Relations Source of Drinking Water, Privy Family and Waste Water Sewer with Genesis Diarrhea in Sub-District Pangkalan Kuras District Pelalawan

Diarrhea is a bowel movement (defecation) by the number of stools more than usually (normal 100-200 ml per hour stool), with fecal liquid or semi-liquid (semi-solid), can also be accompanied by increased defecation. Based on data from the District Health Center Drain Base diarrheal disease is a disease with the highest order of 3 to Pangkalan Kuras people suffered in 2012 to 1645 events. This study aims to determine the relationship of drinking water treatment, the condition of latrines, and SaluranPembuangan Wastewater (SPAL) and the incidence of diarrhea in the community naturally Drain Base in 2013.This study is analitik kuantitatif with cross sectional design of the study, carried out at the Puskesmas Pangkalan Kuras with respondent households totaling 182 people. Data collection is by interview and observation with questionnaires, sampling techniques in this study is random sampling with sampling sytematic method. The results showed that the close relationship between the treatment of river water into drinking water (OR = 2.426), treatment of well water into drinking water (OR = 3.205), with the condition of the family latrine (OR = 3.755), and the condition of SPAL (OR = 3.588) and the incidence of diarrhea in Pangkalan Kuras. This result was obtained through chi-square analysis with 95% confidence leve

What makes brands achieve iconic status?

We propose that brands do not achieve iconic status by chance. This article focuses on how brands manage iconic status effectively. Drawing on an exploratory study of iconic brands, we identify a brand's ability to inspire consumers and connect with them on a personal level as well as its visual identity and presence in consumers' mind as critical elements of brand status. Consumers' perceptions of a sample of brands were investigated through in-depth interviews, followed by the examination of these brands' activities through case-study analyses. The alignment between brand strategies and the relevant features highlighted by consumers was then assessed. A comprehensive framework for achieving iconicity is presented and discussed.Working Pape

DeMalFier: Detection of Malicious web pages using an effective classifier

The web has become an indispensable global platform that glues together daily communication, sharing, trading, collaboration and service delivery. Web users often store and manage critical information that attracts cybercriminals who misuse the web and the internet to exploit vulnerabilities for illegitimate benefits. Malicious web pages are transpiring threatening issue over the internet becaus

A Flexible Sub-block in Region Based Image Retrieval Based on Transition Region

One of the techniques in region based image retrieval (RBIR) is comparing the global feature of an entire image and the local feature of image\u27s sub-block in query and database image. The determined sub-block must be able to detect an object with varying sizes and locations. So the sub-block with flexible size and location is needed. We propose a new method for local feature extraction by determining the flexible size and location of sub-block based on the transition region in region based image retrieval. Global features of both query and database image are extracted using invariant moment. Local features in database and query image are extracted using hue, saturation, and value (HSV) histogram and local binary patterns (LBP). There are several steps to extract the local feature of sub-block in the query image. First, preprocessing is conducted to get the transition region, then the flexible sub-block is determined based on the transition region. Afterward, the local feature of sub-block is extracted. The result of this application is the retrieved images ordered by the most similar to the query image. The local feature extraction with the proposed method is effective for image retrieval with precision and recall value are 57%

ReP-ETD: A Repetitive Preprocessing technique for Embedded Text Detection from images in spam emails

Email service proves to be a convenient and powerful communication tool. As internet continues to grow, the type of information available to user has shifted from text only to multimedia enriched. Embedded text in multimedia content is one of the prevalent means for delivering messages to content viewers. With the increasing importance of emails and the incursions of internet marketers, spam has become a major problem and has given rise to unwanted mails. Spammers are continuously adopting new techniques to evade detection. Image spam is one such technique where in embedded text within images carries the main information of the spam message instead of text based spam. Currently, image spam is evaluated to be roughly 50% of all spam traffic and is still on the rise, thus a serious research issue. Filtering mails is one of the popular approaches used to block spam mails. This work proposes new model ReP-ETD (Repetitive Pre-processing technique for Embedded Text Detection) for efficiently and accurately detecting spam in email images. The performance of the proposed ReP-ETD model has been evaluated across the identified parameters and compared with other existing models. The simulation results demonstrate the effectiveness of the proposed model

Toroidal optical dipole traps for atomic Bose-Einstein condensates using Laguerre-Gaussian beams

We theoretically investigate the use of red-detuned Laguerre-Gaussian (LG) laser beams of varying azimuthal mode index for producing toroidal optical dipole traps in two-dimensional atomic Bose-Einstein condensates. Higher-order LG beams provide deeper potential wells and tighter confinement for a fixed toroid radius and laser power. Numerical simulations of the loading of the toroidal trap from a variety of initial conditions is also given.Comment: 12 pages, 5 figures, submitted to Phys. Rev.

