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In response to the design problems of violent vibration and noise when a stranding machine is running at high speed, this project completed a motion simulation and vibration analysis based on the prototype FB-650C-2 bow-type stranding machine produced by Fuchuan Mechanical and Electrical Technology Co. The modal analysis was carried out in ANSYS to obtain the first eight orders of inherent frequencies and vibration patterns, combined with excitation force analysis to verify whether the rotating parts could avoid the resonant frequency when operating. Harmonic response analysis was carried out based on the modal state to calculate the steady-state forced vibration of the structure, and the variation curve of response value (usual deformation) with frequency and the cloud diagrams of stress distribution of each component at the rotation frequency were obtained. Suggestions for improving vibration and reducing noise were made based on the experimental and analytical results.

Supplementary Material 1

Credit risk in the China's bond market has become increasingly evident, creating a progressively escalating risk of default for credit bond investors. Given the current incomplete and inaccurate bond information disclosure, timely tracking and forecasting the individual credit bond default risks have become essential to maintain market stability and ensure healthy development. This paper proposes an Intelligent Forecasting Framework for Default Risk that provides precise day-by-day default risk prediction. In this framework, we first summarize the factors that impact credit bond defaults and construct a risk index system. Then, we employ a combined default probability annotation method based on the evolutionary characteristics of bond default risk. The method considers the weighted average of Variational Bayesian Gaussian Mixture estimation, Market Index estimation, and Default Trend Backward estimation for daily default risk annotation of matured or defaulted bonds according to the risk index system. Moreover, to mine time-series correlation and cross-sectional index correlation features efficiently, an intelligent prediction model for Chinese credit bond default risk is designed using the ConvLSTM neural network and trained with structured feature data. The experiments demonstrate that the predicted individual bond risk is slightly higher and substantially more responsive to fluctuations than the risk indicated by authoritative ratings, thereby improving on the inadequacies of inflated and untimely bond ratings. Consequently, this study's findings offer multiple insights for regulators, issuers, and investors., Comment: 20 pages, 9 figures, 2 tables

With the expansive aging of global population, service robot with living assistance applied in indoor scenes will serve as a crucial role in the field of elderly care and health in the future. Service robots need to detect multiple targets when completing auxiliary tasks. However, indoor scenes are usually complex and there are many types of interference factors, leading to great challenges in the multiple targets detection. To overcome this technical difficulty, a novel improved Mask RCNN method for multiple targets detection in the indoor complex scenes is proposed in this paper. The improved model utilizes Mask RCNN as the network framework. On this basis, Convolutional Block Attention Module (CBAM) with channel mechanism and space mechanism is integrated, and the influence of different background, distance, angle and interference factors are comprehensively considered. Meanwhile, in order to evaluate the detection and identification effects of the established model, a comprehensive evaluation system based on loss function and Mean Average Precision (mAP) is established. For verification, experiments on the detection and identification effects under different distances, backgrounds, angles and interference factors were conducted. The results show that designed model improves the accuracy to a higher level and has a better anti-interference ability than other methods when the detection speed was nearly the same.

Supplementary Material 2

Background Patients with unilateral cleft lip and palate were associated with different nasomaxillary complex from the normal population. Although the biomechanical effects of conventional rapid palatal expansion (Hyrax expansion) and bone-borne rapid palatal expansion (micro-implant-assisted expansion) in non-cleft patients have been identified by multiple studies, little is known in patients with unilateral cleft lip and palate. The purpose of this study was to investigate and compare the biomechanical effects of the conventional and bone-borne palatal expanders in a late adolescence with unilateral cleft lip and palate. Methods A cone beam CT scan of a late adolescence with unilateral cleft lip and palate was selected to construct the three-dimensional finite element models of teeth and craniofacial structures. The models of conventional and born-borne palatal expanders were established to simulate the clinical maxillary expansion. The geometric nonlinear theory was applied to evaluate the Von Mises stress distribution and displacements in craniofacial structures and teeth. Results Bone-borne palatal expander achieved more transverse movement than conventional palatal expander in the whole mount of craniofacial regions, and the maximum amount of expansion was occurred anteriorly along the alveolar ridge on cleft-side. The expanding force from born-borne palatal expander resulted in more advancement in nasomaxillary complex than it in conventional palatal expander, especially in the anterior area of the minor segment of maxilla. Stresses from the both expanders distributed in similar patterns, but larger magnitudes and ranges were generated using the bone-borne expander around the maxillary buttresses and pterygoid plates of sphenoid bone. The maximum expanding stresses from born-borne palatal expander were concentrated on palatal slope supporting minscrews, whereas those from conventional palatal expander were concentrated on the anchoring molars. In addition, the buccal tipping effect of teeth generated using the bone-borne expander was less than it using the conventional palatal expander. Conclusion Bone-borne expander generated enhanced skeletal expansion at the levels of alveolar and palate in transversal direction, where the miniscrews contributed increased expanding forces to maxillary buttresses and decreased forces to buccal alveolar. Bone-borne expanders presented a superiority in correcting the asymmetric maxilla without surgical assistant in late adolescence with unilateral cleft lip and palate.

