Miracle-dz/UniFAR-FTUs huggingface dataset:
Degradation Analysis and Preventive Maintenance Modelling and Assessment for Improved Resilience of Critical Infrastructures-Application to Torrent Checkdams

Natural threats have become more familiar, to the extent that requires ensuring the resilience of critical infrastructures. Critical infrastructures have always been complicated to study and assess, as they are all characterized by a collection of components that have numerous dependencies and interactions. Recently, several methods and frameworks have been put forward to assess and analyse comprehensively system's resilience. However, these methods have insufficiencies in identifying some hidden risks arising in a complex infrastructure. Therefore, it is essential to go beyond conventional methods and to develop risk strategies and decision-making techniques in order to overcome classical static assessment methods. This paper contributes to analyse the context of critical infrastructures with the ultimate objective of proposing new methods of choosing preventive maintenance strategies. It represents a modelling approach based on Petri nets to study the dynamic behaviour of the system when exposed to deterioration mechanisms and to support maintenance decision-making. The application is carried out on torrential checkdams in which the model results are presented and discussed in the paper.
Bias and Debias in Code Search: Improve Search Results beyond Overall Ranking Performance

Code search engine is an essential tool in soft-001 ware development. Many code search meth-002 ods have sprung up, focusing on the overall 003 ranking performance of code search. In this 004 paper, we study code search from another per-005 spective by analyzing the bias of code search 006 models. Biased code search engines provide 007 poor user experience, even though they show 008 promising overall performance. Due to differ-009 ent development conventions (e.g., prefer long 010 queries or abbreviations), some programmers 011 will ﬁnd the engine useful, while others may 012 ﬁnd it hard to get desirable search results. To 013 mitigate biases, we develop a general debiasing 014 framework that employs reranking to calibrate 015 search results. It can be easily plugged into 016 existing engines and handle new code search 017 biases discovered in the future. Experiments 018 show that our framework can effectively re-019 duce biases. Meanwhile, the overall ranking 020 performance of code search gets improved af-021 ter debiasing. Our implementation is available 022 at: https://anonymous.4open. science/r/ 023 CodeSearchBias-B2AD . 024
Effect of Fish Oil and Linseed Oil on Intake, Milk Yield and Milk Fatty Acid Profile in Goats

Simple Summary Improvements in health-promoting milk fatty acids by feeding oil mixtures have been reported in dairy cows. However, in some cases, oil addition reduces milk yield and milk fat content. It is unknown whether the inclusion of linseed oil and fish oil at a high level in goat diets increases health-promoting fatty acids in milk without affecting milk production. The objective of this study was to investigate the effect of linseed oil added alone at 2.5% or in combination with tuna fish oil at 2.5% or 4.16% in goat diets on intake, milk yield, and milk fatty acid profiles. Compared with the control without oil addition, feeding linseed oil and fish oil at 4.16% markedly increased the levels of health-promoting fatty acids in milk such as c9,t11 conjugated linoleic acid, and docosahexaenoic acid but decreased milk total saturated fatty acids, atherogenicity, and thrombogenicity indices. Oil addition did not have a negative effect on intake, milk yield, and milk fat content. Thus, supplementing linseed oil and fish oil at 4.16% in the diet of lactating goats could have a positive impact on human health without any adverse effects on animal performance. Abstract This study aimed to evaluate the effect of incorporating linseed oil and fish oil in the diet on intake, ruminal fermentation, milk yield, and milk fatty acid profiles in dairy goats. Four crossbred Saanen lactating goats in mid-lactation and milking 1.30 ± 0.28 g/day were used in a 4 × 4 Latin square design. The basal diet contained concentrate and Para grass (C:F 40:60). Treatments included a basal diet without oil supplementation (Ctrl) or with 2.5% linseed oil (LO2.5), 2.5% linseed oil and fish oil (3:2, w/w, LFO2.5), and 4.16% linseed oil and fish oil (3:2, w/w, LFO4.16). Diets had no effect on intake, milk yield, milk composition, or ruminal fermentation (p > 0.05). Compared with Ctrl, lower (p < 0.05) proportions of C10:0–C14:0 in milk fat were observed with LFO4.16. Compared with the Ctrl and linseed oil added alone, feeding LFO4.16 led to a greater (p < 0.01) concentration of C18:1 t11. Compared with both the Ctrl and LO2.5 diets, milk c9,t11 CLA was 4.53 and 2.94 times greater with the LFO4.16 diet. Compared with Ctrl and LO2.5 diets (0.06% and 0.08%), goats fed LFO2.5, and LFO4.16 had greater (p < 0.001) concentrations of C22:6n-3 (0.63% and 0.87%). Overall, the combined data suggested that including 4.16% linseed oil and fish oil in the diet of dairy goats was effective in improving the concentrations of health-promoting fatty acids in milk without affecting milk production.
Family Identity, Place Identity, and Chinese Farmers’ Environment-Friendly Production Behavior

