Usage of NASA's Near Real-Time Solar and Meteorological Data for Monitoring Building Energy Systems Using RETScreen International's Performance Analysis Module

This paper describes building energy system production and usage monitoring using examples from the new RETScreen Performance Analysis Module, called RETScreen Plus. The module uses daily meteorological (i.e., temperature, humidity, wind and solar, etc.) over a period of time to derive a building system function that is used to monitor building performance. The new module can also be used to target building systems with enhanced technologies. If daily ambient meteorological and solar information are not available, these are obtained over the internet from NASA's near-term data products that provide global meteorological and solar information within 3-6 days of real-time. The accuracy of the NASA data are shown to be excellent for this purpose enabling RETScreen Plus to easily detect changes in the system function and efficiency. This is shown by several examples, one of which is a new building at the NASA Langley Research Center that uses solar panels to provide electrical energy for building energy and excess energy for other uses. The system shows steady performance within the uncertainties of the input data. The other example involves assessing the reduction in energy usage by an apartment building in Sweden before and after an energy efficiency upgrade. In this case, savings up to 16% are shown

Two-dimensional carrier density distribution inside a high power tapered laser diode

The spontaneous emission of a GaAs-based tapered laser diode emitting at lambda = 1060 nm was measured through a window in the transparent substrate in order to study the carrier density distribution inside the device. It is shown that the tapered geometry is responsible for nonuniform amplification of the spontaneous/stimulated emission which in turn influences the spatial distribution of the carriers starting from below threshold. The carrier density does not clamp at the lasing threshold and above it the device shows lateral spatial hole-burning caused by high stimulated emission along the cavity center. (C) 2011 American Institute of Physics. (doi: 10.1063/1.3596445

Heart Rate and Energy Expenditure Concurrent Validity of Identical Garmin Wrist Watches During Moderately Heavy Resistance Training

Consistent with previous years, ACSM has found that wearable technology and resistance training (RT) are two of the top 5 fitness trends in 2023. Our lab recently found that wrist-worn devices, such as Garmin Instinct, are neither valid nor reliable at measuring average or maximal heart rate (HR) or estimating energy expenditure (EE) following light intensity circuit RT. We postulated that the errors may have been due to the device’s algorithms assuming higher intensity during RT. PURPOSE: The purpose of this study was to determine the concurrent validity of identical Garmin Instinct wrist-watches to record valid measures of average and maximal HR as well as estimated EE following moderately heavy RT. METHODS: Twenty-one adult participants completed this study (n=10 female, n=11 male). Two Garmin Instinct wrist-watches were evaluated, along with the Polar H10 chest strap and Cosmed K5 portable metabolic unit as the criterion devices for average/maximal HR and EE, respectively. Participants completed 8 supersets of the reverse lunge and shoulder press exercises using dumbbells at a light (4 sets) and moderately heavy (4 sets) intensity with 1 superset of 6 repetitions per exercise (12 repetitions per superset) and 1 min rest between supersets. Data were analyzed for validity (Mean Absolute Percent Error [MAPE] and Lin’s Concordance Coefficient [CCC]), with predetermined thresholds of MAPE\u3c10% and CCC\u3e0.70. A one-way repeated measures ANOVA with Sidak post-hoc test was used to determine differences (p\u3c0.05). RESULTS: The identical Garmin Instinct devices were not considered valid for average HR (MAPE range: 36.5-81.6%; CCC range: 0.07-0.18), maximal HR (MAPE range: 18.6-18.8%; CCC range: 0.15-0.31), or estimated EE (MAPE range: 14.0-16.4%; CCC range: 0.08-0.32) compared to the criterion references. The devices were significantly different than each other for average HR (p=0.005), maximal HR (p\u3c0.001), and estimated EE (p\u3c0.0001). CONCLUSION: The wearable wrist-worn devices tested herein should not be utilized for accurate measurements of HR or EE during RT, and there are even differences between identical devices. People who RT while using these devices should do so with caution if wishing to utilize them for physiological measures

Perceived Fatigue and Physical Activity Enjoyment Following Indoor and Outdoor Moderately Heavy Superset Resistance Training

