Loss Variance Research Articles

Framework for Cyber Risk Loss Distribution of Client‐Server Networks: A Bond Percolation Model and Industry Specific Case Studies

ABSTRACTCyber risk has emerged as a significant threat to businesses that have increasingly relied on new and existing information technologies (IT). Across various businesses in different industries and sectors, a distinct pattern of IT network architectures, such as the client‐server network architecture, may, in principle, expose those businesses, which share it, to similar cyber risks. That is why in this article, we propose a probabilistic structural framework for loss assessments of cyber risks on the class of client‐server network architectures with different client types. To our knowledge, there exist no theoretical models of an aggregate loss distribution for cyber risk in this setting. With this structural framework via the exact mean and variance of losses, we demonstrate how the changing cybersecurity environment of a business's IT network impacts the loss distribution. Furthermore, our framework provides insights into better investment strategies for cybersecurity protection on the client‐server network. Motivated by cyberattacks across industries, we apply our framework to four case studies that utilize the client‐server network architecture. Our first application is implantable medical devices in healthcare. Our second application is the smart buildings domain. Third, we present an application for ride‐sharing services such as Uber and Lyft. The fourth is the application of vehicle‐to‐vehicle cooperation in traffic management. The results are corresponding exact means and variances of cyber risk loss distributions parameterized by various cybersecurity parameters allowing for liability assessments and decisions in cybersecurity protection investments.

Using Experiments, 3D Scanning, and CFD to Analyze the Variance in Energy Losses Through Pipe Elbows

Pipe bends, or elbows, cause energy loss in pipelines due to the flow conditions they create. This energy loss has traditionally been approximated based on published minor loss coefficients, known as k-factors. However, the energy losses of elbows can vary based on geometric characteristics, which may not be accounted for in the published k-factors. The purpose of this research was to quantify the variance in energy loss resulting from elbows with geometric differences and to determine appropriate methods for approximating these variances using computational fluid dynamics (CFD). Eight polyvinyl chloride (PVC) elbows were physically tested in a hydraulic laboratory to determine the individual loss coefficients. The resulting data show that the minor loss coefficient k for two short-radius elbows of the same nominal size can vary by up to 51%. The same tests on two of the elbows were repeated using CFD, in which they were modeled two different ways: 1) using the ideal geometry and 2) using the actual geometry. The actual geometry was captured using 3D scanning. Each geometry was used in a series of simulations, and the results were compared to the experimental data. The CFD simulations were able to reproduce similar variances between the two elbows as displayed in the physical tests, although they were unable to reproduce the same k-factors. When compared to the experimental k-factor data, using the actual geometries captured by 3D scanning was not consistently more accurate than using idealized geometries.

Impacts of early psychological changes on correlates of weight-loss maintenance: Seeking increased precision for sustained behavioural obesity treatment effects.

Obesity is an increasing worldwide health issue. In affluent English-speaking countries, obesity ranges from ~28% (Australia) to ~42% (United States) of the adult population. Enabling weight loss beyond an initial 6 months is an unresolved challenge. Women with obesity participated in community-based obesity treatments incorporating either cognitive-behavioural methods emphasizing self-regulatory skills development (n = 106) or typical information/education processes (n = 47). Psychosocial, behavioural, and weight changes were measured. Significant overall improvements in exercise-related self-regulation and self-efficacy, mood, physical activity/exercise (PA/exercise), diet, and weight were found from baseline-month 6 (weight-loss phase) and baseline-month 12, but not from months 6-12 (weight-loss maintenance phase). Significantly greater improvements were found in the cognitive-behavioural group. Within the weight-loss phase, changes in both PA/exercise and diet significantly contributed to the explained variance in weight loss, whereas within the weight-loss maintenance phase, only change in PA/exercise was a significant predictor. There was no significant relationship of weight loss across phases. Months 6-12 change in PA/exercise significantly mediated relationships of changes in self-regulation→weight, mood→weight, and self-efficacy→weight. Earlier scores and score changes in mood and self-efficacy significantly impacted the subsequent parallel relationships. Cognitive-behavioural methods affect psychosocial changes leading to initial changes in diet, PA/exercise, and weight. Those changes then impact subsequent changes in PA/exercise-a primary malleable correlate of maintained weight loss. SO WHAT?: This research extended previous findings to better-inform behavioural obesity-treatment foci to address the pervasive public health problem of attaining and sustaining weight loss. Findings related to PA/exercise will help impact health-promotion outcomes.

