Due to the complex environment of the university laboratory,personnel flow intensive,personnel irregular behavior is easy to cause security risks.Monitoring using mainstream detection algorithms suffers from low detec...Due to the complex environment of the university laboratory,personnel flow intensive,personnel irregular behavior is easy to cause security risks.Monitoring using mainstream detection algorithms suffers from low detection accuracy and slow speed.Therefore,the current management of personnel behavior mainly relies on institutional constraints,education and training,on-site supervision,etc.,which is time-consuming and ineffective.Given the above situation,this paper proposes an improved You Only Look Once version 7(YOLOv7)to achieve the purpose of quickly detecting irregular behaviors of laboratory personnel while ensuring high detection accuracy.First,to better capture the shape features of the target,deformable convolutional networks(DCN)is used in the backbone part of the model to replace the traditional convolution to improve the detection accuracy and speed.Second,to enhance the extraction of important features and suppress useless features,this paper proposes a new convolutional block attention module_efficient channel attention(CBAM_E)for embedding the neck network to improve the model’s ability to extract features from complex scenes.Finally,to reduce the influence of angle factor and bounding box regression accuracy,this paper proposes a newα-SCYLLA intersection over union(α-SIoU)instead of the complete intersection over union(CIoU),which improves the regression accuracy while increasing the convergence speed.Comparison experiments on public and homemade datasets show that the improved algorithm outperforms the original algorithm in all evaluation indexes,with an increase of 2.92%in the precision rate,4.14%in the recall rate,0.0356 in the weighted harmonic mean,3.60%in the mAP@0.5 value,and a reduction in the number of parameters and complexity.Compared with the mainstream algorithm,the improved algorithm has higher detection accuracy,faster convergence speed,and better actual recognition effect,indicating the effectiveness of the improved algorithm in this paper and its potential for practical application in laboratory scenarios.展开更多
Resilience is the psychological capability to recover from difficulties quickly.Healthcare professionals are especially vulnerable to job-related stress and burnout.Unitary Caring Science is the framework for Watson’...Resilience is the psychological capability to recover from difficulties quickly.Healthcare professionals are especially vulnerable to job-related stress and burnout.Unitary Caring Science is the framework for Watson’s Human Caring Theory,providing a philosophy of practice in healthcare.With the high rates of clinician burnout and psychological issues,it will be significant to unify the human caring theory with research-informed psychological and neuroscience evidence to develop clinicians’resilience-building strategies.The purpose of this article is to introduce a Unitary Caring Science Resilience Model and explain the science behind the core strategies based on Unitary Caring Science philosophy and the psychological and neuroscience research.This model includes six strategies:Embracing loving-kindness for self and others;Nurturing interpersonal and intersubjective connections/relations;Deepening a creative use of self and sense of belonging;Balancing self-learning,self-awareness,and an evolved selfconsciousness;Valuing forgiveness and releasing negativity;Inspiring and maintaining faith-hope.The caring-theory guided resilience-building strategies are proven to alleviate the depletion of clinicians’energy and emotions.Healthcare practices are challenging but rewarding.Clinicians can be emotionally,psychologically,and physically exhausted if they always consider themselves‘giving’and‘doing’institutional tasks without a sense of purpose or fulfillment.The practice can be rewarding if it becomes more aligned with clinicians’value to serve humanity.Through the unitary caring science resilience strategies,clinicians can build resilience as an antidote to clinician burnout and depletion.展开更多
Dear Editor：Quantitative real-time PCR has revolutionized molecular diagnostics with its ease of use,increased sensitivity and specificity and low turnaround time.PCR/quantitative PCR（qPCR）-based assays offer a dis...Dear Editor：Quantitative real-time PCR has revolutionized molecular diagnostics with its ease of use,increased sensitivity and specificity and low turnaround time.PCR/quantitative PCR（qPCR）-based assays offer a distinct advantage over other serological/conventional diagnostic approaches.The ability to diagnose infectious diseases has benefited from the availability of US FDA approved and Conformite Europeenne（CE）-marked qPCR-based in-vitro diagnostic kits from international companies.The high-quality kits are calibrated with the World Health Organization（WHO）reference standards and the National Institute for Biological Standards and Control（NIBSC）standards.展开更多
基金This study was supported by the National Natural Science Foundation of China(No.61861007)Guizhou ProvincialDepartment of Education Innovative Group Project(QianJiaohe KY[2021]012)Guizhou Science and Technology Plan Project(Guizhou Science Support[2023]General 412).
