Cutting-edge Solutions Research Articles

A critical analysis of the role of artificial intelligence and machine learning in enhancing nuclear waste management

Abstract Controlling and managing nuclear waste is a significant challenge due to the harmful effects of radioactive materials on human health. To address this, long-term storage solutions are essential. Artificial Intelligence (AI) and Machine Learning (ML) are being utilized to make nuclear waste management safer, more effective, and efficient. This paper evaluates various applications of AI and ML in the field of nuclear waste, covering aspects such as predictive maintenance, waste sorting, and classification. AI and ML enhance real-time monitoring of storage conditions and optimize waste handling procedures through advanced data processing capabilities. Implementing cutting-edge solutions is crucial to protect public health and the environment from radioactive waste. The purpose of this evaluation is to examine how AI and ML improve nuclear waste management processes. These technologies can reduce human exposure to harmful materials and increase the safety and efficiency of managing nuclear waste through advanced predictive capabilities. The introduction of AI and ML in nuclear waste management is driving significant changes and innovations, addressing current issues, and establishing new guidelines for future policies.

Power electronics for green hydrogen generation with focus on methods, topologies, and comparative analysis.

This research article meticulously examines advanced power electronic converters crucial for optimizing electrolyzer perfor- mance in hydrogen production systems. It conducts a thorough review of mature electrolyzer types, detailing their specifications, electric models, manufacturers, and scalability. To meet the high current and stable DC voltage demands of industrial electrolyzers, the study delves into a broad spectrum of AC-DC and DC-DC converter topologies. It explores cutting-edge solutions like 12-pulse and 20-pulse rectifiers, advanced higher multi-pulse rectifiers utilizing conventional and auto-connected transformer units, Multi-Step Auto-connected Transformers (MSAT), polygon autotransformers, and auxiliary filters and circuits such as pulse multiplication circuits and active power filters. These innovations significantly reduce harmonic distortions and enhance power quality, addressing challenges inherent in conventional 6-pulse diode bridge rectifiers. The research also focuses on the efficiency and power factor correction capabilities of Active Front End (AFE) converters and the 3L-DNPC rectifier. Additionally, it investigates various DC-DC converters, including the Continuous Input Current Non-Isolated Bidirec- tional Interleaved Buck-Boost DC-DC Converter, the Interleaved Buck Converter (IBC) with extended duty cycles, the 3-level buck-boost converter with a coupled inductor, Quadratic converters designed for fault tolerance, the three-level interleaved buck converter, among others. Converter designs like the galvanically isolated half-bridge converter with controlled reverse-blocking switches for achieving zero voltage switching (ZVS), the Push-Pull isolated DC-DC converter prioritizing current stability, and the Isolated Full-Bridge Boost Converter (IFBBC) adept at handling fluctuating power outputs from renewable sources are also explored. Moreover, the study scrutinizes a range of converter configurations such as the Two-stage ZVT boost converter with LCL-Type SRC, multiphase interleaved DC-DC stage, and three-port isolated DC/DC converter for efficient integration with multiple energy sources concurrently. Discussing feature trends in power-to-hydrogen systems, the research reviews multi-cell modular rectifiers and multi-stage rectifiers. Overall, the study underscores the critical role of advanced converter topologies in enhancing efficiency, reliability, and power quality in electrolyzer systems, thereby contributing significantly to the progression of sustainable energy technologies.