Portofolio Management with Markowitz Model to Determine Optimal Investment Values

Investment is a commitment to a number of funds or other resources made at this time with the aim of obtaining a number of benefits in the future. Investments can be channeled through several instruments, such as deposits, gold, property, stocks, and many more. With the various types of investments that exist today, the difficulty of finding an investment cannot be an obstacle. In investment, the appropriate allocation of funds can be an important factor in obtaining profits. By using the correct method, the risk factors that may occur can be minimized as well as possible. The method that can be used in determining funds in investment activities is using the Markowitz Model. Then, the author initiates a method for optimizing assets using the Markowitz method. In this research, used data from 10 stocks in Indonesia PANI, CLEO, DSSA, UNIC, ADRO, CITA, CAKK, TPIA, MYOR, ANTM. Then, the stocks will be arranged optimally portfolio. The optimal investment weight obtained for stocks from January 1, 2019 to December 31, 2023 using the Markowitz model, each share weighting namely 7,6% PANI, 18,05% CLEO, 8,48% DSSA, 16,902% UNIC, 12,471% ADRO, 10,496% CITA, 7,246% CAKK, 12,946% TPIA, 5,877% MYOR, and 0,464% ANTM company stocks and provide a portfolio ratio of 5.694581 which can also be interpreted that the optimal return ratio profit is 5.694581 times greater than the possible loss or portfolio variance.

Enhancing graph convolutional networks with progressive granular ball sampling fusion: A novel approach to efficient and accurate GCN training

Graph convolutional network (GCN) has gained considerable attention and has been widely utilized in graph data analytics. However, training large GCNs presents considerable challenges owing to the inherent complexity of graph-structured data. Previous training algorithms frequently struggle with slow convergence speed caused by full-batch gradient descent on entire graphs and reduced model performance due to inappropriate node sampling methods. To address these issues, we propose a novel framework called Progressive Granular Ball Sampling Fusion (PGBSF). PGBSF leverages granular ball sampling to partition the original graph into a collection of subgraphs, thereby enhancing both training efficiency and detail capture. Then, it applies a progressive approach accompanied by a parameter-sharing strategy for incremental GCN model training, which results in robust performance and rapid convergence speed. This simple yet effective strategy considerably enhances classification accuracy and memory efficiency. The experiment results show that our proposed architecture consistently outperforms other baseline models in terms of accuracy across almost all datasets with different label rates. In addition, PGBSF improves GCN performance significantly on large and complex datasets. Moreover, GCN+PGBSF reduces time complexity by training on subgraphs and achieves the fastest convergence speed among all models, with a relatively small variance in loss during training.

Risk analysis of a multivariate aggregate loss model with dependence

Abstract This paper studies a hierarchical risk model where an accident can cause a combination of different types of claims, whose sizes could be dependent. In addition, the frequencies of accidents that cause the different combinations of claims are dependent. We first derive formulas for computing risk measures, such as the Tail Conditional Expectation and Tail Variance of the aggregate losses for a portfolio of businesses. Then, we present formulas for performing the associated capital allocation to different types of claims in the portfolio. The main tool we used is the moment (or size-biased) transform of the multivariate distributions.

Impact of intra- and extramedullary alignment on blood loss in total knee arthroplasty: a retrospective study.

