Miracle-dz/UniFAR-FTUs huggingface dataset:
State of Health Optimization Based Unequal Clustering in IoT Networks

Energy optimization is an imminent worldwide issues for green computing, it constitutes a major concern and a critical aspect especially for energy constrained wireless networks. To overcome this issue, clustering techniques were introduced as a prominent method that arranges the system operation in correlated manner to attend the energy preservation and prolong the network lifespan. However, existing clustering works only focus on preserving the battery charge to operate until it drains out. This approach is most appropriate for non-rechargeable batteries. However, rechargeable batteries become commonly used and need to be considered. The full discharge of rechargeable battery does not mean the device obsolescence. Therefore, the system lifetime optimization should take into consideration the degradation of the rechargeable batteries performances. In this context, we proposed an improved long-term energy efficient unequal clustering approach based on the battery state of health for IoT networks (ILEC_SOH). This work represents an initial step in the integration of the battery health degradation into the unequal network clustering. The obtained results show that the consideration of battery state of health (SOH) significantly improve the network lifespan in the long term compared to the conventional energy efficient approaches.
From “Sit Still and Listen” to “Get Up and Move,” the Leap May Be One of Educational Paradigms but No Longer One of Faith

INTRODUCTION We live in an era inwhich opportunities for physical activity do not arise naturally in the course of daily life. The industrialized way of life and technological advancements contribute to physical inactivity. If we want children to perform the recommended amount of daily physical activity, it is the responsibility of adults to create appropriate conditions for children to have opportunities for movement. Even more effort should be invested in stimulating the desire andmotivation of children to be and remain physically active. Although the school environment can be an ideal setting for comprehensive physical activity intervention programs, the pressure on schools to prioritize academic achievement has led many school administrators to view calls for increased physical activity opportunities as unrealistic in the context of limited time and resources. However, the body of evidence, while still developing, is strong enough to support the conclusion that physical activity does not interfere with or take time away from learning. To the contrary, it is beneficial for academic achievement and school success (1,2). These benefits have been recognized by several governmental agencies and professional organizations over the last decade (i.e., the American College of Sports Medicine, the Institute of Medicine, and SHAPE America). Evidence-based position papers issued by such organizations have helped in raising awareness and promoting engagement among school staff and policymakers toward increasing opportunities for physical activity at schools. For example, it is remarkable that, for the first time, a collaborative effort emerged between a leading educational agency (the Association for Supervision and Curriculum Development) and a national health organization (the Centers for Disease Control and Prevention), with the joint goal of promoting learning and health through a whole-school, wholecommunity, whole-child model (WSCC model [3]).
Toward an Adaptive Situational Awareness Support System for Urban Driving

A lack of sufficient situational awareness is a primary cause of traffic crashes due to human error. Redirecting a driver’s attention to critical objects is essential, but alerting driver about all critical objects can lead to distraction. This paper develops and evaluates an adaptive support system that incorporates drivers’ fixations as a proxy for their situational awareness. We implement an experimental system that detects a driver’s gaze on important objects in the traffic scene and adapts a cueing strategy in an augmented reality-based driver awareness assistance interface. We collect and analyze data from 15 participants and show that our adaptive support system strategy is effective without increasing the drivers’ cognitive workload. Finally, we show that our system can increase ratio of drivers’ fixations on critical objects in their view without significantly increasing dwell time per object.
Beyond normal: Guidelines on how to identify suitable model input distributions for building performance analysis

Building modelling is a valuable tool in the widespread eﬀorts to decarbonise the built environment. To ensure modelling robustness, uncertainty and sensitivity analysis techniques are often used. Such techniques commonly require model input distributions to be deﬁned. This paper describes a novel approach, within the built environment, for identifying empirically-derived probability distributions of model inputs. Following data cleaning, candidate distributions selected based on measures of skewness and kurtosis are ﬁtted using maximum likelihood estimation. The distribution that best describes the dataset is identiﬁed using Akaike Information Criterion and its derivatives along with goodness-of-ﬁt plots. The method was demonstrated for a dataset of wall U-value measurements in English homes.
C-Myc Expression in Oral Squamous Cell Carcinoma: Molecular Mechanisms in Cell Survival and Cancer Progression

