bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparing canopy density measurement from UAV and hemispherical photography: an evaluation for medium resolution of remote sensing-based mapping
Authors: Umarhadi Deha Agus , Danoedoro Projo
Published: September 2020

UAV and hemispherical photography are common methods used in canopy density measurement. These two methods have opposite viewing angles where hemispherical photography measures canopy density upwardly, while UAV captures images downwardly. This study aims to analyze and compare both methods to be used as the input data for canopy density estimation when linked with a lower spatial resolution of remote sensing data i.e. Landsat image. We correlated the field data of canopy density with vegetation indices (NDVI, MSAVI, and AFRI) from Landsat-8. The canopy density values measured from UAV and hemispherical photography displayed a strong relationship with 0.706 coefficient of correlation. Further results showed that both measurements can be used in canopy density estimation using satellite imagery based on their high correlations with Landsat-based vegetation indices. The highest correlation from downward and upward measurement appeared when linked with NDVI with a correlation of 0.962 and 0.652, respectively. Downward measurement using UAV exhibited a higher relationship compared to hemispherical photography. The strong correlation between UAV data and Landsat data is because both are captured from the vertical direction, and 30 m pixel of Landsat is a downscaled image of the aerial photograph. Moreover, field data collection can be easily conducted by deploying drone to cover inaccessible sample plots.
Design an expert system for students graduation projects in Iraq universities: Basrah University
Authors: Ulkareem Naser Maysaa Abd , Hasen Sajad Mohammed
Published: September 2020

A graduation project is a form or work that the study authority requests from the student to measure what he made during the study. Designed an expert system for students’ graduation projects at the University of Basrah for students who are obligated to submit a project that qualifies them to graduate from the university. The system works according to a set of requirements, the most important is first: The student's possession of a high rate that qualifies him for the project. Second: he must possess half of the skills required for the project provided that it includes at least one programming language example (c ++, java, PHP, c #, etc ...). The system has many features that help the Supervisors and Students Committee to manage students' projects efficiently. System is built as a web-based system, with access limited only to the university's local network.
Resumption of virtual machines after adaptive deduplication of virtual machine images in live migration
Authors: T. Y. J. Naga Malleswari , T. Senthil Kumar , C. JothiKumar
Published: September 2020

In cloud computing, load balancing, energy utilization are the critical problems solved by virtual machine (VM) migration. Live migration is the live movement of VMs from an overloaded/underloaded physical machine to a suitable one. During this process, transferring large disk image files take more time, hence more migration and down time. In the proposed adaptive deduplication, based on the image file size, the file undergoes both fixed, variable length deduplication processes. The significance of this paper is resumption of VMs with reunited deduplicated disk image files. The performance measured by calculating the percentage reduction of VM image size after deduplication, the time taken to migrate the deduplicated file and the time taken for each VM to resume after the migration. The results show that 83%, 89.76% reduction overall image size and migration time respectively. For a deduplication ratio of 92%, it takes an overall time of 3.52 minutes, 7% reduction in resumption time, compared with the time taken for the total QCOW2 files with original size. For VMDK files the resumption time reduced by a maximum 17% (7.63 mins) compared with that of for original files.
Finding Art and the Art of Finding: O que vos nunca cuidei a dizer
Authors: Mestre André Rabello , Núñez-Pacheco Claudia , Carrasco Mauricio
Published: September 2020

The article discusses the making of O que vos nunca cuidei a dizer, a gallery work for interactive garment, transducer-based interface and live electronics. A technical description of the work is framed by an account of the creative process, with reference to media archaeological methodology and a discussion of the role of composed instruments in the new paradigms of artistic research.
A Cerebellar Computational Mechanism for Delay Conditioning at Precise Time Intervals
Authors: Sanger Terence D. , Kawato Mitsuo
Published: September 2020

The cerebellum is known to have an important role in sensing and execution of precise time intervals, but the mechanism by which arbitrary time intervals can be recognized and replicated with high precision is unknown. We propose a computational model in which precise time intervals can be identified from the pattern of individual spike activity in a population of parallel fibers in the cerebellar cortex. The model depends on the presence of repeatable sequences of spikes in response to conditioned stimulus input. We emulate granule cells using a population of Izhikevich neuron approximations driven by random but repeatable mossy fiber input. We emulate long-term depression (LTD) and long-term potentiation (LTP) synaptic plasticity at the parallel fiber to Purkinje cell synapse. We simulate a delay conditioning paradigm with a conditioned stimulus (CS) presented to the mossy fibers and an unconditioned stimulus (US) some time later issued to the Purkinje cells as a teaching signal. We show that Purkinje cells rapidly adapt to decrease firing probability following onset of the CS only at the interval for which the US had occurred. We suggest that detection of replicable spike patterns provides an accurate and easily learned timing structure that could be an important mechanism for behaviors that require identification and production of precise time intervals.
Bicomplex Projection Rule for Complex-Valued Hopfield Neural Networks
Authors: Kobayashi Masaki
Published: September 2020

