Detection Efficiency Research Articles

Multi-slice electron ptychographic tomography for three-dimensional phase-contrast microscopy beyond the depth of focus limits

Abstract Electron ptychography is a powerful computational method for atomic-resolution imaging with high contrast for weakly and strongly scattering elements. Modern algorithms coupled with fast and efficient detectors allow imaging specimens with tens of nanometers thicknesses with sub-0.5 Ångstrom lateral resolution. However, the axial resolution in these approaches is currently limited to a few nanometers, limiting their ability to solve novel atomic structures ab initio. Here, we experimentally demonstrate multi-slice ptychographic electron tomography, which allows atomic resolution three-dimensional phase-contrast imaging in a volume surpassing the depth of field limits. We reconstruct tilt-series 4D-STEM measurements of a Co 3 O 4 nanocube, yielding 2 Å axial and 0.7 Å transverse resolution in a reconstructed volume of ( 18.2 nm ) 3 . Our results demonstrate a 13.5-fold improvement in axial resolution compared to multi-slice ptychography while retaining the atomic lateral resolution and the capability to image volumes beyond the depth of field limit. Multi-slice ptychographic electron tomography significantly expands the volume of materials accessible using high-resolution electron microscopy. We discuss further experimental and algorithmic improvements necessary to also resolve single weakly scattering atoms in 3D.

A novel method for computing the full-energy peak efficiency of coaxial high-purity germanium detectors for cylindrical sources

In this work a novel method for computing the full-energy peak efficiency of coaxial HPGe detectors for cylindrical sources emitting photons in the range of 60–2000 keV is described. It has a solid theoretical basis using an integral expression for computing the full-energy peak efficiency via computed values of the detector response and the attenuation factor of point sources. The method was implemented as a Matlab code written for computing the full-energy peak efficiency of a Canberra detector. The code was extensively verified by intercomparison with GESPECOR and LabSOCS codes and the results are fit purpose e.g. in the measurement of environmental samples.

Characterization of nitric oxide in Octopus maya nervous system and its potential role in sensory perception.

The role of nitric oxide as a neurotransmitter in the olfactory and chemoreception systems of invertebrates has been well documented. This suggests an early and efficient sensory detection system that is evolutionarily preserved in various species, including vertebrates and invertebrates. Additionally, the presence of a nitric oxide neurotransmitter system has been reported in molluscs, particularly in octopus species. In this work, we present evidence for the existence of nitric oxide synthase in neurons and fibers, as well as its anatomical localization in various nuclei involved in chemosensory integration and the motor responses associated with these processes in Octopus maya.

Hybrid Artificial-Intelligence-Based System for Unmanned Aerial Vehicle Detection, Localization, and Tracking Using Software-Defined Radio and Computer Vision Techniques

The proliferation of drones in civilian environments has raised growing concerns about their misuse, highlighting the need to develop efficient detection systems to protect public and private spaces. This article presents a hybrid approach for UAV detection that combines two artificial-intelligence-based methods to improve system accuracy. The first method uses a software-defined radio (SDR) to analyze the radio spectrum, employing autoencoders to detect drone control signals and identify the presence of these devices. The second method is a computer vision module consisting of fixed cameras and a PTZ camera, which uses the YOLOv10 object detection algorithm to identify UAVs in real time from video sequences. Additionally, this module integrates a localization and tracking algorithm, allowing the tracking of the intruding UAV’s position. Experimental results demonstrate high detection accuracy, a significant reduction in false positives for both methods, and remarkable effectiveness in UAV localization and tracking with the PTZ camera. These findings position the proposed system as a promising solution for security applications.

