Timber and forest products made from wood, like furniture, are valuable commodities, and like the global trade of many highly-valued natural resources, face challenges of corruption, fraud, and illegal harvesting. These grey and black market activities in the wood and forest products sector are not limited to the countries where the wood was harvested, but extend throughout the global supply chain and have been tied to illicit financial flows, like trade-based money laundering, document fraud, species mislabeling, and other illegal activities. The task of finding such fraudulent activities using trade data, in the absence of ground truth, can be modelled as an unsupervised anomaly detection problem. However existing approaches suffer from certain shortcomings in their applicability towards large scale trade data. Trade data is heterogeneous, with both categorical and numerical attributes in a tabular format. The overall challenge lies in the complexity, volume and velocity of data, with large number of entities and lack of ground truth labels. To mitigate these, we propose a novel unsupervised anomaly detection -- Contrastive Learning based Heterogeneous Anomaly Detection (CHAD) that is generally applicable for large-scale heterogeneous tabular data. We demonstrate our model CHAD performs favorably against multiple comparable baselines for public benchmark datasets, and outperforms them in the case of trade data. More importantly we demonstrate our approach reduces assumptions and efforts required hyperparameter tuning, which is a key challenging aspect in an unsupervised training paradigm. Specifically, our overarching objective pertains to detecting suspicious timber shipments and patterns using Bill of Lading trade record data. Detecting anomalous transactions in shipment records can enable further investigation by government agencies and supply chain constituents.

A journalist who covers wildfires responds to Premee Mohamed's "All That Burns Unseen." In "All That Burns Unseen," set in a dystopian but not-too-distant future, we finally get the drone sidekick we didn't know we needed. Premee Mohamed's heroine, Vaughn Collins, is a government worker gone rogue as a wildfire burns. Along the way, she rescues a dazed, glitchy fire extinguisher drone. When a funnel of flames heads for Vaughn's truck, threatening everything, her new friend dives into the blaze and sprays.

There are many challenges associated with analysing gas chromatography - mass spectrometry (GC-MS) data. Many of these challenges stem from the fact that electron ionisation can make it difficult to recover molecular information due to the high degree of fragmentation with concomitant loss of molecular ion signal. With GC-MS data there are often many common fragment ions shared among closely-eluting peaks, necessitating sophisticated methods for analysis. Some of these methods are fully automated, but make some assumptions about the data which can introduce artifacts during the analysis. Chemometric methods such as Multivariate Curve Resolution, or Parallel Factor Analysis are particularly attractive, since they are flexible and make relatively few assumptions about the data - ideally resulting in fewer artifacts. These methods do require expert user intervention to determine the most relevant regions of interest and an appropriate number of components, $k$, for each region. Automated region of interest selection is needed to permit automated batch processing of chromatographic data with advanced signal deconvolution. Here, we propose a new method for automated, untargeted region of interest selection that accounts for the multivariate information present in GC-MS data to select regions of interest based on the ratio of the squared first, and second singular values from the Singular Value Decomposition of a window that moves across the chromatogram. Assuming that the first singular value accounts largely for signal, and that the second singular value accounts largely for noise, it is possible to interpret the relationship between these two values as a probabilistic distribution of Fisher Ratios. The sensitivity of the algorithm was tested by investigating the concentration at which the algorithm can no longer pick out chromatographic regions known to contain signal.

FIB/SEM tomography represents an indispensable tool for the characterization of three-dimensional nanostructures in battery research and many other fields. However, contrast and 3D classification/reconstruction problems occur in many cases, which strongly limits the applicability of the technique especially on porous materials, like those used for electrode materials in batteries or fuel cells. Distinguishing the different components like active Li storage particles and carbon/binder materials is difficult and often prevents a reliable quantitative analysis of image data, or may even lead to wrong conclusions about structure-property relationships. In this contribution, we present a novel approach for data classification in three-dimensional image data obtained by FIB/SEM tomography and its applications to NMC battery electrode materials. We use two different image signals, namely the signal of the angled SE2 chamber detector and the Inlens detector signal, combine both signals and train a random forest, i.e. a particular machine learning algorithm. We demonstrate that this approach can overcome current limitations of existing techniques suitable for multi-phase measurements and that it allows for quantitative data reconstruction even where current state-of the art techniques fail, or demand for large training sets. This approach may yield as guideline for future research using FIB/SEM tomography.