Oral squamous cell carcinoma (OSCC) represents 90% of malignant epithelial cancer that occurs in the oral cavity. The c-Myc factor is expressed in multiple types of cancer, comprising head and neck squamous cell carcinoma (HNSCC), where it plays a fundamental role in tumor prognosis and in the self-renewal of tumor stem cells. However, the role of c-Myc in controlling OSCC cells is not well-known. The aim of the present study is the evaluation of the biological roles and regulatory mechanism of c-Myc in the pathogenesis of OSCC. Results indicated that c-Myc, c-Jun, Bcl-2, hypoxia inducible factor-1α (HIF-1α), vascular endothelial growth factor (VEGF), matrix metalloproteinase-9 (MMP-9), ERK 1/2 and pERK1/2 were overexpressed in a cellular model of squamous cell carcinoma, Cal-27. Doxorubicin (Doxo), a common chemotherapeutic agent, inhibited cell invasion, hypoxia, angiogenesis and inflammation in a cellular model of Cal-27 cells as indicated by downregulation of MMP-9, VEGF, ERK 1/2 and pERK 1/2 as well as promoted apoptosis as evidenced by the downregulation of Bcl-2 protein. This work aimed at underlying the functional relevance of c-Myc in OSCC and the HIF-Myc collaboration by integrating the knowledge on this molecular link in an OSCC tumor microenvironment. The results obtained showed for the first time the vital role of c-Myc in Cal-27 in cell survival/proliferation and tumor growth as well as the negative regulatory effect of Doxo against c-Myc signaling pathway.
The Combination Shift $QZ$ Algorithm

An extension of the $QZ$ algorithm, called the combination shift $QZ$ algorithm, is presented for solving the generalized matrix eigenvalue problem $Ax = \lambda Bx$ with real square matrices A and B. Features and properties of the various $QR$-type algorithms which were not practical or possible to implement in the $QZ$ algorithm are generalized and implemented in this new algorithm. The emergence of infinite eigenvalues under exact arithmetic is discussed in detail. Results of numerous test cases are presented to give practical application tests and comparisons for the algorithm.
Indoor Air Pollution and the Health of Vulnerable Groups: A Systematic Review Focused on Particulate Matter (PM), Volatile Organic Compounds (VOCs) and Their Effects on Children and People with Pre-Existing Lung Disease

Air pollution affects health, but much of the focus to this point has been on outdoor air. Higher indoor pollution is anticipated due to increasingly energy-efficient and less leaky buildings together with more indoor activities. Studies of indoor air pollution focusing on children and people with respiratory disease from the database Web of Science (1991–2021) were systemically reviewed according to the PRISMA guidelines, with 69 studies included in the final selection. Emissions from building materials affected indoor air quality, and ventilation also had an influence. The main indoor air pollutants are Volatile Organic Compounds (VOCs) and Particulate Matter (PM). PM sources included smoking, cooking, heating, candles, and insecticides, whereas sources of coarse particles were pets, housework and human movements. VOC sources included household products, cleaning agents, glue, personal care products, building materials and vehicle emissions. Formaldehyde levels were particularly high in new houses. Personal exposure related to both indoor and outdoor pollutant levels, highlighting home characteristics and air exchange rates as important factors. Temperature, humidity, educational level, air purifiers and time near sources were also related to personal exposure. There was an association between PM and Fractional exhaled Nitric Oxide (FeNO), lung function, oxygen saturation, childhood asthma and symptoms of chronic obstructive pulmonary disease (COPD) patients. High VOCs were associated with upper airways and asthma symptoms and cancer. Effective interventional studies for PM in the future might focus on human behavior together with air purifiers and increased ventilation, whereas VOC interventions might center more on building materials and household products, alongside purification and ventilation.
Carbohydrate Status of Mycorrhizal and Nonmycorrhizal Citrus Rootstocks1

