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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Miguel Gutiérrez Gaitán; Luís Almeida; Pedro M. D’orey; Pedro M. Santos; +1 Authors

    This article presents a novel centrality-driven gateway designation framework for the improved real-time performance of low-power wireless sensor networks (WSNs) at system design time. We target time-synchronized channel hopping (TSCH) WSNs with centralized network management and multiple gateways with the objective of enhancing traffic schedulability by design. To this aim, we propose a novel network centrality metric termed minimal-overlap centrality that characterizes the overall number of path overlaps between all the active flows in the network when a given node is selected as gateway. The metric is used as a gateway designation criterion to elect as a gateway the node leading to the minimal number of overlaps. The method is then extended to multiple gateways with the aid of the unsupervised learning method of spectral clustering. Concretely, after a given number of clusters are identified, we use the new metric at each cluster to designate as cluster gateway the node with the least overall number of overlaps. Extensive simulations with random topologies under centralized earliest-deadline-first (EDF) scheduling and shortest-path routing suggest our approach is dominant over traditional centrality metrics from social network analysis, namely, eigenvector, closeness, betweenness, and degree. Notably, our approach reduces by up to 40% the worst-case end-to-end deadline misses achieved by classical centrality-driven gateway designation methods. This work was partially supported by National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit (UIDB/04234/2020); by the Operational Competitiveness Programme and Internationalization (COMPETE 2020) under the PT2020 Agreement, through the European Regional Development Fund (ERDF); also by FCT and the ESF (European Social Fund) through the Regional Operational Programme (ROP) Norte 2020, under PhD grant 2020.06685.BD.

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    ACM Transactions on Embedded Computing Systems
    Article . 2024 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Mohammed Aljebreen; Marwa Obayya; Hany Mahgoub; Saud S. Alotaibi; +2 Authors

    Currently, there is an emerging requirement for applications related to the Internet of Things (IoT). Though the capability of IoT applications is huge, there are frequent limitations namely energy optimization, heterogeneity of devices, memory, security, privacy, and load balancing (LB) that should be solved. Such constraints must be optimised to enhance the network’s efficiency. Hence, the core objective of this study was to formulate the intelligent-related cluster head (CH) selection method to establish green communication in IoT. Therefore, this study develops a chaotic equilibrium optimizer-based green communication with deep learning-enabled load prediction (CEOGC-DLLP) in the IoT environment. The study recognizes the emerging need for IoT applications and acknowledges the critical challenges, such as energy optimization, device heterogeneity, memory constraints, security, privacy, and load balancing, which are essential to enhancing the efficiency of IoT networks. The presented CEOGC-DLLP technique mainly accomplishes green communication via clustering and future load prediction processes. To do so, the presented CEOGC-DLLP model derives the CEOGC technique with a fitness function encompassing multiple parameters. In addition, the presented CEOGC-DLLP technique follows the deep belief network (DBN) model for the load prediction process, which helps to balance the load among the IoT devices for effective green communication. The experimental assessment of the CEOGC-DLLP technique is performed and the outcomes are investigated under different aspects. The comparison study represents the supremacy of the CEOGC-DLLP method compared to existing techniques with a maximum throughput of 64662 packets and minimum MSE of 0.2956.

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    IEEE Access
    Article . 2024
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    IEEE Access
    Article . 2024 . Peer-reviewed
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      IEEE Access
      Article . 2024
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      IEEE Access
      Article . 2024 . Peer-reviewed
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    Authors: Frida I. Piper; Alejandro Cárdenas; Alejandra Zúñiga-Feest; Julieta Orlando; +2 Authors

    Individual tree species might affect underground soil properties to favor the decomposition of their own litter. However, in some studies, monospecific litter decomposed faster underneath multispecific canopies than under canopies of the same species as the litter, suggesting that plant diversity in the overstorey creates a more favorable decomposition microenvironment. We compared the decomposition rate of monospecific litter from eight temperate tree species under the crown of the same species as the litter (conspecific canopy), and a multispecific canopy formed by eight species. Using model selection, we determined the influence of the microenvironments, species, and litter chemistry on decomposition rates. The most robust model included the species and canopy type, but it differed only slightly from the model including only the species. Decomposition rates were significantly different among species and higher in conspecific canopy than multispecific canopy in all species. Models including specific leaf area and different combinations of chemical traits of the litter were less robust than the null model. These results show that the species origin of the litter was significantly more important than the microenvironment to explain litter decomposition rates.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Canadian Journal of ...arrow_drop_down
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    Canadian Journal of Forest Research
    Article . 2024 . Peer-reviewed
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Canadian Journal of ...arrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      Canadian Journal of Forest Research
      Article . 2024 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Sofia Boza; Vicente Zambrano; Camila Jerez; Andrea Rengifo; +1 Authors