There is a scarcity of scientific data regarding the correlation between alignment techniques during total knee arthroplasty (TKA) and blood loss as well as transfusion rates. This study's hypothesis posited that intramedullary-aligned (IM) TKA exhibits higher blood loss and transfusion rates when contrasted with extramedullary-aligned (EM) TKA. We conducted a retrospective examination of 883 patients who underwent total knee arthroplasty (TKA) in 2021 at a solitary orthopedic center in Germany. These patients were divided into two groups based on their tibial alignment technique: extramedullary alignment and intramedullary tibial alignment. In the intramedullary tibial alignment (IM) group, we observed a blood loss of 0.91 L, while in the extramedullary tibial alignment (EM) group, the blood loss was 0.89 L. These values did not demonstrate a significant difference (p = 0.69). Transfusion rates were 0.99% in the IM group and 0.21% in the EM group, and there was no significant distinction between them (Chi-squared test: p > 0.05). We observed no statistically significant variance in blood loss between the IM and EM groups. Likewise, there was no substantial disparity in transfusion rates between these groups. It can be concluded that the selection of a knee arthroplasty system incorporating either intramedullary tibial alignment or extramedullary alignment does not significantly impact blood loss.

Differentiating anorexia nervosa and atypical anorexia nervosa with absolute weight cut-offs results in a skewed distribution for premorbid weight among youth.

Anorexia nervosa (AN) and atypical AN are conceptualized as distinct illnesses, despite similar characteristics and sequelae. Whereas DSM-5 differentiates youth with AN and atypical AN by the presence of clinical 'underweight' (i.e., 5th BMI percentile for age-and-sex (BMI%)), we hypothesized that using this weight cut-off to discern diagnoses creates a skewed distribution for premorbid weight. Participants included hospitalized youth with AN (n = 165, 43.1%) and atypical AN (n = 218, 56.9%). Frequency analyses and chi-square tests assessed the distribution of premorbid BMI z-scores (BMIz) for diagnosis. Non-parametric Spearman correlations and Stepwise Linear regressions examined relationships between premorbid BMIz, admission BMIz, and weight loss in kg. Premorbid BMIz distributions differed significantly for diagnosis (p < .001), with an underrepresentation of 'overweight/obesity' (i.e., BMI% ≥ 85th) in AN. Despite commensurate weight loss in AN and atypical AN, patients with premorbid 'overweight/obesity' were 8.31 times more likely to have atypical AN than patients with premorbid BMI% < 85th. Premorbid BMIz explained 57% and 39% of the variance in admission BMIz and weight loss, respectively. Findings support a homogenous model of AN and atypical AN, with weight loss predicted by premorbid BMI in both illnesses. Accordingly, premorbid BMI and weight loss (versus presenting BMI) may better denote the presence of an AN-like phenotype across the weight spectrum. Findings also suggest that differentiating diagnoses with BMI% < 5th requires that youth with higher BMIs lose disproportionately more weight for an AN diagnosis. This is problematic given unique treatment barriers experienced in atypical AN. Anorexia nervosa (AN) and atypical AN are considered distinct conditions in youth, with differential diagnosis hinging upon a presenting weight status of 'underweight' (i.e., BMI percentile for age-and-sex (BMI%) < 5th). In our study, youth with premorbid 'overweight/obesity' (BMI% ≥ 85th) disproportionately remained above this threshold, despite similar weight loss. Coupled with prior evidence for commensurate characteristics and sequelae in both diagnoses, we propose that DSM-5 differentiation of AN and atypical AN inadvertently reinforces weight stigma and may contribute to treatment disparities in atypical AN.

