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Understanding the drivers of recent change at Greenlandic tidewater glaciers is of great importance if we are to predict how these glaciers will respond to climatic warming. A poorly constrained component of tidewater glacier processes is the near-terminus subglacial hydrology. Here we present a novel method for constraining near-terminus subglacial hydrology with application to marine-terminating Kangiata Nunata Sermia in South-west Greenland. By simulating proglacial plume dynamics using buoyant plume theory and a general circulation model, we assess the critical subglacial discharge, if delivered through a single compact channel, required to generate a plume that reaches the fjord surface. We then compare catchment runoff to a time series of plume visibility acquired from a time-lapse camera. We identify extended periods throughout the 2009 melt season where catchment runoff significantly exceeds the discharge required for a plume to reach the fjord surface, yet we observe no plume. We attribute these observations to spatial spreading of runoff across the grounding line. Persistent distributed drainage near the terminus would lead to more spatially homogeneous submarine melting and may promote more rapid basal sliding during warmer summers, potentially providing a mechanism independent of ocean forcing for increases in atmospheric temperature to drive tidewater glacier acceleration.

Glomus tumors are rare mesenchymal neoplasms of the subcutaneous tissue, most frequently found in the distal extremities. They are typically benign, but malignant glomus tumors have been described in the literature. Here we present a patient found to have a unilateral renal mass with pathology displaying a primary renal glomus tumor with malignant features. Review of the literature reveals only three cases of malignant glomus tumors and five glomus tumors with malignant potential. As such, previous initial presentations, current criteria for glomus tumor malignancy, and previous treatment outcomes of these cases were reviewed.

Tool condition monitoring (TCM) is crucial to ensure good quality products and avoid downtime. Machine learning has proven to be vital for TCM. However, existing works are predominately based on supervised learning, which hinders their applicability in real-world manufacturing settings, where data labeling is cumbersome and costly with in-service machines. Additionally, the existing unsupervised solutions mostly handle binary decision-based TCM which is unable to fully reflect the dynamics of tool wear progression. To address these issues, we propose different unsupervised and semisupervised five-class tool wear recognition frameworks to handle fully unlabeled and partially labeled data, respectively. The underlying methods include Laplacian score, sparse autoencoder (SAE), stacked SAE (SSAE), self-organizing map, Softmax, support vector machine, and random forest. For the semisupervised frameworks, we considered designs where labeled data influence only feature learning, classifier building, or both. We also investigated different training configurations of SSAE regarding the supervision level. We applied the frameworks on two run-to-failure datasets of milling tools, recorded using a microphone and an accelerometer. Single sensor and multisensor data under different percentages of labeled training data were considered in the evaluation. The results showed which of the frameworks led to the best predictive performance under which data settings, and highlighted the significance of sensor fusion and discriminative feature representations in combating the unavailability and scarcity of labels, among other findings. The highest macro-F1 achieved for the two datasets with fully unlabeled data reached 87.52% and 75.80%, respectively, and over 90% when only 25% of the training observations were labeled.

Neighbourhood watch is a concept allowing a community to distribute a complex security task in between all members. Members carry out security tasks in a distributed and cooperative manner ensuring their mutual security and reducing the individual workload while increasing the overall security of the community. Wireless sensor networks (WSNs) are composed of resource-constraint independent battery driven computers as nodes communicating wirelessly. Security in WSNs is essential to prevent attackers from eavesdropping, tampering monitoring results or denying critical nodes from providing their services and potentially cutting off larger network parts. The resource-constraint nature of sensor nodes prevents them from running full-fledged security protocols. Instead, it is necessary to assess the most significant security threats and implement specialised security solutions. A neighbourhood watch inspired distributed security scheme for WSNs has been introduced by Langendörfer aiming to increase the variety of attacks a WSN can fend off. The framework intends to statically distribute requirement-based selections of online security means intended to cooperate in close proximity on large-scale static homogeneous WSNs. A framework of such complexity has to be designed in multiple steps. We determine suitable distributions of security means based on graph partitioning concepts. The partitioning algorithms we provide are NP-hard. To evaluate their computability, we implement them as 0 – 1 linear programs (LPs) and test them on WSN models generated with our novel $\lambda $ -precision unit disk graph (UDG) generator.

