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Food safety is a global concern that deeply affects human health. To ensure the profitability of the food industry and consumer safety, there is an urgent need to develop rapid, sensitive, accurate, and cost-effective detection methods for food contaminants. Recently, the Aggregation-Induced Emission (AIE) has been successfully used to detect food contaminants. AIEgens, fluorescent dyes that cause AIE, have several valuable properties including high quantum yields, photostability, and large Stokes shifts. This review provides a detailed introduction to the principles and advantages of AIE-triggered detection, followed by a focus on the past five years' applications of AIE in detecting various food contaminants including pesticides, veterinary drugs, mycotoxins, food additives, ions, pathogens, and biogenic amines. Each detection principle and component is comprehensively covered and explained. Moreover, the similarities and differences among different types of food contaminants are summarized, aiming to inspire future researchers. Finally, this review concludes with a discussion of the prospects for incorporating AIEgens more effectively into the detection of food contaminants. [ABSTRACT FROM AUTHOR]

X-ray radiography is a valuable, non-destructive tool and can be used to examine the internal components or quality attributes of agricultural commodities, including arecanut. The true quality of an arecanut can be determined using destructive methods through visual inspection. However, dissected arecanuts do not have a shelf life. There is no non-destructive method available for grading arecanuts. We employ X-ray imaging as an aid to conduct internal examinations of arecanuts, allowing for thorough inspection without causing damage. A custom X-ray image dataset of arecanuts is created for automated interpretation of grades. We developed a hybrid arecanut grading model using YOLOv5s architecture incorporating the Stem, the GhostNet and the Transformer blocks. The proposed hybrid architecture outperforms in comparison with state-of-the-art models with a mean average precision (mAP) of 97.30%. The proposed 9.5 MB lightweight model can easily fit into X-ray devices, making it ideal for detecting arecanut grades in the industry. This method could transform the standards of quality inspection for arecanut. Its incorporation could establish a new industry benchmark for unparalleled quality assessment using X-ray technology as a non-destructive tool. [ABSTRACT FROM AUTHOR]

AbstractManganese is an essential metal ion involved in various biological and environmental processes, but its excess can lead to toxicity, particularly affecting the nervous system. Therefore, developing selective and sensitive detection methods for Mn2+ ions is of paramount importance. Colorimetric and fluorimetric chemosensors have emerged as promising tools for the detection of Mn2+ due to their simplicity, cost-effectiveness, and real-time monitoring capabilities. This review discusses recent advances in the colorimetric and fluorimetric chemosensors that exhibit distinct color or fluorescence changes upon interaction with Mn2+ ions. The review explores different organic and nanomaterials, focusing on their mechanisms of sensing, sensitivity, selectivity, and practical applications in environmental monitoring, healthcare, and food safety. The article also provides insights into future research directions aimed at overcoming these challenges, improving chemosensor performance, and expanding the applicability of colorimetric and fluorimetric chemosensors for Mn2+ detection in diverse real-world scenarios. [ABSTRACT FROM AUTHOR]

AbstractThe purpose of this article is to establish mathematical model for an early detection method of polyethylene pipe damage using linear guided waves and then perform a digital analysis to determine the efficiency of the technique in identifying different defects in pipeline materials. This method enhances the reliability of engineering pipeline infrastructures for both industrial and life-support purposes while also enabling the prevention of critical incidents such as ruptures, punctures, fissures, fatigue, and other events that may result in the release of transported agents into the surrounding environment. [ABSTRACT FROM AUTHOR]

AbstractDuring out-of-area military operations, the presence of carcinogenic and/or genotoxic agents has been reported, posing potential health risks to deployed soldiers. Military working dogs (MWDs), trained to detect explosives in the same environments as soldiers, could also serve as sentinel animals, providing valuable information on exposure to hazardous agents. These dogs can help identify environmental and potential adverse effects on their health and that of their handlers, possibly before relevant pathologies manifest. This study aims to evaluate the effectiveness of 33 Italian Army MWDs, deployed to the Lebanese theater for six consecutive months from October 2013 to January 2015, as sentinel animals for detecting exposure to genotoxic agents. The Cytokinesis-Block MicroNucleus (CBMN) assay was used to assess DNA damage, cytostasis, and cytotoxicity in the lymphocytes of these dogs. DNA damage events were specifically scored in once-divided binucleated cells (BCs) and included: a) micronuclei (MNi), indicative of chromosome breakage and/or whole chromosome loss; b) nucleoplasmic bridges (NPBs), a marker of DNA misrepair and/or telomere end-fusions; and c) nuclear buds (NBUDs), which signal the elimination of amplified DNA and/or DNA repair complexes. Our findings revealed an increase in chromosomal damage, assessed before and after deployment, with a statistically significant rise in MNi frequency, thus supporting the use of MWDs as sentinels for human exposure to hazardous agents. [ABSTRACT FROM AUTHOR]

The invasion of trunk-boring insects halts tree growth and causes tree death, making it a key factor in forest destruction. To address the issue of low detection accuracy due to the small size and high concealment of trunk-boring insects, a combined driving algorithm based on BP-UNet is proposed. The BP algorithm exhibits high sensitivity to relative permittivity, enabling the detection of smaller insects. By learning the implicit geometric features from the BP results, UNet can accurately reconstruct the size and shape of the insects. To demonstrate the performance of the proposed algorithm, the BP-UNet is compared with the BP and UNet algorithms, and its generalisation ability in different scenarios is tested. Experimental results show that the BP-UNet algorithm has good generalisation and achieves a detection accuracy of over 80% even in the presence of noise, providing quantitative images of trunk-boring insects within 1 second. [ABSTRACT FROM AUTHOR]

Carbon quantum dots derived from green biomass sources have been an emerging topic in recent times. Various biomass sources such as fruit peels, rice husks, corn cobs, sugarcane bagasse, etc have been employed in green synthesis of carbon dot. In this work, carbon quantum dots were synthesised from Blumea lacera as precursor using hydrothermal method. Blumea lacera is quite popular in many countries and available around the year. Crystal structure, morphology and stability of synthesised quantum dots were also studied with X-ray diffraction, Fourier transmission infrared spectroscopy, Raman spectroscopy, transmission electron microscopy, and Zeta potential measurement. The obtained carbon dots of about 5 nm in diameter presented good stability (Zeta potential of −23 mV) and great dispersion in water. Influence of synthesis duration and incident wavelengths on optical properties of carbon dots were investigated. Based on photoluminescence quenching effect, heavy metal ion (Cd2+ and Mn2+) detection was implemented successfully even at low concentration (10−7 M for both Cd2+ and Mn2+). These results give the boost to accurate quantitative determination of different heavy metal ions. [ABSTRACT FROM AUTHOR]