In response to the sustainable development goal of agriculture put forward by the United Nations, countries have introduced a series of agricultural environmental protection policies. However, the effectiveness of these policies has been hindered by insufficient responses from farmers. This study begins with the fundamental logic of farmers’ production behavior and first introduces family and place identities into the theoretical analysis framework of farmers’ environment-friendly production behavior (EPB). Using primary survey data for farmers from the northwestern Chinese province of Gansu, a hierarchical regression and simple slope analysis models were developed to verify how family and place identities affect farmers’ EPB. The potential moderating effect of place identity on the relationship between family identity and farmers’ EPB is further investigated. The results show that improving family identity has a significantly negative effect on farmers’ EPB, while improving place identity can significantly promote farmers’ EPB. Compared with respondents who have a low place identity, the inhibitory effect of family identity on farmers’ EPB is significantly weakened for those who have a high place identity, which suggests that farmers with a high place identity are more likely to engage in EPBs in agricultural production. This study highlights the importance of considering the economic logic toward maximizing family economic interests and the social logic oriented toward collective interests in the decision-making process of agricultural environmental policies.
Classification of Garlic Land Based on Growth Phase using Convolutional Neural Network

—Indonesian Government needs to monitor the realization of garlic land with production plans in several production areas at growth season. A previous study, which used Sentinel-1A satellite imagery and Convolutional Neural Networks to classify garlic land, needed more information on growth phases. The study aims to address that limitation by creating a garlic land classification model based on the growth phase using Convolutional Neural Networks. The dataset comprises 446 preprocessed Sentinel-2 images cross-referenced with drone ground truth data. The model used both VGG16 and VGG19 architectures. Hyperparameter tuning was applied to obtain optimal values. After evaluating three scenarios (VGG16 base model, modified VGG16, and modified VGG19), the best model was obtained from the modified VGG19, which had an accuracy rate of 81.81% and a loss function of 0.71. The study successfully classified garlic land based on growth phase, with a precision rate of 0.43 for initial growth and vegetation classes, and 0.22 for the harvest class. The study offers an alternative to monitoring garlic production throughout growth phases with satellite imagery and deep learning.
Development and Optimal Immune Strategy of an Alum-Stabilized Pickering emulsion for Cancer Vaccines

Therapeutic cancer vaccines are considered as one of the most cost-effective ways to eliminate cancer cells. Although many efforts have been invested into improving their therapeutic effect, transient maturation and activations of dendritic cells (DCs) cause weak responses and hamper the subsequent T cell responses. Here, we report on an alum-stabilized Pickering emulsion (APE) that can load a high number of antigens and continue to release them for extensive maturation and activations of antigen-presenting cells (APCs). After two vaccinations, APE/OVA induced both IFN-γ-secreting T cells (Th1) and IL-4-secreting T cells (Th2), generating effector CD8+ T cells against tumor growth. Additionally, although they boosted the cellular immune responses in the spleen, we found that multiple administrations of cancer vaccines (three or four times in 3-day intervals) may increase the immunosuppression with more PD-1+ CD8+ and LAG-3+ CD8+ T cells within the tumor environment, leading to the diminished overall anti-tumor efficacy. Combining this with anti-PD-1 antibodies evidently hindered the suppressive effect of multiple vaccine administrations, leading to the amplified tumor regression in B16-OVA-bearing mice.
A hierarchical complementary Lean-Green model and its impact on operational performance of manufacturing organisations

