Revolutionary Potential Research Articles

Fuzzy Logic Empowered NetWatch: Revolutionizing Aquaculture with IoT-based Intelligent Monitoring and Management in Bangladesh

The innovative study "Fuzzy Logic Empowered NetWatch: Revolutionizing Aquaculture with IoT-based Intelligent Monitoring and Management in Bangladesh" is a step towards the dawn of a new era in fish farming techniques that emphasize accuracy and efficiency. Using fuzzy logic controllers in the NetWatch system, stakeholders involved in aquaculture can access a degree of intelligence and adaptability that is not possible with standard management techniques. Fuzzy logic techniques are included in NetWatch, allowing it to make intelligent judgments based on the intricate and frequently unpredictable nature of aquaculture systems, in addition to monitoring and controlling environmental parameters and water quality. Because of its dynamic adaptability, the system can mitigate risks and optimize results in real time while successfully responding to changing situations. Furthermore, NetWatch offers a comprehensive picture of the aquaculture ecosystem by combining pond-specific data with more general environmental insights, facilitating better-informed macro and micro decision-making. With this thorough knowledge, fish farmers can allocate resources more efficiently, reduce waste, and sustainably increase productivity. Moreover, Fuzzy Logic Empowered NetWatch's revolutionary potential offers opportunities for the aquaculture industry, transcending the boundaries of individual fish ponds. Bangladesh can establish itself as a global leader in sustainable aquaculture methods and set new benchmarks for production, efficiency, and environmental stewardship using IoT-based intelligent monitoring and management. Fuzzy Logic Empowered NetWatch catalyzes a systemic shift in how we approach aquaculture management rather than merely technology. Bangladesh may achieve previously unattainable levels of sustainability and productivity by utilizing fuzzy logic and the Internet of Things. This would guarantee a better future for the country's aquaculture sector and the communities it serves.

Enhancing the Identification of False News using Machine Learning Algorithms: A Comparative Study

In today's digital world filled with information overload, preventing the rampant spread of fake news has become an urgent task. Discover the Fake News Prediction System (FNPS), which uses advanced machine learning and technology to provide innovative solutions and powerful methodologies. Natural language processing methods. FNPS uses sophisticated feature engineering from diverse, curated datasets to identify underlying patterns in fraudulent content and significantly improves the ability to recognize authenticity. FNPS achieves outstanding performance using a combination of classifiers combining TF-IDF vectorization, deep learning architecture, and sentiment analysis, demonstrating its ability to accurately predict the legitimacy of news articles. Beyond simple forecasting, FNPS provides an intuitive user interface for evaluating news content in real time. This not only increases accessibility but also promotes media literacy and responsible consumption of information. Provides additional information and promotes robust public discourse. FNPS essentially demonstrates the revolutionary potential of advanced technology in ongoing combat. This will further the important public goal of ensuring the reliability and integrity of information in the digital age.

AI-based Environmental Sustainability: Transforming Conservation Efforts

The use of artificial intelligence (AI) technologies has the potential to enhance sustainability efforts in the face growing environmental issues, especially in the areas of resource management and climate change mitigation. This study focuses at how artificial intelligence (AI) and environmental sustainability interact, examining the various ways AI might help solve urgent environmental issues. AI provides innovative methods to maximize utilization of resources, predict environmental trends, and promote accurate decision-making processes through the use of advanced computational algorithms and data analytics. This study analyses multiple applications of AI in environmental sustainability, from pollution prevention and natural resource conservation to climate modelling and renewable energy optimization. In addition, an evaluation is done about moral implications and possible socio-economic effects of AI-driven environmental solutions. Using an in-depth review of the majority of current research by applying case studies, this research identifies significant challenges and opportunities for more research and application while highlighting the revolutionary potential of AI in achieving environmental sustainability goals. Ultimately, the paper encourages ethical application of artificial intelligence (AI) as important part of larger strategies for mitigating the effects of climate change and developing a future that is environmentally conscious.

