Applications of MATLAB in Natural Sciences: A Comprehensive Review

In the natural sciences, MATLAB is a versatile and essential tool that has revolutionized research across various disciplines, including physics, chemistry, biology, geology, and environmental sciences. This review paper provides a comprehensive overview of MATLAB's applications in data analysis, modeling, simulation, image processing, computational chemistry, environmental sciences, physics, engineering, and data visualization. MATLAB simplifies data analysis by handling complex datasets, performing statistical analyses, and aiding in tasks like curve fitting and spectral analysis. In modeling and simulation, it enables the creation of predictive models for intricate systems, facilitating simulations of physical processes, ecological dynamics, and chemical reactions. In image processing, MATLAB enhances and analyzes images, benefiting fields such as medical imaging and remote sensing. For computational chemistry, MATLAB offers a rich library of tools for exploring molecular structures and simulating chemical reactions. Environmental sciences rely on MATLAB for climate data analysis and ecological modeling. In physics and engineering, it is invaluable for simulating complex systems and analyzing experimental data. Additionally, MATLAB's data visualization capabilities allow scientists to create compelling visuals for effective communication. While challenges like licensing costs exist, efforts are underway to address these issues and enhance integration with other software, including artificial intelligence and machine learning tools. Overall, MATLAB's computational power and versatility are fundamental to advancing natural sciences research, making it an invaluable resource for scientists and researchers across various disciplines

An Agenda-Setting Report: Biome Monitoring For Human Health

In light of the COVID-19 pandemic and the increasing frequency with which zoonotic diseases of pandemic potential have emerged in recent decades, it is vital to seek new methods of preventing disease outbreaks before they occur. Novel information age technologies have the potential to change the game for infectious disease outbreak monitoring. This paper addresses the overall opportunity that exists for biome surveillance as a means for health monitoring, particularly the way that the domains of technological and biological sciences can unite to prevent the next pandemic before it gets a chance to take hold. This report outlines the numerous methods that exist to connect biological health to human health with technology as an intermediary. There are technologies that can monitor at either the individual level or on the scale of entire habitats, either remotely or on-site, and through either invasive measurements or non-invasive sensing. The key opportunities in this space and important next steps are as follows. First, it is vital to integrate predictive capabilities into existing disease mapping and tracking tools. Second, it is necessary to explicitly outline biological health indicators for inclusion in technological processes. Third, we must strengthen methods to quantitatively assess biodiversity loss. Fourth, we should improve indices for species occurrence, leaf area, taxonomic diversity, vegetation height, and above-ground biomass. Fifth, there is a need to refine spatially-explicit and temporally-effective ecosystem health assessments. Last, but certainly not least, it is of the utmost importance to enhance and develop more precise viral sequencing capabilities. If all of these goals are achieved, we will be in a much better position to prevent and respond to future infectious disease outbreaks and to protect the health of human populations around the world

Reflections from the Workshop on AI-Assisted Decision Making for Conservation

In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Research on Computation and Society at Harvard University on October 20-21, 2022. We identify key open research questions in resource allocation, planning, and interventions for biodiversity conservation, highlighting conservation challenges that not only require AI solutions, but also require novel methodological advances. In addition to providing a summary of the workshop talks and discussions, we hope this document serves as a call-to-action to orient the expansion of algorithmic decision-making approaches to prioritize real-world conservation challenges, through collaborative efforts of ecologists, conservation decision-makers, and AI researchers.Comment: Co-authored by participants from the October 2022 workshop: https://crcs.seas.harvard.edu/conservation-worksho

Local Community and Tourism Development: A Study of Rural Mountainous Destinations

Malaysia is internationally regarded as a popular rural destination because of its natural heritage. Rural tourism is increasingly viewed as a panacea for increasing the economic viability of marginalized areas, stimulating social regeneration, and improving the living conditions of rural communities. This study explores local community involvement in a rural tourism development in Kinabalu National Park, Sabah. We explore how the local community perceives their involvement in a local rural tourism development and look to identify the benefit of tourism destination development for this community. To address these objectives, we employed quantitative research methodologies and a sample of 378 respondents drawn from villages surrounding Kinabalu National Park. Sampled residents indicated having positive perceptions of tourism development in the area. Local communities enjoy being involved in the tourism sector because it improves their key income resources and quality of life

