Research on the Application of E-commerce to Small and Medium Enterprises (SMEs): the Case of India

SMEs account for a large proportion and play an important role in the development of each country in the world, including India. The globalization will bring many advantages for enterprises however SMEs will face fierce competition at the local, national and International level. In order to maintain and promote the important role of SMEs in the context of increased competition, SMEs have to change and adopt new technologies. E-commerce and digital technologies are bringing opportunities to help SMEs improve their competitiveness, narrow the gap with big enterprises thanks to their fairness and flexibility of the digital business environment.       According to UNIDO (2017), India is one of the countries successfully applying e-commerce to SMEs. Contributing to this success is the important role of the Indian government. Therefore, this paper focuses on researching the application of e-commerce to SMEs in terms of the role of government in promoting and creating an ecosystem for SMEs and e-commerce development

Constructing Complexity-efficient Features in XCS with Tree-based Rule Conditions

A major goal of machine learning is to create techniques that abstract away irrelevant information. The generalisation property of standard Learning Classifier System (LCS) removes such information at the feature level but not at the feature interaction level. Code Fragments (CFs), a form of tree-based programs, introduced feature manipulation to discover important interactions, but they often contain irrelevant information, which causes structural inefficiency. XOF is a recently introduced LCS that uses CFs to encode building blocks of knowledge about feature interaction. This paper aims to optimise the structural efficiency of CFs in XOF. We propose two measures to improve constructing CFs to achieve this goal. Firstly, a new CF-fitness update estimates the applicability of CFs that also considers the structural complexity. The second measure we can use is a niche-based method of generating CFs. These approaches were tested on Even-parity and Hierarchical problems, which require highly complex combinations of input features to capture the data patterns. The results show that the proposed methods significantly increase the structural efficiency of CFs, which is estimated by the rule "generality rate". This results in faster learning performance in the Hierarchical Majority-on problem. Furthermore, a user-set depth limit for CF generation is not needed as the learning agent will not adopt higher-level CFs once optimal CFs are constructed

Interstate war and food security: Implications from Russia's invasion of Ukraine

In this article, we review and shed light on the interlinkages between interstate war and food insecurity and discuss global policy actions needed to address the challenges of food insecurity due to interstate war. We conceptualize the interlinkages between these two issues with a focus on: (i) the most critical and direct cause of interstate war, namely geo (territorial) political conflict, and (ii) the mechanisms through which interstate war affects four different food security pillars, namely food availability, food access, food utilization, and food stability. We position that, if unsuccessfully addressed, geo (territorial) political conflicts will create a vicious cycle of violence and hunger. This position is illustrated by analyzing recent Russia's invasion of Ukraine. Herein a summary of the root and nature of the invasion and how it has affected global food security is presented, with a discussion on the potential considerations and solutions to avoid the cycle of violence and hunger

SD electronics: simulations on the dynamic range

The surface detector electronics of the Pierre Auger Observatory is characterized by a large dynamic range due to the variation of the signal intensity of the Cherenkov tanks as a function of the distance from the core. In this paper, we present results of simulations and discuss the impact of the dynamic range on the shower reconstruction

Design of the photomultiplier bases for the surface detectors of the Pierre Auger Observatory

The design of the photomultiplier bases for the surface detectors of the Pierre Auger Observatory is presented. The bleeder is purely resistive. The base comprises two outputs: one from the anode and another one from the last dynode followed by an amplifier. The charge ratio between the anode and the amplified dynode is around 30. The design ensures a low consumption (less than 100 mu A at 2 kV), a stability of the gain and of the base line during the whole period of measurement (20 mu s per event) and for the whole dynamic range (max. 1 to 3x10^4 in amplitude). First measurement with a prototype base on the Hamamatsu R5912 photomultiplier tube are presented

Cognitive full-duplex relay networks under the peak interference power constraint of multiple primary users

Abstract This paper investigates the outage performance of cognitive spectrum-sharing multi-relay networks in which the relays operate in a full-duplex (FD) mode and employ the decode-and-forward (DF) protocol. Two relay selection schemes, i.e., partial relay selection (PRS) and optimal relay selection (ORS), are considered to enhance the system performance. New exact expressions for the outage probability (OP) in both schemes are derived based on which an asymptotic analysis is carried out. The results show that the ORS strategy outperforms PRS in terms of OP, and increasing the number of FD relays can significantly improve the system performance. Moreover, novel analytical results provide additional insights for system design. In particular, from the viewpoint of FD concept, the primary network parameters (i.e., peak interference at the primary receivers, number of primary receivers, and their locations) should be carefully considered since they significantly affect the secondary network performance

A novel diagnostic model for tuberculous meningitis using Bayesian latent class analysis

Background Diagnosis of tuberculous meningitis (TBM) is hampered by the lack of a gold standard. Current microbiological tests lack sensitivity and clinical diagnostic approaches are subjective. We therefore built a diagnostic model that can be used before microbiological test results are known. Methods We included 659 individuals aged ≥ 16 years with suspected brain infections from a prospective observational study conducted in Vietnam. We fitted a logistic regression diagnostic model for TBM status, with unknown values estimated via a latent class model on three mycobacterial tests: Ziehl–Neelsen smear, Mycobacterial culture, and GeneXpert. We additionally re-evaluated mycobacterial test performance, estimated individual mycobacillary burden, and quantified the reduction in TBM risk after confirmatory tests were negative. We also fitted a simplified model and developed a scoring table for early screening. All models were compared and validated internally. Results Participants with HIV, miliary TB, long symptom duration, and high cerebrospinal fluid (CSF) lymphocyte count were more likely to have TBM. HIV and higher CSF protein were associated with higher mycobacillary burden. In the simplified model, HIV infection, clinical symptoms with long duration, and clinical or radiological evidence of extra-neural TB were associated with TBM At the cutpoints based on Youden’s Index, the sensitivity and specificity in diagnosing TBM for our full and simplified models were 86.0% and 79.0%, and 88.0% and 75.0% respectively. Conclusion Our diagnostic model shows reliable performance and can be developed as a decision assistant for clinicians to detect patients at high risk of TBM. Summary Diagnosis of tuberculous meningitis is hampered by the lack of gold standard. We developed a diagnostic model using latent class analysis, combining confirmatory test results and risk factors. Models were accurate, well-calibrated, and can support both clinical practice and research