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There are significant challenges in machine learning models due to information security and data privacy issues. In traditional machine learning approaches, the data used to train the centralized model may be sensitive leading to privacy issues. Federated learning overcomes these issues by following the concept of bringing the code to the data instead of data to the code. In today's era of digital transformation, researchers and industries are focusing on cloud-based technologies. New approaches such as microservice models, docker, kubernetes, containers, and virtual machines are being used by IT industries to develop and deploy applications. We make use of software containers to deploy federated learning models and then we identify security vulnerabilities and resource usage in federated learning model. Flask, docker, cAdvisor, prometheus, and grafana are integrated to monitor the resource usage of the federated learning model in terms of CPU utilization, memory utilization, network traffic usage, and disk usage. The generated federated learning model is deployed on docker, and security vulnerabilities and performance are analyzed using open-source tools. The result shows that the generated model is secure and performs efficiently. [ABSTRACT FROM AUTHOR]

The main authorities and practitioners face crucial challenges in safeguarding wildlife and conservation areas due to massive direct anthropogenic disturbances, such as illegal logging, habitat conversion into human development areas, and wildlife poaching. Therefore, measuring the effectiveness of wildlife and habitat protection is essential for wider conservation intervention. This study aimed to examine patrol effectiveness using measurable effort and parameters of SMART-based data collection in Rimbang Baling and Bukit Betabuh, Sumatra. We conducted a series of planned SMART-based data collections in designated patrol blocks of Rimbang Baling from 2014 to 2018. We implemented catch per unit effort (CPUE) measurement by the number of detected illegal activities and then a generalized linear model (GLM) to assess the relationship between patrol efforts and threat numbers. This study covered 209 patrols within 2,129 patrol days, 13,153.05 patrol hours, and 14,864 km. The CPUE value decreased from 0.381 to 0.191. Our GLM showed that patrol efforts significantly reduced threat numbers. This study provides new knowledge regarding SMART-based data collection and its ability to increase the effectiveness of patrols in promoting better protection and threat reduction in conservation areas in Indonesia. [ABSTRACT FROM AUTHOR]

The main authorities and practitioners face crucial challenges in safeguarding wildlife and conservation areas due to massive direct anthropogenic disturbances, such as illegal logging, habitat conversion into human development areas, and wildlife poaching. Therefore, measuring the effectiveness of wildlife and habitat protection is essential for wider conservation intervention. This study aimed to examine patrol effectiveness using measurable effort and parameters of SMART-based data collection in Rimbang Baling and Bukit Betabuh, Sumatra. We conducted a series of planned SMART-based data collections in designated patrol blocks of Rimbang Baling from 2014 to 2018. We implemented catch per unit effort (CPUE) measurement by the number of detected illegal activities and then a generalized linear model (GLM) to assess the relationship between patrol efforts and threat numbers. This study covered 209 patrols within 2,129 patrol days, 13,153.05 patrol hours, and 14,864 km. The CPUE value decreased from 0.381 to 0.191. Our GLM showed that patrol efforts significantly reduced threat numbers. This study provides new knowledge regarding SMART-based data collection and its ability to increase the effectiveness of patrols in promoting better protection and threat reduction in conservation areas in Indonesia.

Utilizing survey information obtained from five irrigation schemes in southeastern Africa, we investigated the influence of agricultural innovation platforms (AIPs) and monitoring tools on a range of farm and household outcome indicators. Doubly robust estimation was used to measure the effects of these interventions, with a variety of other methods used for robustness checks. Involvement in AIP activities and using monitoring tools was found to be statistically associated with increased on-farm income together with an increased capacity to fund child education. Participation in AIPs also had a significant positive influence on off-farm income and reduced food shortages. Moreover, spillover effects were accounted for in the estimations and statistically significant positive effects were found regarding on-farm income for non-participants. These findings suggest that interventions with strong agricultural innovation system approaches in irrigation schemes in Africa could provide significant societal benefits.

