In the realm of Wireless Sensor Networks (WSNs), energy efficiency emerges as a paramount concern due to the inherent limitations in the energy capacity of sensor nodes. The extension of network lifespan is critically dependent on the strategic selection of Cluster Heads (CHs), a process that necessitates a nuanced approach to optimize communication, resource allocation, and network performance overall. This study proposes a novel methodology for CH selection, integrating Multiple Criteria Decision Making (MCDM) with the K-Means algorithm to facilitate a more discerning aggregation and forwarding of data to the network sink. Central to this approach is the application of the Einstein Weighted Averaging Aggregation (EWA) operator, which introduces a layer of sophistication in handling the uncertainties inherent in WSN deployments. The efficiency of CH selection is vital, as CHs serve as pivotal nodes within the network, their selection and operational efficiency directly influencing the network's energy consumption and data processing capabilities. By employing a meticulously designed clustering process via the K-Means algorithm and selecting CHs based on a comprehensive set of parameters, including, but not limited to, residual energy and node proximity, this methodology seeks to substantially enhance the energy efficiency of WSNs. Comparative analysis with the Low-Energy Adaptive Cluster Hierarchy (LEACH)-Fuzzy Clustering (FC) algorithm underscores the efficacy of the proposed approach, demonstrating a 15% improvement in network lifespan. This advancement not only ensures optimal utilization of limited resources but also promotes the sustainability of WSN deployments, a critical consideration for the widespread application of these networks in various fields. The findings of this study underscore the significance of adopting sophisticated, algorithmically driven strategies for CH selection, highlighting the potential for significant enhancements in WSN longevity through methodical, data-informed decision-making processes.
This study introduces an advanced technology for risk analysis in investment projects within the extractive industry, specifically focusing on innovative mining ventures. The research primarily investigates various determinants influencing project risks, including production efficiency, cost, informational content, resource potential, organizational structure, external environmental influences, and environmental impacts. In addressing the research challenge, system-cognitive models from the Eidos intellectual framework are employed. These models quantitatively reflect the informational content observed across different gradations of descriptive scales, predicting the transition of the modelled object into a state corresponding to specific class gradations. A comprehensive analysis of strengths, weaknesses, opportunities and threats (SWOT) has been conducted, unveiling the dynamic interplay of development factors against the backdrop of threats and opportunities within mineral deposits exploitation projects. This analysis facilitates the identification of critical problem areas, bottlenecks, prospects, and risks, considering environmental considerations. The application of this novel intelligent technology significantly streamlines the development process for mining investment projects, guiding the selection of ventures that promise enhanced production efficiency, cost reduction, and minimized environmental harm. The methodological approach adopted in this study aligns with the highest standards of academic rigour, ensuring consistency in the use of professional terminology throughout the article and adhering to the stylistic and structural norms prevalent in leading academic journals. By leveraging an intelligent, systematic framework for risk analysis, this research contributes valuable insights into optimizing investment decisions in the mining sector, emphasizing sustainability and economic viability.
In the realm of high-definition surveillance for dense traffic environments, the accurate detection and classification of vehicles remain paramount challenges, often hindered by missed detections and inaccuracies in vehicle type identification. Addressing these issues, an enhanced version of the You Only Look Once version v5s (YOLOv5s) algorithm is presented, wherein the conventional network structure is optimally modified through the partial integration of the Swin Transformer V2. This innovative approach leverages the convolutional neural networks' (CNNs) proficiency in local feature extraction alongside the Swin Transformer V2's capability in global representation capture, thereby creating a symbiotic system for improved vehicle detection. Furthermore, the introduction of the Similarity-based Attention Module (SimAM) within the CNN framework plays a pivotal role, dynamically refocusing the feature map to accentuate local features critical for accurate detection. An empirical evaluation of this augmented YOLOv5s algorithm demonstrates a significant uplift in performance metrics, evidencing an average detection precision (mAP@0.5:0.95) of 65.7%. Specifically, in the domain of vehicle category identification, a notable increase in the true positive rate by 4.48% is observed, alongside a reduction in the false negative rate by 4.11%. The culmination of these enhancements through the integration of Swin Transformer and SimAM within the YOLOv5s framework marks a substantial advancement in the precision of vehicle type recognition and reduction of target miss detection in densely populated traffic flows. The methodology's success underscores the efficacy of this integrated approach in overcoming the prevalent limitations of existing vehicle detection algorithms under complex surveillance scenarios.
