Miracle-dz/UniFAR-FTUs huggingface dataset:
Resource Management and Task Offloading Issues in the Edge–Cloud Environment

With the increasing number of Internet of Things (IoT) devices connected to the internet, a platform is required to support the enormous amount of data they generate. Since cloud computing is far away from the connected IoT devices, applications that require low-latency, real-time interaction and high quality of service (QoS) may suffer network delay in using the Cloud. Consequently, the concept of edge computing has appeared to complement cloud services, working as an intermediate layer with computation capabilities between the Cloud and IoT devices, to overcome these limitations. Although edge computing is a promising enabler for issues related to latency sensitivity, its deployment produces new challenges. Therefore, this paper presents and discusses the main factors of service latency for IoT applications, considering the impact of application characteristics (e.g., computation and communication demands), as well as resource utilization and heterogeneity at the edge level. Thus, a number of simulation experiments were set up to evaluate the influence of these factors. The outcomes of this research can be used to understand the complex interactions between many factors that affect the overall service time for latency-sensitive applications. Additionally, several open challenges are highlighted, serving as potential directions for future research.
The Role of Public Awareness Campaigns in Sustainable Development

The concept of sustainable development has been present in political discourse for many years. Striving to achieve it has become a worldwide priority. Success in this field depends not only on the right government policies. It is also a matter of society engagement. There are different direct and indirect tools intended to promote and reinforce positive changes in social attitudes. Among the most important ones are public awareness campaigns. The aim of the article is to present the ways in which they can contribute to sustainable development and how their positive impact should be properly assessed.
Welfare parameters and keel bone damage in laying hens reared in different production systems

The aim of this study was to determine the effect of housing system and the age of hens on welfare parameters and the prevalence of keel bone damage in laying hens. In this study two housing systems were evaluated: aviary system and enriched cages. From each system and age, we used 50 randomly selected hens from different cages and tiers. The results showed significant differences between systems in the type of keel bone damage. There was no significant difference in keel deviation between systems, but the higher prevalence of keel fractures was found in aviary system. In addition, significant effect of the age of hens was found on the occurrence of keel fractures. Footpad dermatitis had statistically higher occurrence in enriched cages at 62 weeks of age. Hens from aviary system had significantly better plumage score compared to hens from enriched cages but only at 42 weeks of age.
Decoding of 23 Unique Magnetic Nanobarcodes

The unmet demand for data privacy and brand protection in anti-counterfeiting has urged heavyweight industries to develop new nanomaterials for barcoding. Here, we tailor the remanence spectra of magnetic nanowires (MNWs) to make 23 new unique magnetic nanobarcodes signatures. Using arrays of 30 and 100 nm diameter MNWs, composed of FeCo, Ni, or Co, we establish design rules for generating diverse codes followed by the description of a simple decoding algorithm. When used together, remanence spectra and our decoding algorithm enabled unambiguous identification of 14 of 15 combinations of two nanobarcodes. When three nanobarcodes were present at the readout simultaneously, our algorithm was able to successfully identify 9 of 20 combinations. Our approach opens a promising path toward expandable encoding and reliable decoding of MNWs.
The EU and national sovereignty: the encounter of two concepts of sovereignty. Change or continuity?

The article aims to examine and critically evaluate the idea of sovereignty and the nuances of its verbal expression through the concept of strategic narratives, to reveal different models of sovereignty within the context of European Union Member States (mainly France, Hungary/Poland) and the European Union (EU) since 2017. The article seeks to answer the following questions: What idea of sovereignty has been projected to the public in selected European countries and the EU by their political actors since 2017? What model, functional limits and narratives do actors forge?
Substituent Effect on the Association Phenomenon of an Aqueous Solution of Benzylidene Substituted Aniline at Different Temperatures