Carbohydrates were analyzed in mycorrhizal and nonmycorrhizal citrus rootstock seedlings in greenhouse studies. Inoculated seedlings grew taller or weighed more and their leaves contained greater amounts of total soluble sugar, sucrose, reducing sugars, starch, and total nonstructural carbohydrate per gram of tissue than noninoculated controls, in a low P soil (9–12 ppm). Only the reducing sugars, fructose and glucose, increased slightly in roots of inoculated seedlings over those levels found in uninoculated control seedlings. Levels of reducing sugars were higher in leaves than in roots. Uninoculated seedlings grown in high P soil (210 ppm) were about the same height and their leaves contained levels of total soluble and reducing sugars similar to those in inoculated rootstocks grown in low P soil. It does not appear that these mycorrhizal fungi mobilize sugars as a sink for photosynthate in roots.
Mosquito-Borne Arboviruses in Brazil: Assessment of Apps Based on the Mobile Apps Rating Scale (MARS)

BACKGROUND: In Brazil, the prevalence of arboviral diseases, such as dengue, Zika, and Chikungunya, transmitted mainly by mosquitos, has increased alarmingly. In recent years, numerous free mobile apps tackling this issue have become available for various purposes and users. OBJECTIVES: This study aimed to systematically survey and evaluate these apps using the Mobile App Rating Scale (MARS). METHODS: The survey was performed on Google Play Store and sought to identify these apps adopting the descriptors “Chikungunya”, “Dengue” and “Zika”. The MARS scale was used by two researchers to evaluate the apps following their translation to Portuguese and subsequent validation. Student's T-test, Kappa statistics, and Cronbach's alpha coefficient were employed to evaluate the interobserver agreement and the reliability of the scale. RESULTS: Most apps (20 out of 29 or ~70%) were created to disseminate basic information about arboviral diseases to the population or for entertainment. There was an agreement between the two researchers for all parameters of the MARS scale, except for the engagement (p=0.002). The Cronbach's alpha coefficient indicated good reliability. CONCLUSIONS: The use of the MARS scale has shown that most of the evaluated apps were developed to share information about arboviral diseases in an interactive way, but they do not necessarily have the purpose of influencing their users to change behaviours related to vector control or the prevention of arboviral diseases, which the authors feel would be a more appropriate aim for future app development.
Short-term forecasting of building energy consumption with deep generative learning

Short-term forecasting of building energy consumption is highly valuable from both technical and economic point of views. In this paper, a deep generative learning method taking account of short-term future meteorological data is proposed to forecast building energy consumption in the next 24 hours. A conventional multilayer perceptron and a non-meteorology version of the proposed GAN-based model were developed and comparatively tested as baseline models. Multi-year hourly meteorological data and actual energy consumption measurements from two office buildings in Shanghai were used for modelling and testing. The proposed model significantly outperformed the baseline models in all granularity settings. Decent cross-case generalisability of the proposed GAN-based models were demonstrated.
Comparison of Body Weight between Albanian Children and European Peers in Primary Schools

One of the biggest health challenges today is controlling body weight gain which leads to overweight and obesity. In order to anticipate and monitor the possible problem of overweight/ obesity, this study was undertaken in Albanian children to identify the current status of body weight in primary schools and to compare the children of the same age in Europe for the weight status. A total of 1888 elementary school children (939 boys and 949 girls) aged 6-9 years old) were enrolled in this study. Body mass were measured using a Health O Meter 402 KL professional physician beam scale. The results from this scientific work show that boys in Albania have a higher body weight (p≤0.05) compared to girls (6-9 years). Comparative data from this study on body weight between Albanian and European children show that Albanian boys and girls in elementary school (ages 6-9) are ranked among the lower body weight children compared to European counterparts. This data would be a great help in creating a clear picture of the trends of body weight growth over the years in Albanian children. These data are very important for the consequences on public health given that overweight children have a tendency to become overweight adults.
Quantization Framework for Fast Spiking Neural Networks

