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The current study aimed to evaluate the efficacy of selenium nanoparticles (SeNPs), combined or loaded with chitosan (COS), in broiler chickens reared under standard management protocols. The parameters under investigation were production performance, organ development, components of the intestinal barrier, and ileal microbial count. Two hundred and forty day-old chicks were raised in five groups, with each group containing eight replicates (n＝6/replicate). The control group received a basal diet whereas the other four groups received basal diets supplemented with SeNPs (0.5 mg/kg), COS (200 mg/kg), SeNPs+COS (0.5 mg/kg SeNPs + 200 mg/kg COS), and SeNPs-loaded COS (SeNPs-L-COS) (200 mg/kg) respectively. On day 35, two birds/replicate were sampled to collect the viscera under investigation. The results revealed that dietary inclusion of SeNPs-L-COS increased (p

In the surfactant alternating gas injection, the injected surfactant slug is remained several days under reservoir temperature and salinity conditions. As reservoir temperature is always greater than surface temperature. Therefore, thermal stability of selected surfactants use in the oil industry is almost important for achieving their long-term efficiency. The study deals with the screening of individual and blended surfactants for the applications of enhanced oil recovery that control the gas mobility during the surfactant alternating gas injection. The objective is to check the surfactant compatibility in the presence of formation water under reservoir temperature of 90oC and 120oC. The effects of temperature and salinity on used surfactant solutions were investigated. Anionic surfactant Alpha Olefin Sulfonate (AOSC14-16) and Internal Olefin Sulfonate (IOSC15-18) were selected as primary surfactants. Thermal stability test of AOSC14-16 with different formation water salinity was tested at 90oC and 120oC. Experimental result shows that, no precipitation was observed by surfactant AOSC14-16 when tested with different salinity at 90oC and 120oC. Addition of amphoteric surfactant Lauramidopropylamide Oxide (LMDO) with AOSC14-16 improves the stability in the high percentage of salinity at same temperature, whereas, the surfactant blend of IOSC15-18 and Alcohol Aloxy Sulphate (AAS) was resulted unstable. The solubility and chemical stability at high temperature and high salinity condition is improved by the blend of AOSC14-16+LMDO surfactant solution. This blend of surfactant solution will help for generating stable foam for gas mobility control in the methods of chemical Enhanced Oil Recovery (EOR).

The residual oil after primary or secondary oil recovery can be recovered by the methods of EOR (Enhanced Oil Recovery). The objective of this study is screening the surfactants that generate maximum stable foam in the presence of brine salinity at 92oC. Laboratory experiments have been performed to examine and compare the stability of generated foam by individual and blended surfactants in the synthetic brine water. AOS C14-16 (Alpha Olefin Sulfonate) and SDS (Sodium Dodecyl Sulfonate) were selected as main surfactants. Aqueous stability test of AOS C14-16 and SDS with brine water salinity 62070ppm was performed at 92oC. AAS (Alcohol Alkoxy Sulfate) was blended with SDS and AOS C14-16. The solution was stable in the presence of brine salinity at same conditions. Salt tolerance experimental study revealed that AOS C14-16 did not produce precipitates at 92oC. Further, the foam stability of surfactant blend was performed. Result shows that, the maximum life time of generated foam was observed by using blend of 0.2wt% SDS+0.2wt% AOS+0.2wt% AS-1246 and 0.2wt% AOS+0.2wt% IOSC15-18+0.2wt% AAS surfactants as compared to the foam generated by individual surfactants. The success of generated foam by these surfactant solutions in the presence of brine water is the primary screening of surfactant stability and foamability for EOR applications in reservoirs type of reservoirs.