A fingerprinting structure model for Arabic document plagiarism detection

Plagiarism, which is a significant problem in the academic world worldwide, is particularly challenging to detect in Arabic due to the language's complex structure. Methodology: The ADPDM model and framework for detecting plagiarism in Arabic documents are presented in this dissertation. It is designed to detect plagiarism within academic contexts. By organizing documents logically into paragraphs, sentences, and words, the model seeks to establish a robust system that can identify duplicated content and search for similar documents within identifying corresponding sets. In particular, the study examines preprocessing techniques such as stop word removal, stemming, and rootage processing, followed by content-based methods utilizing fingerprinting and heuristic algorithms that are tailored to Arabic language features. To aid in efficient detection, the BKDR hash function is used for chunk having. To optimize computation time, heuristic algorithms are implemented at different levels of document representation, using metrics such as Longest Common Substring (LCS). To evaluate the ADPDM system, a corpus of 100 documents is utilized, which includes datasets from AraPlagDet and the Decision Support System (DSS). The performance of ADPDM is compared with other plagiarism detection methods using WCopyFind, but the latter has a higher computational speed than ADBDM. Its recall, precision, and F-measure values of 0. 78035, 0. 994264, and 0. 865688 respectively (ADPDM) are particularly notable for its ability to detect plagiarized content in Arabic documents. ADPDM is a successful anti-pluralist solution for Arabic text, even though it requires a longer processing time than WCopyFind.

Apple Defect Detection in Complex Environments

Aiming at the problem of high false detection and missed detection rate of apple surface defects in complex environments, a new apple surface defect detection network: space-to-depth convolution-Multi-scale Empty Attention-Context Guided Feature Pyramid Network-You Only Look Once version 8 nano (SMC-YOLOv8n) is designed. Firstly, space-to-depth convolution (SPD-Conv) is introduced before each Faster Implementation of CSP Bottleneck with 2 convolutions (C2f) in the backbone network as a preprocessing step to improve the quality of input data. Secondly, the Bottleneck in C2f is removed in the neck, and Multi-scale Empty Attention (MSDA) is introduced to enhance the feature extraction ability. Finally, the Context Guided Feature Pyramid Network (CGFPN) is used to replace the Concat method of the neck for feature fusion, thereby improving the expression ability of the features. Compared with the YOLOv8n baseline network, mean Average Precision (mAP) 50 increased by 2.7% and 1.1%, respectively, and mAP50-95 increased by 4.1% and 2.7%, respectively, on the visible light apple surface defect data set and public data set in the self-made complex environments.The experimental results show that SMC-YOLOv8n shows higher efficiency in apple defect detection, which lays a solid foundation for intelligent picking and grading of apples.

The terrestrial isopod Porcellionides pruinosus as a facultative ant guest along foraging trails and inside Messor ebeninus nests in the Negev Desert

AbstractWe report field observations of the cosmopolitan terrestrial isopod species Porcellionides pruinosus both inside the nest and following foraging trails of the seed harvester ant Messor ebeninus in the spring of 2022 and 2023. The isopods inside the nest either traveled to deeper tunnels or joined foraging trails. The density of isopods along foraging trails was as high as 60 individuals along one 22-m foraging trail. The results from 12 preliminary homing assays revealed that the distance isopods were displaced did not affect whether they returned to the trail. Two of the four isopods introduced into a foraging trail of a new nest continued walking on the foreign trail. The isopods were met with minimal aggression from the ants along the trail, and there were no instances of nest guarding observed. The presence of P. pruinosus as a facultative associate inside M. ebeninus nests could be driven by a need for thermal refuge and access to subterranean granaries. The known ability of isopods to cue into ant pheromones is a plausible mechanism for allowing isopods to follow foraging trails. The benefit of trail following behavior needs further investigation. We hypothesize a benefit to walking on a path clear of obstacles, and efficient detection of patchy desert food sources.

