The blurring boundaries and large variations of digestive polyps with complicated anatomical environments severely degrade the performance of automatic polyp segmentation. Though current deep learning-based polyp segmentation methods achieve competitive results, most of these methods often face significant challenges when dealing with out-of-distribution data and real-time performance in clinical applications. In this study, we propose a new lightweight spatial detail fused context reconstruction network (SDCRNet) for real-time polyp segmentation aiming to obtain satisfactory segmenting accuracy while avoiding large-scale computation. Our SDCRNet builds a novel lightweight ConvPFormer block as MetaFormer encoder baseline and then constructs a new multi-resolution spatial detail fused context reconstruction (SDCR) to effectively reconstruct the lost textures and spatial detail features through layer-by-layer interaction of the shallow spatial details with the deep global context, which avoids maintaining high-resolution network propagation while capturing accurate local information and boundary features. Extensive experiments on four benchmark datasets show our SDCRNet obtains satisfactory segmenting results and real-time performance compared with the state-of-the-art methods.

Accurate estimation of AC losses is crucial to successfully implement high-temperature superconductors (HTS) in the armature of electric machines. In this estimation, a purely sinusoidal current is often assumed. However, the connection to power electronic converters will introduce harmonics in the transport current, generally leading to higher losses. In this paper, four separate distorted current waveforms produced by space vector modulation were implemented to assess how harmonics from power electronics influence the HTS AC losses in a 2.5 MW superconducting aviation motor. By varying the switching frequency between 5 and 40 kHz, the amount of harmonic distortion was varied between the four cases. The ReBCO and copper layers were both modeled using a multilayer H-A-formulated model. While the harmonic distortion was found to have a minor impact on ReBCO layer hysteresis losses, it increased eddy current losses in the copper stabilizer by between 250 % and 471 %, depending on the amount of distortion. Consequently, the total losses increased by between 60 % and 123 %, with a higher copper loss fraction.

Gastrointestinal or gastric cancer (GC) classification is a serious field of medical research and healthcare technology, where innovative machine learning (ML) and deep learning (DL) models are employed to categorize and analyze many kinds of GCs like pancreatic, gastric, or colorectal cancer. These models influence features extracted from medical imaging, genetic, and clinical data to distinguish between benign and malignant tumours, define cancer stages, and guide treatment verdicts. By automating cancer detection and classification procedures, DL techniques help healthcare experts make quicker and more exact analyses, leading to superior patient results, modified treatment tactics, and enhanced complete organization of GC cases. This technique has great promise for transforming initial detection and involvement in the battle against dangerous diseases. With this inspiration, this study presents a new snake optimization algorithm with a DL-assisted GC classification (SOADL-GCC) approach. The SOADL-GCC approach aims to examine the gastrointestinal tract images for the detection and classification of GC. To achieve it, the SOADL-GCC model employs a bilateral filtering (BF) approach for the noise removal process and enhances image quality. Besides, the SOADL-GCC technique uses a capsule network (CapsNet) model for deriving the feature vectors from preprocessed images. Moreover, SOA can achieve the optimum assortment of hyperparameters associated with the CapsNet model. Finally, the classification process can be performed using the deep belief network (DBN) model. A sequence of simulations took place on the Kvasir dataset to evaluate improved detection results of SOADL-GCC technology. An extensive comparative study reported that the SOADL-GCC technique effectively performs well with other models with a maximum accuracy of 99.72%.

This letter investigates a system with two identical grid-forming (GFM) converters to give a glimpse of the oscillatory stability characteristics in a power system dominated by multiple GFM converters. It is found that the control interaction of GFM converters can cause the oscillation issues under stiff grid conditions. Particularly, when the network topology is unsymmetrical, the oscillation stability is sensitive to all line impedances and external grid dynamics. When the network topology is symmetrical, the oscillation stability is sensitive to line impedances between converters and point of measurement, but is less sensitive to grid impedances and external grid dynamics.

