Improved Detection System Research Articles

Microfluidics Evolution and Surface Functionalization: A Pathway to Enhanced Heavy Metal Ion Detection

AbstractThis review delves into the significant advancements in microfluidic technology since 2017, highlighting its critical role in shrinking device sizes and integrating advanced surface functionalization techniques. It showcases how microfluidics, an interdisciplinary field, has revolutionized fluid manipulation on a microscale, enabling the creation of cost‐effective, portable devices for on‐the‐spot analyses, like heavy metal ion detection. From its early days rooted in ancient observations to cutting‐edge uses of materials like silicon, glass, polydimethylsiloxane (PDMS), and paper, this review charts microfluidics’ dynamic evolution. It emphasizes the transformative impact of surface functionalization methods, including silanization and plasma treatments, in enhancing device materials' performance. Moreover, this review anticipates the exciting convergence of microfluidics with emerging technologies like droplet microfluidics and three‐dimensional (3D) printing, alongside nanotechnology, forecasting a future of sophisticated analytical tools, point‐of‐care diagnostics, and improved detection systems. It acknowledges the hurdles in scaling production and achieving universal reliability and standardization. This review highlights the transformative impact of microfluidic technology on diagnostics and environmental surveillance, emphasizing its utility in deploying compact sensors for comprehensive and concurrent evaluations of water quality.

Highly Sensitive Measurement of Horseradish Peroxidase Using Surface-Enhanced Raman Scattering of 2,3-Diaminophenazine.

The development of various enzyme-linked immunosorbent assays (ELISAs) coupled with surface-enhanced Raman scattering (SERS) detection is a growing area in analytical chemistry due to their potentially high sensitivity. A SERS-based ELISA with horseradish peroxidase (HRP) as an enzymatic label, an o-phenylenediamine (oPD) substrate, and a 2,3-diaminophenazine (DAP) enzymatic product was one of the first examples of such a system. However, the full capabilities of this long-known approach have yet to be revealed. The current study addresses a previously unrecognized problem of SERS detection stage performance. Using silver nanoparticles and model mixtures of oPD and DAP, the effects of the pH, the concentration of the aggregating agent, and the particle surface chloride stabilizer were extensively evaluated. At the optimal mildly acidic pH of 3, a 0.93 to 1 M citrate buffer, and AgNPs stabilized with 20 mM chloride, a two orders of magnitude advantage in the limits of detection (LODs) for SERS compared to colorimetry was demonstrated for both DAP and HRP. The resulting LOD for HRP of 0.067 pmol/L (1.3 amol per assay) underscores that the developed approach is a highly sensitive technique. We suppose that this improved detection system could become a useful tool for the development of SERS-based ELISA protocols.

Technological Tools and Artificial Intelligence in Estrus Detection of Sows—A Comprehensive Review

In animal farming, timely estrus detection and prediction of the best moment for insemination is crucial. Traditional sow estrus detection depends on the expertise of a farm attendant which can be inconsistent, time-consuming, and labor-intensive. Attempts and trials in developing and implementing technological tools to detect estrus have been explored by researchers. The objective of this review is to assess the automatic methods of estrus recognition in operation for sows and point out their strong and weak points to assist in developing new and improved detection systems. Real-time methods using body and vulvar temperature, posture recognition, and activity measurements show higher precision. Incorporating artificial intelligence with multiple estrus-related parameters is expected to enhance accuracy. Further development of new systems relies mostly upon the improved algorithm and accurate data provided. Future systems should be designed to minimize the misclassification rate, so better detection is achieved.

PhishTransformer: A Novel Approach to Detect Phishing Attacks Using URL Collection and Transformer

Phishing attacks are a major threat to online security, resulting in millions of dollars in losses. These attacks constantly evolve, forcing the cyber security community to improve detection systems. One major problem with current detection systems is that they cannot detect new phishing attacks, such as Browser in the Browser (BiTB) and malvertising attacks. These attacks hide behind legitimate Uniform Resource Locators (URLs) and can evade detection systems that only analyze a web page URL without exploring the page content. To address this problem, we propose PhishTransformer, a deep-learning model that can detect phishing attacks by analyzing URLs and page content. We propose only using URLs embedded within a webpage, such as hyperlinks and JFrames, to train PhishTransformer. This helps reduce the number of features that need to be extracted from the page content, which makes training the model more efficient. PhishTransformer combines convolutional neural networks and transformer encoders to extract features from website URLs and page content. These features are then used to train a classifier that can distinguish between phishing attacks and legitimate websites. We tested PhishTransformer on a dataset of 10,000 URLs. Our results show that PhishTransformer can achieve an F1-score of 99%, precision of 99%, and recall of 99%. This result suggests that PhishTransformer is a promising new approach to phishing detection.

