Introduction to the Research Handbook on the International Court of Justice

This Research Handbook presents an in-depth examination of the International Court of Justice (ICJ) and its jurisprudence. Contributing authors dissect the global governance functions of the ICJ and its impact on national legal orders worldwide.This project was funded by the CaPE Project, Marie Skłodowska-Curie Actions, grant agreement number 708228, Horizon 2020

Social Entropy Informer: A Multi-Scale Model-Data Dual-Driven Approach for Pedestrian Trajectory Prediction

Pedestrian trajectory prediction is fundamental in various applications, such as autonomous driving, intelligent surveillance, and traffic management. Existing methods generally fall into two categories: model-driven approaches and data-driven approaches. However, both approaches have inherent limitations when applied to real-world scenarios, particularly in capturing the complex interactions between pedestrians and modeling the stochastic nature of human motion. Notably, there is a lack of research on integrating the strengths of model-driven and data-driven paradigms, which can better address these challenges. This paper aims to fill these limitations by proposing a novel model-data dual-driven approach, called Social Entropy Informer (SEI), for pedestrian trajectory prediction. SEI simultaneously models local and global pedestrian interactions while incorporating information entropy to capture human motion’s inherent randomness and uncertainty quantitatively, which provides a robust framework for predicting pedestrian trajectories. Furthermore, we propose a new loss function derived from information theory, which accounts for the stochasticity of pedestrian movement and enhances the model’s ability to generalize across diverse scenarios. The SEI framework integrates feature extraction, entropy-based stochastic modeling, and the new loss function, improving prediction accuracy and model interpretability. Experimental results demonstrate that SEI outperforms other benchmark methods in prediction accuracy.10.13039/501100013088-Qinglan Project of Jiangsu Province of China; Natural Science Foundation of Jiangsu Higher Education Institutions of China (Grant Number: 23KJB520038); Research Enhancement Fund of Xi’an Jiaotong-Liverpool University (XJTLU) (Grant Number: REF-23-01-008); Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, Saudi Arabia (Grant Number: GPIP: 108-135-2024)

A Multi-Objective Genetic Programming with Size Diversity for Symbolic Regression Problem

Genetic programming has been positioned as a fit-for-purpose approach for symbolic regression. Researchers tend to select algorithms that produce a model with low complexity and high accuracy. Multi-objective genetic programming (MOGP) is a promising approach for finding appropriate models by considering tradeoffs between accuracy and complexity. The MOGP has gained significant attention for non-dominated sorting genetic algorithm II (NSGA-II). However, NSGA-II tends to excessively select individuals of lower complexity, making NSGA-II inefficient in real world applications. SD can be a strategy to promote the evolutionary process by adapting selection pressures for individuals of various size. It deals with the excessive tendency to select low complexity individuals in NSGA-II.We also introduce a practical industrial case of defect detection for dispensing machines. By modeling the dispensing volume of the fluid dispensing systems, defects in the dispensing machine can be detected under different external environmental factors.For the validation of SD, other MOGP algorithms are compared with the improved NSGA-II algorithm, NSGA-II with SD. By comparing multi-objective optimization methods tested on seven general datasets and an industrial case about defect prediction, the experimental results show that performance of the proposed approach is superior or same to other models in terms of accuracy. In terms of complexity, performance of the proposed approach is satisfactory.10.13039/501100001809-National Natural Science Foundation of China

Intelligent Diagnosis of Closed-Loop Motor Drives Using Interior Control Signals Under Industrial Low Sampling Rate Conditions

Interior control signals derived from motor controllers have gained increasing attention in closed-loop motor drive systems for interturn short-circuit fault diagnosis. Mainstream diagnosis methods generally rely on the extraction of control signals within experimental settings featuring high sampling rates, such as 10 kHz or 40 kHz. However, in practical engineering, the industrial sampling rate of control signals typically reaches only 1 kHz or even lower. This limitation makes it challenging for control signals to intuitively distinguish between healthy and faulty states. To address this practical constraint, an intelligent diagnosis method, termed the prior knowledge integrated contrastive diagnosis model (PK-CDM), is proposed. First, space voltage vectors of interior control signals are extracted as inputs of the PK-CDM to detect the interturn short circuit in a closed-loop motor drive system. Second, the physical variation regularity of space voltage vectors is formulated as the prior diagnostic knowledge to compensate for the lack of information under low sampling rate conditions. Finally, a contrastive pretraining strategy is employed to facilitate the construction of the PK-CDM at an industrially low sampling rate. Experimental results demonstrated that the proposed PK-CDM solves the issue of information loss under industrial low sampling rate conditions by integration of prior diagnostic knowledge with a contrastive learning strategy, thereby yielding superior diagnostic accuracy compared to other state-of-the-art (SOTA) methods.This work was supported in part by the National Key R&D Program of China under Grant 2022YFB3402100, in part by the Key Program of the National Natural Science Foundation of China under Grant 52435003, in part by the National Science Fund for Distinguished Young Scholars of China under Grant 52025056, in part by Shaanxi Science and Technology Innovation Team under Grant 2023-CX-TD-15, in part by the Sanqin Scholar Innovation Team and in part by the Fundamental Research Funds for the Central Universities