In this paper, we develop a daily-frequency measure of economic and policy uncertainty for Chile, employing information obtained from Twitter accounts using web scraping techniques and following closely the methodology proposed by Baker et al. (2016). Our proposed measure, called DEPUC, aims to capture the level of general disagreement -a proxy for economic and policy uncertainty-in topics such as the economy, economic policies, uncertainty about particular events, and Chile's conjuncture situation. The index, available from 2012 onwards, shows significant hikes that coincide with several local and international episodes that provoked extraordinary levels of uncertainty in Chile, especially after the events around the civil protests in mid-October 2019 and the start of the COVID-19 pandemic in mid-March 2020. An empirical exercise reveals that the proposed measure is a significant determinant of the nominal exchange rate dynamics, especially when this variable's magnitude is high and a week after the shock occurs. On the contrary, when the exchange rate is low, the impact of uncertainty on this variable is quantitatively smaller for any forecasting horizon. These features, and others discussed in the paper, highlight the usefulness of the proposed metric as an additional indicator that policymakers can incorporate into their monitoring toolkit. [ABSTRACT FROM AUTHOR]

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While suspended particles play many important roles in the marine environment, their concentrations are very small in the deep sea, making observation difficult with existing methods: water sampling, optical sensors, and special imaging systems. Methods are needed to fill the lack of environmental baseline data in the deep sea, ones that are inexpensive, quick, and intuitive. In this study we applied object detection using deep learning to evaluate the variability of suspended particle abundance from images taken by a common stationary camera, “Edokko Mark 1”. Images were taken in a deep-sea seamount in the Northwest Pacific Ocean for approximately one month. Using the particles in images as training data, an object detection algorithm YOLOv5 was used to construct a suspended particle detection model. The resulting model successfully detected particles in the image with high accuracy (AP50 > 85% and F1 Score > 82%). Similarly high accuracy for a site not used for model training suggests that model detection accuracy was not dependent on one specific shooting condition. During the observation period, the world’s first cobalt-rich ferromanganese crusts excavation test was conducted, providing an ideal situation to test this model’s ability to measure changes in suspended particle concentrations in the deep sea. The time series showed relatively little variability in particle counts under natural conditions, but there were two turbidity events during/after the excavation, and there was a significant difference in numbers of suspended particles before and after the excavation. These results indicate that this method can be used to examine temporal variations both in small amounts of naturally occurring suspended particles and large abrupt changes such as mining impacts. A notable advantage of this method is that it allows for the possible use of existing imaging data and may be a new option for understanding temporal changes of the deep-sea environment without requiring the time and expense of acquiring new data from the deep sea.

Utilizing survey information obtained from five irrigation schemes in southeastern Africa, we investigated the influence of agricultural innovation platforms (AIPs) and monitoring tools on a range of farm and household outcome indicators. Doubly robust estimation was used to measure the effects of these interventions, with a variety of other methods used for robustness checks. Involvement in AIP activities and using monitoring tools was found to be statistically associated with increased on-farm income together with an increased capacity to fund child education. Participation in AIPs also had a significant positive influence on off-farm income and reduced food shortages. Moreover, spillover effects were accounted for in the estimations and statistically significant positive effects were found regarding on-farm income for non-participants. These findings suggest that interventions with strong agricultural innovation system approaches in irrigation schemes in Africa could provide significant societal benefits. [ABSTRACT FROM AUTHOR]

Cloud computing has swiftly established itself as the norm in its field as a result of the advantages described above. In an attempt to reduce the amount of time spent on infrastructure upkeep, an increasing number of businesses are moving their operations to the cloud. As a result, maintaining a cloud environment has proved to be exceedingly difficult. It is necessary to have an efficient cloud monitoring system in order to reduce the workload associated with administration and improve cloud operation. The cloud monitoring service is beneficial since it has the potential to improve performance and make administration easier. The administration of Quality of Service (QoS) parameters for cloudhosted, virtualized, and physical services and applications is one of the most important responsibilities of cloud monitoring. As a result, cloud management software retains a record of both actions and services, and it also conducts dynamic setups of the cloud in order to increase operational effectiveness. The performance of businesses and businesses as a whole was examined in this article, as was the influence that cloud-ready programs and tools have on that performance, as well as the advantages that may be obtained from adopting such programs and products. [ABSTRACT FROM AUTHOR]