Conductivity parameters of an aqueous solution of benzylidene substituted anilines (I-IV) have been determined at five different temperatures in the range of 283.15-318.15 o K, through accurate conductance measurements. The obtained data were analyzed in accordance to Lee-Wheaton equation taking into account the activity of the electrolyte in the solution. The conductance measurement results were used to determine the association constants (KA) of ion-pairs, the mean distance between ions in solution, in addition to the molar conductance at infinite dilution (Λo) for the free ion in the solution. The dependence of (KA) values on the temperature has been examined by plotting Log KA against the reciprocal values of temperature (1/T), which showed linear relationship, where (KA) values decrease with increasing temperature. Moreover, the enthalpy (∆H), Gibbs free energy (∆G) and the entropy (∆S) have been derived from this linear relationship.
Software Defect Prediction Using Supervised Machine Learning Techniques: A Systematic Literature Review

Software defect prediction (SDP) is the process of detecting defectprone software modules before the testing stage. The testing stage in the software development life cycle is expensive and consumes the most resources of all the stages. SDP can minimize the cost of the testing stage, which can ultimately lead to the development of higher-quality software at a lower cost. With this approach, only those modules classified as defective are tested. Over the past two decades, many researchers have proposed methods and frameworks to improve the performance of the SDP process. The main research topics are association, estimation, clustering, classification, and dataset analysis. This study provides a systematic literature review that highlights the latest research trends in the area of SDP by providing a critical review of papers published between 2016 and 2019. Initially, 1012 papers were shortlisted from three online libraries (IEEE Xplore, ACM, and ScienceDirect); following a systematic research protocol, 22 of these papers were selected for detailed critical review. This review will serve researchers by providing the most current picture of the published work on software defect classification.
CrySPY: a crystal structure prediction tool accelerated by machine learning

ABSTRACT We have developed an open-source software called CrySPY, which is a crystal structure prediction tool written in Python 3, and runs on Unix/Linux platforms. CrySPY enables anyone to easily perform crystal structure prediction simulations for materials discovery and design, and automates structure generation, structure optimization, energy evaluation, and efficiently selecting candidates using machine learning. Several searching algorithms are available such as random search, evolutionary algorithm, Bayesian optimization, and Look Ahead based on Quadratic Approximation. Machine learning is employed to efficiently select candidates for priority optimization. CrySPY does not require complex machine learning techniques for users. In the latest version of CrySPY, both atomic and molecular random structures can be generated. CrySPY supports VASP, QUANTUM ESPRESSO, OpenMX, soiap, and LAMMPS for local structure optimization and energy evaluation. CrySPY is distributed under the MIT license at https://github.com/Tomoki-YAMASHITA/CrySPY. Documentation of CrySPY is also available at https://Tomoki-YAMASHITA.github. io/CrySPY_doc. Graphical Abstract
Phosphorene for energy and catalytic application—filling the gap between graphene and 2D metal chalcogenides

Phosphorene, a newly emerging graphene analogous 2D elemental material of phosphorous atoms, is unique on the grounds of its natural direct band gap opening, highly anisotropic and extraordinary physical properties. This review highlights the current status of phosphorene research in energy and catalytic applications. The initial part illustrates the typical physical properties of phosphorene, which successfully bridge the prolonged gap between graphene and 2D metal chalcogenides. Various synthetic methods available for black phosphorus (BP) and the exfoliation/growth techniques for single to few-layer phosphorene are also overviewed. The latter part of this review details the working mechanisms and performances of phosphorene/BP in batteries, supercapacitors, photocatalysis, and electrocatalysis. Special attention has been paid to the research efforts to overcome the inherent shortcomings faced by phosphorene based devices. The relevant device performances are compared with graphene and 2D metal chalcogenides based counterparts. Furthermore, the underlying mechanism behind the unstable nature of phosphorene under ambient condition is discussed along with the various approaches to avoid ambient degradation. Finally, comments are offered for the future prospective explorations and outlook as well as challenges lying in the road ahead for phosphorene research.
Comparative hypoxia responses and oxygen sensing in galaxiid fishes

