Search Results (98)

For human beings, accepting loss and absence is a constant effort, particularly when it comes to accepting their own finitude, which becomes apparent as time passes and people leave us. This is closely linked to nostalgia and the processes of remembrance. While there are many nuances, we can distinguish between constructive and destructive nostalgia. The former cannot accept absence or the passage of time and deludes itself into thinking that it can recover what has been lost. The latter recognizes the temptation to recover everything, but knows that this is impossible, and accepts that the past can only be preserved by transforming it into something else. Contemporary technologies that use algorithms can exacerbate the former tendency by manipulating memory processes and distorting the meaning of the virtual. The aim of this contribution is to shed light on the dynamics and implications of nostalgia as it is influenced by algorithms. To this end, it is divided into three stages. In the first stage, nostalgia is examined for its “restraining” power in relation to deterministically progressive philosophies of history, also through a reference to the original philosophical meaning of the term ‘virtual’. In the second stage, the relation to progress is thematized through a reflection on technologies and artificial intelligence, which uses algorithms and devours our data. In the third stage, it will be shown how thinking about nostalgia and artificial and algorithmic ‘intelligence(s)’ can be a valuable test case for distinguishing between the uses and abuses of nostalgia, between constructive nostalgia and destructive nostalgia. Full article

Clinical application of AI/DL-aided acquisitions for quantitative bi-parametric (q-bp)MRI requires validation and harmonization across vendor platforms. An AI/DL-accelerated q-bpMRI, including 5-echo T₂ and 4-b-value apparent diffusion coefficient (ADC) mapping, was implemented on two 3T clinical scanners by two vendors alongside the qualitative standard-of-care (SOC) MRI protocols for six patients with biopsy-confirmed prostate cancer (PCa). AI/DL versus SOC bpMRI image quality was compared for MR-visible PCa lesions on a 4-point Likert-like scale. Quantitative validation and protocol bias assessment were performed using a multiparametric phantom with reference T₂ and diffusion kurtosis values mimicking prostate tissue ranges. Six-minute q-bpMRI achieved acceptable diagnostic quality comparable to the SOC. Better SNR was observed for DL/AI versus SOC ADC with method-dependent distortion susceptibility and resolution enhancement. The measured biases were unaffected by AI/DL reconstruction and related to acquisition protocol parameters: constant for spin-echo T₂ (−7 ms to +5 ms) and ADC (4b-fit: −0.37 µm²/ms and 2b-fit: −0.19 µm²/ms), while nonlinear for echo-planar T₂ (−37 ms to +14 ms). Measured phantom ADC bias dependence on b-value range was consistent with that observed for PCa lesions. Bias correction harmonized lesion T₂ and ADC values across different AI/DL-aided q-bpMRI acquisitions. The developed workflow enables harmonization of AI/DL-accelerated quantitative T₂ and ADC mapping in multi-vendor clinical settings. Full article

This article presents a novel method for detecting series arc faults (SAFs) in residential applications using time-domain imaging (TDI) and Long Short-Term Memory (LSTM) networks. The proposed method transforms current signals into grayscale images by filtering out the fundamental frequency, allowing key arc fault characteristics—such as high-frequency noise and waveform distortions—to become visually apparent. The use of Ensemble Empirical Mode Decomposition (EEMD) helped isolate meaningful signal components, although it was computationally intensive. To address real-time requirements, a simpler yet effective TDI method was developed for generating 2D images from current data. These images were then used as inputs to an LSTM network, which captures temporal dependencies and classifies both arc faults and appliance types. The proposed TDI-LSTM model was trained and tested on 7000 labeled datasets across five common household appliances. The experimental results show an average detection accuracy of 98.1%, with reduced accuracy for loads using thyristors (e.g., dimmers). The method is robust across different appliance types and conditions; comparisons with prior methods indicate that the proposed TDI-LSTM approach offers superior accuracy and broader applicability. Trade-offs in sampling rates and hardware implementation were discussed to balance accuracy and system cost. Overall, the TDI-LSTM approach offers a highly accurate, efficient, and scalable solution for series arc fault detection in smart home systems. Full article

