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Seeds will display different germination states during the germination process, and their good or bad state directly influences the subsequent growth and yield of the crop. This study aimed to address the difficulties of obtaining the images of seed germination process in all time series and studying the dynamic evolution law of seed germination state under stress conditions. A licorice sprouting experiment was performed using a seed sprouting phenotype acquisition system to obtain images of the sprouting process of licorice in full-time sequence. A labeled dataset of licorice full-time sequence sprouting process images was constructed based on the four states of unsprouted, sprouted, cracked, and shelled in the sprouting process. An optimized model, YOLOv8-Licorice, was developed based on the YOLOv8-n model and its effectiveness was demonstrated by comparative and ablation tests. Different salt stress environments were simulated via NaCl aqueous solution concentration, and germination experiments of licorice seeds were performed under different salt stresses. The germination state of licorice under different salt stress environments was detected using the YOLOv8-Licorice detection model. Percentage curve of licorice seeds in an unsprouted state displayed a continuous decreasing trend. For the percentage curve of licorice seeds in the sprouted state, an increasing and then decreasing trend was observed under the condition of 0-200 mmol/L NaCl solution, and a continuous increasing trend was observed under the condition of 240-300 mmol/L NaCl solution. Licorice seeds in the cracked state demonstrated percentage curves with an increasing and then decreasing trend under the condition of 0-140 mmol/L NaCl solution and a continuous increasing trend under the condition of 160-300 mmol/L NaCl solution. The percentage curve of licorice seeds in shelled state displayed a continuous increasing trend in 0-200 mmol/L NaCl solution condition and remained horizontal in 220-300 mmol/L NaCl solution condition. Overall, this study provides a valuable method involving the seed sprouting phenotype acquisition system and the proposed method for detecting the germination state of licorice seeds. This method serves as a valuable reference to comprehensively understand the seed sprouting process under triggering treatment. [ABSTRACT FROM AUTHOR]

Fiber optic intrusion detection is widely regarded as one of the most effective perimeter defense strategies against intruders or terrorists. Currently, most infiltration detection systems gather data through optical cables and then track intrusions using classification techniques. In this study, we present two intrusion detection algorithms that utilize system recognition to tackle the challenge of unstable fiber optic infiltration features, especially during rainy weather conditions. The primary method involves the computation of the system's disturbance covariance at any given position. This process serves as a foundational step in identifying intrusions within the system. Utilizing the Frobenius norm, we quantify the disparity between the disturbance covariance matrices obtained from different sites. By assessing these differences, we can effectively discern the presence of an intruder and pinpoint their location within the system. The second method employs the Kalman filter to rebuild the output of each point the non-infiltration point state parameters, and in order to detect intrusion, the cross-reconstruction error is determined. Data from experimental tests is used to evaluate the two methods. The experimental outcomes indicate that the proposed methods are both viable. When comparing, the first approach is more solid, while the second is faster when computing. [ABSTRACT FROM AUTHOR]

Wind power has rapidly emerged as a leading renewable energy source, with doubly-fed induction generators gaining attention for their independent power control capability. Integrating doubly-fed induction generator-based wind farms into the grid requires significant upgrades to transmission and distribution infrastructure to fully harness wind energy's potential and address challenges like sub-synchronous control interaction. This phenomenon poses risks such as equipment damage and power supply interruptions. To mitigate these risks, a novel adaptive control strategy incorporating uncertainty and disturbance estimators, alongside H∞ and μ-synthesis controllers, is proposed. By introducing a time delay into the control system, oscillations can be detected and stabilized, ensuring system stability across various operational conditions. The controller parameters are optimized using a dynamic opposite learning-based enhanced mountain gazelle optimization algorithm. This algorithm is utilized to determine the optimal controller parameters that ensure system stability across different operational conditions. Simulation results conducted in MATLAB validate the effectiveness of the proposed controller in damping oscillations under different compensations and wind speeds. Specifically, it demonstrates superior damping capabilities at various wind speeds and compensations, enhancing power system stability and reliability. Results confirm the better damping capability of proposed controller under different compensations such as 75% and 45% and wind speeds such as 13 m/s, 7 m/s, and 11 m/s. This research provides better stability and reliability of power systems by dampening oscillations at 1.5 s, ensuring the sustainable integration of wind energy. [ABSTRACT FROM AUTHOR]

Rifaximin and rifampicin are good broad-spectrum antimicrobials. The irrational use of antimicrobial drugs in veterinary clinics could threaten public health and food safety. It is necessary to develop a reliable detection method of the residue for enhancing the rational supervision of the use of such drugs, reducing and slowing down the generation of bacterial resistance, and promoting animal food safety and human health. So, this study developed an LC-MS/MS method for the detection of rifaximin and rifampicin residues in animal-origin foods. The residual rifaximin and rifampicin of homogenized test materials were extracted with acetonitrile-dichloromethane solution or acetonitrile in the presence of anhydrous sodium sulfate and vitamin C, purified by dispersible solid phase extraction, determined by LC-MS/MS, and quantified by the internal standard method. The specificity, sensitivity, matrix effect, accuracy, and precision of the method were investigated in the edible tissues of cattle, swine, or chicken. In addition, the stability of the standard stock solution and the standard working solution was also investigated. The method was suitable for the muscle, liver, kidney, fat, milk, and eggs of cattle, swine, or chicken, as well as fish and shrimp. The specificity of the method was good, and the detection of the analytes was not affected by different matrices. Both the LOD and LOQ of the two analytes were 5 μg/kg and 10 μg/kg, respectively. The results of matrix effects in each tissue were in the range of 80–120%; there were no significant matrix effects. The average accuracy of rifaximin and rifampicin in different foodstuffs of animal origin was between 80% and 120%, and the method precision was below 20% (RSD). The proposed method showed good performance for determination, which could be employed for the extraction, purification, and detection of residual rifaximin and rifampicin in edible animal tissues. The pretreatment procedure of tissue samples was simple and feasible. The method was highly specific, stable, reliable, and with high sensitivity, accuracy, and precision, which met the requirements of quantitative detection of veterinary drug residues. [ABSTRACT FROM AUTHOR]