An overview of the detection of bovine respiratory disease complex pathogens using immunohistochemistry: emerging trends and opportunities.

The bovine respiratory disease complex (BRDC) is caused by a variety of pathogens, as well as contributing environmental and host-related risk factors. BRDC is the costliest disease for feedlot cattle globally. Immunohistochemistry (IHC) is a valuable tool for enhancing our understanding of BRDC given its specificity, sensitivity, cost-effectiveness, and capacity to provide information on antigen localization and immune response. Emerging trends in IHC include the use of multiplex IHC for the detection of coinfections, the use of digital imaging and automation, improved detection systems using enhanced fluorescent dyes, and the integration of IHC with spatial transcriptomics. Overall, identifying biomarkers for early detection, utilizing high-throughput IHC for large-scale studies, developing standardized protocols and reagents, and integrating IHC with other technologies are some of the opportunities to enhance the accuracy and applicability of IHC. We summarize here the various techniques and protocols used in IHC and highlight their current and potential role in BRDC research.

Moving Target Detection Using CA, SO and GO-CFAR detectors in Nonhomogeneous Environment

Modernization of radar technology and improved signal processing techniques are necessary to improve detection systems in complex situations. A fundamental problem in radar systems is to automatically detect targets while maintaining a desired constant false alarm probability. This work studies two detection approaches, the first with a fixed threshold and the other with an adaptive one. In the latter, we have learned the three types of detectors CA, SO, and GO-CFAR. This research aims to apply intelligent techniques to improve detection performance in a nonhomogeneous environment using standard CFAR detectors. The objective is to maintain the false alarm probability and enhance target detection by combining intelligent techniques. With these objectives in mind, implementing standard CFAR detectors is applied to nonhomogeneous environment data. The primary focus is understanding the reason for the false detection when applying standard CFAR detectors in a nonhomogeneous environment and how to avoid it using intelligent approaches

Dual-Wavelength Smoke Detector Measuring Both Light Scattering and Extinction to Reduce False Alarms

Existing fire smoke detectors use the intensity of scattering light or the light extinction coefficient as the indicator of fire smoke to trigger fire alarms. However, false fire alarms could be triggered by dust and water fog. Achieving reliable early fire detection with minimal false alarms is a challenge. Based on the Mie scattering theory of spherical particles, it is derived that the ratio of scattering intensity and the ratio of optical extinction of two incident lights with different wavelengths only depends on the intrinsic properties of the aerosol (the average particle size and refractive index). This paper then presents an improved dual-wavelength smoke detection by measuring scattering light and extinction simultaneously to reduce false alarms. Simulations and verification with test fires of European Standard EN 54 were performed, demonstrating that fires can be distinguished from nuisance sources without complicated calculations. These results indicate that the improved detection system can be applied for smoke monitoring and fire protection.

A new collimated multichannel modular detection system based on Silicon Drift Detectors

After the manufacture and delivery of a state-of-the-art detection system for the XRF-XAFS beamline of the synchrotron light source SESAME, a new and improved detection system was realized. This new multichannel modular detection system based on Silicon Drift Detectors consists of 8 monolithic multipixel arrays, each comprising 8 SDD cells with a total area of 570 mm2. As the previous one, this 64 channels integrated detection system includes ultra-low-noise front-end electronics, dedicated acquisition system, digital filtering, temperature control and stabilization. With respect to the SESAME version, the new instrument implements a tungsten collimation system yielding a total collimated sensitive area of 500 mm2. Optimized to work in an energy range of 3–30 keV, the system shows an overall energy resolution (sum of its 64 cells) below 180 eV FWHM at the 5.9 Mn Kα line at room temperature. We highlight the system performance and in particular the peak to background ratio, before and after the collimation of the sensors.

1S–3S cw spectroscopy of hydrogen/deuterium atom

We study the 1S-3S two-photon transition of hydrogen in a thermal atomic beam, using a homemade cw laser source at 205 nm. The experimental method is described, leading in 2017 to the measurement of the 1S-3S transition frequency in hydrogen atom with a relative uncertainty of $9 \times 10^{-13}$. This result contributes to the "proton puzzle" resolution but is in disagreement with the ones of some others experiments. We have recently improved our setup with the aim of carrying out the same measurement in deuterium. With the improved detection system, we have observed a broadened fluorescence signal, superimposed on the narrow signal studied so far, and due to the stray accumulation of atoms in the vacuum chamber. The possible resulting systematic effect is discussed.

