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In the present study, an investigation was carried out to analyze vegetable consumption rates by the comprehensive survey at five selected sites (Alipur, Shahdara, Kanjhawala, Mehrauli, and Najafgarh) in Delhi NCR region.Six vegetables, i.e., potato, carrot, tomato, spinach, mustard, and okra mostly cultivated in the peri-urban areas, data were collected from each site of 25 families. Results indicated that the gross per capita per day vegetable consumption was highest at Shahdara (0.314 kg/capita/day), followed by Najafgarh (0.307 kg/capita/day).It was also observed that the per capita per day consumption of vegetables was highest mostly in the case of potato followed by tomato.Tomato was the second-highest utilized vegetable at most of the selected sites. While mustard was consumed lowest at most of the sites except Mehrauli. It was also observed that in selected vegetables, the consumption of root vegetables was higher as compared to other vegetables.

Context: Ideal interaction of dental and facial beauty proportions highly influences a harmonious facial appearance. Racial anatomical variations have been significantly noted and using these norms during prosthodontic rehabilitation will be greatly helpful in successful treatment outcome. Aim: The present study aims to correlate various facial landmarks (interpupillary distance [IPD], intercanthal width, bizygomatic width [BZW], and interalar width) with intercanine distance (ICaD) in Indian young adults to determine the mesiodistal width of the maxillary anterior teeth in edentulous patients. Setting and Design: This is an observational cross-sectional study. Materials and Methods: This study was conducted in 500 individuals ranging in age from 18 to 28 years. IPD, intercanthal width, BZW, and interalar width were measured with a digital caliper (accuracy - 0.01 mm). A “T-” shaped flat metal plate called “canine tip marker” was made to mark the tips of the maxillary canines, which were further measured with the digital caliper. The data were summarized in table form and were statistically analyzed. Statistical Analysis Used: Student's t-test and ANOVA test were used. Results: A significant correlation (P < 0.001) was found between these landmarks with ICaD, in which interalar width shows highest degree of correlation (r = 0.639). ANOVA test showed that maximum number of individuals had < 5% variability range between actual values and calculated values of ICaD for all extraoral facial landmarks except intercanthal width. Conclusion: Combination of various facial and oral factors should be considered while selecting artificial teeth. Regression equations obtained can be well used during teeth selection to achieve a significant result.

Transient cortical blindness (TCB) is a well known but rare complication of administration of contrast agent. In this case report, we present a 53-year-old woman who is a follow-up case of sarcoidosis and developed TCB with focal neurological symptoms following contrast-enhanced computed tomography scan. Magnetic resonance imaging revealed bilateral T2/Flair hyperintensities in parieto-occipital, high frontal, and cerebellar hemispheres with involvement of corpus callosum. Clinically and radiologically patient improved significantly in 4 days. The exact mechanism is still speculative and its possible relationship with posterior reversible encephalopathy syndrome is briefly discussed. The patient′s symptoms were presumed to be exacerbated by presence of hypertension, underlying autoimmune disorder, sepsis, and high osmolality of contrast agent. Though there is no definite evidence to suggest that a certain treatment regimen improves the natural history of this disease but control of risk factors can possibly prevent this rare but devastating complication.

The article explores the use of artificial intelligence tools, specifically ChatGPT‐4 and Google Lens, in dermatological diagnosis. A study compared the diagnostic capabilities of these tools with clinical diagnoses made by dermatologists, showing that ChatGPT‐4 had a higher overall accuracy of 70.15% compared to Google Lens's 45.35%. While ChatGPT‐4 performed better in certain conditions like acne vulgaris and psoriasis, both tools faced challenges with conditions like pityriasis rosea. The study suggests that integrating advanced AI technologies like ChatGPT‐4 could enhance dermatological diagnostic processes, especially in resource‐limited settings, while highlighting the need for further refinement of tools like Google Lens. [Extracted from the article]

The increasing complexity of modern software development necessitates tools and methodologies for code analysis, maintenance, and migration in multi-language Integrated Development Environments (IDEs). The security needs of the software development process also recently led to the introduction of the Software Bill of Materials (SBOM), which vendors must include in their products. Automated tools become imperative to speed up software production and distribution. In the present code development landscape, automated Programming Language Identification (PLI) helps to develop secure software development with its in-depth insights into programming consequences for code maintenance, legacy system management, code analysis, quality assurance, software modernization, migration, and code search. With the increasing success of Machine Learning (ML) models in detection methods, researchers use them for PLI showing superior performance of detection compared to the basic method of identification based on the source file extension. In this paper, we present the first survey for ML-based PLI methods providing insights into the domain’s present status and guiding towards a futuristic tool development for PLI. We evaluate various ML techniques that recognize programming languages. These techniques include conventional, operation-based, and data source-based techniques. Our study examines the advantages and limitations of identifying the programming language in text, images, and videos. Our investigation also emphasizes the capabilities of the existing solutions that improve software development practices. Furthermore, our survey analyzes the strengths and limitations of existing code detection tools such as GitHub’s Linguist, Pygments, highlight.js, Ace, Google’s Code Prettify, SourcererCC, Guesslang, and SonarLint. We also provide some open research problems for future researchers.

