bluuebunny/crossref_metadata_2025 huggingface dataset:
Promising Anti-stroke Signature of Voglibose: Investigation through In- Silico Molecular Docking and Virtual Screening in In-Vivo Animal Studies
Authors: Shah Pooja , Chavda Vishal , Patel Snehal , Bhadada Shraddha , Ashraf Ghulam Md.
Published: July 2020

Background:

Postprandial hyperglycemia considered to be a major risk factor for
cerebrovascular complications.

Objective:

The current study was designed to elucidate the beneficial role of voglibose via in-silico in
vitro to in-vivo studies in improving the postprandial glycaemic state by protection against strokeprone
type 2 diabetes.

Material and Methods:

In-Silico molecular docking and virtual screening were carried out with the
help of iGEMDOCK+ Pymol+docking software and Protein Drug Bank database (PDB). Based on the
results of docking studies, in-vivo investigation was carried out for possible neuroprotective action.
T2DM was induced by a single injection of streptozotocin (90mg/kg, i.v.) to neonates. Six weeks after
induction, voglibose was administered at the dose of 10mg/kg p.o. for two weeks. After eight weeks,
diabetic rats were subjected to middle cerebral artery occlusion, and after 72 hours of surgery,
neurological deficits were determined. The blood was collected for the determination of serum
glucose, CK-MB, LDH and lipid levels. Brains were excised for determination of brain infarct
volume, brain hemisphere weight difference, Na+-K+ ATPase activity, ROS parameters, NO levels,
and aldose reductase activity.

Results:

In-silico docking studies showed good docking binding score for stroke associated proteins,
which possibly hypotheses neuroprotective action of voglibose in stroke. In the present in-vivo study,
pre-treatment with voglibose showed a significant decrease (p<0.05) in serum glucose and lipid levels.
Voglibose has shown significant (p<0.05) reduction in neurological score, brain infarct volume, the
difference in brain hemisphere weight. On biochemical evaluation, treatment with voglibose produced
significant (p<0.05) decrease in CK-MB, LDH, and NO levels in blood and reduction in Na+-K+
ATPase, oxidative stress, and aldose reductase activity in brain homogenate.

Conclusion:

In-silico molecular docking and virtual screening studies and in-vivo studies in MCAo
induced stroke, animal model outcomes support the strong anti-stroke signature for possible
neuroprotective therapeutics.
Hungry Rice-based Biscuits: Effects on Some Biochemical Indices of Alloxan- induced Diabetic Adult Male Wistar Rats
Authors: Chikwendu Justina N. , Adeosun Florence F. , Onwurafor Eucharia U. , Udenta Elizabeth A. , Maduforo Aloysius N.
Published: July 2020

Background:

High costs of hypoglycemic drugs and their side effects have caused an increase
in demand for the production of low glycemic plant foods and snacks from locally available
nutrient dense crops for diabetes management. Hungry rice is known for its high fiber content and
hypoglycemic properties.

Objectives:

The study investigated the effect of Hungry rice-based biscuits on some biochemical indices
of alloxan-induced diabetic Wistar rats.

Methods:

Four groups of six adult male Wistar rats each with a mean weight of 164.59 g were fasted
overnight and induced diabetes by a single intraperitoneal injection of alloxan at a dose of 150
mg/kg body weight and fed biscuits containing hungry rice/unripe plantain/cashew nut
(80:10:10%), hungry rice/unripe-plantain (80:20%), hungry rice/cashew nut (80:20%) or wheat
(100%) and water ad libitum for 21 days. The non-induced rats fed poultry growers’ mash serve as
the control. The rats’ body weight and food intake were evaluated while the lipid profile, liver enzyme
markers, and serum protein were assayed.

Results:

Hungry rice-based biscuits showed decreases in fasting blood glucose, AST, ALT, ALP,
TC, TG, and LDL-C and increases in HDL-C and total serum protein of rats. ALP and serum protein
were more in rats fed Wheat biscuit and 80% hungry rice-20% unripe plantain biscuit. The biscuit
containing 20% cashew nut showed a better desirable effect on all the parameters tested.

Conclusion:

The hungry rice-based-biscuits exhibited hypoglycemic and hypolipidemic potentials
with desirable effect on liver biomarkers and serum proteins; and may be good for diabetes and
weight management.
Naringenin and Ellagic Acid Reduce Tetrazolium Salt in the Absence of Cells
Authors: Tiwary Vaibhav , Sarkar Priyanka , Thirumurugan Kavitha
Published: July 2020

Background:

Plant derived compounds, naringenin and ellagic acid might interfere in MTT (3-(4, 5-
Dimethylthiazol-2-yl)-2, 5-Diphenyltetrazolium Bromide) assay in the form of reducing the tetrazolium salt in the absence
of cells.

Objective:

Effect of naringenin and ellagic acid in MTT assay.

Methods:

MTT assay has been performed at different conditions like, varying the concentration of phytocompounds,
incubation time, and changing the media.

Results:

Ellagic acid at the concentration of 80 μg/ml showed maximum absorbance of 0.579 in
the presence of RPMI media (Roswell Park Memorial Institute Medium) that has 10% newborn
calf serum. Naringenin showed higher absorbance of 2.74 at 80 μg/ml in RPMI media that has 10%
newborn calf serum. Naringenin and Ellagic acid had better ability to reduce the MTT in the presence
of RPMI media than anhydrous ethanol.