Though a reporting mechanism, in which employees report suspicious and/or potentially malicious coworker behavior, is thought to be important to tackling insider risk, the literature on the subject is sparse and unconvincing. Empirical evidence of the actual use and utility of this type of detection mechanism is slim. Our article explores the propensity of employees to report a coworker's concerning behavior suspected to be related to insider activity that would negatively impact an organization. This study uses an inductive approach and qualitative analysis of original interview data collected from 16 financial services organizations to explore attitudes and opinions about reporting a coworker's concerning behavior, providing lessons on countering insider threats useful across industries and national security domains. The results show that there is confusion, uncertainty, and cognitive dissonance surrounding institutional reporting mechanisms, with some participants expressing both affirmative and negative opinions about their personal likelihood of reporting. Employees do want to report concerning coworker behavior that suggests an insider threat, but not at their own expense. These results are consistent with those from other studies and sectors. Our study will assist organizations in refining their assumptions around workforce attitudes regarding the reporting of coworkers. [ABSTRACT FROM AUTHOR]

The multi-frequency signals of the Beidou system are very helpful for positioning, but in data processing, the detection and repair of multi-frequency cycle slips also face certain challenges. A recursive filtering and integer constraint cycle slip detection and repair method for the Beidou six-frequency is proposed in this paper. This method utilises five sets of combination coefficients of six-frequency and three sets of dual-frequency combinations, and detects and repairs cycle slips through integer constraints of cycle slip estimation. The proposed method has been validated by data with a sampling rate of 1 Hz, and can detect the position and size of cycle slips at various frequencies, whether it is random cycle slips or small cycle slips of equal size less than 2 cycles, with a detection success rate of 100%. [ABSTRACT FROM AUTHOR]

The degradation of ATP-related compounds is an important biochemical process that reflects the freshness of aquatic products after death. There has been considerable interest in investigating the factors affecting the degradation of ATP-related compounds in aquatic products and in developing techniques to detect them. This review provides the latest knowledge on the degradation mechanisms of ATP-related compounds during the storage of aquatic products and discusses the latest advances in ATP-related compound detection techniques. The degradation mechanisms discussed include mainly degradation pathways, endogenous enzymes, and microbial mechanisms of action. Microbial activity is the main reason for the degradation of IMP and related products during the mid to late storage of aquatic products, mainly through the related enzymes produced by microorganisms. Further elucidation of the degradation mechanisms of ATP-related compounds provides new ideas for quality control techniques in raw aquatic products during storage. The development of new technologies for the detection of ATP-related compounds has become a significant area of research. And, biosensors further improve the efficiency and accuracy of detection and have potential application prospects. The development of biosensor back-end modalities (test strips, fluorescent probes, and artificial intelligence) has accelerated the practical application of biosensors for the detection of ATP-related compounds. [ABSTRACT FROM AUTHOR]

Diabetes is a prevalent and costly condition, with early diagnosis pivotal in mitigating ‎its progression and complications. The diagnostic process often contends with data ‎ambiguity and decision uncertainty, adding complexity to achieving definitive ‎outcomes. This study addresses the diabetes diagnostic challenge through data mining ‎and machine learning techniques. It involves training various machine learning ‎algorithms and conducting statistical analysis on a dataset comprising 520 patients, ‎encompassing both normal and diabetic cases, to discern influential features.‎ Incorporating 17 features as classifier inputs, this research evaluates the diagnostic ‎performance using four reputable techniques: support vector machine (SVM), random ‎forest (RF), multi-layer perceptron (MLP), and k-nearest neighbor (kNN). The outcomes ‎underscore the SVM model's superior performance, boasting accuracy, specificity, and ‎sensitivity values of 98.78±1.96%, 99.28±1.63%, and 97.32±2.45%, ‎respectively, across 50 iterations. The findings establish SVM as the preferred method ‎for diabetes diagnosis.‎ This study highlights the efficacy of data mining and machine learning models in ‎diabetes diagnosis. While these methods exhibit respectable predictive accuracy, their ‎integration with a physician's assessment promises even better patient outcomes.‎ [ABSTRACT FROM AUTHOR]

Medical imaging research has experienced significant growth over the past decade, particularly in the fields of computer vision and pattern recognition. Computational approaches have been proposed to address the challenges in breast cancer detection, classification and segmentation. However, recent advancements in computational technology such as machine learning (ML) methods have dramatically changed the landscape of breast cancer imaging research. This survey aims to provide a compilation of information for future breast cancer imaging researchers by 1) comprehensively examining how various ML techniques are being used to address the main challenges in breast cancer imaging; 2) providing an in-depth discussion and review of publicly available datasets for the development and evaluation of novel breast cancer detection, classification and segmentation approaches; and 3) outlining current evaluation metrics used by breast cancer imaging researchers. The insights and findings presented in this survey will serve as a valuable resource for researchers and clinicians interested in breast cancer imaging. By providing an overview of the current state-of-the-art techniques and highlighting areas for future research, we hope to facilitate the development of more accurate and effective breast cancer imaging ML techniques, and contribute to advancing our understanding and improving the diagnosis and treatment of breast cancer. [ABSTRACT FROM AUTHOR]

This study aims to tackle the implementation of a single deep learning (DL) technique to the brain tumour recognition, segmentation and classification problem based on MR images. We propose utilising the Mask R-CNN approach along with transfer learning models on images. This was done by assigning bounding boxes and successfully constructing a border on each image for the tumour volume in order to distinguish it from neighbouring tissues and structures. By processing the images in this way, a single DL model could accurately identify, classify and segment three different categories of brain tumours (meningioma, glioma or pituitary). ResNet-50 and ResNet-101 network architectures were trained for 1000 epochs, and their classification and segmentation performances were assessed. A higher classification success was achieved for the ResNet-101 backbone pretrained on COCO images (%75 accuracy) in the testing phase. ResNet-50, on the other hand, achieved a higher classification accuracy (%87) when used with the ImageNet dataset. The classification and segmentation successes of meningioma and pituitary tumours were comparable, while glioma tumours could be segmented at relatively lower success rates (41–73% for all models), despite similar classification performances (73–95%) with other tumour types. [ABSTRACT FROM AUTHOR]

