Karakterisasi Paparan Long Term Particulate Matter di Puspiptek Serpong-Kota Tangerang Selatan

Pencemaran partikulat udara (PM10 dan PM2.5) tidak mengenal batas administrasi wilayah hal ini disebabkan bahwa pencemaran udara tidak hanya berasal dari wilayah lokal namun dapat bertransportasi dari provinsi maupun negara lain. Unsur sampel partikulat yang dikumpulkan di Serpong, Tangerang Selatan selama 24 jam periode tahun 2011 sampai dengan 2013 diidentifikasikan dengan Spekstroskopi Florensensi Sinar-X (XRF). Karekterisasi faktor dilakukan dengan menggunakan reseptor model positive matrix factorization (PMF) dan perkiraan lokasi sumber pencemar menggunakan metode Conditional Probability Function (CPF). Hasil menunjukkan rentang rata-rata konsentrasi massa PM2.5 adalah 12.63 ± 1.60 to 15.89 ± 1.70 μg/m3 sedangkan untuk PM10 berkisar 29.00 ± 3.96 to 31.04 ± 3:28 μg/m3. Multi unsur yang teridentifikasi dengan XRF adalah Al, Ca, Co, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, S, Si, Ti, V dan Zn. Karakterisasi partikulat halus (PM2.5) teridentifikasi 5 faktor yaitu industri peleburan logam Pb (9.61%), debu tanah (17%), campuran industri peleburan logam dan garam laut (13.02%), trasnportasi (44.36%) serta pembakaran biomassa (22.58%).Kata kunci : polusi udara, spektroskopi XRF, sumber pembagian, fungsi probabilitasbersyara

The University Defence Research Collaboration In Signal Processing

This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour

Explorations in anatomy: the remains from Royal London Hospital

This paper considers the faunal remains from recent excavations at the Royal London Hospital. The remains date to the beginning of the 19th century and offer an insight into the life of the hospital's patients and practices of the attached medical school. Many of the animal remains consist of partially dissected skeletons, including the unique finds of Hermann's tortoise (Testudo hermanni) and Cercopithecus monkey. The hospital diet and developments in comparative anatomy are discussed by integrating the results with documentary research. They show that zooarchaeological study of later post-medieval material can significantly enhance our understanding of the exploitation of animals in this perio

Inclusion and release of ant alarm pheromones from metal–organic frameworks

Zinc(II) and zirconium(IV) metal–organic frameworks show uptake and slow release of the ant alarm pheromones 3-octanone and 4-methyl-3-heptanone. Inclusion of N-propyl groups on the MOFs allows for enhanced uptake and release over several months. In preliminary field trials, leaf cutting ants show normal behavioural responses to the released pheromones

Using trained dogs and organic semi-conducting sensors to identify asymptomatic and mild SARS-CoV-2 infections: an observational study

BACKGROUND: A rapid, accurate, non-invasive diagnostic screen is needed to identify people with SARS-CoV-2 infection. We investigated whether organic semi-conducting (OSC) sensors and trained dogs could distinguish between people infected with asymptomatic or mild symptoms, and uninfected individuals, and the impact of screening at ports-of-entry. METHODS: Odour samples were collected from adults, and SARS-CoV-2 infection status confirmed using RT-PCR. OSC sensors captured the volatile organic compound (VOC) profile of odour samples. Trained dogs were tested in a double-blind trial to determine their ability to detect differences in VOCs between infected and uninfected individuals, with sensitivity and specificity as the primary outcome. Mathematical modelling was used to investigate the impact of bio-detection dogs for screening. RESULTS: About, 3921 adults were enrolled in the study and odour samples collected from 1097 SARS-CoV-2 infected and 2031 uninfected individuals. OSC sensors were able to distinguish between SARS-CoV-2 infected individuals and uninfected, with sensitivity from 98% (95% CI 95–100) to 100% and specificity from 99% (95% CI 97–100) to 100%. Six dogs were able to distinguish between samples with sensitivity ranging from 82% (95% CI 76–87) to 94% (95% CI 89–98) and specificity ranging from 76% (95% CI 70–82) to 92% (95% CI 88–96). Mathematical modelling suggests that dog screening plus a confirmatory PCR test could detect up to 89% of SARS-CoV-2 infections, averting up to 2.2 times as much transmission compared to isolation of symptomatic individuals only. CONCLUSIONS: People infected with SARS-CoV-2, with asymptomatic or mild symptoms, have a distinct odour that can be identified by sensors and trained dogs with a high degree of accuracy. Odour-based diagnostics using sensors and/or dogs may prove a rapid and effective tool for screening large numbers of people. Trial Registration NCT04509713 (clinicaltrials.gov)