Fabrication of high gas-tightness SiCN ceramic via PIP process for increasing sensing distance of pressure sensor

Abstract(#br)High-gas-tightness wireless pressure sensors were fabricated by using a silicon carbonitride (SiCN) ceramic material derived from liquid polyvinylsilazane (PVSZ) precursor via precursor infiltration and pyrolysis (PIP) process. In order to increase the density of ceramic disks effectively, two types of infiltration liquids were chosen; PVSZ/Ethanol (2:1) with high viscosity was designed for the first cycle of PIP process, while PVSZ/Ethanol (1:1) with low viscosity was designed for the last two cycles of PIP process (The ratio in the parentheses represents the content of PVSZ and ethanol, respectively). The results confirmed that the density of ceramic disk after three PIP cycles can be increased to 2.155 g/cm 3 . Gas tightness measurement of ceramic disks indicated that the gas tightness was improved obviously after PIP cycles, and ceramic disks after the 2 nd and 3 rd PIP cycles could keep gas-tight condition without loss of pressure after 8 days. In addition, because high density was detected in the ceramic disks after PIP cycles, the wireless pressure sensors with large sensing distance have been fabricated

Tailoring Personality Traits in Large Language Models via Unsupervisedly-Built Personalized Lexicons

Personality plays a pivotal role in shaping human expression patterns, thus regulating the personality of large language models (LLMs) holds significant potential in enhancing the user experience of LLMs. Previous methods either relied on fine-tuning LLMs on specific corpora or necessitated manually crafted prompts to elicit specific personalities from LLMs. However, the former approach is inefficient and costly, while the latter cannot precisely manipulate personality traits at a fine-grained level. To address the above challenges, we have employed a novel Unsupervisedly-Built Personalized Lexicons (UBPL) in a pluggable manner during the decoding phase of LLMs to manipulate their personality traits. UBPL is a lexicon built through an unsupervised approach from a situational judgment test dataset (SJTs4LLM). Users can utilize UBPL to adjust the probability vectors of predicted words in the decoding phase of LLMs, thus influencing the personality expression of LLMs. Extensive experimentation demonstrates the remarkable effectiveness and pluggability of our method for fine-grained manipulation of LLM's personality.Comment: Work in progres

Towards Understanding the Capability of Large Language Models on Code Clone Detection: A Survey

Code cloning, the duplication of code fragments, is common in software development. While some reuse aids productivity, excessive cloning hurts maintainability and introduces bugs. Hence, automatic code clone detection is vital. Meanwhile, large language models (LLMs) possess diverse code-related knowledge, making them versatile for various software engineering challenges. However, LLMs' performance in code clone detection is unclear and needs more study for accurate assessment. In this paper, we provide the first comprehensive evaluation of LLMs for clone detection, covering different clone types, languages, and prompts. We find advanced LLMs excel in detecting complex semantic clones, surpassing existing methods. Adding intermediate reasoning steps via chain-of-thought prompts noticeably enhances performance. Additionally, representing code as vector embeddings, especially with text encoders, effectively aids clone detection.Lastly, the ability of LLMs to detect code clones differs among various programming languages. Our study suggests that LLMs have potential for clone detection due to their language capabilities, offering insights for developing robust LLM-based methods to enhance software engineering.Comment: 13 pages, 3 figure

Virtual Planning and 3D printing modeling for mandibular reconstruction with fibula free flap

This study was to evaluate the use of virtual planning and 3D printing modeling in mandibular reconstruction and compare the operation time and surgical outcome of this technique with conventional method. Between 2014 and 2017, 15 patients underwent vascularized fibula flap mandibular reconstruction using virtual planning and 3D printing modeling. Titanium plates were pre-bent using the models and cutting guides were used for osteotomies. 15 patients who underwent mandibular reconstruction using fibula flap without aid of virtual planning and 3D printing models were selected as control group. The operation time was recorded and compared in two groups. Accuracy of reconstruction was measured by superimposing the preoperative image onto the postoperative image of mandible. The selected bony landmark, distance and angle were measured. The mean total operation time and reconstruction time were 1.60±0.37 and 5.54±0.50 hours in computer-assisted group, respectively; These were 2.58±0.45 and 6.54±0.70 hours in conventional group, respectively. Both operation time and reconstruction time were shorter in computer-assisted group. The difference between the preoperative and postoperative intercondylar distances, intergonial angle distances, anteroposterior distances and gonial angles were 2.92±1.15 and 4.48±1.41mm, 2.93±1.19 and 4.79±1.48mm, 4.31±1.24 and 5.61±1.41mm, 3.85±1.68° and 5.88±2.12° in the computer-assisted and conventional group, respectively. The differences between the preoperative and postoperative mandible is smaller in the computer-assisted group. Virtual planning and 3D printing modeling have the potential to increase mandibular reconstruction accuracy and reduce operation time. we believe that this technology for mandibular reconstruction in selected patients will become a used method and improve the quality of reconstruction

