Phase Extraction Research Articles

Clinical practice guidelines for adolescent and young adult cancer: A literature review.

e13727 Background: Cancers among adolescents and young adults (AYA, aged 15-39) are rising in incidence, yet formal guidelines for diagnosis, treatment, and management of AYA-specific cancers are not widely known or disseminated. Practice patterns vary widely, contributing to a lack of improvement in outcomes for this underserved population. Clinicians and health systems have reported a need for additional guidance in care delivery for AYAs. Methods: We conducted a comprehensive literature review to identify clinical practice guidelines for cancer care delivery specific to AYAs published in PubMed or MEDLINE since 2010. MeSH and other key words were applied, using a search strategy guided by a health science librarian (EM). 795 papers were identified, with 690 excluded based on title and abstract review. 105 papers underwent full text review, and 64 papers underwent complete data extraction. Using Covidence software, two team members independently reviewed articles at the data extraction phase, with disagreements resolved through discussion. Results: We identified 22 papers that met our inclusion criteria as clinical practice guidelines specific to AYA cancer care. Clinical focus included breast cancer (4), sarcoma (4), brain tumors (2), gynecologic malignancies (2), acute lymphoblastic leukemia (1), colorectal cancer (1), NUT carcinoma (1), and proton therapy (1). Six papers covered general principles of caring for AYAs, regardless of cancer diagnosis. Key themes across guidelines included the importance of fertility preservation and psychosocial support, multidisciplinary care provided at specialized centers, and consideration of long-term toxicities of treatment. Conclusions: Clinical practice guidelines exist to inform clinicians and health systems on best practices for caring for AYAs with cancer, a growing and underserved population. Further dissemination of these guidelines may assist with improving outcomes. Comprehensive summary of these and other AYA specific guidelines is forthcoming and may serve as a useful clinical reference. [Table: see text]

Accident Detection and Nearby Patrol Alert System Using AI &ML

Abstract: Intelligent communities are utilizing different creative ideas to improve the quality of human life. Due to fast growing sizes of our cities, need of travelling is constantly increasing, which in turn has increased count of vehicles on the roads. Increasing number of vehicles on the roads has brought about numerous difficulties for Street Traffic Management Authorities. Amongst different traffic related issues, road accidents are something worth giving attention to and have to be on the priority list. This project describes various automatic road accident detection techniques, which automatically detect accidents using surveillance videos in real-time and intimates the event scenario to control room and the main uniqueness of the system is it automatically estimates the crash value of the incident and intimates to the user. The proposed method assumes that traffic accident events are described by visual features occurring through a temporal way. Therefore, a visual features extraction phase, followed by a temporary pattern identification, compose the model architecture. The visual and temporal features are learned in the training phase through convolution and recurrent layers using built-from-scratch and public datasets. An accuracy of 98% is achieved in the detection of accidents in public traffic accident datasets, showing a high capacity in detection independent of the road structure.

Developing a production workflow for 3D-printed temporal bone surgical simulators

Introduction3D-printed temporal bone models enable the training and rehearsal of complex otological procedures. To date, there has been no consolidation of the literature regarding the developmental process of 3D-printed temporal bone models. A brief review of the current literature shows that many of the key surgical landmarks of the temporal bone are poorly represented in models. This study aims to propose a novel design and production workflow to produce high-fidelity 3D-printed temporal bone models for surgical simulation.MethodsDevelopmental phases for data extraction, 3D segmentation and Computer Aided Design (CAD), and fabrication are outlined. The design and fabrication considerations for key anatomical regions, such as the mastoid air cells and course of the facial nerve, are expounded on with the associated strategy and design methods employed. To validate the model, radiological measurements were compared and a senior otolaryngologist performed various surgical procedures on the model.ResultsMeasurements between the original scans and scans of the model demonstrate sub-millimetre accuracy of the model. Assessment by the senior otologist found that the model was satisfactory in simulating multiple surgical procedures.ConclusionThis study offers a systematic method for creating accurate 3D-printed temporal bone models for surgical training. Results show high accuracy and effectiveness in simulating surgical procedures, promising improved training and patient outcomes.