Conclusion:

Naringenin reduced the MTT better than ellagic acid in RPMI-1640 medium. These results show the need to
be cautious of choosing MTT assay to measure the metabolic activity of cells treated with phytocompounds.
Gallic Acid Improved Amytryptiline Effect in Neuropathic Pain Induced by Partial Sciatic Nerve Ligation (PSNL) in Rats
Authors: Neerati Prasad , Enumula Vinay
Published: July 2020

Aim and Backround:

Amytriptiline (AMY), an antidepressant, is used in the treatment
of neuropathic pain. However, with limited therapeutic benefits and side effects of AMY, it is not
helpful to the majority of the neuropathic pain patients. Gallic acid (GA) is a polyphenolic product
with potential antioxidant effects, useful in neuropathic pain.

Objective:

The purpose of the study was to investigate the improved neuropathic pain relief with
GA in combination with AMY in rats.

Methods:

Partial sciatic nerve ligation (PSNL) method is used for neuropathic pain induction in
rats. The rats were randomly divided into 6 groups (n = 8), and treated with drugs or vehicle once
daily for 15 days after the pain induction. The first group served as Normal control and received
normal saline (p.o), the second group served as Sham Control and received Normal Saline (p.o),
the third group served as Surgery Control and received normal saline (p.o), the fourth group received
AMY (25mg/kg, p.o), while the fifth group received GA (100mg/kg,p.o), and the sixth
group received both AMY and GA. Further behavioural studies for pain were carried out on days
1,4,7,10,13,16,19 and 22. Histological studies of sciatic nerves were carried out on days 1, 15 and
22, and on the 22nd day, all the remaining animals were sacrificed for the biochemical estimations
of the sciatic nerve tissues.

Results:

GA attenuates neuropathic pain better in combination with AMY by ameliorating the behavioral,
biochemical and histopathological signs and symptoms. This resultant effect is due to its
antioxidant, antinociceptive and anti-inflammatory actions. It also potentiates AMY antinociceptive
and antioxidant effects when given in combination in a continuous treatment.

Conclusion:

The results suggest that GA in combination with AMYattenuated the neuropathic
pain. Hence, the use of GA as an adjunctive with AMY in the neuropathic pain will be more therapeutically
beneficial; further studies on patients to confirm the results are warranted.
A Novel Static Path Planning Method for Mobile Anchor-Assisted Localization in Wireless Sensor Networks
Authors: Naguib Abdelhady M. , Ali Shahzad
Published: July 2020

Background:

Many applications of Wireless Sensor Networks (WSNs) require awareness
of sensor node’s location but not every sensor node can be equipped with a GPS receiver for
localization, due to cost and energy constraints especially for large-scale networks. For localization,
many algorithms have been proposed to enable a sensor node to be able to determine its location by
utilizing a small number of special nodes called anchors that are equipped with GPS receivers. In
recent years a promising method that significantly reduces the cost is to replace the set of statically
deployed GPS anchors with one mobile anchor node equipped with a GPS unit that moves to cover
the entire network.

Objectives:

This paper proposes a novel static path planning mechanism that enables a single anchor node to follow a predefined static path while periodically broadcasting its current location coordinates to the nearby sensors. This new path type is called
SQUARE_SPIRAL and it is specifically designed to reduce the collinearity during localization.

Results:

Simulation results show that the performance of SQUARE_SPIRAL mechanism is better
than other static path planning methods with respect to multiple performance metrics.

Conclusion:

This work includes an extensive comparative study of the existing static path planning methods then presents a comparison of the proposed mechanism with existing solutions by doing extensive simulations in NS-2.
Warehousing Operation Optimization Model of One Storey New Dangerous Goods Warehouse Based on Warehousing Chain and Its Application
Authors: Li Xiaguang , Lin Xiefang , Zhang Fangwei , Tang Xufeng , Qiu Ruolin , Sun Xue
Published: July 2020

Introduction:

In order to ensure the efficiency and cost of dangerous goods warehouse
under the premise of safety, this study takes the dangerous goods warehouse as the research object
and implements multitask for dangerous goods warehouse with two forklifts.

Methods:

This study takes traversal calculation, novel safety calculation formula and scheduling
scheme evaluation model as tools to research the forklift scheduling scheme of one-storey packed
dangerous goods warehouse.

Results:

Optimal scheme and allocation decision model are obtained through numerical simulation.
The innovation of this study is giving the safety formula of forklift operation in dangerous goods
warehouse and using numerical simulation to obtain the global optimal solution. Furthermore, this
study draws on the concept of the travel chain to propose the idea of warehousing chain. At the same
time, optimal schemes for multiple dangerous goods inbound and outbound the warehouse are studied.

Conclusion:

With the application of actual data in Shanghai Lingang dangerous goods warehouse,
this study combines with the simulation technology to verify the allocation model of warehousing
operations and forklift scheduling model. The validity and feasibility of the novel theory are also
verified.
Biología floral y solapamiento fenológico de las angiospermas de un bosque inundable, cuenca del lago de Maracaibo, Venezuela
Authors: Barrios Orozco Yeni Celeste , Ramírez Nelson
Published: July 2020

