e‐Maintenance Framework for Strategic Asset Management in Tertiary Institutions

Tertiary institutions require buildings such as its senate building, classrooms, laboratories, administrative rooms, hostels and other offices in order to function. Providing and maintaining these buildings require a lot of planning and capital investment. The study examined the prospects of using e‐ Maintenance platform for strategic asset management in tertiary institutions. This study noted that adequate maintenance of the building infrastructural base of tertiary institutions is crucial for sustainability in the face of dwindling funds in the education sector. In order to automate the e‐ Maintenance process for strategic maintenance of the institution’s building maintenance, a use case diagram, system block diagram, sequence diagram and activity diagram were designed and presented in this study. Three (3) main users are essential in the sequence of operation of the e‐Maintenance platform. These users represent the building occupants, the facility manager and the management personnel; for effective oversite and performance monitoring. The methodology of this research includes using the combination of HTML, CSS and the C‐Sharp programming language for the interface design and server side scripting while MySQL was the database platform used for storing and retrieving the data used for the application. In conclusion, the study developed an e‐Maintenance framework for strategic asset management in tertiary institutions. Keywords Asset management Automation Construction industr

Towards Predictive Maintenance for Flexible Manufacturing Using FIWARE

Industry 4.0 has shifted the manufacturing related processes from conventional processes within one organization to collaborative processes across different organizations. For example, product design processes, manufacturing processes, and maintenance processes across different factories and enterprises. This complex and competitive collaboration requires the underlying system architecture and platform to be flexible and extensible to support the demands of dynamic collaborations as well as advanced functionalities such as big data analytics. Both operation and condition of the production equipment are critical to the whole manufacturing process. Failures of any machine tools can easily have impact on the subsequent value-added processes of the collaboration. Predictive maintenance provides a detailed examination of the detection, location and diagnosis of faults in related machineries using various analyses. In this context, this paper explores how the FIWARE framework supports predictive maintenance. Specifically, it looks at applying a data driven approach to the Long Short-Term Memory Network (LSTM) model for machine condition and remaining useful life to support predictive maintenance using FIWARE framework in a modular fashion

Additive Manufacturing Cases and a Vision for a Predictive Analytics and Additive Manufacturing Based Maintenance Business Model

AbstractThis chapter discusses two real-world cases of how additive manufacturing can be used in enhancing results in heart surgery and in cutting costs in the business of maintenance and refurbishing metal dies, both without a radical change in the business model. In addition to the two real-world cases we present a vision of how additive manufacturing technologies, together with predictive analytics, digitalization, and a high level of international networking could revolutionize the business models of international maintenance service businesses

Optimized Hydrophobic Interactions and Hydrogen Bonding at the Target-Ligand Interface Leads the Pathways of Drug-Designing

Weak intermolecular interactions such as hydrogen bonding and hydrophobic interactions are key players in stabilizing energetically-favored ligands, in an open conformational environment of protein structures. However, it is still poorly understood how the binding parameters associated with these interactions facilitate a drug-lead to recognize a specific target and improve drugs efficacy. To understand this, comprehensive analysis of hydrophobic interactions, hydrogen bonding and binding affinity have been analyzed at the interface of c-Src and c-Abl kinases and 4-amino substituted 1H-pyrazolo [3, 4-d] pyrimidine compounds.In-silico docking studies were performed, using Discovery Studio software modules LigandFit, CDOCKER and ZDOCK, to investigate the role of ligand binding affinity at the hydrophobic pocket of c-Src and c-Abl kinase. Hydrophobic and hydrogen bonding interactions of docked molecules were compared using LigPlot program. Furthermore, 3D-QSAR and MFA calculations were scrutinized to quantify the role of weak interactions in binding affinity and drug efficacy.The in-silico method has enabled us to reveal that a multi-targeted small molecule binds with low affinity to its respective targets. But its binding affinity can be altered by integrating the conformationally favored functional groups at the active site of the ligand-target interface. Docking studies of 4-amino-substituted molecules at the bioactive cascade of the c-Src and c-Abl have concluded that 3D structural folding at the protein-ligand groove is also a hallmark for molecular recognition of multi-targeted compounds and for predicting their biological activity. The results presented here demonstrate that hydrogen bonding and optimized hydrophobic interactions both stabilize the ligands at the target site, and help alter binding affinity and drug efficacy

Association between Polymorphisms in Glutathione Peroxidase and Selenoprotein P Genes, Glutathione Peroxidase Activity, HRT Use and Breast Cancer Risk.