PurposeThis research aims to examine the complementary impact of Lean Manufacturing (LM) and Green Manufacturing (GM) on operational and environmental performance.Design/methodology/approachA survey was conducted in the Zimbabwean manufacturing industry. A total of 302 valid responses were obtained and analysed using partial least square structural equation modelling (PLS-SEM). FindingsBoth LM and GM impact environmental and operational performance; however, GM's effect on operational performance is indirect through environmental performance.Research limitations/implicationsThis study only focusses on the Zimbabwean manufacturing industry, and the results may not readily apply to other developing countries.Practical implicationsThe companies that have successfully implemented LM are able to implement GM more easily because of their complementary nature.Social implicationsThe integration of LM and GM reduces most forms of waste, causing an improved environmental and operational performance. In addition, this will improve community relations and customer satisfaction.Originality/valueThis research investigates the complementary nature of LM and GM on how LM and GM impact organisational performance and whether a combined Lean-Green implementation leads to better organisational performance than when LM and GM are implemented individually. The research also examines whether being environmentally compliant leads to improved organisational performance, particularly in a developing country.
Towards A Unified Agent with Foundation Models

Language Models and Vision Language Models have recently demonstrated unprecedented capabilities in terms of understanding human intentions, reasoning, scene understanding, and planning-like behaviour, in text form, among many others. In this work, we investigate how to embed and leverage such abilities in Reinforcement Learning (RL) agents. We design a framework that uses language as the core reasoning tool, exploring how this enables an agent to tackle a series of fundamental RL challenges, such as efficient exploration, reusing experience data, scheduling skills, and learning from observations, which traditionally require separate, vertically designed algorithms. We test our method on a sparse-reward simulated robotic manipulation environment, where a robot needs to stack a set of objects. We demonstrate substantial performance improvements over baselines in exploration efficiency and ability to reuse data from offline datasets, and illustrate how to reuse learned skills to solve novel tasks or imitate videos of human experts.
Semi-supervised learning in perivascular space segmentation using MRI images

Accurate segmentation of perivascular space (PVS) is essential for its quantitative analysis and clinical applications. Various segmentation methods have been proposed, but semi-supervised learning methods have never been attempted. Here, we propose a 3D multi-channel, multi-scale semi-supervised PVS segmentation (M2SS-PVS) network. We incorporated multi-scale image features in the encoder and applied a few strategies to mitigate class imbalance issue. Our M2SS-PVS network segmented PVS with the highest accuracy and high sensitivity among all the tested supervised and semi-supervised methods.
An analysis on the effects of speaker embedding choice in non auto-regressive TTS

In this paper we introduce a first attempt on understanding how a non-autoregressive factorised multi-speaker speech synthesis architecture exploits the information present in different speaker embedding sets. We analyse if jointly learning the representations, and initialising them from pretrained models determine any quality improvements for target speaker identities. In a separate analysis, we investigate how the different sets of embeddings impact the network's core speech abstraction (i.e. zero conditioned) in terms of speaker identity and representation learning. We show that, regardless of the used set of embeddings and learning strategy, the network can handle various speaker identities equally well, with barely noticeable variations in speech output quality, and that speaker leakage within the core structure of the synthesis system is inevitable in the standard training procedures adopted thus far.
Type 2 Diabetes Mellitus: Early Detection using Machine Learning Classification