Early dementia detection with speech analysis and machine learning techniques

This in-depth study journey explores the context of natural language processing and text analysis in dementia detection, revealing their importance in a variety of fields. Beginning with an examination of the widespread and influence of text data. The dataset utilised in this study is from TalkBank's DementiaBank, which is basically a vast database of multimedia interactions built with the goal of examining communication patterns in the context of dementia. The various communication styles dementia patients exhibit when communicating with others are seen from a unique perspective by this specific dataset. Thorough data preprocessing procedures, including cleansing, tokenization, and structuring, are undertaken, with a focus on improving prediction capabilities through the combination of textual and non-textual information in the field of feature engineering. In the subsequent phase, the precision, recall, and F1-score metrics of Support Vector Machines (SVM), K-Nearest Neighbours (KNN), Random Forest, and Artificial Neural Networks (ANN) are assessed. Empirical facts are synthesized using text analysis methods and models to formulate a coherent conclusion. The significance of text data analysis, the revolutionary potential of natural language processing, and the direction for future research are highlighted in this synthesis. Throughout this paper, readers are encouraged to leverage text data to embark on their own adventures in the evolving, data-centric world of dementia detection.

Biohybrid Robots: Integrating Living Tissues with Robotics

Biohybrid robots represent a combination of biological materials and mechanical robots that use the unique resources of tissue to accomplish advanced tasks. This article addresses the emergence of biohybrid robots by describing the integration of living tissues (e.g., muscle cells, neurons, and organoids) with robotic platforms. Exploring the intersection of biology, engineering, and robotics, we examine the processes and challenges associated with the design, control, and management of biohybrid robots. We also examine various applications in medicine, biotechnology, and environmental care that exploit the potential of biohybrid robots. Through a comprehensive review of current research and future prospects, this article aims to uncover the revolutionary potential of biohybrid robots in pushing the boundaries of science and technology.

“The Point Is to Change It”: Marxist Feminist Contradictions and Revolutionary Love

This contribution to a symposium on Marxist-Feminist Theories and Struggles Today—edited by Khayaat Fakier, Diana Mulinari, and Nora Räthzel—follows a thread connecting various chapters with the author’s own work on the “forces of reproduction” and their revolutionary potential for responding to the global crisis of reproduction that characterizes the present time. In the face of unprecedented threats to human and nonhuman life, twenty-first-century Marxist feminists have been striving to turn care into the guiding principle of a revolution from the institutionalized carelessness of capitalism to a postcapitalist society that can finally be careful: a society of caring, by all for all, including the nonhuman life and earth systems that make human lives possible and worth living.

479 BrainPacer: Wireless Battery-Free Neuromodulator Embedded in a Dural Graft

INTRODUCTION: Neuromodulation represents an emerging realm of disruptive technologies for brain/spine disorders with potential applications across disease states, including neurodegenerative, neoplastic, epilepsy, trauma and vascular disorders. However, current implants have limitations inherent to the use of wires, batteries that need replacement, infection, and implant failure that have hindered wider adoption. METHODS: A 100 µm-thick, biocompatible, flexible, and transparent Parylene/PDMS substrate was used. In vivo validation (120 stimulation bursts, 10 pulses, 4 rats and 4 implants) was pursued using motor cortex stimulation after identification of the hindlimb representation via direct cortical stimulation. Hindlimb motion and reliability of stimulation and movement were detected via a video analysis software. A comparison of efficiency was conducted between the proposed dura substitute method and conventional direct stimulation method. RESULTS: Benchtop testing demonstrates reliable monophasic voltage pulses with an amplitude of 20V that can be subsequently regulated. Stimulation through the dura substitute results in reduced efficiency by 60% due to current loss but it offers the same reliability, triggering a motor response at each stimulation onset with an average limb deflection of 5.2 ± 2.986 mm with 99% confidence level. At least 30 seconds of recordings were obtained in each animal at a stimulation frequency of 1 Hz. CONCLUSIONS: We designed, validated and tested both a novel wireless and battery-free implant and a novel embodiment for placement with revolutionary potential. The implant triggers motor response through an FDA-approved dura substitute, without contacting the cortical surface. Studies in free-moving animals for chronic response studies are ongoing.

