An Analysis of Data Driven, Decision-Making Capabilities of Managers in Banks

Organizations are adopting data analytics and Business Intelligence (BI) tools to gain insights from the past data, forecast future events, and to get timely and reliable information for decision making. While the tools are becoming mature, affordable, and more comfortable to use, it is also essential to understand whether the contemporary managers and leaders are ready for Data-Driven Decision Making (DDDM). We explore the extent the Decision Makers (DMs) utilize data and tools, as well as their ability to interpret various forms of outputs from tools and to apply those insights to gain competitive advantage. Our methodology was based on a qualitative survey, where we interviewed 12 DMs of six commercial banks in Sri Lanka at the branch, regional, and CTO, CIO, and Head of IT levels. We identified that on many occasions, DMs' intuition overrules the DDDM due to uncertainty, lack of trust, knowledge, and risk-taking. Moreover, it was identified that the quality of visualizations has a significant impact on the use of intuition by overruling DDDM. We further provide a set of recommendations on the adoption of BI tools and how to overcome the struggles faced while performing DDDM.Comment: 19 pages, 8 figues, 5 table

Wireless sensor network based system for underground chemical plume tracking, A

A real-time subsurface chemical plume monitoring and tracking system is being developed that uses wireless-sensor networking to automatically extract data from underground chemical sensors. This system is aimed at tracking plumes caused by the release of toxic chemicals and biological agents into the environment as a result of accidental spills and improper disposal. Current practice involves manual collection of samples from monitoring wells followed by laboratory analysis, an expensive process taking days to weeks; such a delay reduces the effectiveness of mitigation techniques as well. Virtual Sensor Networks (VSN), a novel resource efficient approach for sensor networking being developed to track the migrating underground plumes, will be applicable to a broad class of problems. Laboratory based experiments and simulations are in progress to demonstrate the feasibility of the approach for large-scale plume tracking.This research is supported in part by Army Research Office and the National Science Foundation.1st place, ISTeC Student Research Poster Contest (April 7, 2008)

Towards Reforming Sri Lanka Railways: Insights from International Experience and Industry Expert Opinion

Growing of the automobile industry and the demand for personal car use and chronic financial deficits in the balance sheets of rail operators have significantly affected the rail industry deterioration since 1970. However, gradual rail reforms were carried out by many countries to eliminate financial and operational issues and to develop their rail transportation systems. Sri Lanka has more than 150 years of history in railway operations, yet it is still in a weak position in terms of the operational efficiency and the financial position. The main purpose of this paper is to explore the key issues and root causes for the operational and financial deficiencies of Sri Lanka Railways and identify the best reform model in the light of world rail reform experiences and rail industry experts’ opinion. A semi-structured questionnaire was employed to interview twelve railway industry experts. Content analysis, Analytical Hierarchy Process (AHP) Method, and Policy Delphi Method were the main analytical techniques employed in the study. The results of the analysis showed that the vertical separation of the ownership between rail service operation and rail infrastructure provision is suitable for Sri Lanka Railways and, given the existing operational and financial characteristics, the reform steps should mostly be designed as in the case of the German- Sweden hybrid model of rail reforms

Effects of tea extracts on the colonization behaviour of Candida species:attachment inhibition and biofilm enhancement

Purpose. We assessed the effects of four different types of tea extracts (green, oolong, black and pu-erh tea) on cellular surface properties (hydrophobicity and auto-aggregation) and the colonization attributes (attachment and biofilm formation) of four strains of Candida albicans and three strains of Candida krusei. Methodology. The cellular surface properties were determined using spectrophotometry. The colonization activities were quantified using colorimetric viability assays and visualized using scanning electron microscopy (SEM) and confocal laser scanning microscopy (CLSM). Results. The tea extracts, in general, reduced the hydrophobicity (by 8-66%) and auto-aggregation (by 20-65%), and inhibited the attachment of two C. krusei strains (by 41-88%). Tea extracts enhanced the biofilm formation of one C. albicans and two C. krusei strains (by 1.4-7.5-fold). The observed reduction in hydrophobicity strongly correlated with the reduction in attachment of the two C. krusei strains (

Fluconazole resistance in Candida albicans is induced by Pseudomonas aeruginosa quorum sensing

Microorganisms employ quorum sensing (QS) mechanisms to communicate with each other within microbial ecosystems. Emerging evidence suggests that intraspecies and interspecies QS plays an important role in antimicrobial resistance in microbial communities. However, the relationship between interkingdom QS and antimicrobial resistance is largely unknown. Here, we demonstrate that interkingdom QS interactions between a bacterium, Pseudomonas aeruginosa and a yeast, Candida albicans, induce the resistance of the latter to a widely used antifungal fluconazole. Phenotypic, transcriptomic, and proteomic analyses reveal that P. aeruginosa’s main QS molecule, N-(3-Oxododecanoyl)-L-homoserine lactone, induces candidal resistance to fluconazole by reversing the antifungal’s effect on the ergosterol biosynthesis pathway. Accessory resistance mechanisms including upregulation of C. albicans drug-efflux, regulation of oxidative stress response, and maintenance of cell membrane integrity, further confirm this phenomenon. These findings demonstrate that P. aeruginosa QS molecules may confer protection to neighboring yeasts against azoles, in turn strengthening their co-existence in hostile polymicrobial infection sites

Maximizing NFT Incentives: References Make You Rich

In this paper, we study how to optimize existing Non-Fungible Token (NFT) incentives. Upon exploring a large number of NFT-related standards and real-world projects, we come across an unexpected finding. That is, the current NFT incentive mechanisms, often organized in an isolated and one-time-use fashion, tend to overlook their potential for scalable organizational structures. We propose, analyze, and implement a novel reference incentive model, which is inherently structured as a Directed Acyclic Graph (DAG)-based NFT network. This model aims to maximize connections (or references) between NFTs, enabling each isolated NFT to expand its network and accumulate rewards derived from subsequent or subscribed ones. We conduct both theoretical and practical analyses of the model, demonstrating its optimal utility

A Tale of Two Cities: Data and Configuration Variances in Robust Deep Learning

Deep neural networks (DNNs), are widely used in many industries such as image recognition, supply chain, medical diagnosis, and autonomous driving. However, prior work has shown the high accuracy of a DNN model does not imply high robustness (i.e., consistent performances on new and future datasets) because the input data and external environment (e.g., software and model configurations) for a deployed model are constantly changing. Hence, ensuring the robustness of deep learning is not an option but a priority to enhance business and consumer confidence. Previous studies mostly focus on the data aspect of model variance. In this article, we systematically summarize DNN robustness issues and formulate them in a holistic view through two important aspects, i.e., data and software configuration variances in DNNs. We also provide a predictive framework to generate representative variances (counterexamples) by considering both data and configurations for robust learning through the lens of search-based optimization