Experimental study on the direct firing of ceramic ware using concentrated solar energy.

In this study, we developed a solar ceramic kiln to address the problem of CO2 emissions caused by traditional ceramic ware firing processes. Ceramic specimens were fired using the proposed setup, and their properties were compared with those of an electric-fired ceramic cup (ECC). The solar-fired ceramic cup (SCC) exhibited no cracks on its surface. Its water absorption was 4.25%, open porosity was 9.93%, bulk density was 2.210 g/cm3, bending strength was 88.96 MPa, and ovalization was 1.46%. These findings suggested that the physical performance of the SCC was comparable to that of the ECC, which had water absorption of 4.03%, an open porosity of 8.79%, a bulk density of 2.180 g/cm3, an ovalization of 1.45%, and a bending strength of 72.4 MPa. The SCC achieved these results in 120 min, which was seven times faster than that of the ECC (firing time of 869 min).

Cognitive impairment and neurocognitive profiles among people living with HIV and HIV-negative individuals older over 50 years: a comparison of IHDS, MMSE and MoCA.

We aimed to examine the l differences in the assessment of neurocognitive impairment (NCI) using cognitive screening tools between PLWH and HIV-negative individuals and further compare the neurocognitive profiles between the two groups. This was baseline evaluation of Pudong HIV Aging Cohort, including 465 people living with HIV (PLWH) and 465 HIV-negative individuals aged over 50 years matched by age (± 3 years), sex and education. NCI was assessed using the Chinese version of Mini-mental State Examination (MMSE), the International HIV Dementia Scale (IHDS) and Beijing version of Montreal Cognitive Assessment (MoCA). In total, 258 (55.5%), 91 (19.6%), 273 (58.7%) of PLWH were classified as having NCI by the IHDS, MMSE and MoCA, compared to 90 (19.4%), 25 (5.4%), 135 (29.0%) of HIV-negative individuals, respectively (p < 0.05); such associations remained significant in multivariable analysis. PLWH showed a larger overlap of NCI detected by IHDS, MMSE, and MoCA. IHDS and MoCA detected almost all of the NCI detected by MMSE. IHDS-motor and psychomotor speeds and MoCA-executive function showed the greatest disparities between two groups. In multivariable analysis, older age and more depressive symptoms were positively associated with NCI regardless of the screening tools or HIV serostatus. PLWH over 50 years old display a higher prevalence of NCI and distinct neurocognitive profiles compared to HIV-negative individuals, despite viral suppression. Given the more considerable overlap in NCI classification in PLWH, it is advisable to choose one screening tool such as IHDS or MoCA to identify those potentially having NCI and then refer to more comprehensive neuropsychological assessment.

Dual-functional ionic liquids additive enables dendrite-free Zn anode with ultra-long cycle life over one year.

Zn anodes suffer from the formation of uncontrolled dendrites aggravated by the uneven electric field and the insulating by-product accumulation in aqueous zinc-ion batteries (AZIBs). Here, an effective strategy implemented by 1-butyl-3-methylimidazolium hydrogen sulfate (BMIHSO4) additive is proposed to synergistically tune the crystallographic orientation of zinc deposition and suppress the formation of zinc hydroxide sulfate for enhancing the reversibility on Zn anode surface. As a competing cation, BMI+ is proved to preferably adsorb on Zn-electrode compared with H2O molecules, which shields the "tip effect" and inhibits the Zn-deposition agglomerations to inducing the horizontal growth along Zn (002) crystallographic texture. Simultaneously, the protonated BMIHSO4 additives could remove the detrimental OH- in real-time to fundamentally eliminate the accumulation of 6Zn(OH)2·ZnSO4·4H2O and Zn4SO4(OH)6·H2O on Zn anode surface. Consequently, Zn anode exhibits an ultra-long cycling stability of one year (8762 h) at 0.2 mA cm-2/0.2 mAh cm-2, 3600 h at 2 mA cm-2/2 mAh cm-2 with a high plating cumulative capacity of 3.6 Ah cm-2, and a high average Coulombic efficiency of 99.6 % throughout 1000 cycles. This work of regulating Zn deposition texture combined with eliminating notorious by-products could offer a desirable way for stabilizing the Zn-anode/electrolyte interface in AZIBs.