A case report of anti-GAD65 antibody-positive autoimmune encephalitis in children associated with autoimmune polyendocrine syndrome type-II and literature review

BackgroundGlutamic acid decarboxylase (GAD) is the rate-limiting enzyme for the synthesis of gamma-aminobutyric acid (GABA), the major inhibitory neurotransmitter in the central nervous system. Antibodies against glutamic acid decarboxylase (GAD) are associated with various neurologic conditions described in patients, including stiff person syndrome, cerebellar ataxia, refractory epilepsy, and limbic and extra limbic encephalitis. While there are few case reports and research on anti-GAD65 antibody-associated encephalitis in adults, such cases are extremely rare in pediatric cases.MethodsFor the first time, we report a case of anti-GAD65-positive autoimmune encephalitis associated with autoimmune polyendocrine syndrome (APS) type II. We reviewed previously published pediatric cases of anti-GAD65 autoimmune encephalitis to discuss their clinical features, laboratory tests, imaging findings, EEG patterns, and prognosis.Case presentationAn 8-year-old, male child presented to the outpatient department after experiencing generalized convulsions for twenty days. The child was admitted for epilepsy and had received oral sodium valproate (500 mg/day) in another center, where investigations such as USG abdomen and MRI brain revealed no abnormalities, however, had abnormal EEG with diffuse mixed activity in the left anterior middle prefrontal temporal region. On the follow-up day, a repeat blood test showed a very low serum drug concentration of sodium valproate hence the dose was increased to 750 mg/day. Then, the child experienced adverse effects including increased sleep, thirst, and poor appetite, prompting the parents to discontinue the medication. A repeat MRI showed increased signals on FLAIR sequences in the right hippocampus hence admitted for further management. The child's past history included a diagnosis of hypothyroidism at the age of 4, and receiving levothyroxine 75 mcg once daily. His parents are healthy with no history of any similar neurological, autoimmune, or genetic diseases, but his uncle had a history of epilepsy. At presentation, he had uncontrolled blood glucose levels with elevated HbA1c levels. Additionally, the serum and CSF autoantibodies were positive against the anti-GAD65 antibody with the titer of 1:100 and 1:32 respectively. The patient was managed with a mixed type of insulin regimen and received first-line immunotherapy (intravenous immunoglobulin, IVIG) for five consecutive days, followed by oral prednisone and sodium valproate as an antiepileptic drug. Upon achieving a favorable clinical outcome, the patient was discharged with oral medications.ResultsAmong the 15 pediatric patients reported in this literature, nine presented with limbic encephalitis (LE), three with extralimbic encephalitis (ELE), and three with a combination of limbic and extralimbic encephalitis. Most of these cases exhibited T2-W FLAIR hyperintensities primarily localized to the temporal lobes in the early phase, progressing to hippocampal sclerosis/atrophy in the later phase on MRI. EEG commonly showed slow or spike waves on frontotemporal lobes with epileptic discharges. Prognostic factors varied among patients, with some experiencing persistent refractory seizures, type-1 diabetes mellitus (T1DM), persistent memory impairment, persistent disability requiring full assistance, and, in severe cases, death.ConclusionOur findings suggest that anti-GAD65 antibody-positive autoimmune encephalitis patients may concurrently present with other APS. Our unique case presented with multiple endocrine syndromes and represents the first reported occurrence in children. Early diagnosis and timely initiation of immunotherapy are crucial for improving clinical symptoms and reducing the likelihood of relapses or permanent disabilities. Therefore, emphasis should be placed on prompt diagnosis and appropriate treatment implementation to achieve better patient outcomes

Study on the influencing factors of digital transformation of construction enterprises from the perspective of dual effects—a hybrid approach based on PLS-SEM and fsQCA

