Peningkatan Kapabilitas Penggunaan Aplikasi Komputer Microsoft Office Bagi Kader Pemberdayaan Dan Kesejahteraan Keluarga (Pkk) Di Kelurahan Cipulir

Kader Pemberdayaan dan kesejahteraan Keluarga (PKK) di Kelurahan Cipulir berlokasi di kecamatan Kebayoran Lama, DKI Jakarta, memiliki salah satu tugas yaitu pendataan profil warga dan pendataan berbagai formulir data program masyarakat yang berasal dari pemerintahan dan saat ini sebagian besar kader PKK belum memiliki kemampuan menggunakan aplikasi komputer Microsoft Office. Sehingga tujuan dari program pengabdian kepada Masyarakat (PPM) ini adalah meningkatkan kapabilitas keterampilan aplikasi komputer Microsoft Office bagi kader PKK di wilayah Cipulir dengan mengadakan pelatihan penggunaan aplikasi Microsoft Word dan Microsoft Excel. Tujuan kegiatan PPM ini adalah membantu kader PKK menjalankan salah satu tugas yaitu untuk mengelola administrasi kegiatan PKK dengan cepat, mudah dan rapi. Kegiatan PPM ini dilakukan selama 2 bulan dari bulan Juli-Agustus 2018 dan metode pelaksanaan kegiatan PPM dilakukan dengan tiga tahap yaitu tahap persiapan, tahap pelaksanaan dan tahap evaluasi dan laporan. Pada tahap persiapan dilakukan proses survey dan perijinan dengan mitra PPM yaitu kelurahan Cipulir khususnya RW.02 RT.012. Pada tahap pelaksanaan dilakukan sosialisasi, pembuatan modul materi pelatihan dan melakukan pelatihan dengan 20% ceramah dan 80% praktikum kemudan tahap ketiga adalah evaluasi dan laporan. Berdasarkan hasil evaluasi kegiatan PPM, kader PKK mendapatkan manfaat dari pelatihan aplikasi komputer sehingga mahir dalam membuat proposal kegiatan dan pendataan kader PKK menggunakan Microsoft Word dan dapat menggunakan Microsoft Excel untuk membuat anggaran dana.

1032A Critical Assessment of Typologies of Conflict and their Relevance to Public Health Research

Background Performing multi-country comparisons of the impact of armed conflict on health is not straightforward given the absence of an agreed definition of armed conflict and the multitude of categorizations/typologies identifying conflict-affected settings. Our analysis provides a critical review of available typologies to assess their usefulness for public health research. Methods Through a two-step comprehensive literature review, we identified available conflict-related typologies and performed a further multiple-database literature review to identify literature critiquing these typologies. Based on this information and our critical systematic assessment, we recommended optimal typologies for use in public health research. Results We identified 28 conflict-related typologies that we evaluated systematically to arrive at four typologies that are most suited for public health researchers examining for example, the effects of conflict on maternal and child health. These are the Uppsala Conflict Data Program, Heidelberg Institute of International Conflict Research (HIIK)-Conflict Barometer, Major Episodes of Political Violence and the Global Peace Index. These typologies are global in scope, address all types of violence, are regularly updated and do not include health/development indicators within their definitions. Conclusion All retrieved typologies have limitations, there are critiques of how conflicts and their intensity rankings are defined. Nevertheless, the four selected typologies provide useful tools for researchers aiming to make multi-country comparisons of the impact of armed conflict on health. Key messages There is no single agreed typology that provides a comprehensive picture of armed conflict across diverse settings. The most suitable typologies need to be complemented by in-country qualitative situation assessments.

The Legacy of Conflict: Reconstruction and Migration in the Aftermath of Civil War in Tajikistan

How do civil war and subsequent reconstruction efforts affect international migration? Although a wealth of evidence points to violent conflict’s effects on contemporaneous migration and although a rich body of literature examines development’s effects on migration, we know less about the intersection of conflict, development, and migration. This article examines the intersection of these factors nearly a decade after the 1992–1997 civil war in Tajikistan, combining data from the 2007 Tajikistan Living Standards Survey, the Uppsala Conflict Data Program, and original interviews. In a series of logistic regression models, I show that conflict fatalities do not have a direct effect on subsequent migration, while the number of years a district has had a development resource center directly increases the likelihood of migrating. However, the interaction between development and conflict is negative and significant. These findings suggest that conflict’s legacy does not directly impact the likelihood of respondents migrating but instead changes the nature of the relationship between development and migration. This finding illuminates conflict’s potential long-term consequences for migration and extends the migration-development nexus by addressing the role of conflict in the relationship between development and migration. In particular, it suggests that migration research in conflict-affected countries should incorporate measures of both conflict and development, even after a given conflict has ended.

Identifikasi masalah kesehatan masyarakat di Desa Banguntapan melalui Data Program Indonesia Sehat Pendekatan Keluarga (PIS-PK)

Noncommunicable diseases and chronic diseases, such as stroke, hypertension and diabetes mellitus has widely founded in Yogyakarta. In order to, the government has developed a health promotion strategy is the healthy Indonesia through a family program approach or Program Indonesia Sehat dengan Pendekatan Keluarga (PIS-PK). The study aimed to analysis determinant factors of public health problems, to obtain of the health situation and public health service in Ponegaran Hamlet, Banguntapan, Bantul. Descriptive research used to interview instruments as 149 respondents. The analysis method used to determine the priority of health problems is Urgency, Seriousness, Growth(USG). Base on three indicators of PIS-PK which were the main problems were the number of family members who smoker (53%), hypertension who did not take regular medication (71%) and health insurance (34%).

