Miracle-dz/UniFAR-FTUs huggingface dataset:
Impaired Bestrophin Channel Activity in an iPSC-RPE Model of Best Vitelliform Macular Dystrophy (BVMD) from an Early Onset Patient Carrying the P77S Dominant Mutation

Best Vitelliform Macular dystrophy (BVMD) is the most prevalent of the distinctive retinal dystrophies caused by mutations in the BEST1 gene. This gene, which encodes for a homopentameric calcium-activated ion channel, is crucial for the homeostasis and function of the retinal pigment epithelia (RPE), the cell type responsible for recycling the visual pigments generated by photoreceptor cells. In BVMD patients, mutations in this gene induce functional problems in the RPE cell layer with an accumulation of lipofucsin that evolves into cell death and loss of sight. In this work, we employ iPSC-RPE cells derived from a patient with the p.Pro77Ser dominant mutation to determine the correlation between this variant and the ocular phenotype. To this purpose, gene and protein expression and localization are evaluated in iPSC-RPE cells along with functional assays like phagocytosis and anion channel activity. Our cell model shows no differences in gene expression, protein expression/localization, or phagocytosis capacity, but presents an increased chloride entrance, indicating that the p.Pro77Ser variant might be a gain-of-function mutation. We hypothesize that this variant disturbs the neck region of the BEST1 channel, affecting channel function but maintaining cell homeostasis in the short term. This data shed new light on the different phenotypes of dominant mutations in BEST1, and emphasize the importance of understanding its molecular mechanisms. Furthermore, the data widen the knowledge of this pathology and open the door for a better diagnosis and prognosis of the disease.
A Hierarchical XGBoost Early Detection Method for Quality and Productivity Improvement of Electronics Manufacturing Systems

This paper presents XGBoost classifier-based methods to solve three tasks proposed by the European Prognostics and Health Management Society (PHME) 2022 conference. These tasks are based on real data from a Surface Mount Technologies line. Each of these tasks aims to improve the efficiency of the Printed Circuit Board (PCB) manufacturing process, facilitate the operator’s work and minimize the cases of manual intervention. Due to the structured nature of the problems proposed for each task, an XGBoost method based on encoding and feature engineering is proposed. The proposed methods utilise the fusion of test values and system characteristics extracted from two different testing equipment of the Surface Mount Technologies lines. This work also explores the problems of generalising prediction at the system level using information from the subsystem data. For this particular industrial case: the challenges with the changes in the number of subsystems. For Industry 4.0, the need for interpretability is very important. This is why the results of the models are analysed using Shapley values. With the proposed method, our team took the first place, capable of successfully detecting at an early stage the defective components for tasks 2 and 3.
Multi-View Clustering on Topological Manifold

Multi-view clustering has received a lot of attentions in data mining recently. Though plenty of works have been investigated on this topic, it is still a severe challenge due to the complex nature of the multiple heterogeneous features. Particularly, existing multi-view clustering algorithms fail to consider the topological structure in the data, which is essential for clustering data on manifold. In this paper, we propose to exploit the implied data manifold by learning the topological relationship between data points. Our method coalesces multiple view-wise graphs with the topological relevance considered, and learns the weights as well as the consensus graph interactively in a unified framework. Furthermore, we manipulate the consensus graph by a connectivity constraint such that the data points from the same cluster are precisely connected into the same component. Substantial experiments on both toy data and real datasets are conducted to validate the effectiveness of the proposed method, compared to the state-of-the-art algorithms over the clustering performance.
Airframe noise predictions using the Ffowcs Williams-Hawkings equation

This paper considers potential sources of error when using the Ffowcs Williams-Hawkings equation to make predictions of airframe noise, which entails a relatively low-speed, uniform incoming flow encountering geometry of varying complexity. Numerical simulations are used to investigate several model problems where Ffowcs Williams-Hawkings integration surfaces are placed on solid surfaces as well as in the flow. Comparisons with the pressure obtained directly from the simulations reveal that when solid surfaces are used, the acoustic calculations can produce erroneous results in upstream directions and when scattering bodies block the line of sight from observers to the source. Using solid surface input data implies ignoring all volumetric source effects, which include noise generation as well as flow effects. Nonuniform flow alone, such as is found in a steady boundary layer, was not found to be a significant source of error, so the amplitude and phase changes induced by turbulent eddies in massively separated flow regions is speculated to be the primary cause of the error.
Learning Modular Structures That Generalize Out-of-Distribution (Student Abstract)

Out-of-distribution (O.O.D.) generalization remains to be a key challenge for real-world machine learning systems. We describe a method for O.O. D. generalization that, through training, encourages models to only preserve features in the network that are well reused across multiple training domains. Our method combines two complementary neuron-level regularizers with a probabilistic differentiable binary mask over the network, to extract a modular sub-network that achieves better O.O. D. performance than the original network. Preliminary evaluation on two benchmark datasets corroborates the promise of our method.
On Dynamic Bitvector Implementations*

