Background Seaweeds are a viable bioresource for suffering plants against salt stress, as they abundant in nutrients, hormones, vitamins, secondary metabolites, and many other phytochemicals that sustain plants' growth under both typical and stressful situations. The alleviating capacity of extracts from three brown algae ( Sargassum vulgare , Colpomenia sinuosa , and Pandia pavonica ) in pea ( Pisum sativum L.) was investigated in this study. Methods Pea seeds were primed for 2 h either with seaweed extracts (SWEs) or distilled water. Seeds were then subjected to salinity levels of 0.0, 50, 100, and 150 mM NaCl. On the 21st day, seedlings were harvested for growth, physiological and molecular investigations. Results SWEs helped reduce the adverse effects of salinity on pea, with S. vulgare extract being the most effective. Furthermore, SWEs diminished the effect of NaCl-salinity on germination, growth rate, and pigment content and raised the osmolytes proline and glycine betaine levels. On the molecular level, two low-molecular-weight proteins were newly synthesized by the NaCl treatments and three by priming pea seeds with SWEs. The number of inter-simple sequence repeats (ISSR) markers increased from 20 in the control to 36 in 150 mM NaCl-treated seedlings, including four unique markers. Priming with SWEs triggered more markers than the control, however about ten of the salinity-induced markers were not detected following seed priming before NaCl treatments. By priming with SWEs, seven unique markers were elicited. Conclusion All in all, priming with SWEs alleviated salinity stress on pea seedlings. Salinity-responsive proteins and ISSR markers are produced in response to salt stress and priming with SWEs.
The technical and economic effects of the two methods of retrofitting with buckling restrain bracing and using concrete shear wall were investigated. The results of this study showed that using reinforcement, the amount of target displacement in the models was significantly reduced and it was observed that the concrete shear wall had a greater impact on the structure in this regard. The reinforcement methods used in this research have a significant impact on improving the technical performance of structures, which has been more in the strengthening method with concrete shear wall. Also, the evaluations showed that despite the fact that the shear wall of reinforced concrete has a better effect on the performance of the structure from a technical point of view, but in terms of weight, it can be seen that using a buckling brace can be more economical.
The successful creation of several unique and intelligent drug delivery systems with improved therapeutic effectiveness, improved patient compliance, and new developments and research in the realm of biopolymers guides cost effectiveness. Several biodegradable polymers are widely employed in the drug delivery industry because they are biologically broken down inside the body into non-toxic components. The design of diverse drug delivery systems based on biopolymers can benefit from a comprehensive understanding of the possible qualities of biopolymers, including extraction techniques and environmentally friendly manufacturing, chemistry, surface properties, rheology, bulk properties, biocompatibility, and biodegradability. Accordingly, new biopolymers were synthesized by free radical and cross-linked molecules and their physical and chemical properties were studied, and some comprehensive examinations were conducted for the task of research, such as FT-IR-EDX, XRD, SEM, TEM, TGA, and BET-BJH. Good results were obtained for adsorption and releasing the adsorbed drug into the aqueous solution with a percentage of 20% at pH 2 and 3% at pH 7, 10. The divorce rate was high when there were different concentrations of salt.