Borehole-to-surface electrical (BTSE) technology plays an important role in detecting mining subsidence area in coalfields. However, the ambiguity of linear inversion and the influence of surrounding rock resistivity reduced the effectiveness and accuracy of BTSE technology in identifying mining subsidence area. In this study, the damped least-squares inversion method constrained by inequality was investigated based on the basic principles of BTSE technology. On this basis, a novel method using the ratio of post- to pre-mining inversion resistivity was proposed to detect mining subsidence area. Finally, the application effect of this method was analyzed by using theoretical model and laboratory mining model, respectively. It was found that the resistivity inversion of the BTSE constrained by inequality could obtain good layered information of the stratum provided by prior information. Using the ratio of post- to pre-mining inversion resistivity can eliminate the influence of surrounding rock on the abnormal resistivity and highlight the resistivity anomalies caused by stratum collapse during coal mining. The application results of the theoretical model and laboratory mining model illustrated the feasibility and effectiveness of this novel method in detecting mining subsidence area, which provides important theoretical basis and reference for its future promotion and application. [ABSTRACT FROM AUTHOR]

Copyright of Guangdong Architecture Civil Engineering is the property of Guangdong Architecture Civil Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Agro-Environment Science is the property of Journal of Agro-Environment Science Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As a newly discovered virus, Decapoda iridovirus 1 (DIV1) can cause a mortality rate of up to 100% in crustaceans, leading to huge economic losses. At present, there is no effective prevention and control measures for this disease. In the present study, the specific primers targeting highly conserved regions of MCP gene were designed, and then a quantitative real-time PCR method was established. The results indicate that DIV1 quantitative real-time PCR established has good specificity and does not cross react with other pathogens including white spot syndrome virus (WSSV), infectious subcutaneous and hematopoietic necrosis virus (IHHNV) and Vibrio parahaemolyticus induced acute hepatopancreatic necrosis disease (VpAHPND). The real-time PCR was capable of detecting DIV1 DNA at a minimum concentration of 10 copies/µL within 34 cycles. The method has good repeatability, with intra group and inter group coefficients of variation both less than 2%. Thirty-two clinical samples were assessed using both the real-time PCR and conventional PCR. The results shown real-time PCR we established are more sensitive than conventional PCR. In conclusion, this method has strong specificity, stable repeatability, and high sensitivity, providing technical support for clinical diagnosis, epidemiology investigation and monitoring of DIV1. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC)-a malignant growth in the colon or rectum-is the second largest cause of cancer deaths worldwide. Early detection may increase therapy choices. Deep learning can improve early medical detection to reduce the risk of unintentional death from an incorrect clinical diagnosis. Histopathological examination of colon cancer is essential in medical research. This paper proposes a deep learning-based colon cancer detection method using stainnormalized images. We use deep learning methods to improve detection accuracy and efficiency. Our solution normalizes image stain variations and uses deep learning models for reliable classification. This research improves colon cancer histopathology analysis, which may enhance diagnosis. Our paper uses DenseNet-121, VGG-16, GoogLeNet, ResNet-50, and ResNet-18 deep learning models. We also analyze how stain normalization (SN) improves our model on histopathology images. The ResNet-50 model with SN yields the highest values (9.94%) compared to the other four models and the nine models from previous studies. [ABSTRACT FROM AUTHOR]

Phthalic acid esters (PAEs) are a class of common environmental endocrine disruptors (EEDs), capable of causing considerable pollution to water, soil, and air and producing a range of adverse health impacts in humans. Although various studies have investigated the pollution characteristics and health hazards of PAEs in different media, a systematic review of PAEs in the broader environmental context is still lacking. In order to comprehensively explore current issues and suggest prospects, the current status, detection technology, toxicity, and health hazards of PAEs were investigated. The results suggest that PAE pollution is a widespread and complex global phenomenon, transported over long distances. The traditional techniques used for determination include high-performance liquid chromatography–mass spectrometry (HPLC-MS), gas chromatography–mass spectrometry (GC-MS), and high-performance liquid chromatography (HPLC). Various detection techniques offer distinct advantages and disadvantages. Moreover, PAEs can cause differing extents of harm to the nervous and reproductive systems of mammals. In the future, it is imperative to improve the detection of PAEs, establish rapid identification approaches, refine toxicological research methods, and investigate more comprehensive health risk assessment methods. These efforts will provide scientific support for the prevention and management of the resulting contaminants. [ABSTRACT FROM AUTHOR]

The commercial cultivation of genetically modified (GM) crops has eased the global food crisis and brought considerable economic and social benefits to countries. Because of the potential safety problems, it is necessary to make clear the molecular genetic characteristics, edible safety, planting, processing, and other aspects of the safety evaluation of GM crops. The safety problems existing in the cultivation of GM crops, safety evaluation and detection of GM crops were introduced in this paper, which provided the basis for safety evaluation and effective supervision of GM crops and their products. Commercial cultivation and reasonable supervision based on safety evaluation have far-reaching significance for ensuring consumer safety, enhancing the credibility of the national political system and enhancing citizens' confidence in the safety of GM crop products for consumption. [ABSTRACT FROM AUTHOR]

