Conventional Constraints Research Articles

VizNav: A Modular Off-Policy Deep Reinforcement Learning Framework for Vision-Based Autonomous UAV Navigation in 3D Dynamic Environments

Unmanned aerial vehicles (UAVs) provide benefits through eco-friendliness, cost-effectiveness, and reduction of human risk. Deep reinforcement learning (DRL) is widely used for autonomous UAV navigation; however, current techniques often oversimplify the environment or impose movement restrictions. Additionally, most vision-based systems lack precise depth perception, while range finders provide a limited environmental overview, and LiDAR is energy-intensive. To address these challenges, this paper proposes VizNav, a modular DRL-based framework for autonomous UAV navigation in dynamic 3D environments without imposing conventional mobility constraints. VizNav incorporates the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm with Prioritized Experience Replay and Importance Sampling (PER) to improve performance in continuous action spaces and mitigate overestimations. Additionally, VizNav employs depth map images (DMIs) to enhance visual navigation by accurately estimating objects’ depth information, thereby improving obstacle avoidance. Empirical results show that VizNav, by leveraging TD3, improves navigation, and the inclusion of PER and DMI further boosts performance. Furthermore, the deployment of VizNav across various experimental settings confirms its flexibility and adaptability. The framework’s architecture separates the agent’s learning from the training process, facilitating integration with various DRL algorithms, simulation environments, and reward functions. This modularity creates a potential to influence RL simulation in various autonomous navigation systems, including robotics control and autonomous vehicles.

Dynamic optimization on quantum hardware: Feasibility for a process industry use case

The quest for real-time dynamic optimization solutions in the process industry represents a formidable computational challenge, particularly within the realm of applications like model-predictive control, where rapid and reliable computations are critical. Conventional methods can struggle to surmount the complexities of such tasks. Quantum computing and quantum annealing emerge as avant-garde contenders to transcend conventional computational constraints. We convert a dynamic optimization problem, characterized by an optimization problem with a system of differential–algebraic equations embedded, into a Quadratic Unconstrained Binary Optimization problem, enabling quantum computational approaches. The empirical findings synthesized from classical methods, simulated annealing, quantum annealing via D-Wave’s quantum annealer, and hybrid solver methodologies, illuminate the intricate landscape of computational prowess essential for tackling complex and high-dimensional dynamic optimization problems. Our findings suggest that while quantum annealing is a maturing technology that currently does not outperform state-of-the-art classical solvers, continuous improvements could eventually aid in increasing efficiency within the chemical process industry.

Narrative review on nanoparticles based on current evidence: therapeutic agents for diabetic foot infection.

Diabetes's effects on wound healing present a major treatment challenge and increase the risk of amputation. When traditional therapies fail, new approaches must be investigated. With their submicron size and improved cellular internalisation, nanoparticles present a viable way to improve diabetic wound healing. They are attractive options because of their innate antibacterial qualities, biocompatibility, and biodegradability. Nanoparticles loaded with organic or inorganic compounds, or embedded in biomimetic matrices such as hydrogels, chitosan, and hyaluronic acid, exhibit excellent anti-inflammatory, antibacterial, and antioxidant properties. Drug delivery systems (DDSs)-more precisely, nanodrug delivery systems (NDDSs)-use the advantages of nanotechnology to get around some of the drawbacks of traditional DDSs. Recent developments show how expertly designed nanocarriers can carry a variety of chemicals, transforming the treatment of diabetic wounds. Biomaterials that deliver customised medications to the wound microenvironment demonstrate potential. Delivery techniques for nanomedicines become more potent than ever, overcoming conventional constraints. Therapeutics for diabetes-induced non-healing wounds are entering a revolutionary era thanks to precisely calibrated nanocarriers that effectively distribute chemicals. This review highlights the therapeutic potential of nanoparticles and outlines the multifunctional nanoparticles of the future that will be used for complete wound healing in diabetics. The investigation of novel nanodrug delivery systems has the potential to revolutionise diabetic wound therapy and provide hope for more efficient and focused therapeutic approaches.

