Two-Dimensional Perovskite Single Crystals for High-Performance X-ray Imaging and Exploring MeV X-ray Detection

Scintillation semiconductors play increasingly important medical diagnosis and industrial inspection roles.Recently,two-dimensional(2D)perovskites have been shown to be promising materials for medical X-ray imaging,but they are mostly used in low-energy(≤130 keV)regions.Direct detection of MeV X-rays,which ensure thorough penetration of the thick shell walls of containers,trucks,and aircraft,is also highly desired in practical industrial applications.Unfortunately,scintillation semiconductors for high-energy X-ray detection are currently scarce.Here,This paper reports a 2D(C_(4)H_(9)NH_(3))_(2)PbBr_(4)single crystal with outstanding sensitivity and stability toward X-ray radiation that provides an ultra-wide detectable X-ray range of between 8.20 nGy_(air)s^(-1)(50 keV)and 15.24 mGy_(air)s^(-1)(9 MeV).The(C_(4)H_(9)NH_(3))_(2)PbBr_(4)single-crystal detector with a vertical structure is used for high-performance X-ray imaging,delivering a good spatial resolution of 4.3 Ip mm^(-1)in a plane-scan imaging system.Low ionic migration in the 2D perovskite enables the vertical device to be operated with hundreds of keV to MeV X-ray radiation at high bias voltages,leading to a sensitivity of 46.90μC Gy_(air)-1 cm^(-2)(-1.16 Vμm^(-1))with 9 MeV X-ray radiation,demonstrating that 2D perovskites have enormous potential for high-energy industrial applications.

Development of Quantitative Analysis Method of Carbendazim in Tomatoes by High-Performance Liquid Chromatography with Fluorescence Detection(HPLC-FLD)

Carbendazim belongs to the benzimidazole fungicides,which can be used for control lots of fungi pathogens.High-performance liquid chromatography is frequently used for the analysis of carbendazim in all kinds of samples.In most occasions,the developed methods were applied for the simultaneous detection of a huge number of pesticides.Thus,an analytical method via UPLC-FLD was developed,and the sample preparation process was optimized by studying the effect of extraction solvent,approach,time and purification absorbent on the recovery rate of carbendazim.The results showed the optimized method for analysis was ultrasonication-assisted extraction with acetonitrile for 1 min,and subsequent purification by C18.In this occasion,the established analytical method of carbendazim in tomato samples displayed good linearity,accuracy and precision.

An Enhanced Lung Cancer Detection Approach Using Dual-Model Deep Learning Technique

Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of successful treatment and survival.However,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung tissue.Single-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate detection.Furthermore,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor characteristics.To overcome these disadvantages,dual-model or multi-model approaches can be employed.This research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of tumors.CNNs automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung cancer.YOLOv8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single image.This is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to detect.Furthermore,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive datasets.The combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applications.The CNN achieved an accuracy of 97.67%in classifying lung tissues into healthy and cancerous categories.The YOLOv8 model achieved an Intersection over Union(IoU)score of 0.85 for tumor localization,reflecting high precision in detecting and marking tumor boundaries within the images.Finally,the incorporation of synthetic images generated by DCGAN led to a 10%improvement in both the CNN classification accuracy and YOLOv8 detection performance.

Determination of Diphenytriazol (DL111-IT) in Rabbit Plasma by High-Performance Liquid Chromatography with Fluorescence Detection

Aim To develop an HPLC method with fluorescence detection for the assay ofDL111-IT in rabbit plasma. Methods DL111-IT and internal standard glybenzcyclamide in rabbit plasmawere extracted with chloroform. The determination was performed on a Diamonsil ODS-C_(18) column(150 mm x 4.6 mm, 5 μm) with a mobile phase of acetonitrile and 0.025 mol·L^(-1) diammoniumhydrogen phosphate buffer (pH 5.0, adjusted by phosphoric acid) (60:40, V/V) at a flow-rate of 1.0mL·min^(-1) . Fluorescence detector was operated at excitation wavelength of 250 nm and emissionwavelength of 332 nm. Results The calibration curve in plasma was linear from 1.00 - 20.00ng·mL^(-1) ( r = 0.999 6, n = 5). The method afforded average extracting recoveries of 85.3% ±1.3%, 84.9% ± 2.7% and 85.8% ± 1.8%, and the average method recoveries were 99.5% ±0.4%, 102.1%± 1.8% and 101.3% ± 2.4% for the high (20.00 ng·mL^(-1)) , middle (10.00 ng·mL^(-1)) and low (1.00 ng·mL^(-1)) check samples, respectively. The intra-day (n = 5) and inter-day (n = 5) precisions(RSD) were less than 3.0% and 7.0%, respectively. The limit of detection and quantitation for themethod were 0.3 ng·mL^(-1) (S/N = 3) and 1 ng·mL^(-1) (S/N = 10, RSD<7.0%) plasma sample,respectively. Conclusion The developed method was accurate, sensitive, simple and could be used forpharmacokinetic study of DL111- IT.

