Time-of-flight Research Articles

Gender-related differences in kinematic parameters of the start and performance in a 50m breaststroke swimming

Introduction: In swimming, the competitions are divided by age categories, with each swimming event beginning at the start. For optimal improvements in swimming performance in the sprint events, the performance at the start and during swimming in each age category should be analyzed. Aim: The aim of our study is to show the differences in kinematic parameters of the start and performance in the sprint event of 50 m breaststroke from the perspective of gender in different age groups at international competitions organized in Slovakia. Materials and Methods: The females were categorized by age into K3 (2010-2011; n=36; 282 WA points), K2 (2008-2009; n=26; 409 WA points) and K1 (2007 and older; n=34; 503 WA points) groups. Males were categorized into K3 (2009-2010; n=27; 265 WA points), K2 (2007-2008; n=33; 372 WA points) and K1 (2006 and older; n=39; 207 WA points) groups. We used SwimPro cameras and Dartgish software to monitor kinematic parameters in the above-water, underwater, and swim phases. The parameters monitored were block time (BT), time (FT), and length (FD) of flight, time (UWT) and length (UWD) underwater, swim time (ST) and distance (SD) to 15 m, time to 15 m (T15), 25 m (T25), and 50 m (T50) distance. Data were tested by Shapiro-Wilk, Kruskal-Wallis ANOVA, and Mann-Whitney U test in Statistica software (version 13.5). Results: Statistically significant differences (p<0.01) in females were found in the above-water phase in BT, FT, and FD between K3 and K1. The underwater phase in UWT showed differences between K3 to K1, K2 to K1 whereas for UWD - between K3 and K2 and between K2 and K1. For T15 and T25, there were differences (p<0.01) between all categories, but in the final time (T50), the differences occurred between K3 and K2 and between K3 and K1. In males, there was a significant difference in the above-water phase between K3 and K1 and between K2 and K1. In UWD, ST, SD, T15, T25, and T50, there were significant differences between all categories. Differences (p>0.01) between males and females by category in the above-water phase were measured in FT (K1) and FD (K2, K1). In the underwater phase, the differences were found in UWT (K2) and UWD (K1). For T15, T25, and T50, significant differences were shown in all categories. Conclusions: The kinematic analysis revealed differences not only in the start phases and the 50 m breaststroke swimming in the male and female categories but also between genders. Differences in swimming performance may be due to performance at the start and therefore more attention should be paid to the start phases in the different categories of swimmers.

Optical crosstalk of protective cover on MPPC array for TOF PET detector

Objective. Time-of-flight (TOF) is an important factor that directly affects the image quality of PET systems, and various attempts have been made to improve the coincidence resolving time (CRT) of PET detectors. For independent readout detectors, the timing is acquired for each silicon photomultiplier (SiPM), so they are less sensitive to diffused scintillation light, resulting in a better CRT. Further improvement can be expected if the light can be focused on a single SiPM. However, existing SiPM arrays have a thin protective cover on the SiPM and the gap between the SiPMs is filled with either air or the protective cover, so the light must diffuse through the cover. In this work, we investigated optical crosstalk in the protective cover to improve the CRT. Approach. We used 3.1 × 3.1 × 20 mm3 fast LGSO crystals and 3 mm square 8 × 8 multi pixel photon counter (MPPC) arrays. Pitch of the MPPCs was 3.2 mm and thickness of the protective cover on them was 150 μm. To reduce diffusion of scintillation light in the protective cover, the part of the inactive areas on the MPPC array were optically separated using reflective material. Specifically, 50, 100, 150, and 350 μm deep grid-shaped slits were made along the inactive area of the MPPCs and they were filled with BaSO4 powder as the reflective material. Main results. Coincidence counts were measured with a pair of TOF detectors, and the CRT was shorter with a deeper slit depth. The CRT before improvement was 235 ps, and using the cover having the 350 μm deep slits filled with reflective material lowered the CRT to 211 ps. Significance. Up to 10% of the scintillation light was diffused to other MPPCs by the protective cover, and the CRT was degraded by 10% due to optical crosstalk of the cover. The proposed method promises to improve the CRT of the TOF detector.