—Type 2 Diabetes Mellitus (T2DM) is a growing global health problem that significantly impacts patient’s quality of life and longevity. Early detection of T2DM is crucial in preventing or delaying the onset of its associated complications. This study aims to evaluate the use of machine learning algorithms for the early detection of T2DM. A classification model is developed using a dataset of patients diagnosed with T2DM and healthy controls, incorporating feature selection techniques. The model will be trained and tested on machine learning algorithms such as Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forest, and Support Vector Machines. The results showed that the Random Forest algorithm achieved the highest accuracy in detecting T2DM, with an accuracy of 98%. This high accuracy rate highlights the potential of machine learning algorithms in early T2DM detection and the importance of incorporating such methods in the clinical decision-making process. The findings of this study will contribute to the development of a more efficient precision medicine screening process for T2DM that can help healthcare providers detect the disease at its earliest stages, leading to improved patient outcomes.
Advancing Dermatological Care: A Comprehensive Narrative Review of Tele-Dermatology and mHealth for Bridging Gaps and Expanding Opportunities beyond the COVID-19 Pandemic

Mobile health (mHealth) has recently had significant advances in tele-dermatology (TD) thanks to the developments following the COVID-19 pandemic. This topic is very important, as telemedicine and mHealth, when applied to dermatology, could improve both the quality of healthcare for citizens and the workflow in the health domain. The proposed study was centered on the last three years. We conducted an overview on the opportunities, the perspectives, and the problems involved in TD integration with mHealth. The methodology of the narrative review was based on: (I) a search of PubMed and Scopus and (II) an eligibility assessment, using properly proposed parameters. The outcome of the study showed that during the COVID-19 pandemic, TD integration with mHealth advanced rapidly. This integration enabled the monitoring of dermatological problems and facilitated remote specialist visits, reducing face-to-face interactions. AI and mobile apps have empowered citizens to take an active role in their healthcare. This differs from other imaging sectors where information exchange is limited to professionals. The opportunities for TD in mHealth include improving service quality, streamlining healthcare processes, reducing costs, and providing more accessible care. It can be applied to various conditions, such as (but not limited to) acne, vitiligo, psoriasis, and skin cancers. Integration with AI and augmented reality (AR), as well as the use of wearable sensors, are anticipated as future developments. However, integrating TD with mHealth also brings about problems and challenges related to regulations, ethics, cybersecurity, data privacy, and device management. Scholars and policymakers need to address these issues while involving citizens in the process.
Error Analysis of Tigrinya - English Machine Translation Systems

Machine translation (MT) is an important language technology that can democratize access to information. In recent years, we have seen some progress in the development and deployment in production of MT systems for a handful of African languages. Evaluating the quality of such systems is fundamental to accelerating progress in this area. Tigrinya is a language that is spoken by more than 10 million native speakers mainly in Tigray, Ethiopia and Eritrea. In this work, we evaluated the current status of state-of-the-art MT systems that support the translation of Tigrinya to and from English: Google translate, Microsoft translator, and Lesan. We systematically collected a dataset for evaluating Tigrinya MT systems across four domains: Arts and Culture, Business and Economics, Politics as well as Science and Technology. The dataset contains snippets from 806 articles gathered from diverse sources. We performed an in-depth analysis of the errors current systems make using MQM-DQF standard error typology. We found that Mistranslation and Omission are the most frequent translation issues. We believe this work gives a methodology for evaluating other machine translation systems for low resource languages and we provide practical suggestions to improve current Tigrinya English MT systems.
Analysis of the Maximum-likelihood Channel Estimator for OFDM Systems with Unknown Interference

