Timing Robustness in the Budding and Fission Yeast Cell Cycles

Robustness of biological models has emerged as an important principle in systems biology. Many past analyses of Boolean models update all pending changes in signals simultaneously (i.e., synchronously), making it impossible to consider robustness to variations in timing that result from noise and different environmental conditions. We checked previously published mathematical models of the cell cycles of budding and fission yeast for robustness to timing variations by constructing Boolean models and analyzing them using model-checking software for the property of speed independence. Surprisingly, the models are nearly, but not totally, speed-independent. In some cases, examination of timing problems discovered in the analysis exposes apparent inaccuracies in the model. Biologically justified revisions to the model eliminate the timing problems. Furthermore, in silico random mutations in the regulatory interactions of a speed-independent Boolean model are shown to be unlikely to preserve speed independence, even in models that are otherwise functional, providing evidence for selection pressure to maintain timing robustness. Multiple cell cycle models exhibit strong robustness to timing variation, apparently due to evolutionary pressure. Thus, timing robustness can be a basis for generating testable hypotheses and can focus attention on aspects of a model that may need refinement

Bayesian optimisation for premise selection in automated theorem proving (student abstract)

Modern theorem provers utilise a wide array of heuristics to control the search space explosion, thereby requiring optimisation of a large set of parameters. An exhaustive search in this multi-dimensional parameter space is intractable in most cases, yet the performance of the provers is highly dependent on the parameter assignment. In this work, we introduce a principled probabilistic framework for heuristic optimisation in theorem provers. We present results using a heuristic for premise selection and the Archive of Formal Proofs (AFP) as a case study.</jats:p

Applying Dijkstras Algorithm in Routing Process

Network is defined as a combination of two or more nodes which are connected with each other. It allows nodes to exchange data from each other along the data connections. Routing is a process of finding the path between source and destination upon request of data transmission. There are various routing algorithms which helps in determining the path and distance over the network traffic. For routing of nodes, we can use many routing protocols. Dijkstrarsquos algorithm is one of the best shortest path search algorithms. Our focus and aim is to find the shortest path from source node to destination node. For finding the minimum path this algorithm uses the connection matrix and weight matrix Thus, a matrix consisting of paths from source node to each node is formed. We then choose a column of destination from path matrix formed and we get the shortest path. In a similar way, we choose a column from a mindis matrix for finding the minimum distance from source node to destination node. It has been applied in computer networking for routing of systems and in google maps to find the shortest possible path from one location to another location.nbs

Analysing the Adoption Barriers of Low-Carbon Operations: A Step Forward for Achieving Net-Zero Emissions

In November 2021, the 26th United Nations Climate Change Conference (COP26) was held in Glasgow, UK, the global leaders from nearly 200 countries stressed taking immediate action on the climate issue and how to ensure global net-zero emissions by 2030. It is possible to accelerate the transition to low-carbon energy systems, the present study seeks to identify and analyse key barriers to Low Carbon Operations (LCO) in emerging economies. A critical literature review was undertaken to recognise the barriers linked to the adoption of LCO. To validate these barriers, an empirical study with a dataset of 127 respondents from the Indian automobile industry was conducted. The validated barriers were analysed using Best Worst Method (BWM) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) techniques. BWM is used to determine the priority ranking of barriers, while the DEMATEL method is employed to elucidate the cause-effect inter-relationships among the listed barriers. The results suggest that ‘Economic’ is the most influential category of barriers followed by ‘Infrastructure’ and ‘Operational’. The results also show that the barriers ‘Economic’, ‘Environmental’, ‘Infrastructure’ and ‘Organizational Governance’ belong to the cause group. Some significant managerial implications are recommended to overcome these barriers and to assist firms in the successful adoption of LCO and achieving net-zero emissions. The work was carried out in the automotive industry in India but provides findings that may have wider applicability in other developing countries and beyond

Investigating enablers to improve transparency in sustainable food supply chain using F-BWM