Recently, biphenylene was successfully synthesized as a novel allotrope of carbon. In this investigation, non-equilibrium molecular dynamics calculations are conducted to explore the intrinsic thermal properties of biphenylene. The isotropic thermal conductivity of biphenylene is obtained, which is also sensitive to size and temperature. Furthermore, the graphene/biphenylene lateral heterostructure is constructed to possess an interfacial thermal conductance of about 2.84 × 109 W K−1 m−2. The external tensile strain can induce a redshift of the vibrational density of states of pristine graphene and biphenylene, and the improved overlap also results in an enhanced heat flux in the biphenylene/graphene heterostructure. Our approach can provide theoretical guidance to design a thermal management device based on graphene and biphenylene.

The tetraruthenium tetrahydrido carbonyl cluster Ru4(µ-H)4(CO)12 (1) reacts with diphenylvinylphosphine under thermal activation to give the substitution products Ru4(µ-H)4(CO)12−n(κ1-Ph2PCH=CH2)n, (2) (n = 3 (a); 4 (b)). Both 2a and 2b have been completely characterized, including by single-crystal X-ray diffraction analysis. Each of the diphenylvinylphosphine ligands in 2a and 2b is terminally bound, via the phosphorus atom, to a different ruthenium metal center, while the hydride positions in the Ru4(µ-H)4 cores retain the D2d symmetry of the parent cluster 1. Clusters 2a and 2b were able to retain their structural integrity at elevated temperatures. Graphic Abstract: Thermolysis of Ru4(μ-H)4(CO)12 (1) in cyclohexane with diphenylvinylphosphine afforded the tri- and tetra-substituted derivatives Ru4(μ-H)4(CO)9(κ1-Ph2PCH=CH2)3 (2a) and Ru4(μ-H)4(CO)8(κ1-Ph2PCH=CH2)4 (2b). For both substituted clusters, the diphenylvinylphosphine ligands are bound terminally to the tetraruthenium core via the phosphorus atoms while the hydride positions retain the D2d symmetry of the parent cluster 1.[Figure not available: see fulltext.] Ministry of Education (MOE) Nanyang Technological University This work was supported by Hwa Chong Institution as well as Nanyang Technological University and the Ministry of Education with a research Grant (No. M4011158).

Peat is known as problematic ground with low bearing capacity and extensively high compressibility. Bio-cementation or commonly known as microbial-induced calcite precipitation (MICP) has been recently introduced as a ground improvement alternative for peat under waterlogged condition. Using isolated bacteria strains P19 and P21 from tropical peat, it is found that unconfined compression strength (UCS) increases with bacteria concentration at a reducing rate. A maximum unconfined compressive strength of 82.05 kPa was measured with bacteria strain P21 at 108 CFU/mL. For the range of cementation reagent varying from 0.1 to 4.0 mol/kg, the largest strength improvement occurred at 1 mol/kg and 2 mol/kg using indigenous bacteria and bacteria strain P21, respectively, for peat with sand content of 25%. At 4.0 mol/kg, the cementation reagent has detrimental effect to MICP resulting in significant reduction in strength. Due to MICP, the UCS of peat increases with sand content. Calcium carbonate precipitation results in a reduction of permeability and an increment of strength of peat–sand mixture under a submerged condition up to 28 days.

Objective: The study addresses whether the growth of the nasal dorsum is disturbed by cleft treatments, for cleft lip only (CL) and cleft lip with cleft palate (CLP). Design: A total of 576 patients with cleft (278 CL, 298 CLP) and 333 individuals without orofacial clefts were retrospectively enrolled. Cleft lip only group was treated with a modified Millard technique combined with Tajima incision for rhinoplasty at 3 to 6 months. The CLP group underwent the same lip repair technique and then underwent a Sommerlad palatoplasty at 9 to 12 months. Lateral cephalometric radiographs of all individuals were taken to evaluate the nasal length and nasal dorsum height. Dunn test was used to analyze the difference ( P < .001). Results: Compared with control, in CL, nasal bone angle and nasal dorsum angle increase by age similarly (5-18 years, P > .05); the total dorsum is significantly shorter (5-18 years, P < .001), while the upper nasal dorsum length is similar (except in 5-6 years), and the lower nasal dorsum is shorter (5-18 years, P < .001). In CLP, nasal bone angle develops insufficiently as children grow (8-18 years, P < .001); the nasal dorsum angle is notably smaller (5-18 years, P < .001); nasal bone length is not significantly different except 11 to 13 years ( P < .05); nasal dorsal length is similar at skeletal maturity (17-18 years, P > .05), although it is shorter during 8 to 16 years ( P < .05); the upper nasal dorsum is overdeveloped (14-18 years, P < .05), whereas the lower nasal dorsum is underdeveloped (5-18 years, P < .001). Conclusion: Treatments in both CL and CLP could be the important factors in disturbing the growth of cartilaginous portion of the nasal dorsum (including nasal tip) and the nasal dorsum height.

