Specific Detector Research Articles

Enriching noise environment analysis with spectrally sensitive event detectors: potential and examples from a citizen science project

Enriching datasets with various indicators adds value to sound environment classification and allows analysing the relation between exposure and noise annoyance and sleep disturbance in more depth. This is in line with the WHO guideline report acknowledging the lack of event based indicators to evaluate the health impact of environmental noise on people. Specific event detectors add value in various ways. Firstly, including spectral windows makes event detection less sensitive to short-term variability of high frequent natural sources. Secondly, a dynamic threshold can adjust to local backgrounds. The event detection then becomes less sensitive to local features related to the diurnal pattern, the distance to curb side and other. In addition, the detectors can be made sensitive to match the source properties (expected emergence and minimum duration of the event). In this manuscript, the potential of spectrally sensitive event detectors will be illustrated on real-life data.

Unsupervised Active Visual Search with Monte Carlo Planning under Uncertain Detections.

We propose a solution for Active Visual Search of objects in an environment, whose 2D floor map is the only known information. Our solution has three key features that make it more plausible and robust to detector failures compared to state-of-the-art methods:(i)itis unsupervised as it does not need any training sessions. (ii)During the exploration, a probability distribution on the 2D floor map is updated according to an intuitive mechanism, while an improved belief update increases the effectiveness of the agent's exploration. (iii) We incorporate the awareness that an object detector may fail into the aforementioned probability modelling by exploiting the success statistics of a specific detector. Our solution is dubbed POMP-BE-PD (Pomcp-based Online Motion Planning with Belief by Exploration and Probabilistic Detection). It uses the current pose of an agent and an RGB-D observation to learn an optimal search policy, exploiting a POMDP solved by a Monte-Carlo planning approach. On the Active Vision Dataset Benchmark, we increase the average success rate over all the environments by a significant 35% while decreasing the average path length by 4% with respect to competing methods. Thus, our results are state-of-the-art, even without any training procedure. Code at https://intelligolabs.github.io/unsupervised_active_visual_search/.

Direct detection of endogenous Gαi activity in cells with a sensitive conformational biosensor.

Activation of heterotrimeric G-proteins (Gαβγ) by G-protein-coupled receptors (GPCRs) is not only a mechanism broadly used by eukaryotes to transduce signals across the plasma membrane, but also the target for a large fraction of clinical drugs. However, approaches typically used to assess this signaling mechanism by directly measuring G-protein activity, like optical biosensors, suffer from limitations. On one hand, many of these biosensors require expression of exogenous GPCRs and/or G-proteins, compromising readout fidelity. On the other hand, biosensors that measure endogenous signaling may still interfere with the signaling process under investigation or suffer from having a small dynamic range of detection, hindering broad applicability. Here, we developed an optical biosensor that detects the endogenous G-protein active species Gαi-GTP upon stimulation of endogenous GPCRs more robustly than current state-of-the-art sensors for the same purpose. Its design is based on the principle of bystander Bioluminescence Resonance Energy Transfer (BRET) and leverages the Gαi-binding protein named GINIP as a high affinity and specific detector module of the GTP-bound conformation of Gαi. We optimized this design to prevent interference with Gi-dependent signaling (cAMP inhibition) and to enable implementation in different experimental systems with endogenous GPCRs, including neurotransmitter receptors in primary astroglial cells or opioid receptors in cell lines, which revealed opioid neuropeptide-mediated activation profiles different from those observed with other biosensors involving exogenous GPCRs and G-proteins. Overall, we introduce a biosensor that directly and sensitively detects endogenous activation of G-proteins by GPCRs across different experimental settings without interfering with the subsequent propagation of signaling.

