Cities exposed their vulnerabilities during COVID-19 pandemic. Unprecedented policies restricted human activities but left an unique opportunity to quantify anthropogenic effects on urban air pollution. This study aimed to address the key questions of urban development behind the restrictions with the goal of supporting sustainable transition. Data from ground stations and Sentinel-5P satellite were used to assess the temporal and spatial anomalies of NO2. Beijing China was selected for a case study because this mega city maintained a “dynamic zero-COVID” policy with adjusted restrictions, allowing us better to track the effects. The time-series decomposition and prediction regression model were employed to estimate the normal NO2 levels in 2020. The anomalies between the observations and predictions as the deviation were identified due to the policy interventions and quantified different effects using spatial stratified heterogeneity statistics. The top three restrictions showing dominant effects were workplace closures, restricted public transport usage, and school closures, accounting for 54.8%, 52.3%, and 46.4% of NO2 anomalies, respectively; and they are directly linked to the mismatch of employment and housing (deter-mining the commuting patterns), educational inequality and the long-term unsolved road con-gestion. Promoting the transformation of urban spatial structure will effectively alleviate air pollution.

This article will briefly review the progress of h-BN based solid-state metal semiconductor metal (MSM) neutron detectors. In the last decade, several groups have been working on hexagonal boron nitride (h-BN) based solid-state neutron detectors. Recently, the detection efficiency of 59% has been reported. Efficient, low-cost neutron detectors made from readily available materials are essential for various applications. Neutron detectors are widely used to detect fissile materials and nuclear power plants for security applications. The most common and widely used neutron detectors are 3He based, which are sometimes bulky, difficult to transport, have high absorption length, need relatively high bias voltage (>1000 V), and have low Q-value (0.764 MeV). Also, 3He is not readily available material. Thus, there is a strong need to find an alternative detection material. 10B isotope has a high neutron absorption cross-section, and it has been tested as a coating on the semiconducting materials. Due to the two-step process, neutron capture through 10B, and then electron-hole pair generation in a typical semiconducting material, the efficiency of these devices is not up to the mark. The progress in h-BN based detectors requires a review to envision the further improvement in this technology.

The paper proposed a modification of the GeoSER(D) model previously developed by us by detailing the age structure of the population, personal schedule on weekdays and working days, and individual health characteristics of the agents, this made it possible to build a more realistic model of the functioning of the city and its residents. The developed model made it possible to simulate the spread of 3 types of the strain of the COVID-19 virus, and to analyze the adequacy of this model in the case of unhindered spread of the virus among city residents. The paper showed that SARS COV 2 spreads mainly from contacts in workplaces and transport, and schoolchildren and preschool children are the consequence, not the initiator of the epidemic. Fluctuations in the dynamics of various indicators of the spread of SARS COV 2 associated with the difference in the daily schedule on weekdays and weekends. It has been shown that people's daily schedules strongly influence the spread of SARS COV 2. For the more contagious "rapid" strains of SARS COV 2 (omicron), immunocompetent people become a significant source of infection. For the less contagious "slow strains" (alpha) of SARS COV 2, the most active source of infection is immunocompromised individuals (pregnant women). The more contagious – "fast" strain of the SARS COV 2 virus (omicron) spreads faster in public transport. For less contagious – "slow" strains of the virus (alpha), the greatest infection occurs due to work and educational contacts.

Background: The advances of genomics have greatly improved the survival rate cancer patients. However, due to genetic heterogeneity, pancreatic ductal adenocarcinoma (PDAC) is still difficult to diagnose early and the survival rate is extremely low. Therefore, we identified biomarkers that predict the prognosis of PDAC patients by using independent cohort data. Methods: To develop a novel prognostic biomarker, we used gene expression and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). In Kaplan-Meier survival curve using median values of genes as cut off, the only statistically significant gene in the three cohorts was EIF4G1. We analyzed prognostic significance of EIF4G1 using the time-dependent area under the curve (AUC) of the Uno's C-index, the AUC value of the receiver operating characteristics (ROC) at 3 years, and multivariate cox analysis. Also, we compare EIF4G1 levels between tumor and matched non-tumor. Results: EIF4G1 is the only prognostic gene patients with PDAC which was selected by Kaplan-Meier survival analysis. Kaplan-Meier survival analysis showed that high expression of EIF4G1 was associated with poor prognosis of PDAC with good discriminative ability in 3 independent cohorts. Risk stratifying ability of EIF4G1 was demonstrated by analyzing C-indices and AUC values. Multivariate cox regression analysis confirmed its prognostic significance. EIF4G1 expression was significantly higher in the PDAC tissues than in the matched normal tissues. Conclusions: Herein, the novel prognostic biomarker EIF4G1 could be used as prognostic maker for PDAC and determining suitable treatment options.

1) Background: We analyzed the corpus of three geoscientific journals to investigate if there are enough locational references in research articles to apply a geographical search method, on the example of New Zealand. 2) Methods: Based on all available abstracts and all freely available papers of the New Zealand Journal of Geology and Geophysics, the New Zealand Journal of Marine and Freshwater Research, and the Journal of Hydrology, New Zealand, we searched title, abstracts and full texts for place name occurrences that match records from the official Land Information New Zealand (LINZ) gazetteer. We generated ISO standard compliant metadata records for each article including the spatial references and make them available in a public catalogue service. This catalogue can be queried for articles based on authors, titles, keywords, topics as well as by spatial reference. We visualize the results in a map to show which area the research articles are about. 3) Results: We outline the methodology and technical framework for the geo-referencing of the journal articles and the platform design for this knowledge inventory. The results indicate that the use of well-crafted abstracts for journal articles with carefully chosen place names of relevance for the article provides a guideline for geographically referencing unstructured information like journal articles and reports in order to make such resources discoverable through geographical queries. 4) Conclusion: This approach can actively support integrated holistic assessment of water resources and support decision making.

Hydrogenated amorphous silicon (a-Si:H) can be produced by plasma-enhanced chemical vapour deposition (PECVD) of SiH4 (Silane) mixed with Hydrogen. The resulting material shows outstanding radiation resistance properties and can be deposited on a wide variety of different substrates. These devices have been used to detect many different kinds of radiation namely: MIPs, x-rays, neutrons and ions as well as low energy protons and alphas. However, MIP detection using planar diodes has always been difficult due to the unsatisfactory S/N ratio arising from a combination of high leakage current, high capacitance and a limited charge collection efficiency (50% at best for a 30 µm planar diode). To overcome these limitations the 3D-SiAm collaboration proposes to use a 3D detector geometry. The use of vertical electrodes allows for a small collection distance to be maintained while conserving a large detector thickness for charge generation. The depletion voltage in this configuration can be kept below 400 V with consequent reduction in the leakage current. In this paper, following a detailed description of the fabrication process, the results of the tests performed on the planar p-i-n structures made with ion implantation of the dopants and with carrier selective contacts will be illustrated.

Climate change is increasing the occurrence of disastrous events in the world, but several disparities in population vulnerability are being registered. One of the causes of these variances is different public risk perception also due to the degree of education and knowledge of the population. In this study, some of the results obtained in a risk perception survey are presented. The survey was carried out in an area of Calabria (Southern Italy) hit by geo-hydrological events that have occurred in recent years with damage to roads, tourism facilities and private houses. A statistical interpretation of the results highlights the importance of education and knowledge to risk perception on the part of the population investigated.

Visualizing geo-spatial data embedded into a three-dimensional terrain is challenging. The problem becomes even more complex when uncertainty information needs to be presented as well. This paper addresses the question of how to visually communicate all three aspects: the 3D terrain, the geo-spatial data, and the data-associated uncertainty. We argue that visualizing all aspects with a high degree of detail will likely exceed the visual budget. Therefore, we propose a visualization strategy based on prioritizing a selected aspect and presenting the remaining two with less detail. We discuss various design options that allow us to obtain differently prioritized visual representations. Our approach has been implemented as a tool for rapid visualization prototyping in the context of avionics applications. Practical solutions are described for a use case related to the visualization of 3D terrain and uncertain weather data.

Solid state sensors having timing capabilities are becoming an absolute need in particle tracking techniques of future experiments at colliders. In this sense, silicon sensors having 3D structure are becoming an interesting solution, due to their intrinsic speed and radiation resistance. A characteristics of such devices is the strict dependence of their performance on their geometric structure, which can be widely optimised by design, thus requiring suitable tools for an accurate modeling of their behaviour. This paper illustrates the development, performance and use of the TCoDe simulator, specifically dedicated to the fast simulation of carrier transportation phenomena in solid state sensors. Some examples of its effectiveness in the design and analysis of 3D sensors is also given.

A lead zirconate titanate [PZT;Pb(Zr_0.52Ti_0.48)O₃] layer embedded infrared (IR) detector decorated with wavelength-selective plasmonic crystals has been investigated for high-performance non-dispersive infrared (NDIR) spectroscopy. A plasmonic IR detector with an enhanced IR absorption band has been designed based on numerical simulations, fabricated by conventional microfabrication techniques, and characterized with a broadly tunable quantum cascade laser. The enhanced responsivity of the plasmonic IR detector at specific wavelength band has improved the performance of NDIR spectroscopy and pushed the limit of detection (LOD) by an order of magnitude. In this paper, a 13 fold enhancement in the LOD of a methane gas sensing using NDIR spectroscopy is demonstrated with the plasmonic IR detector.

