Search

Photovoltaic-powered drip irrigation is a vital approach to address the irrigation requirements in regions with limited water resources and energy deficiencies, thereby ensuring the provision of sustenance and horticultural produce for local inhabitants. However, the susceptibility of the drip irrigation system to clogging as well as the fluctuations in photovoltaic output can significantly impact irrigation quality. Moreover, conventional storage methods commonly employed in photovoltaic-powered drip irrigation systems, such as elevated water tanks and batteries, exhibit notable technological, economic, and environmental limitations. The present study introduces a novel photovoltaic drip irrigation technology (CAES-PVDI) that utilizes solar energy as the exclusive source of power, enabling stable and cost-effective high-quality drip irrigation. This technology actively regulates solar energy through compressed air energy storage, employing a cyclic pulse discharge method to ensure uniformity in irrigation outflow and significantly enhance the anti-clogging performance of the drip irrigation system. The proposed technology was implemented in a solar greenhouse for drip irrigation, and subsequent tests were conducted to assess its hydraulic performance and anti-clogging properties The results demonstrated that the system achieved a discharge uniformity of no less than 91.76 %. Furthermore, there was no blocked emitter in CAES-PVDI system, and the sedimentation inside the capillary tube decreased by 78.95 %-93.36 % compared to traditional drip irrigation system. In comparison to existing photovoltaic-powered drip irrigation technology, the CAES-PVDI system exhibited exceptional technical indicators and offered significant economic and environmental benefits, thereby presenting a novel approach to promote environmentally friendly and efficient operation of drip irrigation systems.

Serratia marcescens is an opportunistic and nosocomial pathogen found in the intensive care unit (ICU), but its antimicrobial resistance (AMR) is rarely addressed. Here, we reported two blaKPC-2-positive S. marcescens strains, SMBC31 and SMBC50, recovered from the ICU of a hospital in Zhengzhou, China. The minimum inhibitory concentration (MIC) was determined using the broth microdilution method, while S1-PFGE was employed to demonstrate plasmid size approximation. Complete genome sequences were obtained through Illumina NovaSeq 6000 and Oxford Nanopore Technologies. Both strains exhibit resistance to meropenem and harbor the blaKPC-2 and blaSRT-1 resistance genes. The plasmid pSMBC31-39K in strain SMBC31 and pSMBC50-107K in strain SMBC50 were identified as carrying the blaKPC-2 gene. Notably, both of these plasmids were successfully transferred to Escherichia coli strain J53. Phylogenetic analysis based on plasmid sequences revealed that pSMBC31-39K exhibited high homology with plasmids found in Aeromonas caviae, Citrobacter sp., and Pseudomonas aeruginosa, while pSMBC50-107K showed significant similarity to those of E. coli and Klebsiella pneumoniae. Notably, the coexistence of blaKPC-2 and blaSRT-1 was observed in all 94 KPC-2-producing S. marcescens strains by mining all genomes available under the GenBank database, which were mainly isolated from hospitalized patients. The emergence of multidrug-resistant S. marcescens poses significant challenges in treating clinical infections, highlighting the need for increased surveillance of this pathogen.

BackgroundFor patients with hilar cholangiocarcinoma (HC) undergoing hemi-hepatectomy, there are controversies regarding the requirement of, indications for, and timing of preoperative biliary drainage (PBD). Dynamic three-dimensional volume reconstruction could effectively evaluate the regeneration of liver after surgery, which may provide assistance for exploring indications for PBD and optimal preoperative bilirubin value. The purpose of this study was to explore the indications for PBD and the optimal preoperative bilirubin value to improve prognosis for HC patients undergoing hemi-hepatectomy.MethodsWe retrospectively analyzed the data of HC patients who underwent hemi-hepatectomy in the First Affiliated Hospital of China Medical University from 2012 to 2023. The liver regeneration rate was calculated using three-dimensional volume reconstruction. We analyzed the factors affecting the liver regeneration rate and occurrence of postoperative liver insufficiency.ResultsThis study involved 83 patients with HC, which were divided into PBD group (n=36) and non-PBD group (n=47). The preoperative bilirubin level may be an independent risk factor affecting the liver regeneration rate (P=0.014) and postoperative liver insufficiency (P=0.016, odds ratio=1.016, β=0.016, 95% CI=1.003–1.029). For patients whose initial bilirubin level was >200 μmol/L (n=45), PBD resulted in better liver regeneration in the early stage (P=0.006) and reduced the incidence of postoperative liver insufficiency [P=0.012, odds ratio=0.144, 95% confidence interval (CI)=0.031–0.657]. The cut-off value of bilirubin was 103.15 μmol/L based on the liver regeneration rate. Patients with a preoperative bilirubin level of ≤103.15 μmol/L shown a better liver regeneration (P200 μmol/L, PBD may result in better liver regeneration and reduce the incidence of postoperative liver insufficiency. Preoperative bilirubin levels ≤103.15 μmol/L maybe recommended for leading to a better liver regeneration and lower incidence of postoperative hepatic insufficiency.

