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Heavy metals in food can pose significant health risks, necessitating regular monitoring and assessment of their concentrations in commonly consumed foods. This study aims to assess the levels of essential and heavy metals and conduct health risk assessments in peanuts and date palms. Using wet acid digestion, the concentrations of heavy metals: iron (Fe), zinc (Zn), copper (Cu), arsenic (As), lead (Pb), and chromium (Cr) were measured in six each sample of peanuts and date palms, via Flame Atomic Absorption Spectroscopy (FAAS). To assess the health risks for adult males and females, the study calculated the estimated daily intake (EDI), target hazard quotient (THQ), hazard index (HI), and incremental lifetime cancer risk (ILCR) based on Maximum Recommended Daily Intake (MRDI) of each metal. The average concentrations of heavy metals in peanuts were: Fe 12.6 ± 0.54, Zn 5.3 ± 0.1, Cu 3.53 ± 0.33, As 0.001 ± 0.0, Pb 0.01 ± 0.0, and Cr 0.02 ± 0.0 in mg/kg. For date palms, the average concentrations were: Fe 40.23 ± 2.5, Zn 48.31 ± 0.8, Cu 10.65 ± 0.05, As 0.05 ± 0.002, Pb 0.2 ± 0.01, and Cr 0.5 ± 0.03 in mg/kg. All measured concentrations were below WHO standards. The estimated daily intake of metals from both food was found to be below the Maximum Recommended Daily Intake (MRDI). The hazard index for peanuts was within safe limits, indicating no significant non-carcinogenic health risks. In contrast, the hazard index for date palms exceeded 1, suggesting potential non-carcinogenic health risks. Incremental lifetime cancer risks were within allowable limits. In conclusion, the concentrations of heavy metals in peanuts and date palms from the Jigjiga market are within safe limits set by the WHO. [ABSTRACT FROM AUTHOR]

Serverless computing is one of the recent compelling paradigms in cloud computing. Serverless computing can quickly run user applications and services regardless of the underlying server architecture. Despite the availability of several commercial and open-source serverless platforms, there are still some open issues and challenges to address. One of the key concerns in serverless computing platforms is security. Therefore, in this paper, we present a multi-layer abstract model of serverless computing for an security investigation. We conduct a quantitative analysis of security risks for each layer. We observe that the Attack Tree and Attack-Defense Tree methodologies are viable approaches in this regard. Consequently, we make use of the Attack Tree and the Attack-Defense Tree to quantify the security risks and countermeasures of serverless computing. We also propose a novel measure called the Relative Risk Matrix (RRM) to quantify the probability of attack success. Stakeholders including application developers, researchers, and cloud providers can potentially apply these findings and implications to better understand and further enhance the security of serverless computing. [ABSTRACT FROM AUTHOR]

Abstract Serverless computing is one of the recent compelling paradigms in cloud computing. Serverless computing can quickly run user applications and services regardless of the underlying server architecture. Despite the availability of several commercial and open-source serverless platforms, there are still some open issues and challenges to address. One of the key concerns in serverless computing platforms is security. Therefore, in this paper, we present a multi-layer abstract model of serverless computing for an security investigation. We conduct a quantitative analysis of security risks for each layer. We observe that the Attack Tree and Attack-Defense Tree methodologies are viable approaches in this regard. Consequently, we make use of the Attack Tree and the Attack-Defense Tree to quantify the security risks and countermeasures of serverless computing. We also propose a novel measure called the Relative Risk Matrix (RRM) to quantify the probability of attack success. Stakeholders including application developers, researchers, and cloud providers can potentially apply these findings and implications to better understand and further enhance the security of serverless computing.

Multimedia streaming has become integral to modern living, reshaping entertainment consumption, information access, and global engagement. The ascent of cloud computing, particularly serverless architectures, plays a pivotal role in this transformation, offering dynamic resource allocation, parallel execution, and automatic scaling-especially beneficial in HTTP Adaptive Streaming (HAS) applications. This study presents an event-driven serverless cloud edge platform with graphics processing units (GPUs), managed by Knative, tailored for video encoding. Two implementations of the High Efficiency Video Coding (HEVC) codec have been encapsulated in the functions: HEVC NVENC (Nvidia Encoder), that uses GPU acceleration, and x265 that only uses CPUs. Experiments focused on measuring the impact of replica requested resources on cold start, scalability and resource consumption with different allocated resources on slim and fat virtual machines (VMs). The best results are obtained when four slim replicas of the functions are deployed on a fat VM with a 8.4% reduction in encoding time for x265 and a 15.2% improvement for HEVC NVENC compared with other deployment scenarios. Comparatively, HEVC NVENC encoding is 8.3 times faster than x265. In multiresolution scenarios, GPU encoding drastically reduces segment encoding time by a factor of 12.4 between non-GPU and GPU-accelerated. These findings are important for live streaming applications where low latency is critical at all stages of the streaming process. [ABSTRACT FROM AUTHOR]

