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Background Monocortical mini-screw-type temporary anchorage devices (TADs), or mini-screws, have significantly impacted orthodontic treatment strategies, especially in severe crowding and protrusion cases. These devices offer flexibility in placement sites, but the chosen location can considerably influence tooth displacement patterns. Key factors include the 'line of force' and the biomechanical properties of orthodontic tools. By analyzing tension distribution and three-dimensional displacements, the finite element method (FEM) provides a thorough means to comprehend these patterns. The Curve of Spee (COS) is a crucial factor potentially affecting displacement. Objective This study aimed to leverage finite element analysis (FEA) to understand the impact of varying mini-implant heights (10 mm, 13 mm, and 16 mm) on the displacements of different tooth types under a consistent force of 150 gm and compare these displacements both in the presence and absence of the COS. Materials and methods A CAD model of the jaw and teeth was developed using CT scan data and a Rexcan III 3D White Light Scanner. This model was meshed in Altair HyperMesh using tetrahedral elements, resulting in a Finite Element Model. The model incorporated various components, including teeth, the periodontal ligament (PDL), alveolar bone, brackets, a titanium mini-screw, and an archwire measuring 0.019 x 0.025 inches. Unique material properties were assigned to the PDL, and the assembly accurately replicated the clinical alignment of the archwire and brackets. Subsequently, stress and strain analyses were conducted on the model using the FEM. Results The displacement patterns of various teeth at implant heights of 10 mm, 13 mm, and 16 mm under a 150-gm force were analyzed in relation to the COS. Notably, for the central incisor, the COS significantly affected displacements in the Y and Z directions. Similarly, the Lateral Incisor and Canine exhibited marked changes in the Z direction with the presence of the COS. The Second Premolar's apex displacement showed significant variation due to the COS, while the First Molar displayed notable changes in the X direction. Generally, the presence of the COS either maintained or slightly increased Z-directional displacements across teeth, particularly at the apices. Conclusion The presence of COS significantly influences tooth displacement patterns when using mini-screws at different implant heights. Central incisors, lateral incisors, and canines are particularly sensitive to changes in the Z direction with the COS. The biomechanical analysis emphasizes the importance of considering COS in treatment planning for optimal results with mini-implants in orthodontics., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Pareek et al.)

Background: Understanding complex systems is made easier with the tools provided by the theory of nonlinear dynamic systems. It provides novel ideas, algorithms, and techniques for signal processing, analysis, and classification. Presently, these ideas are being applied to the investigation of how physiological signals evolve., Objective: The study applies nonlinear dynamics theory to electroencephalogram (EEG) signals to better comprehend the range of alcoholic mental states. One of the main contributions of this paper is an algorithm for automatically distinguishing between sober and drunken EEG signals based on their salient features., Methods: The study utilized various entropy-based features, including ApEn, SampEn, Shannon and Renyi entropies, PE, TS, FE, WE, and KSE, to extract information from EEG signals. To identify the most relevant features, the study employed ranking methods like T-test, Wilcoxon, and Bhattacharyya, and trained SVM classifiers with the selected features. The Bhattacharyya ranking method was found to be the most effective in achieving high classification accuracy, sensitivity, and specificity., Results: Classification accuracy of 95.89%, the sensitivity of 94.43%, and specificity of 96.67% are achieved by the SVM classifier with radial basis function (RBF) for polynomial Kernel using the Bhattacharyya ranking method., Conclusion: From the result, it is clear that the model serves as a cost-effective and accurate decision-support tool for doctors in diagnosing alcoholism and for rehabilitation centres to monitor the effectiveness of interventions aimed at mitigating or reversing brain damage caused by alcoholism.

According to the standard paradigm, white box cryptographic primitives are used to block black box attacks and protect sensitive information. This is performed to safeguard the protected information and keys against black box assaults. An adversary in such a setting is aware of the method and can analyze many system inputs and outputs, but is blind to the specifics of how a critical instantiation primitive is implemented. This is the focus of white-box solutions, which are designed to withstand attacks that come from the execution environment. This is significant because an attacker may obtain unrestricted access to the program's execution in this environment. The purpose of this article is to assess the efficiency of white-box implementations in terms of security. Our contribution is twofold: first, we explore the practical implementations of white-box approaches, and second, we analyze the theoretical foundations upon which these implementations are built. First, a research proposal is crafted that details white-box applications of DES and AES encryption algorithms. To begin, this preparation is necessary. The research effort planned for this project also includes cryptanalysis of these techniques. Once the general cryptanalysis results have been examined, the white-box design approaches will be covered. We have decided to launch an investigation into creating a theoretical model for white box, since no prior formal definitions have been offered, and suggested implementations have not been accompanied by any assurance of security. This is due to the fact that no formal definition of "white box" has ever been provided. In this way lies the explanation for why this is the situation. We define WBC to encompass the security requirements of WBC specified over a white box cryptography technology and a security concept by studying formal models of obfuscation and shown security. This definition is the product of extensive investigation. This state-of-the-art theoretical model provides a setting in which to investigate the security of white-box implementations, leading to a wide range of positive and negative conclusions. As a result, this paper includes the results of a Digital Signature Algorithm (DSA) study which may be put to use in the real world with signature verification. Possible future applications of White Box Cryptography (WBC) research findings are discussed in light of these purposes and areas of investigation.

