Reaksi Hidrogenasi Metoksida Menjadi Metanol pada Klaster Pd6Ni

Kami melakukan kajian teoretis mengenai reaksi hidrogenasi metoksida (H3CO) menjadi metanol (CH3OH) pada katalis klaster Pd6Ni secara teoretis menggunakan perhitungan berbasis teori fungsional kerapatan (DFT). Reaksi ini merupakan salah satu reaksi pembatas laju pada proses konversi gas karbon dioksida (CO2) menjadi metanol. Hasil perhitungan kami menunjukkan bahwa reaksi hidrogenasi metoksida pada katalis klaster Pd6Ni memiliki energi aktivasi yang lebih baik dibandingkan dengan energi aktivasi pada katalis konvensional berbasis permukaan Cu. Hal ini disebabkan karena klaster Pd6Ni mampu menstabilkan adsorpsi molekul metanol dengan baik dan memiliki energi adsorpsi *H yang relatif lemah

Penerapan Model Pembelajaran Discovery Learning Untuk Meningkatkan Aktivitas Dan Prestasi Belajar Pokok Bahasan Larutan Penyangga Pada Siswa Kelas XI IPA Semester II SMA Negeri 1 Ngemplak Tahun Pelajaran 2013/2014

Penelitian ini bertujuan untuk meningkatkan aktivitas dan prestasi belajar siswa kelas XI IPA Semester II SMA Negeri 1 Ngemplak tahun pelajaran 2013/2014 melalui penerapan model Discovery Learning pada pokok bahasan larutan penyangga. Penelitian ini merupakan Penelitian Tindakan Kelas (PTK) yang dilaksanakan dalam dua siklus, dengan tiap siklus terdiri dari perencanaan, pelaksanaan tindakan, observasi, dan refleksi. Subjek penelitian adalah siswa kelas XI IPA 2 SMAN 1 Ngemplak tahun pelajaran 2013/2014. Sumber data adalah guru dan siswa. Teknik pengumpulan data melalui wawancara, observasi, kajian dokumen, angket, dan tes, selanjutnya dianalisis menggunakan deskriptif kualitatif. Berdasarkan hasil penelitian dapat disimpulkan bahwa penerapan model Discovery Learning dapat meningkatkan aktivitas dan prestasi belajar siswa pada materi larutan penyangga. Pada siklus I, persentase ketercapaian aktivitas belajar siswa sebesar 37% yang kemudian meningkat pada siklus II menjadi 77,78%. Peningkatan prestasi belajar dilihat dari aspek kognitif pada siklus I mencapai 63% dan meningkat pada siklus II menjadi 81%, dari aspek afektif persentase ketuntasan untuk siklus I sebesar 89% dan meningkat pada siklus II menjadi 92,6%. Sedangkan untuk prestasi belajar aspek psikomotorik hanya dilakukan pada siklus I dan memberikan hasil ketuntasan sebesar 81,48%

A stabilising control strategy for Cyber-Physical Power Systems

The cyber-physical nature of electric power systems has increased immensely over the last decades, with advanced communication infrastructure paving the way. It is now possible to design wide-area controllers, relying on remote monitor and control of devices, that can tackle power system stability problems more effectively than local controllers. However, their performance and security relies extensively on the communication infrastructure and can make power systems vulnerable to disturbances emerging on the cyber side of the system. In this paper, we investigate the effect of communication delays on the performance of wide-area damping controllers (WADC) designed to stabilise oscillatory modes in a Cyber-Physical Power System (CPPS). We propose a rule-based control strategy that combines wide-area and traditional local stabilising controllers to increase the performance and maintain the stable operation of CPPS. The proposed strategy is validated on a reduced CPPS equivalent model of Great-Britain (GB)

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

Analysis of the Impact of Population Growth in DKI Jakarta Using Logistic Model

The rapid population growth in the DKI Jakarta area has an impact on its population and creates an unfriendly environment. The author is motivated to analyse the effect of the population growth rate in DKI Jakarta over the next 10 years. The process of estimating population growth is calculated by a mathematical model called the logistic model. The logistic model is the model that developed by differential equation like the following . This model illustrates that population growth is determined by the difference between the number of births and deaths of the population. In addition, an analysis of the resulting environmental impact and the impact of its handling will also be discussed. Based on estimation, the population in DKI Jakarta Province in 2022 is predicted around 10,636.685 people and it will reach 10,938.900 in 2030. It means there will be a 3% increase in population from 2019 to 2030 in DKI Jakarta Province. These values increase annually and they are predicted to have an impact on increasing the traffic congestion by 3%, from 70% to 72.1%. Another result has also occurred in air pollution. The average of air pollution increasing by 3%, from 39.6 to 40.79 . These two factors show that the increase of population growth will have an impact on increasing the average traffic congestion and the percentage of air pollution in DKI Jakarta. Pertumbuhan penduduk yang sangat pesat di wilayah DKI Jakarta memiliki dampak pada populasinya serta menciptakan lingkungan yang kurang ramah. Hal ini memotivsi penulis untuk menganalisis dampak dari laju pertumbuhan penduduk di DKI Jakarta selama 10 tahun mendatang. Proses estimasi pertumbuhan penduduk dikalkulasi mengunakan pemodelan matematika yang bernama model logistik.Model logistik adalah sebuah model matematika yang dikembangkan menggunakan persamaan differensial sebagaimana berikut . Model logistik mengilustrasikan pertumbuhan populasi penduduk sebagai selisih antara jumlah populasi yang lahir dengan jumlah populasi yang meninggal. Selain itu juga, akan dipaparkan mengenai dampak pencemaran lingkungan yang mungkin akan muncul di waktu yang akan datang. Berdasarkan hasil estimasi diperoleh prediksi jumlah penduduk di Provinsi DKI Jakarta pada tahun 2022 sebanyak 10,636.685 jiwa dan pada tahun 2030 akan mencapai 10,938.900. Hal tersebut berarti akan ada peningkatan sekitar 3% penduduk dari tahun 2019 hingga tahun 2030 di Provinsi DKI Jakarta. Nilai pertumbuhan populasi tersebut meninggkat setiap tahunnya dan diprediksi dapat meningkatkan kepadatan lalu lintas sebesar 3%, dari 70% menjadi 72.1% di tahun 2030. Hasil lain yang diprediksi akan terjadi ialah peningkatan rata-rata polusi udara sebesar 3%, dari 39.6 menjadi 40.79 pada tahun 2030. Kedua faktor ini menunjukan bahwa peniongkatan jumlah populasi penduduk di Provinsi DKI Jakarta dapat memberikan dampak pada peningkatan rata-rata kepadatan kendaraan dan polusi udara di Provinsi DKI Jakarta