Comparative study of antioxidant activities of Allium sativum (a novel variety, HG17) and Allium ampeloprasum (SMG): Revealing the higher potential of HG17 and analyzing its phytochemicals.

Garlic, belonging to the genus Allium, is renowned for its rich antioxidant potential. Snow Mountain garlic (SMG) (Allium ampeloprasum) has been traditionally used for medicinal purposes because of its higher antioxidant potential. Considering its potential in medical therapies, we compared the antioxidant activity of SMG with a novel variety of Allium sativum, Hisar garlic 17 (HG17). Comparative antioxidant activity data (2,2-diphenyl-1-picrylhydrazyl and 2,2-azinobis (3-ethylbenzothiazoline-6-sulfonic acid) revealed the higher antioxidant activity of HG17 than SMG, which prompted us to conduct a comprehensive phytochemical investigation to elucidate the factors contributing to antioxidant potential of HG17. To get a detailed antioxidant and phytoconstituents profiling, we differentially extracted HG17 by processing it in different forms (fresh, dry, heated, and aged) with two solvents (50% methanol and n-butanol). Our data (antioxidant activities, total phenolics, and flavonoids) showed that dry garlic methanolic extract (DgM) had maximum potential than other HG17 forms/solvents, which concludes that different extraction techniques had direct impact on the phenolics/flavonoids and antioxidant potential of the extracts. Further, phytochemical analysis of HG17 extracts by high resolution liquid chromatograph mass spectrometer quadrupole time of flight validated the maximum potential of DgM. LCMS revealed the presence of garcimangosone C, osmanthuside A, and protoaphin aglucone polyphenols exclusively in DgM compared to other HG17 extracts, which possibly contributing in its high antioxidant potential. The overall differential extraction and LCMS data of HG17 strongly depict that it may be used as an alternative of SMG under diverse medical applications. HG17 higher antioxidant potential and rich array of unique phytochemicals make it valuable for food and pharmaceutical industries to integrate into functional foods/therapeutics. PRACTICAL APPLICATION: Garlic unique phytochemical composition and its remarkable ability to scavenge different radicals make it valuable therapeutic asset to mitigate diseases associated with oxidative stress. SMG is well known for its anti-arthritic and anti-inflammatory properties. HG17 showed higher antioxidant potential than SMG and can be used as an alternative of SMG for anti-arthritic properties.

Validity and Reliability of a Smartphone App for Vertical Jump Height Assessment Using the Marker Displacement Time Method

The correct assessment of the vertical jump height depends on an accurate and reliable measurement tool. This study aimed to determine the concurrent validity and reliability of the My Jump 2 app used for estimating the maximum height (MH) of the counter-movement jump (CMJ). Twenty-one male adults participated in this study. The MH of the CMJ was estimated based on the displacement of the jumper’s center of mass (force platform), the displacement time of the reflective marker placed on the jumper’s sacrum (smartphone, My Jump 2-DT) and the flight time of the jumper (smartphone, My Jump 2-FT). The assessment of the concurrent validity showed a poor agreement (ICC = 0.362; Bland–Altman bias = 12.4 cm) between the My Jump 2-FT and force platform (p < 0.001), and a good agreement (ICC = 0.858; Bland–Altman bias = −0.2 cm) between the My Jump 2-DT and force platform (p < 0.001). The ICC values for internal consistency (>0.9) indicated the excellent reliability of all measurement tools (p < 0.001). The findings revealed the high accuracy and good reliability of the My Jump 2 app for the new method of MH estimation for the CMJ, including the displacement time of the marker placed on the jumper’s sacrum.

Validity of the Hawkin Dynamics Wireless Dual Force Platform System Against a Piezoelectric Laboratory Grade System for Vertical Countermovement Jump Variables.