When Language is Played: Looking into Linguistic Creativity through Language Play

Abstract This article opens a window on linguistic creativity through an in-depth discussion of language play. To this aim, language play is initially conceptualized from a socio-cognitive perspective. ‘Language play for fun’ and ‘language play for practice’ as two dominant approaches in the relevant literature are, then, touched upon. Afterward, the paper elucidates how language play, in its various manifestations, can inform and influence linguistic creativity. In this respect, a theoretical (linguistic) and a socio-pragmatic perspective are presented. In particular, the paper addresses the possibility of playing to ‘language’ outside the box (i.e., to go beyond the conventional systemic constraints of language). Highlighting the ubiquity of language play, it also considers Wittgenstein’s notion of ‘language games’ (or Sprachspiele). Furthermore, the performative power of playful creativity to make and ‘unmake’ language in society is elaborated on. The article contends that language play may offer valuable insights to extend the borders of linguistic creativity. This argument is further developed under the purview of ‘complexity theory,’ ‘soft computing,’ and ‘computational linguistic creativity.’ Finally, the paper advocates an ‘integrated science’ approach to advance play-based research on linguistic creativity.

SAR image classification using deep features and neighborhood heterogeneity confidence refined spatial constraints

ABSTRACT Recently, frameworks fusing the convolutional neural network (CNN) and Markov random field (MRF) models have been successfully used in synthetic aperture radar (SAR) image classification. Over-smoothing of the details and the incapability to eliminate regional-level misclassification are two common drawbacks of these frameworks, which are caused by the use of conventional neighbourhood consistency based spatial constraints. To address these problems, a neighbourhood heterogeneity confidence – Markov random field (NHC-MRF) and CNN-based framework is proposed for SAR image classification. In this framework, an NHC index is constructed in the binary energy function of the NHC-MRF to refine the conventional spatial constraints by adaptively allowing the existence of heterogeneity in the neighbourhood. The NHC index consists of a label coexisting probability term and a top-2 label term, which are constructed based on the outputs of the CNN. By using the label coexisting probability term, the details can be protected by allowing heterogeneity, and the regional-level misclassification can be eliminated by adjusting the impact of the neighbourhoods by using label coexisting probability based weighting. The top-2 label term is used as a correction of the coexisting probability term considering the disturbance of speckle noise. The NHC-MRF is further fused with a CNN by constructing the unary energy term and initial labels based on the outputs of the CNN. The effectiveness and superiority of the proposed framework are experimentally demonstrated using three SAR datasets. The experimental results demonstrate that the superiority of the NHC-MRF is derived from the simultaneous realization of retaining details and eliminating regional-level misclassification.

Improving Unsupervised Object-Based Change Detection via Hierarchical Multi-Scale Binary Partition Tree Segmentation: A Case Study in the Yellow River Source Region

Change detection in remote sensing enables identifying alterations in surface characteristics over time, underpinning diverse applications. However, conventional pixel-based algorithms encounter constraints in terms of accuracy when applied to medium- and high-resolution remote sensing images. Although object-oriented methods offer a step forward, they frequently grapple with missing small objects or handling complex features effectively. To bridge these gaps, this paper proposes an unsupervised object-oriented change detection approach empowered by hierarchical multi-scale segmentation for generating binary ecosystem change maps. This approach meticulously segments images into optimal sizes and leverages multidimensional features to adapt the Iteratively Reweighted Multivariate Alteration Detection (IRMAD) algorithm for GaoFen WFV data. We rigorously evaluated its performance in the Yellow River Source Region, a critical ecosystem conservation zone. The results unveil three key strengths: (1) the approach achieved excellent object-level change detection results, making it particularly suited for identifying changes in subtle features; (2) while simply increasing object features did not lead to a linear accuracy gain, optimized feature space construction effectively mitigated dimensionality issues; and (3) the scalability of our approach is underscored by its success in mapping the entire Yellow River Source Region, achieving an overall accuracy of 90.09% and F-score of 0.8844. Furthermore, our analysis reveals that from 2015 to 2022, changed ecosystems comprised approximately 1.42% of the total area, providing valuable insights into regional ecosystem dynamics.