Machine Learning Based Compton Suppression for Nuclear Fusion Plasma Diagnostics

Diagnostics are critical on the path to commercial fusion reactors, since measurements and characterisation of the plasma is important for sustaining fusion reactions. Gamma spectroscopy is commonly used to provide information about the neutron energy spectrum from activation analysis, which can be used to calculate the neutron flux and fusion power. The detection limits for measuring nuclear dosimetry reactions used in such diagnostics are fundamentally related to Compton scattering events making up a background continuum in measured spectra. This background lies in the same energy region as peaks from low-energy gamma rays, leading to detection and characterisation limitations. This paper presents a digital machine learning Compton suppression algorithm (MLCSA), that uses state-of-the-art machine learning techniques to perform pulse shape discrimination for high purity germanium (HPGe) detectors. The MLCSA identifies key features of individual pulses to differentiate between those that are generated from photopeaks and Compton scatter events. Compton events are then rejected, reducing the low energy background. This novel suppression algorithm improves gamma spectroscopy results by lowering minimum detectable activity (MDA) limits and thus reducing the measurement time required to reach the desired detection limit. In this paper, the performance of the MLCSA is demonstrated using an HPGe detector, with a gamma spectrum containing americium-241 (Am-241) and cobalt-60 (Co-60). The MDA of Am-241 improved by 51% and the signal to background ratio improved by 49%, while the Co-60 peaks were partially preserved (reduced by 78%). The MLCSA requires no modelling of the specific detector and so has the potential to be detector agnostic, meaning the technique could be applied to a variety of detector types and applications.

The great detectives: humans versus AI detectors in catching large language model-generated medical writing

BackgroundThe application of artificial intelligence (AI) in academic writing has raised concerns regarding accuracy, ethics, and scientific rigour. Some AI content detectors may not accurately identify AI-generated texts, especially those that have undergone paraphrasing. Therefore, there is a pressing need for efficacious approaches or guidelines to govern AI usage in specific disciplines.ObjectiveOur study aims to compare the accuracy of mainstream AI content detectors and human reviewers in detecting AI-generated rehabilitation-related articles with or without paraphrasing.Study designThis cross-sectional study purposively chose 50 rehabilitation-related articles from four peer-reviewed journals, and then fabricated another 50 articles using ChatGPT. Specifically, ChatGPT was used to generate the introduction, discussion, and conclusion sections based on the original titles, methods, and results. Wordtune was then used to rephrase the ChatGPT-generated articles. Six common AI content detectors (Originality.ai, Turnitin, ZeroGPT, GPTZero, Content at Scale, and GPT-2 Output Detector) were employed to identify AI content for the original, ChatGPT-generated and AI-rephrased articles. Four human reviewers (two student reviewers and two professorial reviewers) were recruited to differentiate between the original articles and AI-rephrased articles, which were expected to be more difficult to detect. They were instructed to give reasons for their judgements.ResultsOriginality.ai correctly detected 100% of ChatGPT-generated and AI-rephrased texts. ZeroGPT accurately detected 96% of ChatGPT-generated and 88% of AI-rephrased articles. The areas under the receiver operating characteristic curve (AUROC) of ZeroGPT were 0.98 for identifying human-written and AI articles. Turnitin showed a 0% misclassification rate for human-written articles, although it only identified 30% of AI-rephrased articles. Professorial reviewers accurately discriminated at least 96% of AI-rephrased articles, but they misclassified 12% of human-written articles as AI-generated. On average, students only identified 76% of AI-rephrased articles. Reviewers identified AI-rephrased articles based on ‘incoherent content’ (34.36%), followed by ‘grammatical errors’ (20.26%), and ‘insufficient evidence’ (16.15%).Conclusions and relevanceThis study directly compared the accuracy of advanced AI detectors and human reviewers in detecting AI-generated medical writing after paraphrasing. Our findings demonstrate that specific detectors and experienced reviewers can accurately identify articles generated by Large Language Models, even after paraphrasing. The rationale employed by our reviewers in their assessments can inform future evaluation strategies for monitoring AI usage in medical education or publications. AI content detectors may be incorporated as an additional screening tool in the peer-review process of academic journals.

Investigation of Cellulose-Based Materials Applied in Life Sciences Using Laser Light Scattering Methods.