A complex-valued Hopfield neural network (CHNN) with a multistate activation function is a multistate model of neural associative memory. The weight parameters need a lot of memory resources. Twin-multistate activation functions were introduced to quaternion- and bicomplex-valued Hopfield neural networks. Since their architectures are much more complicated than that of CHNN, the architecture should be simplified. In this work, the number of weight parameters is reduced by bicomplex projection rule for CHNNs, which is given by the decomposition of bicomplex-valued Hopfield neural networks. Computer simulations support that the noise tolerance of CHNN with a bicomplex projection rule is equal to or even better than that of quaternion- and bicomplex-valued Hopfield neural networks. By computer simulations, we find that the projection rule for hyperbolic-valued Hopfield neural networks in synchronous mode maintains a high noise tolerance.
Description of new species of Resachus Delève, 1968 (Coleoptera: Limnichidae)
Authors: MATSUMOTO KEITA
Published: September 2020

There are 55 valid species and 8 genera of Afrotropical Limnichidae currently described (Delève, 1968; Spangler et. al. 2001; Hernando & Ribera, 2006; Hernando & Ribera, 2014). Resachus Delève is within the “Mandersia group of genera” according to Hernando & Ribera (2005) with 3 known species: Resachus lineatifrons Delève, 1968 [Democratic Republic of Congo, Zambia]; Resachus schuhi Hernando & Ribera, 2006 [Madagascar]; Resachus striatellus Delève, 1968 [Democratic Republic of Congo, Nigeria] (Delève, 1968; Hernando & Ribera, 2006). Specimens from Malawi have been identified as a new species. The new species have morphological characters, described in this paper, that distinguish them from any other known Resachus.
A new species of Neocerambyx Thomson, 1860 (Coleoptera, Cerambycidae)
Authors: ZHU LI , YAFEI LU , LI CHEN
Published: September 2020

A new species of Neocerambyx Thomson, 1860 is described, N. guangxiensis sp. nov., from Guangxi, China. The description and photographs of adult habitus and male genitalia of this new species are provided.
First record of Epirhyssa Cresson, 1865 (Hymenoptera, Ichneumonidae, Rhyssinae) from Kenya
Authors: VARGA OLEKSANDR
Published: September 2020

The ichneumonid parasitoid wasps of the subfamily Rhyssinae are distributed worldwide with the highest species richness focused in the Oriental region (Yu et al. 2016). The total number of species in the subfamily exceeds 450 species. Nevertheless, the Afrotropical region remains poorly studied, numbering only 13 known species, almost a half of which were described recently (Rousse and van Noort 2014; Hopkins et al. 2019). The majority of the Afrotropical rhyssines belong to the genus Epirhyssa Cresson, 1865. The species of the genus are mostly known from the humid part of Equatorial Africa: Cameroon, Democratic Republic of Congo and Uganda. Since 2011 an extensive Malaise trap collecting programme has been conducted throughout Kenya by Robert Copeland and coordinated by the International Centre of Insect Physiology and Ecology in Nairobi. The present paper provides one of first results from the programme: a description of a new Epirhyssa species from Kenya and, thus expanding the distribution of the genus in Equatorial part of the Afrotropical region eastward (Fig. 1).
Performance of Hoeffding Tree and C4.5 Algorithms to Envisage an Occurrence of Hepatitis–A Liver Disease
Authors: Garg Sharvan Kumar , Sinha Deepak Kumar , Bhatia Nidhi
Published: September 2020

Premature forecasting of hepatitis is extremely imperative to save an individual years and take appropriate steps to control the ailment. Decision Tree algorithms have been effectively useful in a variety of fields particularly in medicinal discipline. This manuscript investigates the premature forecasting of hepatitis by means of a variety of decision tree algorithms. In this manuscript, we build up a Hepatitis prediction model that can aid medical experts in envisaging Hepatitis condition supported on the medicinal data of patients. At the outset, we have chosen 19 imperative medicinal attributes viz., age, sex, antivirals, steroid, fatigue, anorexia, malaise, spleen palpable, etc., in addition to one target class. Secondly, we build up a prediction model using Pruned C4.5-J48 Decision Tree, Unpruned C4.5-J48, Reduced Error Pruned C4.5-J48 and Hoeffding Tree algorithms classifier for classifying Hepatitis based on these clinical attributes. Lastly, the precision of Pruned J48 decision tree approach proves to be more superior then the other approaches. Outcome acquired illustrates that Albumin and Ascites are the foremost predictive attributes which provides enhanced classification in opposition to the supplementary attributes.
Mathematical Modeling and Optimization of Wire Electric Discharge Machining Parameters on Inconel 825 Using Desirability Method
Authors: Kumar Anish , Sharma Renu , Gupta Arun Kumar , Yadav Jay Ram
Published: September 2020