Antecedentes y Objetivos: La biología floral de comunidades tropicales bajo clima estacional ha mostrado el predominio de caracteres relacionados con la polinización generalista. En este contexto, se describe la biología floral de 48 especies de un bosque inundable con el propósito de explorar la relación entre atributos florales, examinar la diversidad de síndromes de polinización y evaluar la efectividad de este concepto para predecir clases de polinizadores. Además, se analizó el solapamiento de los períodos de floración de las especies con síndromes de polinización y/o clases de polinizadores comunes en búsqueda de un patrón que pudiese reflejar efectos adversos del solapamiento en cuanto a polinizadores.
Métodos: En cada especie se describió el sistema sexual, la existencia de dicogamia y/o hercogamia, el tipo de unidad de polinización, las dimensiones, forma, simetría y orientación espacial de las flores, los atrayentes y recompensas florales, el horario de antesis, la longevidad floral, el síndrome de polinización y las clases de polinizadores. El solapamiento interespecífico fue estimado mediante el índice de Pianka y contrastado con valores generados aleatoriamente.
Resultados clave: Las especies en su mayoría fueron hermafroditas, adicógamas y no hercógamas; mostraron unidades de polinización individuales, de dimensiones pequeñas, formas abiertas, simetría radial, orientación horizontal, color blanco y néctar como recompensa principal. La antesis fue principalmente diurna y la longevidad floral corta, generalmente de un día. La melitofilia fue el síndrome predominante y las abejas la principal clase de polinizadores, aunque muchas de las especies fueron polinizadas por más de un tipo de agente. La distribución temporal de los períodos de floración no difirió a la de un esquema aleatorio.
Conclusiones: Los resultados permiten concluir que más allá de lo predicho por los síndromes florales, en la comunidad prevalece un sistema polifílico que posiblemente redunda en mayores oportunidades de polinización para las especies de plantas.
A Companion App to Support Rheumatology Patients Treated with Certolizumab Pegol: Results From a Usability Study
Authors: Domańska Barbara , Vansant Stijn , Mountian Irina
Published: July 2020

Background

Certolizumab pegol (CZP) is an anti-tumor necrosis factor drug approved for the treatment of multiple moderate to severe chronic inflammatory diseases. In the European Union, CZP is approved for administration by subcutaneous self-injection using a prefilled syringe, prefilled pen, or reusable electromechanical auto-injector (electronic device). CimplyMe is a companion app for use alongside CZP self-injection devices, designed to support CZP-treated patients self-managing their treatment and disease.

Objective

This study aimed to validate the usability of the companion app by demonstrating that tasks required for use can be performed successfully by intended end users.

Methods

We recruited 15 patients with moderate to severe rheumatoid arthritis, currently prescribed biologic treatment, and using apps on a smart phone. Patients were assessed on their ability to use the companion app in a setting designed to simulate a location where patients regularly administer biologic treatment. To assess the usability of the key features of the app, 8 critical and 3 noncritical tasks were designed. Patients’ success on each task was recorded through observations or knowledge-based questions. Successes with difficulty and use errors were also recorded. If a patient made a use error at the first attempt, a second attempt was allowed. Second-attempt use errors were recorded as a task failure.

Results

A total of 207 first attempts at the 14 components of the 8 critical tasks were evaluated (3 patients failed to complete one component); 178 (86.0%) critical tasks were successfully completed at the first attempt. The remaining first attempts comprised 16 (7.7%) successes with difficulty and 13 (6.3%) use errors, which had to be repeated. One critical task was not re-attempted by one patient due to time constraints; however, there were no use errors in the 12 completed second attempts. A total of 107 first attempts at the 3 noncritical tasks were made, all of which (107/107, 100.0%) were completed without use errors.

Conclusions

In simulated testing, patients were able to successfully use the companion app without formal training. This study suggests the companion app is easy to use and could help patients prescribed CZP better manage their treatment and disease.
Fueling Clinical and Translational Research in Appalachia: Informatics Platform Approach
Authors: Cecchetti Alfred A , Bhardwaj Niharika , Murughiyan Usha , Kothakapu Gouthami , Sundaram Uma
Published: July 2020

Background

The Appalachian population is distinct, not just culturally and geographically but also in its health care needs, facing the most health care disparities in the United States. To meet these unique demands, Appalachian medical centers need an arsenal of analytics and data science tools with the foundation of a centralized data warehouse to transform health care data into actionable clinical interventions. However, this is an especially challenging task given the fragmented state of medical data within Appalachia and the need for integration of other types of data such as environmental, social, and economic with medical data.

Objective

This paper aims to present the structure and process of the development of an integrated platform at a midlevel Appalachian academic medical center along with its initial uses.

Methods

The Appalachian Informatics Platform was developed by the Appalachian Clinical and Translational Science Institute’s Division of Clinical Informatics and consists of 4 major components: a centralized clinical data warehouse, modeling (statistical and machine learning), visualization, and model evaluation. Data from different clinical systems, billing systems, and state- or national-level data sets were integrated into a centralized data warehouse. The platform supports research efforts by enabling curation and analysis of data using the different components, as appropriate.

Results

The Appalachian Informatics Platform is functional and has supported several research efforts since its implementation for a variety of purposes, such as increasing knowledge of the pathophysiology of diseases, risk identification, risk prediction, and health care resource utilization research and estimation of the economic impact of diseases.

Conclusions

The platform provides an inexpensive yet seamless way to translate clinical and translational research ideas into clinical applications for regions similar to Appalachia that have limited resources and a largely rural population.
Results and Guidelines From a Repeated-Measures Design Experiment Comparing Standing and Seated Full-Body Gesture-Based Immersive Virtual Reality Exergames: Within-Subjects Evaluation
Authors: Xu Wenge , Liang Hai-Ning , He Qiuyu , Li Xiang , Yu Kangyou , Chen Yuzheng
Published: July 2020

Background

Although full-body seated exercises have been studied in a wide range of settings (ie, homes, hospitals, and daycare centers), they have rarely been converted to seated exergames. In addition, there is an increasing number of studies on immersive virtual reality (iVR) full-body gesture-based standing exergames, but the suitability and usefulness of seated exergames remain largely unexplored.

Objective

This study aimed to evaluate the difference between playing a full-body gesture-based iVR standing exergame and seated exergame in terms of gameplay performance, intrinsic motivation, and motion sickness.

