Seven adults with displaced radial head fractures had concurrent dislocation of the distal radioulnar joint. Because support of the radius was lost at both the elbow and wrist, proximal migration of the radius from 5 to 10 mm occurred. Different types of fractures were classified to designate the best method of restoring radial length to prevent chronic wrist pain and stiffness. Type I fractures had large displaced radial head fragments with minimal or no comminution and amenable to interfragmentary fixation. Type II fractures had severe comminution requiring radial head excision and prosthetic replacement. Type III were old injuries with irreducible proximal migration of the radius managed by ulnar shortening and radial head prosthetic replacement. There were three Type I, two Type II, and two Type III fractures. Results of treatment were graded as 3, excellent; 2, good; 1, fair; and 1, poor. The three excellent results were in patients in which restoration of radial length was achieved within one week of injury. Suboptimal results occurred in the remaining four patients when definitive surgery was delayed four to ten weeks. The poor result was in a patient treated only by radial head excision and who refused further surgery. Recommendations include meticulous clinical and roentgenographic examination of the distal radioulnar joint in all patients with displaced radial head fractures. Preservation of the radial head with anatomic reduction and rigid internal fixation is preferred, but radial head replacement may be necessary in cases with extensive comminution. Radial head excision alone, though contraindicated, may be restructured by ulnar shortening and radial head prosthetic replacement.
We first briefly report on the status and recent achievements of the ELPA-AEO (Eigenvalue Solvers for Petaflop Applications - Algorithmic Extensions and Optimizations) and ESSEX II (Equipping Sparse Solvers for Exascale) projects. In both collaboratory efforts, scientists from the application areas, mathematicians, and computer scientists work together to develop and make available efficient highly parallel methods for the solution of eigenvalue problems. Then we focus on a topic addressed in both projects, the use of mixed precision computations to enhance efficiency. We give a more detailed description of our approaches for benefiting from either lower or higher precision in three selected contexts and of the results thus obtained.
Optical-scan voting systems and their supporting ecosystem of people, processes, and technology are fallible. While a substantial body of work examines adversarial threats to such systems, we have encountered jurisdictions where the possibility of tabulator error is not fully internalized. Stakeholders there often find hypothetical attacks unconvincing, but some are persuaded by real-world accounts of equipment and procedural failures. This paper introduces a taxonomy of non-adversarial failure modes organized into intuitive categories: recording votes on paper, reading votes from the paper, combining votes as read into a reported outcome, and testing and verifying, all illustrated with documented incidents. We map common verification mechanisms against this taxonomy, identifying gaps that no paper-based audit can detect or correct, most notably failures that compromise the trustworthiness of the paper trail, such as giving voters the wrong ballot style (omitting contests they are eligible for, or including ones they are not), using ballot-marking devices to record votes, or failing to keep voted ballots secure and organized.
Despite explosive expansion of artificial intelligence based on artificial neural networks (ANNs), these are employed as "black boxes'', as it is unclear how, during learning, they form memories or develop unwanted features, including spurious memories and catastrophic forgetting. Much research is available on isolated aspects of learning ANNs, but due to their high dimensionality and non-linearity, their comprehensive analysis remains a challenge. In ANNs, knowledge is thought to reside in connection weights or in attractor basins, but these two paradigms are not linked explicitly. Here we comprehensively analyse mechanisms of memory formation in an 81-neuron Hopfield network undergoing Hebbian learning by revealing bifurcations leading to formation and destruction of attractors and their basin boundaries. We show that, by affecting evolution of connection weights, the applied stimuli induce a pitchfork and then a cascade of saddle-node bifurcations creating new attractors with their basins that can code true or spurious memories, and an abrupt disappearance of old memories (catastrophic forgetting). With successful learning, new categories are represented by the basins of newly b
This letter illustrates the opinion of the molecular dynamics (MD) community on the need to adopt a new FAIR paradigm for the use of molecular simulations. It highlights the necessity of a collaborative effort to create, establish, and sustain a database that allows findability, accessibility, interoperability, and reusability of molecular dynamics simulation data. Such a development would democratize the field and significantly improve the impact of MD simulations on life science research. This will transform our working paradigm, pushing the field to a new frontier. We invite you to support our initiative at the MDDB community (https://mddbr.eu/community/) Now published as: Amaro, R.E., et al. The need to implement FAIR principles in biomolecular simulations. Nat Methods (2025) https://doi.org/10.1038/s41592-025-02635-0
We demonstrate a method for radionuclide assay that is spectroscopic with 100 % counting efficiency for alpha decay. Advancing both cryogenic decay energy spectrometry (DES) and drop-on-demand inkjet metrology, a solution of Am-241 was assayed for massic activity (of order 100 kBq/g) with a relative combined standard uncertainty less than 1 %. We implement live-timed counting, spectroscopic analysis, validation by liquid scintillation (LS) counting, and confirmation of quantitative solution transfer. Experimental DES spectra are well modeled with a Monte Carlo simulation. The model was further used to simulate Pu-238 and Pu-240 impurities, calculate detection limits, and demonstrate the potential for tracer-free multi-nuclide analysis, which will be valuable for new cancer therapeutics based on decay chains, Standard Reference Materials (SRMs) containing impurities, and more widely in nuclear energy, environmental monitoring, security, and forensics.
