Balanced Metrics Research Articles

Implementing Holistic Review Practices in a Pulmonary and Critical Care Fellowship.

Medical schools have used holistic review in admissions to increase mission-aligned enrollment of students from backgrounds underrepresented in medicine. Graduate medical education programs have increasingly followed suit. However, there is a paucity of literature regarding holistic review at the fellowship level. Here, we share our experience implementing the Association of American Medical Colleges core principles of holistic review during the 2021 recruitment cycle. We used a partially asynchronous and online learning strategy to train division members on the principles of holistic review. Following the match, we conducted a survey of faculty members and fellows to understand their opinions on our holistic review training and implementation. Although few of our colleagues clearly understood holistic review before the training, they were able to identify broad-based criteria that aligned with our division's mission and balanced applicants' experiences, attributes, competencies, and metrics. These were viewed as better selection criteria than traditional measures and were incorporated into the individualized consideration of applicants. Our survey had a 41.5% response rate, with 10 of 22 fellows and 24 of 60 faculty members responding. Most faculty members and fellows agreed that holistic review decreases socioeconomic disparities in fellowship recruitment (79.2% and 80.0%, respectively) and promotes inclusion and diversity (83.3% and 90.0%, respectively). Faculty members appeared more confident than fellows that our training efforts had influenced recruitment. All respondents agreed that it would be critical for such training to be repeated yearly. Although this was a single-institution experience, implementing holistic review was feasible and well received by faculty and fellows.

Differences In Balance Metrics Between Female Volleyball Athletes And Non-athletes

Differences In Balance Metrics Between Female Volleyball Athletes And Non-athletes

Data-Driven Machine Learning Techniques for the Prediction of Cholera Outbreak in West Africa

The cholera epidemic remains a public threat throughout history, affecting vulnerable populations living with unreliable water and substandard sanitary conditions. Various studies have observed that the occurrence of cholera has a strong linkage with environmental factors such as climate change and geographical location. Poor Hygiene has been strongly linked to the seasonal occurrence and widespread of cholera through the creation of weather patterns that favour the disease’s transmission, infection, and the growth of Vibrio cholerae, which cause the disease. Over the past decades, there have been great achievements in developing epidemic models for the proper prediction of cholera. However, machine learning techniques have not been explicitly deployed in modelling cholera epidemics due to the challenges that come with its datasets, such as imbalanced data and missing information. This paper explores the use of machine learning algorithms such as decision tree, random forest, and logistics regression to evaluate the prevalence of cholera epidemics in West African countries while overcoming the data imbalance problem. In addition, mean square error, mean absolute error, F1 score, precision and balanced accuracy metrics were used to evaluate the performance of the three (3) models. The results show that logistic regression has an accuracy of 0.47%, random forest 0.978% and the most efficient model was the decision tree 0.998% with a mean squared error and mean absolute error of 0.001% respectively shows that the model will accurately predict cholera outbreak in Africa. Overall results will improve the understanding of the significant roles of machine learning techniques in healthcare data. The study recommends a review of healthcare systems to facilitate quality data collection and deployment of machine learning techniques

Cow-level factors associated with nitrogen utilization in grazing dairy cows using a cross-sectional analysis of a large database