Brain Tumor Diagnosis using Machine Learning: A Review

Recently, early brain tumor diagnosis has grown in importance as a study area recently. The patient's rateof survival rises with early tumor detection for primarytreatment. Because ofthe high processing overhead caused by the enormous volume regardingimage input to processing system, processing magnetic resonance image (MRI) for the early detection of tumors presents a problem. This led to a significant delay and a decline in system effectiveness. As a result, recently, there has been an increased requirement for an improved detection system for precise representation and segmentationfor accurate and fasterprocessing. Latestliterature has suggested the creation of novel methods depending onenhanced processing and learningfor the detection of brain tumors. This essay provides a succinct overview of the MRI-related advancements. The machine learning (ML)algorithms' capacity for fine processing and learninghas shown an enhancementin the efficiency and accuracy of processing for the detection of the brain tumor in existing automation systems. Restrictions, advantages,and outlook for future regardingthe present approaches for computer-aided diagnostics (CAD)in the detection of the brain tumor are discussed, along with current advances in automation related tobrain tumor detections. In the presented study, researcherexplore the history of numerous methods that have been put forth to image brain tumors across a variety of domains.

Possible Implementation of Ex-Core Measurement in TEPLATOR Graphite Reflector

Abstract An ex-core neutron flux measurement is a crucial system for all common power reactors. It is necessary to monitor the neutron flux and control the chain reaction, therefore, the ex-core neutron flux measurement is one of the main safety and control systems. The main advantage of this arrangement of detectors is a fast response to neutron flux change, which determines the reactor power change. Regarding to the new reactor concepts, it is important to deal with improved detection systems suitable for these reactors. Many of the modern reactor concepts are based on a graphite moderator or reflector, which is also the case of the TEPLATOR. The TEPLATOR is a solution of a district heating system based on heavy water as a moderator and graphite as a reflector. The TEPLATOR is designed to use irradiated fuel from the commercial pressurized water reactor or boiling water reactor, which has low to intermediate burnup. This work is focused on the verification of the possible use of the special neutron measuring system placed in the graphite reflector. The Monte Carlo code serpent was used for the calculations performed in this work.

N_TOF: Measurements of Key Reactions of Interest to AGB Stars

In the last 20 years, the neutron time-of-flight facility n_TOF at CERN has been providing relevant data for the astrophysical slow neutron capture process (s process). At n_TOF, neutron-induced radiative capture (n,γ) as well as (n,p) and (n,α) reaction cross sections are measured as a function of energy, using the time-of-flight method. Improved detection systems, innovative ideas and collaborations with other neutron facilities have lead to a considerable contribution of the n_TOF collaboration to studying the s process in asymptotic giant branch stars. Results have been reported for stable and radioactive samples, i.e., 24,25,26Mg, 26Al, 33S, 54,57Fe, 58,59,62,63Ni, 70,72,73Ge, 90,91,92,93,94,96Zr, 139La, 140Ce, 147Pm, 151Sm, 154,155,157Gd, 171Tm, 186,187,188Os, 197Au, 203,204Tl, 204,206,207Pb and 209Bi isotopes, while others are being studied or planned to be studied in the near future. In this contribution, we present an overview of the most successful achievements, and an outlook of future challenging measurements, including ongoing detection system developments.

Reinforcement learning based data fusion method for multi-sensors

In order to improve detection system robustness and reliability, multi-sensors fusion is used in modern air combat. In this paper, a data fusion method based on reinforcement learning is developed for multi-sensors. Initially, the cubic B-spline interpolation is used to solve time alignment problems of multi-source data. Then, the reinforcement learning based data fusion &#x0028 RLBDF &#x0029 method is proposed to obtain the fusion results. With the case that the priori knowledge of target is obtained, the fusion accuracy reinforcement is realized by the error between fused value and actual value. Furthermore, the Fisher information is instead used as the reward if the priori knowledge is unable to be obtained. Simulations results verify that the developed method is feasible and effective for the multi-sensors data fusion in air combat.

60 GHz Low-Noise Amplifier for Detection Systems

This paper presents a 60 GHz low-noise amplifier (LNA) to improve detection systems performance. The considered system is based on impulse radar technology. The LNA is designed in GaAs metamorphic High Electron Mobility Transistor (m-HEMT) technology. Three stages common source transistors are used with inductive degeneration for a good tradeoff between gain and noise. The post-layout simulation results show a noise factor of 2.06 dB and a gain of 14.5 dB at 60.2 GHz, for a power consumption of 13.5 mW. The simulated nonlinear characteristics show an IP 1dB (Input 1 dB compression Point) of -10 dBm and an IIP3 (Input third-order Intercept Point) of -4.4 dBm. The LNA occupies an area of 1.56 × 1.29 mm2.