A significant study has been undertaken in the areas of health care and administration of cutting-edge artificial intelligence (AI) technologies throughout the previous decade. Healthcare professionals studied smart gadgets and other medical technologies, along with the AI-based Internet of Things (IoT) (AIoT). Connecting the two regions makes sense in terms of improving care for rural and isolated resident individuals. The healthcare industry has made tremendous strides in efficiency, affordability, and usefulness as a result of new research options and major cost reductions. This includes instructions (AIoT-based) medical advancements can be both beneficial and detrimental. While the IoT concept undoubtedly offers a number of benefits, it also poses fundamental security and privacy concerns regarding medical data. However, resource-constrained AIoT devices are vulnerable to a number of assaults, which can significantly impair their performance. Cryptographic algorithms used in the past are inadequate for safeguarding IoT-enabled networks, presenting substantial security risks. The AIoT is made up of three layers: perception, network, and application, all of which are vulnerable to security threats. These threats can be aggressive or passive in nature, and they can originate both within and outside the network. Numerous IoT security issues, including replay, sniffing, and eavesdropping, have the ability to obstruct network communication. The AIoT-H application is likely to be explored in this research article due to its potential to aid with existing and different technologies, as well as bring useful solutions to healthcare security challenges. Additionally, every day, several potential problems and inconsistencies with the AIoT-H technique have been discovered., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Anil Audumbar Pise et al.)

Medical diagnosis is always a time and a sensitive approach to proper medical treatment. Automation systems have been developed to improve these issues. In the process of automation, images are processed and sent to the remote brain for processing and decision making. It is noted that the image is written for compaction to reduce processing and computational costs. Images require large storage and transmission resources to perform their operations. A good strategy for pictures compression can help minimize these requirements. The question of compressing data on accuracy is always a challenge. Therefore, to optimize imaging, it is necessary to reduce inconsistencies in medical imaging. So this document introduces a new image compression scheme called the GenPSOWVQ method that uses a recurrent neural network with wavelet VQ. The codebook is built using a combination of fragments and genetic algorithms. The newly developed image compression model attains precise compression while maintaining image accuracy with lower computational costs when encoding clinical images. The proposed method was tested using real-time medical imaging using PSNR, MSE, SSIM, NMSE, SNR, and CR indicators. Experimental results show that the proposed GenPSOWVQ method yields higher PSNR SSIMM values for a given compression ratio than the existing methods. In addition, the proposed GenPSOWVQ method yields lower values of MSE, RMSE, and SNR for a given compression ratio than the existing methods., Competing Interests: The authors of this manuscript declare that they do not have any conflicts of interest., (Copyright © 2022 Chethana Sridhar et al.)

Due to the increasing number of medical imaging images being utilized for the diagnosis and treatment of diseases, lossy or improper image compression has become more prevalent in recent years. The compression ratio and image quality, which are commonly quantified by PSNR values, are used to evaluate the performance of the lossy compression algorithm. This article introduces the IntOPMICM technique, a new image compression scheme that combines GenPSO and VQ. A combination of fragments and genetic algorithms was used to create the codebook. PSNR, MSE, SSIM, NMSE, SNR, and CR indicators were used to test the suggested technique using real-time medical imaging. The suggested IntOPMICM approach produces higher PSNR SSIM values for a given compression ratio than existing methods, according to experimental data. Furthermore, for a given compression ratio, the suggested IntOPMICM approach produces lower MSE, RMSE, and SNR values than existing methods., Competing Interests: The authors of this manuscript declare that they do not have any conflicts of interest., (Copyright © 2022 Piyush Kumar Pareek et al.)

In the microarray gene expression data, there are a large number of genes that are expressed at varying levels of expression. Given that there are only a few critically significant genes, it is challenging to analyze and categorize datasets that span the whole gene space. In order to aid in the diagnosis of cancer disease and, as a consequence, the suggestion of individualized treatment, the discovery of biomarker genes is essential. Starting with a large pool of candidates, the parallelized minimal redundancy and maximum relevance ensemble (mRMRe) is used to choose the top m informative genes from a huge pool of candidates. A Genetic Algorithm (GA) is used to heuristically compute the ideal set of genes by applying the Mahalanobis Distance (MD) as a distance metric. Once the genes have been identified, they are input into the GA. It is used as a classifier to four microarray datasets using the approved approach (mRMRe-GA), with the Support Vector Machine (SVM) serving as the classification basis. Leave-One-Out-Cross-Validation (LOOCV) is a cross-validation technique for assessing the performance of a classifier. It is now being investigated if the proposed mRMRe-GA strategy can be compared to other approaches. It has been shown that the proposed mRMRe-GA approach enhances classification accuracy while employing less genetic material than previous methods. Microarray, Gene Expression Data, GA, Feature Selection, SVM, and Cancer Classification are some of the terms used in this paper., Competing Interests: The authors of this manuscript declare that they do not have any conflicts of interest., (Copyright © 2022 M. Sathya et al.)

A non-diaryl quinoline scaffold 6,7-dihydropyrazolo[1,5-a]pyrazin-4-one was identified by screening of diverse set of compounds against M. smegmatis ATP synthase. Herein, we disclose our efforts to develop the structure activity relationship against Mycobacterium tuberculosis (Mtb.H37Rv strain) around the identified hit 1. A scaffold hopping approach was used to identify compounds 14a, 14b and 24a with improved activity against MTb.H37Rv., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Background: Dengue, a mosquito-borne viral disease, poses a significant global public health risk. In tropical countries such as India where periodic dengue outbreaks can be correlated to the high prevalence of the mosquito vector, circulation of all four dengue viruses (DENVs) and the high population density, a drug for dengue is being increasingly recognized as an unmet public health need., Methodology/principal Findings: Using the knowledge of traditional Indian medicine, Ayurveda, we developed a systematic bioassay-guided screening approach to explore the indigenous herbal bio-resource to identify plants with pan-DENV inhibitory activity. Our results show that the alcoholic extract of Cissampelos pariera Linn (Cipa extract) was a potent inhibitor of all four DENVs in cell-based assays, assessed in terms of viral NS1 antigen secretion using ELISA, as well as viral replication, based on plaque assays. Virus yield reduction assays showed that Cipa extract could decrease viral titers by an order of magnitude. The extract conferred statistically significant protection against DENV infection using the AG129 mouse model. A preliminary evaluation of the clinical relevance of Cipa extract showed that it had no adverse effects on platelet counts and RBC viability. In addition to inherent antipyretic activity in Wistar rats, it possessed the ability to down-regulate the production of TNF-α, a cytokine implicated in severe dengue disease. Importantly, it showed no evidence of toxicity in Wistar rats, when administered at doses as high as 2g/Kg body weight for up to 1 week., Conclusions/significance: Our findings above, taken in the context of the human safety of Cipa, based on its use in Indian traditional medicine, warrant further work to explore Cipa as a source for the development of an inexpensive herbal formulation for dengue therapy. This may be of practical relevance to a dengue-endemic resource-poor country such as India.

