An Energy-Efficient ECC Processor of UHF RFID Tag for Banknote Anti-Counterfeiting

In this paper, we present the design and analysis of an energy-efficient 163-b elliptic curve cryptographic (ECC) processor suitable for passive ultrahigh frequency (UHF) radio frequency identification (RFID) tags that are usable for banknote authentication and anti-counterfeiting. Even partial public key cryptographic functionality has long been thought to consume too much power and to be too slow to be usable in passive UHF RFID systems. Utilizing a low-power design strategy with optimized register file management and an architecture based on the López-Dahab Algorithm, we designed a low-power ECC processor that is used with a modified ECC-DH authentication protocol. The ECC-DH authentication protocol is compatible with the ISO/IEC 18000-63 (“Gen2”) passive UHF RFID protocol. The ECC processor requires 12 145 gate equivalents. The ECC processor consumes 5.04 nJ/b at a frequency of 960 kHz when implemented in a 0.13-μm standard CMOS process. The tag identity authentication function requires 30 600 cycles to complete all scalar multiplication operations. This size, speed, and power of the ECC processor makes it practical to use within a passive UHF RFID tag and achieve up to 1500 banknote authentications per minute, which is sufficient for use in the fastest banknote counting machines

Edge-aware Hard Clustering Graph Pooling for Brain Imaging Data

Graph Convolutional Networks (GCNs) can capture non-Euclidean spatial dependence between different brain regions, and the graph pooling operator in GCNs is key to enhancing the representation learning capability and acquiring abnormal brain maps. However, the majority of existing research designs graph pooling operators only from the perspective of nodes while disregarding the original edge features, in a way that not only confines graph pooling application scenarios, but also diminishes its ability to capture critical substructures. In this study, a clustering graph pooling method that first supports multidimensional edge features, called Edge-aware hard clustering graph pooling (EHCPool), is developed. EHCPool proposes the first 'Edge-to-node' score evaluation criterion based on edge features to assess node feature significance. To more effectively capture the critical subgraphs, a novel Iteration n-top strategy is further designed to adaptively learn sparse hard clustering assignments for graphs. Subsequently, an innovative N-E Aggregation strategy is presented to aggregate node and edge feature information in each independent subgraph. The proposed model was evaluated on multi-site brain imaging public datasets and yielded state-of-the-art performance. We believe this method is the first deep learning tool with the potential to probe different types of abnormal functional brain networks from data-driven perspective. Core code is at: https://github.com/swfen/EHCPool

(E)-2-[(2-Hydroxy­ethyl)iminiometh­yl]-6-methoxy­phenolate

The title Schiff base compound, C10H13NO3, obtained by the reaction of 2-hydr­oxy-3-methoxy­benzaldehyde and 2-amino­ethanol in methanol solution, crystallizes in a zwitterionic form, in which the mol­ecule adopts a trans configuration about the central C=N bond. An intra­molecular N—H⋯O hydrogen bond occurs. In the crystal structure, mol­ecules are linked into chains by inter­molecular O—H⋯O hydrogen bonding

Multimetric structural covariance in first-episode major depressive disorder: a graph theoretical analysis

Background: Abnormalities of cortical morphology have been consistently reported in major depressive disorder (MDD), with widespread focal alterations in cortical thickness, surface area and gyrification. However, it is unclear whether these distributed focal changes disrupt the system-level architecture (topology) of brain morphology in MDD. If present, such a topological disruption might explain the mechanisms that underlie altered cortical morphology in MDD. Methods: Seventy-six patients with first-episode MDD (33 male, 43 female) and 66 healthy controls (32 male, 34 female) underwent structural MRI scans. We calculated cortical indices, including cortical thickness, surface area and local gyrification index, using FreeSurfer. We constructed morphological covariance networks using the 3 cortical indices separately, and we analyzed the topological properties of these group-level morphological covariance networks using graph theoretical approaches. Results: Topological differences between patients with first-episode MDD and healthy controls were restricted to the thickness-based network. We found a significant decrease in global efficiency but an increase in local efficiency of the left superior frontal gyrus and the right paracentral lobule in patients with first-episode MDD. When we simulated targeted lesions affecting the most highly connected nodes, the thickness-based networks in patients with first-episode MDD disintegrated more rapidly than those in healthy controls. Limitations: Our sample of patients with first-episode MDD has limited generalizability to patients with chronic and recurrent MDD. Conclusion: A systems-level disruption in cortical thickness (but not surface area or gyrification) occurs in patients with first-episode MDD