Evaluating cyber loss in star-ring and star-bus hybrid networks based on the bond percolation model

The rise of cyber risk as a significant concern for businesses and companies has prompted increased attention towards evaluating the loss of cyber risk in networks vulnerable to contagious cyber attacks. Expanding on recent research focusing on standard network topologies, this study investigates two hybrid topologies: star-ring and star-bus. We propose random star-ring and star-bus hybrid network models that incorporate heterogeneous node types and a contagion model based on the bond percolation model to describe the contagion of a cyber attack on them. We derive exact expressions for the mean, variance, and covariance of compromise sizes for different node types. Utilizing these expressions, we further compute the mean and variance of the aggregate cyber loss of the networks, employing a dynamic framework of loss based on the renewal reward process. Moreover, through a numerical study and analysis of mathematical properties, we gain valuable insights into the impact of parameters on the mean, variance, and correlation of compromise sizes.

Iterative Low-Poly Building Model Reconstruction from Mesh Soups Based on Contour

Existing contour-based building-reconstruction methods face the challenge of producing low-poly results. In this study, we introduce a novel iterative contour-based method to reconstruct low-poly meshes with only essential details from mesh soups. Our method focuses on two primary targets that determine the quality of the results: reduce the total number of contours, and generate compact surfaces between contours. Specifically, we implemented an iterative pipeline to gradually extract vital contours by loss and topological variance, and potential redundant contours will be removed in a post-processing procedure. Based on these vital contours, we extracted the planar primitives of buildings as references for contour refinement to obtain compact contours. The connection relationships between these contours are recovered for surface generation by a contour graph, which is constructed using multiple bipartite graphs. Then, a low-poly mesh can be generated from the contour graph using our contour-interpolation algorithm based on polyline splitting. The experiments demonstrated that our method produced satisfactory results and outperformed the previous methods.

Volatility prediction comparison via robust volatility proxies: An empirical deviation perspective

Volatility forecasting is crucial to risk management and portfolio construction. One particular challenge of assessing volatility forecasts is how to construct a robust proxy for the unknown true volatility. In this work, we show that the empirical loss comparison between two volatility predictors hinges on the deviation of the volatility proxy from the true volatility. We then establish non-asymptotic deviation bounds for three robust volatility proxies, two of which are based on clipped data, and the third of which is based on exponentially weighted Huber loss minimization. In particular, in order for the Huber approach to adapt to non-stationary financial returns, we propose to solve a tuning-free weighted Huber loss minimization problem to jointly estimate the volatility and the optimal robustification parameter at each time point. We then inflate this robustification parameter and use it to update the volatility proxy to achieve optimal balance between the bias and variance of the global empirical loss. We also extend this Huber method to construct volatility predictors. Finally, we exploit the proposed robust volatility proxy to compare different volatility predictors on the Bitcoin market data and calibrated synthetic data. It turns out that when the sample size is limited, applying the robust volatility proxy gives more consistent and stable evaluation of volatility forecasts.

Pattern electroretinogram, blue-yellow visual evoked potentials and the risk of developing visual field defects in glaucoma suspects: a longitudinal “survival” analysis with a very long follow-up

PurposeEstimating glaucoma suspects’ risk for visual field defects helps to avoid under- and over-treatment. In this retrospective, longitudinal cohort study with a very long follow-up, we studied whether pattern electroretinograms (PERG) amplitudes and blue-on-yellow visual evoked potential (BY-VEP) latencies can predict visual field defects.MethodsParticipants of the Erlangen Glaucoma Study were examined with PERG and BY-VEP between 9/1991 and 8/2001. Stimuli were created using an optical bench with Maxwellian view and consisted of vertical gratings (0,88 cpd) in a 32° field for both PERG and BY-VEP. Patients were treated according to clinical standards and performed standard automated perimetry (SAP) annually. Retrospectively, patients with normal SAP at baseline were selected. Primary endpoint was conversion to perimetric glaucoma. Predictive value was modeled using Kaplan–Meier analyses and a multivariate cox proportional hazards model with the continuous variables PERG amplitude, BY-VEP peak time and SAP square-root of loss variance (sLV) after stratification for Jonas classification of the optic discs.ResultsOf 412 patients (288: Jonas 0, 103: I, and 21: II; baseline age: 20–60 years), 65 converted to perimetric glaucoma during follow-up (0.5–23.3 years; median 5.5 years). Optic disc classification was a strong risk factor for conversion (log rank p < 0.0001), and patients with more advanced changes progressed earlier. In the multivariate analysis (log rank p = 0.005), only PERG amplitude remained an independent risk factor after stratification for optic disc morphology (p = 0.021), with a ~ 30% higher risk per μV amplitude decrease.ConclusionsPERG helps to estimate glaucoma suspects’ risk for visual field defects.