Primary insomnia (PI) is among the most prevalent sleep-related disorders and has a far-reaching impact on daytime functioning. Repetitive transcranial magnetic stimulation (rTMS) has drawn attention because of its effectiveness and safety. The purpose of the current study was to detect changes in the topological organization of whole-brain functional networks and to determine their associations with the clinical treatment effects of rTMS. Resting-state functional magnetic resonance imaging (rsfMRI) data from 32 patients with PI were collected and compared with findings from 32 age- and gender-matched healthy controls (HCs). The patients were treated with Stanford accelerated intelligent neuromodulation therapy, which is a recently validated neuroscience-informed accelerated intermittent theta-burst stimulation protocol. Graph theoretical analysis was used to construct functional connectivity matrices and to extract the attribute features of small-world networks in insomnia. Scores on the Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index, Self-Rating Anxiety Scale, Self-Rating Depression Scale, and the associations between these clinical characteristics and functional metrics, were the primary outcomes. At baseline, the patients with PI showed inefficient small-world property and aberrant functional segregation and functional integration compared with the HCs. These properties showed renormalization after individualized rTMS treatment. Furthermore, low functional connectivity between the right insula and left medial frontal gyrus correlated with improvement in ISI scores. We highlight functional network dysfunctions in PI patients and provide evidence into the pathophysiological mechanisms involved and the possible mode of action of rTMS.

The problem of multi-object tracking is a fundamental computer vision research focus, widely used in public safety, transport, autonomous vehicles, robotics, and other regions involving artificial intelligence. Because of the complexity of natural scenes, object occlusion and semi-occlusion usually occur in fundamental tracking tasks. These can easily lead to ID switching, object loss, detect errors, and misaligned limitation boxes. These conditions have a significant impact on the precision of multi-object tracking. In this paper, we design a new multi-object tracker for the above issues that contains an object \textbf{Relative Location Mapping} (RLM) model and \textbf{Target Region Density} (TRD) model. The new tracker is more sensitive to the differences in position relationships between objects. It can introduce low-score detection frames into different regions in real-time according to the density of object regions in the video. This improves the accuracy of object tracking without consuming extensive arithmetic resources. Our study shows that the proposed model has considerably enhanced the HOTA and DF1 measurements on the MOT17 and MOT20 data sets when applied to the advanced MOT method.

Supplemental Material for The analgesic effects of β-elemene in rats with neuropathic pain by inhibition of spinal astrocytic ERK activation by Li-Tian Ma, Yang Bai, Peng Cao, Kai-Xi Ren, Jing Chen, Ting Zhang, Bo-Yuan Fan, Yu Qiao, Hong-Yu Yan, Jing-Jie Wang, Yun-Qing Li and Jin Zheng in Molecular Pain

Topology Optimization (TO) provides a systematic approach for obtaining structure design with optimum performance of interest. However, the process requires numerical evaluation of objective function and constraints at each iteration, which is computational expensive especially for large-scale design. Deep learning-based models have been developed to accelerate the process either by acting as surrogate models replacing the simulation process, or completely replacing the optimization process. However, most of them require a large set of labelled training data, which are generated mostly through simulations. The data generation time scales rapidly with the design domain size, decreasing the efficiency of the method itself. Another major issue is the weak generalizability of most deep learning models. Most models are trained to work with the design problem similar to that used for data generation and require retraining if the design problem changes. In this work a scalable deep learning-based model-order-reduction method is proposed to accelerate large-scale TO process, by utilizing MapNet, a neural network which maps the field of interest from coarse-scale to fine-scale. The proposed method allows for each simulation of the TO process to be performed at a coarser mesh, thereby greatly reducing the total computational time. Moreover, by using domain fragmentation, the transferability of the MapNet is largely improved. Specifically, it has been demonstrated that the MapNet trained using data from one cantilever beam design with a specific loading condition can be directly applied to other structure design problems with different domain shapes, sizes, boundary and loading conditions., Comment: 27 pages, 24 figures