Pashmina fiber is one of major specialty animal fiber in India. The quality of Pashmina obtained from Changthangi and Chegu goats in India is very good. Due to restricted availability and high prices, adulteration of natural prized fibers is becoming a common practice by the manufacturers. Sheep wool is a cheap substitute, which is usually used for adulteration and false declaration of Pashmina-based products. Presently, there is lack of cost-effective and readily available methodology to identify the adulteration of Pashmina products from other similar looking substitutes like sheep wool. Polymerase chain reaction (PCR)-based detection method can be used to identify origin of animal fiber. Extraction of quality DNA from dyed and processed animal fiber and textile materials is a limiting factor in the development of such detection methods. In the present study, quality DNA was extracted from textile materials, and PCR-based technique using mitochondrial gene (12S rRNA) specific primers was developed for detection of the Pashmina in textile blends. This technique has been used for detection of the adulteration of the Pashmina products with sheep wool. The technique can detect adulteration level up to 10 % of sheep/goat fibers in textile blends.

Integrating artificial intelligence (AI) into biomedical signal analysis represents a significant breakthrough in enhanced precision and efficiency of disease diagnostics and therapeutics. From traditional computational models to advanced machine learning algorithms, AI technologies have improved signal processing by efficiently handling complexity and interpreting intricate datasets. Understanding physiological data, which requires highly trained professionals, is now more accessible; in regions with limited access, AI tools expand healthcare accessibility by providing high-level diagnostic insights, ultimately improving health outcomes. This review explores various AI methodologies, including supervised, unsupervised, and reinforcement learning, and examines their synergy for biomedical signal analysis and future directions in medical science. By capturing a comprehensive overview of the current state and prospects of AI-driven healthcare, this paper highlights the transformative potential of AI in analyzing biomedical signals. Highlight: • This review shows an extensive examination of the integration of AI in the analysis of biosignals to enhance diagnostics and therapeutics. • The summary details the development and utilization of AI techniques, such as supervised, unsupervised, and reinforcement learning. • This paper also covers the critical factors that impact the design and implementation of AI algorithms for processing bio-signals. [ABSTRACT FROM AUTHOR]

Big data analytics for clinical decision-making has been proposed for various clinical sectors because clinical decisions are more evidence-based and promising. Healthcare data is so vast and readily available that big data analytics has completely transformed this sector and opened up many new prospects. The smart sensor-based big data analysis recommendation system has significant privacy and security concerns when using sensor medical images for suggestions and monitoring. The danger of security breaches and unauthorized access, which might lead to identity theft and privacy violations, increases when sending and storing sensitive medical data on the cloud. Our effort will improve patient care and well-being by creating an anomaly detection system based on machine learning specifically for medical images and providing timely treatments and notifications. Current anomaly detection methods in healthcare systems, such as artificial intelligence and big data analytics-intracerebral hemorrhage (AIBDA-ICH) and parallel conformer neural network (PCNN), face several challenges, including high resource consumption, inefficient feature selection, and an inability to handle temporal data effectively for real-time monitoring. Techniques like support vector machines (SVM) and the hidden Markov model (HMM) struggle with computational overhead and scalability in large datasets, limiting their performance in critical healthcare applications. Additionally, existing methods often fail to provide accurate anomaly detection with low latency, making them unsuitable for time-sensitive environments. We infer the extraction, feature selection, attack detection, and data collection and processing procedures to anticipate anomaly inpatient data. We transfer the data, take care of missing values, and sanitize it using the pre-processing mechanism. We employed the recursive feature elimination (RFE) and dynamic principal component analysis (DPCA) algorithms for feature selection and extraction. In addition, we applied the Auto-encoded genetic recurrent neural network (AGRNN) approach to identify abnormalities. Data arrival rate, resource consumption, propagation delay, transaction epoch, true positive rate, false alarm rate, and root mean square error (RMSE) are some metrics used to evaluate the proposed task. [ABSTRACT FROM AUTHOR]

The emergence of the Internet of Things (IoT) has enabled the proliferation of interconnected devices and sensors, generating vast amounts of often complex and unstructured data. Deep learning (DL), a subfield of machine learning (ML), has shown great promise in addressing the challenges of processing and analyzing such data. Considering the increasing importance of DL and data analysis, we decided to review the articles of the last few years in this field to pave the way for researchers. In this article, we used the systematic literature review (SLR) method, and in line with that, we selected and analyzed 56 articles published from 2019 to April 2024. We first discuss the DL models used in the IoT field and clarify their specific use cases. Secondly, we outline an analysis of research areas in DL-based IoT. In addition, our research extends to the tools and simulators used to evaluate studies in the DL-based IoT domain. We also examine the DL-based IoT research datasets. Finally, our review identifies future directions and open issues in DL-based IoT. We aim to contribute to an accurate understanding of the current state, challenges, and potential breakthroughs at the intersection of DL and the IoT. [ABSTRACT FROM AUTHOR]

We screened 194 Mycobacterium tuberculosis strains isolated from tuberculosis (TB) patients in Delhi and neighboring regions in India to identify the prevalence of extensive drug resistance (XDR) in clinical isolates. Among these, 104 isolates were found to be multidrug resistant (MDR), and 6 were identified as XDR isolates, which was later confirmed by antimicrobial susceptibility testing against the respective drug screening panel. Genotyping was carried out by amplifying and sequencing the following genes: rpoB (rifampin), katG (isoniazid), gyrA (fluoroquinolones), and rrs (amikacin, kanamycin, and capreomycin). Our analyses indicated that mutations at the hot spots of these genes were positively correlated with drug resistance in clinical isolates. The key mutation observed for rpoB was in the codon for amino acid position 531 (S531L), and other mutations were seen in the hot spot, including those encoding Q510P, L511H, D516V, and H526Y mutations. We identified S315T and R463L substitutions encoded in the katG locus. An S95T substitution encoded in the gyrA locus was the most common mutation observed in fluoroquinolone-resistant isolates. In addition, we saw D94G and D94N mutations encoded in the QRDR region. The 16S rRNA (rrs) gene encoded mainly the A1401G mutation and an additional mutation, G1484T, resulting in ribosomal modifications. Taken together, the data in this report clearly establish the presence of phenotypically distinct XDR strains in India by molecular profiling and further identify specific mutational hot spots within key genes of XDR-TB strains.

