scholarly journals A new fine‐grained method for automated visual analysis of herbarium specimens: A case study for phenological data extraction

2020 ◽  
Vol 8 (6) ◽  
Author(s):  
Hervé Goëau ◽  
Adán Mora‐Fallas ◽  
Julien Champ ◽  
Natalie L. Rossington Love ◽  
Susan J. Mazer ◽  
...  
Keyword(s):  
Visual Analysis ◽  
Data Extraction ◽  
Herbarium Specimens ◽  
Phenological Data ◽  
Fine Grained

scholarly journals From Field Observations and Plant Specimens to a Trans-continental Knowledge Base: Efficient, semantically rich integration of highly heterogeneous plant phenological data

2019 ◽  
Vol 3 ◽  
Author(s):  
Brian Stucky ◽  
John Deck ◽  
Ramona Walls ◽  
Robert Guralnick
Keyword(s):  
Data Integration ◽  
General Purpose ◽  
Plant Phenology ◽  
Herbarium Specimens ◽  
Complex Data ◽  
Field Observations ◽  
Phenological Data ◽  
Fine Grained ◽  
Data Integration System ◽  
The Many

Ideally, an information system that automates the integration of disparate datasets should be able to minimize the loss of information from any one dataset, achieve computational complexity suitable for working with large datasets, be flexible enough to easily incorporate new data sources, and produce output that is easily analyzed and understood by data users. Achieving all of these goals within highly heterogeneous and highly complex data domains is a major challenge. In this talk, we present the results of our recent efforts to develop such a system for data about plant phenology. Our data integration system, which is built around the Plant Phenology Ontology, currently supports semantically fine-grained integration of phenological data from both field observations and herbarium specimens. We show that even with a heavily axiomatized ontology and sophisticated, machine-reasoning-based data analysis, it is possible to implement a high-throughput data integration pipeline capable of processing millions of individual records in a matter of minutes while running on modest, server-class hardware. Success requires careful ontology design and judicious application of machine reasoning techniques. We also discuss some of the many challenges that remain for designing efficient, general-purpose data integration systems.

scholarly journals Soil Minerals Serving as Source of Arsenic in Alluvial Aquifers of Holocene: A Case Study from Indus Delta, Sindh, Pakistan

2019 ◽  
Vol 4 (4) ◽  
pp. 227-235
Author(s):  
Adnan Khan ◽  
Viqar Husain ◽  
Suhail Anjum
Keyword(s):  
Visual Analysis ◽  
Soil Samples ◽  
Silty Clay ◽  
X Ray Diffraction ◽  
Plagioclase Feldspar ◽  
Alluvial Aquifers ◽  
Fine Grained ◽  
Indus Delta ◽  
Arsenic Retention

Groundwater arsenic contamination is recently reported in the alluvial aquifers of Indus deltaic plain. Since the source of arsenic is believed to be natural as widely reported in other deltaic aquifers of same age (Holocene), it is imperative to evaluate the soil characteristics for identifying the sources of arsenic and its mobilization mechanism. For this purpose, 49 soil samples were collected from near aquifer sites in all three talukas of Tando Muhammad Khan district. Visual analysis revealed that soil is light grey in color with fine texture ranging from silt to silty-clay. The X-ray diffraction study reveals the occurrence of quartz, mica and clay minerals in all collected soil samples. Plagioclase feldspar is second dominant mineral group in the order of albite (calcian) >albite>albite (disordered) = anorthite > anorthite (sodian) = anorthite (disordered). Calcite is major carbonate mineral which is detected in 40 out of total 49 soil samples. The occurrence of other occasional minerals includes amesite, nitro-calcite, rutile and zinnwaldite. The frequency of micaceous minerals in collected samples is in the order of clinochlore> polylithionite> Biotite > phlogopite> muscovite. Polylithionite is found in about half of the total soil samples, where most of the aquifers contain arsenic >20 μg/L (Khan, 2014). Phlogopite is observed in seven soil samples which are also associated with clinochlore. On the other hand, biotite is found in 14 sediment samples collected from Tando Muhammad Khan and Bhulri Shah Karim talukas and muscovite occurs in three soil samples of Tando Muhammad Khan taluka. It can be concluded from present study that fine-grained Phyllosilicates have strong affinity for arsenic retention. These sediments are important source of arsenic Indus delta and other deltaic plains of the world.