文摘Due to the complex environment of the university laboratory,personnel flow intensive,personnel irregular behavior is easy to cause security risks.Monitoring using mainstream detection algorithms suffers from low detection accuracy and slow speed.Therefore,the current management of personnel behavior mainly relies on institutional constraints,education and training,on-site supervision,etc.,which is time-consuming and ineffective.Given the above situation,this paper proposes an improved You Only Look Once version 7(YOLOv7)to achieve the purpose of quickly detecting irregular behaviors of laboratory personnel while ensuring high detection accuracy.First,to better capture the shape features of the target,deformable convolutional networks(DCN)is used in the backbone part of the model to replace the traditional convolution to improve the detection accuracy and speed.Second,to enhance the extraction of important features and suppress useless features,this paper proposes a new convolutional block attention module_efficient channel attention(CBAM_E)for embedding the neck network to improve the model’s ability to extract features from complex scenes.Finally,to reduce the influence of angle factor and bounding box regression accuracy,this paper proposes a newα-SCYLLA intersection over union(α-SIoU)instead of the complete intersection over union(CIoU),which improves the regression accuracy while increasing the convergence speed.Comparison experiments on public and homemade datasets show that the improved algorithm outperforms the original algorithm in all evaluation indexes,with an increase of 2.92%in the precision rate,4.14%in the recall rate,0.0356 in the weighted harmonic mean,3.60%in the mAP@0.5 value,and a reduction in the number of parameters and complexity.Compared with the mainstream algorithm,the improved algorithm has higher detection accuracy,faster convergence speed,and better actual recognition effect,indicating the effectiveness of the improved algorithm in this paper and its potential for practical application in laboratory scenarios.
文摘Resilience is the psychological capability to recover from difficulties quickly.Healthcare professionals are especially vulnerable to job-related stress and burnout.Unitary Caring Science is the framework for Watson’s Human Caring Theory,providing a philosophy of practice in healthcare.With the high rates of clinician burnout and psychological issues,it will be significant to unify the human caring theory with research-informed psychological and neuroscience evidence to develop clinicians’resilience-building strategies.The purpose of this article is to introduce a Unitary Caring Science Resilience Model and explain the science behind the core strategies based on Unitary Caring Science philosophy and the psychological and neuroscience research.This model includes six strategies:Embracing loving-kindness for self and others;Nurturing interpersonal and intersubjective connections/relations;Deepening a creative use of self and sense of belonging;Balancing self-learning,self-awareness,and an evolved selfconsciousness;Valuing forgiveness and releasing negativity;Inspiring and maintaining faith-hope.The caring-theory guided resilience-building strategies are proven to alleviate the depletion of clinicians’energy and emotions.Healthcare practices are challenging but rewarding.Clinicians can be emotionally,psychologically,and physically exhausted if they always consider themselves‘giving’and‘doing’institutional tasks without a sense of purpose or fulfillment.The practice can be rewarding if it becomes more aligned with clinicians’value to serve humanity.Through the unitary caring science resilience strategies,clinicians can build resilience as an antidote to clinician burnout and depletion.
文摘Dear Editor：Quantitative real-time PCR has revolutionized molecular diagnostics with its ease of use,increased sensitivity and specificity and low turnaround time.PCR/quantitative PCR（qPCR）-based assays offer a distinct advantage over other serological/conventional diagnostic approaches.The ability to diagnose infectious diseases has benefited from the availability of US FDA approved and Conformite Europeenne（CE）-marked qPCR-based in-vitro diagnostic kits from international companies.The high-quality kits are calibrated with the World Health Organization（WHO）reference standards and the National Institute for Biological Standards and Control（NIBSC）standards.
