Miracle-dz/UniFAR-FTUs huggingface dataset:
C-Element Design in Quantum Dot Cellular Automata

The continuous market demands for high-performance and energy-efficient computing systems have steered the computational paradigm and technologies towards nano-scale quantum dot cellular automata (QCA). This paper presents novel simple and complex QCA-based C-element structures. The proposed structures were thoroughly analyzed based on key design parameters, such as area, energy dissipation and robustness against structural defects. Simulation results demonstrate that the proposed simple structures have achieved up to 56% and 66% improvement in area and energy dissipation, respectively. On the other hand, the complex structures have shown a profound immunity against structural defects and achieved up to 143% improvement as compared to the simple structures. The proposed C-element structures can be considered as viable blocks for asynchronous designs.
Implementation of ISO 22000 System in Spaghetti Industry

The ISO 22000 Food Safety Management System has been applied to the spaghetti industry, in order to enhance food protection, protect customers, increase customer trust and improve cost efficiency. This experiment was carried out in North Cairo Flour Mills and Bakeries Company, Pasta Factory, Shubra El Khema, Cairo, Egypt, all stages were followed up from the receiving wheat flour extract 72% to spaghetti production and storage. Beginning with the appointment of the food safety team and that the infrastructure is suitable for the production process, the spaghetti product was described, a flow chart was constructed of the manufacturing process, the hazard was analyzed and identification, the sieving stage, drying stage and packaging stage were identified as critical control points (CCPs), while stage of receiving materials and stage of storing the product have been determined as operational prerequisite programs (OPRPs) and the effectiveness of implementing the HACCP plan is verified. The microbiological analysis of swabs from workers and surfaces was within the allowable limits. The results showed that no significant differences between the chemical composition of spaghetti. The heavy metal concentration of packaging material to use in packing spaghetti was within the allowable limits.
A Finite Mixture GARCH Approach with EM Algorithm for Energy Forecasting Applications

Enhancing forecasting performance in terms of both the expected mean value and variance has been a critical challenging issue for energy industry. In this paper, the novel methodology of finite mixture Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) approach with Expectation–Maximization (EM) algorithm is introduced. The applicability of this methodology is comprehensively evaluated for the forecasting of energy related time series including wind speed, wind power generation, and electricity price. Its forecasting performances are evaluated by various criteria, and also compared with those of the conventional AutoRegressive Moving-Average (ARMA) model and the less conventional ARMA-GARCH model. It is found that the proposed mixture GARCH model outperforms the other two models in terms of volatility modeling for all the energy related time series considered. This is proven to be statistically significant because the p-values of likelihood ratio test are less than 0.0001. On the other hand, in terms of estimations of mean wind speed, mean wind power output, and mean electricity price, no significant improvement from the proposed model is obtained. The results indicate that the proposed finite mixture GARCH model is a viable approach for mitigating the associated risk in energy related predictions thanks to the reduced errors on volatility modeling.
An Experimental Comparison of CNN-based Deep Learning Algorithms for Recognition of Beauty-related Skin Disease

[Abstract] In this paper, we empirically compare the effectiveness of training models to recognize beauty-related skin disease using supervised deep learning algorithms. Recently, deep learning algorithms are being actively applied for various fields such as industry, education, and medical. For instance, in the medical field, the ability to diagnose cutaneous cancer using deep learning based artificial intelligence has improved to the experts level. However, there are still insufficient cases applied to disease related to skin beauty. This study experimentally compares the effectiveness of identifying beauty-related skin disease by applying deep learning algorithms, considering CNN, ResNet, and SE-ResNet. The experimental results using these training models show that the accuracy of CNN is 71.5% on average, ResNet is 90.6% on average, and SE-ResNet is 95.3% on average. In particular, the SE-ResNet-50 model, which is a SE-ResNet algorithm with 50 hierarchical structures, showed the most effective result for identifying beauty-related skin diseases with an average accuracy of 96.2%. The purpose of this paper is to study effective training and methods of deep learning algorithms in consideration of the identification for beauty-related skin disease. Thus, it will be able to contribute to the development of services used to treat and easy the skin disease.
PARAMETRIC SENSITIVITY ASSESSMENT OF CYCLONITE PRODUCTION IN A BATCH REACTOR

This study assessed the parametric sensitivity of cyclonite production in a batch reactor. The objective was to establish safe operation conditions for this highly exothermic reaction. The reaction rate law was adjusted using experimental data published in the related literature. Mass and energy balances were reparametrized. Thus, reaction temperature sensitivity criteria were established as a function of initial reaction and cooling temperatures. Critical reaction conditions were defined using temperature sensitivity, temperature conversion, and the locus of the maximum temperature trajectories. A critical heat generation potential (M) equal to 34 was defined along with the following runaway critical conditions: a Semenov number (ψ) equal to 0.684, a heat of reaction parameter (B) equal to 15, and an Arrhenius-type number (γ) of 20. Therefore, temperature conversion profiles specify a critical relation between the cooling and heat potential (N/M) of 2.5786.
Synthesis of a Silica-bonded Bovine Serum Albumin Carbonyl Chiral Stationary Phase for High-performance Liquid Chromatographic Resolution of Enantiomers