scholarly journals Machine Learning Undercounts Reproductive Organs on Herbarium Specimens but Accurately Derives Their Quantitative Phenological Status: A Case Study of Streptanthus tortuosus

Plants ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 2471
Author(s):  
Natalie L. R. Love ◽  
Pierre Bonnet ◽  
Hervé Goëau ◽  
Alexis Joly ◽  
Susan J. Mazer
Keyword(s):  
Machine Learning ◽  
Reproductive Organs ◽  
Herbarium Specimens ◽  
Phenological Stage ◽  
Phenological Data ◽  
Detection Model ◽  
Ecological Patterns ◽  
Annual Herb ◽  
Phenological Sensitivity

Machine learning (ML) can accelerate the extraction of phenological data from herbarium specimens; however, no studies have assessed whether ML-derived phenological data can be used reliably to evaluate ecological patterns. In this study, 709 herbarium specimens representing a widespread annual herb, Streptanthus tortuosus, were scored both manually by human observers and by a mask R-CNN object detection model to (1) evaluate the concordance between ML and manually-derived phenological data and (2) determine whether ML-derived data can be used to reliably assess phenological patterns. The ML model generally underestimated the number of reproductive structures present on each specimen; however, when these counts were used to provide a quantitative estimate of the phenological stage of plants on a given sheet (i.e., the phenological index or PI), the ML and manually-derived PI’s were highly concordant. Moreover, herbarium specimen age had no effect on the estimated PI of a given sheet. Finally, including ML-derived PIs as predictor variables in phenological models produced estimates of the phenological sensitivity of this species to climate, temporal shifts in flowering time, and the rate of phenological progression that are indistinguishable from those produced by models based on data provided by human observers. This study demonstrates that phenological data extracted using machine learning can be used reliably to estimate the phenological stage of herbarium specimens and to detect phenological patterns.

Forensic Data Extraction and Analysis of Left Artifacts on emulated Android Phones: A Case Study of Instant Messaging Applications

2017 ◽  
Vol 2 (11) ◽  
pp. 8-16
Author(s):  
Moses Ashawa ◽  
Innocent Ogwuche
Keyword(s):  
Mobile Phones ◽  
Instant Messaging ◽  
Data Extraction ◽  
Digital Evidence ◽  
Cyber Attack ◽  
Forensic Evidence ◽  
Digital Forensic ◽  
Proportional Increase ◽  
Forensic Tools

The fast-growing nature of instant messaging applications usage on Android mobile devices brought about a proportional increase on the number of cyber-attack vectors that could be perpetrated on them. Android mobile phones store significant amount of information in the various memory partitions when Instant Messaging (IM) applications (WhatsApp, Skype, and Facebook) are executed on them. As a result of the enormous crimes committed using instant messaging applications, and the amount of electronic based traces of evidence that can be retrieved from the suspect’s device where an investigation could convict or refute a person in the court of law and as such, mobile phones have become a vulnerable ground for digital evidence mining. This paper aims at using forensic tools to extract and analyse left artefacts digital evidence from IM applications on Android phones using android studio as the virtual machine. Digital forensic investigation methodology by Bill Nelson was applied during this research. Some of the key results obtained showed how digital forensic evidence such as call logs, contacts numbers, sent/retrieved messages, and images can be mined from simulated android phones when running these applications. These artefacts can be used in the court of law as evidence during cybercrime investigation.

scholarly journals Potential Impacts of Future Climate Change Scenarios on Ground Subsidence