Coarse- and Fine-Grained Fusion Hierarchical Network for Hole Filling in View Synthesis.

Depth image-based rendering (DIBR) techniques play an essential role in free-viewpoint videos (FVVs), which generate the virtual views from a reference 2D texture video and its associated depth information. However, the background regions occluded by the foreground in the reference view will be exposed in the synthesized view, resulting in obvious irregular holes in the synthesized view. To this end, this paper proposes a novel coarse and fine-grained fusion hierarchical network (CFFHNet) for hole filling, which fills the irregular holes produced by view synthesis using the spatial contextual correlations between the visible and hole regions. CFFHNet adopts recurrent calculation to learn the spatial contextual correlation, while the hierarchical structure and attention mechanism are introduced to guide the fine-grained fusion of cross-scale contextual features. To promote texture generation while maintaining fidelity, we equip CFFHNet with a two-stage framework involving an inference sub-network to generate the coarse synthetic result and a refinement sub-network for refinement. Meanwhile, to make the learned hole-filling model better adaptable and robust to the "foreground penetration" distortion, we trained CFFHNet by generating a batch of training samples by adding irregular holes to the foreground and background connection regions of high-quality images. Extensive experiments show the superiority of our CFFHNet over the current state-of-the-art DIBR methods. The source code will be available at https://github.com/wgc-vsfm/view-synthesis-CFFHNet.

Fine-tuning coreference resolution for different styles of clinical narratives.

OBJECTIVE: Coreference resolution (CR) is a natural language processing (NLP) task that is concerned with finding all expressions within a single document that refer to the same entity. This makes it crucial in supporting downstream NLP tasks such as summarization, question answering and information extraction. Despite great progress in CR, our experiments have highlighted a substandard performance of the existing open-source CR tools in the clinical domain. We set out to explore some practical solutions to fine-tune their performance on clinical data. METHODS: We first explored the possibility of automatically producing silver standards following the success of such an approach in other clinical NLP tasks. We designed an ensemble approach that leverages multiple models to automatically annotate co-referring mentions. Subsequently, we looked into other ways of incorporating human feedback to improve the performance of an existing neural network approach. We proposed a semi-automatic annotation process to facilitate the manual annotation process. We also compared the effectiveness of active learning relative to random sampling in an effort to further reduce the cost of manual annotation. RESULTS: Our experiments demonstrated that the silver standard approach was ineffective in fine-tuning the CR models. Our results indicated that active learning should also be applied with caution. The semi-automatic annotation approach combined with continued training was found to be well suited for the rapid transfer of CR models under low-resource conditions. The ensemble approach demonstrated a potential to further improve accuracy by leveraging multiple fine-tuned models. CONCLUSION: Overall, we have effectively transferred a general CR model to a clinical domain. Our findings based on extensive experimentation have been summarized into practical suggestions for rapid transferring of CR models across different styles of clinical narratives.

A hybrid GA-PSO strategy for computing task offloading towards MES scenarios.

As a new type of computing paradigm closer to service terminals, mobile edge computing (MEC), can meet the requirements of computing-intensive and delay-sensitive applications. In addition, it can also reduce the burden on mobile terminals by offloading computing. Due to cost issues, results in the deployment density of mobile edge servers (MES) is restricted in real scenario, whereas the suitable MES should be chosen for better performance. Therefore, this article proposes a task offloading strategy under the sparse MES density deployment scenario. Commonly, mobile terminals may reach MES through varied access points (AP) based on multi-hop transmitting mode. The transmission delay and processing delay caused by the selection of AP and MES will affect the performance of MEC. For the purpose of reducing the transmission delay due to system load balancing and superfluous multi-hop, we formulated the multi-objective optimization problem. The optimization goals are the workload balancing of edge servers and the completion delay of all task offloading. We express the formulated system as an undirected and unweighted graph, and we propose a hybrid genetic particle swarm algorithm based on two-dimensional genes (GA-PSO). Simulation results show that the hybrid GA-PSO algorithm does not outperform state-of-the-art GA and NSA algorithms in obtaining all task offloading delays. However, the workload by standard deviation approach is about 90% lower than that of the GA and NSA algorithms, which effectively optimizes the performance of load balancing and verifies the effectiveness of the proposed algorithm.