Methods

A total of 52 participants completed the experiment. The order of the game mode (standing and sitting) was counterbalanced. Gameplay performance was evaluated by action or gesture completion time and the number of missed gestures. Exertion was measured by the average heart rate (HR) percentage (AvgHR%), increased HR%, calories burned, and the Borg 6-20 questionnaire. Intrinsic motivation was assessed with the Intrinsic Motivation Inventory (IMI), whereas motion sickness was assessed via the Motion Sickness Assessment Questionnaire (MSAQ). In addition, we measured the fear of falling using a 10-point Likert scale questionnaire.

Results

Players missed more gestures in the seated exergame than in the standing exergame, but the overall miss rate was low (2.3/120, 1.9%). The analysis yielded significantly higher AvgHR%, increased HR%, calories burned, and Borg 6-20 rating of perceived exertion values for the seated exergame (all P<.001). The seated exergame was rated significantly higher on peripheral sickness (P=.02) and sopite-related sickness (MSAQ) (P=.004) than the standing exergame. The score of the subscale “value/usefulness” from IMI was reported to be higher for the seated exergame than the standing exergame. There was no significant difference between the seated exergame and standing exergame in terms of intrinsic motivation (interest/enjoyment, P=.96; perceived competence, P=.26; pressure/tension, P=.42) and the fear of falling (P=.25).

Conclusions

Seated iVR full-body gesture-based exergames can be valuable complements to standing exergames. Seated exergames have the potential to lead to higher exertion, provide higher value to players, and be more applicable in small spaces compared with standing exergames. However, gestures for seated exergames need to be designed carefully to minimize motion sickness, and more time should be given to users to perform gestures in seated exergames compared with standing exergames.
Effectiveness of Virtual Reality in Nursing Education: Meta-Analysis
Authors: Chen Feng-Qin , Leng Yu-Fei , Ge Jian-Feng , Wang Dan-Wen , Li Cheng , Chen Bin , Sun Zhi-Ling
Published: July 2020

Background

Virtual reality (VR) is the use of computer technology to create an interactive three-dimensional (3D) world, which gives users a sense of spatial presence. In nursing education, VR has been used to help optimize teaching and learning processes.

Objective

The purpose of this study was to evaluate the effectiveness of VR in nursing education in the areas of knowledge, skills, satisfaction, confidence, and performance time.

Methods

We conducted a meta-analysis of the effectiveness of VR in nursing education based on the Cochrane methodology. An electronic literature search using the Cochrane Library, Web of Science, PubMed, Embase, and CINAHL (Cumulative Index to Nursing and Allied Health Literature), up to December 2019 was conducted to identify studies that reported the effectiveness of VR on knowledge, skills, satisfaction, confidence, and performance time. The study selection and data extraction were carried out by two independent reviewers. The methodological quality of the selected studies was determined using the Cochrane criteria for risk-of-bias assessment.

Results

A total of 12 studies, including 821 participants, were selected for the final analysis. We found that VR was more effective than the control conditions in improving knowledge (standard mean difference [SMD]=0.58, 95% CI 0.41-0.75, P<.001, I2=47%). However, there was no difference between VR and the control conditions in skills (SMD=0.01, 95% CI –0.24 to 0.26, P=.93, I2=37%), satisfaction (SMD=0.01, 95% CI –0.79 to 0.80, P=.99, I2=86%), confidence (SMD=0.00, 95% CI –0.28 to 0.27, P=.99, I2=0%), and performance time (SMD=–0.55, 95% CI –2.04 to 0.94, P=.47, I2=97%).

Conclusions

The results of this study suggest that VR can effectively improve knowledge in nursing education, but it was not more effective than other education methods in areas of skills, satisfaction, confidence, and performance time. Further rigorous studies with a larger sample size are warranted to confirm these results.
Investigating the Accessibility of Voice Assistants With Impaired Users: Mixed Methods Study
Authors: Masina Fabio , Orso Valeria , Pluchino Patrik , Dainese Giulia , Volpato Stefania , Nelini Cristian , Mapelli Daniela , Spagnolli Anna , Gamberini Luciano
Published: July 2020

Background

Voice assistants allow users to control appliances and functions of a smart home by simply uttering a few words. Such systems hold the potential to significantly help users with motor and cognitive disabilities who currently depend on their caregiver even for basic needs (eg, opening a door). The research on voice assistants is mainly dedicated to able-bodied users, and studies evaluating the accessibility of such systems are still sparse and fail to account for the participants’ actual motor, linguistic, and cognitive abilities.

Objective

The aim of this work is to investigate whether cognitive and/or linguistic functions could predict user performance in operating an off-the-shelf voice assistant (Google Home).

Methods

A group of users with disabilities (n=16) was invited to a living laboratory and asked to interact with the system. Besides collecting data on their performance and experience with the system, their cognitive and linguistic skills were assessed using standardized inventories. The identification of predictors (cognitive and/or linguistic) capable of accounting for an efficient interaction with the voice assistant was investigated by performing multiple linear regression models. The best model was identified by adopting a selection strategy based on the Akaike information criterion (AIC).

Results

For users with disabilities, the effectiveness of interacting with a voice assistant is predicted by the Mini-Mental State Examination (MMSE) and the Robertson Dysarthria Profile (specifically, the ability to repeat sentences), as the best model shows (AIC=130.11).

