Analysis of Waste-Rock Transportation Process Performance in an Open-Pit Mine Based on Statistical Analysis of Cycle Times Data

In this paper, the performance of a waste rock transportation process in an open pit mine was assessed by using cycle time data. A computerized truck-excavator dispatch system was used to record the cycle times. The process was broken into seven steps (or components of the total cycle), durations of which were recorded for a period of 1 month, leading to N = 60,690 data points or dispatches. The open pit mine studied consisted of 12 waste types loaded by 14 excavators and hauled by 49 trucks (at a trucks-to-excavator ratio of 3.5:1) in 75 changing locations. The string-type data was coded using integers to allow a FORTRAN code to extract process performance parameters using statistical analysis. The study established a wide range of parameters including: the waste material generation rate (about 1.73 million t/month, 81% comprising waste rock), truck fill factor, f, total cycle time (Tct), production capacity, theoretical cycle time, non-productive cycle time Tnp, and cycle time performance ratio (CTPR), denoted as Tpr. The factors affecting the process performance include: truck model, excavator model, location (haul distance and road conditions) and material type. For a fixed material type and tonnage, the PDFs of the cycle time components were logarithmic in nature, capable of differentiating performance variations under different factors. It was concluded that the performance of the waste material transportation system in this mine was determined to be acceptable due to mean value of Tpr = 2.432 being closer to unity. Reduction measures were suggested to minimize the cycle time for the process bottlenecks determined from Pareto analysis (that is, full haul, empty haul and loading processes).

Cyber Resilience through Real-Time Threat Analysis in Information Security

This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].

Quantitative Comparative Study of the Performance of Lossless Compression Methods Based on a Text Data Model

Data compression plays a key role in optimizing the use of memory storage space and also reducing latency in data transmission. In this paper, we are interested in lossless compression techniques because their performance is exploited with lossy compression techniques for images and videos generally using a mixed approach. To achieve our intended objective, which is to study the performance of lossless compression methods, we first carried out a literature review, a summary of which enabled us to select the most relevant, namely the following: arithmetic coding, LZW, Tunstall’s algorithm, RLE, BWT, Huffman coding and Shannon-Fano. Secondly, we designed a purposive text dataset with a repeating pattern in order to test the behavior and effectiveness of the selected compression techniques. Thirdly, we designed the compression algorithms and developed the programs (scripts) in Matlab in order to test their performance. Finally, following the tests conducted on relevant data that we constructed according to a deliberate model, the results show that these methods presented in order of performance are very satisfactory:- LZW- Arithmetic coding- Tunstall algorithm- BWT + RLELikewise, it appears that on the one hand, the performance of certain techniques relative to others is strongly linked to the sequencing and/or recurrence of symbols that make up the message, and on the other hand, to the cumulative time of encoding and decoding.

Cyberattack Ramifications, The Hidden Cost of a Security Breach

In this in-depth exploration, I delve into the complex implications and costs of cybersecurity breaches. Venturing beyond just the immediate repercussions, the research unearths both the overt and concealed long-term consequences that businesses encounter. This study integrates findings from various research, including quantitative reports, drawing upon real-world incidents faced by both small and large enterprises. This investigation emphasizes the profound intangible costs, such as trade name devaluation and potential damage to brand reputation, which can persist long after the breach. By collating insights from industry experts and a myriad of research, the study provides a comprehensive perspective on the profound, multi-dimensional impacts of cybersecurity incidents. The overarching aim is to underscore the often-underestimated scope and depth of these breaches, emphasizing the entire timeline post-incident and the urgent need for fortified preventative and reactive measures in the digital domain.