Reducing nitrogen pollution while maintaining milk production is a major challenge of dairy production. One of the keys to delivering on this challenge is to improve the efficiency of how dairy cows use nitrogen. Thus, estimating the nitrogen utilization of lactating grazing dairy cows and exploring the association between animal factors and productivity with nitrogen utilization are the first steps to understanding the nitrogen utilization complex in dairy cows. Nitrogen utilization metrics were derived from milk and body weight records from 1,291 grazing dairy cows of multiple breeds and crossbreeds; all cows had sporadic information on nitrogen intake concurrent with information on nitrogen sinks (and other nitrogen sources, such as body tissue mobilization). Several nitrogen utilization metrics were investigated, including nitrogen use efficiency (nitrogen output as products such as milk and meat divided by nitrogen intake) and nitrogen excreted (nitrogen intake less the nitrogen output as products such as milk and meat). In the present study, a primiparous Holstein-Friesian used, on average, 20.6% of the nitrogen it ate, excreting the surplus as feces and urine, representing 402 g of nitrogen per day. Intercow variability existed, with a between-cow standard deviation of 0.0094 for nitrogen use efficiency and 24 g of nitrogen per day for nitrogen excretion. As lactation progressed, nitrogen use efficiency declined and nitrogen excretion increased. Nevertheless, nitrogen use efficiency improved (i.e., decreased) from first to second parity, even though it did not improve from second to third parity or greater. Furthermore, nitrogen excretion continued to increase from first to third parity or greater. Nitrogen use efficiency and nitrogen excretion were negatively correlated (-0.56 to -0.40), signifying that dairy cows who partition more of the ingested nitrogen into products such as milk and meat, on average, also excrete less nitrogen. Milk urea nitrogen was, at best, weakly correlated with nitrogen use efficiency and nitrogen excretion; the correlations were between -0.01 and 0.06. In conclusion, several cow-level factors such as parity, stage of lactation, and breed were associated with the range of different nitrogen efficiency metrics investigated; moreover, even after accounting for such effects, 4.8% to 6.3% of the remaining variation in the nitrogen use efficiency and nitrogen balance metrics were attributable to intercow differences.

Increasing Line Efficiency By Using Line Balancing In A Steel Manufacturing Company

Line balancing using the Takt Time method is recognized as a modern tool forassisting businesses in reducing costs and increasing profits. It is possible for the enterpriseto smoothly produce the most optimal number of products by rational calculation andresource arrangement. This paper presents a case study on the application of line balancingby the Takt Time method in a steel factory in Vietnam. The methods selected for the studyinclude describing the current situation of the factory, applying Lean Manufacturingimprovement tools, applying modern equipment technology, and rearranging factoryresources, finally calculating Line Balancing metrics and implementing them in practice.According to the findings of the research, using the Takt Time line balancing methodprovides numerous benefits to the factory. The number of operators decreased by 9 people.The percentage of line balance increased by 22% from 77% to 99% and the efficiency ofline balance increased by 6% from 93% to 99%. In addition, the study also helpsVietnamese businesses have a more objective view of the application of Takt Time linebalancing in the future.

Balance and gait in progressive supranuclear palsy: a narrative review of objective metrics and exercise interventions.

The use of objective gait and balance metrics is rapidly expanding for evaluation of atypical parkinsonism, and these measures add to clinical observations. Evidence for rehabilitation interventions to improve objective measures of balance and gait in atypical parkinsonism is needed. Our aim is to review, with a narrative approach, current evidence on objective metrics for gait and balance and exercise interventions in progressive supranuclear palsy (PSP). Literature searches were conducted in four computerized databases from the earliest record up to April 2023: PubMed, ISI's Web of Knowledge, Cochrane's Library, and Embase. Data were extracted for study type (cross-sectional, longitudinal, and rehabilitation interventions), study design (e.g., experimental design and case series), sample characteristics, and gait and balance measurements. Eighteen gait and balance (16 cross-sectional and 4 longitudinal) and 14 rehabilitation intervention studies were included. Cross-sectional studies showed that people with PSP have impairments in gait initiation and steady-state gait using wearable sensors, and in static and dynamic balance assessed by posturography when compared to Parkinson's disease (PD) and healthy controls. Two longitudinal studies observed that wearable sensors can serve as objective measures of PSP progression, using relevant variables of change in turn velocity, stride length variability, toe off angle, cadence, and cycle duration. Rehabilitation studies investigated the effect of different interventions (e.g., balance training, body-weight supported treadmill gait, sensorimotor training, and cerebellar transcranial magnetic stimulation) on gait, clinical balance, and static and dynamic balance assessed by posturography measurements. No rehabilitation study in PSP used wearable sensors to evaluate gait and balance impairments. Although clinical balance was assessed in 6 rehabilitation studies, 3 of these studies used a quasi-experimental design, 2 used a case series, only 1 study used an experimental design, and sample sizes were relatively small. Wearable sensors to quantify balance and gait impairments are emerging as a means of documenting progression of PSP. Robust evidence for improving balance and gait in PSP was not found for rehabilitation studies. Future powered, prospective and robust clinical trials are needed to investigate the effects of rehabilitation interventions on objective gait and balance outcomes in people with PSP.