Study of Spatio-Temporal Modeling in Video Quality Assessment.

Video quality assessment (VQA) has received remarkable attention recently. Most of the popular VQA models employ recurrent neural networks (RNNs) to capture the temporal quality variation of videos. However, each long-term video sequence is commonly labeled with a single quality score, with which RNNs might not be able to learn long-term quality variation well: What's the real role of RNNs in learning the visual quality of videos? Does it learn spatio-temporal representation as expected or just aggregating spatial features redundantly? In this study, we conduct a comprehensive study by training a family of VQA models with carefully designed frame sampling strategies and spatio-temporal fusion methods. Our extensive experiments on four publicly available in- the-wild video quality datasets lead to two main findings. First, the plausible spatio-temporal modeling module (i. e., RNNs) does not facilitate quality-aware spatio-temporal feature learning. Second, sparsely sampled video frames are capable of obtaining the competitive performance against using all video frames as the input. In other words, spatial features play a vital role in capturing video quality variation for VQA. To our best knowledge, this is the first work to explore the issue of spatio-temporal modeling in VQA.

Artificial intelligence for radiological paediatric fracture assessment: a systematic review.

BACKGROUND: Majority of research and commercial efforts have focussed on use of artificial intelligence (AI) for fracture detection in adults, despite the greater long-term clinical and medicolegal implications of missed fractures in children. The objective of this study was to assess the available literature regarding diagnostic performance of AI tools for paediatric fracture assessment on imaging, and where available, how this compares with the performance of human readers. MATERIALS AND METHODS: MEDLINE, Embase and Cochrane Library databases were queried for studies published between 1 January 2011 and 2021 using terms related to 'fracture', 'artificial intelligence', 'imaging' and 'children'. Risk of bias was assessed using a modified QUADAS-2 tool. Descriptive statistics for diagnostic accuracies were collated. RESULTS: Nine eligible articles from 362 publications were included, with most (8/9) evaluating fracture detection on radiographs, with the elbow being the most common body part. Nearly all articles used data derived from a single institution, and used deep learning methodology with only a few (2/9) performing external validation. Accuracy rates generated by AI ranged from 88.8 to 97.9%. In two of the three articles where AI performance was compared to human readers, sensitivity rates for AI were marginally higher, but this was not statistically significant. CONCLUSIONS: Wide heterogeneity in the literature with limited information on algorithm performance on external datasets makes it difficult to understand how such tools may generalise to a wider paediatric population. Further research using a multicentric dataset with real-world evaluation would help to better understand the impact of these tools.

Single Image Super-Resolution Quality Assessment: A Real-World Dataset, Subjective Studies, and an Objective Metric.

Numerous single image super-resolution (SISR) algorithms have been proposed during the past years to reconstruct a high-resolution (HR) image from its low-resolution (LR) observation. However, how to fairly compare the performance of different SISR algorithms/results remains a challenging problem. So far, the lack of comprehensive human subjective study on large-scale real-world SISR datasets and accurate objective SISR quality assessment metrics makes it unreliable to truly understand the performance of different SISR algorithms. We in this paper make efforts to tackle these two issues. Firstly, we construct a real-world SISR quality dataset (i.e., RealSRQ) and conduct human subjective studies to compare the performance of the representative SISR algorithms. Secondly, we propose a new objective metric, i.e., KLTSRQA, based on the Karhunen-Loéve Transform (KLT) to evaluate the quality of SISR images in a no-reference (NR) manner. Experiments on our constructed RealSRQ and the latest synthetic SISR quality dataset (i.e., QADS) have demonstrated the superiority of our proposed KLTSRQA metric, achieving higher consistency with human subjective scores than relevant existing NR image quality assessment (NR-IQA) metrics. The dataset and the code will be made available at https://github.com/Zhentao-Liu/RealSRQ-KLTSRQA.