Copyright of China Brewing is the property of China Brewing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To enhance the effective detection of abnormal points in complex network data flow, perform multi-dimensional dynamic detection, and establish a more stable and reliable data flow abnormal detection method, a continuous abnormal point detection method for complex network data flow based on C-LSTM is proposed. The features of continuous outliers in complex network data streams are extracted, and a data anomaly detection model is established according to the features. The input features of continuous outliers in complex network data streams are qualitatively and quantitatively transformed into multi-scale anomalies, and the outlier detection based on C-LSTM is realized. The experimental results show that the maximum sensitivity of the proposed method reaches 42%, and the average routing overhead is less than 24 Mb. Regardless of the data in any scenario, the detection accuracy is higher than 0.92, the recall is higher than 0.81, and the F1 value is higher than 0.62. Although there may be some misjudgments or omissions due to noise, the overall detection performance is good. [ABSTRACT FROM AUTHOR]

Tilapia stands out as one of the most extensively farmed and consumed fish species globally, valued for its ease of preparation and relative affordability. Although tilapia is a valuable protein source, it can also function as a vector for foodborne pathogens. This literature review reveals that tilapia could carry a variety of contamination with various foodborne pathogens, including Plesiomonas shigelloides, diarrheagenic Escherichia coli, Vibrio parahaemolyticus, Salmonella Weltevreden, Salmonella enterica, Shigella, Staphylococcus aureus, Campylobacter jejuni, Clostridium botulinum, and Listeria monocytogenes. Although guidelines from entities, such as the Global Seafood Alliance, Aquaculture Stewardship Council, and International Organization for Standardization, have been established to ensure the microbiological safety of tilapia, the unique challenges posed by pathogens in tilapia farming call for a more nuanced and targeted approach. Recognizing that contaminants could emerge at various stages of the tilapia supply chain, there is a crucial need for enhanced detection and monitoring of pathogens associated with this fish and its culturing environment. Additionally, it is essential to acknowledge the potential impact of climate change on the safety of tilapia, which may elevate the prevalence and contamination levels of pathogens in this fish. Proactive measures are essential to understand and mitigate the effects of climate change on tilapia production, ensuring the sustainability and safety of this seafood product for both present and future generations. [ABSTRACT FROM AUTHOR]

Abstract Automated detection and identification of vegetable diseases can enhance vegetable quality and increase profits. Images of greenhouse-grown vegetable diseases often feature complex backgrounds, a diverse array of diseases, and subtle symptomatic differences. Previous studies have grappled with accurately pinpointing lesion positions and quantifying infection degrees, resulting in overall low recognition rates. To tackle the challenges posed by insufficient validation datasets and low detection and recognition rates, this study capitalizes on the geographical advantage of Shouguang, renowned as the “Vegetable Town,” to establish a self-built vegetable base for data collection and validation experiments. Concentrating on a broad spectrum of fruit and vegetable crops afflicted with various diseases, we conducted on-site collection of greenhouse disease images, compiled a large-scale dataset, and introduced the Space-Time Fusion Attention Network (STFAN). STFAN integrates multi-source information on vegetable disease occurrences, bolstering the model’s resilience. Additionally, we proposed the Multilayer Encoder-Decoder Feature Fusion Network (MEDFFN) to counteract feature disappearance in deep convolutional blocks, complemented by the Boundary Structure Loss function to guide the model in acquiring more detailed and accurate boundary information. By devising a detection and recognition model that extracts high-resolution feature representations from multiple sources, precise disease detection and identification were achieved. This study offers technical backing for the holistic prevention and control of vegetable diseases, thereby advancing smart agriculture. Results indicate that, on our self-built VDGE dataset, compared to YOLOv7-tiny, YOLOv8n, and YOLOv9, the proposed model (Multisource Information Fusion Method for Vegetable Disease Detection, MIFV) has improved mAP by 3.43%, 3.02%, and 2.15%, respectively, showcasing significant performance advantages. The MIFV model parameters stand at 39.07 M, with a computational complexity of 108.92 GFLOPS, highlighting outstanding real-time performance and detection accuracy compared to mainstream algorithms. This research suggests that the proposed MIFV model can swiftly and accurately detect and identify vegetable diseases in greenhouse environments at a reduced cost.

Milk is an excellent medium for the growth of microorganisms, which is easily contaminated by microorganisms from the environment. Psychrophilic bacteria are the most common microorganisms in cold-stored raw milk. Moreover, psychrophilic bacteria produce heat-resistant proteases and lipases in low-temperature environments, which remain active even after heat treatment, and decompose proteins and fats in liquid milk during storage, thereby affecting the quality of milk. The counting method for psychrophilic bacteria in milk specified by the agricultural industry standard of China takes 10 days to culture samples at 6.5 ℃ for 10 days, which is time-consuming and has low application value in actual production. Therefore, the development of detection technologies for psychrophilic bacteria and their heat-resistant enzymes is very important for ensuring the quality of milk and dairy products. This article reviews a variety of methods for the identification and analysis of psychrophilic bacteria in milk, including polymerase chain reaction (PCR), sequencing, fluorescence in situ hybridization, and loop-mediated isothermal amplification. It provides a reference for the development of new detection technologies for psychrophilic bacteria in milk.

Purposes Rebar plays an indispensable role in construction engineering; however, challenges such as densely packed end faces, non-uniform diameter scales, adhesive boundaries, background fusion, and occlusions between end faces have made precise counting a significant challenge. In recent years, deep learning has made remarkable strides in the field of dense object counting. Nonetheless, deep leaming faces limitations because of the need for large-scale data and computational resources, hindering its practical application. Methods In response to these challenges, an enhanced YOLOv8 model framework is introduced for rebar end detection. The framework incorporates Spatial and Channel reconstruction Convolutional (SCConv) modules and the Normalized Wasserstein Distance (NWD) loss function tailored for small object detection. Findings Experimental results from ablation tests demonstrate that the SCConv module significantly reduces network parameters while maintains network performance. Furthermore, the NWD loss function notably enhances the accuracy of rebar end detection in large models. This re search provides an effective solution for achieving high-precision and lightweight rebar counting.