The present research work mainly emphases on experimentation, analysis and optimization of machining parameters during WEDM on Inconel 825. Based on Box-Behenken Design, 29 experimental runs were executed on WEDM using 0.25 mm dia half hard brass wire and de-ionized water as dielectric.
Controllable parameters pulse on time (Ton), pulse off time (Toff), spark gap voltage (SV) and wire feed (WF) are varied up to three levels to study their influence on response measures cutting speed (CS) and gap current (Ig). Experimental
models were developed by employing RSM. Design expert software 7.0.0 was considered for design of experiments and developing the mathematical model. Graphical model analysis has been done with the help of Analysis of Variance (ANOVA). Ton Toff and SV have
major influencing factors on responses where, as wire Feed was insignificant. To achieve optimal parameter combination, multi-objective optimization of responses is performed using desirability method. Confirmation test is performed at optimal combination of parameters provided by desirability
function to validate the study. Hence, predicted value lies in correspondence with the experimental findings.
Internet of Things: Architecture, Applications, and Security Concerns
Authors: Kaur Rasmeet , Raina B. L. , Sharma Avinash
Published: September 2020

Internet of Things (IoT) appears to be a signature phrase in the field of Information technology. The term immensely strikes our lives. It becomes more recognized in recent years. Today we are experiencing technology called IoT which is revolutionizing almost every branch of activity.
Internet of things envisaged as a network of billions of objects amenable to communicate with one another. The Internet of Things strives to a malgamate all the devices under one roof. These devices can be monitored and controlled at any moment. They can communicate, exchange information and
perform the desired operations. Taking note of this, the paper addresses the IoT concept using analysis of research papers, corporate persuasive reports, scholarly review articles, and online databases. Furthermore, the paper comes up with an overview of IoT including definition, Genesis,
the chronology of IoT, Obligations for IoT, Aliases, architecture and different technologies regularly used in IoT in a systematic approach. The paper also discusses the application areas of its and security concerns.
A Quantum Approach Using Stochastic Simulation and Schrodinger with Multi-Layer Learning for Finding Features from Dataset
Authors: Prashar Kapil , Talwar Rajneesh , Kant Chander
Published: September 2020

Quantum computing relies on the quantity of the mechanical phenomenon, such as interference and overlap. It aims to solve issues which are not realistically possible on computers. The research work introduces the new quantum-based model from a provided dataset for forecasting the infection.
This technique is beneficial in describing the association among different statistical models. Our study has resulted in highest precision than ever applied technique, which was differentiated and calculated from the defined dataset and results. Such suggested strategies were evaluated and
reviewed against multiple state-of-the-art methods to demonstrate efficacy. The qualitative and graphical results are provided for the verification of the current approach. The suggested model is more robust than existing mathematical models due to the findings.
An Efficient Load Balancing Method by Using Machine Learning-Based VM Distribution and Dynamic Resource Mapping
Authors: Lilhore Umesh Kumar , Simaiya Sarita , Guleria Kalpna , Prasad Devendra
Published: September 2020

In cloud computing, balancing the load among VMs and resources is a major research area, which still needs attention. The primary aim of this research is to expand an effective cloud load balancing approach, enhance the reaction time, lessen the ready time, premiere utilization of sources
as well lessen the activity rejection time. The proposed MLBL method is primarily based on the SVM and Ksuggest clustering method. In the proposed MLBL technique SVM classification method used to create activity businesses based totally at the size. Later Kmethod clustering technique
is used to create the institution of Virtual machines based totally on their usage of CPU and number one memory (RAM). We are providing an MLBL method based totally on system learning-based VM distribution and dynamic aid mapping. In cloud computing, VMs are scheduled to hosts as in keeping
with their utilization (a host which has better availability of memory) without thinking about common utilizations. The proposed technique divides the assets into various organizations as well as VMs and then follow dynamic aid mapping. The proposed MLBL technique creates VMs clusters to execute
comparable task agencies that enhance the QoS and ideal utilization of sources in addition to lessen the process rejection time.
The Validity of Mobile Learning Management System (M-LMS) at University
Authors: Rini Faiza , Mulyadi R. , Surya Junaidi , Louis Ahmad
Published: September 2020

This study aims to find out the results of expert validation on Mobile Learning Management System (M-LMS) in the subjects of Human and Computer Interaction at university. Mobile Learning Management System (M-LMS) is developed in order to create more dynamic communication between lecturers
and students. Learning management system is a model and system that runs an administration which serves as an e-learning platform, which delivers and manages content, identifies, assesses, tracks progress, collects and presents data to monitor the learning process as a whole. The development
procedure of this research uses Borg and Gall which is simplified into five stages as follow: (1) Conducting product analysis that will be developed, (2) Developing the initial product/ prototype, (3) Expert validation and revision, (4) Small scale field trial and product revision, (5) Large-scale
field trials and final products. The stage of expert validation on Borg and Gall method is done at the third stage, namely the stage of expert validation and revision. This study produces a valid mobile learning management system (M-LMS) software for the course of Human and Computer Interaction.
Dynamic Reconfiguration of Network Partition Under the Constraints of Hostile Environment
Authors: Kaur Jaspreet , Bindal Amit Kumar
Published: September 2020