This review emphasizes the practical importance of laser light scattering methods for characterizing cellulose and its derivatives. The physicochemical parameters like molecular weights, the radius of gyration, hydrodynamic radius, and conformation will be considered when the reproducibility of polymer behavior in solution is necessary for the subsequent optimization of the property profile of a designed product. Since there are various sources of cellulose, and the methods of cellulose extraction and chemical modification have variable yields, materials with variable molecular weights, and size polydispersity will often result. Later, the molecular masses will influence other physicochemical properties of cellulosic materials, both in solution and solid state. Consequently, the most rigorous determination of these quantities is imperative. In this regard, the following are presented and discussed in this review: the theoretical foundations of the light scattering phenomenon, the evolution of the specific instrumentation and detectors, the development of the detector-coupling techniques which include a light scattering detector, and finally, the importance of the specific parameters of polymers in solution, resulting from the data analysis of light scattering signals. All these aspects are summarized according to the chemical classification of the materials: celluloses, esters of cellulose, co-esters of cellulose, alkyl esters of cellulose, ethers of cellulose, and other heterogeneous cellulose derivatives with applications in life sciences.

Dosimetric Systems in Pre-Treatment QA for Stereotactic Treatments: Correlation Agreements and Target Volume Dependency.

This study comprehensively investigated pre-treatment quality assurance (QA) for 100 cancer patients undergoing stereotactic treatments (SRS/SRT) using various detectors. The study conducted QA for SRS/SRT treatments planned with a 6MV SRS beam at a dose rate of 1,000 MU/min, utilizing Eclipse v13.6 Treatment Planning System (TPS). Point dose measurements employed 0.01cm3 and 0.13cm3 cylindrical ionization chambers, while planar dose verification utilized Gafchromic EBT-XD Film and Portal Imager (aS1000). Plans were categorized by target volume, and a thorough analysis compared point dose agreements, planar dose gamma pass rates, and their correlations with chamber volume mean dose, detector type, and point dose agreement. Additionally, the consistency between different ionization chambers was assessed. Point dose agreement generally improved with increasing target volume, except for volumes over 10cm3 with 0.01cm3 chambers, showing a contrary trend. Significant differences (p<0.05) were observed between TPS and measured doses for both chambers. Gamma pass rate improved with increasing target volume in EBT XD and aS1000 analyses, except for the >10cm3 group in EBT XD. EBT XD demonstrated better agreement with TPS for target volumes up to 10cm3 compared to aS1000, with a statistically significant difference (p<0.05) between the detectors. Strong correlations were found between chamber point dose and chamber volume mean dose agreement, as well as between the two gamma criteria analyses of the same detector type in the planar dose correlation analysis. However, weak correlations were discovered for other analyses. This study found weak correlation between different detector types in pre-treatment QA for point dose and planar dose evaluation. However, within a specific detector type, strong correlation was observed for different point dose evaluation methods and gamma criteria. This highlights the importance of cautious interpretation of QA results, particularly for SRS QA, due to the lack of correlation between detector types.

Human sensory adaptation to the ecological structure of environmental statistics.

Humans acquire sensory information via fast, highly specialized detectors: For example, edge detectors monitor restricted regions of visual space over timescales of 100-200ms. Surprisingly, this study demonstrates that their operation is nevertheless shaped by the ecological consistency of slow global statistical structure in the environment. In the experiments, humans acquired feature information from brief localized elements embedded within a virtual environment. Cast shadows are important for determining the appearance and layout of the environment. When the statistical reliability of shadows was manipulated, human feature detectors implicitly adapted to these changes over minutes, adjusting their response properties to emphasize either "image-based" or "object-based" anchoring of local visual elements. More specifically, local visual operators were more firmly anchored around object representations when shadows were reliable. As shadow reliability was reduced, visual operators disengaged from objects and became anchored around image features. These results indicate that the notion of sensory adaptation must be reframed around complex statistical constructs with ecological validity. These constructs far exceed the spatiotemporal selectivity bandwidth of sensory detectors, thus demonstrating the highly integrated nature of sensory processing during natural behavior.

