Background: Providing nursing care for patients and relatives requires a great effort from a nurse. It is therefore important for the nurse to have the opportunity to reflect on the caring acts included in process-ori...Background: Providing nursing care for patients and relatives requires a great effort from a nurse. It is therefore important for the nurse to have the opportunity to reflect on the caring acts included in process-oriented nursing supervision (PRONS). The overall objective of the nursing supervision is to support the professional development identity, competences, skills and ethics in different situations in nursing care. Aim: To investigate nurses’ experiences of the model of purging, playing and learning (PPL) according to Eriksson theory in a (PRONS) related to strengthen safe care, quality and professional development. Method: A qualitative study with individual interviews and the data was analyzed using qualitative interpretive content analysis. Participants: All eleven registered participated nurses were from southwestern Sweden and worked in different hospital units. Ethical Considerations: The study carried out in accordance with the ethical guidelines laid down in the Helsinki Declaration and according to the recommendations of the regional ethics committee. Findings: Three categories were identified, valuable purging in the process, responsive playing and awareness of learning. The study found that by participating in PRONS the nurses had developed new approaches with different “tools” when difficult situations occurred in daily nursing practice. Conclusion: PRONS with the model PPL has an important role to support nurses in daily nursing practice. The study highlights that there are residual successful effects after PRONS for the nurses in managing care situations that experienced strengthen quality in care and professional development.展开更多
To rationalize the design of D-π-A type organic small-molecule nonlinear optical materials,a theory guided machine learning framework is constructed.Such an approach is based on the recognition that the optical prope...To rationalize the design of D-π-A type organic small-molecule nonlinear optical materials,a theory guided machine learning framework is constructed.Such an approach is based on the recognition that the optical property of the molecule is predictable upon accumulating the contribution of each component,which is in line with the concept of group contribution method in thermodynamics.To realize this,a Lewis-mode group contribution method(LGC)has been developed in this work,which is combined with the multistage Bayesian neural network and the evolutionary algorithm to constitute an interactive framework(LGC-msBNN-EA).Thus,different optical properties of molecules are afforded accurately and efficientlyby using only a small data set for training.Moreover,by employing the EA model designed specifically for LGC,structural search is well achievable.The origins of the satisfying performance of the framework are discussed in detail.Considering that such a framework combines chemical principles and data-driven tools,most likely,it will be proven to be rational and efficient to complete mission regarding structure design in related fields.展开更多
Big data are regarded as a tremendous technology for processing a huge variety of data in a short time and with a large storage capacity.The user’s access over the internet creates massive data processing over the in...Big data are regarded as a tremendous technology for processing a huge variety of data in a short time and with a large storage capacity.The user’s access over the internet creates massive data processing over the internet.Big data require an intelligent feature selection model by addressing huge varieties of data.Traditional feature selection techniques are only applicable to simple data mining.Intelligent techniques are needed in big data processing and machine learning for an efficient classification.Major feature selection algorithms read the input features as they are.Then,the features are preprocessed and classified.Here,an algorithm does not consider the relatedness.During feature selection,all features are misread as outputs.Accordingly,a less optimal solution is achieved.In our proposed research,we focus on the feature selection by using supervised learning techniques called grey wolf optimization(GWO)with decomposed random differential grouping(DrnDG-GWO).First,decomposition of features into subsets based on relatedness in variables is performed.Random differential grouping is performed using a fitness value of two variables.Now,every subset is regarded as a population in GWO techniques.The combination of supervised machine learning with swarm intelligence techniques produces best feature optimization results in this research.Once the features are optimized,we classify using advanced kNN process for accurate data classification.The result of DrnDGGWO is compared with those of the standard GWO and GWO with PSO for feature selection to compare the efficiency of the proposed algorithm.The accuracy and time complexity of the proposed algorithm are 98%and 5 s,which are better than the existing techniques.展开更多
Social Work students typically enroll in the required practice methods courses that are specified by the Council on Social Work Education. At least in the majority of BSW Programs, these practice methods courses are d...Social Work students typically enroll in the required practice methods courses that are specified by the Council on Social Work Education. At least in the majority of BSW Programs, these practice methods courses are divided into micro-level and macro-level sequences. Individual and family systems are covered in the former sequence, while communities and organizations are the systems of focus in the latter sequence. The group system is often minimized and/or completely ignored in the process, even though there may be some reference made to groups in both practice methods sequences. Consequently, the mezzo-level system of groups is only given ＂lip-service＂ in the dyadic division of micro and macro-level practice. Furthermore, students often do not have the opportunity to practice in group settings, much less, develop competency in these professional venues. This paper describes a teaching format used in a BSW Program in an effort to ensure student competency as required in a Groups Practice course. The triangular model includes： （1） Classroom teaching of theory; （2） Practice of group dynamics; and （3） Supervision by instructors and MSW students.展开更多