Anaerobic digestion (AD) is widely used for the sustainable treatment of biological wastes and the production of biogas. Its byproduct, digestate, is a valuable organic waste and needs appropriate management, which is one of the major concerns with a negative impact on the efficiency of biogas installations. One approach to extend the utilization of digestate as well as improve its handling and storage characteristics is compaction into pellets. This study aimed to evaluate the behavior of digestate during cyclic loading and unloading in a closed matrix. The findings presented here may provide insights into the mechanisms of pellet formation for optimizing the production of pellets and improving their sustainable management. The study can be considered novel as it applied cyclic loading, for the first time, in view of densification modeling and pelleting prediction. A Zwick universal machine was used in the experiments. The moisture content of digestate was found to be 10–22%. Samples were loaded with a constant amplitude of 20 kN for 10 cycles. The distribution of energy inputs, including the total energy, energy of permanent deformations, and energy lost to elastic ones, was thoroughly evaluated. A decrease in the total loading energy was observed in the first cycle, in cycles 2–10, and after all 10 applied cycles due to the rise in the moisture content of digestate. Similar relations were also found for the nonrecoverable energy part. In subsequent cycles of loading/unloading, the values of total energy and permanent deformation energy fell asymptotically. One of the most noteworthy findings of the study was that the absolute values of elastic deformation energy were consistent across all the cycles and moisture levels. However, it was noted that the percentage of energy dissipated to elastic deformation in all cycles significantly increased as the moisture content increased. Loading, which contributed to elastic deformations, was identified as the key factor causing an increase in cumulative energy inputs, and the majority of the energy expended was dissipated. Dissipated energy was the only component that permanently altered the total energy required for compaction. Another important finding, which resulted from the analysis of successive courses of loading and unloading curves, was that the shape of the areas enclosed between the loading/unloading curves was significantly influenced by the moisture content of the digestate.
The main purpose of Sentiment Analysis (SA) is to derive useful insights from large amounts of unstructured data compiled from various sources. This analysis helps to interpret and classify textual data using different techniques applied in machine learning (ML) models. In this paper, we compared simple and ensemble ML methods as classifiers for SA: Random Forest (RF), K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), Gradient Boosting (GB), Support Vector Machine (SVM), AdaBoost, Extreme Gradient Boosting (XGBoost), Decision Tree (DT), Light GBM, Stochastic Gradient Descent (SGD) and Bagging. For this, we considered a test set database of 50,000 movie reviews, of which 25,000 were rated positive and 25,000 negatives. We have chosen 20,000 words that have an impact on the feelings of the documents. This work aims to propose a new rating prediction approach based on a textual customer review. We consider term frequency (TF) characteristics and term frequency-inverse document frequency (TF-IDF) from the large-scale and serial trials to compare the results obtained by various classifiers using feature extraction techniques. For the decision phase, we applied the Fuzzy Decision by Opinion Score Method (FDOSM), one of the most recent methods for multi-criteria decision-making (MCDM). To evaluate and quantify the performance of the different ML methods we considered, we apply six standard measures namely precision, accuracy, recall, F-score, AUC, and Kappa-measure. The results we obtained, at the end of the experimental work that we conducted, indicated that the SVM classier is the best with 88,333% as a precision rate followed by the FDOSM method, with 0.800 for the same measurement.
Recent research and developments in the field of Sentiment Analysis (SA) have made it possible to simplify the detection and classification of sentiments from the textual content. This type of analysis classifies the text according to its positive, negative, or neutral polarity. Recently, researchers have focused on film reviews and aim to extract personal information about text reviews that can, for example, be used to determine the listener’s position on a number of different topics. The main contributions, proposed in the literature, focused on three categories of approaches: (i) a first category based on the lexicon, (ii) a second category based on machine learning, and (iii) a third based on a hybridization of the two previous categories. To our knowledge, and until the elaboration of this study, no previous study has examined the approaches and levels of sentiment in the field of film reviews. In this article, we propose to review and analyze the main works in this field. We begin by giving a methodological review of our study. Then, we present a taxonomy on the domain of sentiment analysis and a generic view of the main families of sentiment classification techniques. As a next step, we describe the different levels of sentiment analysis considered in the literature, then we expose the process of pre-processing, extracting, and selecting the characteristics necessary for the sentiment analysis. We then propose an analysis and a discussion of the results of the main works studied on sentiment analysis. This presentation will then be followed by a discussion of some research questions and a proposal for a number of future directions in this area that we believe are essential to contribute to solving the problem addressed in this article.