A new method of measuring Forbush decreases

Context. Forbush decreases (FDs) are short-term depressions in the galactic cosmic ray (GCR) flux and one of the common signatures of coronal mass ejections (CMEs) in the heliosphere. They often show a two-step profile, the second one associated with the CME’s magnetic structure. This second step can be described by the recently developed analytical FD model for flux ropes (FRs) – ForbMod. Aims. The aim of this study is to utilise ForbMod to develop a best-fit procedure to be applied on FR-related FDs as a convenient measurement tool. Our motivation is to develop a best-fit procedure that can be applied to a data series from an arbitrary detector. Thus, the basic procedure would facilitate measurement estimation of the magnitude of the FR-related FD, with the possibility of being adapted for the energy response of a specific detector for a more advanced analysis. Methods. The non-linear fitting was performed by calculating all possible ForbMod curves constrained within the FR borders to the designated dataset and minimising the mean square error (MSE). In order to evaluate the performance of the ForbMod best-fit procedure, we used synthetic measurements produced by calculating the theoretical ForbMod curve for a specific example CME and then applying various effects to the data to mimic the imperfection of the real measurements. We also tested the ForbMod best-fit function on the real data, measured by detector F of the SOHO/EPHIN instrument on a sample containing 30 events, all of which have a distinct FD corresponding to the magnetic obstacle. The extraction of FD profiles (from the onset to the end) was performed manually by an observer, whereby we applied two different versions of border selection and assigned a quality index to each event. Results. We do not find notable differences between events marked by a different quality index. For events with a selection of two different borders, we find that the best fit applied on extended interplanetary coronal mass ejection (ICME) structure borders results in a slightly larger MSE and differences compared to the traditional method due to a larger scatter of the data points. We find that the best-fit results can visually be categorised into six different FD profile types. Although some profiles do not show a visually pleasing FD, the ForbMod best-fit function still manages to find a solution with a relatively small MSE. Conclusions. Overall, we find that the ForbMod best-fit procedure performs similar to the traditional algorithm-based observational method, but with slightly smaller values for the FD amplitude, as it’s taking into account the noise in the data. Furthermore, we find that the best-fit procedure has an advantage compared to the traditional method as it can estimate the FD amplitude even when there is a data gap at the onset of the FD.

Smart Grid Security: An Effective Hybrid CNN-Based Approach for Detecting Energy Theft Using Consumption Patterns.

In Internet of Things-based smart grids, smart meters record and report a massive number of power consumption data at certain intervals to the data center of the utility for load monitoring and energy management. Energy theft is a big problem for smart meters and causes non-technical losses. Energy theft attacks can be launched by malicious consumers by compromising the smart meters to report manipulated consumption data for less billing. It is a global issue causing technical and financial damage to governments and operators. Deep learning-based techniques can effectively identify consumers involved in energy theft through power consumption data. In this study, a hybrid convolutional neural network (CNN)-based energy-theft-detection system is proposed to detect data-tampering cyber-attack vectors. CNN is a commonly employed method that automates the extraction of features and the classification process. We employed CNN for feature extraction and traditional machine learning algorithms for classification. In this work, honest data were obtained from a real dataset. Six attack vectors causing data tampering were utilized. Tampered data were synthetically generated through these attack vectors. Six separate datasets were created for each attack vector to design a specialized detector tailored for that specific attack. Additionally, a dataset containing all attack vectors was also generated for the purpose of designing a general detector. Furthermore, the imbalanced dataset problem was addressed through the application of the generative adversarial network (GAN) method. GAN was chosen due to its ability to generate new data closely resembling real data, and its application in this field has not been extensively explored. The data generated with GAN ensured better training for the hybrid CNN-based detector on honest and malicious consumption patterns. Finally, the results indicate that the proposed general detector could classify both honest and malicious users with satisfactory accuracy.

Reliability and Robustness analysis of Machine Learning based Phishing URL Detectors