Magnetotelluric (MT) sounding is a crucial technique in mineral exploration. However, MT data are highly susceptible to various types of noise. Traditional data processing methods, which rely on the assumption of signal stationarity, often result in severe distortion when suppressing non-stationary noise. In this study, we propose a novel, adaptive, and less parameter-dependent signal decomposition method for MT signal denoising, based on time–frequency domain analysis and the application of modal decomposition. The method uses Variational Mode Decomposition (VMD) to adaptively decompose the MT signal into several intrinsic mode functions (IMFs), obtaining the instantaneous time–frequency energy distribution of the signal. Subsequently, robust statistical methods are introduced to extract the independent components of each IMF, thereby identifying signal and noise components within the decomposition results. Synthetic data experiments show that our method accurately separates high-amplitude non-stationary interference. Furthermore, it maintains stable decomposition results under various parameter settings, exhibiting strong robustness and low parameter dependency. When applied to field MT data, the method effectively filters out non-stationary noise, leading to significant improvements in both apparent resistivity and phase curves, indicating its practical value in mineral exploration. Full article

Past archaeological practices have resulted in a distorted history of Native American cultures based upon western-biased research. This has been especially apparent in the rock art of the Klamath Basin in southern Oregon and northern California. In response to this, Native and non-Native scholars are striving to develop a counter-discourse that both challenges and replaces western constructs in research on Native American communities. The result of this approach is a growing trend within the discipline that has come to be called “Indigenous Archaeology.” Critical to this approach is that Native voices are transported from the margins of the research to its center, where they are intended to replace the Western colonialist narrative. Unfortunately, Native American tribal communities have been the targets of federal assimilation policies for the past few centuries, and as a result, much of their cultural knowledge unwittingly carries forward this distorted past. In this paper I explore a framework built upon ethnographic accounts of shamanism and rock art, along with a robust familiarity with local myth, and how this provides a foundation of traditional cultural knowledge against which to compare and evaluate the interpretive statements made in contemporary tribal members about rock art and other sacred material culture. Full article

Intra-tissue endoscopy, providing real-time images at all scales, from macroscopic to microscopic, from inside living tissue during surgical procedures, has revealed the existence of a body-wide fibrillar architecture that extends from the surface of the skin to the cell. Different types of cells are housed within this fibrillar architecture and gather together to carry out specific functions. This challenges the commonly accepted notion of the organization of living matter that associates separate organs with connective tissue packaging. We are thus confronted with the global nature of the living human body and its vital processes. This paper sets out to describe the architecture of this fibrillar network which could be assimilated with the fascial tissue and which attributes a more constitutive role to connective tissue. It also demonstrates how movements within this fibrillar network can occur with minimal local distortion while maintaining tissue continuity. The authors propose that the gliding of tissues can be explained by the existence of a highly adaptable fibrillar network that enables the gliding of distinct anatomical structures such as tendons and muscles, without any dynamic influence on the surrounding tissues. The authors propose a new model of tissue movement based on the observation of a ubiquitous dynamic polyhedric fibrillar network with an apparently dispersed and complex pattern of organization, that forms fluid-filled microvolumes, and is found everywhere in the human body. Furthermore, this fibrillar network appears to act as a force absorption system, in addition to providing a framework or scaffolding for cells throughout the body. Observation during intra-tissue endoscopy suggests that this fundamental architectural organization extends into the extracellular matrix that is the natural environment of all cells in the living body, regardless of their size, location or specific function. Full article

