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Deep learning has been revolutionizing many fields in computer vision, and facial informatics is one of the major fields. Novel approaches and performance breakthroughs are often reported on existing benchmarks. As the performances on existing benchmarks are close to saturation, larger and more challenging databases are being made and considered as new benchmarks, further pushing the advancement of the technologies. Considering face recognition, for example, the VGG-Face2 and Dual-Agent GAN report nearly perfect and better-than-human performances on the IARPA Janus Benchmark A (IJB-A) benchmark. More challenging benchmarks, e.g., the IARPA Janus Benchmark A (IJB-C), QMUL-SurvFace and MegaFace, are accepted as new standards for evaluating the performance of a new approach. Such an evolution is also seen in other branches of face informatics. In this Special Issue, we have selected the papers that report the latest progresses made in the following topics: 1. Face liveness detection 2. Emotion classification 3. Facial age estimation 4. Facial landmark detection We are hoping that this Special Issue will be beneficial to all fields of facial informatics.

History of engineering & technology --- deep learning --- RGB --- depth --- facial landmarking --- merging networks --- 3D geometry data --- 2D attribute maps --- fused CNN feature --- coarse-to-fine --- convolutional neural network (CNN) --- deep metric learning --- multi-task learning --- image classification --- age estimation --- generative adversarial network --- emotion classification --- facial key point detection --- facial images processing --- convolutional neural networks --- face liveness detection --- convolutional neural network --- thermal image --- external knowledge

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Myxobacteria are fascinating and important prokaryotes. They have large genomes and exhibit a wide range of interesting behaviors, including multicellular fruiting body formation, social interaction, predation, and secondary metabolite production. Substantial progress is being made in understanding their ecological roles and the evolutionary forces that have shaped their phenotypes and behaviors. Novel species of myxobacteria are regularly described, often producing unusual metabolites and enzymes which can be of significant biotechnological interest. Molecular studies, ranging in subject from individual enzymes to entire ‘omes, continue to provide rich insights into myxobacterial biology. This collected volume brings together five research articles and three reviews, to provide a snapshot of current myxobacterial research in all its diversity.

carotenoids --- comparative genomics --- development --- fruiting body formation --- one-component systems --- quorum signalling --- two-component systems --- myxobacteria --- Myxococcales --- germination --- bacterial cell wall --- sporulation --- morphology --- photoreceptor --- photosensitizer --- photoregulation --- singlet oxygen --- plasmalogens --- CarF --- vitamin B12 --- CarH --- ECF-sigma --- CarD-CdnL --- microbial food web --- trophic interactions --- predator–prey interactions --- mesopredator --- social bacteria --- nematodes --- experimental community --- behavior --- Myxococcus sp. --- Corallococcus sp. --- Melittangium sp. --- Archangium sp. --- biosynthetic gene clusters --- Myxococcus xanthus --- phase contrast microscopy --- fluorescence microscopy --- aggregation --- rippling --- deep learning --- generative adversarial network --- pretator-prey coevolution --- antagonism --- mucoidy --- predatory bacteria --- bacterial predation --- prey diversity --- negative frequency dependence --- experimental evolution --- MyxoEE-6 --- functional genomics --- genome evolution --- genome organisation --- pan-genome --- proteomics --- taxonomy --- transcriptomics --- n/a --- predator-prey interactions

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Machine learning (ML) technologies are emerging in Mechanical Engineering, driven by the increasing availability of datasets, coupled with the exponential growth in computer performance. In fact, there has been a growing interest in evaluating the capabilities of ML algorithms to approach topics related to metal forming processes, such as: Classification, detection and prediction of forming defects; Material parameters identification; Material modelling; Process classification and selection; Process design and optimization. The purpose of this Special Issue is to disseminate state-of-the-art ML applications in metal forming processes, covering 10 papers about the abovementioned and related topics.