A novel approach has been developed for the solid phase extraction of chromium(VI) based on the adsorption of its diphenylcarbazide complex on a mixture of acid activated montmorillonite (AAM)-silica gel column. The effect of various parameters such as acidity, stability of the column, sample volume, interfering ions, etc., were studied in detail. The adsorbed complex could be easily eluted using polyethylene glycol-sulfuric acid mixture and the concentration of chromium has been determined using visible spectrophotometry. The calibration graph was linear in the range 0-1microgmL(-1) chromium(VI) with a detection limit of 6microgL(-1). A highest preconcentration factor of 25 could be obtained for 250mL sample volume using glass wool as support for the mixed bed adsorbent. Chromium(VI) could be effectively separated from other ions such as nickel, copper, zinc, chloride, sulfate, nitrate, etc., and the method has been successfully applied to study the recovery of chromium in electroplating waste water and spiked water samples.

Recognising human activities using smart devices has led to countless inventions in various domains like healthcare, security, sports, etc. Sensor-based human activity recognition (HAR), especially smartphone-based HAR, has become popular among the research community due to lightweight computation and user privacy protection. Deep learning models are the most preferred solutions in developing smartphone-based HAR as they can automatically capture salient and distinctive features from input signals and classify them into respective activity classes. However, in most cases, the architecture of these models needs to be deep and complex for better classification performance. Furthermore, training these models requires extensive computational resources. Hence, this research proposes a hybrid lightweight model that integrates an enhanced Temporal Convolutional Network (TCN) with Gated Recurrent Unit (GRU) layers for salient spatiotemporal feature extraction without tedious manual feature extraction. Essentially, dilations are incorporated into each convolutional kernel in the TCN-GRU model to extend the kernel's field of view without imposing additional model parameters. Moreover, fewer short filters are applied for each convolutional layer to alleviate excess parameters. Despite reducing computational cost, the proposed model utilises dilations, residual connections, and GRU layers for longer-term time dependency modelling by retaining longer implicit features of the input inertial sequences throughout training to provide sufficient information for future prediction. The performance of the TCN-GRU model is verified on two benchmark smartphone-based HAR databases, i.e., UCI HAR and UniMiB SHAR. The model attains promising accuracy in recognising human activities with 97.25% on UCI HAR and 93.51% on UniMiB SHAR. Since the current study exclusively works on the inertial signals captured by smartphones, future studies will explore the generalisation of the proposed TCN-GRU across diverse datasets, including various sensor types, to ensure its adaptability across different applications. [ABSTRACT FROM AUTHOR]

Uterine involution and follicular dynamics during postpartum period were studied ultrasonographically in French jennies. For the study of uterine involution in postpartum jennies (n = 6, Group S), sonographic measurements of different parts of the uterus and endometrium were made at three-day interval, starting from the day of foaling and continued up to 33 days postpartum. Uterine dimensions were also recorded in non-pregnant jennies (n = 3, Group C) throughout a cycle and compared with the dimensions of Group S jennies observed on the day of complete involution. Follicular dynamics of first and second postpartum ovulatory cycles were studied and compared with that of the single estrous cycle of Group C jennies. Jugular venous blood samples of Group S jennies were collected at weekly intervals for 49 days, commencing at the appearance of first preovulatory follicle, to support the sonographic findings. The average involution period was 22.5 +/- 1.7 days. However, it was significantly delayed (P < 0.05) in jennies which came into first postpartum ovulatory heat within Day 9 than those who came later (25.0 +/- 1.0 versus 20.0 +/- 1.0). The endometrial layer was not discernible beyond Day 15 postpartum and thus was found to be unreliable index of uterine involution. The follicular growth rate (mm per day) and diameter (mm) of preovulatory follicle in postpartum jennies were similar to that in normal cycling jennies (P > 0.05). The first and second ovulations occurred at 14.6 +/- 0.8 and 39.0 +/- 0.8 days postpartum in Group S jennies. All the corpora lutea, either echogenic or centrally non-echogenic were functionally similar and had similar life span (P > 0.05). In conclusion, the postpartum reproductive events related to uterine involution and ovarian cyclicity apparently resemble that of mares.

The rapid advancement of deepfake technology poses an escalating threat of misinformation and fraud enabled by manipulated media. Despite the risks, a comprehensive understanding of deepfake detection techniques has not materialized. This research tackles this knowledge gap by providing an up-to-date systematic survey of the digital forensic methods used to detect deepfakes. A rigorous methodology is followed, consolidating findings from recent publications on deepfake detection innovation. Prevalent datasets that underpin new techniques are analyzed. The effectiveness and limitations of established and emerging detection approaches across modalities including image, video, text and audio are evaluated. Insights into real-world performance are shared through case studies of high-profile deepfake incidents. Current research limitations around aspects like cross-modality detection are highlighted to inform future work. This timely survey furnishes researchers, practitioners and policymakers with a holistic overview of the state-of-the-art in deepfake detection. It concludes that continuous innovation is imperative to counter the rapidly evolving technological landscape enabling deepfakes. [ABSTRACT FROM AUTHOR]

Present investigation was conducted to study the ovarian response and embryo recovery using different PMSG dose levels. Six rabbits each were assigned randomly to treatment 1 (PMSG 50 IU + hCG 100 IU), treatment 2 (PMSG 75 IU + hCG 100 IU) and the control group (no hormone administered). PMSG injection (im) was followed 68 hr later by natural mating to a fertile rabbit buck and human chorionic gonadotrophin (hCG) injection iv post coitum. Embryos were recovered 96 hr post coitum by a modified surgical method. Mean number of ovulations in the control group differed significantly from treatment 1 and 2, but no significant difference was observed between treatments 1 and 2. Mean embryo recovery percentage was lowest in treatment 2 and highest in the control group. The higher dose PMSG (treatment 2) was observed to be more disturbing in terms of recovery of embryos as well as their morphology.