Food Supply Chains (FSC) are complex and dynamic in behavior and prone to increasing risks of unsustainability. Consumers increasingly demand food quality, safety, and sustainability, which are fast becoming issues of great importance in FSC. Lack of real-time information sharing and connectivity among stakeholders make these issues tougher to mitigate. Supply chain transparency (SCT) is thus an essential attribute to manage these supply chain complexities and enhance the sustainability of FSC. The paper identifies and analyses key enablers for SCT in FSC. Several technical, as well as sustainability-related enablers, contribute to the implementation of SCT. The identified enablers are analyzed using Fuzzy-best worst methodology (F-BWM), which determine the most critical factors using the decision maker’s opinion. Extending BWM with fuzzy logic incorporates the vagueness of human-behaviour into decision making approach. The results of this research provides decision makers with the priority of enablers to the decision maker. Enhancing these enablers in will help improve the transparency for better management of FSC. The article expands upon the practical as well as theoretical implications of SCT on sustainability in FSC. It addresses the requirement of including sustainability in the decision-making process. The results demonstrate the effectiveness of the F-BWM for the decision making process. The study is conducted by considering downstream supply chain activities in Indian context. It is one of the first studies that analyzes SCT enablers using F-BWM method in Indian context. The study contributes towards improving the environmental, economical, and social sustainability of FSC

Virulence of Oomycete Pathogens from \u3cem\u3ePhragmites australis\u3c/em\u3e-Invaded and Noninvaded Soils to Seedlings of Wetland Plant Species

Soil pathogens affect plant community structure and function through negative plant-soil feedbacks that may contribute to the invasiveness of non-native plant species. Our understanding of these pathogen-induced soil feedbacks has relied largely on observations of the collective impact of the soil biota on plant populations, with few observations of accompanying changes in populations of specific soil pathogens and their impacts on invasive and noninvasive species. As a result, the roles of specific soil pathogens in plant invasions remain unknown. In this study, we examine the diversity and virulence of soil oomycete pathogens in freshwater wetland soils invaded by non-native Phragmites australis (European common reed) to better understand the potential for soil pathogen communities to impact a range of native and non-native species and influence invasiveness. We isolated oomycetes from four sites over a 2-year period, collecting nearly 500 isolates belonging to 36 different species. These sites were dominated by species of Pythium, many of which decreased seedling survival of a range of native and invasive plants. Despite any clear host specialization, many of the Pythium species were differentially virulent to the native and non-native plant species tested. Isolates from invaded and noninvaded soils were equally virulent to given individual plant species, and no apparent differences in susceptibility were observed between the collective groups of native and non-native plant species

A Fuzzy AHP-TOPSIS Approach to Supply Partner Selection in Continuous Aid Humanitarian Supply Chains

The selection of suitable supply partners is a strategic issue for managers working in humanitarian operations and has received little attention in the literature. In humanitarian operations, complexity characterizes the continuous-aid procurement operations, and the selection criteria can differ from those used in commercial supply chain settings. This paper advances knowledge by introducing a supply partner selection framework for continuous-aid procurement. A proposed multi-criteria decision-making model uses selection criteria attributes verified by the extant literature and by field experts. A fuzzy Analytic Hierarchy Process is then used to compute criterion weights, and a fuzzy Technique for Order Performance by Similarity to Ideal Solution is used to rank supply partner alternatives. Even with elevated levels of subjectivity, these techniques enable humanitarian operation stakeholders to select the best supply partner effectively. An actual case illustrates how the proposed framework efficiently identifies the most suitable continuous-aid supply partner for the prevailing situation

Predicting changing pattern: building model for consumer decision making in digital market

YesConsumers have the multiple options to choose their products and services, which have a significant impact on the pattern of consumer decision making in digital market and further increases the challenges for the service providers to predict their buying pattern. In this sense, the purpose of this paper is to propose a structural hierarchy model for analyzing the changing pattern of consumer decision making in digital market by taking an Indian context. Design/methodology/approach: To accomplish the objectives, the research is conducted in two phases. An extensive literature review is performed in the first phase to list the factors related to the changing pattern of consumer decision making in digital market and then fuzzy Delphi method is applied to finalize the factors. In the second phase, fuzzy analytic hierarchy process (AHP) is employed to find the priority weights of finalized factors. The fuzzy set theory allows capturing the vagueness in the data. Findings: The findings obtained in this study shows that consumers are much conscious about innovative and trendy products as well as brand and quality; therefore, the service providers must think about these two most important factors so that they can able to retain their consumer in their online portal. Practical implications: The analysis shows that “innovative and trendy” is the first priority factor for the consumers followed by “brand and quality” and “fulfilment and time energy.” The proposed model can help the marketers and service providers in predicting customers’ preferences and their changing pattern efficiently under vague surroundings. The outcomes of this research work not only help the service provider to update their products and services according to consumers’ needs but can also help them to increase profit and minimize their risk. Originality/value: This work contributes to consumer research literature focusing on problem evaluation in the context of changing pattern of consumer decision making in digital era