A szervezeti elköteleződés és a machiavellizmus kapcsolata

Committed employees are essential for organizations. Numerous studies look for factors that lead to organizational commitment, although most of former research focused primarily on job-specific factors like income, socialization process, or workplace atmosphere. However, individual characteristics should also be taken into account, because various personal factors could also have significant impact on development of commitment. These factors are like social orientation, social value orientation or Machiavellianism. Present study focuses on high and low level Machiavellian individuals’ engagement processes. According to the results of the investigation attached to this paper the engagement due negative or positive reasons both are in a contrary relationship with high Machiavellianism. Lower Machiavellian scores suggest easier commitment process and more committed employees. In addition to the above the effects of some demographic factors on commitment will also be presented in the article

The relationship between social orientation and organizational commitment

In today rapidly changing business environment committed workers are indispensable for companies to stay in competition. Identification of factors that support the commitment represents an advantage for organizations. Research so far has put great emphasis on job-specific factors, but this article has the focus on personal factors like social orientation. With regard to social orientation, science distinct individualistic people who are less binding as a result of positive or negative reasons, and collectivists, who become committed more easily because of positive reasons like they love to be part of the team, or negatives like they have a fear that they might lose their important relationships. In this article we use our own results of testing to confirm that collectivists become committed more ja positive or negative reasons than other

Investigating ferroelectric and metal-insulator phase transition devices for neuromorphic computing

Neuromorphic computing has been proposed to accelerate the computation for deep neural networks (DNNs). The objective of this thesis work is to investigate the ferroelectric and metal-insulator phase transition devices for neuromorphic computing. This thesis proposed and experimentally demonstrated the drain erase scheme in FeFET to enable the individual cell program/erase/inhibition for in-situ training in 3D NAND-like FeFET array. To achieve multi-level states for analog in-memory computing, the ferroelectric thin film needs to be partially switched. This thesis identified a new challenge of ferroelectric partial switching, namely “history effect” in minor loop dynamics. The experimental characterization of both FeCap and FeFET validated the history effect, suggesting that the intermediate states programming condition depends on the prior states that the device has gone through. A phase-field model was constructed to understand the origin. Such history effect was then modelled into the FeFET based neural network simulation and analyze its negative impact on the training accuracy and then propose a possible mitigation strategy. Apart from using FeFET as synaptic devices, using metal-insulator phase transition device, as neuron was also explored experimentally. A NbOx metal-insulator phase transition threshold switch was integrated at the edge of the crossbar array as an oscillation neuron. One promising application for FeFET+NbOx neuromorphic system is to implement quantum error correction (QEC) circuitry at 4K. Cryo-NeuroSim, a device-to-system modeling framework that calibrates data at cryogenic temperature was developed to benchmark the performance of the FeFET+NbOx neuromorphic system.Ph.D

