Miracle-dz/UniFAR-FTUs huggingface dataset:
Thermal Protective Properties and Breathability of Multilayer Protective Woven Fabrics for Wildland Firefighting

A firefighter in the wildland fields spends an average of 8 to 16 h during which he encounters enormous physical effort and very demanding outdoor conditions of high temperatures. Research shows that the most common injuries are due to the occurrence of heat stress, and not due to lack of protection against burns. Therefore, for this very specific field of firefighting, it is necessary to provide clothing that will, in addition to adequate flame protection, provide good comfort properties such as lightweight suits, good porosity and breathability, so that gaseous sweat and heat generated by body heating can be released into the environment. The aim of this study was to determine the influence of structural parameters of multi-weft woven fabrics on two mutually contradictory properties—breathability and thermal protection. When designing fabrics, the goal was to produce a structure with a high proportion of volume pores, which, regardless of the increased volume of the fabric, insure the fabric mass would be acceptably small. Volume pores in the fabric have two roles—as a heat insulator and as an inhibitor of the breathability of the material. The analysis of the obtained results showed that the thickness and mass of the fabric have a greater influence on the water vapor resistance, while the heat transmission property is more affected by the thickness, porosity and fiber content.
A Resilience-Oriented Methodology for Transformation of the Distribution Networks into MicroGrid

Transformation of the distribution network, integrated with Distributed Energy Resources (DER), into a MicroGrid (MG) is an attractive approach in improving the reliability and resilience of the network. In this paper, by introducing the important and necessary aspects of the MG formation problem, an effective methodology for transformation of a distribution network into the MG is proposed. In the presented methodology, optimal allocation of DERs within MG is obtained by optimization of power losses and voltage stabilization along feeders. Moreover, the short-circuit constraints are also taken into account simultaneously while allocating DERs. The reason for the latter is that the integration of DERs alters the amplitude and direction of the short circuit current, which can cause the overall fault current to exceed the designed fault-level capability of the circuit breakers. This issue may put CBs at risk of failure and degrading MG resilience. In this regard, resilience-oriented evaluation of the MG formation, from the perspective of considering short circuit level and coordination of OCRs, is also performed. Another advantage of short circuit studies is devising complete and economic overcurrent-based protection plan for MG in both operating modes (grid-connected and islanded). It should be pointed out that adopting an appropriate control routine is a key element in the MG formation. Hence, a control strategy, based on Energy Storage System (ESS) utilization, is suggested in this paper. This feature, which is another contribution of the paper, will guarantee seamless transition to island mode and stable operation of the MG. The proposed formulation of the MG formation is solved using a modified multi-objective PSO and the Pareto-optimal set is obtained. However, since the optimal solutions are nondominated to each other, a Fuzzy Satisfaction Method (FSM) is utilized to find the optimal solution among the Pareto-set. Utilization of the proposed methodology leads to the successful transformation of the distribution network into a MG, which not only considers the load flow constraints but also preserves the optimal coordination of the protective relays and resilience of the MG. In addition, seamless transition and stable operation of the MG in island mode is achieved.
Hill Climbing Artificial Electric Field Algorithm for Maximum Power Point Tracking of Photovoltaics

In this paper, maximum power point tracking (MPPT) of a photovoltaic (PV) system is performed under partial shading conditions (PSCs) using a hill climbing (HC)–artificial electric field algorithm (AEFA) considering a DC/DC buck converter. The AEFA is inspired by Coulomb’s law of electrostatic force and has a high speed and optimization accuracy. Because the traditional HC method cannot perform global search tracking and instead performs local search tracking, the AEFA is used for a global search in the proposed HC-AEFA. The critical advantage of the HC-AEFA is that it is desirable performing local and global searches. The proposed hybrid method is implemented to derive an MPP by tuning the converter duty cycle, considering the objective function for maximizing the PV system extracted power. Its capability is evaluated and compared with well-known particle swarm optimization (PSO), considering standards, PSCs, and radiation changes conditions. The tracking efficiency for the most challenging shading pattern (third pattern) using the HC-AEFA, HC, AEFA and PSO is obtained at 99.93, 90.35, 98.85, 99.80%, respectively. The analysis of the population-based optimization process for different algorithms proved the HC-AEFA faster convergence at lower iterations than the other methods. So, the superiority of the proposed HC-AEFA subjected to different patterns is confirmed with higher tracking efficiency and global power peak, fewer fluctuations, higher convergence speed, and higher dynamic and Static-efficiency compared to the other methods.
Secured Protocol with Collaborative IoT-Enabled Sustainable Communication Using Artificial Intelligence Technique