In situ atomic-scale observation of size-dependent (de) potassiation and reversible phase transformation in tetragonal FeSe anodes

Potassium-ion batteries(PIBs)are considered promising alternatives to lithium-ion batteries owing to cost-effective potassium resources and a suitable redox potential of-2.93 V(vs.-3.04 V for Li+/Li).However,the exploration of appro-priate electrode materials with the correct size for reversibly accommodating large K+ions presents a significant challenge.In addition,the reaction mecha-nisms and origins of enhanced performance remain elusive.Here,tetragonal FeSe nanoflakes of different sizes are designed to serve as an anode for PIBs,and their live and atomic-scale potassiation/depotassiation mechanisms are revealed for the first time through in situ high-resolution transmission electron micros-copy.We found that FeSe undergoes two distinct structural evolutions,sequen-tially characterized by intercalation and conversion reactions,and the initial intercalation behavior is size-dependent.Apparent expansion induced by the intercalation of K+ions is observed in small-sized FeSe nanoflakes,whereas unexpected cracks are formed along the direction of ionic diffusion in large-sized nanoflakes.The significant stress generation and crack extension originating from the combined effect of mechanical and electrochemical interactions are elucidated by geometric phase analysis and finite-element analysis.Despite the different intercalation behaviors,the formed products of Fe and K_(2)Se after full potassiation can be converted back into the original FeSe phase upon depotassiation.In particular,small-sized nanoflakes exhibit better cycling perfor-mance with well-maintained structural integrity.This article presents the first successful demonstration of atomic-scale visualization that can reveal size-dependent potassiation dynamics.Moreover,it provides valuable guidelines for optimizing the dimensions of electrode materials for advanced PIBs.

Spratlies Archipelago as the Australasian Tektite Impact Crater, Details of Formation &Richard Muller’s Dust Cloud Explanation for the Mid-Pleistocene Ice Age Cycle Transition

Several significant events of a geological nature occurred approximately 800 ka before the present: (1) Australasian tektite fall (AA), (2) Brunhes-Matuyama geomagnetic reversal (BMR), (3) mid-Pleistocene changes in ice age cycles. Add to these the undated fault system (4) in the South-West (SW) of the South China Sea (SCS). Here we offer a unified cause for all four of these in (5), an impact in the SCS of a large, massive cosmic object, likely a comet, obliquely coming from the SW at an extremely shallow angle, striking the Sunda shelf yet unexploded with the shock of its compressed air bow wave, and causing the continual shelf and slope to collapse, resulting in the fault system (4), then traveling almost tangentially to the surface, exploding at impact with the sea surface, ejecting the tektites (1), creating the formation underlying the later atolls of Spratlies Archipelago (6), Nansha Islands in Chinese, & causing the BMR (2). An explanation of event (3) was Richard Muller’s hypothesis of planet Earth passing through an interplanetary dust cloud periodically due to ecliptic precession. Here we hypothesize this cloud actually is a belt of Australasian tektites ejected into space at super-orbital velocities that Earth encounters about every 100 ka.

Expression of p27Kip1, A Cell Cycle Repressor Protein with Dual Roles for Both Cancer Prevention and Promotion, Is Regulated Primarily at the Level of Unusual p27Kip1 mRNA—A Short Concept Proposal

The p27Kip1 is a cell cycle repressor protein that regulates primarily the cell cycle transition from G1 to S phase and hence the DNA replication is in the S phase and cell division in the M phase. Expression of p27Kip1 protein has dual roles for both cancer prevention and promotion. For example, numerous nutritional and chemopreventive anti-cancer agents specifically increase the expression of p27Kip1 protein without directly affecting the expression of any other cell cycle regulatory proteins. On the other hand, pro-cancer agents (like glucose, insulin and other growth factors frequently seen in obesity and/or diabetes) specifically decrease the expression of p27Kip1 protein without directly affecting the expression of any other cell cycle regulatory proteins. Unlike expression of any other cell cycle regulatory proteins, expression of p27Kip1 protein is very unusual. The mRNA of p27Kip1 has a very long and unusual 5’-untranslated region (from -575 to -1 in human). It appears that the 5’-untranslated region of p27Kip1 mRNA forms two alternative secondary structures. One increases the expression of p27Kip1 protein when anti-cancer agents are added and another decrease the expression of p27K1p1 when pro-cancer agents are added. For this short concept proposal, Dr. Albert Einstein’s “visualized thought experiments (German: Gedanken experiment)” were used as a fundamental tool for understanding how either anti- or pro-cancer agents bring the primary structure of the 5’-untranslated region of p27Kip1 mRNA into two alternative secondary structures, thereby either increasing or decreasing, respectively, the translation initiation of p27Kip1 protein.

