Miracle-dz/UniFAR-FTUs huggingface dataset:
Bibliometric Studies on Multi-Criteria Decision Analysis (MCDA) Applied in Personnel Selection

Personnel selection is increasingly proving to be an essential factor for the success of organizations. These issues almost universally involve multiple conflicting objectives, uncertainties, costs and benefits in the decision-making process. In this context, due to the characteristics of human resources problems, composed of several subjective evaluation criteria, such problems can be solved by applying Multiple Criteria Decision Aid (MCDA) methods. This paper aims to present a literature review on the main applications of MCDA in the personnel selection area, considering the tactical, operational and strategic spheres. The methodology includes a bibliometric study and a literature review of documents from the Scopus database. We identified the document type, year of publication, affiliation, authors, author’s H-index, the field of knowledge, country and keyword clusters. The literature review allowed us to verify that the Fuzzy Logic is the most applied methodology in personnel selection problems with MCDA, due to its capacity to handle vague, imprecise and subjective data.
Performance Evaluation of Homing Guidance Laws for Tactical Missiles

Abstract : A number of homing guidance laws, designed using principles of modern stochastic control theory, are compared using a recently developed analytical technique (CADET) for propagating the statistics of nonlinear, randomly excited systems. The guidance laws include proportional guidance, optimal linear guidance, and optimal nonlinear guidance. Each law is evaluated in a realistic nonlinear model of the missile guidance system and the homing geometry. This work provides a direct comparative evaluation of several important guidance techniques, and extends the usefulness of CADET for analyzing nonlinear guidance systems.
In-Situ Growth of Nucleus Geometry to Dual Types of Periodically Ringed Assemblies in Poly(nonamethylene terephthalate)

Monitoring of nucleus geometry and growth into dual types of periodically ring-banded morphology in poly(nonamethylene terephthalate) (PNT), respectively, Type-1 and Type-2, are done with detailed analyses using polarized-light optical microscopy (POM) in-situ CCD recording; the periodic assembly morphologies are characterized using atomic-force microscopy (AFM) and scanning electron microscopy (SEM). Different annealing treatments (Tmax = 110, 120, 130 °C) are accomplished at a crystallization temperature of 85 °C; effects on the nucleus geometry, number (25–10%) and volume fractions (33–15%) of Type-2 among two types of banded PNT spherulites are expounded. Growth of a specific type of periodically banded PNT spherulite is initiated from either highly elongated sheaf-like or well-rounded nuclei, with the final grown lamellae being self-packed as multi-shell structures. Nucleation geometry and crystallization parameters collectively lead to development of multiple types of banded PNT spherulites of different relative fractions.
MeF-RAM: A New Non-Volatile Cache Memory Based on Magneto-Electric FET

Magneto-Electric FET (MEFET) is a recently developed post-CMOS FET, which offers intriguing characteristics for high-speed and low-power design in both logic and memory applications. In this article, we present MeF-RAM, a non-volatile cache memory design based on 2-Transistor-1-MEFET (2T1M) memory bit-cell with separate read and write paths. We show that with proper co-design across MEFET device, memory cell circuit, and array architecture, MeF-RAM is a promising candidate for fast non-volatile memory (NVM). To evaluate its cache performance in the memory system, we, for the first time, build a device-to-architecture cross-layer evaluation framework to quantitatively analyze and benchmark the MeF-RAM design with other memory technologies, including both volatile memory (i.e., SRAM, eDRAM) and other popular non-volatile emerging memory (i.e., ReRAM, STT-MRAM, and SOT-MRAM). The experiment results for the PARSEC benchmark suite indicate that, as an L2 cache memory, MeF-RAM reduces Energy Area Latency (EAT) product on average by ~98% and ~70% compared with typical 6T-SRAM and 2T1R SOT-MRAM counterparts, respectively.
Efficient hyperparameter-tuned machine learning approach for estimation of supercapacitor performance attributes