Exploring the Potential of Deep Learning in Healthcare: A perspective

Deep learning has received much interest in the field of healthcare in recent years. Health care plays a significant role in delivering services and practices that promote, maintain, and restore health of an individual. However, applying deep learning in healthcare is still an exciting area of research. This paper explores the application of deep learning, and henceforth, it highlights new perspectives in healthcare by reviewing published state-of-the-art research works from four scholarly databases, including Scopus, Web of Science, Pubmed, and Google Scholar. The selected studies were from April 2014 to April 2024, and based on the predefined quality assessment criteria, 16 articles were thoroughly reviewed after the preliminary extraction, review, and screening phases. The study’s findings indicate that deep learning has been applied in healthcare, particularly in medical images, digital consultation, Electronic medical records, and genomics. Furthermore, challenges such as deep learning cannot replicate the human touch and emotional connection that patients often seek in their healthcare journey, and data privacy are highlighted. Lastly, new perspectives, such as leveraging emerging technologies like Augmented Reality (AR), Virtual Reality (VR), and federated learning, are suggested to address these challenges

Detection of 13 emerging soil pollutant compounds using a dual extraction method (QuEChERS and solid phase extraction) and a liquid chromatography/mass spectrometry LC-MS/MS method

Emerging pollutants derived from human and animal sources, are present in soils and pose significant environmental and health impacts, even at low concentrations. Their detection in soil is analytically complex due to soil interference and the rapid degradation of compounds in the matrix. In this study, a protocol was optimized for quantifying hormonal steroids (n = 7), human drugs (n = 3), and antibiotics (n = 3) by a dual-phase extraction using QuEChERS and Solid Phase Extraction (SPE), followed by analysis using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The double extraction phase allows an accurate and effective purification of the target compounds while eliminating the interference in the soil matrix. The method is optimized to detect environmental concentrations of these pollutants, to suit large-scale sampling campaigns and to maintain the efficiency of extraction while reducing analysis time. The limits of detection (LODs) of these compounds ranged between 0.0043 and 0.13 ng/g and recovery rates between 75.9 % and 105.39 %.•Enhanced Analyte Purification: Implements QuEChERS and SPE for robust removal of matrix interferences, optimizing target compound isolation.•Precision at Trace Levels: Secures LODs as minimal as 0.0043 ng/g, enabling accurate detection of low-concentration contaminants.•Adapted for Broad-scale sampling: Modifies extraction and analysis durations to accommodate large-scale environmental assessments.

Hybrid machine learning-based breast cancer segmentation framework using ultrasound images with optimal weighted features.

One of the most dangerous conditions in clinical practice is breast cancer because it affects the entire life of women in recent days. Nevertheless, the existing techniques for diagnosing breast cancer are complicated, expensive, and inaccurate. Many trans-disciplinary and computerized systems are recently created to prevent human errors in both quantification and diagnosis. Ultrasonography is a crucial imaging technique for cancer detection. Therefore, it is essential to develop a system that enables the healthcare sector to rapidly and effectively detect breast cancer. Due to its benefits in predicting crucial feature identification from complicated breast cancer datasets, machine learning is widely employed in the categorization of breast cancer patterns. The performance of machine learning models is limited by the absence of a successful feature enhancement strategy. There are a few issues that need to be handled with the traditional breast cancer detection method. Thus, a novel breast cancer detection model is designed based on machine learning approaches and employing ultrasonic images. At first, ultrasound images utilized for the analysis is acquired from the benchmark resources and offered as the input to preprocessing phase. The images are preprocessed by utilizing a filtering and contrast enhancement approach and attained the preprocessed image. Then, the preprocessed images are subjected to the segmentation phase. In this phase, segmentation is performed by employing Fuzzy C-Means, active counter, and watershed algorithm and also attained the segmented images. Later, the segmented images are provided to the pixel selection phase. Here, the pixels are selected by the developed hybrid model Conglomerated Aphid with Galactic Swarm Optimization (CAGSO) to attain the final segmented pixels. Then, the selected segmented pixel is fed in to feature extraction phase for attaining the shape features and the textual features. Further, the acquired features are offered to the optimal weighted feature selection phase, and also their weights are tuned tune by the developed CAGSO. Finally, the optimal weighted features are offered to the breast cancer detection phase. Finally, the developed breast cancer detection model secured an enhanced performance rate than the classical approaches throughout the experimental analysis.