Detecting N-Phenyl-2-Naphthylamine, L-Arabinose, D-Mannose, L-Phenylalanine, L-Methionine, and D-Trehalose via Photocurrent Measurement

The concentration of small molecules reflects the normality of physiological processes in the human body, making the development of simple and efficient detection equipment essential. In this work, inspired by a facile strategy in point-of-care detection, two devices were fabricated to detect small molecules via photocurrent measurement. A linear response of the photocurrent against the concentration of the small molecules was found. In the first device, metal ions were introduced into gel substrates made by xanthan gum to enhance photocurrent response. N-phenyl-2-naphthylamine was measured when iron or manganese ions were used. L-Phenylalanine was measured when the gel was modified by samarium, iron, cerium, or ytterbium ions. L-(+)-Arabinose was detected via the gels modified by iron or holmium ions. D-(+)-Mannose was detected when the gel was modified by ytterbium, manganese, chromium, or sodium ions. L-Methionine was detected in the gels modified by samarium, zinc, or chromium ions. The second device was based on a paper sheet. A sugar-like molecule was first synthesized, which was then used to modify the paper. The detection was possible since the photocurrent showed a linear trend against the concentration of D-Trehalose. A linear fit was conducted to derive the sensitivity, whose value was found to be 5542.4. This work offers a novel, simple, and environmentally sustainable platform that is potentially useful in remote areas lacking medical devices.

Advances in Single Particle Mass Analysis.

Single particle mass analysis methods allow the measurement and characterization of individual nanoparticles, viral particles, as well as biomolecules like protein aggregates and complexes. Several key benefits are associated with the ability to analyze individual particles rather than bulk samples, such as high sensitivity and low detection limits, and virtually unlimited dynamic range, as this figure of merit strictly depends on analysis time. However, data processing and interpretation of single particle data can be complex, often requiring advanced algorithms and machine learning approaches. In addition, particle ionization, transfer, and detection efficiency can be limiting factors for certain types of analytes. Ongoing developments in the field aim to address these challenges and expand the capabilities of single particle mass analysis techniques. Charge detection mass spectrometry is a single particle version of mass spectrometry in which the charge (z) is determine independently from m/z. Nano-electromechanical resonator mass analysis relies on changes in a nanoscale device's resonance frequency upon deposition of a particle to directly derive its inertial mass. Mass photometry uses interferometric video-microscopy to derive particle mass from the intensity of the scattered light. A common feature of these approaches is the acquisition of single particle data, which can be filtered and concatenated in the form of a particle mass distribution. In the present article, dedicated to our honored colleague Richard Cole, we cover the latest technological advances and applications of these single particle mass analysis approaches.

Image processing‐based noise‐resilient insulator defect detection using YOLOv8x

AbstractAccurate and efficient insulator defect detection is critical for power grid reliability, but it can be affected by the presence of noises in captured images and can be difficult to employ for real‐time operation due to the slow processing of the detection scheme. This paper proposes a novel framework based on the YOLOv8x object detection scheme, addressing the challenge of detecting small defects in complex aerial images and providing a noise mitigation scheme. A Gaussian blur and Laplacian sharpening‐based hybrid scheme is proposed to mitigate the impacts of noises in insulator images. Experimental results indicate that the proposed framework can achieve a mean average precision (mAP) of 98.4% on noise‐free images, surpassing benchmark models, such as YOLOv5x and YOLOv7 by 2.1% and 3.9%, respectively. Also, while the performance of a conventional system can decrease to a mAP of 93.3% in the worst case, the implementation of the proposed mitigation scheme ensures a mAP of 96.7% for that case. With an inference speed of 56.9 ms per image, this approach offers a promising solution for real‐time power line inspection, contributing to enhanced power grid maintenance and safety.

Standardization and advancements efforts in breast diffusion-weighted imaging.