Recent years have witnessed the rise of supercapacitor as effective energy storage device. Specifically, carbon-based electrodes have been experimentally well studied and used in the fabrication of supercapacitors due to their excellent electrochemical properties. Recent publications have reported the use of Machine Learning (ML) techniques to study the correlation between the structural features of electrodes and supercapacitor performance metrics. However, the poor R-squared values (i.e., large deviations from the ideal value of unity) and large RMSE values reported in these works reflect the lack of accurate models’ development. This work reports the development and utilization of highly tuned and efficient ML models using hyperparameter tuning, that give insights into correlation between the structural features of electrodes and supercapacitor performance metrics namely specific capacitance, power density and energy density. Artificial Neural Networks (ANN) and Random Forest (RF) models have been employed to predict the various in-operando performance metrics of carbon-based supercapacitors based on three input features such as mesopore surface area, micropore surface area and scan rate. Experimentally measured values of these parameters used for training and testing these two models have been extracted from a set of research papers reported in literature. The optimization techniques and various tuning methodologies adopted for identifying model hyperparameters are discussed in this paper. The R2 values obtained for prediction of specific capacitance, power density and energy density using RF model are in the range from 0.8612 to 0.9353 respectively, while the RMSE values of the above parameters are 18.651, 0.2732 and 0.5764 for respective input parameters. Similarly, the R2 values obtained for prediction of specific capacitance, power density and energy density using ANN model are in the range from 0.9211 to 0.9644 respectively, while the RMSE values of the above parameters are 18.132, 0.1601 and 0.5764 for respective input parameters. Thus, the highly tuned ANN and RF models depict higher R-squared and lower RMSE values in comparison to those previously reported in literature, thereby demonstrating the importance of hyperparameter tuning and optimization in building accurate and reliable computational models.
Algorithms for the Reduction of Wind-Tunnel Data Derived from Strain Gauge Force Balances.

Abstract : Algorithms and procedures are presented for the reduction of force and moment data derived from wind-tunnel models supported by internal strain-gauge balances. The algorithms are developed in their most general forms, suitable for implementation on the new generation of powerful minicomputers currently being included in wind-tunnel data acquisition systems. Although the emphasis of the report is on the treatment of data derived from sting mounted strain-gauge force balances, the analysis is applicable, with only minor modifications, to data derived from modern load-cell based external force balances. Keywords: Data reduction; Wind tunnel apparatus; Loads(forces); Moments. (Australia)
Microencapsulation of β-Carotene by Self-Aggregated Caseinates

β -Carotene was encapsulated by self-aggregated caseinates, and the encapsulation efficiencies and storage stabilities of the resultant spray-dried powder were investigated. The pH of the sodium caseinate solution, which determined the degree of aggregation, tremendously affected the encapsulation yield and storage stability of β -carotene in the spray-dried powder. We found that at pH 6.0, the resultant powder showed excellent encapsulation property compared to other pH conditions (pH ＝ 6.5, 5.5). Small angle X-ray scattering analysis revealed that the representative sizes of the aggregates in the sodium caseinate solution at pH 6.5, 6.0, and 5.5 were approximately 91, 111, and 166 nm, respectively. This analysis also clarified that our system represented fractal growth aggregation while the surface fractal dimension increased with decreasing pH. Apparently, the encapsulation property was affected by the physical properties of the aggregates, and there existed an optimal aggregation degree, which maximized the performance of an encapsulated system.
Genotoxicity Assessment of Metal-Based Nanocomposites Applied in Drug Delivery