Malware software now encrypts the data of Internet of Things (IoT) enabled fog nodes, preventing the victim from accessing it unless they pay a ransom to the attacker. The ransom injunction is constantly accompanied by a deadline. These days, ransomware attacks are too common on IoT healthcare devices. On the other hand, IoT-based heartbeat digital healthcare applications have been steadily increasing in popularity. These applications make a lot of data, which they send to the fog cloud to be processed further. In healthcare networks, it is critical to examine healthcare data for malicious intent. The malware is a peace code with polymorphic and metamorphic attack forms. Existing malware analysis techniques did not find malware in the content-aware heartbeat data. The Adaptive Malware Analysis Dynamic Machine Learning (AMDML) algorithm for content-aware heartbeat data in fog cloud computing is described in this article. Based on heartbeat data from health records, an adaptive method can train both pre-and post-train malware models. AMDML is based on a rule called 'federated learning,' which says that malware analysis models are made at both the local fog node and the remote cloud to meet the performance workload safely. The simulation results show that AMDML out-performs machine learning malware analysis models in terms of accuracy by 60%, delay by 50%, and detection of original heartbeat data by 66% compared to existing malware analysis schemes. K E Y W O R D S big data, Internet of Things, machine learning This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Key Performance Indicator (KPI) gives potential information that needs for successful network deploying, performance study, and enhanced networks. But, insufficient KPI data may prevent effective network design, which would increase operating costs and have a negative impact on network users. As known, Iraq launched the 4 G-LTE license very recently. Therefore, this work demonstrates Long Term-Evolution (LTE) data measurements and performance analysis of the KPI at the 2100MHz frequency band via a bandwidth of 20MHz for three mobile operators in Iraq, including Najaf city called (OP1, OP2, and OP3) for data confidentiality. Data collection is done by drive tests, from routes to characterize the cellular network. The data measurements have been focused on the parameters that affect the network directly, such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Signal to Interference and noise Ratio (SINR), Received Signal Strength Indicator (RSSI), and Downlink Throughput (DL Throughput). Studying the analysis behavior of these parameters and the probability distribution function (PDF) for the KPIs demonstrates the relationship and dependency among these parameters for the three mentioned operators. Finally, these KPIs provide useful information for network management, assessment, and suitable requirements for Cellular network operators for voice and data services.
Garden cress (Lepidium sativum L.), a member of the Cruciferae family, is widely planted across the world, especially in India, Europe, and the United States. It has been respected as a key medicinal plant from the time of the Vedic culture. Lepidium sativum Linn. seeds were tested for their efficacy as an antibacterial agent against pathogens found in food. The active components were extracted from the powdered dry seeds using chloroform, ethyl acetate, methanol, and dichloromethane. The antibacterial activity of various doses of the extracts was evaluated using agar well diffusion. We also estimated the MIC and MBC for the most effective extract using the tube dilution technique and the subculturing method, respectively. One of the most common mosquito species that carries Plasmodium falciparum is the Anopheles gambiae sensu lato, which may be combated by sprinkling fields with Lepidium sativum seeds. Scientists have begun to extract essential oils from Lepidium sativum and evaluate their bio-potential against juvenile and adult Anopheles gambiae as part of an attempt to identify ecologically viable vector control tactics. Based on the findings, it is clear that L. sativum essential oil effectively kills An. gambiae. Although field application on a wide scale is necessary for An. gambiae population control, more work has to be done in formulation and assessment.
This research aims to review and evaluate the most relevant scientific studies about deep learning (DL) models in the omics field. It also aims to realize the potential of DL techniques in omics data analysis fully by demonstrating this potential and identifying the key challenges that must be addressed. Numerous elements are essential for comprehending numerous studies by surveying the existing literature. For example, the clinical applications and datasets from the literature are essential elements. The published literature highlights the difficulties encountered by other researchers. In addition to looking for other studies, such as guidelines, comparative studies, and review papers, a systematic approach is used to search all relevant publications on omics and DL using different keyword variants. From 2018 to 2022, the search procedure was conducted on four Internet search engines: IEEE Xplore, Web of Science, ScienceDirect, and PubMed. These indexes were chosen because they offer enough coverage and linkages to numerous papers in the biological field. A total of 65 articles were added to the final list. The inclusion and exclusion criteria were specified. Of the 65 publications, 42 are clinical applications of DL in omics data. Furthermore, 16 out of 65 articles comprised the review publications based on single-and multi-omics data from the proposed taxonomy. Finally, only a small number of articles (7/65) were included in papers focusing on comparative analysis and guidelines. The use of DL in studying omics data presented several obstacles related to DL itself, preprocessing procedures, datasets, model validation, and testbed applications. Numerous relevant investigations were performed to address these issues. Unlike other review papers, our study distinctly reflects different observations on omics with DL model areas. We believe that the result of this study can be a useful guideline for practitioners who look for a comprehensive view of the role of DL in omics data analysis.