Imidazo[1,2-a] pyridine-based two probes with different substituents containing oxime and hydroxyl functional groups were synthesised and evaluated for detection of tabun nerve agent simulant diethylcyanophosphonate (DCNP). The nucleophile hydroxyl group present on both the probes can be rapidly phosphorylated by DCNP through a nucleophilic substitution reaction. The probes were fluorescent and on reacting with DCNP underwent fluorescence OFF, which can be visualised under a UV lamp at 365 nm wavelength. The limit of detection for probes 1 and 2 was calculated as 0.66 μM and 0.54 μM, respectively. The mechanism of detection was studied using spectroscopic techniques revealed that the hydroxyl group is involved in the reaction with DCNP. The probes were found selective for the detection of DCNP in presence of other electrophilic interfering agents. Results were obtained in aqueous media and probes can be used directly for the rapid and sensitive detection of DCNP in water without any pretreatment. [ABSTRACT FROM AUTHOR]

The surge in demand for advanced operations in sports video analysis has underscored the crucial role of multiple object tracking. This study addresses the escalating need for efficient and accurate player and referee identification in sports video analysis. The challenge of identity switching among players, especially those with similar appearances, complicates multi-player tracking. Existing algorithms relying on manually labeled data face limitations, particularly with changes in jersey colors. This paper introduces an automated algorithm employing Intersection over Union (IoU) loss and Euclidean Distance (EUD), termed EIoU-Distance Loss, to track players and referees. The method prioritizes identity coherence, aiming to mitigate challenges associated with player and referee recognition. Comprising BackgroundSubtractionMOG2 for player and referee detection and IoU with EUD for connecting nodes across frames, the proposed approach enhances tracking performance, ensuring a clear distinction between different identities. This innovative method addresses critical issues in sports video analysis, offering a robust solution for tracking players and referees in dynamic game scenarios. [ABSTRACT FROM AUTHOR]

Generative AI can be a powerful research tool, but researchers must employ it ethically and transparently. This commentary addresses how the editors of pharmacy practice journals can identify manuscripts generated by generative AI and AI-assisted technologies. Editors and reviewers must stay well-informed about developments in AI technologies to effectively recognise AI-written papers. Editors should safeguard the reliability of journal publishing and sustain industry standards for pharmacy practice by implementing the crucial strategies outlined in this editorial. Although obstacles, including ignorance, time constraints, and protean AI strategies, might hinder detection efforts, several facilitators can help overcome those obstacles. Pharmacy practice journal editors and reviewers would benefit from educational programmes, collaborations with AI experts, and sophisticated plagiarism-detection techniques geared toward accurately identifying AI-generated text. Academics and practitioners can further uphold the integrity of published research through transparent reporting and ethical standards. Pharmacy practice journal staffs can sustain academic rigour and guarantee the validity of scholarly work by recognising and addressing the relevant barriers and utilising the proper enablers. Navigating the changing world of AI-generated content and preserving standards of excellence in pharmaceutical research and practice requires a proactive strategy of constant learning and community participation. [ABSTRACT FROM AUTHOR]

Fibrillation (Fib) is the most well-known sort of sporadic heartbeat. The sporadic termination of electrical impulses causes the atria (the top chambers in the heart) to tremble (or fibrillate). Cardiovascular arrhythmias happen in older people with coronary disease. Now and again, individuals with Fib have no manifestations, and their condition is just recognizable upon actual assessment. The issues include inconsistent heartbeats when the heart is reliably in a sporadic mood, as anticipated through the patient well-being observation framework (PHMS). The PHMS proposed calculation is ready to anticipate the inconsistent pulses by the location of the R top in electrocardiogram signals (ECG) and the unpredictable beat anticipated by utilizing the P wave along with the QRS complex with the T wave for one heart cycle. Hence, this paper proposes a hybrid Convolutional Neural Network (CNN) and Recurrent Neural Networks (RNN), termed hybrid C-RNN, to proceed with the characterization and determination of the arrhythmic beats. This work also analyzes the QRS peak variations, followed by classification. The practicability of the heartbeat characterization utilizing hybrid C-RNN is completed by metrics like accuracy, sensitivity, and specificity. The proposed model achieved an accuracy of 98.57%, sensitivity of 1%, and specificity of 9.565217e-01%. This is compared best with some existing approaches in the result section. [ABSTRACT FROM AUTHOR]

Tylosin (TYL) is a kind of antibiotics which promotes the development of animal husbandry, but it poses potential threats to food safety and human health. Therefore, it is necessary to establish a highly sensitive method for the detection of TYL. Here, an enzyme-linked immunosorbent assay (ELISA) combined with biotin–streptavidin (Bio-SA) system and hybridization chain reaction (HCR) technique (Bio-SA-HCR-ELISA) was developed. In the absence of the TYL, a strong fluorescence was generated through double signal amplification as signal output, while in the presence of TYL, the fluorescence intensity decreased. The results showed that the limit of detection for TYL was 0.39 ng/mL, and this method had a good specificity for 9 common antiboics. Compared to the traditional indirect competitive ELISA, the sensitivity of this method was 6.8 times higher. Additionally, the established double signal amplification Bio-SA-HCR-ELISA was used for detecting TYL in milk and honey, the LOD of this method were 4.9 and 2.5 ng/mL, respectively. [ABSTRACT FROM AUTHOR]

Pesticides are applied widely to increase agricultural output and quality, however, this practice results in residual issues that not only harm the environment but also put people and animals' lives and health at risk. As a result, it is critical to find pesticide residues in a variety of sources, including crops, water supplies, and soil. Aptamers are more flexible in their synthesis and modification, have a high level of specificity, are inexpensive, and have good stability compared to conventional detection methods. They have therefore attracted a lot of interest in the industry. This study reviews the most recent aptasensor advancements in the detection of pesticide residues. Firstly, aptamers specifically binding to many pesticides are summarized. Secondly, the combination of aptasensors with colorimetric, fluorescent, surface enhanced Raman spectroscopy (SERS), resonance Light Scattering (RLS), chemiluminescence (CL), electrochemical, and electrochemiluminescence (ECL) technologies are systematically introduced, and their advantages and disadvantages are expounded. Importantly, the aptasensors for the detection of various pesticides (organochlorine, organophosphorus, neonicotinoids, carbamates, and pyrethroids) that have been developed so far are systematically analyzed and discussed. Finally, the furture prospects and challenges of the aptasensors are highlighted. It is expected to offer suggestions for the later creation of novel, highly effective and sensitive aptasensors for the detection of pesticide residues. [ABSTRACT FROM AUTHOR]