Quantization of symplectic fibrations and canonical metrics

We relate Berezin–Toeplitz quantization of higher rank vector bundles to quantum-classical hybrid systems and quantization in stages of symplectic fibrations. We apply this picture to the analysis and geometry of vector bundles, including the spectral gap of the Berezin transform and the convergence rate of Donaldson’s iterations toward balanced metrics on stable vector bundles. We also establish refined estimates in the scalar case to compute the rate of Donaldson’s iterations toward balanced metrics on Kähler manifolds with constant scalar curvature.

Practice walking on a treadmill-mounted balance beam modifies beam walking sacral movement and alters performance in other balance tasks.

The goals of this study were to determine if a single 30-minute session of practice walking on a treadmill mounted balance beam: 1) altered sacral marker movement kinematics during beam walking, and 2) affected measures of balance during treadmill walking and standing balance. Two groups of young, healthy human subjects practiced walking on a treadmill mounted balance beam for thirty minutes. One group trained with intermittent visual occlusions and the other group trained with unperturbed vision. We hypothesized that the subjects would show changes in sacrum movement kinematics after training and that there would be group differences due to larger improvements in beam walking performance by the visual occlusions group. We also investigated if there was any balance transfer from training on the beam to treadmill walking (margin of stability) and to standing static balance (center of pressure excursion). We found significant differences in sacral marker maximal velocity after training for both groups, but no significant differences between the two groups from training. There was limited evidence of balance transfer from beam-walking practice to gait margin of stability for treadmill walking and for single leg standing balance, but not for tandem stance balance. The number of step-offs while walking on a narrow beam had the largest change with training (partial η2 = 0.7), in accord with task specificity. Other balance metrics indicative of transfer had lower effect sizes (partial η2<0.5). Given the limited transfer across balance training tasks, future work should examine how intermittent visual occlusions during multi-task training improve real world functional outcomes.

A-16 Self-reported balance disturbance and performance-based balance measures in varsity level athletes with and without concussion history

Abstract [Outstanding Trainee Award Winner] Purpose Assessment of postural balance in varsity athletes is essential to managing sport-related concussion. The current study characterizes the relationship between subjective and objective measures of balance in varsity athletes. Methods A total of 208 varsity athletes across 12 sports participated in pre-season testing from June 2019 to January 2020 (no history of concussion= 117; 1+ past concussion= 91). Measures included the Sport Concussion Assessment Tool (SCAT-5) and the AccuGait force plate. Relationships between objective and subjective balance measures were assessed using correlations, parametric, and non-parametric tests. Results Of the total sample, 9.6% (n=20) endorsed subjective balance issues and 5.3% (n=11) reported subjective dizziness. Those with a history of concussion, endorsed greater total number of symptoms at baseline (M=2.95, SD=4.03) than those without (M=1.90, SD=3.07; p=.04; Cohen’s d=.30). However, there were no differences on subjective or objective balance measures (i.e., total balance error on the MBESS or force plate measures; ps&amp;gt;0.05) between athletes with and without a history of concussion. For those with no history of concussion, total balance errors from the Modified Balance Error Scoring system were significantly associated with force plate metrics in the eyes closed condition (ps&amp;lt;0.05) but not the eyes open condition. These associations were not observed in athletes with history of concussion (ps&amp;gt;0.05). Similarly, no associations were found between subjective balance issues and objective balance metrics. Conclusions Overall, findings suggest poor alignment between subjective and objective balance measures in varsity athletes, regardless of concussion history. Associations were observed across the objective balance measures in those with no history of injury, but not in athletes with a history of concussion.

Influence of transcutaneous electrical nerve stimulation on walking kinematics and standing balance of older adults who differ in walking speed.

