Towards developing an intergrated maturity model framework for managing an enterprise business intelligence

There has been a great deal of recent interest that is driving research and development in the area of Business Intelligence (BI),but the issues regarding the implementation of enterprise scale of BI is still concern among BI academics and practitioners.Therefore, an Enterprise Business Intelligence Maturity Model(EBI2M) is proposed to serve as useful guideline for enterprises which are planning or undertaking large scale BI initiatives.In this paper, the author utilizes a Delphi study to conduct two stages of enquiries with a panel of BI experts, and then refines the research into a preliminary EBI2M model

Positive Solutions of Two-point right focal boundary value problems on time scales

AbstractWe consider the following boundary value problem,(−1)n−1yΔn(t)=(−1)p+1F(t,y(σn−1(t))),t∈[a,b]∩T,yΔi(a)=0,0≤i≤p−1,yΔi(σ(b))=0,p≤i≤n−1,where n ≥ 2, 1 ⩽ p ⩽ n - 1 is fixed and T is a time scale. Criteria for the existence of single, double, and multiple positive solutions of the boundary value problem are developed. Upper and lower bounds for these positive solutions are established for two special cases that arise from some physical phenomena. We also include several examples to illustrate the usefulness of the results obtained

Genetic based discrete particle swarm optimization for elderly day care center timetabling

The timetabling problem of local Elderly Day Care Centers (EDCCs) is formulated into a weighted maximum constraint satisfaction problem (Max-CSP) in this study. The EDCC timetabling problem is a multi-dimensional assignment problem, where users (elderly) are required to perform activities that require different venues and timeslots, depending on operational constraints. These constraints are categorized into two: hard constraints, which must be fulfilled strictly, and soft constraints, which may be violated but with a penalty. Numerous methods have been successfully applied to the weighted Max-CSP; these methods include exact algorithms based on branch and bound techniques, and approximation methods based on repair heuristics, such as the min-conflict heuristic. This study aims to explore the potential of evolutionary algorithms by proposing a genetic-based discrete particle swarm optimization (GDPSO) to solve the EDCC timetabling problem. The proposed method is compared with the min-conflict random-walk algorithm (MCRW), Tabu search (TS), standard particle swarm optimization (SPSO), and a guided genetic algorithm (GGA). Computational evidence shows that GDPSO significantly outperforms the other algorithms in terms of solution quality and efficiency

The global spread and invasion capacities of alien ants

Many alien species are neither cultivated nor traded but spread unintentionally, and their global movements, capacities to invade ecosystems, and susceptibility to detection by biosecurity measures are poorly known.1,2,3,4 We addressed these key knowledge gaps for ants, a ubiquitous group of stowaway and contaminant organisms that include some of the world’s most damaging invasive species.5,6,7,8,9,10 We assembled a dataset of over 146,000 occurrence records to comprehensively map the human-mediated spread of 520 alien ant species across 525 regions globally. From descriptions of the environments in which species were collected within individual regions—such as in imported cargoes, buildings, and outdoor settings—we determined whether different barriers to invasion had been overcome11 and classified alien ant species under three levels of invasion capacity corresponding to increasing biosecurity threat. We found that alien species of different invasion capacities had different sources and sinks globally. For instance, although the diversity of indoor-confined species peaked in the Palearctic realm, that of species able to establish outdoors peaked in the Nearctic and Oceanian realms, and these mainly originated from the Neotropical and Oriental realms. We also found that border interceptions worldwide missed two-thirds of alien species with naturalization capacity, many associated with litter and soil. Our study documents the vast spread of alien ants globally while highlighting avenues for more targeted biosecurity responses, such as prioritizing the screening of imports from regions that are diversity hotspots for species of high invasion capacity and improving the detection of cryptic alien invertebrates dwelling in substrates.journal articl

Design and application of genetically-encoded malonyl-CoA biosensors for metabolic engineering of microbial cell factories

Malonyl-CoA is the basic building block for synthesizing a range of important compounds including fatty acids, phenylpropanoids, flavonoids and non-ribosomal polyketides. Centering around malonyl-CoA, we summarized here the various metabolic engineering strategies employed recently to regulate and control malonyl-CoA metabolism and improve cellular productivity. Effective metabolic engineering of microorganisms requires the introduction of heterologous pathways and dynamically rerouting metabolic flux towards products of interest. Transcriptional factor-based biosensors translate an internal cellular signal to a transcriptional output and drive the expression of the designed genetic/biomolecular circuits to compensate the activity loss of the engineered biosystem. Recent development of genetically-encoded malonyl-CoA sensor has stood out as a classical example to dynamically reprogram cell metabolism for various biotechnological applications. Here, we reviewed the design principles of constructing a transcriptional factor-based malonyl-CoA sensor with superior detection limit, high sensitivity and broad dynamic range. We discussed various synthetic biology strategies to remove pathway bottleneck and how genetically-encoded metabolite sensor could be deployed to improve pathway efficiency. Particularly, we emphasized that integration of malonyl-CoA sensing capability with biocatalytic function would be critical to engineer efficient microbial cell factory. Biosensors have also advanced beyond its classical function of a sensor actuator for in situ monitoring of intracellular metabolite concentration. Applications of malonyl-CoA biosensors as a sensor-invertor for negative feedback regulation of metabolic flux, a metabolic switch for oscillatory balancing of malonyl-CoA sink pathway and source pathway and a screening tool for engineering more efficient biocatalyst are also presented in this review. We envision the genetically-encoded malonyl-CoA sensor will be an indispensable tool to optimize cell metabolism and cost-competitively manufacture malonyl-CoA-derived compounds