Innovations in Drug Delivery: From Microtechnology to Targeted Therapeutics

Drug delivery systems are developed to maximize drug efficacy and minimize side effects. As drug delivery technologies improve, the drug becomes safer and more comfortable for patients to use. The study on advanced drug delivery systems aims to increase patient compliance, decrease adverse effects, and improve treatment efficacy. Furthermore, the goal of these systems is to deliver drugs to particular body tissues or cells in a targeted manner, which should enhance treatment outcomes. During the last seven decades, extraordinary progress has been made in drug delivery technologies, such as systems for long-term delivery for months and years, localized delivery, and targeted delivery. In this review, we explain the general principles of several advanced drug delivery systems, their introduction objective, components, mechanism, functioning principle, and application. The implantable polymeric drug delivery system, cardiac pacemaker, prefilled dual chamber device, closed-loop insulin delivery system, hepatic infusion pumps, MEMS devices, inhaled insulin devices, and hydrogel-forming microneedles are among the medical devices that are being discussed in this article as being at the forefront of technological innovation in medicine. For a variety of medical diseases, these devices provide minimally invasive procedures, real-time monitoring, and more accurate drug delivery. The scope of medical treatment and monitoring procedures is changing due to the integration of cutting-edge materials and technology. By increasing convenience, decreasing adverse effects, and increasing efficacy, these cutting-edge solutions directly help patients. Furthermore, the adoption of advanced drug delivery systems has demonstrated promising results in enhancing patient compliance and treatment outcomes. By increasing the bioavailability and pharmacokinetics of pharmaceuticals, these systems have the potential to transform the fields of drug delivery and personalized medicine. However, the field also faces challenges in overcoming physicochemical barriers and biological unknowns. Challenges facing the advanced drug delivery systems industry include complicated formulation requirements, regulatory obstacles, and the need for new technologies to enhance patient outcomes and therapeutic efficacy. Strict intellectual property rules and competition from generic drug manufacturers can also directly hinder the expansion and financial success of businesses in this field. To progress, the field should focus on translatable research ideas, develop realistic goals, and most importantly, diversify technologies. This emphasis on diversity will inspire new ideas and approaches, leading to the development of more effective, patient-friendly drug delivery systems.

2024 International Conference on Education, Economics, Management, and Social Sciences(EMSS 2024)

Welcome to the official website of the 2024 International Conference on Education, Economics, Management, and Social Sciences (EMSS 2024), scheduled to take place from September 25-26, 2024 in Melbourne, Australia! EMSS 2024 offers a prestigious platform for scholars, educators, researchers, and practitioners in the fields of education, economics, management, and social sciences to share their latest research insights, innovative methodologies, and cutting-edge solutions. This conference serves as a global forum to foster interdisciplinary discussions, collaborations, and the exchange of ideas on some of the most critical and emerging issues in these fields. We are proud to gather participants from various sectors who will engage in thought-provoking discussions and debates on topics of major societal relevance, including advances in educational practices, economic strategies, management approaches, and social sciences. The conference aims to provide valuable contributions to both academic research and practical applications. EMSS 2024 is committed to highlighting original, high-quality research papers that advance theoretical understanding, offer practical implications, and contribute to policy-making. Each paper has been rigorously reviewed based on its technical merit, research significance, and real-world impact, ensuring a high standard of academic excellence. We would like to extend our deepest gratitude to the esteemed members of the technical and organizing committees, whose diligent efforts in reviewing and improving the submissions have significantly elevated the quality of the conference proceedings. Additionally, we express our heartfelt thanks to the authors for their contributions, collaboration in adhering to the conference guidelines, and for sharing their unique insights and innovative research. We look forward to the intellectual exchange, networking opportunities, and collaborative potential that EMSS 2024 will bring. We hope you enjoy the conference! EMSS 2024 Conference Chairs