Water ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 219 ◽  
Author(s):  
Antonio-Juan Collados-Lara ◽  
David Pulido-Velazquez ◽  
Rosa María Mateos ◽  
Pablo Ezquerro
Keyword(s):  
Climate Change ◽  
Land Subsidence ◽  
Ground Subsidence ◽  
Lower Percentage ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
Fine Grained ◽  
Fine Grained Material ◽  
The Impact

In this work, we developed a new method to assess the impact of climate change (CC) scenarios on land subsidence related to groundwater level depletion in detrital aquifers. The main goal of this work was to propose a parsimonious approach that could be applied for any case study. We also evaluated the methodology in a case study, the Vega de Granada aquifer (southern Spain). Historical subsidence rates were estimated using remote sensing techniques (differential interferometric synthetic aperture radar, DInSAR). Local CC scenarios were generated by applying a bias correction approach. An equifeasible ensemble of the generated projections from different climatic models was also proposed. A simple water balance approach was applied to assess CC impacts on lumped global drawdowns due to future potential rainfall recharge and pumping. CC impacts were propagated to drawdowns within piezometers by applying the global delta change observed with the lumped assessment. Regression models were employed to estimate the impacts of these drawdowns in terms of land subsidence, as well as to analyze the influence of the fine-grained material in the aquifer. The results showed that a more linear behavior was observed for the cases with lower percentage of fine-grained material. The mean increase of the maximum subsidence rates in the considered wells for the future horizon (2016–2045) and the Representative Concentration Pathway (RCP) scenario 8.5 was 54%. The main advantage of the proposed method is its applicability in cases with limited information. It is also appropriate for the study of wide areas to identify potential hot spots where more exhaustive analyses should be performed. The method will allow sustainable adaptation strategies in vulnerable areas during drought-critical periods to be assessed.

Historical and phenological observations on the spread of Chaenorrhinum minus across North America

1983 ◽  
Vol 61 (1) ◽  
pp. 179-187 ◽  
Author(s):  
Mark P. Widrlechner
Keyword(s):  
North America ◽  
Rapid Onset ◽  
Herbarium Specimens ◽  
Reproductive Maturity ◽  
Phenological Data ◽  
Dispersal Mechanism ◽  
Rapid Spread ◽  
The Relationship

Through a review of floristic and taxonomic literature and an examination of over 1500 herbarium specimens, this report documents the rapid spread of Chaenorrhinum minus (L.) Lange along railroads across North America. The relationship between C. minus and railroads is described and phenological data on flowering and fruiting are presented. The combination of an effective dispersal mechanism and the rapid onset of reproductive maturity contributes to the species' adaptive success.

scholarly journals Neural networks for increased accuracy of allergenic pollen monitoring

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marcel Polling ◽  
Chen Li ◽  
Lu Cao ◽  
Fons Verbeek ◽  
Letty A. de Weger ◽  
...  
Keyword(s):  
Neural Networks ◽  
Airborne Pollen ◽  
Pollen Grains ◽  
Allergenic Pollen ◽  
Herbarium Specimens ◽  
Pollen Monitoring ◽  
Pollen Concentrations ◽  
Source Of Information ◽  
Urticaceae Pollen

AbstractMonitoring of airborne pollen concentrations provides an important source of information for the globally increasing number of hay fever patients. Airborne pollen is traditionally counted under the microscope, but with the latest developments in image recognition methods, automating this process has become feasible. A challenge that persists, however, is that many pollen grains cannot be distinguished beyond the genus or family level using a microscope. Here, we assess the use of Convolutional Neural Networks (CNNs) to increase taxonomic accuracy for airborne pollen. As a case study we use the nettle family (Urticaceae), which contains two main genera (Urtica and Parietaria) common in European landscapes which pollen cannot be separated by trained specialists. While pollen from Urtica species has very low allergenic relevance, pollen from several species of Parietaria is severely allergenic. We collect pollen from both fresh as well as from herbarium specimens and use these without the often used acetolysis step to train the CNN model. The models show that unacetolyzed Urticaceae pollen grains can be distinguished with > 98% accuracy. We then apply our model on before unseen Urticaceae pollen collected from aerobiological samples and show that the genera can be confidently distinguished, despite the more challenging input images that are often overlain by debris. Our method can also be applied to other pollen families in the future and will thus help to make allergenic pollen monitoring more specific.