Abstract Background Strong artificial and natural selection causes the formation of highly conserved haplotypes that harbor agronomically important genes. GWAS combination with haplotype analysis has evolved as an effective method to dissect the genetic architecture of complex traits in crop species. Results We used the 60 K Brassica Infinium SNP array to perform a genome-wide analysis of haplotype blocks associated with oleic acid (C18:1) in rapeseed. Six haplotype regions were identified as significantly associated with oleic acid (C18:1) that mapped to chromosomes A02, A07, A08, C01, C02, and C03. Additionally, whole-genome sequencing of 50 rapeseed accessions revealed three genes (BnmtACP2-A02, BnABCI13-A02 and BnECI1-A02) in the A02 chromosome haplotype region and two genes (BnFAD8-C02 and BnSDP1-C02) in the C02 chromosome haplotype region that were closely linked to oleic acid content phenotypic variation. Moreover, the co-expression network analysis uncovered candidate genes from these two different haplotype regions with potential regulatory interrelationships with oleic acid content accumulation. Conclusions Our results suggest that several candidate genes are closely linked, which provides us with an opportunity to develop functional haplotype markers for the improvement of the oleic acid content in rapeseed.

Background: The failure to detect "red flag" signs in patients presenting with acute low back pain can adversely affect the outcome of management. This can seriously affect the quality of life and productivity of the patient. Objective: The present questionnaire-based study was performed to assess the knowledge and awareness of red flag signs among primary health care physicians managing patients with acute back pain in Riyadh, Saudi Arabia. Materials and Methods: The study sample size was comprised of 80 subjects. The level of knowledge was assessed by means of a new structured self-administered questionnaire. The design of this questionnaire was based on the Agency for Health Care Research and Policy (AHCRP) guidelines for detection of red flag signs. Physicians were asked about red flag signs that indicate the presence of tumor, infection, spinal fracture, or cauda equina syndrome. Results: Sixty-eight (85% of total) physicians were aware of red flag signs. Of the 68 physicians who were aware of the red flag signs, 58 (72%) were aware of neurological deficit, 36 (45% of total) were aware of extremes of age (50 years), and 33 (41% of total) were aware of and routinely inquired about the history of spinal trauma, whereas only 24 (30% of total) were aware of and inquired about constitutional symptoms in their patients with acute back pain. Conclusion: Although low back pain is extremely common, knowledge and awareness of red flag signs of primary health care physicians managing patients with acute back pain in Riyadh appear to be inadequate. This indicates a lack of adherence to the international guidelines. Specific educational programs should target these deficiencies and increase awareness.

Genomic selection (GS) is a modern breeding approach where genome-wide single-nucleotide polymorphism (SNP) marker profiles are simultaneously used to estimate performance of untested genotypes. In this study, the potential of genomic selection methods to predict testcross performance for hybrid canola breeding was applied for various agronomic traits based on genome-wide marker profiles. A total of 475 genetically diverse spring-type canola pollinator lines were genotyped at 24,403 single-copy, genome-wide SNP loci. In parallel, the 950 F1 testcross combinations between the pollinators and two representative testers were evaluated for a number of important agronomic traits including seedling emergence, days to flowering, lodging, oil yield and seed yield along with essential seed quality characters including seed oil content and seed glucosinolate content. A ridge-regression best linear unbiased prediction (RR-BLUP) model was applied in combination with 500 cross-validations for each trait to predict testcross performance, both across the whole population as well as within individual subpopulations or clusters, based solely on SNP profiles. Subpopulations were determined using multidimensional scaling and K-means clustering. Genomic prediction accuracy across the whole population was highest for seed oil content (0.81) followed by oil yield (0.75) and lowest for seedling emergence (0.29). For seed yieId, seed glucosinolate, lodging resistance and days to onset of flowering (DTF), prediction accuracies were 0.45, 0.61, 0.39 and 0.56, respectively. Prediction accuracies could be increased for some traits by treating subpopulations separately; a strategy which only led to moderate improvements for some traits with low heritability, like seedling emergence. No useful or consistent increase in accuracy was obtained by inclusion of a population substructure covariate in the model. Testcross performance prediction using genome-wide SNP markers shows considerable potential for pre-selection of promising hybrid combinations prior to resource-intensive field testing over multiple locations and years.

Birt-Hogg-Dubé syndrome is an autosomal dominant genodermatosis caused by germline mutations in the folliculin gene and characterized by facial papules, pulmonary cysts, kidney tumours and recurrent pneumothoraces. Several distinct mutations in the folliculin gene resulting in a truncated protein have been described. The present report describes a new mutation, which has not been reported in individuals with Birt-Hogg-Dubé syndrome but is of a type predicted to cause disease.