Recent advancements in breast magnetic resonance imaging (MRI) have significantly enhanced breast cancer detection and characterization. Breast MRI offers superior sensitivity, particularly valuable for high-risk screening and assessing disease extent. Abbreviated protocols have emerged, providing efficient cancer detection while reducing scan time and cost. Diffusion-weighted imaging (DWI), a non-contrast technique, has shown promise in differentiating malignant from benign lesions. It offers shorter scanning times and eliminates contrast agent risks. Apparent diffusion coefficient (ADC) values provide quantitative measures for lesion characterization, potentially reducing unnecessary biopsies. Studies have revealed some correlations between ADC values and hormone receptor status in breast cancers, although substantial variability exists among studies. However, standardization remains challenging. Initiatives such as European Society of Breast Imaging (EUSOBI), Diffusion-Weighted Imaging Screening Trial (DWIST), Quantitative Imaging Biomarkers Alliance (QIBA) have proposed guidelines to ensure consistency in imaging protocols and equipment specifications, addressing variability in ADC measurements across different sites and vendors. Advanced techniques like Intravoxel incoherent motion (IVIM) and non-Gaussian DWI offer insights into tissue microvasculature and microstructure. Despite ongoing challenges, the integration of these advanced MRI techniques shows great promise for improving breast cancer diagnosis, characterization, and treatment planning. Continued research and standardization efforts are crucial for maximizing the potential of breast DWI in enhancing patient care and outcomes.

An Innovative 3D-IDE Design and Adaptive Signal Extraction Algorithm for Efficient Ovarian Cancer Detection.

This work presents a facile, ultrasensitive, and selective chemiresistive biosensor assisted by an adaptive signal extraction algorithm (ASEA) for detecting vimentin, a potential biomarker for ovarian cancer detection. The low-cost device, fabricated on a PCB substrate through sacrificial copper etching, features a 3D-IDE design with interwoven comb-like structures mimicking the natural symmetry of a droplet. An unequal count of positive and negative concentric circle fingers ensures a uniform, higher electric field over the sensor's surface, as verified by COMSOL Multiphysics 3D simulation. This optimal electric field elegantly reflects changes in the IV characteristics, even with minor variations in surface charge density from probe-target interactions. Graphene oxide, functionalized with a heterobifunctional linker, serves as the sensing nanomaterial. A detailed study examines the device's response with interdigitated gaps from 30 to 150 μm. A wider interdigitated gap introduces greater variability in the response across different voltage levels. To address this, the Python-based ASEA meticulously scans the entire voltage range, isolating the segment of the signal that best balances both the intensity and extension for optimal expression. ASEA boosts the limit of detection (LOD) by five times for sensors with gaps of over 100 μm. The biosensor achieves a minimum LOD of 9.45 fg mL-1 and a highest sensitivity of (ΔR/R) 477 ng mL-1 cm2. The biosensor exhibits excellent selectivity, strong resistance to interfering proteins, and commendable stability, showcasing its potential for on-field and point-of-care testing.

Viral concentration method biases in the detection of viral profiles in wastewater.

Viral detection methodologies used for wastewater-based epidemiology (WBE) studies have a broad range of efficacies. The complex matrix and low viral particle load in wastewater emphasize the importance of the concentration method. This study focused on comparing three commonly used virus concentration methods: polyethylene glycol precipitation (PEG), immuno-magnetic nanoparticles (IMNP), and electronegative membrane filtration (EMF). Influent and effluent wastewater samples were processed by the methods and analyzed by DNA/RNA quantification and sequencing for the detection of human viruses. SARS-COV-2, Astrovirus, and Hepatitis C virus were detected by all the methods in both sample types. PEG precipitation resulted in the detection of 20 types of viruses in influent and 16 types in effluent samples. The corresponding number of virus types detected was 21 and 11 for IMNP, and 16 and 8 for EMF. Certain viruses were unique to only one concentration method. For example, PEG detected three types of viruses in influent and six types in effluent compared to IMNP, which detected seven types in influent and one type in effluent samples. However, the EMF method appeared to be the least effective, detecting three types in influent and none in effluent samples. Rotavirus was detected in influent sample using IMNP method, whereas EMF and PEG methods failed to yield a similar outcome. Consequently, the potential false negative results pose a risk to the credibility of WBE applications. Therefore, implementation of a proper concentration technique is critical to minimize method biases and ensure accurate viral profiling in WBE studies.IMPORTANCEIn recent years, significant research efforts have been focused on the development of viral detection methodology for wastewater-based epidemiology studies, showing a range of variability in detection efficacies. A proper methodology is essential for an appropriate evaluation of disease prevalence and community health in such studies and necessitates designing a concentration method based on the target pathogenic virus. There remains a need for comparative performance evaluations of methods in the context of detection efficiencies. This study highlights the significant impact of sample matrix, viral structure, and nucleic acid composition on the efficacy of viral concentration methods. Assessing WBE techniques to ensure accurate detection and understanding of viral presence within wastewater samples is critical for revealing viral profiles in municipality wastewater samples.