Peste des petits ruminants (PPR) is a serious acute, highly contagious disease caused by the peste des petits ruminants virus (PPRV). This study aims to establish a qRT-PCR assay with an internal amplification control for the rapid and accurate detection of PPRV. The primers and probes for PPRV N were based on the national standard of the diagnostic techniques for PPR of China, and a pair of primers and TaqMan probes for the internal reference gene of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was designed. Optimisation of the reaction conditions, specificity, sensitivity and reproducibility tests, and clinical sample detection were conducted. The results showed that the optimal primers and probe concentrations of PPRV were 0.4 μmol/l and 0.4 μmol/l, respectively, and were 0.4 μmol/l and 0.2 μmol/l for the reference gene GAPDH, respectively. The established method has no cross-reaction with other viruses. The minimum detection limit was 6.8 copies/µl for PPRV and 190 copies/µl for GAPDH. The coefficients of variation (CV%) of PPRV and GAPDH were both lower than 2%. The results suggest that the PPRV qRT-PCR method containing internal reference genes has strong specificity, high sensitivity, and good reproducibility. The addition of internal reference genes for the sample quality control improves the accuracy of the detection.

Abstract The damage of road base course has the characteristics of strong concealment and difficulty in detecting. For this reason, the impact imaging method has been used for detection of road base course. This paper discussed systematically collection points setting, excitation mode and data processing method. Through the application in testing for highway pavement base before and after grouting maintenance, the results show that the method is simple and accurate. The detection results can be displayed in a two-dimensional image form and it is easy to be used in road maintenance. This method can be used to identify and locate the damages of the pavement base, to judge the uniformity of the pavement base structure. It can also be used to evaluate the effectiveness of internal damage after grouting repairing.

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Porcine deltacoronavirus (PDCoV) is a newly discovered porcine intestinal pathogenic coronavirus with a single-stranded positive-sense RNA genome and an envelope. PDCoV infects pigs of different ages and causes acute diarrhea and vomiting in newborn piglets. In severe cases, infection leads to dehydration, exhaustion, and death in sick piglets, entailing great economic losses on pig farms. The clinical symptoms of PDCoV infection are very similar to those of other porcine enteroviruses. Although it is difficult to distinguish these viral infections without testing, monitoring PDCoV is very important because it can spread in populations. The most commonly used methods for the detection of PDCoV is qPCR, which is time-consuming and require skilled personnel and equipment. Many farms cannot meet the conditions required for detection. Therefore, it is necessary to establish a faster and more convenient method for detecting PDCoV. Aims: To establish a rapid and convenient detection method for PDCoV by combining RPA (Recombinase Polymerase Isothermal Amplification) with CRISPR/Cas13a. Methods: Specific RPA primers and crRNA for PDCoV were designed, and the nucleic acids in the samples were amplified with RPA. Fluorescent CRISPR/Cas13a detection was performed. We evaluated the sensitivity and specificity of the RPA-CRISPR/Cas13a assay using qPCR as the control method. Results: CRISPR/Cas13a-assisted detection was completed within 90 min. The minimum detection limit of PDCoV was 5.7 × 101 copies/µL. A specificity analysis showed that the assay did not cross-react with three other porcine enteroviruses. Conclusion: The RPA-CRISPR/Cas13a method has the advantages of high sensitivity, strong specificity, fast response, and readily accessible results, and can be used for the detection of PDCoV. [ABSTRACT FROM AUTHOR]

The characteristics of automatic dependent surveillance-broadcast (ADS-B) technology, such as unencrypted channels and open data protocols, make it extremely vulnerable to various intentional or unintentional spoofing and jamming, which seriously threatens air traffic safety. Traditional approaches do not update their high-bit data for a long time, which causes the situations of the same encoding results to have different aircraft positions and it is difficult to determine the real position of the aircraft and effectively detect ADS-B jamming. Therefore, it is urgent to propose more effective detection methods for ADS-B spoofing and jamming. First, the methods and principles of frequency shift calculation and automatic monitoring algorithm are introduced. Then, signal consistency detection and error robustness are analyzed, and a track-data-based detection model for abnormal ADS-B data are proposed. The track-data-based detection methods can use multi-point sampling judgment algorithm to realize the reception and processing of data bits. This method makes full use of the information of multiple sampling values of each data bit and the reference power value obtained in the preamble detection, and determines the data value and confidence value through the relationship between the two. The detection methods for ADS-B spoofing and jamming distinguishes the true and false signals and the authenticity of target positions by comparing the arrival time difference of each signal at the ground station. Finally, the research results show that, with the increase of signal to noise ratio, no matter the estimation based on virtual array translation or the estimation based on circular stationary signal, their estimation accuracies are improved with gradually increasing trends. [ABSTRACT FROM AUTHOR]

Microplastics are emerging contaminants that undergo progressive aging under environmental conditions such as sunlight irradiation, mechanical forces, temperature variations, and the presence of biological organisms. Since aging modifies microplastic properties, such as their own toxicity and the toxicity of trapped pollutants, advanced methods to analyze microplastics are required. Here we review methods to analyze microplastic aging with focus on the aging process, qualitative identification, quantitative characterization, and chemometrics. Qualitative identification is done by mechanical techniques, thermal techniques, e.g., thermal degradation and gas chromatography–mass spectrometry, and spectral techniques, e.g., infrared, Raman, fluorescent, and laser techniques. Quantitative characterization is done by microscopy and mass spectrometry. Microplastic aging results in a series of surface physical changes, biofilm formation, chemical oxidation, thermal alternation, and mechanical deterioration. Changes in mechanical and thermal properties allow to differentiate aged microplastics. Infrared and Raman spectroscopy are rapid and sensitive for chemical identification of microplastics in complex environmental samples. Combining two techniques is preferable for accurate detection and categorization. [ABSTRACT FROM AUTHOR]