    ABSTRACT: Small-scale farmers are essential food suppliers, especially in developing countries, but they face many constraints that limit their productivity and returns. Associating with other farmers (for instance, through cooperatives) has been shown to be a strategy to mitigate these constraints; however, there are limitations in farmers’ participation and commitment to associations. This research explored small-scale farmers’ attitudes and perceptions of associations. This paper considered the district of Cauquenes, a rural area in central Chile. A total of 71 small-scale farmers were surveyed. The data were processed using descriptive, multivariate, and qualitative techniques. The results showed that the farmers had limited knowledge of and experience with associations. They also did not see participation in an association as necessary for improving their business outcomes. They were also reluctant to accept a possible loss in decision-making power or the possibility of being scammed or tricked. The factors underlying farmers’ attitudes toward associations were “Uncertainty of economic benefits” (23.3% of variance), “Technical-economic limitations perception” (15.2%), “Distrust” (10.8%), and “Individualism” (9.8%). These results suggested that policies are needed to improve farmers’ experience with and knowledge of associations, considering the cultural variables that affect distrust and focusing on existing uncertainties. The associative processes need to allow for progressive commitment, expedite tangible results, and provide continuous technical and motivational support. RESUMO: Os pequenos agricultores são essenciais para o abastecimento de alimentos, especialmente nos países em desenvolvimento, mas enfrentam muitas restrições que limitam sua produtividade e retorno. A associação com outros agricultores (por exemplo, por cooperativas) tem se mostrado uma estratégia para mitigar essas deficiências, no entanto, há entraves na participação e comprometimento dos agricultores com as associações. O objetivo desta pesquisa é explorar as atitudes e percepções dos pequenos agricultores em relação às associações. Para isso, consideramos o caso do distrito de Cauquenes, uma área rural no centro do Chile, em que se entrevistou um total de 71 pequenos agricultores. Os dados foram processados por meio de técnicas descritivas, multivariadas e qualitativas. Como resultados, os agricultores têm um conhecimento e experiência limitados sobre as associações e, além disso, não entendem a relevância do vínculo social como uma alternativa para melhorar o seu desempenho. Por outro lado, relutam em uma possível perda de poder decisório, bem como em serem enganados. Os fatores subjacentes às atitudes dos agricultores em relação às associações foram “incerteza dos benefícios econômicos” (23,3%), “percepção de limitações técnico-econômicas” (15,2%), “desconfiança” (10,8%) e “individualismo” (9,8%). Esses resultados nos demonstram serem necessárias políticas que promovam o conhecimento direto, concreto e experiencial dos agricultores sobre o associativismo, com foco nas incertezas existentes. Os processos associativos devem estar atrelados a compromissos progressivos, com foco em resultados tangíveis e suporte contínuo, não apenas técnico, mas também motivacional.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Ciência Ruralarrow_drop_down
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    Ciência Rural
    Article . 2023
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    Ciência Rural
    Article . 2024 . Peer-reviewed
    License: CC BY
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Ciência Ruralarrow_drop_down
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      Ciência Rural
      Article . 2023
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      Ciência Rural
      Article . 2024 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Z. H. Mahmoud; H. N. K. AL Salman; H. H. Hussein; A. H. Adhab; +6 Authors

    Abstract Nanosensors work on the “Nano” scale. “Nano” is a unit of measurement around 10− 9 m. A nanosensor is a device capable of carrying data and information about the behavior and characteristics of particles at the nanoscale level to the macroscopic level. Nanosensors can be used to detect chemical or mechanical information such as the presence of chemical species and nanoparticles or monitor physical parameters such as temperature on the nanoscale. Nanosensors are emerging as promising tools for applications in agriculture. They offer an enormous upgrade in selectivity, speed, and sensitivity compared to traditional chemical and biological methods. Nanosensors can be used for the determination of microbe and contaminants. With the advancement of science in the world and the advent of electronic equipment and the great changes that have taken place in recent decades, the need to build more accurate, smaller and more capable sensors was felt. Today, high-sensitivity sensors are used that are sensitive to small amounts of gas, heat, or radiation. Increasing the sensitivity, efficiency and accuracy of these sensors requires the discovery of new materials and tools. Nano sensors are nanometer-sized sensors that, due to their small size and nanometer size, have such high accuracy and responsiveness that they react even to the presence of several atoms of a gas. Nano sensors are inherently smaller and more sensitive than other sensors. Resumo Os nanossensores funcionam na escala “Nano”. “Nano” é uma unidade de medida em torno de 10-9 m. Um nanosensor é um dispositivo capaz de transportar dados e informações sobre o comportamento e as características das partículas no nível da nanoescala para o nível macroscópico. Os nanossensores podem ser usados para detectar informações químicas ou mecânicas, como a presença de espécies químicas e nanopartículas, ou monitorar parâmetros físicos, como temperatura em nanoescala. Os nanossensores estão surgindo como ferramentas promissoras para aplicações na agricultura. Eles oferecem uma abrangente atualização em seletividade, velocidade e sensibilidade em comparação com os métodos químicos e biológicos tradicionais. Os nanossensores podem ser usados para a determinação de micróbios e contaminantes. Com o avanço da ciência no mundo e o advento dos equipamentos eletrônicos e as grandes mudanças ocorridas nas últimas décadas, sentiu-se a necessidade de construir sensores mais precisos, menores e mais capazes. Hoje, são usados sensores de alta sensibilidade que são sensíveis a pequenas quantidades de gás, calor ou radiação. Aumentar a sensibilidade, eficiência e precisão desses sensores requer a descoberta de novos materiais e ferramentas. Os nanossensores são sensores de tamanho nanométrico que, devido ao seu tamanho pequeno e tamanho nanométrico, possuem uma precisão e capacidade de resposta tão altas que reagem até mesmo na presença de vários átomos de um gás. Os nanossensores são inerentemente menores e mais sensíveis do que outros sensores.

