Miracle-dz/UniFAR-FTUs huggingface dataset:
Post-Processing of Cold Sprayed CoNiCrAlY Coatings on Inconel 718 by Rapid Induction Heating

Cold spray is a solid-state additive manufacturing process that has been increasingly used for restoration of damaged parts. Due to the nature of powder solid-state bonding, cold spray coatings usually possess pores in the microstructures. Heat treatment has been widely used as a post-processing method to reduce the porosity of cold sprayed coatings. However, it usually requires the whole component to be treated in the furnace and typically takes a few hours to finish the treatment. This study aims to develop a localized and rapid post-processing method for cold sprayed coatings. Multi-layer cold sprayed coatings of CoNiCrAlY on an Inconel 718 substrate were produced. Then the coatings were heat treated at 800 °C, 900 °C, 1000 °C and 1100 °C by rapid induction heating. The porosity level of the coatings reduced significantly after induction heating for a short period of merely 10 min. This is attributed to the preferential Joule heating at pore regions, which results in pore closures within the coatings. The potential application of induction heating as a rapid post-processing method for cold spray coatings has been demonstrated in this work.
A Linear Programming Model for Latency Minimization and Fault Tolerance in Software Defined Network Controller Placement

This study proposes a mathematical model for the controller placement problem(CPP) in Software Defined Networks (SDN). For SDN networks, multiple controllers are often suitable, and the location of these controllers affects the network performance. In case the controller is at fault, the resilience and reliability of the network should be ensured. Therefore, the recommended model optimizes the number and location of the controller(s) in the network while controller(s) is at fault. The model aims to minimize the latency by considering the reliability and resilience of the network. The simulation results show that the average latency is slightly increased whilst providing network resilience. The proposed model can be used to integrate SDN into existing networks or plan a new SDN network.
Cost-Effectiveness Analysis from a Societal Perspective of Recurrence Index for Distant Recurrence (RecurIndex) in Women with Hormone Receptor-Positive and HER2-Negative Early-Stage Breast Cancer

Purpose A clinical-genomic prognostic multigene panel (RI-DR assay, RecurIndex®), predicting the risk level of distant recurrence (DR) in early-stage breast cancer (EBC) patients with an Asian background, has been validated as a valuable tool for identifying high-risk patients to develop distant recurrence (metastasis). Although the clinical benefit of adjuvant chemotherapy from the assay’s prediction is already proved, its affordability remains uncertain. This study is the first time in which the long-term cost-effectiveness of the RI-DR assay is evaluated. Patients and Methods A lifetime Markov decision-analytic model was developed from a societal perspective to estimate the life-years gained (LYGs), quality-adjusted life-years (QALYs), medical costs, and incremental cost-effectiveness ratios (ICERs), comparing EBC women with and without RI-DR genomic testing. A decision tree was used to classify patients in one of the fifteen end nodes (by order, each arm was stratified by a patient being tested or not with the RI-DR assay, being treated or not with adjuvant chemotherapy and had no, minor, major, or fatal toxicity after adjuvant chemotherapy). Health utilities, costs, transition probabilities, and survival data were extracted from the scientific literature. Deterministic sensitivity analysis (DSA) and probabilistic sensitivity analysis (PSA) were performed on variables to assess the robustness of the model. A willingness-to-pay (WTP) threshold of 790,000 NT$ per QALY gained was considered as a cost-effectiveness criterion. Results The incremental cost per QALY gained under base-case assumptions of the model was 173,842 NT$. Findings on the variation in model input parameters were robust and confirmed that every key variable was cost-effective for the benefit of RI-DR testing. Conclusion The clinical-genomic RI-DR assay is cost-effective in guiding adjuvant chemotherapy decisions compared to current clinical practice guidelines.
Four-Frequency Small-Signal Model for High-Bandwidth Voltage Regulator With Current-Mode Control