Kidney cancer is a significant health concern worldwide, with early detection crucial for successful treatment outcomes. Recently, there has been an increasing focus on investigating affordable and non-intrusive methods for detecting cancer at its early stages. This proposed approach delves into the utilization of antenna arrays to identify kidney cancer. Specifically, antenna array with circular polarization has been crafted to identify tumors in the human kidney. This array design has been implemented using CST software, operating at 2.4 GHz. The antenna array having a dimension of 150 mm x 150 mm. The array consists of four-square patches each having a length of 30 mm and the spacing between the patches is preferred to be 20 mm for ensuring the mutual coupling. FR4 substrate is used with the relative permittivity 4.3 and 1.6 mm thickness. The axial ratio of the circularly polarized antenna is 0.9 dB at 2.4 GHz. The reflection coefficient S 11 = –30dB. The renal phantom in humans has been built with the kidney, adrenal gland, ureter, skin, fat, and muscle components to allow for precise simulation. Following that, four stages of cancer tumors were built employing all of the necessary qualities. For every stage of the cancer tumor, the reflection coefficient growth and resonance frequency shift are calculated. For the initial stages, the resonance frequency shift is very low. Consequently, the primary determinant of detection is the rise in S 11 . For 3rd and 4th stages of the tumor, there is a significant change in resonance frequency and increase S 11 , which facilitates identification. The calculated specific absorption rate (SAR) is a smaller amount than the safety thresholds, indicating that using this method is safe. Overall, this research points to a novel, easy-to-use method for kidney cancer screening. This method has the following benefits: it is non-invasive, safe, quick, affordable, and comfortable, and it emits no ionizing radiation during the measurement process. Furthermore, a range of output parameters such as gain, directivity, and polar radiation have been accurately computed. [ABSTRACT FROM AUTHOR]

Background: Pseudomonas aeruginosa (P. aeruginosa) is widely distributed in air, soil, and water, human respiratory tract, intestinal tract, and skin. It can induce bloodstream infection, urinary tract infection, gastrointestinal tract infection, respiratory tract infection, etc. Conventional bacterial isolation, culture, and identification are time-consuming, and many false negative results, which cannot meet the needs of precise clinical diagnosis，and proper treatment. This study aims to develop a rapid isothermal amplification assay of Pseudomonas aeruginosa. Methods: Specific primers were designed according to the National Center for Biotechnology Information (NCBI) database based on the highly conserved sequence of Pseudomonas aeruginosa virulence factor gene lecA, and a recombinase polymerase amplification (RPA) detection method was established. The sensitivity and specificity were calculated, as well as the collection and processing of clinical samples. Results: The thermostatic amplification technique for Pseudomonas aeruginosa established in this paper allows nucleic acid detection within 10 minutes without cross-amplification with other bacterial strains. 27 P. aeruginosa infections were accurately detected in 300 clinical samples. Conclusion: The rapid detection system based on thermostatic amplification had shown high sensitivity and specificity in this study, indicated that this method can effectively assist clinical bacterial detection. [ABSTRACT FROM AUTHOR]

A Software-defined network paradigm provides flexibility and programmability to deal with the growing users of future networks. As a result of the centralized control attribute, it could be regarded as a single point of failure that is vulnerable to various forms of attacks, such as Distributed denial of service (DDOS) attacks. This study attempts to show a mathematical representation of DDOS attacks in SDN, together with how some five-tuple features contribute to the attacks. The studied features were used to detect DDOS using a random forest classifier. The result shows 96.3% detection accuracy and 96.45% precision. [ABSTRACT FROM AUTHOR]

Cervical cancer (CC) is a major health concern for women due to the high mortality rate. Conventional treatments frequently have significant adverse effects and dismal survival rates, particularly in advanced stages. Nanotechnology provides intriguing options for precise detection and customized medicine delivery. Nanoparticles, when paired with immunotherapy or chemotherapy, can efficiently target cancer cells, resulting in better therapeutic outcomes. This review examines literature published over the past decade, focusing on various types of nanoparticles utilized for CC detection and drug administration, including polymers, biomimetics, and inorganic nanoparticles. This study aims to illumine recent advancements, novel methodologies, and promising prospects in using nanoparticles for the detection and treatment of cervical cancer. [ABSTRACT FROM AUTHOR]

Technological solutions cannot completely protect companies and governments from human actors driven by money, greed, or simply the thrill of the steal. In fact, each technological countermeasure for industrial espionage is itself the product of human decision-making attempting to preempt and inhibit the decisions and actions of the industrial spy. Nobody knows for sure who the spy is. And the spy doesn't know for sure that he hasn't already been detected or, even if he hasn't, whether his plans will succeed, and his desired payoffs attained. Searching, deciding, detecting, and stopping the industrial spy when both sides of the game face risk and uncertainty is the subject matter of this paper. We focus on closing the spy's window of opportunity. [ABSTRACT FROM AUTHOR]