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    Brazilian Journal of Biology
    Article . 2024 . Peer-reviewed
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    Brazilian Journal of Biology
    Article . 2023
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      Brazilian Journal of Biology
      Article . 2024 . Peer-reviewed
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      Brazilian Journal of Biology
      Article . 2023
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    Authors: Munya A. Arasi; Latifah Almuqren; Imène Issaoui; Nabil Sharaf Almalki; +2 Authors

    In recent times, mostly in the Middle East region, Red Palm Weevils (RPW) are one of the most dangerous pests of palm trees worldwide. The RPW causes significant harm to several palm species. The existing detection method includes the symptoms detection of RPW through sound or visual assessment and chemical recognition of volatile signatures created by diseased palm trees. However, an effective recognition of RPW disease at earlier stages is assumed that a very complex problem for cultivating date palms. This is another reason why the use of state-of-the-art technologies is supported in the avoidance of the spread of the RPW on palm trees. Several researchers are working on determining the correct process for the localization, classification, and detection of RPW pests. Therefore, this paper presents an intelligent Red Palm Weevil Detection using the Bird Swarm Algorithm with Deep Learning (IRPWD-BSADL) model. The major aim of the IRPWD-BSADL technique focuses on the identification and classification of RPW using CV and DL models. Primarily, the bilateral filtering (BF) approach can be utilized to remove the noise that exists in the images. In the presented IRPWD-BSADL technique, an improved ShuffleNet model can be applied for feature extraction purposes. To enhance the recognition results, the IRPWD-BSADL technique makes use of BSA for the hyperparameter tuning process. For RPW detection and classification, an extreme gradient boosting (XGBoost) classifier can be used. The simulation analysis of the IRPWD-BSADL method can be tested on the RPW dataset. An extensive comparison study stated the improved performance of the IRPWD-BSADL algorithm on the RPW detection method.

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    IEEE Access
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    Authors: A. Majrashi; M. M. Khandaker;

    Abstract The presence of weeds in areas of agricultural activities is a hinderance to the development of these activities. It is important to take advantage of the vast open spaces suitable for agriculture and provide food security for humans, and also it is an important indicator for determining the feasibility of growing crops, benefiting from yield and determining the percentage of loss, clearing fields through agricultural practices, that protect crops from weed attack and agricultural practice method must be followed that will reduce weed presence. This study was conducted during the years 2018 to 2020 to evaluate Portulacaceae of Flora in the Taif area in the Kingdom of Saudi Arabia at different altitudes (Area 1 =1700 m, Area 2 =1500 m, Area 3 =1500 m, Area 4 =500 m ِ Area 5 = 2200 m, and Area 6 = 2200 m). The results show that there were 2,816 individuals of Portulaca oleracea weed, with the highest density found in A 1, followed by A 2, while in A 5 and A 6, no weeds were recorded. The highest density of weeds were in the Pomegranate fields, followed by Grape fields. The lowest density was found in A man field. The results of this study will help to take the necessary measures to combat weeds and its management in areas of agricultural activity, while more studies are needed to survey the ecology of weeds of Taif in The Kingdom of Saudi Arabia. Resumo A presença de plantas daninhas em áreas de atividades agrícolas é um entrave ao desenvolvimento dessas atividades. É importante aproveitar os vastos espaços abertos adequados para a agricultura e dar segurança alimentar para o homem. Também é um indicador importante para determinar a viabilidade de cultivo de lavouras, beneficiando-se da produtividade e determinando o percentual de perda, desmatando campos agrícolas, práticas que protegem as lavouras do ataque de ervas daninhas, e métodos de práticas agrícolas devem ser seguidos para reduzir a presença de ervas daninhas. Este estudo foi realizado durante os anos de 2018 a 2020 para avaliar Portulacaceae de flora na área de Taif, no Reino da Arábia Saudita, em diferentes altitudes (Área 1 = 1.700 m, Área 2 = 1.500 m, Área 3 = 1.500 m, Área 4 = 500 m, Área 5 = 2.200 m, e Área 6 = 2.200 m). Os resultados mostram que houve 2.816 indivíduos de planta daninha Portulaca oleracea, com a maior densidade encontrada em A 1, seguida de A 2, enquanto em A 5 e A 6, nas plantas daninhas foram registrados. A maior densidade de ervas daninhas estava nos campos de romã, seguido pelos campos de uva. A densidade mais baixa foi encontrada no campo A man. Os resultados deste estudo ajudarão a tomar as medidas necessárias para combater as ervas daninhas e seu manejo em áreas de atividade agrícola, enquanto mais estudos são necessários para levantar a ecologia das ervas daninhas de Taif na Arábia Saudita.