ML-based Phishing URL (MLPU) detectors serve as the first level of defence to protect users and organisations from being victims of phishing attacks. Lately, few studies have launched successful adversarial attacks against specific MLPU detectors raising questions on their practical reliability and usage. Nevertheless, the robustness of these systems has not been extensively investigated. Therefore, the security vulnerabilities of these systems, in general, remain primarily unknown that calls for testing the robustness of these systems. In this article, we have proposed a methodology to investigate the reliability and robustness of 50 representative state-of-the-art MLPU models. First, we have proposed a cost-effective Adversarial URL generator URLBUG that created an Adversarial URL dataset ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Adv_\text{data}$</tex-math></inline-formula> ) . Subsequently, we reproduced 50 MLPU (traditional ML and Deep learning) systems and recorded their baseline performance. Lastly, we tested the considered MLPU systems on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Adv_\text{data}$</tex-math></inline-formula> and analyzed their robustness and reliability using box plots and heat maps. Our results showed that the generated adversarial URLs have valid syntax and can be registered at a median annual price of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\$}$</tex-math></inline-formula> 11.99, and out of 13% of the already registered adversarial URLs, 63.94% were used for malicious purposes. Moreover, the considered MLPU models Matthew Correlation Coefficient (MCC) dropped from median 0.92 to 0.02 when tested against <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Adv_\text{data}$</tex-math></inline-formula> , indicating that the baseline MLPU models are unreliable in their current form. Further, our findings identified several security vulnerabilities of these systems and provided future directions for researchers to design dependable and secure MLPU systems.

Multi-institutional consensus on machine QA for isochronous cyclotron-based systems delivering ultra-high dose rate (FLASH) pencil beam scanning proton therapy in transmission mode.

The first clinical trials to assess the feasibility of FLASH radiotherapy in humans have started (FAST-01, FAST-02) and more trials are foreseen. To increase comparability between trials it is important to assure treatment quality and therefore establish a standard for machine quality assurance (QA). Currently, the AAPM TG-224 report is considered as the standard on machine QA for proton therapy, however, it was not intended to be used for ultra-high dose rate (UHDR) proton beams, which have gained interest due to the observation of the FLASH effect. The aim of this study is to find consensus on practical guidelines on machine QA for UHDR proton beams in transmission mode in terms of which QA is required, how they should be done, which detectors are suitable for UHDR machine QA, and what tolerance limits should be applied. A risk assessment to determine the gaps in the current standard for machine QA was performed by an international group of medical physicists. Based on that, practical guidelines on how to perform machine QA for UHDR proton beams were proposed. The risk assessment clearly identified the need for additional guidance on temporal dosimetry, addressing dose rate (constancy), dose spillage, and scanning speed. In addition, several minor changes from AAPM TG-224 were identified; define required dose rate levels, the use of clinically relevant dose levels, and the use of adapted beam settings to minimize activation of detector and phantom materials or to avoid saturation effects of specific detectors. The final report was created based on discussions and consensus. Consensus was reached on what QA is required for UHDR scanning proton beams in transmission mode for isochronous cyclotron-based systems and how they should be performed. However, the group discussions also showed that there is a lack of high temporal resolution detectors and sufficient QA data to set appropriate limits for some of the proposed QA procedures.

Optimizing deep learning-based piled-up pulse height correction method for high radiation-field application

In high-radiation environments, measured pile-up pulses can lead to unavoidable issues such as total count loss and spectrum distortion. Additionally, the recording of large volumes of data within a short period makes real-time processing difficult. In this study, a deep learning-based pulse height estimation (PHE) method was optimized to perform pile-up signal correction in high-radiation fields. First, we adopted a previous deep-learning-based PHE method that allows for fast correction without being restricted to specific detectors. However, the peak-finding method was slightly modified to improve the count restoration rate. Moreover, the input data length of the deep learning model was optimized for convolutional neural networks (CNN) and deep neural networks (DNN) to achieve the maximum correction performance using minimal input data. A series of single pulses was experimentally obtained from a LaBr3 detector with a short decay time to prepare a dataset for training the deep learning models. The pile-up signals were generated by randomly synthesizing single pulses. Samples around their peaks were sliced using the peak-finding method and used as input data for the deep learning models. As a result of the optimization, the modified peak-finding method improved the count restoration rate compared to the previous method by effectively detecting the peaks of tail pile-up, and peak pile-up pulses. Furthermore, the input data length and region were optimized based on the performance evaluation of each deep learning model. Despite having a simpler architecture than the CNN model, the DNN model demonstrated excellent PHE performance. The results of this study showed the efficient and practical considerations necessary for applying pile-up signal correction in high-radiation fields.