Nanocomposites as drug delivery systems (e.g., metal nanoparticles) are being exploited for several applications in the biomedical field, from therapeutics to diagnostics. Green nanocomposites stand for nanoparticles of biocompatible, biodegradable and non-toxic profiles. When using metal nanoparticles for drug delivery, the question of how hazardous these “virus-sized particles” can be is posed, due to their nanometer size range with enhanced reactivity compared to their respective bulk counterparts. These structures exhibit a high risk of being internalized by cells and interacting with the genetic material, with the possibility of inducing DNA damage. The Comet Assay, or Single-Cell Gel Electrophoresis (SCGE), stands out for its capacity to detect DNA strand breaks in eukaryotic cells. It has huge potential in the genotoxicity assessment of nanoparticles and respective cells’ interactions. In this review, the Comet assay is described, discussing several examples of its application in the genotoxicity evaluation of nanoparticles commonly administered in a set of routes (oral, skin, inhaled, ocular and parenteral administration). In the nanoparticles boom era, where guidelines for their evaluation are still very limited, it is urgent to ensure their safety, alongside their quality and efficacy. Comet assay or SCGE can be considered an essential tool and a reliable source to achieve a better nanotoxicology assessment of metal nanoparticles used in drug delivery.
Upper bounds on the node numbers of hidden layers in MLPs

: It is one of the fundamental and challenging problems to determine the node numbers of hidden layers in neural networks. Various efforts have been made to study the relations between the approximation ability and the number of hidden nodes of some specific neural networks, such as single-hidden-layer and two-hidden-layer feedforward neural networks with specific or conditional activation functions. However, for arbitrary feedforward neural networks, there are few theoretical results on such issues. This paper gives an upper bound on the node number of each hidden layer for the most general feedforward neural networks called multilayer perceptrons (MLP), from an algebraic point of view. First, we put forward the method of expansion linear spaces to investigate the algebraic structure and properties of the outputs of MLPs. Then it is proved that given k distinct training samples, for any MLP with k nodes in each hidden layer, if a certain optimization problem has solutions, the approximation error keeps invariant with adding nodes to hidden layers. Furthermore, it is shown that for any MLP whose activation function for the output layer is bounded on R , at most k hidden nodes in each hidden layer are needed to learn k training samples.
Economic algorithms for the management of resources in computer systems

Cloud computing and distributed Grid computations in the e-science and commercial spheres are beginning to make accessible huge amounts of computing power with “just in time” availability. However, the economic models surrounding these systems are static and uniform, with charging models that, for web-based cloud systems work on a price per unit per hour basis, whilst for educational type resources, fixed contractual arrangements and multi-year projects are more prevalent. The common place practice of using just-in-time capacity planning and variable pricing algorithms, such as those pioneered by airlines like EasyJet, tells us that the cost of delivering these services and the price that should be paid for them is a much more complex beast. Future Grid and Cloud Computing computations will be enabled by participants trading resources in order to construct bundles of goods or services in both new commercial arenas and the more well established “e-science” experiments in science, engineering and, now emerging, social sciences. A combinatorial auction (CA) is a natural choice for determining the optimal allocation for a bundle of required goods and services, but the space and time dimensions that characterise a Grid compute cloud would appear to indicate they are incompatible. This thesis proposes that an analogue of a physical commodities market is more appropriate for distributed resource allocation and that there is a class of bundling problems whose complexity properties appear to make the utilisation of a CA impractical. We therefore compare the two techniques for resource bundling and investigate the crossover point, to enrich our understanding of how combinatorial auctions and distributed markets may be used together to improve distributed resource allocation practices.
A global spatially Continuous Solar Induced Fluorescence (CSIF) 
dataset using neural networks