Microfluidic enabled flexible sensors based on self-diffusion MWCNTs dispersion

Flexible microfluidic pressure sensors have recently attracted a lot of attention due to its revolutionary potential in the field of healthcare. These sensors provide non-invasive, highly sensitive, and adaptable options for tracking various aspects of human movement and health. Nevertheless, there are a number of limitations that affect the effectiveness of microfluidic pressure sensors, including material selection, sensor packaging techniques, sensitivity, and stability. The research outlined in this paper, which aims to address these issues head-on and significantly improve the functionality of microfluidic pressure sensors in order to increase their usefulness in the fields of medical and biomedical applications.

Beyond Borders: CBAM’s Revolutionary Potential and Challenges in Achieving Carbon Neutrality

The Carbon Border Adjustment Mechanism (CBAM) is a ground-breaking method for attaining carbon neutrality in international trade as the world community struggles with the pressing need to address climate change. This research investigates how the CBAM might influence the dynamics of international trade and promote the shift to low-carbon, sustainable economies. A critical analysis has been conducted to explore the CBAM's transformational potential in bringing trade practices into line with climate goals by examining its economic, environmental, and political components and its implication practice, covering issues with trade equity, enforcement protocols, and possible geopolitical conflicts. The information has been gathered from several reliable sources, including the WTO, the European Commission for trade input, the World Bank, and the EPA for emission information. It has been compiled into a tabular format for simplicity of reading. The graphs have been plotted using Python (3.11.6) to improve visual comprehension. Stakeholders may help CBAM implement successfully by adopting these suggestions, maximising its revolutionary potential, and overcoming the difficulties in reaching carbon neutrality in international trade. By using an interdisciplinary approach, the study seeks to offer a thorough grasp of CBAM's function in becoming carbon neutral, revealing the complex obstacles that must be overcome for it to integrate into the world economy successfully.

L’univers de la Blockchain : étude d’une terminologie émergente aux contours nébuleux à partir d’un corpus secondaire

The World of Blockchain: a Study of an Emerging Terminology with Nebulous Contours from a Secondary Corpus. Defined in 2008 as a large database with the unique feature of being shared simultaneously by all its users, the blockchain interests various actors as it presents itself as a technology with revolutionary potential. The terms of this new field of knowledge are increasingly penetrating the world’s institutional, economic and financial discourses, although this is not always matched by a real awareness of the concepts included in the world of blockchain, which nevertheless remains very nebulous. In this contribution we have analysed the emerging terms used in French in the constitution of the specialised lexicon of this field of knowledge from the point of view of language planning actions relating to these terms. To do so, we took into consideration lexicographic and terminographic resources available online and constituting a secondary corpus of study, which enabled us to show that the emerging terminology of blockchain lends well itself to studies in terminological neology. In fact, this terminology cannot be considered completely stable either at the formal level or at the semantic level. As for the terminological resources consulted, although they are very useful for understanding blockchain concepts and for linguistic orientation, our study reveals that there is no unanimity with regard to some of the linguistic equivalents proposed in French for these English terminological units. This leads us to consider the need for in-depth analyses of the French terminology of blockchain based on a primary study corpus. Keywords: blockchain, terminology, terminography, lexicology, terminological neology

Advancements in Lightweight Materials for Aerospace Structures: A Comprehensive Review