To address the rate matching issue between high-bandwidth and high-sampling-rate analog-to-digital converters (ADCs) and low-bandwidth and low-sampling-rate baseband processors, the key technology of digital downconversion is introduced. This approach relocates the intermediate-frequency baseband signal to a vicinity of the baseband, laying a foundation for subsequent Digital Signal Processor (DSP) analysis and processing. In an innovative application of the Coordinate Rotation Digital Computer (CORDIC) algorithm for Numerically Controlled Oscillator (NCO) in a pipeline design, the phase differences of five parallel signals are measured, facilitating real-time parallel processing of the phase and amplitude relationships of multiple signals. The Field Programmable Gate Array (FPGA) design and implementation of the digital mixer module and filter bank for digital downconversion have been accomplished. A test board for the direction-finding application of five digital downconversion channels has been constructed, with the FMQL45T900 as its core. The correctness of the direction-finding data has been validated through practical application, demonstrating a significant improvement in power consumption compared to methods documented in other literature, thereby enhancing overall efficiency. The digital downconversion technology based on the CORDIC algorithm is applicable in various fields, including military communications, broadcasting, and radar navigation systems.
The transition from traditional production activities to a manufacturing-dominated economy has been a hallmark of industrial evolution, culminating in the advent of the fourth industrial revolution. This phase is characterized by the seamless integration of digital advancements across all sectors of global industry, heralding significant strides in meeting the evolving demands of markets and consumers. The concept of the smart factory stands at the forefront of this transformation, embedding sustainability, which is defined as economic viability, environmental stewardship, and social responsibility, into its core principles. This research focuses on the critical role of autonomous material handling technologies within these smart manufacturing environments, emphasizing their contribution to enhancing industrial productivity. The automation of material handling, propelled by the exigencies of reducing material damage, minimizing human intervention in repetitive tasks, and mitigating errors and service delays, is increasingly viewed as indispensable for achieving sustainable industrial operations. The employment of artificial intelligence (AI) in material handling not only offers substantial benefits in terms of operational efficiency and sustainability but also introduces specific challenges that must be navigated to align with the smart factory paradigm. By examining the integration of autonomous material handling solutions, traditionally epitomized by the utilization of forklifts in industrial settings, this study delineates the essential benchmarks for their implementation, ensuring compatibility with the overarching objectives of smart manufacturing systems. Through this lens, the paper articulates the dual imperative of aligning material handling technologies with environmental and social sustainability criteria, while also ensuring their economic feasibility.
In the realm of ground transportation, high-speed maglev trains stand out due to their exceptional stability, rapid velocity, and environmental benefits, such as low pollution and noise. However, the aerodynamic challenges faced by these lightweight, high-velocity trains significantly impact their safety and comfort, making aerodynamics a critical aspect in their design. This research delves into the dynamic aerodynamic behavior of high-speed maglev trains in the presence of crosswinds. A simulation analysis was conducted on a simplified model of a three-car maglev train, with an established aerodynamic model for the train and track beam in crosswind scenarios. The study employed three-dimensional, steady-state, incompressible $N-S$ equations, complemented by a $k-\varepsilon$ dual-equation turbulence model. The finite volume method was utilized to assess the flow field structure around the train and the pressure distribution on its surface under varying combinations of train speed and wind velocity. The investigation summarized the patterns and trends in aerodynamic loads across diverse conditions. Results demonstrate that at a speed of 600 km/h, the tail car is subjected to the highest aerodynamic drag, while the head car bears the maximum lateral force and overturning moment. As crosswind speeds increase from 5 m/s to 20 m/s, the tail car exhibits the largest increment in drag, reaching 16.6 kN. The front car shows the most significant rise in lateral force and overturning moment, measured at 34.11 kN and 52.45 kN·m, respectively. It is observed that the behavior of aerodynamic forces at lower and medium speeds aligns fundamentally with the patterns noted at higher speeds.
When cutting the hard cortical bone layer, orthopedic robots are prone to cutting chatter and thermal damage due to force and heat. Accurately establishing a model of cortical bone milling force and assessing the milling force in suppressing cortical bone cutting chatter, reducing cutting thermal damage, and optimizing process parameters is of great significance. This study aims to deeply explore the issues of modeling and coefficient identification of the milling force model of the orthopedic robot ball-end milling cutter for cortical bone, and to establish a theoretical model related to the milling state for analyzing the stability of robot milling chatter. The milling force model of the orthopedic robot ball-end milling cutter was constructed using the micro-element method, and a milling coefficient identification model was established based on the average milling force model. The coefficients were identified using the least squares method, and the cortical bone milling force model for the orthopedic robot ball-end milling cutter was established and experimentally verified. The experimental results show that the milling force curve calculated is basically consistent with the actual measured curve in terms of values and trend, verifying the accuracy of the established milling force model, and providing a theoretical basis for the study of robot cortical bone milling chatter.
