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Abstract Synthetic Aperture Radar (SAR) integrated with deep learning has been widely used in several military and civilian applications, such as border patrolling, to monitor and regulate the movement of people and goods across land, air, and maritime borders. Amongst these, maritime borders confront different threats and challenges. Therefore, SAR-based ship detection becomes essential for naval surveillance in marine traffic management, oil spill detection, illegal fishing, and maritime piracy. However, the model becomes insensitive to small ships due to the wide-scale variance and uneven distribution of ship sizes in SAR images. This increases the difficulties associated with ship recognition, which triggers several false alarms. To effectively address these difficulties, the present work proposes an ensemble model (eYOLO) based on YOLOv4 and YOLOv5. The model utilizes a weighted box fusion technique to fuse the outputs of YOLOv4 and YOLOv5. Also, a generalized intersection over union loss has been adopted in eYOLO which ensures the increased generalization capability of the model with reduced scale sensitivity. The model has been developed end-to-end, and its performance has been validated against other reported results using an open-source SAR-ship dataset. The obtained results authorize the effectiveness of eYOLO in multi-scale ship detection with an F 1 score and mAP of 91.49% and 92.00%, respectively. This highlights the efficacy of eYOLO in multi-scale ship detection using SAR imagery.

The tight control of the process parameters through appropriate tuning of controllers is an art that imperatively employed to various process industries. Most of these industries are influenced by the nonlinearity that occurred due to the input parameter variation and presence of disturbances. The aim of this work is to investigate the nonlinear dynamics of a paper industry based energy intensive unit named Multiple Stage Evaporator (MSE) in presence of different Energy Reduction Schemes. MSE is used to concentrate the weak Black Liquor (BL), a biomass based byproduct. Hence, to extract the bioenergy from the BL, the quality of the product liquor needs to be appropriately controlled. The quality of BL is measured by two process parameters, product concentration and temperature. Hence, in this work, an intelligent controller Fraction Order Proportional-Integral-Derivative controller has been studied and employed to resolve the servo and the regulatory problem occurred during the process. A state-of-art metaheuristic approach, Black Widow Optimization Algorithm has been proposed here to tune the controller parameters and compared with another optimization approaches named Water Cycle Algorithm. The simulated result demonstrates the usefulness of the proposed strategy and confirm the performance improvement for the process parameters. To enlighten the advantages of the proposed control scheme, a comparative analysis have also been performed with conventional PID, 2-DOF-PID and FOPID controllers.

Internet explosion and penetration have amplified the fake news problem that existed even before Internet penetration. This becomes more of a concern, if the news is health-related. To address this issue, this research proposes Content Based Models (CBM) and Feature Based Models (FBM). The difference between the two models lies in the input provided. The CBM only takes news content as the input, whereas the FBM along with the content also takes two readability features as the input. Under each category, the performance of five traditional machine learning techniques: - Decision Tree, Random Forest, Support Vector Machine, AdaBoost-Decision Tree and AdaBoost-Random Forest is compared with two hybrid Deep Learning approaches, namely CNN-LSTM and CNN-BiLSTM. The Fake News Healthcare dataset comprising 9581 articles was utilized for the study. Easy Data Augmentation technique is used to balance this highly imbalanced dataset. The experimental results demonstrate that Feature Based Models perform better than Content Based Models. Among the proposed FBM, the Hybrid CNN - LSTM model had a F1 score of 97.09% and AdaBoost-Random Forest had a F1 Score of 98.9%. Thus, Adaboost-Random Forest under FBM is the best-performing model for the classification of fake news.