Conclusions

Users with motor, linguistic, and cognitive impairments can effectively interact with voice assistants, given specific levels of residual cognitive and linguistic skills. More specifically, our paper advances practical indicators to predict the level of accessibility of speech-based interactive systems. Finally, accessibility design guidelines are introduced based on the performance results observed in users with disabilities.
Respiratory Health of Pacific Youth: An Observational Study of Associated Risk and Protective Factors Throughout Childhood
Authors: Tautolo El-Shadan , Wong Conroy , Vandal Alain , Jalili-Moghaddam Shabnam , Griffiths Emily , Iusitini Leon , Trenholme Adrian , Byrnes Catherine
Published: July 2020

Background

Respiratory disease is the third most common cause of death in New Zealand, with Pacific people living in New Zealand bearing the greatest burden of this type of disease. Although some epidemiological outcomes are known, we lack the specifics required to formulate targeted and effective public health interventions. The Pacific Islands Families (PIF) birth cohort study is a study that provides a unique source of data to assess lung function and current respiratory health among participants entering early adulthood and to examine associations with early life events during critical periods of growth.

Objective

This paper aims to provide an overview of the design, methods, and scope of the Respiratory Health of Pacific Youth Study, which uses the overall PIF study cohort aged 18-19 years.

Methods

From 2000-2019, the PIF study has followed, from birth, the growth, and the development of 1398 Pacific children born in Auckland, New Zealand. Participants were nested within the overall PIF study (at ages 18-19 years) from June 2018, and assessments were undertaken until mid-November 2019. The assessments included respiratory and general medical histories, a general physical examination, assessment of lung function (forced expiratory volume and forced vital capacity), self-completed questionnaires (St George’s Respiratory Questionnaire, European Quality of Life 5 Dimensions-3 Level, Epworth Sleepiness Scale for Children and Adolescents, and Leicester Cough Questionnaire), blood tests (eosinophils, Immunoglobulin E, Immunoglobulin G, Immunoglobulin A, Immunoglobulin M, and C-reactive protein), and chest x-rays. Noninferential analyses will be carried out on dimensionally reduced risk and protective factors and confounders.

Results

Data collection began in June 2018 and ended in November 2019, with a total of 466 participants recruited for submission of the paper. Collection and collation of chest x-ray data is still underway, and data analysis and expected results will be published by November 2020.

Conclusions

This is the first longitudinal observational study to address the burden of respiratory disease among Pacific youth by determining factors in early life that impose long-term detriments in lung function and are associated with the presence of respiratory illness. Identifying risk factors and the magnitude of their effects will help in adopting preventative measures, establishing whether any avoidable risks can be modified by later resilient behaviors, and provide baseline measurements for the development of respiratory disease in later adult life. The study results can be translated into practice guidelines and inform health strategies with immediate national and international impact.

International Registered Report Identifier (IRRID)

DERR1-10.2196/18916
Marrying Medical Domain Knowledge With Deep Learning on Electronic Health Records: A Deep Visual Analytics Approach
Authors: Li Rui , Yin Changchang , Yang Samuel , Qian Buyue , Zhang Ping
Published: July 2020

Background

Deep learning models have attracted significant interest from health care researchers during the last few decades. There have been many studies that apply deep learning to medical applications and achieve promising results. However, there are three limitations to the existing models: (1) most clinicians are unable to interpret the results from the existing models, (2) existing models cannot incorporate complicated medical domain knowledge (eg, a disease causes another disease), and (3) most existing models lack visual exploration and interaction. Both the electronic health record (EHR) data set and the deep model results are complex and abstract, which impedes clinicians from exploring and communicating with the model directly.

Objective

The objective of this study is to develop an interpretable and accurate risk prediction model as well as an interactive clinical prediction system to support EHR data exploration, knowledge graph demonstration, and model interpretation.

Methods

A domain-knowledge–guided recurrent neural network (DG-RNN) model is proposed to predict clinical risks. The model takes medical event sequences as input and incorporates medical domain knowledge by attending to a subgraph of the whole medical knowledge graph. A global pooling operation and a fully connected layer are used to output the clinical outcomes. The middle results and the parameters of the fully connected layer are helpful in identifying which medical events cause clinical risks. DG-Viz is also designed to support EHR data exploration, knowledge graph demonstration, and model interpretation.

Results

We conducted both risk prediction experiments and a case study on a real-world data set. A total of 554 patients with heart failure and 1662 control patients without heart failure were selected from the data set. The experimental results show that the proposed DG-RNN outperforms the state-of-the-art approaches by approximately 1.5%. The case study demonstrates how our medical physician collaborator can effectively explore the data and interpret the prediction results using DG-Viz.

Conclusions

In this study, we present DG-Viz, an interactive clinical prediction system, which brings together the power of deep learning (ie, a DG-RNN–based model) and visual analytics to predict clinical risks and visually interpret the EHR prediction results. Experimental results and a case study on heart failure risk prediction tasks demonstrate the effectiveness and usefulness of the DG-Viz system. This study will pave the way for interactive, interpretable, and accurate clinical risk predictions.
An Artificial Intelligence Fusion Model for Cardiac Emergency Decision Making: Application and Robustness Analysis (Preprint)
Authors: Gong Liheng , Zhang Xiao , Li Ling
Published: July 2020

BACKGROUND

During cardiac emergency medical treatment, reducing the incidence of avoidable adverse events, ensuring the safety of patients, and generally improving the quality and efficiency of medical treatment have been important research topics in theoretical and practical circles.

OBJECTIVE

This paper examines the robustness of the decision-making reasoning process from the overall perspective of the cardiac emergency medical system.

METHODS

The principle of robustness was introduced into our study on the quality and efficiency of cardiac emergency decision making. We propose the concept of robustness for complex medical decision making by targeting the problem of low reasoning efficiency and accuracy in cardiac emergency decision making. The key bottlenecks such as anti-interference capability, fault tolerance, and redundancy were studied. The rules of knowledge acquisition and transfer in the decision-making process were systematically analyzed to reveal the core role of knowledge reasoning.