Aims: To develop a colourimetric loop-mediated isothermal amplification (LAMP) assay for the detection of Pasteurella multocida in clinical poultry samples and compare the performance of this assay with PCR. A secondary aim was to evaluate a simple DNA extraction method that could enable LAMP-based testing in the field without the need for specialised laboratory equipment. Methods: Primer sets for both LAMP and PCR were designed to amplify the KMT1 gene of P. multocida. DNA was extracted from 12 P. multocida isolates using a commercial extraction kit, and subjected to analysis using both LAMP and PCR. The analytical specificity of the LAMP assay was evaluated by testing it against a panel of 12 unrelated bacterial species, and the analytical sensitivity (limit of detection) was determined through testing of serial dilutions of the target DNA and compared to that of PCR. Subsequently, cloacal swabs (n = 40) from a commercial turkey flock were subjected to analysis using both LAMP and PCR assays, using a rapid DNA extraction method and a commercial extraction kit. Clinical sensitivity and specificity of the LAMP assay were calculated in comparison to PCR. Results: A single DNA fragment of the expected size (∼ 200 base pairs), was amplified by PCR from 12 P. multocida isolates, which were also all positive by the LAMP assay. The identity of all PCR amplicons was confirmed by sequencing. Both PCR and LAMP showed similar analytical sensitivity, with a LOD of 20 pg of target DNA. As neither PCR nor LAMP assays produced positive results with 12 non-related bacterial species, the analytical specificity was assessed as 100%. However, LAMP demonstrated lower clinical specificity (94.74%) compared to PCR (100%) when 40 clinical samples were tested. None of the DNA samples extracted using the simplified DNA extraction method were amplified by either LAMP or PCR. Conclusion: The LAMP assay developed in this study exhibits comparable performance to PCR in detecting P. multocida. Clinical relevance: The use of a rapid and portable DNA extraction method, in conjunction with LAMP assays, could create opportunities for point-of-care testing for fowl cholera in field settings. [ABSTRACT FROM AUTHOR]

In this study, data spanning 60 years on the distributions and richness of 19 bird species and 108 vascular plant species in China were utilized to detect and attribute multidirectional changes in distribution and richness. Between 1961 and 2020, approximately two-thirds of the studied species exhibited northward shifts in their northern boundaries or latitudinal centres of distribution, while significant statistical shifts in range variables occurred for less than one-third of the species. Changes in northern boundaries and distribution centres were observed for more than half of the studied species, with other directional shifts attributed to climate change affecting less than one-third of the species; additionally, some range shifts were linked to land-use activities. Furthermore, a significant shift was noted in the statistical distribution of richness among the studied species, accompanied by increases in both mean and median values; these changes in richness were associated with climate change as well as land-use factors. [ABSTRACT FROM AUTHOR]

A robust environmental monitoring system is highly essential for the instant detection of environmental microfiber pollutants for the sustainable management of the environment and human health. The extent of microfiber pollution is growing exponentially across the globe in both terrestrial and marine environments. An immediate and accurate environmental monitoring system is crucial to investigate the composition and distribution of these micropollutants. Fourier Transform Infrared Spectroscopy and Raman Spectroscopy are vibrational spectroscopic techniques that have the novel ability to detect microfibers within a minute concentration from diverse environmental samples. The major micropollutants which have been analyzed are polyethylene, polypropylene, nylon 6, polystyrene, and polyethylene terephthalate. After a detailed and critical study of the various aspects of spectroscopic analysis, the review is concluded with a comprehensive discussion of the significance of these robust methods and their application in future aspects for further preventing microfiber pollution in the marine environment. This study highlights the utilities and significance of vibrational spectroscopic detection techniques for the immediate and accurate identification of synthetic microfibers. This review also evaluated the implementation of spectroscopic methods as a precise tool for the characterization and monitoring of microfiber pollutants in the environment. [ABSTRACT FROM AUTHOR]

Current approaches used by educational institutions to address the problem of student cheating are not working. This is because the discourse of academic integrity that currently dominates is, on its own, inadequate for addressing the problem. We propose that in order for higher education institutions to challenge cheating effectively, they need to learn from history and to look outside their own sector to find a new way of responding to student cheating behaviours. We propose a new framework for this. We take the theory of responsive regulation, developed by Ayres and Braithwaite, and combine it with the discourse of assessment security introduced by Dawson to form the Educational Integrity Enforcement Pyramid. We use this pyramid to show how institutions can operationalize this new approach and use it to identify gaps in their strategic response to the challenging problem of student cheating. [ABSTRACT FROM AUTHOR]

In food safety analysis, the detection and control of foodborne pathogens and their toxins are of great importance. Monitoring of virus transmission is equally important, especially in light of recent findings that coronaviruses have been detected in frozen foods and packages during the current global epidemic of coronavirus disease 2019. In recent years, field-effect transistor (FET) biosensors have attracted considerable scholarly attention for pathogenic microorganisms and toxins detection and sensing due to their rapid response time, high sensitivity, wide dynamic range, high specificity, label-free detection, portability, and cost-effectiveness. FET-based biosensors can be modified with specific recognition elements, thus providing real-time qualitative and semiquantitative analysis. Furthermore, with advances in nanotechnology and device design, various high-performance nanomaterials are gradually applied in the detection of FET-based biosensors. In this article, we review specific detection in different biological recognition elements are immobilized on FET biosensors for the detection of pathogenic microorganisms and toxins, and we also discuss nonspecific detection by FET biosensors. In addition, there are still unresolved challenges in the development and application of FET biosensors for achieving efficient, multiplexed, in situ detection of pathogenic microorganisms and toxins. Therefore, directions for future FET biosensor research and applications are discussed. [ABSTRACT FROM AUTHOR]

AbstractPathogens contamination is a pressing global public issue that has garnered significant attention worldwide, especially in light of recent outbreaks of foodborne illnesses. Programmable nucleases like CRISPR/Cas and Argonaute hold promise as tools for nucleic acid testing owning to programmability and the precise target sequence specificity, which has been utilized for the development pathogens detection. At present, fluorescence, as the main signal output method, provides a simple response mode for sensing analysis. However, the dependence of fluorescence output on large instruments and correct analysis of output data limited its use in remote areas. Lateral flow strips (LFS), emerging as a novel flexible substrate, offer a plethora of advantages, encompassing easy-to-use, rapidity, visualization, low-cost, portability, etc. The integration of CRISPR/Cas and Argonaute with LFS, lateral flow assay (LFA), rendered a new and on-site mode for pathogens detection. In the review, we introduced two programmable nucleases CRISPR/Cas and Argonaute, followed by the structure, principle and advantages of LFA. Then diversified engineering detection pattens for viruses, bacteria, parasites, and fungi based on CRISPR/Cas and Argonaute were introduced and summarized. Finally, the challenge and perspectives involved in on-site diagnostic assays were discussed. [ABSTRACT FROM AUTHOR]