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    Brazilian Journal of Biology
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      Brazilian Journal of Biology
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    Authors: Youseef Alotaibi; R Deepa; K Shankar; Surendran Rajendran;

    <abstract> <p>Internet of Things (IoT) edge devices are becoming extremely popular because of their ability to process data locally, conserve bandwidth, and reduce latency. However, with the developing count of IoT devices, threat detection, and security are becoming major concerns. IoT edge devices must avoid cyber threats and protect user data. These devices frequently take limited resources and can run on lightweight operating systems, which makes them vulnerable to security attacks. Intrusion detection systems (IDS) can be run on edge devices to recognize suspicious actions and possible risks. These systems monitor traffic patterns, and behavior, and identify attack signatures to detect and report on possible attacks. This study presents a design for an inverse chi square-based flamingo search optimization algorithm with machine learning (ICSFSO-ML) as a security solution for Internet of Things edge devices. The goal of the ICSFSO-ML technique is to apply ML and metaheuristics for threat recognition in IoT edge devices. To reduce the high dimensionality problem, the ICSFSO-ML technique uses the ICSFSO algorithm for feature selection purposes. Further, the ICSFSO-ML technique exploits the stacked bidirectional long short-term memory (SBiLSTM) model for the threat detection process. To enhance the efficacy of the SBiLSTM model, an arithmetic optimization algorithm (AOA) is applied for the hyperparameter selection process. The simulation performance of the ICSFSO-ML technique can be tested on a benchmark threat database. The performance analysis showed the benefits of the ICSFSO-ML methodology compared to existing methodologies with a maximum accuracy of 98.22%.</p> </abstract>

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    AIMS Mathematics
    Article . 2024 . Peer-reviewed
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    AIMS Mathematics
    Article . 2024
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    Authors: M. M. S. Bakry; Nagdy F. Abdel-Baky;

    Abstract The fall armyworm [FAW; Spodoptera frugiperda (J.E Smith, 1797)] is an invasive and polyphagous insect that infests cereal crops, causing economic losses, and may be led to pose a threat to future the global maize crop in the future. Field trials were conducted to study the negative impacts of S. frugiperda on vegetative growth measurements, yield, and the components of the maize cultivar (Single-Hybrid 168 Yellow) in Luxor Governorate, Egypt. S. frugiperda larvae infestation to maize plants was observed in the 3rd week of June and so continued till the harvest in both 2021 and 2022 seasons. S. frugiperda had three peaks of the seasonal activity/season in the untreated (pesticide-free, control) and in the treated main plots by pesticides. Maize vegetative growth attributes (averages of plant height, stem diameter, and the number of green leaves per plant) displayed higher rates of the treated maize plants by insecticides against S. frugiperda. Maize grain, straw, and biological yield (kg/ha) were decreased in the untreated maize plants (insecticides free) than in the treated by insecticides. Concerning maize yield components, the treated plants were to outperform in the average length of a plant stem (cm), stem diameter (cm), and weight of cob (g), as well as, number of rows/cob, number of grains/ cob, number of grains/cob, maize cob grain weight (g) and weight of 1000-grains (g)], in comparison with the untreated plants. Also, the FAW infestation to untreated maize plants was decreased well in all calculated maize growth attributes, i.e., grain yield, and components. Regarding the relationship between variations in a given variable and the changes in S. frugiperda larvae numbers and plant damage percentage, the simple correlation and regression coefficient revealed a highly significant negative relationship in all the parameters tested. The obtained information may help farmers and decision-makers in the management of FAW populations based on an effective plan related to control measures that should be implemented.

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    Brazilian Journal of Biology
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    Brazilian Journal of Biology
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      Brazilian Journal of Biology
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      Brazilian Journal of Biology
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    Authors: Khaled H. Alyoubi; Adil O. Khadidos; Abdulrhman M. Alshareef; Diaa Hamed; +2 Authors

    Internet of Things (IoT) devices generate enormous quantities of data, and ensuring the authenticity and integrity of this data is essential. Blockchain (BC) serves as a transparent and secure ledger for recording each data transaction, which prevents unauthorized modification and provides a trust layer for the IoT ecosystem. Data edge verification for BC-enabled IoT platforms is a fundamental aspect of ensuring data trustworthiness, integrity, and security in the system. In such an environment, IoT devices generate vast amounts of data, and BC technology can be used to create a decentralized and immutable ledger that records all the transactions and data exchanges Machine learning (ML) in the context of BC and the IoT offers a powerful toolkit for optimizing and securing these technologies. It enables the analysis of vast and complex data generated by IoT devices, allowing for predictive maintenance, anomaly detection, and pattern recognition. ML also enhances security by developing intrusion detection systems and supporting smart contracts in BC networks. This article introduces a new Capuchin Search Algorithm with a Deep Learning based Data Edge Verification model (CSADL-DEVM) for the Blockchain assisted IoT platform. The purpose of the CSADL-DEVM technique is to integrate the BC with DL and hyperparameter tuning concepts for data edge verification in the IoT environment. In addition, IoT devices comprise a considerable level of decentralized decision-making ability, which accomplishes a consensus on the performance of intra-block transactions. In addition, the CSADL-DEVM technique applies the Elman recurrent neural network (ERNN) model for the identification and classification of faults. Moreover, the hyperparameters related to the ERNN model can be adjusted by the use of CSA which in turn better the detection results. A comprehensive set of simulations has been conducted to exhibit the enhanced results of the CSADL-DEVM method. The obtained outcomes exhibit the superior outcome of the CSADL-DEVM algorithm.