RESULTS

The robustness threshold method was adopted to construct the robustness criteria group of the system, and the fusion and coordination mechanism was realized through information entropy, information gain, and mutual information methods.

CONCLUSIONS

A set of fusion models and robust threshold methods such as the R2CMIFS (treatment mode of fibroblastic sarcoma) model and the RTCRF (clinical trial observation mode) model were proposed. Our study enriches the theoretical research on robustness in this field.
Against the Misuse of the ICCPR Act: Protest and Activism by Sri Lankan Political Cartoonists
Authors: Perera Vihanga
Published: July 2020

This paper investigates the work of three cartoonists – Awantha Artigala, Gihan de Chickera, and Shanika Somathilake – in response to the misuse of the International Convention on Civil and Political Rights (ICCPR) Act by the Sri Lankan police in a series of arrests between April and May 2019.
Wireless Body Area Network (WBAN) untuk Detak Jantung dan Suhu Tubuh
Authors: Lestari Rahayu Puji , Risqiwati Diah , Sari Zamah
Published: July 2020

Wireless Body Area Network (WBAN) adalah suatu sistem terpadu yang terdiri atas sekelompok modul sensor yang terdistribusi dan terhubung secara nirkabel pada suatu topologi jaringan tertentu dan berfungsi untuk mengekstrak dan berbagi informasi untuk diolah sesuai bidang aplikasinya. Kesehatan adalah hal yang sangat penting untuk selalu diperhatikan mengingat kondisi tubuh manusia tidak selalu dalam kondisi sehat. Sekarang permasalahan dari kesehatan tubuh manusia tidak hanya dari kuman, bakteri ataupun virus yang dibawa oleh udara. Melainkan, dari pola hidup dan asupan yang masuk kedalam tubuh kita. Dalam jangka panjang atau pendek akan berpangaruh buruk dalam tubuh kita bila dalam pola hidup kita kurang atau tidak sehat, tanpa megenal usia, penyakit apa saja bisa menyerang kita. Yakni serangan jantung dan suhu tubuh. Untuk menjamin ketepatan diagnosis dari dokter, dibutuhkan suatu mekanisme untuk mengirimkan sinyal fisiologis dari pasien melalui jaringan telekomunikasi Berangkat dari permasalahan yang ada, dapat dibuat sebuah sistem untuk menghitung detak jantung dan suhu tubuh secara berkala. Dengan Sensor detak jantung (pulse sensor) dan sensor suhu tubuh (ds18b20) akan dipasang di tubuh si pasien, data yang dibaca oleh sensor kemudian dikirimkan ke android dengan menggunakan modul wireless ESP8266 yang tersambung dengan sensor. Android menerima data yang dikirimkan oleh modul wireless dan menampilkan data. Mengimplementasikan sistem dengan aplikasi berbasis android dengan fitur history.
Implementasi Metode Personal Extreme Programming dalam Pengembangan Sistem Manajemen Transaksi Perusahaan (Studi Kasus : CV. Todjoe Sinar Group)
Authors: Ulfi Muhammad , Marthasari Gita Indah , Nuryasin Ilyas
Published: July 2020

CV. Todjoe Sinar Group merupakan salah satu perusahaan perusahaan yang bergerak di bidang percetakan. Beberapa masalah yang terjadi dalam pelaksanaan proses bisnis disebabkan oleh pengelolaan proses bisnis yang dijalankan secara manual. Oleh karena itu, diperlukan suatu sistem informasi yang dapat dibangun secara tepat dan cepat. Metode pengembangan perangkat lunak yang dapat memenuhi hal tersebut termasuk kedalam agile software development. Salah satu metode yang termasuk kedalam agile software development adalah Personal Extreme Programming atau PXP. PXP merupakan metode pengembangan dari Extreme Programming yang disesuaikan agar dapat dikerjakan oleh pengembang tunggal. Sistem yang akan dibangun dalam penelitian ini merupakan sistem manajemen transaksi perusahaan. Kebutuhan sistem didapat pada fase requirements. Tahap pengembangan akan dilanjutkan dengan perencanaan iterasi dengan menentukan prioritas kebutuhan dan diskusi bersama. Pengembangan sistem akan dilakukan secara iteratif berdasarkan perencanaan yang telah dilakukan. Apabila terdapat perubahan atau penambahan kebutuhan, maka akan dilakukan perencanaan ulang. Berdasarkan hasil penelitian ini, PXP dapat diimplementasikan dalam studi kasus ini. Kemampuan, pemahaman, dan pengalaman dari seorang pengembang dalam mengerjakan suatu sistem dapat menentukan kesesuaian estimasi pengerjaan sistem dalam metode PXP. Interaksi dengan klien berperan penting dalam proses pengembangan sistem agar proses pengembangan sistem tetap terarah sesuai dengan kebutuhan klien.
Sistem Pendukung Keputusan Kelayakan Kredit pada PT. BPR Mitra Catur Mandiri Menggunakan Metode Credit Scoring
Authors: Fata Haris Diyaul , Marthasari Gita Indah , Azhar Yufis
Published: July 2020