The purpose was to determine the impact of transcutaneous electrical nerve stimulation (TENS) on measures of walking kinematics and standing balance of healthy older adults who were stratified into two groups based on differences in the distance walked during the 6-min test of walking endurance. Regression models were developed to explain the variance in the 6-min distance and to assess the predictive power of balance metrics to categorize the 26 older adults (72 ± 5.4 yrs) as either slow or fast walkers. Walking kinematics were measured during 6- and 2-min walk tests that were performed with and without the concurrent application of TENS to the hip flexor and ankle dorsiflexor muscles. Participants walked briskly during the 6-min test and at a preferred pace during the 2-min test. The supplementary sensory stimulation provided by TENS did not alter the power of the models to explain the variance in the Baseline 6-min distance: Baseline, R2 = 0.85; TENS, R2 = 0.83. In contrast, TENS improved the explanatory power of the data obtained during the 2-min walk to account for the variance in the Baseline 6-min distance: no TENS, R2 = 0.40; TENS, R2 = 0.64. Logistic regression models based on force-plate and kinematic data obtained during the balance tasks were able to discriminate between the two groups with excellent certainty. The impact of TENS was greatest when older adults walked at a preferred speed but not when they walked at a brisk pace or performed tests of standing balance.

An Integrated Ensemble Network Model for Skin Abnormality Detection with Combined Textural Features.

Melanoma is the most lethal of all skin cancers. This necessitates the need for a machine learning-driven skin cancer detection system to help medical professionals with early detection. We propose an integrated multi-modal ensemble framework that combines deep convolution neural representations with extracted lesion characteristics and patient meta-data. This study intends to integrate transfer-learned image features, global and local textural information, and patient data using a custom generator to diagnose skin cancer accurately. The architecture combines multiple models in a weighted ensemble strategy, which was trained and validated on specific and distinct datasets, namely, HAM10000, BCN20000 + MSK, and the ISIC2020 challenge datasets. They were evaluated on the mean values of precision, recall or sensitivity, specificity, and balanced accuracy metrics. Sensitivity and specificity play a major role in diagnostics. The model achieved sensitivities of 94.15%, 86.69%, and 86.48% and specificity of 99.24%, 97.73%, and 98.51% for each dataset, respectively. Additionally, the accuracy on the malignant classes of the three datasets was 94%, 87.33%, and 89%, which is significantly higher than the physician recognition rate. The results demonstrate that our weighted voting integrated ensemble strategy outperforms existing models and could serve as an initial diagnostic tool for skin cancer.

NZ-RugbyHealth Study: Current Postural Control Ability of Former Rugby Union and Non-contact Sport Players.

Players in contact sports frequently experience mild traumatic brain (concussion) injuries (TBI). While there are known disruptions to balance following acute head trauma, it is uncertain if sport-related concussion injuries have a lasting impact on postural control. To assess postural control in retired rugby players in comparison to retired non-contact sport players, and to evaluate any association with self-reported sport-related concussion history. Using a cross-sectional design, 75 players in the NZ-RugbyHealth study from three sports groups (44 ± 8years; 24 elite rugby, 30 community rugby, 21 non-contact sport) took part in this study. The SMART EquiTest® Balance Master was used to assess participant's ability to make effective use of visual, vestibular and proprioceptive information using standardised tests. Postural sway was also quantified using centre of pressure (COP) path length. The relationship among sports group, sport-related concussion history and postural control was evaluated using mixed regression models while controlling for age and body mass index. Limited significant differences in balance metrics were found between the sports groups. A statistically significant (p < 0.001) interaction indicated a relationship between COP path length and sport-related concussion history in the most challenging balance condition, such that path length increased as the number of previous sport-related concussions increased. There was some evidence for a relationship between sport-related concussion recurrence in sports players and postural stability in challenging balance conditions. There was no evidence of impaired balance ability in retired rugby players compared with non-contact sport athletes.

A variational approach to SKT and balanced metrics

We investigate compact complex manifolds endowed with SKT or balanced metrics. In each case, we define a new functional whose critical points are proved to be precisely the Kähler metrics, if any, on the manifold. As general manifolds of either type need not admit Kähler metrics, this provides an approach to new obstructions to Kählerianity within these two families of metrics.

On the geometry of the Heisenberg group with a balanced metric

We study the geometry of the Heisenberg group Nil3 with a balanced metric, the sum of the left and right invariant metrics. We prove that with this metric, Nil3 splits as a Riemannian product T×Z, where T is a totally geodesic surface and Z the center of Nil3. So we prove the existence of complete properly embedded minimal surfaces in Nil3 by solving the asymptotic Dirichlet problem for the minimal surface equation on T. We also show the existence of complete properly embedded minimal surfaces foliating an open set of Nil3 having as boundary a given curve Γ in T, satisfying the exterior circle condition, by solving the exterior Dirichlet problem for the minimal surface equation in the unbounded connected component of T∖Γ.