In recent years, 5G and the Internet of Things (IoT) have been integrated into a variety of applications to support sustainable communication systems. In the presence of intermediate hardware, IoT devices collect the network data and transfer them to cloud technologies. The interconnect machines provide essential information to the connected devices over the Internet. Many solutions have been proposed to address the dynamic and unexpected characteristics of IoT-based networks and to support smart developments. However, more work needs to explore efficient quality-aware data routing for distributed processing. Additionally, to handle the massive amount of data created by smart cities and achieve the transportation objectives for resource restrictions, artificial intelligence (AI)-oriented approaches are necessary. This research proposes a secured protocol with collaborative learning for IoT-enabled sustainable communication using AI techniques. This approach increases systems’ reaction times in critical conditions and also controls the smart functionalities for inter-device communication. Furthermore, fitness computing can help in balancing the contribution of quality-aware metrics to achieve load balancing and efficient energy consumption. To deal with security, IoT communication is broken down into stages, resulting in a more dependable network for unpredictable environments. The simulation results of the proposed protocol have been compared to existing approaches and improved the performance of response time by 17%, energy consumption by 14%, number of re-transmissions by 16%, and computing overhead by 16%, under a varying number of nodes and data packets.
Deep Reinforcement Learning for Online VRC Deployment in Mobile Edge Computing

The improvement of communication capabilities and the surge in the number of mobile devices have led to the continuous emergence of a large number of computing-intensive mobile applications. Users have higher requirements for the services provided by the applications, which need high bandwidth and low latency communications. However, the existing traditional network architecture may not be able to achieve this requirement well. As a new network architecture and an extension of cloud computing, Mobile Edge Computing (MEC) is regarded as a promising solution to provide cloud-like computing services for users with low latency. While the number of MEC servers in a region is limited, so for services provider applying virtual machine replicas (VRCs) of the application on the MEC server is a good choice, which give rise to the VRCs deployment problem in the edge network. In this paper, we propose an online deep reinforcement learning method to deploy the VRCs on edge servers so as to minimize the data traffic. Extensive experimental results show that the proposed online deep reinforcement learning method can achieve near-optimal solution with a rather short computing time compared with the enumeration method and have a much better performance from both computing time and transmission delay perspective compared with the available algorithms in the literature.
Cloth manipulation based on category classification and landmark detection

Cloth manipulation remains a challenging problem for the robotic community. Recently, there has been an increased interest in applying deep learning techniques to problems in the fashion industry. As a result, large annotated data sets for cloth category classification and landmark detection were created. In this work, we leverage these advances in deep learning to perform cloth manipulation. We propose a full cloth manipulation framework that, performs category classification and landmark detection based on an image of a garment, followed by a manipulation strategy. The process is performed iteratively to achieve a stretching task where the goal is to bring a crumbled cloth into a stretched out position. We extensively evaluate our learning pipeline and show a detailed evaluation of our framework on different types of garments in a total of 140 recorded and available experiments. Finally, we demonstrate the benefits of training a network on augmented fashion data over using a small robotic-specific data set.
INFLUENCE OF CHANGING WAY OF THE ROTATIONAL MOUNTING POSITION ON CALIBRATION RESULTS IN REFERENCE TORQUE WRENCHES

In the calibration of torque measuring devices, measurement is to be repeated by changing the rotational mounting position concerned with the measurement axis as evenly as possible to the direction of rotation because the axi-asymmetric of sensing element and mechanical parts in the transducer contribute to the measurement uncertainty. However, in the calibration of reference torque wrenches, there are some kinds of changing way to change the rotational mounting position. Typical way is to change the angle of detachable square drive parts connected to the front of sensing elements in the transducer of torque wrench form. The other is to change the mounting position of sensing elements themselves. The author investigated to compare the calibration results between two ways for the mounting positions using various transducers. As a result, somewhat large differences of calibration results were occurred between two ways depending on transducers.
A Comparative Study of Benchtop and Portable NIR and Raman Spectroscopic Methods for the Quantitative Determination of Curcuminoids in Turmeric Powder

Turmeric consumption is continually increasing worldwide. Curcuminoids are major active constituents in turmeric and are associated with numerous health benefits. A combination of spectroscopic methods and chemometrics shows the suitability of turmeric for food quality control due to advantages such as speed, versatility, portability, and no need for sample preparation. Five calibration models to quantify curcuminoids in turmeric were proposed using benchtop and portable devices. The most remarkable results showed that Raman and NIR calibration models present an excellent performance reporting RMSEP of 0.44% w/w and 0.41% w/w, respectively. In addition, the five proposed methods (FT-IR, Raman, and NIR) were compared in terms of precision and accuracy. The results showed that benchtop and portable methods were in good agreement and that there are no significant differences between them. This study aims to foster the use of portable devices for food quality control in situ by demonstrating their suitability for the purpose.
Quo vadis MFA? Integrated material flow analysis to support material efficiency

