The increasing demand for electricity, coupled with the limitations of centralised power generation, has necessitated the transition towards smart grid technologies as a critical evolution of traditional power systems. The smart grid represents a significant transformation from the conventional grid, offering a pathway towards modernising energy infrastructure. This review aims to present a comprehensive analysis of the advantages and challenges of smart grid implementation, particularly within the context of the Kurdistan Region of Iraq. Key benefits such as improved grid intelligence, enhanced reliability, and sustainability were highlighted. However, several challenges were identified, including cybersecurity risks, regulatory complexities, and issues of interoperability, which collectively pose obstacles to widespread adoption. Furthermore, the review examines the current energy network in the Kurdistan region and proposes a framework for integrating smart grid technologies. Strategies for addressing the identified challenges were discussed, emphasising the importance of overcoming these barriers to facilitate the region's transition to a more advanced and efficient energy infrastructure.
The rapid advancement of technology has correspondingly escalated the sophistication of cyber threats. In response, the integration of artificial intelligence (AI) into cybersecurity (CS) frameworks has been recognized as a crucial strategy to bolster defenses against these evolving challenges. This analysis scrutinizes the effects of AI implementation on CS effectiveness, focusing on a case study involving company XYZ's adoption of an AI-driven threat detection system. The evaluation centers on several pivotal metrics, including False Positive Rate (FPR), Detection Accuracy (DA), Mean Time to Detect (MTTD), and Operational Efficiency (OE). Findings from this study illustrate a marked reduction in false positives, enhanced DA, and more streamlined security operations. The integration of AI has demonstrably fortified CS resilience and expedited incident response capabilities. Such improvements not only underscore the potential of AI-driven solutions to significantly enhance CS measures but also highlight their necessity in safeguarding digital assets within a continuously evolving threat landscape. The implications of these findings are profound, suggesting that leveraging AI technologies is imperative for effectively mitigating cyber threats and ensuring robust digital security in contemporary settings.
The burgeoning application of artificial intelligence (AI) technologies for the diagnosis and detection of defects has marked a significant area of interest among researchers in recent years. This study presents a fuzzy logic-based approach to identify failures within industrial systems, with a focus on operational anomalies in a real-world context, particularly within the competitive landscape of Omar Benamour, in Al-Fajjouj region, Guelma, Algeria. The analysis has been started with the employment of the Activity-Based Costing (ABC) method to identify the critical machinery within the K-short dough production line. Subsequently, an elaborate failure tree analysis has been conducted on the pressing machine, enabling the deployment of a fuzzy logic approach for the detection of failures in the dough cutter of AMOR BENAMOR's K production line press. The effectiveness of the proposed method has been validated through an evaluation conducted with an authentic and real-time data from the facility, where the study took place. The results underscore the efficacy of the fuzzy logic approach in enhancing fault detection within industrial systems, offering substantial implications for the advancement of defect diagnosis methodologies. The study advocates for the integration of fuzzy logic principles in the operational oversight of industrial machinery, aiming to mitigate potential failures and optimize production efficiency.
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.
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