Aquatic ecosystems are inherently characterised by limited oxygen availability and fluctuations in dissolved oxygen concentration, and therefore, environmental hypoxia (i.e. low oxygen conditions) is common. However, in recent decades, the frequency and global expansion of hypoxic environments have increased significantly, owing to intensified anthropogenic impacts on the natural environment. Fish depend on environmental oxygen for oxidative metabolism. In the absence of oxygen, alternative anaerobic mechanisms are in place to maintain energy metabolism. Efficiency and productivity of anaerobic metabolism are, however, comparatively low and may become rapidly insufficient in light of high physiological and metabolic oxygen demand. Correspondingly, substrates used in the oxygen-independent metabolism often are limited, and are converted into metabolites detrimental for the organism. When anaerobic mechanisms fail to meet the energy demand and lethal levels of anaerobic by-products have accumulated, hypoxic death is initiated. In this context, a variety of adaptive hypoxia-response strategies and hypoxia-sensitivities are established in fish, in dependence on species-specific oxygen demand and life strategies, thereby potentially creating very specific environmental niches occupied by distinct species. Accordingly, fish may demonstrate distinct habitat requirements and preferences in relation to the oxygen environment, potentially reflecting speciesspecific hypoxia sensitivities and response capacities, that may ultimately initiate adaptive radiation. In this context, a range of studies has been undertaken previously in a variety of species, however these are difficult to evaluate in a comparative context due to pronounced taxonomic differences between study
Joint Sparse Observation and Coding Design for Multiple Phenomena Monitoring

Energy-efficient designs play an important role in the Internet of Things (IoT) that monitors multiple phenomena, due to the limited power supply and complicated observation. In this paper, taking into account the power consumptions of observation, coding, and communication, we propose a joint sparse observation and coding scheme for energy-efficient monitoring of multiple phenomena using IoT. Through the analysis of outage performance, we find that the sparse observation and coding scheme can achieve the performance of the full observation scheme in which all nodes observe all phenomena with lower power consumption due to the dynamic and selective observation and coding. With the derived achievable rates and network power consumption, we study the trade-off between achievable rates and network power consumption that is determined by both the observation matrix and the coding matrix. For given rate constraints, we propose an optimization problem to minimize the network power consumption by jointly designing the observation and coding matrices. To solve this NP-hard problem efficiently, we propose a low-complexity algorithm with the convex-concave procedure. Moreover, to improve performance in high noise environment, we adopt collaboration among nodes to suppress observation noises and equalize bad observations by utilizing observation diversity. Finally, simulation results illustrate the superior performance of the proposed schemes.
Quantitative Genes Controlling Chlorophyll Fluorescence Attributes in Barley (Hordeum vulgare L.)

Chlorophyll fluorescence is one of the very useful techniques in plant physiology because of the ease with which the user can gain detailed information on the state of photosystem II (PSII) at a relatively low cost. Detection of quantitative traits loci related to chlorophyll fluorescence have a major role in understanding the genetic mechanisms of photosynthesis. In the present research, to mapping, the genome regions controlling chlorophyll fluorescence traits, barley (Hordeum vulgare L) from 106 F8 recombinant inbred lines caused by crossing two cultivars of Badia × Kavir was used and these lines were cultured in a complementary randomized design with two replications. Traits studied include ABS/CSo, TRo/CSo, DIo/CSo, ABS/CSm, DIo/CSm, psi (Eo), TRo/RC, REo/RC, ABS/RC, DIo/RC, Area, Fv/Fm, Sm. Linkage maps were prepared using 152 SSR polymorphic markers, 72 ISSR, 7 IRAP, 29 CAAT, 27 Scot, and 15 iPBS alleles. Molecular markers were assigned on 7 chromosomes of barley. The linkage map covered 999.2 cM of the barley genome and the average distance between two flanking markers was 3.387 cM. Three major QTLs were identified for Area, psi (Eo), and Dio/Rc on Chromosome 6 between ISSR31-1-Bmag0867 in position 62 Centimorgan that explained 17.2%, 31.5%, and 15.9%, respectively. Also, another colocation was detected for ABS/CSo, TRo/CSo, ABS/CSm, and DIo/CSm QTLs on chromosome 6 in position 72 Centimorgan. The results obtained in the present research provide valuable information on the genetic basis of the Chlorophyll fluorescence parameters that can be used in the barley breeding program, including marker-assisted selection.
CHEMICAL CONSTITUENTS ISOLATED FROM ORİGANUM SOLYMICUM WITH ANTIOXIDANT ACTIVITITES