Due to their designable properties, microstructural materials have emerged as an important class of materials that have the potential for used in a variety of applications. The design of such materials is challenged by the multifunctionality requirements and various constraints stemmed from manufacturing limitations and other practical considerations. Traditional design methods such as those based on topological optimization techniques rely heavily on high-dimensional physical simulations and can be inefficient. In addition, it is difficult to impose geometrical constraints in those methods. In this work, we propose a deep learning model based on deep convolutional generative adversarial network (DCGAN) and convolutional neural network (CNN) for the design of microstructural materials. The DCGAN is used to generate design candidates that satisfy geometrical constraints and the CNN is used as a surrogate model to link the microstructure to its properties. Once trained, the two networks are combined to form the design network which is utilized to for the inverse design. The advantages of the method include its high efficiency and the simplicity in handling geometrical constraints. In addition, no high-dimensional sensitivity simulations are required. The performance of the method is demonstrated on the design of microstructural materials with desired compliance tensor, subject to specified geometrical constraints.

Purpose The precise segmentation of brain tumors is the most important and crucial step in their diagnosis and treatment. Due to the presence of noise, uneven gray levels, blurred boundaries and edema around the brain tumor region, the brain tumor image has indistinct features in the tumor region, which pose a problem for diagnostics. The paper aims to discuss these issues. Design/methodology/approach In this paper, the authors propose an original solution for segmentation using Tamura Texture and ensemble Support Vector Machine (SVM) structure. In the proposed technique, 124 features of each voxel are extracted, including Tamura texture features and grayscale features. Then, these features are ranked using the SVM-Recursive Feature Elimination method, which is also adopted to optimize the parameters of the Radial Basis Function kernel of SVMs. Finally, the bagging random sampling method is utilized to construct the ensemble SVM classifier based on a weighted voting mechanism to classify the types of voxel. Findings The experiments are conducted over a sample data set to be called BraTS2015. The experiments demonstrate that Tamura texture is very useful in the segmentation of brain tumors, especially the feature of line-likeness. The superior performance of the proposed ensemble SVM classifier is demonstrated by comparison with single SVM classifiers as well as other methods. Originality/value The authors propose an original solution for segmentation using Tamura Texture and ensemble SVM structure.

Internal audit quality is the foundation for a company's survival and development across the world. As a result, global efforts have been made to develop a scientific and accurate evaluation index for internal audit quality. However, literature shows that existing internal audit quality evaluation indices have many flaws, such as a lack of systematic internal audit evaluation indicators, poor execution, and inability to identify priority areas. To address gaps in the literature, this study intended to construct an internal audit quality evaluation index utilizing the joint approaches of the Balanced Scorecard, Delphi Process, and Analytical Hierarchy Process. A systematic literature review, examination of companies' internal audit guidelines, Balanced Scorecard, and Delphi approaches resulted in a multilevel internal audit quality evaluation index with five dimensions (stakeholder satisfaction, stakeholder contribution, financial results, internal audit process, and learning and growth) and 36 indicators. The Analytical Hierarchy Process revealed that the most prioritized dimension is the internal audit process. The consistency ratio and evaluation feedback from the listed companies' internal auditors, management, and audit committee members revealed that the results are valid and reliable.

Background and Objective: Citrus grandis ‘Tomentosa’, as the fruit epicarp of C. grandis ‘Tomentosa’ or C. grandis (L.) Osbeck, is widely used in health food and medicine. Actually, based on our survey results, there are also rich essential oils with bioativities in leaves, but the chemical compounds in this part and relevant pharmacological activities have never been studied systematically yet. Therefore, this study was to preliminarily decipher the pharmacological activities and mechanisms of the essential oil in leaves of C. grandis ‘Tomentosa’ by an integrated network pharmacology approach. Methods: Essential oil compositions from leaves of C. grandis ‘Tomentosa’ were identified using GC-MS/MS. And then the targets of these oil compositions were predicted and screened from TCMSP, SwissTargetPrediction, STITCH and SEA databases. STRING database was used to construct the protein-protein interaction networks, and the eligible protein targets were input into WebGestalt 2019 to carry out GO enrichment and KEGG pathway enrichment analysis. Based on the potential targets, disease enrichment information was obtained by TTD databases. Cytoscape software was used to construct the component-target-disease network diagrams. Results: Finally, 61 essential oil chemical components were identified by GC-MS/MS, which correspond to 679 potential targets. Biological function analysis showed that there were 12, 19, and 12 GO entries related to biological processes, cell components and molecular functions, respectively. 43 KEGG pathways were identified, of which the most significant categories were terpenoid backbone biosynthesis, TNF signaling pathway and leishmaniasis. The component-target-disease network diagram revealed that the essential oil compositions in leaves of C. grandis ‘Tomentosa’ could treat tumors, immune diseases, neurodegenerative diseases and respiratory diseases, which were highly related to CHRM1, PTGS2, CASP3, MAP2K1 and CDC25B.Conclusion: This study may provide a new insight into C. grandis ‘Tomentosa’ or C. grandis (L.) Osbeck and may provide useful information for future utilization and development.