Open-source Programmable Logic Controller (OpenPLC) software is designed to be vendor-natural and run on almost any computer or low-cost embedded devices e.g., Raspberry Pi, Ardunio, and other controllers. The aim of this project is to introduce an affordable and practical alternative solution for the high-cost of real hardware PLCs, and has successfully gained substantial interest within both the research and industrial communities. Due to its popularity grows, understanding its security vulnerabilities and implementing effective mitigation strategies become crucial. Through a combination of threat modeling, vulnerability analysis, and practical experiments, this article provides valuable insights for developers, researchers, and engineers aiming to deploy OpenPLC securely in industrial environments. To this end, we first conducted an in-depth analysis aimed to shed light on various security challenges and vulnerabilities within the OpenPLC project. These encompass issues such as unauthorized access, vulnerabilities in communication protocols, concerns regarding data integrity, the absence of robust encryption mechanisms, etc. After that, we showed the research community what the consequences of those vulnerabilities would be if they are exploited. To this end, we performed a sophisticated control logic injection attack that maliciously modifies the user program run on the OpenPLC Runtime. Our injection was stealthy and not detected by the legitimate user. Finally, we introduced a security-enhanced OpenPLC software called OpenPLC Aqua. Our developed software is equipped with a set of security solutions designed specifically to address the vulnerabilities to which current OpenPLC versions are prone. All our attack codes as well OpenPLC Aqua software are publically available.

Programmable logic controllers (PLCs) are indispensable in critical infrastructures and industrial control systems. The increasing demand for enhanced cost-effectiveness and production efficiency has driven automation manufacturers to integrate PLC-based applications and systems with external networks, such as Internet. Unfortunately, this connectivity has exposed systems to potential malicious attacks from motivated adversaries. Addressing this pressing issue necessitates a comprehensive summary of ongoing research related to PLCs and their related systems. This summary should classify these systems based on disclosed vulnerabilities, potential threats, and proposed security solutions, catering to both scientists and industrial engineers. While several recent surveys have reviewed and discussed PLC security and related topics, they often fell short of covering all essential aspects comprehensively. Furthermore, prior surveys tended to focus on analyzing vulnerabilities at the system level, overlooking the vulnerabilities specific to PLCs themselves. Consequently, their findings failed to effectively secure current operational systems or propose improved solutions for future PLC designs. In this article, we bridge this research gap by providing a detailed review of all aspects concerning the security of PLCs and related systems. This includes vulnerabilities, potential attacks, and security solutions including digital forensics. We aim to offer a precise analysis, addressing the shortcomings of previous studies. Finally, we conclude this article by presenting our recommendations tailored for PLC manufacturers, researchers, and engineers. We hope that these recommendations will contribute to the development of more secure PLCs in the future.

Many technical solutions are bio-inspired. Octopus-inspired robotic arms belong to continuum robots which are used in minimally invasive surgery or for technical system restoration in areas difficult-to-access. Continuum robot missions are bounded with their motions, whereby the motion of the robots is controlled by humans via wireless communication. In case of a lost connection, robot autonomy is required. Distributed control and distributed decision-making mechanisms based on artificial intelligence approaches can be a promising solution to achieve autonomy of technical systems and to increase their resilience. However these methods are not well investigated yet. Octopuses are the living example of natural distributed intelligence but their learning and decision-making mechanisms are also not fully investigated and understood yet. Our major interest is investigating mechanisms of Distributed Artificial Intelligence as a basis for improving resilience of complex systems. We decided to use a physical continuum robot prototype that is able to perform some basic movements for our research. The idea is to research how a technical system can be empowered to combine movements into sequences of motions by itself. For the experimental investigations a suitable physical prototype has to be selected, its motion control has to be implemented and automated. In this paper, we give an overview combining different fields of research, such as Distributed Artificial Intelligence and continuum robots based on 98 publications. We provide a detailed description of the basic motion control models of continuum robots based on the literature reviewed, discuss different aspects of autonomy and give an overview of physical prototypes of continuum robots.

Programmable logic controllers (PLCs) make up a substantial part of critical infrastructures (CIs) and industrial control systems (ICSs). They are programmed with a control logic that defines how to drive and operate critical processes such as nuclear power plants, petrochemical factories, water treatment systems, and other facilities. Unfortunately, these devices are not fully secure and are prone to malicious threats, especially those exploiting vulnerabilities in the control logic of PLCs. Such threats are known as control logic injection attacks. They mainly aim at sabotaging physical processes controlled by exposed PLCs, causing catastrophic damage to target systems as shown by Stuxnet. Looking back over the last decade, many research endeavors exploring and discussing these threats have been published. In this article, we present a flashback on the recent works related to control logic injection attacks against PLCs. To this end, we provide the security research community with a new systematization based on the attacker techniques under three main attack scenarios. For each study presented in this work, we overview the attack strategies, tools, security goals, infected devices, and underlying vulnerabilities. Based on our analysis, we highlight the current security challenges in protecting PLCs from such severe attacks and suggest security recommendations for future research directions.