Land use characteristics affect the sub-basinal scale urban fish community identified by environmental DNA metabarcoding

The heterogeneity of urban landscapes has effects on the environmental characteristics and fish composition of individual urban streams, even within a single water system. It is, therefore, imperative to assess the influence of physiochemical properties on urban streams by analyzing the spatial distribution of fish communities at the local scale. However, conventional fish surveys encounter time and labor constraints when selecting and surveying dense sampling points under 2 km in stream networks. In this study, environmental DNA (eDNA) metabarcoding was used as an innovative survey methodology to identify the effects of land use and stream order on fish composition and tolerance guild in an urban area. The eDNA sampling was conducted in 31 sites of the Anyang stream network in Korea, including part of the stream undergoing ecological restoration. The eDNA survey detected 12 of 17 species (70.6%) that appeared in the historical data, and 12 of 18 species (66.7%) identified in a conventional field survey with kick nets and casting nets. The proportions of urban area, forest and grassland were positively correlated with abundance (p < 0.05) and richness (p < 0.05) in multiple regression analyses, while the proportion of agricultural area showed a negative correlation (p < 0.05). For abundance, richness, and diversity within the fish community from first- to third-order streams, there was a significant decrease in sensitive species (p < 0.05) alongside a significant increase in tolerant species (p < 0.01) across all three indices. The results of this study highlight variations in fish composition across sites within the local scale of the urban stream network, underscoring the need for detailed monitoring to understand the ecological function of urban streams.

Application of Innovative 3D Pathological Tactic for Diagnosis of Organizing Pneumonia.

The pathological diagnosis of organizing pneumonia (OP) relies on conventional traditional histopathological analysis, which involves examining stained thin slices of tissue. However, this method often results in suboptimal diagnostic objectivity due to low tissue sampling rates. This study aimed to assess the efficacy of tissue-clearing and infiltration-enhanced 3D spatial imaging techniques for elucidating the tissue architecture of OP. H&E staining, 3D imaging technology, and AI-assisted analysis were employed to facilitate the construction of a multidimensional tissue architecture using six OP patient specimens procured from Taichung Veterans General Hospital, enabling a comprehensive morphological assessment. Specimens underwent H&E staining and exhibited Masson bodies and varying degrees of interstitial fibrosis. Furthermore, we conducted a comprehensive study of 3D images of the pulmonary histology reconstructed through an in-depth pathology analysis, and uncovered heterogenous distributions of fibrosis and Masson bodies across different depths of the OP specimens. Integrating 3D imaging for OP with AI-assisted analysis permits a substantially enhanced visualization and delineation of complex histological pulmonary disorders such as OP. The synergistic application of conventional histopathology with novel 3D imaging elucidated the sophisticated spatial configuration of OP, revealing the presence of Masson bodies and interstitial fibrosis. This methodology transcends conventional pathology constraints and paves the way for advanced algorithmic approaches to enhance precision in the detection, classification, and clinical management of lung pathologies.

MA'AT analysis of the O-glycosidic linkages of oligosaccharides using nonconventional NMR J-couplings: MA'AT and MD models of phi.

MA'AT analysis (Meredith et al., J. Chem. Inf. Model. 2022, 62, 3135-3141) is a new NMR-based method to treat ensembles of redundant NMR spin-coupling constants (J-couplings) to obtain experiment-based probability distributions of molecular torsion angles in solution. Work reported to date on modeling the conformations of O-glycosidic linkages of oligosaccharides using three conventional J-coupling constraints (2 J COC, 3 J COCH, 3 J COCC) has shown that the method gives mean torsion angles and circular standard deviations (CSDs) for psi in very good agreement with those obtained by MD simulation. On the other hand, CSDs for phi determined by MA'AT analysis have consistently been much larger than those determined by MD, calling into question either the reliability of MA'AT analysis or MD to accurately predict this behavior. Prior work has shown that this discrepancy does not stem from the limitations of DFT-based J-coupling equation parameterization where secondary conformational dependencies can introduce uncertainties. The present work re-visits this problem by incorporating a new nonconventional J-coupling constraint into MA'AT analyses of phi, namely, a geminal (two-bond) 2 J CCH J-value that exhibits a strong primary dependence on phi. The latter property pertains explicitly to linkages contributed by GlcNAc pyranosyl rings and pyranosyl rings devoid of substituents at C2 (i.e., deoxy residues) where known secondary contributions to 2 J CCH magnitude caused by C-O bond rotation involving the coupled carbon are negligible or absent. The results show that when 2 J CCH values are added to the analysis, phi CSDs reduce considerably, bringing them into better alignment with those obtained by MD simulation. The cause of the discrepancy when only three conventional J-couplings are used to treat phi appears to be associated with the two-bond 2 J COC, which has properties that make it less effective than the non-conventional 2 J CCH as a discriminator of different conformational models of phi.

