Efficient Monitoring Strategies Research Articles

Occurrence and Behavior Analysis of Duponchelia fovealis on Strawberry Plants: Insights for Integrated Pest Management

The European pepper moth, Duponchelia fovealis (Lepidoptera: Crambidae), is a key pest to strawberries in America and Europe. Understanding its behavior in the field can support integrated management strategies. In this work, field surveys were conducted to confirm the presence of this pest in commercial areas within the State of Paraná (Brazil) and to determine on which plant organ it prevailed. Semi-field experiments evaluated oviposition preference as a function of strawberry cultivar. Based on pest behavior, insecticide distribution was assessed following conventional field applications. Our results determined that D. fovealis spread within a 400 km radius from the site in which it was first recorded in Paraná, and the infested area reached 68.2% by the end of the survey. This species concentrated on basal leaves and crowns, where more than 90% of the larvae were collected. Moreover, the number of eggs per plant was significantly higher in the ‘Albion’ cultivar. The sprayed insecticide remained in the upper and middle thirds of the strawberry plant canopy, not reaching the organs where D. fovealis larvae were mainly detected. This study provides useful information on the cryptic habit of this pest that may help in designing efficient monitoring and control strategies.

Physiological and intestinal microbiota responses of sea cucumber Apostichopus japonicus to various stress and signatures of intestinal microbiota dysbiosis.

Identifying the signatures of intestinal dysbiosis caused by common stresses is fundamental to establishing efficient health monitoring strategies for sea cucumber. This study investigated the impact of six common stress experienced frequently in aquaculture on the growth performance, intestinal homeostasis and microbiota of sea cucumber, including thermal (23°C), hypoosmotic (22‰ salinity), ammonium (0.5 mg/L NH4 +-N), and nitrite (0.25 mg/L NO2 --N) stress exposure for 30 days, as well as starvation and crowding (6 kg/m3 density) stress exposure for 60 days. Results demonstrated that all stress led to reduced growth performance and digestive capacity of sea cucumber, along with varying degrees of oxidative stress and immune responses. Various stresses significantly altered the diversity, community structure (except for crowding stress), and composition of intestinal microbiota. The ratios of Bacteroidota: Proteobacteria (B: P) and Firmicutes: Proteobacteria (F: P) declined markedly compared to the control. Potentially pathogenic bacteria of Shewanellaceae, Vibrionaceae, and Moraxellaceae significantly increased under crowding, ammonium, and nitrite stress, respectively, whereas beneficial microbes of Achromobacter and Rhodobacteraceae were, respectively, enriched under hypoosmotic and starvation stresses. The complexity and stability of microbial ecological networks were further altered by these stresses. KEGG predictions revealed the reduced functional pathways of intestinal microbiota involved in host immunity under different stresses. Correlation analysis further confirmed a strong link between microbiota response and host immunity under different stresses. The increased abundance of Verrucomicrobia species could also be identified as the sensitive indicator for diagnosing whether the host was under stressful pressure by random forest analysis.

Biomimetic FeNi-MOF assisted intelligent theranostic hydrogels for pH identification and treatment of wounds

Wound healing poses a significant challenge because of prolonged treatment and compromised tissue regeneration caused by bacterial infections. Timely and efficient wound monitoring strategies are urgently needed to assess wound progression and healing in real time. To address this, we present an intelligent theranostic hydrogel system based on dual-site biomimetic FeNi metal–organic frameworks (MOFs) that enables intelligent wound pH monitoring and effective prevention of infection. Notably, the quasi-MOFs among the pristine bimetallic FeNi-MOFs and their derivatives demonstrate remarkable performance in mimicking superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) activities, along with exceptional stability. The synthesized MOF hydrogels act as wound dressings for monitoring wound status and promoting healing. By incorporating the quasi-MOF and a pH indicator (phenol red) into the hydrogel, the hybrid system allows accurate pH detection, reflecting dynamic wound conditions. Furthermore, visual changes captured by smartphones provide real-time signals for remote assessment of wound pH, facilitating precise antimicrobial therapy and on-demand pH monitoring. This multifunctional theranostic hydrogel represents a significant advancement in the development and optimization of MOF hydrogels, offering a platform for efficient wound healing and comprehensive wound assessment.