Lignocellulosic carbon sheets-based hybrid electrochemical sensor for ultra-sensitive detection of chloramphenicol.

Efficient detection of chloramphenicol (CAP) in the environment and food products is crucial for addressing global health and environmental safety concerns. This study presents the development of a cost-effective hybrid electrocatalyst comprising lignocellulosic carbon sheets, graphene oxide, and manganese oxide (LCSs/GO@MnO2) for CAP detection using a simple electrochemical sensor fabricated on a glassy carbon electrode (GCE) substrate. The synergistic interaction between LCSs, GO, and MnO2 enhance the electroactive surface area of GCE, facilitating effective dispersion and electrode modification. This composite material significantly improves electrical conductivity and provides numerous electroactive sites for electrochemical CAP detection via voltammetric techniques. The developed sensor demonstrates a rapid electron transfer rate, enhancing electrode sensitivity for CAP detection at a low overpotential (-0.5717 V) and an optimal pH (7.0). The sensor exhibits a wide linear range (0.017-477.247 μM), excellent sensitivity (105.22 μA μM-1 cm-2), and a low limit of detection (1.2 nM) with enhanced charge carrier efficiency. Additionally, the sensor shows good cycle stability, reproducibility, selectivity, and trace-level CAP sensing applicability in food samples at a low cost. These features make the sensor a promising platform for monitoring antibiotics in various applications.

A new method to assess the performance of anti-scatter grids in x-ray projection imaging

Purpose.This work proposes a new method to assess the performance of radiographic anti-scatter grids (ASGs) without the use of a narrow primary beam, which is difficult to achieve.Method.Three general purpose ASGs were evaluated, two marketed ASGs and a low frequency and high ratio prototype ASG with molybdenum lamellae. A range of high scatter x-ray beams were used in a standardized geometry, with energies ranging from 60 kV to 121 kV, for five beam sizes between 10 × 10 and 30 × 30 cm2. The scatter fraction (SF) of each beam was measured in the image plane with and without ASG using the lead beam stop method with an extrapolation function derived from the scatter point spread function (PSF).Results.The primary, scatter and total transmissions of the three ASGs measured for the different x-ray beams allowed the calculation of the grid factor, contrast improvement factor and detective quantum efficiency (DQE) as functions of the input SF. The results obtained for the three ASGs are consistent with those obtained with the standard narrow-beam method and data published in the literature, confirmed the prime importance of the ASG primary transmission and revealed important variations in ASG performance, especially as a function of the input SF and beam size. The break-even input SFs at which the imaging system efficiency was improved by the ASG ranged between 0.18 and 0.52 for the different ASGs and beam characteristics.Significance.The method is proposed as an alternative to current ASG characterization techniques.

Efficient feature envy detection and refactoring based on graph neural network

Efficient feature envy detection and refactoring based on graph neural network

Transforming Breast Cancer Detection: AI as a Catalyst for Enhanced Clinical Outcomes

A revolutionary development in contemporary healthcare, especially in oncology, is the application of artificial intelligence (AI) to breast cancer diagnosis. The use of AI-powered diagnostic tools in breast cancer screening programs is examined in detail in this article, which shows notable gains in workflow efficiency, accuracy, and early detection. Along with quantitative performance indicators and system reliability measures, it examines the technological framework, which includes deep learning architectures and integration workflows. Through a thorough analysis of data processing issues, fixes, and clinical implications, the article shows how AI enhances radiologists' skills without replacing human knowledge. It also discusses future technological advancements and scaling issues, emphasizing the possibility of integrating multi-modal imaging and improving predictive capacities. This approach makes a strong case for wider deployment across healthcare systems and is a blueprint for future AI integration in medical diagnostics.