Floral color and scent profiles vary across species, geographical locations, and developmental stages. The exclusive floral color and fragrance of Chimonanthus praecox is contributed by a range of endogenous chemicals that distinguish it from other flowers and present amazing ornamental value. This comprehensive review explores the intricate interplay of environmental factors, chemicals and genes shaping the flower color and fragrance of Chimonanthus praecox. Genetic and physiological factors control morpho-anatomical attributes as well as pigment synthesis, while environmental factors such as temperature, light intensity, and soil composition influence flower characteristics. Specific genes control pigment synthesis, and environmental factors such as temperature, light intensity, and soil composition influence flower characteristics. Physiological processes including plant hormone contribute to flower color and fragrance. Hormones, notably ethylene, exert a profound influence on varioustraits. Pigment investigations have spotlighted specific flavonoids, including kaempferol 3-O-rutinoside, quercetin, and rutin. Red tepals exhibit unique composition with cyanidin-3-O-rutinoside and cyanidin-3-O-glucoside being distinctive components. Elucidating the molecular basis of tepal color variation, particularly in red and yellow varieties, involves the identification of crucial regulatory genes. In conclusion, this review unravels the mysteries of Chimonanthus praecox, providing a holistic understanding of its flower color and fragrance for landscape applications. This comprehensive review uniquely explores the genetic intricacies, chemical and environmental influences that govern the mesmerizing flower color and fragrance of Chimonanthus praecox, providing valuable insights for its landscape applications. This review article is designed for a diverse audience, including plant geneticists, horticulturists, environmental scientists, urban planners, and students, offering understandings into the genetic intricacies, ecological significance, and practical applications of Chimonanthus praecox across various disciplines. Its appeal extends to professionals and enthusiasts interested in plant biology, conservation, and industries dependent on unique floral characteristics.

Background:The diagnosis of Precancerous Lesions of Gastric Cancer (PLGC) is challenging in clinical practice. We conducted a clinical study by analyzing the information of relevant chromosome copy number variations (CNV) in the TCGA database followed by the UCAD technique to evaluate the value of Chromosomal Instability (CIN) assay in the diagnosis of PLGC.Methods:Based on the screening of gastric cancer related data in TCGA database, CNV analysis was performed to explore the information of chromosome CNV related to gastric cancer. Based on the gastroscopic pathology results, 12 specimens of patients with severe atrophy were screened to analyze the paraffin specimens of gastric mucosa by UCAD technology, and to explore the influence of related factors on them.Results:The results of CNV in TCGA database suggested that chromosome 7, 8, and 17 amplification was obvious in patients with gastric cancer. UCAD results confirmed that in 12 patients with pathologic diagnosis of severe atrophy, five of them had positive results of CIN, with a positive detection rate of 41.7%, which was mainly manifested in chromosome seven and chromosome eight segments amplification. We also found that intestinalization and HP infection were less associated with CIN. And the sensitivity of CIN measurement results was significantly better than that of tumor indicators.Conclusion:The findings suggest that the diagnosis of PLGC can be aided by UCAD detection of CIN, of which Chr7 and 8 may be closely related to PLGC.

Abstract Interfaces of metal oxide heterojunctions display a variety of intriguing physical properties that enable novel applications in spintronics, quantum information, neuromorphic computing, and high‐temperature superconductivity. One such LaAlO3/SrTiO3 (LAO/STO) heterojunction hosts a 2D electron liquid (2DEL) presenting remarkable 2D superconductivity and magnetism. However, these remarkable properties emerge only at very low temperatures, while the heterostructure fabrication is challenging even at the laboratory scale, thus impeding practical applications. Here, a novel plasma‐enabled fabrication concept is presented to develop the TiO2/Ti3O4 hetero‐phase bilayer with a 2DEL that exhibits features of a weakly localized Fermi liquid even at room temperature. The hetero‐phase bilayer is fabricated by applying a rapid plasma‐induced phase transition that transforms a specific portion of anatase TiO2 thin film into vacancy‐prone Ti3O4 in seconds. The underlying mechanism relies on the screening effect of the achieved high‐density electron liquid that suppresses the electron‐phonon interactions. The achieved “adiabatic” electron transport in the hetero‐phase bilayer offers strong potential for low‐loss electric or plasmonic circuits and hot electron harvesting and utilization. These findings open new horizons for fabricating diverse multifunctional metal oxide heterostructures as an innovative platform for emerging clean energy, integrated photonics, spintronics, and quantum information technologies.