The need for sustainable concrete with low carbon dioxide emissions and exceptional performance has recently increased in the building industry. Many distinct types of industrial byproducts and ecologically safe wastes have shown promise as ingredients for this kind of concrete. Meanwhile, as industrialization and lifestyle modernization continue to rise, ceramic waste becomes an increasingly serious threat to the natural environment. It is well known that free cement binder that incorporates tile ceramic wastes (TCWs) can significantly improve the material’s sustainability. We used this information to create a variety of geopolymer mortars by mixing TCWs with varied proportions of ground blast furnace slag (GBFS) and fly ash (FA). Analytical techniques were used to evaluate the mechanical properties and impact resistance (IR) of each designed mixture. TCWs were substituted for binders at percentages between 50 and 70 percent, and the resultant mixes were strong enough for real-world usage. Evidence suggests that the IR and ductility of the proposed mortars might be greatly improved by the addition of TCWs to a geopolymer matrix. It was found that there is a trend for both initial and failure impact energy to increase with increasing TCWs and FA content in the matrix. The results show that the raising of TCWs from 0% to 50, 60 and 70% significantly led to an increase in the failure impact energy from 397.3 J to 456.8, 496.6 and 595.9 J, respectively.
Given a rectangle [Formula: see text] containing points, we consider the problem of detecting the largest rectangle that is totally contained in [Formula: see text] and does not include any of the points. In other words, we want to find the biggest hole in the dataset that can contain the biggest possible rectangle. A new algorithm for dealing with this problem is, therefore, suggested. Existing algorithms are exact but cannot deal efficiently with problems in high dimensions and large instances. In fact, only computing maximum empty rectangles in a set of points in [Formula: see text] has been well addressed. In high dimensions, the problem is shown to be NP-complete. Our suggested approach is evolutionary in nature and is an innovative implementation of the genetic algorithm. The approach solves a more general form of the stated problem in that it ignores the axis-parallel condition imposed on the hyper-rectangles to be found. This paper includes computational results and their discussions.
The experimental data is used to investigate the level structures of 130–136Ce isotopes. The values from the Dynamic deformation model (DDM) and the Interacting Boson Model-1 (IBM-1) are compared to the level energies, E2 moments, and B(E2) ratios for transitions from the K=2 and 02 bands (IBM-1). On the basis of the expected K-components and the decay mode, the newly assigned 1.672 MeV Jπ = (2,3,4)⁺ state in 130Ce is tentatively associated with Jπ = 4⁺. This K = 4 band is correlated with the 2.068 MeV J = (4,5,6)⁺ state. The 1.932 MeV Jπ = 4⁺ level in ¹³²Ce is associated with band.
This study was designated to investigate the chemical composition, the antifungal activity and antibiofilm properties of Glycyrrhiza foetida (Desf.) growing in Tunisia and recognized for its pharmacological and therapeutic effects. The chemical analysis of essential oil samples prepared via hydrodistillation of the aerial parts was performed by Gas‐Chromatography‐Mass Spectrometry (GC‐MS) procedure. Moreover, the antifungal activity of G. foetida essential oil was developed against three dermatophyte strains, two molds and Candida spp. yeasts using the broth microdilution assay. According to the percentages, the main constituents are δ‐cadinene (13.9%), (E)‐caryophyllene (13.2%) and γ‐cadinene (8.3%). The efficiency of the essential oil to inhibit Candida albicans biofilms formation was also evaluated in terms of inhibitory percentages. The results showed that Candida albicans and Microsporum canis are the most sensitive to G. foetida essential oil with a complete inhibition at 0.4 and 0.2 mg mL‐1, respectively. C. albicans biofilm development is reduced by 80% by the volatile oil at a concentration of 0.8 mg.mL−1. The essential oil of G. foetida has a promising role in the control of fungal agent with medical interest and a pertinent inhibition of the Candida biofilm development.