Synthetic Aperture Radar Image Change Detection Based on Principal Component Analysis and Two-Level Clustering

Synthetic aperture radar (SAR) change detection provides a powerful tool for continuous, reliable, and objective observation of the Earth, supporting a wide range of applications that require regular monitoring and assessment of changes in the natural and built environment. In this paper, we introduce a novel SAR image change detection method based on principal component analysis and two-level clustering. First, two difference images of the log-ratio and mean-ratio operators are computed, then the principal component analysis fusion model is used to fuse the two difference images, and a new difference image is generated. To incorporate contextual information during the feature extraction phase, Gabor wavelets are used to obtain the representation of the difference image across multiple scales and orientations. The maximum magnitude across all orientations at each scale is then concatenated to form the Gabor feature vector. Following this, a cascading clustering algorithm is developed within this discriminative feature space by merging the first-level fuzzy c-means clustering with the second-level neighbor rule. Ultimately, the two-level combination of the changed and unchanged results produces the final change map. Five SAR datasets are used for the experiment, and the results show that our algorithm has significant advantages in SAR change detection.

Dual-Frequency Lidar for Compressed Sensing 3D Imaging Based on All-Phase Fast Fourier Transform

Lidar, with its advantages of high measurement accuracy, fine angular resolution, and strong anti-interference capability, plays a pivotal role in the field of scene depth information acquisition. Traditional approaches to achieving lateral spatial resolution in imaging include raster scanning and array detectors. The former necessitates frequent scanning to acquire depth maps, resulting in time consumption and instability. The latter encounters challenges such as high dark count rates, pixel crosstalk, and excessive costs for obtaining high-resolution images using array detectors. The introduction of compressed sensing (CS) offers a novel perspective on realizing non-scanning three-dimensional imaging. In this context, we propose a novel three-dimensional imaging system that combines CS with coherent dual-frequency continuous-wave lidar and utilizes the all-phase fast Fourier transform to extract both amplitude and phase information. This system requires only M measurements, and through a reconstruction algorithm, it achieves the inversion of depth information for N-pixel scenes (M << N). Integrating cost-effective components such as digital micromirrors and single-point detectors, this affordable system accomplishes three-dimensional imaging of a single target. Notably, it significantly reduces the required number of measurements while concurrently ensuring enhanced eye safety and signal-to-noise ratio.

Novel electrospun-based extractive probes for rapid determination of clinically important compounds in human plasma

BackgroundCoated blade spray (CBS) represents an innovative approach that utilizes solid-phase microextraction principles for sampling and sample preparation. When combined with ambient mass spectrometry (MS), it can also serve as an electrospray ionization source. Therefore, it became a promising tool in analytical applications as it can significantly reduce the analysis time. However, the current CBS coatings are based on the immobilization of extractive particles in bulk polymeric glue, which constrains the diffusion of the analytes to reach the extractive phase; therefore, the full reward of the system cannot be taken at pre-equilibrium. This has sparked the notion of developing new CBS probes that exhibit enhanced kinetics. ResultsWith this aim, to generate a new extractive phase with improved extraction kinetics, poly(divinylbenzene) (PDVB) nanoparticles were synthesized by mini-emulsion polymerization and then immobilized into sub-micrometer (in diameter) sized polyacrylonitrile fibers which were obtained by electrospinning method. Following the optimization and characterization studies, the electrospun-coated blades were used to determine cholesterol, testosterone, and progesterone in plasma spots using the CBS-MS approach. For testosterone and progesterone, 10 ng mL−1 limits of quantification could be obtained, which was 200 ng mL−1 for cholesterol in spot-sized samples without including any pre-treatment steps to samples prior to extraction. SignificanceThe comparison of the initial kinetics for dip-coated and electrospun-coated CBS probes proved that the electrospinning process could enhance the extraction kinetics; therefore, it can be used for more sensitive analyses. The total analysis time with this method, from sample preparation to instrumental analysis, takes only 7 min, which suggests that the new probes are promising for fast diagnostic applications.