An HPLC chiral stationary phase was synthesized by the in situ covalent immobilization of bovine serum albumin on a aminopropyl silica packed column activated with 1,1 carbonyldiimidazole in a short time. The effects of mobile phase pH and the content of 1 propanol on k  and α of D,L tryptophan were investigated, and the resolution of seven chiral compounds was successfully achieved.
Reference-Free DIBR-Synthesized Video Quality Metric in Spatial and Temporal Domains

Depth image-based rendering (DIBR) techniques play an important role in free viewpoint videos (FVVs), which have a wide range of applications including immersive entertainment, remote monitoring, education, etc. FVVs are usually synthesized by DIBR techniques in a “blind” environment (without a reference video). Thus, an effective reference-free synthesized video quality assessment (VQA) metric is vital. At present, many image quality assessment (IQA) algorithms for DIBR-synthesized images have been proposed, but limited researches have been concerned about the quality assessment of DIBR-synthesized videos. To this end, this paper proposes a novel reference-free VQA method for synthesized videos, which operates in Spatial and Temporal Domains, dubbed as STD. The design fundamental of the proposed STD metric considers the effects of two major distortions introduced by DIBR techniques on the visual quality of synthesized videos. First, considering the geometric distortion introduced by DIBR technologies can increase high-frequency contents of the synthesized frame, the influence of the geometric distortion on the visual quality of a synthesized video can be effectively evaluated by estimating high-frequency energies of each synthesized frame in spatial domain. Second, temporal inconsistency caused by DIBR techniques brings the temporal flicker distortion, which is one of the most annoying artifacts in DIBR-synthesized videos. In temporal domain, we quantify temporal inconsistency by measuring motion differences between consecutive frames. Specifically, optical flow method is first used to estimate the motion field between adjacent frames. Then, we calculate the structural similarity of adjacent optical flow fields and further adopt the structural similarity value to weight the pixel differences of adjacent optical flow fields. Experiments show that the above two features are able to well perceive the visual quality of DIBR-synthesized videos. Furthermore, since the two features are extracted from spatial and temporal domains, respectively, we integrate them using a linear weighting strategy to obtain our STD metric, which proves advantageous over two components and the competing state-of-the-art I/VQA methods. The source code is available at https://github.com/wgc-vsfm/DIBR-video-quality-assessment.
SHORT-TERM HYDROTHERMAL COORDINATION BASED ON PRIMAL-DUAL INTERIOR POINT METHOD

A b s t r a c t: This paper proposes a Primal-Dual Interior Point (PDIP) algorithm for solving short-term hydrothermal power system (STHTS) scheduling problem considering power losses in transmission system, forecasted wind power and transmission line constraint. A mathematical model for solving the problem of STHTS has been developed and implemented in the MATLAB software package. The PDIP forms Lagrange function by adding an internal (barrier) penalty function, and converting all inequality constraints to equality constraints, and then Newton's iterative method is used to solve the system equations. The main advantages of the PDIP method are the large scale type and the ability to solve optimization problems with sparse matrices. The effectiveness of the proposed PDIP has been previously tested on different hydrothermal power systems and the obtained test results have been compared to those from other methods in the literature. The main purpose of this paper is to highlight the impact of transmission line constraint on the operating costs of the thermal power plants, i
BIM-Supported Knowledge Management: Potentials and Expectations

: Building information modeling (BIM) is a currently prevalent term for a new way of 4 managing design, construction, operation and maintenance (O&M) of a project. It is regarded as 5 an accumulation of information about a building anchored to a model. Consequently, BIM has been 6 widely applied to manage information in construction projects. On the other hand, knowledge 7 integrates context, experience, rules and capability, which is at a higher level than information. 8 Knowledge has also been widely recognized as one of the most important factors in construction 9 projects. Therefore, close attention should be paid to knowledge management (KM) in construction 10 projects. Compared to traditional information technology (IT) systems, BIM is characterized by 11 object-oriented and parameter-driven modeling. How these features can improve KM is a matter 12 that has not been fully explored. To bridge the knowledge gap, this study first explores the existing 13 tools used for KM in construction projects. Following this, the potentials of BIM for the 14 improvement of KM are identified and the expectations for BIM-supported KM are outlined. A 15 total of 30 industrial experts in the UK and Ireland were interviewed as the main research method 16 in this study. Based on the analysis of interview results, a conceptual model is developed in this 17 study. The conceptual model presents the features of BIM, such as 3D visualization, life cycle 18 management, collaborative environment and proactive detection and analysis, which can improve 19 various KM activities. This study not only reveals the role of BIM for KM, but also gives insights 20 into an innovative way of managing knowledge in construction projects. 21
MFC-MZI Type Ultraviolet Sensor Based on ZnO and Composite Graphene

