Miracle-dz/UniFAR-FTUs huggingface dataset:
Scoping Study of Machine Learning Techniques for Visualization and Analysis of Multi-source Data in Nuclear Safeguards

In implementation of nuclear safeguards, many different techniques are being used to monitor operation of nuclear facilities and safeguard nuclear materials, ranging from radiation detectors, flow monitors, video surveillance, satellite imagers, digital seals to open source search and reports of onsite inspections/verifications. Each technique measures one or more unique properties related to nuclear materials or operation processes. Because these data sets have no or loose correlations, it could be beneficial to analyze the data sets together to improve the effectiveness and efficiency of safeguards processes. Advanced visualization techniques and machine ‐ learning based multi ‐ modality analysis could be effective tools in such integrated analysis. In this project, we will conduct a survey of existing visualization and analysis techniques for multi ‐ source data and assess their potential values in nuclear safeguards
An Integrated Delphi-Agent-Based Model (ABM) For Simulating Postharvest Loss Behavior Management

Decision-making is often a complex task. This paper proposes a decision support system (DSS) which aims at greatly improving decision making for postharvest management. The decision support system consists of two main components: an integrated Delphi-agent-based model (ABM) for simulating postharvest loss behaviour, an evaluation model based on the fuzzy analytic hierarchy process (FAHP) together with a fuzzyDecision Making Trial and Evaluation Laboratory (DEMATEL). The latter is responsible for assessing alternative strategies based on the simulation outputs generated by the former. The integrated model is able to keep track of not only biophysical feedback effects, but also complex socio-economic benefits or non-market values that are reflected in market transactions. In the outcome evaluation component of the system, the Fuzzy AHP is used to structure and rate all related criteria, the method is used to obtain final rankings in terms of simulated socioeconomic and biophysical outcomes.
An Equitable, Acceptable and Socially Beneficial Congestion Pricing Design: A Continuum Modeling Approach

Consider a region of an arbitrary shape with multiple Central Business Districts (CBDs) competing for multi-class users that are distributed continuously over the region. Within this region, the road network is represented as a continuum and users patronize in a two-dimensional continuum transportation system to travel to their chosen CBD. A logit-type distribution function is specified to model the probabilistic destination choices made by the different classes of users. In this paper, a cordon-based congestion-pricing model for this continuum network is developed and the optimal location and toll level of the charging cordon is selected based on the equity, acceptability and social benefit of the charging schemes. The cordon-based congestion-pricing model is solved by using the finite element method and a promising Newtonian-based solution algorithm. A numerical example is given to show the usefulness of the proposed model, algorithm and criteria for cordon design and selection.
Seasonal patterns of vegetative growth and competition with reproductive sinks in peach (Prunus persica)

Summary Growth of leaves, wood, and stems were studied over an entire growing season on four year old peach (Prunus persica (L.) Batsch. ) trees having no crop, commercial crop loads, or heavy crop loads. Leaf, wood, and stem growth were reduced on cropping trees relative to defruited trees. The presence of fruit reduced leaf biomass growth during Stage I and II of fruit growth but not thereafter. Wood biomass growth was reduced by the presence of fruit during all stages of fruit growth. Stem biomass growth was most strongly affected by fruit during Stage III of fruit growth. Carbon partitioning to stems appeared to be influenced by both fruit sink demands and set developmental capacity for secondary radial growth. Total above-ground biomass production was similar in all three treatments, despite significantly greater leaf area in defruited trees. The total carbohydrate cost of the above ground biomass (the sum of biomass costs, calculated growth and maintenance respiration costs) was estimated to be similar for all three treatments. Cropping reduced root starch content and flowering density but did not influence percent fruit set during the subsequent growing season relative to non-cropped trees.
Abstract: Maximum Entropy Acquisition Design and Optimal Interpolation; #90187 (2014)

Acquisition design plays a very significant role in seismic data processing and imaging. An optimized seismic acquisition design requires less resources and therefore, it can reduce the total cost of seismic exploration. Non-linear optimization methods can be used to deduce the position of sources and receivers that can lead to an optimal grid. By optimal grid, we understand a feasible distribution of sources and receivers that is amenable of reconstruction via known wavefield restoration methods. For this purpose, we propose to maximize the entropy of the sampling function of the grid subject to acquisition constraints. Maximum entropy grids produce sampling functions that can be easily reconstructed by existing interpolators. The design of source-receiver patterns that lead to maximum entropy mid-point offset grids is a non-linear optimization problem that we would like to explore.
Study of Different Methods of Voltage Stability Analysis