Urban transportation systems, characterized by inherent uncertainty and ambiguity, present a formidable challenge in decision-making due to their complex interplay of factors. This complexity arises from dynamically shifting commuter behaviors, a diverse array of transit options, and variable traffic patterns. Such unpredictability hinders the formulation and implementation of effective strategies. Addressing this challenge necessitates innovative problem-solving methodologies capable of handling the nuanced uncertainties present in these systems. This study introduces the multidimensional neutrosophic fuzzy hypersoft set (MDNFHS) as a groundbreaking method for managing ambiguity in urban transportation planning. MDNFHS, emerging from the integration of neutrosophic fuzzy sets (NFSs) and hypersoft sets (HSs), uniquely encapsulates both the degrees of membership and non-membership. It is demonstrated that the tailored set-theoretic operations and distance measurements specific to MDNFHS enable enhanced manipulation and analysis, making it a potent tool in complex decision-making scenarios. The efficacy of MDNFHS in decision-making is exemplified through a compelling case study, showcasing its ability to offer clarity in situations marred by ambiguity. This novel approach is posited to revolutionize decision-making processes, offering a new level of certainty in environments traditionally dominated by uncertain elements.
This investigation explores the dynamics of logistics information traceability within the realm of e-commerce, emphasizing the simultaneous existence of diverse sales channels in the digital landscape. It adopts Stackelberg game theory to dissect multi-channel pricing strategies, underscoring the significance of consumer preferences pertaining to logistics information traceability and pricing structures. The study meticulously constructs a supply chain framework, predominantly supplier-driven, integrating both platform-based retail and direct sales channels. This framework serves as the basis for examining fluctuations in retail pricing and the aggregate profit margins under varying decision-making scenarios. It is revealed that platforms operating independently and opting for third-party logistics services for information traceability tend to achieve elevated traceability levels. In contrast, direct sales models managed by suppliers and utilizing e-commerce platform logistics services are associated with enhanced traceability. These insights contribute to a nuanced understanding of the strategic choices in e-commerce logistics, especially in the context of information traceability. This study's findings have broad implications for designing efficient logistics systems in the e-commerce sector, catering to the evolving demands of the digital economy.
In the realm of engineering, the significance of minichannels has escalated, especially in micro-scale multiphase fluid dynamics. This study conducts an extensive numerical analysis of two-phase flow in minichannels, utilizing the level-set method coupled with COMSOL Multiphysics®. Focusing on the minutiae of the liquid-gas interface, the research employs a two-dimensional grid to solve the incompressible Navier-Stokes equations, thereby illuminating the complex formation of diverse flow patterns in minichannels. A critical aspect of this investigation is the exploration of various geometric configurations at the inlet, particularly the examination of serrated air and water inlet channels. The findings reveal that serrated air inlets, when designed internally, effectively mitigate the buoyancy force across diverse channel configurations, ensuring stable and predictable flow patterns. Conversely, the configuration of water inlets plays a less significant role in controlling this force, underscoring the paramount importance of air inlet design in achieving optimal flow regulation. These insights not only deepen the understanding of minichannel flow dynamics but also provide practical knowledge for enhancing the efficiency of micro-scale systems. The implications of this study extend to the design of more effective minichannel applications, such as cooling systems, heat sinks, and heat exchangers used as evaporators. Moreover, the research highlights the necessity of considering geometric factors in minichannel flow analyses and sets the stage for future advancements in this evolving domain of engineering.
The paramountcy of English in the contemporary global landscape necessitates the enhancement of English language proficiency, especially in academic settings. This study addresses the disparate levels of English proficiency among college students by proposing a novel approach to evaluate English text readability, tailored for the higher education context. Employing a deep learning (DL) framework, the research focuses on developing a model based on convolutional neural networks (CNNs) to assess the readability of English texts. This model diverges from traditional methods by evaluating the difficulty of individual sentences and extending its capability to ascertain the readability of entire texts through adaptive weight learning. The methodology's effectiveness is underscored by an impressive 72% accuracy rate in readability assessment, demonstrating its potential as a transformative tool in English language education. The application of this DL-based text readability evaluation model in college English training is explored, highlighting its potential to facilitate a more nuanced understanding of text complexity. Furthermore, the study contributes to the broader discourse on enhancing English language instruction in higher education, proposing a method that not only evaluates text comprehensibility but also aligns with diverse educational needs. The findings suggest that this approach could significantly support the enhancement of English teaching methodologies, thereby promoting a deeper, more accessible learning experience for students with varying levels of proficiency.
