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While the human population is increasing globally, the sustainability of ecosystem services is declining. Okalma Natural Forest Reserve in Sudan hosts high woody plant species richness that support ecosystem services, soil health, and local livelihood. This study aims to assess the relationship between woody plant species richness, carbon stock, dendrometric features, soil chemical properties, recreation services, and income sources. Data were collected from 178 circular sample plots with a radius of 17.84 m (area of 1000 m 3 each) along 17 transect lines, complemented by 510 questionnaires and soil analysis. We recorded 30 woody species (tree and shrubs), with species richness positively correlated with carbon stock ( R 2  = 0.88), tree height ( R 2  = 0.82) and recreation preferences (R 2  = 0.90), but negatively correlated with soil sodium and nitrogen (R 2  = - 0.91). Importance value index (IVI), basal area, and seedling density varied significantly ( P  < 0.05) among sites. Outdoor recreation activities such as enjoying fresh air and forest fruit were preferred over hunting and games. However, the high dependence on non-timber forest products highlights the need for sustainable use and industrialization of these resources. We recommend conserving species with low density, enhancing recreation facilities, and maintaining soil health for sustainable management of the reserve.

The advancements in Industry 4.0 have opened up new ways for the structural deployment of Smart Grids (SGs) to face the endlessly rising challenges of the 21st century. SGs for Industry 4.0 can be better managed by optimized routing techniques. In Mobile Ad hoc Networks (MANETs), the topology is not fixed and can be encountered by interference, mobility of nodes, propagation of multi-paths, and path loss. To extenuate these concerns for SGs, in this paper, we have presented a new version of the standard Optimized Link State Routing (OLSR) protocol for SGs to improve the management of control intervals that enhance the efficiency of the standard OLSR protocol without affecting its reliability. The adapted fault tolerant approach makes the proposed protocol more reliable for industrial applications. The process of grouping of nodes supports managing the total network cost by reducing severe flooding and evaluating an optimized head of clusters. The head of the unit is nominated according to the first defined expectation factor. With a sequence of rigorous performance evaluations under simulation parameters, the simulation results show that the proposed version of OLSR has proliferated Quality of Service (QoS) metrics when it is compared against the state-of-the-art-based conventional protocols, namely, standard OLSR, DSDV, AOMDV and hybrid routing technique.

As more people utilize the cloud, more employment opportunities become available. With constraints such as a limited make-span, a high utilization rate of available resources, minimal execution costs, and a rapid turnaround time for scheduling, this becomes an NP-hard optimization issue. The number of solutions/combinations increases exponentially with the magnitude of the challenge, such as the number of tasks and the number of computing resources, making the task scheduling problem NP-hard. As a result, achieving the optimum scheduling of user tasks is difficult. An intelligent resource allocation system can significantly cut down the costs and waste of resources. For instance, binary particle swarm optimization (BPSO) was created to combat ineffective heuristic approaches. However, the optimal solution will not be produced if these algorithms are not paired with additional heuristic or meta-heuristic algorithms. Due to the high temporal complexity of these algorithms, they are less useful in real-world settings. For the NP problem, the binary variation of PSO is presented for workload scheduling and balancing in cloud computing. Considering the updating and optimization constraints stated in this research, our objective function determines if heterogeneous virtual machines (VMs) Phave the most significant difference in completion time. In conjunction with load balancing, we developed a method for updating the placements of particles. According to the experiment results, the proposed method surpasses existing metaheuristic and heuristic algorithms regarding work scheduling and load balancing. This level of success has been attainable because of the application of Artificial Neural Networks (ANN). ANN has demonstrated promising outcomes in resource distribution. ANN is more accurate and faster than multilayer perceptron networks at predicting targets.

Internet of Things (IoT) performs a vital role in providing connectivity between computing devices, processes, and things. It significantly increases the communication facilities and giving up-to-date information to distributed networks. On the other hand, the techniques of artificial intelligence offer numerous and valuable services in emerging fields. An IoT-based healthcare solution facilitates patients, hospitals, and professionals to observe real-time and critical data. In the literature, most of the solution suffers from data intermission, high ethical standards, and trustworthiness communication. Moreover, network interruption with recurrent expose of sensitive and personal health data decreases the reliance on network systems. Therefore, this paper intends to propose an IoT solution for AI-enabled privacy-preserving with big data transferring using blockchain. Firstly, the proposed algorithm uses a graph-modeling to develop a scalable and reliable system for gathering and transmitting data. In addition, it extracts the subset of nodes using the artificial intelligence approach and achieves efficient services for the healthcare system. Secondly, symmetric-based digital certificates are utilized to offer authentic and confidential transmission with communication resources using blockchain. The proposed algorithm is explored with existing solutions through multiple simulations and proved improvement in terms of realistic parameters.