One of the unfortunate findings from the ongoing COVID-19 crisis is the disproportionate impact the crisis has had on people and communities who were already socioeconomically disadvantaged. It has, however, been difficult to study this issue at scale and in greater detail using social media platforms like Twitter. Several COVID-19 Twitter datasets have been released, but they have very broad scope, both topically and geographically. In this paper, we present a more controlled and compact dataset that can be used to answer a range of potential research questions (especially pertaining to computational social science) without requiring extensive preprocessing or tweet-hydration from the earlier datasets. The proposed dataset comprises tens of thousands of geotagged (and in many cases, reverse-geocoded) tweets originally collected over a 255-day period in 2020 over 10 metropolitan areas in North America. Since there are socioeconomic disparities within these cities (sometimes to an extreme extent, as witnessed in `inner city neighborhoods’ in some of these cities), the dataset can be used to assess such socioeconomic disparities from a social media lens, in addition to comparing and contrasting behavior across cities.

Urban cycling is a sustainable transport mode that cities currently try to expand. However, cities still do not take advantage of geospatial technologies to understand cycling mobility based on the behavioural patterns and difficulties faced by cyclists. This study analyses a geospatial dataset crowdsourced by urban cyclists using an experimental geo-game. About 20 participants per city recorded bicycle trips during one week and, by aggregating them, we found not only the cyclists’ preferred streets but also the frictions faced during cycling. We successfully identified 284 places potentially having frictions: 71 in Münster, Germany; 70 in Castelló, Spain; and 143 in Valletta, Malta. At such places, Participants not only recorded bicycle trips but also segments at lower speeds indicating a deviation from an ideal cycling scenario. We described the potential frictions preventing from bicycle commuting based on the distance to bicycle paths, surrounding infrastructure, and location in the urban area.

CdZnTe (CZT) is a new type of compound semiconductor that has emerged in recent years. Compared to other semiconductor materials, it possesses an ideal bandgap, high density, and high electron mobility, rendering it an excellent room-temperature composite semiconductor material for X-ray and γ-ray detectors. Due to the exceptional performance of CZT material, detectors manufactured using it exhibit high energy resolution, spatial resolution, and detection efficiency. They also have the advantage of operating at room temperature. CZT array detectors, furthermore, demonstrate outstanding spatial detection and three-dimensional imaging capabilities. Researchers worldwide have conducted extensive studies on this subject. This paper, building upon this foundation, provides a comprehensive analysis of CZT crystals and CZT array detectors, summarizes existing research to offer valuable insights for envisioning new detector methodologies.

This study seeks to measure the degree of agglomeration of educational quality in Colombia, based on the non-socialization of the population that exhibits low educational quality, with the population that exhibits high educational quality, and thus determine how such agglomeration affects the phenomenon of academic segregation. To this end, we perform a spatial analysis of the educational quality in Colombia and of variables that may influence to the phenomenon of educational agglomeration. The level of agglomeration in educational quality in Colombia is demonstrated by the calculation of the Moran’s Index, in which a result of 0.62 was obtained. High educational quality is concentrated in the Andean region, while low educational quality is agglomerated in the periphery of the country, in areas such as the Pacific region. A spatial regression model was carried out to measure the dependence of municipalities on their neighbors, and to determine the main socio-economic factors affecting the phenomenon of educational agglomeration in Colombia, finding that living conditions, unsatisfied basic needs and fiscal transparency have all an impact on the educational quality of the municipalities. It is also found that the number of homicides in the municipalities does not seem to have a significant relationship with education.

Monitoring of the surface operations using movement and surveying radar (MSR) can prevent loss of life, equipment, production and loss of the mine. Slope monitoring using MSR is an important aspect of open-pit mining as it provides real-time movement of deformation data for the slope. It is therefore important that the radar is accurately geo-referenced in order to provide accurate real-time movement data. Geo-referencing is defined as the process of determining an instrument’s position (in the form of Easting, Northing, Height) as well as the orientation with respect to the mine’s local coordinate system. This helps in getting geo-referenced data points from the radar that are identified by a unique set of coordinates in relation to the mine’s coordinate system which allows the radar to track movement for a specific set of coordinates. In this research, we assess the performance of geo-referencing a radar using the total station method and compare it with the integration of Advance Navigation – Spatial Dual GPS system connected via RS422 on the MSR. This includes usage of the Spatial Dual navigation coordinates output to calculate the radar’s position relative to the mine local coordinates and mapping the radar’s azimuth, elevation and Range (Az, El and Rl) values to the measured pit-slope data points. Furthermore, a comparison of key attributes of both methods of geo-referencing is performed using a matrix system and giving an overall performance appraisal of both systems. Integrating a navigation system allows the radar to have an auto geo-referencing functionality that will reduce the time spent in completing this process. The findings reveal that the GPS obtained a higher score than the total station with prism method on the weighted matrix system. The total station was found to be more accurate than the GPS however, the deployment time for the GPS is quicker than that of the total station. This is important for different operation such as strip and open-pit mining to choose the preferred method of geo-referencing depending on the level of accuracy required.

As the amount of sensor data made available online increases, it becomes more difficult for users to identify useful datasets. Semantic web technologies improve discovery with meaningful ontologies, but the decision of suitability remains with the users. The GEO label provides a visual summary of the standardised metadata to aid users in this process. This work presents novel rules for deriving the information for the GEO label's multiple facets, such as user feedback or quality information, based on the Semantic Sensor Network Ontology and related ontologies. It enhances an existing implementation of the GEO label API to generate labels for resources of the Semantic Sensor Web. The prototype is deployed to serverless cloud infrastructures. We find that serverless GEO label generation is capable of handling two evaluation scenarios for concurrent users and burst generation. More real-world semantic sensor descriptions and an integration into large scale discovery platforms are needed to develop the presented solutions further.

In the moist environment of soil-water-air, there is a problem of low accuracy in monitoring Volatile Organic Compounds (VOCs) using a Photoionization Detector (PID). This paper analyzes the reasons for the low accuracy of the traditional Support Vector Machine (SVM) regression method. To address the issue, the PID signal is subjected to feature extraction and Principal Component Analysis (PCA) to reduce the data dimensionality. Moreover, the optimal SVM parameters are selected using a Genetic Algorithm (GA), and a combined approach of SVM regression with PCA and GA is utilized for PID signal regression analysis. And the effectiveness of the method is validated through extensive experiments and simulations. Furthermore, the influence of the sample quantity on the regression accuracy is analyzed, enabling accurate monitoring of VOCs concentration in a moist environment.

Loop detectors are probably the widest-used technology for traffic state estimation. Previous research has shown that loop detector positions within the link significantly affect the estimation of the macroscopic fundamental diagram (MFD) of a given network. This paper examines the biases produced by the positioning of loop detectors on the MFD, using both analytical and simulation methods, as well as empirical data from UTD-19. We confirm earlier results that a uniform distribution of loop detector positions reduces the bias. For non-uniform distribution, we found that: (i) the subsets of the MFD by the loop detector position help estimate whether the loop detector MFD will have a bias; (ii) if the detectors in the network are positioned more downstream with a larger variation, the loop detector MFD is more likely to have a discrepancy in position subsets of the MFD; (iii) a lower ratio of link length to green signal time elevates the MFD bias as well. This research opens the possibility for the bias of MFD induced by the loop detector data to be corrected by only using itself.

Neutron computed tomography (nCT) has been established at many major neutron sources worldwide, using high-end equipment requiring major investment and development. Many older and smaller reactors would be capable of doing nCT as well, but cannot afford the investment before feasibility is proven. We have developed a compact low-cost but high-quality detection system using a new cooled CMOS camera that can either be fully integrated into a sophisticated setup, or used with a rudimentary CT control and motion system to quickly evaluate feasibility of neutron CT at a given beam line facility. Exchanging the scintillation screen makes it feasible for X-rays as well, even for visible light (and transparent samples) using a matte screen. The control system uses a hack to combine motion control with existing imaging software so it can be used to test several dozen different cameras without writing specific drivers. Freeware software can do reconstruction and 3D imaging.

The refractive index of seawater is one of the essential parameters in ocean observation, so it is necessary to achieve high precision seawater refractive index measurement. In this paper, we propose a method for measuring the refractive index of seawater based on a Position Sensitive Detector (PSD). Theoretical model is established to depict the correlation between laser spot displacement and refractive index change, utilizing a combination of position sensitive detector and laser beam deflection principle. Based on this optical measurement method, a seawater refractive index measurement system was established. To effectively enhance the sensitivity of refractive index detection, a focusing lens was incorporated into the optical path of the measuring system, and simulations were conducted to investigate the impact of focal length on refractive index sensitivity. The calibration experiment of the measuring system was performed based on the relationship between refractive index of seawater and underwater pressure (depth). By measuring laser spot displacement at different depths, changes in displacement with respect to both refractive index and depth were determined. Experimental results demonstrate that the system exhibits a sensitivity of 9.9277×10−9RIU (refractive index unit), the refractive index deviation due to stability is calculated as ±7.545×10−9RIU. Thereby the feasibility of highly sensitive measurement of seawater refractive index is verified.

We demonstrate the use of a nanotechnology-based approach for the investigation of enzymatic activity of a single molecule of horseradish peroxidase with a solid-state nanopore. The artificial 5 nm solid-state nanopore has been formed in a 40-nm-thick silicon nitride structure. Single molecule of HRP has been entrapped into the nanopore. The activity of horseradish peroxidase (HRP) enzyme molecule inserted in the nanopore has been monitored by recording the time dependence of the ion current through the nanopore in the course of the reaction of 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonate) (ABTS) oxidation reaction. We have found that in the process of ABTS oxidation in the presence of 2.5 mM hydrogen peroxide, individual HRP enzyme molecules are able to retain activity for approximately 700 seconds before a decrease in the ion current through the nanopore, which can be explained by structural changes of the enzyme.