Magnetic components are recently being researched and believed to be a main source of losses, and increasing the weight of power converters, which has become a real challenge for designers. Controlled magnetics for power electronic applications is a promising but still-evolving technology that started to gain momentum as it enabled circuit designers to achieve greater optimization in terms of cost, losses, and size during the design stages of different power converters. In this paper, a comprehensive review is carried out on practical tunable magnetic technologies that may turn out to be a viable solution and find their usage in power electronic applications. In addition, several techniques that can be used in the realization of controlled magnetic components, including but not limited to current-controlled magnetics, voltage-controlled magnetics, and self-regulated magnetics, are being investigated to define the merits and drawbacks of each technology for potential future usage. This article is mainly focused on controlled power inductors for various power electronic applications, and its main goal is to assist designers in selecting the suitable technology for their applications.

Traditionally, power system flexibility in economic dispatch is measured by the ramping up/down capacity with given total demand and locational distributional factors in a system. In this paper, we present the elasticity metrics for the optimal security-constrained economic dispatch (SCED) restoration using the sequential convergence speed of a deformed SCED. The degree of the residual deformation of the SCED solution is denoted by the norm of the Euclid distance between the optimal dispatch point without initial point and some trajectory constraints and sequential dispatches starting from the current operating point. The mathematical proof of the convergence of the sequential SCED is given in this paper. Numerical examples are based on a SCED simulation tool running on a practical power system to verify the convergence and calculate the metrics of the proposed elasticity.

In this work, a modeling methodology is proposed for building up a quasi-analytical equivalent circuit of semiconductor diodes. In particular, it is demonstrated how this nonlinear intrinsic model can be directly extracted from port current and voltage waveform responses under single-tone injections through a formulation which relates the spectral coefficients of the state-functions involved. The extraction technique also includes a multi-bias S-parameters-based flow for characterizing the extrinsic linear network whose proper estimation is essential in high-frequency applications. Another strength of the proposed solution is that the quasi-static version can be extended to a first-order nonquasi-static approach for the modeling of typical relaxation times in high-frequency electron devices. These models have been analytically formulated and implemented in a Computer-Aided Design tool for a commercial Schottky diode and a varactor. Experimental tests for both devices at microwave frequencies prove how the nonquasi-static equivalent diode successfully responds when frequency grows up. In addition, extracted circuits are able to extrapolate in frequency under several bias and power conditions. This strategy provides a solution to overcome some foundry models' limitations under modern power and frequency operating conditions and enhances reconfigurable prototypes design.

Anomaly detection is an important application in factory environments. The sounds emitted by a manufacturing machine during runtime can be indicative of either normal performance or of mechanical failure. Traditionally, cosine losses are frequently utilized in anomalous sound detection (ASD) algorithms. In this work, we evaluate cosine losses within the context and scope of Deep Metric Learning (DML), and we investigate various ways to modify and potentially boost their performance. The impact of each modification on the performance of ASD systems is studied under extensive experimental settings and ablations utilizing both the DCASE 2022 and DCASE 2023 machine ASD datasets. Additionally, under the framework of DML, we develop a key new insight into the inner workings of cosine losses, and we verify it with an ablation.

A fundamental radiation limit for quasi-isotropic antennas is rigorously presented utilizing vector spherical modes within the framework of a mode-matching technique. The theoretical derivation is substantiated through diverse numerical analyses demonstrating that quasi-isotropic radiation employing an omnidirectional antenna is realizable with unity gain across nearly all angles and a maximal gain of 1.1929 (0.7660 dBi) at specific finite angles attributable to the Gibbs phenomenon. Practical implementation necessitates the introduction of a linear array comprising traditional element antennas such as Hertzian dipoles, half-wave dipole antennas, and rectangular patch antennas. Our array configuration is validated using commercial software packages, and the quasi-isotropic radiation patterns they generate exhibit favorable agreement. The ratio between the numbers of element antennas and effective Legendre modes governs a relative convergence that influences radiation characteristics, thereby indicating the existence of an optimal ratio for achieving quasiisotropic radiation while minimizing the mean absolute error (MAE).

