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Milk is an excellent source of many essential nutrients, including Ca, proteins and vitamin D. Nine metals (Ca, Cd, Cu, Fe, Mg, Mn, Zn, Ni and Pb) in raw and pasteurized milk of cow, sheep and goat were determined by atomic absorption spectrometry, flame technique. The levels of Cd, Fe, Mn, Ni and Pb were below the detection limit of the used method. The concentration ranges for Ca, Mg, Zn and Cu, in all milk samples, were as follows: 459.8-992.8, 59.5-206.8, Mg>Zn>Cu. The highest content of Ca and Mg was found in goat milk. In the case of cow's milk, the content of Ca, Mg and Zn increased with increasing amount of milk fat in processed milk. Matrix correlation analysis showed that there is a significant correlation for the following pairs: Ca-Mg (r = 0.830), Zn-Cu (r = 0.799) and Ca-Zn (r = 0.624). Also, a strong correlation was found between milk fat and Ca, Mg and Zn (Pearson factor, r > 0.600). Based on daily consumption of 200 mL of milk, the milk consumption does not meet the daily requirements for determined elements. Most significant intake was in case of Ca. [ABSTRACT FROM AUTHOR]

The Function-as-a-Service (FaaS) service model has aroused great interest since its introduction in the context of cloud computing. Although FaaS can be used to perform isolated tasks, it is in the composition of applications that this service model can promote significant performance improvements. This work presents an evolution in the Node2FaaS framework, whose objective is to assist in the conversion of originally monolithic node.js applications to work with FaaS in the Remote Procedure Call (RPC) communication mechanism. The new implementations in the Node2FaaS framework allowed new experiments to be conducted. Those showed significant gains in runtime for applications with CPU-bound, memory-bound, I/O-bound characteristics, especially for a Bioinformatics application that aims to align genetic sequences.

The subject of study in this article is the evaluation of the performance issues of cloud services implemented using FPGA technology. The goal is to improve the performance of cloud services built on top of multiple FPGA platforms known as FPGA-as-a-Service (FaaS). Task: to analyze the delays in communications between host computer and FPGA; propose the steps of development to reduce the delay and perform the evaluation of the response time for the FPGA-based accelerator depending on number of involved cards; consider the reliability aspect of such systems implemented using programmable logic. According to the tasks, the following results were obtained. The FPGA-as-a-Service where FPGA resources are provided through a set of hardware/software toolset is considered. The usage of queueing theory for cloud-based services is analyzed. The contribution of the parts of FPGA-as-a-Service to the final delay of the service is discussed. The process of modeling of work the services based on FPGA accelerator cards with use of Jackson's network is analyzed in detail. The model of the delays of FaaS that considers the parameters of accelerator FPGA cards is offered. The formula of the total response time of the service combined based on the response of the components of is obtained. The proposed steps of reduce data processing delays include increase the size of data blocks for processing in FPGA by each kernel, change the communication model with kernel from sequential to pipelined, following timing closure technique and use more FPGA accelerator cards in parallel to divide the enquiring delay. Based on the proposed model the evaluation of response time of FaaS was done. The advantage of the use of many FPGAs in parallel for same data processing task instead of implementation of requests thread for each accelerator card is shown. Conclusions. The main contribution of this study is a step forward to the modeling of FPGA-based services that can be used for FPGA-based artificial intelligence (AI) applications. It helps to improve the performance of the system by means of reducing the delays at different stages of requests processing. Another side of this result is the reliability aspect that is based on modified manner of service operation in case of use the proposed steps of system optimization. It helps to improve the processing of requests to FaaS. The proposed method is the next step after prototyping of such systems because it helps to turn the FaaS from the object for development to the tool for deployment of new technologies like AI applications.

Abstract Honey is a natural, sugary and sticky liquid that is produced from the nectars of flowers by the bees. This study aimed to analyze the concentration of some selected heavy Metals in honey samples. 1 g of honey sample was digested by a hot plate using 9ml of HNO3 and 3ml of H2O2. The concentrations of the heavy metals in the digested were detected using a flame atomic absorption spectrometer. The results of this study found that the concentrations of the heavy metals in the honey samples were ranged from 1.97 to 2.04 µg/g for Zn, 1.93 µg/g to 2 µg/g for Cu, 0.83 to 1.01 µg/g for Mn, 0.25 to 0.45 µg/g for Cr, 0.025–0.031 µg/g for Cd. However, Pb was not detected in all honey samples. Hence, the levels of heavy metals found were below the permitted levels set by the World Health Organization. From the results, the levels of heavy metals found were below the permitted levels set by the World Health Organization. Thus, the heavy metals in the sampled honey are safe for human consumption in these selected areas.