Removal of heavy metals becomes increasingly crucial for public health and the environment. In this regard, Bi2S3 nanoflowers were produced for heavy metal ion removal applications. Nanoflowers are fabricated using hydrothermal synthesis, and then tested as adsorbents for the removal of lead (Pb2+) ions from aqueous systems. To assess the morphological and chemical characteristics, Bi2S3 nanoflowers were investigated using X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and X-ray photoelectron spectroscopy (XPS) techniques. Flame atomic adsorption spectroscopy (FAAS) was used in the assessment of heavy metal ion recovery. To determine the optimum conditions of the adsorption system, the effect of different parameters such as pH, eluent type and amount, adsorbent dose, adsorbent time, etc. were investigated. For lead removal, the optimum pH value was determined as 5.0, eluent dose and type were found 1 M HNO3. For 20 mL, the optimum adsorbent dose and time were found 50.0 mg, and 30 min, respectively. The adsorption capacity of the Bi2S3 nanoflowers was found to be quite higher than the results reported in similar works. It was illustrated that Bi2S3 nanoflowers have great potential to be used in water remediation applications as sorbent material. [ABSTRACT FROM AUTHOR]

Cr (III) is an essential element for maintaining glucose, lipid, and protein metabolism in mammals. Removal/recovery of such heavy metals is crucial. For this purpose, dendrimer-based novel magnetic nanoparticles to enrich Cr (III) were synthesized and characterized by spectroscopic methods such as SEM-EDX, TEM, FT-IR, XRD, and VSM. The optimum parameters such as pH, eluent type-concentration, adsorbent amount, and time, which are important in the recovery of Cr (III) with solid magnetic-based nanoparticles (MNPs), were determined by flame atomic absorption spectroscopy. After the enrichment of Cr (III) with MNPs, the device's detection power enhancements were increased by 8653 ± 145 times. The optimum conditions obtained for Cr (III) ions with MNPs were successfully applied to water, honey, and hardaliye samples. Furthermore, the reusability results of the MNPs were determined to be approximately 99% for Cr (III) up to the 10th use. [ABSTRACT FROM AUTHOR]

An environmentally friendly and simple dispersive liquid–liquid microextraction method based on a hydrophobic natural deep eutectic solvent (NADES) was developed for the simultaneous preconcentration of Cu and Ni from water samples. The NADES composed of a 1:1 molar ratio of menthol and decanoic acid, presents hydrophobic characteristics and allows to simultaneously extract both analytes avoiding the addition of ligands or emulsifier agents. The extraction was performed in 1.0 min using 0.500 mL of NADES and a sample volume up to 25.0 mL in 0.5 M ammonium chloride-ammonia buffer solution at pH 8.2. The content of Ni and Cu in the NADES phase was determined using Flame Atomic Absorption Spectroscopy. Under optimal conditions, limits of detection of 1.9 µg L−1 for Cu and 4.6 µg L−1 for Ni were obtained, with precision (expressed as RSD% value) better than 5 % for both metals (n = 3). The method was applied to extract and determine Cu and Ni in water samples with different characteristics: tap water, bottled water, river water, dam water, and seawater. The recoveries for spiked water samples ranged between 97 and 110 % for Cu and 92–98 % for Ni, demonstrating the suitability of the method. The environmental impact of the method was assessed using the AGREE, AGREEprep, and ChlorTox metric tools, resulting in scores of 0.58, 0.61, and 0.48 g, respectively.

Petroleum-produced water is the primary effluent generated during the oil extraction process, presenting high flow rates, and containing a series of chemical components that are harmful to the environment, such as dissolved manganese. The proof of metrological compatibility between laboratories and different test methods or techniques is an analytical challenge when complying with environmental laws and regulations, which is the aim of this study. The first step of this study was to compare the results of manganese content in real test samples of produced water between some laboratories that used different test methods, that is, flame atomic absorption spectrophotometry, inductively coupled plasma optical emission spectroscopy, and inductively coupled plasma mass spectrometry. Based on one-way analysis of variance, systematic errors were found. The following step was to visit a specific lab, where one could confirm the relevance of the wavelength parameters, operating conditions for the view configuration, and effects of the saline matrices. Finally, a compatibility assessment between the reference laboratory and a new laboratory, which implemented and validated its analytical methodology based on these analytical adjustments, and the experiences and lessons learned in the previous step, was undertaken by a paired t-test.