The capacity for nanopatterning and functionality is a promising facet of supramolecular self‐assembly. However, the formation of molecular frameworks entirely dependent on van der Waals (vdW) interactions is infrequently explored. Herein, 2D vdW supramolecular structures are synthesized through the self‐assembled cocrystallization of C60 and decanethiol (DT) molecules on Au(111) surface. Notably, the system eliminates the need of functional groups for specific bonding between adjacent units. The conformation C60/DT is delicately manipulated by adjusting molecular coverage and annealing temperature. The absence of directional bonding between C60 and DT molecules facilitates the formation of a variety of stable phases at room temperature (RT), such as 1) porous C60 networks with thiol‐filled pores and 2) self‐synthesized (C60)n nanochains with thiol spacers interspersed between the chains are achieved and visualized by scanning tunneling microscopic imaging under RT. This innovative integration of the vdW interaction unveils new avenues for developing supramolecular patterns characterized by their comparatively weak but exceptionally adaptable bonding.

Background: Pulsatile varicose veins of the lower extremities are rare in clinical practice. The indications and therapy for pulsating varicose veins remain unclear, and most cases are managed conservatively, with fewer reports of endovenous approaches. However, the treatment of pulsating varicose veins combined with ulcer bleeding remains unclear in the relevant literature. Case presentation: We report a case of sudden rupture and bleeding from a pulsatile varicose vein with a lower extremity ulcer. The patient was a 74-year-old male with atrial fibrillation, pulmonary hypertension, and tricuspid regurgitation. Emergency compression was first applied to stop the bleeding. Wound cleansing, antimicrobial treatment and bacterial control were performed. Elastic compression stockings (23–32 mmHg) were also prescribed. After a comprehensive duplex ultrasound examination and heart disease evaluation, high ligation of the great saphenous vein, radiofrequency ablation and foam sclerotherapy were performed. After the procedure, the chronic venous insufficiency symptoms improved significantly at the 1-month follow-up, and the ulcer was completely healed by postoperative month 5. The patient had mild lower limb swelling, and no ulcer recurrence was reported during the 18-month follow-up. Duplex scans at the 3- and 18-month follow-ups showed complete obliteration of the treated great saphenous vein. Conclusions: Pulsatile varicose veins with ulcer bleeding are a rare pathological condition. It usually results from reflux from the heart and may also be a consequence of hydraulic transmission dysfunction. We believe that comprehensive duplex ultrasound examination of the peripheral vascular system and complex surgical treatment allow the correction of superficial venous reflux and achieve favorable early and mid-term outcomes.

In the stage of aircraft development and airworthiness verification, it is necessary to master the influence of lithium-ion battery (LIB) thermal runaway (TR) propagation. In this paper, the battery TR propagation behavior under different trigger positions and modes is studied experimentally, and the calculation and comparison are carried out from the parameters of real-time temperature, voltage, propagation speed, total energy released, and solid ejecta. When the two adjacent cells at the top corner, side, and center of the module are overheated, TR occurs at about 1000 s for the triggered cells, while the whole-overheating trigger mode takes a longer time. The latter’s transmission speed is extremely fast, spreading 2.67 cells per second on average. The heat generated by the solid ejecta of the whole-overheating trigger mode is 82,437 J, which is more destructive. The voltage of the triggered cell fluctuates abnormally in a precursor manner when the internal active substances in the cell undergo a self-generated thermal reaction. This work can provide a reference for the safety and economical design of system installations and the correct setting of airworthiness verification Method of Compliance (MoC) experiments to verify whether the aircraft can bear and contain the adverse effects caused by LIB TR.

In recent years, semantic segmentation has made significant progress in visual place recognition (VPR) by using semantic information that is relatively invariant to appearance and viewpoint, demonstrating great potential. However, in some extreme scenarios, there may be semantic occlusion and semantic sparsity, which can lead to confusion when relying solely on semantic information for localization. Therefore, this paper proposes a novel VPR framework that employs a coarse-to-fine image matching strategy, combining semantic and appearance information to improve algorithm performance. First, we construct SemLook global descriptors using semantic contours, which can preliminarily screen images to enhance the accuracy and real-time performance of the algorithm. Based on this, we introduce SemLook local descriptors for fine screening, combining robust appearance information extracted by deep learning with semantic information. These local descriptors can address issues such as semantic overlap and sparsity in urban environments, further improving the accuracy of the algorithm. Through this refined screening process, we can effectively handle the challenges of complex image matching in urban environments and obtain more accurate results. The performance of SemLook descriptors is evaluated on three public datasets (Extended-CMU Season, Robot-Car Seasons v2, and SYNTHIA) and compared with six state-of-the-art VPR algorithms (HOG, CoHOG, AlexNet_VPR, Region VLAD, Patch-NetVLAD, Forest). In the experimental comparison, considering both real-time performance and evaluation metrics, the SemLook descriptors are found to outperform the other six algorithms. Evaluation metrics include the area under the curve (AUC) based on the precision–recall curve, Recall@100%Precision, and Precision@100%Recall. On the Extended-CMU Season dataset, SemLook descriptors achieve a 100% AUC value, and on the SYNTHIA dataset, they achieve a 99% AUC value, demonstrating outstanding performance. The experimental results indicate that introducing global descriptors for initial screening and utilizing local descriptors combining both semantic and appearance information for precise matching can effectively address the issue of location recognition in scenarios with semantic ambiguity or sparsity. This algorithm enhances descriptor performance, making it more accurate and robust in scenes with variations in appearance and viewpoint.