Modern Corrosion Mapping of Storage Tank Bottoms--Notable Advancements in Critical Zone Coverage,Inspection Efficiency and Data Integrity

Every day,an NDT(Non-Destructive Testing)report will govern key decisions and inform inspection strategies that could affect the flow of millions of dollars which ultimately affects local environments and potential risk to life.There is a direct correlation between report quality and equipment capability.The more able the equipment is-in terms of efficient data gathering,signal to noise ratio,positioning,and coverage-the more actionable the report is.This results in optimal maintenance and repair strategies providing the report is clear and well presented.Furthermore,when considering tank floor storage inspection it is essential that asset owners have total confidence in inspection findings and the ensuing reports.Tank floor inspection equipment must not only be efficient and highly capable,but data sets should be traceable and integrity maintained throughout.Corrosion mapping of large surface areas such as storage tank bottoms is an inherently arduous and time-consuming process.MFL(magnetic flux leakage)based tank bottom scanners present a well-established and highly rated method for inspection.There are many benefits of using modern MFL technology to generate actionable reports.Chief among these includes efficiency of coverage while gaining valuable information regarding defect location,severity,surface origin and the extent of coverage.More recent advancements in modern MFL tank bottom scanners afford the ability to scan and record data sets at areas of the tank bottom which were previously classed as dead zones or areas not scanned due to physical restraints.An example of this includes scanning the CZ(critical zone)which is the area close to the annular to shell junction weld.Inclusion of these additional dead zones increases overall inspection coverage,quality and traceability.Inspection of the CZ areas allows engineers to quickly determine the integrity of arguably the most important area of the tank bottom.Herein we discuss notable developments in CZ coverage,inspection efficiency and data integrity that combines to deliver an actionable report.The asset owner can interrogate this report to develop pertinent and accurate maintenance and repair strategies.

Statistical Methods of SNP Data Analysis and Applications

We develop various statistical methods important for multidimensional genetic data analysis. Theorems justifying application of these methods are established. We concentrate on the multifactor dimensionality reduction, logic regression, random forests, stochastic gradient boosting along with their new modifications. We use complementary approaches to study the risk of complex diseases such as cardiovascular ones. The roles of certain combinations of single nucleotide polymorphisms and non-genetic risk factors are examined. To perform the data analysis concerning the coronary heart disease and myocardial infarction the Lomonosov Moscow State University supercomputer “Chebyshev” was employed.

δ^(13)C_(org) perturbations preserved by the interglacial Datangpo Formation in South China with implications for stratigraphic correlation and carbon cycle

Palaeoclimatic and palaeoenvironmental reconstructions of the Cryogenian Period have attracted attention in relation to the debated“Snowball Earth”hypothesis and the early evolution of metazoan life.The carbon cycle and redox conditions of the Sturtian-Marinoan non-glacial interval have been subjected to much controversy in the past decades because of the lack of a high-resolution stratigraphic correlation scheme.As one of the typical Sturtian-Marinoan interglacial deposits,the Datangpo Formation was widely distributed in South China with shales continuously deposited.The previous zircon dating data of the Datangpo Formation provide important ages for global constrain of the Sturtian-Marinoan non-glacial interval.Here we present a high-resolution straitigraphic study of the organic carbon isotopes of the Datangpo Formation from a drill core section in northern Guizhou Province.Based on measured episodicδ^(13)C_(org) perturbations,three positive shifts and three negative excursions are identified.Aδ^(13)C_(org)-based chemostratigraphic correlation scheme is proposed herein that works well for the Datangpo Formation regionally.Meanwhile,theδ^(13)C_(org) vertical gradients changed dynamically throughout the formation.This discovery implies that a significant ocean circulation overturn might have occurred in the upper Datangpo Formation,coinciding with the potential oxygenation.