A review on progress trends of machining of Carbon Fiber Reinforced Plastics

In contemporary manufacturing industries, composite materials such as carbon fiber reinforced plastics (CFRPs) have become indispensable, finding extensive applications in aerospace, automotive, shipbuilding, high-tech sports equipment, and more. The exceptional chemical, physical, and mechanical properties of CFRPs, including high corrosion resistance, superior strength-to-weight ratio, high fatigue strength, and oxidation resistance, make them highly sought after. However, these very advantages pose significant challenges during machining due to the abrasive nature of composites, anisotropic mechanical properties, and poor thermal conductivity, which collectively exacerbate tool wear. This review paper addresses the critical need for innovative solutions to improve the machinability of CFRPs, a subject of paramount importance for advancing manufacturing efficiency and product quality. It comprehensively examines the latest research progress, current practices, and emerging trends in machining techniques such as turning, drilling (including reaming and countersinking), milling and grinding. The review delves into the influence of various cutting conditions, environments, and tool geometries. It also examines tool textures, materials, and coatings. Additionally, advanced machining methods, including vibration, thermal, and hybrid-assisted machining, are discussed. These factors impact key performance metrics such as cutting forces and torques, tool wear and life, chip morphology, surface roughness, and the quality of machined surfaces, including defect analysis. By synthesizing a vast array of literature for the first time, this paper highlights the most effective strategies to enhance machinability while extending tool life and improving surface finish. Notably, it underscores the innovative use of dry and flood lubrication, minimum quantity lubrication, cryogenic lubrication, and high-pressure cooling. Additionally, it emphasizes optimizing cutting tool geometry, employing diamond tools, and coating tools with TiN and TiAlN, alongside the application of heat treatment and hybrid machining methods, particularly those incorporating vibration machining techniques. This review not only identifies the current challenges but also proposes cutting-edge solutions, making it an essential resource for researchers and practitioners aiming to push the boundaries of CFRP machining technology.

Radiation Hardened Read-Stability and Speed Enhanced SRAM for Space Applications

With the advancement of CMOS technology, the susceptibility of SRAM to single node upset (SNU), double node upset (DNU), and multiple node upset (MNU) induced by radiation has increased. To address this issue, various cutting-edge solutions, such as radiation hardened sextuple cross coupled (RHSCC)-16T and DNU-completely-tolerant memory (DNUCTM) cells, have been proposed. While the RHSCC-16T cell is robust against SNU, it may be vulnerable to DNU. The DNUCTM cell is resistant to both SNU and DNU, but it remains susceptible to MNU. In this paper, we propose a radiation hardened read-stability and speed enhanced (RHRSE)-20T SRAM, which is immune to all potential cases of SNU, DNU, and MNU. Additionally, the proposed design demonstrates improvements in read and write delays compared to conventional SRAM designs. Experimental results confirm that the RHRSE-20T SRAM maintains stability under various charge levels for SEU, DNU, and MNU. The proposed integrated circuit is implemented in a 90-nm CMOS process and operates on a 1 V supply voltage, offering significant advantages for next-generation radiation-hardened memory applications.

Technological Greenery. Exploring cutting-edge solutions for performant Greenery integration in building envelope design

In recent years, there has been a growing integration between nature and technology, which has led to the emergence of Greenery as a bio-based building technology that can provide not only aesthetics but also ecosystem services. Similar to photovoltaic in its early days, Greenery is undergoing extensive design experimentation to make it an expression of contemporary culture rather than a mere ’greenwashing’ practice. In urban areas, competition for space is driving the development of innovative envelope designs, often stemming from the combination of different disciplines and, therefore, not easy to be systematized.This study aims to explore potential technological systems from both practice and academic research to maximise the integration of Greenery into building envelopes. The systems are categorised as Technological Greenery and are classified according to their primary function, such as insulation, air purification, electricity generation, and food production. This classification makes it easier to identify the most pressing and innovative technological research topics related to building envelopes and greenery integration. The resulting abacus of solutions accompanied by performance data might be helpful for building designers and architects.