The current investigation has been carried out to determine the potentially dangerous glacial lakes (PDGLs) and glacier recession and in the Bhut and Warwen sub-basins of Chenab basin using the Landsat series of data. In order to assess the glacier dynamics from 2000 to 2020 the consecutive set of 5-year intervals data were used and it is found that, glaciers are shrinked in area as 1049.94 km2 in 2000, to 988.07 km2 in 2020, indicating a notable decrease in glacier extent over the twenty-year period. Further, an empirical approach was used to identify the PDGLs in the study area. The results of PDGLs shows that there are a total of 55 glacial lakes have the potential to be hazardous. Out of them, 11 are classified as high hazard, 18 as moderate hazard; and the remaining 26 are designated as low hazard. The PDGLs in the region have seen a surge in their numbers in the last few decades due to topographical as well as climate variables. The receding of glaciers and the risk of landslides in the region greatly influences PDGLs. The dangers among these lakes in the region have accumulated so much sediments and the glacial melt and their bearing capacity has come up to certain threshold causing the GLOFs and landslide lake outburst floods (LLOFs) in the region.

We present the synthesis, analysis, and utility of polyvinylpyrrolidone (PVP) stabilized monometallic silver nanospheres for liquefied petroleum gas sensing at room temperature. We have synthesised silver nanospheres (Ag-NSs) in the diameter range of 13–21 nm using a microwave assisted method, which is both easy and fast, because microwave irradiation only takes 30 s. A solution of 0.1 M silver nitrate (AgNO3) in an ethanolic medium was microwave-irradiated to produce Ag-NSs. PVP was used as a stabilising agent throughout the process. Spin coating method was used to produce thin films of synthesised monometallic Ag-NSs. X-ray diffraction (XRD), scanning electron microscopy (SEM), acoustic particle sizer (APS), UV–visible absorption (UV–visible) and Fourier Transform Infrared (FT-IR) spectroscopy were used for characterising Ag-NSs. Based on the acoustic attenuation of the prepared sample, the acoustic particle sizer estimated the diameter of Ag-NSs to be around 17 nm. The deposited films were examined for LPG leakage detection at ambient temperature. This is the first report on monometallic Ag-NSs for leakage detection of LPG at room temperature operation below its lower explosive limit (LEL). The significant finding of the developed sensor is that it prompts the quick response (∼16 s) and recovery (∼64 s) as Ag enhances the reaction rate between the sensing film surface and adsorbed LPG, thereby, augments the sensitivity of sensor even below LEL. Moreover, the fabricated LPG sensor shows noteworthy reproducibility (measured up to six cycles) and stability up to 06 months after fabrication.

Objective: To estimate the maximum voluntary molar biting force (MBF) and incisor biting force (IBF) and their relationship to morphological variables in subjects with different vertical skeletal patterns. Materials and Methods: Maximum voluntary MBF, IBF, and morphological variables were recorded in 120 subjects (60 males and 60 females) with skeletal class I pattern in the age range of 14 to 24 years. All subjects were divided into 3 groups: Normodivergent, hypodivergent, and hyperdivergent, according to the maxillomandibular plane angle and Jarabak ratio. Bite force measurements were undertaken using a custom-made portable digital gnathodynamometer on the left and the right sides of the jaw in the molar and incisor regions during maximal clenching. Statistical analysis was performed using independent t-test, chi-square test, and ANOVA test using SPSS version 22.0.0.0 software. Results: MBF and IBF are influenced by gender with higher values obtained for male subjects in all groups in the following order: hypodivergent > normodivergent > hyperdivergent. No significant changes were seen with morphological variables in different groups. Conclusion: Molar and incisor biting forces are highest in hypodivergent subjects and least in hyperdivergent subjects as a reflection of jaw morphology and muscular efficiency. In all groups, males exhibit higher force values than females, underlining a strong gender influence on biting force and facial pattern. [ABSTRACT FROM AUTHOR]