Dos'Santos, T, Evans, DT, and Read, DB. Validity of the Hawkin dynamics wireless dual force platform system against a piezoelectric laboratory grade system for vertical countermovement jump variables. J Strength Cond Res 38(6): 1144-1148, 2024-The aim of this study was to determine the criterion validity of the Hawkin Dynamics (HD) wireless dual force platform system for assessing vertical countermovement jump (CMJ) variables, compared with those derived from a Kistler piezoelectric laboratory grade force platform system. During a single testing session, HD force platforms were placed directly on top of 2 adjacent Kistler force platforms to simultaneously collect vertical ground reaction forces produced by 2 male recreational soccer players (age: 29.0 ± 2.8 years, height: 1.79 ± 0.01 m, mass: 85.6 ± 4.7 kg) that performed 25 vertical CMJs each. Sixteen vertical CMJ variables pertaining to jump height (JH), flight time (FT), time-to-take off (TTT), countermovement depth, body weight (BW), propulsive and braking mean, and peak powers, forces, and impulses were compared between systems. Fixed bias was observed for 6 of 16 variables (peak and mean braking power, mean propulsion force, TTT, FT, and BW), while proportional bias was present for 10 of 16 variables (peak and mean propulsive and braking force, TTT, FT, peak and mean braking power, mean propulsive power, and BW). For all variables regardless of fixed or proportional bias, percentage differences were ≤3.4% between force platform systems, with near perfect to perfect correlations (r or ρ = 0.977-1.000) observed for 15 of 16 variables. The HD dual wireless force platform system can be considered a valid alternative to a piezoelectric laboratory grade force platform system for the collection of vertical CMJ variables, particularly outcome (i.e., JH, reactive strength index modified) and strategy variables (countermovement depth).

On the Generalizability of Time-of-Flight Convolutional Neural Networks for Noninvasive Acoustic Measurements.

Bulk wave acoustic time-of-flight (ToF) measurements in pipes and closed containers can be hindered by guided waves with similar arrival times propagating in the container wall, especially when a low excitation frequency is used to mitigate sound attenuation from the material. Convolutional neural networks (CNNs) have emerged as a new paradigm for obtaining accurate ToF in non-destructive evaluation (NDE) and have been demonstrated for such complicated conditions. However, the generalizability of ToF-CNNs has not been investigated. In this work, we analyze the generalizability of the ToF-CNN for broader applications, given limited training data. We first investigate the CNN performance with respect to training dataset size and different training data and test data parameters (container dimensions and material properties). Furthermore, we perform a series of tests to understand the distribution of data parameters that need to be incorporated in training for enhanced model generalizability. This is investigated by training the model on a set of small- and large-container datasets regardless of the test data. We observe that the quantity of data partitioned for training must be of a good representation of the entire sets and sufficient to span through the input space. The result of the network also shows that the learning model with the training data on small containers delivers a sufficiently stable result on different feature interactions compared to the learning model with the training data on large containers. To check the robustness of the model, we tested the trained model to predict the ToF of different sound speed mediums, which shows excellent accuracy. Furthermore, to mimic real experimental scenarios, data are augmented by adding noise. We envision that the proposed approach will extend the applications of CNNs for ToF prediction in a broader range.

Simulation and experiment of complex residual stress detection of metal plate based on critical refracted longitudinal wave (LCR) method

This study is based on the principles of acoustoelasticity, analyzing the variation in sound wave time-of-flight(TOF) under different conditions using the critical refracted longitudinal(LCR) wave method. A simulation model was developed using the COMSOL finite element simulation software to mimic the propagation of LCR wave in a stress-free plate. The model was then subjected to uniaxial loading, and the change in wave TOF was observed by varying the angle between the stress direction and the sound wave propagation direction under constant load. The results were compared with theoretical calculations, showing a high degree of agreement, validating the accuracy of the designed model. Subsequently, during the tensile testing, the influence of different uniaxial loads and angle variations on the TOF of LCR wave was observed. Upon comparing the experiment data with theoretical calculations, a high degree of consistency in the trend of variation is observed, this demonstrates that the detection platform established in this study is feasible for the assessment of residual stresses in practice.

Seasonal patterns and processes of migration in a long-distance migratory bird: energy or time minimization?

Optimal migration theory prescribes adaptive strategies of energy, time or mortality minimization. To test alternative hypotheses of energy- and time-minimization migration we used multisensory data loggers that record time-resolved flight activity and light for positioning by geolocation in a long-distance migratory shorebird, the little ringed plover, Charadrius dubius. We could reject the hypothesis of energy minimization based on a relationship between stopover duration and subsequent flight time as predicted for a time minimizer. We found seasonally diverging slopes between stopover and flight durations in relation to the progress (time) of migration, which follows a time-minimizing policy if resource gradients along the migration route increase in autumn and decrease in spring. Total flight duration did not differ significantly between autumn and spring migration, although spring migration was 6% shorter. Overall duration of autumn migration was longer than that in spring, mainly owing to a mid-migration stop in most birds, when they likely initiated moult. Overall migration speed was significantly different between autumn and spring. Migratory flights often occurred as runs of two to seven nocturnal flights on adjacent days, which may be countering a time-minimization strategy. Other factors may influence a preference for nocturnal migration, such as avoiding flight in turbulent conditions, heat stress and diurnal predators.