Adherence to proper environmental cleaning practices is critical in food establishments. To validate cleanliness, cleaning practices should be routinely monitored, preferably by a rapid, reliable, and cost-effective method. The aim of this study was to determine whether a correlation exists between ATP bioluminescence measurements and selected microbial assessments in studies conducted in food establishments. A systematic literature review and meta-analysis was conducted using the principles of preferred reporting items for systematic reviews and meta-analyses. Twelve online databases and search engines were selected for the review. Peer-reviewed articles published in English between January 2000 and July 2020 were included in the search. From a total of 19 eligible studies, 3 that included Pearson correlation coefficients (r) between ATP bioluminescence measurements and microbial assessments were used for the meta-analysis calculations. Only the fixed-effect model produced a strong correlation because one value dominated the estimates: r = 0.9339 (0.9278, 0.9399). In contrast, both the random effects model, 0.2978 (0.24, 0.3471), and the mixed effects model, r = 0.3162 (−0.0387, 0.6711), indicated a weak relationship between ATP bioluminescence and microbial assessments, with no evidence of a strong correlation. The meta-analysis results indicated no sufficient evidence of a strong correlation between ATP bioluminescence measurements and microbial assessments when applied within food establishments. This lack of evidence for a strong correlation between the results of these two monitoring tools suggests that food establishments cannot depend on only one method. Yet, with immediate feedback and quantification of organic soiling, ATP bioluminescence could be an effective monitoring tool to use in food establishments. [ABSTRACT FROM AUTHOR]

Seabirds are the most endangered avian taxa on Earth, with over a third of species globally threatened. To help slow their decline, conservation physiology seeks to determine how seabird responses to climatic and anthropogenic threats influence demographic processes, but it is not widely utilized in monitoring.A wide variety of metrics and methods are available to the conservation practitioner; thus, the correct method selection is paramount. This is a review of physiological tools to assess both individual and population health in seabirds, outlining which tools could be accessible enough to incorporate into conservation management strategies to increase the efficacy and range of population monitoring.Ultimately, the cost and expertise required limits the use of some tools in a community‐based management context, but they are useful in academic research in collaboration with conservation projects to generate data to inform management strategies for threatened species.The value of the data available from particular tools is weighed against the invasiveness of the methodology to assess the practicality of incorporating physiological tools into routine seabird monitoring programmes.A broader application of conservation physiology tools in a monitoring context could help manage threatened species; this paper summarizes a set of physiological variables from minimally invasive samples that have potential to assist in monitoring population health for seabird conservation. The full potential of these physiological tools is yet to be realized in seabirds. [ABSTRACT FROM AUTHOR]

The article is devoted to the development of methodological tools for tracking the parameters of technological transformation in the production sector of the regional economy. The definition of the concept of monitoring the technological transformation of industry is given, the means, methods, information base and observed parameters of the monitoring process are identified and described. The types of tools for monitoring the technological transformation of industrial enterprises in the region are highlighted. In the article, as part of the justification for monitoring the technological transformation of industry, its purpose, object, subjects, as well as consumers of monitoring results are defined, which include the relevant executive authorities of the subject of the Russian Federation, scientific organizations specializing in conducting scientific research in the field of regional industrial development, and regional industrial enterprises. The general scheme of monitoring of technological transformation in industry is given. Two tools for monitoring the technological transformation of the Omsk Region industry are proposed — the information and analytical system and the expert platform. The tools are based on automating the processing of information about the components of technological transformation using web tools, as well as attracting experts to monitor the transformation processes in the production base of industrial enterprises and analyze unstructured material. The typical difficulties of using tools for monitoring the technological transformation of regional industry are summarized. The results of the analysis of the technological transformation of the regional industry determined by the use of monitoring serve as the basis for the implementation by consumers of the results of monitoring regulatory measures to improve the regulatory and organizational and managerial conditions for the participation of industrial enterprises of the Omsk region in the processes of technological transformation of the production base.