The correctness and the true validated data in Human Resource Management (HRM) are important for organizations as the data plays an impactful role in recruiting, developing, and retaining a skilled workforce. On one hand, the validated data in an organization helps in recruiting legitimate skillful employees; on the other hand, keeping the employee's data safe and maintaining privacy laws such as compliance with the General Data Protection Regulation (GDPR) is also an organization's responsibility. Besides, transparency in human resource management operations is crucial because it promotes trust and fairness within an organization. The present HRM systems are centralized in nature and their verifiable credential system is ineffective; this leads to the intentions of internal data sabotage or internal threats. Besides, the organizations' biases also become more prominent.In this paper, we address the above-mentioned problems with a blockchain framework for HRM to utilize the privacy of data access through a Privacy Information Retrieval (PIR) process. To be specific, our proposed framework called Blockchained piR of resOurces as humaN (BRON), is the first blockchain framework to show an effective mechanism to access data from organizations globally without hampering privacy. BRON uses a generalized user registration process to use the services of data access and in the background, it uses Zero-Knowledge Proofs (ZKPs) for global verification and PIR for privacy-based data retrieval. More specifically, credential verification and ZKP-based PIR are the highlights of our proposed BRON. Another interesting aspect of BRON is the use of Proof-of-Authority (PoA) to validate the anonymity and unlinkability of any HR operation. Finally, BRON has also contributed with a smart contract to incentivize the employees. BRON is very generic and easily be customizable as per the HR requirements. We run a set of experiments on BRON and observe that it is successful in providing privacy-assured data access and decentralized human resource data management. Overall, BRON provides 30% reduced latency and 35% better throughput as compared to the existing blockchain solutions in the direction of HRM.

Blockchain networks provide a reliable and secure mode of communication due to their decentralized and distributed nature. The emergence of amalgamated blockchain-based internet-of-things (IoT) systems has generated a huge amount of data to be online. Though blockchains show potential for ensuring transparency, traceability, and immutable records, the privacy of online data in blockchains becomes a question. The privacy of blockchain transactions is at stake as various privacy breaching methods are used by attackers such as linking the transactions, deanonymization, etc. However, the benefits of blockchain make the technology a dominator in the present and future technological paradigms. The other side of the coin deals with privacy information retrieval (PIR), which is necessary to retrieve private information from servers without much revealing. However, the conjunction of blockchain privacy and PIR is very critical and an important aspect of blockchain solutions. In this present survey, we pioneer in analyzing the privacy factors of existing blockchain solutions. We discuss the privacy parameters and important privacy enhancement techniques for blockchains comprehensively. We show the applicability of privacy in various domains including e-commerce, supply chain, healthcare, and IoT. We also discuss PIR-related issues and solutions in the existing literature. We highlight open research problems and discuss the benefits of collaborating with PIR and blockchain systems to improve privacy in blockchains. Our survey is beneficial for academia and industries to be aware of the present status of privacy solutions in blockchains and to address the identified loopholes to make the systems better.

Vehicular networks are expanding their applications for future sustainability. The reported increasing rate of data breaches through vehicular networks by Distributed Denial of Service (DDoS) type of intrusion creates concern for such networks. Existing security solutions focus only on intrusion detection. However, prevention solutions are more proactive and provide security by probabilistic analysis. Existing prevention models for vehicular networks have low accuracy and are unable to handle zero-day attacks and advanced persistent threats. In this paper, we solve the problems mentioned above and introduce Predictive Risk Evaluation for Vehicular Infrastructure Resilience (PREVIR), the first amalgamated model of logit method (statistical analysis) and LogitBoost method (machine learning) to prevent DDoS attacks in vehicular networks. In PREVIR, the logit model predicts the packet probabilities for identifying maliciousness. The machine learning method improves PREVIR’s performance through iterative refinement of the model’s periodic updates based on new traffic parameters. We run a set of experiments on PREVIR. We use our NS3-generated dataset, NSL-KDD public dataset, and CIC-DDoS public dataset. PREVIR analyses multiple attack types, including UDP flood, TCP flood, mixed flooding, U2R, Probe, and R2L attacks. The results show that PREVIR classifies packets with accuracy up to 99.99%. Our proposed PREVIR model achieves a True Positive Ratio (TPR) up to 100% and an average False Positive Ratio (FPR) of 35%. The comparative analysis shows that PREVIR’s efficiency is 20% better on average in the prevention of malicious packets as compared to the state-of-the-art models.