This paper proposes a novel Ultraviolet (UV) sensor in a Mach-Zehnder Interference (MZI) structure consisting of a coupler and a microfiber coupler (MFC) coated with zinc oxide (ZnO) and ZnO composite graphene. Since the MFC is highly sensitive to the surrounding refractive index (RI), and the RI of ZnO is sensitive to UV light, the UV sensing function of this structure is realized. ZnO and ZnO composite graphene materials were prepared by hydrothermal method and coated on the surface of MFC as a thin layer. The effects of various shapes of ZnO and its composites on the sensitivity of UV sensors were investigated. The experimental results show that the higher sensitivity of the sensor for ultraviolet sensing ranging from 4.6310 nW/cm2 to 7.1995 nW/cm2 can reach 2443.7 pm/nW·cm-2 with the flake ZnO composite graphene. The MFC-MZI structure combined with UV sensitive materials provides a simplified platform for UV detection technology with potential applications in partial discharge, open fire detection, and solar illumination monitoring.
Combining PSR theory with distributional reinforcement learning

This work focuses on using Distributional Reinforcement Learning (DRL) in a partially observable environment that is modelled via Predictive State Representation Theory (PSR). We aim to integrate the bene ts of DRL and PSR to obtain a model-based reinforcement learning method that is capable of providing complete (distributional) performance information about a policy using an observation-only environment model. PSR theory is one of the advanced techniques used to model a dynamical system on a partially observable environment. Unlike traditional partially observable Markov models, such as POMDP, which capture the uncertainty of the environment using belief states, PSR model describes the partially observable environment based on probabilities of executable and observable future events. Distributional Reinforcement Learning (DRL), proposed by MG Bellemare, is a learning paradigm that aims to improve learning by modelling the rewards as probability distributions instead of scalar expectations.
Design of an Analog Multi-Neuronal Spike-sequence Detector (MNSD) based on a 180nm CMOS Leaky Integrate & Fire with Latency Neuron

In this work, an analog hardware implementation of the Multi Neuronal Spike-sequence Detector (MNSD), is presented.The MNSD is a neuromorphic system for spike pattern detection based on the Leaky Integrate-and-Fire with Latency (LIFL) spiking neuron model. The LIFL neuron model is able to encode more information than simpler models like the Integrate-and- Fire thanks to the spike latency, a feature that can be exploited to tackle spatio-temporal pattern recognition tasks. The LIFL is here designed using 180nm CMOS technology, obtaining values of power consumption, area and complexity comparable to those of simpler neuron models with less biological resemblance and encoding capabilities. The LIFL is finally embedded in a spiking neural network based on the MNSD topology, and the operation of the overall system is tested for a simple character recognition task. Accuracy, precision and recall of 100% is obtained considering the one-versus-all classification scheme, for different noise levels. This confirms the validity of the overall system and acts as a proof of concept for future extension to more complex hardware recognition systems based on the MNSD
Use of the Brief Observation of Symptoms of Autism (BOSA) as a new clinical approach to assessing patients with suspected spectrum disorder during the COVID-19 pandemic

COVID-19 has imposed major changes to the delivery of healthcare worldwide In many settings, healthcare is now predominantly transitioned to telehealth, creating challenges and unique benefits In 2020, the Centers for Disease Control and Prevention has reported that 1 in 54 children in the U S are diagnosed with autism spectrum disorder (ASD) The Brief Observation of Symptoms of Autism (BOSA) is a new clinical approach that has been developed to ensure that patients suspected of ASD can receive valid diagnostic assessments during the pandemic BOSA is offered when a diagnostician is unable to administer the Autism Diagnostic Observation Schedule-Second Edition (ADOS-2) either because it is not appropriate to administer the ADOS-2 via telehealth or, in person, face coverings confound ASD assessment Only trained ADOS-2 diagnosticians can administer BOSA, because BOSA relies on ADOS-2 codes The article describes BOSA to prepare the field for future research during the COVID-19 crisis
Proximate Relay Propagated Deep Learning for Power Efficient Data Transmission in Wireless Networks