In today’s scenario, as the development is taking place simultaneously the demand for the electricity in the world is also increasing. Operation and planning of large interconnected power systems are becoming more and more complex. In power system, voltage stability plays a very important role. Voltage instability may cause blackouts and collapse of the power system. In last decade, many countries have experienced the problem of blackouts due to voltage instability. The problem of voltage stability is mainly caused due to stressed loading condition of the system. It is essential to analyse the voltage stability for improvement in the efficiency of power system. Various authors have conducted research on analysis of voltage stability and methods to improve it. This paper, presents a review on research and development in the voltage stability analysis and methods for improving the voltage stability.
Antigenic Individuality of Certain Papilloma Viruses

Papillomas of the skin and mucous membranes are frequent in man and many of the lower animals. That certain of these growths are caused by filterable viruses has been demonstrated, notably verruca vulgaris in man, 1 oral papillomatosis of dogs, 2 cattle warts 3 and the papillomatosis of American cottontail rabbits. 4 All these growths are readily induced in susceptible hosts by inoculation with the respective viruses. In general, animals of other species than that furnishing the virus prove resistant on inoculation, though some problematic transfers have been reported. The virus of the Shope papilloma of cottontails is undoubtedly effective in other kinds of rabbits, 5 but guinea pigs, dogs, cats, rats and mice have proved insusceptible. Despite the limitations thus illustrated in the pathogenic range of the viruses, the strikingly similar characters of the growth produced by those deriving from various animals suggest that they may be related, like the viruses causing the pox diseases and the swine and human influenza viruses. We have tested this possibility immunologically, seeking for evidence of cross neutralization between the virus of rabbit papillomatosis and the sera of dogs, rabbits, cattle, and men bearing virus-induced papillomas or recently recovered from them. It is known that these growths induce neutralizing antibodies for the homologous agents with fair regularity. It was impracticable for us to test most of the sera against the homologous agents; and hence we have assumed that they were actively neutralizing. Serum was procured from a cottontail rabbit trapped in Texas and carrying many cutaneous papillomas, as also from 2 cows of the same Texas neighborhood,—one a yearling heifer bearing characteristic papillomas of the kind due to virus, the other a cow about 2 years old in which similar growths had retrogressed about 4 months previously.
Exerting 2D-Space of Sentiment Lexicons with Machine Learning Techniques: A Hybrid Approach for Sentiment Analysis

Sentiment mining from the textual content on the web can give valuable insights for discernment, strategic decision making, targeted advertisement, and much more. Supervised machine learning (ML) approaches do not capture the sentiment inherent in the individual terms. Whereas the unsupervised sen-timent lexicon (SL) based approaches lag behind ML approaches because of a bias they have towards one sentiment than the other. In this paper, we propose a hybrid approach that uses unsuper-vised sentiment lexicons to transform the term space into a two-dimensional sentiment space on which a discriminative classifier is trained in a supervised fashion. This hybrid approach yields higher accuracy, faster training, and lower memory footprint than the ML approaches. It is more suitable for scenarios where training data is scarce. We support our claim by reporting results on six social media datasets using five sentiment lexicons and four ML algorithms.
Cercopid spittle as a microhabitat

Some observations and experiments are described which support the generally accepted view that >>cuckoo-spit< has the function of protecting cercopid nymphs from desiccation, natural enemies and also from high temperatures. Although the nymphs do not control the quantity of spittle produced, evaporation rate gradients ensure that spittles are largest near the ground surface where nymphs are likely to be most easily available to non-flying predators, and where a greater insulation from high temperatures is required.
Link Discovery Attacks in Software-Defined Networks: Topology Poisoning and Impact Analysis

Software Defined Networking (SDN) has become a popular technology that offers advantages of programmable and flexible network management over the legacy practice. The centralized SDN controller is an important enabler of these benefits. One of the most crucial tasks of the SDN controller is link discovery as it provides topology of the network essential for the controller to direct or create rule forwarding and routing mechanisms. Much research on SDN security has been studied but only recently that security of OpenFlow link discovery protocols and topology poisoning have been addressed. Existing work includes link fabrication attacks via compromised hosts and defense systems with authentication. This paper discusses SDN link discovery process and its vulnerability to link discovery attacks including new attacks via compromised switches. We present a simple but effective defense mechanism using active ports that can detect both host-based and switchbased link discovery attacks. Finally, the paper presents an analytical and empirical analysis of the impacts of topology attacks on routing. The paper discusses attack details, proposed methods and results of these analyses. 
A Review of Contrast Enhancement Techniques