Abstract Background The burden of the COVID-19 pandemic resulted in a reduction of available health care capacity for regular care. To guide prioritisation of semielective surgery in times of scarcity, we previously developed a decision model to quantify the expected health loss due to delay of surgery, in an academic hospital setting. The aim of this study is to validate our decision model in a nonacademic setting and include additional elective surgical procedures. Methods In this study, we used the previously published three-state cohort state-transition model, to evaluate the health effects of surgery postponement for 28 surgical procedures commonly performed in nonacademic hospitals. Scientific literature and national registries yielded nearly all input parameters, except for the quality of life (QoL) estimates which were obtained from experts using the Delphi method. Two expert panels, one from a single nonacademic hospital and one from different nonacademic hospitals in the Netherlands, were invited to estimate QoL weights. We compared estimated model results (disability adjusted life years (DALY)/month of surgical delay) based on the QoL estimates from the two panels by calculating the mean difference and the correlation between the ranks of the different surgical procedures. The eventual model was based on the combined QoL estimates from both panels. Results Pacemaker implantation was associated with the most DALY/month of surgical delay (0.054 DALY/month, 95% CI: 0.025–0.103) and hemithyreoidectomy with the least DALY/month (0.006 DALY/month, 95% CI: 0.002–0.009). The overall mean difference of QoL estimates between the two panels was 0.005 (95% CI -0.014–0.004). The correlation between ranks was 0.983 (p

While IoT technology makes industries, cities, and homes smarter, it also opens the door to security risks. With the right equipment and physical access to the devices, the attacker can leverage side-channel information, like timing, power consumption, or electromagnetic emanation, to compromise cryptographic operations and extract the secret key. This work presents a side channel analysis of a cryptographic hardware accelerator for the Elliptic Curve Scalar Multiplication operation, implemented in a Field-Programmable Gate Array and as an Application-Specific Integrated Circuit. The presented framework consists of initial key extraction using a state-of-the-art statistical horizontal attack and is followed by regularized Artificial Neural Networks, which take, as input, the partially incorrect key guesses from the horizontal attack and correct them iteratively. The initial correctness of the horizontal attack, measured as the fraction of correctly extracted bits of the secret key, was improved from 75% to 98% by applying the iterative learning.

Programmable Logic Controllers (PLCs) are increasingly connected and integrated into the Industrial Internet of Things (IIoT) for a better network connectivity and a more streamlined control process. But in fact, this brings also its security challenges and exposes them to various cyber-attacks targeting the physical process controlled by such devices. In this work, we investigate whether the newest S7 PLCs are vulnerable by design and can be exploited. In contrast to the typical control logic injection attacks existing in the research community, which require from adversaries to be online along the ongoing attack, this article introduces a new exploit strategy that aims at disrupting the physical process controlled by the infected PLC when adversaries are not connected neither to the target nor to its network at the point zero for the attack. Our exploit approach is comprised of two steps: 1) Patching the PLC with a malicious Time-of-Day interrupt block once an attacker gains access to an exposed PLC, 2) Triggering the interrupt at a later time on the attacker will, when he is disconnected to the system’s network. For a real attack scenario, we implemented our attack approach on a Fischertechnik training system based on S7-1500 PLC using the latest version of S7CommPlus protocol. Our experimental results showed that we could keep the patched interrupt block in idle mode and hidden in the PLC memory for a long time without being revealed before being activated at the specific date and time that the attacker defined. Finally, we suggested some potential security recommendations to protect industrial environments from such a threat.

Abstract Background Recent developments in machine learning have shown its potential impact for clinical use such as risk prediction, prognosis, and treatment selection. However, relevant data are often scattered across different stakeholders and their use is regulated, e.g. by GDPR or HIPAA. As a concrete use-case, hospital Erasmus MC and health insurance company Achmea have data on individuals in the city of Rotterdam, which would in theory enable them to train a regression model in order to identify high-impact lifestyle factors for heart failure. However, privacy and confidentiality concerns make it unfeasible to exchange these data. Methods This article describes a solution where vertically-partitioned synthetic data of Achmea and of Erasmus MC are combined using Secure Multi-Party Computation. First, a secure inner join protocol takes place to securely determine the identifiers of the patients that are represented in both datasets. Then, a secure Lasso Regression model is trained on the securely combined data. The involved parties thus obtain the prediction model but no further information on the input data of the other parties. Results We implement our secure solution and describe its performance and scalability: we can train a prediction model on two datasets with 5000 records each and a total of 30 features in less than one hour, with a minimal difference from the results of standard (non-secure) methods. Conclusions This article shows that it is possible to combine datasets and train a Lasso regression model on this combination in a secure way. Such a solution thus further expands the potential of privacy-preserving data analysis in the medical domain.