A multimodal sensing network based on synergistically sensitized polyaniline composites strategy for safety monitoring in pesticide spraying

Ensuring the safety of farmers during pesticide spraying is crucial in smart agricultural practices due to the potential risks of pesticide poison and the associated irreversible damages. However, there is a notable deficiency of efficient monitoring strategies for providing timely and cost-effective safety warnings. In this study, we have devised an advanced and real-time sensing network using polyaniline (PANI) composites to address this pressing concern. To enhance sensing performance, diverse quantum dots (QDs) sensitizers and rigid or flexible substrates are strategically integrated with PANI, resulting in sensors tailored to different sensing capabilities. Specifically, the proton conduction is heightened by incorporating carboxyl graphene QDs (GQDs) into PANI, obtaining the superior gas-sensing performance for NH3 detection. Moreover, a strain sensor employing Ti3C2Tx QDs (MQDs) and a flexible thermoplastic polyurethane (TPU) substrate has been developed for real-time monitoring of physical biosignals. Finally, a smart sensing network with real-time display integrated with the above sensors is designed to detection NH3 concentration released from pesticide volatilization and physiological tracking in cases of mild pesticide poisoning. This sensing network can wirelessly interface with a laptop for convenient safety monitoring of farmers engaged in smart agriculture. The proof of concept demonstrated by this sensing network holds promise for enhancing safety monitoring in smart agriculture.

A risk assessment framework utilizing bivariate copula for contaminate monitoring in groundwater.

Regular groundwater quality monitoring in resource-constrained regions present formidable challenges in terms of funding, testing facilities and manpower; necessitating the development of easily implementable monitoring techniques. This study proposes a copula-based risk assessment model utilizing easily measurable indicators (e.g., turbidity, alkalinity, pH, total dissolved solids (TDS), conductivity), to monitor the contaminates in groundwater which are otherwise difficult to measure (i.e., iron, nitrate, sulfate, fluoride, etc.). Preliminary correlation between the indicators and the target contaminates were identified using Pearson coefficient. Best representative univariate distributions for these pairs were selected using the Akaike Information Criterion (AIC), which were used in the formulation of the copula model. Validation against observed data showcased the model's high accuracy, supported by consistent Kendall Tau correlation coefficients. Through this model, conditional probabilities of the contaminants not exceeding the permissible limits set by the Bureau of Indian Standards (BIS) were calculated using indicator concentration. Notably, an inverse correlation between iron concentration and conductivity was noted, with the likelihood of iron exceeding BIS limits decreasing from 90 to 50% as conductivity rose from 500 to 2000 micromhos/cm. TDS emerged as a pivotal indicator for nitrate and sulfate concentrations, with the probability of sulfate surpassing 10mg/l decreasing from 75 to 25% as TDS increased from 250 to 750mg/l. Likewise, the probability of nitrate exceeding 1mg/l decreased from 90 to 60% with TDS levels reaching 1500mg/l. Furthermore, a 63% probability of fluoride concentrations remaining below 1mg/l was observed at turbidity levels of 0-10 NTU. These findings hold significant implications for policymakers and researchers since the model can provide crucial insights into the risks associated with the contaminates exceeding the permissible limit, facilitating the development of an efficient monitoring and management strategies to ensure safe drinking water access for vulnerable populations.