MARIE:One-Stage Object Detection Mechanism for Real-Time Identifying of Firearms

Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable to society.Consequently,there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces,thereby preventing potential attacks or violent incidents.Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection,particularly in identifying firearms.This paper introduces a novel automatic firearm detection surveillance system,utilizing a one-stage detection approach named MARIE(Mechanism for Realtime Identification of Firearms).MARIE incorporates the Single Shot Multibox Detector(SSD)model,which has been specifically optimized to balance the speed-accuracy trade-off critical in firearm detection applications.The SSD model was further refined by integrating MobileNetV2 and InceptionV2 architectures for superior feature extraction capabilities.The experimental results demonstrate that this modified SSD configuration provides highly satisfactory performance,surpassing existing methods trained on the same dataset in terms of the critical speedaccuracy trade-off.Through these innovations,MARIE sets a new standard in surveillance technology,offering a robust solution to enhance public safety effectively.

Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment planning.Liver Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and challenging.This review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and resilience.Performance metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical contexts.The review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging conditions.It suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular methods.This paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

A novel detection method for warhead fragment targets in optical images under dynamic strong interference environments

A measurement system for the scattering characteristics of warhead fragments based on high-speed imaging systems offers advantages such as simple deployment,flexible maneuverability,and high spatiotemporal resolution,enabling the acquisition of full-process data of the fragment scattering process.However,mismatches between camera frame rates and target velocities can lead to long motion blur tails of high-speed fragment targets,resulting in low signal-to-noise ratios and rendering conventional detection algorithms ineffective in dynamic strong interference testing environments.In this study,we propose a detection framework centered on dynamic strong interference disturbance signal separation and suppression.We introduce a mixture Gaussian model constrained under a joint spatialtemporal-transform domain Dirichlet process,combined with total variation regularization to achieve disturbance signal suppression.Experimental results demonstrate that the proposed disturbance suppression method can be integrated with certain conventional motion target detection tasks,enabling adaptation to real-world data to a certain extent.Moreover,we provide a specific implementation of this process,which achieves a detection rate close to 100%with an approximate 0%false alarm rate in multiple sets of real target field test data.This research effectively advances the development of the field of damage parameter testing.

Development of NIR Responsive Upconversion Nanosensor for Turn-on Detection of 4-Nonylphenol

4-Nonylphenol(NP)is a kind of estrogen belonging to the endocrine disrupter,widely used in various agricultural and industrial goods.However,extensive use of NP with direct release to environment poses high risks to both human health and ecosystems.Herein,for the first time,we developed near-infrared(NIR)responsive upconversion luminescence nanosensor for NP detection.The Förster resonance energy transfer based upconversion nanoparticles(UCNPs)-graphene oxide sensor offers highly selective and sensitive detection of NP in linear ranges of 5−200 ng/mL and 200−1000 ng/mL under 980 nm and 808 nm excitation,respectively,with LOD at 4.2 ng/mL.The sensors were successfully tested for NP detection in real liquid milk samples with excellent recovery results.The rare-earth fluoride based upconversion luminescence nanosensor with NIR excitation wavelength,holds promise for sensing food,environmental,and biological samples due to their high sensitivity,specific recognition,low LOD,negligible autofluorescence,along with the deep penetration of NIR excitation sources.

Oversampling-Enhanced Feature Fusion-Based Hybrid ViT-1DCNN Model for Ransomware Cyber Attack Detection

Ransomware attacks pose a significant threat to critical infrastructures,demanding robust detection mechanisms.This study introduces a hybrid model that combines vision transformer(ViT)and one-dimensional convolutional neural network(1DCNN)architectures to enhance ransomware detection capabilities.Addressing common challenges in ransomware detection,particularly dataset class imbalance,the synthetic minority oversampling technique(SMOTE)is employed to generate synthetic samples for minority class,thereby improving detection accuracy.The integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features,resulting in comprehensive ransomware classification.Tested on the UNSW-NB15 dataset,the proposed ViT-1DCNN model achieved 98%detection accuracy with precision,recall,and F1-score metrics surpassing conventional methods.This approach not only reduces false positives and negatives but also offers scalability and robustness for real-world cybersecurity applications.The results demonstrate the model’s potential as an effective tool for proactive ransomware detection,especially in environments where evolving threats require adaptable and high-accuracy solutions.