Arthroscopic treatment for rotator cuff injury and frozen shoulder with concomitant rotator cuff injury: analysis of efficacy and factors influencing prognosis.

To analyze the efficacy of arthroscopic treatment for patients with rotator cuff injuries and frozen shoulder combined with rotator cuff injuries and assess the factors influencing patient prognosis. A retrospective analysis was performed on 85 patients who underwent arthroscopic surgery at Hanzhong Central Hospital between October 2016 and October 2021, including 42 patients treated for rotator cuff injuries alone (Group A), and 43 patients for frozen shoulder combined with rotator cuff injuries (Group B). Both groups underwent general anesthesia with controlled hypotension during surgery. Treatment outcomes, including shoulder joint functional scores, pain scores, shoulder joint range of motion, and muscle strength were assessed and compared between the two groups before treatment, as well as at 2 weeks and 2 months post-treatment. Quality of life was also evaluated and compared at 2 months post-treatment. Patients were categorized into good and poor prognosis groups based on their outcome, and factors influencing patient prognosis were analyzed. Before treatment, both groups exhibited relatively low shoulder joint function scores and external rotation angles, coupled with higher pain scores; however, these differences were not significant between groups (all P>0.05). The surgery duration for Group B was notably longer than that of Group A (P<0.05). Nevertheless, there was no significant variance in intraoperative blood loss between the two groups (P>0.05). After a 2-week treatment duration, both groups demonstrated a significant improvement in shoulder joint function score, pain score, and shoulder joint range of motion compared to baseline, but with no statistically significant intergroup differences. However, two months after the treatment, patients in Group A exhibited marked improvements in shoulder joint function score, pain score, shoulder joint range of motion, and overall quality of life compared to Group B (all P<0.05). Furthermore, the therapeutic efficacy in Group A was superior to that in Group B at the 2-month follow-up (P<0.05). Age, comorbid diabetes, metabolic disorders such as thyroid dysfunction, and the extent of shoulder cuff injury were identified as independent risk factors influencing prognosis. Arthroscopic treatment is effective for both frozen shoulder combined with rotator cuff injury and rotator cuff injury alone, with better outcomes observed in patients with rotator cuff injury only. This technique warrants further promotion.

Detección y caracterización de defectos del campo visual mediante perimetría Octopus en glaucoma congénito primario

PurposeTo detect and characterise visual field (VF) defects using static Octopus perimetry in patients with primary congenital glaucoma (PCG) and to determine VF quality and time duration. Material and methodsEighty-eight eyes of 70 patients diagnosed with PCG were included. Assessments were performed using an Octopus 900 and each eye was assessed with the tendency-oriented perimetry (G-TOP) algorithm. Quantitative VF data were collected: quality data (false positive and negative response, and time duration) and results of mean deviation (MD) and square root of loss variance (sLV). Qualitative data were collected: the presence of diffuse and localized defects, the affected hemifield and grade of defects using the Aulhorn and Karmeyer classification. Correlations between perimetric results and clinical variables were analysed. ResultsMedian age was 11 (8-17) years. 65.9% (58/88) of PCG eyes showed VF defects. Diffuse defects were observed in 10/58 eyes (16.94%) (mean MD=23.92 [SD: 2.52]) dB) and localized defects in 48/58 eyes (82.75%). The most frequent defect was spot-like/stroke-like/incipient paracentral scotoma (n=15), nasal step (n=8), adding arcuate defect (n=2), half ring-shaped (n=13) and concentric defect with a central island (n=9). And the most frequent affected visual hemifield was inferior hemifield. Mean test duration was 2min 12s (SD: 21.6s). MD and sLV values were correlated with best corrected visual acuity (BCVA), cup to disc ratio and number of antiglaucoma surgeries (all P<.001). ConclusionA high number of diffuse and localized defects were identified using Octopus perimetry in PCG patients. The most frequent defect was paracentral scotoma and inferior hemifield was the most affected.