Abstract Internet of Things (IoT) platforms have become the building blocks of any automated system but they are more important in case of industrial systems where sensitive data are captured and handled by the information system. Therefore, it is imperative to deploy the right IoT platform to perform the computational and operational tasks in a better way. During the last few years, an array of IoT technologies/platforms with different capabilities and features were introduced in the markets. This abrupt rise created selection and decision-making issues particularly for the network engineers, designers, and industrial managers due to a lack of technical understanding and skill in this area. Therefore, we present an integrated assessment model focusing on evaluating and ranking IoT platforms in the industrial environment. It encompasses multiple methods such as the proposed model leverages a well-known data collection technique such as Delphi for data collection related to the criteria features. It adopts the Analytic Hierarchy Process (AHP) for giving weights to the criteria features. The technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method has been applied for the evaluation of the top twenty (20) Industrial IoT(IIoT) platform alternatives according to the proposed criteria. It selects the most rational choice of IoT platform that can be employed in the Industry 4.0 setting. The proposed integrated assessment model produces the most accurate and consistent outcomes. Hence, it is believed that it can be used as a guideline by different stakeholders like researchers, developers, network engineers, and policymakers for the assessment and deployment of IoT platforms in the industrial environment. It is believed that it is the first kind of multi-methods integrated assessment mode for the assessment, decision-making, and prioritization of IoT technologies in the industry 4.0 domain.

Abstract Researchers are now avoiding perovskite materials containing lead due to their toxicity and instability in open air and heat. In this study, our research group worked on potassium-based K2MgSiCl6 & K2MgSiBr6 perovskite compounds and thoroughly investigated these materials’ basic behaviours. Goldschmidt and formation energy results certify the structure and thermodynamic stabilities of the studied compounds. The band gaps noted for the K2MgSiCl6 & K2MgSiBr6 perovskites were 2.42 eV & 1.88 eV, respectively. After the mechanical observations of the investigated compounds, it became apparent that both the studied compounds own brittle and anisotropic nature. Based on the tendency to absorb electromagnetic radiations both in the ultra-violet and visible range, it enables the K2MgSiCl6 & K2MgSiBr6 perovskites suitable for detectors, solar cells, and many other optoelectronic devices. The K2MgSiBr6 material showed the first response to the electromagnetic radiations in the visible range and acquired the optical conductivity value of 1219 (Ω.cm)-1; however, K2MgSiCl6 material showed its response to a bit higher energetic photons in the visible range than K2MgSiBr6 material, but its optical conductivity was higher (i.e., 1270 (Ω.cm)-1) than the K2MgSiBr6 material in the visible range.

Abstract Solar cells are increasingly being utilised for both energy harvesting and reception in free‐space optical (FSO) communication networks. The authors focus on the implementation of a mid‐band p‐In0.01Ga0.99 N/p‐In0.5Ga0.5 N/n‐In0.5Ga0.5 N (PPN) solar cell, boasting an impressive 26.36% conversion efficiency (under 1.5AM conditions) as a receiver within an indoor FSO communication network. Employing a solar cell with dimensions of 1 mm in length and width, the FSO system underwent simulation using Optisystm software, while the solar cell's behaviour was simulated using SCAPS‐1D. The received power from the solar cell was then compared to that of four commercially available avalanche photodiode (APD) receivers. Exploring incident wavelengths spanning 400–700 nm within the visible spectrum, across transmission distances of 5, 10, 15, and 20 m, the study presented current‐voltage (IV) and power‐voltage curves. Notably, the InGaN solar cell exhibited superior electrical power output compared to all commercial APDs. In conclusion, the findings underscore that augmenting received power has the potential to enhance FSO network quality and support extended transmission distances.