Advances and challenges in molecular understanding, early detection, and targeted treatment of liver cancer

In this review,we explore the application of next-generation sequencing in liver cancer research,highlighting its potential in modern oncology.Liver cancer,particularly hepatocellular carcinoma,is driven by a complex interplay of genetic,epigenetic,and environmental factors.Key genetic alterations,such as mutations in TERT,TP53,and CTNNB1,alongside epigenetic modifications such as DNA methylation and histone remodeling,disrupt regulatory pathways and promote tumorigenesis.Environmental factors,including viral infections,alcohol consum-ption,and metabolic disorders such as nonalcoholic fatty liver disease,enhance hepatocarcinogenesis.The tumor microenvironment plays a pivotal role in liver cancer progression and therapy resistance,with immune cell infiltration,fibrosis,and angiogenesis supporting cancer cell survival.Advances in immune check-point inhibitors and chimeric antigen receptor T-cell therapies have shown po-tential,but the unique immunosuppressive milieu in liver cancer presents challenges.Dysregulation in pathways such as Wnt/β-catenin underscores the need for targeted therapeutic strategies.Next-generation sequencing is accele-rating the identification of genetic and epigenetic alterations,enabling more precise diagnosis and personalized treatment plans.A deeper understanding of these molecular mechanisms is essential for advancing early detection and developing effective therapies against liver cancer.

Topology Data Analysis-Based Error Detection for Semantic Image Transmission with Incremental Knowledge-Based HARQ

Semantic communication(SemCom)aims to achieve high-fidelity information delivery under low communication consumption by only guaranteeing semantic accuracy.Nevertheless,semantic communication still suffers from unexpected channel volatility and thus developing a re-transmission mechanism(e.g.,hybrid automatic repeat request[HARQ])becomes indispensable.In that regard,instead of discarding previously transmitted information,the incremental knowledge-based HARQ(IK-HARQ)is deemed as a more effective mechanism that could sufficiently utilize the information semantics.However,considering the possible existence of semantic ambiguity in image transmission,a simple bit-level cyclic redundancy check(CRC)might compromise the performance of IK-HARQ.Therefore,there emerges a strong incentive to revolutionize the CRC mechanism,thus more effectively reaping the benefits of both SemCom and HARQ.In this paper,built on top of swin transformer-based joint source-channel coding(JSCC)and IK-HARQ,we propose a semantic image transmission framework SC-TDA-HARQ.In particular,different from the conventional CRC,we introduce a topological data analysis(TDA)-based error detection method,which capably digs out the inner topological and geometric information of images,to capture semantic information and determine the necessity for re-transmission.Extensive numerical results validate the effectiveness and efficiency of the proposed SC-TDA-HARQ framework,especially under the limited bandwidth condition,and manifest the superiority of TDA-based error detection method in image transmission.

Comparison of liquid-liquid extraction-thin layer chromatography with solid-phase extraction-high-performance thin layer chromatography in detection of urinary morphine

Liquid-liquid extraction-thin layer chromatography （LLE-TLC） has been a common and routine combined method for detection of drugs in biological materials. Solid-phase extraction （SPE） is gradually replacing the tra- ditional LLE method. High performance thin layer chromatography （HPTLC） has several advantages over TLC. The present work studied the higher efficiency of a new SPE-HPTLC method over that of a routine LLE-TLC method, in extraction and detection of urinary morphine. Fifty-eight urine samples, primarily identified as mor- phine-positive samples by a strip test, ＇were re-screened by LLE-TLC and SPE-HPTLC. The results of LLE-TLC and SPE-HPTLC were then compared with each other. The results showed that the SPE-HPTLC detected 74% of total samples as morphine-positive samples whereas the LLE-TLC detected 48% of the same samples. We further discussed the effect of codeine abuse on TLC analysis of urinary morphine. Regarding the importance of morphine detection in urine, the present combined SPE-HPTLC method is suggested as a replacement method for detection of urinary morphine by many reference laboratories.

Trace Determination of Tamoxifen in Biological Fluids Using Hollow Fiber Liquid-Phase Microextraction Followed by High-Performance Liquid Chromatography-Ultraviolet Detection

The applicability of hollow fiber liquid-phase microextraction (HF-LPME) combined with high-performance liquid chromatography-ultraviolet detection (HPLC-UV) was evaluated for the extraction and determination of tamoxifen (TAM) in biological fluids including human urine and plasma. The drug was extracted from a 15 mL aqueous sample (source phase;SP) into an organic phase impregnated in the pores of the hollow fiber (membrane phase;MP) followed by the back-extraction into a second aqueous solution (receiving phase;RP) located in the lumen of the hollow fiber. The effects of several factors such as the nature of organic solvent, compositions of SP and RP solutions, extraction time, ionic strength and stirring rate on the extraction efficiency were examined and optimized. An enrichment factor of 360 along with substantial sample clean up was obtained under the optimized conditions. The calibration curve showed linearity in the range of 1 - 500 ng?mL–1 and the limit of detection was found to be 0.5 ng?mL–1 in aqueous medium. A reasonable relative recovery (≥89%) and satisfactory intra-assay (3.7% - 4.2%, n = 3) and inter-assay (7.5% - 7.8%, n = 3) precision illustrated good performance of the analytical procedure in spiked human urine and plasma samples.