Potential Applications of Milk Fractions and Valorization of Dairy By-Products: A Review of the State-of-the-Art Available Data, Outlining the Innovation Potential from a Bigger Data Standpoint

The unique composition of milk makes this basic foodstuff into an exceptional raw material for the production of new ingredients with desired properties and diverse applications in the food industry. The fractionation of milk is the key in the development of those ingredients and products;hence continuous research and development on this field, especially various levels of fractionation and separation by filtration, have been carried out. This review focuses on the production of milk fractions as well as their particular properties, applications and processes that increase their exploitation. Whey proteins and caseins from the protein fraction are excellent emulsifiers and protein supplements. Besides, they can be chemically or enzymatically modified to obtain bioactive peptides with numerous functional and nutritional properties. In this context, valorization techniques of cheese-whey proteins, by-product of dairy industry that constitutes both economic and environmental problems, are being developed. Phospholipids from the milk fat fraction are powerful emulsifiers and also have exclusive nutraceutical properties. In addition, enzyme modification of milk phospholipids makes it possible to tailor emulsifiers with particular properties. However, several aspects remain to be overcome;those refer to a deeper understanding of the healthy, functional and nutritional properties of these new ingredients that might be barriers for its use and acceptability. Additionally, in this review, alternative applications of milk constituents in the non-food area such as in the manufacture of plastic materials and textile fibers are also introduced. The unmet needs, the cross-fertilization in between various protein domains,the carbon footprint requirements, the environmental necessities, the health and wellness new demand, etc., are dominant factors in the search for innovation approaches;these factors are also outlining the further innovation potential deriving from those “apparent” constrains obliging science and technology to take them into account.

A Review: On Smart Materials Based on Some Polysaccharides;within the Contextual Bigger Data, Insiders, “Improvisation” and Said Artificial Intelligence Trends

Smart Materials are along with Innovation attributes and Artificial Intelligence among the most used “buzz” words in all media. Central to their practical occurrence, many talents are to be gathered within new contextual data influxes. Has this, in the last 20 years, changed some of the essential fundamental dimensions and the required skills of the actors such as providers, users, insiders, etc.? This is a preliminary focus and prelude of this review. As an example, polysaccharide materials are the most abundant macromolecules present as an integral part of the natural system of our planet. They are renewable, biodegradable, carbon neutral with low environmental, health and safety risks and serve as structural materials in the cell walls of plants. Most of them are used, for many years, as engineering materials in many important industrial processes, such as pulp and papermaking and manufacture of synthetic textile fibres. They are also used in other domains such as conversion into biofuels and, more recently, in the design of processes using polysaccharide nanoparticles. The main properties of polysaccharides (e.g. low density, thermal stability, chemical resistance, high mechanical strength…), together with their biocompatibility, biodegradability, functionality, durability and uniformity, allow their use for manufacturing smart materials such as blends and composites, electroactive polymers and hydrogels which can be obtained 1) through direct utilization and/or 2) after chemical or physical modifications of the polysaccharides. This paper reviews recent works developed on polysaccharides, mainly on cellulose, hemicelluloses, chitin, chitosans, alginates, and their by-products (blends and composites), with the objectives of manufacturing smart materials. It is worth noting that, today, the fundamental understanding of the molecular level interactions that confer smartness to polysaccharides remains poor and one can predict that new experimental and theoretical tools will emerge to develop the necessary understanding of the structure-property-function relationships that will enable polysaccharide-smartness to be better understood and controlled, giving rise to the development of new and innovative applications such as nanotechnology, foods, cosmetics and medicine (e.g. controlled drug release and regenerative medicine) and so, opening up major commercial markets in the context of green chemistry.