Objective The outcome of congenital clubfoot treatment is still challenging if the feet deformities are not completely corrected. Here we explore a minimal invasive procedure with an eight‐plate implant to correct the residual forefoot adduction deformity after treatment of neglected or relapsed clubfoot. Methods We retrospectively reviewed patients with residual forefoot adduction deformity after clubfoot treatment between January 2013 and June 2016. The patients underwent temporary epiphysiodesis of the lateral column of the mid‐foot, which in detail, an eight‐plate was placed on each side of the calcaneocuboid joint. The foot deformities were recorded according to the weight‐bearing radiographic measurements including talo‐first metatarsal angle, calcaneo‐fifth metatarsal angle and medial‐to‐lateral column length. Results A total of 13 patients (20 feet) with an average age of 7.8 years old were located with an average duration of 40.8 months follow‐up (range, 28 to 54 months). The average talo‐first metatarsal angle improved from 28.3° (range, 19° to 47°) preoperatively to 8.3° (range, 3° to 18°) and the calcaneo‐fifth metatarsal angle improved from 29.1° (range, 19° to 40°) preoperatively to 8.4° (range, 0° to 21°) at final follow‐up. The mean ratio of the medial‐to‐lateral column length improved from 1.14 ± 0.06 to 1.55 ± 0.09 with statistical significance (t = 3.566; P

Abstract Crowd counting aims to count the number of people in crowded scenes, which is important to the security systems, traffic control and so on. The existing methods typically using local features cannot properly handle the perspective distortion and the varying scales in congested scene images, and henceforth perform wrong people counting. To alleviate this issue, this study proposes a multi‐scale residual feature‐aware network (MSR‐FAN) that combines multi‐scale features using multiple receptive field sizes and learns the feature‐aware information on each image. The MSR‐FAN is trained end‐to‐end to generate high‐quality density map and evaluate the crowd number. The method consists of three parts. To handle the perspective changes problem, the first part, the direction‐based feature‐enhanced network, is designed to encode the perspective information in four directions based on the initial image feature. The second part, the proposed multi‐scale residual block module, gets the global information to handle the represent the regional feature better. This module explores features of different scales as well as reinforce the global feature. The third part, the feature‐aware block, is designed to extract the feature hidden in the different channels. Experiment results based on benchmark datasets show that the proposed approach outperforms the existing state‐of‐the‐art methods.

Abstract Introduction To compare the clinical outcomes and the radiographic features between tibial condylar valgus osteotomy (TCVO) and open wedge high tibial osteotomy (OWHTO). New insight into the indication criteria for TCVO was also clarified for achieving satisfactory results. Materials and methods Sixty-three knees with medial-compartment osteoarthritis were retrospectively studied. Thirty-four knees with subluxated lateral joint and depression of the medial tibial plateau underwent TCVO and the rest underwent OWHTO. Among the 63 knees included, 27 knees with a pre-operative femorotibial angle (FTA) ≥ 185° were defined as severe varus (subgroup S, 15 in STCVO group and 12 in SHTO group). Lower limb alignment, intra-, and extra-articular congruency were evaluated according to the radiograph obtained before and 24 months after surgery. The visual analog scale (VAS) score and Hospital for Special Surgery (HSS) score were obtained to assess the clinical results. Opening angle and distance of the opening gap in each group were measured by intra-operative fluoroscopy. Results During the 2-year follow-up period, the mean HSS score increased from 70.3 to 81.4 in HTO group and 65.9 to 87.3 in TCVO group (p < 0.05). The mean VAS score decreased from 5.9 to 2.6 and 6.0 to 2.1, respectively (p < 0.01). Pre-operative FTA was restored to 172.9° in HTO group and 171.3° in TCVO group, and percentage of mechanical axis (%MA) was improved to 59.7% and 61.2%, respectively. Joint line convergence angle (JLCA) was slightly restored and medial tibial plateau depression (MTPD) was relatively the same before and after OWHTO, while these parameters improved greatly (from 6.4° to 1.2° and − 8.0° to 5.9°, p < 0.01) in TCVO group. More undercorrected knees were observed in SHTO group than STCVO group (58.3% and 13.3%, p < 0.05). Opening angle and distance of the opening gap were larger in TCVO group (19.1° and 14.0 mm) than those in OWHTO group (9.3° and 10.1 mm, p < 0.05). Conclusion Compared to OWHTO, TCVO had priority in treating advanced knee OA with intra-articular deformity. However, TCVO had a limited capacity to correct the varus angle. Besides, TCVO might be suitable for medial-compartment OA with a pre-operative FTA ≥ 185°.