Understanding eco-innovation in the Hungarian wine sector

PurposeA growing body of literature analyzes eco-innovation in the wine industry, specifically focusing on the internal and external factors that contribute to sustainable innovation in wineries. The purpose of this study is to classify wineries according to their eco-innovation strategy and investigate variations in innovation groups concerning networking and socio-demographic characteristics.Design/methodology/approachThe authors use an original firm-level survey conducted between June 2022 and January 2023 to study eco-innovation practices of 234 Hungarian winemakers using four groups of indicators: marketing, organizational, process and product innovations. The authors apply principal component analysis and cluster analysis to identify eco-innovation dimensions and group wineries. The authors also investigate the relationship between eco-innovation strategies, determinants and firm-specific characteristics.FindingsThe estimations identify two innovation modes: with Cluster 1 (characterized by innovative wineries) surpassing Cluster 2 (comprising less innovative wineries) in all aspects. Marketing and process innovations were more widespread than product and organizational innovations. The results confirm the importance of firm and manager characteristics, corporate organization and size, age and education of management influence innovation activity.Research limitations/implicationsThe cross-sectional nature of the survey provides a snapshot of the wine industry. To identify changing trends, it is beneficial to conduct follow-up research and ongoing monitoring for a more comprehensive understanding of the dynamics of eco-innovation over time. The study offers valuable information about the Hungarian wine sector, but generalization of results to other geographic areas or wine-producing regions should be careful. The unique characteristics of particular regions might have a different effect on the dynamics of eco-innovation. Comparative studies across different wine regions or countries may reveal regional variations in eco-innovation strategies and the impact of local contexts.Practical implicationsThe study highlights the priority of human elements, such as the commitment of owners and managers to sustainability in driving force for innovation. The ability of businesses to absorb new knowledge and the exchange of information within organizations is crucial in promoting innovation. Considering the results of the survey, managers should proactively strive to improve knowledge transfer and information sharing within their organizations and establish cooperative alliances with universities, research institutes, suppliers, industry bodies and associations and global consultancy firms. Such knowledge-based cooperation can counterweight the innovative inertia of the micro and small enterprises representing the Hungarian wine sector.Social implicationsTo address the issue of “innovative inertia” faced by micro and small businesses in the wine sector, policymakers may implement focused supports, such as educational initiatives and grants, to augment their understanding and promote sustainable innovation. They should initiate intra- and inter-cluster connections and cooperations. Incentives, such as tax reduction or financial support, may stimulate wineries to introduce eco-innovation practices. Policymakers could also streamline the process of obtaining funding or grants for wineries interested in investing in cutting-edge solutions, such as renewable energy or advanced cultivation technologies to accelerate the adoption of sustainable practices.Originality/valueThis study contributes to the eco-innovation literature by providing insights into the drivers and practices of wineries in Hungary. The results emphasize the significance of networking, sharing of knowledge and firm/manager-specific characteristics in influencing eco-innovation in the wine industry.

Malaria diagnostics: from traditional techniques to cutting-edge solutions

Recent advancements in malaria diagnostics have revolutionized the detection and management of this deadly disease. From traditional microscopy to rapid diagnostic tests and currently, to cutting-edge molecular techniques, such as isothermal amplification and different types of polymerase chain reactions, significant progress has been witnessed in enhancing the sensitivity, specificity, and accessibility of diagnostic tools. These innovations have enabled rapid and more accurate detection of malarial parasites, especially in regions with limited healthcare infrastructure. Furthermore, integrating information technology- and artificial intelligence-based applications with point of care devices has facilitated realtime data collection and decision-making, ultimately aiding global efforts toward malaria elimination. Although conventional techniques are still employed at field sites, challenges such as high sensitivity, species specificity, cost-effectiveness, scalability, and the emergence of drug-resistant strains persist. These challenges underscore the need for continuous research and development of novel malaria diagnostics.

Vehicle Ego-Trajectory Segmentation Using Guidance Cues

Computer vision has significantly influenced recent advancements in autonomous driving by providing cutting-edge solutions for various challenges, including object detection, semantic segmentation, and comprehensive scene understanding. One specific challenge is ego-vehicle trajectory segmentation, which involves learning the vehicle’s path and describing it with a segmentation map. This can play an important role in both autonomous driving and advanced driver assistance systems, as it enhances the accuracy of perceiving and forecasting the vehicle’s movements across different driving scenarios. In this work, we propose a deep learning approach for ego-trajectory segmentation that leverages a state-of-the-art segmentation network augmented with guidance cues provided through various merging mechanisms. These mechanisms are designed to direct the vehicle’s path as intended, utilizing training data obtained with a self-supervised approach. Our results demonstrate the feasibility of using self-supervised labels for ego-trajectory segmentation and embedding directional intentions within the network’s decisions through image and guidance input concatenation, feature concatenation, or cross-attention between pixel features and various types of guidance cues. We also analyze the effectiveness of our approach in constraining the segmentation outputs and prove that our proposed improvements bring major boosts in the segmentation metrics, increasing IoU by more than 12% and 5% compared with our two baseline models. This work paves the way for further exploration into ego-trajectory segmentation methods aimed at better predicting the behavior of autonomous vehicles.