Electric vehicles are considered as an emerging solution to mitigate the environmental footprint of transportation sector. Therefore, researchers and automotive developers devote significant efforts to enhance the performance of electric vehicles to promote broader adoption of such technology. One of the critical challenges of the electric vehicle is limited battery lifetime and entailed range anxiety. In his context, development of counter-aging control strategies based on precise battery modeling is regarded as an emerging approach that has a significant potential to address battery degradation challenges. This paper presents a combined trade-off strategy to minimize battery degradation while maintaining acceptable driving performance and charge retention in electric vehicles. A battery aging model has been developed and integrated into a full vehicle model. An optimal control problem has been formulated to tackle the afore-mentioned challenges. Non-dominant sorting genetic algorithms have been implemented to yield the optimal solution through the Pareto-front of three contending objectives, based upon which an online simulation has been conducted considering three standard driving cycles. The results reveal the ability of the proposed strategy to prolong the life cycle of the battery and extend the driving range by 25 % and 8 % respectively with minimal influence of 0.6 % on the driveability.

COVID-19 or 2019-nCoV is no longer pandemic but rather endemic, with more than 651,247 people around world having lost their lives after contracting the disease. Currently, there is no specific treatment or cure for COVID-19, and thus living with the disease and its symptoms is inevitable. This reality has placed a massive burden on limited healthcare systems worldwide especially in the developing nations. Although neither an effective, clinically proven antiviral agents' strategy nor an approved vaccine exist to eradicate the COVID-19 pandemic, there are alternatives that may reduce the huge burden on not only limited healthcare systems but also the economic sector; the most promising include harnessing non-clinical techniques such as machine learning, data mining, deep learning and other artificial intelligence. These alternatives would facilitate diagnosis and prognosis for 2019-nCoV pandemic patients. Supervised machine learning models for COVID-19 infection were developed in this work with learning algorithms which include logistic regression, decision tree, support vector machine, naive Bayes, and artificial neutral network using epidemiology labeled dataset for positive and negative COVID-19 cases of Mexico. The correlation coefficient analysis between various dependent and independent features was carried out to determine a strength relationship between each dependent feature and independent feature of the dataset prior to developing the models. The 80% of the training dataset were used for training the models while the remaining 20% were used for testing the models. The result of the performance evaluation of the models showed that decision tree model has the highest accuracy of 94.99% while the Support Vector Machine Model has the highest sensitivity of 93.34% and Naïve Bayes Model has the highest specificity of 94.30%.

Gastric ulcer is sores that form in the stomach mucosal layer because of erosion caused by high acid secretion and excessive use of non-steroidal anti-inflammatory drugs. Prodigiosins (PdGs) are red-pigmented secondary metabolites produced by bacteria, including actinomycetes. Butylcycloheptylprodigiosin (1) and undecylprodigiosin (2) were identified and isolated from a crude extract of the actinomycete RA2 isolated from the Red Sea Sponge Spheciospongia mastoidea. Chemical structure of 1 and 2 was determined by NMR and mass spectroscopy. Although their antioxidant and anti-inflammatory properties are known, their effect on gastric lesion is unknown. Therefore, this study aimed to investigate gastroprotective effects of PdGs against HCl/ethanol-induced gastric lesion in rats. Oral pretreatment with PdGs (100, 200, and 300 mg/kg) attenuated severity of HCl/ethanol-induced gastric mucosal injury, as evidenced by decreases in gastric lesion index scores, ulceration area, histopathologic abnormality, and neutrophil infiltration. These effects were comparable to those of omeprazole, a standard anti-gastric ulcer agent. HCl/ethanol-induced gastric erosions was associated with tremendous increases in lipid peroxidation, nitric oxide, and pro-inflammatory cytokines and mediators (myeloperoxidase, interleukin-1β, tumor necrosis factor-α, and cyclooxygenase-2), and with significant decreases in enzymatic and non-enzymatic antioxidant activities. However, PdGs ameliorated gastric inflammation and oxidative stress by downregulating nuclear factor kappa B and inducible nitric oxide synthase expression and upregulating heme oxygenase-1 expression. PdGs prevented gastric mucosal apoptosis by downregulating Bax and caspase-3 expression and upregulating Bcl-2 expression, thereby increasing prostaglandin E2 production. Our results suggested that PdGs exerted gastroprotective effects by decreasing the levels of inflammatory mediators, apoptotic markers, and antioxidants.