Origanum genus includes 43 species and 18 hybrids. Sixteen species are endemic for Turkey. Due to the spicy fragrance, Origanum has been widely used in food products as flavoring agents. These species are well known for their essential oils which have been applied in food and cosmetic industries. Origanum Solymicum P.H. Davis was collected from Kemer, Antalya. The aerial part of plant materials were dried and powdered then boiled in water. After filtration, water phase was partitioned with ethyl acetate and n-butanol sequentially. After removal of the solvent by rotary evaporator, the chromatographic methods were applied for ethyl acetate extract to isolate the bioactive compounds. 3,4-dihdroxy benzoic acid (protocatechuic acid) (1), 3,4-dihydroxy benzaldehyde (protocatechuic aldehyde) (2), 2- O-s -D- glycopyronozyl-4,6-dihydroxyacetophenone (myrciaphenone) (3), caffeic acid (4), rosmarinic acid (5), lithospermic acid B (6) were isolated by chromatographic techniques (column chromatography, preparative HPLC) and spectroscopic methods (1D, 2D NMR, LC-TOF/MS) were used for elucidation of isolated compounds. All isolated natural products revealed the outstanding activities on antioxidant assays including DPPH • free radical scavenging, ABTS •+ radical cation scavenging, and reducing power (FRAP).
Developing a Resource Allocation Approach for Resource-Constrained Construction Operation under Multi-Objective Operation

In the construction industry, it is of great importance for project managers (PM) to consider the resource allocation arrangement problem based on different perspectives. In this situation, the management of resources in construction becomes a challenge. Generally speaking, there are many objectives that need to be optimized in construction that are in conflict with each other, including time, cost, and energy consumption (EC). This paper proposed a multi-objective optimization framework based on the quantum genetic algorithm (QGA) to obtain the best trade-off relationship among these goals. The construction resources allocated in each construction activity would eventually determine its execution time, cost, and EC, and a complexed time-cost-energy consumption trade-off framework of the project is finally generated due to correlations between construction activities. QGA was performed to find the best combination among time, cost, and EC and the optimal scheme of resource arrangement under this state. The construction process is simulated in BIM to check the rationality of this resource allocation mode. An industrial plant office building in China is presented as an example to illustrate the implementation of the proposed model. The results show that the presented method could effectively reduce 7% of cost, 17% of time, and 21% of energy consumption. This developed model is expected to help PMs to solve the problem of multi-objective optimization with limited resource allocation.
Toothwise Health Monitoring of Planetary Gearbox Under Time-Varying Speed Condition Based on Rotating Encoder Signal

To meet industrial demand, plenty of research works have been dedicated to monitoring the health status of planetary gearboxes. For the same purpose, a new path is explored based on rotating encoder signal (RES) by considering time-varying speed condition. In proposed method, generalized demodulation is firstly applied on second-order derivative of RES to eliminate the frequency variability caused by time-varying speed, and a combined comb filter is performed in this process to improve the signal-to-noise ratio of demodulated signal. The toothwise meshing information is then integrated as individual samples for the detection of local anomalies induced by gear defect. After reducing the dimensionality of these toothwise samples by reordered kernel principal component analysis, a normalized robust statistical distance is constructed to indicate the anomalies contained in RES. Simulation and experimental cases are provided to reveal the efficacy of the proposed method.
Determining the Sex-Specific Distributions of Average Daily Alcohol Consumption Using a Machine Learning Method: Is There a Separate Distribution for People With Alcohol Dependence?