The arising of the Cyber–Physical Systems' vision and concepts drives technological evolution toward a new architectural design for the infrastructure of an environment referred to as a Smart Environment. This perspective alters the way systems within Smart City landscapes are conceived, designed, and ultimately realized. Modular architecture, resource-sharing techniques, and precise deployment approaches (such as microservices-oriented or reliant on the FaaS paradigm) serve as the cornerstones of a Smart City cognizant of multiple Cyber–Physical Systems composing it. This paper presents a framework integrating Digital Decisioning, encompassing the automated combination of human-derived knowledge and data-derived knowledge (e.g., business rules and machine learning), to enhance decision-making processes and application definition within the Smart City context. [ABSTRACT FROM AUTHOR]

Server-less computing is a novel cloud-based paradigm that is gaining popularity today for running widely distributed applications. When it comes to server-less computing, features are available via subscription. Server-less computing is advantageous to developers since it lets them install and run programs without worrying about the underlying architecture. A common choice for code deployment these days, server-less design is preferred because of its independence, affordability, and simplicity. The healthcare industry is one excellent setting in which server-less computing can shine. In the existing literature, we can see that fewer studies have been put forward or explored in the area of server-less computing with respect to smart healthcare systems. A cloud infrastructure can help deliver services to both users and healthcare providers. The main aim of our research is to cover various topics on the implementation of server-less computing in the current healthcare sector. We have carried out our studies, which are adopted in the healthcare domain and reported on an in-depth analysis in this article. We have listed various issues and challenges, and various recommendations to adopt server-less computing in the healthcare sector. [ABSTRACT FROM AUTHOR]

In this work, a procedure based on dispersive liquid-liquid micro-extraction for lead (Pb) pre-concentration and quantification in an environmental matrix by flame atomic absorption spectroscopy (FAAS) was applied. A case-study approach was chosen to obtain further in-depth information on the Pb levels. The green chemistry principles have been applied for the pretreatment and preparation of real samples by focusing on some features such as the volume of reagents/sample, employ of energy efficient equipment, and production of waste. A univariate strategy was utilized to achieve the optimum extraction conditions. 1750 mL of acetonitrile containing 100 mL of carbon tetrachloride, were rapid injection into: 12.0 mL of a sample solution adjusted the pH at 9.0 and 0.07% (m v-1) Dithizone. The estimated limits of detection (LOD) and quantification (LOQ) under optimum conditions were 2.85 and 9.5 mg L-1, respectively. The enrichment factors EFs were found to be 60.6 (aqueous standards/DLLME-FAAS) and 30.8 (matrix matched/DLLME-FAAS). It was applied for the analyzing eleven environmental samples. The detectable Pb levels for water samples were ranged from 1.87 to 8.11 mg L-1, while in solid samples were ranged 0.39e4.66 mg g-1. The relative standard deviation for three concentrations (low, medium, and high) were found to be 10.64, 6.87, and 2.7, respectively. The presented system was validated by analyzing eleven environmental samples (water, bivalve molluscs, lettuce, and medicinal plants). The results indicated that high recoveries were obtained in the range of 92e104 %. The lead concentration was found in these samples always lower than permissible maximum levels stipulated by Iraqi Standards Specifications 2009 (number 417) for neutral water. The DLLME/FAAS procedure has proven to be a fast, simple, low-cost, and efficient analytical protocol, therefore allowing for employing this protocol in routine analysis of trace metals in such matrixes. [ABSTRACT FROM AUTHOR]

Here, a simple/efficient analytical strategy based on dispersive solid phase extraction was developed using CoSn(OH)6 nanocubes for lead ions from environmental water samples by FAAS (flame atomic absorption spectrometry). CoSn(OH)6 was synthesized as an adsorbent using the co‐precipitation method and characterized. The characterization studies proved the cube‐shaped morphology and uniform size distribution. The optimal experimental conditions were found by optimization studies and, the limit of quantification (LOQ), limit of detection (LOD), linear dynamic range were recorded as 37.6 μg/L, 11.3 μg/L, and 40–600 μg/L, respectively. The enhancement factor of the presented method was calculated as 158.7‐folds compared to the LOD of the conventional FAAS system. Recovery experiments were performed to check the method's applicability/accuracy and the percent recovery results varied from 89.0 % to 112.9 %. According to these findings, the developed method was successfully applied for the quantitative determination of the target analyte in a seawater sample matrix. [ABSTRACT FROM AUTHOR]

Petroleum-produced water is the primary effluent generated during the oil extraction process, presenting high flow rates, and containing a series of chemical components that are harmful to the environment, such as dissolved manganese. The proof of metrological compatibility between laboratories and different test methods or techniques is an analytical challenge when complying with environmental laws and regulations, which is the aim of this study. The first step of this study was to compare the results of manganese content in real test samples of produced water between some laboratories that used different test methods, that is, flame atomic absorption spectrophotometry, inductively coupled plasma optical emission spectroscopy, and inductively coupled plasma mass spectrometry. Based on one-way analysis of variance, systematic errors were found. The following step was to visit a specific lab, where one could confirm the relevance of the wavelength parameters, operating conditions for the view configuration, and effects of the saline matrices. Finally, a compatibility assessment between the reference laboratory and a new laboratory, which implemented and validated its analytical methodology based on these analytical adjustments, and the experiences and lessons learned in the previous step, was undertaken by a paired t-test. [ABSTRACT FROM AUTHOR]