ABSTRACT Development of extended-spectrum-β-lactamase (ESBL)-producing Escherichia coli is one the greatest threats faced by mankind. Among animals, chickens, pigs, and cattle are reservoirs of these pathogens worldwide. Nevertheless, there is a knowledge gap on ESBL-producing E. coli from small ruminants (i.e., sheep and goats) in China. The aim of this study was to identify and characterize the resistance profiles, resistomes, and sequence features of 67 ESBL-producing E. coli isolates from sheep in northwest China. The findings showed that blaCTX-M and blaTEM were the most prevalent. Interestingly, we found that the resistance gene mcr-1 was widespread in sheep merely from Shaanxi areas, accounting for 19.2% (5/26). The highly prevalent serotypes and FumC-FimH (CH) typing isolates were O8 and C4H32, respectively. High-risk E. coli clones, such as sequence type 10 (ST10), ST23, ST44, and ST58, were also found in China’s sheep population. A total of 67 ESBL-producing isolates were divided into five phylogenetic groups, namely, B1 (n = 47, 70.1%), B2 (n = 1, 1.5%), C (n = 14, 20.9%), E (n = 1, 1.5%), and F (n = 1, 1.5%), with the phylogenetic groups for 3 isolates (4.5%) remaining unknown. Moreover, ESBL-producing E. coli isolates were also characterized by the abundance and diversity of biocide/metal resistance genes and insert sequences. We found that in ESBL-producing E. coli isolates, there were two different types of isolates, those containing ESBL genes or not, which led to large discrepancies between resistance phenotypes and resistomes. In summary, our study provides a comprehensive overview of resistance profiles and genome sequence features in ESBL-producing E. coli and highlights the possible role of sheep as antibiotic resistance gene disseminators into humans. IMPORTANCE Antimicrobial resistance (AMR), especially the simultaneous resistance to several antibiotics (multidrug resistance [MDR]), is one of the greatest threats to global public health in the 21st century. Among animals, chickens, pigs, and cattle are reservoirs of these pathogens worldwide. Nevertheless, there is a knowledge gap on ESBL-producing E. coli from small ruminants in China. This study is the largest and most comprehensive analysis of ESBL-producing E. coli isolates from sheep, including antibiotic resistance profiles, phylogenetic groups, serotypes, multilocus sequence types (MLST), insert sequences (IS), antibiotic resistance genes, disinfectant resistance genes, and heavy metal resistance genes. We recommend extending the surveillance of AMR of sheep-origin E. coli to prevent future public health risks.

The vehicle logo contains the vehicle’s identity information, so vehicle logo detection (VLD) technology has extremely important significance. Although the VLD field has been studied for many years, the detection task is still difficult due to the small size of the vehicle logo and the background interference problem. To solve these problems, this paper proposes a method of VLD based on the YOLO-T model and the correlation of the vehicle space structure. Aiming at the small size of the vehicle logo, we propose a vehicle logo detection network called YOLO-T. It integrates multiple receptive fields and establishes a multi-scale detection structure suitable for VLD tasks. In addition, we design an effective pre-training strategy to improve the detection accuracy of YOLO-T. Aiming at the background interference, we use the position correlation between the vehicle lights and the vehicle logo to extract the region of interest of the vehicle logo. This measure not only reduces the search area but also weakens the background interference. We have labeled a new vehicle logo dataset named LOGO-17, which contains 17 different categories of vehicle logos. The experimental results show that our proposed method achieves high detection accuracy and outperforms the existing vehicle logo detection methods.

Team composition is one of the most important and challenging directions in the recommendation problem. Compared with a single person, the advantage of a team is mainly reflected in the synergy of team members’ complementary collaboration. To build a high-efficiency team, how to choose the team members has become a tricky problem. However, there is a lack of quantitative algorithms and validation methods for team member selection. In this paper, we put forward three indicators to measure a team’s ability and formulate the selection of football team members as a multi-objective optimization problem. Subsequently, an evolutionary player selection algorithm based on the genetic algorithm is proposed to solve the team composition problem. We verify the effectiveness of the team member recommendation algorithm via data analysis, football game simulation under different budget constraints and provide comparisons with existing methods.

Deep image embedding learns how to map images onto feature vectors. Image retrieval performance is often used to evaluate embedding quality. In this study, the authors proposed a wise deep image embedding optimisation (WDIEO) algorithm based on informative pair weighting and ranked list learning (IPWRLL) for network optimisation of fine‐grained image retrieval. First, a hard sample mining method Top‐k is proposed to select positive and negative samples. Then, for the selected query sample, a ranking list is obtained by comparing the similarity between samples in the data set and the query sample, and the sample is labelled according to the similarity. Finally, for positive samples, two optimisation rules with different functions are used, while ensuring two key issues of instance weighting and intra‐class data distribution. For negative samples, different from the widely adopted methods based on the weight of sample information, the authors’ algorithm's weights are set according to the ranking list, which keeps the inter‐class data distribution and the optimisation direction consistent with the loss reduction direction. The WDIEO‐IPWRLL model is an end‐to‐end optimisation that can share parameters in the testing process. Experiments show that their proposed model achieves the state‐of‐the‐art performance on the benchmark data set.

