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Achieving maximum utilization in optimal time for learning or convergence in the Kolkata Paise Restaurant problem
No full textIn the original version of the Kolkata Paise Restaurant (KPR) problem, where each of the N agents (or players) chooses independently every day (updating their strategy based on past experience of failures) among the N restaurants, where he/she will be alone or lucky enough to be picked up randomly from the crowd who arrived at that restaurant that day, to get the only food plate served there. The objective of the agents is to learn themselves in the minimum (learning) time to have maximum success or utilization probability (f). A dictator can easily solve the problem with f=1 in no time, by asking every one to form a queue and go to the respective restaurant, resulting in no fluctuation and full utilization from the first day (convergence time τ=0). It has already been shown that if each agent chooses randomly the restaurants, f=1-e-1≃0.63 (where e≃2.718 denotes the Euler number) in zero time (τ=0). With the only available information about yesterday’s crowd size in the restaurant visited by the agent (as assumed for the rest of the strategies studied here), the crowd avoiding (CA) strategies can give higher values of f but also of τ. Several numerical studies of modified learning strategies actually indicated increased value of f=1-α for α→0, with τ∼1/α. We show here using Monte Carlo technique that a modified Greedy Crowd Avoiding (GCA) Strategy can assure full utilization (f=1) in convergence time τ≃eN, with of course non-zero probability for an even larger convergence time. All these observations suggest that the strategies with single step memory of the individuals can never collectively achieve full utilization (f=1) in finite convergence time and perhaps the maximum possible utilization that can be achieved is about eighty percent (f≃0.80) in an optimal time τ of order ten, even when N the number of customers or of the restaurants goes to infinity
Affinity Propagation in Semi-Supervised Segmentation: A Biomedical Application
No full textGiven the scarcity of sufficient annotated data, using small sets of labeled samples under semi-supervision in biomedical imaging becomes necessary. Despite being highly successful, deep learning algorithms demand plenty of data to obtain significant performance. Complex data models make the usage of these methods costly. Selecting the correct model and tuning the hyperparameters of a model are also difficult jobs. Hence, a novel approach namely affinity propagation-based semi-supervised segmentation (APSS) is proposed. Here, affinity propagation clustering is modified and integrated with the advanced learning techniques that can efficiently use limited training data by discarding the completely exploited labeled data points. Moreover, a novel affinity calculation method is proposed considering both the Euclidean and geodesic distances to compute the distance between the two points on the histogram. This twofold contribution is tested using the three standard datasets (the International Skin Imaging Collaboration (ISIC) dermoscopic image dataset, the retinal fundus image dataset, and the liver tumor segmentation (LiTS) dataset). Results are compared with the three standard semi-supervised algorithms and four supervised algorithms. The effectiveness of the APSS approach in finding and exploiting the relationship between the labeled and unlabeled datasets is demonstrated in terms of qualitative (subjective evaluation and visual inspection) and quantitative performance (objective evaluation and numerical measurements)
An Approach to Synergise the Management of Lean, Green, Quality and Waste for Achieving ZED Emphasising SDG12
No full textThe advent of the Zero Defect and Zero Effect (ZED) concept has thrown a significant challenge in integrating quality management, lean philosophy, waste management, and green management within industrial operations. Achieving quality outcomes requires fulfilling both external and internal customer expectations through effective requirement analysis and meeting specific quality dimensions. At the same time, organizations must minimize resource usage—such as time, energy, manpower, and materials—while maintaining product quality. This is a core principle of lean management, which aims to improve margins and ensure organizational growth and survival. Simultaneously, environmental sustainability must be prioritized by minimizing air, water, and soil pollution, in line with green management principles. This study employs the Fuzzy Analytical Hierarchy Process (FAHP) to prioritize key parameters for assessing ZED performance across three industrial sectors in India. Additionally, it proposes practical methods for improving vital components using Six Sigma metrics to enhance operational excellence and sustainability. The novelty of this research lies in the integration of a ZED Maturity Model with the Business Excellence Model, along with the analysis of vital components through Six Sigma metrics to develop a comprehensive framework for improving operational performance and sustainability. This paper provides a theoretical framework and actionable strategies for researchers and practitioners to effectively integrate ZED principles, fostering sustainability and operational performance in manufacturing industries while supporting Sustainable Development Goal 12