The aerospace engineering field is witnessing an evolution towards lightweight materials driven by the unwavering pursuit of improved performance and efficiency. Lightweight materials play a critical role in aerospace structures, and this thorough examination explores their significant influence on mission success, design, and fuel efficiency. Key developments in several material categories such as composites, metals, and polymers are methodically examined in this study, revealing their special qualities and potential uses. The historical viewpoint lays out a chronology of the development of materials, charting significant turning points that have influenced the use of lightweight materials in aerospace. The following sections delve into each lightweight material in great detail, explaining composites, alloys (including titanium, aluminium, and high-strength steel), and sophisticated polymers, along with their features, production methods, and uses. An examination of particular varieties and production nuances in the context of aerospace composite materials highlights their benefits in achieving unmatched strength-to-weight ratios. By elucidating their qualities and expanding uses in aircraft constructions, advanced metallic alloy research reveals the most recent advancements in materials like titanium and aluminium. A thorough examination of polymeric materials and nanocomposites reveals their critical role in the creation of lightweight structures. Their distinctive qualities and new uses are emphasized, highlighting their role in the ongoing revolution in aeronautical design. With a comprehensive examination of additive manufacturing and sophisticated machining methods essential to the realization of lightweight aircraft structures, innovative manufacturing technologies take center stage. Additionally, the assessment thoroughly evaluates the longevity and structural integrity of lightweight materials, addressing issues related to corrosion, fatigue, and other crucial elements. Investigations from actual applications provide concrete instances of lightweight materials being successfully used in aerospace projects, shedding light on the materials' performance and tangible advantages in various situations. Looking ahead, the paper's conclusion forecasts forthcoming developments in lightweight materials and clarifies possible obstacles and future research avenues that may influence the course of aerospace engineering. To underscore the critical role that lightweight materials play in aeronautical constructions, this paper synthesizes a variety of knowledge. It highlights the revolutionary potential of lightweight materials in influencing the future of aeronautical engineering, not only synthesizing current knowledge but also laying out a path for further study and innovation.

Adaptive magnification network for precise tumor analysis in histopathological images

The variable magnification levels in histopathology images make it difficult to accurately categorize tumor regions in breast cancer histology. In this study, a novel architecture for accurate image interpretation MagNet is presented. With specific modules like Separable Dilation Convolution (SDC), Separable Dilation Skip Block (SDSB), and Point-wise Reformation Block (PRB), MagNet uses a Parallel U-Net (PU-Net) infrastructure. SDC in the PU-Net encoder ensures downsampled generalized feature representations by capturing characteristic attributes at varying magnifications. Using feature upsampling, attribute mapping merging, and PRB for precise feature capture, the decoder improves reconstruction. While PRB combines data from several decoder levels, SDSB creates vital links between the encoder and decoder layers. MagNet requires less processing of histopathology images and improves multi-magnification feature maps. MagNet performs exceptionally well, constantly outperforming rivals in terms of accuracy (0.98), F1 score (0.97), sensitivity (0.96), and specificity (0.97). The effectiveness of MagNet and its revolutionary potential in cancer diagnostics are shown by these quantitative data.

Exploring the Impact and Future of the Internet of Things: A Comprehensive Review

A revolutionary force altering many facets of our everyday lives, industries, and communities is the Internet of Things (IoT). This in-depth analysis seeks to assess the diverse effects of IoT technologies and provide predictions about their future developments. We examine the state of IoT applications already available in industries like manufacturing, smart cities, transportation, healthcare, and agriculture, emphasising the advantages, difficulties, and new developments. We also explore the technology developments, such as blockchain, edge computing, and artificial intelligence, that are propelling IoT innovation. We also go over important issues with security, privacy, interoperability, and ethical ramifications of the growing number of IoT devices. In conclusion, we offer perspectives on the future course of the Internet of Things, imagining a state in which human endeavours are harmoniously merged with interconnected ecosystems, promoting effectiveness, durability, and improved user experiences. This assessment adds to a better understanding of the Internet of Things' revolutionary potential and alerts stakeholders to the opportunities and difficulties associated with using this disruptive technology to enhance society.

