Probability Of Identification Research Articles

Potential plasma biomarkers for the onset of heart and skeletal muscle inflammation from Piscine orthoreovirus-1 infection in Atlantic salmon (Salmo salar)

Biomarkers that give quantifiable information about disease development are valuable diagnostic indicators providing opportunities to thwart adverse outcomes through timely detection and initiation of countermeasures. Whereas human medicine already relies extensively on biomarker analysis, their application in the diagnostics of fish diseases is still at a tentative beginning. However, the increased utilisation of broad-range analytical approaches such as untargeted proteomics enhances the chances to discover changes in the levels of proteins, which are connected to fish health and could serve as novel biomarkers. In the present study, proteomic analysis accompanied by biostatistical data processing was employed with the aim of identifying plasma biomarker candidates for the onset of heart and skeletal muscle inflammation (HSMI) caused by Piscine orthoreovirus (PRV)-1 infection in Atlantic salmon. HSMI is a common viral disease and threat to fish welfare and productivity in Norwegian aquaculture, so that the early detection of HSMI development would allow a timely intervention to limit the damage. In the search for biomarker candidates, salmon plasma was sampled five and eight weeks after a PRV-1 cohabitation challenge experiment and from adequate control fish, and analysed by mass spectrometry-based proteomics. The proteome dataset contained 695 proteins with >90% probability of correct identification according to the set cut-off criteria for peptide quality. Time- and condition-related differences in protein abundance levels were revealed by multivariate analysis. Comparison of the proteins identified as significant using three different biostatistical models resulted in a short list of four potential biomarkers: galectin-3-binding protein (Gal-3BP), fucolectin-6, urokinase plasminogen activator surface receptor and ryanodine receptor 3. The physiological functions described for these proteins in mammals designate them as interesting indicators of inflammatory diseases also in other species, such as PRV-1 pathogenesis in salmon. In situ-hybridisation targeting Gal-3BP and PRV-1 mRNA in heart tissue sections from the same challenge trial showed a significant upregulation of Gal-3BP mRNA in infected salmon hearts as compared to controls, supporting the findings of the proteomic analysis and suggesting a role in the onset of heart- and skeletal muscle inflammation.

Simulating the LOcal Web (SLOW) II. Properties of local galaxy clusters

This is the second paper in a series presenting the results from a 500 $h^ $Mpc large constrained simulation of the local Universe (SLOW). The initial conditions for this cosmological hydro-dynamical simulation are based on peculiar velocities derived from the CosmicFlows-2 catalog. The simulation follows cooling, star formation, and the evolution of super-massive black holes. This allows one to directly predict observable properties of the intracluster medium (ICM) within galaxy clusters, including X-ray luminosity, temperatures, and the Compton-y signal. Comparing the properties of observed galaxy clusters within the local Universe with the properties of their simulated counterparts enables us to assess the effectiveness of the initial condition constraints in accurately replicating the mildly nonlinear properties of the largest, collapsed objects within the simulation. Based on the combination of several, publicly available surveys we compiled a sample of galaxy clusters within the local Universe, of which we were able to cross-identify 46 of them with an associated counterpart within the SLOW simulation. We then derived the probability of the cross identification based on mass, X-ray luminosity, temperature, and Compton-y by comparing it to a random selection. Our set of 46 cross-identified local Universe clusters contains the 13 most massive clusters from the Planck SZ catalog as well as 70<!PCT!> of clusters with $M_ $ larger than $2 $ M$_ odot $. Compared to previous constrained simulations of the local volume, we found in SLOW a much larger amount of replicated galaxy clusters, where their simulation-based mass prediction falls within the uncertainties of the observational mass estimates. Comparing the median observed and simulated masses of our cross-identified sample allows us to independently deduce a hydrostatic mass bias of $(1-b) approx0.87$. The SLOW constrained simulation of the local Universe faithfully reproduces numerous fundamental characteristics of a sizable number of galaxy clusters within our local neighborhood, opening a new avenue for studying the formation and evolution of a large set of individual galaxy clusters as well as testing our understanding of physical processes governing the ICM.

Exploring the role of the human microbiome in forensic identification: opportunities and challenges.