Multi-Task AI-driven undetermined blind source identification for modal identification of large scale structures

The effective management of aging infrastructure represents a critical concern globally, exerting profound socio-economic influences and occupying a prominent position on the political agenda. The escalating occurrences of catastrophic collapses in recent years underscore the pressing requirement for efficient Structural Health Monitoring (SHM) strategies. These strategies play a pivotal role in aiding decision-making processes related to prioritizing interventions and rehabilitation efforts. Among SHM systems, those utilizing Operational Modal Analysis (OMA) through vibration-based techniques have gained popularity due to their non-destructive nature, comprehensive damage assessment capabilities, and relative ease of automation. Despite their advantages, current OMA methods encounter significant scalability challenges, primarily attributable to extensive computational demands and the necessity for substantial expert involvement. In this context, recent advancements in the field of artificial intelligence (AI) present a promising solution to address these scalability issues, opening the door for the development of next-generation SHM systems. This study introduces an innovative Multitask Learning Deep Neural Network (MTL-DNN) model designed for rapid and automated blind source modal identification of structures. Integrating the concepts of undetermined second-order blind source identification (SOBI) into the architecture of the network, the proposed model can reveal the independent modal components present in the raw response acceleration data with minimum computational burden. The presented results demonstrate the quasi-instantaneous modal identification capabilities of the proposed architecture, enabling the estimation of more components than the number of sensors.

Estimation of Oil Palm Tree Quantity Using Oil Palm Ecognition and SAGA GIS Based on UAV Imagery (Case Study: Dumai, Riau)

Abstract Oil palm is a plantation commodity in Indonesia that drives the growth of the national economy. The growth of oil palm is rapidly expanding every year, thus requiring efficient land monitoring strategies. Tree counting is one technique used to monitor current conditions in vast plantation areas. Utilizing UAV technology in agricultural mapping is the best option as a tool for capturing high-resolution aerial photos. The objective of this research is to automatically count the number of oil palm trees by comparing two methods and algorithms. GEOBIA (Geography-Based Image Analysis) and Deep Learning classification are methods used for automatic object counting. In this study, high-resolution aerial photo data was utilized, making object detection much more feasible. The research applied the Template Matching and Watershed Segmentation algorithm models in a sample area of 32.5 hectares divided into two blocks: Unknown-2 and E-1. It can be concluded that both methods, with their respective algorithm models, are sufficiently relevant for use in the automatic counting process of oil palm trees. This is evidenced by the accuracy test error values, which are not more than 15%, indicating that the counted oil palm tree results can be used for further data analysis.

Environmental DNA metabarcoding reflects spatiotemporal fish community shifts in the Scheldt estuary

Estuarine ecosystems face increasing anthropogenic pressures, necessitating effective monitoring methods to mitigate their impacts on the biodiversity they harbour. The use of environmental DNA (eDNA) based detection methods is increasingly recognized as a promising tool to complement other, potentially invasive monitoring techniques. Integrating such eDNA analyses into monitoring frameworks for large ecosystems is still challenging and requires a deeper understanding of the scale and resolution at which eDNA patterns may offer insights in species presence and community composition space and time. The Scheldt estuary, characterized by its diverse habitats and complex currents, is one of the largest Western European tidal river systems. Until now, it remains challenging to obtain accurate information on fish communities living in and migrating through this ecosystem, consequently confining our knowledge to specific locations. To explore the potential of eDNA based monitoring, we simultaneously combine stow net fishing with eDNA metabarcoding, to assess spatiotemporal shifts in the Scheldt estuary's fish communities. In total, we detected 71 fish species in the estuary using eDNA metabarcoding, partly overlapping with historic fish community data gathered at the different study locations and in contrast to only 42 species using stow net fishing during the same survey period. Community compositions found by both detection methods varied among sampling locations, driven by a clear correlation to the salinity gradient. Limited effects of sampling depth and tide were observed on the eDNA metabarcoding data, allowing a significant reduction of the eDNA sampling effort for future eDNA fish monitoring campaigns in this study system. Our results further demonstrate that seasonal shifts in fish species occurrence can be detected using eDNA metabarcoding. Combining eDNA metabarcoding and stow net fishing further enhances our understanding of this vital waterway's diverse fish populations, allowing a higher resolution and more efficient monitoring strategy.