Accommodating maintenance in prognostics

Error on title page - year of award is 2021Steam turbines are an important asset of nuclear power plants, and are required to operate reliably and efficiently. Unplanned outages have a significant impact on the ability of the plant to generate electricity. Therefore, condition-based maintenance (CBM) can be used for predictive and proactive maintenance to avoid unplanned outages while reducing operating costs and increasing the reliability and availability of the plant. In CBM, the information gathered can be interpreted for prognostics (the prediction of failure time or remaining useful life (RUL)). The aim of this project was to address two areas of challenges in prognostics, the selection of predictive technique and accommodation of post-maintenance effects, to improve the efficacy of prognostics. The selection of an appropriate predictive algorithm is a key activity for an effective development of prognostics. In this research, a formal approach for the evaluation and selection of predictive techniques is developed to facilitate a methodic selection process of predictive techniques by engineering experts. This approach is then implemented for a case study provided by the engineering experts. Therefore, as a result of formal evaluation, a probabilistic technique the Bayesian Linear Regression (BLR) and a non-probabilistic technique the Support Vector Regression (SVR) were selected for prognostics implementation. In this project, the knowledge of prognostics implementation is extended by including post maintenance affects into prognostics. Maintenance aims to restore a machine into a state where it is safe and reliable to operate while recovering the health of the machine. However, such activities result in introduction of uncertainties that are associated with predictions due to deviations in degradation model. Thus, affecting accuracy and efficacy of predictions. Therefore, such vulnerabilities must be addressed by incorporating the information from maintenance events for accurate and reliable predictions. This thesis presents two frameworks which are adapted for probabilistic and non-probabilistic prognostic techniques to accommodate maintenance. Two case studies: a real-world case study from a nuclear power plant in the UK and a synthetic case study which was generated based on the characteristics of a real-world case study are used for the implementation and validation of the frameworks. The results of the implementation hold a promise for predicting remaining useful life while accommodating maintenance repairs. Therefore, ensuring increased asset availability with higher reliability, maintenance cost effectiveness and operational safety.Steam turbines are an important asset of nuclear power plants, and are required to operate reliably and efficiently. Unplanned outages have a significant impact on the ability of the plant to generate electricity. Therefore, condition-based maintenance (CBM) can be used for predictive and proactive maintenance to avoid unplanned outages while reducing operating costs and increasing the reliability and availability of the plant. In CBM, the information gathered can be interpreted for prognostics (the prediction of failure time or remaining useful life (RUL)). The aim of this project was to address two areas of challenges in prognostics, the selection of predictive technique and accommodation of post-maintenance effects, to improve the efficacy of prognostics. The selection of an appropriate predictive algorithm is a key activity for an effective development of prognostics. In this research, a formal approach for the evaluation and selection of predictive techniques is developed to facilitate a methodic selection process of predictive techniques by engineering experts. This approach is then implemented for a case study provided by the engineering experts. Therefore, as a result of formal evaluation, a probabilistic technique the Bayesian Linear Regression (BLR) and a non-probabilistic technique the Support Vector Regression (SVR) were selected for prognostics implementation. In this project, the knowledge of prognostics implementation is extended by including post maintenance affects into prognostics. Maintenance aims to restore a machine into a state where it is safe and reliable to operate while recovering the health of the machine. However, such activities result in introduction of uncertainties that are associated with predictions due to deviations in degradation model. Thus, affecting accuracy and efficacy of predictions. Therefore, such vulnerabilities must be addressed by incorporating the information from maintenance events for accurate and reliable predictions. This thesis presents two frameworks which are adapted for probabilistic and non-probabilistic prognostic techniques to accommodate maintenance. Two case studies: a real-world case study from a nuclear power plant in the UK and a synthetic case study which was generated based on the characteristics of a real-world case study are used for the implementation and validation of the frameworks. The results of the implementation hold a promise for predicting remaining useful life while accommodating maintenance repairs. Therefore, ensuring increased asset availability with higher reliability, maintenance cost effectiveness and operational safety

In Brief: Myeloid-derived suppressor cells in cancer

The role of myeloid-derived suppressor cells (MDSCs) in cancer development has become clear over recent years, and MDSC targeting is an emerging opportunity for enhancing the effectiveness of current anticancer therapies. As MDSCs are not only able to limit anti-tumour T-cell responses, but also to promote tumour angiogenesis and invasion, their monitoring has prognostic and predictive value. Herein, we review the key features of MDSCs in cancer promotion

HUBUNGAN ADVERSITY QUOTIENT DAN DUKUNGAN SOSIAL DENGAN PROKRASTINASI AKADEMIK DALAM PENYELESAIAN SKRIPSI PADA MAHASISWA YANG BEKERJA DI PTS STMIK-STIE MIKROSKIL MEDAN

Tujuan penelitian ini untuk menguji hubungan adversity quotient dan dukungan sosial dengan prokrastinasi akademik dalam penyelesaian skripsi pada mahasiswa yang bekerja. Subjek penelitian adalah mahasiswa STMIK-STIE Mikroskil Medan yang belum lulus sejumlah 83 mahasiswa dari 2 angkatan yaitu 2008 dan 2009. Pendekatan yang dipergunakan dalam penelitian ini adalah pendekatan kuantitatif. Pengambilan sampel dalam penelitian ini dengan total sampling. Pada penelitian ini, peneliti menggunakan 3 (tiga) jenis skala yaitu skala prokrastinasi akademik, skala adversity quotient dan skala dukungan sosial. Adapun teknik analisis data yang digunakan dalam penelitian ini adalah analisis regresi berganda. Berdasarkan hasil analisis data diperoleh koefisien Freg=97,952 dimana p<0,05. Menunjukkan bahwa terdapat hubungan yang signifikan antara adversity quotient dan dukungan sosial dengan prokrastinasi akademik dalam menyelesaikan skripsi pada mahasiswa yang bekerja di STMIK-STIE Mikroskil. variabel adversity quotient dengan prokrastinasi akademik memiliki nilai sebesar 0,276 dengan nilai p<0,05, artinya arah hubungan variabel negatif yaitu semakin tinggi adversity quotient akan semakin rendah prokrastinasi akademik. Sedangkan variabel dukungan sosial dengan prokrastinasi akademik memiliki nilai sebesar -0,787 dengan p<0,05, artinya arah hubungan kedua variabel negatif, artinya semakin tinggi dukungan sosial akan semakin rendah prokrastinasi akademik.Kata Kunci : Prokrastinasi akademik, Adversity Quotient, Dukungan sosia