Despite the strong decrease in antimicrobial use in the French poultry and pig sectors over the last decade, room for improvement remains. A participatory approach was set up in France, involving representatives of veterinarians, the pig and poultry industries, technical institutes, the French Ministry of Agriculture, and researchers, to further improve how antimicrobials are used on farms. By successively defining a shared, long-term vision of future antimicrobial use on farms, identifying lock-in mechanisms impeding this future vision from being realized, and articulating practical questions on how to move in the desired direction, the group rapidly reached a consensus. The results highlight the need for consensual standardized monitoring tools that would allow farmers and veterinarians to jointly monitor the health, welfare, antimicrobial resistance, and antimicrobial use on farms. Other results relate to better communication and training for citizens regarding animal health, animal welfare, and proper antimicrobial use; some benefits but also counterproductive effects of antibiotic-free labels that imperil animal health and welfare; the economic competitiveness of farms on international markets; and the economic sustainability of farm animal veterinary practices. These results call for a concerted way to produce tools for farmers and veterinarians and the broader involvement of other food sector actors. [ABSTRACT FROM AUTHOR]

As in many other countries, Peru has the Water Quality Standard (WQS) as the primary tool for managing and diagnosing water resources. An analysis of variable by variable was applied to define water quality as poor or good by setting concentration limits. A second group of tools commonly used are Biotic Indexes based on tolerance of benthic macroinvertebrates to pollution, that reflect the impacts caused by a group of variables, even though they cannot identify which variables determine the viability of the ecosystem. This research proposes to include the Stable States approach to evaluate the ecological integrity in central‐Andes rivers to explore an alternative approach with the capacity to represent a broader number of factors through multivariate analyses. A ten‐year database of biological, physical, and chemical variables measured in five Andean Rivers was evaluated. Our results suggest these rivers fluctuate into two seasonal stable states (wet and dry season), accounting for approximately 41% of the system variability. The wet season stable state was defined by high levels of suspended solids and turbidity, coliform, phosphorus, and some metals. During the dry season, the key variables were dissolved solids, barium, pH, and dissolved oxygen. Likewise, there seems to be a third alternative state influenced by human pressures driven by organic pollution variables that exceed the WQS. Regarding water quality, the concentrations of coliforms, phosphorus, and lead usually exceeded the limits in two stations, but not every year. Between bioindicator indexes, the ABI represented the ecological condition more accurately than EPT. [ABSTRACT FROM AUTHOR]

In a quickly growing world, there is increasing demand for a secure food supply, a reduction in the intensive use of natural resources, and the enhancement of sustainability for future long-term maintenance. In this regard, plant health, including fruit and foliar diseases, which can cause a vast amount of crop loss, potentially has a huge effect on food security. The integration of new, innovative technological tools and data management techniques into the traditional agricultural practices is a promising approach to combat future food shortages. The use of the same principles of precision agriculture to "do the right thing, at the right time, in the right place" will allow for providing detailed, real-time information that will help farmers to protect their crops and choose healthier, as well as more productive, farming methods. The presented narrative review reports on several items of innovation, including monitoring and surveillance, diagnostic, and decision-making tools, with a specific focus devoted to digital solutions that can be applied in agriculture in order to improve the quality and the speed of the decision-making process and specifically, to set up a digital collaboration that can be crucial under certain circumstances to reach sustainability goals, particularly in the Near East and North Africa (NENA) Region, where an effective and rapid solution for phytosanitary control is needed. [ABSTRACT FROM AUTHOR]

The elaboration and development of monitoring (diagnostic and prognostic) tools for industrial systems has been one of the main concerns of the researchers for many years, so that many researches and studies have been developed and proposed, especially concerning discrete event systems (DES), which occupy an important class of industrial systems. However, the use of modeling tools to ensure these operations become a complex and exhausting task, while the complexity of industrial systems has been increasing incessantly. Therefore, the development of more and more sophisticated techniques is required. In this context, the use of artificial neural networks (NN) seems interesting, because thanks to their automatics and intelligent algorithms, the NN could handle perfectly DES diagnosis and prognosis problems. For this purpose, in the following papers, we propose an intelligent approach based on feed-forward neural network, which will deal with fault diagnosis and prognosis in DES, so that the events generated by the DES, will be presented and analyzed by the neural network in real-time, in order to perform an online diagnosis and prognosis. [ABSTRACT FROM AUTHOR]