Object detection is a popular image processing technique, widely used in numerous applications for detecting and locating objects in images or videos. While being one of the fastest algorithms for object detection, Single-Shot Multibox Detection (SSD) networks are also computationally very demanding, which limits their usage in real-time edge applications. Even though the SSD post-processing algorithm is not the most complex segment of the overall SSD object detection network, it is still computationally demanding and can become a bottleneck with respect to processing latency and power consumption, especially in edge applications with limited resources. When using hardware accelerators to accelerate backbone CNN processing, the SSD post-processing step implemented in software can become critical for high-end applications where high frame rates are required, as this paper shows. To overcome this problem, we propose Puppis, an architecture for hardware acceleration of SSD post-processing algorithm. As experiments will show, our solution will lead to an average SSD post-processing speedup of 34.42 when compared with a software implementation. Furthermore, execution of a complete SSD network will be on average 45.39 times faster than software implementation when the proposed Puppis SSD hardware accelerator is used together with some existing CNN accelerators.

To overcome the temperature effect of NaI(Tl) detectors for energy spectrometry without additional hardware, a new correction method was put forward based on pulse deconvolution, trapezoidal shaping and amplitude correction, named DTSAC. To verify this method, actual pulses from a NaI(Tl)-PMT detector were measured at different temperatures from -20℃ to 50℃. Pulse processing and spectrum synthesis showed that the position drift of the 662 keV peak was less than 3 keV, and the corresponding resolution at 662 keV of the sum spectra ranges from 6.9% to 10.60% with trapezoidal width set as 1000 ns to 100 ns. The DTSAC method corrects temperature effect by pulse processing, and need no reference peak, reference spectrum and additional circuits. The method solves the problem of correction of pulse shape and pulse amplitude at the same time, and can be used even at high counting rate.

Many corner detector techniques have already been used in extracting information from UAV images to perform various photogrammetric and mapping activities. Among these techniques is the Feature from Accelerated Segment Test (FAST) and the Harris corner detector. It is widely agreed that the evaluation of detectors is of great importance because it evaluates and enhances the accuracy of the detected features. This research evaluates the performance of FAST-9 and FAST-12 as well as the Harris detector in terms of the repeatability rate, completeness, and correctness under different threshold values. Each method is evaluated in terms of its ability for detection UAV objects (crowd and cars features). Then the common detected features between both FAST versions and the Harris detector are extracted. This is to determine which method performs best under different image conditions (e.g., illumination variations, camera position and orientation, and image noise). The results show that the size of the threshold plays a crucial role in determining the number of detected feature points. An increase in the threshold value leads to a decrease in the number of detected points and vice versa. Thus, the correctness decreases whereas the completeness increases as a function of the threshold values. Furthermore, the relationship between the FAST-9 and the Harris detector is slightly better than those between the FAST-12 and the Harris detector. This is because the number of common features between the FAST-9 and the Harris detector are relatively higher than those between the FAST-12 and the Harris detector.

Air quality has been a major concern throughout the world, Nigeria inclusive. The monitoring of air quality involves indoor and outdoor air quality. In this study, our concern was on indoor air quality. The aim of this study was to assess the air quality of residential homes (17), classrooms (3), hospitals (2), offices (5), Shops (2), and laboratories (5) in Akure, Nigeria in terms of formaldehyde (HCHO), total volatile organic compound (TVOC), Particulate matter (PM_1.0; PM_2.5, and PM₁₀). A Multifunction Air Detector was used for the assessment using the manufacturers’ procedures and the locations were identified using a Mini GPS. The results revealed as follows: HCHO (0.001-0.030 mg/m³), TVOC (0.003-362 mg/m³), PM_1.0 (004-014 µg/m³), PM_2.5 (006-020 µg/m³), and PM₁₀ (006-022 µg/m³). The results obtained were below the 24 h pollution recommended standards (0.1 mg/m³- HCHO; TVOC; 10-20 μ/m³ PM) of EPA and WHO. Statistically, there were correlations within the pollutants and weather. The Indoor air quality (IAQ) depicted the areas as ‘good,’ and toxicity potential (TP) were below unity. Although the locations looked safe, it is recommended that constant monitoring of the indoors should be ensured and proper ventilation should be provided.

Inflammatory bowel diseases, familial adenomatous polyposis (FAP) and colorectal cancer (CRC) are associated with alterations of the intestinal microbiota. However, few data are available on the perpetuation of FAP and ulcerative colitis (UC) in relation to microbial dysbiosis. This study evaluated the UC and genetically confirmed FAP patients’ gut microbial balance in concordance to clinical outcome. Fecal materials (average mass of 0.54 g) were collected from three FAP and five UC patients to compare with healthy individuals as control group. Genomic materials of micro-biota were isolated for next generation sequencing of 16S rRNA that was performed by using QIAseq 16S/ITS panel in Illumina Miseq Platform. Data processing and bioinformatics analysis were performed via CLC Genomic Workbench bioinformatics tool. The comparison between FAP, UC and control group revealed an alteration in the intestinal microbial composition. More in details, relative abundance of class levels showed statistical significance differences among FAP, UC and control groups. Our preliminary data focused on the explanation of how dysbiosis can lead to inflammation and drive processes together with host genetic profile that leads to colorectal carcinogenesis.

The increasing residential space differentiation is an important practical problem for Chinese cities. The different choice of living space for urban residents is a concrete reflection of social class divide, which reflects the differences in the choices and demands of different social groups for urban housing. Taking Wuhan, an important city in central China, as an example, this research divides the residential space based on the social resource structure, and analyzes and compares the spatial differentiation and influencing factors of different residential areas and houses with different prices in Wuhan by spatial differentiation indicators and geographic detector. The results show clear differences in resource structure among residential areas, and there are few high-quality residential areas with a concentrated distribution. The spatial differentiation levels of different residential areas are distinct, and the pursuit of high-quality and scarce resources is the key to the residential space differentiation of most large cities in China. In addition, residential space differentiation can lead to inter-generational problems such as housing wealth inequality, resource deprivation and solidified class identity. Exploring the mechanisms and social effect of residential space differentiation in most large cities in China can provide some reference for China's urban space planning and real estate market regulation in the new era.

High-energy small electron beams generated by linear accelerators are used for radiotherapy of localized superficial tumors. The aim of the present study is to assess the dosimetric performance under small radiation therapy electron beams of the novel PTW microSilicon detector by comparison with commercially available dosimeters. Relative dose measurements of circular fields with 20, 30, 40 and 50 mm aperture diameters were performed for 4 to 12 MeV energy range of electron beams generated by an Elekta Synergy linac. Percentage depth dose, transverse profiles and output factors normalized to the 10 × 10 cm2 reference field were measured. All dosimetric data were collected in a PTW MP3 motorized water phantom at SSD of 100cm by using the novel PTW microSilicon detector. The PTW diode E and the PTW microDiamond were also used in all beam aperture for benchmarking. Data for the biggest field size were also measured by the PTW Advanced Markus ionization chamber.Measurements performed by the microSilicon are in good agreement with the reference values for all the tubular applicators and beam energies, within the stated uncertainties. This confirms the reliability of the microSilicon detector for relative dosimetry of small radiation therapy electron beams collimated by tubular applicators.

A metering roundabout where traffic is controlled by traffic lights with phase times influenced by queue detector occupancy might be the solution to enhance performance when there are unbalanced traffic flows at roundabouts. There have, however, been minimal studies on how the distance of the queue detector from the stop line affects signal phase time durations and the queuing lengths. This research, therefore, seeks to develop a Cuckoo Search/Local search Algorithm using parameters such as arrival volumes, conflicting volumes, detector distance and phase time to investigate the relationship of signal setting, detector location and queuing formulations. Also, some additional statistical tests were performed for the fitness of the data. In order to conduct solid model validations, model output data was compared against the AIMSUN model. The results from the analyses demonstrated that the queue detector distance can affect phase time durations and vehicle queuing lengths on the controlling approach as well as queuing lengths on the metered approach. This study showed that, based on the study for the Old Belair Road roundabout in Adelaide, South Australia, the total queue length (controlling + metered) will be minimized when the detector is relocated at 209 meters from the roundabout stop line, giving longer phase green times and resulting in decreased intersection queuing lengths.

In order to improve contrast and restore color for underwater image captured by camera sensors without suffering from insufficient details and color cast, a fusion algorithm for image enhancement in different color spaces based on contrast limited adaptive histogram equalization (CLAHE) is proposed in this article. The original color image is first converted from RGB color space to two different special color spaces: YIQ and HSI. The color space conversion from RGB to YIQ is a linear transformation, while the RGB to HSI conversion is nonlinear. Then, the algorithm separately operates CLAHE in YIQ and HSI color spaces to obtain two different enhancement images. The luminance component (Y) in the YIQ color space and the intensity component (I) in the HSI color space are enhanced with CLAHE algorithm. The CLAHE has two key parameters: Block Size and Clip Limit, which mainly control the quality of CLAHE enhancement image. After that, the YIQ and HSI enhancement images are respectively converted backward to RGB color. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, with 4 direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse YIQ-RGB and HSI-RGB images together to achieve the final fused image. The enhancement fusion algorithm has two key factors: average of Sobel edge detector and fusion coefficient, and these two factors determine the effects of enhancement fusion algorithm. A series of evaluate metrics such as mean, contrast, entropy, colorfulness metric (CM), mean square error (MSE) and peak signal to noise ratio (PSNR) are used to assess the proposed enhancement algorithm. The experiments results showed that the proposed algorithm provides more detail enhancement and higher values of colorfulness restoration as compared to other existing image enhancement algorithms. The proposed algorithm can suppress effectively noise interference, improve the image quality for underwater image availably.