High-bandwidth buck voltage regulator with peak current-mode control is a commonly-used scheme, especially for CPU power applications. However, the conventional single-frequency and three-frequency models are inaccurate in modeling the loop gain stability when the control bandwidth is close or higher than one-third of the converter switching frequency. A more accurate four-frequency small-signal model for a high-bandwidth buck converter with peak current-mode control is proposed in the present paper. The describing function approach is used to model the duty-cycle modulator. The effects of the four-frequency components, including the modulating frequency, the two sideband harmonics, and the switching frequency, are considered. Simulations and experiments verified the results.
Hyaluronic Acid–Zein Core-Shell Nanoparticles Improve the Anticancer Effect of Curcumin Alone or in Combination with Oxaliplatin against Colorectal Cancer via CD44-Mediated Cellular Uptake

Curcumin (CUR) has been reported to enhance the chemotherapeutic efficacy of oxaliplatin (OXA) in colorectal cancer (CRC) and inhibit OXA-induced side effects. However, shortcomings, including poor solubility and sensitivity to metabolic transformation, have greatly undermined its value in clinical applications. In this study, the potential of CUR-encapsulated hyaluronic acid (HA)–zein composite nanoparticles (HZ-CUR) as an oral adjuvant for OXA-based chemotherapy was assessed in representative CRC models in mice. Cell viability and colony formation assays in three human CRC cell lines showed that HZ-CUR had a stronger anti-CRC effect than free CUR when given alone and a stronger synergistic effect when combined with OXA, especially in HCT116 and HT29 cell lines. Western blotting, cellular uptake, and RNA interference assays revealed that OXA-induced upregulation of CD44 likely contributed to enhanced cellular uptake of HZ-CUR and thus the enhanced anticancer effect. The significantly improved anti-CRC effects and potential underlying mechanism of HZ-CUR alone and in combination with OXA were further validated in a subcutaneous xenograft and an in situ CRC model in mice. These findings support that HZ-CUR may be an effective oral adjuvant for OXA-based CRC chemotherapy that would not only improve its efficacy but also help reduce the associated side effects.
Brokerage System for Integration of LrWPAN Technologies

The prevalent demand for remote data sharing and connectivity has catalysed the development of many wireless network technologies. However, low-power and low-rate wireless network technologies have emerged as the preferred choice (due to cheap procurement and maintenance cost, efficiency, and adaptability). Currently, these groups of wireless networks are adopted in homes, health, and business sectors. The increase in existing WSNs has resulted in the incompatibility of wireless network protocols and poses a problem that results in high acquisition or maintenance costs, increased complexity, reliability inadequacies in some instances, lack of uniformity within similar standards, and high energy consumption. To address this problem, we develop a novel machine-to-machine software-based brokerage application (known as JosNet) for interoperability and integration between Bluetooth LE, Zigbee, and Thread wireless network technologies. JosNet allows one network protocol to exchange data packets or commands with each other. In this paper, we present a novel working network brokerage model for a one-to-one network protocol to communication (e.g., from Zigbee to Bluetooth) or one-to-many network protocol communication (e.g., from Bluetooth to Zigbee, Thread, etc.) to securely send messages in a large-scale routing process for short or long-range connections. We also present a large-scale implementation of JosNet using a routing table for large areas. The results show an industry standard performance for end-to-end latency time and throughput.
Analysis and modelling of mistriage in the Stockholm stroke triage system