This article discusses the issues of ensuring the effectiveness of monitoring for correct estimates of parameter manifestations (start time, intensity and severity) of catastrophic natural (earthquakes, floods and others) and man-made (water and air pollution) processes. Organizational structure of monitoring with reasonable time and address of the measurement mode taking into account the main features and stages of forming controlled natural and technogenic processes should ensure the acquisition of data needed for further calculations of the start time of earthquakes, territorial distribution and levels of water or air pollution, etc. Besides, the article outlines requirements for the relevant qualification of engaged staff capable of correctly interpreting the results of monitoring measurements and performing the necessary calculation of numerous parameters, for example, in the case of pollution evaluation. As a result, all this allows to ensure the compliance of obtained assessments with the real condition of monitored object, which is the basis for informed decisions on a set of actions to further ensure technosphere security. Along with this, the proposed monitoring approach allows to optimize the measuring base in order to achieve the necessary efficiency in assessing the status of controlled objects and the layout of the base in terms of the characteristics of buildings and their intrinsic properties, as well as the specificity of technogenic impact and natural processes on technosphere objects.

The necessity of rangers and other frontline staff for ensuring the protection and effective management of protected areas is well documented. The Spatial Monitoring and Reporting Tool (SMART) was developed as a law enforcement monitoring tool with the aim of assisting management in the allocation of resources and strategic deployment of ranger patrols. The ability of SMART to contribute to improved protection depends heavily on effective application through an adaptive management framework. Royal Manas National Park (RMNP), Bhutan, presents an opportunity to review the use of a protected area law enforcement monitoring tool for improving the detection and reduction of illegal activities within a protected area through effective ranger patrols. Results from the analysis of 5 years of monitoring data collected by rangers indicates a marked improvement in patrol effort in RMNP and suggests that the effective deployment of law enforcement monitoring tools, when used as part of an adaptive management framework, can improve the detectability of poaching related threats, potentially resulting in an overall reduction of poaching threats through enabling the informed and targeted allocation of staff and resources. [ABSTRACT FROM AUTHOR]

Abstract The necessity of rangers and other frontline staff for ensuring the protection and effective management of protected areas is well documented. The Spatial Monitoring and Reporting Tool (SMART) was developed as a law enforcement monitoring tool with the aim of assisting management in the allocation of resources and strategic deployment of ranger patrols. The ability of SMART to contribute to improved protection depends heavily on effective application through an adaptive management framework. Royal Manas National Park (RMNP), Bhutan, presents an opportunity to review the use of a protected area law enforcement monitoring tool for improving the detection and reduction of illegal activities within a protected area through effective ranger patrols. Results from the analysis of 5 years of monitoring data collected by rangers indicates a marked improvement in patrol effort in RMNP and suggests that the effective deployment of law enforcement monitoring tools, when used as part of an adaptive management framework, can improve the detectability of poaching related threats, potentially resulting in an overall reduction of poaching threats through enabling the informed and targeted allocation of staff and resources.

Bat populations are thought to have suffered significant declines in the past century throughout Europe. Fortunately, there are some signs of recovery; for instance, of the 11 species monitored in the UK, population trends of five are increasing. The drivers of past losses and recent trends are unclear; identifying them will enable targeted conservation strategies to support further recovery.We review the evidence linking proposed drivers to impacts on bat populations in Europe, using the results of a previous cross‐taxa semi‐quantitative assessment as a framework. Broadly, the drivers reviewed relate to land‐use practices, climate change, pollution, development and infrastructure, and human disturbance. We highlight where evidence gaps or conflicts present barriers to successful conservation and review emerging opportunities to address these gaps.We find that the relative importance or impacts of the potential drivers of bat population change are not well understood or quantified, with conflicting evidence in many cases. To close key gaps in the evidence for responses of bat populations to environmental change, future studies should focus on the impacts of climate change, urbanisation, offshore wind turbines, and water pollution, as well as on mitigation measures and the synergistic effects of putative drivers.To increase available evidence of drivers of bat population change, we propose utilising advances in monitoring tools and statistical methods, together with robust quantitative assessment of conservation interventions to mitigate threats and enable the effective conservation of these protected species. [ABSTRACT FROM AUTHOR]