This study explores the profound impact of emerging technologies on the Zero Trust paradigm and the challenges they present in the evolving cybersecurity landscape. As organizations grapple with increasingly complex threats, the integration of innovative technologies with Zero Trust principles offers both promising solutions and new hurdles. The research investigates how artificial intelligence, machine learning, blockchain, quantum computing, and cloud/edge technologies are reshaping the implementation and efficacy of Zero Trust architectures. These technologies enable more sophisticated trust evaluation algorithms, enhanced threat intelligence, and dynamic access control mechanisms, pushing the boundaries of traditional Zero Trust models. The rapid pace of innovation introduces complexities in maintaining continuous verification and least-privilege access across hybrid and multi-cloud environments. Moreover, the integration of AI and machine learning in Zero Trust frameworks raises questions about data privacy, algorithmic bias, and the need for explainable security decisions. The paper proposes a methodology for addressing these challenges, emphasizing the need for adaptive Zero Trust strategies that can evolve with technological advancements. By examining real-world case studies and conducting empirical research, this study provides insights into the practical implications of emerging technologies on Zero Trust implementation. It offers guidance on how enterprises can harness these technologies to create more resilient, responsive, and effective cybersecurity measures. Ultimately, this review aims to equip organizations with the knowledge and strategies needed to embrace emerging technologies within a Zero Trust framework, enabling them to navigate the complex interplay between innovation and security in the digital age.

The trend of global economic integration has fostered the prosperity of the maritime transportation industry, which has placed higher demands on the construction of automated container terminals, and the optimization of the integrated  berth allocation and crane assignment problems (BACAPs) is a key link of. Currently, population-based computational intelligence methods have attracted attention on BACAPs, but on small-scale case and simplified problem models. In this paper,  we propose a novel swarm optimization with intra- and inter-hierarchical competition (I2HCSO) for addressing large-scale BACAPs, which is a major challenge in container terminals. First, we construct a hierarchical model with better particles at higher hierarchy, and the population at different hierarchy are divided into several sub-swarm. Then, we design a intra- and inter-hierarchical competitive mechanism to balance the exploration and exploitation of the population, in which intra-hierarchical competition is carried out within sub-swarm at any hierarchy, whereas inter-hierarchical competition occurs in different sub-swarms of neighboring hierarchies. Third, we consider optimizing the priorities of vessels for efficient use of resources berth and crane for the first time in BACAPs, and employ ε-constraints to search for feasible regions. Additionally, we develop a local search operator as a repair strategy to improve the quality of the solution. Finally, we test I2HCSO in a set of cases consisting of 25 BACAPs. Compared with the several typical optimizers with experimental results, I2HCSO is more competitive on BACAPs with different scales.

The global climate crisis has accelerated the search for sustainable energy alternatives, positioning green hydrogen as a promising solution for the decarbonisation of various industrial sectors. This article takes a comprehensive look at the role of green hydrogen in Chile's energy transition, analysing its potential to reduce greenhouse gas emissions, promote sustainable economic development and position the country as a world leader in clean energy. It assesses the World Bank's recent investment of US$150 million in the 'Green Hydrogen Facility in Chile' programme, exploring its implications for the Chilean energy industry and the Latin American and Caribbean region. The study addresses the technical, economic, political and social aspects of green hydrogen development in Chile, including technological challenges, market opportunities, necessary public policies and expected socioeconomic impacts. It analyses the current energy context in Chile, characterised by an increasing share of renewable energies, and projects the country's potential to become a leading exporter of green hydrogen globally. It also examines the role of green hydrogen in the decarbonisation of key sectors such as mining, transport and heavy industry, as well as its potential to drive the creation of green jobs and the development of new value chains. The article concludes with a critical assessment of the prospects for green hydrogen in Chile, offering recommendations to maximise its benefits and mitigate the risks associated with its large-scale implementation.

In this comment, it is shown that Zhu et. al. had misinterpreted some double difference bases (DDBs) as twofold redundancy arrays (TFRAs). The baselines of most DDBs listed in Table I of the above communication get disrupted/broken in the event of single-element failures, thereby failing on the very promise of TFRAs to be robust against single-sensor failures. The use of robust minimum redundancy arrays (RMRAs) is suggested as an alternative solution.

Optimal current setpoints in the form of Maximum torque per ampere (MTPA) and Field Weakening (FW) curves are well-known for three-phase machines. Fivephase machines have more freedom in searching for optimum since the current waveform contains except the first harmonic also the third harmonic, which has a role in torque enhancement and waveform shaping. The problem does not allow for an analytical solution and has to be solved numerically. In this contribution, we analyze the resulting solution and provide guidance for its implementation. Specifically, we show that the optimal operation has MTPA and FW regimes that have an inner structure with multiple sub-regimes. This variety comes from the limits imposed on the maximum of the waveforms of the phase voltage and phase current (peak shaving). A taxonomy of these regimes is provided, and their mutual relations are demonstrated on two exemplary machines. Moreover, we show that commonly used assumptions on the symmetry of the waveforms yield suboptimal results. The theory is validated experimentally on a laboratory prototype of a fivephase PMSM drive of 3 kW.