Good, Bad, Ugly? The Opportunities of AI in Finance

Artificial Intelligence (A.I.) has become increasingly popular due to emerging technologies, including generative AI, big data, deep learning, etc. It can provide insights from data that are hard to determine from a human perspective. Artificial Intelligence (A.I.) in finance helps to provide more personal and safer experiences for customers and develop cutting-edge solutions for a company. This paper surveys the challenges and opportunities in applying Artificial Intelligence (A.I.) to finance. It provides a state-of-the-art review of financial technologies, algorithmic trading, and fraud detection. Also, the paper identifies two research topics. One is how to use generative AI in algorithmic trading. The other is how to apply it to fraud detection. Last but not least, this paper discusses the challenges posed by generative AI, such as the ethical considerations, potential biases, and data security

Preparing and Assessment of Biocidal La Nano-complex Treated Filter Capacity against Isolated Microbes from Air Conditioning Systems in COVID-19 Rehabilitation Rooms

Mucormycosis is a severe fungal infection which mainly caused by filamentous fungi of the Absidia sp., Rhizopus sp., Cunninghamella sp, Mucor sp., and Rhizomucor sp. Moreover, the pandemic of the SARS-CoV-2 virus expands the need to interfere with spread of the airborne respiratory infections. Accordingly, developing cutting-edge solutions to restrict and/or prevent air contamination by infectious microbes are very warranted. The current work aims to prepare biocidal La-nano complex treated filters and assess their anti-fungal capacity against 20 Rhizopus oryzae, 10 Candida albicans, and 11 Aspergillus fumigatus. These fungi were isolated from the inside parts of the air conditioning systems in the rehabilitation rooms for COVID-19 patients. The obtained results demonstrated that the prepared were able to significantly decrease the invading microbes and eradicate Rhizopus, Aspergillus, Mucor, Candida albicans isolates at 0.64 mg/ml concentration. DFT study compares the electronic properties and reactivity of a ligand in its uncoordinated form with its lanthanum complex. The ligand exhibits lower binding energy, ionization potential, electron affinity, absolute electronegativity, and chemical potential when coordinated with lanthanum. In contrast, the lanthanum complex has a smaller energy gap, absolute hardness, and global softness.

A fourfold-objective-based cloud privacy preservation model with proposed association rule hiding and deep learning assisted optimal key generation

ABSTRACT Numerous studies have been conducted in an attempt to preserve cloud privacy, yet the majority of cutting-edge solutions fall short when it comes to handling sensitive data. This research proposes a “privacy preservation model in the cloud environment”. The four stages of recommended security preservation methodology are “identification of sensitive data, generation of an optimal tuned key, suggested data sanitization, and data restoration”. Initially, owner’s data enters the Sensitive data identification process. The sensitive information in the input (owner’s data) is identified via Augmented Dynamic Itemset Counting (ADIC) based Associative Rule Mining Model. Subsequently, the identified sensitive data are sanitized via the newly created tuned key. The generated tuned key is formulated with new fourfold objective-hybrid optimization approach-based deep learning approach. The optimally tuned key is generated with LSTM on the basis of fourfold objectives and the new hybrid MUAOA. The created keys, as well as generated sensitive rules, are fed into the deep learning model. The MUAOA technique is a conceptual blend of standard AOA and CMBO, respectively. As a result, unauthorized people will be unable to access information. Finally, comparative evaluation is undergone and proposed LSTM+MUAOA has achieved higher values on privacy about 5.21 compared to other existing models.

