Understanding International Agenda using Media Analytics: The case of Disaster News Coverage in Indonesia

Recently, the flow of data has increased rapidly in terms of volume, velocity, variety, veracity, and value (5Vs). One of the potential sources of data is the worldwide news provided by the Global Data on Event Location and Tone (GDELT) project that reports events. The large database on global events allow the platform as a source for big data analysis that potentially signify our understanding on the extent of event reports. This paper questions the inter-news coverage between foreign and domestic media on the earthquake and tsunami in Palu and Donggala. The paper conducts two main analysis; first, exploratory of news reported from selected countries with highest size of aid (USA, Australia, Europe and Japan) and vice versa, Indonesia news on the respective countries aid activities. Second, media linkages are captured using visualization with statistical graph and spatial flow of news. The paper found that the dynamic of news broadcast on disaster are varied between countries and its coverage fluctuated in all countries. The paper contributes to presence of newsworthiness on disaster news coverage, and consequently lack of coverage on disaster meaning and mitigation plans.

Down-regulation LncRNA-SNHG15 Contributes to Proliferation and Invasion of Bladder Cancer Cells

OBJECTIVES: This study aimed to investigate effect of lncRNA-SNHG15 in bladder carcinoma using cell lines experiments and the relationship between clinical characteristics and lncRNA-SNHG15 expression was analyzed.Methods. Bladder cancer tissues and near-cancer tissues were collected. The expression of lncRNA-SNHG15 in tissues and cell lines was detected by real-time PCR (RT-PCR). The expression of lncRNA-SNHG15 was downregulated by interference (siRNA) as detected by RT-PCR that was used to detect the interference efficiency. CCK-8, and Transwell assays were used to evaluate the effect of lncRNA-SNHG15 on the proliferation, invasion capa­bility of bladder cancer cells.t-test was used for Statistical analyses, were performed using the Statistical Graph pad 8.0.1.224 software.Result: The expression of lncRNA-SNHG15 was up regulated in 5637, UMUC3 and T24 cell lines compared with corresponding normal controls (P<0.05). up regulation was positively related to tumor stage (P = 0.015), and tumor size (P =0.0465) . The down-regulation of lncRNA-SNHG15 with siRNA significantly inhibited UMUC3 and T24 cell proliferation and invasion.Conclusion: This study showed that lncRNA-SNHG15 is overexpressed in bladder cancer tissues and (5637, UMUC3 T24) cell lines. up regulation was positively related to tumor stage (P = 0.015), and tumor size (P =0.0465) .The down-regulation of lncRNA-SNHG15 with siRNA significantly inhibited UMUC3 and T24 cell proliferation and invasion, which provides a potential molecular target for future tumor targeted therapy.

Enhanced Attendance Monitoring System using Biometric Fingerprint Recognition

In this study, an enhanced attendance monitoring system using biometric fingerprint recognition in tracking and monitoring employees’ attendances for Callang National High School, District 04, San Manuel, Isabela was introduced. For most organizations, handling people is a daunting job in which it is very important to maintain an accurate record of attendance. Taking and maintaining the attendance of employee manually on a regular basis is a big activity that requires time. For this reason an effective system was designed. The system was designed and developed primarily to improve the monitoring of employees attendances and leave management through the use of biometric technology. It records the data of the employees, handles leave management, tracks employee attendance and encourages participation through fingerprint recognition. The system is equipped with a dashboard monitoring system that can be viewed by school heads to track the list of employees, early birds (employees who arrived early), on-leave staff, on-official business and a statistical graph of the monthly attendance rate of employees. Moreover, the system provides an auto-generated DTR for employees which saved time compared to the manual process. The innovation greatly affects the improvement of employees’ attendance through its automated attendance monitoring, leave management and report generated by the system. The impact of EAMS to the employees was identified through first quarter attendance report of SY 2028-2019 which served as a bases of comparison with the attendance rate of SY 2019-2020 when the system was implemented. The outcome shows that through the usage of the system, employees’ attendance has improved.