: Most of the time, the digital images while capturing are affected by weather, acquiring the device itself and by poor lighting, they suffer from poor contrast. Sufficient Contrast in an image makes an object distinguishable from the other objects and the background. Contrast enhancement basically improves the quality of images for human observer by expanding the dynamic range of input gray level. In such a scenario, it plays important role to provide a comprehensive survey of these contrast enhancement techniques used in digital image processing.
A Hybrid Filtering Methodology for Nonlinear Estimation

We are concerned with Bayesian estimation of a discrete stochastic process governed by a nonlinear model. We employ marginalized particle filter (MPF, [5])—which is also known as Rao-Blackwellised particle filter, [2]—for on-line tuning of nuisance parameters of analytical filters. Particle filtering [1] is a general filtering methodology applicable to nonlinear and non-Gaussian systems. However, intensive sampling is in high-dimensional spaces computationally prohibitive. MPF arises when the structure of the model allows marginalization over a subset of state variables [5]. Selected state variables are then estimated with an analytical filter and the rest is treated using particle filter. The marginalization substantially reduces the dimension of the space we sample from. This is of particular significance in high-dimensional estimation problems emerging, e.g., in geoscientific applications. The methodology is demonstrated on estimation of a three-dimensional chaotic nonlinear system given by the Lorenz attractor [4]. We use the extended Kalman filter (EKF, [3]) for estimation of the three coordinates of the attractor and the particle filter for adaptive tuning of model error covariance in EKF. EKF is the nonlinear version of the Kalman filter [6]. It uses linearized version of a nonlinear differentiable state transition function to propagate posterior covariance matrix. Jacobian of the Lorenz attractor is approximated at each time step using forward differences. Observations are simulated by the attractor, which is integrated forward with the fourth-order Runge-Kutta scheme, and perturbed with a Gaussian noise. Satisfactory accuracy of estimation was achieved even with a small number of particles.
Sociopolitical Silos: Environmental Views and the Multiplicative Effect of Same-Party Friends

Analysis of climate and other environmental questions on regional surveys conducted over 2017–2020 (9,000+ live telephone interviews) elaborate on the well-known importance of political factors by adding support for three newer but replicated propositions. (1) Strength of political identification predicts climate-change views within the ranks of conservatives to a much greater degree than within ranks of liberals, evidence that conservatives’ climate-change views are more politically determined. (2) Factor analysis suggests that climate-change beliefs meet statistical criteria for being an indicator of sociopolitical identity, alongside the traditional indicators of ideology and party, or the newer one of Trump support. This result underlines the need for caution when interpreting models that include climate beliefs in addition to other identity indicators such as party or ideology among their independent variables. (3) Having mostly same-party friends intensifies the already-strong effects of political identity on views regarding climate change, local weather, conservation, renewable energy, and wildlife management. Twelve examples show multiplicative (interaction) effects of political identity and same-party friends across different dependent variables, political indicators and datasets. These identity×friends results build upon other recent studies, confirming that sociopolitical feedbacks accentuate political divisions on the environment, and particularly so among conservatives.
Strategy Of Stimulating Purchase Intention Of Private Label Brands (Plb)

This research aims to find out some factors which can lead the consumer's purchase intention towards Private Label Brand (PLB) product. Moreover, this research is intended to bridge the gap of research results between store image perception and purchase intention of private label brands.The amount of the sample used are 147 respondents of Alfamart customers who know the Alfamart private label product, and the data were analyzed by using SEM Amos 22. The result shows that there are positive and significant impacts of Store Image Perception (SIP) towards Private Label Brands image (PLB Image), SIP towards PQ PLB, PQ PLB towards PLB Image, and PQ PLB towards PI PLB; whereas the impact of SIP towards Purchase Intention of PLB and the impact of PLB Image towards Purchase intention of PLB show insignificant results.
Enhance Pyrene Degradation of Sodium Alginate Embedding Immobilization by Adding PAC