Forensic microbiology is rapidly emerging as a novel tool for human identification. The human microbiome, comprising diverse microbial communities including fungi, bacteria, protozoa, and viruses, is unique to each individual, offering a new dimension to forensic investigations. While traditional identification methods primarily rely on DNA profiling and fingerprint analysis, they face limitations when complete DNA or fingerprints profiles are unattainable or degraded. In this context, the microbial signatures of the human skin microbiome present a promising alternative due to their resilience to environmental stresses and individual-specific composition. This review explores the potential of microbiome analysis in forensic human identification, evaluating its applications, advantages, limitations, and future prospects. The uniqueness of an individual's microbial community, particularly the skin microbiota, can provide distinctive biological markers for identification purposes, while technological advancements like 16S rRNA sequencing and metagenomic shotgun sequencing are enhancing the specificity of microbial identification, enabling detailed analysis of these complex ecological communities. Despite these promising findings, current research has not yet achieved a level of identification probability that could establish microbial analysis as a stand-alone evidence tool. Therefore, it is presently considered ancillary to traditional methods, contributing to a more comprehensive biological profile of individuals.

ДІАГНОСТИКА ЙМОВІРНОСТІ БАНКРУТСТВА ПІДПРИЄМСТВ

Formulation of the problem. In the period of today's complex challenges - the conduct of military operations on the territory of Ukraine, the consequences of the pandemic, an unfavorable investment climate, and others - the detection of the probability of bankruptcy of enterprises is of particular importance. Timely detection of the onset of crisis phenomena using various methods and models and the adoption of precautionary measures ensure the avoidance of financial instability in the future. Therefore, conducting a timely diagnosis of the probability of bankruptcy is relevant, because there is a growing need for effective financial management in order to achieve the goals set by enterprises in the long term. The purpose of the research is to build a methodology for diagnosing the probability of bankruptcy of enterprises based on various bankruptcy models, based on the conditions of their activity, and to form forecasts of their further development on this basis. The object of the research is the process of constructing a diagnosis of the probability of bankruptcy of aviation industry enterprises. The method of the research - its became modern methods and principles of scientific knowledge, methods and techniques for evaluating bankruptcy diagnostics, tools of financial and economic analysis, models for forecasting further development, which allowed to combine existing approaches for multifaceted assessment of the probability of bankruptcy and identification of factors affecting the unstable activity of enterprises. The hypothesis of the research there was an assumption about the possibility of building a methodology for diagnosing the probability of bankruptcy of enterprises using modern, widely used methods of assessing bankruptcy, financial analysis of the results of operations and forming forecasts of further stable development. The statement of basic materials. The article examines the main stages of diagnosing the probability of bankruptcy of aviation enterprises, which provides an opportunity to determine the factors that generate unstable activity, develop appropriate countermeasures to avoid problem areas, and predict the improvement of development conditions. It is emphasized that each individual enterprise can use its own methods of assessing the probability of bankruptcy, which require the development of further specific anti-crisis measures, but the proposed method allows a comprehensive and thorough approach to the prompt detection and avoidance of crisis phenomena at enterprises. The originality and practical significance of the research is confirmed by the approbation of the proposed methodology for diagnosing the probability of bankruptcy at aviation industry enterprises. Conclusions and perspectives of further research. It has been proven that the use of the proposed methodology for diagnosing the probability of bankruptcy at enterprises allows recognizing and identifying threats, predicting their consequences, making operational decisions regarding the elimination of threats and adjusting the strategic goals of the enterprise's development. Further research will be aimed at assessing the probability of bankruptcy of enterprises in priority sectors of the national economy and developing measures for their recovery activity.

Predicting ion mobility collision cross sections and assessing prediction variation by combining conventional and data driven modeling

The use of collision cross section (CCS) values derived from ion mobility studies is proving to be an increasingly important tool in the characterization and identification of molecules detected in complex mixtures. Here, a novel machine learning (ML) based method for predicting CCS integrating both molecular modeling (MM) and ML methodologies has been devised and shown to be able to accurately predict CCS values for singly charged small molecular weight molecules from a broad range of chemical classes. The model performed favorably compared to existing models, improving compound identifications for isobaric analytes in terms of ranking and assigning identification probability values to the annotation. Furthermore, charge localization was seen to be correlated with CCS prediction accuracy and with gas-phase proton affinity demonstrating the potential to provide a proxy for prediction error based on chemical structural properties. The presented approach and findings represent a further step towards accurate prediction and application of computationally generated CCS values.

The Safety of Injections in the Infraorbital Region.