Copyright of China Brewing is the property of China Brewing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to further improve the efficiency and automation level of sliver evenness detection, reducing the influences of environmental temperature and humidity, sliver structure status, light intensity and other factors on the detection method of sliver evenness, the principles and characteristics of sliver evenness capacitive detection, photoelectric detection and each detection methods were analyzed. The advantages and disadvantages of various detection methods in different environmental conditions, detection speed, accuracy, effective detection area, etc. were discussed. The research focuses and improvement measures of each sliver evenness detection methods were summarized. It is considered that the detection method should be developed in the direction of high-precision, highspeed and on-line detection so as to meet the requirements of modern textiles for large weight per unit, rapid detection and on-site detection of sliver. [ABSTRACT FROM AUTHOR]

Addressing the limitations of current railway track foreign object detection techniques, which suffer from inadequate real-time performance and diminished accuracy in detecting small objects, this paper introduces an innovative vision-based perception methodology harnessing the power of deep learning. Central to this approach is the construction of a railway boundary model utilizing a sophisticated track detection method, along with an enhanced UNet semantic segmentation network to achieve autonomous segmentation of diverse track categories. By employing equal interval division and row-by-row traversal, critical track feature points are precisely extracted, and the track linear equation is derived through the least squares method, thus establishing an accurate railway boundary model. We optimized the YOLOv5s detection model in four aspects: incorporating the SE attention mechanism into the Neck network layer to enhance the model's feature extraction capabilities, adding a prediction layer to improve the detection performance for small objects, proposing a linear size scaling method to obtain suitable anchor boxes, and utilizing Inner-IoU to refine the boundary regression loss function, thereby increasing the positioning accuracy of the bounding boxes. We conducted a detection accuracy validation for railway track foreign object intrusion using a self-constructed image dataset. The results indicate that the proposed semantic segmentation model achieved an MIoU of 91.8%, representing a 3.9% improvement over the previous model, effectively segmenting railway tracks. Additionally, the optimized detection model could effectively detect foreign object intrusions on the tracks, reducing missed and false alarms and achieving a 7.4% increase in the mean average precision (IoU = 0.5) compared to the original YOLOv5s model. The model exhibits strong generalization capabilities in scenarios involving small objects. This proposed approach represents an effective exploration of deep learning techniques for railway track foreign object intrusion detection, suitable for use in complex environments to ensure the operational safety of rail lines. [ABSTRACT FROM AUTHOR]

Peste des petits ruminants (PPR) is a serious acute, highly contagious disease caused by the peste des petits ruminants virus (PPRV). This study aims to establish a qRT-PCR assay with an internal amplification control for the rapid and accurate detection of PPRV. The primers and probes for PPRV N were based on the national standard of the diagnostic techniques for PPR of China, and a pair of primers and TaqMan probes for the internal reference gene of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was designed. Optimisation of the reaction conditions, specificity, sensitivity and reproducibility tests, and clinical sample detection were conducted. The results showed that the optimal primers and probe concentrations of PPRV were 0.4 μmol/l and 0.4 μmol/l, respectively, and were 0.4 μmol/l and 0.2 μmol/l for the reference gene GAPDH, respectively. The established method has no cross-reaction with other viruses. The minimum detection limit was 6.8 copies/µl for PPRV and 190 copies/µl for GAPDH. The coefficients of variation (CV%) of PPRV and GAPDH were both lower than 2%. The results suggest that the PPRV qRT-PCR method containing internal reference genes has strong specificity, high sensitivity, and good reproducibility. The addition of internal reference genes for the sample quality control improves the accuracy of the detection. [ABSTRACT FROM AUTHOR]

The research status of microplastics leaching solution is reviewed. The detection methods of mi- croplastic leaching solution are summarized, the main components of microplastic leaching solution are summarized, the basic composition is summarized, the role of additive leaching in water and their harm to water and aquatic life are summarized. It is proposed that we should strengthen the research on the leaching behavior of additives from aging MPs, explore the relationship between the release rate of additives and the aging degree of microplastics, and pay attention to the enrichment of microplastic leaching liquid components in organisms and the transmission rules between different nutritional levels in the food chain. It is proposed to strengthen the supervision of the treatment and treatment of plastic products, actively look for replaceable materials, and research and develop effective waste plastics and microplastics disposal technology. [ABSTRACT FROM AUTHOR]

Bladder cancer (BC) is the tenth most prevalent malignancy globally, presenting significant clinical and societal challenges because of its high incidence, rapid progression, and frequent recurrence. Presently, cystoscopy and urine cytology serve as the established diagnostic methods for BC. However, their efficacy is limited by their invasive nature and low sensitivity. Therefore, the development of highly specific biomarkers and effective noninvasive detection strategies is imperative for achieving a precise and timely diagnosis of BC, as well as for facilitating an optimal tumor treatment and an improved prognosis. microRNAs (miRNAs), short noncoding RNA molecules spanning around 20–25 nucleotides, are implicated in the regulation of diverse carcinogenic pathways. Substantially altered miRNAs form robust functional regulatory networks that exert a notable influence on the tumorigenesis and progression of BC. Investigations into aberrant miRNAs derived from blood, urine, or extracellular vesicles indicate their potential roles as diagnostic biomarkers and prognostic indicators in BC, enabling miRNAs to monitor the progression and predict the recurrence of the disease. Simultaneously, the investigation centered on miRNA as a potential therapeutic agent presents a novel approach for the treatment of BC. This review comprehensively analyzes biological roles of miRNAs in tumorigenesis and progression, and systematically summarizes their potential as diagnostic and prognostic biomarkers, as well as therapeutic targets for BC. Additionally, we evaluate the progress made in laboratory techniques within this field and discuss the prospects. [ABSTRACT FROM AUTHOR]