The removal of reservoir silt and the restoration of existing reservoir capacities through land excavation and hydraulic sediment flushing have become necessary. Hydraulic sediment flushing discharge changes flow and sediment conditions of the downstream river channel. In the Tamsui River estuary in Taiwan, sediment flushing from the Shihmen reservoir upstream has potential impacts on the morphology of the navigation channels and the adjacent coasts. This study employed a validated coastal and estuarine processes model to investigate: (1) the influence of sediment flushing and tidal levels on morphological changes during flood and flushing-discharge operations of the reservoir, and (2) the differences in morphological changes on the estuary between monsoon and typhoon seasons. The prediction of the morphological changes was carried out by simulating hydrodynamic and morphodynamic processes under multi-year synthetic conditions combined by northeast monsoon and three historical typhoon events. The simulation results reveal that during the operation of sediment flushing when the peak discharge of river flood flow reaches the estuary section at ebb tides, more sediment can be transported to the open sea than that at flood tides. Additionally, the nature reserve area on the left bank of the estuary is eroded during monsoon and silted in typhoon seasons.

The aim of the present study was to investigate the incidence of postoperative fistula formation from a hybrid cleft palate repair compared to that from two well-established techniques. We performed a modified technique, Sommerlad-Furlow (SF), which combined the repositioning of the levator veli palatini muscles as described by Sommerlad with the double opposing Z-plasty of Furlow to lengthen the soft palate. A retrospective cohort study was conducted to evaluate patients who underwent cleft palate repair utilizing SF, Sommerlad, or Furlow techniques with the incidence of palatal fistula as the target endpoint. A total of 1,164 patients were included in the present study and underwent the following techniques: 603 cases with SF, 244 cases with Furlow, and 317 cases with Sommerlad. In addition to not requiring relaxing incisions, SF advantages included a consistently lower fistula rate compared to that of the Sommerlad technique, as well as the lowest fistula rate in patients with both hard and soft palate clefts without a cleft lip (OR:2.62 95% CI: 1.35, 5.09). However, the differences among the three techniques did not reach statistical significance in terms of a bilateral or unilateral cleft lip/palate, or in patients with a soft palate only or a submucosal cleft palate(OR: 2.22,95% CI:0.77, 6.37). Based on the results of our study, the Somerlad-Furlow technique should be preferred whenever possible.

Background and objective As the pathological mechanisms of AD are complex, increasing evidence have demonstrated Chinese Medicine with multi-ingredients and multi-targets may be more suitable for the treatment of diseases with complex pathogenesis. Therefore, the study was to preliminarily decipher the bioactive compounds and potential mechanisms of Qiong Yu Gao (QYG) for AD prevention and treatment by an integrated network pharmacology approach. Methods Putative ingredients of QYG and significant genes of AD were retrieved from public database after screening. Then QYG ingredients target proteins/genes were obtained by target fishing. Compound-target-disease network was constructed using Cytoscape to decipher the mechanism of QYG for AD. KEGG pathway and GO enrichment analysis were performed to investigate the molecular mechanisms and pathways related to QYG for AD treatments. Results Finally, 70 compounds and 511 relative drug targets were collected. In which, 17 representative direct targets were found. Gene ontology enrichment analysis revealed that the adenylate cyclase-inhibiting G-protein coupled acetylcholine receptor signaling pathway was the key biological processes and were regulated simultaneously by the 17 direct targets. The KEGG pathway enrichment analysis found that three signaling pathways were closely related to AD prevention and treatment by QYG, including PI3K-Akt signaling pathway, regulation of actin cytoskeleton pathway and insulin resistance pathway. Conclusion This study demonstrated that QYG exerted the effect of preventing and treating AD by regulating multi-targets with multi-components. Furthermore, the study demonstrated that a network pharmacology-based approach was useful for elucidation of the interrelationship between complex diseases and interventions of Chinese herbal medicines.