Text-to-3D Non-Deformable Animation: A Proof-of-Concept System using Natural Language Descriptions

The ability to generate 3D animations from natural language descriptions has significant potential in various fields, including entertainment, education, and computer-aided design. This research paper presents a novel proof-of-concept system that transforms text prompts into complete and simple 3D animations. The system architecture comprises two main components: extraction and scene creation. In the extraction phase, a transformer model identifies the necessary objects from the text prompt, generates their geometric representations as vertices and edges, and creates a script describing the animations. The scene creation phase involves constructing the 3D objects, adding them to the scene, and rendering the animation based on the generated script. The implementation details, including the technologies and tools used, code snippets, and challenges faced, are thoroughly discussed. Qualitative and quantitative evaluations are conducted to assess the system's performance, highlighting its strengths and limitations compared to existing text-to-animation approaches. Potential applications of the proposed system are explored, ranging from rapid prototyping of 3D animations to interactive storytelling and educational tools. Furthermore, future research directions, such as improving scalability, generalization, and real-world deployment, are outlined. The research presented in this paper contributes to the emerging field of text-to-animation generation and paves the way for further advancements in transforming natural language descriptions into engaging and immersive 3D experiences.

Development of optimized cascaded LSTM with Seq2seqNet and transformer net for aspect-based sentiment analysis framework

The recent development of communication technologies made it possible for people to share opinions on various social media platforms. The opinion of the people is converted into small-sized textual data. Aspect Based Sentiment Analysis (ABSA) is a process used by businesses and other organizations to assess these textual data in order to comprehend people’s opinions about the services or products offered by them. The majority of earlier Sentiment Analysis (SA) research uses lexicons, word frequencies, or black box techniques to obtain the sentiment in the text. It should be highlighted that these methods disregard the relationships and interdependence between words in terms of semantics. Hence, an efficient ABSA framework to determine the sentiment from the textual reviews of the customers is developed in this work. Initially, the raw text review data is collected from the standard benchmark datasets. The gathered text reviews undergo text pre-processing to neglect the unwanted words and characters from the input text document. The pre-processed data is directly provided to the feature extraction phase in which the seq2seq network and transformer network are employed. Further, the optimal features from the two resultant features are chosen by utilizing the proposed Modified Bird Swarm-Ladybug Beetle Optimization (MBS-LBO). After obtaining optimal features, these features are fused together and given to the final detection model. Consequently, the Optimized Cascaded Long Short Term Memory (OCas-LSTM) is proposed for predicting the sentiments from the given review by the users. Here, the parameters are tuned optimally by the MBS-LBO algorithm, and also it is utilized for enhancing the performance rate. The experimental evaluation is made to reveal the excellent performance of the developed SA model by contrasting it with conventional models.

Magnetic Resonance Images Segmentation of Multifidus based on Dense-unet and Superpixel.

Lumbar disc herniation (LDH) is a common clinical condition causing lower back and leg pain. Accurate segmentation of the lumbar discs is crucial for assessing and diagnosing LDH. Magnetic resonance imaging (MRI) can reveal the condition of articular cartilage. However, manual segmentation of MRI images is burdensome for physicians and needs to be more efficient. In this study, we propose a method that combines UNet and superpixel segmentation to address the problem of loss of detailed information in the feature extraction phase, leading to poor segmentation results at object edges. The aim is to provide a reproducible solution for diagnosing patients with lumbar disc herniation. We suggest using the network structure of UNet. Firstly, dense blocks are inserted into the UNet network, and training is performed using the Swish activation function. The Dense-UNet model extracts semantic features from the images and obtains rough semantic segmentation results. Then, an adaptive-scale superpixel segmentation algorithm is applied to segment the input images into superpixel images. Finally, high-level abstract semantic features are fused with the detailed information of the superpixels to obtain edge-optimized semantic segmentation results. Evaluation of a private dataset of multifidus muscles in magnetic resonance images demonstrates that compared to other segmentation algorithms, this algorithm exhibits better semantic segmentation performance in detailed areas such as object edges. Compared to UNet, it achieves a 9.5% improvement in the Dice Similarity Coefficient (DSC) and an 11.3% improvement in the Jaccard Index (JAC). The experimental results indicate that this algorithm improves segmentation performance while reducing computational complexity.