Abstract. Satellite-retrieved Solar Induced Chlorophyll Fluorescence (SIF) has shown great potential to monitor the photosynthetic activity of terrestrial ecosystems. However, several issues, including low spatial and temporal resolution of the gridded datasets and high uncertainty of the individual retrievals, limit the applications of SIF. In addition, inconsistency in measurements footprints also hinder the direct comparison between gross primary production (GPP) from eddy covariance (EC) flux towers and satellite-retrieved SIF. In this study, by training a neural network (NN) with surface reflectance from the MODerate-resolution Imaging Spectroradiometer (MODIS) and SIF from Orbiting Carbon Observatory-2 (OCO-2), we generated two global spatially continuous SIF (CSIF) datasets at moderate spatio-temporal resolutions (0.05 degree 4-day) during 2001–2016, one for clear-sky conditions and the other one in all-sky conditions. The clear-sky instantaneous CSIF (CSIFclear-inst) shows high accuracy against the clear-sky OCO-2 SIF and little bias across biome types. The all-sky daily average CSIF (CSIFall-daily) dataset exhibits strong spatial, seasonal and interannual dynamics that are consistent with daily SIF from OCO-2 and the Global Ozone Monitoring Experiment-2 (GOME-2). An increasing trend (0.39 %) of annual average CSIFall-daily is also found, confirming the greening of Earth in most regions. Since the difference between satellite observed SIF and CSIF is mostly caused by the environmental down-regulation on SIFyield, the ratio between OCO-2 SIF and CSIFclear-inst can be an effective indicator of drought stress that is more sensitive than normalized difference vegetation index and enhanced vegetation index. By comparing CSIFall-daily with gross primary production (GPP) estimates from 40 EC flux towers across the globe, we find a large cross-site variation (c.v. = 0.36) of GPP-SIF relationship with the highest regression slopes for evergreen needleleaf forest. However, the cross-biome variation is relatively limited (c.v. = 0.15). These two continuous SIF datasets and the derived GPP-SIF relationship enable a better understanding of the spatial and temporal variations of the GPP across biomes and climate.
AI-Based Approach for Optimal Placement of EVCS and DG With Reliability Analysis

It is expected that future transport will rely on electric vehicles (EVs) due to their sustainability and reduced greenhouse gas emissions. However, the rapid increase in electric load penetration causes several other concerns, including a generation-demand mismatch, increased network active power loss, a degradation in voltage profile, and decreased voltage stability margin. To overcome the issues mentioned earlier, proper integration of electric vehicle charging stations (EVCS) at appropriate locations is essential. The connection of an EVCS to the electricity grid will bring new challenges. Distributed generation (DG) sources are incorporated with EVCS to lessen the impact of EV charging load. In this study, charging stations are combined with DG units, which increases the motivation to use EVs. This study proposes an artificial intelligence (AI) approach, the hybrid of grey wolf optimization and particle swarm optimization, i.e., HGWOPSO, to investigate the suitable nodes for EVCS and DGs in a balanced distribution system. The proposed methodology is verified on the IEEE-33 bus and IEEE-69 bus systems. According to the findings, the obtained results are consistent as compared to other existing techniques. These findings are taken into consideration to analyze the reliability of electrical distribution networks. It is stated that using adequate reliability data of appropriately integrated DG and EVs increases the electrical system’s reliability.
Improving the Virtual Trichrome Assessment through Bridge Category Models

Non-alcoholic steatohepatitis (NASH) is a liver disease characterized by excessive lipid accumulation and disease progression is typically assessed through inspection of a Trichrome stain for Fibrosis staging. As the public health burden of NASH worsens due to evolving lifestyle habits, pathology laboratory resources will become increasingly strained due to rising demand for specialized stains. Virtual staining processes, computational methods which can synthesize the application of chemical staining reagents, can potentially provide resource savings by obviating the need to acquire specialized stains. Virtual staining technologies are assessed by comparing virtual and real tissue stains for their realism and ability to stage. However, these assessment methods are rife with statistical mistreatment of observed phenomena that are difficult to account for. Bridge category ratings represent a phenomenon where a pathologist may assign two adjacent stages simultaneously, which may bias and/or reduce the power of research findings. Such stage assignments were frequently reported in a large-scale assessment of Virtual Trichrome technologies yet were unaccounted for since no statistical adjustment procedures existed. In this work, we provide an updated assessment of Virtual Trichrome technologies using Bridge Category Models, which account for these bridge ratings. We report that two of four pathologists tended to assign lower Fibrosis stages to virtually stained tissue while the other two pathologists assigned similar stages. These research findings differ when bridge ratings are not accounted for. While promising, these results indicate further room for algorithmic finetuning of Virtual Trichrome technologies.
Sequence variation and regulatory variation in acetylcholinesterase genes contribute to insecticide resistance in different populations of Leptinotarsa decemlineata