Breast cancer (BC) is one of the most common cancers in women. Evidence suggests that genetic variation in antioxidant enzymes could influence BC risk, but to date the relationship between selenoproteins and BC risk remains unclear. In this report, a study population including 975 Danish cases and 975 controls matched for age and hormone replacement therapy (HRT) use was genotyped for five functional single nucleotide polymorphisms (SNPs) in SEPP1, GPX1, GPX4 and the antioxidant enzyme SOD2 genes. The influence of genetic polymorphisms on breast cancer risk was assessed using conditional logistic regression. Additionally pre-diagnosis erythrocyte GPx (eGPx) activity was measured in a sub-group of the population. A 60% reduction in risk of developing overall BC and ductal BC was observed in women who were homozygous Thr carriers for SEPP1 rs3877899. Additionally, Leu carriers for GPX1 Pro198Leu polymorphism (rs1050450) were at ∼2 fold increased risk of developing a non-ductal BC. Pre-diagnosis eGPx activity was found to depend on genotype for rs713041 (GPX4), rs3877899 (SEPP1), and rs1050450 (GPX1) and on HRT use. Moreover, depending on genotype and HRT use, eGPx activity was significantly lower in women who developed BC later in life compared with controls. Furthermore, GPx1 protein levels increased in human breast adenocarcinoma MCF7 cells exposed to β-estradiol and sodium selenite.In conclusion, our data provide evidence that SNPs in SEPP1 and GPX1 modulate risk of BC and that eGPx activity is modified by SNPs in SEPP1, GPX4 and GPX1 and by estrogens. Our data thus suggest a role of selenoproteins in BC development

Genomic reconstruction of the SARS-CoV-2 epidemic in England.

The evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus leads to new variants that warrant timely epidemiological characterization. Here we use the dense genomic surveillance data generated by the COVID-19 Genomics UK Consortium to reconstruct the dynamics of 71 different lineages in each of 315 English local authorities between September 2020 and June 2021. This analysis reveals a series of subepidemics that peaked in early autumn 2020, followed by a jump in transmissibility of the B.1.1.7/Alpha lineage. The Alpha variant grew when other lineages declined during the second national lockdown and regionally tiered restrictions between November and December 2020. A third more stringent national lockdown suppressed the Alpha variant and eliminated nearly all other lineages in early 2021. Yet a series of variants (most of which contained the spike E484K mutation) defied these trends and persisted at moderately increasing proportions. However, by accounting for sustained introductions, we found that the transmissibility of these variants is unlikely to have exceeded the transmissibility of the Alpha variant. Finally, B.1.617.2/Delta was repeatedly introduced in England and grew rapidly in early summer 2021, constituting approximately 98% of sampled SARS-CoV-2 genomes on 26 June 2021

Proactive Learning for Intelligent Maintenance in Industry 4.0

Manufacturing companies require efficient maintenance practices in order to improve business performance, ensure equipment availability and reduce process downtime. With the advent of new technology, manufacturing processes are evolving from the traditional ways into digitalized manufacturing. This transformation enables systems and machines to be connected in complex networks as a collaborative community through the industrial internet of things (IIoT) and cyber-physical system (CPS). Hence, advanced maintenance strategies should be developed in order to ensure the successful implementation of Industry 4.0, which aims to transform traditional product-oriented systems into product-service systems (PSS). Today, machines and systems are expected to gain self-awareness and self-predictiveness in order to provide management with more insight on the status of the factory. In this regards, real-time monitoring along with the application of advanced machine learning algorithms based on historical data will enable systems to understand the current operating conditions, predict the remaining useful life and detect anomalies in the process. This paper discusses the necessity of predictive maintenance to achieve a sustainable and service-oriented manufacturing system and provides a methodology to be followed for implementing proactive maintenance in the context of Industry 4.0