The digital transformation of Chinese construction enterprises is crucial for achieving sustainable and high-quality development in the construction industry. However, there is still a lack of in-depth research on the impact mechanism of digital transformation in construction enterprises. The purpose of this study is to explore the multiple influencing factors and complex causal relationships of digital transformation in construction enterprises and promote the deep integration of digitalization and construction enterprises. To this end, based on the dual-effect perspective (net effect perspective of a single influencing factor and configuration effect perspective of multiple influencing factors), using the “technology–organization–environment” framework (TOE framework) to construct a research model of influencing factors for digital transformation in construction enterprises. A sample of 236 construction enterprise managers was surveyed, and partial least squares structural equation modeling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA) methods were used to empirically analyze the dual effects of influencing factors for digital transformation in construction enterprises. The results show that: (1) from the net effect perspective, there are seven factors that significantly impact digital transformation in construction enterprises; (2) from the configuration effect perspective, there are three paths that can achieve high-level digital transformation in construction enterprises, and one path that leads to low-level digital transformation; (3) from the dual-effect perspective, top management support and policy support are key factors for digital transformation in Chinese construction enterprises. The research results enrich the relevant research on digital transformation in construction enterprises and provide a reference basis for promoting digital transformation in construction enterprises

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

High-Efficient Micro Reacting Pipe with 3D Internal Structure: Design, Flow Simulation, and Metal Additive Manufacturing

The micro reacting pipe with 3D internal structure, which is a micromixer with the shape of the pipe, has shown great advantages regarding mass transfer and heat transfer. Since the fluid flow is mostly laminar at the micro-scale, which is unfavorable to the diffusion of reactants, it is important to understand the influence of the geometry of the microchannel on the fluid flow for improving the diffusion of the reactants and mixing efficiency. On the other hand, it is a convenient method to manufacture a micro reacting pipe in one piece through metal additive manufacturing without many post-processing processes. In this paper, a basis for the design of a micromixer model was provided by combining the metal additive manufacturing process constraints with computational fluid dynamics (CFD) simulation. The effects of microchannel structures on fluid flow and mixing efficiency were studied by CFD simulation whose results showed that the internal micro-structure had a significantly positive effect on the mixing efficiency. Based on the simulation results, the splitting-collision mechanism was discussed, and several design rules were obtained. Two different materials were selected for manufacturing with the laser powder bed fusion (L-PBF) technology. After applying pressure tests to evaluate the quality of the formed parts and comparing the corrosion-resistance of the two materials, one material was picked out for the industrial application. Additionally, the chemical experiment was conducted to evaluate the accuracy of the simulation. The experimental results showed that the mixing efficiency of the micro reacting pipe increased by 56.6%, and the optimal determining size of the micro reacting pipe was 0.2 mm. The study can be widely used in the design and manufacture of a micromixer, which can improve efficiency and reacting stability in this field

Research on Key Technology of Ship Re-Identification Based on the USV-UAV Collaboration

Distinguishing ship identities is critical in ensuring the safety and supervision of the marine agriculture and transportation industry. In this paper, we present a comprehensive investigation and validation of the progression of ship re-identification technology within a cooperative framework predominantly governed by UAVs. Our research revolves around the creation of a ship ReID dataset, the creation of a ship ReID dataset, the development of a feature extraction network, ranking optimization, and the establishment of a ship identity re-identification system built upon the collaboration of unmanned surface vehicles (USVs) and unmanned aerial vehicles (UAVs). We introduce a ship ReID dataset named VesselID-700, comprising 56,069 images covering seven classes of typical ships. We also simulated the multi-angle acquisition state of UAVs to categorize the ship orientations within this dataset. To address the challenge of distinguishing between ships with small inter-class differences and large intra-class variations, we propose a fine-grained feature extraction network called FGFN. FGFN enhances the ResNet architecture with a self-attentive mechanism and generalized mean pooling. We also introduce a multi-task loss function that combines classification and triplet loss, incorporating hard sample mining. Ablation experiments on the VesselID-700 dataset demonstrate that the FGFN network achieves outstanding performance, with a Rank-1 accuracy of 89.78% and mAP of 65.72% at a state-of-the-art level. Generalization experiments on pedestrian and vehicle ReID datasets reveal that FGFN excels in recognizing other rigid body targets and diverse viewpoints. Furthermore, to further enhance the advantages of UAV-USV synergy in ship ReID performance, we propose a ranking optimization method based on the homologous fusion of multi-angle UAVs and heterologous fusion of USV-UAV collaborative architecture. This optimization leads to a significant 3% improvement in Rank-1 performance, accompanied by a 73% reduction in retrieval time cost