Elemental ratios (δ 13 C, δ 15 N and C/N) and carbon and nitrogen concentrations in macrophytes, sediments and sponges of the hypersaline Al-Kharrar Lagoon (KL), central eastern Red Sea coast, were measured to distinguish their sources, pathways and see how they have been influenced by biogeochemical processes and terrestrial inputs. The mangroves and halophytes showed the most depleted δ 13 C values of –27.07±0.2 ‰ and –28.34±0.4 ‰, respectively, indicating their preferential 12 C uptake, similar to C3-photosynthetic plants, except for the halophytes Atriplex sp. and Suaeda vermiculata which showed δ 13 C of –14.31±0.6 ‰, similar to C4-plants. Macroalgae were divided into A and B groups based on their δ 13 C values. The δ 13 C of macroalgae A averaged –15.41±0.4 ‰, whereas macroalgae B and seagrasses showed values of –7.41±0.8 ‰ and –7.98 ‰, suggesting uptake of HCO 3 – as a source for CO 2 during photosynthesis. The δ 13 C of sponges was –10.7±0.3 ‰, suggesting that macroalgae and seagrasses are their main favoured diets. Substrates of all these taxa showed δ 13 C of –15.52±0.8 ‰, suggesting the KL is at present a macroalgae-dominated lagoon. The δ 15 N in taxa/sediments averaged 1.68 ‰, suggesting that atmospheric N 2 -fixation is the main source of nitrogen in/around the lagoon. The heaviest δ 15 N (10.58 ‰) in halophytes growing in algal mats and sabkha is possibly due to denitrification and ammonia evaporation. The macrophytes in the KL showed high C %, N %, and C/N ratios, but this is not indicated in their substrates due possibly to a rapid turnover of dense, hypersaline waters carrying most of the detached organic materials out into the Red Sea. The δ 13 C allowed separation of subaerial from aquatic macrophytes, a proxy that could be used when interpreting paleo-sea level or paleoclimatic changes from the coastal marine sediments.

Internet of Things (IoT) networks has recently become an important component of smart cities, smart buildings, health care, and other applications. It finds it beneficial due to the inherent characteristics of low cost, compact, and low-powered IoT devices. At the same time, security remains a challenging issue in the design of IoT networks. Intrusion detection systems (IDS) can be used to identify the occurrence of intrusions in the network, i.e., abnormal activities in the network. The latest advances in machine learning (ML) and metaheuristics can be employed to design effective IDS models for IoT networks. This article develops a novel political optimizer with cascade forward neural network (PO-CFNN-)-based IDS in the IoT environment. The major intention of the PO-CFNN technique is to determine the occurrence of intrusions from the IoT environment. The PO-CFNN technique follows three major processes, namely, preprocessing, classification, and parameter optimization. Initially, the networking data is preprocessed to transform it into a useful format. Following that, the CFNN technique is employed for the identification and classification of intrusions in the IoT environment. In the final stage, the PO algorithm is applied for the optimal adjustment of the parameters involved in the CFNN model. The experimental validation of the PO-CFNN technique on a benchmark dataset stated the better outcomes of the PO-CFNN technique over recent approaches.

Pyrazole has been recognized as a pharmacologically important privileged scaffold whose derivatives produce almost all types of pharmacological activities and have attracted much attention in the last decades. Of the various pyrazole derivatives reported as potential therapeutic agents, this article focuses on pyrazole-based kinase inhibitors. Pyrazole-possessing kinase inhibitors play a crucial role in various disease areas, especially in many cancer types such as lymphoma, breast cancer, melanoma, cervical cancer, and others in addition to inflammation and neurodegenerative disorders. In this article, we reviewed the structural and biological characteristics of the pyrazole derivatives recently reported as kinase inhibitors and classified them according to their target kinases in a chronological order. We reviewed the reports including pyrazole derivatives as kinase inhibitors published during the past decade (2011–2020).