This study aimed to determine the content of elemental levels of eight plants species originating from Mauritania and used as medicinal plant and to assess their risk for human health. The range of elemental content in the studied plant parts of these plant species were 0.51–16.1 mg/g for Ca, 2.63–6.49 mg/g for Mg, 11.2–201 μg/g for Al, 6.5–28.2 μg/g for Zn, 5.6–453 μg/g for Fe, 1.1–6.4 μg/g for Ni, 11.0–302 μg/g for Mn and 0.7–9.0 μg/g for Cu. The Cr, Pb, Cd and Co levels were below the limits of detection in all samples. The estimated weekly intake (EWI) and target hazard quotient (THQ) were calculated. Except for Al the THQ values were below 1, so it could be concluded that these plant species have low probability of causing non-cancer health problems for the consumer. [ABSTRACT FROM AUTHOR]

Circular business models have been developed in recent years, driven by concerns over environmental sustainability. The circular business models are initially tested domestically, but businesses may aspire to expand internationally with it. However, entering foreign markets with circular business models, which include novel products and innovative services, may be challenging. Moreover, generalized foreign market entry criteria for circular business models are lacking. This in-depth case study utilized a mixed-method approach to shed light on the tactical adaptations businesses need to make in their circular business model when entering foreign markets while also ensuring economic competitiveness. The case includes foreign market entry with a circular “service" business model; therefore, we emphasized the customer side of the business model canvas related to customer relationships. In our conclusion, we identify five tactical adaptations, namely implementing a co-creation tool for customization, offering tangible customized benefits, increasing investment in social media marketing, utilizing start-up incubators/accelerators/co-working spaces as a marketing channel and offering adjustable contract lengths. Foreign markets with circular business models and involving the value network partners of the business are also expected to expand the positive externalities of circular economy internationally.

Iron deficiency anemia is one of the main health issues that has an impact on cognitive function, physical ability, immune function, and reproductive performance. Therefore, this study focused on the determination of iron content in leafy plants cultivated in Bosnia and Herzegovina, and the average daily intake of iron via consumption of these plants. The plant samples were prepared by wet digestion with nitric acid (HNO3), and iron content was determined using flame atomic absorption spectrometry (FAAS). The mean concentration of Fe ranged from 41.97 mg/kg for Brassica oleracea var. acephala to 338.73 mg/kg for Spinacia oleracea. Daily intakes for different leafy plants were also calculated. The leafy plants were arranged by daily iron intake in the following decreasing order Spinacia oleracea > Beta vulgaris > Atriplex hortensis > Urtica dioica > Brassica oleracea var. acephala. Results from this study indicate that leafy plants can be a significant source of iron. The findings conclusively suggest that our local leafy plants are good source of iron through diet.

The subject of study in this article is the technologies of Field Programmable Gate Array (FPGA), methods, and tools for prototyping of hardware accelerators of Artificial Intelligence (AI) and providing it as a service. The goal is to reduce the efforts of creation and modification of FPGA implementation of Artificial Intelligent projects and provide such solutions as a service. Task: to analyze the possibilities of heterogeneous computing for the implementation of AI projects; analyze advanced FPGA technologies and accelerator cards that allow the organization of a service; analyze the languages, frameworks, and integrated environments for the creation of Artificial Intelligence projects for FPGA implementation; propose a technique for modifiable FPGA project prototyping to ensure a long period of compatibility with integrated environments and target devices; propose a technique for the prototyping of FPGA services with high performance to improve the efficiency of FPGA based AI projects; propose a sequence of optimization of neural networks for FPGA implementation; and provide an example of the practical implementation of the research results. According to the tasks, the following results were obtained. Analysis of the biggest companies and vendors of FPGA technology is performed. Existing heterogeneous technologies and potential non-electronic mediums for AI computations are discussed. FPGA accelerator cards with a large amount of High Bandwidth Memory (HBM) on the same chip package for implementation of AI projects are analyzed and compared. Languages, frameworks, and technologies as well as the capabilities of libraries and integrated environments for prototyping of FPGA projects for the AI applications are analyzed in detail. The sequence of prototyping of FPGA projects that are stable to changes in the environment is proposed. The sequence of prototyping of highly efficient pipelined projects for data processing is proposed. The steps of optimization of neural networks for FPGA implementation of AI applications are provided. An example of practical use of the results of research, including the use of sequences is provided. Conclusions. One of the main contributions of this research is the proposed method of creation of FPGA based implementation of AI projects in the form of services. Proposed sequence of neural network optimization for FPGA allows the reduction of the complexity of the initial program model by more than five times for hardware implementation depending on the required accuracy. The described solutions allow the construction of completely scalable and modifiable FPGA implementations of AI projects to provide it as a service.