ACSM has again determined that resistance training (RT) and outdoor activities are two of the top ten worldwide fitness trends for 2023. We previously found that RT outdoors had a significantly lower perception of effort (RPE) compared to indoor RT, despite no physiological differences in heart rate (HR) and energy expenditure (EE). However, no study has examined other feelings during RT in indoor or outdoor settings. PURPOSE: To determine how indoor or outdoor environments effect perceptions of fatigue and physical activity enjoyment following RT in recreationally resistance trained adults. METHODS: Twenty-three adult participants (n=10 female, n=13 male) completed this study. The Visual Analog Scale Fatigue (VAS-F) measured perceived fatigue and the Physical Activity Enjoyment Scale – Short Version (PACES-S) measured PA enjoyment, and both were measured at baseline and then immediately following an acute session of indoor or outdoor RT. HR was obtained from a chest strap (Polar H10) and EE from a Portable Metabolic Cart (COSMED K5). Randomly in indoor and outdoor settings, participants completed 4 supersets of the reverse lunge and shoulder press exercises using dumbbells at a light (2 sets) and moderately heavy (2 sets) intensity with 1 superset of 6 repetitions per exercise and 1 min rest between supersets. A paired T-test (for HR & EE comparisons) or one-way repeated measures ANOVA with Sidak post-hoc test (for VAS-F & PACES-S comparisons) were used to determine differences (p\u3c0.05). RESULTS: No significant differences were observed between indoor and outdoor RT for the physiological variables of average HR (129.4±17.2 and 127.75±23.3 bpm, respectively, p=0.66) and EE (30.6±11.5 and 28.3±9.9 kcals, respectively, p=0.06). Perceived fatigue significantly (p\u3c0.0001) increased from baseline (1.13±0.94 arbitrary units, AU’s) following indoor (4.54±1.91 AU’s) and outdoor (3.99±1.54 AU’s) RT, but no environmental differences (p=0.36) were observed. PA enjoyment was not significantly (p range: 0.27-0.93) different between baseline (18.73±1.83 AU’s) and following indoor (18.18±1.99 AU’s) or outdoor (18.36±1.99 AU’s) RT. CONCLUSION: In recreationally resistance trained adults, moderately heavy superset RT in indoor or outdoor settings does not alter perceived fatigue or physical activity enjoyment

Repetition Count Concurrent Validity of Various Garmin Wrist Watches During Light Circuit Resistance Training

Wearable technology and strength training with free weights are two of the top 5 fitness trends worldwide. However, minimal physiological research has been conducted on the two together and none have measured the accuracy of devices measuring repetition counts across exercises. PURPOSE: The purpose of this study was to determine the concurrent validity of four wrist-worn Garmin devices, Instinct (x2), Fenix 6 Pro, and Vivoactive 3, to record repetition counts while performing 4 different exercises during circuit resistance training. METHODS: Twenty participants (n=10 female, n=10 male; age: 23.2 ± 7.7 years) completed this study. Participants completed 4 circuits of 4 exercises (front squat, reverse lunge, push-ups, and shoulder press) using dumbbells at a light intensity with 1 set of 10 repetitions per exercise and 30 seconds rest between exercises and 1-1.5 min rest between circuits. Mean absolute percent error (MAPE, ≤10%) and Lin’s Concordance Coefficient (CCC, ρ≥0.7) were used to validate the device’s repetitions counts in all exercises compared to the criterion reference manual count. Dependent T-tests determined differences (p≤0.05). RESULTS: No devices were considered valid (meeting both the threshold for MAPE and CCC) for measuring repetition counts during front squats (MAPE range: 3.0-18.5% and CCC range: 0.27-0.68, p value range: 0.00-0.94), reverse lunge (MAPE range: 44.5-67.0% and CCC range: 0.19-0.31, p value range: 0.00-0.28), push-ups (MAPE range: 12.5-67.5% and CCC range: 0.10-0.34, p value range: 0.07-0.83), and shoulder press (MAPE range: 18.0-51.0% and CCC range: 0.11-0.43, p value range: 0.00-0.79) exercises. CONCLUSION: The wearable wrist-worn devices were not considered accurate for repetition counts and thus manual counting should be utilized. People who strength train using free weights will need to wait for either improved repetition counting algorithms or increased sensitivity of devices before this measure can be obtained with confidence

Evaluation of Average and Maximum Heart Rate of Wrist-worn Wearable Technology Devices During Trail Running

It has been estimated that there are 20 million people who participate in trail running, and these numbers are expected to increase by 15% each year. Our laboratory group has conducted studies on the validity of wearable technology watches and heart rate (HR) during trail running. The previous generation devices were mostly inaccurate, and a limitation was that reliability was not measured. PURPOSE: To determine both validity and reliability in newer models of wearable devices during trail running. METHODS: Seventeen participants (F = 7) ran on the Thunderbird Gardens Lightning Switch trail in Cedar City, UT. Demographic characteristics: Age = 25 (9) years (mean [standard deviation]), ht = 168 (9) cm, mass = 72 (14) kg. Two Garmin Instincts and two Polar Vantage M2s were evaluated, along with the Polar H10 chest strap as the criterion measure. Participants ran out on the trail for 10-minutes, and then returned to the trailhead. Maximum HR and average HR were measured during the run. Data were analyzed for validity (Mean Absolute Percent Error [MAPE] and Lin’s Concordance [CCC]) and reliability (Coefficient of Variation [CV] and Intraclass Correlation Coefficient [ICC]). Predetermined thresholds were: MAPE0.70, CV0.70. RESULTS: The Garmin Instinct met the threshold for both reliability tests for average and maximum HR (see table). The Garmin Instinct and Polar Vantage met the threshold for both validity tests for maximum HR. CONCLUSION: In order for a device to be considered valid, it must meet the predetermined thresholds for both validity and reliability. These results indicate that only the Garmin Instinct is valid and reliable, but only for measuring maximum HR. This is challenging for those who wish to track their HR while trail running, because neither of the studied devices were valid and reliable for maximum and average HR