Detection and characterisation of visual field defects using Octopus perimetry in congenital glaucoma

PurposeTo detect and characterise visual field (VF) defects using static Octopus perimetry in patients with primary congenital glaucoma (PCG) and to determine VF quality and time duration. Material and methodsEighty-eight eyes of 70 patients diagnosed with PCG were included. Assessments were performed using an Octopus 900 and each eye was assessed with the tendency-oriented perimetry (G-TOP) algorithm. Quantitative VF data were collected: quality data (false positive and negative response, and time duration) and results of mean deviation (MD) and square root of loss variance (sLV). Qualitative data were collected: the presence of diffuse and localized defects, the affected hemifield and grade of defects using the Aulhorn and Karmeyer classification. Correlations between perimetric results and clinical variables were analysed. ResultsMedian age was 11 (8–17) years. 65.9% (58/88) of PCG eyes showed VF defects. Diffuse defects were observed in 10/58 eyes (16.94%) (mean MD = 23.92 [SD: 2.52]) dB) and localized defects in 48/58 eyes (82.75%). The most frequent defect was spot-like/stroke-like/incipient paracentral scotoma (n = 15), nasal step (n = 8), adding arcuate defect (n = 2), half ring-shaped (n = 13) and concentric defect with a central island (n = 9). And the most frequent affected visual hemifield was inferior hemifield. Mean test duration was 2 min 12 s (SD: 21.6 s). MD and sLV values were correlated with best corrected visual acuity (BCVA), cup to disc ratio and number of antiglaucoma surgeries (all P < .001). ConclusionA high number of diffuse and localized defects were identified using Octopus perimetry in PCG patients. The most frequent defect was paracentral scotoma and inferior hemifield was the most affected.

Who participates in corporate income tax consolidation?: Evidence from Japan

When a group of affiliated corporations can file a single tax return based on a combined income, what types of groups would take up the option? This study empirically analyzes decisions to participate in a single-jurisdiction consolidated tax filing. The data consists of 2,782 Japanese corporate groups headed by publicly-traded corporations observed from 2002 to 2007. Results indicate a higher likelihood of participation among groups characterized by low correlation in returns among group members, high variance in returns, many subsidiaries, and losses accumulated in parents. The significant influence of variance and covariance of returns suggests that a consolidation scheme improves the efficiency of a corporate income tax by reducing profit shifting.

Inverse Problem Algorithm-Based Time-Resolved Imaging of Head and Neck Computed Tomography Angiography Contrast Kinetics with Clinical Testification.

This study mitigated the challenge of head and neck CT angiography by IPA-based time-resolved imaging of contrast kinetics. To this end, 627 cerebral hemorrhage patients with dizziness, brain aneurysm, stroke, or hemorrhagic stroke diagnosis were randomly categorized into three groups, namely, the original dataset (450), verification group (112), and in vivo testified group (65), in the Affiliated BenQ Hospital of Nanjing Medical University. In the first stage, seven risk factors were assigned: age, CTA tube voltage, body surface area, heart rate per minute, cardiac output blood per minute, the actual injected amount of contrast media, and CTA delayed trigger timing. The expectation value of the semi-empirical formula was the CTA number of the patient's left artery (LA). Accordingly, 29 items of the first-order nonlinear equation were calculated via the inverse problem analysis (IPA) technique run in the STATISTICA 7.0 program, yielding a loss function and variance of 3.1837 and 0.8892, respectively. A dimensionless AT was proposed to imply the coincidence, with a lower AT indicating a smaller deviation between theoretical and practical values. The derived formula was confirmed for the verification group of 112 patients, reaching high coincidence, with average ATavg and standard deviation values of 3.57% and 3.06%, respectively. In the second stage, the formula was refined to find the optimal amount of contrast media for the CTA number of LA approaching 400. Finally, the above procedure was applied to head and neck CTA images of the third group of 65 patients, reaching an average CTA number of LA of 407.8 ± 16.2 and finding no significant fluctuations.