Pilot Assistance Systems for Energy-Optimized Approaches: Is It Possible to Reduce Fuel Consumption and Noise at the Same Time?

Air traffic has appreciable environmental impacts, especially regarding gaseous emissions and noise. Recent studies have shown that the energy management during approach is a driving factor regarding environmental impact and is especially challenging for pilots. In a previous project, a newly developed pilot assistance system called LNAS (Low Noise Augmentation System) showed the potential of energy-optimized approaches to reduce fuel consumption and noise. Within the SESAR Exploratory Research project DYNCAT, novel functions based on LNAS have been integrated in the flight management system. In this contribution, results from real-time simulations with the enhanced FMS are presented, and mitigation of the environmental impact is analyzed. It was shown that with DYNCAT, the energy management could be improved, resulting in a later configuration and engines mostly in idle. With DYNCAT, procedures were also flown more uniformly and the variability in noise and fuel outcomes was reduced. However, the results revealed a trade-off for optimizing noise and fuel consumption simultaneously, whereby both parameters can be improved along specific optimum curves. A perfect strategy to minimize noise would be to first reduce speed and only secondly height, as high speeds lead to higher levels of airframe noise and sound exposure increases with decreasing distance. In contrast, saving fuel might be achieved by reducing the flight time, as the engines consume fuel even when being in idle.

An efficient deep learning-based workflow for real-time CO2 plume visualization in saline aquifer using distributed pressure and temperature measurements

Underground carbon dioxide (CO2) sequestration is widely accepted as a proven and established technology to respond to global warming from greenhouse gas emissions. It is crucial to monitor the CO2 plume effectively throughout the life cycle of a geologic CO2 sequestration project to ensure safety and storage efficacy. We propose a fast and efficient deep learning workflow for near real-time data assimilation, forecasting and visualization of CO2 plume evolution in saline aquifer and demonstrate its application at a field site.Our proposed workflow integrates field measurements, including distributed pressure and temperature at wells to visualize spatial and temporal migration of the CO2 plume in the subsurface. The deep learning framework has two key concepts that enhance the efficiency of the training and robustness of the CO2 plume prediction: first, instead of using multiple saturation images as output of the deep-learning model, a single Time of Flight (TOF) image are used to represent CO2 plume propagation; second, we use variational autoencoder-decoder (VAE) for high dimensional image data compression considering uncertainties in the predicted images. Time-of-Flight (TOF) is the travel time of a neural particle from the injection point, which is calculated along streamline based on the velocity field by considering reservoir heterogeneity and driving forces. The latent variables of VAE are estimated through a deep neural network model with inputs of distributed pressure and temperature measurements. Subsequently, the decoder part of the VAE expands the estimated latent variables to the original dimension of the TOF images. We demonstrate the efficacy of the proposed method by comparison with the more traditional pareto-based multi-objective Genetic Algorithm (MOGA) for the assimilation of the field measurements and forecasting of the CO2 plume migration.The power and utility of our proposed workflow are demonstrated by application to the Illinois Basin-Decatur Project (IBDP). The IBDP is a large scale 3-year CO2 storage test and is part of the Midwest Regional Carbon Sequestration Partnership (MRCSP). The field measurements include bottom-hole pressure and distributed temperature sensing (DTS) data at the injection well, and the distributed pressure measurements at several locations along the monitoring well. The dynamic model is a compositional model with 1.7 million cells. First, we identify the influential parameters using sensitivity analysis based on the pressure and temperature responses. Next, the sensitive parameters are sampled from a pre-specified range and a comprehensive training dataset is generated, including the pressure and temperature measurements and TOF images based on flow simulation results from a variety of geological model realizations. The trained neural network model can identify geological model realizations that capture the spatial and temporal migration of pressure and CO2 saturation based on Euclidean distance within the VAE latent space. For comparison with traditional history matching workflow, the selected sensitive parameters are also tuned using MOGA with the pressure and temperature measurements. A reasonable agreement is obtained for the predicted CO2 plume migration between two workflows. The proposed deep learning workflow shows significant speed-up compared to the traditional multi-objective optimization-based history matching workflow (5 h for training and seconds for prediction; compared to several days/weeks for the MOGA).The novelty of this work is the application of an efficient deep learning-based workflow to a large-scale CO2 storage test site for assimilating field measurements and predicting CO2 plume propagation in a near real-time and comparison with traditional history matching approach.