Introduction The Stockholm Stroke Triage System (SSTS) is a prehospital triage system for detection of patients eligible for endovascular thrombectomy (EVT). Assessment of hemiparesis combined with ambulance-hospital teleconsultation is used to route patients directly to the thrombectomy centre. Some patients are not identified and require secondary transport for EVT (undertriage) while others taken to the thrombectomy centre do not undergo EVT (overtriage). The aims of this study were to characterize mistriaged patients, model for and evaluate alternative triage algorithms. Patients and methods Patients with suspected stroke transported by priority 1 ground ambulance between October 2017 and October 2018 (n = 2905) were included. Three triage algorithms were modelled using prehospital data. Decision curve analysis was performed to calculate net benefit (correctly routing patients for EVT without increasing mistriage) of alternative models vs SSTS. Results Undertriage for EVT occurred in n = 35/2582 (1.4%) and overtriage in n = 239/323 (74.0%). Compared to correct thrombectomy triages, undertriaged patients were younger and had lower median NIHSS (10 vs 18), despite 62.9% with an M1 occlusion. In overtriaged patients, 77.0% had a stroke diagnosis (29.7% haemorrhagic). Hemiparesis and FAST items face and speech were included in all models. Decision curve analysis showed highest net benefit for SSTS for EVT, but lower for large artery occlusion (LAO) stroke. Discussion Undertriaged patients had lower NIHSS, likely due to better compensated proximal occlusions. SSTS was superior to other models for identifying EVT candidates, but lacked information allowing comparison to other prehospital scales. Conclusion Using prehospital data, alternative models did not outperform the SSTS in finding EVT candidates.
High-Risk Clone of Klebsiella pneumoniae Co-Harbouring Class A and D Carbapenemases in Italy

Background: Carbapenem-resistant Klebsiella pneumoniae (CR-Kp) is endemic globally, causing severe infections in hospitalized patients. Surveillance programs help monitor and promptly identify the emergence of new clones. We reported the rapid spread of a novel clone of K. pneumoniae co-harbouring class A and D carbapenemases in colonized patients, and the potential risk factors involved in the development of infections. Methods: Rectal swabs were used for microbiological analyses and detection of the most common carbapenemase encoding genes by real-time PCR (i.e., blaKPC, blaOXA-48, blaNDM, blaVIM, and blaIMP). All strains co-harbouring KPC and OXA-48 genes were evaluated. For each patient, the following variables were collected: age, sex, length and ward of stay, device use, and outcome. Clonality of CR-Kp was assessed by preliminary pulsed field gel electrophoresis (PFGE), followed by multi-locus sequence typing (MLST) analyses. Results: A total of 127 isolates of K. pneumoniae co-harbouring KPC and OXA-48 were collected between September 2019 and December 2020. The median age (IQR) of patients was 70 (61–77). More than 40% of patients were admitted to intensive care unit (ICU). Around 25% of patients developed an invasive infection, the majority of which were respiratory tract infections (17/31; 54.8%). ICU stay and invasive infection increased the risk of mortality (OR: 5.39, 95% CI: 2.42–12.00; OR 6.12, 95% CI: 2.55–14.69, respectively; p-value ≤ 0.001). The antibiotic susceptibility test showed a resistance profile for almost all antibiotics considered. Monoclonal origin was confirmed by PFGE and MLST showing a similar restriction pattern and belonging to ST-512. Conclusions: We report the spread and the marked antibiotic resistance profiles of K. pneumoniae strains co-producing KPC and OXA-48. Further study could clarify the roles of clinical and microbiological variables in the development of invasive infection and increasing risk of mortality, in colonized patients.
Care Infrastructures for Digital Security in Intimate Partner Violence

Survivors of intimate partner violence (IPV) face complex threats to their digital privacy and security. Prior work has established protocols for directly helping them mitigate these harms; however, there remains a need for flexible and pluralistic systems that can support survivors’ long-term needs. This paper describes the design and development of sociotechnical infrastructure that incorporates feminist notions of care to connect IPV survivors experiencing technology abuse with volunteer computer security consultants. We present findings from a mixed methods study that draws on data from an 8-month, real-world deployment, as well as interviews with 7 volunteer technology consultants and 18 IPV professionals. Our findings illuminate emergent challenges in safely and adaptively providing computer security advice as care. We discuss implications of these findings for feminist approaches to computer security and privacy, and provide broader lessons for interventions that aim to directly assist at-risk and marginalized people experiencing digital insecurity.
Performance Characterization of Serverless Computing

