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\"This book provides an in-depth analysis of a selection of thirty building types: looking closely at the property's background, the transformation of the motivation, the transformation strategy, as well as the problems encountered in the transformation. The pages within are filled with practical insights, including information on the latest features of contemporary renovations and adaptations of older buildings (some up to 300 years old), including some previous designs by revered practitioners, such as Antoni Gaudâi and Zaha Hadid. Internationally renowned architects discuss in detail about key problems they've encountered when renovating an old building or conducting an urban renewal project, and provide readers with helpful outlines of a range of projects, what to look out for, and useful, practical approaches in each scenario\"--Back cover.

The paper will focus on the topic of adapting digital humanities methods from architectural history to technical history, considering mapping and image analysis for increasing disaster resilience in Art Nouveau and Modernist buildings in different geographical areas—including lessons from Europe to the USA. The project proposes the transformation of the collection of photographs of early 20th-century architecture gathered by the applicant over about 30 years of travel into a database by answering the research question on how threats from the hazards of earthquakes, floods, and fires can be answered by taking into account the local culture in the European countries covered, for buildings from a period when the architecture styles were already global at that time. For this purpose, digital humanities methods of image annotation (including architectural volumetric analysis) and mapping are employed. From the knowledge gathered and the resulting database, a prototyping ontology and taxonomy is derived. This outcome can be further developed into a set of evaluation criteria, considering the decisions that can be taken to prioritize the retrofit interventions depending on the geographic positions of the buildings.

Contemporary processes of urbanisation present major challenges for urban research and theory as urban areas expand and interweave. In this process, urban forms are constantly changing and new urban configurations are frequently evolving. An adequate understanding of urbanisation must derive its empirical and theoretical inspirations from the multitude of urban experiences across the various divides that shape the contemporary world. New concepts and terms are urgently required that would help, both analytically and cartographically, to decipher the differentiated and rapidly mutating landscapes of urbanisation that are being produced today. One of the key procedures to address these challenges is the application of comparative strategies. Based on postcolonial critiques of urban theory and on the epistemologies of planetary urbanisation, this paper introduces and discusses the theoretical and methodological framework of a collaborative comparative study of urbanisation processes in eight large metropolitan territories across the world: Tokyo, Hong Kong/Shenzhen/Dongguan, Kolkata, Istanbul, Lagos, Paris, Mexico City and Los Angeles. In order to approach these large territories, a specific methodological design is applied mainly based on qualitative methods and a newly developed method of mapping. After the presentation of the main lines of our theoretical and methodological approach we discuss some of the new comparative concepts that we developed through this process: popular urbanisation, plotting urbanism, multilayered patchwork urbanisation and the incorporation of urban differences. 随着城市地区的扩大和相互交织，当代城市化进程给城市研宄和理论带来了重大挑战。在这个 过程中，城市形态不断变化，新的城市形态在不断演进。要充分理解城市化，我们必须从塑造 当代世界的大量纷纭歧出的城市经验中获得经验和理论层面的启发。如今正在产生的城市化格 局千差万别，并迅速变化，我们迫切需要新的概念和术语，在分析层面和图解层面帮助破译这 些格局。应对这些挑战的关键手段是应用比较策略。本文基于城市理论的后殖民批判和全域城 市化的认识论，介绍并讨论了对世界八大都市圈城市化进程进行协同比较研宄的理论和方法论 框架，这八大都市圈是：东京、香港/深圳/东莞、加尔各答、伊斯坦布尔、拉各斯、巴黎、墨 西哥城和洛杉矶。为着手探讨这些广大的都市圈，我们主要基于定性方法和新开发的测绘方法， 应用了一套特定的方法论设计。在介绍了我们主要的理论和方法论进路之后，我们讨论了在这 个过程中发展起来的一些新的比较概念：大众城市化、小块圈地式城市化、多层拼凑城市化以 及城市差异的纳入。

We run a selection of algorithms on two state-of-the-art 5-qubit quantum computers that are based on different technology platforms. One is a publicly accessible superconducting transmon device (www.research.ibm.com/ibm-q) with limited connectivity, and the other is a fully connected trapped-ion system. Even though the two systems have different native quantum interactions, both can be programed in a way that is blind to the underlying hardware, thus allowing a comparison of identical quantum algorithms between different physical systems. We show that quantum algorithms and circuits that use more connectivity clearly benefit from a better-connected system of qubits. Although the quantum systems here are not yet large enough to eclipse classical computers, this experiment exposes critical factors of scaling quantum computers, such as qubit connectivity and gate expressivity. In addition, the results suggest that codesigning particular quantum applications with the hardware itself will be paramount in successfully using quantum computers in the future.