Illegal ship identification system based on trajectory similarity measurement

The Automatic Identification System (AIS) relies on the identification code (MMSI) of a vessel to identify it and records its trajectory data, which is an important technical means for modern maritime management. However, there are some vessels that violate regulations by opening multiple AIS transponders, resulting in multiple location trajectory records for the same vessel. This behavior is difficult to distinguish through traditional means and has a negative impact on the accuracy and effectiveness of maritime management, thereby threatening the safety of navigation. This system uses big data processing tool Spark to efficiently process and analyze large amounts of AIS data and detects the similarity of vessel trajectories to automatically identify the violating vessels that open multiple AIS transponders. In this way, the system not only improves the detection efficiency of violations, but also provides more reliable evidence for maritime management departments to ensure the safety and order of navigation routes.

Molecular Probe for Specific Recognition of TKX-50: 'Luminescence-ON' Response and its Integration to a Smart Device for Surveillance.

In response to the growing concerns about the unauthorized use of advanced secondary explosives such as TKX-50 against non-combatant targets, there is an urgent need for effective detection methods or techniques to ensure efficient security screening, homeland security, and public safety. Herein, a new polymeric receptor (IV) derived from functionalized tetraphenylethylene moiety (TPE) and 1,3,5-tris(4-aminophenyl)benzene (TAPB) moietiesfor the efficient detection of TKX-50 through a 'switch ON' luminescence response upon specific binding to the explosive, is reported. The observed 'luminescence ON' response is rationalized based on a charge transfer complex formation between TKX-50 and the polymeric receptor IV (Ka = 1.7 × 104 m-1). This is validated by the steady and excited-state luminescence studies, along with detailed computational studies. The authors' presumptions are further validated with adequate control studies using an appropriate monomeric derivative (III) of TPE. Moreover, this 'luminescence ON' response can be integrated into a smart and user-friendly Internet of Things (IoT)-based prototype device. This device can effectively convert optical responses into digital output to develop an optical device for real-time detection of TKX-50 in solution. This lightweight, portable device is ideally suited for remote surveillance and monitoring of TKX-50; such examples are rare in contemporary literature.

A Cu/β-cyclodextrin/reduced graphene oxide nanocomposite for efficient and multi-aflatoxin detection in rice, ginger and bean samples.

Aflatoxins (AFs) are some of the most important mycotoxins or fungal toxins that cause contamination of food products and are considered a threat to human and animal health. An efficient Cu/β-cyclodextrin/reduced graphene oxide nanocomposite (Cu/β-CD/rGO) has been prepared and applied as a new solid-phase extraction adsorbent for the separation and preconcentration of four AFs (B1, B2, G1, and G2) using high-performance liquid chromatography with fluorescence detection (HPLC-FLD). The successful synthesis of the prepared nanocomposite was confirmed using Fourier transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), and transmission electron microscopy (TEM). The impacts of pH, amount of adsorbent, sample volume, desorption solvent volume, and salt concentration on the recovery of AFs were precisely investigated and optimized by central composite design (CCD). Under the optimal conditions, the introduced method demonstrated good linearity in the range of 0.4-5.4, 0.08-1.08, 0.4-5.4, and 0.08-1.08 ng g-1 for AFs B1, B2, G1 and G2, respectively. The limits of detection and quantification for the four AFs were obtained in the range of 0.06-0.53 and 0.20-1.62 ng g-1, respectively. The accuracy of the method was evaluated using recovery measurements in spiked real samples such as rice, bean, and ginger samples, and satisfactory recoveries were obtained in the range of 83.5-109.0% with good precision (RSDs between 2.4 and 8.6%). The results of this research revealed that our developed method is sensitive, highly effective, and convenient to perform for the trace analysis of AFs in different real samples.