Establishment of an electronic integrated disease surveillance and response system in Sierra Leone

Purpose: The 2014–16 Ebola epidemic exposed weaknesses within Sierra Leone's disease surveillance system and served as a catalyst to improve detection systems. In 2015, the national Integrated Disease Surveillance and Response (IDSR) system consisted of health facilities (HF) calling/texting or hand-delivering reports to their District Health Management Team (DHMT), DHMT staff entering IDSR data from all HF into a spreadsheet, and each DHMT emailing the file weekly to the national level. When districts lacked adequate bandwidth to upload attachments, DHMT staff emailed a curtailed IDSR data summary to the national level, which jeopardized data timeliness and accuracy. In 2016, Sierra Leone introduced an electronic IDSR (eIDSR) system to gauge feasibility and efficacy in a resource-constrained setting. Methods & Materials: The U.S. Centers for Disease Control and Prevention and eHealth Africa supported the Ministry of Health and Sanitation to establish eIDSR, which transmits data into the web-based District Health Information System (DHIS2). In 2016, district-level eIDSR was piloted then rolled out to all fourteen districts. District-level eIDSR entails HF staff calling the DHMT, then DHMT staff entering data directly into the national DHIS2 database. From 2016–17, facility-level eIDSR was piloted in one district to enable data entry using tablets at all 112 HF in the district, and in 2018, a national scale-up of facility-level eIDSR is underway to reach all 1285 HF in Sierra Leone. Results: After implementing district-level eIDSR, IDSR reporting increased from <40% of HF in 2016 to >97% in 2017, an increase maintained in 2018. The district-level pilot revealed that data-entry time decreased by 63% and errors decreased by 45%. Analyses from the facility-level eIDSR pilot found that data accuracy was 12% higher than data reported through district-level eIDSR. The Joint External Evaluation (JEE) score for real-time surveillance went from “limited” to “developed” capacity between 2016 and 2017. Conclusion: District-level eIDSR improved the timeliness and accuracy of surveillance data. Additional benefits are anticipated with completion of the national scale-up of facility-level eIDSR, and JEE scores are projected to reach the next level of “demonstrated” capacity. Lessons learned in Sierra Leone can inform other low-resource settings considering implementation of electronic disease surveillance systems.

Estudio de los factores determinantes para el desarrollo de sensores SERS efectivos y económicos

La nanotecnología ha aportado a la ciencia un sinfín de herramientas para la mejora de los sistemas de detección que pueden aplicarse en los más diversos campos. Una de esas herramientas es la aparición de la espectroscopía Raman amplificada en superficie (SERS por sus siglas en inglés), que ha permitido aumentar en 8-10 órdenes de magnitud las señales obtenidas mediante la técnica tradicional. Sin embargo, el éxito de esta técnica analítica depende de muchos factores como el material del que están formadas las nanopartículas, de su morfología e incluso de la adsorción de los analitos sobre las mismas, aspectos que no siempre se tienen en cuenta a la hora de diseñar este tipo de sensores. En este trabajo se ha explorado la influencia de estos factores mediante el uso de una molécula modelo como el azul de metileno (MB) y dos metabolitos del café, trigonelina y ácidos clorogénicos (CGAs), conocidos por ser potenciales marcadores de calidad de este producto agropecuario tan importante en el Perú. Así, mediante los experimentos que aquí se presentan, se ha podido determinar la mejor señal obtenida al comparar nanoestructuras de plata y oro, así como para las señales obtenidas con triángulos versus esferas del mismo material, debido a la distribución no homogénea del campo electromagnético descrito para nanoestructuras anisotrópicas. Por último, al analizar los dos metabolitos seleccionados, se pudo observar como sólo en uno de los dos casos, se produce un aumento de las señales respectivas, lo cual demuestra la gran influencia de la interacción entre la molécula y la superficie del metal, ya que esta adsorción puede limitar alguno de los modos vibracionales de la molécula en cuestión, disminuyendo su aplicabilidad.