This study aimed to assess the presence of metal contamination in the sediments of the Narmada River by considering seasonal variations. In this context, samples were gathered from six stations along the river in 2021–22. To assess the various metals including Li, Na, Mg, Al, Ca, Fe, Cu, Zn, Co, and Ni by utilizing ICP-MS. Sediment contamination was assessed by utilizing indices like contamination factor (CF), pollution load index (PLI), geo-accumulation factor (Igeo), and enrichment factor (EF). The observed order of metals concentration revealed that metals Fe>Al>Mg>Ca>Na>Cu>Zn>Co>Ni>Li. The seasonal variation reveals metals Al, Fe, Cu, Zn, and Ni exhibit the highest concentrations during the monsoon and metals Li and Co, the highest concentration in the post-monsoon, while metals Na, Mg, and Ca, exhibit their peak concentration during the pre-monsoon. The findings from CF, PLI, Igeo, and EF indicate that there were low to moderate contaminations, metal pollution exists, uncontaminated to moderate contaminations, and minor enrichment. The cluster analysis revealed the source of contamination was anthropogenic, marine, and fertilizer activities. Additionally, Correlation matrix was employed to establish connections between metals. The findings of this research are to understand the metal contamination and potential carcinogenicity in the sediment of Narmada River.

Reactive transport (RT) couples bio-geo-chemical reactions and transport. RT is important to understand numerous scientific questions and solve some engineering problems. RT is highly multidisciplinary, which hinders the development of a body of knowledge shared by RT modelers and developers. The goal of this paper is to review the basic conceptual issues shared by all RT problems, so as to facilitate advance along the current frontier: biochemical reactions. To this end, we review the basic equations to point that chemical systems are controlled by the set of equilibrium reactions, which are easy to model, but whose rate is controlled by mixing. Since mixing is not properly represented by the standard advection-dispersion equation (ADE), we conclude that this equation is poor for RT. This leads us to review alternative transport formulations, and the methods to solve RT problems using both the ADE and alternative equations. Since equilibrium is easy, difficulties arise for kinetic reactions, which is especially true for biochemistry, where numerous frontiers are open (how to represent microbial communities, impact of genomics, effect of biofilms on flow and transport, etc.). We conclude with the basic 10 issues that we consider fundamental for any conceptually sound RT effort.

The object of research is an optical-electronic system for recording and processing signals from fiber-optic sensors. Due to the use of optical fibers as sensitive elements, there is no influence on the measurement result of electromagnetic fields, spurious electromagnetic radiation, channel crosstalk, there are no problems, electrical safety is significantly increased, there are no problems of arcing and sparking. A scheme of a receiving-transmitting module has been developed, which is additionally equipped with a preliminary protection and filtering system. Further, measuring tracks, microcontroller software and functional tests were ordered to determine the metrological properties of the proposed system.Signal integrity analysis is presented, which allows you to check whether the signals present in the device are correctly transmitted between their sources and receivers. The analysis of signal integrity is investigated, which is performed in two stages of the device design process. Modeling of the studied module in the specialized program Altium Designer. With the help of this program, potential problems were found, such as the occurrence of crosstalk between paths, the exact values of some elements, accurate analytical calculation. Learned an impedance analysis trace for a given connection's parameters, where the value affects the shape of the given path, its physical dimensions, distance from other paths, and other physical properties of the board. Optoelectronic system for recording and processing signals from fiber optic sensors based on Bragg fiber gratings resistant to electromagnetic interference and temperature, for use in modern systems for monitoring building structures, bridges, tunnels, dams, high-rise buildings, railways, oil platforms, buildings, pipelines.

During the COVID-19 pandemic face masks grew in importance as their use by the general population was recommended by health officials in order to minimize the risk of infection and prevent further spread of the virus. To ensure health protection of medical personal and other system relevant staff, it is of considerable interest to quickly test if a certain lot of filtering facepiece masks meets the requirements or if the permeability changes under different conditions. As certified penetrometers are rather expensive and were difficult to obtain during the COVID-19 pandemic, we describe two quite simple and cheap methods to quickly test the filter permeability based on an electronic cigarette. The first method uses a precision scale, the second method uses a light scattering detector to measure the filter penetration. To make sure these two methods yield reliable results, both were tested with freshly cut filter samples covering the range of approx. 7% to 60% permeability and compared to the results of a certified penetrometer. The comparison of the two methods with the certified penetrometer showed a good correlation and therefore allow a quick and rather reliable estimation of the permeability. Several examples about the use of faulty masks and the resulting health risks show that simple, fast, cheap and broadly available methods for filter characterization might be useful in these days.

Background: Hepatocellular carcinoma (HCC) is one of the most prevalent cancers in human populations worldwide. Conversely, Huanglian Decoction is one of the most important Chinese medicine formulas, with the potential to treat cancer. Methods: To identify differentially expressed genes (DEG), we herein downloaded gene expression profile data from the TCGA (TCGA-LIHC) and GEO (GSE45436) databases. We obtained phytochemicals of the four constituent herbs of Huanglian Decoction from the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP). We also established a regulatory network of DEG and their drug target genes and subsequently analyzed key genes using bioinformatic approaches. Furthermore, we explored the effect of Huanglian Decoction by conducting in vitro experiments so as to verify the prediction. In particular, the CCNB1 gene was knockdown to verify the primary target of this Decoction. Results: Based on the results of network pharmacological analysis, we revealed that there are 5 bioactive compounds in Huanglian Decoction acting on HCC. In addition, our findings confirmed that CCNB1 was the primary key gene, which can be highly expressed in tumors and was significantly associated with a worse prognosis (P = 0.002) according to PPI network analysis of the target genes of these five compounds, as well as expression and prognosis analyses in tumors. We also noted that CCNB1 can be used as an independent prognostic indicator of HCC (P < 0.01). Moreover, in vitro experimental results demonstrate that Huanglian Decoction can significantly inhibit the growth, migration, and invasion of HCC cells. Finally, further analysis showed that this Decoction may inhibit the growth of HCC cells by down-regulating the expression level of CCNB1.

Flood is identified as one of the major disasters in the world; it destroys both human and properties across the world, where lives are lost, properties, public infrastructure, farmlands and agricultural produce with farm crops carted away as a result of flood disaster. Studies revealed that the flood in itself is not the danger, but the level of human vulnerability to flooding disaster risk, which enhances its destructive capabilities. However, based on the challenges poses by flood disaster risk, this research identifies Ala river in Akure as a potential cause of flood, considering its location and other human activities around the river. Therefore, the research used Ala-river a case study to identify and mapped out areas susceptible to flood disaster risk. The research made use of both literature review and conducted goe-data gathering with the application GIS-computer database to retrieve georeferencing relevant data from the fieldwork in the study area of Ala-river basin to mapped out locations vulnerable to achieve the research aim. The research adopted a Geo-mapping of the vulnerable area to Ala-River basin using arc-GIS tool in combination with other software such as IKONAS and OLI (Operation Land Imager) for the production of the study area imagery, ER-ITERIM was used for the collection of rainfall data and FAO was applied for digital soil mapping. These applications produced; the land use/land cover map, digital elevation map, buffer map using 30 meters setback, annual rainfall map, soil types map, vulnerability map and soil textural table for the study area. Analysis of the produced and generated maps shows 316 buildings vulnerability to flood disaster risk; the soil texture and types, and alternative use to which the soil types can be useful. The research recommends that demolition of the identified 316 buildings prone to flood disaster and compliance of building construction to 30 meters setback by developers. Others are the conversion of the future land setback for urban agricultural purposes and preservation of water retention areas for agricultural activities during the dry season among others. The study concludes that relevant government agencies in the State and in particular in Akure South Local Government should ensure prompt compliance and implementation of the recommendations to avoid potential flood disaster risks.

The interaction between different regions normally is reflected by the form of the stream. For example, the interaction of the flow of people and flow of information between different regions can reflect the structure of cities’ network, and also can reflect how the cities function and connect to each other. Since big data has become increasingly popular, it is much easier to acquire flow data for various types of individuals. Currently, it is a hot research topic to apply the regional interaction model, which is based on the summary level of individual flow data mining. So far, previous research on spatial interaction methods focused on point-to-point and area-to-area interaction patterns. However, there are a few scholars who study the hotspot interaction pattern between two regional groups with some predefined neighborhood relationship by starting with two regions. In this paper, a method for identifying a similar hotspot interaction pattern between two regional groups has been proposed, and the Geo-Information-Tupu methods are applied to visualize the interaction patterns. For an example of an empirical analysis, we discuss China’s air traffic flow data, so this method can be used to find and analyze any hotspot interaction patterns between regional groups with adjoining relationships across China. Our research results indicate that this method is efficient in identifying hotspot interaction flow patterns between regional groups. Moreover, it can be applied to any analysis of flow space that is used to excavate regional group hotspot interaction patterns.

(1) Background: The imaging energy range of a typical Compton camera is limited due to the fact that scattered gamma photons are seldom fully absorbed when the incident energies are above 3 MeV. Further improving the upper energy limit of gamma-ray imaging has important application significance in active interrogation of special nuclear materials and chemical warfare agents, as well as range verification of proton therapy; (2) Methods: To realize gamma-ray imag-ing in a wide energy range of 0.3~7 MeV, a principle prototype, named a portable three-layer Compton camera, was developed using the scintillation detector that consist of an silicon photo-multiplier array coupled with a Gd3Al2Ga3O12:Ce pixelated scintillator array. Implemented in a list-mode maximum likelihood expectation maximization algorithm, a far-field energy-domain imaging method based on two-interaction and three-interaction events was applied to estimate the initial energy and spatial distribution of gamma-ray sources. The simulation model of detec-tors was established based on Monte Carlo simulation toolkit Geant4. The reconstructed images of a 133Ba, a 137Cs and a 60Co point-like sources were successfully obtained with our prototype in laboratory tests and compared with simulation studies; (3) Results: The proportion of effective imaging events accounted for about 2%, which made our prototype realize reconstructing the distribution of a 0.05 μSv/h 137Cs source in 10 seconds. The angular resolution for resolving two 137Cs point-like sources was 15°. Additional simulated imaging of the 6.13 MeV gamma-rays from 14.1 MeV neutron scattering with water preliminarily demonstrated the imaging capability for high incident energy; (4) Conclusions: We concluded that the prototype had good imaging per-formance in a wide energy range (0.3~7 MeV), which showed potential in several MeV gam-ma-ray imaging applications.