Compared with artificial neural networks (ANNs), spiking neural networks (SNNs) offer additional temporal dynamics with the compromise of lower information transmission rates through the use of spikes. When using an ANN-to-SNN conversion technique there is a direct link between the activation bit precision of the artificial neurons and the time required by the spiking neurons to represent the same bit precision. This implicit link suggests that techniques used to reduce the activation bit precision of ANNs, such as quantization, can help shorten the inference latency of SNNs. However, carrying ANN quantization knowledge over to SNNs is not straightforward, as there are many fundamental differences between them. Here we propose a quantization framework for fast SNNs (QFFS) to overcome these difficulties, providing a method to build SNNs with enhanced latency and reduced loss of accuracy relative to the baseline ANN model. In this framework, we promote the compatibility of ANN information quantization techniques with SNNs, and suppress “occasional noise” to minimize accuracy loss. The resulting SNNs overcome the accuracy degeneration observed previously in SNNs with a limited number of time steps and achieve an accuracy of 70.18% on ImageNet within 8 time steps. This is the first demonstration that SNNs built by ANN-to-SNN conversion can achieve a similar latency to SNNs built by direct training.
By Draconis and RS Canum Venaticorum Stars: The Discoveries of Classical Photometry and Spectroscopy

The RS CVn group, as defined by Hall, consists of detached binaries both components of types F, G, or K. The orbital periods are from a few days to a few weeks, and the mass ratios are generally near unity. Spectroscopically, the stars are remarkable in that they show very strong Ca II H and K emission outside eclipse. Masses and radii are known for many of these systems; physical parameters indicate these systems contain a moderately evolved subgiant, with mass slightly greater than 1 M@. The ages (which may be verified by data on visual companions) are a few 10 years.
The Effect of Capping Agents on Gold Nanostar Stability, Functionalization, and Colorimetric Biosensing Capability

Capping agents (organic ligands, polymers, and surfactants) are pivotal for stabilizing nanoparticles; however, they may influence the surface chemistry, as well as the physico-chemical and biological characteristics, of gold nanostar (AuNS)-based biosensors. In this study, we proved that various capping agents affected capped and bioconjugated AuNS stability, functionality, biocatalysis, and colorimetric readouts. Capped and bioconjugated AuNSs were applied as localized surface plasmon resonance (LSPR)-based H2O2 sensors using glucose oxidase (GOx) as a model enzyme. Furthermore, our analyses revealed that the choice of capping agent influenced the properties of the AuNSs, their stability, and their downstream applications. Our analyses provide new insights into factors governing the choice of capping agents for gold nanostars and their influences on downstream applications with conjugated enzymes in confined environments.
University of Birmingham Critical percolation on random regular graphs

We show that for all d ∈ { 3 , . , n − 1 } the size of the largest component of a random d -regular graph on n vertices around the percolation threshold p = 1 / ( d − 1) is Θ( n 2 / 3 ), with high probability. This extends known results for ﬁxed d ≥ 3 and for d = n − 1, conﬁrming a prediction of Nachmias and Peres on a question of Benjamini. As a corollary, for the largest component of the percolated random d -regular graph, we also determine the diameter and the mixing time of the lazy random walk. In contrast to previous approaches, our proof is based on a simple application of the switching method.
Soil pH, Calcium Content and Bacteria as Major Factors Responsible for the Distribution of the Known Fraction of the DNA Bacteriophage Populations in Soils of Luxembourg