A strategy integrating GC–MS, UPLC profiling, and DNA metabarcoding for characterization and discrimination of the medicinal and culinary pieces from four Curcuma species

Curcumae pieces prepared in ready-to-use forms, consumed extensively for their significant medicinal and culinary benefits, present a challenge in discrimination due to their heterogeneous origins, analogous morphologies, and varied components. This study explored the metabolites of nine Curcumae pieces (NCPs) by Gas Chromatography-Mass Spectrometry (GC–MS) and Ultra-Performance Liquid Chromatography/Ultra High-Performance Liquid Chromatography–Quadrupole Time of Flight–Mass Spectrometry (UPLC/UHPLC–QTOF–MS) technologies, augmented by multivariate statistical analysis (Principal Component Analysis, PCA; Orthogonal Partial Least Squares Discriminant Analysis, OPLS–DA) and machine learning algorithms (Random Forest, RF). Twenty-six key differential markers in volatile and non-volatile metabolites each were found subsequently. Additionally, DNA metabarcoding was employed to develop specific Single Nucleotide Polymorphism (SNP) markers for the precise identification of Curcuma species in single and mixed samples, and for CRa even contained in Chinese patent prescriptions. The integration of these three methodologies facilitates a more reliable authentication of NCPs, enhancing the overall strategy for their global discrimination. This research contributes to the advancement of quality control and the development of functional foods or products derived from Curcumae pieces, tailored to meet specific health and dietary requirements.

Transformer-CNN hybrid network for improving PET time of flight prediction

Objective. In positron emission tomography (PET) reconstruction, the integration of time-of-flight (TOF) information, known as TOF-PET, has been a major research focus. Compared to traditional reconstruction methods, the introduction of TOF enhances the signal-to-noise ratio of images. Precision in TOF is measured by full width at half maximum (FWHM) and the offset from ground truth, referred to as coincidence time resolution (CTR) and bias. Approach. This study proposes a network combining transformer and convolutional neural network (CNN) to utilize TOF information from detector waveforms, using event waveform pairs as inputs. This approach integrates the global self-attention mechanism of Transformer, which focuses on temporal relationships, with the local receptive field of CNN. The combination of global and local information allows the network to assign greater weight to the rising edges of waveforms, thereby extracting valuable temporal information for precise TOF predictions. Experiments were conducted using lutetium yttrium oxyorthosilicate (LYSO) scintillators and silicon photomultiplier (SiPM) detectors. The network was trained and tested using the waveform datasets after cropping. Main results. Compared to the constant fraction discriminator (CFD), CNN, CNN with attention, long short-term memory (LSTM) and Transformer, our network achieved an average CTR of 189 ps, reducing it by 82 ps (more than 30%), 13 ps (6.4%), 12 ps (6.0%), 16 ps (7.8%) and 9 ps (4.6%), respectively. Additionally, a reduction of 10.3, 8.7, 6.7 and 4 ps in average bias was achieved compared to CNN, CNN with attention, LSTM and Transformer. Significance. This work demonstrates the potential of applying the Transformer for PET TOF estimation using real experimental data. Through the integration of both CNN and Transformer with local and global attention, it achieves optimal performance, thereby presenting a novel direction for future research in this field.

Systematic Research and Application of a 5G Medical Unmanned Aerial Vehicle to Deliver COVID-19 Nucleic Acid Samples.