Abstract Although insect herbivores are known to evolve resistance to insecticides through multiple genetic mechanisms, resistance in individual species has been assumed to follow the same mechanism. While both mutations in the target site insensitivity and increased amplification are known to contribute to insecticide resistance, little is known about the degree to which geographic populations of the same species differ at the target site in a response to insecticides. We tested structural (e.g., mutation profiles) and regulatory (e.g., the gene expression of Ldace1 and Ldace2, AChE activity) differences between two populations (Vermont, USA and Belchow, Poland) of the Colorado potato beetle, Leptinotarsa decemlineata in their resistance to two commonly used groups of insecticides, organophosphates, and carbamates. We established that Vermont beetles were more resistant to azinphos‐methyl and carbaryl insecticides than Belchow beetles, despite a similar frequency of resistance‐associated alleles (i.e., S291G) in the Ldace2 gene. However, the Vermont population had two additional amino acid replacements (G192S and F402Y) in the Ldace1 gene, which were absent in the Belchow population. Moreover, the Vermont population showed higher expression of Ldace1 and was less sensitive to AChE inhibition by azinphos‐methyl oxon than the Belchow population. Therefore, the two populations have evolved different genetic mechanisms to adapt to organophosphate and carbamate insecticides.
Progress Toward an Industrywide Safety Program

ter industry figures were based on conditions in only 33 utilities, less than 0.2 per cent of the total of 17,000 operating units involved. Although the figures represent only a minute segment of the water industry, they depict the situation at its best, for it may be presumed that the experience in water works not even safety conscious enough to keep records was worse than in the few utilities with established
Repot: Transferable Reinforcement Learning for Quality-centric Networked Monitoring in Various Environments

Collecting and monitoring data in low-latency from numerous sensing devices is one of the key foundations in networked cyber-physical applications such as industrial process control, intelligent traffic control, and networked robots. As the delay in data updates can degrade the quality of networked monitoring, it is desirable to continuously maintain the optimal setting on sensing devices in terms of transmission rates and bandwidth allocation, taking into account application requirements as well as time-varying conditions of underlying network environments. In this paper, we adapt deep reinforcement learning (RL) to achieve a bandwidth allocation policy in networked monitoring. We present a transferable RL model Repot in which a policy trained in an easy-to-learn network environment can be readily adjusted in various target network environments. Specifically, we employ flow embedding and action shaping schemes in Repot that enable the systematic adaptation of a bandwidth allocation policy to the conditions of a target environment. Through experiments with the NS-3 network simulator, we show that Repot achieves stable and high monitoring performance across different network conditions, e.g., outperforming other heuristics and learning-based solutions by 14.5~20.8% in quality-of-experience (QoE) for a target network environment. We also demonstrate the sample-efficient adaptation in Repot by exploiting only 6.25% of the sample amount required for model training from scratch. We present a case study with the SUMO mobility simulator and verify the benefits of Repot in practical scenarios, showing performance gains over the others, e.g., 6.5% in urban-scale and 12.6% in suburb-scale.
A speed measurement method for underwater robots using an artificial lateral line sensor

Underwater robot technology has made considerable progress in recent years. However, due to the harsh environment and noise in the flow field near the underwater robots, it is difficult to measure some basic parameters, including swimming speed. The traditional speed measurement methods for underwater robots have the disadvantages of being limited by the environment and bulky. In order to overcome these shortcomings, an artificial lateral line (ALL) sensor based on cantilever structure was developed in this paper. According to the deformation of cantilever beam under water impact, the swimming speed of underwater robots can be measured. In addition, an ‘end-to-end’ calibration algorithm was proposed to calibrate the ALL sensor in the noisy environment, avoiding the complicated noise modeling and filter design process. To reduce the risk of overfitting, a hybrid loss function based on physical model was adopted. Compared with the classical calibration method, our method can reduce the error by 47.8%. Our sensor achieved an average absolute error of 0.07897 m s−1, and can measure water speed up to 3 m s−1.