Formulation and Structural Optimisation of PVA-Fibre Biopolymer Composites for 3D Printing in Drug Delivery Applications

Data Availability Statement: The dataset is available upon request from the authors.Supplementary Materials: The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/polym17182502/s1, Figure S1: Macroscopic images of M10, S10, P10, P10F5, P10F5T5, P10F5E5, and P10F5E5T5 filaments.Additive manufacturing using fused deposition modelling (FDM) is increasingly explored for personalised drug delivery, but the lack of suitable biodegradable and printable filaments limits its pharmaceutical application. In this study, we investigated the influence of formulation and structural design on the performance of polyvinyl alcohol (PVA)-based filaments doped with theophylline anhydrous for 3D printing. To address the intrinsic brittleness and poor printability of PVA, cassava pulp-derived fibres—a sustainable and underutilised agricultural by-product—were incorporated together with polyethylene glycol (PEG 400), Eudragit® NE 30 D, and calcium stearate. The addition of fibres modified the mechanical properties of PVA filaments through hydrogen bonding, improving flexibility but increasing surface roughness. This drawback was mitigated by Eudragit® NE 30 D, which enhanced surface smoothness and drug distribution uniformity. The optimised composite formulation (P10F5E5T5) was successfully extruded and used to fabricate 3D-printed constructs. Release studies demonstrated that drug release could be modulated by pore geometry and construct thickness: wider pores enabled rapid Fickian diffusion, while narrower pores and thicker constructs shifted release kinetics toward anomalous transport governed by polymer swelling. These findings demonstrate, for the first time, the potential of cassava fibre as a functional additive in pharmaceutical FDM and provide a rational formulation–structure–performance framework for developing sustainable, geometry-tuneable drug delivery systems.This research project was supported by the Fundamental Fund 2025, Chiang Mai University and Thailand Science Research and Innovation (TSRI) (FRB680102/0162)

Can Immersive Training Complement On-Road Cycle Training for Children? Two Intervention Studies in Urban and Rural UK Communities

Data availability: We have provided the Mendeley Data doi, and we have uploaded additional files with the manuscript.Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S2214140525000684?via%3Dihub#appsec1 .Introduction: Cyclists are frequent casualties in road traffic collisions; failure to look is a contributory factor. Recent research shows that immersive training may improve children's performance, including their observational skills, when cycling on roads. However, robust data in this regard are scarce. Methods: In two related studies, we collected data from 95 children aged 9–11 years across two different UK locations – a cycling-supportive city and a rural town – to ascertain the effects of immersive cycle training on their cycling attitudes and confidence, their situation awareness, and on-road performance. In the urban study we employed a traditional control group design (immersive intervention vs. control); in the rural study, we compared two immersive interventions (with verbal prompts vs. without). At pre-intervention, post-intervention, and 4–6 weeks later (retention), the children reported their attitudes and confidence and completed video-based situation awareness tests (SATs) and on-road cycling assessments (ORCAs). Changes in parental confidence and attitudes were also recorded. Findings: In both studies, ORCA performance improved pre-to-post-intervention, irrespective of group. SATs scores did not improve but were somewhat correlated with ORCA performance. Although the children's cycling attitudes did not change, their confidence increased post-intervention. Parents' confidence in their child's ability to cycle increased significantly from pre-intervention to follow-up, after watching POV footage recorded during their child's retention phase ORCA. Conclusions: The contribution of immersive training to young children's on-road cycling ability is indeterminate. We tentatively suggest that a combination of independent on-road, immersive, and video-based cycling experiences may improve this ability and consequently increase parental confidence.These studies were part-funded by The Road Safety Trust via a Strategic Priority Grant (grant number 302_0_23), for which a related report is available here: https://www.roadsafetytrust.org.uk/small-grants-awarded/bikeability-trust