The physical phenomena in which an extra electron is removed from a negative ion is called photo‐detachment. Photo‐detachment is important phenomena, used to find the structure of anions, particularly to find the electron affinities. In this paper, we present theoretically the induced effects in the photo‐detached of triatomic anion H3− near hard reflecting wall or surface. For the photo‐detachment process, a z‐polarized coherent source of radiations (laser) is used to kick electrons from H3− anion in the domain of a hard reflecting surface. Imaging method is adopted to derive the generalized detached electron wave, differential cross‐section and the total photo detachment cross‐section analytically. Numerical solutions (simulations) for total electron flux and the total cross‐section is presented. The electron flux, shows visible oscillations and hence the induced effect of surface in the interference. It is depicted that the reflecting hard wall strongly affects the flux and total photo‐detachment cross‐section. The analytical results are extended for triiodide I3− anion numerically, which also show interference. The total photo detachment cross‐section for I3− is calculated numerically and compared with cross‐section of H3−. [ABSTRACT FROM AUTHOR]

Nine Paleozoic sedimentary formations from western Tasmania and two from the Lachlan Orogen—one in northeast Tasmania and one in Waratah Bay in Victoria—contain detrital zircons that fall into major U–Pb age clusters at 2.0–1.4, 1.25–0.95 and 0.62–0.49 Ga. The zircon Th/U ratios and rare earth element (REE) patterns suggest a magmatic origin for detrital zircons in all these rock sequences. The REE geochemical signatures on bivariate discrimination diagrams indicate that most of the zircons originated in continental orogenic settings. The oldest group of zircons have a southwest Laurentian signature previously recognised from Proterozoic metasedimentary rocks in Tasmania. The 1.25–0.5 Ga zircons from all the samples have very similar εHfi values. They are not statistically different from those of typical Lachlan Orogen sandstones and resemble those in Rodinia and early Gondwana orogenic belts. The εHfi values of the Cambrian zircons within the Pioneer Sandstone are similar to the Mount Read Volcanics but cannot be distinguished on εHfi values from other sources in East Gondwana. The detrital zircon provenance of the Bear Gully Chert Bed at the base of the Digger Island Marlstone at Waratah Bay includes the west Tasmanian Proterozoic, the East Gondwana margin and the Macquarie Arc. This mixed provenance provides evidence for the late Cambrian docking of VanDieland with East Gondwana. New zircon Hf isotope data are reported for early Paleozoic sedimentary rocks in Tasmania and Waratah Bay, southern Victoria. The western Tasmania basement was the dominant source for Tasmanian sandstones and a minor source for the Lachlan Orogen sandstones on the northeast margin of VanDieland. The mixed provenance of the Bear Gully Chert supports a late Cambrian accretion of VanDieland to the East Gondwana margin. The youngest zircons in the Bear Gully Chert at Waratah Bay were potentially sourced from the Macquarie Arc. [ABSTRACT FROM AUTHOR]

Phytobiotics are promising diet alternatives, yet their effectiveness in high-risk aquaculture conditions remains underexplored. Therefore, a 90-day feeding trial was conducted based on dietary supplementation of Nile tilapia, Oreochromis niloticus, with herbal extracts, namely, lemon balm [Melissa officinalis (MOE)], marjoram [Origanum majorana (OME)], and chamomile [Matricaria chamomilla (MCE)] with 0% water change. The treated groups were compared to groups untreated with herbs or control groups [positive control (PC; 0% water change) and negative control (NC; 20% water exchange per day]. Fish were cultured at stocking density (20 fish m-3: 1.8kg of biomass/m3). We conducted a physicochemical analysis of the water and the clinical responses, growth, and immune responses of the fish were evaluated. Furthermore, the herbal-supplemented fish were then challenged with a pathogenic Edwardseilla tarda strain and mortality was monitored. In the 1st and 2nd months, the water parameters were within the permissible limits. After that, a fatally low dissolved oxygen concentration and the highest levels of ammonia, nitrite, nitrate, and pH were recorded during the 3rd month. Blood and immune assays were conducted in the treated groups and control groups. The herbal-treated groups appeared healthy, but during the 3rd month, lethargy and decreased appetite were evident. Generally, the herbal-treated fish showed improved growth performance parameters, survival rates, and resistance against pathogenic bacteria E. tarda, particularly in the OME and MOE-treated groups compared to the positive control group. Finally, phytobiotic supplements were shown to improve fish stress tolerance and immune activation for a certain period under stressful conditions or unchanged water, based on the stocking density, dosages of herbs used, and the extent of deterioration of the water quality.