A Deep Reinforcement-Based Anomaly Intrusion Detection for Enhancing Network Cybersecurity

Conventional protection methods, such as rules-based firewalls and signature-based detection, are not cutting it in today's environment of increasingly sophisticated and frequent cyberattacks. Cyberattacks nowadays are extremely dynamic and complex, calling for cutting-edge solutions that can change and adapt as the threat does. DRL is an AI subfield that has been successfully addressing difficult decision-making challenges in several fields, including cybersecurity. Here, we make a step forward by using a DRL framework to model cyberattacks; by incorporating real-world events, we make the models more realistic and applicable. We provide a customized approach that greatly improves existing approaches by carefully tailoring DRL (deep reinforcement algorithms to the complex needs of cybersecurity situations, including adversarial training, dynamic environments, bespoke structure of reward and actions, and more. In this study, we provide an anomaly detection method to detect attacks on network CPS using Deep Reinforcement Learning. Our proposed methodology was tested using several publicly available research datasets to ensure its efficacy.

Design and Analysis of Architectural Systems for Earthquake Resilience

This research investigates the impact of wind and gravity loads on the seismic performance of a G+20 reinforced concrete building in Jordan. The study highlights the importance of non-linearities in earthquake-resistant design, with non-linear models showing a 39.66% increase in displacement compared to linear-elastic models. The analysis was conducted using the OpenSees platform. Using a fiber model to account for reinforcement plasticity and inelastic behavior, such as concrete cracking, a single element was used to represent an entire beam or column in the force-based finite element modeling technique. Geometric non-linearities, including large rotations, were addressed through the corotational formulation, and the P-Delta approach was used for result validation. Significant variations in load distribution across floors further highlight the importance of non-linearity in seismic analysis. The results of this study demonstrate how important it is to improve high-rise structures' earthquake resistance through the use of non-linear modeling and real-time electronic monitoring systems. Architects and engineers may greatly decrease structural damage during seismic events by incorporating these cutting-edge solutions into future building designs. This will improve building performance and safety in earthquake-prone areas.

Designing an absorber for solar photovoltaic thermal systems: a heat pipe approach and comparative evaluation

Abstract Renewable energy power generation is extensively promoting for the global objective of mitigating greenhouse gas emissions. Solar energy leads all renewables due to its ubiquitous nature which can be harvested by solar thermal and solar photovoltaic devices. Solar PV is used widely because of its flexibility in working with both direct and diffuse radiation. However, PV systems experience efficiency loss for every degree rise of solar cell temperature above room temperature. Solar photovoltaic-thermal (PVT) collectors are proposed for co-generation of electrical and thermal energy by utilizing excess heat generated in the PV layer. The low heat transfer rate, energy-intensive nature, and freezing of working fluid in colder regions are some problems that persist in water- and air-based PV/Ts, leading us to consider alternatives. Thus, the present study addresses the current opportunities by utilizing heat pipes as PV/T absorbers. Heat pipe PVT is a passive system which operates in phase transition facilitating a tremendous amount of heat transfer enhancement. To enhance the efficiency of PV/T systems, the integration of advanced technologies, like heat pipe thermal absorbers, becomes imperative. Incorporating cutting-edge solutions, such as heat pipe thermal absorbers, is essential to optimize the performance of PV/T systems. Hence, the current study provides insight into heat pipe design through a case study on the application of heat pipes as thermal absorbers for photovoltaic systems. The existing absorbers with fluid flow configurations like Raster, Spiral or Rectangular spiral, etc. used in PV/T are considered as reference. Copper as tube material and water as working fluid are found to be compatible with each other for the proposed heat pipe. The maximum heat input of 0.55kW is computed by the inadequacy of the mentioned existing PVTs to extract the maximum available heat from the PV surface. Additionally, an analytical approach used to define the optimum size of the system and calculation of minimum requirement of tubes. The optimized design of copper heat pipes was found to be ½ inch in diameter and at least ten heat pipes were required to transfer a computed heat flux of 466kW/m 2 per pipe to outperform the existing PV/Ts.