Infraorbital filler injection is a commonly used minimally invasive cosmetic procedure on the face, which can cause vascular complications. In this study, we aimed to explore the anatomical structure of the infraorbital vasculature and to establish an accurate protocol for infraorbital filler injection. The vascular structure of the infraorbital region was evaluated in 84 hemifacial specimens using computed tomography. Four segments (P1-P4) and five sections (C1-C5) were considered. We recorded the number of identified arteries in each slice and at each location and the number of deep arteries. Furthermore, we also measured the infraorbital artery (IOA) distribution. At P1-P4, the lowest number of arteries was detected in segment P4, with a 317/1727 (18.4%) and 65/338 (2.3%) probability of total and deep arterial identification, respectively. The probabilities of encountering an identified artery at the five designated locations (C1-C5) were 277/1727 (16%), 318/1727 (18.4%), 410/1727 (23.7%), 397/1727 (23%), and 325/1727 (18.8%), respectively. The probability of an IOA being identified at C2 was 68/84 (81%). We described an effective filler injection technique in the infraorbital region to minimize the associated risks. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

On the derivation of the delta formulation for decision value

This paper contains decision analytical approaches, conditions and models for the quantification of decision values for built environment systems. Specifically, (1) delta formulations of objective functions for decision value quantification are introduced, (2) conditions for decision value are identified and (3) action value analysis formulations are further developed. The delta objective functions are formulated with differences in utility, cost, and probabilities for consistent decision identification by expected utility and value. The delta formulations facilitate the direct calculation of action and information values and the explication of conditions for a positive decision value. Action value objective functions are derived in delta formulation for the action types of utility and system state actions and with action implementation states and action uncertainty models. The delta formulations are exemplified for predicted information and predicted actions values. The paper closes with a synthesis and discussion of decision values and with findings encompassing a computational effort reduction and the identification of predicted information value mechanisms.

ULTRA-WIDEFIELD IMAGING DETECTION RATE IN IDENTIFYING PERIPHERAL RETINAL TEARS IN SINGLE VERSUS MONTAGE OF PERIPHERAL STEERING.

To compare the detection rate of orthogonal, directed peripheral steering, and automontaged images with ultra-widefield imaging and the factors influencing the ability to identify retinal breaks. Retrospective cohort study. Three hundred and seventy-six treatment-naive eyes (349 patients) that underwent laser retinopexy for retinal breaks between 2015 and 2021 were included. Pretreatment ultra-widefield orthogonal, peripheral steering, and automontage were cross-referenced to scleral-depressed examination to determine whether images successfully visualized all retinal breaks. Total relative retinal area (RRA) visualized was divided by its optic disk area (pixels) to calculate relative retinal area. Potential associations were assessed by linear regression analysis. One hundred and sixty two eyes (154 patients) met inclusion criteria. Orthogonal, peripheral steering, and automontage images showed detection rates of 47.5%, 90.7%, and 80.0%, respectively. Relative retinal area increased from orthogonal versus montage by 34.7% ± 26.5% (mean ± SD), which increased the detection rate by 90.8% ( P = 0.006). In linear probability models, vertical meridian tears decreased probability of identification in orthogonal, peripheral steering, and automontage by -26.6%, -86.2%, and -68.7%, respectively ( P < 0.001), and horizontal meridian tears increased the probability by 62.2%, 92.9%, and 85.5%, respectively, ( P < 0.001). Tears posterior to the equator in orthogonal images increased the probability (91.4%, P < 0.001). Artifacts such as lids/lashes, reflection, and face guard decreased the probability in directed peripheral steering by -28.6%, -50.0%, and -66.7%, respectively, ( P = 0.020, P = 0.049, and P = 0.016). Using directed peripheral steering and automontage increases RRA and detection rate of identifying peripheral retinal breaks. Tears in horizontal meridians or posterior to the equator increase the probability of identification. Common ultra-widefield imaging artifacts can significantly limit the probability of identifying retinal tears.

Selective vital dyes in macular surgery : Do they increase the probability of intraoperative identification of the internal limiting membrane also for an experienced surgeon?