Multi-channel quantum noise suppression and phase-sensitive modulation in a hybrid optical resonant cavity system

Quantum noise suppression and phase-sensitive modulation of continuously variable in vacuum and squeezed fields in a hybrid resonant cavity system are investigated theoretically. Multiple dark windows similar to electromagnetic induction transparency (EIT) are observed in quantum noise fluctuation curve. The effects of pumping light on both suppression of quantum noise and control the widths of dark windows are carefully analyzed, and the saturation point of pumping light for nonlinear crystal conversion is obtained. We find that the noise suppression effect is strongly sensitive to the pumping light power. The degree of noise suppression can be up to 13.9 dB when the pumping light power is 6.5 Beta_th. Moreover, a phase-sensitive modulation scheme is demonstrated, which well fills the gap that multi-channel quantum noise suppression is difficult to realize at the quadrature amplitude of squeezed field. Our result is meaningful for various applications in precise measurement physics, quantum information processing and quantum communications of system-on-a-chip

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in the deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. In this paper, we provide an extensive survey of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods, publicly available datasets, and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are described in detail. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.Comment: Survey, 41 page

A real-world pharmacovigilance study of drug-induced QT interval prolongation: analysis of spontaneous reports submitted to FAERS

PurposeTo identify the most commonly reported drugs associated with QT interval prolongation in the FDA Adverse Event Reporting System (FAERS) and evaluate their risk for QT interval prolongation.MethodsWe employed the preferred term (PT) “electrocardiogram QT prolonged” from the Medical Dictionary for Regulatory Activities (MedDRA) 26.0 to identify adverse drug events (ADEs) of QT interval prolongation in the FAERS database from the period 2004–2022. Reporting odds ratio (ROR) was performed to quantify the signals of ADEs.ResultsWe listed the top 40 drugs that caused QT interval prolongation. Among them, the 3 drugs with the highest number of cases were quetiapine (1,151 cases, ROR = 7.62), olanzapine (754 cases, ROR = 7.92), and citalopram (720 cases, ROR = 13.63). The two most frequently reported first-level Anatomical Therapeutic Chemical (ATC) groups were the drugs for the nervous system (n = 19, 47.50%) and antiinfectives for systemic use (n = 7, 17.50%). Patients with missing gender (n = 3,482, 23.68%) aside, there were more females (7,536, 51.24%) than males (5,158, 35.07%) were involved. 3,720 patients (25.29%) suffered serious clinical outcomes resulting in deaths or life-threatening conditions. Overall, most drugs that caused QT interval prolongation had early failure types according to the assessment of the Weibull's shape parameter (WSP) analysis.ConclusionsOur study offered a list of drugs that frequently caused QT interval prolongation based on the FAERS system, along with a description of some risk profiles for QT interval prolongation brought on by these drugs. When prescribing these drugs in clinical practice, we should closely monitor the occurrence of ADE for QT interval prolongation

Synthesis and Characterization of Iron Oxide Nanoparticles and Applications in the Removal of Heavy Metals from Industrial Wastewater

This study investigated the applicability of maghemite (γ-Fe 2 O 3 ) nanoparticles for the selective removal of toxic heavy metals from electroplating wastewater. The maghemite nanoparticles of 60 nm were synthesized using a coprecipitation method and characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM) equipped with energy dispersive X-ray spectroscopy (EDX). Batch experiments were carried out for the removal of Pb 2+ ions from aqueous solutions by maghemite nanoparticles. The effects of contact time, initial concentration of Pb 2+ ions, solution pH, and salinity on the amount of Pb 2+ removed were investigated. The adsorption process was found to be highly pH dependent, which made the nanoparticles selectively adsorb this metal from wastewater. The adsorption of Pb 2+ reached equilibrium rapidly within 15 min and the adsorption data were well fitted with the Langmuir isotherm