Investigation of Excavator Performance Factors in an Open-Pit Mine Using Loading Cycle Time

This study presents the effect of excavator model, loading operation location, shift availability and truck-shovel combination on loading cycle time and productivity of an open-pit mine. The loading cycle time was used to assess the material loading system performance which is one of the key components of the total cycle time for material transportation in an open-pit mine. Loading is among the components of cycle time during which material is being handled. The data analyzed?was?collected from a computerized dispatch system at GGM from which 62,000 loading dispatches per month involving several shifts, 14 excavators and 49 trucks were loaded. About 4465 dispatches per excavator and 1276 dispatches per truck were assessed using loading cycle time data for each dispatch for a period of four months (between August and December). Under fixed tonnage loaded and waste type (33 t of non-acid forming waste rock),?it was observed that loading cycle time depends on excavator model, location and truck being loaded. Average cycle times, PDFS?and CDFS of loading cycle time series were used to identify differences in performance under different situations. It was concluded that shift availability for excavators, loading location, excavator model and truck-shovel combinations strongly affect the productivity during loading process in an open-pit mine.

A Study of EM Algorithm as an Imputation Method: A Model-Based Simulation Study with Application to a Synthetic Compositional Data

Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.

Modeling and Simulation Study of Space Data Link Protocol

This research paper describes the design and implementation of the Consultative Committee for Space Data Systems (CCSDS) standards REF _Ref401069962 \r \h \* MERGEFORMAT [1] for Space Data Link Layer Protocol (SDLP). The primer focus is the telecommand (TC) part of the standard. The implementation of the standard was in the form of DLL functions using C++ programming language. The second objective of this paper was to use the DLL functions with OMNeT++ simulating environment to create a simulator in order to analyze the mean end-to-end Packet Delay, maximum achievable application layer throughput for a given fixed link capacity and normalized protocol overhead, defined as the total number of bytes transmitted on the link in a given period of time (e.g. per second) divided by the number of bytes of application data received at the application layer model data sink. In addition, the DLL was also integrated with Ground Support Equipment Operating System (GSEOS), a software system for space instruments and small spacecrafts especially suited for low budget missions. The SDLP is designed for rapid test system design and high flexibility for changing telemetry and command requirements. GSEOS can be seamlessly moved from EM/FM development (bench testing) to flight operations. It features the Python programming language as a configuration/scripting tool and can easily be extended to accommodate custom hardware interfaces. This paper also shows the results of the simulations and its analysis.

Data Mining Based Research of Development Direction of Waist Protection Equipment

[Objectives]To explore the trend of brands towards the design of waist protection products through data mining,and to provide reference for the design concept of the contour of waist protection pillow.[Methods]The structural design information of all waist protection equipment was collected from the national Internet platform,and the data were classified and a database was established.IBM SPSS 26.0 and MATLAB 2018a were used to analyze the data and tabulate them in Tableau 2022.4.After the association rules were clarified,the data were imported into Cinema 4D R21 to create the concept contour of waist protection pillow.[Results]The average and standard deviation of the single airbag design were the highest in all groups,with an average of 0.511 and a standard deviation of 0.502.The average and standard deviation of the upper and lower dual airbags were the lowest in all groups,with an average of 0.015 and a standard deviation of 0.120;the correlation coefficient between single airbag and 120°arc stretching was 0.325,which was positively correlated with each other(P<0.01);the correlation coefficient between multiple airbags and 360°encircling fitting was 0.501,which was positively correlated with each other and had the highest correlation degree(P<0.01).[Conclusions]The single airbag design is well recognized by companies,and has received the highest attention among all brand products.While focusing on single airbag design,most brands will consider the need to add 120°arc stretching elements in product design.At the time of focusing on multiple airbag design,some brands believe that 360°encircling fitting elements need to be added to the product,and the correlation between the two is the highest among all groups.