An Integrative Salt Marsh Conceptual Framework for Global Comparisons

Salt marshes occur globally across climatic and coastal settings, providing key linkages between terrestrial and marine ecosystems. However, salt marsh science lacks a unifying conceptual framework; consequently, historically well-studied locations have been used as normative benchmarks. To allow for more effective comparisons across the diversity of salt marshes, we developed an integrative salt marsh conceptual framework. We review ecosystem-relevant drivers from global to local spatial scales, integrate these multi-scale settings into a framework, and provide guidance on applying the framework using specific variables on 11 global examples. Overall, this framework allows for appropriate comparison of study sites by accounting for global, coastal, inter-, and intra-system spatial settings unique to each salt marsh. We anticipate that incorporating this framework into salt marsh science will provide a mechanism to critically evaluate research questions and a foundation for effective quantitative studies that deepen our understanding of salt marsh function across spatial scales

Interactions among mitochondrial proteins altered in glioblastoma

Mitochondrial dysfunction is putatively central to glioblastoma (GBM) pathophysiology but there has been no systematic analysis in GBM of the proteins which are integral to mitochondrial function. Alterations in proteins in mitochondrial enriched fractions from patients with GBM were defined with label-free liquid chromatography mass spectrometry. 256 mitochondrially-associated proteins were identified in mitochondrial enriched fractions and 117 of these mitochondrial proteins were markedly (fold-change ≥2) and significantly altered in GBM (p ≤ 0.05). Proteins associated with oxidative damage (including catalase, superoxide dismutase 2, peroxiredoxin 1 and peroxiredoxin 4) were increased in GBM. Protein–protein interaction analysis highlighted a reduction in multiple proteins coupled to energy metabolism (in particular respiratory chain proteins, including 23 complex-I proteins). Qualitative ultrastructural analysis in GBM with electron microscopy showed a notably higher prevalence of mitochondria with cristolysis in GBM. This study highlights the complex mitochondrial proteomic adjustments which occur in GBM pathophysiology

Average Heart Rate and Energy Expenditure Validity of Garmin Vivoactive 3 and Fenix 6 Wrist Watches During Light Circuit Resistance Training

Our laboratory recently found wrist-worn wearable technology devices to be valid for measuring average heart rate (HR), but not valid for estimated energy expenditure (EE) compared to criterion devices, during steady state aerobic training (walking, running, biking). However, the validity of wrist-worn devices for HR and EE measures during resistance training is largely unknown. PURPOSE: The purpose of this study was to determine if two wrist-worn devices, Garmin Vivoactive 3 and Garmin Fenix 6 Pro, record valid measures of average HR and EE while performing circuit resistance training. METHODS: Twenty participants (n=10 female, n=10 male; age: 23.2 ± 7.7 years) completed this study. The Garmin Vivoactive 3 and Garmin Fenix 6 Pro were tested along with the Polar H10 chest strap and Cosmed K5 portable metabolic unit as the criterions for average HR and EE, respectively. Participants completed 4 circuits of 4 exercises (front squat, reverse lunge, push-ups, and shoulder press) using dumbbells at a light intensity with 1 set of 10 repetitions per exercise and 30 seconds rest between exercises and 1-1.5 min. rest between circuits. Mean absolute percent error (MAPE, ≤10%) and Lin’s Concordance (ρ≥0.7) were used to validate the device’s average HR (in bpm) and estimated EE (in kcals) compared to criterion reference devices. Dependent T-tests determined differences (p≤0.05). RESULTS: Average HR for Garmin Vivoactive 3 and Fenix 6 Pro were significantly different (p\u3c0.01) than the Polar H10 (115.0±23.9 and 124.5±15.4 vs 128.9±19.0 bpm, respectively), and were not considered valid (MAPE: 44.8% and 25.1%; Lin’s Concordance: 0.50 and 0.63, respectively). Estimated EE for Garmin Vivoactive 3 and Fenix 6 Pro were significantly different (p\u3c0.0001) than the Cosmed K5 (31.7±12.3 and 39.7±13.1 vs 20.3±5.5 kcals, respectively), and were not considered valid (MAPE: 309.7% and 322.1%; Lin’s Concordance: 0.04 and 0.15, respectively). CONCLUSION: Anyone involved in any resistance training aspect should be aware of the limitations of these wrist-worn devices in measuring average HR or EE