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Land Use/Cover Dynamics and Associated Impacts on Eutrophication, Land Surface Temperature, and Ecosystem Service Values: An Eco-Climatological Investigation of Chilika Lake, India
No full textChilika Lake, the largest lagoon in Asia, located in Odisha, India, has been subjected to substantial anthropogenic pressures over the past 30 years, necessitating a comprehensive examination of its evolving landscape. Employing Landsat data spanning from 1991 to 2021, our research focuses on meticulous Land use/Land cover (LULC) classification, revealing an alarming 11.7% reduction in the lake area in the last 30 years. This decline is attributed to the conversion of vital mangrove and wetland areas into urban and agricultural expenses. The consequences extend to a 9.3% reduction in plant cover and a 7.8% decrease in catchment area. Notably, visible eutrophication patches and escalating nutrient concentrations in the past decade underscore the lake's vulnerability to environmental stressors. In parallel, the study integrates key indicators such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Land Surface Temperature (LST), revealing a significant 4°C increase in surface temperature from 1991 to 2021. A forward-looking CA-Markov model forecasts a continued reduction in the lake's area until 2026, coupled with an expansion of urban and agricultural domains. While the pandemic-induced restrictions temporarily improved lake levels in 2021, our overarching findings underscore the urgent need for strategic interventions to safeguard this vital natural resource. This research contributes valuable insights into the ongoing transition and future trajectory of Chilika Lake's landscape, offering a nuanced understanding for stakeholders and policymakers to formulate evidence-based strategies for the preservation and sustainable management of this ecologically significant ecosystem
Self-Supervised Learning for Pre-training Capsule Networks: Overcoming Medical Imaging Dataset Challenges
No full textDeep learning techniques are increasingly being adopted in diagnostic medical imaging. However, the limited availability of high-quality, large-scale medical datasets presents a significant challenge, often necessitating the use of transfer learning approaches. This study investigates self-supervised learning methods for pre-training capsule networks in polyp diagnostics for colon cancer. We used the PICCOLO dataset, comprising 3,433 samples, which exemplifies typical challenges in medical datasets: small size, class imbalance and distribution shifts between data splits. Capsule networks offer inherent interpretability due to their architecture and inter-layer information routing mechanism. However, their limited native implementation in mainstream deep learning frameworks and the lack of pre-trained versions pose a significant challenge. This is particularly true if aiming to train them on small medical datasets, where leveraging pre-trained weights as initial parameters would be beneficial. We explored two auxiliary self-supervised learning tasks—colourisation and contrastive learning—for capsule network pre-training. We compared self-supervised pre-trained models against alternative initialisation strategies. Our findings suggest that contrastive learning and in-painting techniques are suitable auxiliary tasks for self-supervised learning in the medical domain. These techniques helped guide the model to capture important visual features that are beneficial for the downstream task of polyp classification, increasing its accuracy by 5.26% compared to other weight initialisation methods.</p
A comprehensive review of the influence of various parameters on convection in different enclosures and heat sinks
No full textOwing to their good efficiency and use as heat exchangers, heat sinks support electronic devices in effective heating dissipation. However, heat dissipation remains a huge challenge to optimum thermal performance of heat sinks. The paper divides into two main broad categories. The first part deals with thermal design and thermal modeling of some various aspects of heat sinks: effects of natural convection heat dissipation mechanisms; geometrical configurations of heat sinks; and intake and outflow positions. The study broadens to the core materials, flat fins (particularly FPFHS and PFHS fins), and porous fins. Multi-wicks and multi-medium heat sinks are investigated, and a comprehensive analysis of the attributes affecting heat transfer and the efficacy of heat dissipation in these mechanisms is also provided. The latter portion examines interior heating by examining several indoor geometries, including cylindrical, circular, rectangular, and hexagonal forms, and evaluating their influence on heat transport. Additionally, empirical investigations examining enclosures with diverse fin designs are evaluated, along with the