Levan, a β-(2,6)-linked fructose polymer, exhibits diverse properties that impart versatility, rendering it a highly sought-after biopolymer with various industrial applications. Levan can be produced by various microorganisms using sucrose, food industry byproducts and agricultural wastes. Microbial levan represents the most potent cost-effective process for commercial-scale levan production. This study reviews the optimization of levan production by understanding its biosynthesis, physicochemical properties and the fermentation process. In addition, genetic and protein engineering for its increased production and emerging methods for its detection are introduced and discussed. All of these comprehensive studies could serve as powerful tools to optimize levan production and broaden its applications across various industries. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Detection and enumeration of coliform bacteria using traditional methods and current molecular techniques against E. coli usually involve long processes with less sensitivity and specificity to distinguish between viable and non-viable bacteria for microbiological water analysis. This approach involves developing and validating an immunosensor comprising ring resonators functionalized with specific antibodies surrounded by a network of microchannels as an alternative method for detecting and indirectly enumerating Escherichia coli in samples of water for consumption. Different ELISA assays were conducted to characterize monoclonal and polyclonal antibodies selected as detection probes for specific B-galactosidase enzymes and membrane LPS antigens of E. coli. An immobilization control study was performed on silicon nitride surfaces used in the immunosensor, immobilized with the selected antibodies from the ELISA assays. The specificity of this method was confirmed by detecting as few as 10 CFU/mL of E. coli from viable and non-viable target bacteria after applying various disinfection methods to water samples intended for human consumption. The 100% detection rate and a 100 CFU/mL Limit of Quantification of the proposed method were validated through a comprehensive assessment of the immunosensor-coupled microfluidic system, involving at least 50 replicates with a concentration range of 10 to 106 CFU/mL of the target bacteria and 50 real samples contaminated with and without disinfection treatment. The correlation coefficient of around one calculated for each calibration curve obtained from the results demonstrated sensitive and rapid detection capabilities suitable for application in water resources intended for human consumption within the food industry. The biosensor was shown to provide results in less than 4 h, allowing for rapid identification of microbial contamination crucial for ensuring water monitoring related to food safety or environmental diagnosis and allowing for timely interventions to mitigate contamination risks. Indeed, the achieved setup facilitates the in situ execution of laboratory processes, allowing for the detection of both viable and non-viable bacteria, and it implies future developments of simultaneous detection of pathogens in the same contaminated sample. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The damage of road base course has the characteristics of strong concealment and difficulty in detecting. For this reason, the impact imaging method has been used for detection of road base course. This paper discussed systematically collection points setting, excitation mode and data processing method. Through the application in testing for highway pavement base before and after grouting maintenance, the results show that the method is simple and accurate. The detection results can be displayed in a two-dimensional image form and it is easy to be used in road maintenance. This method can be used to identify and locate the damages of the pavement base, to judge the uniformity of the pavement base structure. It can also be used to evaluate the effectiveness of internal damage after grouting repairing. [ABSTRACT FROM AUTHOR]

Purpose: More than 20 genetically modified (GM) food crops including rice have been approved in many countries. GM rice and derived products have not yet been approved in India so they are considered as unauthorized genetically modified organisms (GMOs) in the country. Therefore it is important to track whether the rice containing food items, available in the marketplace are GMO-free. Methods: A pilot study was conducted to check the GM status of 30 samples of packed rice grains and processed food products with rice as an ingredient, using polymerase chain reaction (PCR) assays targeting Cauliflower Mosaic Virus 35 S promoter (P-35 S), nopaline synthase terminator (T-nos), phosphinothricin-N-acetyltransferase (pat) and cry1Ac gene, which could cover screening for all the globally approved GM rice events. Results: Based on the results, none of the samples tested were found positive for P-35 S, T-nos, pat and cry1Ac. Conclusion: The unauthorized presence of GM rice ingredients was not detected in the samples tested. Such studies may further be conducted for the testing of GM ingredients derived from cereals other than rice in the food products imported from the country where GM events of respective cereal crop are approved, as a part of regulatory requirement. [ABSTRACT FROM AUTHOR]

Copyright of Acta Agriculturae Zhejiangensis is the property of Acta Agriculturae Zhejiangensis Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A great proportion of neuromuscular diseases are immune-mediated, included myasthenia gravis, Lambert-Eaton myasthenic syndrome, acute- and chronic-onset autoimmune neuropathies (anti-MAG neuropathy, multifocal motor neuropathy, Guillain-Barré syndromes, chronic inflammatory demyelinating polyradiculoneuropathy, CANDA and autoimmune nodopathies), autoimmune neuronopathies, peripheral nerve hyperexcitability syndromes and idiopathic inflammatory myopathies. The detection of autoantibodies against neuromuscular structures has many diagnostic and therapeutic implications and, over time, allowed a better understanding of the physiopathology of those disorders. In this paper, we will review the main autoantibodies described in neuromuscular diseases and focus on their use in clinical practice.