Bitvectors that support rank and select queries are the workhorses of succinct data structures, implementations of which are now widespread, for example, in bioinformatics software. To date, however, most bitvector implementations are static, thus forcing more complex data structures built from them to be static too. In this paper we explore dynamic bitvectors, which, in addition to rank and select queries, also support update operations, specifically: insert, remove, and modify. We first provide several practical optimizations to the recent B-tree based bitvectors of Prezza (Proc. SEA 2017), including the use of buffers at leaves to speed update operations at the cost of a small overhead to query times. We then consider a common use case of succinct data structures, where queries and updates come in separate batches, and examine the efficacy of query support data structures that are fast to construct and speed rank and select queries, but become out of date when update operations are made. Finally, we explore several methods for leaf compression.
Solutions to the Endless Addition of Transaction Volume in Blockchain

—In blockchain system, the problem of endless addition of transaction volume results in larger space occupation, heavier network transmission burden and the like. But the way of abandoning historical data is hindered by the characteristic of blockchain-tamper-proof. To solve this problem, this dissertation takes bitcoin system as an example, and gives a definition of expired transaction. By abandoning expired transactions and packing the rest transactions in several blocks into a new substitute block to replace old blocks, help to overcome the difficulty in clearing historical data. However, this solution fails to clear ineffective intermediate transactions. Thus, a follow-up solution is proposed, abandon the transactions whose outputs have been run out and retain the transactions where not all the outputs have been spent. Additionally, include records of the spending details of each transaction output, which allows us to clear ineffective intermediate transactions. In the end, an experiment is conducted to confirm the effectiveness of the two solutions as to clearing transactions.
Failures Mapping for Aircraft Electrical Actuation System Health Management

This paper presents the different types of failure that may occur in flight control electrical actuation systems. Within an aircraft, actuation systems are essential to deliver physical actions. Large actuators operate the landing gears and small actuators adjust passenger seats. As developing, aircraft systems have become more electrical to reduce the weight and complexity of hydraulic circuits, which could improve fuel efficiency and lower NOx emissions. Electrical Actuation (EA) are one of those newly electrified systems. It can be categorized into two types, Electro-Hydraulic Actuation (EHA) and Electro-Mechanical Actuation (EMA) systems. Emerging electric and hydrogen fuel aircraft will rely on all-electric actuation. While electrical actuation seems simpler than hydraulic at the systems level, the subsystems and components are more varied and complex. The aim of the overall project is to develop a highly representative Digital Twin (DT) for predictive maintenance of electrical flight control systems. A comprehensive understanding of actuation system failure characteristics is fundamental for effective design and maintenance. This research focuses on the flight control systems including the ailerons, rudders, flaps, spoilers, and related systems. The study uses the Cranfield University Boeing 737 as the basis to elaborate the different types of actuators in the flight control system. The Aircraft Maintenance Manual (AMM) provides a baseline for current maintenance practices, effort, and costs. Equivalent EHA and EMA to replace the 737 systems are evaluated. In this paper, the components and their failure characteristics are elaborated in a matrix. The approach to model these characteristics in DT for aircraft flight control system health management is discussed. This paper contributes to the design, operation and support of aircraft systems.
Eigenfettinjektion in das Sattelgelenk zur Behandlung der Rhizarthrose – eine viel versprechende Therapieoption

Zusammenfassung Klassische chirurgische Operationstechniken zur Behandlung der Rhizarthrose führen zu einer unwiderruflichen Veränderung der Anatomie der Handwurzel. Auch die Arbeitsunfähigkeit und das Wiedererlangen der Handfunktion nach diesen Eingriffen dauert in der Regel 12–16 Wochen. Daher erscheint eine nicht ablative und gering invasive chirurgische Therapieoption der Sattelgelenksarthrose eine wünschenswerte Alternative. In einer Pilotstudie injizieren wir Fettgewebe in das arthrotisch veränderte Daumensattelgelenk. Die ersten Ergebnisse sind viel versprechend. Der durchschnittliche präoperative Schmerz nach VAS betrug 7,4 bei Belastung und 3,8 in Ruhe. Der durchschnittliche postoperative Schmerz nach einem Monat betrug 2,2 bei Belastung und 0 in Ruhe, sowie 2,4 und 0,8 nach 3 Monaten. Die Schmerzminderung einen Monat nach Injektion bei Belastung war statistisch signifikant (p=0,042). Die Kraft des Grobgriffes betrug 78% und des Pinch Griffes 74% im Vergleich zu der gesunden Seite präoperativ. Die Kraft des Grobgriffes betrug 89% und die des Pinch Griffes 80% im Vergleich zu der gesunden Seite einen Monat postoperativ und 93% bzw. 89% 3 Monate postoperativ. Entsprechend reduzierte sich der DASH Score von präoperativ durchschnittlich 58 auf 36 nach einem bzw. 33 nach 3 Monaten. Die Verbesserungen der Handfunktion waren statistisch signifikant (p=0,042 und p=0,043). Es traten keine Komplikationen auf und alle Patienten waren zufrieden. Diese vorläufigen Ergebnisse sind viel versprechend, da die Eigenfettinjektion auch im Falle eines möglichen Therapieversagens, anders als die meisten bekannten Operationstechniken, den Wechsel auf eine andere klassische Operationsalternative nicht ausschließt.
Romanian dental practitioners’ knowledge of the methods used to evaluate implant stability