Facilitation cascades across space: Monitoring estuarine foundation species from satellites to the microscope

AbstractEstuaries are productive ecosystems that are vulnerable to human impacts and environmental disturbances. Estuaries are often inhabited by foundation species that facilitate other organisms by creating and modifying habitats. However, spatiotemporal variability and underpinning drivers of facilitation from estuarine foundation species are poorly studied, especially in the context of monitoring and conservation. Here, we combined the analyses of satellite, drone, and camera images, close‐up field sampling of quadrats and individuals, and laboratory microscopy, to quantify co‐occurrences between the cockle Austrovenus stutchburyi and green macroalga Ulva spp., and their associated invertebrate communities, across spatial resolutions and environmental gradients in a New Zealand estuary. We found higher abundance of Ulva in winter and higher abundance of Austrovenus at a site near the ocean. Furthermore, Ulva was, across space and time, often attached to Austrovenus, either live or dead shells, suggesting a hierarchy between a primary (cockle) and secondary (macroalgae) foundation species. Finally, Ulva provided a better habitat for mobile invertebrates than Austrovenus, and thereby increased estuarine diversity through a density‐dependent facilitation cascade, which also was consistent in space and time. Overall, our study showed that facilitation cascades can be indirectly inferred and monitored through remote sensing of foundation species using supplementary imaging methods, and that integration of data across scales allows researchers to better understand the drivers and consequences of facilitation from foundation species. These insights may aid in the development of efficient monitoring strategies and ultimately improve the management of estuarine ecosystems.

Wastewater-Based Epidemiology for Viral Surveillance from an Endemic Perspective: Evidence and Challenges.

Wastewater-based epidemiology (WBE) is currently used to monitor not only the spread of the viral SARS-CoV-2 pandemic but also that of other viruses in endemic conditions, particularly in the absence of syndromic surveillance. The continuous monitoring of sewage requires high expenditure and significant time investments, highlighting the need for standardized methods and structured monitoring strategies. In this context, we conducted weekly wastewater monitoring in northwestern Tuscany (Italy) and targeted human adenovirus (HAdV), norovirus genogroup II (NoVggII), enterovirus (EV), and SARS-CoV-2. Samples were collected at the entrances of treatment plants and concentrated using PEG/NaCl precipitation, and viral nucleic acids were extracted and detected through real-time reverse transcription qPCR. NoVggII was the most identified target (84.4%), followed by HAdV, SARS-CoV-2, and EV. Only HAdV and EV exhibited seasonal peaks in spring and summer. Compared with data that were previously collected in the same study area (from February 2021 to September 2021), the results for SARS-CoV-2 revealed a shift from an epidemic to an endemic pattern, at least in the region under investigation, which was likely due to viral mutations that led to the spreading of new variants with increased resistance to summer environmental conditions. In conclusion, using standardized methods and an efficient monitoring strategy, WBE proves valuable for viral surveillance in pandemic and epidemic scenarios, enabling the identification of temporal-local distribution patterns that are useful for making informed public health decisions.

Monkeypox virus: insights into pathogenesis and laboratory testing methods.

The monkeypox virus (MPXV) is a zoonotic pathogen that transmits between monkeys and humans, exhibiting clinical similarities with the smallpox virus. Studies on the immunopathogenesis of MPXV revealed that an initial strong innate immune response is elicited on viral infection that subsequently helps in circumventing the host defense. Once the World Health Organization (WHO) declared it a global public health emergency in July 2022, it became essential to clearly demarcate the MPXV-induced symptoms from other viral infections. We have exhaustively searched the various databases involving Google Scholar, PubMed, and Medline to extract the information comprehensively compiled in this review. The primary focus of this review is to describe the diagnostic methods for MPXV such as polymerase chain reaction (PCR), and serological assays, along with developments in viral isolation, imaging techniques, and next-generation sequencing. These innovative technologies have the potential to greatly enhance the accuracy of diagnostic procedures. Significant discoveries involving MPXV immunopathogenesis have also been highlighted. Overall, this will be a knowledge repertoire that will be crucial for the development of efficient monitoring and control strategies in response to the MPXV infection helping clinicians and researchers in formulating healthcare strategies.