Insights into Modern Intrusion Detection Strategies for Internet of Things Ecosystems

The swift explosion of Internet of Things (IoT) devices has brought about a new era of interconnectivity and ease of use while simultaneously presenting significant security concerns. Intrusion Detection Systems (IDS) play a critical role in the protection of IoT ecosystems against a wide range of cyber threats. Despite research advancements, challenges persist in improving IDS detection accuracy, reducing false positives (FPs), and identifying new types of attacks. This paper presents a comprehensive analysis of recent developments in IoT, shedding light on detection methodologies, threat types, performance metrics, datasets, challenges, and future directions. We systematically analyze the existing literature from 2016 to 2023, focusing on both machine learning (ML) and non-ML IDS strategies involving signature, anomaly, specification, and hybrid models to counteract IoT-specific threats. The findings include the deployment models from edge to cloud computing and evaluating IDS performance based on measures such as accuracy, FP rates, and computational costs, utilizing various IoT benchmark datasets. The study also explores methods to enhance IDS accuracy and efficiency, including feature engineering, optimization, and cutting-edge solutions such as cryptographic and blockchain technologies. Equally, it identifies key challenges such as the resource-constrained nature of IoT devices, scalability, and privacy issues and proposes future research directions to enhance IoT-based IDS and overall ecosystem security.

Innovative Solutions to Environmental Challenges: Exploring Fishermen's Perspectives on Technology-driven Conservation Efforts in Noveleta, Cavite

This phenomenological study explores the viewpoints of fishermen in Noveleta, Cavite, on technology-based conservation initiatives and their effects on local marine ecosystems and fishing methods. Using purposive sampling, seven participants offered valuable insights into the present condition of marine life, the implementation of cutting-edge solutions, and the impact of these technologies on their means of living. The study revealed several themes: The challenges of modern fishing conditions, including stricter laws, and the consequences of environmental changes, such as pollution and habitat damage. Fishermen also discussed how government rules impacted them and how new technologies like GPS and fish finders brought new challenges even as productivity rose. Furthermore, the discussion underscored significant concerns such as the necessity for efficient marine conservation, the impact of infrastructure developments on local communities compared to traditional practices, and the economic viability amidst shifting circumstances. This study highlights the complex connection between policy, technology, and traditional fishing methods, highlighting the importance of inclusive and sustainable approaches to conservation.

The dynamic integration of computational approaches and machine learning for cutting-edge solutions in photonics

The dynamic integration of computational approaches and machine learning for cutting-edge solutions in photonics

Research on sustainable green building space design model integrating IoT technology.

"How can the integration of Internet of Things (IoT) technology enhance the sustainability and efficiency of green building (G.B.) design?" is the central research question that this study attempts to answer. This investigation is important because it examines how green building and IoT technology can work together. It also provides important information about how to use contemporary technologies for environmental sustainability in the building sector. The paper examines a range of IoT applications in green buildings, focusing on this intersection. These applications include energy monitoring, occupant engagement, smart building automation, predictive maintenance, renewable energy integration, and data analytics for energy efficiency enhancements. The objective is to create a thorough and sustainable model for designing green building spaces that successfully incorporates IoT, offering industry professionals cutting-edge solutions and practical advice. The study uses a mixed-methods approach, integrating quantitative data analysis with qualitative case studies and literature reviews. It evaluates how IoT can improve energy management, indoor environmental quality, and resource optimization in diverse geographic contexts. The findings show that there has been a noticeable improvement in waste reduction, energy and water efficiency, and the upkeep of high-quality indoor environments after IoT integration. This study fills a major gap in the literature by offering a comprehensive model for IoT integration in green building design, which indicates its impact. This model positions IoT as a critical element in advancing sustainable urban development and offers a ground-breaking framework for the practical application of IoT in sustainable building practices. It also emphasizes the need for customized IoT solutions in green buildings. The paper identifies future research directions, including the investigation of advanced IoT applications in renewable energy and the evaluation of IoT's impact on occupant behavior and well-being, along with addressing cybersecurity concerns. It acknowledges the challenges associated with IoT implementation, such as the initial costs and specialized skills needed.