Generalizing to new geometries with Geometry-Aware Autoregressive Models (GAAMs) for fast calorimeter simulation

Generation of simulated detector response to collision products is crucial to data analysis in particle physics, but computationally very expensive. One subdetector, the calorimeter, dominates the computational time due to the high granularity of its cells and complexity of the interactions. Generative models can provide more rapid sample production, but currently require significant effort to optimize performance for specific detector geometries, often requiring many models to describe the varying cell sizes and arrangements, without the ability to generalize to other geometries. We develop a geometry-aware autoregressive model, which learns how the calorimeter response varies with geometry, and is capable of generating simulated responses to unseen geometries without additional training. The geometry-aware model outperforms a baseline unaware model by over 50% in several metrics such as the Wasserstein distance between the generated and the true distributions of key quantities which summarize the simulated response. A single geometry-aware model could replace the hundreds of generative models currently designed for calorimeter simulation by physicists analyzing data collected at the Large Hadron Collider. This proof-of-concept study motivates the design of a foundational model that will be a crucial tool for the study of future detectors, dramatically reducing the large upfront investment usually needed to develop generative calorimeter models.

Biomass derived green carbon dots for sensing applications of effective detection of metallic contaminants in the environment

The rapid consumption of metals and unorganized disposal have led to unprecedented increases in heavy metal ion concentrations in the ecosystem, which disrupts environmental homeostasis and results in agricultural biodiversity loss. Mitigation and remediation plans for heavy metal pollution are largely dependent on the discovery of cost-effective, biocompatible, specific, and robust detectors because conventional methods involve sophisticated electronics and sample preparation procedures. Carbon dots (CDs) have gained significant importance in sensing applications related to environmental sustainability. Fluorescence sensor applications have been enhanced by their distinctive spectral properties and the potential for developing efficient photonic devices. With the recent development of biomass-functionalized carbon dots, a wide spectrum of multivalent and bivalent transition metal ions responsible for water quality degradation can be detected with high efficiency and minimal toxicity. This review explores the various methods of manufacturing carbon dots and the biochemical mechanisms involved in metal detection using green carbon dots for sensing applications involving Cu (II), Fe (III), Hg (II), and Cr (VI) ions in aqueous systems. A detailed discussion of practical challenges and future recommendations is presented to identify feasible design routes.

Investigation of modified uni-traveling carrier photodiode for cryogenic microwave photonic links

Quantum devices present the potential for unparalleled computing and communications capabilities; however, the cryogenic temperatures required to successfully control and read out many qubit platforms can prove to be very challenging to scale. Recently, there has emerged an interest in using microwave photonics to deliver control signals down to ultracold stages via optical fiber, thereby reducing thermal load and facilitating dense wavelength multiplexing. Photodetectors can then convert this optical energy to electrical signals for qubit control. The fidelity of the quantum operations of interest therefore depend heavily upon the characteristics of the photodiode, yet experimental demonstrations of fiber-coupled photodetection systems at low temperatures are relatively few in number, leaving important open questions regarding how specific detectors may perform in real-world cryogenic settings. In this work, we examine a highly linear modified uni-traveling carrier photodiode (MUTC-PD) under C-band illumination (1530–1565 nm) at three temperature regimes (300 K, 80 K, and ∼4 K) and multiple bias conditions. Our findings of reduced responsivity but preserved bandwidth are consistent with previous studies, while our saturation tests suggest a variety of potential applications for MUTC-PDs in cryogenic microwave photonics with and without electrical bias. Overall, our results should provide a valuable foundation for the continued and expanding use of this detector technology in quantum information processing.

Optimization strategies for the greenhouse gas consumption of the resistive plate chamber detectors at the CERN LHC experiments

Resistive Plate Chamber (RPC) detectors at the Large Hadron Collider (LHC) experiments are operated with a gas mixture containing two greenhouse gases (GHG): C2H2F4 (R134a) and SF6. These gases are used because they allow to achieve specific detector performance. However, due to their high Global Warming Potential (GWP) they are currently subject to a phase down policy that affects price and availability. To reduce the GHG usage, different strategies have been identified. As immediate actions, during the LHC Long Shutdown 2 (LS2) the RPC gas systems was upgraded to cope with new detector requirements and, in parallel, extensive campaigns for fixing leaks at detector level were performed. Since R134a dominates the GHG consumption, the development of a R134a gas recuperation plant is ongoing. A first prototype system was tested with encouraging results. A second prototype is under construction and it will be ready by beginning of 2023. For future long-term detector operation, R&D studies are ongoing for finding “green” alternatives to the currently used gases. Unfortunately, new gases developed by industry as refrigerant fluids are not behaving as the R134a in particle detectors which makes the replacement for present experiments difficult.