ABSTRACT The swift uptake and the advancing demand of wireless services cause unparalleled requirements on wireless networking environment. Upcoming wireless networks have to sustain accelerating wireless traffic volumes with higher data rate and wide range of network coverage to increase user experience. Accelerating higher data rate for unmanned vehicle node can be performed using Long Term Evolution. However, in wireless networks, it is not possible for unmanned vehicle node to be connected directly to LTE all the time. Also, as substantial fragment of energy is utilized during data transmission, methods are required to minimize power consumption. To address these issues in this work, an immense investigation between power saving methods and QoS support, called, Power-efficient Proximate Linear Regression and Relay-propagated Deep Learning (PPLR-RDL) framework is designed for improving data transmission rates and quality of service in wireless networks. The efficiency of PPLR-RDL framework is estimated in terms of average end-to-end delay, energy consumption, packet delivery ratio and latency and compared with the existing methods.
Impacts, Adaptation, Vulnerability, and Decision-Making

Climate change is a broad-reaching, global challenge. It impacts most human and natural systems, from agriculture and ecosystems to energy and health, and exacerbates other preexisting issues, from poverty to political instability. Evaluating these impacts and our vulnerability to them increases awareness of the need for adaptation and resilience. This chapter provides a brief history of impact assessments, focusing on the models, tools, and information that are needed and are available to quantify future impacts across a wide range of systems and scales, and to provide valuable input to adaptation and resilience planning. key infrastructure against extreme weather-related stressors.
Construction of an Interconnected Nanostructured Carbon Black Network: Development of Highly Stretchable and Robust Elastomeric Conductors

In the present work, a strong filler–filler network of conductive carbon black was strategically established in an elastomer matrix, which leads to a unique combination of electrical and mechanical properties. The novelty of our composites was the development of a strong percolated morphology of nanostructured conducting carbon black particles by the incorporation of relatively large nonreinforcing spherical silica particles, inside the soft elastomer matrix. This technique allowed us to fabricate solution styrene butadiene rubber (S-SBR) composites with outstanding electrical conductivity of 40 S/m, tensile strength ∼10 MPa, and extensibility up to 200%. Furthermore, the electrical conductivity was strain-independent up to 50% elongation strain. The electrical conductivity was found to be unaltered after 2000 loading–unloading cycles. This is the first ever report of a robust elastomeric system with such high electrical conductivity where all the basic ingredients used were selected from well-known comme.
Application of Transition Metal Catalysts in Organic Synthesis

untersuchen. Für graduierte Studenten kann der Band eine Fundgrube fur neue wissenschaftliche Anregungen auf diesem Gebiet sein. Auch in den Planungsabteilungen der Ministerien für Wissenschaft und Forschung des Bundes und der Lander solite der Band nicht fehlen, damit die Referenten Anregungen bekommen konnen, die Organisation von durch Ministerien gefòrderten naturwissenschaftlichen Schwerpunktprogrammen nachhaltig zu verbessern, um den wissenschaftlich-technischen Erkenntniszuwachs eines Schwerpunktprogramms in einer Förderperiode zu verbessern.
The Future of Physician Advocacy: Attitudes and Intentions of U.S. Medical Students

Background: Advocacy is a core component of medical professionalism. Medical organizations frequently emphasize advocacy, particularly around just resource distribution and social determinants of health, but it is unclear how educators can best prepare trainees for this professional obligation. Objective: We sought to assess medical students’ attitudes toward advocacy, including activities and issues of interest, and to determine congruence with professional obligations.Design, Participants, and Measures: A cross-sectional, web-based survey probed U.S. medical students’ attitudes toward advocacy around 7 issues directly related to health (e.g. nutrition/obesity, addiction) and 11 indirect determinants of health (e.g. housing, transportation, education). Descriptive statistics, Kruskal-Wallis tests, and regression analysis investigated associations with gender, race, political identification, and intended future field.Key Results: Of 240 students completing the survey, 53% were female; most were white (62%) or Asian (28%). Clinical/non-primary-care was the most common intended future field (61%). Most agreed it is very important that physicians encourage medical organizations to advocate for public health (76%) and provide health-related expertise to the community (57%). More participants rated advocacy for medical issues as very important (e.g. drug addiction [83%], nutrition [81%]), compared to issues with indirect connections to health (e.g. national security [22%], transportation [36%], criminal justice [40%]) (p<0.001). Generally, liberals and non-whites were more likely than others to value advocacy. Conclusions: Medical students reported strong interest in advocacy, particularly around health issues, consistent with professional standards. Many attitudes were associated with political affiliation and race. To optimize future physician advocacy, educators should provide opportunities for learning and engagement in issues of interest.
Solvent‐free three‐component synthesis of 7‐aryl‐1,1‐dioxothieno[3,2‐b]pyran derivatives catalyzed by ammonium acetate

An efficient synthesis of 7-aryl-1,1-dioxothieno[3,2-b]pyran derivatives via the reaction of aryl aldehyde, tetrahydrothiophene-3-one-1,1-dioxide and malononitrile or ethyl 2-cyanoacetate was performed at room temperature catalyzed by ammonium acetate under solvent-free conditions. Compared with the conventional methods, this protocol features mild reaction conditions, high yields, and eco-friendliness. J. Heterocyclic Chem., (2011).