Research on seismic technology of civil engineering structure based on GIS system

The development and application of seismic technology in civil engineering structures is of great significance to extend the service life of buildings and improve the overall quality of buildings, so it is necessary to further strengthen the research on it. Geographic Information System (GIS) is a new subject integrating computer science, informatics, geography and other sciences. Because of its rapid and convenient management of massive data, GIS has been widely used in the fields of resource development, environmental protection, urban planning and construction, disaster monitoring and evaluation, etc. GIS is used to manage and analyze the data, and the damage detection module in the system is used to realize the structural damage identification. And use that special thematic analysis function of GIS to display the data on the map in the form of statistical graph, so that users can find the damage position more directly and clearly.

Research on post-match score mechanism of players based on artificial intelligence and clustering regression model

Currently, the athletes’ post-match scores are mostly manual methods, and artificial intelligence is still less used in athletes’ post-match scores. Based on this, this study is based on machine learning algorithms and combined with athletes’ scores for analysis. At the same time, this study uses the reptile technology to conduct real-time mining of athletes’ data and proposes a model-based regression algorithm in the construction of scoring algorithm. Moreover, based on the actual situation, a comprehensive model combining clustering and regression is proposed. In addition, in order to study the validity of the model, this paper designs a performance simulation test, compares the proposed algorithm model with the traditional algorithm model, and collects relevant experimental data and draws the corresponding statistical graph. The experimental results show that the combination of clustering and regression can improve the model’s effect and the results are like the expert scores, which verifies the practicality of the proposed algorithm and provides a theoretical reference for subsequent related research.

Simulation of English speech emotion recognition based on transfer learning and CNN neural network

The difference between English and Chinese expressions is that English emphasizes the stress of syllables, so the recognition of English speech emotions plays an important role in learning English. This study uses transfer learning as the technical support to study English speech emotion recognition. The acoustic model based on weight transfer has two different training strategies: single-stage training and two-stage training strategy. By comparing the performance of the English speech emotion recognition model based on CNN neural network and the model proposed in this paper, the statistical comparison data is drawn into a statistical graph. The research results show that transfer learning has certain advantages over other algorithms in English speech emotion recognition. In the subsequent teaching and real-time translation equipment research, transfer learning can be applied to English models.

Gut microbiota in hospitalized children under 5 years with acute infective gastroenteritis caused by virus or bacteria in regional Peruvian Hospital

Objetivo el objetivo de este estudio fue describir la prevalencia de 6 géneros de bacterias y 7 Phyla más comunes de la microbiota intestinal (MI) en las muestras fecales de niños menores de 5 años con gastroenteritis infecciosa aguda (GIA) y los patrones clínicos y demográficos poblacionales. Materiales y métodos: se analizó la base de datos de un estudio en donde las muestras fueron obtenidas en niños menores de 5 años hospitalizados con gastroenteritis aguda infecciosa en el “Hospital Docente de Cajamarca” al norte del Perú. Las variables cuantitativas fueron descritas en frecuencias y porcentajes de cada grupo usando el GraphPad Prism3 statistical (Graph Pad Software Inc., San Diego, USA). Resultados: las bacterias aisladas más comunes de la MI fueron Firmicutes (74/117) Bacteriodetes (73/117), Lactobacillus (70/117), Prevotella (67/117) Proteobacterium (63/117), sin tomar en cuenta el agente etiológico responsable de la GIA. Sin embargo, a pesar de la alta prevalencia de Firmicutes, Bacteroidetes, Lactobacillus y Prevotella en todas las muestras, observandose una notable reducción de éstos, especialmente entre los pacientes con una infección bateriana a comparación de las infecciones virales. Los pacientes con lactancia materna exclusiva o mixta denotaron mayor cantidad de bacterias en la MI a comparación de aquellos que recibieron fórmula o no recibieron. Conclusiones A pesar de que en este estudio solo se realizó la detección de algunas bacterias patógenas mediante un método cualitativo, se debe tener en cuenta que existen cambios notorios en la microbiota intestinal, ya sea que se curse con una infección viral o bacteriana.