Pioneering the Way: The Revolutionary Potential of Antibody-Drug Conjugates in NSCLC.

Despite targeted therapy and immunotherapy being recognized as established frontline treatments for advanced non-small cell lung cancer (NSCLC), the unavoidable development of resistance and disease progression poses ongoing challenges. Antibody-drug conjugates (ADCs) offer a potent treatment option for NSCLC through the specific delivery of cytotoxic agents to tumor cells that display distinct antigens. This review delves into the latest evidence regarding promising ADC agents for NSCLC, focusing on their targets, effectiveness, and safety assessments. Additionally, our study provides insights into managing toxicities, identifying biomarkers, devising methods to counter resistance mechanisms, tackling prevailing challenges, and outlining prospects for the clinical implementation of these innovative ADCs and combination regimens in NSCLC.

Homely yet dissonant. Cultural identity in Leopold Buczkowski’s early novels

The article examines the notion of cultural identity in the fi rst three novels by Leopold Buczkowski:Wertepy, Czarny potok (Black Torrent, MiT 1969) and Dorycki krużganek. Although Buczkowski’swriting possesses a strong subversive potential, its disruptiveness is predominantly studiedthrough poetics or historiosophical refl ection. In this article anthropology serves as the main focalpoint to extricate from the setting of the novels a set of certain qualities that disturb the representationalunambiguity of certain topoi, such as national identity or borderland identity. These qualitiesconvey an image of a specifi c cultural formation – Galicia, however, this formation is a by-productof the locality and only constituted via contact with, and opposition to, the hegemonic discourse.Cultural relations defi ne the space of internal familiarity, while remaining „unfamiliar” to an externalobserver, a dissonance in the cultural landscape.

Optical communication enhanced IDMBOC for maximizing backhaul-effect & maintaining optimum cell sizes

Abstract The increasing need for high data rates and low latency in optical communication networks necessitates innovative solution for system efficiency enhancements. With the persistent increase in data demand and the emergence of diverse applications in 5G networks, minimizing the backhaul-effect while maintaining optimal cell sizes has become a formidable obstacle. Existing methods are incapable of achieving a comprehensive optimization of network parameters, resulting in degraded performance metrics. To address these constraints, our proposed approach integrates optical communication infrastructure into IDMBOC systems which maximizes backhaul-effect and preserves optimal cell sizes. This work is primarily motivated by the need to improve the efficiency and quality of 5G networks in the face of rising data traffic. Existing methods frequently struggle to optimize concurrently multiple 5G network aspects, such as carrier aggregation, dynamic spectrum sharing, packet prioritization, network function virtualization, frequency planning, HetNets deployments, and network slicing process. As a result, these methods are incapable of delivering robust and scalable solutions. To solve these issues, we present an Iterative dual metaheuristic method that combines ant lion optimization (ALO) and grey wolf optimization (GWO) in a synergistic manner for 5G deployments. The proposed method is functionally superior to existing models. By capitalizing on the strengths of both ALO and GWO, our approach achieves superior performance metrics in comparison to recently proposed methods for maximizing backhaul-effect and maintaining optimal cell sizes. The preliminary results reveal a remarkable 8.3 % reduction in bit error rate (BER), 4.9 % reduction in energy consumption, 8.5 % increase in throughput, and 4.5 % reduction in communication delay. The achieved results demonstrate the revolutionary potential of our 5G network optimization approach and pave the way for future research and advancements in the field for different scenarios. These enhancements will revolutionize optical communication networks in order to accommodate the requirements of 5G, IoT, and other contemporary applications.