In the nickel and cobalt production plant located in Punta-Gorda Cuba, an effluent liquor is obtained with several pollutants species of Ni(II)–NH3–CO2–SO2–H2O system. In previous studies the performance of electrocoagulation (EC) for the nickel removal from this effluent was demonstrated on a lab-scale and bench-scale. In this research, geometric, electrical and equilibrium parameters used as criteria for scaling-up the EC process were evaluated through hydrodynamic, kinetic and operating cost analysis. For this purpose, four semi-continuous stirred-tank electrochemical reactors of equal sized with 200 L useful volume were design. Based on the residence time distribution (RTD) curve, the hydrodynamic behavior of EC systems followed multi-branch tanks-in-series model. Experimental mean residence time was of 92 min and hydraulic efficiency 92% for a liquor flow rate 2 L∙min−1, due to the presence of short-circuit, internal recirculation and 3% dead zone. Then, at the operating parameters: nickel concentration 277 ± 52 mg L−1, current density 6.26 mA cm−2, pH 8.40 ± 0.28 and temperature 50 ± 2 °C, the maximum nickel removal was 97.8% with an operating cost $1.008 kg−1 Ni. Combination of RTD model and the conversion time kinetic model (CTV) was presented to predict the removal efficiency with successful outcomes, and the time for complete conversion was 23.3 min under control of chemical reaction. The precipitate presented a nickel concentration of 35.71 ± 2.13%, aluminum 6.05 ± 0.83%, true density 2836 ± 449 kg m−3 and typical layered structure. These results suggest the opportunity for the projection of an industrial production unit of double lamellar hydroxide and reduce the adverse environmental impact.
The present review systematically investigates and illustrates the effect of multilayered membrane spacers on the features of fluid dynamics that influence all performance metrics. Multilayer spacers are frequently composed of three sets of filaments (i.e., top, middle, and bottom layers), which has the benefit of increasing mass transfer and decreasing membrane surface fouling when compared to ordinary mono (e.g., extruded spacer) and two‐layer spacers. The review found that the multilayer spacer's middle layer disperses primary flow to the side thin spacers placed near the membrane's surfaces. The thin side spacers will then form narrow passageways to keep the solution in situ for as long as mass transfer is achievable. The employment of thin spacers close to the membranes at satisfactory operational conditions (e.g., adequate flow velocity) results in swirling flows and incorporation of transversal and longitudinal eddies near to the membranes, reducing the boundary layer's width and making the associated ion concentration domain at the membranes much more consistent. The concept and implementation of multilayer geometry in feed channels appears to be promising, since a multilayered spacer can function at a lower maximum flow velocity than normal 2‐layer spacers, saving operational energy while minimizing concentration gradients at the membrane surfaces. Furthermore, the multilayer structure's durability and mechanical strength may help to reduce membrane deformation and maintain long processes. Next studies might look at significantly reducing spacer thickness for industrial uses. This article is protected by copyright. All rights reserved.
Susceptibility analysis is an intelligent technique that not only assists decision makers in assessing the suspected severity of any sort of brain tumour in a patient but also helps them diagnose and cure these tumours. This technique has been proven more useful in those developing countries where the available health-based and funding-based resources are limited. By employing set-based operations of an arithmetical model, namely fuzzy parameterised complex intuitionistic fuzzy hypersoft set (FPCIFHSS), this study seeks to develop a robust multi-attribute decision support mechanism for appraising patients’ susceptibility to brain tumours. The FPCIFHSS is regarded as more reliable and generalised for handling information-based uncertainties because its complex components and fuzzy parameterisation are designed to deal with the periodic nature of the data and dubious parameters (sub-parameters), respectively. In the proposed FPCIFHSS-susceptibility model, some suitable types of brain tumours are approximated with respect to the most relevant symptoms (parameters) based on the expert opinions of decision makers in terms of complex intuitionistic fuzzy numbers (CIFNs). After determining the fuzzy parameterised values of multi-argument-based tuples and converting the CIFNs into fuzzy values, the scores for such types of tumours are computed based on a core matrix which relates them with fuzzy parameterised multi-argument-based tuples. The sub-intervals within [0, 1] denote the susceptibility degrees of patients corresponding to these types of brain tumours. The susceptibility of patients is examined by observing the membership of score values in the sub-intervals.