Technology: general issues --- History of engineering & technology --- Mining technology & engineering --- sheet metal forming --- uncertainty analysis --- metamodeling --- machine learning --- hot rolling strip --- edge defects --- intelligent recognition --- convolutional neural networks --- deep-drawing --- kriging metamodeling --- multi-objective optimization --- FE (Finite Element) AutoForm robust analysis --- defect prediction --- mechanical properties prediction --- high-dimensional data --- feature selection --- maximum information coefficient --- complex network clustering --- ring rolling --- process energy estimation --- metal forming --- thermo-mechanical FEM analysis --- artificial neural network --- aluminum alloy --- mechanical property --- UTS --- topological optimization --- artificial neural networks (ANN) --- machine learning (ML) --- press-brake bending --- air-bending --- three-point bending test --- sheet metal --- buckling instability --- oil canning --- artificial intelligence --- convolution neural network --- hot rolled strip steel --- defect classification --- generative adversarial network --- attention mechanism --- deep learning --- mechanical constitutive model --- finite element analysis --- plasticity --- parameter identification --- full-field measurements --- n/a

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Animal production (e.g., milk, meat, and eggs) provides valuable protein production for human beings and animals. However, animal production is facing several challenges worldwide such as environmental impacts and animal welfare/health concerns. In animal farming operations, accurate and efficient monitoring of animal information and behavior can help analyze the health and welfare status of animals and identify sick or abnormal individuals at an early stage to reduce economic losses and protect animal welfare. In recent years, there has been growing interest in animal welfare. At present, sensors, big data, machine learning, and artificial intelligence are used to improve management efficiency, reduce production costs, and enhance animal welfare. Although these technologies still have challenges and limitations, the application and exploration of these technologies in animal farms will greatly promote the intelligent management of farms. Therefore, this Special Issue will collect original papers with novel contributions based on technologies such as sensors, big data, machine learning, and artificial intelligence to study animal behavior monitoring and recognition, environmental monitoring, health evaluation, etc., to promote intelligent and accurate animal farm management.

Technology: general issues --- History of engineering & technology --- pig weight --- body size --- estimation --- deep learning --- convolutional neural network --- pig identification --- mask scoring R-CNN --- soft-NMS --- group-housed pigs --- audio --- dairy cow --- mastication --- jaw movement --- forage management --- precision livestock management --- equine behavior --- wearable sensor --- intermodality interaction --- class-balanced focal loss --- absorbing Markov chain --- cow behavior analysis --- prediction of calving time --- cow identification --- EfficientDet --- YOLACT++ --- cascaded model --- instance segmentation --- generative adversarial network --- machine learning --- automated medical image processing --- deep neural network --- animal science --- CT scans --- computer vision --- cow --- extensive livestock --- sensorized wearable device --- monitoring --- parturition prediction --- radar sensors --- radar signal processing --- animal farming --- computational ethology --- signal classification --- wavelet analysis --- dairy welfare --- hierarchical clustering --- mutual information --- precision livestock farming --- time budgets --- unsupervised machine learning --- wearables design --- animal-centered design --- animal telemetry --- modularity --- smart collar --- design contributions --- additive manufacturing --- low-frequency tracking --- commercial aviary --- laying hens --- false registrations --- tree-based classifier --- animal behaviour

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Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology.

Technology: general issues --- History of engineering & technology --- artificial intelligence --- machine learning --- artificial neural networks --- tribology --- condition monitoring --- semi-supervised learning --- random forest classifier --- self-lubricating journal bearings --- reduced order modelling --- dynamic friction --- rubber seal applications --- tensor decomposition --- laser surface texturing --- texturing during moulding --- digital twin --- PINN --- reynolds equation --- triboinformatics --- databases --- data mining --- meta-modeling --- monitoring --- analysis --- prediction --- optimization --- fault data generation --- Convolutional Neural Network (CNN) --- Generative Adversarial Network (GAN) --- bearing fault diagnosis --- unbalanced datasets --- tribo-testing --- tribo-informatics --- natural language processing --- tribAIn --- BERT --- amorphous carbon coatings --- UHWMPE --- total knee replacement --- Gaussian processes --- rolling bearing dynamics --- cage instability --- regression --- neural networks --- random forest --- gradient boosting --- evolutionary algorithms --- rolling bearings --- remaining useful life --- feature engineering --- structure-borne sound --- n/a