The Valley of Tammaro River lies between the regions of Molise and Campania in central southern Italy. The area has been inhabited since ancient times due to its fertile soil and plentiful water resources. The interest in this region is enhanced by the many urban centers and the isolated and rural building complexes that date back to the Samnite era and are connected by a road system that is still in use today. Saepinum, regarded as the symbol of Roman civilization in the Molise area (Italy), is one of these. Before becoming a Roman municipium and then a medieval and contemporary rural community, it was a Samnite trade forum and service center. A suburban villa belonging to the Gens Neratia, a family originally from the Roman municipality of Saepinum, is connected to it approximately 2 km northeast. Both sites were partially excavated, and much more can be learned from the material still available. To this end, geoelectrical studies using the tensor acquisition mode were used to conduct geophysical surveys in certain sectors. The data were processed using Data-Adaptive Probability-Based Electrical Resistivity Tomography, here adapted for the first time to Apparent Resistivity Tensor Analysis. The trace of the apparent resistivity tensor provides distortion-free maps and demonstrates that the anomalies are closely constrained on the source bodies. Full article

Solar energy-absorbing and -storing integrated ceramics are a new type of material that absorbs sunlight and stores it as heat energy, with properties such as high absorptivity, high thermal storage density, and high temperature stability. In this study, forsterite ceramics were prepared from fused magnesia, quartz, α-Al₂O₃, and Sm₂O₃, and concurrently, two additives of Fe₂O₃ and CuO were doped to improve the absorptivity, and the effects of the composite additives on the performance of forsterite ceramics were investigated. The results showed that the optimal Fe₂O₃/CuO content ratio was 8:2, at which time the apparent porosity, bulk density, and thermal storage density of the sample were 0.21%, 3.08 g/cm³, and 1516.71 kJ/kg (1000 °C), respectively. After 30 thermal shock cycles, the precipitation of samarium silicate in the samples resulted in a tighter grain bonding, increased the bending strength by 70.6%, and exhibited excellent thermal shock resistance. The solar absorptivity reached 93.80% in the 0.3–2.5 μm wavelength range. Fe₂O₃ doping replaced part of the positions of Al³⁺ in MgAl₂O₄ to form MgFe_0.6Al_1.4O₄ phase. This replacement caused lattice distortion, which triggered electronic transition and augmented the intrinsic absorption capacity, thereby enhancing the sample’s absorptivity. CuO’s low reflectivity across the spectrum further reduced sample reflectivity. Full article

The static shift effect is a distortion in electromagnetic data that severely impacts exploration results. Traditional static effect correction methods are often ineffective, prone to overcorrection or undercorrection, and make it difficult to accurately assess the applicability of the correction. Furthermore, some correction processes require additional data, which increases correction costs. This paper first presents the theoretical foundation for correcting static shift effects in the electric field components using magnetic field component information. Based on time-frequency electromagnetic exploration technology, a method is proposed to correct static shift effects in the electric field by using simultaneously collected magnetic field data, aiming to address the distortion issues caused by static shift effects in the electric field and apparent resistivity. The method is validated through both theoretical models and field data, demonstrating its excellent correction performance. Additionally, the paper introduces the use of the multifractal spectrum analysis algorithm to analyze profile measurement points and study the fractal dimension characteristics of static shift effects, providing an effective way to evaluate the appropriateness and potential overcorrection of the correction. Finally, the multifractal features of field data are discussed, validating the ability of the multifractal spectrum to identify subsurface electrical complexity. Full article