Key performance indicators for measuring sustainability in health care industry in Malaysia

The health care industry in Malaysia was the fastest-growth industry over the past few years. In today’s competitive business environment, companies focus on improving sustainability to reduce cost and improve well-being of the environment and society. However, there are limited published studies on the evaluation of sustainability performance for the healthcare sector. This paper aims to formulate a list of key performance indicators (KPI) for the sustainability performance. First, a literature study of KPIs from various industries was carried out. Next, an in-depth meeting was conducted to gain insights and feedbacks with the management of a private hospital. Finally, a set of 70 KPIs which can be used for measuring sustainability performance in health care industry was developed. These 70 KPIs were used to design a questionnaire which is then distributed to the private hospital.Keywords: key performance indicators; sustainability; health care industr

Crafting genetic diversity: unlocking the potential of protein evolution

Genetic diversity is the foundation of evolutionary resilience, adaptive potential, and the flourishing vitality of living organisms, serving as the cornerstone for robust ecosystems and the continuous evolution of life on Earth. The landscape of directed evolution, a powerful biotechnological tool inspired by natural evolutionary processes, has undergone a transformative shift propelled by innovative strategies for generating genetic diversity. This shift is fuelled by several factors, encompassing the utilization of advanced toolkits like CRISPR-Cas and base editors, the enhanced comprehension of biological mechanisms, cost-effective custom oligo pool synthesis, and the seamless integration of artificial intelligence and automation. This comprehensive review looks into the myriad of methodologies employed for constructing gene libraries, both in vitro and in vivo, categorized into three major classes: random mutagenesis, focused mutagenesis, and DNA recombination. The objectives of this review are threefold: firstly, to present a panoramic overview of recent advances in genetic diversity creation; secondly, to inspire novel ideas for further innovation in genetic diversity generation; and thirdly, to provide a valuable resource for individuals entering the field of directed evolution

Cryptococcal osteomyelitis of the femur: A case report and review of literature

Fungal osteomyelitis is a rare opportunistic infection. It exhibits some clinical and radiological similarities to several other bone pathologies. A diagnostic delay may result in significant increase in morbidity. We report a case of a 37-year-old man with underlying hypogammaglobulinaemia presented with isolated cryptococcal osteomyelitis of the femur

Analysis of vortex ring formation in the heart chamber by instantaneous vortex deviation based on convolutional neural network

The formation of vortex rings during the left ventricle (LV) filling is an optimized mechanism for blood transport, and the vorticity is an important measure of a healthy heart and LV. There is a relationship between abnormal diastolic vortex structure and impaired LV, and hence vortex identification is vital for understanding the underlying physical mechanism of blood flow. However, due to lack of quantitative methods, defining, computing and mapping the left ventricular vortices has not been rigorously studied previously. In this paper, a novel method of vortex detection based on the convolutional neural network (CNN) is created, which enables determination of the boundary of vortex and integrates the local and global flow fields. We have used the CNN-based vortex identification and vector flow mapping (VFM) to quantify left ventricular vorticity. In the clinical application of our methodology to healthy subjects and uremic patients, we find differences in the strength and position of the vortices between healthy and patients with uremia cardiomyopathy. Our results can accurately indicate the role of vortex formation in intracardiac flow, and provide new insights into the blood flow within the heart structure

Adaptive laboratory evolution of cupriavidus necator H16 for carbon co-utilization with glycerol

Cupriavidus necator H16 is a non-pathogenic Gram-negative betaproteobacterium that can utilize a broad range of renewable heterotrophic resources to produce chemicals ranging from polyhydroxybutyrate (biopolymer) to alcohols, alkanes, and alkenes. However, C. necator H16 utilizes carbon sources to different efficiency, for example its growth in glycerol is 11.4 times slower than a favorable substrate like gluconate. This work used adaptive laboratory evolution to enhance the glycerol assimilation in C. necator H16 and identified a variant (v6C6) that can co-utilize gluconate and glycerol. The v6C6 variant has a specific growth rate in glycerol 9.5 times faster than the wild-type strain and grows faster in mixed gluconate–glycerol carbon sources compared to gluconate alone. It also accumulated more PHB when cultivated in glycerol medium compared to gluconate medium while the inverse is true for the wild-type strain. Through genome sequencing and expression studies, glycerol kinase was identified as the key enzyme for its improved glycerol utilization. The superior performance of v6C6 in assimilating pure glycerol was extended to crude glycerol (sweetwater) from an industrial fat splitting process. These results highlight the robustness of adaptive laboratory evolution for strain engineering and the versatility and potential of C. necator H16 for industrial waste glycerol valorization