Corporate tax avoidance practices of multinationals and country responses to improve quality of compliance

The aim of this systematic review was to review the methods adopted by corporates to avoid corporate tax, factors related to it and responses of countries to improve their compliance. Using Google Scholar with the topic as the search terms and different overlapping timeframes, the search yielded 68 papers. These were listed and briefly described. The common factors in these studies were tabulated for easy reference. It was a revelation in the way the corporates adopted methods to avoid sales tax. Papers dealing with policies, strategies and impact of sales tax on these corporates were in majority. Economic growth variables and their linkage to sales tax were the basis of study of some papers. Corporate social responsibility is an essential part of corporate finance and an attempt to link it with their tax compliance practice was the subject in some. Studies also covered the role of civic and interest groups in preventing evasion of tax as detrimental to society at large. An important aspect which came to light was that as long as there is competition among countries on tax matters and the existence of tax havens, a defiance of the tax laws and the tendency to avoid tax was noticed. Only a thorough reform of the tax laws in the countries will check these large scale evasions and bring more revenue where it is required. The need of the hour is a vigilant civil rights and interest group which can add pressure on the corporates to behave responsibly and ethically towards tax compliance. The implications that these changes will bring is huge as it prevents leakage of income which is rightly due to the governments. This study will help in plugging the loop holes and ensuring stricter compliance with the laws of the land

Food security and transition towards sustainability

In the light of linkages in various scales and targets, the complex and nuanced design of the sustainable development goals (SDG) raises more challenges in their implementation on the ground. This paper reviewed 25 food security indicators, proposed improvements to facilitate operationalization, and illustrated practical implementation. The research focused on three essential blind spots that arise from the potential interactions between sustainable food production, consumption, and domestic material consumption (DMC). Projection of latent structure regression was applied to link food security and sustainable development goals. Findings revealed that the key target in reducing trade-offs was the integration of DMC with sustainable food production and consumption. DMC was positively correlated with the creation of coherent SDG strategies and sustainable food security. Practical implications were discussed by highlighting how to achieve food security across contrasting development contexts and the challenges of addressing the links between targets and indicators within and beyond SDGs 2 and 12. The results are useful for setting a proper strategy for sustainable production and consumption that can improve the efficient use of resources in the eight Central European countries

Deep learning-enabled technologies for bioimage analysis.

Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases

Characterization of soil organic matter in aggregates and size-density fractions by solid state C-13 CPMAS NMR spectroscopy.

Understanding the changes in soil organic matter (SOM) composition during aggregate formation is crucial to explain the stabilization of SOM in aggregates. The objectives of this study were to investigate (i) the composition of SOM associated with different aggregates and size-density fractions and (ii) the role of selective preservation in determining the composition of organic matter in aggregate and size-density fractions. Surface soil samples were collected from an Alfisol on the Northern Tablelands of NSW, Australia with contrasting land uses native pasture, crop-pasture rotation and woodland. Solid state 13C cross-polarization and magic angle spinning (CPMAS) Nuclear Magnetic Resonance (NMR) spectroscopy was used to determine the SOM composition in macroaggregates (250-2000 µm), microaggregates (53-250 µm), and <53 µm fraction. The chemical composition of light fraction (LF), coarse particulate organic matter (cPOM), fine particulate organic matter (fPOM) and mineral associated soil organic matter (mSOM) were also determined. The major constituent of SOM of aggregate size fractions was O-alkyl carbon, which represented 44-57% of the total signal acquired, whereas alkyl carbon contributed 16-27%. There was a progressive increase in alkyl carbon content with decrease in aggregate size. Results suggest that SOM associated with <53 µm fraction was at a more advanced stage of decomposition than that of macroaggregates and microaggregates. The LF and cPOM were dominated by O-alkyl carbon while alkyl carbon content was high in fPOM and mSOM. Interestingly, the relative change in O-alkyl, alkyl and aromatic carbon between aggregates and SOM fractions revealed that microbial synthesis and decomposition of organic matter along with selective preservation of alkyl and aromatic carbon plays a significant role in determining the composition of organic matter in aggregates

Numerical simulation of periodic MHD casson nanofluid flow through porous stretching sheet