Background/Objectives: This study compared the image quality of conventional multiplexed sensitivity-encoding diffusion-weighted imaging (MUSE-DWI) and deep learning MUSE-DWI with that of vendor-specific deep learning (DL) reconstruction applied to bladder MRI. Methods: This retrospective study included 57 patients with a visible bladder mass. DWI images were reconstructed using a vendor-provided DL algorithm (AIR^TM Recon DL; GE Healthcare)—a CNN-based algorithm that reduces noise and enhances image quality—applied here as a prototype for MUSE-DWI. Two radiologists independently assessed qualitative features using a 4-point scale. For the quantitative analysis, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), signal intensity ratio (SIR), and apparent diffusion coefficient (ADC) of the bladder lesions were recorded by two radiologists. The weighted kappa test and intraclass correlation were used to evaluate the interobserver agreement in the qualitative and quantitative analyses, respectively. Wilcoxon signed-rank test was used to compare the image quality of the two sequences. Results: DL MUSE-DWI demonstrated significantly improved qualitative image quality, with superior sharpness and lesion conspicuity. There were no significant differences in the distortion or artifacts. The qualitative analysis of the images by the two radiologists was in good to excellent agreement (κ ≥ 0.61). Quantitative analysis revealed higher SNR, CNR, and SIR in DL MUSE-DWI than in MUSE-DWI. The ADC values were significantly higher in DL MUSE-DWI. Interobserver agreement was poor (ICC ≤ 0.32) for SNR and CNR and excellent (ICC ≥ 0.85) for SIR and ADC values in both DL MUSE-DWI and MUSE-DWI. Conclusions: DL MUSE-DWI significantly enhanced the image quality in terms of lesion sharpness, conspicuity, SNR, CNR, and SIR, making it a promising tool for clinical imaging. Full article

This study introduces and examines the principle of accentuation as a novel mechanism in perceptual organization, analyzing its effects through the framework of Grossberg’s Adaptive Resonance Theory (ART). We demonstrate that localized accentuators, manifesting as minimal dissimilarities or discontinuities, can significantly modulate global perceptions, inducing illusions of geometric distortion, orientation shifts, and apparent motion. Through a series of phenomenological experiments, we establish that accentuation can supersede classical Gestalt principles, influencing figure-ground segregation, shape perception, and lexical processing. Our findings suggest that accentuation functions as an autonomous organizing principle, leveraging salience-driven attentional capture to generate perceptual effects. We then apply the ART model to elucidate these phenomena, focusing on its core constructs of complementary computing, boundary–surface interactions, and resonant states. Specifically, we show how accentuation-induced asymmetries in boundary signals within the boundary contour system (BCS) can propagate through laminar cortical circuits, biasing figure-ground assignments and shape representations. The interaction between these biased signals and top–down expectations, as modeled by ART’s resonance mechanisms, provides a neurally plausible account for the observed illusions. This integration of accentuation effects with ART offers novel insights into the neural substrates of visual perception and presents a unifying theoretical framework for a diverse array of perceptual phenomena, bridging low-level feature processing with high-level cognitive representations. Full article

A novel coupled processing method (PDCT) that associated deep cryogenic treatment (DCT) with a high pulsed magnetic field (PMT) was investigated to improve the performance of an as-cast TC4 aero-titanium alloy. Through XRD, SEM, TEM, EBSD, and a properties test, its microstructure and tensile properties and their relationship were investigated. The results show that in comparison with the untreated samples, in the PDCT alloys, the amount of nano-sized precipitates and dislocation density are increased, and this phenomenon is characterized by their combed dislocation morphology. The grain sizes are refined and rounded, and the deformed grains are enhanced, together with the enhancement of low-angle grain boundaries in grains and the transformation from the β phase to the α phase. The (112) crystal orientation is apparently strengthened. The tensile strength, elongation, and fracture energy of the optimized PDCT sample are 921.4 MPa, 7.6% and 5.47 × 10⁷ J/m³, which increased by 4.9%, 28.8% and 80.5% compared with the untreated sample, respectively. The tensile fracture exhibits rheological deformation along the phase boundaries. The strength–toughness mechanisms are mainly attributed to the texture, precipitation, dislocation and fine grain strengthening, which stem from the cold contraction and lattice distortion of DCT and the main magneto-plasticity effect of PMT, together with their coupling effects. Full article