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    IEEE Access
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    IEEE Access
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Miguel Gutiérrez Gaitán; Luís Almeida; Pedro M. D’orey; Pedro M. Santos; +1 Authors

    This article presents a novel centrality-driven gateway designation framework for the improved real-time performance of low-power wireless sensor networks (WSNs) at system design time. We target time-synchronized channel hopping (TSCH) WSNs with centralized network management and multiple gateways with the objective of enhancing traffic schedulability by design. To this aim, we propose a novel network centrality metric termed minimal-overlap centrality that characterizes the overall number of path overlaps between all the active flows in the network when a given node is selected as gateway. The metric is used as a gateway designation criterion to elect as a gateway the node leading to the minimal number of overlaps. The method is then extended to multiple gateways with the aid of the unsupervised learning method of spectral clustering. Concretely, after a given number of clusters are identified, we use the new metric at each cluster to designate as cluster gateway the node with the least overall number of overlaps. Extensive simulations with random topologies under centralized earliest-deadline-first (EDF) scheduling and shortest-path routing suggest our approach is dominant over traditional centrality metrics from social network analysis, namely, eigenvector, closeness, betweenness, and degree. Notably, our approach reduces by up to 40% the worst-case end-to-end deadline misses achieved by classical centrality-driven gateway designation methods. This work was partially supported by National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit (UIDB/04234/2020); by the Operational Competitiveness Programme and Internationalization (COMPETE 2020) under the PT2020 Agreement, through the European Regional Development Fund (ERDF); also by FCT and the ESF (European Social Fund) through the Regional Operational Programme (ROP) Norte 2020, under PhD grant 2020.06685.BD.

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    ACM Transactions on Embedded Computing Systems
    Article . 2024 . Peer-reviewed
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    Authors: Mohammed Aljebreen; Marwa Obayya; Hany Mahgoub; Saud S. Alotaibi; +2 Authors

    Currently, there is an emerging requirement for applications related to the Internet of Things (IoT). Though the capability of IoT applications is huge, there are frequent limitations namely energy optimization, heterogeneity of devices, memory, security, privacy, and load balancing (LB) that should be solved. Such constraints must be optimised to enhance the network’s efficiency. Hence, the core objective of this study was to formulate the intelligent-related cluster head (CH) selection method to establish green communication in IoT. Therefore, this study develops a chaotic equilibrium optimizer-based green communication with deep learning-enabled load prediction (CEOGC-DLLP) in the IoT environment. The study recognizes the emerging need for IoT applications and acknowledges the critical challenges, such as energy optimization, device heterogeneity, memory constraints, security, privacy, and load balancing, which are essential to enhancing the efficiency of IoT networks. The presented CEOGC-DLLP technique mainly accomplishes green communication via clustering and future load prediction processes. To do so, the presented CEOGC-DLLP model derives the CEOGC technique with a fitness function encompassing multiple parameters. In addition, the presented CEOGC-DLLP technique follows the deep belief network (DBN) model for the load prediction process, which helps to balance the load among the IoT devices for effective green communication. The experimental assessment of the CEOGC-DLLP technique is performed and the outcomes are investigated under different aspects. The comparison study represents the supremacy of the CEOGC-DLLP method compared to existing techniques with a maximum throughput of 64662 packets and minimum MSE of 0.2956.

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    IEEE Access
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    IEEE Access
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      IEEE Access
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Frida I. Piper; Alejandro Cárdenas; Alejandra Zúñiga-Feest; Julieta Orlando; +2 Authors

    Individual tree species might affect underground soil properties to favor the decomposition of their own litter. However, in some studies, monospecific litter decomposed faster underneath multispecific canopies than under canopies of the same species as the litter, suggesting that plant diversity in the overstorey creates a more favorable decomposition microenvironment. We compared the decomposition rate of monospecific litter from eight temperate tree species under the crown of the same species as the litter (conspecific canopy), and a multispecific canopy formed by eight species. Using model selection, we determined the influence of the microenvironments, species, and litter chemistry on decomposition rates. The most robust model included the species and canopy type, but it differed only slightly from the model including only the species. Decomposition rates were significantly different among species and higher in conspecific canopy than multispecific canopy in all species. Models including specific leaf area and different combinations of chemical traits of the litter were less robust than the null model. These results show that the species origin of the litter was significantly more important than the microenvironment to explain litter decomposition rates.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Canadian Journal of ...arrow_drop_down
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    Canadian Journal of Forest Research
    Article . 2024 . Peer-reviewed
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Canadian Journal of ...arrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      Canadian Journal of Forest Research
      Article . 2024 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Sofia Boza; Vicente Zambrano; Camila Jerez; Andrea Rengifo; +1 Authors