With the increasing popularity of IoT (Internet-of-Things) devices, their security becomes an increasingly important issue. Buffer overflow vulnerabilities have been known for decades, but are still relevant, especially for embedded devices where certain security measures cannot be implemented due to hardware restrictions or simply due to their impact on performance. Therefore, many buffer overflow detection mechanisms check for overflows only before critical data are used. All data that an attacker could use for his own purposes can be considered critical. It is, therefore, essential that all critical data are checked between writing a buffer and its usage. This paper presents a vulnerability of the ESP32 microcontroller, used in millions of IoT devices, that is based on a pointer that is not protected by classic buffer overflow detection mechanisms such as Stack Canaries or Shadow Stacks. This paper discusses the implications of vulnerability and presents mitigation techniques, including a patch, that fixes the vulnerability. The overhead of the patch is evaluated using simulation as well as an ESP32-WROVER-E development board. We showed that, in the simulation with 32 general-purpose registers, the overhead for the CoreMark benchmark ranges between 0.1% and 0.4%. On the ESP32, which uses an Xtensa LX6 core with 64 general-purpose registers, the overhead went down to below 0.01%. A worst-case scenario, modeled by a synthetic benchmark, showed overheads up to 9.68%.

Feature selection (FS) represents an essential step for many machine learning-based predictive maintenance (PdM) applications, including various industrial processes, components, and monitoring tasks. The selected features not only serve as inputs to the learning models but also can influence further decisions and analysis, e.g., sensor selection and understandability of the PdM system. Hence, before deploying the PdM system, it is crucial to examine the reproducibility and robustness of the selected features under variations in the input data. This is particularly critical for real-world datasets with a low sample-to-dimension ratio (SDR). However, to the best of our knowledge, stability of the FS methods under data variations has not been considered yet in the field of PdM. This paper addresses this issue with an application to tool condition monitoring in milling, where classifiers based on support vector machines and random forest were employed. We used a five-fold cross-validation to evaluate three popular filter-based FS methods, namely Fisher score, minimum redundancy maximum relevance (mRMR), and ReliefF, in terms of both stability and macro-F1. Further, for each method, we investigated the impact of the homogeneous FS ensemble on both performance indicators. To gain broad insights, we used four (2:2) milling datasets obtained from our experiments and NASA’s repository, which differ in the operating conditions, sensors, SDR, number of classes, etc. For each dataset, the study was conducted for two individual sensors and their fusion. Among the conclusions: (1) Different FS methods can yield comparable macro-F1 yet considerably different FS stability values. (2) Fisher score (single and/or ensemble) is superior in most of the cases. (3) mRMR’s stability is overall the lowest, the most variable over different settings (e.g., sensor(s), subset cardinality), and the one that benefits the most from the ensemble.

Anomaly detection modeled as a one-class classification is an essential task for tool condition monitoring (TCM) when only the normal data are available. To confront with the real-world settings, it is crucial to take the different operating conditions, e.g., rotation speed, into account when approaching TCM solutions. This work mainly addresses issues related to multi-operating-condition TCM models, namely the varying discriminability of sensory features with different operating conditions; the overlap between normal and anomalous data; and the complex structure of input data. A feature selection scheme is proposed in which the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is presented as a tool to aid the multi-objective selection of sensory features. In addition, four anomaly detection approaches based on Self-Organizing Map (SOM) are studied. To examine the stability of the four approaches, they are applied on different single-operating-condition models. Further, to examine their robustness when dealing with complex data structures, they are applied on multi-operating-condition models. The experimental results using the NASA Milling Data Set showed that all the studied anomaly detection approaches achieved a higher assessment accuracy with our feature selection scheme as compared to the Principal Component Analysis (PCA), Laplacian Score (LS), and extended LS in which we added a final step to the original LS method in order to eliminate redundant features.

Many studies showed the feasibility of detecting Freezing of Gait (FOG) of Parkinson’s patients by using several numbers of inertial sensors worn on the body and in back-end computing power. This work uses machine learning approaches for analyzing the data of one single body-worn inertial sensor system to classify and detect FOG. Long-Short-Term-Memory (LSTM) is employed as the FOG detection algorithm and the Daphnet (FOG and normal gait) dataset provides the data for model training and testing in this paper. The model considers raw data from three channels of the acceleration sensor mounted on the patient’s shank and ignores all other data from other sensors. The model is patient dependent and uses sensitivity and specificity metrics to evaluate the model’s performance. In this paper, we propose a novel padding method that is applied to the windows of FOG and non-FOG with zero overlaps on the training set and adapts the padding to the individual regions. This method produces windows of only one type of data and label. The proposed padding method reduces the padding amount by two orders of magnitude compared to bigger batch sizes in the sequence splitting method offered by MATLAB 2019a. The padding amount is independent of the batch size. Raw data is fed to the model in the testing mode without any pre-processing or data transformation. The standard rolling window generates fixed-size windows for the test set without overlap and the higher amount of FOG or Normal walking data which defines the label of the individual window. The model for one-second long windows applied in this work outperformed the literature results with a sensitivity of 92.57% and a specificity of 95.62% compared to 82% and 94% reported by Masiala et al.