Rapid developments in the Internet of Things (IoT) have opened the door for game-changing applications in numerous sectors, especially the vehicle industry. There is a rising demand for efficient assessment and decision-making methodologies to pinpoint the most promising choices for the vehicle sector with the introduction of IoT-based maritime vehicles. To overcome this issue, the integrated multi-criteria decision-making analysis (MCDA) paradigm proposed in this research combines the additive ratio assessment (ARAS) and analytic hierarchy process (AHP) approaches to evaluate and choose IoT-based maritime vehicles based on their performance- and authenticity-related criteria in the vehicle sector. The selection issue is hierarchically organized, and the assessment criteria are prioritized using the AHP approach. There are seven performance and authentication related criteria are selected that might aid in the selection procedure. Using the AHP, we are assigned these criteria proportionate weights that reflect their respective significance and interrelationships. AHP, however, falls short of offering a thorough analysis of the alternatives that exist. To overcome these restrictions, this research presents the integration of AHP with the ARAS approach for the ranking of alternatives according to how well they perform against the set criteria. By using the ARAS technique, it is possible to get over the restrictions of AHP and achieve a more thorough assessment of maritime IoT-based vehicles. The efficiency of the framework is proven using empirical data and professional judgment. The findings show that the hybrid method successfully encapsulates the intricate relationships between the factors being evaluated and objectively appraises the potential of IoT-based maritime vehicles for the automotive sector. This study extends to the area by providing an organized and thorough method for assessing and choosing IoT-based maritime vehicles. Considering several factors and their mutual dependence, the hybrid AHP and ARAS technique gives decision-makers a powerful tool for evaluating the potential of IoT-based maritime vehicles in the automotive sector. Smart decisions on the deployment of IoT-based marine vehicles and maximizing the potential they present may be made by beneficiaries in the automotive sector using the study’s results.

Abstract The line of sight (LoS) probability is a key factor for the channel modeling of air‐to‐ground (A2G) communication. However, the existing LoS probability models do not account for the effects of airframe shadowing (AS) and building density, which can cause serious link obstruction and performance loss due to the six‐dimensional (6D) mobility and self‐body of unmanned aerial vehicle (UAV). In this article, a new LoS probability model is proposed that considers the AS and building density for different UAV heights. Adding to this, the AS is derived in terms of UAV framework and 6D mobility. Next, the machine learning (ML) based graph neural network (GNN) method is developed to learn the features and structure of the urban environment and predict the LoS probability. Then, the GNN model is trained and evaluated based on the ray tracing (RT) data to establish the relationship between model parameters and UAV heights under the building density and AS factors. The interpretation and explanation of the proposed GNN model and prediction are also discussed in this article. It is shown from the simulation analysis that the GNN model accurately captures the effects of AS, building height distributions, and UAV heights, with high accuracy compared to the baseline 3GPP, GCM and NYU models.

Brassinosteroid (BR), a plant hormone regulating growth, development, and stress responses, emerges as a promising tool for maintaining agricultural production under abiotic stress conditions. In this study, we conducted RNA-seq profiling and morpho-physiological analysis to investigate the molecular cross-talk involved in 24-epibrassinolide (EBR) mediating alleviation of chromium (Cr) stress. EBR inhibited Cr accumulation and reversed Cr-induced phytotoxicity, thereby promoting plant growth. The photosynthetic pigments, chlorophyll fluorescence a, electron transport rate (ETR) and non-photochemical quenching (NPQ) were significantly higher in EBR+Cr treated plants compared to Cr alone. EBR application facilitated the recovery from Cr-induced structural deformities, including the disintegration of cell walls and membranes. Furthermore, under Cr stress, EBR application reduced malondialdehyde (MDA) and reactive oxygen species (ROS) production and accumulation. The levels of glutathione reductase (GR) and the activities of antioxidant enzymes were notably higher in plants subjected to EBR application following Cr stress. In addition, we established a transcriptomic database comprising 2345 differentially expressed genes (DEGs) (1255 upregulated and 1090 downregulated) as a result of EBR application under Cr stress. The transcriptome analysis unveiled key DEGs and the associated pathways, emphasizing the importance of defense responses, genes encoding photosystem I and II, jasmonate signaling, aquaporins, ABC transporters, and cell wall biogenesis-related genes in the response of EBR to Cr stress.