Traditional Chinese medicine residue-derived micropore-rich porous carbon frameworks as efficient sulfur hosts for high-performance lithium-sulfur batteries.

Biomass-derived carbon-based energy materials are receiving extensive attention nowadays. With the widespread use of traditional Chinese medicines in the treatment of diseases and health care, a great deal of herb residues are thrown away after the unique decoction process. Here, through hydrothermal carbonization combined with KOH activation, a micropore-rich and nitrogen-doped porous carbon framework (MRNCF) is prepared from the waste roots of a kind of well-known and widely used traditional Chinese medicine, Acanthopanax senticosus. Compared with ordinary carbon-based sulfur host materials, the MRNCFs can effectively hinder the shuttling effect and dissolution of polysulfides through the synergistic action of physical confinement in micropores and chemical anchoring for nitrogen doping, and the lithium-sulfur batteries using MRNCF as the host present superior electrochemical performance. In a high sulfur content of over 75%, the as-prepared electrodes exhibit a highly reversible specific capacity of 540.4 mA h g-1 at a current density of 0.5C after 150 cycles and an excellent rate capability at different current densities.

Comparative study of the methodologies used for subjective medical image quality assessment.

Healthcare professionals have been increasingly viewing medical images and videos in their routine clinical practice, and this in a wide variety of environments. Both the perception and interpretation of medical visual information, across all branches of practice or medical specialties (e.g. diagnostic, therapeutic, or surgical medicine), career stages, and practice settings (e.g. emergency care), appear to be critical for patient care. However, medical images and videos are not self-explanatory and, therefore, need to be interpreted by humans, i.e. medical experts. In addition, various types of degradations and artifacts may appear during image acquisition or processing, and consequently affect medical imaging data. Such distortions tend to impact viewers' quality of experience, as well as their clinical practice. It is accordingly essential to better understand how medical experts perceive the quality of visual content. Thankfully, progress has been made in the recent literature towards such understanding. In this article, we present an up-to-date state-of the-art of relatively recent (i.e. not older than ten years old) existing studies on the subjective quality assessment of medical images and videos, as well as research works using task-based approaches. Furthermore, we discuss the merits and drawbacks of the methodologies used, and we provide recommendations about experimental designs and statistical processes to evaluate the perception of medical images and videos for future studies, which could then be used to optimise the visual experience of image readers in real clinical practice. Finally, we tackle the issue of the lack of available annotated medical image and video quality databases, which appear to be indispensable for the development of new dedicated objective metrics.

High-efficiency recovery, regeneration and recycling of 1-ethyl-3-methylimidazolium hydrogen sulfate for levulinic acid production from sugarcane bagasse with membrane-based techniques.

Ionic liquids have been proven efficient and environmental medium for producing platform chemical levulinic acid. Lack of high-efficiency, stable and low-cost recovery strategy with complex electrolyte form restricts the further scale-up of ionic liquids for platform chemicals production. Membrane-based techniques including ultrafiltration (UF) and bipolar membrane electrodialysis (BMED) were employed for the high-efficiency recovery, regeneration and recycling of 1-ethyl-3-methylimidazolium hydrogen sulfate [Emim][HSO4] for levulinic acid production from sugarcane bagasse. UF-BMED treatment works based on the interception of macromolecule biomass degradation products by UF treatment with regional recovery of Emim+ and SO42- by BMED treatment. Effect of major parameters on [Emim][HSO4] recovery performance was determined. Recovery ratio for Emim+ and SO42- approached 95.4% and 95.9%. Energy consumption of specific [Emim][HSO4] recovery was closed to 5.8 kWh/kg. Insight gained from this study suggests a high-efficiency and economical strategy for platform chemicals production with green solvent ionic liquids.