Serverless computing has seen rapid adoption because of its instant scalability, ﬂex-ible billing model, and economies of scale. In serverless, developers structure their applications as a collection of functions, sporadically invoked by various events like clicks. The high variability in function image sizes, invocation inter-arrival times, and request burst sizes motivates vendors to scrutinize their infrastructure to ensure a seamless user experience across all of their services. To monitor serverless platform performance, identify pitfalls, and compare different providers, the public attention has turned to benchmarking, whereby measurement functions are deployed to the cloud to gather insights regarding response latencies, transfer speeds, and vendor policies. This work introduces our open-source framework for serverless performance evaluation, intending to enable researchers and developers to benchmark multiple cloud platforms. Using this framework, we conduct one of the biggest serverless measurements to date, launching over 320,000 function instances to characterize system performance in AWS Lambda and vHive - the Edinburgh Architecture and Systems (EASE) virtual machine orchestrator system. We ﬁnd that server response times for cold function instances are, on average, ten times slower and more unpredictable than for warm function instances. When requests arrive in bursts of over 100, we show that while warm instances incur a penalty, AWS optimizes cold instances by fetching resources in batches. We achieve this by incorporating tail latency into our studies, a metric that is often overlooked in the literature. Our analysis further reveals that AWS allocates resources aggressively to avoid queueing and that invocations do not share function instances even when receiving up to 500 concurrent requests. We also discover that inter-function transfer latencies are much higher in AWS than in vHive and that AWS caps network bandwidth for low-memory conﬁgurations and throttles it for larger payloads regardless of allocated memory. Based on these insights, we stress the importance of regular platform performance monitoring for improved customer satisfaction. Finally, to boost performance and economic efﬁciency from a user’s perspective, we suggest that serverless applications are designed with a trade-off in mind between the function image size and the transfer chain length.
UvA-DARE (Digital Academic Facilitating PSS workshops: a conceptual framework and findings from interviews with facilitators

Recent research has emphasized the importance of workshops as a venue where planning support systems (PSS) are used in planning processes. Empirical studies of these workshops have previously largely overlooked facilitation, in particular the role of a moderator (steering the discussion) and/or a chauffeur (steering the PSS). Drawing on existing facilitation research, we identify four main categories of facilitation interventions: substantive, procedural, relational, and tool-related. We use these categories to develop a novel conceptual framework for facilitation at PSS workshops. We test and develop this framework through semi-structured interviews with eight experienced facilitators of PSS workshops in the US and the Netherlands. The interviews con ﬁ rm the validity of the intervention categories, but also revealed a wider range of PSS-speci ﬁ c workshop outcomes. We conclude that successful facilitation of PSS workshops requires two different types of facilitation interventions: some to encourage PSS use, and others to prevent PSS domination of the group discussion. Facilitating PSS workshops is mainly about ﬁ nding the delicate and context-dependent balance between these two extremes.
CoverDrop: Blowing the Whistle Through A News App

Abstract Whistleblowing is hazardous in a world of pervasive surveillance, yet many leading newspapers expect sources to contact them with methods that are either insecure or barely usable. In an attempt to do better, we conducted two workshops with British news organisations and surveyed whistleblowing options and guidelines at major media outlets. We concluded that the soft spot is a system for initial contact and trust establishment between sources and reporters. CoverDrop is a two-way, secure system to do this. We support secure messaging within a news app, so that all its other users provide cover traffic, which we channel through a threshold mix instantiated in a Trusted Execution Environment within the news organisation. CoverDrop is designed to resist a powerful global adversary with the ability to issue warrants against infrastructure providers, yet it can easily be integrated into existing infrastructure. We present the results from our workshops, describe CoverDrop’s design and demonstrate its security and performance.
Analog and digital approaches to uwb narrowband interference cancellation