Bacteriophages participate in soil life by influencing bacterial community structure and function, biogeochemical cycling and horizontal gene transfer. Despite their great abundance, diversity, and importance in microbial processes, they remain little explored in environmental studies. The influence of abiotic factors on the persistence of bacteriophages is now recognized; however, it has been mainly studied under experimental conditions. This study aimed to determine whether the abiotic factors well-known to influence bacteriophage persistence also control the natural distribution of the known DNA bacteriophage populations. To this end, soil from eight study sites including forests and grasslands located in the Attert River basin (Grand Duchy of Luxembourg) were sampled, covering different soil and land cover characteristics. Shotgun metagenomics, reference-based bioinformatics and statistical analyses allowed characterising the diversity of known DNA bacteriophage and bacterial communities. After combining soil properties with the identified DNA bacteriophage populations, our in-situ study highlighted the influence of pH and calcium cations on the diversity of the known fraction of the soil DNA bacteriophages. More interestingly, significant relationships were established between bacteriophage and bacterial populations. This study provides new insights into the importance of abiotic and biotic factors in the distribution of DNA bacteriophages and the natural ecology of terrestrial bacteriophages.
Artificial Neural Networks and Knowledge-Based Systems: Complementary AI Techniques for Civil Engineering

This paper considers the feasibility of integrating Artificial Neural Networks (ANNs) and Knowledge-Based Systems (KBSs) to solve civil engineering problems. KBSs are superior at representing human judgments, and in solving problems by reasoning with heuristic knowledge. ANNs offer different problem-solving characteristics including learning from example, recognizing patterns, and processing data in parallel. The integration of both technologies provides a hybrid system with the merits of both techniques, and with a broader scope of application within civil engineering. Consideration is given to the advantages of ANN and KBS hybrids, alternative methods of integration, and their potential applications.
Influence of the ears canals location on spherical head model for the individualized interaural time difference

The spatialization rendering with binaural synthesis is still limited by the individualization of the spatial filters, which are usually composed of a pure delay for the interaural time difference (ITD) and a minimumphase filter for the spectral cues. This paper deals with the individualization of the ITD based on physical modeling. Individualized ITDs are well predicted by a spherical-head model with a customized radius derived from an empiric linear combination of three head anthropometric parameters describing the listener’s head. However it can be observed that the constant ITD contours computed from the spherical-head model are concentric circles, whereas the constant ITD contours extracted from measured HRTFs show deviations from these circles, especially for azimuths near to the interaural axis. The location of the ears with respect to the center of the head seems to be the main factor to reproduce these deviations. The authors propose to introduce a parameter governing the ears’ shifting, so as to break the symmetry of the spherical-head model. This idea leads to a new formula for the computation of individualized ITDs. The formula is tested by comparison with measured data. The authors also examine to what extent the deviations of the measured ITD along the circles contribute to the individualization. Psycho-acoustical experiments are performed to answer this question.
Prevalence, clinical features and associated factors of keratoconus patients attending ophthalmology department, King Khalid Hospital, Hail city, Saudi Arabia

Introduction: Keratoconus is a non-inflammatory, progressive and usually bilateral corneal disorder that leads to considerable alterations in the shape, thickness and biomechanical properties of the cornea. The incidence varies globally depending upon the geographic location, the ethnic groups and diagnostic criteria used. Methods: Retrospective study, review the prevalence, demographic features and associated factors of the outpatients who have been diagnosed as keratoconus through the years 2010 2014. Results: Forty-Four eyes reviewed, the prevalence of keratoconus among ophthalmology department was 5-6 per 12,000 patients each year. The greatest age group affected was 20 29 years old (60.8%), slightly common in female compared to male (52.2%) with majority affected in both eyes (78.3%). The commonest presenting symptom was painless progressive decrease of vision (95.65%), itching (56.21%) and intolerance lenses (39.13%). Clinical profile started by visual acuity the greatest had 6/30 meter (29.3%) then count finger (14.9%), for K-reading mostly they had severe K-reading > 52 (56%), intraocular pressure was within normal (95.2%); high myopia > 6.00 for around half of the patients (41.7%) with absolute astigmatism in all patients. Slit Lamb examination the commonest found cunning (61%) then scaring of cornea (24.4%) and one case with acute bilateral hydro-drop. Discussion: To our knowledge, the current study is the first study of keratoconus in northern region of Saudi Arabia. Demographic features were parallel to the pervious published studies of keratoconus. Moreover, in this study was found a significant relationship between increasing in the age and the severity of the presenting features of keratoconus.