Various vital dyes exist on the market for intraoperative internal limiting membrane (ILM) identification. The aim of this study was to verify the added value of these dyes for ILM identification and in the difficulty of ILM peeling during pars plana vitrectomy (ppV) by asingle surgeon highly experienced in this operation. In this study 400 ppV surgical reports involving ILM peeling were retrospectively analyzed. Intraoperative assessment of identification or difficulty of intraoperative ILM peeling had to be documented in the surgical report. The total group consisted of 2 cohorts each with 200 surgical reports (firstcohort without selective vital dyes, period 2004-2006; secondcohort with vital dyes in the majority of ppVs, period 2013-2020). The difference between both groups in terms of intraoperative identification of ILM was statistically significant (p < 0.001); however, no statistically significant difference (p = 0.951) was found between the two groups in terms of difficulty of ILM peeling. In logistic regression analysis neither patient gender, age, eye side, lens status nor posterior vitreous limiting membrane status were significantly associated with ILM identification. The introduction of intravital dyes represents adecisive advancement in retinal surgery. In the investigated sample this benefit was evident from two precisely defined surgical cohorts of asingle highly experienced surgeon. This underlines the additional benefit of using selective vital dyes to identify ILM in macular surgery for less experienced surgeons.

Asymmetric imaging through engineered Janus particle obscurants using a Monte Carlo approach for highly asymmetric scattering media

The advancement of imaging systems has significantly ameliorated various technologies, including Intelligence Surveillance Reconnaissance Systems and Guidance Systems, by enhancing target detection, recognition, identification, positioning, and tracking capabilities. These systems can be countered by deploying obscurants like smoke, dust, or fog to hinder visibility and communication. However, these counter-systems affect the visibility of both sides of the cloud. In this sense, this manuscript introduces a new concept of a smoke cloud composed of engineered Janus particles to conceal the target image on one side while providing clear vision from the other. The proposed method exploits the unique scattering properties of Janus particles, which selectively interact with photons from different directions to open up the possibility of asymmetric imaging. This approach employs a model that combines a genetic algorithm with Discrete Dipole Approximation to optimize the Janus particles' geometrical parameters for the desired scattering properties. Moreover, we propose a Monte Carlo-based approach to calculate the image formed as photons pass through the cloud, considering highly asymmetric particles, such as Janus particles. The effectiveness of the cloud in disguising a target is evaluated by calculating the Probability of Detection (PD) and the Probability of Identification (PID) based on the constructed image. The optimized Janus particles can produce a cloud where it is possible to identify a target more than 50% of the time from one side (PID > 50%) while the target is not detected more than 50% of the time from the other side (PD < 50%). The results demonstrate that the Janus particle-engineered smoke enables asymmetric imaging with simultaneous concealment from one side and clear visualization from the other. This research opens intriguing possibilities for modern obscurant design and imaging systems through highly asymmetric and inhomogeneous particles besides target detection and identification capabilities in challenging environments.

Detection and performance analysis for MIMO visible light communication system using joint optical spatial and pulse amplitude width modulation

Conventional optical spatial modulation (SM) scheme activates one of the light-emitting diodes (LEDs) to transmit an intensity-modulated optical signal, in which the index of the activated LED is determined by spatial symbol and the emitted intensity is controlled by temporal symbol. In order to enhance the spectral efficiency (bits per channel use), we propose a joint SM and pulse amplitude width modulation (PAWM) as a novel optical spatial–temporal signaling scheme. In this paper, the proposed SM-PAWM optical signaling scheme is applied in a multi-input multi-output (MIMO) visible light communication (VLC) system. Employing optimal maximum likelihood (ML) algorithm to extract the spatial and temporal symbols is computationally prohibitive; hence, we develop a novel low-complexity detection scheme that converts the joint optimization problem separately to decode the spatial and temporal symbols. Moreover, theoretical results in terms of the successful identification probability of activated LED as well as the overall symbol error rate are derived. Extensive computer simulations are performed to validate the analytical results. It is shown that the proposed detection scheme is a feasible alternative to the ML detector in the VLC-MIMO system employing SM-PAWM.

The comparative analysis of different two-wavelength laser altimeter versions for forest monitoring

This study presents a capability analysis of the various options for the technical implementation of a two-wavelength laser altimeter for monitoring forests. The results of woodlands pieces identification statistical simulation for a neuronic net using experimentally measured spectral reflectance are presented. It is shown that the neuronic net provides a strong probability of correct identification when using information about the reflectivity and the height of woodlands pieces. Two wavelengths 532 and 1064 nm and the neuronic provide the probability of correct identification of green broad-leaved and needle-leaved trees understory, wetlands and ground vegetation of greater than 0.89 and the misidentification probability of lower than 0.055.