The impact of ultraviolet (UV) radiation on public health is severe and can cause sunburn, skin aging and cancer, immunosuppression, and eye damage. Minimization of exposure to solar UV is required in order to reduce the risks of these illnesses to the public. Greater public awareness and a prediction of the ultraviolet index (UVI) is considered an essential task for the minimization of solar UV exposures. This research has designed artificial intelligence based deep learning models to predict multistep UVI index. It has developed a convolutional neural network integrated with long short-term memory network (CLSTM) as the main model to forecast UVI for Brisbane with latitude − 27.47 and longitude 153.02, the capital city of Queensland, Australia. Solar zenith angle (SZA) data were used together with UVI as inputs in the CLSTM for 10-min, 30-min and 60-min UVI prediction. The CLSTM model was benchmarked against long short-term memory network (LSTM), convolutional neural network (CNN), Deep Neural Network (DNN), multilayer perceptron (MLP), extreme learning machine (ELM), random forest regression (RFR), Extreme Gradient Boosting (XGB), and Pro6UV Deterministic models. The experimental results showed that the CLSTM model outperformed these models with RMSE = 0.3817, MAE = 0.1887, RRMSE = 8.0086%, MAPE = 4.6172% and APB = 3.9586 for 10-min prediction. In addition to that, these metrics for 30-min and 60-min prediction were RMSE = 0.4866/0.5146, MAE = 0.2763/0.3038, RRMSE = 10.4860%/11.5840%, MAPE = 8.1037%/9.6558% and APB = 5.9546/6.8386, respectively. Thus, the CLSTM model can yield improved UVI prediction for both the public and the government agencies.
This study examined >140 relevant publications from the last few years (2018–2021). In this study, classification was reviewed depending on the operation's progress. Electrocoagulation (EC), electrooxidation (EO), electroflotation (EF), electrodialysis (ED), and electro-Fenton (EFN) processes have received considerable attention. The type of action (individual or hybrid) for each electrochemical procedure was evaluated, and statistical analysis was performed to compare them as a new manner of reviewing cited papers providing a massive amount of information efficiently to the readers. Individual or hybrid operation progress of the electrochemical techniques is critical issues. Their design, operation, and maintenance costs vary depending on the in-situ conditions, as evidenced by surveyed articles and statistical analyses. This work also examines the variables affecting the elimination efficacy, such as the applied current, reaction time, pH, type of electrolyte, initial pollutant concentration, and energy consumption. In addition, owing to its efficacy in removing toxins, the hybrid activity showed a good percentage among the studies reviewed. The promise of each wastewater treatment technology depends on the type of contamination. In some cases, EO requires additives to oxidise the pollutants. EF and EFN eliminated lightweight organic pollutants. ED has been used to treat saline water. Compared to other methods, EC has been extensively employed to remove a wide variety of contaminants.
The study explores the challenge of food waste in the commercial foodservice sector of a major developing economy in the Middle East, Iraq. It responds to the Food Waste Index report 2021 by the United Nations Environment Programme which has called for more accurate data-based assessments of wastage in national foodservice sectors of developing nations. Via mixed methods research design consisting of direct measurements, in-situ observations and managerial interviews, the study provides first preliminary benchmarks of food waste in three categories of Iraqi restaurants and explains the reasons for discrepancy in estimates. The study showcases national culture as one of the main underlying causes of food waste in Iraqi foodservices. The study highlights the approaches to food waste management that can be categorised as novel/effective for the market of the Middle East. The study elaborates on how intra-sectorial and cross-country adoption of these approaches can be operationalised and promoted.
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General Street, 66001, Samawa, Iraq
Head of institution
Prof. Amer Ali Al-Atwi, PhD