Feature Extraction vs Fine-tuning for Cyber Intrusion Detection Model

This study investigates the effectiveness of feature extraction and fine-tuning approaches in developing robust cyber intrusion detection models using the Network-based Security Lab - KDD dataset (NSL-KDD). The role of cyber intrusion detection is pivotal in securing computer networks from unauthorized access and malicious activities. Feature extraction, involving methods such as PCA, LDA, and Autoencoders, aims to transform raw data into informative representations, while fine-tuning leverages pre-trained models for task-specific adaptation. The study follows a comprehensive research method encompassing data collection, preprocessing, model development, and experimental evaluation. Results indicate that LDA and Autoencoders excel in the feature extraction phase, demonstrating precision, high accuracy, F1-Score, and recall. However, fine-tuning a pre-trained Multilayer Perceptron model surpasses individual feature extraction methods, achieving superior performance across all metrics. The discussion emphasizes the complexity and flexibility of these approaches, with fine-tuned models showcasing higher adaptability. In conclusion, this study provides valuable insights into the comparative effectiveness of feature extraction and fine-tuning for cyber intrusion detection. The findings underscore the importance of leveraging pre-trained knowledge and adapting models to specific tasks, offering a foundation for further advancements in enhancing network security through advanced machine learning techniques.

The impact of normothermic and hypothermic preservation methods on kidney lipidome-comparative study using chemical biopsy with microextraction probes.

Normothermic ex vivo kidney perfusion (NEVKP) is designed to replicate physiological conditions to improve graft outcomes. A comparison of the impact of hypothermic and normothermic preservation techniques on graft quality was performed by lipidomic profiling using solid-phase microextraction (SPME) chemical biopsy as a minimally invasive sampling approach. Direct kidney sampling was conducted using SPME probes coated with a mixed-mode extraction phase in a porcine autotransplantation model of the renal donor after cardiac death, comparing three preservation methods: static cold storage (SCS), NEVKP, and hypothermic machine perfusion (HMP). The lipidomic analysis was done using ultra-high-performance liquid chromatography coupled with a Q-Exactive Focus Orbitrap mass spectrometer. Chemometric analysis showed that the NEVLP group was separated from SCS and HMP groups. Further in-depth analyses indicated significantly (p < 0.05, VIP > 1) higher levels of acylcarnitines, phosphocholines, ether-linked and longer-chain phosphoethanolamines, triacylglycerols and most lysophosphocholines and lysophosphoethanolamines in the hypothermic preservation group. The results showed that the preservation temperature has a more significant impact on the lipidomic profile of the kidney than the preservation method's mechanical characteristics. Higher levels of lipids detected in the hypothermic preservation group may be related to ischemia-reperfusion injury, mitochondrial dysfunction, pro-inflammatory effect, and oxidative stress. Obtained results suggest the NEVKP method's beneficial effect on graft function and confirm that SPME chemical biopsy enables low-invasive and repeated sampling of the same tissue, allowing tracking alterations in the graft throughout the entire transplantation procedure.