Towards Digital Twins in Healthcare: Optimizing Operating Room and Recovery Room Dynamics

Efficient resource monitoring and organizational strategies are crucial for improving healthcare processes. Digital twins have been applied in many healthcare applications, and offer promising solutions. In our paper, we present a new digital twins-based approach that helps to improve the entire operating block management. We developed a prototype that allows for easy tracking of patients among the block, monitoring key areas: entrances, exits, and operating and recovery rooms. The system provides helpful services to medical staff, including statistical reports and scheduling assistance. Our solution was developed in partnership with the local hospital in Parma, and it seamlessly integrates with the existing legacy system to simplify healthcare operations.

Antibiotic resistance determinants among carbapenemase producing bacteria isolated from wastewaters of Kathmandu, Nepal

The emergence of carbapenem resistant bacteria (CRB) possesses a remarkable threat to the health of humans. CRB and carbapenem resistance genes (CRGs) have frequently been reported in clinical isolates from hospitals, however, their occurrence and distribution in wastewaters from various sources and river water have not been emphasized in Nepal. So, this study aimed to detect carbapenem resistant bacterial isolates and their resistance determinants in river water and different types of wastewaters. River water and both untreated and treated wastewater samples from hospitals, pharmaceutical industries, and municipal sewage were collected in summer and winter seasons. From 68 grab wastewater samples, CRB were detected only in 16 samples, which included eight hospital wastewater, and four each from untreated municipal sewage and river water. A total of 25 CRB isolates were detected with dominance of E. coli (44.0%) and K. pneumoniae (24.0%). The majority of the isolates harbored blaNDM-1 (76.0%), followed by blaOXA (36.0%) and blaKPC (20.0%) genes. Hospital wastewater majorly contributed to the presence of blaNDM-1, blaKPC, and blaOXA along with intI1 genes compared to river water and untreated municipal sewage, especially during the winter season. However, CRB were not detected in treated effluents of hospitals and municipal sewage, and both influents and effluents from pharmaceutical industries. The combined presence of each blaNDM-1 &blaOXA and blaKPC &blaOXA occurred in 16.0% of the bacterial isolates. The increased minimum inhibitory concentration (MIC) of meropenem was significantly associated with the presence of CRGs. The results of this study highlight the significance of carbapenem resistance in bacteria isolated from wastewater and river water, and underscore the necessity for efficient monitoring and control strategies to prevent the dispersion of carbapenem resistance in the environment and its potential consequences on human health.

Proposal for priority emerging pollutants in the Nakdong river, Korea: Application of EU watch list mechanisms

The Nakdong River, the longest in Korea, has received numerous pollutants from heavily industrialized and densely populated areas while being used as a drinking water source. A number of research have reported occurrences of emerging pollutants (EPs) in the river. The results requested efficient monitoring and systematic management strategies such as EU watch list under Water Framework Directive. The aim of this study is to propose a watch list through preliminary monitoring of the river and risk-based prioritization approach. As candidates for monitoring target, 632 substances were selected based on literature and database searches. Among them, 175 substances were subjected to target screening method whereas 457 were evaluated via suspect screening. A risk-based prioritization was applied to substances quantified through target screening based on concentrations, and a scoring-based prioritization was applied to substances tentatively identified through suspect screening. Sampling campaigns (n = 12) were conducted from October 2020 to September 2021, at 8 sampling sites along the river. As a result, 130 target substances were quantified above the LOQ. Among the 21 substances whose priority score was assigned through risk-based prioritization, telmisartan and iprobenfos were identified with very high environmental risk while candesartan, TBEP, imidacloprid, azithromycin and clotrimazole were classified with high or intermediate risk. As result of the scoring system for 39 tentatively identified substances, 6 substances (benzophenone, caprolactam, metolachlor oxanilic acid, heptaethylene glycol, octaethylene glycol and pentaethylene glycol), which were then confirmed with reference standards, showed a potential environmental risk. Those substances prioritized through target and suspect screening followed by scoring systems can be a subset for the watch list and potential targets for nationwide water quality monitoring program in the future.