Quantitative recovery and regeneration of acidic ionic liquid 1-butyl-3-methylimidazolium hydrogen sulphate via industrial strategy for sustainable biomass processing.

Quantitative recovery is necessary for scale-up application of acidic ionic liquids (AILs). Ultrafiltration and bipolar membrane electrodialysis (BMED) was employed for the recovery and regeneration of acidic ionic liquid 1-butyl-3-methylimidazolium hydrogen sulphate (Bmim[HSO4]) after biomass pretreatment. Ultrafiltration was designed for the purification of BMED feed solution. During BMED treatment, Bmim+ retention with OH- generation occurred in mixing section and SO42- immigration with H+ generation occurred in aciding section. Resulting aqueous Bmim[OH] in mixing section and H2SO4 in aciding section could be utilized for quantitative synthesis of Bmim[HSO4]. Influence of BMED operating mode and major parameters including BMED feed concentration and current density of BMED module were studied in detail. The highest recovery ratio for Bmim+ and SO42- reached 96.2% and 96.0%. And the lowest energy consumption of specific Bmim[HSO4] recovery approached 9.0 kw∙h/kg. Insight gained from this study suggested a sustainable biomass processing methodology using AILs.

Accurately-controlled recovery and regeneration of protic ionic liquid after Ionosolv pretreatment via bipolar membrane electrodialysis with ultrafiltration.

Efficient recovery and regeneration of ionic liquid is significant for industrial Ionosolv pretreatment. Complicated electrolyte composition restricts the scale-up recovery and application of protic ionic liquid such as triethylammonium hydrogen sulfate [TEA][HSO4] in biomass-related research. Recovery of [TEA][HSO4] after Ionosolv pretreatment for miscanthus powder was studied using bipolar membrane electrodialysis (BMED) assisted with ultrafiltration (UF) by the divisional recovery of TEA+ as TEA and recovery of SO42- as H2SO4 in different BMED compartments. Hence accurately-controlled regeneration of [TEA][HSO4] could be realized. Influence of current density and feed concentration of BMED module was studied in detail. In this study, the highest recovery ratio for TEA+ and SO42- reached 93.7% and 96.4%. The lowest energy consumption of specific [TEA][HSO4] recovery was about 6.2 kwh/kg. Insight gained from this study suggests a potentially industrial methodology for complicated protic ionic liquid recovery after biomass processing.

PAC1 receptor blockade reduces central nociceptive activity: new approach for primary headache?

Pituitary adenylate cyclase activating polypeptide-38 (PACAP38) may play an important role in primary headaches. Preclinical evidence suggests that PACAP38 modulates trigeminal nociceptive activity mainly through PAC1 receptors while clinical studies report that plasma concentrations of PACAP38 are elevated in spontaneous attacks of cluster headache and migraine and normalize after treatment with sumatriptan. Intravenous infusion of PACAP38 induces migraine-like attacks in migraineurs and cluster-like attacks in cluster headache patients. A rodent-specific PAC1 receptor antibody Ab181 was developed, and its effect on nociceptive neuronal activity in the trigeminocervical complex was investigated in vivo in an electrophysiological model relevant to primary headaches. Ab181 is potent and selective at the rat PAC1 receptor and provides near-maximum target coverage at 10 mg/kg for more than 48 hours. Without affecting spontaneous neuronal activity, Ab181 effectively inhibits stimulus-evoked activity in the trigeminocervical complex. Immunohistochemical analysis revealed its binding in the trigeminal ganglion and sphenopalatine ganglion but not within the central nervous system suggesting a peripheral site of action. The pharmacological approach using a specific PAC1 receptor antibody could provide a novel mechanism with a potential clinical efficacy in the treatment of primary headaches.

A Metric for Video Blending Quality Assessment.