Node failure may interrupt the smooth network operations of a sensor network. A faulty node may choke the entire network. Fault detection and recovery provisions may extend the network life span. In this paper, a recovery mechanism is proposed to handle the partitioned network using
instant reconfiguration method based on minimum spanning tree. Performance of the proposed method is analyzed using different routing protocols (AODV/LEACH/DSDV) under the constraints of partitioned network.
Dimensionality Reduction Techniques: Comparative Analysis
Authors: Vats Deepak , Sharma Avinash
Published: September 2020

It has been spotted an exponential growth in terms of dimension in real world data. Some example of higher dimensional data may includes speech signal, sensor data, medical data, criminal data and data related to recommendation process for different field like news, movies (Netflix)
and e-commerce. To empowering learning accuracy in the area of machine learning and enhancing mining performance one need to remove redundant feature and feature not relevant for mining and learning task from this high dimension dataset. There exist many supervised and unsupervised methodologies
in literature to perform dimension reduction. The objective of paper is to present most prominent methodologies related to the field of dimension reduction and highlight advantages along with disadvantages of these algorithms which can act as starting point for beginners of this field.
Architectural Analysis of Wireless Sensor Network and Underwater Wireless Sensor Network with Issues and Challenges
Authors: Monika  , Mamta  , Goyal Nitin
Published: September 2020

Wireless Sensor Network (WSN) now days become a promising technology in which the set of sensor nodes are installed to form a network and these sensor nodes send data to a storage centre called sink or off shore base station. This data helps in revolutionizing many sensor network based
application areas like military, sciences, automobile industry etc. Further, there exists another category of this broad field that is Underwater Wireless Sensor Network abbreviated as UWSN in which sensor nodes are deployed under the water and they use acoustic signals for communication and
data transmission. In this paper, some concepts and routing protocols for both WSN and UWSN are presented. Also various issues are highlighted; those can come during communication in both the technologies. Further, issues during routing protocols design those plays important role for communication
in establishing and maintenance of network are also discussed.
I-MaxStd: Toward Improving MaxStd Task Scheduling Technique for Cloud Computing Environment
Authors: Banga Puneet , Rana Sanjeev
Published: September 2020

Job scheduling process under the roof of Cloud is consisting of three phases: Resource Discovery, Resource Selection and Task Scheduling (Yousif, A., et al., 2011. A taxonomy of grid resource selection mechanisms. International Journal of Grid and Distributed Computing, 4(3),
pp.107.117). Among them, task scheduling is always treated a cumbersome activity because it mapped task(s) to their assigned resource(s) based on various constraints and impositions according to requirements. Task scheduling in Cloud environment is broadly categorized into two streams that
are: Heuristic and Meta-Heuristic (sometimes combination of both). Heuristic approach is further categorized into two streams: Immediate mode or online approach and Batch mode or offline scheduling technique. MaxStd, heuristic mapping, is one of the efficient batch modes scheduling technique
for independent task(s) due to its inherent efficiency and performance. In this paper, we have proposed an improved version of MaxStd (I-MaxStd) that refines the mapping process of conventional MaxStd to yields an efficient output in the form of reduced makespan and better resource average
utilization rate without compromising its legacy. The validation of proposed work has been done for heterogeneous types of ETC matrices being used as dataset.
Comparative Analysis of Classification Methods Based on Medical Datasets
Authors: Barwal Ravi Kumar , Raheja Neeraj
Published: September 2020

Data mining can be used to predict the disease for the medical database to authenticate hidden strategies. It normally extracts the useful knowledge domain from medical datasets. Various classifi- cation and feature selection methods are available to predict diseases. This paper examines
on five different diseases which include the BCD, PID, HS and CID databases. It normally considered the existing related work to search out important knowledge domains in this field and explained different methods such as ELM, CNN, SVM, PSOA and KNN used in disease prediction. The main discus-
sion about the classification methods is used to improve the accuracy rate, recall, and precision rate. Data mining methods are very efficient for the job of classification. The current classification methods are used for classifying the diseases (Medical Databases). Various classification
tech- niques have experimented on different databases of the UCI machine learning repository site for managing medical database classification. The simulation result depicts that various methods can attain better performance and compare to the results of other classifiers.
Comparison of Microphysical Characteristics between the Southern Korean Peninsula and Oklahoma Using Two-Dimensional Video Disdrometer Data
Authors: Bang Wonbae , Lee GyuWon , Ryzhkov Alexander , Schuur Terry , Sunny Lim Kyo-Sun
Published: September 2020