Causes of evolutionary divergence in prostate cancer

Data Availability: Components of the PPCG data set can be accessed through different portals in accordance with the required level of data protection for each data type. The main data constituents, and respective modes of access, are listed in detail in the companion manuscript by GM Jakobsdottir [ref. 19].A preprint version of the article is available at arXiv:2503.13189v1 [q-bio.GN], https://arxiv.org/abs/2503.13189 . It has not been certified by peer review. Submission history: From: Emre Esenturk [v1] Mon, 17 Mar 2025 14:00:02 UTC (818 KB).Code Availability: Codes are available to reviewers. Open-source repository will be made available at the time of publication.Cancer progression involves the sequential accumulation of genetic alterations that cumulatively shape the tumour phenotype. In prostate cancer, tumours can follow divergent evolutionary trajectories that lead to distinct subtypes, but the causes of this divergence remain unclear. While causal inference could elucidate the factors involved, conventional methods are unsuitable due to the possibility of unobserved confounders and ambiguity in the direction of causality. Here, we propose a method that circumvents these issues and apply it to genomic data from 829 prostate cancer patients. We identify several genetic alterations that drive divergence as well as others that prevent this transition, locking tumours into one trajectory. Further analysis reveals that these genetic alterations may cause each other, implying a positive-feedback loop that accelerates divergence. Our findings provide insights into how cancer subtypes emerge and offer a foundation for genomic surveillance strategies aimed at monitoring the progression of prostate cancer.Centro Nacional de Investigaciones Oncológicas (CNIO), which is funded by the Instituto de Salud Carlos III and recognized by the Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011033) as a ‘Severo Ochoa’ Centre of Excellence (ref. CEX2019000891-S). Both A.F.-S. and G.M. also received support from Spanish Ministry of Science and Innovation grants PID2019-111356RA-I00 and PID2023-151298OB-I00 (MCIN/AEI/ 10.13039/501100011033). Additionally, A.F.-S. was awarded a fellowship from La Caixa Foundation (ID 100010434; LCF/BQ/DR21/11880009). V.J.G. acknowledges infrastructure backing from the NIHR Cambridge Biomedical Research Centre (BRC-1215–20014). V.M.H. received support from the Petre Foundation via the University of Sydney Foundation (Australia). H.H.H. is supported by project grants from the Canadian Institutes of Health Research (CIHR) (142246, 152863, 152864, 159567 and 438793). This work was also funded by NHMRC project grants 1104010 (C.M.H., N.M.C.) and 1047581 (C.M.H., N.M.C.), as well as through a federal grant from the Australian Department of Health and Ageing awarded to the Epworth Cancer Centre, Epworth Hospital (N.M.C., C.M.H.). We acknowledge further financial support from Australian Prostate Cancer Research and the University of Melbourne, Australia. M.L. received funding from National Cancer Institute grants P50CA211024, P01CA265768, R01 CA259200, from the U.S.A. Department of Defense (DoD) grants PC160357 and PC200390, as well as from the Prostate Cancer Foundation (22CHAL05). Additional support for SAPCS analytical costs came from the U.S. National Institute of Health (NIH) National Cancer Institute (NCI) Award R01CA285772-01 and a U.S. Prostate Cancer Foundation (PCF) Challenge Award (2023CHAL4150). Genomic sequencing and investigation of Southern African Prostate Cancer Study (SAPCS) data received funding from the U.S. Congressionally Directed Medical Research Programs (CDMRP) Prostate Cancer Research Program (PCRP), which included an Idea Development Award (PC200390, TARGET Africa) and HEROIC Consortium Awards (PC210168 and PC230673, HEROIC PCaPH Africa1K). R.M. and A.T.P. are supported by The Lorenzo and Pamela Galli Medical Research Trust, and A.T.P. also holds an Investigator Grant (2026643) from the National Health and Medical Research Council (NHMRC). B.P. is the recipient of a Victorian Health and Medical Research Fellowship awarded by the Victoria State Government, Australia. K.D.S. is funded by The Novo Nordisk Foundation (grant nos. NNF20OC0059410, NNF21OC0071712), The Danish Cancer Society (grant no. R352-A20573), and Independent Research Fund Denmark (grant no. 9039-00084B). J.R. acknowledges support from a CIHR Project Grant (grant no. PJT-162410) and an Investigator Award from the Ontario Institute for Cancer Research (OICR), which is itself funded by the Government of Ontario. Work at the University of Konstanz was supported by the university and an Exploration Grant from the Boehringer Ingelheim Foundation to A.J.G. J.W. received grants from the Danish Cancer Society (#R147-A9843, #R374-A22518), the Danish Council for Independent Research (#8020-00282, #3101-00177A), the Novo Nordisk Foundation (#NNF200C0060141), and Sygeforsikringen Danmark (#2022-0198). A.L. is supported by Cancer Research UK (C57899/A25812), The John Fell Fund (0012782), the Health Technology Assessment (NIHR 131233), and the John Black Charitable Foundation (TRANSLATE Triallinked biobank). Y-J.L. receives funding from Orchid, Prostate Cancer Research UK & Movember (MA-CT20-011, RIA22-ST2-006) and Cancer Research UK (C16420/A18066). A full list of funding organizations for the Pan Prostate Cancer Group is provided in a companion manuscript [ref. 19: to be published as supplementary material in due course]