Compatibility of Balanced and SKT Metrics on Two-Step Solvable Lie Groups

AbstractIt has been conjectured by Fino and Vezzoni that a compact complex manifold admitting both a compatible SKT and a compatible balanced metric also admits a compatible Kähler metric. Using the shear construction and classification results for two-step solvable SKT Lie algebras from our previous work, we prove this conjecture for compact two-step solvmanifolds endowed with an invariant complex structure which is either (a) of pure type or (b) of dimension six. In contrast, we provide two counterexamples for a natural generalisation of this conjecture in the homogeneous invariant setting. As part of the work, we obtain further classification results for invariant SKT, balanced and Kähler structures on two-step solvable Lie groups. In particular, we give the full classification of left-invariant SKT structures on two-step solvable Lie groups in dimension six.

Load Balancing Algorithms in Fog Computing

Recently, fog computing has been introduced as a modern distributed paradigm and complement to cloud computing to provide services. The fog system extends storing and computing to the edge of the network, which can remarkably solve the problem of service computing in delay-sensitive applications besides enabling location awareness and mobility support. Load balancing is an important aspect of fog networks that avoids a situation with some under-loaded or overloaded fog nodes. Quality of service parameters such as resource utilization, throughput, cost, response time, performance, and energy consumption can be improved by load balancing. In recent years, some research in load balancing algorithms in fog networks has been carried out, but there is no systematic study to consolidate these works. This article investigates the load-balancing algorithms systematically in fog computing in four classifications, including approximate, exact, fundamental, and hybrid algorithms. Also, this article investigates load balancing metrics with all advantages and disadvantages related to chosen load balancing algorithms in fog networks. The evaluation techniques and tools applied for each reviewed study are explored as well. Additionally, the essential open challenges and future trends of these algorithms are discussed.

Balanced Hermitian structures on almost abelian Lie algebras

We study balanced Hermitian structures on almost abelian Lie algebras, i.e. on Lie algebras with a codimension-one abelian ideal. In particular, we classify six-dimensional almost abelian Lie algebras which carry a balanced structure. It has been conjectured in [1] that a compact complex manifold admitting both a balanced metric and an SKT metric necessarily has a Kähler metric: we prove this conjecture for compact almost abelian solvmanifolds with left-invariant complex structures. Moreover, we investigate the behaviour of the flow of balanced metrics introduced in [2] and of the anomaly flow [3] on almost abelian Lie groups. In particular, we show that the anomaly flow preserves the balanced condition and that locally conformally Kähler metrics are fixed points.

Executive function and relation to static balance metrics in chronic mild TBI: A LIMBIC-CENC secondary analysis.

Among patients with traumatic brain injury (TBI), postural instability often persists chronically with negative consequences such as higher fall risk. One explanation may be reduced executive function (EF) required to effectively process, interpret and combine, sensory information. In other populations, a decline in higher cognitive functions are associated with a decline in walking and balance skills. Considering the link between EF decline and reduction in functional capacity, we investigated whether specific tests of executive function could predict balance function in a cohort of individuals with a history of chronic mild TBI (mTBI) and compared to individuals with a negative history of mTBI. Secondary analysis was performed on the local LIMBIC-CENC cohort (N = 338, 259 mTBI, mean 45 ± STD 10 age). Static balance was assessed with the sensory organization test (SOT). Hierarchical regression was used for each EF test outcome using the following blocks: (1) the number of TBIs sustained, age, and sex; (2) the separate Trail making test (TMT); (3) anti-saccade eye tracking items (error, latency, and accuracy); (4) Oddball distractor stimulus P300 and N200 at PZ and FZ response; and (5) Oddball target stimulus P300 and N200 at PZ and FZ response. The full model with all predictors accounted for between 15.2% and 21.5% of the variability in the balance measures. The number of TBI's) showed a negative association with the SOT2 score (p = 0.002). Additionally, longer times to complete TMT part B were shown to be related to a worse SOT1 score (p = 0.038). EEG distractors had the most influence on the SOT3 score (p = 0.019). Lastly, the SOT-composite and SOT5 scores were shown to be associated with longer inhibition latencies and errors (anti-saccade latency and error, p = 0.026 and p = 0.043 respectively). These findings show that integration and re-weighting of sensory input when vision is occluded or corrupted is most related to EF. This indicates that combat-exposed Veterans and Service Members have greater problems when they need to differentiate between cues when vision is not a reliable input. In sum, these findings suggest that EF could be important for interpreting sensory information to identify balance challenges in chronic mTBI.