The pivotal role of suction cup handling systems within various industrial and commercial applications, notably in the lifting and manoeuvring of glass window panels and the secure retention of specimens, is underscored by myriad practical implementations. The present research endeavours to meticulously design and rigorously assess the efficacy of suction cup holding systems, employing Catia design software for the creation of the CAD design and utilising the ANSYS simulation package for structural analysis. Particular attention is accorded to the investigation of the suitability of disparate materials for the suction cup, specifically emphasising Nitrile Butadiene Rubber (NBR) and polyurethane, whilst the plate material undergoes examination utilising a carbon fibre composite. Contrastive assessments, grounded in parameters such as stress, deformation, and equivalent elastic strain, are elucidated for these varied material applications. Preliminary findings indicate that, amid numerous suction cup diameters explored, a 141 mm diameter manifests the lowest equivalent stress (ES), whilst a diameter of 118 mm reveals the maximal ES. A 141 mm diameter emerges as optimal in suction cup design and, to minimise deformation, polyurethane rubber (PR) is identified as the most propitious material. Pertaining to the suction cup body, carbon composite material (CCM) is delineated as the pre-eminent selection, offering an enhancement in the strength-to-weight ratio that is notably superior when compared with a carbon steel suction cup apparatus.
Microelectromechanical systems (MEMS) have instigated transformative advancements, notably in controlled microdroplet generation, offering applications across diverse industrial sectors. Precise control of fluid quantities at microscales has emerged as pivotal for myriad fields, from microfluidics to biomedical engineering. In this investigation, the impacts of fluid viscosity and surface tension on microdroplet formation were meticulously studied. For this purpose, a microdispenser, actuated piezoelectrically and fitted with an 18-micrometer diameter nozzle, was employed. This setup facilitated precise fluid manipulation, enabling a systematic study of fluid behavior during droplet creation. Three fluids, specifically water, ink, and ethanol, were examined to decipher the influences of their inherent properties on microdroplet generation. Emphasis was laid on both primary and satellite droplets due to their direct implications in industrial applications. Observations revealed that fluids with elevated surface tension and diminished viscosity yielded larger microdroplets. Conversely, fluids manifesting greater surface tension underwent rapid breakup upon ejection, culminating in the genesis of several diminutive droplets. Such findings underscore the intricate relationship between fluid properties and droplet formation dynamics. This newly acquired understanding holds the potential to guide MEMS device design, ensuring the desired droplet size and distribution. Furthermore, these insights are poised to facilitate optimal microdispenser design and judicious fluid selection for applications spanning inkjet printing, microreactors, and drug delivery mechanisms.
The transition from manufacturing activities to an economy dominated by industrial production signifies the evolution of the Industrial Revolution. Presently, the global landscape is undergoing the Fourth Industrial Revolution, a natural progression from its digital predecessor, Industry 3.0. This era is distinguished by an influx of innovations across global business sectors. Solutions, particularly aiming to enhance industrial production and address volatile consumer demands, have gained paramount importance. These global shifts lead to profound structural transformations in industrial production. With the rapid integration of contemporary technologies in Industry 4.0, coupled with significant technical advancements, the notion of “smart factories” has emerged as a focal point in research, engineering, and practical applications. In the realm of sustainable business operations, the concept of the smart factory is frequently debated. While its intent is the diminution of manual tasks and the enhancement of customer service, it inherently demands the incorporation of various technologies inherent to Industry 4.0. Recognizing the profound impact of Industry 4.0 on production methodologies and the workforce's perspective, emphasis is placed on understanding the pivotal role of advanced technologies within the smart factory paradigm. This abstract seeks to elucidate the core processes in the smart factory concept with a spotlight on refining intralogistics activities through the lens of Industry 4.0 technologies.
Amid the COVID-19 pandemic, the imperative for alternative biometric attendance systems has arisen. Traditionally, fingerprint and facial recognition have been employed; however, these methods posed challenges in adherence to Standard Operational Procedures (SOPs) set during the pandemic. In response to these limitations, iris detection has been advanced as a superior alternative. This research introduces a novel machine learning approach to iris detection, tailored specifically for educational environments. Addressing the restrictions posed by COVID-19 SOPs, which permitted only 50% of student occupancy, an automated e-attendance mechanism has been proposed. The methodology comprises four distinct phases: initial registration of the student's iris, subsequent identity verification upon institutional entry, evaluation of individual attendance during examinations to assess exam eligibility, and the maintenance of a defaulter list. To validate the efficiency and accuracy of the proposed system, a series of experiments were conducted. Results indicate that the proposed system exhibits remarkable accuracy in comparison to conventional methods. Furthermore, a desktop application was developed to facilitate real-time iris detection.
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