BackgroundIt remains unclear whether alcohol use disorders (AUDs) can be characterized by specific levels of average daily alcohol consumption. The aim of the current study was to model the distributions of average daily alcohol consumption among those who consume alcohol and those with alcohol dependence, the most severe AUD, using various machine learning techniques. MethodsData from Wave 1 and Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions were used in the current analyses. Clustering algorithms were applied in order to group a set of data points that represent the average daily amount of alcohol consumed. Gaussian Mixture Models (GMMs) were then used to estimate the likelihood of a data point belonging to one of the mixture distributions. Individuals were assigned to the clusters which had the highest posterior probabilities from the GMMs, and their treatment utilization rate was examined for each of the clusters. ResultsModeling alcohol consumption via machine learning technique was feasible. The clusters identified did not point to alcohol dependence as a separate cluster characterized by a higher level of alcohol consumption. Among both females and males with alcohol dependence, daily alcohol consumption was relatively low. ConclusionsOverall, we found little evidence for clusters of people with the same drinking distribution, which could be characterized as clinically relevant for people with alcohol use disorders as currently defined.
Peritoneal dialysis in Tunisia: complications, technique and patient’s survival (twenty-seven years of experience in a single center)

Introduction peritoneal dialysis (PD) is a renal replacement therapy method that offers various advantages to end-stage renal disease patients. The aim of our study was to analyze patient characteristics, peritonitis and clinical outcome over a 27-year period of PD in our center. Methods retrospective study of incident patients on PD from January 1990 to December 2017. A total of 304 patients were enrolled in the study group. All patients over 15 years of age entering the dialysis program were included in the study. Patients dropping out from PD within three months were all excluded. Biochemical and demographic variables, peritonitis episodes and patient and technique survival were analyzed. Results the PD prevalence in our center was 4.5% during the study period; the mean age was 46.47 ± 18.6 years; diabetic nephropathy was the main cause of chronic kidney disease: 35.5% (n=108). Cardiovascular disease was the main cause of death: 39.6% (n=34). The peritonitis rate was 0.68 episode per patient-year. Ultrafiltration failure was the most important cause of PD withdrawal: 43% (n=60). Occurrence of peritonitis was the only independent predictor of technique failure: adjusted relative risk [aRR] 5.07, 95% CI 2.69-9.58; p<0.001. The overall non-adjusted patient survival was around 100%, 95% and less than 20% at 1, 4 and 25 years respectively basing on the Kaplan-Meier analysis. The group undergoing renal transplantation had the best survival rate. Conclusion peritonitis remains the most common complication as well as the most provider of technique failure and patient´s transfer to hemodialysis. The technique survival was better in case of absence of peritonitis. However, our series showed the superiority of hemodialysis over PD in terms of overall patient survival.
Green cross-dock based supply chain network design under demand uncertainty using new metaheuristic algorithms

This study concerns the optimization of green supply chain network design under demand uncertainty. The issue of demand uncertainty has been addressed using a scenario-based analysis approach. The main contribution of this research is to investigate the optimization of cross-dock based supply chain under uncertainty using scenario-based formulation and metaheuristic algorithms. The problem has been formulated as a two-objective mathematical model with the objectives of minimizing the costs and minimizing the environmental impact of the supply chain. Two metaheuristic algorithms, namely non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective invasive weed optimization (MOIWO), have been developed to optimize this mathematical model. This paper focuses on the use of new metaheuristic algorithms such as MOIWO in green supply chain network design, which has received less attention in previous studies. The performance of the two solution methods has been evaluated in terms of three indices, which measure the quality, spacing, and diversification of solutions. Evaluations indicate that the developed MOIWO algorithm produces more Pareto solutions and solutions of higher quality than NSGA-II. A performance test carried out with 31 problem instances of different sizes shows that the two methods perform similarly in terms of the spread of solutions on the Pareto front, but MOIWO provides higher quality solutions than NSGA-II.