The Kraft recovery process in the paper mills is highly complex and nonlinear. The evaporation of black liquor using Multiple Effect Evaporators (MEE) in the recovery unit of the Kraft process is an energy-consuming procedure. The challenge lies in designing a proficient control strategy to conserve energy and guarantee good product quality. In this article, the 2-DOF-PI controller is designed to enable efficient control of MEE and eliminate process uncertainties. The Heptads’ Effect Falling Film Evaporator in the backward feed flow configuration is used as a working platform. The steady-state and transient behavior of MEE is modeled and analyzed to assist in controller design. The steady-state process parameters, that guarantee optimum energy efficiency, are estimated using Moth–Flame Optimization (MFO). MFO is also employed to estimate the optimal 2-DOF-PI controller parameters to attain improved product quality and energy-efficient performance of MEE. The competence of MFO towards controller tuning is shown by a fair comparison with some well-known optimization techniques. The quantitative investigation of the statistical results demonstrates that MFO outperforms the other algorithms. To validate the proficiency of the designed control scheme, its performance is compared with basic PI/PID control strategy for set-point tracking, noise suppression, and process uncertainties. The simulation results indicate that the designed MFO-tuned 2-DOF-PI controller offers efficient control action and, therefore, proves to be an appropriate control algorithm to ensure sustainable production.

The System of Rice Intensification (SRI), an agro-ecological approach to rice cultivation developed in Madagascar, has generated considerable interest worldwide. Having not been developed at a research establishment but rather from observation and testing on farmers’ fields, SRI attracted considerable controversy, for example, with unwarranted objections that it lacked of scientific evidence, and being characterized as based on ‘unconfirmed field observations’ (UFOs). One 2004 article concluded that “the system of rice intensification does not fundamentally change the physiological yield potential of rice”. This assertion was not based on any physiological examination of rice plants grown using SRI methodology, however, or on any systematic comparison with what would be considered as best management practices (BMPs), recommended practices (RPs), or farmer practices (FPs). Other dismissals of SRI have had contestable data selection, analytical methods, and presentation of results. The published literature provides abundant evidence that the earlier evaluations of SRI were either not well-informed or objective, and possibly, they discouraged others from embarking on systematic evaluations of their own. This article examines the results of 78 studies in the published literature where SRI results were explicitly compared with RPs, including BMPs or FPs. The results from 27 countries, plus several large-scale evaluations, show that in 80% of the evaluations, grain yield was higher under SRI than with RPs or FPs. SRI gave 24% higher grain yield than RPs and 56% more than FPs, while reducing seed, water, and fertilizer inputs. Beyond the descriptive evidence, this paper considers that the phenotypical changes and physiological improvements in SRI-grown rice plants could account for the reported enhancement in yield. More research remains to be undertaken to elucidate casual mechanisms, but abundant evidence shows that this is a subject deserving considerable scientific effort.

Chemotherapy is one of the most extensively utilized cancer treatment strategies worldwide. It is intended to eliminate fast-developing cancer cells in a patient’s body. The amount of chemotherapeutic drug that must be administered precisely into a patient’s body determines the efficacy of the treatment and governs the patient survival during chemotherapy. Therefore, controlling the chemotherapeutic drug dose delivered to the patient is essential. This research aims to propose a two-degree-of-freedom fractional order proportional–integral–derivative (2FOPID) controller with a set point filter for implementing an automatic drug delivery control scheme during chemotherapy. The Whale optimization algorithm (WOA) is used to tune the parameters of the 2FOPID controller, resulting in a WOA-tuned 2FOPID controller (W2FOPID). The performance of the proposed W2FOPID is compared with the Integral–Proportional–Derivative (IPD), Internal Model Control (IMC), and Fractional Order IMC (FOIMC) schemes. The experimental results demonstrate that the proposed W2FOPID controller is effective, accurate, and robust for drug concentration control during chemotherapy. W2FOPID outperforms IPD, IMC, and FOIMC schemes in terms of Integral Absolute Error by 79.9%, 25.3%, and 23.36%, respectively. In addition, W2FOPID exhibits excellent set-point tracking, noise suppression and uncertainty handling capabilities.