    ABSTRACT: Small-scale farmers are essential food suppliers, especially in developing countries, but they face many constraints that limit their productivity and returns. Associating with other farmers (for instance, through cooperatives) has been shown to be a strategy to mitigate these constraints; however, there are limitations in farmers’ participation and commitment to associations. This research explored small-scale farmers’ attitudes and perceptions of associations. This paper considered the district of Cauquenes, a rural area in central Chile. A total of 71 small-scale farmers were surveyed. The data were processed using descriptive, multivariate, and qualitative techniques. The results showed that the farmers had limited knowledge of and experience with associations. They also did not see participation in an association as necessary for improving their business outcomes. They were also reluctant to accept a possible loss in decision-making power or the possibility of being scammed or tricked. The factors underlying farmers’ attitudes toward associations were “Uncertainty of economic benefits” (23.3% of variance), “Technical-economic limitations perception” (15.2%), “Distrust” (10.8%), and “Individualism” (9.8%). These results suggested that policies are needed to improve farmers’ experience with and knowledge of associations, considering the cultural variables that affect distrust and focusing on existing uncertainties. The associative processes need to allow for progressive commitment, expedite tangible results, and provide continuous technical and motivational support. RESUMO: Os pequenos agricultores são essenciais para o abastecimento de alimentos, especialmente nos países em desenvolvimento, mas enfrentam muitas restrições que limitam sua produtividade e retorno. A associação com outros agricultores (por exemplo, por cooperativas) tem se mostrado uma estratégia para mitigar essas deficiências, no entanto, há entraves na participação e comprometimento dos agricultores com as associações. O objetivo desta pesquisa é explorar as atitudes e percepções dos pequenos agricultores em relação às associações. Para isso, consideramos o caso do distrito de Cauquenes, uma área rural no centro do Chile, em que se entrevistou um total de 71 pequenos agricultores. Os dados foram processados por meio de técnicas descritivas, multivariadas e qualitativas. Como resultados, os agricultores têm um conhecimento e experiência limitados sobre as associações e, além disso, não entendem a relevância do vínculo social como uma alternativa para melhorar o seu desempenho. Por outro lado, relutam em uma possível perda de poder decisório, bem como em serem enganados. Os fatores subjacentes às atitudes dos agricultores em relação às associações foram “incerteza dos benefícios econômicos” (23,3%), “percepção de limitações técnico-econômicas” (15,2%), “desconfiança” (10,8%) e “individualismo” (9,8%). Esses resultados nos demonstram serem necessárias políticas que promovam o conhecimento direto, concreto e experiencial dos agricultores sobre o associativismo, com foco nas incertezas existentes. Os processos associativos devem estar atrelados a compromissos progressivos, com foco em resultados tangíveis e suporte contínuo, não apenas técnico, mas também motivacional.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Ciência Ruralarrow_drop_down
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    Ciência Rural
    Article . 2023
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    Ciência Rural
    Article . 2024 . Peer-reviewed
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      Ciência Rural
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      Ciência Rural
      Article . 2024 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Z. H. Mahmoud; H. N. K. AL Salman; H. H. Hussein; A. H. Adhab; +6 Authors

    Abstract Nanosensors work on the “Nano” scale. “Nano” is a unit of measurement around 10− 9 m. A nanosensor is a device capable of carrying data and information about the behavior and characteristics of particles at the nanoscale level to the macroscopic level. Nanosensors can be used to detect chemical or mechanical information such as the presence of chemical species and nanoparticles or monitor physical parameters such as temperature on the nanoscale. Nanosensors are emerging as promising tools for applications in agriculture. They offer an enormous upgrade in selectivity, speed, and sensitivity compared to traditional chemical and biological methods. Nanosensors can be used for the determination of microbe and contaminants. With the advancement of science in the world and the advent of electronic equipment and the great changes that have taken place in recent decades, the need to build more accurate, smaller and more capable sensors was felt. Today, high-sensitivity sensors are used that are sensitive to small amounts of gas, heat, or radiation. Increasing the sensitivity, efficiency and accuracy of these sensors requires the discovery of new materials and tools. Nano sensors are nanometer-sized sensors that, due to their small size and nanometer size, have such high accuracy and responsiveness that they react even to the presence of several atoms of a gas. Nano sensors are inherently smaller and more sensitive than other sensors. Resumo Os nanossensores funcionam na escala “Nano”. “Nano” é uma unidade de medida em torno de 10-9 m. Um nanosensor é um dispositivo capaz de transportar dados e informações sobre o comportamento e as características das partículas no nível da nanoescala para o nível macroscópico. Os nanossensores podem ser usados para detectar informações químicas ou mecânicas, como a presença de espécies químicas e nanopartículas, ou monitorar parâmetros físicos, como temperatura em nanoescala. Os nanossensores estão surgindo como ferramentas promissoras para aplicações na agricultura. Eles oferecem uma abrangente atualização em seletividade, velocidade e sensibilidade em comparação com os métodos químicos e biológicos tradicionais. Os nanossensores podem ser usados para a determinação de micróbios e contaminantes. Com o avanço da ciência no mundo e o advento dos equipamentos eletrônicos e as grandes mudanças ocorridas nas últimas décadas, sentiu-se a necessidade de construir sensores mais precisos, menores e mais capazes. Hoje, são usados sensores de alta sensibilidade que são sensíveis a pequenas quantidades de gás, calor ou radiação. Aumentar a sensibilidade, eficiência e precisão desses sensores requer a descoberta de novos materiais e ferramentas. Os nanossensores são sensores de tamanho nanométrico que, devido ao seu tamanho pequeno e tamanho nanométrico, possuem uma precisão e capacidade de resposta tão altas que reagem até mesmo na presença de vários átomos de um gás. Os nanossensores são inerentemente menores e mais sensíveis do que outros sensores.