Current progress in developing a MARPOL Annex VI enforcement strategy in the Bonn Agreement through remote measurements

The harmful environmental and human health consequences of air pollution from ships are widely acknowledged, leading to their regulation under IMO’s MARPOL Annex VI, setting limits on the emission of SOx and NOx from ships. This article provides an overview of the current progress made by the Bonn Agreement (BA) in adopting a MARPOL Annex VI monitoring strategy. In 2019, the North Sea Ministers of the parties of the BA formally agreed to include MARPOL Annex VI within the jurisdiction of the BA. Through cooperation on MARPOL Annex VI enforcement at sea, the BA aims to attain a significant decrease in air pollution violations, similar to the accomplished outcomes observed in the past three decades regarding oil pollution. Subsequently, in 2021, a BA MARPOL Annex VI expert workshop was convened, which identified a series of action points. To effectively address these action points, two working groups (WGs) were established: the Strategic Operational WG and the Technical WG. The primary objective of the WGs is the exchange of best practices, monitoring outcomes and the harmonization of enforcement practices among the Parties of the BA, with the aim of implementing an efficient monitoring and enforcement strategy within the BA. The BA has recognized that remote monitoring operations play a pivotal role within this strategy. This article explores the extent to which remote measurements can contribute to the development of a regional enforcement strategy for MARPOL Annex VI within the framework of the BA.

Current Status and Emerging Trends in Colorectal Cancer Screening and Diagnostics.

Colorectal cancer (CRC) is a prevalent and potentially fatal disease categorized based on its high incidences and mortality rates, which raised the need for effective diagnostic strategies for the early detection and management of CRC. While there are several conventional cancer diagnostics available, they have certain limitations that hinder their effectiveness. Significant research efforts are currently being dedicated to elucidating novel methodologies that aim at comprehending the intricate molecular mechanism that underlies CRC. Recently, microfluidic diagnostics have emerged as a pivotal solution, offering non-invasive approaches to real-time monitoring of disease progression and treatment response. Microfluidic devices enable the integration of multiple sample preparation steps into a single platform, which speeds up processing and improves sensitivity. Such advancements in diagnostic technologies hold immense promise for revolutionizing the field of CRC diagnosis and enabling efficient detection and monitoring strategies. This article elucidates several of the latest developments in microfluidic technology for CRC diagnostics. In addition to the advancements in microfluidic technology for CRC diagnostics, the integration of artificial intelligence (AI) holds great promise for further enhancing diagnostic capabilities. Advancements in microfluidic systems and AI-driven approaches can revolutionize colorectal cancer diagnostics, offering accurate, efficient, and personalized strategies to improve patient outcomes and transform cancer management.

Real-time monitoring of biomass during Escherichia coli high-cell-density cultivations by in-line photon density wave spectroscopy.

An efficient monitoring and control strategy is the basis for a reliable production process. Conventional optical density (OD) measurements involve superpositions of light absorption and scattering, and the results are only given in arbitrary units. In contrast, photon density wave (PDW) spectroscopy is a dilution-free method that allows independent quantification of both effects with defined units. For the first time, PDW spectroscopy was evaluated as a novel optical process analytical technology tool for real-time monitoring of biomass formation in Escherichia coli high-cell-density fed-batch cultivations. Inline PDW measurements were compared to a commercially available inline turbidity probe and with offline measurements of OD and cell dry weight (CDW). An accurate correlation of the reduced PDW scattering coefficient µs ' with CDW was observed in the range of 5-69 g L-1 (R2 = 0.98). The growth rates calculated based on µs ' were comparable to the rates determined with all reference methods. Furthermore, quantification of the reduced PDW scattering coefficient µs ' as a function of the absorption coefficient µa allowed direct detection of unintended process trends caused by overfeeding and subsequent acetate accumulation. Inline PDW spectroscopy can contribute to more robust bioprocess monitoring and consequently improved process performance.