Transthyretin amyloid cardiomyopathy (ATTR‐CM) is a life‐threatening condition with a heterogeneous clinical presentation. The recent availability of treatment for ATTR‐CM has stimulated increased awareness of the disease and patient identification. Stratification of patients with ATTR‐CM is critical for optimal management and treatment; however, monitoring disease progression is challenging and currently lacks best‐practice guidance. In this report, experts with experience in treating amyloidosis and ATTR‐CM developed consensus recommendations for monitoring the course of patients with ATTR‐CM and proposed meaningful thresholds and frequency for specific parameters. A set of 11 measurable features across three separate domains were evaluated: (i) clinical and functional endpoints, (ii) biomarkers and laboratory markers, and (iii) imaging and electrocardiographic parameters. Experts recommended that one marker from each of the three domains provides the minimum requirements for assessing disease progression. Assessment of cardiac disease status should be part of a multiparametric evaluation in which progression, stability or improvement of other involved systems in transthyretin amyloidosis should also be considered. Additional data from placebo arms of clinical trials and future studies assessing ATTR‐CM will help to elucidate, refine and define these and other measurements. [ABSTRACT FROM AUTHOR]

This study reports, for the first time, the baseline levels for fifteen trace metals in M. galloprovincialis tissue from around the Mediterranean, Marmara and Black Seas. The environmental quality in the surrounding seawater was assessed i.e., a mussel farm was investigated by using CF and DC indices, and the water quality was qualified as good for the aquacultural activities. A strong Cu-regulation in the transplanted mussels was observed and it ranged between 3.20 and 3.60 μg/g d.w. The highest bioavailability and bioconcentration of the particulate Fe fraction could present a health risk to consumers with a low risk level (1 < THQ < 9.9). Cr is considered the limiting metal for mussel consumption (< 2 kg/day). The metal contamination gradient was assessed using TEPI and TESVI indices that identified seven reference stations on the large scale and revealed that Cd is the most investigated metal in the literature databases, and found that Pb was the most bioavailable contaminant in the areas examined. [Display omitted] • First proposed background levels for 15 metals in M. galloprovincialis. • The reviewing of 50 years of metals biomonitoring in mussels on large scale. • Pb highlighted as a potential critical environmental impact (TESVI = 1536). • M. galloprovincialis was noted as an ineffective bioindicator of Cu exposure. • Daily ingestion rate limit is set at 2 kg of edible soft parts of mussel. [ABSTRACT FROM AUTHOR]

Background: Monitoring serves to maintain physiologic variables within normal limits and when a parameter crosses a set threshold, an alarm is triggered. Therefore, this study aimed to determine whether alarm limits are properly adjusted in the neonatal intensive care unit (NICU) by nursing staff or not.Methods: The data concerning alarm limits corresponding to the three shifts of nursing work were recorded for the newborns with cardiorespiratory problems, such as hyaline membrane disease, transient tachypnea of the newborns, and pneumonia, who were admitted to the NICU of Imam Reza Hospital, Mashhad, Iran during March 2016-December 2016.Results: The findings of this study showed that 75.1% of the 95 subjects of this study were preterm infants, while the others were cases with a gestational age of more than 37 weeks. The mean birth weight of the neonates was 1939.15±899.2 g. The upper alarm limit of pulse oximetry (95%) had been set correctly just in almost 26% of the patients. On the other hand, only at about 21% of all the cases, a normal lower alarm limit (85%) was observed for this variable. Compliance with the normal lower and upper limits of alarm for heart rate (i.e., 90 and 180 beats/min, respectively) was reported only in 10.46% and 18.6% of the infants, respectively.Conclusion: This study revealed that the alarm limits in NICU for unstable neonates were frequently set outside the normal range.