Measuring political legitimacy with Twitter: Insights from India’s Aadhaar program

As emerging powers forge ahead with big data initiatives, questions arise regarding the implications of these programs for governance in the Global South more broadly. One understudied aspect deals with how actors attribute legitimacy to governments’ big data activities. We explore actors’ agency in one crucial case: the world’s largest demographic and biometric data program, India’s Aadhaar. Analyzing roughly 250,000 tweets collected in the first 10 years of Aadhaar’s operation, we find that both normative acceptance and cost–benefit calculations are crucial for legitimacy attribution. This finding challenges mainstream theoretical approaches, which prioritize normative factors and often fail to examine how normative and material factors interact during legitimacy attribution. In addition, our study demonstrates a new, mixed-methods approach to measuring legitimacy attribution using Twitter data, which overcomes traditional challenges. As such, we underline the viability of Twitter data as a tool for social measurement.

The Effects of Conflicts and Self-Reported Insecurity on Maternal Healthcare Utilisation and Children Health Outcomes in the Democratic Republic of Congo (DRC)

Background The Democratic Republic of Congo (DRC) has experienced political unrest, civil insecurity, and military disputes, resulting in extreme poverty and a severely impaired health care system. To reduce the morbidity and mortality in women and children by strengthening healthcare, this study aimed at exploring the relationship between self-reported insecurity of mothers and maternal health-seeking behaviours and diseases in children in the DRC. Method Data collected from 8144 mothers and 14,403 children from the Multiple Indicators Cluster Survey (MICS) conducted by the National Institute of Statistics in 2017–2018, in collaboration with the United Nations Children’s Fund (UNICEF), was used. The severity of the conflict in different provinces was measured using the Uppsala Conflict Data Program (UCDP) reports. Multivariate logistic regression and stratified analysis were utilized to explore the association between conflicts with maternal health-seeking behaviours and diseases among children. Results High self-reported insecurity was positively associated with skilled antenatal care (OR1.93, 95%CI 1.50–2.49), skilled attendants at delivery (OR1.42, 95%CI 1.08–1.87), and early initiation of breastfeeding (OR1.32, 95%CI 1.04–1.68). These associations were more significant in regions with more armed conflict. It was also found that children of mothers with high self-reported insecurity were more likely to suffer from diarrhoea (OR1.47, 95%CI: 1.14–1.88), fever (OR1.23, 95%CI 1.01–1.50), cough (OR1.45, 95%CI 1.19–1.77), and dyspnea (OR2.04, 95%CI 1.52–2.73), than children of mothers with low self-reported insecurity. Conclusions Conflicts increases mothers’ insecurities and negatively affects children’s development. However, high conflict regions have to increase governmental and international assistance to promote the availability and access to maternal and child health services.

Organized violence 1989–2020, with a special emphasis on Syria

This article reports on trends in organized violence, building on new data by the Uppsala Conflict Data Program (UCDP). The falling trend in fatalities stemming from organized violence in the world, observed for five consecutive years, broke upwards in 2020 and deaths in organized violence seem to have settled on a high plateau. UCDP registered more than 80,100 deaths in organized violence in 2020, compared to 76,300 in 2019. The decrease in violence in Afghanistan and Syria was countered by escalating conflicts in, for example, Artsakh (Nagorno-Karabakh), Azerbaijan and Tigray, Ethiopia. Moreover, the call for a global ceasefire following the outbreak of the COVID-19 pandemic failed to produce any results. In fact, the number of active state-based and non-state conflicts, as well as the number of actors carrying out one-sided violence against civilians, increased when compared to 2019. UCDP noted a record-high number of 56 state-based conflicts in 2020, including eight wars. Most of the conflicts occurred in Africa, as the region registered 30 state-based conflicts, including nine new or restarted ones.

Deep learning based automatic vertical height adjustment of incorrectly fastened seat belts for driver and passenger safety in fleet vehicles

The recognition of incorrect fastening of seat belts is significant in passenger and driver safety for the automotive industry and public health. It should be made sure that the passenger’s seat belt is not only fastened but also correctly fastened across the body so that the passenger is adequately protected in the event of an accident. Current technology employs the buckle effect sensor, which merely solves the buckling detection problem, but there is no reliable solution for the correct positioning of the seat belt. Additionally, computer vision-based systems are still incapable of recognizing the incorrect positioning of seat belts when the training is performed by employing the subjects out of the fleet. Considering this fact, in this study, we propose a novel solution that employs a vision-based incorrect fastening seat belt detector to perform automatic vertical height adjustment independent from drivers and passengers for the fleet vehicles. We recognize the incorrect positioning of the seat belt inside the car by the acceptable distance of the seat belt from the neck of drivers or passengers to avoid neck injuries and the deaths caused by neck cuts. An extensive benchmarking is performed by comparing the three CNN architectures such as; DenseNet121, GoogLeNet (Inception-v3), ResNet50 with respect to sensitivity, specificity, precision, false-positive rate, false-negative rate, F1 score, and accuracy. Additionally, training and validation loss curves and accuracy curves are plotted for all the models. Later, the three models are evaluated with a precision-recall (PR) curve at the end. According to the results, the DenseNet121 achieved the highest classification accuracy among the tested models with 99.95%. This paper includes information about the proposed system elements, registration of data, elaboration of data, program algorithm, testing the system in the lab, and on the vehicle.