We propose an objective approach to assess the quality of video blending. Blending is a fundamental operation in video editing, which can smooth the intensity changes of relevant regions. However blending also generates artefacts such as bleeding and ghosting. To assess the quality of the blended videos, our approach considers the illuminance consistency as a positive aspect while regard the artefacts as a negative aspect. Temporal coherence between frames is also considered. We evaluate our metric on a video blending dataset where the results of subjective evaluation are available. Experimental results validate the effectiveness of our proposed metric, and shows that this metric gives superior performance over existing video quality metrics.

PAC1 receptor mRNA and protein distribution in rat and human trigeminal and sphenopalatine ganglia, spinal trigeminal nucleus and in dura mater.

BACKGROUND: To further understand the role of pituitary adenylate cyclase-activating polypeptide 1 (PAC1) receptors in headache disorders, we mapped their expression in tissues of the trigemino-autonomic system by immunohistochemistry and in situ hybridization. METHODS: To optimize screening for monoclonal antibodies suitable for immunohistochemistry on formalin-fixed, paraffin-embedded tissues, we developed a new enzyme-linked immunosorbent assay using formalin-fixed, paraffin-embedded cells overexpressing human PAC1 receptors. 169G4.1 was selected from these studies for analysis of rat and human tissues and chimerized onto a mouse backbone to avoid human-on-human cross-reactivity. Immunoreactivity was compared to PAC1 receptor mRNA by in situ hybridization in both species. RESULTS: 169G4.1 immunoreactivity delineated neuronal cell bodies in the sphenopalatine ganglion in both rat and human, whereas no staining was detected in the trigeminal ganglion. The spinal trigeminal nucleus in both species showed immunoreactivity as especially strong in the upper laminae with both cell bodies and neuropil being labelled. No immunoreactivity was seen in either rat or human dura mater vessels. In situ hybridization in both species revealed mRNA in sphenopalatine ganglion neurons and the spinal trigeminal nucleus, a weak signal in the trigeminal nucleus and no signal in dural vessels. CONCLUSION: Taken together, these data support a role for PAC1 receptors in the trigemino-autonomic system as it relates to headache pathophysiology.

A Comparative Study of Algorithms for Realtime Panoramic Video Blending.

Unlike image blending algorithms, video blending algorithms have been little studied. In this paper, we investigate 6 popular blending algorithms-feather blending, multi-band blending, modified Poisson blending, mean value coordinate blending, multi-spline blending and convolution pyramid blending. We consider their application to blending realtime panoramic videos, a key problem in various virtual reality tasks. To evaluate the performances and suitabilities of the 6 algorithms for this problem, we have created a video benchmark with several videos captured under various conditions. We analyze the time and memory needed by the above 6 algorithms, for both CPU and GPU implementations (where readily parallelizable). The visual quality provided by these algorithms is also evaluated both objectively and subjectively. The video benchmark and algorithm implementations are publicly available1.

Mechanical properties of hollow and water-filled graphyne nanotube and carbon nanotube hybrid structure.

By performing molecular dynamics simulations, a GNT/CNT hybrid structure constructed via combing (6, 6) graphyne nanotube (GNT) with (6, 6) carbon nanotube (CNT) has been designed and investigated. The mechanical properties induced by the percentage of GNT, water content and electric field were examined. Calculation results reveal that the fracture strain and strength of hollow hybrid structure are remarkably smaller than that of perfect (6, 6) CNT. In addition, the Young's modulus decreases monotonously with the increase of percentage of GNT. More importantly, the tunable mechanical properties of hybrid structure can be achieved through filling with water molecules and applying an electric field along tensile direction. Specifically, increasing water content from 0.0 to 8.70 mmol g-1 in the absence of electric field could result in fracture strain and strength reducing by 15.09% and 12.87%, respectively. Besides, enhancing fracture strain and strength of water-filled hybrid structure with water content of 8.70 mmol g-1 can also be obtained with rising electric field intensity. These findings would provide a valuable theoretical basis for designing and fabricating a nanodevice with controllable mechanical performances.