Scientific relevance. Zinc content is a quality attribute of insulin products. The State Pharmacopoeia of the Russian Federation requires that it should be determined by flame atomic absorption spectrometry (FAAS). However, many pharmaceutical manufacturers currently prefer inductively coupled plasma mass spectrometry (ICP-MS), which is considered the most promising method for pharmaceutical and biomedical elemental analysis.Aim. The study aimed to develop and validate an ICP-MS-based analytical procedure for zinc content determination in insulin products.Materials and methods. The study focused on human insulin, insulin lispro, insulin aspart, and insulin glargine in the form of active substances, suspensions for subcutaneous injection, and solutions for injection from different manufacturers. Zinc content was determined on an Agilent 7900 ICP-MS; the analysis included 66Zn signal intensity registration.Results. The study compared the results of zinc content determination in test samples with either hydrochloric or nitric acid used as the solvent for sample preparation. The authors selected the experimental conditions to achieve relative standard deviations (RSDs) of not more than 2.5% for the measurements. The ICP-MSbased analytical procedure was validated for its specificity, linearity, accuracy, and precision in the range of 0.4–1.6 mg/L. The authors compared the measurements of zinc content made using FAAS and ICP-MS.Conclusions. The ICP-MS-based analytical procedure for zinc content determination in insulin products meets the validation criteria. This analytical procedure, as developed and validated, may be used in the quality control of medicinal products in the Russian healthcare system and at the batch release stage of pharmaceutical manufacturing.

Abstract Function-as-a-Service (FaaS) is a popular programming model for building serverless applications, supported by all major cloud providers and many open-source software frameworks. One of the main challenges for FaaS providers is providing fault tolerance for the deployed applications, that is, providing the ability to mask failures of function invocations from clients. The basic fault tolerance approach in current FaaS platforms is automatically retrying function invocations. Although the retry approach is well suited for transient failures, it incurs delays in recovering from other types of failures, such as node crashes. This paper proposes the integration of a Request Replication mechanism in FaaS platforms and describes how this integration was implemented in Fission, a well-known, open-source platform. It provides a detailed experimental comparison of the proposed approach with the retry approach and an Active-Standby approach in terms of performance, availability, and resource consumption under different failure scenarios.

In this work, flower-like zinc oxide (ZnO) nanostructures were synthesized using hydrothermal synthesis. The fabricated nanoflowers were used as adsorbents for the removal of heavy metal ions such as lead and chrome. Morphological assessments and chemical investigations of the ZnO nanoflowers were conducted by using X-ray diffraction, field emission electron microscopy, energy dispersive X-ray spectroscopy, and Fourier transform infrared spectroscopy techniques. To optimize the heavy metal recovery process optimum parameters like pH, eluent type, adsorbent amount, solution volume, and adsorption time were studied; flame atomic absorption spectrometry was used in the optimization process. The optimum conditions for Cr(VI) and Pb(II) were determined as 3 M HNO3, pH 6.5, 20 mL, 30 min. But the amount of ZnO nanoflowers for Cr(VI) and Pb(II) was determined as 200 and 50 mg, respectively. Outstanding heavy metal ion removal performance of the ZnO nanoflowers was achieved on spinach (NIST-SRM1570a), and apple leaf (NIST-SRM 1515) for the method validation where the solid phase extraction technique was used as the main enrichment method. The flame AAS method was applied to determine for Cr(VI) and Pb(II) metal ions in real samples (spinach, rocket leaf, collard leaf, parsley, bottled drinking water and hardaliye), after and before enrichment on the present ZnO. Relative standard deviation (%RSD) was found in the range of 0.2–5.3. [ABSTRACT FROM AUTHOR]

Background. In the present study, the migration of elements from and to bentonites after processing Chardonnay white wine from the Danube Plain region, Bulgaria, was investigated. Materials and methods. Sixteen of the most common brands and types (natural, activated, sodium, calcium, sodium-calcium) of bentonites available on the Bulgarian market were selected. Inductively coupled plasmaoptical emission spectrometry (ICP-OES), inductively coupled plasma with mass spectrometry (ICP-MS), flame atomic emission spectrometry (FAES) and flame atomic absorption spectrometry (FAAS) were used for element content determination. Results. For some elements (Al, Ca, Cr, Fe, Hg, P, Pb and Si), an increase in their concentration compared to the untreated wine was reported in most cases. For others (B, Cu, Li, Mg, Mn, Ni and Zn), the changes in concentration were more often not significant. Only the K content decreased as a result of treatment with all studied bentonites. Conclusion. It was possible to conclude that bentonite treatment of wines leads to different changes in the analyzed wine. It is quite possible that with a different dose of bentonite application, results that deviate from those described above would be obtained. This is to be the subject of further research. [ABSTRACT FROM AUTHOR]