In recent years, the Chinese government pays more and more attention to agricultural development and ecological protection. While improving the cultivated land green use efficiency(CLGUE) is the key to promote the sustainable development of agriculture. This study aims to study the current situation and influencing factors of agricultural production from the perspective of green utilization efficiency of cultivated land. It takes 39 cities in the upper, middle and lower reaches of the Yangtze River basin in China as an example. The CLGUE values in those 39 cities from 2011 to 2020 were specifically measured, using the Super-SBM model, kernel density estimation and geographic detector method. Their temporal and spatial heterogeneity was described, and the influencing factors were detected at both single and interactive levels. The results showed that: （1）From 2011 to 2020, the green utilization efficiency value of cultivated land in the Yangtze River Basin showed an upward trend on the whole; （2）There is a clear spatial heterogeneity the CLGUE values in the Yangtze River Basin cities, as shown by: downstream region > midstream region > upstream region; (3) Cultivated land resource endowment, socioeconomic development, and agricultural production technology are important factors affecting the variability of CLGUE values. However, there are some differences in the degree and direction of influence of different influencing factors on different sample subgroups.

The diamagnetic levitation technique can be applied to non-destructive testing for identifying cracks and defects in magnetic materials. Pyrolytic graphite is a material that can be leveraged in micromachines due to its no-power diamagnetic levitation on a permanent magnet (PM) array. However, the damping force applied to pyrolytic graphite prevents it from maintaining con-tinuous motion along the PM array. This paper investigates the in-plane motion and damping characteristics of pyrolytic graphite sheets on the PM array. The centroid of pyrolytic graphite will maintain a stable levitation when positioned on the intersecting point of the PM array. Fur-thermore, the in-plane motion of two different pyrolytic graphite sizes - PG10 (10×10×0.5mm3) and PG20 (20×20×0.5mm3) - has revealed that the in-plane forces applied to them are approximately 61.4μN and 28.2μN, respectively. The smaller sheet has a shorter oscillation time while oscillating before it stabilizes. The damping ratio of the oscillation was between 0.1-0.2. The in-plane force and the stable time of pyrolytic graphite are related to its size ratio in comparison with the PM. This relationship is believed to promote stability, as the force acting on the graphite sheet will be distributed over a larger area when the sheet size increases. During the fixed-axis rotation process, the study found that the friction coefficient and friction force decreased as the rotational speed decreased. The diamagnetic levitation of pyrolytic diamagnetic levitation is also unable to maintain long-term axial rotation. Finally, friction force was tested using PG10 during fixed-axis rotation above a circular PM array. The results showed that the order of the friction coefficient is 10-3, and the friction force is about 0.8μN, decreasing with graphite rotation speed. We hope this technique will be used in crack detection, magnetic detection and other micromachine.

Neural networks require a large quantity of training spectra and detector responses in order to learn to solve the inverse problem of neutron spectrum unfolding. In addition, due to the under-determined nature of unfolding, non-physical spectra which would not be encountered in usage should not be included in the training set. While physically realistic training spectra are commonly determined experimentally or generated through Monte Carlo simulation, this can become prohibitively expensive when considering the quantity of spectra needed to effectively train an unfolding network. In this paper, we present three algorithms for the generation of large quantities of realistic and physically motivated neutron energy spectra. Using an IAEA compendium of 251 spectra, we compare the unfolding performance of neural networks trained on spectra from these algorithms, when unfolding real-world spectra, to two baselines. We also investigate general methods for evaluating the performance of and optimizing feature engineering algorithms.

The 21st century, where all things are depending upon technology, almost all the human tasks are being done with the help of technology to save a lot of time and make our life much more comfortable. The monitoring of students’ attendance is a crucial task for faculty in today’s world. There are more chances of errors while entering the students’ attendance records in the primary system, primarily when the class was over. The main concern of this study is to build an IoT-based automated attendance management system for educational institutes by biometric recognition to incorporate fake/proxy attendance and errors of entry and to replace old manual methods of taking students’ attendance by calling their names or roll numbers. The AAS will click the image of the classroom, and it will automatically detect the faces of students sitting in the lecture room and recognize them during lectures then mark their attendance daily to keep a record of their presence and also maintain and manage it for the management staff of the institution for future by using web services.

Searching for neutrinoless double beta decay is a top priority in particle and astroparticle physics, being the most sensitive test of lepton number violation and the only suitable process to probe the Majorana nature of neutrinos. In order to increase the experimental sensitivity for this particular search, ton-scale detectors operated at nearly zero-background conditions with a few keV energy resolution are required. In this scenario, cryogenic detectors have proven effective in addressing many of these issues simultaneously. After long technical developments, the CUORE experiment established the possibility to operate large scale detectors based on this technology. Parallel studies pointed out that scintillating cryogenic detectors represent a suitable upgrade for the CUORE design, directed towards higher sensitivities. In this work, we review the recent development of cryogenic detectors, starting from the status of the art and outlying the path toward next-generation experiments.

The NA61/SHINE experiment studies hadron production in hadron-hadron, hadron-nucleus and nucleus-nucleus collisions. The physics programme includes the study of the onset of deconfinement and search for the critical point as well as reference measurements for neutrino and cosmic ray experiments. For strong interactions, future plans are to extend the programme of study of the onset of deconfinement by measurements of open-charm and possibly other short-lived, exotic particle production in nucleus-nucleus collisions. This new programme is planned to start after 2020 and requires upgrades to the present NA61/SHINE detector setup. Besides the construction of a large acceptance silicon detector, a 10-fold increase of the event recording rate is foreseen, which will necessitate a general upgrade of most detectors.

The extensive use of carrier-aided smoothing code (CSC) filter has led to reduce the noise level of raw code measurements in GNSS positioning and navigation applications. However, the existing CSC technique is sensitive to the changes of the integer ambiguity and then the smoothing proce-dure needs to be restarted in the presence of cycle-slips. As the Doppler shift is instantaneous ob-servation and immune to cycle-slips, Doppler-aided smoothing code (DSC) algorithm would be more promising in challenged environment. Based on the Hatch-filter, an optimal DSC approach is proposed with the principle of minimum variance. Meanwhile, to inhibit the effect of integral cumulative error of Doppler, a balance factor is adopted to adjust the contributions of raw code and DSC. The noise level of code observable is not only affected by thermal noise, but also limited by systematic bias. Satellite code bias (SCB) has been identified in the raw code observable on each frequency for each BDS-2 satellite. By minimizing the sum of absolute value of residuals, polyno-mial segment fitting algorithm as a function of elevation-angles is applied to establish the SCB cor-rection model based on epoch-differenced Multipath (MP) deviations. Finally, numerical experi-ments demonstrate the validity and efficiency of the refined DSC filter with SCB corrections on each available frequency for BDS-2 un-GEO satellites.

The work presented in this paper represents a preliminary study on the performance of the new Silicon tracker layer, the Layer 0 (L0), that will be installed on top of the Alpha Magnetic Spectrometer (AMS-02), at the end of 2024. AMS-02 is a cosmic rays (CR) detector that has been operating on the International Space Stations (ISS) since May 2011. Thanks to its 9-layer Silicon tracker, this apparatus can perform high energy CR measurements with an unprecedented level of statistics and precision. However, high Z (Z $\ge 15$) CR nuclei statistic is strongly affected by fragmentation along the detector: with the installation of the new Silicon layer will be possible to achieve new unique high energy (TeV region) measures for those nuclei along with increased statistics for all nuclei up to Zinc. To achieve this, a Silicon ladder prototype, that will be part of the final Silicon layer, has been exposed to an ions test beam at the super-proton-synchrotron (SPS) of the CERN to characterize its charge resolution and the readout electronics. Preliminary results have shown a charge resolution of 10 $\%$ for nuclei up to Z=7.

Desertification is a global environmental and socio-economical issue threatening humanity's survival and development. The Shiyang River Basin ecosystem is vulnerable and prone to desertification. In addition, establishing the quantitative analysis of desertification driving factors and understanding their relative contribution, separately or combined, is still an unresolved problem. The present study applied geographic information system (GIS) techniques and a geographic detector model to quantify desertification spatial extent and driving mechanisms. This research utilized Fractional Vegetation Cover (FVC) to elucidate desertification spatial heterogeneity. The 30 years Coefficient of Variation (CV) of the Normalized Difference Vegetation Index (NDVI) was a dependent variable and indicator of ecosystem terrestrial conditions; Elevation, near-surface air temperature, precipitation, wind velocity, land cover change, soil salinity, road buffers, waterway buffers, and soil types were independent variables. The results showed that 89.41% of the total area is under desertification risk, where 20.99% is extremely desertified, 34.45% is severely desertified, 12.05% is moderately, and 21.92% is slightly desertified. The results from the Geodetector model showed that Power Determinant (PD) values ranged between 0.004 and 0.270. Elevation and soil types had the highest contributing factors with PD values of 0.270 and 0.227, whereas precipitation, soil salinity, the buffer of the waterway, and wind velocity played a moderate role with PD values of 0.146, 0.117, 0.107, and 0.071. Near-surface air temperature, road buffer, and land cover dynamics exhibited lower impact with PD values of 0.028, 0.013, and 0.004. In most cases, investigating the interaction between driving factors resulted in a mutual or non-linear enhancement. There was an apparent linear and mutual enhancement between elevation and soil salinity, precipitation, and soil types with values of 0.3513, 0.3232, and 0.3204, respectively. In addition, there was a mutual enhancement between soil salinity and soil types with a value of 0.2962. On the other hand, a non-linear enhancement was observed between Elevation and near-surface air temperature (0.3116), Elevation and Land cover dynamics (0.2759), soil types and near-surface air temperature (0.2687), land cover dynamics and soil types (0.234), precipitation and near-surface air temperature (0.2248), precipitation and wind velocity (0.2248), and between land cover dynamics and precipitation (0.223). This research revealed irrefutable evidence that environmental factors might be the primary drivers of ecosystem disturbance, provided the basis for the environmental footprint of desertification mechanism, and might be a cornerstone for future policy on ecological restoration sustainability in the Shiyang River Basin.