Factors Affecting Consumers’ Green Purchasing Behavior: An Integrated Conceptual Framework

In this modern era of societal marketing business ethics and social responsibility are becoming the guiding themes for marketing strategies and practices. Within the field of ethics and social responsibility environmental and green marketing topics are the central topics, which are closely related to biodiversity and sustainability. This paper suggests a different approach to assessing the variables of consumers’ green purchasing behavior. Based on thoroughly researched secondary data, this conceptual paper suggests a framework integrating the so far incoherent frameworks as proposed by previous authors. Emanating from this eclectic and chronological literature review, the paper will also propose further missing links that need to be included in the proposed integrated framework. Based on this holistic framework, in a future study, the authors will explain a sustainability index of green consumer behavior, which will be tested empirically in the study. In fact, from the proposed integrated framework, in total eight vital factors/aspects of green/environmental issues are likely to have an impact on consumer green purchasing behavior. Demographic variables will play an intervening or mediating role in the framework.pro-environmental consumer behaviour, sustainability, green consumer behavior, green purchasing

Integrating the finite element method and genetic algorithms to solve structural damage detection and design optimisation problems

This thesis documents fundamental new research in to a specific application of structural box-section beams, for which weight reduction is highly desirable. It is proposed and demonstrated that the weight of these beams can be significantly reduced by using advanced, laminated fibre-reinforced composites in place of steel. Of the many issues raised during this investigation two, of particular importance, are considered in detail; (a) the detection and quantification of damage in composite structures and (b) the optimisation of laminate design to maximise the performance of loaded composite structuress ubject to given constraints. It is demonstrated that both these issues can be formulated and solved as optimisation problems using the finite element method, in which an appropriate objective function is minimised (or maximised). In case (a) the difference in static response obtained from a loaded structure containing damage and an equivalent mathematical model of the structure is minimised by iteratively updating the model. This reveals the damage within the model and subsequently allows the residual properties of the damaged structure to be quantified. Within the scope of this work is the ability to resolve damage, that consists of either penny-shaped sub-surface flaws or tearing damage of box-section beams from surface experimental data. In case (b) an objective function is formulated in terms of a given structural response, or combination of responses that is optimised in order to return an optimal structure, rather than just a satisfactory structure. For the solution of these optimisation problems a novel software tool, based on the integration of genetic algorithms and a commercially available finite element (FE) package, has been developed. A particular advantage of the described method is its applicability to a wide range of engineering problems. The tool is described and its effectiveness demonstrated with reference to two inverse damage detection and quantification problems and one laminate design optimisation problem. The tool allows the full suite of functions within the FE software to be used to solve non-convex optimisation problems, formulated in terms of both discrete and continuous variables, without explicitly stating the form of the stiffness matrix. Furthermore, a priori knowledge about the problem may be readily incorporated in to the method

MENINGKATKAN HASIL BELAJAR SISWA MELALUI MODEL PEMBELAJARAN WORD SQUARE PADA MATA PELAJARAN PENDIDIKAN KEWARGANEGARAAN MATERI ORGANISASI KELAS V SDN 066045 MEDAN HELVETIA TAHUN AJARAN 2018/2019

The problem in this study is the result of student learning in learning citizenship education is still low because the teacher uses the lecture method and emphasizes memorization and making notes, student activeness to ask and answer questions in KBM activities is still not optimal so students are less motivated to learn. This research is a classroom action research in class V even semester 066045 Medan Helvetia field in the 2018/2019 school year with a total of 28 students. The acquisition of the results of the first cycle need to follow up on the second cycle, so that significantly the learning outcomes of the second cycle in the meta subjects of citizenship education in organizational material using the word square model increased to 28 students who completed their learning outcomes with a percentage of 100% with an average value of 85.00 from the management of learning outcomes tests in the second cycle the percentage of students' mastery learning has met the target as determined by the percentage of mastery learning ideal of 75% of students who have achieved the KKM score of 75. Therefore, it can be concluded that learning activities using the word square learning model can improve student learning outcomes in fifth grade SD Negeri 066045 Medan Helvetia. on learning civic education, organizational material