Medicinal plants have worldwide applications in the treatment of different types of human diseases. Among the medicinal plants of Ethiopia, Ocimum lamiifoliumHochst. Ex Benth(Damakese, in Amharic) is one of the well-celebrated and most widely used home remedies for the treatment of a disease locally known as “Mitch” which is characterized by headache, fever, inflammation, joint pain, sweat, loss of appetite, etc. The aim of the present study on this medicinal plant was to determine the levels of heavy and trace metals in the leaves using the flame atomic absorption spectrometer (FAAS), which is novAA model. The sampling technique used to carry out the analysis was purposive for the community in the selected area use the plant widely to treat different diseases. In addition, for each of three kebeles, selected from Duguna Fango District, three sites were selected to homogenize the samples. The concentrations or levels of heavy and trace metals, Cd, Co,Pb, Cr, Cu, and Zn, in the leaves of the selected medicinal plant were found to be (inmg/L) 0.0489, 0.0579, 0.0936, 0.153, 0.214 and 0.847, respectively. The results revealed that the selected medicinal plant accumulated these metals at different concentration levels in different sites. The results also confirmed that the concentration levels of the metals in the leaves of the selected medicinal plant were not higher than the internationally accepted permissible limits. Thus, the results indicated that the medicinal plant under the study is safe for medicinal uses. Furthermore, monitoring such medicinal plants for heavy and trace metals concentrations is of great importance in protecting the community from the adverse effects of heavy metals.

A simple and selective method for the preconcentration of Fe(III) and Zn ions prior to flame atomic absorption spectrometric (FAAS) determinations using a column packed with nano graphene oxide (GO) as a solid-phase extractant has been presented in this manuscript. The method is based on the sorption of mentioned ions on synthesized GO with use of Methyl-2-(4-methoxy-benzoyl)-3- (4-methoxyphenyl)-3-oxopropanoylcarbamate) as chelating agent. Some effective parameters on the extraction and complex formation were selected and optimized. Under the optimized conditions (pH 9, flow rate 9 mL min−1), metal ions were retained on the column, then quantitatively eluted by 5 mL 3.0 mol L−1 HNO3 solution. The preconcentration factor was calculated as 240. The detection limits for the understudy analyte ions were found in the range of 0.22 ng mL-1 to 0.28 ng mL-1 (for Fe3+ and Zn2+, respectively). The column packed with GO was good enough for metal ions separation in matrixes containing alkali, alkaline earth, transition and heavy metal ions. This method was also used to determine the recovery rate of the studied ions in real samples.

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 FROM AUTHOR]

Background and Aims: In this study, calcium (Ca) element concentration changes of Salvia species in simulated body fluid (SBF) medium conditions were investigated and the results in these medium conditions were compared with each other. Methods: Salvia species samples were air-dried and ground into powder. Salvia species was prepared as a herbal tea. Prepared teas were left in three different SBF medium conditions. The samples were analyzed using Flame Atomic Absorption Spectroscopy (FAAS) method to determine the Ca absorptions. Also, a correlation analysis of the results obtained in three different SBF medium conditions and the species in simulated gastric fluid (SGF) medium conditions was performed. Results: When the SBF medium conditions were compared, it was determined that the highest Ca absorption of all Salvia species occurred in the SGF conditions. Thus, it can be said that the Ca in plants and foods occurs in the gastric fluid medium and its absorption occurs there. When the result of the correlation analysis was evaluated, it was determined that there was a stronger correlation between the SGF and simulated intestinal fluid (SIF) mediums compared to other mediums. Conclusion: The Ca absorption was determined according to what remained in the SGF the most. Based on the results obtained from SBF medium conditions, it can be said which element is taken in which body fluid medium. Information on the differences between samples belonging to different SBF medium conditions was not obtained. However, when the simulated fluid medium conditions were evaluated individually, information was obtained for two or more samples. [ABSTRACT FROM AUTHOR]

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 ), 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. [ABSTRACT FROM AUTHOR]