In emergency situations, every second counts for an ambulance navigating through traffic. Efficient use of traffic light systems can play a crucial role in minimizing response time. This paper introduces a novel automated Real-Time Ambulance in an Emergency Detector (RTAIAED). The proposed system uses special Lookout Stations (LS) suitably positioned at a certain distance from each involved traffic light (TL), to obtain timely and safe transitions to green lights as the Ambulance in an Emergency (AIAE) approaches. The RTAIAED is particularly pertinent on one-way roads, addressing the challenge of regulating the sequence of traffic-lights-signals so as to minimize the time needed to safely grant the green signal to the AIAE. The proposed solution leverages a part-based model made of elementary detectors for video analysis, specifically realized with a customized YOLOv8 model, and for audio analysis, thanks to an additional neural network based on Mel Frequency Cepstral Coefficients (MFCCs). This way, the RTAIAED ensures the accurate and robust identification of an AIAE heading towards a traffic light in time to ensure a green light thanks to the strategic positioning of the LS detectors. Extensive experiments demonstrate the robustness of the approach and its reliable application in real-world scenarios thanks to its predictions in real-time, showcasing the ability to detect AIAEs even in challenging conditions such as noisy environments, nighttime, or adverse weather conditions, provided a suitable-quality camera is appropriately positioned. The proposed system can also find application in traffic flow management.

Radon gas represents the major contributor to human health risk from environmental radiological exposure. In confined spaces radon can accumulate to relatively high levels so that mitigation actions are necessary. The Italian legislation on radiation protection has set a reference value for the activity concentration of radon at 300 Bq/m3. In this study, measurements of the annual radon concentration in 62 bank buildings, spread on Campania region (Southern Italy), were carried out. Using CR-39 solid-state nuclear track detectors, radon level was assessed in 136 confined spaces (127 at underground and 9 at ground floors), frequented by workers and/or the public. The survey parameters considered in the analysis of the results were: floor types, wall cladding materials, number of openings, door/window opening duration for air exchange. Radon levels were found to be between 17 and 680 Bq/m3, with an average value of 130 Bq/m3 and a standard deviation of 120 Bq/m3. About 7% of the results gave a radon activity concentration above 300 Bq/m3. The analysis showed that the floor level and air exchange have the most significant influence. This study highlighted the importance to assess the indoor radon levels, even in particular environments, to protect public and workers by radon-induced effects on health.

The emerging use of visualization techniques in pathology and microbiol- ogy has been accelerated by machine learning (ML) approaches towards image preprocessing, classification, and feature extraction in an increasingly complex series of datasets. Modern Convolutional Neural Network (CNN) architectures have developed into an umbrella of vast image reinforcement and recognition methods, including a combined classification-localization of single/multi-object featured images. As a subtype neural network, CNN cre- ates a rapid order of complexity by initially detecting borderlines, edges, and colours in images for dataset construction, eventually capable in mapping intricate objects and conformities. This paper investigates the disparities between Tensorflow object detection APIs, exclusively, Single Shot Detector (SSD) Mobilenet V1 and the Faster RCNN Inception V2 model, to sample computational drawbacks in accuracy-precision vs. real time visualization capabilities. The situation of rapid ML medical image analysis is theoretically framed in regions with limited access to pathology and disease prevention departments (e.g. 3rd world and impoverished countries). Dark field mi- croscopy datasets of an initial 62 XML-JPG annotated training files were processed under Malaria and Syphilis classes. Model trainings were halted as soon as loss values were regularized and converged.

The Direction-of-Arrival (DoA) estimation methods are highly versatile and find extensive applications in satellite communication. The DoA methods are employed across a range of orbits, from Low Earth Orbits (LEO) to Geostationary Earth Orbits (GEO). They serve multiple applications, including altitude determination, geolocation and estimation accuracy, target localization, and relative and collaborative positioning. This paper presents a novel approach for modeling the DoA angle using a closed-form expression, incorporating the boresight angle and satellite and Earth station position data. The method uses the geographic coordinate system in the satellite communication system, precisely the latitude and longitude of the Earth station and altitude parameters of the satellite stations, to calculate the Earth station’s elevation angle and accurately model the DoA angle. Furthermore, this paper performs a comprehensive comparative analysis of various DoA methods to gain deeper insights into the performance of DoA estimation in multi-antenna systems operating under spatially correlated channels. Accordingly, this paper evaluates DoA estimation performance using root-mean-square-error (RMSE) statistics for uplink and downlink conditions through extensive Monte Carlo simulations. The simulation’s effectiveness is validated against the Cramer-Rao Bound (CRB) performance metric for the Additive white Gaussian noise (AWGN) case. (i.e., thermal noise). The simulation results demonstrate improved RMSE performance in satellite systems by incorporating spatial correlation into the system model.

In this research, the other reasonable explanations for the cosmic microwave background radiation is revealed. Due to the microwave resolution, it very roughly shows the image of galaxies in the universe. Moreover, the intensity measurement on each pixel of the image is the sum of the incident microwaves from different directions, so the microwave image cannot represent the microwave sources clearly far away from the Earth. Hence, we propose a simulation after removing several strongest microwave sources, the remaining microwave radiation sources can establish a very uniform intensity distribution over a range of several ten light years. On the other hand, Sloan Digital Sky Survey reveals 200 million galaxies in the universe and, in fact, only to eliminate the contributions of all galaxies from the microwave image is impossible. The way to further obtain the fine-scale structure by only removing the few strongest microwave sources as the foreground effect will keep the other contributions from all the rest galaxies and stars. Therefore, the Cosmic Microwave Background cannot be uniquely explained the radiation which was left after the initial formation of the universe. Moreover, it is the mainly residual radiation from the un-calculated galaxies and inaccurate estimation of the microwave source strength.

Under optimal conditions, just 3–6 ms of visual stimulation suffices for humans to see motion. Motion perception on this time scale implies that the visual system under these conditions reliably encodes, transmits, and processes neural signals with near-millisecond precision. Motivated by in vitro evidence for high temporal precision of motion signals in the primate retina, we investigated how neuronal and perceptual limits of motion encoding relate. Specifically, we examined the correspondence between the time scale at which cat retinal ganglion cells in vivo represent motion information and temporal thresholds for human motion discrimination. The time scale for motion encoding by ganglion cells ranged from 4.6–91 ms, depended nonlinearly on temporal frequency but not on contrast. Human psychophysics revealed that minimal stimulus durations required for perceiving motion direction were similarly brief, 5.6–65 ms, similarly depended on temporal frequency but, above ~10%, not on contrast. Notably, physiological and psychophysical measurements corresponded closely throughout (r = 0.99), despite more than a 20-fold variation in both human thresholds and optimal time scales for motion encoding in the retina. These results demonstrate that neural circuits for motion vision in cortex can maintain and make use of the high temporal fidelity of the retinal output signals.

This paper presents an improved indoor localization system based on RF and ultrasonic signal which we named SNSH system. This system composes of a transmitter mounted in a mobile target, and a series of receiver nodes which are managed by a coordinator. By measuring Time Delay of Arrival (TDoA) of RF and ultrasonic signal from the transmitter, distance from target to receiver node is calculated and sent to the coordinator through CAN network, and all the information are gathered in PC to estimate 3D position of the target. Sine wave detector and dynamic threshold filter are applied to provide excellent accuracy of range measurement from TDoA result, and multilateration algorithms is realized to optimize coordinate determination accuracy. Specifically, Linear Least Square and Non-linear Least Square techniques are implemented to contrast their performances in target coordinate estimation. RF signal encoding/decoding time, time delay in CAN network and math calculating are carefully considered to ensure optimal system performance and get ready for field application. Experiments show that sine wave detector algorithm has greatly improved range measurement accuracy, with mean error at 2.2mm and maximum error at 6.7 mm, for distance below 5m. In addition, 3D position accuracy is greatly enhanced by multilateration methods, with mean error in position stay under 15mm, and 90% confident error values at 23mm for LLS, 20mm for NLS. Overall system update period has been verified in real system operation, with maximum rate at 25ms, and compared with other existing researches.

A novel metaheuristic optimization method is proposed based on an earthquake that is a geology phenomenon. The novel Earthquake Algorithm (EA) proposed, adapts the principle of propagation of geology waves P and S through the earth material composed by random density to ensure the dynamic balance between exploration and exploitation, in order to reach the best solution to optimization complex problems by searching for the optimum into the search space. The performance and validation of the EA are compared against the Bat Algorithm (BA) and the Particle Swarm Optimization (PSO) by using 10 diverse benchmark functions. In addition, an experimental engineering application is implemented to evaluate the proposed algorithm. Early results show a feasibility of the proposed method with a clearly constancy and stability. It is important highlight the fact that the main purpose of this paper is to present a new line of research, which is opened from the novel EA.

