the control accuracy of the warp tension of the rapier loom and meets the actual needs of industrial applications. The whole system has a good application prospect in the warp tension control of rapier looms.

Highly electrocatalytic Cu2ZnSn(S1-xSex)4 counter electrodes for quantum-dot-sensitized solar cells.

Traditional Pt counter electrode in quantum-dot-sensitized solar cells suffers from a low electrocatalytic activity and instability due to irreversible surface adsorption of sulfur species incurred while regenerating polysulfide (S(n)(2-)/S(2-)) electrolytes. To overcome such constraints, chemically synthesized Cu(2)ZnSn(S(1-x)Se(x))(4) nanocrystals were evaluated as an alternative to Pt. The resulting chalcogenides exhibited remarkable electrocatalytic activities for reduction of polysulfide (S(n)(2-)) to sulfide (S(2-)), which were dictated by the ratios of S/Se. In this study, a quantum dot sensitized solar cell constructed with Cu(2)ZnSn(S(0.5)Se(0.5))(4) as a counter electrode showed the highest energy conversion efficiency of 3.01%, which was even higher than that using Pt (1.24%). The compositional variations in between Cu(2)ZnSnS(4) (x = 0) and Cu(2)ZnSnSe(4) (x = 1) revealed that the solar cell performances were closely related to a difference in electrocatalytic activities for polysulfide reduction governed by the S/Se ratios.

Upgraded silicon nanowires by metal-assisted etching of metallurgical silicon: a new route to nanostructured solar-grade silicon.

Through metal-assisted chemical etching (MaCE), superior purification of dirty Si is observed, from 99.74 to 99.9884% for metallurgical Si and from 99.999772 to 99.999899% for upgraded metallurgical Si. In addition, large area of silicon nanowires (SiNW) are fabricated. The purification effect induces a ∼35% increase in photocurrent for SiNW based photoelectrochemical cell.