Heavy metal ions threaten public health and marine life; thus, removal of heavy metals is essential for the environment. Bi2S3 nanostructures are active materials which used in various applications. In this work, we produced Bi2S3 nanoflowers using hydrothermal synthesis and assessed their Cd2+ ions removal capability. Structural properties of the Bi2S3 nanoflowers were assessed by various like scanning electron microscopy, X-ray diffraction. Chemical characteristics of the materials were confirmed using energy dispersive X-ray spectra. Different parameters affecting the Cd2+ ion recovery was worked some of which are adsorbent amount, Cd2+ ion concentration, pH, adsorbent time, eluent type and amount, etc. For cadmium, the optimum pH was determined 8.0 for 1 M HNO3 eluent. Optimum adsorbent amount was determined as 150 mg while optimum recovery duration was found as 30 min. Bi2S3 nanoparticles managed to achieve 99.3% Cd2+ recovery rate in 5 mL eluent volume, for optimum condition. The adsorption capacity of the Bi2S3 nanoflowers was found to be promising and therefore, it was illustrated that Bi2S3 nanoflowers are good candidates as sorbent for water remediation applications. [ABSTRACT FROM AUTHOR]

Abstract Function-as-a-Service (FaaS) is an event-based reactive programming model where functions run in ephemeral stateless containers for short duration. For building complex serverless applications, function composition is crucial to coordinate and synchronize the workflow of an application. Some serverless orchestration systems exist, but they are in their primitive state and do not provide inherent support for non-trivial workflows like, Fork-Join. To address this gap, we propose a fully serverless and scalable design model ReactiveFnJ for Fork-Join workflow. The intent of this work is to illustrate a design which is completely choreographed, reactive, asynchronous, and represents a dynamic composition model for serverless applications based on Fork-Join workflow. Our design uses two innovative patterns, namely, Relay Composition and Master-Worker Composition to solve execution time-out challenges. As a Proof-of-Concept (PoC), the prototypical implementation of Split-Sort-Merge use case, based on Fork-Join workflow is discussed and evaluated. The ReactiveFnJ handles embarrassingly parallel computations, and its design does not depend on any external orchestration services, messaging services, and queue services. ReactiveFnJ facilitates in designing fully automated pipelines for distributed data processing systems, satisfying the Serverless Trilemma in true essence. A file of any size can be processed using our effective and extensible design without facing execution time-out challenges. The proposed model is generic and can be applied to a wide range of serverless applications that are based on the Fork-Join workflow pattern. It fosters the choreographed serverless composition for complex workflows. The proposed design model is useful for software engineers and developers in industry and commercial organizations, total solution vendors and academic researchers.

Abstract Serverless computing is stepping forward to provide a cloud environment that mainly focuses on managing infrastructure, resources and configurations on the behalf of a user. Research in this field can’t rely on commercial providers such as AWS and Azure, as their inflexibility and cost often limits the required levels of reproducibility and scalability. Therefore, simulators have been opted as an alternative solution by the research community. They offer a reduced-cost and easy-setup environment. To get respectable precision, simulators use real traces collected and offered by commercial providers. These traces represent comprehensive information of executed tasks that reflect user behaviour. Due to serverless computing’s recency, typical workload traces employed by IaaS simulators are not well adoptable to the new computing model. In this paper, we propose an approach for generating realistic serverless traces. We enhance our previous generator approach that was based on the Azure Functions dataset. Our new, genetic algorithm based approach improves the statistical properties of the generated traces. We also enabled arbitrary scaling of the workload, while maintaining real users’ behaviour. These advances further support reproducibility in the serverless research community. We validated the results of our generator approach using the coefficient of determination ( $$R^2$$ R 2 ), which shows that our generated workload closely matches the original dataset’s characteristics in terms of execution time, memory utilisation as well as user participation percentage. To demonstrate the benefits of the reusability of the generated traces, we applied them with a diverse set of simulators and shown that they offer reproducible results independently of the simulator used.

The arising of the Cyber–Physical Systems’ vision and concepts drives technological evolution toward a new architectural design for the infrastructure of an environment referred to as a Smart Environment. This perspective alters the way systems within Smart City landscapes are conceived, designed, and ultimately realized. Modular architecture, resource-sharing techniques, and precise deployment approaches (such as microservices-oriented or reliant on the FaaS paradigm) serve as the cornerstones of a Smart City cognizant of multiple Cyber–Physical Systems composing it. This paper presents a framework integrating Digital Decisioning, encompassing the automated combination of human-derived knowledge and data-derived knowledge (e.g., business rules and machine learning), to enhance decision-making processes and application definition within the Smart City context.

Server-less computing is a novel cloud-based paradigm that is gaining popularity today for running widely distributed applications. When it comes to server-less computing, features are available via subscription. Server-less computing is advantageous to developers since it lets them install and run programs without worrying about the underlying architecture. A common choice for code deployment these days, server-less design is preferred because of its independence, affordability, and simplicity. The healthcare industry is one excellent setting in which server-less computing can shine. In the existing literature, we can see that fewer studies have been put forward or explored in the area of server-less computing with respect to smart healthcare systems. A cloud infrastructure can help deliver services to both users and healthcare providers. The main aim of our research is to cover various topics on the implementation of server-less computing in the current healthcare sector. We have carried out our studies, which are adopted in the healthcare domain and reported on an in-depth analysis in this article. We have listed various issues and challenges, and various recommendations to adopt server-less computing in the healthcare sector.