Kredit adalah suatu cara yang dapat dilakukan untuk mendapatkan modal usaha. Tetapi terkadang pihak bank mengalami kesulitan dalam melakukan penentuan kredit, hal ini dikarenakan terdapat beberapa kriteria yang tidak terpenuhi oleh calon nasabah. Maka dibutuhkan suatu sistem yang dapat mempermudah petugas bank dalam melakukan penentuan kelayakan kredit, yaitu dengan membangun sistem pendukung keputusan kelayakan kredit menggunakan metode credit scoring. Dalam penentuan kredit, metode credit scoring melakukan perhitungan berdasarkan kriteria-kriteria yang ada, sehingga dapat dihasilkan rekomendasi diterima atau ditolaknya sebuah pengajuan kredit. Berdasarkan penelitian yang telah dilakukan, hasil pembuatan sistem pedukung keputusan kelayakan kredit menggunakan metode credit scoring ini adalah mempermudah petugas bank dalam melakukan penentuan kredit. Berdasarkan pengujian menggunakan metode confusion matrix, sistem ini mempunyai performa yang sangat baik dengan tinggkat akurasi 93%.
Rekomendasi Guest house Dan Villa Kota Wisata Batu Berbasis Android Dengan Metode Profile Matching
Authors: Saputra Rizky Irwan , Marthasari Gita Indah , Suharso Wildan
Published: July 2020

Bisnis Penginapan Villa dan Guest House di daerah Pariwisata seperti Kota Batu telah menjadi satu industri yang memberikan hasil yang besar untuk suatu daerah. Sehingga objek Penginapan perlu dikembangkan untuk mendapatkan hasil yang lebih maksimal. Ada berbagai pengaruh dari perkembangan Penginapan Villa dan Guest House , salah satunya adalah perbaikan ekonomi dari warga sekitar tempat. Kota Batu merupakan salah satu kota yang memiliki berbagai pariwisata dan menjadi tempat yang populer untuk dituju oleh masyarakat yang tinggal didaerah Jawa Timur khususnya. Akan tetapi, wisatawan seiring kebingungan mengenai masalah penginapan dari mulai harga, jarak tempat wisata dan fasilitas. Keputusan rekomendasi Penginapan Villa dan Guest House dilihat dari berbagai faktor, seperti; harga, fasilitas, jarak dan layanan dari lokasi user berada saat ini dalam bentuk maps, fasilitas. Profile Matching merupakan sebuah mekanisme pengambilan keputusan dengan mengasumsikan bahwa terdapat tingkat variabel prediktor yang ideal yang harus dipenuhi oleh subyek yang diteliti, Selain itu untuk membantu dalam penentuan jalur akan digunakan Google API .Untuk Core factor adalah kriteria (kompetensi) yang paling penting atau menonjol atau paling dibutuhkan oleh suatu penilaian yang diharapkan dapat menghasilkan hasil yang optimal sedangkan untuk secondary factor merupakan faktor pendukung yang kurang dibutuhkan oleh suatu penilaian.
Pemodelan Proses Bisnis Menggunakan Pendekatan Agile Requirement Elicitation (Studi Kasus : Lab Informatika Universitas Muhammadiyah Malang)
Authors: Aksan Maulana F , Kusuma Wahyu Andhyka , Wahyuni Evi Dwi
Published: July 2020

Dalam pemodelan proses bisnis dapat membantu dalam pembuatan prosedur kerja perusahaan untuk menangani permintaan bisnis. Pemodelan proses bisnis ini juga merupakan aktivitas-aktivitas yang dilakukan untuk membantu dalam memahami proses yang sedang berlangsung. Terdapat kendala dikarenakan tidak adanya pemodelan bisnis pada sistem, serta waktu pembuatan sistem yang sangat singkat sehingga mengakibatkan tidak adanya kesempatan untuk memodelkan proses bisnis. Tujuan daripada penelitian ini yaitu memodelkan sebuah proses bisnis serta dapat menerapkan pemodelan proses bisnis pada sistem menggunakan pendekatan Inisiasi, Elisitasi, Verifikasi, dan Validasi. Secara keseluruhan dari penelitian ini adalah pemodelan proses bisnis pada sistem yang digunakan pada Lab Informatika Universitas Muhammadiyah Malang, dengan melakukan pendekatan terhadap stakeholder untuk mendapatkan kebutuhan-kebutuhan dari sistem tersebut. Kemudian dalam proses pembuatannya menggunakan pendekatan Inisiasi untuk proses penggalian kebutuhan, Elisitasi sebagai pembuatan proses bisinis, Verifikasi untuk menentukan hasil dari elisitasi sudah sesuai, dan Validasi yang dilakukan bersama stakeholder untuk menentukan kesesuaian serta hasil akhir dan pembuatan proses bisnis.
SISTEM INFORMASI AKADEMIK BERBASIS WEB DENGAN IMLEMENTASI HMVC (HIERARCHICAL, MODEL, VIEW, CONTROLLER) (STUDI KASUS: SMK MUHAMMADIYAH 8 SILIRAGUNG BANYUWANGI)
Authors: Yuana Ayu Siska , Aminuddin Aminuddin , Nuryasin Ilyas -
Published: July 2020