Purpose. The study aims to evaluate Romanian dentists’ knowledge of the methods used to assess dental implant stability. Materials and method. 192 dental practitioners who graduated from dental schools in Romania completed an online questionnaire designed to assess their level of knowledge regarding the methods used to assess the osseointegration of dental implants. Data collected from the participants was centralized in a database and statistically analysed. Results. The response rate was 100%, 55% of the participants being females and 45% males. The best-known method for assessing the stability of dental implants was radiological analysis and the main source of information was from university courses. Only 16% of the dentists who completed this survey practice oral implantology. However, the majority (60%) intends to practice implant dentistry in the future. Conclusions. Most of the dentists who took part in this study don’t have knowledge about the current methods used to assess the osseointegration of dental implants. More information about these methods can be gained by deepening one’s knowledge during college, promoting the reading of medical journals and attending medical conferences focused on oral implantology.
VisualNet: An End-to-End Human Visual System Inspired Framework to Reduce Inference Latency of Deep Neural Networks

Acceleration of deep neural network (DNN) inference has gained increasing attention recently with the wide adoption of DNNs for practical applications. For computer vision tasks where inputs are images, existing works mostly focus on improving the throughput of inference for multiple images. However, in many real-time applications, it is critical to reduce the latency of a single image inference, which is more complicated than improving the throughput because of the inherent data dependencies. On the other hand, from human brain's perspective, the complexity in our visual surroundings is first encoded as a pattern of light on a two dimensional array of photoreceptors, with little direct resemblance to the original input or the ultimate percept. Within just a few hundred microns of retinal thickness, this initial signal encoded by our photoreceptors must be transformed into an adequate representation of the entire visual scene. Inspired by how the retina helps human brain incept new information efficiently, we present an end-to-end structured framework built using any existing convolutional neural network (CNN) as the backbone. The proposed framework, called VisualNet, can create task parallelism for the backbone during the inference of a single image. Experiments using a number of neural networks for the ImageNet classification task and the CIFAR-10 classification task on GPUs and CPUs show that the proposed VisualNet reduces the latency of the regular network it builds on by up to 80.6% when both are fully parallelized with state-of-the-art acceleration libraries. At the same time, VisualNet can achieve similar or slightly higher accuracy.
Case-study Led Investigation of Explainable AI (XAI) to Support Deployment of Prognostics in the industry

Civil nuclear generation plant must maximise it’s operational uptime in order to maintain it’s viability. With aging plant and heavily regulated operating constraints, monitoring is commonplace, but identifying health indicators to pre-empt disruptive faults is challenging owing to the volumes of data involved. Machine learning (ML) models are increasingly deployed in prognostics and health management (PHM) systems in various industrial applications, however, many of these are black box models that provide good performance but little or no insight into how predictions are reached. In nuclear generation, there is significant regulatory oversight and therefore a necessity to explain decisions based on outputs from predictive models. These explanations can then enable stakeholders to trust these outputs, satisfy regulatory bodies and subsequently make more effective operational decisions. How ML model outputs convey explanations to stakeholders is important, so these explanations must be in human (and technical domain related) understandable terms. Consequently, stakeholders can rapidly interpret, then trust predictions better, and will be able to act on them more effectively. The main contributions of this paper are: 1. introduce XAI into the PHM of industrial assets and provide a novel set of algorithms that translate the explanations produced by SHAP to text-based human-interpretable explanations; and 2. consider the context of these explanations as intended for application to prognostics of critical assets in industrial applications. The use of XAI will not only help in understanding how these ML models work, but also describe the most important features contributing to predicted degradation of the nuclear generation asset.
Damage Prediction and Remaining Useful Lifetime Assessment of a Discrete Power Electronic Component Using a Multi-Layer Perceptron based on Mission Profile Data