Environment-Aware Energy Efficient and Reliable Routing in Real-Time Multi-Sink Wireless Sensor Networks for Smart Cities Applications

Internet of things (IoT) is one of the leading technologies that have been used in many fields, such as environmental monitoring, healthcare, and smart cities. The core of IoT technologies is sensors; sensors in IoT form an autonomous network that is able to route messages from one place to another to the base station or the sink. Recently, due to the rapid technological development of sensors, wireless sensor networks (WSNs) have become an important part of IoT. However, in applications such as smart cities, WSNs with one sink might not be suitable due to the limited communication range of sensors and the wide area to be covered. Therefore, multi-sink WSN solutions seem to be suitable for such applications. The multi-sink WSNs are gaining popularity because they increase network throughput, network lifetime, and energy usage. At the same time, multi-hop routing is essential for the WSNS to collect data from sensor nodes and route it to the sink node for decision-making. Many routing algorithms developed for multi-sink WSNs focus on being energy efficient to extend the network lifetime, but the delay was not the main concern. However, these algorithms are unable to deal with such applications in which the data packets have to reach sink nodes within predefined real-time information. On the other hand, in the most existing routing schemes, the effects of the external environmental factors such as temperature and humidity and the reliability of real-time data delivery have largely been ignored. These issues can dramatically influence the network performance. Therefore, this paper designs a routing algorithm that satisfies three critical conditions: energy-efficient, real-time, environment-aware, and reliable routing. Therefore, the routing decisions are made according to different parameters. Such parameters include environmental impact metrics, energy balance metrics to balance the energy consumption among sensor nodes and sink nodes, desired deadline time (required delivery time), and wireless link quality. The problem is formed in integer linear programming (ILP) for optimal solution. The problem formulation is designed to fully understand the problem with its major constraints by the sensor networks research community. In addition, the optimal solution for small-scale problems could be used to measure the quality of any given heuristic that might be used to solve the same problem. Then, the paper proposes swarm intelligence to solve the optimization problem for large-scale multi-sink WSNs as a heuristic algorithm. The proposed algorithm is evaluated and analyzed compared with two recent algorithms, which are the most related to our proposal, SMRP and EERP protocols using an extensive set of experiments. The obtained results prove the superiority of the proposed algorithm over the compared algorithms in terms of packet delivery ratio, deadline miss ratio, average end-to-end delay, network lifetime, and energy imbalance factor under different aspects. In particular, the proposed algorithm requires more computational energy compared to comparison algorithms.

CtP2ISP: Protein-Protein Interaction Sites Prediction Using Convolution and Transformer With Data Augmentation.

Protein-protein interactions are the basis of many cellular biological processes, such as cellular organization, signal transduction, and immune response. Identifying protein-protein interaction sites is essential for understanding the mechanisms of various biological processes, disease development, and drug design. However, it remains a challenging task to make accurate predictions, as the small amount of training data and severe imbalanced classification reduce the performance of computational methods. We design a deep learning method named ctP2ISP to improve the prediction of protein-protein interaction sites. ctP2ISP employs Convolution and Transformer to extract information and enhance information perception so that semantic features can be mined to identify protein-protein interaction sites. A weighting loss function with different sample weights is designed to suppress the preference of the model toward multi-category prediction. To efficiently reuse the information in the training set, a preprocessing of data augmentation with an improved sample-oriented sampling strategy is applied. The trained ctP2ISP was evaluated against current state-of-the-art methods on six public datasets. The results show that ctP2ISP outperforms all other competing methods on the balance metrics: F1, MCC, and AUPRC. In particular, our prediction on open tests related to viruses may also be consistent with biological insights. The source code and data can be obtained from https://github.com/lennylv/ctP2ISP.