Sustainable cashmere production in Mongolia needs to reduce the negative impact of production on the environment and society. However, the industry still bears responsibility for significant resource consumption and disposal and has not yet taken the necessary transformative measures to fully sustainable production transition. In this context, the production method enhancement is still insufficient although it is preparing for the transition to sustainable practices. This study aims to determine the valid attributes of sustainable production as a hierarchical structure and the interdependence relationships using a hybrid of the fuzzy Delphi method, fuzzy decision-making trial and evaluation laboratory, and analytic network process. As a result, 4 aspects and 15 criteria are validated as a sustainable production hierarchical structure. Cleaner production practices and supply chain greening are indicated as the major aspects. The practical criteria are carbon accounting, wastewater reduction, investment in recycling-related research and development, eco-design, and green manufacturing., Competing Interests: Declarations. Ethics approval: Not applicable. Consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: Not applicable., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Skin cancer is a pervasive and deadly disease, prompting a surge in research efforts towards utilizing computer-based techniques to analyze skin lesion images to identify malignancies. This paper introduces an optimized vision transformer approach for effectively classifying skin tumors. The methodology begins with a pre-processing step aimed at preserving color constancy, eliminating hair artifacts, and reducing image noise. Here, a combination of techniques such as piecewise linear bottom hat filtering, adaptive median filtering, Gaussian filtering, and an enhanced gradient intensity method is used for pre-processing. Afterwards, the segmentation phase is initiated using the self-sparse watershed algorithm on the pre-processed image. Subsequently, the segmented image is passed through a feature extraction stage where the hybrid Walsh-Hadamard Karhunen–Loeve expansion technique is employed. The final step involves the application of an improved vision transformer for skin cancer classification. The entire methodology is implemented using the Python programming language, and the International Skin Imaging Collaboration (ISIC) 2019 database is utilized for experimentation. The experimental results demonstrate remarkable performance with the different performance metrics is accuracy 99.81%, precision 96.65%, sensitivity 98.21%, F-measure 97.42%, specificity 99.88%, recall 98.21%, Jaccard coefficient 98.54%, and Mathew's correlation coefficient (MCC) 98.89%. The proposed methodology outperforms the existing methodology. [ABSTRACT FROM AUTHOR]

Medical image denoising has been a subject of extensive research, with various techniques employed to enhance image quality and facilitate more accurate diagnostics. The evolution of denoising methods has highlighted impressive results but struggled to strike equilibrium between noise reduction and edge preservation which limits its applicability in various domains. This paper manifests the novel methodology that integrates an adaptive masking strategy, transformer-based U-Net Prior generator, edge enhancement module, and modified non-local block (MNLB) for denoising brain MRI clinical images. The adaptive masking strategy maintains the vital information through dynamic mask generation while the prior generator by capturing hierarchical features regenerates the high-quality prior MRI images. Finally, these images are fed to the edge enhancement module to boost structural information by maintaining crucial edge details, and the MNLB produces the denoised output by deriving non-local contextual information. The comprehensive experimental assessment is performed by employing two datasets namely the brain tumor MRI dataset and Alzheimer's dataset for diverse metrics and compared with conventional denoising approaches. The proposed denoising methodology achieves a PSNR of 40.965 and SSIM of 0.938 on the Alzheimer's dataset and also achieves a PSNR of 40.002 and SSIM of 0.926 on the brain tumor MRI dataset at a noise level of 50% revealing its supremacy in noise minimization. Furthermore, the impact of different masking ratios on denoising performance is analyzed which reveals that the proposed method showed PSNR of 40.965, SSIM of 0.938, MAE of 5.847, and MSE of 3.672 at the masking ratio of 60%. Moreover, the findings pave the way for the advancement of clinical image processing, facilitating precise detection of tumors in clinical MRI images., (© 2024. International Federation for Medical and Biological Engineering.)

In 5G, the next generation of technologies like the Internet of Medical Things (IoMT) cooperate with networking artifacts to provide seamless connectivity. To provide effective information sharing, the Internet infrastructure integrates various components of medical information systems such as computing devices, software tools, and application services. The information systems access a massive Internet of Medical Things (mIoMT) to discover a series of network infrastructures with technological innovations to offer remote diagnosis and treatment. In collecting healthcare information, innovative applications utilize medical sensor networks to drive extensive information processing including mobility support. However, the convergence of IoT and the cloud addresses the security gap while centralizing the devices into edge computing systems to protect the transmission flow through the device gateway. Considering this fact, an authentication scheme with privacy preservation was proposed using a key exchange protocol and digital signature by Soleymani et al. to guarantee data confidentiality and integrity. However, this scheme is still incapable of validating the integrity of data with the edge systems properly. Thus, this paper presents privacy-preserving authentication with device verification (PP-ADV) for securing healthcare systems in 5G networks. To protect the device privacy and server identities with registered users, the proposed PP-ADV utilizes two cryptographic primitives namely elliptic curve arithmetic and a collision-free hash function to achieve the significant properties of key agreement protocol including authenticity and confidentiality. Formal and informal analyses show that the proposed PP-ADV can restrict vulnerabilities such as privileged insider and forgery to achieve high-level privacy protection with seamless verification to improve system efficiency compared with other state-of-the-art approaches. Also, a board simulation testbed was designed using Raspberry Pi with Arduino IDE to examine the quality metrics including data transmission ratio, authentication delay, and throughput rate. The testbed analysis demonstrates that the proposed PP-ADV gains less delay ≈ 292.28 s and improved throughput ≈ 87.73 to meet the design criteria of 5G-enabled remote healthcare systems. [ABSTRACT FROM AUTHOR]