There is global demand for methods to prioritize child sexual exploitation material (CSEM) investigations. Previous research comparing online CSEM offenders based on how they were detected found potentially meaningful differences in offense and individual characteristics, including factors relating to targets for prioritization, such as risk of other offending. The present study builds on this work by providing an in-depth comparison of the individual characteristics and offending behavior of a sample of 336 men convicted of CSEM offenses, divided into four detection groups: (1) those reported by others; (2) those identified during another police investigation; (3) those identified due to their online web purchases or downloads, and; (4) those detected during proactive online police investigations. As a group, the riskiest individuals were detected by reports of others and during other investigations (Cohen's f =.25). This finding suggests that it is important to search for CSEM when doing other police investigations, particularly those involving allegations of sexual offending or crimes against children. Risk relevant information may also assist prioritization, though it will depend on the information available at different points in an investigation and may require the use of professional judgement in approximating evidence of robust risk factors. [ABSTRACT FROM AUTHOR]

AbstractIn recent years, biomass carbon materials have received widespread attention in the field of electrochemical sensors. As a new type of renewable green energy, biomass carbon has the advantages of low cost and abundant resources. After special treatment, it can be used as an ideal electrode material. Since biomass carbon materials have diverse sources and their morphology is difficult to control, researchers have conducted in-depth research on their preparation process, morphology regulation and application. This review summarizes different biomass carbon structures and their preparation methods and explores the applications of these materials in electrochemical sensors. Modification of biomass carbon materials through pretreatment, physical and chemical activation, heteroatom doping, metal compound composite and other methods can make up for the deficiencies in its pore structure, electrical conductivity and surface wettability, thereby improving its electrochemical performance. The effects of different biomass sources, functional groups, constituent elements and modification methods on the morphology, structure and electrochemical properties of biomass carbon materials are discussed, and the applications of this type of material in biological molecules, heavy metal ions and pesticide residues are reviewed. Biomass carbon-based materials show great application potential and development prospects in the field of electrochemical sensors. [ABSTRACT FROM AUTHOR]

Nanozymes, made of emerging nanomaterials, have similar activity to natural enzyme and exhibit promising applications in in the fields of environment, biology and medicine, and food safety science. In recent years, with the deep finding and research to nanozymes by researchers, its application in field of pharmaceutical analysis has emerged gradually, possessing great significance in drug safety evaluation and quality control. This review summarizes the construction of metal nanozymes, strategies to improve their performance and their application in pharmaceutical detection and analysis, especially in detection of target analytes consisting of small molecule medicine macromolecule, toxic and others, which proposes theoretical foundation for development of nanozymes in this field. At the same time, it also provides opportunities and challenges for the construction and application of new nanozymes. [ABSTRACT FROM AUTHOR]

AbstractThe human living environment serves as a habitat for microorganisms and the presence of ubiquitous airborne microbes significantly impacts the natural material cycle. Through ongoing experimentation with beneficial microorganisms, humans have greatly benefited from airborne microbes. However, airborne pathogens endanger human health and have the potential to induce fatal diseases. Tracking airborne microbes is a critical prerequisite for a better understanding of bioaerosols, harnessing their potential advantages, and mitigating associated risks. Although technological breakthroughs have enabled significant advancements in accurately monitoring airborne pathogens, many puzzles about these microbes remain unanswered due to their high variability and environmental diffusibility. Consequently, advanced techniques and strategies for special identification, early warning, and efficient eradication of microbial contamination are continuously being sought. This review presents a comprehensive overview of the research status of airborne microbes, concentrating on the recent advances and challenges in sampling, detection, and inactivation. Particularly, the fundamental design principles for the collection and timely detection of airborne pathogens are described in detail, as well as critical factors for eliminating microbial contamination and enhancing indoor air quality. In addition, future research directions and perspectives for controlling airborne microbes are also suggested to promote the translation of basic research into real products. [ABSTRACT FROM AUTHOR]

The field of academic stress detection has gained significant attention recently because mental and physical health is crucial for academic success. The goal of academic stress detection is to identify a student's level of stress during the learning process using observable markers including physiological, behavioral, and psychological data. In recent years, detection methods that utilize wearable and nonwearable sensors have gained increased attention owing to their rich functionalities. In order to discover contemporary developments, coping strategies, limitations, difficulties, and potential research areas for addressing academic stress in educational settings, this study conducted an exhaustive review of the existing literature. First, we discussed how stressful events influence students’ psychological and physical health as well as the statistics frequently utilized to monitor academic stress. Then, using machine learning and deep learning methods, we described academic stress detection models. In addition, we described self-regulated strategy, computer-supported strategy and interactive learning technology-supported strategy. This comprehensive analysis of the latest techniques and recommendations for potential research avenues for tackling academic stress in educational settings will help other researchers in this field carry out and assess user research and build academic stress detection systems. [ABSTRACT FROM AUTHOR]

AbstractThe continuing development of heavy industry worldwide has led to an exponential increase in the amount of wastewater discharged from factories and entering the natural world in the form of rivers and air. As the top of the food chain in the natural world, toxic ions penetrate the human body through the skin, nose, and a few milligrams of toxic ions can often cause irreversible damage to the human body, so ion detection and adsorption is related to the health and safety of human beings. Hydrogel is a hydrophilic three-dimensional reticulated polymer material that first synthesized by Wichterle and Lim in 1960, which is rich in porous structure and has a variety of active adsorption sites as a new type of adsorbent and can be used to detect ions through the introduction of photonic crystals, DNA, fluorescent probe, and other materials. This review describes several synthetic and natural hydrogels for the adsorption and detection of ions and discusses the mechanism of ion adsorption by hydrogels, and provide a perspective for the future development. [ABSTRACT FROM AUTHOR]

AbstractA commonly employed method for identifying aspirin involves the use of either HPLC or electrochemical analysis. In this article, a highly selective and ultrasensitive fluorescent sensor with convenient synthesis steps for the detection of aspirin was reported. This study introduced a novel hybrid organic-inorganic optical network hydrogel achieved through the integration of a complex of Tb-xanthan gum (XG) into the backbone of polyvinyl alcohol (PVA). This hydrogel fabricated in a water-based environment, exhibits weak green luminescence at the beginning owing to the chelation and sensitization of lanthanide (Tb3+) ions by ligands. Interestingly, the green emission intensity observed at λex = 346 nm, is enhanced by dual energy-transfer channels from XG and aspirin. We experimentally demonstrate that luminescent Tb3+-XG complex-binding aptamer hydrogels can selectively detect aspirin from 250 nM to 1 μM with a detection limit of 150 nM in this study. Furthermore, we discuss the resulting structure, thermal stability, excellent swelling properties, mechanical characteristics, and morphology of this light-emission material in detail. The success of this method will open up new opportunities for the use of hydrogels in visual fluorescence sensing applications. [ABSTRACT FROM AUTHOR]