The concentrations of potentially toxic metals like lead (Pb), cadmium (Cd), and mercury (Hg) in healthy lactating mothers of Lahore city were estimated during 2020–2021 with the help of a flame atomic absorption spectrometer (FAAS). Seventy samples of breast milk were collected from two different age groups, namely, G-1 (25–30 years) and G-2 (31–40 years). The results showed that Cd contents were below the detection limit of the measuring instrument; however, the Pb and Hg contents were detected with great ease. The mean concentrations of Pb and Hg in the G-1 corresponding to the age between 25 and 30 years were observed to be 1.914 ± 0.493 μg/L and 10.432 ± 3.249 μg/L, respectively. For the G-2 with an age limit between 31 and 40 years, the concentrations of Pb and Hg were estimated to be 2.045 ± 0.502 μg/L and 11.527 ± 3.231 μg/L, respectively. The T-test analysis of concentrations of these toxic metals indicated a significant correlation between the content levels of Pb and Hg (p < 0.05). The observed values of toxic metals were significantly more frequent than those suggested by WHO (World Health Organization). An increase in the concentrations of Pb and Hg is directly associated with the location of the target population. As a result, it is turned out that the majority of the lactating woman with significantly high levels of Pb and Hg belongs to industrial areas of Lahore. To avoid such conditions, residential areas must be at larger distances, in addition, to strictly abiding by the environmental policies of the government. [ABSTRACT FROM AUTHOR]

Serverless computing is a new way of delivering cloud services that abstracts a user from the burden of managing resources and infrastructure. Although a few serverless simulators have been developed, they do not compare to services that are offered by commercial providers. Developing a comprehensive serverless simulation framework is essential towards evaluating scenarios reliant on the serverless paradigm. This paper focuses on introducing a serverless computing model to the DIScrete event baSed Energy Consumption simulaTor for Clouds and Federations (DISSECT-CF) simulator to enable simulating realistic function-as-a-service (FaaS) solutions. This new model is capable of imitating serverless providers in terms of cost model, associating triggers, extracting performance metrics, and customising the configurations of functions. [ABSTRACT FROM AUTHOR]

Laryngeal cancer, a rare form of head and neck cancer, is more common in men, and various factors play important roles in its etiology. In the current study, trace elemental contents in the scalp hair of laryngeal cancer patients and healthy subjects were evaluated. Selected essential and toxic elements were measured using flame atomic absorption spectrophotometry after the hair samples were digested with HNO3–HClO4. The mean/median levels of Pb in the scalp hair of patients were significantly higher than the healthy donors, but Sr, Zn, and Co exhibited comparatively lower levels in the laryngeal cancer patients. In comparison to healthy donors, the correlation pattern among the elements in the hair of the patients exhibited diverse mutual relationships. The multivariate apportionment of the elemental levels in the scalp hair of both donor groups was significantly divergent. The average elemental levels varied significantly among the cancer types (glottic, supraglottic, transglottic, and subglottic) as well as the cancer stages (I, II, III, IV). The average levels of the elements also exhibited considerable variations depending on the donors' habitat, gender, food, and smoking habits. Overall, the study revealed significantly divergent disparities among the elemental contents in the scalp hair of the laryngeal cancer patients in comparison with counterpart controls. [ABSTRACT FROM AUTHOR]

Serverless computing and Function-as-a-Service (FaaS) are programming paradigms that have many advantages for modern, distributed and highly modular applications. However, the process of transforming a legacy, monolithic application into a set of functions suitable for a FaaS environment can be a complex task. It may be questionable whether the obvious advantages received from such a transformation outweigh the effort and resources spent on it. In this paper we present our continuing research aimed at the transformation of legacy applications into the FaaS paradigm. Our test subject is an airport visibility system, a sub-class of the meteorological services required for airport operations. We have chosen to modularize the application, divide it into parts that can be implemented as functions in the FaaS paradigm, and provide it with a simple cloud-based data management layer. The tools that we are using are Apache OpenWhisk for FaaS and Airflow for workflow management, Apache Airflow for workflow management and NextCloud for cloud storage. Only a part of the original application has been transformed, but it already allows us to draw some conclusions and especially start forming a generalized picture of a Function-as-a-Service application. [ABSTRACT FROM AUTHOR]