Electricity theft presents a substantial threat to distributed power networks, leading to non-technical losses (NTLs) that can significantly disrupt grid functionality. As power grids supply centralized electricity to connected consumers, any unauthorized consumption can harm the grids and jeopardize overall power supply quality. Detecting such fraudulent behavior becomes challenging when dealing with extensive data volumes. Smart grids provide a solution by enabling two-way electricity flow, thereby facilitating the detection, analysis, and implementation of new measures to address data flow issues. The key objective is to provide a deep learning-based amalgamated model to detect electricity theft and secure the smart grid. This research introduces an innovative approach to overcome the limitations of current electricity theft detection systems, which predominantly rely on analyzing one-dimensional (1-D) electric data. These approaches often exhibit insufficient accuracy when identifying instances of theft. To address this challenge, the article proposes an ensemble model known as the RNN-BiLSTM-CRF model. This model amalgamates the strengths of recurrent neural network (RNN) and bidirectional long short-term memory (BiLSTM) architectures. Notably, the proposed model harnesses both one-dimensional (1-D) and two-dimensional (2-D) electricity consumption data, thereby enhancing the effectiveness of the theft detection process. The experimental results showcase an impressive accuracy rate of 93.05% in detecting electricity theft, surpassing the performance of existing models in this domain. [ABSTRACT FROM AUTHOR]

Dengue fever is a mosquito-borne viral illness that affects over 100 nations around the world, including Africa, America, the Eastern Mediterranean, Southeast Asia, and the Western Pacific. Those who get infected by virus for the second time are at greater risk of having persistent dengue symptoms. Dengue fever has yet to be treated with a long-lasting vaccination or medication. Because of their ease of use, mosquito repellents have become popular as a dengue prevention technique. However, this has resulted in environmental degradation and harm, as well as bioaccumulation and biomagnification of hazardous residues in the ecosystem. Synthetic pesticides have caused a plethora of serious problems that were not foreseen when they were originally introduced. The harm caused by the allopathic medications/synthetic pesticides/chemical mosquito repellents has paved the door to employment of eco-friendly/green approaches in order to reduce dengue cases while protecting the integrity of the nearby environment too. Since the cases of dengue have become rampant these days, hence, starting the medication obtained from green approaches as soon as the disease is detected is advisable. In the present paper, we recommend environmentally friendly dengue management strategies, which, when combined with a reasonable number of vector control approaches, may help to avoid the dengue havoc as well as help in maintaining the integrity of the ecosystem. [ABSTRACT FROM AUTHOR]

Copyright of Boletín Latinoamericano y del Caribe de Plantas Medicinales y Aromáticas is the property of Universidad de Santiago de Chile and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

There is a noticeable growing interest in the use of natural dyes in contemporary textile dyeing, motivated by the requirements imposed, in favour of environmental prosperity. The use of natural dyes reduces significantly both the energy requirements and the environmental impact of the process. In the present work natural and synthetic fibres were dyed with the dyestuff isolated from Crocus sativus L., after aqueous extraction of the dried stigmas of the plant. Additionally, part of the powder was purified by using ultrafiltration technology. The saffron extract and the ultrafiltrated saffron retentate were used to dye cotton, wool, nylon and polyester, in various depths of shade and temperatures. Both saffron and ultrafiltrated saffron successfully dyed not only the natural substrates, but also the synthetic ones, while higher dyeing temperatures produced level dyeing with all substrates used. Ultrafiltrated saffron powder produced brighter and much stronger dyeing to the original saffron powder, due to the elimination of extraction by‐products. Isothermal adsorptions for both colourants were performed on all substrates in order to investigate their adsorption mechanism. It was found that saffron and ultrafiltrated saffron follow a Freundlich‐type adsorption isotherm on cotton, wool and nylon which is a typical mechanism for a planar‐directed dye of big molecular weight. Nernst‐type adsorption was found to occur on polyester which again is typical for the adsorption of disperse dyes on polyester. Thus, saffron can be claimed as a universal dye, able to successfully dye natural and synthetic substrates. [ABSTRACT FROM AUTHOR]

Pyrazolo[1,5-a]pyrazin-4(5H)-ones react regioselectively with N-iodosuccinimide in the presence of N-methylmorpholine to form 7-iodo derivatives. Their carbonylation catalyzed with Pd(dppf)Cl2 under pressure in MeOH solution yielded methyl 4-oxo-4,5-dihydropyrazolo-[1,5-a]pyrazine-7-carboxylates, which were transformed into the corresponding carboxylic acids by alkaline hydrolysis. [ABSTRACT FROM AUTHOR]

Release of GOT was determined during different treatments in ram semen extended (1:10) with lactose-egg yolk diluter containing 3.5% and 5% glycerol (v/v). Semen samples were collected by the electro-ejaculation technique and GOT was analysed colorimetrically. Highly significant difference (p less than 0.01) was found in the post-thaw percent motile spermatozoa and GOT release (IU/10(9) spermatozoa) between 3.5% and 5% glycerol containing equilibrated samples. The difference in percent motile spermatozoa was not significant between 3.5% and 5% glycerol levels in control, post-thaw unequilibrated and prefreeze equilibrated samples. Whereas the difference in GOT release between 3.5% and 5% glycerol concentrations was not significant in control and post-thaw unequilibrated samples and highly significant (p less than 0.01) in prefreeze equilibrated samples.