The vegetation types on the Qinghai-Tibet Plateau are complex and diverse, and the ecological environment is fragile and sensitive. It is very important to study the dynamic changes in vegetation and the main factors related to these changes to grasp the present state of the regional ecosystem, maintain the balance of the ecosystem and promote the sustainable development of the ecosystem. Therefore, this paper is based on SPOT/VEG NDVI (normalized difference vegetation index) data, land use data, topographic data and meteorological data from 1999 to 2019. The spatiotemporal variation in the NDVI over the Tibetan Plateau in the last 21 years and its response to different driving factors were investigated by using the Sen slope method, Mann–Kendall mutation test, partial correlation analysis and geographical detector method. The results showed that (1) the vegetation coverage on the Qinghai-Tibet Plateau showed an increasing trend from 1999 to 2019, with increases in approximately 67.00% of the plateau area. (2) The spatial differences in vegetation coverage were large; notably, low-density vegetation areas decreased obviously, moderate-density vegetation areas accounted for approximately 50% of the total area, high-density vegetation areas were the least common, and the overall growth rate was significant. (3) The NDVI was positively correlated with temperature and precipitation, and a positive correlation was observed in more than 66% of the region. (4) The order of the influence of single driving factors on the NDVI was as follows: precipitation > soil type > altitude > temperature > gradient > slope > population density > GDP. (5) The effect of multiple factors was significantly higher than that of single driving factors, with a notable nonlinear influence. The interactions between meteorological factors, such as precipitation, and topographic factors, such as altitude, were important, with a q value over 0.79.

This article introduces the design and analysis of a slope detection circuit tailored for on-chip sensing of the load current in DC-DC converters. The novel indirect sensing approach is grounded in measuring the output voltage slope across the filtering capacitor during discharge phases. Consequently, this slope data can be effectively harnessed to control the converter switches. The slope detector circuit was designed in standard 65 nm CMOS technology using the nominal supply voltage of 1.2 V and can be used for current sensing within the range from one to hundreds of mA. Since the proposed circuit has mainly digital character, the robustness to process, temperature and supply voltage variations can be ensured rather conveniently.

Swift and diligent resilience response is mandatory in sustainable geo-distributed ecosystems. The real-time geo-spatial resilience requires agility in millions of parallel and distributed data processing tasks on data acquired from regional condition monitoring(RCM) systems. These tasks include expiditous resolution of complex sustainability conflict sets, promptly anomalies characterization in chaos sets, and resilience response uniqueness. This work is an archetype of a paragon geo-resilience support system(GRSS) for regional sustainability using a novel melioration in DADO production machine. The proposed expert system capitalized the synergic strengths of RETE, TREAT, LEAPS and GATOR networks was designed and implemented as a synthetic DADO machine(SDM). The generic architecture of DADO machine was improved in this work by enrichment in rule set conditions and solution set for regional scale resilience rule set conditions. The condition left-hand side(LHS) X equal to the solution set 2X for right-hand side (RHS) was the goal achieved by working memory(WM) optimization and conflict resolution strategy(CRS) in alpha and beta networks rules. A round-trip time of 80.2 seconds for first event response set using 1492 segment size and sequence number 360,000 with maximum packets at a single geospatial structure was 21 packets/sec was a noticeable landmark in this work. LEAPS and Concurrent-read algorithm for GATOR cluster networks in the proposed synthetic DADO machine architecture was the overall implementation that enabled urban scale resilient system practically possible on physical SHM deployment.

Nanling Mountain region is a typical southern hilly region, which plays an important ecological and environmental protection role in China's overall land protection pattern. Based on the remote sensing image data of Longnan City in Nanling Mountain in 2013, 2018 and 2023, this paper interpreted the land use type and analyzed the land use transfer situation by using Land use transfer flow, and Land use transfer matrix. At the same time, based on the remote sensing Ecological index (RSEI) model, the ecological environment quality of Longnan City from 2013 to 2023 was retrieved. The temporal and spatial response model of ecological environment quality to land use transfer in Longnan City from 2013 to 2023 was discussed based on Spatial autocorrelation and Geographic detector. The results show that from 2013 to 2023, the decrease of forest land (16.23km²) and the increase of construction land (13.25km²) were the main land use transfer in Longnan City. The ecological environment index of Longnan City in 2013, 2018 and 2023 was 0.789, 0.917 and 0.872, respectively, showing a trend of "first rising and then decreasing". The ecological environment quality in the north of Longnan City was significantly lower than that in the south, and the poor ecological quality area appeared in and around the northern main urban area, showing a trend of "inward contraction". Forest land, garden land, grassland, cultivated land and water area have a positive impact on ecological environment quality, while traffic land, construction land and other land have a negative impact on ecological environment quality. The response of ecological environment quality to different land use transfer modes is related to the change of the overall ecological environment quality. The interaction between LUCC and other factors had a great impact on the evolution of ecological environment quality in Longnan City.

Present work investigates, in photon energy domains of specific interest for medical imaging, the effect of the escape of X-ray fluorescence from the scintillator materials on the energy resolution of scintillation detector itself. Monolithic as well as pixelated crystals differently sized are considered, in the case of the NaI because it is the gold standard in the field. The volumetric ratio is introduced, as a figure of merit, taking into account, alongside the surface-to-volume ratio, the geometric shape, the elemental composition and also the mean-free-path for the given scintillator and energy. The inter-crystal XRF escape is finally suggested to be considered among the effects producing image blurring in segmented scintillatros due to the spread of events between the primary pixel and the first neighbors ones.

Radar transmit signal design is a critical factor for the radar performance. In this paper, we investigate the problem of radar signal waveform design under the small signal power conditions for detecting a doubly spread target, whose impulse response can be modeled as a random process, in a colored noise environment. The doubly spread target spans multiple range bins (range-spread) and its impulse response is time-varying due to fluctuation (hence also Doppler-spread), such that the target impulse response is both time-selective and frequency-selective. Instead of adopting the conventional assumption that the target is wide-sense stationary uncorrelated scattering,we assume that the target impulse response is both wide-sense stationary in range and in time to account for the possible correlation between the impulse responses corresponding to close range intervals. The locally most powerful detector, which is asymptotically optimal for small signal cases, is then derived for detecting such targets. The signal waveform is optimized to maximizing the detection performance of the detector or equivalently maximizing the Kullback-Leibler divergence. Numerical simulations validate the effectiveness of the proposed waveform design for the small signal power conditions and performance of optimum waveform design are shown in comparison to the frequency modulated waveform.

We report a novel dual-band barrier infrared detector (DBIRD) design using type-II superlattices (T2SLs). The DBIRD structure consists of back-to-back barrier diodes: a "blue channel" (BC) diode which has an nBp architecture, an n-type layer of larger bandgap for absorbing blue band infrared/barrier/p-type layer, and a "red channel" (RC) diode which has an pBn architecture, a p-type layer of smaller bandgap for absorbing red band infrared/barrier/n-type layer. Each has a unipolar barrier using a T2SL lattice matched to a GaSb substrate to impede the flow of majority carriers from the absorbing layer. Each channel in the DBIRD can be independently accessed with a low bias voltage, as is preferable for high-speed thermal imaging. Device modeling of DBIRDs and simulation results of the current-voltage characteristics under dark and illuminated conditions are also presented. They predict that dual-band operation of the DBIRD will produce low dark currents and 27-33% quantum efficiencies for the in-band photons in the BC with λc=5.58 μm, and a nearly-constant 33% in the RC with λc=8.05 μm. The spectral quantum efficiency of the BC for 500 K blackbody radiation is approximately 20% over the range of λ=3 − 4.2 μm, while that of the RC has a peak of 42% at 5.7 μm. The DBIRD may provide improved high-speed dual-band imaging in comparison with NBn dual-band detectors.

Small-scale facilities play a significant role in the landscape of nuclear physics research in Europe. They address a wide range of fundamental questions and are essential for teaching and training personnel in accelerator technology and science, providing them with diverse skill sets, complementary to large projects. The current status and perspectives of nuclear physics research at small-scale facilities in Europe will be given.

Transportation of goods is as old as human civilizations : past networks and their evolution shed light on long term trends. Transportation impact on climate change is measured as major, as well as the impact on spreading a pandemic. These two reasons motivate the importance of providing relevant and reliable historical geographic datasets of these networks. This paper focuses on reconstructing the railway network in France at its maximal extent, a century ago. The active stations and lines are well documented by the French SNCF, in open public data. However, that information ignores past stations (ante 1980), which represent probably more than what is recorded in public data. Additional open data, individual or collaborative (eg. Wikipedia) are particularly valuable, but they are not always geo-coded, and two more sources are necessary to completing that geo-coding: ancient maps and aerial photography. Therefore, remote sensing and volunteer geographic information are the two pillars of past railway reconstruction. The methods developed are adapted to the extraction of information from these sources: automated parsing of Wikipedia Infoboxes, data extraction from simple tables, even from simple text. That series of sparse procedures can be merged into a comprehensive computer-assisted process. Beyond this, a huge effort in quality control is necessary when merging these data: automated wherever possible, or finally visually controlled by observation of remote sensing information. The main output is a reliable dataset, under ODbl, of more than 9100 stations, which can be combined with the information about the 35000 communes of France, for a large variety of studies. This work demonstrates two thesis: (a) it is possible to reconstruct transport network data from the past, and generic computer assisted methods can be developed; (b) the value of remote sensing and volunteered geo info is considerable (what archeologists already know).