Deep reinforcement learning (DRL) has exhibited considerable promise in the training of control agents for mapless robot navigation. However, DRL-trained agents are limited to the specific robot dimensions used during training, hindering their applicability when the robot's dimension changes for task-specific requirements. To overcome this limitation, we propose a dimension-variable robot navigation method based on DRL. Our approach involves training a meta agent in simulation and subsequently transferring the meta skill to a dimension-varied robot using a technique called dimension-variable skill transfer (DVST). During the training phase, the meta agent for the meta robot learns self-navigation skills with DRL. In the skill-transfer phase, observations from the dimension-varied robot are scaled and transferred to the meta agent, and the resulting control policy is scaled back to the dimension-varied robot. Through extensive simulated and real-world experiments, we demonstrated that the dimension-varied robots could successfully navigate in unknown and dynamic environments without any retraining. The results show that our work substantially expands the applicability of DRL-based navigation methods, enabling them to be used on robots with different dimensions without the limitation of a fixed dimension. The video of our experiments can be found in the supplementary file., Comment: 9 pages, 15 figures. This work will be submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

This paper proposes a combined network structure between convolutional neural network (CNN) and long-short term memory (LSTM) quantifier for WiFi fingerprinting indoor localization. In contrast to conventional methods that utilize only spatial data with classification models, our CNN-LSTM network extracts both space and time features of the received channel state information (CSI) from a single router. Furthermore, the proposed network builds a quantification model rather than a limited classification model as in most of the literature work, which enables the estimation of testing points that are not identical to the reference points. We analyze the instability of CSI and demonstrate a mitigation solution using a comprehensive filter and normalization scheme. The localization accuracy is investigated through extensive on-site experiments with several mobile devices including mobile phone (Nexus 5) and laptop (Intel 5300 NIC) on hundreds of testing locations. Using only a single WiFi router, our structure achieves an average localization error of 2.5~m with $\mathrm{80\%}$ of the errors under 4~m, which outperforms the other reported algorithms by approximately $\mathrm{50\%}$ under the same test environment., Comment: Channel state information (CSI), WiFi indoor localization, convolutional neural network, long short-term memory, fingerprint-based localization

Additional file 1 : Table S1. The 1981 significant genes associated with AD. Table S2. The putative major chemical ingredients and ADME parameters in QYG. Table S3. The detailed target information of the ingredients of herbs

Traditional approaches to each image simply depend on detectors of the classification network and the bounding box regression network. These approaches are, although working well, fail to exploit the satisfied accuracy for specific object detection or many methods sacrifice the accuracy for the speed of real-time detection. The reason for the unsatisfied results is that the relationship between the various objects is not effectively considered. In this paper, we propose a decisive fusion network for object detection. After the network extracts the features from the backbone first, the last five layers are treated as the feature pyramid structure. Each layer is responsible for the bounding box by the border regression algorithm, and the Deep Q-Learning (DQN) algorithm is responsible for the classification of the target. The fusion function connects the DQN algorithm with the object detection network, and considers the selection relationship between objects through the DQN algorithm to improve the overall accuracy. Through a lot of experiments, the parameters of DQN are better adjusted. The experiments show our method is better than tradition approaches.

This study reports that the C2–C2 aldolization in ethanol conversion to C4 products, particularly butadiene, can be catalyzed by silica-supported LaMnO3 catalysts. The concentration and strength of Mn4+ was discovered to be related to the particle size of supported LaMnO3: the smaller the particle size is, the higher the concentration and acidity of Mn4+ are. The presence of high concentration and acidity of Mn4+ of small LaMnO3 particles concurrently increases the amount of weak basic nonstoichiometric oxygen, with which the surface concentration of Lewis acid–base adducts can be elevated. The Mn4+/nonstoichiometric oxygen pair is intrinsically active in C2–C2 aldolization, and the concentration of the paired site is positively correlated to the selectivity of C4 products. By coreacting ethanol with its evolved intermediates, that is, acetaldehyde and crotonaldehyde, we discovered the aldol condensation of acetaldehyde molecules to be rate-limiting. Accordingly, a plausible mechanism of aldolization of a...

Urease were known to catalyze the conversion of urea to ammonia and carbon dioxide. Microbial urease has demonstrated its benefits in wide biotechnological, agricultural, medicinal and engineering application. There are number of diverse microbial species contribute to urease activity in different natural habitats like soil, ocean and in various geological formation. For this study, urease bacteria were screened and isolate from acidic peat in Sarawak, Malaysia. Five distinct and diverse bacterial strains that were able to produce urease constitutively were selected to be characterized with respect to morphology, biochemical test, growth conditions and urease activity. The selected strain showed their capability to precipitate calcium carbonate (CaCO3). Hence, the isolates could be potential source of acid ureases that can be use in various industrial utilizations. 16S rRNA sequencing and phylogenetic analysis found that the selected isolates belong to the genus of Bacillus.