Development of a reversed dispersive based graphene functionalized with multiwalled carbon nanotubes for detection ofβ2-agonists in pork by high-performance liquid chromatography

A reliable and inexpensive pretreatment procedure in the determination ofβ2-agonists in pork was developped.The procedure used a nanocomposite of multiwalled carbon nanotubes(CNTs)functionalized with graphene(rGO)as the reversed dispersive sorbent.It was analyzed after purification by highperformance liquid chromatography,the extraction time and the properties of the nanocomposite were optimized.Under optimized conditions,present method has linear response over concentration range of 0.5–50 ng/mL in pork samples with a satisfactory detection limit close to 0.1 ng/mL.The precisions of the current method(coefficient of variation)are lower than 5%,while recoveries are more than 98.3%.The nanocomposite exhibited high adsorptivity,long-term storage stability,satisfactory anti-interfering activity and high selectivity toward2-agonists compared with those of rGO and CNTs.

High-Performance Structure of Guard Ring in Avalanche Diode for Single Photon Detection

Avalanche photon diode and avalanche diode array, working in Geiger mode, have single photon detection capability. The structure of guard ring is the key factor to avoid the premature edge breakdown of the avalanche diode and increase the maximum bias voltage. A new structure of the guard ring is proposed in this letter, in which the floating guard ring is put outside the p-well guard ring. Simulation results indicate that the maximum bias voltage of the proposed guard ring is higher than that of the state-of-the-art methods.

基于改进Detection Transformer的棉花幼苗与杂草检测模型研究

基于深度学习的目标检测技术在棉花幼苗与杂草检测领域已取得一定进展。论文提出了基于改进Detection Transformer的棉花幼苗与杂草检测模型,以提高杂草目标检测的准确率和效率。首先,引入了可变形注意力模块替代原始模型中的Transformer注意力模块,提高模型对特征图目标形变的处理能力。提出新的降噪训练机制,解决了二分图匹配不稳定问题。提出混合查询选择策略,提高解码器对目标类别和位置信息的利用效率。使用Swin Transformer作为网络主干,提高模型特征提取能力。通过对比原网络,论文提出的模型方法在训练过程中表现出更快的收敛速度,并且在准确率方面提高了6.7%。

Accelerated design of high-performance Mg-Mn-based magnesium alloys based on novel bayesian optimization

Magnesium(Mg),being the lightest structural metal,holds immense potential for widespread applications in various fields.The development of high-performance and cost-effective Mg alloys is crucial to further advancing their commercial utilization.With the rapid advancement of machine learning(ML)technology in recent years,the“data-driven''approach for alloy design has provided new perspectives and opportunities for enhancing the performance of Mg alloys.This paper introduces a novel regression-based Bayesian optimization active learning model(RBOALM)for the development of high-performance Mg-Mn-based wrought alloys.RBOALM employs active learning to automatically explore optimal alloy compositions and process parameters within predefined ranges,facilitating the discovery of superior alloy combinations.This model further integrates pre-established regression models as surrogate functions in Bayesian optimization,significantly enhancing the precision of the design process.Leveraging RBOALM,several new high-performance alloys have been successfully designed and prepared.Notably,after mechanical property testing of the designed alloys,the Mg-2.1Zn-2.0Mn-0.5Sn-0.1Ca alloy demonstrates exceptional mechanical properties,including an ultimate tensile strength of 406 MPa,a yield strength of 287 MPa,and a 23%fracture elongation.Furthermore,the Mg-2.7Mn-0.5Al-0.1Ca alloy exhibits an ultimate tensile strength of 211 MPa,coupled with a remarkable 41%fracture elongation.

Improvement of High-Speed Detection Algorithm for Nonwoven Material Defects Based on Machine Vision

Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.

A Hybrid Intrusion Detection Method Based on Convolutional Neural Network and AdaBoost

To solve the problem of poor detection and limited application range of current intrusion detection methods,this paper attempts to use deep learning neural network technology to study a new type of intrusion detection method.Hence,we proposed an intrusion detection algorithm based on convolutional neural network(CNN)and AdaBoost algorithm.This algorithm uses CNN to extract the characteristics of network traffic data,which is particularly suitable for the analysis of continuous and classified attack data.The AdaBoost algorithm is used to classify network attack data that improved the detection effect of unbalanced data classification.We adopt the UNSW-NB15 dataset to test of this algorithm in the PyCharm environment.The results show that the detection rate of algorithm is99.27%and the false positive rate is lower than 0.98%.Comparative analysis shows that this algorithm has advantages over existing methods in terms of detection rate and false positive rate for small proportion of attack data.

IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.