A novel probiotic Pediococcus acidilactici AS185, isolated from traditional Chinese fermented foods, was used as an adjunct culture for probiotic cheddar cheese production. The physicochemical composition, textural, free amino acids (FAAs), short‐chain fatty acids (SCFAs) profiles, sensory properties, and microbial survival, was evaluated during the 90‐day ripening period. The addition of P. acidilactici AS185 did not influence the physicochemical composition of cheddar cheese but significantly decreased the hardness without affecting its textural profile. During ripening, P. acidilactici AS185 was able to grow and promote the generation of FAAs and SCFAs, but did not alter the overall sensory properties; it rather improved the flavor and taste of cheese. In addition, the cheese matrix protected strain P. acidilactici AS185 during transit throughout the simulated gastrointestinal system. These results demonstrated that P. acidilactici AS185 adjunct cultures might be useful for producing high‐quality probiotic cheddar cheese. [ABSTRACT FROM AUTHOR]

A new and sensitive enrichment membrane filtration method was applied for the preconcentration and separation of trace amounts of Cd(II), Co(II), Cu(II), Ni(II), and Pb(II) ions from environmental (water and food) samples prior to their flame atomic absorption spectrometric (FAAS) determinations. The method was based on the collection of analyte ions as quinalizarin complexes on cellulose nitrate membrane filter and then eluted using nitric acid. The concentration levels of Cd(II), Co(II), Cu(II), Ni(II), and Pb(II) ions in the final solutions were determined by FAAS. Various analytical parameters which affect the collection and determination of metal ions including pH, amounts of quinalizarin, sample volume, type and size of the membrane filter, flow rates and matrix effects were investigated. The analyte ions recovery values were measured and found to be in the range of 95–101%. The relative standard deviation (RSD%) was measured to determine the precision of the proposed approach and was found to be ≤5.0%. The detection limits for the proposed method were in the range of 0.1–0.7 μg L−1. The preconcentration factors were 100 and 125 for (Cd(II), Co(II), Cu(II) and Ni(II)) and Pb(II), respectively. The validation of the proposed method was verified by the analysis of certified reference materials. Satisfactory results for separation and preconcentration trace levels of the analyte ions in water and food samples. [ABSTRACT FROM AUTHOR]

This study presents a simple, sensitive, and accurate method for the determination of nickel by flame atomic absorption spectrometry (FAAS). Prior to instrumental measurement, a deep eutectic solvent-based simultaneous complexation and preconcentration (DES-SCP) method was used to preconcentrate nickel from aqueous solution into measurable quantities. The efficiency of the extraction method was enhanced by forming a non-ionic complex of nickel using dithizone as ligand. By mixing the ligand with the DES extractant, simultaneous complexation and preconcentration of nickel were achieved in a single step. Under optimum conditions of the extraction method, the limit of detection (LOD) and the limit of quantification (LOQ) values were found to be 2.4 and 8.0 ng/mL, respectively. With respect to direct FAAS measurement, the developed method enhanced the sensitivity of nickel determination by about 169 folds. The accuracy and applicability of the developed method were evaluated by performing spike recovery experiments with lake water sampled from Antarctica. Satisfactory recovery results in the range of 94.0–113.7% were recorded and this validated the developed method as an efficient and green alternative for nickel determination. [ABSTRACT FROM AUTHOR]

In this study, a green, simple and effective preconcentration method named as spray-assisted fine droplet formation-liquid phase microextraction (SAFDF-LPME) before the flame atomic absorption spectrophotometry (FAAS) measurement for cobalt determination was developed. The method reduces the external dispersive solvent usage by using a simple spraying apparatus to obtain fine droplets of the extraction solvent. SAFDF-LPME method also consists of simultaneous complexation and extraction which indicates the environmental benevolence of the developed method. This method minimized the relative errors with high repeatability and accuracy by reducing the experimental steps. The influential parameters such as buffer type, buffer solution volume, extraction solvent/ligand solution volume (spraying cycle), and mixing period were systematically optimized by the univariate optimization procedure. With the optimum parameters applied, the detection power of the FAAS system was enhanced to about 110-folds with respect to 2.2 ng mL−1 detection limit calculated for the proposed method. Bottled drinking water samples from Fiji Islands were used to demonstrate the applicability of the developed method for the accurate determination of trace cobalt in real sample matrices. Percent recovery results obtained between 95.5 and 88.5% showed the suitability of the developed method in the determination of cobalt at trace levels even in complex sample matrices. [ABSTRACT FROM AUTHOR]