Ultra wide band (UWB) is an extremely promising wireless technology for researchers and industrials. One of the most interesting is its high data rate and fading robustness due to selective frequency fading. However, beside such advantages, UWB system performance is highly affected by existing narrowband interference (NBI), undesired UWB signals and tone/multi-tone noises. For this reason, research about NBI cancellation is still a challenge to improve the system performance vs. receiver complexity, power consumption, linearity, etc. In this work, the two major receiver sections, i.e., analog (radiofrequency or RF) and digital (digital signal processing or DSP), were considered and new techniques proposed to reduce circuit complexity and power consumption, while improving signal parameters. In the RF section, different multiband UWB low-noise amplifier key design parameters were investigated like circuit configuration, input matching and desired/undesired frequency band filtering, highlighting the most suitable filtering package for efficient UWB NBI cancellation. In the DSP section, due to pulse transmitter signals, different issues like modulation type and level, pulse variety, shape and color noise/tone noise assumptions, were addressed for efficient NBI cancelation. A comparison was performed in terms of bit-error rate, signal-to-interference ratio, signal-to-noise ratio, and channel capacity to highlight the most suitable parameters for efficient DSP design. The optimum number of filters that allows the filter bandwidth to be reduced by following the required low sampling rate and thus improving the system bit error rate was also investigated.
Exploring Language Models for Motif Discovery in Immunopeptidomics Datasets

Immunopeptides are the short protein sequences that bind to molecules to be presented to the immune system. The discovery of sequence patterns (motifs) in Immunopep-tidomics datasets is a crucial step for the development of cancer vaccines, since these patterns can be used to indicate which parts of a protein are likely to be viewed by the immune system. Current motif discovery techniques in this area fail to capture the possible relationships within peptides that could affect their binding afﬁnity. This research project explores the use of language modelling as a candidate for motif discovery in immunopeptides. The main contribution is a new motif discovery approach for immunopeptides that uses the transformers based language model RoBERTa and it’s self-attention mechanism. The new approach was carried out on 40,689 immunopep-tide sequences, and the motifs found were compared to an existing motif discovery method. In addition, part of the comparison utilised a novel peptide visualisation technique. It was found that the motifs shared some similarity with the other technique, but some key conserved regions were missed by the new technique. Also, the approach was able to ﬁnd new sequence patterns in the same datasets indicative of new motifs learned by the model. This project also explored RoBERTa’s prediction of a missing amino acid in a peptide, thus evaluating the model’s ability to pick up relationships within a sequence. It was found that the RoBERTa model was more likely to predict a physio-chemically similar amino acid when compared to a baseline. Also, the prediction accuracy varied considerably by peptide position and amino acid, and moreover, this variation was linked to some of the conserved amino acids in the motifs found by both methods.
Implementation and Analysis of Approximation Algorithms For One-Counter Markov Decision Processes

We implement and analyze several algorithms of some key analysis problems for One-Counter Markov Decision Processes (OC-MDPs), a type of ﬁnitely-presented inﬁnite state MDPs. One-Counter Markov Decision Processes are a mathematical model inspired from Quasi Birth Death processes, a randomized model studied in stochastic modelling theory and applied probability. OC-MDPs have probabilistic and control states, where every probabilistic state deﬁnes a probability distribution over the set of its outgoing transition, while in control states, the agent is required to choose a particular action which dictates which probabilistic states he/she will visit. OC-MDPs also have an un-bounded counter which can be interpreted at the total reward the player has achieved at a given time step. At every control state, an agent must pick between its allowed number of actions in order to achieve a certain objective. Examples of such objectives include the long-run average reward obtained per transition, and covering very low total reward values in the long-run. In this study we implement algorithms in Python to approximate the optimal probability of the agent “terminating” given that (s)he starts above a counter value N . That is, if the agent starts with a given counter value j > N , our algorithms are able to approximate the probability that the agent ensures that the counter value (the total reward) will hit 0 upto a certain error ε . We also implement algorithms to compute an upper bound on the counter value N and illustrate the challenges behind implementing these algorithms. We also empirically analyze the performance of these algorithms by experimenting them on a well known subclass of OC-MDPs known as solvency games. Solvency games are extremely relevant to mathematical ﬁnance as they can model the behaviour of risk-averse investors. Our results show that the upper bound on the counter value N is not tight enough and we provide a heuristic that allows one to calculate a tighter bound on the counter value than currently implemented. We also show that some of the subproblems used to approximate the optimal termination probabilities can be greatly simpliﬁed for an average OC-MDP. Our algorithms combine theory from linear programming, stochastic