IDENTIFIKASI TEKSTUR DAN WARNA MINERAL UNTUK KLASIFIKASI BATUAN BEKU DENGAN MENGGUNAKAN METODE HISTOGRAM OF ORIENTED GRADIENT DAN LINEAR DISCRIMINANT ANALYSIS

Batuan terdiri dari tiga jenis, yaitu batuan beku, batuan sedimen, dan batuan metamorf. Pada lapisan kerak bumi, sebagian besar lapisannya tersusun oleh batuan beku dan batuan metamorf, sedangkan batuan sedimen pada umumnya terdapat di permukaan kerak bumi. Hal ini menjadikan para ahli petrologi harus mampu mengidentifikasi batuan sesuai dengan karakteristik batuan tersebut. Namun untuk dapat mengidentifikasi dan menentukan komponen mineral apa saja yang terdapat dalam batuan, khususnya batuan beku, para ahli petrologi tersebut masih melakukannya dengan cara yang konvensional yang dibekali oleh ilmu yang mereka miliki. Hal inilah yang menjadi latar belakang topik Tugas akhir ini yaitu klasifikasi mineral apa saja yang terdapat pada batuan beku meliputi batuan andesit, batuan basalt, dan batuan granit. Kemampuan system pada penelitian ini dapat membantu para ahli petrologi untuk mengidentifikasi kandungan mineral apa saja yang ada pada batuan beku, sehingga dapat menjadi standar akurasi yang tepat. Pada tugas akhir ini, telah dibahas mengenai teknik pengolahan citra digital untuk klasifikasi mineral dalam batuan beku yang dapat dilakukan dengan menggunakan metode tertentu yang dapat mengenali objek. Dalam Tugas Akhir ini, penulis menggunakan metode ekstraksi ciri Histogram of Oriented Gradient (HOG) dan klasifikasi Linear Discriminant Analysis (LDA) yang dimulai dengan proses preprocessing, ekstraksi ciri, dan klasifikasi batuan beku. Dari hasil pengujian diperoleh nilai akurasi dari sistem. Dengan menggunakan metode ekstraksi Histogram of Oriented Gradient dengan ukuran dimensi blok = 2 diperoleh nilai akurasi sebesar 79.12 % untuk data citra parallel dan akurasi sebesar 73.99 % untuk data citra cross nikol. Kata Kunci : Batuan Beku, Histogram of Oriented Gradient, Linear Discriminant Analysi

Fælleskjøb og Fællessalg i det britiske Landbrug.

Fælleskjøb og Fællessalg i det britiske Landbrug

Testing the Potential of Deep Learning in Earthquake Forecasting

Reliable earthquake forecasting methods have long been sought after, and so the rise of modern data science techniques raises a new question: does deep learning have the potential to learn this pattern? In this study, we leverage the large amount of earthquakes reported via good seismic station coverage in the subduction zone of Japan. We pose earthquake forecasting as a classification problem and train a Deep Learning Network to decide, whether a timeseries of length greater than 2 years will end in an earthquake on the following day with magnitude greater than 5 or not. Our method is based on spatiotemporal b value data, on which we train an autoencoder to learn the normal seismic behaviour. We then take the pixel by pixel reconstruction error as input for a Convolutional Dilated Network classifier, whose model output could serve for earthquake forecasting. We develop a special progressive training method for this model to mimic real life use. The trained network is then evaluated over the actual dataseries of Japan from 2002 to 2020 to simulate a real life application scenario. The overall accuracy of the model is 72.3 percent. The accuracy of this classification is significantly above the baseline and can likely be improved with more data in the futur

Review of the First Fraunhofer Life Science Symposium on Cell Therapy and Immunology

This report covers recent advances in Regenerative Medicine with a special focus on (i) imaging of regeneration, (ii) nanotechnology and tissue engineering, (iii) immunological cell tolerance, (iv) cell therapies in cardiovascular, neurodegenerative, and liver diseases and in spinal regeneration

