Klasterisasi Puskesmas dengan K-Means Berdasarkan Data Kualitas Kesehatan Keluarga dan Gizi Masyarakat

One of the fundamental principles followed by the Jember Health Office for decision-making is data. Data plays a crucial role in the decision-making process. Raw data is more difficult to interpret and needs to be analyzed. Clustering is one of the techniques used for analysis. This study discusses using K-Means to cluster Public Health Center data based on AKI, AKB, and stunting prevalence. The data is processed by reducing dimensions and normalizing them. The clustering process is performed using the K-Means method, where the maximum k-value is obtained by calculating WCSS. The clustering process results in three clusters of Public Health Centers in the Jember Regency. These clusters can serve as a reference for the Jember Health Office to formulate family health and community nutrition quality policies.Keywords: data mining, K-Means, clustering, Maternal Mortality Rate, Infant Mortality Rate, the prevalence of stunting   Salah satu dasar pengambilan kebijakan oleh Dinas Kesehatan Jember adalah data. Data memiliki peran dalam proses pengambilan keputusan. Data mentah yang didapatkan lebih sulit untuk diinterpretasikan sehingga diperlukan analisis terhadap data tesebut. Salah satu analisis yang dapat digunakan adalah teknik klasterisasi. Padapenelitian ini akan dibahas penggunaan K-Means untuk klasterisasi data puskesmas berdasarkan AKI, AKB, dan prevalensi stunting. Data diproses dengan melakukan reduksi dimensi dan normalisasi. Proses klasterisasi dilakukan dengan metode K-Means dimana nilai k maksimal diperoleh dengan menghitung WCSS. Adapun hasil proses klasterisasi didapatkan tiga kelompok klaster puskesmas yang terdapat di Kabupaten Jember. Hasil klasterisasi dapat digunakan sebagai referensi Dinas Kesehatan Jember dalam mengambil kebijakan terkait kualitas kesehatan keluarga dan gizi masyarakatKata Kunci: data mining, K-Means, klasterisasi, Angka Kematian Ibu, Angka Kematian Bayi, prevalensi stuntin

Predicting Corrosion Damage in the Human Body Using Artificial Intelligence: In Vitro Progress and Future Applications Applications

Artificial intelligence (AI) is used in the clinic to improve patient care. While the successes illustrate the impact AI can have, few studies have led to improved clinical outcomes. A gap in translational studies, beginning at the basic science level, exists. In this review, we focus on how AI models implemented in non-orthopedic fields of corrosion science may apply to the study of orthopedic alloys. We first define and introduce fundamental AI concepts and models, as well as physiologically relevant corrosion damage modes. We then systematically review the corrosion/AI literature. Finally, we identify several AI models that may be Preprint implemented to study fretting, crevice, and pitting corrosion of titanium and cobalt chrome alloys

Ti-6Al-4V β Phase Selective Dissolution: In Vitro Mechanism and Prediction

Retrieval studies document Ti-6Al-4V β phase dissolution within total hip replacement systems. A gap persists in our mechanistic understanding and existing standards fail to reproduce this damage. This thesis aims to (1) elucidate the Ti-6Al-4V selective dissolution mechanism as functions of solution chemistry, electrode potential and temperature; (2) investigate the effects of adverse electrochemical conditions on additively manufactured (AM) titanium alloys and (3) apply machine learning to predict the Ti-6Al-4V dissolution state. We hypothesized that (1) cathodic activation and inflammatory species (H2O2) would degrade the Ti-6Al-4V oxide, promoting dissolution; (2) AM Ti-6Al-4V selective dissolution would occur and (3) near field electrochemical impedance spectra (nEIS) would distinguish between dissolved and polished Ti-6Al-4V, allowing for deep neural network prediction. First, we show a combinatorial effect of cathodic activation and inflammatory species, degrading the oxide film’s polarization resistance (Rp) by a factor of 105 Ωcm2 (p = 0.000) and inducing selective dissolution. Next, we establish a potential range (-0.3 V to –1 V) where inflammatory species, cathodic activation and increasing solution temperatures (24 oC to 55 oC) synergistically affect the oxide film. Then, we evaluate the effect of solution temperature on the dissolution rate, documenting a logarithmic dependence. In our second aim, we show decreased AM Ti-6Al-4V Rp when compared with AM Ti-29Nb-21Zr in H2O2. AM Ti-6Al-4V oxide degradation preceded pit nucleation in the β phase. Finally, in our third aim, we identified gaps in the application of artificial intelligence to metallic biomaterial corrosion. With an input of nEIS spectra, a deep neural network predicted the surface dissolution state with 96% accuracy. In total, these results support the inclusion of inflammatory species and cathodic activation in pre-clinical titanium devices and biomaterial testing