An Accurate Ladder-Type Ramp Rate Constraint Derived From Field Test Data for Thermal Power Unit With Deep Peak Regulation

The power system experiences a significant increase in its flexibility in recent years, along with the increase in renewable energy penetration. Implementing the flexibility retrofit of thermal power units (TPUs) is an effective way to enhance power system flexibility. However, the conventional ramp rate constraint cannot reveal the ramping characteristics of TPUs during deep peak regulation after the retrofit. In this paper, to establish a constraint that is more consistent with the engineering practice, the relationship between ramping capability and unit load of TPUs is firstly analyzed, based on the field test data of a TPU in China. Then, a ladder-type ramp rate of the TPU is proposed before a unified expression for the constraint is derived for the first time. When a specific dispatch time scale is satisfied, an equivalent expression for this constraint is also derived. Finally, the dimensionality of the binary variables in ladder-type ramp rate constraint is discussed, and the effectiveness of the ladder-type ramp rate constraint of the TPU is initially verified, with the actual 1-year operating data of two TPUs of a power plant in Dalian, Liaoning Province, China.

Women in Trouble: Much Ado About Nothing, Pride &amp; Prejudice and Bridget Jones’s Diary

Shakespeare’s Beatrice, Jane Austen’s Lizzie Bennet and Helen Fielding’s Bridget Jones are three literary incarnations of the same female comic character. They share characteristics that make them all funny for the same reasons at vastly different times. This is due to an inextricable bond between gender and comedy that targets the audience’s expectations of normative femininity. The comic heroines in these three texts are all funny because they deliberately and consciously defy conventional constraints of femininity. The humor of each text results from the comic incongruity that is created by these women’s refusal to be what they ought to be and all three authors reward them for it.

Ultrasonic Vibrator Glove for Enhanced Mobility and Safety in Visually Impaired Individuals

Abstract: This paper explores the conception, production, and actual use of an ultrasonic vibrator glove intended to aid the blind. The glove accomplishes real-time obstacle detection and offers tactile feedback by merging cutting-edge components such an ultrasonic sensor, haptic motor, and Arduino UNO board. For people who are blind, this breakthrough dramatically improves mobility, independence, and safety. The glove's integration of haptic vibration and ultrasonic detection overcomes conventional constraints and gives users a natural approach to understand their environment. The Ultrasonic Vibrator Glove redefines the tactile experience, allowing those who are blind to navigate their environment more successfully through quick obstacle detection and intuitive vibrations. The end result is a solution that empowers people and encourages self-assurance, independence, and security in their daily lives.

Enhancing Myeloid Cancer Genomic Profiling through Comprehensive Whole Genome Sequencing Using the Isabl Gxt Heme Analytical Platform