On the foundation of a general theory of stocks

This essay develops the "concept of stocks" – a conceptual notion designed to enable a clearer understanding of the interaction between the dynamics of ecosystems and the economy. The notion of stocks is formulated in a general manner based on set theory. The central attribute of a stock is its temporal durability. Seen thus, stocks are suitable for depicting the influences a system’s history has on its present – and hence for analysing temporal developments. Since permanency is a temporal attribute, the concept of stocks is not specifically limited to individual scientific disciplines and is suitable for interdisciplinary analysis. The notion is applied to economic and ecological examples and generalised for stochastic sets. The hierarchical structure of actual ecological-economic systems can be analysed by distinguishing the stock perspective from a system view. The theory of stocks is a building block for the conceptual foundations of ecological economics

Eine allgemeine Theorie der Bestände

Im vorliegenden Aufsatz wird ein Begriffskonzept entwickelt, das zum besseren Verständnis des Zusammenspiels der Dynamiken von Ökosystemen und Wirtschaft dient: das Konzept des Bestandes. Der Bestandsbegriff wird allgemein mengentheoretisch formuliert. Die zentrale Eigenschaft eines Bestandes liegt in seiner zeitlichen Dauerhaftigkeit. Damit eignen sich Bestände zur Abbildung von Einflüssen, die die Vergangenheit von Systemen auf deren Gegenwart ausübt, und damit zur Analyse von zeitlichen Entwicklungen. Da Beständigkeit eine Eigenschaft in der Zeit darstellt, ist das Konzept des Bestandes nicht spezifisch auf den Gegenstandsbereich einzelner wissenschaftlicher Disziplinen beschränkt und so für die interdisziplinäre Analyse geeignet. Der Begriff wird auf ökonomische und ökologische Beispiele angewandt und dabei auf stochastische Mengen verallgemeinert. Durch die Abgrenzung der Bestandsperspektive von einer Systemsicht kann die hierarchische Struktur realer ökologisch-ökonomischer Systeme analysiert werden. Die Theorie der Bestände stellt einen Baustein für die konzeptionellen Grundlagen der Ökologischen Ökonomie dar. --Dynamik,Beständigkeit,System,Zeitskalen,Population,Persistenz

SAIPy: A Python Package for single station Earthquake Monitoring using Deep Learning

Seismology has witnessed significant advancements in recent years with the application of deep learning methods to address a broad range of problems. These techniques have demonstrated their remarkable ability to effectively extract statistical properties from extensive datasets, surpassing the capabilities of traditional approaches to an extent. In this study, we present SAIPy, an open source Python package specifically developed for fast data processing by implementing deep learning. SAIPy offers solutions for multiple seismological tasks, including earthquake detection, magnitude estimation, seismic phase picking, and polarity identification. We introduce upgraded versions of previously published models such as CREIMERT capable of identifying earthquakes with an accuracy above 99.8 percent and a root mean squared error of 0.38 unit in magnitude estimation. These upgraded models outperform state of the art approaches like the Vision Transformer network. SAIPy provides an API that simplifies the integration of these advanced models, including CREIMERT, DynaPickerv2, and PolarCAP, along with benchmark datasets. The package has the potential to be used for real time earthquake monitoring to enable timely actions to mitigate the impact of seismic events. Ongoing development efforts aim to enhance the performance of SAIPy and incorporate additional features that enhance exploration efforts, and it also would be interesting to approach the retraining of the whole package as a multi-task learning problem

Tissue specific and androgen-regulated expression of human prostate-specific transglutaminase

Transglutaminases (TGases) are calcium-dependent enzymes catalysing the post-translational cross-linking of proteins. In the prostate at least two TGases are present, the ubiquitously expressed tissue-type TGase (TGC), and a prostate-restricted TGase (TGP). This paper deals with the molecular cloning and characterization of the cDNA encoding the human prostate TGase (hTGP). For this purpose we have screened a human prostate cDNA library with a probe from the active-site region of TGC. The largest isolated cDNA contained an open reading frame encoding a protein of 684 amino acids with a predicted molecular mass of 77 kDa as confirmed by in vitro transcription-translation and subsequent SDS/PAGE. The hTGP gene was tissue-specifically expressed in the prostate, yielding an mRNA of approx. 3.5 kb. Furthermore, a 3-fold androgen-induced upregulation of hTGP mRNA expression has been demonstrated in the recently developed human prostate cancer cell line, PC346C. Other well established human prostate cancer cell lines, LNCaP and PC-3, showed no detectable hTGP mRNA expression on a Northern bolt. The gene coding for prostate TGase was assigned to chromosome 3