To improve and sustain the quality of software products, software process improvement (SPI) is needed. Currently, small and medium software enterprises (SMSEs) represent a high proportion of companies around the world and become a cornerstone in the worldwide industry economy. These companies have realized that improving their process is crucial for success, but they are facing difficulties to implement it due to limited resources, limited knowledge, and time constraints. This study aimed to identify the sustainability success factors (SSFs) that have a positive impact on implementing SPI efforts in SMSEs. Data were collected through a systematic literature review (SLR) approach and quantitatively through a survey questionnaire. A list of 44 SSFs was identified during SLR and empirical study. Results illustrate that there is a positive correlation between the ranks obtained from both dataset (rs (44) =.548, ρ =.001). Therefore, there would be significant differences between the SSFs identified in both datasets. In conclusion, the top‐ranked factors can then be used to guide the SPI coordinators on where they should focus their attention to reach the desired SPI goals, which is crucial to deliver the software products and also facilitates in development of model for SPIs in the future. [ABSTRACT FROM AUTHOR]

The induced ovulatory nature and low breeding efficiency of camels makes it much important to have timely pregnancy diagnosis so that measures could be implemented to make them pregnant as soon as possible. The managemental methods of pregnancy diagnosis include response to male and persistent raised tail (tail reflex). Clinical method involves rectal palpation (palpation of corpus luteum, palpable uterine swelling following one month of breeding among bactrian camels and 8 weeks onwards among dromedary camels, foetal skeleton palpation from 2.5 to 3.0 months and then after 6-7 months onward and other uterine changes), vaginal examination (mucous membrane and cervical plug examination) and ultrasonography (examination of corpus luteum, echoic embryo surrounded by non-echoic fluid and heart beat following 25 days of pregnancy). Laboratory methods involve hormone estimation (progesterone, oestradiol 17β, PGFA, relaxin, thyroid and testosterone), cervical mucus examination for changes in its pH to alkaline side, increased viscosity and specific gravity, chemical methods including Cuboni's test for increased oestrone sulphate and barium chloride test etc. where sure positive reactions of Cuboni's test are expected during final third of pregnancy among bactrian camels and during second third pregnancy among dromedary camels. The most practical, instant diagnosis oriented and accurate approach among all is ultrasonography. Few recent advanced techniques like pregnancy-associated glycosylation changes of human IgG have also been introduced to carry out pregnancy diagnosis. [ABSTRACT FROM AUTHOR]

Background: Viral infections have a history of abrupt and severe eruptions through the years in the form of pandemics. And yet, definitive therapies or preventive measures are not present. Herbal medicines have been a source of various antiviral compounds such as Oseltamivir, extracted using shikimic acid from star anise (Illicium verum) and Acyclovir from Carissa edulis are FDA (Food and Drug Administration) approved antiviral drugs. In this study, we dissect the anti-coronavirus infection activity of Cissampelos pareira L (Cipa) extract using an integrative approach. Methods: We analysed the signature similarities between predicted antiviral agents and Cipa using the connectivity map (https://clue.io/). Next, we tested the anti-SARS-COV-2 activity of Cipa in vitro. Molecular docking analyses of constituents of with key targets of SARS-CoV2 protein viz. spike protein, RNA‑dependent RNA‑polymerase (RdRp) and 3C‑like proteinase. was also performed. A three-way comparative analysis of Cipa transcriptome, COVID-19 BALF transcriptome and CMAP signatures of small compounds was also performed. Results: Several predicted antivirals showed a high positive connectivity score with Cipa such as apcidin, emetine, homoharringtonine etc. We also observed 98% inhibition of SARS-COV-2 replication in infected Vero cell cultures with the whole extract. Some of its prominent pure constituents e.g. pareirarine, cissamine, magnoflorine exhibited 40–80% inhibition. Comparison of genes between BALF and Cipa showed an enrichment of biological processes like transcription regulation and response to lipids, to be downregulated in Cipa while being upregulated in COVID-19. CMAP also showed that Triciribine, torin-1 and VU-0365114–2 had positive connectivity with BALF 1 and 2, and negative connectivity with Cipa. Amongst all the tested compounds, Magnoflorine and Salutaridine exhibited the most potent and consistent strong in silico binding profiles with SARS-CoV2 therapeutic targets. [ABSTRACT FROM AUTHOR]

Starting from the 3,5-dimethyl pyrazole ring and acetophenone derivatives, five different N-propargylated C-3 substituted pyrazoles were obtained. These derivatives were reacted with different amine derivatives using Cs2CO3 in methanol and 11 different pyrazolo [1,5-a] pyrazine-4(5H)-one derivatives were obtained, which are not found in the literature. The cytotoxic effects of these derivatives in the A549 cell line were investigated. The 160 µM concentration of two derivatives was found to increase cell death rate to 50%, and two derivatives increased cell death rate by up to 40%. The structure–activity relationship (SAR) study revealed an amide group with a long alkyl chain and benzene ring with a p-CF3 group could be important for efficiency. With theoretical ADMET studies of pyrazolopyrazine derivatives, pharmacokinetic phases were predicted to be suitable. [ABSTRACT FROM AUTHOR]

A number of bisbenzyilisoquinoline alkaloids have been previously isolated from Cissampelos sympodialis (Menispermaceae). The tertiary alkaloid fraction of the rhizomes (TAFrz) was prepared and the major alkaloid warifteine was isolated. Five TAFrz subfractions in addition to warifteine were tested against Dengue virus (DENV). We then used an epithelial (Vero) cell line to evaluate the cytotoxicity and effective concentrations of the samples against DENV. All TAFrz subfractions were active, but subfraction 6 (a mixture of the alkaloids methylwarifteine and warifteine) in particular showed a promising antiviral effect against DENV-2 with an IC50 of 2.00 µg/mL and a selectivity index (SI) of 10.74. Warifteine was the second most active sample and had an IC50 of 8.13 µg/mL and SI = 10.94. The antiviral activity of the samples compared favorably with that of 6-methylmercaptopurine riboside (IC50 = 7.31 µg/mL and SI = 11.8). These results suggest that bisbenzylisoquinoline alkaloids may prove interesting leading antiviral compounds. [ABSTRACT FROM AUTHOR]