Laryngeal carcinoma is one of the common types of head and neck cancer, with men being more likely than women to develop it. Diet, age, gender, smoking habits, and environmental factors play important roles in its development. The goal of this study was to ascertain if there were imbalances in essential and toxic trace metals owing to the initiation and progression of laryngeal cancer. Atomic absorption spectrometry was employed to quantify selected macroelements, and essential/toxic trace metals in blood of the cancerous patients and matching controls. Significantly higher concentrations of Pb, Cu, Fe, and Sr while substantially lower levels of Na, K, Ca, and Mg were observed in the cancer patients compared with the controls. Considerably disparate mutual relationships among the macroelements, and essential/toxic trace metals in the patients and controls were manifested by their correlation coefficients. Similarly, multivariate apportionment of the metal levels showed appreciably diverse associations and grouping in the patients and controls. The laryngeal cancer patients exhibited significant disparities in the metal levels among various sub-types (supraglottic, subglottic, transglottic, and glottic cancer) and stages (I, II, III, and IV) of the disease. Most of the metals revealed distinct differences based on the gender, habitat, age, eating preferences, and smoking habits in both donor groups. Overall, the study demonstrated significant imbalances among the macroelements, and essential/toxic trace metal levels in the blood of laryngeal cancer patients compared to the controls. [ABSTRACT FROM AUTHOR]

A new, simple, and accurate carrier element-free coprecipitation method has been developed to separate and preconcentrate of Cd(II), Cu(II), Ni(II), and Pb(II) ions from water and food samples. The proposed method was based on utilisation of 3-benzyl-4-p-nitrobenzylidenamino-4,5-dihydro-1,2,4-triazole-5-thiol (BNBATT) as a new organic co-precipitant at pH 7.0 and the determination of the metal ions being examined using FAAS. To recover the metal ions analysed, The influencce of many parameters including pH, sample volume, co-precipitant quantity, and the rate and time of centrifugation, was tested. There was also monitoring the effect of matrix ions, and no interferences were found. The validity of the proposed method was verified by test of certified reference materials. The analyte ions recovery values were measured and found to be in the range of 95–100%. The relative standard deviation (RSD%) was measured to determine the precision of the proposed approach and was found to be ≤ 5.0%. The limits of detection (K = 3.0, N = 10), were determined in the range of 0.3–0.8 μg L−1. The calculated preconcentration factor was 100 for Cd(II), Cu(II) and Ni(II) and 80 for Pb(II). Satisfactory results for separation and preconcentration trace amounts of the studied metal ions in water, food, and certified reference materials samples. [ABSTRACT FROM AUTHOR]

Accumulation of hexavalent chromium in the environment affects our ecosystem. Physico-chemical treatment did not solve the problem, due to high requirement of operational costs & energy, insolubility of Cr (VI) at any pH & sludge accumulation. The study aim is screening and evaluating fungi for chromium biosorption for mycoremediation. Soil and effluent sample were collected. PDA media was used for fungi purification. Fungal chromium tolerance evaluation and optimization was conducted at pH (4, 7, 10), T0 (25, 27.5, 35 °C), Cr concentration (5000–25000 ppm) using Full Factorial experimental design. Spore count, biomass production, Cr tolerance index, mycelia growth were evaluated. Flame Absorptive Spectrophotometer was used for quantification of fungal chromium removal. Fungi species were identified by MALDI-TOF. Fungi growth & degradation of tannery shaving waste was evaluated. The result revealed 6 potential fungi, T. longibrachiatum, T, koningii, A. niger, G. candidum, P. rubens, T. orientale were identified. Optimium mean biomass & spore count were 0.514 ± 0.051& 1.39 ± 0.038 at pH4 and 27.5 °C respectively. MTL test indicate that Trichoderma species, A. niger & P. rubens survive up to 10,000 ppm, 15, 000 ppm & 20,000 ppm respectively. FAAS analysis of fungi chromium removal from aqueous media result indicate that A. niger (99%). T. longibrachiatum (85%) and P. ruben (84.6%). These 6 fungi are efficient in tannery shaving waste degradation but A. niger was superior in spore count 15.1 Log/mL and 7 g substrate weight loss. In conclusion three fungi have high chromium tolerance and removal capacity and shaving waste degradation promising for mycoremediation. [Display omitted] • Chromium (VI) & tannery shaving waste severely pollute our environment. • Eco friendly mycoremediation approach for Chromium pollution is advantageous over conventional methods. • Aspergillus niger , Trichoderma longbranchium, and Pencilium rubens showed best Chromium biosorption capacity. • Tannery shaving waste used as substrate for fungal cultivation for large scale & chromium recovery. [ABSTRACT FROM AUTHOR]