Perspectives in machine learning for wildlife conservation

Data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging devices. These new technologies and the data they generate hold great potential for large-scale environmental monitoring and understanding, but are limited by current data processing approaches which are inefficient in how they ingest, digest, and distill data into relevant information. We argue that machine learning, and especially deep learning approaches, can meet this analytic challenge to enhance our understanding, monitoring capacity, and conservation of wildlife species. Incorporating machine learning into ecological workflows could improve inputs for population and behavior models and eventually lead to integrated hybrid modeling tools, with ecological models acting as constraints for machine learning models and the latter providing data-supported insights. In essence, by combining new machine learning approaches with ecological domain knowledge, animal ecologists can capitalize on the abundance of data generated by modern sensor technologies in order to reliably estimate population abundances, study animal behavior and mitigate human/wildlife conflicts. To succeed, this approach will require close collaboration and cross-disciplinary education between the computer science and animal ecology communities in order to ensure the quality of machine learning approaches and train a new generation of data scientists in ecology and conservation

INVESTIGATION OF INDUSTRY 5.0 HURDLES AND THEIR MITIGATION TACTICS IN EMERGING ECONOMIES BY TODIM ARITHMETIC AND GEOMETRIC AGGREGATION OPERATORS IN SINGLE VALUE NEUTROSOPHIC ENVIRONMENT

Industry 5.0 acceptance is accelerating, but research is still in its infancy, and existing research covers a small subset of context-specific obstacles. This study aims to enumerate all potential obstacles, quantitatively rank them, and assess interdependencies at the organizational level for Industry 5.0 adoption. To achieve this, we thoroughly review the literature, identify obstacles, and investigate causal relationships using a multi-criteria decision-making approach called single value Neutrosophic TODIM. Single-valued Neutrosophic sets (SVNS) ensembles are employed in a real-world setting to deal with uncertainty and indeterminacy. The suggested strategy enables the experts to conduct group decision-making by focusing on ranking the smaller collection of criterion values and the comparison with the decision-making trial and evaluation laboratory method (DEMATEL). According to the findings, the most significant hurdles are expenses and the funding system, capacity scalability, upskilling, and reskilling of human labor. As a result, a comfortable atmosphere is produced for decision-making, enabling the experts to handle an acceptable amount of data while still making choices

Towards developing algal synthetic biology.

The genetic, physiological and metabolic diversity of microalgae has driven fundamental research into photosynthesis, flagella structure and function, and eukaryotic evolution. Within the last 10 years these organisms have also been investigated as potential biotechnology platforms, for example to produce high value compounds such as long chain polyunsaturated fatty acids, pigments and antioxidants, and for biodiesel precursors, in particular triacylglycerols (TAGs). Transformation protocols, molecular tools and genome sequences are available for a number of model species including the green alga Chlamydomonas reinhardtii and the diatom Phaeodactylum tricornutum, although for both species there are bottlenecks to be overcome to allow rapid and predictable genetic manipulation. One approach to do this would be to apply the principles of synthetic biology to microalgae, namely the cycle of Design-Build-Test, which requires more robust, predictable and high throughput methods. In this mini-review we highlight recent progress in the areas of improving transgene expression, genome editing, identification and design of standard genetic elements (parts), and the use of microfluidics to increase throughput. We suggest that combining these approaches will provide the means to establish algal synthetic biology, and that application of standard parts and workflows will avoid parallel development and capitalize on lessons learned from other systems.We thank the following for funding: the Biotechnology and Biological Sciences Research Council (BBSRC) of the UK grant BB/I00680X/1 and the European Commission 7th Framework Program (FP7) project SPLASH (Sustainable 8 PoLymers from Algae Sugars and Hydrocarbons), grant agreement number 311956.This is the author accepted manuscript. The final version is available from Portland Press via http://dx.doi.org/10.1042/BST20160061