impact of interior layouts on different fin arrangements for both natural and mixed convection. The project encompasses multiple research initiatives aimed at developing a framework for the continued investigation of heat sinks within cavities. This work offers insightful recommendations for scientists and researchers, providing fundamental understanding of heat sinks and cavitation Procedures. The use of cavitation-based technologies in operations enhances the heat transfer efficiency of heat sinks, specifically heat exchangers employed for cooling electrical equipment, hence accelerating the research process. Their principal attributes, including cost efficiency, good heat dissipation, and ease of production, account for the advantages
Systems thinking for sustainability: shifting to a higher level of systems consciousness
No full textThe grand challenges encapsulated in the seventeen UN Sustainable Development Goals to be achieved by 2030, are complex, messy and interconnected. Fulfilling these goals necessitates a shift in mindset from ego-to-ecosystems awareness and an imperative for stakeholder collaboration. Systems thinking is crucial to address sustainability challenges and an agenda for sustainable development. While some management approaches, like Doughnut Economics and Circular Economy, have roots in systems thinking, there is limited research into system thinking for sustainability. Nevertheless, the authors suggest we can learn from many systems-based contributions in the environmental science/studies literature that address ecological/Earth issues (e.g., Gaia, autopoiesis) and the Operational Research/Systems literature rich in a tradition of engaging communities in analysis and taking action. We ask, “How can systems thinking help businesses to meaningfully engage their stakeholders in a shared sense of purpose, value and impact?” The “systemic sustainability” framework (SSF) is proposed to address this, extending Laszlo’s concept and incorporating traditional systems thinking principles. The SSF emphasises that organisations and their stakeholders engage at four levels of systems awareness, reflecting on organisational purpose, and balancing organisational viability with planetary pressures. Interdependence, legitimacy and thrivability are highlighted as critical concepts in systems thinking for sustainability
Strategies for Success? Market entry strategies of new craft beer producers
No full textFewer than half of UK start-up businesses survive beyond five years (ONS, 2020). The Scottish Small Business Survey of 2019 found competition in the market and uncertainty as to how to face it were considered the most significant barrier to success by almost half of SMEs (Scottish Government, 2020). This chapter considers how four Scottish breweries have formulated start-up strategies to respond to competition in an everincreasingly crowded marketplace in order to maximise their likelihood of survival. The findings from each of these case studies are presented in an accessible format, and indicate that a variety of approaches to the development of the businesses can be adopted, albeit planned approaches dominate. Drawing on real life experiences of four successful businesses, the practical choices they took provide guidance and inspiration for other aspiring craft beer entrepreneurs in selecting an appropriate approach to and content of their founding strategy
A group-theoretic approach to elimination measurements of qubit sequences
No full textMost measurements are designed to tell you which of several alternatives have occurred, but it is also possible to make measurements that eliminate possibilities and tell you an alternative that did not occur. Measurements of this type have proven useful in quantum foundations and in quantum cryptography. Here we show how group theory can be used to design such measurements. This requires that the set of states being considered possesses a symmetry described by a group. After some general considerations, we focus on the case of measurements on two-qubit states that eliminate one state. We then move on to construct measurements that eliminate two three-qubit states and four four-qubit states. The sets of states eliminated constitute cosets of a subgroup. A condition that constrains the construction of elimination measurements is then presented. Finally, in an appendix, we briefly consider the case of elimination measurements with failure probabilities and an elimination measurement on n-qubit states
An investigation of conservation of fluid oscillation in a continuous oscillatory baffled reactor