Preliminary Design, Drive-Cycle Simulation and Energy Analysis of a Hybrid Transit Bus

Modern metropolises are increasingly affected by air quality problems. Transportation is one of the largest sources of several pollutants emissions, such as nitrogen oxides (NOx) and carbon monoxide (CO). Today in the EU, vehicles' emissions are strictly limited by Euro 6 norm-Euro VI for heavy-duty vehicles-which is periodically upgraded. To match such limits, manufacturers are forced in developing new technologies to perform new sustainable vehicles design strategies, such as EVs and HEVs. Present work's aim is to provide the design of series-hybrid urban transportation bus, equipped with a novel thermal power unit, namely a small gas turbine, to exploit its cleaner combustion process in comparison with an ICE. The control logic is described, while the main drivetrain components are chosen, and suitable models from suppliers are selected as well. Then, some simulations of the resulting vehicle are performed on opportune drive cycles, using Advisor, a free software based on Matlab-Simulink environment, published by US' National Renewable Energy Laboratory (NREL). Two different final configurations are environmentally and economically analysed, with the thermal power unit being respectively fuelled by compressed natural gas (CNG) and liquefied petroleum gas (LPG). Both satisfy the Euro VI norms, showing a substantial emission reduction (-89% and -43% in CO and THC releases respectively) in comparison to pollutants' threshold values.

Life Cycle Assessment of CCA-Treated Wood Highway Guard Rail Posts in the US with Comparisons to Galvanized Steel Guard Rail Posts

A cradle-to-grave life cycle assessment is done to identify the environmental impacts of chromated copper arsenate (CCA)-treated timber used for highway guard rail posts, to understand the processes that contribute to the total impacts, and to determine how the impacts compare to the primary alternative product, galvanized steel posts. Guard rail posts are the supporting structures for highway guard rails. Transportation engineers, as well as public and regulatory interests, have increasing need to understand the environmental implications of guard rail post selection, in addition to factors such as costs and service performance. This study uses a life cycle inventory (LCI) to catalogue the input and output data from guard rail post manufacture, service life, and disposition, and a life cycle impact assessment (LCIA) to assess anthropogenic and net greenhouse gas (GHG), acidification, smog, ecotoxicity, and eutrophication potentially resulting from life cycle air emissions. Other indicators of interest also are tracked, such as fossil fuel and water use. Comparisons of guard rail post products are made at a functional unit of one post per year of service. This life cycle assessment (LCA) finds that the manufacture, use, and disposition of CCA-treated wood guard rails offers lower fossil fuel use and lower anthropogenic and net GHG emissions, acidification, smog potential, and ecotoxicity environmental impacts than impact indicator values for galvanized steel posts. Water use and eutrophication impact indicator values for CCA-treated guard rail posts are greater than impact indicator values for galvanized steel guard rail posts.

Intelligent Biometric Information Management

We advance here a novel methodology for robust intelligent biometric information management with inferences and predictions made using randomness and complexity concepts. Intelligence refers to learning, adap- tation, and functionality, and robustness refers to the ability to handle incomplete and/or corrupt adversarial information, on one side, and image and or device variability, on the other side. The proposed methodology is model-free and non-parametric. It draws support from discriminative methods using likelihood ratios to link at the conceptual level biometrics and forensics. It further links, at the modeling and implementation level, the Bayesian framework, statistical learning theory (SLT) using transduction and semi-supervised lea- rning, and Information Theory (IY) using mutual information. The key concepts supporting the proposed methodology are a) local estimation to facilitate learning and prediction using both labeled and unlabeled data;b) similarity metrics using regularity of patterns, randomness deficiency, and Kolmogorov complexity (similar to MDL) using strangeness/typicality and ranking p-values;and c) the Cover – Hart theorem on the asymptotical performance of k-nearest neighbors approaching the optimal Bayes error. Several topics on biometric inference and prediction related to 1) multi-level and multi-layer data fusion including quality and multi-modal biometrics;2) score normalization and revision theory;3) face selection and tracking;and 4) identity management, are described here using an integrated approach that includes transduction and boosting for ranking and sequential fusion/aggregation, respectively, on one side, and active learning and change/ outlier/intrusion detection realized using information gain and martingale, respectively, on the other side. The methodology proposed can be mapped to additional types of information beyond biometrics.