Differences in atmospheric environments can have a significant impact on microphysical processes of precipitation. Dominant warm (cold) rain processes in East Asia (southern Great Plains of the United States) are implied by a large (small or constant) gradient of reflectivity at low levels in vertical reflectivity profiles. Long-term ground observations using two-dimensional video disdrometers were conducted in the southern Korean Peninsula (KOR) and Norman, Oklahoma, United States (OKL). Raindrop size distributions (RSD) and their moments in the two regions were analyzed in the framework of scaling normalized RSDs. Results show that the concentrations of small (big) raindrops were higher (smaller) in KOR than in OKL. KOR RSDs were also found to be characterized by relatively high characteristic number concentrations and small characteristic diameters when compared to OKL RSDs. The increases with increasing in both KOR and OKL at lower Z with the opposite trend at higher Z. In addition, OKL RSDs with indicate the existence of an equilibrium between coalescence and breakup processes. Rainfall estimation relationships between the rain rate R and radar variables were retrieved from scattering simulations at S-, C-, and X-band wavelengths. KOR RSDs showed relatively small horizontal reflectivity and specific differential phase shift at the same R and same wavelength when compared to OKL RSDs. The regional dependency was significant due to the different microphysical process in KOR and OKL. The specific attenuation of KOR was, however, similar to that of OKL only at S band, indicating an advantage of using specific attenuation in S band in rainfall estimation.
Composite Vertical-Motion Patterns near North American Polar–Subtropical Jet Superposition Events
Authors: Winters Andrew C. , Keyser Daniel , Bosart Lance F.
Published: September 2020

A polar–subtropical jet superposition is preceded by the development of a polar cyclonic potential vorticity (PV) anomaly at high latitudes and a tropical anticyclonic PV anomaly at subtropical latitudes. A confluent large-scale flow pattern can lead to the juxtaposition of these respective PV anomalies at middle latitudes, resulting in the addition of the nondivergent circulations induced by each PV anomaly and an increase in upper-tropospheric wind speeds at the location of jet superposition. Once these PV anomalies become juxtaposed, vertical motion within the near-jet environment facilitates the advection and diabatic redistribution of tropopause-level PV, and the subsequent formation of the steep, single-step tropopause structure that characterizes a jet superposition. Given the importance of vertical motion during the formation of jet superpositions, this study adopts a quasigeostrophic (QG) diagnostic approach to quantify the production of vertical motion during three types of jet superposition events: polar dominant, eastern subtropical dominant, and western subtropical dominant. The diagnosis reveals that the geostrophic wind induced by polar cyclonic QGPV anomalies is predominantly responsible for QG vertical motion in the vicinity of jet superpositions. The QG vertical motion diagnosed from the along-isotherm component of theQvector, which represents the vertical motion associated with synoptic-scale waves, is dominant within the near-jet environment. The QG vertical motion diagnosed from the across-isotherm component of theQvector, which represents the vertical motion associated with frontal circulations in the vicinity of the jet, is subordinate within the near-jet environment, but is relatively more important during eastern subtropical dominant events compared to polar dominant and western subtropical dominant events.
Innovative Nonparametric Method for Data Outlier Filtering
Authors: Wu Zifeng , Wu Zhouxiang , Rilett Laurence R.
Published: September 2020

Outlier filtering of empirical travel time data is essential for traffic analyses. Most of the widely applied outlier filtering algorithms are parametric in nature and based on assumed data distributions. The assumption, however, might not hold under unstable traffic conditions. This paper proposes a nonparametric outlier filtering method based on a robust locally weighted regression scatterplot smoothing model. The proposed method identifies outliers based on a data point’s standard residual in the robust local regression model. This approach fits a regression surface with no constraint on parametric distributions and limited influence from outliers. The proposed outlier filtering algorithm can be applied to various data collection technologies and for real-time applications. The performance of the new outlier filtering algorithm is compared with the moving standard deviation method and other traditional filtering algorithms. The test sites include GPS data of an Interstate highway in Indiana and Bluetooth data of an urban arterial roadway in Texas. It is shown that the proposed filtering algorithm has several advantages over the traditional filtering algorithms.
Liability and artificial intelligence in the EU: Assessing the adequacy of the current Product Liability Directive
Authors: Cabral Tiago Sérgio
Published: September 2020

The European Union selected achieving a leadership position in the AI sector as one of the priorities for the future of the bloc as a whole. Economic reasons are behind this decision, but they are not the exclusive motive behind this objective. Undeniably, AI will have an enormous impact on world’s economy and if the EU falls behind, the standards of living that citizens of the Union currently enjoy may be at risk. Furthermore, there is also the question of keeping European values, principles and ethical standards alive in this technological transition. To achieve the leadership position it desires, it is essential for the EU to possess an updated, producer-friendly legal framework, that manages, at the same time, to ensure consumer protection and safe development of AI. One of the legal instruments that may need to be amended is the Product Liability Directive. In this paper we will study the Product Liability Directive and its shortcomings on AI regulation, along with the possible solutions to adapt the EU product liability legislation to this new technological challenge. We will assess what solutions are best suited to apply in the EU and fulfil the objective of achieving leadership in the sector.
Tiered Systems of Adaptive Supports and the Individualization of Intervention: Merging Developmental Cascades and Correlated Constraints Perspectives
Authors: Farmer Thomas W. , Bierman Karen L. , Hall Cristin M. , Brooks Debbie S. , Lee David L.
Published: September 2020