An approximate equivalence for the GNS representation of the Haar state of SUq(2)
No full textWe use the crystallised C∗-algebra C(SUq(2)) at q=0 to obtain a unitary that gives an approximate equivalence involving the GNS representation on the L2 space of the Haar state of the quantum SU(2) group and the direct integral of all the infinite dimensional irreducible representations of the C∗-algebra C(SUq(2)) for nonzero values of the parameter q. This approximate equivalence gives a KK class via the Cuntz picture in terms of quasihomomorphisms as well as a Fredholm representation of the dual quantum group SUq(2)^ with coefficients in a C∗-algebra in the sense of Mishchenko
An efficient and secure quantum blind signature-based electronic cash transaction scheme
No full textThe authors present a novel token exchange scheme with an example of an electronic cash (eCash) transaction scheme that ensures quantum security, addressing the vulnerabilities of existing models in the face of quantum computing threats. The authors’ comprehensive analysis of various quantum blind signature mechanisms revealed significant shortcomings in their applicability to eCash transactions and their resilience against quantum adversaries. In response, the authors drew inspiration from D. Chaum\u27s original classical eCash scheme and innovated a quantum-secure transaction framework. The authors detail the developed protocol and rigorously evaluate its security aspects. The protocol\u27s adherence to critical security requirements such as blindness, non-forgeability, non-deniability, and prevention of double spending is analysed. Moreover, the scheme against Intercept and Resend, Denial of Service, Man-in-the-Middle, and Entangle-and-Measure attacks is rigorously tested. The authors’ findings indicate a robust eCash transaction model capable of withstanding the challenges posed by quantum computing advancements
An end-to-end model for multi-view scene text recognition
No full textDue to the increasing applications of surveillance and monitoring such as person re-identification, vehicle re-identification and sports events tracking, the necessity of text detection and end-to-end recognition is also growing. Although the past deep learning-based models have addressed several challenges such as arbitrary-shaped text, multiple scripts, and variations in the geometric structure of characters, the scope of the models is limited to a single view. This paper presents an end-to-end model for text recognition through refining the multi-views of the same scene, which is called E2EMVSTR (End-to-End Model for Multi-View Scene Text Recognition). Considering the common characteristics shared in multi-view texts, we propose a cycle consistency pairwise similarity-based deep learning model to find texts more efficiently in three input views. Further, the extracted texts are supplied to a Siamese network and semi-supervised attention embedding combinational network for obtaining recognition results. The proposed model combines natural language processing and genetic algorithm models to restore missing character information and correct wrong recognition results. In experiments on our multi-view dataset and several benchmark datasets, the proposed method is proven effective compared to the state-of-the-art methods. The dataset and codes will be made available to the public upon acceptance
An explainable machine learning technique to forecast lightning density over North-Eastern India
No full textIncreasing lightning fatalities over India is a concerning subject. Especially, it is pretty crucial over North-Eastern part of the country where lightning is extremely frequent. Given the complex nature of the problem, machine learning can be an excellent option in such forecasting scenarios. However, such dynamic processes seek proper transparency of the model. The current work attempts to devise a model for short range prediction (one month ahead) of lightning density based on primary atmospheric parameters from satellite data with a lead time of one month over North –Eastern and Eastern part of the country. Random Forest regression seems to outperform other models explored, with a R2 of 0.86 and an MAE of 0.0071. The interpretation of the model output using SHAP index reveals that 2 m temperature at previous two months and CAPE and K-index at previous month has a positive impact on the output of the model whereas, instantaneous surface heat flux of previous month and two month prior K-index has an inhibiting effect on model\u27s output. The use of machine learning techniques for atmospheric predictions without the shed of the black box can be of importance to the scientific community. Such studies especially over lightning prone tropical regions can be crucial in meteorological forecasting applications