The perspective of this paper is to characterize a Casson type of Non-Newtonian fluid flow through heat as well as mass conduction towards a stretching surface with thermophoresis and radiation absorption impacts in association with periodic hydromagnetic effect. Here heat absorption is also integrated with the heat absorbing parameter. A time dependent fundamental set of equations, i.e. momentum, energy and concentration have been established to discuss the fluid flow system. Explicit finite difference technique is occupied here by executing a procedure in Compaq Visual Fortran 6.6a to elucidate the mathematical model of liquid flow. The stability and convergence inspection has been accomplished. It has observed that the present work converged at, Pr ≥ 0.447 indicates the value of Prandtl number and Le ≥ 0.163 indicates the value of Lewis number. Impact of useful physical parameters has been illustrated graphically on various flow fields. It has inspected that the periodic magnetic field has helped to increase the interaction of the nanoparticles in the velocity field significantly. The field has been depicted in a vibrating form which is also done newly in this work. Subsequently, the Lorentz force has also represented a great impact in the updated visualization (streamlines and isotherms) of the flow field. The respective fields appeared with more wave for the larger values of magnetic parameter. These results help to visualize a theoretical idea of the effect of modern electromagnetic induction use in industry instead of traditional energy sources. Moreover, it has a great application in lung and prostate cancer therapy

Fuzzy Logic Controller Design for Intelligent Air-Conditioning System

Inefficient air cooling systems may cause of wasting energy in a great amount specially in the urban area. Being the most popular cooling system, air-conditioners have been used in domestic usage as well as in industrial applications. However, the unintelligent nature of such cooling system gives rise to excess energy consumption which causes a huge problem in the current energy hungry world. In this paper, we present design of a fuzzy logic controller for the intelligent air-conditioning system. The performance of the controller is also simulated. The proposed controller has the adaptive nature to control fan and compressor speed which leads to reducing power consumption. Also, the system controls the operation mode to retain the healthy oxygen level and humid condition of the indoor environment

Genetic diversity and population structure of Striga hermonthica populations from Kenya and Nigeria

Article purchasedStriga hermonthica is a parasitic weed that poses a serious threat to the production of economically important cereals in sub-Saharan Africa. The existence of genetic diversity within and between S. hermonthica populations presents a challenge to the successful development and deployment of effective control technologies against this parasitic weed. Understanding the extent of diversity between S. hermonthica populations will facilitate the design and deployment of effective control technologies against the parasite. In the present study, S. hermonthica plants collected from different locations and host crops in Kenya and Nigeria were genotyped using single nucleotide polymorphisms. Statistically significant genetic differentiation (FST = 0.15, P = 0.001) was uncovered between populations collected from the two countries. Also, the populations collected in Nigeria formed three distinct subgroups. Unique loci undergoing selection were observed between the Kenyan and Nigerian populations and among the three subgroups found in Nigeria. Striga hermonthica populations parasitising rice in Kenya appeared to be genetically distinct from those parasitising maize and sorghum. The presence of distinct populations in East and West Africa and in different regions in Nigeria highlights the importance of developing and testing Striga control technologies in multiple locations, including locations representing the geographic regions in Nigeria where genetically distinct subpopulations of the parasite were found. Efforts should also be made to develop relevant control technologies for areas infested with ‘rice-specific’ Striga spp. populations in Kenya

Leveraging Multi-Modal Sensing for Mobile Health: A Case Review in Chronic Pain

Active and passive mobile sensing has garnered much attention in recent years. In this paper, we focus on chronic pain measurement and management as a case application to exemplify the state of the art. We present a consolidated discussion on the leveraging of various sensing modalities along with modular server-side and on-device architectures required for this task. Modalities included are: activity monitoring from accelerometry and location sensing, audio analysis of speech, image processing for facial expressions as well as modern methods for effective patient self-reporting. We review examples that deliver actionable information to clinicians and patients while addressing privacy, usability, and computational constraints. We also discuss open challenges in the higher level inferencing of patient state and effective feedback with potential directions to address them. The methods and challenges presented here are also generalizable and relevant to a broad range of other applications in mobile sensing