Although tremendous advances have been made in the development of evidence-based services and strategies to prevent and treat emotional and behavioral disorders (EBDs) in children, often such programs may be necessary but not sufficient to address the circumstances and needs of a specific student. The purpose of this introductory article and this broader special issue on the adaptation and individualization of evidence-based approaches for students with EBD is to consider innovations for tailoring multifactored interventions within tiered systems of support. In this article, we discuss potential ways that current tiered models may be strengthened using information and research strategies from developmental science to more rigorously link intervention to long-term outcomes of youth. We center this discussion on the concepts of developmental cascades and correlated constraints. We then present Tiered Systems of Adaptive Supports (TSAS) as a framework that is developmentally informed to guide individualized intervention. In this model, Tier 1 focuses on adapting general classroom strategies to support the routine daily functioning of all students; Tier 2 focuses on preventing the negative reorganization of a student’s developmental system; and Tier 3 focuses on promoting the positive reorganization of a student’s developmental system.
No Evidence for a Relationship between Intelligence and Ejaculate Quality
Authors: DeLecce Tara , Fink Bernhard , Shackelford Todd , Abed Mohaned G.
Published: September 2020

Genetic quality may be expressed through many traits simultaneously, and this would suggest a phenotype-wide fitness factor. In humans, intelligence has been positively associated with several potential indicators of genetic quality, including ejaculate quality. We conducted a conceptual replication of one such study by investigating the relationship between intelligence (assessed by the Raven Advanced Progressive Matrices Test–Short Form) and ejaculate quality (indexed by sperm count, sperm concentration, and sperm motility) in a sample of 41 men (ages ranging 18 to 33 years; M = 23.33; SD = 3.60). By self-report, participants had not had a vasectomy, and had never sought infertility treatment. We controlled for several covariates known to affect ejaculate quality (e.g., abstinence duration before providing an ejaculate) and found no statistically significant relationship between intelligence and ejaculate quality; our findings, therefore, do not match those of Arden, Gottfredson, Miller et al. or those of previous studies. We discuss limitations of this study and the general research area and highlight the need for future research in this area, especially the need for larger data sets to address questions around phenotypic quality and ejaculate quality.
HLA-mismatched donor and high ferritin level showed poor clinical outcomes after allogeneic hematopoietic cell transplantation in patients with advanced myelofibrosis
Authors: Yoon Jae-Ho , Min Gi June , Park Sung-Soo , Park Silvia , Lee Sung-Eun , Cho Byung-Sik , Kim Yoo-Jin , Lee Seok , Kim Hee-Je , Min Chang-Ki , Cho Seok-Goo , Lee Jong Wook , Eom Ki-Seong
Published: September 2020

Background:

Preconditioning intensity, donor choice and graft- versus-host disease (GVHD) prophylaxis of allogeneic hematopoietic cell transplantation (allo-HCT) for advanced myelofibrosis (MF) have not been fully elucidated.

Methods:

Thirty-five patients with advanced MF were treated with reduced-intensity conditioning (RIC) allo-HCT. We searched for matched sibling donors first, followed by matched or mismatched unrelated donors and familial mismatched donors. Preconditioning regimen consisted of fludarabine (total 150 mg/m2) and busulfan (total 6.4 mg/kg) with total body irradiation ⩽400cGy.

Results:

All showed engraftments, but four showed either leukemic relapse or delayed graft failure. Two-year overall survival (OS) and non-relapse mortality (NRM) was 60.0% and 29.9%, respectively. Acute GVHD was observed in 19 patients, and grade III–IV acute GVHD (eight grade III and four grade IV) was higher in human leukocyte antigen (HLA)-mismatched donor HCT compared with HLA-matched HCT (70% versus 20%). Chronic GVHD was observed in 16 patients, and a cumulative incidence of severe chronic GVHD was 33% in HLA-mismatched donor HCT and 7.7% in HLA-matched HCT. Significant hepatic GVHD was observed in nine patients (five acute, four chronic) and six of them died. Multivariate analysis revealed inferior OS in HLA-mismatched donor HCT (hazard ratio (HR) = 6.40, 95% confidence interval (CI) 1.6–25.7, p = 0.009) and in patients with high ferritin level at the time of pre-conditioning period (HR = 7.22, 95% CI 1.9–27.5, p = 0.004), which were related to higher incidence of hepatic GVHD with high NRM rate.

Conclusion:

RIC allo-HCT can be a valid choice providing graft- versus-fibrosis effect for advanced MF patients. However, HLA-mismatched donor and high pre-HCT ferritin level related to fatal hepatic GVHD should be regarded as poor-risk parameters.
Regional brain gray matter volume in world-class artistic gymnasts
Authors: Fukuo Makoto , Kamagata Koji , Kuramochi Mana , Andica Christina , Tomita Hiroyuki , Waki Hidefumi , Sugano Hidenori , Tange Yuichi , Mitsuhashi Takumi , Uchida Wataru , Takenaka Yuki , Hagiwara Akifumi , Harada Mutsumi , Goto Masami , Hori Masaaki , Aoki Shigeki , Naito Hisashi
Published: September 2020

