Interpretive structural model of trust factors in construction virtual project teams

Purpose Organisational dependence on virtual project teams (VPTs) is growing dramatically due to the substantial benefits they offer, such as efficiently achieving objectives and improving organisational performance. One of the major issues that influence the effectiveness of VPTs is trust building. This study aims to determine the key factors of trust in VPTs and design a model by identifying the interrelationships among the trust factors. Design/methodology/approach Focus group discussion was used to gather data on factors affecting trust in VPTs and their interrelationships. Interpretive structural modelling (ISM) was used to establish the relationship among the factors. Cross-impact matrix multiplication applied to classification analysis was conducted to identify the driving power and the dependence power towards effective VPTs in the construction sector. Findings The finding revealed that “characteristics of team members” (such as ability, integrity, benevolence, competence, reliability and professionalism) is the most significant factor for building trust in virtual team members. Some factors were further identified as having high driving power, while others were defined as having high dependence variables. Practical implications The findings will assist construction managers and practitioners dealing with VPTs identify the factors influencing trust among team members. Taking cognisance of the factors that influence trust will enable them to design more effective virtual team arrangements. Originality/value As the first research of its kind using ISM technique, the study offers insights into interrelationships between trust factors in the construction VPTs. It provides guides for construction managers on the effective management of trustworthy VPTs.

Fast Barycentric-Based Evaluation Over Spectral/hp Elements

As the use of spectral/hp element methods, and high-order finite element methods in general, continues to spread, community efforts to create efficient, optimized algorithms associated with fundamental high-order operations have grown. Core tasks such as solution expansion evaluation at quadrature points, stiffness and mass matrix generation, and matrix assembly have received tremendous attention. With the expansion of the types of problems to which high-order methods are applied, and correspondingly the growth in types of numerical tasks accomplished through high-order methods, the number and types of these core operations broaden. This work focuses on solution expansion evaluation at arbitrary points within an element. This operation is core to many postprocessing applications such as evaluation of streamlines and pathlines, as well as to field projection techniques such as mortaring. We expand barycentric interpolation techniques developed on an interval to 2D (triangles and quadrilaterals) and 3D (tetrahedra, prisms, pyramids, and hexahedra) spectral/hp element methods. We provide efficient algorithms for their implementations, and demonstrate their effectiveness using the spectral/hp element library Nektar++ by running a series of baseline evaluations against the ‘standard’ Lagrangian method, where an interpolation matrix is generated and matrix-multiplication applied to evaluate a point at a given location. We present results from a rigorous series of benchmarking tests for a variety of element shapes, polynomial orders and dimensions. We show that when the point of interest is to be repeatedly evaluated, the barycentric method performs at worst $$50\%$$ 50 % slower, when compared to a cached matrix evaluation. However, when the point of interest changes repeatedly so that the interpolation matrix must be regenerated in the ‘standard’ approach, the barycentric method yields far greater performance, with a minimum speedup factor of $$7\times $$ 7 × . Furthermore, when derivatives of the solution evaluation are also required, the barycentric method in general slightly outperforms the cached interpolation matrix method across all elements and orders, with an up to $$30\%$$ 30 % speedup. Finally we investigate a real-world example of scalar transport using a non-conformal discontinuous Galerkin simulation, in which we observe around $$6\times $$ 6 × speedup in computational time for the barycentric method compared to the matrix-based approach. We also explore the complexity of both interpolation methods and show that the barycentric interpolation method requires $${\mathcal {O}}(k)$$ O ( k ) storage compared to a best case space complexity of $${\mathcal {O}}(k^2)$$ O ( k 2 ) for the Lagrangian interpolation matrix method.

Listening to Caregivers’ Voices: The Informal Family Caregiver Burden of Caring for Chronically Ill Bedridden Elderly Patients

The informal family caregiver burden (IFCB) for chronically ill bedridden elderly patients (CIBEPs) is a major issue worldwide. It is a significant challenge due to the ongoing increased palliative care in the family setting; therefore, we explored the IFCB of caring for CIBEPs in Thailand. This article utilized a qualitative method, the total interpretive structural modeling (TISM) approach, with purposive sampling of thirty respondents between September and December 2020. The data were analyzed using cross-impact matrix multiplication applied to classification (MICMAC) to determine the relationship between the driving and dependence power of the enabling factors. The IFCB of the palliative care of CIBEPs was associated with primary care, nursing, extrinsic monitoring and complication prevention. The results showed that the IFCB involves taking responsibility, daily workload, follow-up caring, caring tasks, caregiving strain, financial distress, patient support, external support and caregiving strategy; thus, assistance with taking responsibility, extrinsic monitoring and follow-up care daily tasks may reduce the caregiver burden.

Unimodular matrix and bernoulli map on text encryption algorithm using python

One of the encryption algorithms is the Hill Cipher. The square key matrix in the Hill Cipher method must have an inverse modulo. The unimodular matrix is one of the few matrices that must have an inverse. A unimodular matrix can be utilized as a key in the encryption process. This research aims to demonstrate that there is another approach to protect text message data. Symmetric cryptography is the sort of encryption utilized. A Bernoulli Map is used to create a unimodular matrix. To begin, the researchers use an identity matrix to generate a unimodular matrix. The Bernoulli Map series of real values in (0,1) is translated to integers between 0 and 255. The numbers are then inserted into the unimodular matrix's top triangular entries. To acquire the full matrix as the key, the researchers utilize Elementary Row Operations. The data is then encrypted using modulo matrix multiplication.

cuSCNN: A Secure and Batch-Processing Framework for Privacy-Preserving Convolutional Neural Network Prediction on GPU

The emerging topic of privacy-preserving deep learning as a service has attracted increasing attention in recent years, which focuses on building an efficient and practical neural network prediction framework to secure client and model-holder data privately on the cloud. In such a task, the time cost of performing the secure linear layers is expensive, where matrix multiplication is the atomic operation. Most existing mix-based solutions heavily emphasized employing BGV-based homomorphic encryption schemes to secure the linear layer on the CPU platform. However, they suffer an efficiency and energy loss when dealing with a larger-scale dataset, due to the complicated encoded methods and intractable ciphertext operations. To address it, we propose cuSCNN, a secure and efficient framework to perform the privacy prediction task of a convolutional neural network (CNN), which can flexibly perform on the GPU platform. Its main idea is 2-fold: (1) To avoid the trivia and complicated homomorphic matrix computations brought by BGV-based solutions, it adopts GSW-based homomorphic matrix encryption to efficiently enable the linear layers of CNN, which is a naive method to secure matrix computation operations. (2) To improve the computation efficiency on GPU, a hybrid optimization approach based on CUDA (Compute Unified Device Architecture) has been proposed to improve the parallelism level and memory access speed when performing the matrix multiplication on GPU. Extensive experiments are conducted on industrial datasets and have shown the superior performance of the proposed cuSCNN framework in terms of runtime and power consumption compared to the other frameworks.