Introduction: Genomic profiling in myeloid cancers is vital for disease classification, risk stratification, and treatment decisions. Whole genome sequencing (WGS) captures a complete range of biomarkers, overcoming conventional diagnostic constraints of multiple assays (Duncavage et al, 2021). Duncavage et al's seminal work demonstrated WGS as an alternative and sensitive approach to cytogenetics. Due to emerging evidence, NCCN integrated WGS in their AML testing guidelines (v2.2023). Given the importance of WGS in myeloid cancers, we developed Isabl GxT Heme, an analytical platform, that uses unmatched WGS data as input, assesses all mutation types across the entire genome (structural variants (SVs) and copy number variations (CNVs), small mutations) and delivers an automated report of clinically relevant findings in accordance to latest clinical guidelines. Here we use the Duncavage dataset to analytically validate this WGS approach for the Isabl platform. Methods:Data for 263 samples from Duncavage et al were processed using Isabl GxT Heme analytical framework. The cohort included 175 AML, 81 myelodysplastic syndrome (MDS), and 7 other myeloid malignancies, with a median coverage of 50x (19x - 90x). 2 samples were excluded due to low coverage (&amp;lt;20x). Sensitivity analysis for Isabl GxT Heme and ChromoSeq (reference test; Duncavage et al., 2021) involved comparing against clinical panel sequencing for single nucleotide variations (SNVs) and indels, and against cytogenetics for SVs and CNVs (restricted to clonal events). Results:Isabl GxT analytical workflow returned results within one day from FASTQ generation (&amp;lt;19 hours). Isabl GxT Heme and the reference test detected 100% of recurrent SVs (41/41) and clonal CNVs (67/67) that were reported by cytogenetics. Isabl GxT Heme sensitivity for SNVs and indels was 93% (219/236) and 93% (109/117) for all events called by panel sequencing. Sensitivities for the reference test were 85% and 92%, respectively. A total of 850 calls from 261 samples from the reference test were evaluated next (41 SVs, 205 SNVs, 113 Indels, and 491 CNVs). Isabl GxT Heme identified 100% of SVs, while sensitivity for SNVs was 99.5% (204), and that for indels was 98% (111). Isabl GxT Heme called 97% (475) of CNVs with clonality &amp;gt;20%. Our sensitivity in calling FLT3 internal tandem duplication (ITD) as compared to polymerase-chain-reaction (PCR)-based assay was 84% (27/32) as compared to the reference test at 87% (28/32). Of the 46 genes from the latest guidelines for myeloid disease classification and risk stratification (guideline genes; WHO-2022, IPSSM-2022), 12 were not reported by the targeted analytical approach of the reference test and panel sequencing. A total of 32 guideline related small mutations in 29 samples (11%) were called by Isabl GxT Heme alone . In 13/29 samples, the called events were deemed oncogenic/likely oncogenic by OncoKB (Chakravarty et al. 2017). Isabl GxT Heme's allele-specific approach detects copy-neutral loss of heterozygosity (cnLOH), identifying 7 samples (3%) with bi-allelic TP53 loss due to a small mutation and cnLOH. Additionally, 9 focal CNVs (&amp;lt;5Mbp) were detected in 6 samples (2%). Partial tandem duplication eventsinKMT2A(MLL PTD) in 4 AML and 2 MDS samples were uniquely reported by Isabl GxT Heme. 9 SVs were reported affecting guideline genes (Table 1) . Cytogenetics and panel sequencing information was available for 94 samples in this cohort, enabling an assessment of the validity of Isabl GxT Heme. Additional myeloid-relevant driver events not detected by panel sequencing/cytogenetics were found in 27% of patients by Isabl GxT Heme and 12% by the reference test. Isabl GxT Heme called events in 30% of patients as compared to the reference test (Fig 1). All events missed by Isabl GxT Heme were small mutations detected by panel sequencing of which 69% were absent in the aligned BAM file (11/16) and 31% (5/16) had a median variant allele fraction of 5%. Conclusion:Isabl GxT Heme represents a myeloid cancer profiling solution that reports all clinically relevant biomarkers in AML and MDS inclusive of TP53 allelic state, MLL PTD, disease defining and risk stratifying biomarkers across mutation types. Isabl GxT Heme had comparable sensitivity to the reference test and additionally detected myeloid-relevant driver events in 27% of patients. This work demonstrates the validity and added benefit of Isabl GxT Heme in characterizing myeloid cancers.

조지 엘리엇의 리얼리즘에서 관습과 스테레오타입의 문제

This paper examines the issue of stereotypes and conventions as constitutive elements of the realist form, focusing on chapter 17 of Adam Bede, which is most often cited when discussing the realist aesthetic of 19th-century English writer George Eliot. The emphasis on stereotypes and conventions is centered on Eliot’s observation of the still life. I argue that Eliot’s fiction aims for moral truth rather than mechanical objectivity in its representations in order to elicit reader’s sympathy. As such, Eliot’s realism emphasizes empirical observation while simultaneously embracing idealism. In addressing the often-debated “question of objectivity in the aesthetic representation of reality” in realism, the focus then needs to be on the word “aesthetic” as much as on “objectivity,” and this paper argues that Eliot’s aesthetic achievement is not sufficiently free from the constraints of convention and stereotype.