Animal telemetry is a powerful tool for observing marine animals and the physical environments that they inhabit, from coastal and continental shelf ecosystems to polar seas and open oceans. Satellite-linked biologgers and networks of acoustic receivers allow animals to be reliably monitored over scales of tens of meters to thousands of kilometres, giving insight into their habitat use, home range size, the phenology of migratory patterns and the biotic and abiotic factors that drive their distributions. Furthermore, physical environmental variables can be collected using animals as autonomous sampling platforms, increasing spatial and temporal coverage of global oceanographic observation systems. The use of animal telemetry therefore has the capacity to provide measures from a suite of essential ocean variables (EOVs) for improved monitoring of Earth’s oceans. Here we outline the design features of animal telemetry systems, describe current applications and their benefits and challenges, and discuss future directions. We describe new analytical techniques that improve our ability to not only quantify animal movements but to also provide a powerful framework for comparative studies across taxa. We discuss the application of animal telemetry and its capacity to collect biotic and abiotic data, how the data collected can be incorporated into ocean observing systems, and the role these data can play in improved ocean management.

Regional rainfall–runoff modeling is an old but still mostly outstanding problem in the hydrological sciences. The problem currently is that traditional hydrological models degrade significantly in performance when calibrated for multiple basins together instead of for a single basin alone. In this paper, we propose a novel, data-driven approach using Long Short-Term Memory networks (LSTMs) and demonstrate that under a “big data” paradigm, this is not necessarily the case. By training a single LSTM model on 531 basins from the CAMELS dataset using meteorological time series data and static catchment attributes, we were able to significantly improve performance compared to a set of several different hydrological benchmark models. Our proposed approach not only significantly outperforms hydrological models that were calibrated regionally, but also achieves better performance than hydrological models that were calibrated for each basin individually. Furthermore, we propose an adaption to the standard LSTM architecture, which we call an Entity-Aware-LSTM (EA-LSTM), that allows for learning catchment similarities as a feature layer in a deep learning model. We show that these learned catchment similarities correspond well to what we would expect from prior hydrological understanding.

The performance of six existing deep learning architectures were compared for the task of detection of mango fruit in images of tree canopies. Images of trees (n = 1 515) from across five orchards were acquired at night using a 5 Mega-pixel RGB digital camera and 720 W of LED flood lighting in a rig mounted on a farm utility vehicle operating at 6 km/h. The two stage deep learning architectures of Faster R-CNN(VGG) and Faster R-CNN(ZF), and the single stage techniques YOLOv3, YOLOv2, YOLOv2(tiny) and SSD were trained both with original resolution and 512 × 512 pixel versions of 1 300 training tiles, while YOLOv3 was run only with 512 × 512 pixel images, giving a total of eleven models. A new architecture was also developed, based on features of YOLOv3 and YOLOv2(tiny), on the design criteria of accuracy and speed for the current application. This architecture, termed ‘MangoYOLO’, was trained using: (i) the 1 300 tile training set, (ii) the COCO dataset before training on the mango training set, and (iii) a daytime image training set of a previous publication, to create the MangoYOLO models ‘s’, ‘pt’ and ‘bu’, respectively. Average Precision plateaued with use of around 400 training tiles. MangoYOLO(pt) achieved a F1 score of 0.968 and Average Precision of 0.983 on a test set independent of the training set, outperforming other algorithms, with a detection speed of 8 ms per 512 × 512 pixel image tile while using just 833 Mb GPU memory per image (on a NVIDIA GeForce GTX 1070 Ti GPU) used for in-field application. The MangoYOLO model also outperformed other models in processing of full images, requiring just 70 ms per image (2 048 × 2 048 pixels) (i.e., capable of processing ~ 14 fps) with use of 4 417 Mb of GPU memory. The model was robust in use with images of other orchards, cultivars and lighting conditions. MangoYOLO(bu) achieved a F1 score of 0.89 on a day-time mango image dataset. With use of a correction factor estimated from the ratio of human count of fruit in images of the two sides of sample trees per orchard and a hand harvest count of all fruit on those trees, MangoYOLO(pt) achieved orchard fruit load estimates of between 4.6 and 15.2% of packhouse fruit counts for the five orchards considered. The labelled images (1 300 training, 130 validation and 300 test) of this study are available for comparative studies.