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    Brazilian Journal of Biology
    Article . 2024 . Peer-reviewed
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    Brazilian Journal of Biology
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      Brazilian Journal of Biology
      Article . 2024 . Peer-reviewed
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      Brazilian Journal of Biology
      Article . 2023
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    Authors: Munya A. Arasi; Latifah Almuqren; Imène Issaoui; Nabil Sharaf Almalki; +2 Authors

    In recent times, mostly in the Middle East region, Red Palm Weevils (RPW) are one of the most dangerous pests of palm trees worldwide. The RPW causes significant harm to several palm species. The existing detection method includes the symptoms detection of RPW through sound or visual assessment and chemical recognition of volatile signatures created by diseased palm trees. However, an effective recognition of RPW disease at earlier stages is assumed that a very complex problem for cultivating date palms. This is another reason why the use of state-of-the-art technologies is supported in the avoidance of the spread of the RPW on palm trees. Several researchers are working on determining the correct process for the localization, classification, and detection of RPW pests. Therefore, this paper presents an intelligent Red Palm Weevil Detection using the Bird Swarm Algorithm with Deep Learning (IRPWD-BSADL) model. The major aim of the IRPWD-BSADL technique focuses on the identification and classification of RPW using CV and DL models. Primarily, the bilateral filtering (BF) approach can be utilized to remove the noise that exists in the images. In the presented IRPWD-BSADL technique, an improved ShuffleNet model can be applied for feature extraction purposes. To enhance the recognition results, the IRPWD-BSADL technique makes use of BSA for the hyperparameter tuning process. For RPW detection and classification, an extreme gradient boosting (XGBoost) classifier can be used. The simulation analysis of the IRPWD-BSADL method can be tested on the RPW dataset. An extensive comparison study stated the improved performance of the IRPWD-BSADL algorithm on the RPW detection method.

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    IEEE Access
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    IEEE Access
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      IEEE Access
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      IEEE Access
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    Authors: A. Majrashi; M. M. Khandaker;

    Abstract The presence of weeds in areas of agricultural activities is a hinderance to the development of these activities. It is important to take advantage of the vast open spaces suitable for agriculture and provide food security for humans, and also it is an important indicator for determining the feasibility of growing crops, benefiting from yield and determining the percentage of loss, clearing fields through agricultural practices, that protect crops from weed attack and agricultural practice method must be followed that will reduce weed presence. This study was conducted during the years 2018 to 2020 to evaluate Portulacaceae of Flora in the Taif area in the Kingdom of Saudi Arabia at different altitudes (Area 1 =1700 m, Area 2 =1500 m, Area 3 =1500 m, Area 4 =500 m ِ Area 5 = 2200 m, and Area 6 = 2200 m). The results show that there were 2,816 individuals of Portulaca oleracea weed, with the highest density found in A 1, followed by A 2, while in A 5 and A 6, no weeds were recorded. The highest density of weeds were in the Pomegranate fields, followed by Grape fields. The lowest density was found in A man field. The results of this study will help to take the necessary measures to combat weeds and its management in areas of agricultural activity, while more studies are needed to survey the ecology of weeds of Taif in The Kingdom of Saudi Arabia. Resumo A presença de plantas daninhas em áreas de atividades agrícolas é um entrave ao desenvolvimento dessas atividades. É importante aproveitar os vastos espaços abertos adequados para a agricultura e dar segurança alimentar para o homem. Também é um indicador importante para determinar a viabilidade de cultivo de lavouras, beneficiando-se da produtividade e determinando o percentual de perda, desmatando campos agrícolas, práticas que protegem as lavouras do ataque de ervas daninhas, e métodos de práticas agrícolas devem ser seguidos para reduzir a presença de ervas daninhas. Este estudo foi realizado durante os anos de 2018 a 2020 para avaliar Portulacaceae de flora na área de Taif, no Reino da Arábia Saudita, em diferentes altitudes (Área 1 = 1.700 m, Área 2 = 1.500 m, Área 3 = 1.500 m, Área 4 = 500 m, Área 5 = 2.200 m, e Área 6 = 2.200 m). Os resultados mostram que houve 2.816 indivíduos de planta daninha Portulaca oleracea, com a maior densidade encontrada em A 1, seguida de A 2, enquanto em A 5 e A 6, nas plantas daninhas foram registrados. A maior densidade de ervas daninhas estava nos campos de romã, seguido pelos campos de uva. A densidade mais baixa foi encontrada no campo A man. Os resultados deste estudo ajudarão a tomar as medidas necessárias para combater as ervas daninhas e seu manejo em áreas de atividade agrícola, enquanto mais estudos são necessários para levantar a ecologia das ervas daninhas de Taif na Arábia Saudita.