The relationship between long-term intensive training and brain plasticity in gymnasts has recently been reported. However, the relationship between abilities in different gymnastic events and brain structural changes has not been explored. This study aimed to evaluate the correlation between world-class gymnasts (WCGs)’ specific abilities in different gymnastics events and their gray matter (GM) volume. Ten right-handed Japanese male WCGs and 10 right-handed gender- and age-matched controls with no history of gymnastic training participated in this study. Whole brain three-dimensional T1-weighted images (magnetization-prepared rapid gradient-echo sequence) with 0.90 mm3 voxels were obtained using a 3 T-MRI scanner from each subject. Volume-based morphometry (VolBM) was used to compare GM volume differences between WCGs and controls. We then explored the correlation between specific gymnastic abilities using different gymnastic apparatuses, and GM volume. Significantly higher GM volumes (false discovery rate-corrected p < 0.05) in the inferior parietal lobule, middle temporal gyrus, precentral gyrus, rostral middle frontal gyrus, and superior frontal gyrus were demonstrated in WCGs, compared with controls using VolBM. Moreover, significant positive correlations were observed between brain regions and the difficulty scores for each gymnastic event, for example, rings and inferior parietal lobule and parallel bars and rostral middle frontal gyrus. These results may reflect the neural basis of an outstanding gymnastic ability resulting from brain plasticity in areas associated with spatial perception, vision, working memory, and motor control.
Estimating the number of usability problems affecting medical devices: modelling the discovery matrix
Authors: Vandewalle Vincent , Caron Alexandre , Delettrez Coralie , Périchon Renaud , Pelayo Sylvia , Duhamel Alain , Dervaux Benoit
Published: September 2020

Background

Usability testing of medical devices are mandatory for market access. The testings’ goal is to identify usability problems that could cause harm to the user or limit the device’s effectiveness. In practice, human factor engineers study participants under actual conditions of use and list the problems encountered. This results in a binary discovery matrix in which each row corresponds to a participant, and each column corresponds to a usability problem. One of the main challenges in usability testing is estimating the total number of problems, in order to assess the completeness of the discovery process. Today’s margin-based methods fit the column sums to a binomial model of problem detection. However, the discovery matrix actually observed is truncated because of undiscovered problems, which corresponds to fitting the marginal sums without the zeros. Margin-based methods fail to overcome the bias related to truncation of the matrix. The objective of the present study was to develop and test a matrix-based method for estimating the total number of usability problems.

Methods

The matrix-based model was based on the full discovery matrix (including unobserved columns) and not solely on a summary of the data (e.g. the margins). This model also circumvents a drawback of margin-based methods by simultaneously estimating the model’s parameters and the total number of problems. Furthermore, the matrix-based method takes account of a heterogeneous probability of detection, which reflects a real-life setting. As suggested in the usability literature, we assumed that the probability of detection had a logit-normal distribution.

Results

We assessed the matrix-based method’s performance in a range of settings reflecting real-life usability testing and with heterogeneous probabilities of problem detection. In our simulations, the matrix-based method improved the estimation of the number of problems (in terms of bias, consistency, and coverage probability) in a wide range of settings. We also applied our method to five real datasets from usability testing.

Conclusions

Estimation models (and particularly matrix-based models) are of value in estimating and monitoring the detection process during usability testing. Matrix-based models have a solid mathematical grounding and, with a view to facilitating the decision-making process for both regulators and device manufacturers, should be incorporated into current standards.
Tuberculosis clinical presentation and treatment outcomes in pregnancy: a prospective cohort study
Authors: van de Water Brittney J. , Brooks Meredith B. , Huang Chuan-Chin , Trevisi Letizia , Lecca Leonid , Contreras Carmen , Galea Jerome , Calderon Roger , Yataco Rosa , Murray Megan , Becerra Mercedes C.
Published: September 2020

Background

There is limited research to guide TB treatment specifically in pregnant women and few studies have described the presentation of TB in pregnant women. We aimed to understand TB presentation and treatment outcomes in pregnant women in a low HIV burden setting. We describe a cohort of women of childbearing age treated for TB disease in Lima, Peru, and compare clinical presentation and treatment outcomes among pregnant and non-pregnant women between 2009 and 2012, including 36 pregnant women.

Methods

This is a prospective cohort study. Subjects were recruited from across 106 public health centers in Lima, Peru. Baseline demographic, medical history, and drug-susceptibility test results were collected. We used descriptive statistics to describe demographic and clinical characteristics of the women using Pearson chi-squared, Fisher’s exact tests, or Kruskal-Wallis.

Results

Among 4500 individuals with pulmonary TB disease, 1334 women were included in analysis with 36 (2.69%) pregnant women. Pregnant women had similar demographics, past medical histories, and clinical presentation to non-pregnant women, except being more likely to be married (p = 0.01) and have cardiac disease (p = 0.04) and less likely to have weight loss (p = 0.05). Twenty (71.4%) pregnant women had pan-susceptible TB compared with 616 (63.1%) non-pregnant women; four (14.3%) pregnant women had mono-resistant TB compared with 154 (15.8%) non-pregnant women; and four (14.3%) pregnant women had multi-drug-resistant TB compared with 140 (14.3%) of non-pregnant women (p = 0.53). Twenty-eight (96.6%) pregnant women had a successful outcome (cure, completed treatment, treatment ended early by clinical team) while one (3.4%) had an unsuccessful outcome (treatment failed) and 1074 (97.3%) non-pregnant women had a successful outcome while 30 (2.7%) had an unsuccessful outcome (p = 0.56).