Recent Advances in the 3D Printing of Pure Copper Functional Structures for Thermal Management Devices

Thermal management devices such as heat exchangers and heat pipes are integral to safe and efficient performance in multiple engineering applications, including lithium-ion batteries, electric vehicles, electronics, and renewable energy. However, the functional designs of these devices have until now been created around conventional manufacturing constraints, and thermal performance has plateaued as a result. While 3D printing offers the design freedom to address these limitations, there has been a notable lack in high thermal conductivity materials beyond aluminium alloys. Recently, the 3D printing of pure copper to sufficiently high densities has finally taken off, due to the emergence of commercial-grade printers which are now equipped with 1 kW high-power lasers or short-wavelength lasers. Although the capabilities of these new systems appear ideal for processing pure copper as a bulk material, the performance of advanced thermal management devices are strongly dependent on topology-optimised filigree structures, which can require a very different processing window. Hence, this article presents a broad overview of the state-of-the-art in various additive manufacturing technologies used to fabricate pure copper functional filigree geometries comprising thin walls, lattice structures, and porous foams, and identifies opportunities for future developments in the 3D printing of pure copper for advanced thermal management devices.

Decoding the 'Artificial' Epoch: “Embracing Speculative Architectural-Design Methodologies in the era of AI by addressing the Computational Continuum”

In the contemporary epoch marked by paradigm shifts, as articulated by Saggio in "The IT Revolution in Architecture" (Saggio, 2013), our world is enmeshed in a tapestry of evolving architectural ideologies. The current correlative relationship between those, are blurred superimpositions of influential waves which define a multi-dimensional complexity which resembles a simplified fragrance of the quantum state. The integration of AI into architecture marks a seismic shift, challenging the very narrative that has historically defined architectural discourse. As AI methodologies continue to unfold, they introduce a paradoxical dynamic, redefining conventional perspectives and prompting a reexamination of architectural legacies. The roots of speculative design, deeply embedded in avant-garde movements of yore, now intersect intriguingly with the emergence of AI. Speculation, an inherent aspect of architectural exploration, discovers resonance in the iterative and generative capacities of AI methodologies. These technologies, acting as speculative agents themselves, transcend conventional design constraints, engendering a novel dialogue between human creativity and computational ingenuity. This unfolding narrative opens up new dimensions within the ever-evolving saga of architectural expression by reading and defining this dynamic tension, simultaneously challenging and aligning with tradition, propelling the discipline into unexplored territories that also embrace individualities. Within this metamorphic landscape, this research will try to articulate the correlation of generative design thinking in approaching AI generative methodologies in addressing new architectural design processes; precipitated as an emergence in the ever-evolving Architecture, Engineering, and Construction (AEC) industry. This study lays a foundation for understanding AI's role in architecture but also points to significant areas for further inquiry.

An Accelerated Algorithm for 3D Inversion of Gravity Data Based on Improved Conjugate Gradient Method

The 3D inversion algorithm for gravity data based on a smooth model constraint has been proven to yield a reasonable density distribution. However, as the amount of observed data and model parameters increases, the algorithm experiences issues with high memory consumption and prolonged computation time. Therefore, the corresponding problem in interpreting gravity inversion lies in developing a fast inversion algorithm. The conventional smooth model constraint inversion algorithm, based on regularization theory, requires the introduction of a model weighting function with a large matrix, and involves storage and operation of a large matrix with intermediate variables during inversion iteration, contributing significantly to the prolonged computation time. In this paper, a diagonal weight matrix is represented by vectorization, and the intermediate variable of the large matrix type in the iteration is replaced with the combination of a small matrix and a vector. Additionally, the algorithm flow of the conjugate gradient method is further optimized to minimize the number of vectors that need to be stored during iteration. As a result of these optimizations, the memory consumption of the algorithm during the operation process is successfully reduced. Finally, the experiments demonstrate the successful development of a fast 3D inversion algorithm for gravity data. Specifically, for a 80 × 80 × 20 mesh number inversion, our accelerated algorithm achieves an average speed of ~0.5 s per iteration, and the iterative process speeds up by a factor of 1000, providing an effective strategy for the fast inversion of large-scale data.