Advancing glaucoma detection with convolutional neural networks: a paradigm shift in ophthalmology.

A leading cause of irreversible vision loss, glaucoma needs early detection for effective management. Intraocular Pressure (IOP) is a significant risk factor for glaucoma. Convolutional Neural Networks (CNN) demonstrate exceptional capabilities in analyzing retinal fundus images, a non-invasive and cost-effective imaging technique widely used in glaucoma diagnosis. By learning from large datasets of annotated images, CNN can identify subtle changes in the optic nerve head and retinal structures indicative of glaucoma. This enables early and precise glaucoma diagnosis, empowering clinicians to implement timely interventions. CNNs excel in analyzing complex medical images, detecting subtle changes indicative of glaucoma with high precision. Another valuable diagnostic tool for glaucoma evaluation, Optical Coherence Tomography (OCT), provides high-resolution cross-sectional images of the retina. CNN can effectively analyze OCT scans and extract meaningful features, facilitating the identification of structural abnormalities associated with glaucoma. Visual field testing, performed using devices like the Humphrey Field Analyzer, is crucial for assessing functional vision loss in glaucoma. The integration of CNN with retinal fundus images, OCT scans, visual field testing, and IOP measurements represents a transformative approach to glaucoma detection. These advanced technologies have the potential to revolutionize ophthalmology by enabling early detection, personalized management, and improved patient outcomes. CNNs facilitate remote expert opinions and enhance treatment monitoring. Overcoming challenges such as data scarcity and interpretability can optimize CNN utilization in glaucoma diagnosis. Measuring retinal nerve fiber layer thickness as a diagnostic marker proves valuable. CNN implementation reduces healthcare costs and improves access to quality eye care. Future research should focus on optimizing architectures and incorporating novel biomarkers. CNN integration in glaucoma detection revolutionizes ophthalmology, improving patient outcomes and access to care. This review paves the way for innovative CNN-based glaucoma detection methods. Abbreviations: CNN = Convolutional Neural Networks, AI = Artificial Intelligence, IOP = Intraocular Pressure, OCT = Optical Coherence Tomography, CLSO = Confocal Scanning Laser Ophthalmoscopy, AUC-ROC = Area Under the Receiver Operating Characteristic Curve, RNFL = Retinal Nerve Fiber Layer, RNN = Recurrent Neural Networks, VF = Visual Field, AP = Average Precision, MD = Mean Defect, sLV = square-root of Loss Variance, NN = Neural Network, WHO = World Health Organization.