Material efficiency (ME) strategies are pursued by governments and companies to reduce environmental emissions, improve supply chain resilience, and enhance cost competitiveness. Material flow analysis (MFA) methods are useful to quantify raw material stocks and flows and assess the potential impacts of ME strategies. Although the popularity of MFA methods is increasing, there are methodological limitations that hinder its use as a decision support tool for prospective assessments. To overcome some of these, MFA is increasingly integrated with other methods from different research fields. This paper categorizes integrated MFA methods by identifying and explaining the methods and their applications. A semi‐structured literature review screened a wide range of methods integrated with MFA prospective analysis and evaluated their applications from 158 studies. This showed that integrated MFA can be used to: (1) include economic, social, and environmental layers; (2) improve the technical foundation of MFA by including entropy and exergy analyses and process engineering methods; (3) include economic mechanisms and link the economic system to the material system; and (4) improve the representation of materials in existing methods and models. Our research demonstrates that integrated MFA should be a central method in planning and designing ME strategies for companies and governments. This paper provides an important knowledge base of integrated MFA methods and creates a discussion point on MFA, where the research field is currently at or indeed where it could be heading in the future, that is, “Quo Vadis” MFA.
From Theory to Practice: Development and Calibration of Micro Pellet Extruder for Additive Manufacturing

Pellet additive manufacturing (PAM) is in terms of printing process very similar to the widely used fused deposition modelling (FDM) systems. The main difference is the use of pellets instead of filament. In this study, a pellet, single screw extruder is developed. A screw design with variable pitch and depth is modelled analytically to predict the melting behavior of acrylonitrile butadiene styrene (ABS) during steady state extrusion. The extruder screw was designed unconventionally short with a length of 85 mm and a diameter of 20 mm, giving an L:D ratio of 4.25:1. The model predicted the melting profile for ABS being extruder at 235°C at 10 RPM, the result of which was confirmed to a degree by experimentation. The extrusion rate of the screw extruder was measured at barrel temperatures of 225°C to 245°C with 5°C increments and at 5 RPM and 10 RPM. The extrusion was found to increase non-linearly with barrel temperature and screw speeds. The extruder printed adequately on an FDM style motion system with minor upgrades.
Hawkeye: Eliminating Kernel Address Leakage in Normal Data Flows

The confidentiality of the operating system kernel addresses is crucial to keeping the kernel secure from malicious users. To avoid leaking this position, researchers have proposed various techniques to defeat the exploits of abnormal data flows embedded in the kernel's memory safety loopholes, such as uninitialized memory read and buffer over-reads. However, this is far from complete. The kernel address can be leaked even in normal data flows without exploiting memory safety loopholes. We have designed a static analysis tool named Hawkeye to fill the gap. It searches for kernel address leakages in normal data flows that reveal the kernel address clues. Hawkeye precisely identifies the kernel addresses with minimum manual annotation and scales to analyze the whole kernel source code. It requires nearly ten times fewer memory resources and 40 times less inspection time than the state-of-the-art tool that analyzes kernel address leakage. Hawkeye unveils hundreds of leakages in various versioned kernels. It has even discovered 20 bugs in the mainline Linux kernel with the kernel pointer hashing mechanism already deployed and three bugs in FreeBSD. All the corresponding patches have been accepted by the developers.
Restoration of euthyroidism with levothyroxine: implications of etiology of hypothyroidism and the degree of residual endogenous thyroid function

There are many thyroid-related factors that combine with non-thyroid-related factors in order to affect the patient response to treatment of hypothyroidism, in terms of their satisfaction with therapy. Some of the thyroid-derived factors include the etiology of the hypothyroidism and the amount of residual thyroid function that the patient retains. These two factors may be intertwined and affected by a third influence, the presence of thyroid peroxidase antibodies. The downstream consequences of the interactions between these three factors may influence both free thyroxine and free triiodothyronine levels, TSH concentrations, and various thyroid biomarkers. Evidence of the widespread importance of thyroid hormones can be inferred from the multiple genes that are regulated, with their regulation affecting multiple serum biomarkers. Thyroid biomarkers may extend from various well-known serum markers such as lipids and sex hormone-binding globulin to serum levels of thyroid hormone metabolites. Moreover, the interplay between thyroid hormones and biomarkers and their relative ratios may be different depending on the hypothyroidism etiology and degree of residual thyroid function. The ultimate significance of these relationships and their effect on determining patient-reported outcomes, quality of life, and patient satisfaction is, as yet, poorly understood. However, identification of better biomarkers of thyroid function would advance the field. These biomarkers could be studied and correlated with patient-reported outcomes in future prospective studies comparing the impact of various thyroid hormone therapies.
Outliers in Time Series