The gram pod borer is a major pest of chickpea, accounting for average annual yield losses to the tune of 40-50%. VIP3Aa, a class of insecticidal protein with different receptor binding site in the insect's midgut compared to Bt-crystal protein, offers an alternative protection strategy against Lepidopteran insects. Here, we report evaluation of genetically engineered chickpea lines harboring codon modified Vip3Aa (cmVip3Aa) against the Lepidopteran insect pest, gram pod borer. The synthetic codon modified, cmVip3Aa gene of 2,370 bp was sub-cloned in modified plant expression vector and used for direct transformation of embryonic axis explants of chickpea (cv. DCP 92-3), with transformation efficiency of 4.30%. Presence and transmission of transgene across two generations were confirmed by PCR and Southern blot analyses in the five selected transgenic chickpea lines. Real Time PCR analyses indicated variable levels of cmVip3Aa expression in the transgenic chickpea lines (average Cq values 15.01±0.86 to 19.32±0.10), which were absent in the non-transgenic counterpart. Detached leaf insect bioassay indicate larval mortality (up to 39.75%), reduced larval feeding (up to 82.91%) and reduced larval weight gain (up to 68.23%), compared to control lines. Evaluation of gene offers a platform to identify efficacious insecticidal gene that can be used for insect resistance management in chickpea.

Mosquitoes are the potential vectors of several viral diseases such as filariasis, malaria, dengue, yellow fever, Zika fever and encephalitis in humans as well as other species. Dengue, the most common mosquito-borne disease in humans caused by the dengue virus is transmitted by the vector Ae. aegypti. Fever, chills, nausea and neurological disorders are the frequent symptoms of Zika and dengue. Thanks to various anthropogenic activities such as deforestation, industrialized farming and poor drainage facilities there has been a significant rise in mosquitoes and vector-borne diseases. Control measures such as the destruction of mosquito breeding places, a reduction in global warming, as well as the use of natural and chemical repellents, mainly DEET, picaridin, temephos and IR-3535 have proven to be effective in many instances. Although potent, these chemicals cause swelling, rashes, and eye irritation in adults and children, and are also toxic to the skin and nervous system. Due to their shorter protection period and harmful nature towards non-target organisms, the use of chemical repellents is greatly reduced, and more research and development is taking place in the field of plant-derived repellents, which are found to be selective, biodegradable and harmless to non-target species. Many tribal and rural communities across the world have been using plant-based extracts since ancient times for various traditional and medical purposes, and to ward off mosquitoes and various other insects. In this regard, new species of plants are being identified through ethnobotanical surveys and tested for their repellency against Ae. aegypti. This review aims to provide insight into many such plant extracts, essential oils and their metabolites, which have been tested for their mosquitocidal activity against different life cycle forms of Ae. Aegypti, as well as for their efficacy in controlling mosquitoes.

Black liquor, a residual stream from the Kraft recovery process of paper mills is an incipient biomass energy resource which finds prospective biofuel-based industrial applications to ensure process self-sufficiency and sustainability. Black liquor is concentrated using Multiple Stage Evaporator, the utmost energy intensive unit, before using it as biofuel. Pertaining to the contemporary global energy scenario, improvement in energy efficiency of Multiple Stage Evaporator becomes indispensable. The present work investigates the non-linear modeling and simulation-based optimization of Heptads' stage based Multiple Stage Evaporator in backward feed flow configuration integrated with various energy saving strategies. A novel metaheuristic approach, Water Cycle Algorithm has been employed to search the optimum estimates of unknown process variables and therefore, the optimum energy efficiency parameters. The optimization results demonstrate the efficiency of Water Cycle Algorithm in screening the most appropriate operating strategy, i.e., hybrid model of all energy saving strategies (steam-split, feed-split and feed-preheating) with optimum energy efficiency i.e. Steam Economy of 7.092 and Steam Consumption of 1.919 kg/s. Moreover, a comparative analysis of the results with previous literature and real-time plant estimates reveal that the hybrid model offers improvement of 52.84% in Steam Economy and reduction in Steam Consumption by 28.13% when compared to the real plant data.