The detrimental effects of increased amounts of heavy metals in food and the discovery of sources from which these metals come into food is an increasingly challenging task for analysts. As milk plays an important role as the only source of nutrients for young mammals, providing a complex mixture of lipids, proteins, carbohydrates, vitamins and minerals it is important to determine its quality based on the content of metals, via the soil-grass-milk chain. Many authors consider that the content of heavy metals in milk samples is associated with contaminated pastures, industrial influences and mining. Therefore, the main objective of this study was to determine the concentration of essential minerals (Ca, Na, K, Zn, Fe, Cu, Mn, Ni and Mg) and potentially toxic metals (Cd, Co, Cr and Pb) in milk, as well as their content in soil and grass in order to conduct the correlation study, from three diferrent locations. Iron and manganese were present in highest content (mg/g), while other metals were in µg/g, within all soil-grass samples. In general, the correlation for total content of Fe, Cu, Ni, Zn and K between soil - grass was evident, where among listed Cu, Zn and K are very important for plant growth. However, zinc occured as the main constituent in all milk samples, while the other metals followed the order: Ca>K>Fe>Mg>Mn, indicating weak or none correlation with soil-grass content. Moreover, milk samples did not contain Co, Cr, Ni and Pb despite their relatively high content in grass. By comparing the total content of Co, Cr, Cu, Ni, Pb and Zn in the soil with the values from the "Regulations on determination of permitted amounts of harmful and dangerous substances in the soil and their testing methods", published in the "Official Gazette of the Federation of Bosnia and Herzegovina", number 08- 24-480-1/99, it can be stated that the tested soils are not contaminated with heavy metals. [ABSTRACT FROM AUTHOR]

Spices are used both in the food industry during processing and in households for the purpose of achieving certain sensorial properties of food. In addition to giving food a certain taste or smell, many spices show positive effects on the state of the organism. Cinnamon is one of the oldest and most widely used spices. Besides its pronounced smell and pleasant taste, recent research shows that cinnamon is an antioxidant, that it improves digestion, helps people with type II diabetes, and has a beneficial effect on cancerous diseases, autoimmune diseases and various viruses. Spices are significant sources of heavy metals which are present in different concentrations. Despite their positive effects, spices can have a high content of heavy metals, originating from the environment, during production, processing and storage. In this paper, the content of Ca, Cd, Cu, Fe, Mg, Mn, Ni, Pb and Zn in 14 samples of cinnamon (powder or stick) was determined by using flame atomic absorption spectrometry (FAAS). All samples are purchased from the Sarajevo market and prepared by acid digestion. Metal content in cinnamon samples ranged from: 2.26-11.36 μg/g for Cu, 39.63-252 μg/g for Mn, 20.17-443 μg/g for Fe, 0.66-0.72 µg/g for Ni, 0.96-50.67 µg/g for Zn, 1297-2677 µg/g for Ca and 52.49-103 µg/g for Mg. Cd and Pb concentrations were below the detection limit of the used method. Based on the obtained results, it can be concluded that cinnamon samples are a significant source of metals. However, consumers should be careful with excessive consumption of cinnamon as this research did not include its organic components nor other elements, which is certainly recommended for further research. [ABSTRACT FROM AUTHOR]

Tea is one of the most popular and consumed beverages in the world, whose beneficial effect on human health is particularly emphasized by researchers. One of the parameters that is very often determined in teas is the metal content. It is necessary to monitor metal presence in food because some metals are essential while others are toxic; moreover, it is important to point out that both the lack and elevated concentrations of essential elements can have a negative impact on human health. In this work, the content of Co, Cd, Cu, Fe, Mn, Ni, Pb and Zn in 13 air dried tea samples was investigated by using atomic absorption spectrometry - flame technique (FAAS). The collected samples are commercially available on the Sarajevo market or are collected in the wider area of the Sarajevo basin. Samples were prepared by using acid digestion. The metal content in the tea samples ranged from: (0.85-12.92) mg/kg for Cu,