Green Surfactant Assisted-Solidified Floating Organic Drop Micro-Extraction (SA-SFODME) for the Preconcentration of Trace Lead with Determination by Flame Atomic Absorption Spectrometry (FAAS)

Determination of trace lead from environmental samples was carried out by solidified floating organic drop microextraction for preconcentration before analysis by flame atomic absorption spectrometry. The determination of lead from environmental samples, even at trace levels, was possible with an ordinary flame atomic absorption spectrometer (FAAS). The lead formed a hydrophobic complex with the anionic surfactant sodium dodecyl benzene sulfonate (SDBS), and this hydrophobic complex was extracted into the 1-dodecanol drop. Parameters affecting analytical performance have been studied. The optimum pH value was 3.5. The optimum sample volume, SDBS concentration, and extraction solvent volume were 75 mL, 50 mM in dodecanol, and 75 µL, respectively. The optimum extraction time was determined as 30 min, the optimum extraction temperature was 45 °C, the optimum mixing speed was 650 rpm and the ideal extraction phase volume was 0.50 mL. Methanol has been shown to be effective when used as a diluent. Using the optimum conditions, the enhancement factor was 106, and the limit of detection (3s) and precision were 3.3 ng/mL and 2.3% (n = 9,15 ng/mL), respectively. The limit of quantification (LOQ) (10s) was 10 ng/mL and the linear working range was 15-100 ng/mL. The accuracy was evaluated by certified reference material analysis. The optimized method was applied to the determination of Pb(II) from environmental water samples. This method follows the principles of green analytical chemistry.

Detecting emotions through EEG signals based on modified convolutional fuzzy neural network

Emotion is a human sense that can influence an individual’s life quality in both positive and negative ways. The ability to distinguish different types of emotion can lead researchers to estimate the current situation of patients or the probability of future disease. Recognizing emotions from images have problems concealing their feeling by modifying their facial expressions. This led researchers to consider Electroencephalography (EEG) signals for more accurate emotion detection. However, the complexity of EEG recordings and data analysis using conventional machine learning algorithms caused inconsistent emotion recognition. Therefore, utilizing hybrid deep learning models and other techniques has become common due to their ability to analyze complicated data and achieve higher performance by integrating diverse features of the models. However, researchers prioritize models with fewer parameters to achieve the highest average accuracy. This study improves the Convolutional Fuzzy Neural Network (CFNN) for emotion recognition using EEG signals to achieve a reliable detection system. Initially, the pre-processing and feature extraction phases are implemented to obtain noiseless and informative data. Then, the CFNN with modified architecture is trained to classify emotions. Several parametric and comparative experiments are performed. The proposed model achieved reliable performance for emotion recognition with an average accuracy of 98.21% and 98.08% for valence (pleasantness) and arousal (intensity), respectively, and outperformed state-of-the-art methods.

Acid-hydrolyzed phenolic extract of parsley (Petroselinum crispum L.) leaves inhibits lipid oxidation in soybean oil-in-water emulsions

The antioxidant activity of the natural phenolic extracts is limited in particular food systems due to the existence of phenolic compounds in glycoside form. Acid hydrolysis post-treatment could be a tool to convert the glycosidic polyphenols in the extracts to aglycones. Therefore, this research investigated the effects of an acid hydrolysis post-treatment on the composition and antioxidant activity of parsley extracts obtained by an ultrasound-assisted extraction method to delay lipid oxidation in a real food system (i.e., soybean oil-in-water emulsion). Acid hydrolysis conditions were varied to maximize total phenolic content (TPC) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity. When extracts were exposed to 0.6 M HCl for 2 h at 80 ℃, TPC was 716.92 ± 24.43 µmol gallic acid equivalent (GAE)/L, and DPPH radical scavenging activity was 66.89 ± 1.63 %. Not only did acid hydrolysis increase the concentrations of individual polyphenols, but it also resulted in the release of new phenolics such as myricetin and gallic acid. The extract's metal chelating and ferric-reducing activity increased significantly after acid hydrolysis. In soybean oil-in-water emulsion containing a TPC of 400 µmol GAE/L, the acid-hydrolyzed extract had an 11-day lag phase for headspace hexanal compared to the 6-day lag phase of unhydrolyzed extract. The findings indicated that the conversion of glycosidic polyphenols to aglycones in phenolic extracts can help extend the shelf-life of emulsion-based foods.