We propose a simple estimator that allows to calculate the absolute value of a system's partition function from a finite sampling of its canonical ensemble. The estimator utilizes a volume correction term to compensate the effect that the finite sampling cannot cover the whole configuration space. As a proof of concept, the estimator is applied to calculate the partition function for several model systems, and the results are compared with the numerically exact solutions. Excellent agreement is found, demonstrating that a solution for an efficient calculation of partition functions is possible.
In this article we survey the main research topics of our group at the University of Essex. Our research interests lie at the intersection of theoretical computer science, artificial intelligence, and economic theory. In particular, we focus on the design and analysis of mechanisms for systems involving multiple strategic agents, both from a theoretical and an applied perspective. We present an overview of our group's activities, as well as its members, and then discuss in detail past, present, and future work in multi-agent systems.
We present a Quality by Design (QbD) styled approach for optimizing lipid nanoparticle (LNP) formulations, aiming to offer scientists an accessible workflow. The inherent restriction in these studies, where the molar ratios of ionizable, helper, and PEG lipids must add up to 100%, requires specialized design and analysis methods to accommodate this mixture constraint. Focusing on lipid and process factors that are commonly used in LNP design optimization, we provide steps that avoid many of the difficulties that traditionally arise in the design and analysis of mixture-process experiments by employing space-filling designs and utilizing the recently developed statistical framework of self-validated ensemble models (SVEM). In addition to producing candidate optimal formulations, the workflow also builds graphical summaries of the fitted statistical models that simplify the interpretation of the results. The newly identified candidate formulations are assessed with confirmation runs and optionally can be conducted in the context of a more comprehensive second-phase study.
This paper presents the results of a series of penetration tests performed on the OpenStack Essex Cloud Management Software. Several different types of penetration tests were performed including network protocol and command line fuzzing, session hijacking and credential theft. Using these techniques exploitable vulnerabilities were discovered that could enable an attacker to gain access to restricted information contained on the OpenStack server, or to gain full administrative privileges on the server. Key recommendations to address these vulnerabilities are to use a secure protocol, such as HTTPS, for communications between a cloud user and the OpenStack Horizon Dashboard, to encrypt all files that store user or administrative login credentials, and to correct a software bug found in the OpenStack Cinder typedelete command.
When noisy intermediate scalable quantum (NISQ) devices are applied in information processing, all of the stages through preparation, manipulation, and measurement of multipartite qubit states contain various types of noise that are generally hard to be verified in practice. In this work, we present a scheme to deal with unknown quantum noise and show that it can be used to mitigate errors in measurement readout with NISQ devices. Quantum detector tomography that identifies a type of noise in a measurement can be circumvented. The scheme applies single-qubit operations only, that are with relatively higher precision than measurement readout or two-qubit gates. A classical post-processing is then performed with measurement outcomes. The scheme is implemented in quantum algorithms with NISQ devices: the Bernstein-Vazirani algorithm and a quantum amplitude estimation algorithm in IBMQ yorktown and IBMQ essex. The enhancement in the statistics of the measurement outcomes is presented for both of the algorithms with NISQ devices.