THE detection of outliers has mainly been considered for single random samples, although some recent work deals also with standard linear models; see, for example, Anscombe (1960) and Kruskal (1960). Essentially similar problems arise in time series (Burman, 1965) but there seems no published work taking into account correlations between successive observations. In the past, the search for outliers in time series has been based on the assumption that the observations are independently and identically normally distributed. This assumption leads to analyses which will be called random sample procedures. Two types of outlier that may occur in a time series are considered in this paper. A Type I outlier corresponds to the situation in which a gross error of observation or recording error affects a single observation. A Type II outlier corresponds to the situation in which a single "innovation" is extreme. This will affect not only the particular observation but also subsequent observations. For the development of tests and the interpretation of outliers, it is necessary to distinguish among the types of outlier likely to be contained in the process. The present approach is based on four possible formulations of the problem: the outliers are all of Type I; the outliers are all of Type II; the outliers are all of the same type but whether they are of Type I or of Type II is not known; and the outliers are a mixture of the two types. Since more practical solutions than those given by likelihood ratio methods are often obtained from simplifications of likelihood ratio criteria, some simpler criteria are derived. These criteria are of the form /&2a, where A is the estimated error in the observation tested and ^ is the estimated standard error of A. Throughout this paper, trend and seasonal components are assumed either negligible or to have been eliminated. The method adopted to remove these components might affect the results in some way.
High-Power Ridge Waveguide Four-Port Junction Circulator Based on Composite Post Resonators

An X-band high-power ridge waveguide (RW) four-port junction circulator is proposed in this article. It adopts two three-port RW junction circulators connected by a compact RW-to- coaxial-line transition in series with a parallel configuration topology. The composite post resonators are utilized in the three-port circulators. The theoretical analysis of composite post resonator and the design methods of the circulator have been demonstrated in detail. The RW transmission line, parallel configuration topology structure, and compact transition form make the cross-sectional size of the four-port circulator less than $0.9\lambda \times 0.95\lambda $ at the central frequency of 10 GHz. Measurements show that the 12-dB isolation bandwidths of the circulator are 40% with an insertion loss of 0.2–0.7 dB. High-power tests show that its peak power capacity can reach about 7 kW.
A QUALITY ASSURANCE FRAMEWORK FOR OERs BASED ON QUALITY SEALS AND THE PHOTODENTRO SEALS REPOSITORY

"Open Educational Resources (OERs) offer unique opportunities to enhance the quality of education and ensure equitable access to learning resources. A major concern, however, is the quality of OERs. This article presents a generic conceptual framework for the Quality Assurance (QA) of OERs based on Quality Seals and introduces the Photodentro Quality Seals (QS) Repository and e-service (photodentro.edu.gr/seals), which was initiated and developed to support the quality assurance processes of the Greek national digital OER repositories for primary and secondary education. The proposed QA framework is based on three main entities: Quality Seals (describing either a set of quality criteria, a well-defined quality assurance procedure, or a reliable source of origin); Quality Seal holders (e.g., OER certifiers); and Sealings (representing e.g. certificates for OERs). The Photodentro QS repository hosts and manages Quality Seals and Sealings for OERs, which reside on various OER repositories, and provides a registry of OER certifiers. Our experiences from its nationwide use are also discussed. "
An IT strategic decision-making framework in the midst of disruptive technologies

Technological disruption enabled by the internet revolution has resulted in an exponential increase in the growth of new technologies and the resultant technology disruptions in the workplace. Disruptive technologies are changing the rules of competition in organisations. Most chief information officers agree that there could be significant value in utilising new technology to create a competitive advantage in an agile world; however, in practice, the adoption and implementation of newer technology occur relatively slowly. There is very little research on factors influencing strategic IT decisions from a perspective of disruptive technologies. The objective of this study was to investigate critical factors that guide strategic IT decision-making in an agile business context. Using a literature review, various factors that had an influence on strategic IT decision-making in organisations were identified. The factors were extended and confirmed with data from semi-structured interviews resulting in the Framework for IT decision-making and a model for the classification of IT systems, which provide CIOs with comprehensive guidelines to make strategic IT decisions in the midst of disruptive technologies.