The subject of study in this article is the technologies of Field Programmable Gate Array (FPGA), methods, and tools for prototyping of hardware accelerators of Artificial Intelligence (AI) and providing it as a service. The goal is to reduce the efforts of creation and modification of FPGA implementation of Artificial Intelligent projects and provide such solutions as a service. Task: to analyze the possibilities of heterogeneous computing for the implementation of AI projects; analyze advanced FPGA technologies and accelerator cards that allow the organization of a service; analyze the languages, frameworks, and integrated environments for the creation of Artificial Intelligence projects for FPGA implementation; propose a technique for modifiable FPGA project prototyping to ensure a long period of compatibility with integrated environments and target devices; propose a technique for the prototyping of FPGA services with high performance to improve the efficiency of FPGA based AI projects; propose a sequence of optimization of neural networks for FPGA implementation; and provide an example of the practical implementation of the research results. According to the tasks, the following results were obtained. Analysis of the biggest companies and vendors of FPGA technology is performed. Existing heterogeneous technologies and potential non-electronic mediums for AI computations are discussed. FPGA accelerator cards with a large amount of High Bandwidth Memory (HBM) on the same chip package for implementation of AI projects are analyzed and compared. Languages, frameworks, and technologies as well as the capabilities of libraries and integrated environments for prototyping of FPGA projects for the AI applications are analyzed in detail. The sequence of prototyping of FPGA projects that are stable to changes in the environment is proposed. The sequence of prototyping of highly efficient pipelined projects for data processing is proposed. The steps of optimization of neural networks for FPGA implementation of AI applications are provided. An example of practical use of the results of research, including the use of sequences is provided. Conclusions. One of the main contributions of this research is the proposed method of creation of FPGA based implementation of AI projects in the form of services. Proposed sequence of neural network optimization for FPGA allows the reduction of the complexity of the initial program model by more than five times for hardware implementation depending on the required accuracy. The described solutions allow the construction of completely scalable and modifiable FPGA implementations of AI projects to provide it as a service. [ABSTRACT FROM AUTHOR]

In order to eliminate the effects of seawater matrix on the precise/accurate determination of elements, new and efficient analytical procedure requires. In this study, co-precipitation method based on the triethylamine (TEA)-assisted Mg(OH)2 was performed to eliminate side-effects of seawater medium on the determination with flame atomic absorption spectrometry (FAAS) prior to the preconcentration of nickel by an optimized dispersive liquid–liquid microextraction (DLLME) method. Under the optimum conditions of the presented method, the limit of detection and quantification (LOD, LOQ) values obtained for nickel were found as 16.1 and 53.8 μg kg−1, respectively. Seawater samples collected from West Antarctic region were used for real sample applications to check the accuracy and applicability of developed method, and satisfying recovery results (86–97%) were obtained. In addition to this, the digital image-based colorimetric detection system and the UV-Vis system were applied to confirm the applicability of the developed DLLME-FAAS method in other analytical systems. [ABSTRACT FROM AUTHOR]

Function-as-a-Service (FaaS) is a popular programming model for building serverless applications, supported by all major cloud providers and many open-source software frameworks. One of the main challenges for FaaS providers is providing fault tolerance for the deployed applications, that is, providing the ability to mask failures of function invocations from clients. The basic fault tolerance approach in current FaaS platforms is automatically retrying function invocations. Although the retry approach is well suited for transient failures, it incurs delays in recovering from other types of failures, such as node crashes. This paper proposes the integration of a Request Replication mechanism in FaaS platforms and describes how this integration was implemented in Fission, a well-known, open-source platform. It provides a detailed experimental comparison of the proposed approach with the retry approach and an Active-Standby approach in terms of performance, availability, and resource consumption under different failure scenarios. [ABSTRACT FROM AUTHOR]

The aim of this study was to determine the potential relative bioaccessibility of zinc (Zn) from selected dietary supplements during in vitro digestion. The bioaccessibility of Zn was evaluated in dietary supplements differing in the pharmaceutical form, content, dose, and chemical form of the element. The content of Zn was determined by flame atomic absorption spectrometry. The applied method was validated, and results were characterised by good linearity (R2 = 0.998), recovery (109%), and accuracy (0.02%). As a result of the tests conducted, it was found that the bioaccessibility of Zn from dietary supplements varied and ranged from 1.1% to 9.4%. The highest bioaccessibility was found for zinc diglycinate and the lowest for zinc sulphate. In 9 out of 10 tested dietary supplements, the determined Zn content was higher than the one declared by the producer (up to 161%). The estimated tolerable upper intake level (UL) was exceeded by five of the analysed dietary supplements (123–146%). The analysed dietary supplements were assessed in terms of compliance with the information contained on the product packaging, based on current Polish and European legal regulations. The qualitative assessment was performed according to the United States Pharmacopoeia (USP) guidelines. [ABSTRACT FROM AUTHOR]

The natural radioactivity of 238U, 232Th, and 40K was measured in selected samples from 11 monitoring stations along the Euphrates River in Elanbar governorate in 2022, using a NaI (Tl) gamma-ray spectroscopy detector 3" in diameter. The results showed that the average activity concentrations of 238U (214Bi), 232Th (208Tl), and 40K in the water samples were 4.29, 1.62, and 10.95 Bq/L, respectively. Cations and heavy metals were measured using flame and flame atomic absorption spectroscopy (FAAS). The concentrations of Pb2+, Na+, and K+ were 0.02, 207, and 22 mg/L, respectively. According to the information provided by the World Health Organization, the samples studied in this work revealed the correlation between the heavy metals and cations. On the whole, waters of Elanbar governorate are unsafe in terms of the concentration of natural radioactive and heavy metals. [ABSTRACT FROM AUTHOR]