What Happens After Nomination? Evaluating the Probability of Gifted Identification With the Torrance Test of Creative Thinking

The Torrance Test of Creative Thinking (TTCT) is the most widely used norm-referenced creativity test used in gifted identification. Although commonly used for identifying talent, little is known about how creativity tests, like the TTCT-Figural, contribute to the probability of being identified as gifted especially with underrepresented populations. Using nominated students ( n = 1,191) from a diverse midsized urban school district, this study examined the differential predictive validity among student demographics (i.e., race/ethnicity, free/reduced price lunch status, English learning status, sex) and the TTCT-Figural to the probability of being identified as gifted. Results of a multilevel hierarchical generalized linear regression indicated underrepresented groups showed no difference in the probability of being identified after controlling for cognitive ability and academic achievement; the same was true when the TTCT-Figural was included within the model. The inclusion of the TTCT-Figural does contribute to the probability of identification; however, the disproportionality of underrepresented student groups remains in this school district. Gifted administrators looking to enhance equity may not find the solution with the mere inclusion of a creativity assessment. Implications for practice and future directions are discussed.

Developmental characteristics of fractures in deep tight sandstone reservoirs in the second Member of the Xujiahe Formation of Zhongjiang Gas Field

The development characteristics, scale and control factors of fractures are the core subjects of reservoir sweet spot prediction. The sandstone reservoir of the TX2 gas reservoir in the Zhongjiang Gas Field is a typical low porosity and low permeability tight reservoir with strong heterogeneity, but relatively high-quality reservoirs can be found in different well areas and well segments. In this paper, taking the second Member of the Xujiahe Formation (TX2) as an example, the control factors of fractures were systemically investigated via core observation, thin section, logging data, and fracture logging identifications. The results show that shear fractures are mainly developed in the cores, and they generally have high filling rate and poor effectiveness; microfractures can be found based on the vitrinite and cast thin section results. The intersection diagram (semi-quantitative) and the principal component and BP comprehensive identification (quantitative) methods can effectively identify different types of fractures. The combined application of principal component and BP comprehensive identification methods results in an 83.3 % fracture identification probability. Finally, we found that the development of fractures in TX2 is comprehensively affected by lithology, rock thickness, porosity, and faults.

Role of Forensic Osteolgy in positive identification for Forensic investigations to solve medicolgal cases in Punjab, Pakistan

Background: Positive identification of human skeletal remains is prime importance to solve the medicolegal cases in unidentified dead bodies. In case of mass disasters, genocide, mass graves, manslaughter and other medicolegal cases, the skeletal data provides enough information to solve the question of identification of the deceased human beings to provide justice even many years after their death. Aim: To see evidentiary value of skeletal data to determine the positive identification by using race ,age sex, stature and other parameters of identification in medicolagal cases of human skeletal remains in medicolegal forensic investigations in Punjab from 2015 to 2022. Study design: In this retrospective study 100 medico-legal cases where positive identification was done from skeletal remains in different medico-legal work performing Forensic departments of Punjab, were analyzed. Methods: In this retrospective study 100 medico-legal cases where positive identification was done from skeletal remains. This skeletal data was collected from different medico-legal cases where the identification was done on bones from five district of Punjab. This data was collected from the District Head Quarter hospitals of these cities from their medico-legal offices to conduct this research study. All the information collected were interred in predesigned Performa before analyzing the data. Descriptive statistics was implied on all the medico-legal cases for data collections and analysis of skeletal data from different bones recovered from the crime-scenes in this research study. Furthermore, standard deviation, F-statistics, and Chi test p-value were also applied to see the association of these parameters of identification with different skeletal remains with highest probability of positive identification from different bones.

Non Fungible Gratings as Physical Unclonable Functions

Physical Unclonable Functions (PUFs) are physical devices exploiting intrinsic randomness through the fabrication process that can be used for authentication and secure key generation. In this article, we describe a novel weak PUF architecture based on the inscription of random gratings in an optical fiber, which can be easily interrogated by a commercial instrument due to the enhanced backscatter. With PUF lengths of only 40 mm, we show that the false positive and false negative probabilities can reach below 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-27</sup> . We also describe a temperature and strain compensation algorithm to mitigate environmental fluctuations. Furthermore, we describe an alternative technique which increases the number of bits used for digitizing the PUF's signature, thus significantly decreasing the false identification probabilities by almost two orders of magnitude.