A novel Doppler Effect reduction method for wayside acoustic train bearing fault detection systems

Wayside acoustic detection of train bearing faults plays a significant role in maintaining safety in the railway transport system. Due to the relative movement between the train and the detection system, the collected acoustic signals are distorted by the Doppler Effect which results in frequency-domain distortion. Combining the multi-scale chirplet path pursuit (MSCPP) method, a variable digital filter (VDF), and a new motion parameter estimation method, a novel Doppler Effect reduction method is proposed. This can be used by wayside acoustic monitoring systems to improve detection system for train bearing faults, as illustrated in this paper. The MSCPP method with the build-in criterions is firstly used to estimate the instantaneous frequencies (IFs) of harmonic components in the wayside acoustic signals. Next, VDFs whose centre frequencies are the fitted IFs are constructed to exclude harmonic components. Using these, residual signals, free of strong harmonic interferences, can be obtained. At the same time, the motion parameters can be obtained by using a recently developed estimation method based on fitted IFs. The residual signal is then resampled to reduce the Doppler Effect by using the resampling time vector constructed using those estimated motion parameters. Finally, any bearing fault features can be extracted using the spectral kurtosis (SK) method. The effectiveness of the proposed signal processing method is verified by simulation and field-based experiments, as demonstrated in this paper.

Mycoplasma pneumoniae in the Immunocompromised Host

Abstract Pneumonia due to bacteria that do not grow well on routine culture media have been difficult to diagnose. Mycoplasma pneumoniae is an infrequent cause of pneumonia in the adult population. As a cause of atypical pneumonia, along with Chlamydia pneumoniae and Legionella pneumophila, it still makes up about a third of the cases of community acquired pneumonia (CAP). Mycoplasma pneumoniae can cause mild atypical respiratory illness to severe disease requiring hospitalization. It has largely been seen as a disease of children and young adults younger than 30 years. With improved detection systems, more cases in older age groups are expected. This is especially true for immunosuppressed cancer patients who undergo polymerase chain reaction testing of the nasopharynx for respiratory infections. Although detection of Mycoplasma by polymerase chain reaction does not always indicate active infection, immunosuppressed patients who are at higher risk for severe disease and coinfection are more likely to require prompt therapy.

Effect of PpIX photoproducts formation on pO2 measurement by time-resolved delayed fluorescence spectroscopy of PpIX in solution and in vivo

The measurement of Protoporphyrin IX delayed fluorescence lifetime is a minimally invasive method for monitoring the levels of oxygen in cells and tissues. The excitation of Protoporphyrin IX during this measurement can lead to the formation of photoproducts in vitro and in vivo. The influence of their luminescence on the measured Protoporphyrin IX delayed fluorescence lifetimes was studied in solution and in vivo on the Chick's chorioallantoic membrane (CAM) model under various oxygen enriched air conditions (0mmHg, 37mmHg and 155mmHg). The presence of photoproducts disturbs such measurements since the delayed fluorescence emission of some of them spectrally overlaps with that of Protoporphyrin IX. One possible way to avoid this obstacle is to detect Protoporphyrin IX's delayed fluorescence lifetime in a very specific spectral range (620–640nm). Another possibility is to excite Protoporphyrin IX with light doses much lower than 10J/cm2, quite possibly as low as a fraction 1J/cm2 at 405nm. This leads to an increased accuracy of pO2 detection. Furthermore, this method allows combination of diagnosis and therapy in one step. This helps to improve detection systems and real-time identification of tissue respiration, which is tuned for the detection of PpIX luminescence and not its photoproducts.

Time-resolved fast-neutron radiography of air-water two-phase flows in a rectangular channel by an improved detection system.

In a previous work, we have demonstrated the feasibility of high-frame-rate, fast-neutron radiography of generic air-water two-phase flows in a 1.5 cm thick, rectangular flow channel. The experiments have been carried out at the high-intensity, white-beam facility of the Physikalisch-Technische Bundesanstalt, Germany, using an multi-frame, time-resolved detector developed for fast neutron resonance radiography. The results were however not fully optimal and therefore we have decided to modify the detector and optimize it for the given application, which is described in the present work. Furthermore, we managed to improve the image post-processing methodology and the noise suppression. Using the tailored detector and the improved post-processing, significant increase in the image quality and an order of magnitude lower exposure times, down to 3.33 ms, have been achieved with minimized motion artifacts. Similar to the previous study, different two-phase flow regimes such as bubbly slug and churn flows have been examined. The enhanced imaging quality enables an improved prediction of two-phase flow parameters like the instantaneous volumetric gas fraction, bubble size, and bubble velocities. Instantaneous velocity fields around the gas enclosures can also be more robustly predicted using optical flow methods as previously.