Rapid analysis of multiple food allergens is required to confirm the appropriateness of food allergen labelling in processed foods. This study aimed to develop a rapid and reliable method to simultaneously detect trace amounts of seven food allergenic proteins (wheat, buckwheat, milk, egg, crustacean, peanut, and walnut) in processed foods using LC-MS/MS. Suspension-trapping (S-Trap) columns and on-line automated solid-phase extraction were used to improve the complex and time-consuming pretreatment process previously required for allergen analysis using LC-MS/MS. The developed method enabled the simultaneous detection of selected marker peptides for specific proteins derived from seven food ingredients in five types of incurred samples amended with trace amounts of allergenic proteins. The limit of detection values of the method for each protein were estimated to be

Copyright of Food Science & Technology & Economy is the property of Grain Science & Technology & Economy Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

旨在为芝麻木酚素相关产品、特殊用途油脂等的研发提供参考，对芝麻木酚素中主要的脂溶性活性成分芝麻素及其转化产物细辛素的含量分布、转化条件、检测方法和生物活性进行综述。芝麻素是芝麻中含量最多的脂溶性木酚素，而芝麻中几乎不含细辛素，但芝麻素可在酸性和受热条件下转化为细辛素；芝麻素与细辛素的检测方法有高效液相色谱法、薄层色谱法、气相色谱-质谱联用法、液相色谱-质谱联用法等；芝麻素具有抑菌、抗癌、降血脂等生物活性，细辛素具有抗炎、抑菌、抗病毒、抗癌、抗肿瘤、抗氧化等生物活性。In order to provide references for the research and development of sesame lignan-related products and oils for special purposes, the contents, convertion conditions, detection methods and physiological activities of sesamin and asarinin were reviewed. Sesamin is the most abundant fat-soluble lignan in sesame, while asarinin is almost absent in sesame, but sesamin can be converted to asarinin under acidic and heat conditions. Sesamin and asarinin can be detected by high performance liquid chromatography, thin layer chromatography, gas chromatography-mass spectrometry, liquid chromatography-mass spectrometry, etc. Sesamin has bioactivities such as antibacterial, anticancer and hypolipidemic, and asarinin has bioactivities such as anti-inflammatory, antibacterial, antiviral, anticancer, antitumor, and antioxidant.

Background and Aim: Feline coronavirus (FCoV), feline panleukopenia virus (FPV), and feline leukemia virus (FeLV) are prevalent throughout China and significantly threaten cat health. These viruses cause similar manifestations and pathological damage. Rapid and accurate diagnosis depends on detection in the laboratory. This study aimed to establish a reliable and rapid method for accurate detection of FCoV, FPV, and FeLV so that a definite diagnosis can be made and effective measures can be taken to prevent and control viral infection. Materials and Methods: We designed three pairs of specific primers and probes for the detection of FCoV 5′ untranslated region, FPV viral protein 2, and FeLV pol genes. Recombinant plasmid constructs were generated for use as standard plasmid constructs. Optimal reaction conditions, including primer and probe concentrations, reaction cycles, and annealing temperatures, were obtained on the basis of optimization tests. One-step triplex real-time reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was successfully established to simultaneously detect FCoV, FPV, and FeLV. The specificity, sensitivity, and repeatability of the assay were analyzed, and its applicability was validated by testing 1175 clinical samples. Results: One-step triplex RT-qPCR had a high degree of specificity only for the detection of FCoV, FPV, and FeLV; it had high sensitivity with limits of detection of 139.904, 143.099, and 152.079 copies/reaction for p-FCoV, p-FPV, and p-FeLV standard plasmid constructs, respectively, and it had reliable repeatability with 0.06%–0.87% intra-assay coefficients of variations. A total of 1175 clinical samples were examined for FCoV, FPV, and FeLV using triplex RT-qPCR, and the FCoV, FPV, and FeLV positivity rates were 18.47%, 19.91%, and 47.57%, respectively. The clinical sensitivity and specificity of one-step triplex RT-qPCR were 93.07% and 97.99%, respectively. Conclusion: We developed a rapid and reliable one-step triplex RT-qPCR method for the detection of FCoV, FPV, and FeLV, which could be used as a diagnostic tool for clinical monitoring and diagnosis.

Signal detection in orthogonal time frequency space modulation is one of the critical aspects for enhancing the throughput of the beyond fifth-generation framework, especially in challenging environments characterized by high mobility and multipath propagation. In this work, we proposed a hybrid detection algorithm by combining zero-forcing equalization (ZFE) and minimum square error equalization (MMSE), also known as ZFE–MMSE, with Rician and Rayleigh channels for 64 and 256 quadrature amplitude modulation. The combination of ZFE and MMSE allows for a more comprehensive approach to equalization. ZFE addresses channel-induced distortions, while MMSE tackles the impact of noise. Together, they enhance the overall performance of the equalization process, resulting in improved signal detection accuracy. The analysis and comparison of the simulation parameters with traditional detection systems include bit error rate, power spectral density (PSD), and complexity. The projected algorithms achieve a signal-to-noise ratio and a PSD gain of 2 dB and −800, respectively, outperforming the traditional detection techniques.

In addition to containing rich nutrients and biologically active substances, the pulses contain phytic acid, saponins, trypsin inhibitors, lectins and other anti-nutritional factors, which have multiple types and entail complex detection processes, with different detection capabilities required. In certain treatment methods, their structures can be destroyed, and their content can be lowered, furthermore, the nutrients in pulses can be preserved to the maximum extent during processing. This paper evaluates the detection methods of anti-nutritional factors in detail, analyzes and compares the advantages, disadvantages, applicability and sensitivity of different detection methods, and introduces the principles, processes, inhibition effects, advantages and disadvantages of several common anti-nutritional factor elimination methods, focuses mainly on heat treatment. This study aims to provide some references for the analytical method selection, improvement and control of anti-nutritional factors in pulses.