Sistem informasi akademik ialah sebuah sistem yang mampu memberikan sebuah informasi mengenai segala hal mengenai pelayanan akademik suatu instansi pendidikan. Sistem informasi akademik dapat menjadi solusi dalam upaya meningkatkan sebuah kualitas pendidikan dengan cara memperlancar informasi akademik siswa dengan pihak sekolah sehingga lebih efesien. SMK Muhammadiyah 8 siliragung adalah suatu instansi pendidikan kejuruan dengan matapelajaran adaptif,normative serta produktif sehingga sistem informasi akademik akan sangat menunjang proses pembelajaran di sekolah. Penelitian ini akan membahas sebuah pembangunan sistem informasi akademik SMK Muhammadiyah 8 Siliragung berbasis web dengan mengimpelemntasikan metodel arsitektur HMVC (Hierarchical, Model, View, Controller) yang mampu membuat aplikasi dalam skala besar yang akan dibangun dalam modul-modul yang nantinya setiap modul dapat diimplementasikan kembali ke dalam modul lain ataupun pengembangan . Sistem informasi akademik SMK Muhammaiyah 8 Siliragung dikembangkan dengan pengujian black-box, pengujian UAT (User Acceptance Testing) dan integrasi testing dengan (PHPUnit 8) sebagai sarana untuk menguji model HMVC. Dalam rangka memudahkan dalam pembangunan sistem maka akan digunakannya sebuah metode Waterfall  sehingga sistem informasi akan tersusun secara sistematik dan berurutan yang akan meminimalisir sebuah kesalahan.
PENERAPAN METODE K-MEANS DENGAN METODE ELBOW UNTUK SEGMENTASI PELANGGAN MENGGUNAKAN MODEL RFM(Recency, Frequency, & Monetary)
Authors: Kiat Adnan Burhan Hidayat , Azhar Yufiz , Rahmayanti Vinna
Published: July 2020

Segmentasi pelanggan pada perusahaan merupakan tindakan yang dapat mempermudah perusahaan dalam mengambil keputusan ke depan. Pada penelitian ini data yang digunakan berasal dari perusahaan otomotif, PT Hasjrat Abadi Ambon. Data yang dipakai terdiri dari data transaksi dan pelanggan kendaraan bermotor. Penerapan model RFM dapat mengelompokkan pelanggan-pelanggan berdasarkan nilai variabel Recency, Frequency dan Monetary. Hasil dari model RFM akan memperoleh status baru pada tiap pelanggan dari skala terbaik sampai terburuk. Pelanggan yang telah memiliki status akan dikelompokkan menggunakan metode K-Means menjadi beberapa Cluster(kelompok). Dalam menentukan jumlah Cluster yang optimal maka diterapkan metode Elbow. Algoritma yang digunakan dalam pembentukan Cluster terdiri dari Euclidean Distance dan Manhattan Distance. Kedua algoritma akan dibandingkan kualitas pembentukan Clusternya menggunakan metode Silhoutte Coefficient. Hasil yang diberikan pada penelitian ini berupa data yang terbagi atas 5 kelompok dengan dilakukannya lima kali pengujian untuk menentukan centroid yang unggul. Cluster yang unggul akan dibuatkan visualisasi datanya untuk memudahkan perusahaan dalam mengambil keputusan. Berdasarkan penerapan Silhoutte Coefficient, algoritma yang lebih unggul yaitu Manhattan Distance dengan nilai s(i) sebesar 0.152695. Customer segmentation at the company is an action that can facilitate the company in making decisions going forward. In this study the data used came from an automotive company, PT Hasjrat Abadi Ambon. The data used consists of transaction data and motor vehicle customers. The application of the RFM model can classify customers based on the value of the Recency, Frequency and Monetary variables. The results of the RFM model will obtain a new status on each customer from the best to the worst scale. Customers who already have status will be grouped using the K-Means method into several Clusters (groups). In determining the optimal number of Clusters, the Elbow method is applied. The algorithm used in Cluster formation consists of Euclidean Distance and Manhattan Distance. The two algorithms will be compared the quality of the Cluster formation using the Silhoutte Coefficient method. The results given in this study are in the form of data divided into 5 groups by conducting five tests to determine superior centroids. Excellent clusters will be made of data visualization to facilitate the company in making decisions. Based on the application of Silhoutte Coefficient, a superior algorithm is Manhattan Distance with value s(i) : 0.152695.
Incorporation of Carbon Fiber and Silica Fume in the Production of Conductive Concrete
Authors:   Department of Civil and Structural Engineering, Annamalai University, Annamalai Nagar–608002, Tamilnadu, India, Purushothaman M. , Natarajan K.
Published: July 2020

Concrete is regarded as a composite material that has good mechanical and durability properties for construction. However normal concrete is poor in electrical conductivity. An endeavour has been made with concrete to have all these three properties. The addition of small amounts of short carbon fibers and a nanomaterial silica fume to the concrete mixture causes an increase in strength and durability properties as well as electrical conductivity. In this paper, experimental results of compressive strength and electrical resistivity are presented. This Concrete technology can be applied with low voltage current for de-icing on highways and airfields, during snowfall in the winter season. This technique will help to reduce the cost and environmental issues of roads in snow fall region.
An Extended C4.5 Classification Algorithm using Mathematical Series
Authors:   Department of Computer Science, S.T. Hindu College, Nagercoil, India, Aswathi R. Raja , Kumar K. Pazhani , Ramakrishnan B.
Published: July 2020

The algorithm C4.5 is an efficient decision tree based classification, which is derived from the ID3 approach. C4.5 is also a rule based classification algorithm. The main importance of the C4.5 algorithm is that it can deal with categorical data, over fitting of data and handling of missing values. The performance of C4.5 is superior to ID3 even with equal number of attributes. The EC4.5 (Exponential C4.5) is an extension of C4.5 algorithm which uses exponential of split value to predict the gain of attributes and handled the set back reported in C4.5. However the EC4.5 has some misclassification of data and to avoid this problem a new technique is introduced. This paper proposes a proficient technique TMC4.5 (Taylor-Madhava C4.5) to reduce the uncertainty in classification of data by integrating an exponential split value in EC4.5 and sin splitting value derived from the Madhava series. By using this technique an optimized gain value is obtained that reduces uncertainty. From the obtained result the TMC4.5 has far better results than the C4.5 and EC4.5 algorithms.