文摘Background: Providing nursing care for patients and relatives requires a great effort from a nurse. It is therefore important for the nurse to have the opportunity to reflect on the caring acts included in process-oriented nursing supervision (PRONS). The overall objective of the nursing supervision is to support the professional development identity, competences, skills and ethics in different situations in nursing care. Aim: To investigate nurses’ experiences of the model of purging, playing and learning (PPL) according to Eriksson theory in a (PRONS) related to strengthen safe care, quality and professional development. Method: A qualitative study with individual interviews and the data was analyzed using qualitative interpretive content analysis. Participants: All eleven registered participated nurses were from southwestern Sweden and worked in different hospital units. Ethical Considerations: The study carried out in accordance with the ethical guidelines laid down in the Helsinki Declaration and according to the recommendations of the regional ethics committee. Findings: Three categories were identified, valuable purging in the process, responsive playing and awareness of learning. The study found that by participating in PRONS the nurses had developed new approaches with different “tools” when difficult situations occurred in daily nursing practice. Conclusion: PRONS with the model PPL has an important role to support nurses in daily nursing practice. The study highlights that there are residual successful effects after PRONS for the nurses in managing care situations that experienced strengthen quality in care and professional development.
基金support by the Key Research and Development Program of Zhejiang Province(2023C01102,2023C01208,2022C01208)。
文摘To rationalize the design of D-π-A type organic small-molecule nonlinear optical materials,a theory guided machine learning framework is constructed.Such an approach is based on the recognition that the optical property of the molecule is predictable upon accumulating the contribution of each component,which is in line with the concept of group contribution method in thermodynamics.To realize this,a Lewis-mode group contribution method(LGC)has been developed in this work,which is combined with the multistage Bayesian neural network and the evolutionary algorithm to constitute an interactive framework(LGC-msBNN-EA).Thus,different optical properties of molecules are afforded accurately and efficientlyby using only a small data set for training.Moreover,by employing the EA model designed specifically for LGC,structural search is well achievable.The origins of the satisfying performance of the framework are discussed in detail.Considering that such a framework combines chemical principles and data-driven tools,most likely,it will be proven to be rational and efficient to complete mission regarding structure design in related fields.
文摘Big data are regarded as a tremendous technology for processing a huge variety of data in a short time and with a large storage capacity.The user’s access over the internet creates massive data processing over the internet.Big data require an intelligent feature selection model by addressing huge varieties of data.Traditional feature selection techniques are only applicable to simple data mining.Intelligent techniques are needed in big data processing and machine learning for an efficient classification.Major feature selection algorithms read the input features as they are.Then,the features are preprocessed and classified.Here,an algorithm does not consider the relatedness.During feature selection,all features are misread as outputs.Accordingly,a less optimal solution is achieved.In our proposed research,we focus on the feature selection by using supervised learning techniques called grey wolf optimization(GWO)with decomposed random differential grouping(DrnDG-GWO).First,decomposition of features into subsets based on relatedness in variables is performed.Random differential grouping is performed using a fitness value of two variables.Now,every subset is regarded as a population in GWO techniques.The combination of supervised machine learning with swarm intelligence techniques produces best feature optimization results in this research.Once the features are optimized,we classify using advanced kNN process for accurate data classification.The result of DrnDGGWO is compared with those of the standard GWO and GWO with PSO for feature selection to compare the efficiency of the proposed algorithm.The accuracy and time complexity of the proposed algorithm are 98%and 5 s,which are better than the existing techniques.
文摘Social Work students typically enroll in the required practice methods courses that are specified by the Council on Social Work Education. At least in the majority of BSW Programs, these practice methods courses are divided into micro-level and macro-level sequences. Individual and family systems are covered in the former sequence, while communities and organizations are the systems of focus in the latter sequence. The group system is often minimized and/or completely ignored in the process, even though there may be some reference made to groups in both practice methods sequences. Consequently, the mezzo-level system of groups is only given ＂lip-service＂ in the dyadic division of micro and macro-level practice. Furthermore, students often do not have the opportunity to practice in group settings, much less, develop competency in these professional venues. This paper describes a teaching format used in a BSW Program in an effort to ensure student competency as required in a Groups Practice course. The triangular model includes： （1） Classroom teaching of theory; （2） Practice of group dynamics; and （3） Supervision by instructors and MSW students.