In this contribution, an automated routine is presented to assess the reliability of a discrete power electronic component used In electric bikes based on a data driven approach. Real temperature profiles are acquired from the field under different loading conditions of electric bikes and its features are extracted using a custom algorithm. A Multilayer Perceptron trained with synthetic but realistic temperature loads is used to predict creep strains induced in the solder joints of a chip resistor. In addition, the remaining useful lifetime has been evaluated at various stages of the mission profile. This methodology provided a solution for real-time capable remaining useful lifetime estimation method.
Risk Assessment of Dichlorodiphenyltrichloroethane Residues in Soil and Rice Samples Harvested in Selected Rice Farms of Benue State, Nigeria

This work investigates the level of Dichlorodiphenyltrichloroethane (DDT) residues in soil and rice samples collected from Otukpo farmland area of Benue State. Ten soil samples and five different species of rice samples were collected from the farmlands to determine the concentration of DDT residues. Standard analytical methods were employed for the determination of some physicochemical parameters (pH, total organic carbon, moisture content and cation exchange capacity) of the samples. Collected samples were analyzed for residues of total dichlorodiphenyltrichloroethane using gas chromatography mass spectrometry (GC-MS) after careful extraction and cleanup. Risk assessment was carried out by determining the hazard indices. The results of the physicochemical analysis showed that the mean pH value of soil samples is 6.2 indicating slight acidity as compared to rice 7.34 which is approximately neutral and within WHO acceptable limits. The mean total organic carbon (TOC) value is 14.57% while the mean cation exchange capacity is 7.85 meq/100g in soil. DDT was detected with average concentration of 10.5 mg/kg in soil and 3.41 mg/kg in rice which is above the EU/WHO and MRL recommended rate of 0.1 mg/kg and 0.05 mg/kg. Harzard indices employed in this research shows that rice species are fit for consumption with the exception of those with Hazard Index level greater than one in children. The contamination levels of DDT in the analyzed soil based on national standards are considered toxic for crop production but analysis shows that the uptake level of DDT from soil to crop is very low which makes the rice cultivaled fit for consumption.
A Simple Beamforming Technique for Intelligent Reflecting Surfaces in 5G Scenarios

This contribution proposes a simple closed-form formula to compute the phase-shift distribution on Intelligent Reflecting Surfaces (IRS), which can be implemented using metasurfaces or reflectarray panels. The proposed technique is based on introducing a quadratic phase correction to the phase distribution that generates a collimated beam. The phase correction allows to independently control the half-power beamwidth (HPBW) in azimuth and elevation for low-cost passive IRS panels. The capabilities of the technique have been demonstrated by computing the phase distribution and radiation patterns of a 40 cm × 40 cm IRS that redirects the beam in azimuth and elevation and broadens the beam from 2º to 4º in elevation and from 2º to 18º in azimuth. This technique is very useful to the design of low-cost passive IRS panels.
Proposal for a new local recurrence score in patients with recurrent malignant pleural mesothelioma

Background Malignant pleural mesothelioma (MPM) is associated with high rates of local recurrence (LR) up to 75%. Second line treatment should be applied tailored to relapse pattern. We aimed to establish a new score for LR pattern with prognostic impact in this observational study of retrospective nature. Methods MPM patients with LR after surgery, verified by serial imaging during follow-up visits or biopsy were included in a retrospective analysis using a new local recurrence score (LRS). We divided the thoracic cavity into six sections and calculated the LRS according to the tumor burden. We assessed the impact on survival after recurrence using cox regression model. Results From 2001 until 2017, 128 consecutive MPM patients with LR who underwent macroscopic complete resection (MCR) by extrapleural pneumonectomy (EPP, n=61) or by (extended) pleurectomy/decortication [(E)PD, n=67], were included in the present analysis; 104 patients received second line therapy. Patients with chest wall (CW) recurrence had the shortest survival after recurrence (9 vs. 16 months, P=0.05) as well as patients with affected lymph nodes (LN) (9 vs. 17 months, P=0.02). In subgroup analysis, the (E)PD group had a significantly higher LRS (P≤0.001) despite a longer survival time after recurrence of 12.4 months (IQR, 6.45–20.32) compared to 9.3 months (IQR, 2.93–17.40, EPP group) (P=0.04). Patients with LRS ≤4 had a longer survival undergoing radiotherapy or local surgery for second line treatment whereas patients with LRS >4 only if they underwent chemotherapy. Conclusions LRS might be a useful prognostic tool in MPM patients with LR after multimodality therapy to guide second line treatment allocation.
Investigation of Modeling of Anomalous Reflecting Metasurfaces Using Characteristic Modes

A method is proposed to design a metasurface applying characteristic modes by decomposition of the scattered farfield. The additional insight into the behavior of the metasurface structure gained by the modal analysis is utilized to further optimize the unit cells of a period of the metasurface that allows anomalous reflection into a predefined direction from a normal incident wave. It is shown that the proposed method works for metal backed dielectric containing metal patches as unit cells.