This paper explores the challenges and potential solutions related to data collection, integration, processing, and utilization in defense manufacturing within the context of Industry 4.0. While Industry 4.0 envisions the integration of various technologies to achieve seamless operations in industries, the unique characteristics of defense manufacturing, such as stringent data limitations and security requirements, make direct translation challenging. Through a comprehensive review of academic literature, key themes were identified, including quality control, digitalization, cyber–physical aspects, sustainability, risk management, ownership of information, and security. Drawing from the reviewed publications, potential solutions were distilled into related approaches, such as data governance frameworks, data exchange standards, blockchain, additive manufacturing, transparent digital supply chains, and smart factories. These solutions present opportunities for the Australian defense manufacturing industry to overcome the identified challenges and leverage the benefits of Industry 4.0, including improved quality control, increased efficiency, enhanced security, and optimized supply chains. By embracing these opportunities, the defense manufacturing sector can successfully navigate the complexities of Industry 4.0 and realize its vision of seamless integration for continued growth and success.

Abstract The intensification of the Internet of Health Things devices created security concerns due to the limitations of these devices and the nature of the healthcare data. While dealing with the security challenges, several authentication schemes, protocols, processes, and standards have been adopted. Consequently, making the right decision regarding the installation of a secure authentication solution or procedure becomes tricky and challenging due to the large number of security protocols, complexity, and lack of understanding. The major objective of this study is to propose an IoHT-based assessment framework for evaluating and prioritizing authentication schemes in the healthcare domain. Initially, in the proposed work, the security issues related to authentication are collected from the literature and consulting experts’ groups. In the second step, features of various authentication schemes are collected under the supervision of an Internet of Things security expert using the Delphi approach. The collected features are used to design suitable criteria for assessment and then Graph Theory and Matrix approach applies for the evaluation of authentication alternatives. Finally, the proposed framework is tested and validated to ensure the results are consistent and accurate by using other multi-criteria decision-making methods. The framework produces promising results such as 93%, 94%, and 95% for precision, accuracy, and recall, respectively in comparison to the existing approaches in this area. The proposed framework can be picked as a guideline by healthcare security experts and stakeholders for the evaluation and decision-making related to authentication issues in IoHT systems

Large orifices are constructed for dams to release water and sediments from reservoirs. Such structures are called submerged spillways. Numerous studies have investigated discharge coefficient, velocity coefficient, and head loss coefficient of large orifices; however, the literature lacks data on the upper and lower nappes of the jets from these orifices. In the present experimental study, the upper and lower nappes are investigated up to 80 m head at different gate openings. The observed minor deviation between the lower nappe profile and trajectory profile equation suggests sensitivity to different factors. The significant role of the coefficient of velocity, averaging at 0.926, highlights its impact on minor deviation. Subsequently, the impact of the solid bottom profile on the discharge coefficient and upper nappe profile are also examined. The results show improvement in discharge coefficient of a sharp-edged large orifice, which increased from 0.69 to 0.74. The results also indicate that the upper nappe profiles and United States Bureau of Reclamation (USBR) profiles are similar. The improvement in the upper nappe profile indicates the significant role of the solid bottom profile, which consequently was found to be helpful in defining the roof profile of an orifice spillway. . HIGHLIGHTS Hydraulic analyses of lower and upper nappe profiles of sharp-edged large orifices.; Impact evaluation of a solid bottom profile on an upper nappe profile.; Hydraulic performance evaluation of solid bottom profile at various operational scenarios.;