BackgroundCanadians are increasingly gaining web-based access to digital health services, and they expect to access their data from these services through a central patient access channel. Implementing data sharing between these services will require patient trust that is fostered through meaningful consent and consent management. Understanding user consent requirements and information needs is necessary for developing a trustworthy and transparent consent management system. ObjectiveThe objective of this study is to explore consent management preferences and information needs to support meaningful consent. MethodsA secondary analysis of a national survey was conducted using a retrospective descriptive study design. The 2019 cross-sectional survey used a series of vignettes and consent scenarios to explore Canadians’ privacy perspectives and preferences regarding consent management. Nonparametric tests and logistic regression analyses were conducted to identify the differences and associations between various factors. ResultsOf the 1017 total responses, 716 (70.4%) participants self-identified as potential users. Of the potential users, almost all (672/716, 93.8%) felt that the ability to control their data was important, whereas some (385/716, 53.8%) believed that an all or none control at the data source level was adequate. Most potential users preferred new data sources to be accessible by health care providers (546/716, 76.3%) and delegated parties (389/716, 54.3%) by default. Prior digital health use was associated with greater odds of granting default access when compared with no prior use, with the greatest odds of granting default access to digital health service providers (odds ratio 2.17, 95% CI 1.36-3.46). From a list of 9 information elements found in consent forms, potential users selected an average of 5.64 (SD 2.68) and 5.54 (SD 2.85) items to feel informed in consenting to data access by care partners and commercial digital health service providers, respectively. There was no significant difference in the number of items selected between the 2 scenarios (P>.05); however, there were significant differences (P

The existing deep learning methods for human fall detection have difficulties to distinguish falls from similar daily activities such as lying down because of not using the 3D network. Meanwhile, they are not suitable for mobile devices because they are heavyweight methods and consume a large number of memories. In order to alleviate these problems, a two-stream approach to fall detection with the MobileVGG is proposed in this paper. One stream is based on the motion characteristics of the human body for detection of falls, while the other is an improved lightweight VGG network, named the MobileVGG, put forward in the paper. The MobileVGG is constructed as a lightweight network model through replacing the traditional convolution with a simplified and efficient combination of point convolution, depth convolution and point convolution. The residual connection between layers is designed to overcome the gradient disappeared and the obstruction of gradient reflux in the deep model. The experimental results show that the proposed two-stream lightweight fall classification model outperforms the existing methods in distinguishing falls from similar daily activities such as lying and reducing the occupied memory. Therefore, it is suitable for mobile devices.

A quality detection system for the “Red Fuji” apple in Luochuan was designed for automatic grading. According to the Chinese national standard, the grading principles of apple appearance quality and Brix detection were determined. Based on machine vision and image processing, the classifier models of apple defect, contour, and size were constructed. And then, the grading thresholds were set to detect the defective pixel ratio t, aspect ratio λ, and the cross-sectional diameter Wp in the image of the apple. Spectral information of apples in the wavelength range of 400 nm~1000 nm was collected and the multiple scattering correction (MSC) and standard normal variable (SNV) transformation methods were used to preprocess spectral reflectance data. The competitive adaptive reweighted sampling (CARS) algorithm and the successive projections algorithm (SPA) were used to extract characteristic wavelength points containing Brix information, and the CARS-PLS (partial least squares) algorithm was used to establish a Brix prediction model. Apple defect, contour, size, and Brix were combined as grading indicators. The apple quality online grading detection platform was built, and apple’s comprehensive grading detection algorithm and upper computer software were designed. The experiments showed that the average accuracy of apple defect, contour, and size grading detection was 96.67%, 95.00%, and 94.67% respectively, and the correlation coefficient Rp of the Brix prediction set was 0.9469. The total accuracy of apple defect, contour, size, and Brix grading was 96.67%, indicating that the detection system designed in this paper is feasible to classify “Red Fuji” apple in Luochuan.

At present, most quadruped robots can move quickly and steadily on both flat and undulating ground; however, natural environments are complex and changeable, so it is important for a quadruped robot to be able to jump over obstacles immediately. Inspired by the jumping movement of quadruped animals, we present aerial body posture adjustment laws and generate animal-like jumping trajectories for a quadruped robot. Then, the bionic reference trajectories are optimized to build a trajectory library of a variety of jumping motions based on the kinematic and dynamic constraints of the quadruped robot. The model predictive control (MPC) method is employed by the quadruped robot to track the optimized trajectory to achieve jumping behavior. The simulations show that the quadruped robot can jump over an obstacle of 40 cm in height. The effectiveness of the animal-like jump control method is verified.