SISTEM PENDUKUNG KEPUTUSAN PEMBERIAN KREDIT MENGGUNAKAN METODE ELECTRE (Studi Kasus : Koperasi Kredit Immaculata)

The Immaculata Credit Cooperative is a cooperative organization whose mission is to advance the Immaculata Credit Cooperative as a reliable, independent and professional microfinance empowerment institution. One of the cooperative sectors is the credit sector because credit is a source of financing for cooperatives. Lending at the Immaculata Credit Cooperative is currently still subject to manual analysis by the credit committee, so that an inaccurate determination of credit granting can increase the number of bad or default loans. Therefore the researcher proposes to build a decision support system using the electre method where the output of the electre method calculation is in the form of ranking so that it can determine recommended prospective customers by looking at 5 assessment criteria including income, length of time to return, occupation, age, and collateral. The results of this study are in the form of desktop-based applications that can make it easier for cooperatives, especially credit committees, to determine which customers to recommend

Improving time efficiency in big data through progressive sampling-based classification model

The proposed system aims to overcome challenges posed by large databases, data imbalance, heterogeneity, and multidimensionality through progressive sampling as a novel classification model. It leverages sampling techniques to enhance processing performance and overcome memory restrictions. The random forest regressor feature importance technique with the gini significance method is employed to identify important characteristics, reducing the data’s features for classification. The system utilizes diverse classifiers such as random forest, ensemble learning, support vector machine (SVM), k-nearest neighbors’ algorithm (KNN), and logistic regression, allowing flexibility in handling different data types and achieving high accuracy in classification tasks. By iteratively applying progressive sampling to the dataset with the best features, the proposed technique aims to significantly improve performance compared to using the entire dataset. This approach focuses computational resources on the most informative subsets of data, reducing time complexity. Results show that the system can achieve over 85% accuracy even with only 5-10% of the original data size, providing accurate predictions while reducing data processing requirements. In conclusion, the proposed system combines progressive sampling, feature selection using random forest regressor feature importance (RFRFI-PS), and a range of classifiers to address challenges in large databases and improve classification accuracy. It demonstrates promising results in accuracy and time complexity reduction

Prevalence and Risk Factors of Gastrointestinal Disorders in Patients with Rheumatoid Arthritis: Results from a Population-Based Survey in Olmsted County, Minnesota

Objectives. To compare the prevalence of gastrointestinal (GI) disorders in rheumatoid arthritis (RA) versus non-RA subjects and to describe determinants of GI disorders in RA. Methods. The bowel disease questionnaire was completed by RA and non-RA subjects. RA patients also completed the health assessment questionnaire (HAQ). Results. The study responders included 284 RA and 233 non-RA subjects. Abdominal pain/discomfort, postprandial fullness, nausea, and stool leakage were significantly more common in RA versus non-RA (odds ratios [OR] = 1.8; 1.9; 4.0; 8.2, resp.). The use of laxatives, proton pump inhibitors, NSAIDs, acetaminophen, and narcotics was more commonly reported in RA versus non-RA (OR = 2.0; 1.7; 3.0; 2.0; 1.9, resp.). Age < 60 and HAQ ≥ 1 were associated with dyspepsia, irritable bowel syndrome, gastroesophageal reflux disease, and GI symptom complex overlap in RA. Conclusion. Several upper and lower GI disorders were significantly more prevalent in RA versus non-RA subjects. Age <60 and physical function impairment (HAQ ≥ 1) were associated with GI disorders in RA

Sistem Pendukung Keputusan Penerimaan Bantuan Sosial Perikanan untuk Nelayan menggunakan Metode Simple Additive Weighting (SAW)

Nelayan merupakan penduduk yang tinggal di pesisir pantai dan sumber kehidupan ekonominya bergantung secara langsung pada kegiatan mengolah sumber daya laut, komunitas nelayan atau kelompok dan orang  yang mata pencarian hasil laut dan tinggal di desa, Pantai atau pesisir Aset nelayan bagi Indonesia juga salah satu factor yang penting serta menjadi ujung tombak dalam pengembangan di bidang kelautan dan perikanan Aktivitas  nelayan yang berada di dalam laut, untuk menangkap ikan  memilik resiko tinggi yang bahkan mengancam keselamatan jiwa. Proses pemberian bantuan sosial kepada nelayan di Kabupaten Belu masih dilakukan secara manual, menyebabkan penumpukan dokumen, keterlambatan, dan kesalahan dalam pengajuan bantuan. Untuk mengatasi masalah ini, penelitian ini mengembangkan sistem pendukung keputusan (SPK) berbasis metode Simple Additive Weighting (SAW) yang dapat mengotomatisasi proses seleksi calon penerima bantuan. Metode SAW dipilih karena kemudahannya dalam implementasi, efisiensi perhitungan, serta transparansi dan fleksibilitas dalam menyesuaikan kriteria dan bobot. Hasil penelitian menunjukkan menunjukkan ranking 13 alternatif penerima bantuan sosial kepada nelayan berdasarkan dari 5 kriteria penilaian yang telah ditentukan oleh pengambil keputusan, sehingga dapat menghasilkan keputusan obyektif dengan mempertimbangkan setiap nilai alternatif pada setiap kriteria sehingga dapat menunjangkan penerima bantuan yang paling sesuai