SonicFace

2021 ◽  
Vol 5 (4) ◽  
pp. 1-33
Author(s):  
Yang Gao ◽  
Yincheng Jin ◽  
Seokmin Choi ◽  
Jiyang Li ◽  
Junjie Pan ◽  
...  
Keyword(s):  
Facial Expressions ◽  
Microphone Array ◽  
Wearable Sensors ◽  
Real Life ◽  
Privacy Concerns ◽  
Fine Grained ◽  
Recognition Of Facial Expressions ◽  
Ubiquitous Sensing ◽  
Contact Free

Accurate recognition of facial expressions and emotional gestures is promising to understand the audience's feedback and engagement on the entertainment content. Existing methods are primarily based on various cameras or wearable sensors, which either raise privacy concerns or demand extra devices. To this aim, we propose a novel ubiquitous sensing system based on the commodity microphone array --- SonicFace, which provides an accessible, unobtrusive, contact-free, and privacy-preserving solution to monitor the user's emotional expressions continuously without playing hearable sound. SonicFace utilizes a pair of speaker and microphone array to recognize various fine-grained facial expressions and emotional hand gestures by emitted ultrasound and received echoes. Based on a set of experimental evaluations, the accuracy of recognizing 6 common facial expressions and 4 emotional gestures can reach around 80%. Besides, the extensive system evaluations with distinct configurations and an extended real-life case study have demonstrated the robustness and generalizability of the proposed SonicFace system.

The impact of business networks on foreign subsidiaries development

IMP Journal ◽  
2018 ◽  
Vol 12 (3) ◽  
pp. 427-443
Author(s):  
Enrico Baraldi ◽  
Francesco Ciabuschi ◽  
Olof Lindahl ◽  
Andrea Perna ◽  
Gian Luca Gregori
Keyword(s):  
Single Case ◽  
Foreign Market ◽  
Business Networks ◽  
International Expansion ◽  
Content Type ◽  
Fine Grained ◽  
Automatic Control Systems ◽  
Industrial Networks ◽  
The Impact

Purpose The purpose of this paper is to explore two specific areas pertaining to industrial networks and international business (IB). First, the authors look at how business relationships influence the internationalization in time, from the establishment of the first subsidiary in a foreign market to the following ones, and in space, that is, across different markets. Second, the authors investigate how an increasing external network dependence of subsidiaries in their internationalization may cause a detachment of a subsidiary from the mother company as its knowledge becomes insufficient to guide a subsidiary’s internationalization. Design/methodology/approach This paper utilizes an exploratory, longitudinal, single-case study of Loccioni – a manufacturer of measuring and automatic control systems for industrial customers – to illustrate the specific dynamics of the influences of industrial networks on the internationalization of subsidiaries. Findings The case study helps to elucidate the roles, entailing also free will and own initiative, of small suppliers’ subsidiaries which operate inside several global factories, and how “surfing” on many different global factories, by means of several local subsidiaries, actually supports these suppliers’ own international developments. This notion adds to our understanding of the global factory phenomenon a supplier focus that stresses how the role of suppliers is not merely that of being passive recipients of activities and directions from a focal orchestrating firm, but can also be that of initiative-takers themselves. Originality/value The paper contributes to the IMP tradition by providing a multi-layered and geographically more fine-grained view of the network embedding companies that operate on internationalized markets. This paper thereby sheds light on a less investigated area of research within the IMP tradition: the link between internationalization in different countries and the interconnectedness between the industrial networks spanning these countries. At the same time, this paper contributes to IB theories by showing how a late-internationalizing SME can enter highly international markets by “plugging into” several established “Global Factories” as a way to exploit further opportunities for international expansion.

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