Saffron (Crocus sativus L.) is a monocotyledonous herbaceous triploid plant that produces the most expensive spice in the world. Its main constituents, crocin, picrocrocin, and safranal, are responsible for color, taste, and aroma, respectively. The saffron plant produces a red-colored spice that is important in pharmaceutics, cosmetics, perfumery, and textile dye-producing industries. Iran produces almost 90% of the total world production. The saffron market is expected to grow by 12.09% in the forecast period 2020 to 2027. This paper reviews the current knowledge about the taxonomy, geographical distribution, reproductive biology, chemical composition, therapeutic and traditional uses, and agro-technology of the world's most expensive spice crop, saffron. [ABSTRACT FROM AUTHOR]

Catalytic degradation based on microwave irradiation is an emerging technique which promises prompt and efficient catalytic degradation of organic pollutants. Calcium ferrite (CaFe2O4), poly(1-napththylamine) (PNA), and PNA/CaFe2O4 nanohybrids were synthesized via microwave-assisted technique. The properties of the as-prepared CaFe2O4, PNA, and PNA/CaFe2O4 nanohybrids were characterized by the thermogravimetric analysis (TGA), FTIR, XRD, SEM, and ultraviolet–visible spectrophotometry (UV-vis) analyses. The formation of inorganic-organic hybrids was confirmed by the FTIR and XRD studies. Loading of PNA was confirmed to be 8%, 16%, 32%, and 40% in CaFe2O4 which was established by TGA studies and the thermal stability was found to follow the order: CaFe2O4 > 8-PNA/CaFe2O4 > 16-PNA/CaFe2O4 > 32-PNA/CaFe2O4 > 40-PNA/CaFe2O4 > PNA. CaFe2O4 and PNA revealed band gap values of 3.42 eV and 2.60 eV respectively while for the PNA/CaFe2O4 nanohybrids, the values were found to be ranging between 2.46 and 3.00 eV. The PNA modified CaFe2O4 nanohybrids showed higher degradation efficiency towards metronidazole (MTZ) drug as compared with PNA and pure CaFe2O4. MTZ drug showed around 94% degradation within 21 min of microwave irradiation using 40-PNA/CaFe2O4 as catalyst. The enhanced catalytic activity was attributed to the high surface area of the nanohybrid catalyst as well as improved microwave catalytic activity of PNA. The reactive species responsible for degradation were confirmed by scavenger studies which formation of ·OH and O2·− radicals. Recyclability tests showed that the 40-PNA/CaFe2O4 nanohybrid exhibited 86% degradation of MTZ (90 mg/l) even after the third cycle, which reflected higher reusability of the catalyst. The MTZ fragments were identified using liquid chromatography-mass spectrometry (LC-MS). [ABSTRACT FROM AUTHOR]

In this study, characterization of KAP13.1 gene and polymorphism analysis in Changthangi goat population was attempted. Association of genotype and sex with Pashmina production traits (diameter, yield, and down thickness) was also assessed. After PCR amplification, restriction digestion with PvuII and sequencing (in both directions) of KAP13.1 gene were done. Mutation at nucleotide position g. 171 T > G was observed in KAP13.1 gene. This SNP created a restriction site for PvuII and an important missense mutation in KAP13.1 protein, causing a change of codon from CTG (Leucine) >CGG (Arginine). The frequencies of A1A1, A2A2 and A1A2 genotypes based on the observed polymorphic patterns of KAP13.1 gene of Changthangi goats were 0.00, 0.427 and 0.573, respectively. Genotypic distribution of the genotypes of a 501bp fragment of KAP13.1 gene varied significantly (P < 0.05) from Hardy-Weinberg equilibrium. The variability parameters for the two gene loci analyzed were as heterozygosity: 0.3360; PIC: 0.27950; Effective allele no: 1.5059; and Shannon index I: 0.5187. The effect of sex on fiber production was statistically significant. The nucleotide and protein sequences showed close homology among bovine, ovine and caprine sequences. The present study revealed the genetic polymorphism of KAP13.1 gene locus and negative association with fiber traits. [ABSTRACT FROM AUTHOR]

Copyright of Al-Mustansiriyah Journal for Pharmaceutical Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The phytochemical and biological investigation of Cissampelos pareira leads to the isolation of one new isoquinoline alkaloid (7) along with six known isoquinoline alkaloids, namely, magnoflorine (1), magnocurarine (2), cissamine (3), curine (4), hayatinine (5) and cycleanine (6). Magnoflorine (1) and magnocurarine (2) were isolated for the first time from C. pareira. A new, rapid, simple and sensitive UPLC method was developed for simultaneous quantification of five pure compounds (1–5). Seasonal variation study revealed higher content of these compounds during the rainy season. The chloroform (CPCF) and n-butanol (CPBF) fractions showed cytotoxic efficacy against KB cells. Among pure compounds, hayatinine (5) was found to be most active against KB and A549, while, cycleanine (6) against KB cells. [ABSTRACT FROM AUTHOR]

This study was conducted between January to July 2015 in Junagadh and surrounding villages to document the use of medicinal plants for veterinary practices by people living in this area. A total 121 farmers, cowshed owners or livestock owners were contacted personally in the survey with a semi-structured questionnaire. Out of 121, 82 male informants of age 40 to 70 years have responded well and were included in this study. Sixty-seven medicinal plants from 40 different families have been reported to be effective in 13 different animal ailments. A total of 13 medicinal plants were cited 47 times for gastrointestinal problems, which were the highest. Leaves were used prominently (27.59 %) whereas, epicarp was the least used (0.86 %). Large number of medicinal plants reported were from the Fabaceae family (15.38 %). Junagadh area is important due to a large plant biodiversity. [ABSTRACT FROM AUTHOR]