Abstract Trace metals are naturally occurring metals found in very small concentrations in the environment. In the context of fish flesh, metals such as copper, calcium, potassium, sodium, zinc, iron, and manganese are absorbed by fish and play vital roles in various physiological functions. However, if these metals exceed the recommended limits set by WHO/FAO, they are termed 'toxic metals' due to their harmful impacts on both the fish and its consumers. Therefore, the present study aims to analyze the levels of protein, lipids, and certain metals—Aluminum (Al), Sodium (Na), Zinc (Zn), Titanium (Ti), Iron (Fe), Copper (Cu), Potassium (K), and Calcium (Ca) in three commercially important marine fishes i.e. Rastrelliger kanagurta, Sardinella abella, and Otolithes ruber. The study also aims to assess their potential impact on human health. The macro-Kjeldhal method and Soxhlet apparatus were used to estimate protein and lipid contents, while atomic absorption spectroscopy (AAS) was used to estimate trace metals found in fishes. The study found that these fish species are valuable sources of protein, lipids, and certain essential minerals. The protein content (CP) in these three species ranged from 63.35 to 86.57%, while lipid content was from 21.05 to 23.86%. The overall results of the trace metal concentrations analyzed in the present study revealed that Aluminum (Al), Sodium (Na), Zinc (Zn), Titanium (Ti), Copper (Cu), Potassium (K), and Calcium (Ca) were found in low concentration or traces and also within suitable ranges as set by WHO/FAO. However, Iron (Fe) was absent in all three species. Moreover, both copper and potassium were found in all three species, while Zinc was present in Rastrelliger kanagurta and Sardinella abella, calcium in Sardinella abella, and sodium in Otolithes ruber only. Titanium was recorded for the first time in S. abella. However, the total health risk assessment associated with these fish food consumption was measured by THQ and TTHQ and found to be less than 1, which shows no potential risk related to trace metals found in these fishes on human health upon their consumption. In conclusion, these commercially important marine fish species were found valuable sources of protein, lipids, and essential trace minerals that are necessary for human health. Thus, the current study provides useful information for the local population to make informed decisions about their daily diets and highlights the importance of sustainable fishing practices to maintain these valuable marine resources by periodical monitoring of their ecosystem.

Context:: Software undergoes a constant evolution driven by ongoing changes in customer requirements, which enhances the competitive advantage. Regression testing plays a pivotal role by ensuring that modifications have not introduced detrimental effects on the system under test. Problem:: However, regression testing becomes prohibitively expensive as the software grows in complexity and the size of the test suite also expands. Moreover, keeping the test cases up-to-date and managing the relevant test data can become a laborious and challenging task. Hence, it is required to optimize the test suite by finding a diverse subset of test cases having high code coverage, fault-detection rate, and minimal execution time. Objective:: To solve the regression test optimization problem, the researchers have proposed various approaches including greedy algorithms, search-based algorithms, and clustering algorithms. However, existing approaches lack in finding the global optimal solution and are mostly focused on the single-objective test optimization problem. Inspired by this, we propose a Similarity-based Multi-Objective Optimization Technique (SMOOT) for test suite reduction using a Grey Wolf Optimizer (GWO) algorithm. The proposed technique employs different similarity metrics, including Cosine Similarity, Euclidean Distance, Jaccard Similarity, Manhattan Distance, and Minkowski Distance, to evaluate the similarity score of the tests. This ensures a comprehensive assessment of test diversity to achieve high code coverage and fault-detection rate while minimizing the test execution cost. Method:: We evaluated the performance of GWO with state-of-the-art search-based algorithms using three varying types of case studies. Similarly, to evaluate the similarity score of the considered search algorithms, we employed state-of-the-art similarity measures. Results:: The experimental results revealed that GWO significantly outperformed the considered search algorithms by attaining high code coverage and fault-detection rate while minimizing the test execution time. Moreover, we found that GWO attained a higher similarity score than the other considered search algorithms using the employed similarity measures. Conclusion:: Based on the attained results, we believe that the proposed technique could be useful for the researchers and practitioners by effectively handling multi-objective regression test optimization problem.

ABSTRACTMoney laundering (ML) is a critical source of extracting the money illegally from the financial system. It is linked to various types of crimes, including corruption, exploitation of a specific community, drug use, and many others. Detection of ML operations is a difficult task on a global scale due to the large volume of financial transactions. However, it also allows criminals to use financial systems to carry out fraudulent transactions. It mainly concern minimizing the potentially risks associated with money laundering. Anti-money laundering-(AML) tools based on AI-driven applications are now tracking transactions to overcome this challenge. A total of 112 research papers are assessed to identify the literature’s gaps and suggest new directions for the research area accordingly. The findings of this systemic literature review work will not only open new paths for the research community, but will also assist the state agencies in developing an optimal AML system to counter these major issues and provide a healthy environment for their residents. This article seeks to assess the existing situation from various angles and open up new pathways for future research directions to investigate and build high levels of authenticity and security in the financial industry using artificial intelligence (AI).