An important area in a gathering place is a region attracting the constant attention of people and has evident visual features, such as a flexible stage or an open-air show. Finding such areas can help security supervisors locate the abnormal regions automatically. The existing related methods lack an efficient means to find important area candidates from a scene and have failed to judge whether or not a candidate attracts people’s attention. To realize the detection of an important area, this study proposes a two-stage method with a novel multi-input attention network (MAN). The first stage, called important area candidate generation, aims to generate candidate important areas with an image-processing algorithm (i.e., K-means++, image dilation, median filtering, and the RLSA algorithm). The candidate areas can be selected automatically for further analysis. The second stage, called important area candidate classification, aims to detect an important area from candidates with MAN. In particular, MAN is designed as a multi-input network structure, which fuses global and local image features to judge whether or not an area attracts people’s attention. To enhance the representation of candidate areas, two modules (i.e., channel attention and spatial attention modules) are proposed on the basis of the attention mechanism. These modules are mainly based on multi-layer perceptron and pooling operation to reconstruct the image feature and provide considerably efficient representation. This study also contributes to a new dataset called gathering place important area detection for testing the proposed two-stage method. Lastly, experimental results show that the proposed method has good performance and can correctly detect an important area.

Negative curvature hollow core fiber (NCHCF) is a promising candidate for sensing applications; however, research on NCHCF based fiber sensors starts only in the recent two years. In this work, an all-fiber interferometer based on an NCHCF structure is proposed for the first time. The interferometer was fabricated by simple fusion splicing of a short section of an NCHCF between two singlemode fibers (SMFs). Both simulation and experimental results show that multiple modes and modal interferences are excited within the NCHCF structure. Periodic transmission dips with high spectral extinction ratio (up to 30 dB) and wide free spectral range (FSR) are produced, which is mainly introduced by the modes coupling between HE11 and HE12. A small portion of light guiding by means of Anti-resonant reflecting optical waveguide (ARROW) mechanism is also observed. The transmission dips, resulting from multimode interferences (MMI) and ARROW effect have a big difference in sensitivities to strain and temperature, thus making it possible to monitor these two parameters with a single sensor head by using a characteristic matrix approach. In addition, the proposed sensor structure is experimentally proven to have a good reproducibility.

Remote sensing images are featured by massiveness, diversity and complexity. These features put forward higher requirements for the speed and accuracy of remote sensing image retrieval. The extraction method plays a key role in retrieving remote sensing images. Deep metric learning (DML) captures the semantic similarity information between data points by learning embedding in vector space. However, due to the uneven distribution of sample data in remote sensing image datasets, the pair-based loss currently used in DML is not suitable. To improve this, we propose a novel distribution consistency loss to solve this problem. First, we define a new way to mine samples by selecting five in-class hard samples and five inter-class hard samples to form an informative set. This method can make the network extract more useful information in a short time. Secondly, in order to avoid inaccurate feature extraction due to sample imbalance, we assign dynamic weight to the positive samples according to the ratio of the number of hard samples and easy samples in the class, and name the loss caused by the positive sample as the sample balance loss. We combine the sample balance of the positive samples with the ranking consistency of the negative samples to form our distribution consistency loss. Finally, we built an end-to-end fine-tuning network suitable for remote sensing image retrieval. We display comprehensive experimental results drawing on three remote sensing image datasets that are publicly available and show that our method achieves the state-of-the-art performance.

Recently, with the rapid growth of the number of datasets with remote sensing images, it is urgent to propose an effective image retrieval method to manage and use such image data. In this paper, we propose a deep metric learning strategy based on Similarity Retention Loss (SRL) for content-based remote sensing image retrieval. We have improved the current metric learning methods from the following aspects—sample mining, network model structure and metric loss function. On the basis of redefining the hard samples and easy samples, we mine the positive and negative samples according to the size and spatial distribution of the dataset classes. At the same time, Similarity Retention Loss is proposed and the ratio of easy samples to hard samples in the class is used to assign dynamic weights to the hard samples selected in the experiment to learn the sample structure characteristics within the class. For negative samples, different weights are set based on the spatial distribution of the surrounding samples to maintain the consistency of similar structures among classes. Finally, we conduct a large number of comprehensive experiments on two remote sensing datasets with the fine-tuning network. The experiment results show that the method used in this paper achieves the state-of-the-art performance.

Introduction Since 2007, Cancer Care Ontario (CCO) has systematically collected patient-reported outcomes (PROs) in the form of symptom data, for cancer outpatients visiting regional cancer centres or affiliate institutions. Data are used in real-time to facilitate conversation between clinicians and patients and have recently been combined with provincial administrative databases. Objectives and Approach CCO collects PROs using the Edmonton Symptom Assessment System (ESAS), which scores 9 symptoms on a scale of 0 (no symptoms) to 10 (worst symptom severity). Data were imported from CCO in 2015 and linked to a cancer cohort at ICES. We investigated differences between patients who completed $\geq$1 ESAS record and patients who did not, as well as the number of records, timing of data collection and missingness. We describe our experience linking and using the PRO data to administrative data, including presenting trajectories of symptoms over time and combining scores into composite indices. Results 120,745 cancer patients had 729,861 symptom records between 2007 and 2014. Not all patients with a cancer diagnosis had $\geq$1 ESAS record and this varied by patient, disease and system level factors. Because implementation occurred from a clinical perspective, data collection was irregular within and across patients and depended on treatment and other factors; the number of records per patient varied, as well the number of contributing patients in each time period following diagnosis. Attempts were made to create meaningful composite indices by combining all symptom scores as well as combining multiple high scores for each individual symptom. As a result, selecting the best statistical analysis to use these PRO data as an exposure or outcome is still uncertain. Conclusion/Implications PRO data linked to provincial, administrative data holdings represent a new frontier for population-based cancer research, both in their challenging structure as well as their implications for clinical practice and health system. These lessons learned will hopefully support other researchers rigorous use of these data in the future.