This study explored the development of immobilized microorganism carriers for pyrene degradation. Two bacterial strains Klebsiella pneumoniae (Kp) and Pseudomonas aeruginosa (Pa), along with bacterial consortium were cultured for 14 d. Powdered activated carbon (PAC), binder CaCl2, and sodium alginate (SA) were used as beads to immobilize the bacterial consortium for improving the degradation effects of the pyrene. Removal rate, mass transfer properties, embedding ratio, crosslinking time, pH, and temperature effects on Pyrene removal efficiency were studied for the immobilized particles. The obtained results indicate that the removal rate of pyrene was reached up to 91.6 %, which improved 89.26 % with compared to the control conditions. Scanning electron microscopy (SEM) characterization showed that the PAC has increased more porous structure in the immobilization SA beads, which was responsible for maintaining bacterial activity and improving mass transferability. PAC added SA immobilization can enhance the effect of pyrene degradation by improving the bacterial absorption ability and nutrient permeability.
Autophagy Mitochondrial respiratory chain deficiency inhibits lysosomal hydrolysis

Mitochondria are key organelles for cellular metabolism, and regulate several processes including cell death and macroautophagy/autophagy. Here, we show that mitochondrial respiratory chain (RC) deficiency deactivates AMP-activated protein kinase (AMPK, a key regulator of energy homeostasis) signaling in tissue and in cultured cells. The deactivation of AMPK in RC-deficiency is due to increased expression of the AMPK-inhibiting protein FLCN (folliculin). AMPK is found to be necessary for basal lysosomal function, and AMPK deactivation in RC-deficiency inhibits lysosomal function by decreasing the activity of the lysosomal Ca channel MCOLN1 (mucolipin 1). MCOLN1 is regulated by phosphoinositide kinase PIKFYVE and its product PtdIns(3,5)P2, which is also decreased in RC-deficiency. Notably, reactivation of AMPK, in a PIKFYVE-dependent manner, or of MCOLN1 in RCdeficient cells, restores lysosomal hydrolytic capacity. Building on these data and the literature, we propose that downregulation of the AMPK-PIKFYVE-PtdIns(3,5)P2-MCOLN1 pathway causes lysosomal Ca accumulation and impaired lysosomal catabolism. Besides unveiling a novel role of AMPK in lysosomal function, this study points to the mechanism that links mitochondrial malfunction to impaired lysosomal catabolism, underscoring the importance of AMPK and the complexity of organelle cross-talk in the regulation of cellular
Vehicle Counting using Deep Learning Models: A Comparative Study

Recently, there has been a shift to deep learning architectures for better application in vehicle traffic control systems. One popular deep learning library used for detecting vehicle is TensorFlow. In TensorFlow, the pre-trained model is very efficient and can be transferred easily to solve other similar problems. However, due to inconsistency between the original dataset used in the pre-trained model and the target dataset for testing, this can lead to low-accuracy detection and hinder vehicle counting performance. One major obstacle in retraining deep learning architectures is that the network requires a large corpus training dataset to secure good results. Therefore, we propose to perform data annotation and transfer learning from an existing model to construct a new model for vehicle detection and counting in the real world urban traffic scenes. Then, the new model is compared with the experimental data to verify the validity of the new model. Besides, this paper reports some experimental results, comprising a set of innovative tests to identify the best detection algorithm and system performance. Furthermore, a simple vehicle tracking method is proposed to aid the vehicle counting process in challenging illumination and traffic conditions. The results showed a significant improvement of the proposed system with the average vehicle counting of 80.90%.
The Benefit Finding Questionnaire (BFQ): Scale Development, Validation, and Its Psychometric Properties Among People with Mental Illness

Benefit finding has been defined as positive life changes that result from a stressful event, such as the diagnosis of chronic illness. The present study aimed to develop a benefit finding questionnaire (BFQ) and examine its psychometric property among people with chronic mental illness in Japan. This study adopted a mixed method composed of three phases, including Phase 1: To draft the item pool and design the BFQ based on literature review and discussion among the authors, Phase 2: To revise and refine the drafted items through feedback from focus group interviews and further consideration, and Phase 3: To examine the psychometric properties of the BFQ following the questionnaire survey for people with chronic mental illness and validation of the questionnaire. In Phase 3, a cross-sectional, self-administered questionnaire survey was conducted for mental health service users. Among the 373 eligible participants, we used data from 265 respondents for the analyses (valid response rate = 71.0%). About 65% were male, and the average age was 45.3 years (SD = 12.9). Around 70% were diagnosed with schizophrenia. Factorial, concurrent, and divergent validities, as well as reliability were explored. The 21-item BFQ demonstrated good factorial validity, concurrent and divergent validities, and sufficient internal consistency reliability among people with chronic mental illness. It appears to be a useful scale to assess experience of benefit finding among people with chronic mental illness. Further large-scale research will ensure verification of the scale among people with other illnesses or difficulties.