Taraxacum officinale, or Dandelion, is a plant that can be found in various ecosystems. Traditionally, Taraxacum officinale is picked up by people in Bosnia and Herzegovina and eaten as salad or used for preparing homemade dandelion honey; moreover, it is used as a medicinal plant, and it has been frequently used in environmental studies. The aim of this study was to assess the safety of using Taraxacum officinale in traditional medicine and as food by determining the Fe, Co, Ni, and Zn content in the leaves and roots of Taraxacum officinale. Sampling was performed at seven urban locations in the municipality of Visoko in Bosnia and Herzegovina. Root and leaf samples were washed with distilled water and air-dried. The method of wet acid digestion was used. Flame Atomic Absorption Spectrometry (FAAS) was used to perform the metal analysis. Metal content in leaf samples was in the following range: Co (0.41 µg/g - 2.05 µg/g), Fe (75.4 µg/g - 278 µg/g), Ni (4.39 µg/ g - 6.19 µg/g), and Zn (13.88 µg/g - 36 µg/g). Metal content in root samples was in the following range: Co (

In this study, the proximate composition, levels of essential and heavy metals; and health risk assessment were evaluated in sesame seed (Sesamum indicum L.) samples purchased randomly from local markets of three study sites in Ethiopia. A 0.15 g of dried and homogenized samples were digested using 6 mL of HNO3 (69–72%) and 3 mL of HClO4 (70%) at 120 °C for 3 h. The levels of proximate composition and essential metals in the sesame samples were determined by the Association of Official Agricultural Chemists (AOAC) and Flame Atomic Absorption Spectrometer (FAAS), respectively. The moisture content, crude fiber, ash content, fat, crude proteins and carbohydrate of sesame samples ranged between 3.15% and 5.52%, 4.21–4.40%, 3.10–4.75%, 55.75–56.9%, 22.65–23.39% and 8.34–8.80%, respectively. The accuracy of the procedure was validated by percent recovery of samples for all metals varied from 85.60% to 102.00%. The mean levels of the metals (mg/kg) were found in the range of 37.20–70.10 (Na), 315.1–338.00 (K), 209.00–216.50 (Mg), 328.00–341.30 (Ca), 12.60–12.90 (Fe), 4.90–5.00 (Cu) and 8.30–8.70 (Zn) mg/kg. The THQ values of heavy metals were found

Machine Learning Operations (MLOps) is an approach to managing the entire lifecycle of a machine learning model. It has evolved over the last years and has started attracting many people in research and businesses in the industry. It supports the development of machine learning (ML) pipelines typical in the phases of data collection, data pre-processing, building datasets, model training, hyper-parameters refinement, testing, and deployment to production. This complex pipeline workflow is a tedious process of iterative experimentation. Moreover, cloud computing services provide advanced features for managing ML stages and deploying them efficiently to production. Specifically, serverless computing has been applied in different stages of the machine learning pipeline. However, to the best of our knowledge, it is missing to know the serverless suitability and benefits it can provide to the ML pipeline. In this paper, we provide a systematic mapping study of machine learning systems applied on serverless architecture that include 53 relevant studies. During this study, we focused on (1) exploring the evolution trend and the main venues; (2) determining the researchers’ focus and interest in using serverless on machine learning; (3) discussing solutions that serverless computing provides to machine learning. Our results show that serverless usage is growing, and several venues are interested in the topic. In addition, we found that the most widely used serverless provider is AWS Lambda, where the primary application was used in the deployment of the ML model. Additionally, several challenges were explored, such as reducing cost, resource scalability, and reducing latency. We also discuss the potential challenges of adopting ML on serverless, such as respecting service level agreement, the cold start problem, security, and privacy. Finally, our contribution provides foundations for future research and applications that involve machine learning in serverless computing.

Serverless computing is a cloud computing paradigm that allows developers to focus exclusively on business logic as cloud service providers manage resource management tasks. Serverless applications based on this model are often composed of several fine-grained and ephemeral Function-as-a-Service (FaaS) functions that implement complex business processes via mutual interaction and interaction with Backend-as-a-Services (BaaS) such as databases. FaaS functions suffer from the cold start problem because of the scale to zero instances feature. In this work, we use neural Temporal Point Processes (TPPs) to model function invocations in FaaS compositions. A probability distribution over the time and class of the following invocations for a given history of invocations is predicted using these probabilistic models. The prediction can avoid cold starts by scaling functions in advance and reduce network load by optimizing the function-server assignment. In this regard, we developed a python-based tool called TppFaaS on top of OpenWhisk open-source serverless platform. TppFaaS uses the neural TPPs LogNormMix for modeling the time using a log-normal mixture distribution and TruncNorm for predicting a single value for the time. Furthermore, we built a custom trace data collector for OpenWhisk embedded into TppFaaS and created datasets for multiple FaaS compositions to train and test our models. For datasets without cold starts, the models achieved for most compositions a mean absolute error below 22ms and a percentage of correctly predicted function classes above 94%.