Weightless Infrastructures

Considered as an artistic medium, airwaves are not neutral, nor are they immaterial. The decision to broadcast is linked to the decision to activate electronic circuits. Radio holds both propagandistic and subversive potentials. But even the most experimental broad- or webcasts rely on electronic or digital technologies. Thus, responsible radio productions cannot shy away from a self-critical, and political positioning in this regard. What political implications does it have for radio art that transmission rests on a system of energy-consuming technologies? This question calls for a theorization that nuances the idea of podcasting as an ephemeral and intimate medium. This article proposes the term weightless infrastructures in rethinking satellites as atmospheric, free-floating and free-falling technological infrastructures. This notion is abbreviated and used interchangeably with the following terms: weightless technology, weightless hardware and free-floating infrastructure.

Discussing Sharing Investment in Vietnam

The emergence of sharing investment within the landscape of the sharing economy presents a transformative market model blending ownership and resource utilization. This research delves into the tech-driven and collaborative nature of sharing investment, drawing upon seminal works and the evolving economic terrain of Vietnam. It offers nuanced insights into the multifaceted dimensions, implications, and challenges stemming from regulatory gaps and complex responsibilities. Highlighting the necessity for an inclusive and transparent framework, the research advocates for flexible policies to harness the potential benefits of sharing investments while navigating inherent challenges. Through a thorough analysis, this study lays a foundation for future research and policy development in the dynamic realm of investment sharing in Vietnam.In addition to the foundational aspects, recent empirical findings underscore the revolutionary potential of sharing investment in Vietnam's by embracing cooperative ownership structures, technological integration, and community-centric approaches, sharing investment promises to foster financial inclusion, catalyze economic growth, and bolster societal welfare. However, realizing this potential necessitates overcoming several obstacles and establishing robust legislative frameworks. Regulatory adaptations must account for the innovative nature of sharing investments, safeguarding investors and ensuring market stability. In leveraging these strategies and capitalizing on the educated and technologically literate populace, Vietnam can harness the benefits of cooperative financial strategies, fostering equitable economic growth and societal advancement.

The Role of Base in Reaction Performance of Photochemical Synthesis of Thiazoles: An Integrated Theoretical and Experimental Study.

Artificial intelligence (AI)/machine learning (ML) is emerging as pivotal in synthetic chemistry, offering revolutionary potential in retrosynthetic analysis, reaction conditions and reaction prediction. We have combined chemical descriptors, primarily based on Density Functional Theory (DFT) calculations, with various AI/ML tools such as Multi-Layer Perceptron (MLP) and Random Forest (RF), to predict the synthesis of 2-arylbenzothiazole in photoredox reactions. Significantly, our models underscore the critical role of the molecular structure and physicochemical characteristics of the base, especially the total atomic polarizabilities, in the rate-determining steps involving cyclohexyl and phenethyl moieties of the substrate. Moreover, we validated our findings in articles through experimental studies. It showcases the power of AI/ML and quantum chemistry in shaping the future of organic chemistry.

Exploring the Efficacy of LightGBM, AdaBoost, and CatBoost in Alzheimer’s Disease Classification

Alzheimer’s disease (AD) is a major public health concern that requires prompt and accurate diagnosis to intervene effectively. We present a comprehensive machine learning system in our work that is specifically designed to classify AD. It incorporates various neuroimaging and clinical features, including air time1, disp index1, gmrt in air1, max x extension1, and max y extension1. We can decipher complex data correlations suggesting AD pathology using rigorous preprocessing and visualization methods, such as correlation heatmaps and 3D scatter plots. We can distinguish minute changes between AD, moderate cognitive impairment, and healthy controls using CatBoost, LightGBM, and AdaBoost classifiers. A thorough assessment of the model’s performance is provided by the rigorous evaluation metrics of accuracy, precision, recall, and F1-score, which are supplemented by detailed classification reports and confusion matrices. Learning curves also provide information about the generalization and flexibility of models. Our findings support integrated analysis approaches across many data modalities and highlight the revolutionary potential of machine learning in AD diagnosis. Developing customized treatment plans and cutting-edge clinical decision support systems is expected to improve patient outcomes and the standard of care for neurodegenerative diseases.