—In this paper, a comprehensive theoretical analysis of the maximum-likelihood (ML) channel estimator for OFDM systems with unknown interference is presented. The uniqueness of the solution is analytically demonstrated when the number of transmitted OFDM symbols is strictly greater than one. This solution is derived from the iterative conditional ML (CML) algorithm. It is shown that the channel estimate can be described as an algebraic function whose inputs are the initial value and the means and variances of the received samples. It is also theoretically demonstrated that the channel estimator is not biased, and that the second moment exists as long as the number of transmitted OFDM symbols is strictly greater than three. Furthermore, this is confirmed by numerical results.
A Bandits Approach to Intelligent Tutoring Systems using Concept Evolution Estimation

With the huge number of learning resources available online today, the Intelligent Tutoring Systems (ITS) are of great need more than ever. An ITS is a system that personalizes the course contents to each learner. In this paper, we address the problem of suggesting an effective & efficient learning sequences to learners based on their knowledge levels. We take a multi-armed bandits approach to action choosing where we suggest that action which has the highest estimated learning outcome at each step. We model the actions as Beta distributions & the learners’ knowledge level as concept vectors. We also learn the prerequisite relationships that can exist among the concepts automatically. We propose a novel algorithm that achieves the goal efficiently. Our experimental results show that our algorithm’s performance is comparable to that of the optimal algorithm.
A Methodology for Task-Driven Guidance Design

Mixed-initiative Visual Analytics (VA) systems are becoming increasingly important; however, the design of such systems still needs to be formulated. We present a methodology to aid and structure the design of guidance for mixed-initiative VA systems consisting of four steps: (1) defining the target of analysis, (2) identifying the user search tasks, (3) describing the system guidance tasks, and (4) specifying which and when guidance is provided. In summary, it specifies a space of possible user tasks and then maps it to the corresponding space of guidance tasks, using recent VA task typologies for guidance and visualization. We illustrate these steps through a case study in a real-world model-building task involving decision-making with unevenly-spaced time-oriented data. Our methodology’s goal is to enrich existing VA systems with guidance, being its output a structured description of a complex guidance task schema.
Bayesian inference for high-speed train dynamics and speed optimization under uncertainty for energy saving

The train is a complex nonlinear system, whose dynamic behavior is difficult to predict accurately because of its environmental sensitivity. Indeed, in spite of a relative fine modeling of the vehicle and its rolling environment (track and wind), the slightest uncontrolled disturbance can modify the dynamic comportment of the train. For this reason, uncertainty must be considered in the physical models. The industrial objective of this work is twofold. Firstly, the construction of a longitudinal dynamic model for high-speed trains able to take into account the fluctuations inherent to the system. Secondly, the optimization under uncertainty of the driver’s command with the objective of reducing the energy consumed by the train, under a set of punctuality and physical nonlinear constraints (speed limitation, final speed, and final position constraints).
Zero-Watermarking for Medical Images Based on Regions of Interest Detection using K-Means Clustering and Discrete Fourier Transform

—Watermarking schemes ensure digital image security and copyright protection to prevent unauthorized distribution. Zero-watermarking methods do not modify the image. This characteristic is a requirement in some tasks that need image integrity, such as medical images. Zero-watermarking methods obtain specific features for the master share construction to protect the digital image. This paper proposed a zero-watermarking scheme based on K-means clustering for ROI detection to obtain specific features. The K-means algorithm classifies the data according to the proximity of the generated clusters. K-means clustering is applied for image segmentation to identify ROI and detect areas that contain important information from the image. Therefore, the Discrete Fourier Transform (DFT) is applied to the ROI features, using the high frequencies to increase its robustness against geometric attacks. In addition, an edge detection based on the Sobel operator is applied for the QR code creation. This type of watermark avoids errors in watermark detection and increases the robustness of the watermark system. The master share creation is based on an XOR logic operation between extracted features from the selected ROI and the watermark. This method focuses on the protection of the image despite it being tampered with. Many proposed schemes focus on protection against advanced image processing attacks. The experiments demonstrate that the presented algorithm is robust against geometric and advanced signal-processing attacks. The DFT coefficients from the extracted ROI features increase the efficiency and robustness.