Source identification via contact tracing in the presence of asymptomatic patients

Inferring the source of a diffusion in a large network of agents is a difficult but feasible task, if a few agents act as sensors revealing the time at which they got hit by the diffusion. One of the main limitations of current source identification algorithms is that they assume full knowledge of the contact network, which is rarely the case, especially for epidemics, where the source is called patient zero. Inspired by recent implementations of contact tracing algorithms, we propose a new framework, which we call Source Identification via Contact Tracing Framework (SICTF). In the SICTF, the source identification task starts at the time of the first hospitalization, and initially we have no knowledge about the contact network other than the identity of the first hospitalized agent. We may then explore the network by contact queries, and obtain symptom onset times by test queries in an adaptive way, i.e., both contact and test queries can depend on the outcome of previous queries. We also assume that some of the agents may be asymptomatic, and therefore cannot reveal their symptom onset time. Our goal is to find patient zero with as few contact and test queries as possible. We implement two local search algorithms for the SICTF: the LS algorithm, which has recently been proposed by Waniek et al. in a similar framework, is more data-efficient, but can fail to find the true source if many asymptomatic agents are present, whereas the LS+ algorithm is more robust to asymptomatic agents. By simulations we show that both LS and LS+ outperform previously proposed adaptive and non-adaptive source identification algorithms adapted to the SICTF, even though these baseline algorithms have full access to the contact network. Extending the theory of random exponential trees, we analytically approximate the source identification probability of the LS/ LS+ algorithms, and we show that our analytic results match the simulations. Finally, we benchmark our algorithms on the Data-driven COVID-19 Simulator (DCS) developed by Lorch et al., which is the first time source identification algorithms are tested on such a complex dataset.

Prefiltering-based reduced rank multiuser detection in uplink generalized spatial modulation massive MIMO system

Exploiting generalized spatial modulation in massive MIMO system, we develop an efficient uplink multiuser detection scheme. Different from the maximum likelihood detection algorithm, the spatial and temporal data in the proposed detection scheme are decoded in a sequential manner: At the front end of base station receiver, a bank of spatial filters is first employed to mitigate the multiuser interference using the benefits of large-scale antenna array. There then, a reduced rank generalized Eigen-value decomposition-based algorithm is proposed to identify the antennas activated at each user terminal. In the second stage, a least-squares-based multiuser detector is employed to decode the multiplexed temporal symbols. Both theoretical analysis and computer simulations are conducted to evaluate system performance in terms of correct identification probability and overall symbol error rate. Simulation results demonstrate that the proposed algorithm outperforms most of the existing algorithms yet the complexity is extensively reduced.

Idle-Less Slotted ALOHA Protocol for Drone Swarm Identification

In this paper, we modify the slotted ALOHA protocol to quickly identify a swarm of drones in unmanned aerial vehicle (UAV) communication environments. Considering the unreliable UAV channel and the fundamental tradeoff between collision and idle slots in slotted ALOHA, we propose an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">idle-less slotted ALOHA</i> (ILS-ALOHA) protocol that detects and utilizes idle slots to transmit control messages to drones. In ILS-ALOHA, the ground control station recognizes the idle slot promptly at the beginning of each slot and then transmits acknowledgment (ACK) and beacon messages during the remaining period of the idle slot. Therefore, it can control the access of the drone swarm more adaptively and feed back the ACK to the drones more frequently in an error-prone UAV environment without allocating additional slots. The analysis and simulation results show that the proposed ILS-ALOHA protocol outperforms the conventional slotted ALOHA-based protocols in terms of identification time, channel throughput, and probability of successful identification.

The New Reality: Non-Eyewitness Identifications in a Surveillance World

Cognitive scientists have firmly established the risk of eyewitness misidentifications, and in response, courts have adopted legal safeguards to forestall this possibility; however, there are few safeguards against misidentifications by non-eyewitnesses. We define non-eyewitnesses as people who did not actually observe an event but nonetheless testify about who they think is depicted in a video of the event on the basis of their prior familiarity with the person they believe is depicted. In this review, we discuss the accuracy with which people typically recognize people from videos; in fact, these non-eyewitness identifications are far from perfect. We then explore seven factors that affect the accuracy of non-eyewitness identifications and organize these factors around three categories of bias—case-specific bias, person-specific bias, and general cognitive bias—that are likely to reduce the probability of a correct identification. We conclude with a proposed two-prong framework for determining the admissibility of non-eyewitness identifications in court and call for the adoption of legal safeguards against non-eyewitness misidentifications.