Developing countries like India and Indonesia struggle with proper management of plastic waste, causing rampant plastic pollution that adversely impacts the ecosystem and potentially human health. In this study, life cycle assessment (LCA) was adopted to compare the environmental impact of end-of-life (EOL) treatment of 1 kg plastic waste in India and Indonesia based on the EOL mix, which includes mechanical recycling, co-processing in cement kilns, incineration, sanitary landfill, open dumping and open burning. Nine environmental impacts were considered, namely climate change, cumulative energy demand, water depletion and marine eco-toxicity, human toxicity, terrestrial acidification, fossil depletion, particulate matter formation and urban land occupation. Waste plastics EOL treatment in India was found to have a lower environmental impact than Indonesia among all nine categories, which was attributed to higher mechanical recycling rates in India. Hotspot analysis revealed that open burning is a major contributor to climate change, while landfills are the major contributor to marine eco-toxicity. A sensitivity analysis found that the percentage of plastic waste collection, percentage of uncollected plastic waste openly burnt, percentage of plastic rejects from recycling and percentage replacement of virgin plastic from recycled plastic granules were key sensitive parameters. The results of a future scenario analysis showed that further investments in mechanical recycling by 2030 can not only reduce mismanaged plastic waste, but also contribute towards the Paris Agreement carbon reduction pledges for both India and Indonesia. The results from this study can be used to support future waste management investment decisions in both countries.

A large eddy simulation (LES) model was used to examine the wind-driven cross ventilation of gable-roof greenhouses containing vegetation. The obstruction of air flow by vegetation was described by a porous drag model in the numerical model, and the simulation results were validated using wind tunnel experiments. The numerical model was then utilised to inspect the influences of vegetation and greenhouse length (in the wind direction) on the ventilation rate. The results revealed that the diminishing effects of the vegetation, insect screen and internal friction on the ventilation rate can all be quantified by a physical-based resistance model. The driving force (the difference between windward and leeward pressures) of long, multi-span greenhouses was found to be less than that of a short, single-span greenhouse leading to a lower ventilation rate. The resistance factor of the vegetation and the insect screen depends on their porosity, while the resistance factor of the internal friction increased as the greenhouse length increased. In addition, the internal friction of multi-span greenhouses should be considered when the length of the greenhouse was greater than six times the greenhouse height.

This work was devoted to three Ir(III) complexes having large conjugation planes and various numbers of fluorine substituents for optical oxygen sensing purpose. These F atoms might broaden electronic distribution of excited electrons and increase their decay lifetime, hoping to increase sensing collision probability of excited electrons with O 2 molecules. These Ir(III) complexes were analyzed by single crystal analysis and density functional theory calculation. Photophysical measurement suggested that their phosphorescent emission was quenchable by O 2 gas. By doping these Ir(III) complexes into fibrous films through electrospinning method, their oxygen sensing performance was explored. The presence of F atoms improved oxygen sensing performance. High sensitivity (12.89), short response (8 s), good stability and, more importantly, linear response were observed.

Tubes with various cross-sectional shapes are widely used in automotive and bicycle manufacture for lightweight considerations. This study proposes a compound forming process for hydroforming a rectangular cross-sectional tube of aluminum alloys AA6061-O. The compound forming process is divided into crushing, hydroforming, and calibration. The finite element simulation software DEFORM was used to analyze the plastic deformation pattern of the tube during the compound forming processes and determine feasible loading paths that can generate a uniform thickness distribution, a lower internal pressure, and a smaller clamping force. The effects of varied internal pressures on the product shapes, thickness distributions, and forming loads are discussed. A self-designed hydroforming test machine with an internal pressure capacity of 30 MPa and axial feeding force of 40 tons was used to conduct hydroforming experiments. The experimental results of the product thickness distributions and dimensions were compared with simulation results, and the validity of the FE modeling is confirmed. Sound products with the required shape and dimensions can be obtained using the proposed hydraulic compound forming process with a low internal pressure of 8 MPa.

In this paper, two up-conversion optical sensing systems for cysteine detection were constructed, using two rhodamine derivatives as chemosensors. To improve chemosensor photostability, up-conversion NaYF 4 :Yb 3+ /Er 3+ nanocrystals were used as excitation host. This host was then modified with α-cyclodextrin to improve its water-dispersibility. The energy transfer between this excitation host and chemosensors was confirmed through spectral analysis and emission decay lifetime comparison. Job's plot experiment suggested that these chemosensors followed a simple recognition mechanism towards Cys with binding stoichiometry of 1:1. These chemosensors showed emission “turn-on” effect towards Cys with good selectivity and linear response. Sulfur substituent effect was found positive to improve chemosensor selectivity and linearity.