Terahertz tomography is a promising method among non-destructive inspection techniques to detect faults and defects in dielectric samples. Image quality recently significantly improved through the incorporation of a priori information and off-axis data. However, this improvement has come at the cost of increased measurement time. To aim toward industrial applications, it is therefore necessary to speed up the measurement by parallelizing the data acquisition employing multi-channel setups. In this work, we present two tomographic frequency-modulated continuous wave (FMCW) systems working at a bandwidth of 230-320 GHz, equipped with an eight-channel detector array, and compare their imaging results with a single-pixel setup. While in the first system, the additional channels are used exclusively to detect radiation refracted by the sample, the second system features an f-θ lens, focusing the beam at different positions on its flat focal plane, and thus utilizing the whole detector array directly. The usage of the f-θ lens in combination with a scanning mirror eliminates the necessity of the formerly used slow translation of a single-pixel transmitter. This opens up the potential for a significant increase in acquisition speed, in our case by a factor of four to five, respectively.

The detection performance of small objects in remote sensing images has not been satisfactory compared to large objects, especially in low-resolution and noisy images. A generative adversarial network (GAN)-based model called enhanced super-resolution GAN (ESRGAN) showed remarkable image enhancement performance, but reconstructed images usually miss high-frequency edge information. Therefore, object detection performance showed degradation for small objects on recovered noisy and low-resolution remote sensing images. Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we applied a new edge-enhanced super-resolution GAN (EESRGAN) to improve the quality of remote sensing images and used different detector networks in an end-to-end manner where detector loss was backpropagated into the EESRGAN to improve the detection performance. We proposed an architecture with three components: ESRGAN, EEN, and Detection network. We used residual-in-residual dense blocks (RRDB) for both the ESRGAN and EEN, and for the detector network, we used a faster region-based convolutional network (FRCNN) (two-stage detector) and a single-shot multibox detector (SSD) (one stage detector). Extensive experiments on a public (car overhead with context) dataset and another self-assembled (oil and gas storage tank) satellite dataset showed superior performance of our method compared to the standalone state-of-the-art object detectors.

In Geo Polymer bricks (GPB), fly ash content which is a waste from power plants is converted into bricks by chemical treatment. GPB can be dried by using appropriate curing methods. Conven-tionally electric oven curing is one of the prominent methods. Using solar dryer instead of elec-trical oven gives added advantage of saving high grade electrical energy. So in this work solar dryer with PCM (Phase Change materials) Paraffin Wax and without PCM is used for curing ap-plication. GPB are gaining added advantage when compared to conventional bricks like cement and sand bricks in terms of strength. GPB has been taken as specimen for comparing the com-pressive, tensile split strength and flexural strength by electrical energy based curing and solar energy based curing. It is experimentally observed that solar energy based curing with PCM and without PCM exhibits higher compressive strength, higher tensile split strength and flexural strength when compared to electrical energy based curing. Solar curing with PCM shows higher compressive strength, higher tensile split strength and flexural strength when compared to Solar curing without PCM. Open solar curing is a traditional technique but nowadays aggressive cli-matic conditions can lead to severe damage of Geo polymers. The novelty of this work is to suggest to study the effect of PCM like paraffin wax in solar drying on curing time, mechanical properties of GPB.

Human Face and facial parts are the most significant parts as it reveals a person’s true identity. It plays an important role in various biometric applications like crowd analysis, human tracking, photography, cosmetic surgery, etc. There are many techniques are available to detect a facial image. Among them, skin detection is the most popular one. The aim of this paper is to detect first the person's identity from facial image and finally check any spot present the the detected person. The first step is to detect the maximum skin region based on a combination method of RGB and HSV color space model. Next it is to verify the skin areas of human through machine learning approach. The Aggregated Channel Features (ACF) detector is used to identify the different facial parts like eye pairs, nose, and mouth. Bootstrap aggregation decision tree classifier is applied to classify the person’s identity based on Histogram Oriented Gradient (HOG) features value. The experimental results show that the proposed method gives the average 97% accuracy.

The evaluation of HAADF-STEM images of a sample with the composition Nb18W16O93 provided new insights in its real structure. The basic structure comprises an intact octahedral framework of the tetragonal tungsten bronze (TTB) type. The partial occupation of the pentagonal tunnels (PT) by metal-oxygen strings determines the oxygen to metal ratio (O/SM with M = Nb,W). For a large area, the O/SM was determined to be 2.755 which is smaller than the value of Nb18W16O93 which is O/SM = 2. 735. To a large extent, the threefold TTB superstructure structure of Nb8W9O47 with a high oxygen content is present (O/SM = 2.765). In addition, a new fourfold TTB superstructure was found in small domains: Nb12W11O63 with an O/SM = 2.739 obviously accommodates a part of the sample’s metal excess compared to the stable phase Nb8W9O47.

Carbon nanomaterials with a different character of the chemical bond – graphene (sp2) and nanodiamond (sp3) are the building bricks for a new class of all-carbon hybrid nanomaterials, where the two different carbon networks with the sp3 and sp2 hybridization coexist, interact and even transform into one another. The unique electronic, mechanical, and chemical properties of the two border nanoallotropes of carbon ensure the immense application potential and versatility of these all-carbon graphene – diamond nanomaterials. The review gives an overview of the current state of the art of graphene – diamond nanomaterials, including their composites, heterojunctions, and other hybrids for sensing, electronic, energy storage, and other applications. Also, the graphene-to-diamond and diamond-to-graphene transformations at the nanoscale, essential for innovative fabrication, and stability and chemical reactivity assessment are discussed based on extensive theoretical, computational, and experimental studies.

The sPHENIX experiment is a state-of-the-art jet and heavy flavor physics detector which successfully recorded its first Au + Au collision data at 200 GeV at RHIC. sPHENIX will provide heavy flavor physics measurements covering an unexplored kinematic region and unprecedented precision at RHIC to probe the parton energy loss mechanism, parton transport coefficients in quark-gluon plasma, and the hadronization process under various medium conditions. At the center of sPHENIX, the Monolithic-Active-Pixel-Sensor (MAPS) based VerTeX detector (MVTX) is a high-precision silicon pixel detector. The MVTX provides excellent position resolution and the capability of operating in continuous streaming readout mode, allowing precise vertex determination and recording a large data sample, both of which are particularly crucial for heavy flavor physics measurements. In this work, we will show the general performance of heavy-flavor hadron reconstruction. In addition, we will discuss the commissioning experience with sPHENIX. Finally, we will provide the projection of b-hadron and jet observables and discuss the estimated constraints on theoretical models.

We report a small footprint cost-effective isothermal rapid DNA amplification system, with integrated microfluidics for automated sample analysis and detection of SARS-CoV-2 in human and environmental samples. Our system measures low-level fluorescent signal in real-time during amplification, while maintaining the desired assay temperature, on a low power, portable system footprint. A unique soft microfluidic chip design was implemented to mitigate thermocapillary effects and facilitate optical alignment for automated image capture and signal analysis. The system-on-board prototype coupled with the LAMP primers designed by BioCoS, is sensitive enough to detect viral loads of SARS-CoV-2 in a broad concentration range corresponding to a threshold cycle range of 16 to 39. Clinical specimens were tested at Kingston Health Sciences using a clinically validated PCR assay and variants were determined using whole genome sequencing.

The illegal use of explosives by terrorists and other criminals is an increasing issue in public spaces, such as airports, railway stations, highways, sports arenas, theaters, and other large buildings. Security in these environments can be achieved by a set of different means, including the installation of scanners and other analytical devices to detect ultra-small traces of explosives in a very short time-frame to be able to take action as early as possible to prevent the detonation of such devices. Unfortunately, an ideal explosive detection system still does not exist, which means that a compromise is needed in practice. Most detection devices lack the extreme analytical sensitivity, which is nevertheless necessary due to the low vapor pressure of nearly all explosives. In addition, the rate of false positives needs to be virtually zero, which is also very difficult to achieve. Here we present an immunosensor system based on kinetic competition, which is known to be very fast and may even overcome affinity limitation, which impairs the performance of many traditional competitive assays. This immunosensor consists of a monolithic glass column with a vast excess of immobilized hapten, which traps the fluorescently labeled antibody as long as no explosive is present. In the case of TNT occurring, some binding sites of the antibody will be blocked, which leads to an immediate breakthrough of the labeled protein, detectable by highly sensitive laser-induced fluorescence with the help of a Peltier-cooled CMOS camera. Liquid handling is performed with high-precision syringe pumps and chip-based mixing-devices and flow-cells. The system achieved limits of detection of 1 pM (1 ppt) of the fluorescent label and around 100 pM (20 ppt) of the explosive 2,4,6-trinitrotoluene (TNT). The total assay time is less than 8 min. A cross-reactivity test with 5000 pM solutions showed no signal by PETN, RDX, and HMX. This immunosensor belongs to the most sensitive and fastest detectors for TNT with no significant cross-reactivity by non-related compounds.

The trafficking of illegal drugs by criminal networks at borders, harbors, or airports is an increasing issue in public health as these routes ensure the main supply of illegal drugs. The prevention of drug smuggling, including the installation of scanners and other analytical devices to detect ultra-small traces of drugs within a reasonable time frame, remains a challenge. The presented immunosensor is based on a monolithic affinity column with a large excess of immobilized hapten, which traps fluorescently labeled antibodies as long as the analyte cocaine is absent. In the presence of the drug, some binding sites of the antibody will be blocked, which leads to an immediate breakthrough of the labeled protein, detectable by highly sensitive laser-induced fluorescence with the help of a Peltier-cooled complementary metal-oxide-semiconductor (CMOS) camera. Liquid handling is performed with high-precision syringe pumps and microfluidic chip-based mixing devices and flow cells. The biosensor achieved limits of detection of 23 pM (7 ppt) of cocaine with a response time of 90 seconds and a total assay time below 3 minutes. With surface wipe sampling, the biosensor was able to detect 300 pg of cocaine. This immunosensor belongs to the most sensitive and fastest detectors for cocaine and offers near-continuous analyte measurement.