27 Quality Variation in Beef Chuck Eye Rolls Sourced from a Commercial Processor

Abstract The objective of this study was to evaluate quality variation in beef Chuck Eye rolls (IMPS 116D). Beef Chuck Rolls (IMPS 116A) were purchased from a commercial meat packer and fabricated to obtain beef Chuck Eye Rolls (IMPS 116D). Cases were transported to the Illinois State University Meat Lab and hand-sliced into 2.54 cm boneless beef Chuck Eye steaks (IMPS 1116D) (n = 161). Each roll contained between 7 to 10 steaks for use in further meat quality analyses in accordance with in-plant procedures. Upon slicing, steaks were allowed to bloom for 15 minutes and then used in subsequent quality analysis including color, cook loss, and tenderness evaluation. Instrumental color measurements [L* (lightness), a* (redness), b* (yellowness)] were taken for each steak in duplicate with a HunterLab Miniscan XE Plus Spectrophotometer using a D65 illuminant, 100 observer, and 35-mm aperture. All steaks were cooked to a final internal temperature of 71° C. Steaks then underwent cook loss evaluation as well as tenderness analysis via Warner-Bratzler shear force (WBSF). Statistical analysis was performed using the Proc MIXED procedure of SAS to estimate variance components of steak quality in a nested design (steak within roll within case). For all response variables, the standard error was large for the case term. Ranges of observed response values were: L* 28.03-52.15, a* 14.55-27.82, b* 12.78-25.32, cook loss 17.58-61.11, shearforce 2.10-5.86. Variance components in L* and a* values followed similar patterns in which the greatest amount of variance was contributed by steak, followed by case, followed by roll (L* 13.541 (P &amp;lt; 0.001) vs. 9.035 (P = 0.1790) vs. 1.958 (P = 0.1839) and a* 4.055 (P &amp;lt; 0.0001) vs. 1.605 ( P= 0.2124) vs. 0.632 (P = 0.1746). Case, roll, and steak contributed variance almost equally for b* though two variance components were statistically different from zero σ2case =2.133 (P = 0.2344) σ2roll = 1.627 (P = 0.0335) σ2steak = 2.37 (P &amp;lt; 0.0001). Case contributed approximately 1.5 times the variance in cook loss when compared with steak (σ2case = 43.687 (P = 0.1387) vs. σ2steak=29.471 (P &amp;lt; 0.0001)) and approximately 3.5 times more than roll ( σ2case = 43.687 vs. σ2roll = 12.077 (P = 0.0565)). The largest impact on variance in shearforce values came from steak whereas case and roll contributed almost equally (shearforce σ2case = 0.0256 (P = 0.3894)σ2roll = 0.0333 (P = 0.2040) σ2steak = 0.2454 (P &amp;lt; 0.0001)). Some correlations were observed: L*:a* (-0.46, P &amp;lt; 0.0001), L*:b* (-0.07, P = 0.3979), L*:cookloss (0.166, P = 0.0422), and cookloss:shearforce (0.274, P = 0.0007). As lower-cost alternatives to traditional center of the plate cuts continue to grow in popularity among large volume purchasers, it is important to evaluate potential differences in quality attributes. Further evaluation of the impact of case as well as steak within Chuck Eye roll on individual steak quality is warranted.

SOCIAL MEDIA AND ENTREPRENEURSHIP DEVELOPMENT NEXUS IN BENUE STATE NIGERIA: EVIDENCE FROM A BINARY LOGISTIC REGRESSION MODEL

This study carried out an analysis of social media and entrepreneurship development nexus in Benue State. The researcher used mainly primary data from a sample of three hundred entrepreneurs in Zone A, B &amp; C in Benue State Nigeria. The primary data was obtained using a structured questionnaire. The data collected were analyzed using logit regression to establish the relationship between social media and entrepreneurship development while principal component analysis was used to identify the most important factors that affects the use of social media in entrepreneurship development. The result of the binary logistic regression analysis indicates that Social Media added significantly to the model/prediction. This means that the logistic model was statistically significant at 5 percent level of significance. Increasing the use of social media was associated with an increased likelihood of entrepreneurship development in Benue State. The logistic regression model was statistically significant, using values from the Omnibus Test of Model Coefficient. The model explained of the variance in post harvest losses of orange in the studied areas and correctly classified of cases. Use of social media was 0.018 times more likely to bring about entrepreneurship development in the studied areas. It was concluded that social media have a positive and significant effect on entrepreneurship development in Benue State Nigeria. It was recommended among others that entrepreneurs in the study area should engage more qualified personnel who can create the right content needed to engage the diverse customers of the business. When this is done, the likelihood of the use of social media in bringing about entrepreneurship development in Benue State can be increased.