Several classes of advanced Internet of Things (IoT) applications, e.g., in the industrial manufacturing domain, call for Quality of Service (QoS) management to guarantee/control performance indicators, even in presence of many sources of “stochastic noise” in real deployment environments, from scarcely available bandwidth in a time window to concurrent usage of virtualized processing resources. This paper proposes a novel IoT-oriented middleware that i) considers and coordinates together different aspects of QoS monitoring, control, and management for different kinds of virtualized resources (from networking to processing) in a holistic way, and ii) specifically targets deployment environments where edge cloud resources are employed to enable the Serverless paradigm in the cloud continuum. The reported experimental results show how it is possible to achieve the desired QoS differentiation by coordinating heterogeneous mechanisms and technologies already available in the market. This demonstrates the feasibility of effective QoS-aware management of virtualized resources in the cloud-to-things continuum when considering a Serverless provisioning scenario, which is completely original in the related literature to the best of our knowledge.

In Edge and Fog Computing environments, it is usual to design and test distributed algorithms that implement scheduling and load balancing solutions. The operation paradigm that usually fits the context requires the users to make calls to the closer node for executing a task, and since the service must be distributed among a set of nodes, the serverless paradigm with the FaaS (Function-as-a-Service) is the most promising strategy to use. In light of these preconditions, we designed and implemented a framework called P2PFaaS. The framework, built upon Docker containers, allows the implementation of fully decentralised scheduling or load balancing algorithms among a set of nodes. By relying on three basic services, such as the scheduling service, the discovery service, and the learner service, the framework allows the implementation of any kind of scheduling solution, even if based on Reinforcement Learning. Finally, the framework provides a ready-to-go solution that can be installed and has been tested both on x86 servers and ARM-based edge nodes (like, for example, the Raspberry Pi).

Honey is a natural, sugary and sticky liquid that is produced from the nectars of flowers by the bees. This study aimed to analyze the concentration of some selected heavy Metals in honey samples. 1 g of honey sample was digested by a hot plate using 9ml of HNO3 and 3ml of H2O2. The concentrations of the heavy metals in the digested were detected using a flame atomic absorption spectrometer. The results of this study found that the concentrations of the heavy metals in the honey samples were ranged from 1.97 to 2.04 µg/g for Zn, 1.93 µg/g to 2 µg/g for Cu, 0.83 to 1.01 µg/g for Mn, 0.25 to 0.45 µg/g for Cr, 0.025–0.031 µg/g for Cd. However, Pb was not detected in all honey samples. Hence, the levels of heavy metals found were below the permitted levels set by the World Health Organization. From the results, the levels of heavy metals found were below the permitted levels set by the World Health Organization. Thus, the heavy metals in the sampled honey are safe for human consumption in these selected areas. [ABSTRACT FROM AUTHOR]

The importance of plants in folk medicine and scientific studies is increasing day by day. In this context, the plants pose a danger to human health when they are eaten by the public, consumed as herbal tea or used as medicinal plants especially the toxic metals in their composition. For this reason, determining the macro, micro and toxic element content in the plants is important for health. In this study; the metal contents of root and aerial parts of nine different Salvia species were determined and the chemometric evaluation of the obtained results was made. In the principal component analysis (PCA) made with 20 elements of Salvia species, it was determined that the first two principal components explained 62.60% of the variance and the first six principal components explained 92.70%. When PCA and hierarchical cluster analysis (HCA) results are evaluated; the parts of Salvia species root and aerial parts were determined that were not clearly separated and there was no regional grouping. In addition, Salvia species prepared as herbal tea was left to different simulated body fluid medium conditions, and the changes in metal contents were examined. It was determined that were taken up Cr, Cd, Pb, Cu, Zn metals in the simulated saliva fluid (SSF), Ni, Mn metals in the simulated gastric fluid (SGF), and Na, K metals in the simulated intestinal fluid (SIF). It was determined that Fe metal in almost all Salvia species was not taken from three simulated body fluid mediums. Thus, elements determined which were taken up in body fluid mediums. [ABSTRACT FROM AUTHOR]