Abstract Only three immediate early genes (IE) BICP0, BICP4 and BICP22 of Bovine herpesvirus 1 (BoHV-1) are known. These genes are expressed coordinately and their promoters are well characterized. We provide evidence for expression of three additional IE genes of BoHV-1 i.e. UL21, UL33 and UL34. These genes are expressed in the presence of cycloheximide (CH) at the same time as known IE genes. Surprisingly, the promoters of newly identified IE genes (UL21, UL33, UL34) lack the OCT-1 binding site, a considered site of transactivation of the BoHV-1 IE genes. The other difference in the promoters of the newly identified IE genes is the presence of TATA box at near optimal site. However, all the IE genes have similar spatial placements of C/EBPα, DPE and INR elements.

Recommender systems are information filtering tools that aspire to predict the rating for users and items, predominantly from big data to recommend their likes. Movie recommendation systems provide a mechanism to assist users in classifying users with similar interests. This makes recommender systems essentially a central part of websites and e-commerce applications. This article focuses on the movie recommendation systems whose primary objective is to suggest a recommender system through data clustering and computational intelligence. In this research article, a novel recommender system has been discussed which makes use of k-means clustering by adopting cuckoo search optimization algorithm applied on the Movielens dataset. Our approach has been explained systematically, and the subsequent results have been discussed. It is also compared with existing approaches, and the results have been analyzed and interpreted. Evaluation metrics such as mean absolute error (MAE), standard deviation (SD), root mean square error (RMSE) and t-value for the movie recommender system delivers better results as our approach offers lesser value of the mean absolute error, standard deviation, and root mean square error. The experiment results obtained on Movielens dataset stipulate that the proposed approach may provide high performance regarding reliability, efficiency and delivers accurate personalized movie recommendations when compared with existing methods. Our proposed system (K-mean Cuckoo) has 0.68 MAE, which is superior to existing work (0.78 MAE) [1] and also has improvement of our previous work (0.75 MAE) [2].

Satellite image enhancement suffers from edge localization and distortion of detail. A novel satellite image enhancement is presented which enhance the edge detail as well as a quality of low-resolution satellite images. In the proposed approach, we improve and preserve the edge detail of a significant region using particle swarm optimization and fuzzy logic in the high spectral region. Further, the fusion of detail images is performed to conquer the limitation of intensity distortion and over saturation in the region using an estimation of weights. The magnitude of weights is estimated by fuzzy membership functions to extract optimal spectral information. The measure of enhancement and contrast assessment function criterion are used to examine the performance of proposed method in comparison with other two recent techniques.

Cerca de 46% da água potável consumida na região de Belém, provém de fontes subterrâneas. Contribui para isso, a existência de uma enorme quantidade desses recursos nos sedimentos Barreiras e na Formação Pirabas. Os primeiros foram depositados em ambiente fluvial e estuarino, enquanto que a Formação Pirabas possui fácies de origem estuarina, de mangue e marinha. No contexto apresentado, mostra-se então a necessidade de investigar tais recursos com o intuito de melhor conhecer o que se tem e assim fazer ótimo proveito deste. Para tanto, foram usadas duas metodologias geofísicas: a Perfilagem Geofísica de Poço, usando raios gama, potencial espontâneo e resistência elétrica, e a Sondagem Elétrica Vertical (SEV). Ao final do processamento de todas as informações obtidas, pode-se discernir dois ambientes aqüíferos. O primeiro até cerca de 160 m de profundidade, em que prevalecem corpos arenosos entrecortados com argila. O segundo, a partir de 160 m, é caracterizado por pacotes arenosos bastante extensos, com lentes de argila. Tais pacotes têm continuidade lateral por quilômetros e espessuras variáveis até a aproximadamente 285 m de profundidade, onde findam os dados, e por isso seria a zona mais adequada a explotação com fim ao abastecimento público.