Plant-derived antioxidants (PD-AOs) are important for food preservation, as well as for human health and nutrition. However, the poor chemical stability and water solubility of many PD-AOs currently limit their application as functional ingredients in foods and pharmaceuticals. Moreover, it is often difficult to isolate and detect specific antioxidants in multi-component systems, which again limits their potential in the food and medical industries. In this review, we highlight recent advances in the use of cyclodextrins (CDs) to overcome these limitations by forming simple, modified and competitive host–guest interactions with PD-AO. The host–guest properties of CDs can be used to enhance the separation efficiency of PD-AOs, as well as to improve their dispersion and stability in food systems. Moreover, the competitive complexation properties of CDs with target molecules can be used to selectively isolate PD-AOs from multi-component systems and develop detection technologies for PD-AOs. Overall, CD-antioxidant interactions have great potential for addressing isolation, detection, and food quality issues. [ABSTRACT FROM AUTHOR]

Ridge Estimation (RE) is a widespread method to overcome the problem of collinearity defining a class of estimators depending on the non-negative scalar parameter k. A great number of papers focus on the estimation of this biasing parameter. Traditionally, the mean squared error criterion is used to compare the performance of the different proposed estimators. However, the minimization of the mean squared error (MSE) does not always guarantee the mitigation of collinearity, meaning it is possible, for example, to obtain a variance inflation factor (VIF) higher than 10 for the k that minimizes the MSE. In this paper, we propose the VIF criteria to select the biased ridge parameter. A Monte Carlo simulation is presented with results that support this idea. Also, two real life empirical applications are used to illustrate the contribution of this paper. [ABSTRACT FROM AUTHOR]

Detecting and monitoring prisoners who present a risk of committing terrorism is an important objective of prison authorities. A key practice in many prison services is for prison staff to observe prisoner behaviours that may indicate such risk. However, there is a dearth of research that systematically examines which behaviours signify, or prison staff consider to signify, terrorism risk. This study addresses this issue by systematically examining the nature and prevalence of different behaviours indicated across a large and unique international data set of both open and closed sources, which primarily focusses on Islamist groups and ideologies. Reflexive thematic analysis identifies 29 distinct behaviours (subthemes), brigaded under 6 domains of functioning (main themes). Tentative findings suggest that many behaviours are consistently identified across open and closed data sources, indicating these are similar in different jurisdictions – although how they manifest locally may not be – and in both the fields of research and practice. Whilst some behaviours may (also) signify general non-compliance in prisons, others appear more distinct in potentially signifying terrorism risk. Finally, this practice appears over-focussed on prisoner behaviours that signify risk, rather than protection or resilience. Findings are discussed in relation to theoretical, methodological, and operational issues. [ABSTRACT FROM AUTHOR]

AbstractMXenes (Mn+1XnTx), a subgroup of 2-dimensional (2D) materials, specifically comprise transition metal carbides, nitrides, and carbonitrides. They exhibit exceptional electrocatalytic and photocatalytic properties, making them well-suited for the detection and removal of pollutants from aqueous environments. Because of their high surface area and remarkable properties, they are being utilized in various applications, including catalysis, sensing, and adsorption, to combat pollution and mitigate its adverse effects. Different characterization techniques like XRD, SEM, TEM, UV-Visible spectroscopy, and Raman spectroscopy have been used for the structural elucidation of 2D MXene. Current responses against applied potential were measured during the electrochemical sensing of the hazardous pollutants in an aqueous system using a variety of electroanalytical techniques, including differential pulse voltammetry, amperometry, square wave anodic stripping voltammetry, etc. In this review, a comprehensive discussion on structural patterns, synthesis, properties of MXene and their application for electrochemical detection of lethal pollutants like hydroquionone, phenol, catechol, mercury and lead, etc. are presented. This review will be helpful to critically understand the methods of synthesis and application of MXenes for the removal of environmental pollutants. [ABSTRACT FROM AUTHOR]

AbstractFood additives are essential constituents of food products in the modern world. The necessity of food processing went up rapidly as to meet requirements including, imparting desirable properties like preservation, enhancement and regulation of color and taste. The methods of identification and analysis of such substances are crucial. With the advancement of technology, a variety of techniques are emerging for this purpose which have many advantages over the existing conventional ways. This review is on different kinds of additives used in the food industry and few prominent methods for their determination ranging from conventional chromatographic techniques to the recently evolved nano-sensor techniques. [ABSTRACT FROM AUTHOR]

Milk contaminated with trace amounts of foodborne pathogens can considerably threaten food safety and public health. Therefore, rapid and accurate detection techniques for foodborne pathogens in milk are essential. Nucleic acid amplification (NAA)-based strategies are widely used to detect foodborne pathogens in milk. This review article covers the mechanisms of the NAA-based detection of foodborne pathogens in milk, including polymerase chain reaction (PCR), loop-mediated isothermal amplification (LAMP), recombinase polymerase amplification (RPA), rolling circle amplification (RCA), and enzyme-free amplification, among others. Key factors affecting detection efficiency and the advantages and disadvantages of the above techniques are analyzed. Potential on-site detection tools based on NAA are outlined. We found that NAA-based strategies were effective in detecting foodborne pathogens in milk. Among them, PCR was the most reliable. LAMP showed high specificity, whereas RPA and RCA were most suitable for on-site and in-situ detection, respectively, and enzyme-free amplification was more economical. However, factors such as sample separation, nucleic acid target conversion, and signal transduction affected efficiency of NAA-based strategies. The lack of simple and effective sample separation methods to reduce the effect of milk matrices on detection efficiency was noteworthy. Further research should focus on simplifying, integrating, and miniaturizing microfluidic on-site detection platforms. [ABSTRACT FROM AUTHOR]

Escherichia coli in phosphate-buffered saline was treated with high hydrostatic pressures (HHPs) of 400, 500, and 600 MPa at 25 °C for 10 min. The cell suspension was then plated on nonselective agar and incubated at 20, 25, 30, 35, 37, and 40 °C to study the effect of the incubation temperatures on the detection of HHP-injured populations. The number of detected cells was maximum when incubated at 25 °C for 72 h. Cold storage at 4 °C for 7 d after HHP-treatment improved the detection efficacies of incubations at 30–40 °C, implicating increased tolerance of HHP-injured cells against the mild heat. Combined incubation at 25 °C for more than 6 h with subsequent incubation at 35 °C shortened total incubation period from 72 h to 24 h while achieving the maximized colony number obtained by the incubation at 25 °C for 72 h. [ABSTRACT FROM AUTHOR]