An investigation on the prevalence and patterns of multi-morbidity among a group of slum-dwelling older women of Kolkata, India
No full textBackground: Multi-morbidity is a pervasive and growing issue worldwide. The prevalence of multi-morbidity varies across different populations and settings, but it is particularly common among older adults. It poses substantial physical, psychological, and socio-economic burdens on individuals, caregivers and healthcare systems. In this context, the present study aims to provide an insight on the prevalence and degree of multi-morbidity; and also, on the relationship between level of multi-morbidity and morbid conditions among a group of slum-dwelling older women. Methods: This community based cross-sectional study was conducted in the slum areas of urban Kolkata, West Bengal, India. It includes total 500 older women, aged 60 years or above. Pre-tested schedules on so-demographic and morbidity profile have canvassed to obtain the information by door-to-door survey. To determine the relationship between the level of multi-morbidity and morbid conditions, correspondence analysis has performed. Results: The study revealed three most prevalent morbid conditions- back and/or joint pain, dental caries/cavity and hypertension. The overall prevalence of multi-morbidity was 95.8% in this group of older women. It was highly over-represented by the oldest-old age group (80 years and above). Majority were found to suffer from five simultaneous morbid conditions that accounted for 15.2% of the total respondents. All of the oldest-old women of this study reported to suffer from more than two medical conditions simultaneously. Three distinct groups were formed based on the inter-relationship between level of multi-morbidity and morbid conditions. The group 1 and 2 represents only 27.8% and 18% of the total sample. Whereas, group 3 comprises the highest level of morbidities (≥ 6) and 52.8% of total sample, and strongly related with general debilities, cardiac problems, asthma/COPD, gastrointestinal, musculoskeletal problems, neurological disorders, hypothyroidism and oral health issues. Conclusion: The findings confirmed the assertion that multi-morbidity in slum living older adults is a problem with high prevalence and complexity. This study proposes an easily replicable approach of understanding complex interaction of morbidities that can help further in identifying the healthcare needs of older adults to provide them with healthy and more productive life expectancy
Antibiofilm activity of mesoporous silica nanoparticles against the biofilm associated infections
No full textIn pharmaceutical industries, various chemical carriers are present which are used for drug delivery to the correct target sites. The most popular and upcoming drug delivery carriers are mesoporous silica nanoparticles (MSN). The main reason for its popularity is its ability to be specific and optimize the drug delivery process in a controlled manner. Nowadays, MSNs are widely used to eradicate various microbial infections, especially the ones related to biofilms. Biofilms are sessile groups of cells that live by forming a consortium and exhibit antibacterial resistance (AMR). They exhibit AMR by extracellular polymeric substances (EPS) and various quorum sensing (QS) signaling molecules. Usually, bacterial and fungal cells are capable of forming biofilms. These biofilms are pathogenic. In the majority of the cases, biofilms cause nosocomial diseases. This review will focus on the antibiofilm activities of MSN, its mechanism of target-specific drug delivery, and its ability to disrupt the bacterial biofilms inhibiting the infection. The review will also discuss various mechanisms for the delivery of pharmaceutical molecules by the MSNs to inhibit the bacterial biofilms, and lastly, we will talk about the different types of MSNs and their antibiofilm activities
Arithmetic Progressions of r-Primitive Elements in a Field
No full textIn this paper, we deal with the existence of r-primitive elements, a generalisation of primitive elements, in arithmetic progression by using a new formulation of the characteristic function for r-primitive elements in Fq. In fact, we find a condition on q for the existence of α∈Fq× for a given n⩾2 and β∈Fq× such that each of α,α+β,α+2β,⋯,α+(n-1)β⊂Fq× is r-primitive in Fq×. This result is utilized with the help of an inequality due to Robin also to produce an explicit bound on q; this, in turn, shows that for any n,r∈N, for all but finitely many prime powers q, for any β∈Fq×, there exists α∈Fq such that α,α+β,⋯,α+(n-1)β are all r-primitive whenever r∣q-1. The number of arithmetic progressions in Fq consisting of r-primitive elements of length n, is asymptotic to q(q-1)nφ(q-1r)n