A ratiometric wavelength measurement based on a Silicon-on-Insulator (SOI) integrated device is proposed and designed, which consists of directional couplers acting as two edge filters with opposite spectral responses. The optimal separation distance between two parallel silicon waveguides and the interaction length of the directional coupler are designed to meet the desired spectral response by using local supermodes. The wavelength discrimination ability of the designed ratiometric structure is demonstrated by a beam propagation method numerically and then is verified experimentally. The experimental results have shown a general agreement with the theoretical models. The ratiometric wavelength system demonstrates a resolution of better than 50 pm at a wavelength around 1550 nm with ease of assembly and calibration.

The inference of a general Bayesian network has been shown to be an NP-hard problem, even for approximate solutions. Although k-dependence Bayesian (KDB) classifier can construct at arbitrary points (values of k) along the attribute dependence spectrum, it cannot identify the changes of interdependencies when attributes take different values. Local KDB, which learns in the framework of KDB, is proposed in this study to describe the local dependencies implicated in each test instance. Based on the analysis of functional dependencies, substitution-elimination resolution, a new type of semi-naive Bayesian operation, is proposed to substitute or eliminate generalization to achieve accurate estimation of conditional probability distribution while reducing computational complexity. The final classifier, averaged k-dependence Bayesian (AKDB) classifiers, will average the output of KDB and local KDB. Experimental results on the repository of machine learning databases from the University of California Irvine (UCI) showed that AKDB has significant advantages in zero-one loss and bias relative to naive Bayes (NB), tree augmented naive Bayes (TAN), Averaged one-dependence estimators (AODE), and KDB. Moreover, KDB and local KDB show mutually complementary characteristics with respect to variance.

As one of the most common types of graphical models, the Bayesian classifier has become an extremely popular approach to dealing with uncertainty and complexity. The scoring functions once proposed and widely used for a Bayesian network are not appropriate for a Bayesian classifier, in which class variable C is considered as a distinguished one. In this paper, we aim to clarify the working mechanism of Bayesian classifiers from the perspective of the chain rule of joint probability distribution. By establishing the mapping relationship between conditional probability distribution and mutual information, a new scoring function, Sum_MI, is derived and applied to evaluate the rationality of the Bayesian classifiers. To achieve global optimization and high dependence representation, the proposed learning algorithm, the flexible K-dependence Bayesian (FKDB) classifier, applies greedy search to extract more information from the K-dependence network structure. Meanwhile, during the learning procedure, the optimal attribute order is determined dynamically, rather than rigidly. In the experimental study, functional dependency analysis is used to improve model interpretability when the structure complexity is restricted.

The massive number of images demands highly efficient image retrieval tools. Deep distance metric learning (DDML) is proposed to learn image similarity metrics in an end-to-end manner based on the convolution neural network, which has achieved encouraging results. The loss function is crucial in DDML frameworks. However, we found limitations to this model. When learning the similarity of positive and negative examples, the current methods aim to pull positive pairs as close as possible and separate negative pairs into equal distances in the embedding space. Consequently, the data distribution might be omitted. In this work, we focus on the distribution structure learning loss (DSLL) algorithm that aims to preserve the geometric information of images. To achieve this, we firstly propose a metric distance learning for highly matching figures to preserve the similarity structure inside it. Second, we introduce an entropy weight-based structural distribution to set the weight of the representative negative samples. Third, we incorporate their weights into the process of learning to rank. So, the negative samples can preserve the consistency of their structural distribution. Generally, we display comprehensive experimental results drawing on three popular landmark building datasets and demonstrate that our method achieves state-of-the-art performance.

Image retrieval applying deep convolutional features has achieved the most advanced performance in most standard benchmark tests. In image retrieval, deep metric learning (DML) plays a key role and aims to capture semantic similarity information carried by data points. However, two factors may impede the accuracy of image retrieval. First, when learning the similarity of negative examples, current methods separate negative pairs into equal distance in the embedding space. Thus, the intraclass data distribution might be missed. Second, given a query, either a fraction of data points, or all of them, are incorporated to build up the similarity structure, which makes it rather complex to calculate similarity or to choose example pairs. In this study, in order to achieve more accurate image retrieval, we proposed a method based on learning to rank and multiple loss (LRML). To address the first problem, through learning the ranking sequence, we separate the negative pairs from the query image into different distance. To tackle the second problem, we used a positive example in the gallery and negative sets from the bottom five ranked by similarity, thereby enhancing training efficiency. Our significant experimental results demonstrate that the proposed method achieves state-of-the-art performance on three widely used benchmarks.