Dose distributions of the INTRABEAM electronic brachytherapy system measured with traceable detectors

In this work, a methodology to determine the absorbed dose rate to water distribution from electronic brachytherapy (eBT) units is suggested. It involves measuring directly in a full scatter water tank with small active detectors whose energy response at low X-ray photon energies have been previously characterized. Dose distributions of an INTRABEAM PRS 500 system in two configurations — bare needle and with the spherical applicator of 40 mm diameter — were measured in water using the Exradin A26 ionization chamber and the PTW 60019 microDiamond detector. The continuous energy response of these two detectors in terms of air kerma from 5 keV to 70 keV (eBT energy range) was available from a previous calibration at several X-ray radiation qualities. Here, it is converted to the response in absorbed dose to water using correction factors obtained with Monte Carlo (MC) particle transport simulations. The detectors were modelled using the MCNP code. The air kerma calibration factors were simulated and compared with measurements to refine and validate the models. A series of simulations of the experimental arrangement of the INTRABEAM and the detector were carried out for determining the system specific detector correction factors. These values are distance-dependent due to the steep dose gradient and the remarkable spectral changes around the eBT source in water. The dose rate with distance from the source, the radial and the polar distributions of the INTRABEAM are reported and compared with the manufacturers supplied data. This work is an effort to provide traceability for detectors and measurement procedures for the determination of 3D dose distributions as part of the European joint research project “Primary standards and traceable measurement methods for X-ray emitting electronic brachytherapy devices” (18NRM02 PRISM-eBT).

Prospects of testing late-time cosmology with weak lensing of gravitational waves and galaxy surveys

We investigate the synergy of upcoming galaxy surveys and gravitational wave (GW) experiments in constraining late-time cosmology, examining the cross-correlations between the weak lensing of gravitational waves (GW-WL) and the galaxy fields. Without focusing on any specific GW detector configuration, we benchmark the requirements for the high precision measurement of cosmological parameters by considering several scenarios, varying the number of detected GW events and the uncertainty on the inference of the source luminosity distance and redshift.We focus on ΛCDM and scalar-tensor cosmologies, using the Effective Field Theory formalism as a unifying language. We find that, in some of the explored setups, GW-WL contributes to the galaxy signal by doubling the accuracy on non-ΛCDM parameters, allowing in the most favourable scenarios to reach even percent and sub-percent level bounds. Though the most extreme cases presented here are likely beyond the observational capabilities of currently planned individual GW detectors, we show nonetheless that — provided that enough statistics of events can be accumulated — GW-WL offers the potential to become a cosmological probe complementary to LSS surveys, particularly for those parameters that cannot be constrained by other GW probes such as standard sirens.

Catalytic degradation of propetamphos and azamethiphos using silver ion.

In this study, the degradation of two organophosphate pesticides, namely, propetamphos and azamethiphos, in the presence of Ag+ at different mole ratios was investigated. Moreover, the kinetic and degradation pathways of both chemicals in the range of 0-60min were explored. Gas chromatography equipped with a thermionic specific detector was used to investigate the pesticide degradation kinetics and mechanism. The results show that the degradation rate of both pesticides follows first-order kinetic. The first-order rate constant and the half-life of reaction were in the range of 0.002-0.143min-1, 187-2.1min, and 0.005-0.164min-1 and 60-1.8min, for propetamphos and azamethiphos, respectively, at ambient temperature (25 ºC). Because group containing sulfur atom is a better leaving group than group containing nitrogen, the rate of degradation of azamethiphos is higher than propetamphos. In a higher mole ratio of Ag+ to pesticides, the degradation rate was increased, and it is possible to predict the rate of degradation of pesticides according to the chemical composition of leaving group.