Currently, greenhouses are widely applied for plant growth, and environmental parameters can also be controlled in the modern greenhouse to guarantee the maximum crop yield. In order to optimally ...control greenhouses’ environmental parameters, one indispensable requirement is to accurately predict crop yields based on given environmental parameter settings. In addition, crop yield forecasting in greenhouses plays an important role in greenhouse farming planning and management, which allows cultivators and farmers to utilize the yield prediction results to make knowledgeable management and financial decisions. It is thus important to accurately predict the crop yield in a greenhouse considering the benefits that can be brought by accurate greenhouse crop yield prediction. In this work, we have developed a new greenhouse crop yield prediction technique, by combining two state-of-the-arts networks for temporal sequence processing—temporal convolutional network (TCN) and recurrent neural network (RNN). Comprehensive evaluations of the proposed algorithm have been made on multiple datasets obtained from multiple real greenhouse sites for tomato growing. Based on a statistical analysis of the root mean square errors (RMSEs) between the predicted and actual crop yields, it is shown that the proposed approach achieves more accurate yield prediction performance than both traditional machine learning methods and other classical deep neural networks. Moreover, the experimental study also shows that the historical yield information is the most important factor for accurately predicting future crop yields.
Abstract
Falling poses significant risks, especially for the geriatric population. In this study, the authors introduce an innovative approach to privacy‐preserving fall detection using computer ...vision. The authors’ technique leverages a deep neural network (DNN) to accurately identify falling events in input images, while simultaneously prioritizing privacy through the implementation of an optical element. The experimental results establish that the authors’ proposed method outperforms alternative hardware and software‐based privacy‐preserving approaches in terms of encryption level and accuracy. These results are derived from an extensive dataset encompassing diverse falling scenarios.
Background
Patients with percutaneous transhepatic biliary drainage (PTBD) need regular drainage tube care after discharge, and transitional care can help solve this problem. However, few studies ...have focused on the quality of transitional care, the perceptions of patients with drainage tubes after discharge and those of healthcare professionals.
Aim
This study is aimed at exploring the real experience and perceptions of transitional care services among healthcare professionals and PTBD patients who have been discharged with tubes and at providing references for future transitional care service development.
Design
The study uses a qualitative descriptive design. The reporting method followed Consolidated Criteria for Reporting Qualitative Research guidelines.
Methods
Semistructured interviews were conducted with PTBD patients who had been discharged with tubes and multicentre healthcare professionals using the purpose sampling method. The thematic analysis method was used for analysis.
Results
Thirteen PTBD patients from one hospital and 12 healthcare professionals from three hospitals were interviewed. The analysis of the patient interview data revealed three themes, namely, recognition of the value of transitional care services, patients have some unmet needs and perception of transitional care service pathways. Six subthemes were also identified. The analysis of the interview data of healthcare professionals revealed two themes, namely, harvest and challenges in transitional care services work and expectations for future development of transitional care services. Four subthemes were also identified.
Conclusions
The transitional care of discharged patients with PTBD tubes deserves the attention of clinical workers, and a series of measures should be taken to improve transitional care services.
Patient/Public Contribution
Patients were involved in the formulation of interview questions for this study, and during the interviews, patients presented their suggestions for transitional care services. Healthcare professionals participated in this study as interviewees, and no members of the public were involved in this study.
Liver transplantation, the only effective treatment for end stage liver disease, is characterized by complicated surgery, long surgery time, and high trauma. Patients may experience a variety of ...difficulties following surgery, including infection, abdominal bleeding and rejection, all of which directly affect the quality of rehabilitation. Enhanced Recovery After Surgery (ERAS), a novel perioperative management strategy, can effectively promote postoperative recovery of patients and has been extensively implemented in various fields of surgery. However, there are no scientific and universal ERAS protocols in the fields of liver transplantation in China. The first Consensus Recommendations of Enhanced Recovery for Liver Transplantation was issued by the International Liver Transplantation Society in December 2022, offering recommendations about ERAS strategies for liver transplantation recipients who receive deceased and living organ donations, and for living donors of liver transplantation. This paper provide
In this study, we present a novel smart greenhouse control algorithm that optimizes crop yield while minimizing energy consumption costs. To achieve this, we relied on both a greenhouse climate model ...and a greenhouse crop yield model. Our approach involves applying the model predictive control (MPC) method, which utilizes the particle swarm optimization (PSO) algorithm to identify optimal controllable parameters such as heating, lighting, ventilation levels. The objective of the optimization is to maximize crop yield while minimizing energy consumption costs. We demonstrate the superiority of our proposed control algorithm in terms of performance and energy efficiency compared to the traditional control algorithm. The effectiveness of the PSO-based optimization strategy for finding optimal controllable parameters for MPC control is also demonstrated, outperforming the traditional genetic algorithm optimization. This study provides a promising approach to smart greenhouse control with the potential for increasing crop yield while minimizing energy costs.
Necrotising funisitis (NF) is a rare, chronic stage of funisitis, a severe inflammation of the umbilical cord and an important risk factor for fetal adverse outcomes. NF is characterized by ...yellow-white bands running parallel to the umbilical blood vessels. These bands consist of inflammatory cells, necrotic debris, and calcium deposits. Calcification is visible in ultrasonography, which makes it possible to suspect NF when umbilical vascular wall calcification is detected by prenatal ultrasonography.