No full textContinuous oscillatory baffled reactors (COBRs) have been utilised in organic synthesis and crystallisation, however, no validation work has yet been conducted to determine if the fluid displacement caused by the oscillation at the start of the COBR is conserved at the end of the COBR. This work reports, for the first time, both experimental measurements and theoretical evaluations of the displacement. The experimental validation involves physically measuring the fluid displacement using a laser distance sensor located at the end of the COBR. Surprisingly, the measured displacement values at some operating conditions differ from the initial settings. The theoretical evaluation entails the determination of power dissipation across the COBR using the pressure measurements at four locations along the COBR. The model evaluated displacements agree well with the experimental measurements at all operational conditions, validating the methodologies used in this work
Natural Deep Eutectic Solvent Integrated with Bulk Liquid Membrane System for Salicylic Acid Removal from Wastewater
No full textThis work describes an innovative approach for salicylic acid (SA) removal from wastewater using a bulk liquid membrane (BLM) technique incorporated with natural deep eutectic solvents (NADES) as the stripping phase. The preparation of NADES was achieved by mixing of choline chloride (ChCl) as a hydrogen bond acceptor (HBA) with lactic acid (LA) as a hydrogen bond donor (HBD). Screening studies were performed to select the best NADES molar ratio as a stripping agent for SA removal. The impact of various parameters such as the pH of the feed phase, initial concentration of SA, carrier concentration, mixing speed and temperature were investigated. The conductor-like screening model for real solvents (COSMO-RS) is used to analyse the SA removal and molecular interaction. The SA achieves an extraction efficacy of 73% and a stripping efficacy of 90.28% under the optimum conditions: HBA: HBD of 1:1, pH 2 of feed, 0.001 M of SA, 4 wt.% of carrier concentration, 150 rpm of mixing speed and at 50 ºC. The SA extraction using the NADES-based BLM technique follows the consecutive first-order kinetic model with an extraction (K1) and stripping rate constants (K2) of 0.0128 and 0.0627 min-1, respectively. The SA transport mechanism through the NADES-based BLM adheres to the film theory for mass transfer. COSMO-RS confirmed the extraction efficiency through molecular interactions validated by a sigma profile and a sigma potential between the SA molecules and NADES. This study provides a new and cleaner route for using NADES as the stripping agent in the BLM system for water treatment
Design-of-experiments based Modeling & Optimization of LGA Cooling Crystallization via Continuous Oscillatory Baffled Crystallizer
No full textA novel data-driven modeling and optimization method is proposed in this paper for cooling crystallization of L-glutamic acid (LGA) via a continuous oscillatory baffled crystallizer (COBC), based on the design of experiments (DoEs) for the main operating conditions of zone temperature setting and volume net flowrate. The crystal size distribution (CSD) can be effectively predicted by constructing a data-mapping model with double-layer basis functions, where the first layer is composed of wavelet basis functions for reshaping the steady-state CSD in each operating zone of COBC, and the second layer consists of polynomial basis functions for reflecting the nonlinear relationship between the above operating conditions and the corresponding CSD in each zone. Furthermore, a comprehensive cost function related to the desired crystal size, the distribution variance of product crystals and throughput is introduced to design an optimization method for the above operating conditions. A guaranteed convergence particle swarm optimization (GCPSO) algorithm is offered to solve the nonconvex optimization problem based on the established CSD prediction model. Experimental results on the continuous crystallization of LGA demonstrate that the above cost function and the desired crystal product yield can be improved over 23% and 9%, respectively, in comparison with all tests by DoEs
Type synthesis of 3-RSR equivalent 2R1T parallel mechanisms based on screw theory
No full textThis paper systematically synthesizes a family of parallel mechanisms (PMs) kinematically equivalent to the 3-RSR (R and S are short for revolute joint and spherical joint, respectively) PM with two rotational and one translational (2R1T) degrees of freedom (DOFs). Based on the parallel virtual chain and subchain replacement methodology, three classes of branches with different symmetry characteristics are designed. The PMs designed by these branches can break the constraint of original 3-RSR PM that the moving platform and the base must be symmetrical about a mid-plane where all axes of rotation lie. Consequently, the relationship between the moving platform and the axis of rotation can be more flexible. This increased flexibility enhances the applicability of the 3-RSR equivalent PMs in fields such as pointing mechanisms, rehabilitation robots, motion simulators, medical robots
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