Learning a discriminative model to distinguish a target from its surrounding distractors is essential to generic visual object tracking. Dynamic target representation adaptation against distractors is challenging due to the limited discriminative capabilities of prevailing trackers. We present a new visual Prompting mechanism for generic Visual Object Tracking (PiVOT) to address this issue. PiVOT proposes a prompt generation network with the pre-trained foundation model CLIP to automatically generate and refine visual prompts, enabling the transfer of foundation model knowledge for tracking. While CLIP offers broad category-level knowledge, the tracker, trained on instance-specific data, excels at recognizing unique object instances. Thus, PiVOT first compiles a visual prompt highlighting potential target locations. To transfer the knowledge of CLIP to the tracker, PiVOT leverages CLIP to refine the visual prompt based on the similarities between candidate objects and the reference templates across potential targets. Once the visual prompt is refined, it can better highlight potential target locations, thereby reducing irrelevant prompt information. With the proposed prompting mechanism, the tracker can generate improved instance-aware feature maps through the guidance of the visual prompt, thus effectively reducing distractors. The proposed method does not involve CLIP during training, thereby keeping the same training complexity and preserving the generalization capability of the pretrained foundation model. Extensive experiments across multiple benchmarks indicate that PiVOT, using the proposed prompting method can suppress distracting objects and enhance the tracker. The code is publicly available. 

This paper presents a novel approach for joint Terahertz (THz) communication and three-dimensional (3D) map reconstruction using an Unmanned Aerial Vehicle (UAV). Due to the need for precise mapping to provide Line-of-Sight (LoS) THz communication services, our approach leverages the UAV's trajectory to initiate the reconstruction and updating of the 3D environment in real-time. The UAV starts without any prior knowledge of the target area, establishing THz communications with users scattered among 3D obstacles. A comprehensive system model is developed, incorporating realistic antenna patterns, UAV oscillations, and received signal power modeling for accurate analysis. We establish an adaptive methodology for 3D environment modeling, dynamically constructing and refining a gridbased obstacle map. A dynamic algorithm for real-time 3D map reconstruction is implemented, utilizing received signal strengths to update the environmental model. Detailed simulations validate the effectiveness of our proposed methods, demonstrating significant improvements in computational efficiency and realtime decision-making in UAV-assisted communication networks. Our results highlight the potential of this joint approach in enhancing operational efficiency and communication reliability under varying environmental conditions.

In this letter, we propose an extremely high scanning-rate slot array antenna utilizing uniplanar-coupled waveguide cavities, allowing for easy fabrication and a lowprofile planar implementation. In contrast to current techniques employing alternating electric and magnetic coupling topologies, our novel approach relies on a pure magnetic coupling topology, serving as the key for uniplanar implementation. We design and fabricate both metallic waveguide and substrate-integrated waveguide implementations to showcase the effectiveness of the proposed technique. Experimental results demonstrate that the two uniplanar-coupled waveguide antennas can scan angle ranges of 65 • and 70 • , respectively, within a 40 MHz frequency band centered at 4 GHz (equivalent to a 1% bandwidth). Compared to state-of-the-art methods, these proposed frequency-scanning antennas exhibit exceptionally high scanning rates, ease of fabrication, and a low-profile implementation.

A large-scale IT outage on July 19th, 2024, primarily affecting Windows systems with CrowdStrike software, caused significant disruptions in airlines, finances, and government services. The culprit was a faulty CrowdStrike update, triggering system crashes. We analyze this incident through the lens of the "release first, fix later" approach discussed in the literature, emphasizing technology managers' need for alternative mitigation strategies, including patch management solutions and disaster recovery plans. Additionally, policymakers may explore measures to incentivize thorough testing, while consumers require robust business continuity plans. Vigilance from all parties is crucial in the ever-evolving software landscape.