Ultrasonography revealed calcification of the umbilical venous wall in an expectant 31-year-old woman who was gravida 1, para 0. The woman required emergency cesarean section because of fetal distress and suspected umbilical cord torsion at 31 weeks gestation. The root of the umbilical cord was quite fragile and broke during the operation. The pathological results on the placenta showed histologic chorioamnionitis and NF. The infant was diagnosed to have neonatal sepsis and acidosis after delivery but was discharged without severe complications after a one-month hospitalization that included antibiotic and supportive therapy.
NF is a rare and severe inflammation of the umbilical cord. Umbilical vascular wall calcification discovered in prenatal ultrasonography is diagnostically helpful.
In this work, we have proposed a novel methodology for greenhouse tomato yield prediction, which is based on a hybrid of an explanatory biophysical model—the Tomgro model, and a machine learning ...model called CNN-RNN. The Tomgro and CNN-RNN models are calibrated/trained for predicting tomato yields while different fusion approaches (linear, Bayesian, neural network, random forest and gradient boosting) are exploited for fusing the prediction result of individual models for obtaining the final prediction results. The experimental results have shown that the model fusion approach achieves more accurate prediction results than the explanatory biophysical model or the machine learning model. Moreover, out of different model fusion approaches, the neural network one produced the most accurate tomato prediction results, with means and standard deviations of root mean square error (RMSE), r2-coefficient, Nash-Sutcliffe efficiency (NSE) and percent bias (PBIAS) being 17.69 ± 3.47 g/m2, 0.9995 ± 0.0002, 0.9989 ± 0.0004 and 0.1791 ± 0.6837, respectively.
There exist various types of information on retail food packages, including
use by
date, food product name and so on. The correct coding of
use by
dates on food packages is vitally important for ...avoiding potential health risks to customers caused by erroneous mislabelling of
use by
dates. It is extremely tedious and laborious to check the
use by
dates coding manually by a human operator, which is prone to generate errors thus an automatic system for validating the correctness of the coding of
use by
dates is needed. In order to construct such a system, firstly it needs to correctly automatic recognize
use by
dates on food packages. In this work, we propose a novel dual deep neural networks-based methodology for automatic recognition of
use by
dates in food package photographs recorded by a camera, which is a combination of two networks: a fully convolutional network for
use by
date ROI detection and a convolutional recurrent neuron network for date character recognition. The proposed methodology is the first attempt to apply deep learning for automatic
use by
date recognition. From comprehensive experimental evaluations, it is shown that the proposed method can achieve high accuracies in
use by
date recognition (more than 95% on our testing dataset), given food package images with varying lighting conditions, poor printing quality and varied textual/pictorial contents collected from multiple real retailer sites.
Jinmaitong (JMT) is a Traditional Chinese Compound Prescription for the treatment of diabetic peripheral neuropathy (DPN). This study aims to investigate the effect of JMT on the insulin-like growth ...factor 1 (IGF-1) and the insulin like growth factor 1 receptor (IGF-1R) expression in sciatic nerves of diabetic rats. Firstly, the chemical profile of JMT was characterized by UPLC/Q-TOF-MS analysis. A total of 72 compounds were putatively identified. Secondly, streptozotocin (STZ)-induced diabetic rats were treated with neurotropin (NTP, 2.67 NU/kg/day) or JMT at low-dosage (0.4375 g/kg/day), medium-dosage (0.875 g/kg/day), and high-dosage (1.75 g/kg/day) for continuous 16 weeks. Blood glucose and body weight were detected every 4 weeks during the experiment. The mechanical pain and morphological change on sciatic nerves were detected by pain measurement instrument and microscopy. The IGF-1 level in serum and tissues were measured though ELISA and immunohistochemistry. The mRNA and protein expressions of IGF-1, IGF-1R, peripheral myelin protein zero (P0), and peripheral myelin protein 22 (PMP22) in the tissues were measured by qRT-PCR and western blot. As a result, JMT had no significant effect on body weight, but reduced the fasting blood glucose levels of diabetic rats. Besides, the pathological morphology, mechanical pain thresholds, serum level and tissue expression of IGF-1, mRNA, and protein levels of IGF-1R, P0, and PMP22 were significantly improved in JMT group at middle dosage. In conclusion, JMT could ameliorate the behavioristics and morphology changes in DPN rats by promoting IGF-1 and IGF-1R gene and protein expressions in sciatic nerves, as well as regulating the peripheral nerve remyelination genes P0 and PMP22 expressions, which provides scientific evidence for the clinical application of JMT in DPN patients.
This study proposes a novel approach for the analysis of brain responses in the modality of ongoing EEG elicited by the naturalistic and continuous music stimulus. The 512-second long EEG data ...(recorded with 64 electrodes) are first decomposed into 64 components by independent component analysis (ICA) for each participant. Then, the spatial maps showing dipolar brain activity are selected in terms of the residual dipole variance through a single dipole model in brain imaging, and clustered into a pre-defined number (estimated by the minimum description length) of clusters. Subsequently, the temporal courses of the EEG theta and alpha oscillations of each component for each cluster are produced and correlated with the temporal courses of tonal and rhythmic features of the music. Using this approach, we found that the extracted temporal courses of the theta and alpha oscillations along central and occipital area of scalp in two of the selected clusters significantly correlated with the musical features representing progressions in the rhythmic content of the stimulus. We suggest that this demonstrates that with the proposed approach, we have managed to discover what kinds of brain responses were elicited when a participant was listening continuously to the long piece of naturalistic music.
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