Conclusion

In this cohort with low HIV co-infection, we found high TB treatment success rates in both pregnant and non-pregnant women, irrespective of drug-susceptibility profiles. If treated appropriately, pregnant women with TB disease can have successful outcomes.
Intraperitoneal ampicillin treatment for peritoneal dialysis- related peritonitis with Listeria monocytogenes – a case report
Authors: Boss Kristina , Wiegard-Szramek Ina , Dziobaka Jan , Kribben Andreas , Dolff Sebastian
Published: September 2020

Background

Peritoneal dialysis (PD)-related peritonitis is a rare but serious complication and is associated with increased morbidity and mortality rates. It is most commonly caused by Staphylococcus aureus or Staphylococcus epidermidis, but infection with Listeria monocytogenes may also occur. Recommendations for antibiotic treatment of a Listeria infection are currently based on a small number of case reports and suggest the administration of ampicillin. But unlike vancomycin or gentamicin, for ampicillin the route of application, the dosage, and the duration of treatment have not yet been established. We report a case in which PD-associated peritonitis due to Listeria infection was treated with ampicillin administered intravenously and intraperitoneally, separately and in combination.

Case presentation

A 72-year-old man with chronic kidney disease stage 5 dialysis (CKDG5D) secondary to hypertension and diabetes was hospitalised in April 2020 because of PD-related peritonitis caused by a Listeria infection. In accordance with the results of resistance tests, the patient was treated with intravenous ampicillin at a dosage of 6 g twice daily. After initial treatment the leukocyte count in the PD effluent had decreased substantially, but it was permanently reduced only with the addition of intraperitoneal ampicillin (4 g daily). Efficient serum concentrations of ampicillin were determined for both routes of administration, intravenous and intraperitoneal.

Conclusion

This is the first case report demonstrating that PD-related peritonitis due to Listeria monocytogenes infection can be treated with intraperitoneal ampicillin and monitored by the determination of peripheral serum concentrations of ampicillin.
Lower versus higher diagnostic criteria for the detection of gestational diabetes for reducing maternal and perinatal morbidity: study protocol for the GEMS randomised trial
Authors: Crowther Caroline A. , McCowan Lesley M. E. , Rowan Janet A. , Edlin Richard , McKinlay Christopher J. D. ,   for the GEMS Study Group
Published: September 2020

Background

Gestational diabetes mellitus (GDM) has lifelong implications for the woman and her infant. Treatment reduces adverse maternal and perinatal outcomes although uncertainty remains about the optimal diagnostic criteria. The GEMS Trial aims to assess whether detection and treatment of women with GDM using the lower International Association of Diabetes in Pregnancy Study Groups diagnostic criteria compared with the higher criteria recommended in New Zealand reduces infant morbidity without increasing maternal morbidity.

Methods

GEMS is a multicentre, randomised trial. Women with a singleton pregnancy at 24 to 34 weeks’ gestation are eligible who give written informed consent. Women are randomly allocated to the Lower Criteria Group or the Higher Criteria Group. Women with a normal OGTT by their allocated criteria receive routine care (Higher criteria: fasting plasma glucose < 5.5 mmol/L, AND 2 hour < 9.0 mmol/L; Lower criteria: fasting plasma glucose < 5.1 mmol/L, AND 1 hour < 10.0 mmol/L, AND 2 hour < 8.5 mmol/l). Women with GDM on OGTT by their allocated criteria receive standard care for GDM (Higher criteria: fasting plasma glucose ≥ 5.5 mmol/L, OR 2 hour ≥ 9.0 mmol/L; Lower criteria: fasting plasma glucose ≥ 5.1 mmol/L, OR 1 hour ≥ 10.0 mmol/L, OR 2 hour ≥ 8.5 mmol/L). The primary outcome is large for gestational age (birth weight > 90th centile). Secondary outcomes for the infant include a composite of serious outcomes, gestational age, anthropometry, Apgar score < 4 at 5 minutes, lung disease, use of respiratory support, hypoglycaemia, hyperbilirubinaemia, infection, and encephalopathy; and for the woman, a composite of serious outcomes, preeclampsia, induction of labour, mode of birth, weight gain, postpartum haemorrhage and infectious morbidity. A study with 4,158 women will detect an absolute difference of 2.9% in the proportion of large for gestational age infants from 10.0% using the lower criteria to 12.9% with the higher criteria.

Discussion

The GEMS Trial will provide high-level evidence relevant for clinical practice. If use of the lower diagnostic criteria results in significantly fewer large for gestational age infants and/or improves maternal and perinatal outcomes these criteria should be recommended for diagnosis of gestational diabetes.

Trial registration

Australian New Zealand Clinical Trials Registry registration number ACTRN12615000290594. Date registered: 27th March 2015.