The solubility of d -phenylglycine methyl ester hydrochloride in water, methanol, ethanol, acetone, ethyl acetate, and toluene as well as in methanol-ethyl acetate and methanol-toluene binary mixtures with different compositions was measured under atmospheric pressure and from 283.15 K to 333.15 K using a dynamic laser method. Experimental data were correlated with the modified Apelblat, combined nearly ideal binary solvent/Redlich-Kister, and nonrandom two-liquid models. Computational results showed that the solubility of d -phenylglycine methyl ester hydrochloride increased monotonically with increasing temperature in individual and mixed solvents. It also increased monotonically with increasing methanol concentration in binary mixtures. The evaluation of model accuracy and applicability demonstrated that all models provided good fits. Changes in the standard enthalpy, entropy, and Gibbs free energy of d -phenylglycine methyl ester hydrochloride upon dissolution were calculated to obtain thermodynamics data. The results showed that the dissolution followed a non-spontaneous and endothermic process, and in most cases enthalpy was the main contributing parameter.

Fire extinction in a closed ship cabin is one of the efficient measures for fire fighting. It would be of significant values for engineering application to study the smoke characteristics of a fire within a closed cabin. This paper constructs a fire model with analyses of smoke height, self-extinction time, the cabin temperature at the quenching time and CO density based on the study of the fire behavior and the smoke characteristics in a confined cabin. It also presents a simulation verification for the theoretical model. The results demonstrates that this model does well to study the characteristics of smoke production and flow in a closed cabin. So it could be a good reference for crews to control damage while suppressing fire in closed cabins.

In this paper, eight chemosensors derived from rhodamine 6G were synthesized for Hg(II) detection. To minimize their photobleaching, we used an up-conversion material β-NaYF 4 :Yb 3+ /Er 3+ as excitation host. Under near-IR laser of 980 nm, up-conversion emission of this excitation host overlapped well with absorption of our chemosensors. Efficient energy transfer from this excitation host to our chemosensors was confirmed by emission decay lifetime analysis. Job's plot measurement suggested that the complexation equilibrium between our chemosensors and Hg(II) was a simple one with binding stoichiometry of 1:1. Host:chemosensor systems exhibited fluorescence “turn-on” effect toward Hg(II) with good selectivity and linear response. We tentatively discussed the correlation between sensing characteristics and chemosensor structure. It was concluded that S substituent increased association constant and selectivity, but decreased photostability of our chemosensors.

This study is to investigate morphological changes in the Tamsui River Estuary in Taiwan driven by multiple physical processes, such as river flows, tides, waves, and storm surges, and then to study the impacts of sediment flushing operated at the Shihmen reservoir upstream on the river estuary. An integrated coastal and estuarine processes model (CCHE2D-Coast) (Center for Computational Hydroscience and Engineering Two-Dimensional-Coast) was validated by simulating these physical processes in the estuary driven by three historical typhoons in 2008. The site-specifically validated model was then applied to simulate morphological changes in the estuary in response to reservoir sediment flush scenarios from the upstream. For the impact assessment of sediment flushing, a synthetic hydrological event was designed by including a historical typhoon and a typical monsoon. It was found that during the typhoon, the sediments will be mostly deposited in the estuarine river reach of Tamsui and the Wazihwei sandy beach. During the monsoon period, most of the sediments tend to be deposited in the second fishing port of Tamsui, the northern breakwater, and the estuary, while the Wazihwei sandy beach in the river mouth would be scoured by backflow. Simulations of the complex flow fields and morphological changes will facilitate the best practice of sediment management in the coastal and estuarine regions.

In this paper, the research of life bench test and fatigue probability reliability of the buffer block used in the damper of car(Chery in China) was carried out. In the work of life investigation, the constitutive model of stress and strain for the material of the buffer block was revised and fitted, fatigue damage properties of the buffer block was calculated with finite element simulation method and then the bench test was conducted to verify the life of buffer block and the constitutive model. In the work of reliability study, based on the constitutive model revised by experiment, the dispersion characteristics of composite fatigue stress at dangerous part and composite fatigue strength of buffer block were calculated, the fatigue probability reliability of buffer blocks was theoretically analyzed. Finally, the life bench test was also carried out to validate the reliability of buffer block.

Recently, Odelu et al. proposed a dynamic group password-based authenticated key agreement scheme for the integrated EPR information system. They claimed that their scheme could ensure user anonymity, perfect forward security and known-key security. After analyzing their scheme, we find that it suffers from some flaws. First, their scheme cannot work. Second, their scheme cannot ensure privacy as claimed. In this paper, we will show how these flaws threaten Odelu et al.'s scheme.