Significant parameters of mining properties in arctic and subarctic areas of North America

Editors: Bruce W. Campbell and John J. DiMarchiThis paper is a review of those factors unique to mining in the Arctic and subarctic. The information was developed from an exhaustive Literature search and personal visits to several northern mines in North America. The intent is to present a broad overview of many of these factors, to identify and stimulate consideration of parameters that are likely to be overlooked by companies end persons wlthout p rior arctic experience. Topics of discussion include exploration, cold weather plant design, blasting in permafrost, living conditions and employees relations. The appendices are a brief discussion of a number of the arctic and subarctic operations in North America. In brief, minlng in northern regions is practical provided the deposit has sufficient value to support the higher construction, transportation and operating costs associated with the remoteness and cold weather. Hiring and retaining good employees and integrating the native labor force into the operation have proven to be the most difficult problems. Equipment and plant operation are problems more easily solved.This report is a revised and edited form of the report by the same authors and originally titled "Identification of and Significant Parameters of Mining Properties located in Arctic and Subarctic areas of Norther America", under U.S. Bureau of Mines Contract No. S0144117

Validation of a commercially available markerless motion-capture system for trunk and lower extremity kinematics during a jump-landing assessment

Context: Field-based, portable motion-capture systems can be used to help identify individuals at greater risk of lower extremity injury. Microsoft Kinect-based markerless motion-capture systems meet these requirements; however, until recently, these systems were generally not automated, required substantial data postprocessing, and were not commercially available. Objective: To validate the kinematic measures of a commercially available markerless motion-capture system. Design: Descriptive laboratory study. Setting: Laboratory. Patients or Other Participants: A total of 20 healthy, physically active university students (10 males, 10 females; age ¼ 20.50 6 2.78 years, height ¼ 170.36 6 9.82 cm, mass ¼ 68.38 6 10.07 kg, body mass index ¼ 23.50 6 2.40 kg/m2). Intervention(s): Participants completed 5 jump-landing trials. Kinematic data were simultaneously recorded using Kinect-based markerless and stereophotogrammetric motion-capture systems. Main Outcome Measure(s): Sagittal- and frontal-plane trunk, hip-joint, and knee-joint angles were identified at initial ground contact of the jump landing (IC), for the maximum joint angle during the landing phase of the initial landing (MAX), and for the joint-angle displacement from IC to MAX (DSP). Outliers were removed, and data were averaged across trials. We used intraclass correlation coefficients (ICCs [2,1]) to assess intersystem reliability and the paired-samples t test to examine mean differences (a < .05). Results: Agreement existed between the systems (ICC range ¼1.52 to 0.96; ICC average ¼ 0.58), with 75.00% (n ¼ 24/ 32) of the measures being validated (P < .05). Agreement was better for sagittal- (ICC average ¼ 0.84) than frontal- (ICC average ¼ 0.35) plane measures. Agreement was best for MAX (ICC average ¼ 0.77) compared with IC (ICC average ¼ 0.56) and DSP (ICC average ¼ 0.41) measures. Pairwise comparisons identified differences for 18.75% (6/32) of the measures. Fewer differences were observed for sagittal- (0.00%; 0/15) than for frontal- (35.29%; 6/17) plane measures. Between-systems differences were equivalent for MAX (18.18%; 2/11), DSP (18.18%; 2/11), and IC (20.00%; 2/10) measures. The markerless system underestimated sagittal-plane measures (86.67%; 13/15) and overestimated frontal-plane measures (76.47%; 13/ 17). No trends were observed for overestimating or underestimating IC, MAX, or DSP measures. Conclusions: Moderate agreement existed between markerless and stereophotogrammetric motion-capture systems. Better agreement existed for larger (eg, sagittal-plane, MAX) than for smaller (eg, frontal-plane, IC) joint angles. The DSP angles had the worst agreement. Markerless motion-capture systems may help clinicians identify individuals at greater risk of lower extremity injury

Movement profile influences systemic stress and biomechanical resilience to high training load exposure