Why did you do this work?Diagnostic errors, often arising from inadequate clinical reasoning skills, remain a concern in healthcare (Lawson & Daniel, 2011). Artificial intelligence (AI) has the potential to revolutionize medical training though innovative curricular elements to finetune trainee aptitude to help perform metacognitive tasks (Gordon et al., 2024). AI presents a promising avenue to revolutionize medical training by enhancing clinical diagnostic reasoning (CDR) skills in postgraduate paediatric training (Ba et al., 2024). This study sought to develop an AI-based tool to support paediatric trainees in enhancing their CDR skills, thereby improving diagnostic accuracy.What did you do?A multicenter case-control study was conducted among 48 postgraduate paediatric trainees. Phase 1 constituted the development of a CDR framework (Croskerry 2009) by educational experts. A software developers team integrated the framework into an AI-powered tool accessible via a web-based platform (figure 1). The interface employed client-server architecture with the back end consisting of a system for storing and processing clinical data points. Algorithms were developed to query this database and dynamically generate case details based on user interactions. Trainees engaged with the AI tool through chatbot function to gather virtual case history, physical findings, and lab results. A training workshop was conducted to orient trainees to CDR concepts (Phase-2). Participants completed a 12-week clinical posting in general paediatrics during which they used the AI tool. Trainees’ CDR skills were evaluated post-intervention by script concordance approach and feedback obtained using a pre-validated questionnaire.What did you find?Overall CDR skills of trainees assessed using script concordance test showed a significant improvement compared to control group (mean difference = 2.5 points, p=0.007). Specific improvements were observed in generating and testing hypotheses (mean difference = 2.7 points, p=0.004), identifying and prioritizing differential diagnoses (mean difference = 1.9 points, p=0.001), and justifying provisional diagnoses (mean difference = 2.3 points, p=0.005). The percentage of trainees able to generate at least four relevant hypotheses improved from 73.6% to 87.7% while those accurately prioritising differential diagnoses rose from 67.4% to 82.5%.Thematic analysis of trainee feedback suggested the tool’s impact on ‘enhancing structured critical thinking’ and ‘facilitating diagnostic accuracy through safe, iterative practice.’ The majority agreed that the tool significantly improved diagnostic reasoning in a supportive, practice-based environment. Analysis of feedback pre-post intervention corroborated these sentiments, citing the tool’s ability to foster ‘confidence in decision-making’ and provide ‘critical reflection opportunities’ on diagnostic errors. Data triangulation confirmed the practical utility of the tool.What does it mean?Our findings demonstrate the potential of AI-driven tools to enhance CDR skills in postgraduate education. Significant performance gains validated the AI tool’s potential for improving CDR. Receptive attitudes of trainees suggest that integrating AI into paediatric curricula could improve clinical training. Adequate resource allocation, interdisciplinary coordination, and continued refinement based on user feedback are key for optimising such innovations (Liang at al., 2019). Future research should explore the scalability of AI-driven educational tools in medical training.Abstract 7838 Figure 1[Image Omitted. See PDF.]ReferencesBa H, Zhang L, Yi Z. Enhancing clinical skills in pediatric trainees: a comparative study of ChatGPT-assisted and traditional teaching methods. BMC Med Educ. 2024;24(1):558.Croskerry P. A universal model of diagnostic reasoning. Acad Med. 2009;84(8):1022–1-28.Gordon M, Daniel M, Ajiboye A, et al. A scoping review of artificial intelligence in medical education: BEME guide no. 84. Med Teach. 2024;46(4):446–470.Lawson AE, Daniel ES. Inferences of clinical diagnostic reasoning and diagnostic error. J Biomed Inform. 2011;44(3):402–412.Liang H, Tsui BY, Ni H, et al. Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nat Med. 2019;25:433–438.

The rapid development of abnormal brain cells that characterizes a brain tumor is a major health risk for adults since it can cause severe impairment of organ function and even death. These tumors come in a wide variety of sizes, textures, and locations. When trying to locate cancerous tumors, magnetic resonance imaging (MRI) is a crucial tool. However, detecting brain tumors manually is a difficult and time-consuming activity that might lead to inaccuracies. In order to solve this, we provide a refined You Only Look Once version 7 (YOLOv7) model for the accurate detection of meningioma, glioma, and pituitary gland tumors within an improved detection of brain tumors system. The visual representation of the MRI scans is enhanced by the use of image enhancement methods that apply different filters to the original pictures. To further improve the training of our proposed model, we apply data augmentation techniques to the openly accessible brain tumor dataset. The curated data include a wide variety of cases, such as 2548 images of gliomas, 2658 images of pituitary, 2582 images of meningioma, and 2500 images of non-tumors. We included the Convolutional Block Attention Module (CBAM) attention mechanism into YOLOv7 to further enhance its feature extraction capabilities, allowing for better emphasis on salient regions linked with brain malignancies. To further improve the model’s sensitivity, we have added a Spatial Pyramid Pooling Fast+ (SPPF+) layer to the network’s core infrastructure. YOLOv7 now includes decoupled heads, which allow it to efficiently glean useful insights from a wide variety of data. In addition, a Bi-directional Feature Pyramid Network (BiFPN) is used to speed up multi-scale feature fusion and to better collect features associated with tumors. The outcomes verify the efficiency of our suggested method, which achieves a higher overall accuracy in tumor detection than previous state-of-the-art models. As a result, this framework has a lot of potential as a helpful decision-making tool for experts in the field of diagnosing brain tumors.

Internet of Things (IoT) is a disruptive technology for the future decades. Due to its pervasive growth, it is susceptible to cyber-attacks, and hence the significance of Intrusion Detection Systems (IDSs) for IoT is pertinent. The viability of machine learning has encouraged analysts to apply learning techniques to intelligently discover and recognize cyber attacks and unusual behavior among the IoTs. This paper proposes an enhanced anomaly-based Intrusion Detection Deep learning Multi-class classification model (EIDM) that can classify 15 traffic behaviors including 14 attack types with the accuracy of 95% contained in the CICIDS2017 dataset. Four state-of-the-art deep learning models are also customized to classify six classes of network traffic behavior. An extensive comparative study in terms of classification accuracy and efficiency metrics is conducted between EIDM and several state-of-the-art deep learning-based IDSs showing that EIDM has achieved accurate detection results.