Advanced Manufacturability of Electrical Machine Architecture through 3D Printing Technology

The rapid evolution of electric machines requires innovative approaches to boost performance, efficiency, and sustainability. Additive Manufacturing (AM) has emerged as a transformative technique, reshaping the landscape of electric machine components, ranging from magnetic materials to windings and extending to thermal management. In the area of magnetic materials, AM’s capacity to fabricate intricate structures optimizes magnetic flux dynamics, yielding advanced shape-profile cores and self-coating laminations for superior performance. In windings, AM’s prowess is evident through innovative concepts, effectively mitigating AC conduction effects while reducing weight. Furthermore, AM revolutionizes thermal management, as exemplified by 3D-printed ceramic heat exchangers, intricate cooling channels, and novel housing designs, all contributing to enhanced thermal efficiency and power density. The integration of AM not only transcends conventional manufacturing constraints but also promises to usher in an era of unprecedented electric machine innovation, addressing the intricate interplay of magnetic, winding, and thermal dynamics.

Chance-constrained optimization for active distribution networks with virtual power lines

Active distribution networks (ADNs) experience uncertainties because of the high integration of distributed renewable energy resources (RESs). Stochastic analysis approaches are attracting increasing attention owing to the uncertain nature of RESs and load demands. This study proposes a chance-constrained optimal operation framework for ADNs considering virtual power lines (VPLs) solved via the second- order cone programming (SOCP) optimal power flow (OPF) problem. VPLs comprise virtual lines connected to each network’s energy storage systems (ESSs). If one ESS is charged, another ESS is discharged by the same amount. We reformulate the constraints into tractable forms using a simplified Z-bus sensitivity matrix and first-order Taylor expansion to solve via SOCP OPF because the chance constraints on voltage magnitudes are generally non-convex and intractable. The efficacy of the proposed approach was verified through its application to two modified IEEE 33-bus systems, each featuring different consumer units, including commercial, residential, and industrial units. The effectiveness of the proposed methods was demonstrated by comparing three different types of constraints: conventional hard constraints, soft constraints with penalties, and chance constraints. The results showed that although the chance constraints case had a slightly higher voltage violation rate, the network power loss was reduced by 3.4% compared to the conventional hard constraints case. Furthermore, modified 85-bus and 65-bus systems are carried out to verify the algorithm’s scalability. The proposed approach achieves a trade-off between voltage regulation and power loss minimization, with a voltage violation rate of 0.88% and a power loss reduction to 1.02%.

High–Linear Energy Transfer Irradiation in Clinical Carbon-Ion Beam With the Linear Energy Transfer Painting Technique for Patients With Head and Neck Cancer

Dose-averaged linear energy transfer (LETd) is one of the important factors in determining clinical outcomes for carbon-ion radiation therapy. Innovative LET painting (LP) has been developed as an advanced form of conventional intensity modulated carbon-ion radiation therapy (IMIT) at the QST Hospital. The study had 2 motivations: to increase the minimum LETd (LETdmin) and to improve uniformity of the LETd distribution within the gross tumor volume (GTV) by using LP treatment plans for patients with head and neck cancer while maintaining the relative biologic effectiveness (RBE)-weighted dose coverage within the planning tumor volume (PTV) the same as in the conventional IMIT plan. The LP treatment plans were designed with the in-house treatment planning system. For the plans, LETd constraints and LETdmin, goal-LETd, and maximum-LETd (LETdmax) constraints for the GTV were added to the conventional dose constraints in the IMIT prescription. For 13 patients with head and neck cancer, the RBE-weighted dose to 90% (D90) and 50% (D50) of the PTV and the LETdmin, mean (LETdmean), and LETdmax values within the GTV in the LP plans were evaluated by comparing them with those in the conventional IMIT plans. The LP for 13 patients with head and neck cancer could keep D90s and D50s for the PTV within 1.0% of those by the conventional IMIT. Among the 13 patients, the mean LETdmin of the LP plans for the GTV was 59.2 ± 7.9 keV/μm, whereas that of the IMIT plans was 45.9 ± 6.0 keV/μm. The LP increased the LETdmin to 8 to 24 keV/μm for the GTV compared with IMIT. While maintaining the dose coverage to the PTV as comparable to that for IMIT, the LP increased the mean LETdmin to 13.2 keV/μm for the GTV. For a GTV up to 170 cm3, LETd > 44 keV/μm could be achieved using LP, which according to previous studies was associated with lower recurrence. In addition, the LP method delivered more uniform LETd distributions compared with IMIT.