Gold mining is one of the very oldest human occupations. The earliest known underground gold mine, in what is now the country of Georgia, dates back at least 5,000 years, when people were just starting to develop written language. Over the centuries, a number of innovations have emerged that disrupted and forever changed how we explore and mine for gold and other metals. Think dynamite, or the steam engine. Lately, however, innovation has slowed.

Domestic giant Tata Steel on Wednesday said it has inked a pact with a Bengaluru-based startup for drone-based mining solutions for effective mine management. The primary goal of this collaboration is to jointly develop and offer sustainable and end-to-end integrated solutions that will focus on efficiency, safety, and productivity of open cast mining operations. "Tata Steel has signed a Memorandum of Understanding with Aarav Unmanned Systems, a Bangaluru-based startup, providing end-to-end drone solutions... for effective mine management," the company said in a statement. Tata Steel will also work jointly with AUS to provide exclusive drone-based solutions, including mine analytics and geo-technical mapping, to Tata Steel group companies across mining locations in India, it said. On the partnership, D B Sundara Ramam, Vice President, Raw Materials, Tata Steel, said: "Drone survey enabled digitalisation and other technology will assist in gathering impactful and actionable insights. We see enormous potential in redefining core mining processes such as exploration and mine planning using drone data and adequate analytics."

Think you know what lurks beneath you when you take a dip in the ocean? Scientists have discovered 39 species that are'potentially new to science', while exploring up to 16,700 feet (5,100 metres) underwater. A robot was sent down to the abyssal plains of the Clarion-Clipperton Zone (CCZ) in the central Pacific Ocean - one of the least explored regions of the world - to collect specimens of deep sea creatures. The researchers, from the Natural History Museum in London, recovered 39 brand new species of megafauna as well as nine known species. Amongst those found were spindly starfish, tulip-shaped sea sponges, prickly urchins and'gummy squirrel' sea cucumbers.

Graph neural networks (GNNs) are emerging in chemical engineering for the end-to-end learning of physicochemical properties based on molecular graphs. A key element of GNNs is the pooling function which combines atom feature vectors into molecular fingerprints. Most previous works use a standard pooling function to predict a variety of properties. However, unsuitable pooling functions can lead to unphysical GNNs that poorly generalize. We compare and select meaningful GNN pooling methods based on physical knowledge about the learned properties. The impact of physical pooling functions is demonstrated with molecular properties calculated from quantum mechanical computations. We also compare our results to the recent set2set pooling approach. We recommend using sum pooling for the prediction of properties that depend on molecular size and compare pooling functions for properties that are molecular size-independent. Overall, we show that the use of physical pooling functions significantly enhances generalization.

Slope stability is essential in mining operations since slope failure endangers safety and productivity. The complexity of conventional geotechnical methods makes slope failure prediction challenging. Artificial intelligence (AI) has helped mining companies forecast slope failures quickly and efficiently through detailed analysis. Due to the development of more advanced mining techniques and the growing demand for mineral resources, most mines are constructed to extract more minerals from steeper or deeper areas. The steeper slope angle makes these mines more vulnerable to slope failure. It can cause injury to workers, damage to mine equipment, and halt production, negatively influencing mining productivity.

Process synthesis experiences a disruptive transformation accelerated by digitization and artificial intelligence. We propose a reinforcement learning algorithm for chemical process design based on a state-of-the-art actor-critic logic. Our proposed algorithm represents chemical processes as graphs and uses graph convolutional neural networks to learn from process graphs. In particular, the graph neural networks are implemented within the agent architecture to process the states and make decisions. Moreover, we implement a hierarchical and hybrid decision-making process to generate flowsheets, where unit operations are placed iteratively as discrete decisions and corresponding design variables are selected as continuous decisions. We demonstrate the potential of our method to design economically viable flowsheets in an illustrative case study comprising equilibrium reactions, azeotropic separation, and recycles. The results show quick learning in discrete, continuous, and hybrid action spaces. Due to the flexible architecture of the proposed reinforcement learning agent, the method is predestined to include large action-state spaces and an interface to process simulators in future research.