The 4th edition of the Montreal AI Ethics Institute's The State of AI Ethics captures the most relevant developments in the field of AI Ethics since January 2021. This report aims to help anyone, from machine learning experts to human rights activists and policymakers, quickly digest and understand the ever-changing developments in the field. Through research and article summaries, as well as expert commentary, this report distills the research and reporting surrounding various domains related to the ethics of AI, with a particular focus on four key themes: Ethical AI, Fairness & Justice, Humans & Tech, and Privacy. In addition, The State of AI Ethics includes exclusive content written by world-class AI Ethics experts from universities, research institutes, consulting firms, and governments. Opening the report is a long-form piece by Edward Higgs (Professor of History, University of Essex) titled "AI and the Face: A Historian's View." In it, Higgs examines the unscientific history of facial analysis and how AI might be repeating some of those mistakes at scale. The report also features chapter introductions by Alexa Hagerty (Anthropologist, University of Cambridge), Mariann
Human face recognition is, indeed, a challenging task, especially under the illumination and pose variations. We examine in the present paper effectiveness of two simple algorithms using coiflet packet and Radon transforms to recognize human faces from some databases of still gray level images, under the environment of illumination and pose variations. Both the algorithms convert 2-D gray level training face images into their respective depth maps or physical shape which are subsequently transformed by Coiflet packet and Radon transforms to compute energy for feature extraction. Experiments show that such transformed shape features are robust to illumination and pose variations. With the features extracted, training classes are optimally separated through linear discriminant analysis (LDA), while classification for test face images is made through a k-NN classifier, based on L1 norm and Mahalanobis distance measures. Proposed algorithms are then tested on face images that differ in illumination,expression or pose separately, obtained from three databases,namely, ORL, Yale and Essex-Grimace databases. Results, so obtained, are compared with two different existing algorithms.Performa
The AI chatbot was more effective at creating “exploitable trust” than the humans
Scientists have created an “electron lighthouse” that uses laser light to launch and steer electrons through a semiconductor without an applied electrical field。 The quantum effect could eventually improve optical sensors, communications, imaging, and information storage
A quantum problem once described as impossible for classical computers has now been solved using relatively modest hardware。 Researchers used tensor networks to compress the overwhelming wave function created by hundreds of entangled qubits, allowing some calculations to run on a laptop。 Their results matched both theoretical predictions and simula
Rice University chemists have found a new way to make neodymium, a rare-earth metal, interact with oxygen。 Using a specially designed molecular structure described as a “basket,” the team positioned the atoms so they could form a bond once thought unlikely。 The breakthrough produced highly reactive compounds that could eventually give chemists alte
Dark matter particles may exert a hidden force on one another, but its effects are stranger than expected。 An extra attraction helps the particles cluster, yet it also makes dark matter effectively lighter as the Universe expands。 That weakens its gravitational impact and usually slows the growth of cosmic structure rather than accelerating it
Bayesian Networks (BN) are probabilistic graphical models that are widely used for uncertainty modeling, stochastic prediction and probabilistic inference. A Quantum Bayesian Network (QBN) is a quantum version of the Bayesian network that utilizes the principles of quantum mechanical systems to improve the computational performance of various analyses. In this paper, we experimentally evaluate the performance of QBN on various IBM QX hardware against Qiskit simulator and classical analysis. We consider a 4-node BN for stock prediction for our experimental evaluation. We construct a quantum circuit to represent the 4-node BN using Qiskit, and run the circuit on nine IBM quantum devices: Yorktown, Vigo, Ourense, Essex, Burlington, London, Rome, Athens and Melbourne. We will also compare the performance of each device across the four levels of optimization performed by the IBM Transpiler when mapping a given quantum circuit to a given device. We use the root mean square percentage error as the metric for performance comparison of various hardware.
A black hole observed during a dramatic 2023 eruption did not simply devour gas from its nearby companion star。 It also expelled large amounts of material through powerful jets and winds, even after the outburst had nearly faded。 The results suggest black holes may continue reshaping their surroundings long after their brightest fireworks end