This review paper describes and compares the practical methods that make it possible to calculate an average value of apparent, active, and reactive (i.e., blind and distorted) power in each calculation step. In addition to two methods, $p–q$ and $ip–iq$, it deals with the application of the $id–iq$ method for determining power components’ mean values in a discrete step. The results are important and needed for the right dimensioning and sizing of power electronic and electrical systems (PEESs), which those power components produce. This is because the integral calculation for the mean values of the product of voltage u(t) and current i(t) always gives a value lower than the actual value of the apparent power. Using moving average and moving root mean square (rms) techniques (or digital filtering), one obtains the right values, although with a time delay. Using sliding filtering, these techniques calculate the average or rms values, respectively, of the power components in each step k. By calculating the moving average value of the power components in both transient and steady states (on/off as well), we achieve the correct design of the system. The transients for the three- and single-phase power electronic systems are modeled, simulated, and theoretically supported in this study. Any PEES can be determined and sized using the calculated data. The real-time HW simulator Plecs RT Box 1 and Matlab/Simulink 2024a simulations validate the comprehensive time waveform produced by the suggested method. Full article

A new aluminum complex (NSO)AlMe₂ featuring a hydrogen bond donor on the ligand backbone has been synthesized via the reaction of AlMe₃ with 1-((2-(isopropylamino)phenyl)thio)propan-2-ol (NSO-H) and spectroscopically characterized. In the complex, the aluminum atom is in a distorted tetrahedral coordination sphere determined by the anionic oxygen and neutral nitrogen atoms of the ligand and by the two carbon atoms of the alkyl groups. After proper activation, the complex (NSO)AlMe₂ was able to promote the ring-opening polymerization of L-, rac-lactide, ε-caprolactone and rac-β-butyrolactone. The polymerization of rac-lactide was faster than that of L-lactide: in a toluene solution at 80 °C, the high monomer conversion of 100 equivalents was achieved in 1.5 h, reaching a turnover frequency of 63 mol_LA·mol_Al^–1·h^–1. The experimental molecular weights of the obtained polymers were close to those calculated, assuming the growth of one polymer chain for one added alcohol equivalent and the polydispersity indexes were monomodal and narrow. The kinetic investigation of the polymerization led to the determination of the apparent propagation constants and the Gibbs free energies of activation for the reaction; the terminal groups of the polymers were also identified. The complex (NSO)AlMe₂ was active in harsh conditions such as at a very low concentration or in the melt using technical-grade rac-lactide. A relatively high level of activity was observed in the ring-opening polymerization of ε-caprolactone and rac-β-butyrolactone. DFT calculations were performed and revealed the central role of the NH function of the coordinated ligand. Acting as a hydrogen bond donor, it docks the monomer in the proximity of the metal center and activates it toward the nucleophilic attack of the growing polymer chain. Full article

Background/Objectives: Apparent Diffusion Coefficient (ADC) maps in prostate MRI can reveal tumor characteristics, but their accuracy can be compromised by artifacts related with patient motion or rectal gas associated distortions. To address these challenges, we propose a novel approach that utilizes a Generative Adversarial Network to synthesize ADC maps from T2-weighted magnetic resonance images (T2W MRI). Methods: By leveraging contrastive learning, our model accurately maps axial T2W MRI to ADC maps within the cropped region of the prostate organ boundary, capturing subtle variations and intricate structural details by learning similar and dissimilar pairs from two imaging modalities. We trained our model on a comprehensive dataset of unpaired T2-weighted images and ADC maps from 506 patients. In evaluating our model, named AI-ADC, we compared it against three state-of-the-art methods: CycleGAN, CUT, and StyTr2. Results: Our model demonstrated a higher mean Structural Similarity Index (SSIM) of 0.863 on a test dataset of 3240 2D MRI slices from 195 patients, compared to values of 0.855, 0.797, and 0.824 for CycleGAN, CUT, and StyTr2, respectively. Similarly, our model achieved a significantly lower Fréchet Inception Distance (FID) value of 31.992, compared to values of 43.458, 179.983, and 58.784 for the other three models, indicating its superior performance in generating ADC maps. Furthermore, we evaluated our model on 147 patients from the publicly available ProstateX dataset, where it demonstrated a higher SSIM of 0.647 and a lower FID of 113.876 compared to the other three models. Conclusions: These results highlight the efficacy of our proposed model in generating ADC maps from T2W MRI, showcasing its potential for enhancing clinical diagnostics and radiological workflows. Full article