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    Brazilian Journal of Biology
    Article . 2024 . Peer-reviewed
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    DOAJ
    Article . 2021
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      Brazilian Journal of Biology
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    Authors: Youseef Alotaibi; R Deepa; K Shankar; Surendran Rajendran;

    <abstract> <p>Internet of Things (IoT) edge devices are becoming extremely popular because of their ability to process data locally, conserve bandwidth, and reduce latency. However, with the developing count of IoT devices, threat detection, and security are becoming major concerns. IoT edge devices must avoid cyber threats and protect user data. These devices frequently take limited resources and can run on lightweight operating systems, which makes them vulnerable to security attacks. Intrusion detection systems (IDS) can be run on edge devices to recognize suspicious actions and possible risks. These systems monitor traffic patterns, and behavior, and identify attack signatures to detect and report on possible attacks. This study presents a design for an inverse chi square-based flamingo search optimization algorithm with machine learning (ICSFSO-ML) as a security solution for Internet of Things edge devices. The goal of the ICSFSO-ML technique is to apply ML and metaheuristics for threat recognition in IoT edge devices. To reduce the high dimensionality problem, the ICSFSO-ML technique uses the ICSFSO algorithm for feature selection purposes. Further, the ICSFSO-ML technique exploits the stacked bidirectional long short-term memory (SBiLSTM) model for the threat detection process. To enhance the efficacy of the SBiLSTM model, an arithmetic optimization algorithm (AOA) is applied for the hyperparameter selection process. The simulation performance of the ICSFSO-ML technique can be tested on a benchmark threat database. The performance analysis showed the benefits of the ICSFSO-ML methodology compared to existing methodologies with a maximum accuracy of 98.22%.</p> </abstract>

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    AIMS Mathematics
    Article . 2024 . Peer-reviewed
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    AIMS Mathematics
    Article . 2024
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      AIMS Mathematics
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    Authors: M. M. S. Bakry; Nagdy F. Abdel-Baky;

    Abstract The fall armyworm [FAW; Spodoptera frugiperda (J.E Smith, 1797)] is an invasive and polyphagous insect that infests cereal crops, causing economic losses, and may be led to pose a threat to future the global maize crop in the future. Field trials were conducted to study the negative impacts of S. frugiperda on vegetative growth measurements, yield, and the components of the maize cultivar (Single-Hybrid 168 Yellow) in Luxor Governorate, Egypt. S. frugiperda larvae infestation to maize plants was observed in the 3rd week of June and so continued till the harvest in both 2021 and 2022 seasons. S. frugiperda had three peaks of the seasonal activity/season in the untreated (pesticide-free, control) and in the treated main plots by pesticides. Maize vegetative growth attributes (averages of plant height, stem diameter, and the number of green leaves per plant) displayed higher rates of the treated maize plants by insecticides against S. frugiperda. Maize grain, straw, and biological yield (kg/ha) were decreased in the untreated maize plants (insecticides free) than in the treated by insecticides. Concerning maize yield components, the treated plants were to outperform in the average length of a plant stem (cm), stem diameter (cm), and weight of cob (g), as well as, number of rows/cob, number of grains/ cob, number of grains/cob, maize cob grain weight (g) and weight of 1000-grains (g)], in comparison with the untreated plants. Also, the FAW infestation to untreated maize plants was decreased well in all calculated maize growth attributes, i.e., grain yield, and components. Regarding the relationship between variations in a given variable and the changes in S. frugiperda larvae numbers and plant damage percentage, the simple correlation and regression coefficient revealed a highly significant negative relationship in all the parameters tested. The obtained information may help farmers and decision-makers in the management of FAW populations based on an effective plan related to control measures that should be implemented.

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    Brazilian Journal of Biology
    Article . 2024 . Peer-reviewed
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    Brazilian Journal of Biology
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      Brazilian Journal of Biology
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      Brazilian Journal of Biology
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    Authors: Khaled H. Alyoubi; Adil O. Khadidos; Abdulrhman M. Alshareef; Diaa Hamed; +2 Authors

    Internet of Things (IoT) devices generate enormous quantities of data, and ensuring the authenticity and integrity of this data is essential. Blockchain (BC) serves as a transparent and secure ledger for recording each data transaction, which prevents unauthorized modification and provides a trust layer for the IoT ecosystem. Data edge verification for BC-enabled IoT platforms is a fundamental aspect of ensuring data trustworthiness, integrity, and security in the system. In such an environment, IoT devices generate vast amounts of data, and BC technology can be used to create a decentralized and immutable ledger that records all the transactions and data exchanges Machine learning (ML) in the context of BC and the IoT offers a powerful toolkit for optimizing and securing these technologies. It enables the analysis of vast and complex data generated by IoT devices, allowing for predictive maintenance, anomaly detection, and pattern recognition. ML also enhances security by developing intrusion detection systems and supporting smart contracts in BC networks. This article introduces a new Capuchin Search Algorithm with a Deep Learning based Data Edge Verification model (CSADL-DEVM) for the Blockchain assisted IoT platform. The purpose of the CSADL-DEVM technique is to integrate the BC with DL and hyperparameter tuning concepts for data edge verification in the IoT environment. In addition, IoT devices comprise a considerable level of decentralized decision-making ability, which accomplishes a consensus on the performance of intra-block transactions. In addition, the CSADL-DEVM technique applies the Elman recurrent neural network (ERNN) model for the identification and classification of faults. Moreover, the hyperparameters related to the ERNN model can be adjusted by the use of CSA which in turn better the detection results. A comprehensive set of simulations has been conducted to exhibit the enhanced results of the CSADL-DEVM method. The obtained outcomes exhibit the superior outcome of the CSADL-DEVM algorithm.

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    IEEE Access
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ IEEE Accessarrow_drop_down
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      IEEE Access
      Article . 2024
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      IEEE Access
      Article . 2024 . Peer-reviewed
      License: CC BY NC ND
      Data sources: Crossref
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      This Research product is the result of merged Research products in OpenAIRE.

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