Rising compound hot-dry extremes engendering more inequality in human exposure risks

Data availability: Data analyzed during the current study is the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) gridded dataset distributed data archive [https://ds.nccs.nasa.gov/].Supplementary information is available online at: https://www.nature.com/articles/s44304-025-00119-x#Sec16 .Compound hot-dry events, with their amplified negative impacts on ecosystems and societies, are attracting growing attention. This study investigates the global-scale inequality and risks of hot-dry compound events under various shared socioeconomic pathways (SSP) scenarios, considering hazards, exposure, and vulnerability. Results show a worldwide increase in hot-dry extreme events and population exposure by mid-century (2041–2070), with variations among scenarios and regions. Climate factors are identified as the primary contributors to future changes in population exposure. SSP1-2.6 shows lower risks than SSP5-8.5 notably. Spatially, ASIA and the Middle East and Africa (MAF) will likely face higher exposure risks due to large populations, lower income levels and aging demographics, which amplify climate impacts. Under SSP3-7.0, rapid population growth introduces greater uncertainty in exposure estimates, particularly in ASIA, MAF, Latin America and the Caribbean (LAM). Aging populations, especially under SSP3-7.0 and SSP5-8.5 scenarios, exacerbate exposure risks through climate-demographic interactions.This paper is supported by the Academy of Medical Sciences, British Academy, Royal Academy of Engineering, Royal Society and the International Science Partnerships Fund (NGR2\1867)

Conceptual Aircraft Design and AI: Developing a functional relationship for the rapid realisation of future drone concepts

The use of Unmanned Aerial Vehicles(UAVs) has expanded rapidly over the last decade. These systems have an almost limitless scope of application with resupply, surveillance, monitoring, and logistics representing but a few. Having such a wide scope, a means to rapidly, efficiently and accurately develop new designs fit-for-purpose would offer a significant advantage to developers given their inherent need to maximise potential within a competitive marketplace. This paper attempts to leverage the capabilities of Artificial Intelligence(AI) for this purpose through the development of functional synergies to predict maximum rated engine power from limited inputs and datasets. Overall, the use of AI techniques was found to offer the potential to substantial improve and enhance the design process with also the possibility for the creation of more cost-effective and efficient software tools that could significantly streamline the process.The work was financially supported under project “DATA3: Drone Design using AI for Transport Applications 3(Grant No 10126519)” as part of the UKRI Innovate UK Feasibility studies for AI solutions: Series 2 competition

The Impact of Regulatory Changes on Rating Shopping and Rating Catering Behavior in the European Securitization Market

JEL classification: G21; G28.Data Availability: The dataset used is available from the corresponding author on request.A version of the article was developed as the Working Paper Series no. 2290, available online at: https://www.ecb.europa.eu/pub/pdf/scpwps/ecb.wp2920~f44cdd68b2.en.pdf . © European Central Bank, 2024. All rights reserved. Any reproduction, publication and reprint in the form of a different publication, whether printed or produced electronically, in whole or in part, is permitted only with the explicit written authorisation of the ECB or the authors. This paper can be downloaded without charge from https://www.ecb.europa.eu, from the Social Science Research Network electronic library ( https://ssrn.com/ ) or from RePEc: Research Papers in Economics. Information on all of the papers published in the ECB Working Paper Series can be found on the ECB’s website ( https://www.ecb.europa.eu/pub/research/working-papers/html/index.en.html ). PDF ISBN 978-92-899-6400-5 ISSN 1725-2806 doi:10.2866/23087 QB-AR-24-037-EN-N.We examine whether rating shopping and rating catering behaviors two mechanisms associated with credit rating inflation remained prevalent in the European securitization market following the Global Financial Crisis (GFC) and the subsequent introduction of regulatory reforms targeting credit rating agencies (CRAs). Using a dataset of 12,469 asset-backed security (ABS) tranches issued between 1998 and 2018, we analyze the information content of yield spreads at issuance and compare patterns across pre- and post-reform periods. Our findings suggest that rating catering is no longer reflected in pricing after the reforms, while indicators of rating shopping persist, particularly among tranches with fewer published ratings. We also find continued signs of investor over-reliance on ratings, especially for high-quality ABS. These results are consistent with a shift in investor perceptions and market practices post-GFC, although the extent to which this shift reflects regulatory changes versus broader crisis-related adjustments remains open to interpretation.The authors did not receive support from any organization for the submitted work