In a quickly growing world, there is increasing demand for a secure food supply, a reduction in the intensive use of natural resources, and the enhancement of sustainability for future long-term maintenance. In this regard, plant health, including fruit and foliar diseases, which can cause a vast amount of crop loss, potentially has a huge effect on food security. The integration of new, innovative technological tools and data management techniques into the traditional agricultural practices is a promising approach to combat future food shortages. The use of the same principles of precision agriculture to “do the right thing, at the right time, in the right place” will allow for providing detailed, real-time information that will help farmers to protect their crops and choose healthier, as well as more productive, farming methods. The presented narrative review reports on several items of innovation, including monitoring and surveillance, diagnostic, and decision-making tools, with a specific focus devoted to digital solutions that can be applied in agriculture in order to improve the quality and the speed of the decision-making process and specifically, to set up a digital collaboration that can be crucial under certain circumstances to reach sustainability goals, particularly in the Near East and North Africa (NENA) Region, where an effective and rapid solution for phytosanitary control is needed.

Aquatic ecologists routinely count animals to provide critical information for conservation and management. Increased accessibility to underwater recording equipment such as action cameras and unmanned underwater devices has allowed footage to be captured efficiently and safely, without the logistical difficulties manual data collection often presents. It has, however, led to immense volumes of data being collected that require manual processing and thus significant time, labor, and money. The use of deep learning to automate image processing has substantial benefits but has rarely been adopted within the field of aquatic ecology. To test its efficacy and utility, we compared the accuracy and speed of deep learning techniques against human counterparts for quantifying fish abundance in underwater images and video footage. We collected footage of fish assemblages in seagrass meadows in Queensland, Australia. We produced three models using an object detection framework to detect the target species, an ecologically important fish, luderick (Girella tricuspidata). Our models were trained on three randomized 80:20 ratios of training:validation datasets from a total of 6,080 annotations. The computer accurately determined abundance from videos with high performance using unseen footage from the same estuary as the training data (F1 = 92.4%, mAP50 = 92.5%) and from novel footage collected from a different estuary (F1 = 92.3%, mAP50 = 93.4%). The computer’s performance in determining abundance was 7.1% better than human marine experts and 13.4% better than citizen scientists in single image test datasets, and 1.5 and 7.8% higher in video datasets, respectively. We show that deep learning can be a more accurate tool than humans at determining abundance and that results are consistent and transferable across survey locations. Deep learning methods provide a faster, cheaper, and more accurate alternative to manual data analysis methods currently used to monitor and assess animal abundance and have much to offer the field of aquatic ecology.

Smallholder irrigation is an important pathway towards better livelihoods and food security in sub-Saharan Africa. This article assesses the contribution of farmer-friendly soil and water monitoring tools, and agricultural innovation platforms, towards household income and food security in two small-scale irrigation schemes in Tanzania. Quantitative and qualitative data from farmer's field books, household surveys and focus groups were used to assess the impacts of the two interventions. The two interventions together contributed to enhancing smallholders' food security and household income in the two schemes, as did the agricultural innovation platform on its own. [ABSTRACT FROM AUTHOR]

As part of the Austrian Transition to Open Access (AT2OA) project, subproject TP1-B is working on designing a monitoring solution for the output of Open Access publications in Austria. This report on a potential Open Access monitoring approach in Austria is one of the results of these efforts and can serve as a basis for discussion on an international level. [ABSTRACT FROM AUTHOR]

BACKGROUND: Vine weevil, Otiorhynchus sulcatus F. (Coleoptera: Curculionidae), is one of the most economically important pest species of berry and ornamental crops globally. Monitoring this nocturnal pest can be difficult and time consuming and the efficacy of current tools is uncertain. Without effective monitoring tools, implementation of integrated pest management strategies is challenging. This study tests the relative efficacy of a range of vine weevil monitoring tools. Whether host‐plant volatiles and weevil feeding experience influence vine weevil capture is also tested. RESULTS: Monitoring tool efficacy differed overall between the six monitoring tool designs tested and ranged from catches of 0.4% to 26.7% under semi‐field conditions. Previous feeding experience influenced vine weevil behavior. In yew conditioned populations, 39% of the weevils responded to and were retained in the trap baited with yew foliage while 37% of weevils from Euonymus fortunei conditioned populations responded to and were retained in the trap baited with E. forunei foliage. A simple synthetic lure consisting of (Z)‐2‐pentenol + methyl eugenol also increased vine weevil catches compared with an unbaited trap. CONCLUSION: Demonstrating differences in the efficacy of different monitoring tool designs is an important first step for developing improved methods for monitoring vine weevil populations within crops. This study presents the first direct comparison of vine weevil monitoring tool designs and indicates that trap efficacy can be improved by baiting with host‐plant material or a synthetic lure based on host‐plant volatiles. © 2019 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