Objectives: Determine the influence of movement profile on systemic stress and mechanical loading before and after high training load exposure. Design: Cross-sectional cohort study. Methods: 43 physically active, college-aged field or court sport female athletes participated in this study. Participants were assigned to a “excellent” (n = 22; age = 20.5 ± 1.9 yrs, height = 1.67 ± 0.67 m, mass = 64.5 ± 7.8 kg) or “poor” (n = 21; age = 20.4 ± 1.3 yrs, height = 1.69 ± 0.67 m, mass = 60.9 ± 6.1 kg) movement group defined by The Landing Error Scoring System. Participants completed five cycles of high training load exercise of 5-min treadmill-running at a speed coincident with 100–120% ventilatory threshold and 10 jump-landings from a 30-cm box. Jump-landing vertical ground reaction force and serum cortisol were evaluated prior to and following exercise. Vertical ground reaction force ensemble averages and 95% confidence interval waveforms were generated for pre-exercise, post-exercise, and pre-post exercise changes. A two-way mixed model ANOVA was used to evaluate the effect of movement profile on systemic stress before and after exercise. Results: There was no significant difference in changes in serum cortisol between the poor and excellent groups (p = 0.69) in response to exercise. Overall, individuals in the poor group exhibited a higher serum cortisol level (p < 0.05, d = 0.85 [0.19,1.48]). The poor group exhibited higher magnitude vertical ground reaction force prior to (d = 1.02–1.26) and after exercise (d = 1.15) during a majority of the stance phase. Conclusions: Individuals with poor movement profiles experience greater mechanical loads compared to individuals with excellent movement profiles. A poor movement profile is associated with greater overall concentrations of circulating cortisol, representative of greater systemic stress

Trunk and lower extremity movement patterns, stress fracture risk factors, and biomarkers of bone turnover in military trainees

Context: Military service members commonly sustain lower extremity stress fractures (SFx). How SFx risk factors influence bone metabolism is unknown. Understanding how SFx risk factors influence bone metabolism may help to optimize risk-mitigation strategies. Objective: To determine how SFx risk factors influence bone metabolism. Design: Cross-sectional study. Setting: Military service academy. Patients or Other Participants: Forty-five men (agepre ¼ 18.56 6 1.39 years, heightpre ¼ 176.95 6 7.29 cm, masspre ¼ 77.20 6 9.40 kg; body mass indexpre ¼ 24.68 6 2.87) who completed Cadet Basic Training (CBT). Individuals with neurologic or metabolic disorders were excluded. Intervention(s): We assessed SFx risk factors (independent variables) with (1) the Landing Error Scoring System (LESS), (2) self-reported injury and physical activity questionnaires, and (3) physical fitness tests. We assessed bone biomarkers (dependent variables; procollagen type I amino-terminal propeptide [PINP] and cross-linked collagen telopeptide [CTx-1]) via serum. Main Outcome Measure(s): A markerless motion-capture system was used to analyze trunk and lower extremity biomechanics via the LESS. Serum samples were collected post-CBT; enzyme-linked immunosorbent assays determined PINP and CTx-1 concentrations, and PINP: CTx-1 ratios were calculated. Linear regression models demonstrated associations between SFx risk factors and PINP and CTx-1 concentrations and PINP: CTx-1 ratio. Biomarker concentration mean differences with 95% confidence intervals were calculated. Significance was set a priori using a ≤ .10 for simple and a ≤ .05 for multiple regression analyses. Results: The multiple regression models incorporating LESS and SFx risk factor data predicted the PINP concentration (R2 ¼ 0.47, P ¼ .02) and PINP: CTx-1 ratio (R2 ¼ 0.66, P ¼ .01). The PINP concentration was increased by foot internal rotation, trunk flexion, CBT injury, sit-up score, and pre- to post-CBT mass changes. The CTx-1 concentration was increased by heel-to-toe landing and post-CBT mass. The PINP: CTx-1 ratio was increased by foot internal rotation, lower extremity sagittal-plane displacement (inversely), CBT injury, sit-up score, and pre- to post-CBT mass changes. Conclusions: Stress fracture risk factors accounted for 66% of the PINP: CTx-1 ratio variability, a potential surrogate for bone health. Our findings provide insight into how SFx risk factors influence bone health. This information can help guide SFx risk-mitigation strategies

Informing UK Information Management Pedagogic Practice: the nature of contemporary Higher Education culture

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