An investigation into the use of quality management techniques in NZ IT projects : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Information Systems at Massey University

The risks in an IT project are very high both because of its complexity and also because the context of rapidly-developing technology leads to a high degree of uncertainty. IT projects should have comprehensive formal quality management fully integrated within all aspects of project management. A review of the quality management in IT project literature suggests, customer-focused TQM is now synonymous with good management. TQM combines the use of computerised data collection and statistical experimentation with a focus on teamwork, group participation and a culture of continuous improvement in operating systems (Robert. 1993). Using the survey methodology and through two case studies, qualitative data was gathered to develop a model of quality management implementation process in New Zealand. Key words: Quality. Total Quality Management (TQM), Quality Control (QC), Quality Assurance (QA), Quality Model

Applications for Drowning Identification by Planktonic Diatom Test on Rats in Forensic Medicine

AbstractWe established a model of drowning, and by investigating diatoms in lung, liver, kidney, and long bone marrow of rats at different time to discuss the cause of death. The organs of 35 rats were extracted 0.5h, 1h, 6h, 12h, 24h and 48h after drowning and the organs of sham-drowning group killed by mechanical asphyxia were extracted 1h after body immersed in water. The organs were digested by acid, and the diatoms were analyzed by statistics. Results shown the detection rate was 100% in lung, and the positive rate of all the extracted organs was 100% 6hours after drowning except the sham-drowning group. No diatoms were detected in the liver, kidney and bone marrow of the sham-drowning group, just only one case was positive in the lung. So it is concluded that the detection rate of diatoms could be considered as important evidence in drowning determination

Turkish-Russian Relations in 21st Century: Problems of Development

This article provides a brief overview of the development of Russian-Turkish relations in the 21st century against the backdrop of changes in Turkey’s domestic political situation at the turn of the 20th and 21st centuries. Special attention is paid to the evolution of Turkey’s foreign policy strategy. It is noted that the Russian Federation is a particularly important partner for the Republic of Turkey. The relevance of the topic lies in the fact that currently, the relationship between the two countries continues to develop actively, despite external political challenges. At the same time, the history of Russia-Turkey contacts requires further study, as this period is not well represented in the existing literature. The novelty of this research lies in the use of new sources to study state relations. The authors of the article come to a justified conclusion that the Turkish government seeks to develop its relationship with the Russian Federation while maintaining ties with both Western and Eastern countries. This demonstrates their efforts to utilize the opportunities provided by Turkey’s geographical location, which serves as a “corridor” between various parts of Eurasia

CONSS: Contrastive Learning Approach for Semi-Supervised Seismic Facies Classification

Recently, seismic facies classification based on convolutional neural networks (CNN) has garnered significant research interest. However, existing CNN-based supervised learning approaches necessitate massive labeled data. Labeling is laborious and time-consuming, particularly for 3D seismic data volumes. To overcome this challenge, we propose a semi-supervised method based on pixel-level contrastive learning, termed CONSS, which can efficiently identify seismic facies using only 1% of the original annotations. Furthermore, the absence of a unified data division and standardized metrics hinders the fair comparison of various facies classification approaches. To this end, we develop an objective benchmark for the evaluation of semi-supervised methods, including self-training, consistency regularization, and the proposed CONSS. Our benchmark is publicly available to enable researchers to objectively compare different approaches. Experimental results demonstrate that our approach achieves state-of-the-art performance on the F3 survey

Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges

Deep learning is a subfield of state-of-the-art artificial intelligence (AI) technology, and multiple deep learning-based AI models have been applied to musculoskeletal diseases. Deep learning has shown the capability to assist clinical diagnosis and prognosis prediction in a spectrum of musculoskeletal disorders, including fracture detection, cartilage and spinal lesions identification, and osteoarthritis severity assessment. Meanwhile, deep learning has also been extensively explored in diverse tumors such as prostate, breast, and lung cancers. Recently, the application of deep learning emerges in bone tumors. A growing number of deep learning models have demonstrated good performance in detection, segmentation, classification, volume calculation, grading, and assessment of tumor necrosis rate in primary and metastatic bone tumors based on both radiological (such as X-ray, CT, MRI, SPECT) and pathological images, implicating a potential for diagnosis assistance and prognosis prediction of deep learning in bone tumors. In this review, we first summarized the workflows of deep learning methods in medical images and the current applications of deep learning-based AI for diagnosis and prognosis prediction in bone tumors. Moreover, the current challenges in the implementation of the deep learning method and future perspectives in this field were extensively discussed

Formation Flight in Dense Environments

Formation flight has a vast potential for aerial robot swarms in various applications. However, existing methods lack the capability to achieve fully autonomous large-scale formation flight in dense environments. To bridge the gap, we present a complete formation flight system that effectively integrates real-world constraints into aerial formation navigation. This paper proposes a differentiable graph-based metric to quantify the overall similarity error between formations. This metric is invariant to rotation, translation, and scaling, providing more freedom for formation coordination. We design a distributed trajectory optimization framework that considers formation similarity, obstacle avoidance, and dynamic feasibility. The optimization is decoupled to make large-scale formation flights computationally feasible. To improve the elasticity of formation navigation in highly constrained scenes, we present a swarm reorganization method which adaptively adjusts the formation parameters and task assignments by generating local navigation goals. A novel swarm agreement strategy called global-remap-local-replan and a formation-level path planner is proposed in this work to coordinate the swarm global planning and local trajectory optimizations efficiently. To validate the proposed method, we design comprehensive benchmarks and simulations with other cutting-edge works in terms of adaptability, predictability, elasticity, resilience, and efficiency. Finally, integrated with palm-sized swarm platforms with onboard computers and sensors, the proposed method demonstrates its efficiency and robustness by achieving the largest scale formation flight in dense outdoor environments.Comment: Submitted for IEEE Transactions on Robotic

Association between acute kidney injury and prognoses of cardiac surgery patients: Analysis of the MIMIC-III database

BackgroundAcute kidney injury (AKI) is the most common major complication of cardiac surgery field. The purpose of this study is to investigate the association between acute kidney injury and the prognoses of cardiac surgery patients in the Medical Information Mart for Intensive Care III (MIMIC-III) database.MethodsClinical data were extracted from the MIMIC-III database. Adult (≥18 years) cardiac surgery patients in the database were enrolled. Multivariable logistic regression analyses were employed to assess the associations between acute kidney injury (AKI) comorbidity and 30-day mortality, 90-day mortality and hospital mortality. Different adjusting models were used to adjust for potential confounders.ResultsA total of 6,002 patients were involved, among which 485 patients (8.08%) had comorbid AKI. Patients with AKI were at higher risks of prolonged ICU stay, hospital mortality, 90-day mortality (all P < 0.001), and 30-day mortality (P = 0.008). AKI was a risk factor for hospital mortality [Model 1, OR (95% CI) = 2.50 (1.45–4.33); Model 2, OR (95% CI) = 2.44 (1.48–4.02)], 30-day mortality [Model 1, OR (95% CI) = 1.84 (1.05–3.24); Model 2, OR (95% CI) = 1.96 (1.13–3.22)] and 90-day mortality [Model 1, OR (95% CI) = 2.05 (1.37–3.01); Model 2, OR (95% CI) = 2.76 (1.93–3.94)]. Higher hospital mortality, 30-day mortality and 90-day mortality was observed in higher KDIGO grade for cardiac surgery patients with AKI (all P < 0.05).ConclusionComorbid AKI increased the risk of hospital mortality, 30-day mortality, and 90-day mortality of cardiac surgery patients in the MIMIC-III database