γ-aminobutyric acid (GABA) is a non-protein amino acid discovered in animals, plants, and microorganisms that was approved as the "new resource food" by the National Health Commission of the People's Republic of China (NHC) in 2009. It has a wide range of applications in food, medicine, feed, and other industries, and the research has grown increasingly popular in recent years. The paper reviews the bio-synthesis and metabolic processes of GABA, summarizes the methods of chemical synthesis, plant enrichment, and present GABA detection techniques, and discusses their advantages and limitations. Furthermore, the main physiological functions and mechanism of GABA are summarized, and GABA’s research and development trend is also presented, in order to provide reference for future research and application of GABA.

In this study, a fluorescence detection method combining aptamer-modified up-conversion nanoparticles (UCNPs) and magnetic nanoparticles (MNPs) was developed for detection of Clenbuterol (CLB). The aptamer-modified magnetic NPs captured CLB, which reacted with the aptamer-modified UCNPs and generated a sandwich complex. The aptamer-modified UCNPs acted as a fluorescence source. The MNP-CLB-UCNP complex was retrieved from the solution using an magnetic field, and the fluorescence intensity was detected by fluorescence spectrophotometry with excitation and emission spectra at 980 nm and in the 400–800 nm region, respectively. The results showed that the fluorescence intensity gradually increased with increasing concentrations of CLB with a good specify. The method was highly sensitive for the quantification of CLB, with a limit of detection of 0.304 ng mL−1. The recovery rate of CLB from pork samples ranged from 84 % to 94.87 %. This fluorescence method enables the sensitive, precise, and accurate quantification of CLB residues in pork samples.

World is presently facing major challenges in energy industry to reduce or replace greenhouse gases (CO 2 , CH 4 , NO x etc.) emissions to net zero in the coming decades while decreasing the reliance on traditional fossil fuels. In the current scenario, hydrogen (H 2) appears to be an excellent alternative energy source to meet the criteria. Storage of H 2 over a long period of time in large quantities poses a significant challenge for its widespread uses. In order to overcome this issue, the idea of using large-scale depleted underground hydrocarbon reservoirs, salt caverns, underground aquifers, etc. is considered to be a suitable option for underground hydrogen storage. However, underground storage of H 2 for a longer time period comes up with a potentially high microbial activity where H 2 can be utilized in the microbial metabolism and converted into undesirable gases like CH 4 and H 2 S which in turn, damage the porous medium due to clogging, acid formation, and corrosion activity. This review paper explores the various scientific challenges during large-scale H 2 storage in porous medium to facilitate the global H 2 economy. Moreover, a detailed description of the effect of subsurface environment conditions such as pressure, temperature, pH, and salinity on microbes is included in this study. In addition to this, steps to counter microbial activity in stored H 2 monitoring and mitigation approaches are also discussed in this article. • Underground hydrogen storage and its mechanism are discussed. • Effect of microorganisms on stored hydrogen and porous media properties comprehensively reviewed. • The impact of microbial activity on subsurface storage environment conditions. • Monitoring and mitigation strategies to counter microbial activities comprehensively reviewed. • Latest treatment proposition of microbial activities and role of biocides in underground hydrogen storage discussed. [ABSTRACT FROM AUTHOR]

This article summarizes the sources, hazards, detection, and removal technology of perchlorate, and looks forward to the future research directions and development trends・ Perchlorate is an important inorganic pollutant widely used as a strong oxidant in pyrotechnics, munitions, and aerospace industries. Perchlorate can be exposed to the human body through air, food, drinking water, etc., causing thyroid dysfunction in humans and mammals, which exhibits toxicity and affects the normal physiological functions of organisms ・ With its frequent detection in water, soil, and food, it has attracted the attention of researchers・ Currently, the research on perchlorate still lacks comprehensive and in-depth survey data・ This review is expected to provide some useful references for further exploring the pollution problem of perchlorate. [ABSTRACT FROM AUTHOR]

In the Japanese official detection method for unauthorized genetically modified (GM) papayas, one of two types of real-time PCR reagents with DNA polymerase (TaqMan Gene Master Mix [Taq-Man Gene] or FastGene QPCR Probe Mastermix w/ROX [FastGene]) is primarily used for measurement. In 2022, we conducted a laboratory performance study on the unauthorized GM papaya line PRSV-YK, and the results revealed that high threshold cycle (Cq) values for the PRSV-YK detection test were obtained using TaqMan Gene with the 7500 Fast & 7500 Real-Time PCR System (ABI7500) and QuantStudio 12K Flex (QS12K), indicating the possibility of false negatives. The possibility of similar problems with all unauthorized GM papaya lines detection tests needs to be evaluated. In this study, we performed detection tests on unauthorized GM papaya lines (PRSV-YK, PRSV-SC, and PRSV-HN), the cauliflower mosaic virus 35S promotor (CaM), and a papaya positive control (Chy), and examined how the limits of detection (LOD) for each test are affected by two types of DNA polymerases (TaqMan Gene and FastGene) and three types of real-time PCR instruments (ABI7500, QS12K, and LightCycler 480 Instrument II [LC480]). In the PRSV-YK and PRSV-SC detection tests using ABI7500 and QS12K, measurement with TaqMan Gene showed a higher LOD than FastGene. In this case, an exponential amplification curve was confirmed on the amplification plot; however, the amplification curve did not cross the ARn threshold line and the correct Cq value was not obtained with a threshold line = 0.2. The other tests (PRSV-HN, CaM, and Chy with ABI7500 and QS12K, and all detection tests with LC480) showed no important differences in the LOD for each test using either DNA polymerase. Therefore, when performing PRSV-YK and PRSV-SC detection tests with the ABI7500 or QS12K, FastGene should be used to avoid false negatives for foods containing GM papaya lines PRSV-YK and PRSV-SC at low mixing levels. [ABSTRACT FROM AUTHOR]