In recent years, across tropical regions of the world, there has been an expansion of integrated farming systems that combine rice and shrimp production. While these systems were developed as a form of crop-rotation -growing rice in the wet season and shrimp in the dry season -some farmers grow both rice and brackish-water shrimp simultaneously during the wet season. Climatic variability has resulted in considerable crop losses in this system across many regions. Research has yet to identify the complete array of key risk factors, and their potential interactions, for integrated rice-shrimp farming. Consequently, different farming practices and environmental factors that may affect crop production need to be clarified to guide research efforts. We applied a staged, iterative process to develop a probabilistic Bayesian belief network based on expert knowledge that describes the relationships that contribute to the risk of failure of both crops in integrated rice-shrimp farming systems during the wet season. We applied the approach in the Southern Mekong Delta, Vietnam, in the context of a broader research program into the sustainability of the rice-shrimp farming system. The resulting network represents the experts' perceptions of the key risk factors to production and the interactions among them. While both farmers and extension officers contributed to the identification of the processes included in the network, the farmers alone provided estimates of the probability of the relationships among them. The network identified the challenges to minimise the risk of failure for both crops, and the steps farmers can take to mitigate some of them. Overall, farmers perceived they have a better chance to minimise risk of failure for shrimp rather than rice crops, and limited opportunities appear to exist for successful production of both. By engaging the farmers in this process of model development, we were able to identify additional research questions for the broader research team and to identify simple steps the farmers could take to reduce the risk of crop failure.Integrating additional empirical data into this network, as it becomes available, will help Highlights • We converted knowledge from rice-shrimp farmers in the Mekong Delta into a probabilistic model• Key risks to production for both crops revolved around soil and water quality • On average, rice crop failure was perceived to be more likely than for shrimp • Capturing extant agricultural knowledge improves outcomes for research and practice

Skin cancer as the most common cancer diagnosis tend to be increasing. This condition is a particularly significant issue in developed countries. This study aimed to describe the clinical features, histopathological features, complications, and early surgical treatment outcomes of skin cancer in CanTho Oncology Hospital from 2014 to 2015. This descriptive prospective study involved all patients with non-melanoma skin cancer that were examined and treated at Can Tho Oncology Hospital from July 2014 to March 2015. There were 78 cases selected. Skin cancer was found to be more common among older patients. The prevalence of basal cell carcinoma was found higher than squamous cell carcinoma with percentage worth 76.9% and 23.1% respectively. Worth 73.1% of all the patients in the study underwent surgery with wide resection and reconstruction. In this study, most patients were the elderly. The basal cell carcinoma was the most common. The main treatment was surgery with wide resection and reconstruction. The complication was rare 1.3% with skin flap necrosis Penelitian pada Fitur Klinis, Histopatologis dan Hasil Evaluasi PengobatanKanker di Rumah Sakit Onkologi Can ThoKanker kulit, diagnosis kanker paling umum, cenderung mengalami peningkatan. Kondisi ini secara khusus merupakan isu penting di negara-negara maju. Penelitian ini bertujuan untuk mendeskripsikan fitur klinis, fitur hispatologis, komplikasi dan hasil pengobatan bedah awal kanker kulit di Rumah Sakit Onkologi Can Tho dari tahun 2014 sampai 2015. Penelitian deskriptif prospektif ini melibatkan seluruh pasien dengan kanker kulit nonmelanoma yang diuji dan diobati di Rumah Sakit Onkologi Can Tho dari Juli 2014 sampai Maret 2015. Terdapat 78 kasus terpilih. Kanker kulit ditemukan lebih umum pada pasien yang lebih tua. Prevalensi karsinoma sel basal ditemukan lebih tinggi dibandingkan karsinoma sel skuamosa dengan persentase masing-masing 76,9% dan 23,1%. Sebesar 73,1% dari seluruh pasien dalam penelitian ini menjalani bedah dengan rekonstruksi dan reseksi yang lebar. Dalam penelitian ini, sebagian besar pasien adalah lanjut usia. Karsinoma sel basal adalah yang paling umum. Pengobatan utama adalah bedah dengan rekonstruksi dan reseksi yang lebar. Komplikasi jarang terjadi 1,3% dengan nekrosis lipatan kulit.

In many parts of the world, conditions for small scale agriculture are worsening, creating challenges in achieving consistent yields. The use of automated decision support tools, such as Bayesian Belief Networks (BBNs), can assist producers to respond to these factors. This paper describes a decision support system developed to assist farmers on the Mekong Delta, Vietnam, who grow both rice and shrimp crops in the same pond, based on an existing BBN. The BBN was previously developed in collaboration with local farmers and extension officers to represent their collective perceptions and understanding of their farming system and the risks to production that they face. This BBN can be used to provide insight into the probable consequences of farming decisions, given prevailing environmental conditions, however, it does not provide direct guidance on the optimal decision given those decisions. In this paper, the BBN is analysed using a novel, temporally-inspired data mining approach to systematically determine the agricultural decisions that farmers perceive as optimal at distinct periods in the growing and harvesting cycle, given the prevailing agricultural conditions. Using a novel form of data mining that combines with visual analytics, the results of this analysis allow the farmer to input the environmental conditions in a given growing period. They then receive recommendations that represent the collective view of the expert knowledge encoded in the BBN allowing them to maximise the probability of successful crops. Encoding the results of the data mining/inspection approach into the mobile Decision Support System helps farmers access explicit recommendations from the collective local farming community as to the optimal farming decisions, given the prevailing environmental conditions.