We studied food fraud detection and the reporting of suspected cases using a questionnaire survey and interviews with Finnish food control officers (FCOs). In total, 95 FCOs responded to the questionnaire, and 17 were interviewed. We found that even though many respondents had either suspected (69.2%) or detected (43.4%) food fraud or other food-related crime during the past five years, 46.8% thought they had no realistic chance of detecting food fraud during inspections. Challenges raised by the FCOs we interviewed included inadequate resources (8/17) and difficulties in inspecting documents or establishing their authenticity (14/17). Moreover, many interviewees highlighted difficulties in assessing whether to inform the police about a suspected case (7/17), and 62% (18/29) of respondents who had detected fraud had not reported it to the police. Training in food fraud detection, increased resources and guidelines on reporting suspected food fraud would improve food fraud detection and harmonize reporting. [ABSTRACT FROM AUTHOR]

Cyber-attacks using malicious uniform resource locator (URL) propagation are very common and serious. Statistics indicate that there is a need to research and apply techniques and methods for identifying and preventing malicious URLs. The main objective of this research is to train machine learning models on selected dataset to predict phishing websites based on URL-related features. The accuracy level of each model is measured and compared. Finally, the best performing model will be used to develop a web application that provide internet users with an easy way to check suspicious URLs. We have used five different machine learning models to classify URLs as legitimate or phishing, these models are eXtreme Gradient Boosting (XGBoost), k-nearest neighbors (KNN), support vector machine (SVM), Decision Tree, and Random Forest. Finally, we used Voting Classifier to combine the work of Random Forest (RF) algorithm with other two models, Gaussian Naive Bayes, and Logistic Regression, to check if we can increase the accuracy of RF as suggested in the literature, but we found that the accuracy of RF alone was higher than the accuracy of the combined models. This project can be implemented as a browser extension or mobile application to classify suspicious URLs to legitimate or phishing with the use of the saved model. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) is one of the trending topics in the research field in recent years. Facial expressions are the direct way in which humans communicate their emotions and identifying these emotions requires complex cognitive skills. Because of continual advances in image processing and machine learning techniques, several novel strategies are presented each year. Their performance still needs to be enhanced, and it is a challenging task. So, we aim to propose a novel method called Ensemble Convolutional Recurrent Neural Network (CRNN), and this model is proposed to enhance the prediction accuracy of facial expressions. The methods such as GRU, LSTM, and CNN prediction outputs are incorporated. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is constructed as the ensemble model's topmost layer to adequately examine the few sub-models. The final output is predicted by the fuzzy inference system. The facial Recognition Dataset and EMOTIC dataset are utilized in the method for evaluating the results. The various performance metrics are utilized to test the efficiency of the proposed model such as precision, False Positive Rate, F1-score, True Positive Rate, and accuracy. Our proposed method achieves 99.52% for precision and 99.35% for F1-score. Compared with other existing such our proposed method achieves 99.75% and the value of the Area under the ROC curve (AUC) is 0.95. [ABSTRACT FROM AUTHOR]

Matrix-associated mycotoxins that bind with macromolecular components through covalent or non-covalent interactions easily occur in various cereals, cereal-based products, and cereal-based feedstuff. They are "masked" by macro-components, causing the underestimation of total exposure risk of mycotoxins. Most of the current reports focus on the free and modified mycotoxins, while the matrix-associated forms are ignored but still can exert toxic effects after ingestion. In this paper, current researches and future prospects of matrix-associated mycotoxins are reviewed. Especially, a focus is set on the transformation of matrix-associated mycotoxins with their free forms during metabolism and food processing. Enzymes, temperature and pH levels during food processing can induce the interconversion of matrix-associated mycotoxins with free mycotoxins. Furthermore, the analytical methods targeted on matrix-associated mycotoxins are discussed. Due to the lack of efficient methods releasing the mycotoxins from matrix, the standard analytical methods has not developed so far. Also, we further analyzed the challenges of matrix-associated mycotoxins about variety, occurrence, toxicity and transformation, exposure assessment, which contributes to establish preventive measures to control their hazards for consumers. Overall, this overview is significant for perfecting risk assessment, as well as developing effective prevention and control actions to matrix-associated mycotoxins. [ABSTRACT FROM AUTHOR]

In recent years, deepfakes have become part and parcel of contemporary algorithmic culture. It is regularly claimed that they have the potential to introduce novel modes of societal disruption, violence, and harm. Yet, over-emphasising the power of deepfakes risks occluding frictions, struggles, and logics that already persist in the digital landscape. Arguing for a conceptualisation of deepfakes as an assemblage of differential tensions in society, we explore how they represent both a rupture and a continuation of the variegated politics of the image in the social world. The paper analyses the tensions of deepfakes through three distinct case studies: bodies, politics, and ideas of objectivity. Ultimately, we argue that the tensions and ethicopolitical implications of deepfakes are not reducible to a problem that can be solved through a logic of algorithmic detection and verification. [ABSTRACT FROM AUTHOR]

AbstractDiabetes is a chronic health condition that is characterized by increased levels of glucose (sugar) in the blood. It can have harmful effects on different parts of the body, such as the retina of the eyes, skin, nervous system, kidneys, and heart. Diabetes affects the structure of electrocardiogram (ECG) impulses by causing cardiovascular autonomic dysfunction. Multi-resolution analysis of the input ECG signal is utilized in this paper to develop a machine learning-based system for the automated detection of diabetic patients. In the first step, the input ECG signal is decomposed into sub-bands utilizing the tunable Q-factor wavelet transform (TQWT) technique. In the second step, four entropy-based characteristics are evaluated from each SB and elected using the K-W test method. To develop an automatic diabetes detection system, selected features are given as input with 10-fold validation to a SVM classifier using various kernel functions. The 3rd sub-band of TQWT with the Coarse Gaussian kernel function kernel of the SVM classifier yields a classification accuracy of 91.5%. In the same dataset, the comparative analysis demonstrates that the proposed method outperforms other existing methods. [ABSTRACT FROM AUTHOR]