Circadian Rhythm Effects on the Extraction and Identification of Semen on Semen-Stained Fabrics Using Seminal Fluid Biochemistry

The rate of rape and other sexually induced crimes is rapidly on the increase worldwide with weak empirical confirmational tools, and expensive financial implications This is further compounded by poor proof of the time of the committal of the crime and its attendant effect on the parameter of interest. This called for a cheap and accurate approach involving the use of biochemical parameters indicative of seminal fluids on fabrics and other ancillary materials found in a crime scene. This study was therefore designed to validate a suitable seminal fluid biochemical approach to detecting seminal stained fabrics and the attendant circadian rhythm effects. Seminal fluid biochemical parameters used for the study include prostate-specific antigen, gamma-glutamyl transferase, acid phosphatase, magnesium, zinc, and inorganic phosphorus. These parameters were analyzed using WHO-approved methods after the extraction phase from dried stained fabrics. Forty (40) participants were recruited for the study divided into two groups; nocturnal and diurnal. The mathematical and empirical analysis data were analyzed using One-way ANOVA (Post Hoc) on an SPSS 25 version platform. The result showed a significant increase (p&lt; 0.05) in seminal PSA, GGT, and ACP in the semen-stained fabrics when compared to distilled water-stained fabrics. In a similar vein, there was a significant increase in seminal ACP and PSA concentrations in the nocturnal group when compared to the diurnal, whereas seminal GGT decreased. In conclusion, this study has shown that semen-stained fabric can be detected for the presence of seminal fluids using seminal PSA, ACP, and GGT. Also, the effect of circadian rhythm should be considered during result interpretation.

Robust 3D object watermarking scheme using shape features for copyright protection

This article utilizes the discrete wavelet transformation to introduce an advanced 3D object watermarking model depending on the characteristics of the object’s vertices. The model entails two different phases: integration and extraction. In the integration phase, a novel technique is proposed, which embeds the secret grayscale image three times using both the encrypted pixels and the vertices’ coefficients of the original 3D object. In the extraction phase, the secret image is randomly extracted and recaptured using the inverse phase of the integration technique. Four common 3D objects (Stanford bunny, horse, cat figurine, and angel), with different faces and different vertices, are used in this model as a dataset. The performance of the proposed technique is evaluated using different metrics to show its superiority in terms of execution time and imperceptibility. The results demonstrated that the proposed method achieved high imperceptibility and transparency with low distortion. Moreover, the extracted secret grayscale image perfectly matched the original watermark with a structural similarity index of 1 for all testing models.

Development and validation of a health practitioner survey on ocular allergy

Survey studies have played a significant role in understanding the gaps in the knowledge and practices of health practitioners. However, there have been no such survey studies on Ocular Allergy (OA). Thus, the purpose of this study was to develop and validate a survey on OA to better understand the gaps in the diagnostic, treatment, and collaborative care approaches of health practitioners in OA. The survey is titled “Survey on Ocular Allergy for Health Practitioners (SOAHP)”. SOAHP was developed in a five-stage process. First, item extraction via the use of a literature review, second, face and content validity, third, a pilot study, fourth, test–retest reliability, and fifth, finalisation of the survey. 65 items under 6 domains were initially generated in the item extraction phase. Content validity was conducted on 15 experts in the field. This was conducted twice to reach consensus whereby items and domains were added, edited, kept, or removed, resulting in 50 items under 7 domains. The pilot study was conducted on 15 participants from the five relevant health practitioner fields (Allergists/Immunologists, General Practitioners (GPs), Ophthalmologists, Optometrists and Pharmacists). This altered the survey further to 40 items under 7 domains. Test–retest reliability was conducted on 25 participants from the five health practitioner fields. Reliability was moderate to almost perfect for most (97%) investigated items. The finalised survey was 40 items under 7 domains. SOAHP is the first survey created to assess diagnostic, treatment and collaborative care approaches of Allergists/Immunologists, GPs, Ophthalmologists, Optometrists and Pharmacists on OA. SOAHP will be a useful tool in clinical research on OA.