With the rapid development of clinical pharmacy in China, therapeutic drug monitoring (TDM) has become an essential tool for guiding rational clinical drug use and is widely concerned. TDM is a tool that combines pharmacokinetic and pharmacodynamic knowledge to optimize personalized drug therapy, which can improve treatment outcomes, reduce drug-drug toxicity, and avoid the risk of developing drug resistance. To effectively implement TDM, accurate and sophisticated analytical methods are required. By researching the literature published in recent years, we summarize the types of commonly monitored drugs, therapeutic windows, and clinical assays and track the trends and hot spots of therapeutic drug monitoring. The purpose is to provide guidelines for clinical blood drug concentration monitoring, to implement individualized drug delivery programs better, to ensure the rational use of drugs for patients, and to provide a reference for the group to carry out related topics in the future. [Display omitted] • Systematically analyze the commonly used clinical means of TDM and discuss the key points of personalized treatment. • We compared the sensitivity and specificity, advantages and limitations of different TDM methods in applications. • We emphasize our personal views on the current limitations and future trends of personalized therapy. [ABSTRACT FROM AUTHOR]

Background: Monitoring serves to maintain physiologic variables within normal limits and when a parameter crosses a set threshold, an alarm is triggered. Therefore, this study aimed to determine whether alarm limits are properly adjusted in the neonatal intensive care unit (NICU) by nursing staff or not. Methods: The data concerning alarm limits corresponding to the three shifts of nursing work were recorded for the newborns with cardiorespiratory problems, such as hyaline membrane disease, transient tachypnea of the newborns, and pneumonia, who were admitted to the NICU of Imam Reza Hospital, Mashhad, Iran during March 2016-December 2016. Results: The findings of this study showed that 75.1% of the 95 subjects of this study were preterm infants, while the others were cases with a gestational age of more than 37 weeks. The mean birth weight of the neonates was 1939.15±899.2 g. The upper alarm limit of pulse oximetry (95%) had been set correctly just in almost 26% of the patients. On the other hand, only at about 21% of all the cases, a normal lower alarm limit (85%) was observed for this variable. Compliance with the normal lower and upper limits of alarm for heart rate (i.e., 90 and 180 beats/min, respectively) was reported only in 10.46% and 18.6% of the infants, respectively. Conclusion: This study revealed that the alarm limits in NICU for unstable neonates were frequently set outside the normal range. [ABSTRACT FROM AUTHOR]

The muon system of the CERN Compact Muon Solenoid (CMS) experiment includes more than a thousand Resistive Plate Chambers (RPC). They are gaseous detectors operated in the hostile environment of the CMS underground cavern on the Large Hadron Collider where pp luminosities of up to 2 × 1 0 34 cm − 2 s − 1 are routinely achieved. The CMS RPC system performance is constantly monitored and the detector is regularly maintained to ensure stable operation. The main monitorable characteristics are dark current, efficiency for muon detection, noise rate etc. Herein we describe an automated tool for CMS RPC current monitoring which uses Machine Learning techniques. We further elaborate on the dedicated generalized linear model proposed already and add autoencoder models for self-consistent predictions as well as hybrid models to allow for RPC current predictions in a distant future. [ABSTRACT FROM AUTHOR]

The management of distributed systems infrastructure requires dedicated set of tools. The one tool that helps visualize current operational state of all systems and notify when failure occurs is available within monitoring solution. This paper provides an overview of monitoring approaches for gathering data from distributed systems and what are the major factors to consider when choosing a monitoring solution. Finally we discuss the tools currently available on the market.