Designing a Long-term Occupancy Monitoring Plan for a Cryptic Reptile

Monitoring populations of cryptic reptiles is challenging because of their crypticity. Occupancy monitoring is a useful technique for local populations, but seasonal unavailability for detection can result in large swings in apparent annual occupancy. We used data from 5 yr of occupancy surveys, and the observed sampling and process error, to evaluate the power to detect true change in occupancy under a range of sampling scenarios and occupancy trends for a cryptic reptile, Mojave Desert Tortoise (Gopherus agassizii) None of the sampling strategies that we tested had the power to detect a 1% per annum increase in true occupancy over a 10-yr period and required ≥70 sites to detect a 1% decline in occupancy. For a 2% per annum change in true occupancy, 50 sites were needed and for a 3–4% per annum change 20–30 sites were needed. Power to detect a 4% per annum decline in occupancy was >80% for all number of secondary visits and number of sites if the number of sites was ≥50, but required seven visits to ≥60 sites to detect a 4% increase in occupancy Multiple sampling designs provided equivalent power. For example, both designs of 80 sites with three secondary visits and 50 sites with seven secondary visits yielded >50% power on average to detect 1–4% per annum changes in true occupancy. These results can help local managers of Mojave Desert Tortoises and other cryptic reptiles to design optimally efficient occupancy monitoring strategies.

An Intelligent Secure Monitoring Phase in Blockchain Framework for Large Transaction

Blockchain is the key concept for security purposes for digital applications. But, in some cases, the effectiveness of the malicious behavior has degraded the security function of the blockchain. So, to enrich the blockchain process prediction and to neglect the malicious event from the data broadcasting medium is very important. So, the current research article intends to develop an efficient monitoring strategy based on incorporating deep features. Hence, the designed paradigm is termed as Lion-based Convolutional Neural Model (LbCNM) with serpent encryption. Before performing the encryption process, the novel LbCNM parameters have been activated to monitor the data process channel in the blockchain environment. Here, the malicious behaviors were estimated by incorporating the known and unknown user behavior in the Lion fitness model. During the execution, the fitness formulation of Lion is acted in the classification layer of the convolutional model. Once the present malicious characteristics have been detected, it is neglected from the data broadcasting channel. Hereafter, the transactional data has been encrypted and stored in the specific cloud. The planned strategy is verified in the python platform. The successful performance of the LbCNM with serpent has been analyzed with some key parameters like confidential rate, accuracy, data overhead, and processing time.

Soil Salinity Patterns in an Olive Grove Irrigated with Reclaimed Table Olive Processing Wastewater

The agricultural use of saline table olive processing wastewater enables the implementation of closed water cycles in this socioeconomically important industry for rural southern Spain and relieves environmental, economic, and legal burdens. To allow growers to evaluate and guarantee adequate long-term soil and plant conditions when irrigating with such regenerated wastewaters, efficient soil monitoring strategies are needed. Field-scale monitoring with electromagnetic induction sensing, after one (2013) and five years (2017) of irrigation with regenerated wastewater with average electrical conductivity (EC) near 6 dS m−1 in an olive orchard in southern Spain, showed accumulation of highly conductive material in the subsoil in relation to local topography and soil characteristics. Laboratory analysis of the soil water revealed strongly varying patterns of EC during the growing season and across the olive grove, which were attributed to dilution and concentration effects due to rainfall and evaporation, respectively. Visual inspection and leaf analyses revealed no negative effects on the olive trees. Apparent electrical conductivity (ECa), measured in between the tree rows in 2013, showed a linear relationship with surface soil EC1:5 under the drippers and allowed identification of areas with high ECa in the low elevation zones of the farm, due to the presence of shallow perched saline water tables. A second ECa measurement in 2017 showed similar spatial ECa patterns and was used to estimate the distribution of soil EC across the soil profile using inversion software, although no unique field-wide relationships with soil properties could be inferred, possibly as a consequence of spatially variable soil clay and water contents, due to the influence of the topography. Despite the implementation of a more conservative irrigation strategy since 2015, results showed that the salinity has increased since 2013 in about 15% of the study area, with the largest increments in the deepest horizons.