The purpose of this study was to investigate the feasibility and effectiveness of COVID-19 throat swab samples delivered by medical drones in epidemic prevention and control. This study was carried out in both southern and northern hospital districts of the Affiliated Hospital of Jiangnan University from May to October 2022. The main participants were the Affiliated Hospital of Jiangnan University and Zhejiang Antwork Technology Co., Ltd. We first constructed an urban medical unmanned aerial vehicle (UAV) delivery system and developed a UAV-specific storage box for COVID-19 samples. The UAV system was used to transport COVID-19 throat swab samples from the northern hospital district to the southern hospital district, and the following indexes were obtained: (1) flight time of COVID-19 samples delivered by UAV, (2) real-time temperature of COVID-19 nucleic acid samples during transportation, and (3) the time of distribution of COVID-19 nucleic acid samples by road traffic as measured using the Baidu Maps application, compared with the flight time of UAV. The COVID-19 sample delivery system for urban medical UAV mainly consists of intelligent logistics UAV, low-temperature COVID-19 throat swab sample storage box, unmanned logistics hub, and cloud operation control platform. The flight distance between the northern and southern districts of the Affiliated Hospital of Jiangnan University was 10 km, and the ground distance was 24 km. From May 11 to October 28, 2022, a total of 1,190 UAV flights occurred. The average flight time was 13 minutes, which was 40 to 70 minutes faster than the average road travel time required for manual delivery of COVID-19 throat swab samples. At different time points in the day, UAV delivery efficiency increased by 67.5% to 82%. The use of 5G with the Internet of Things and UAV technology to deliver nucleic acid samples has the characteristics of fast speed, being unaffected by ground traffic conditions, and the ability to ensure the safety of nucleic acid samples in the transportation process, which is worthy of further study.

Comparative Antioxidant Activity and Untargeted Metabolomic Analyses of Sour Cherry Cultivars Based on Ultra-Performance-Time of Flight-Mass Spectrometry.

This study was conducted for the comparative analysis of antioxidant activity and untargeted metabolomics of dark- and light-colored sour cherry cultivars grown in Canada. Based on our previous study, we selected four cultivars-'Heimann R', 'Gorsemska', V70142, and 'Montmorency'-to determine the untargeted metabolites and their role in antioxidant activities. A total of 473 metabolites were identified from four sour cherry genotypes using UPLC-ToF-MS. Untargeted metabolomics revealed the dominant chemical groups present in sour cherries. PCA showed that the diversity in sour cherry metabolites was due to the genotype differences indicating iditol, malic acid, chlorobenzene, 2-mercaptobenzothiazole, and pyroglutamic acid as the predominant contributors. The variable importance in the projection (VIP > 1.0) in partial least-squares-discriminant analysis described 20 biomarker metabolites representing the cherry metabolome profiles. A heatmap of Pearson's correlation analysis between the 20 biomarker metabolites and antioxidant activities identified seven antioxidant determinants that displayed the highest correlations with different types of antioxidant activities. TPC and TAC were evaluated using the Folin-Ciocalteu method. The total antioxidant activity was performed using three different assays (ABTS, FRAP, and DPPH). This study of correlating metabolomics and antioxidant activities elucidated that the higher nutritional value and biological functions of sour cherry genotypes can be useful for the development of nutraceutical and functional foods.

A Novel Rolling Horizon Based Solution Framework for Scheduling Airplane Maintenance

Maintenance, Repair and Overhaul (MRO) activities on aircraft and systems in aviation is a service sector that relies heavily on skilled workforce. The output of MRO activities is basically bringing the system reliability values, which decrease because of the use of aircraft and systems in certain flight times and landing and take-off numbers, to the default levels determined during the design phase. MRO companies are accountable to the civil aviation authorities who directly authorize them to ensure the required levels of reliability of their products. Airline companies request MRO services at the most convenient time and cost. However, the maintenance of an aircraft is a process that can take up to five weeks, includes plenty of jobs some of which may have stochastic durations, and many over-costed qualified technicians spend thousands of man-hours. In addition, each MRO company is involved in the maintenance of several airplanes arriving at different time intervals. In the study such a problem faced by an MRO company is addressed. The company’s aim is to schedule several incoming airplane maintenance projects. A framework that employs an integer programming (IP) model working on a rolling horizon (RH) setting is used.

Vertical and temporal flight patterns of coffee berry borer (Coleoptera: Curculionidae) in Hawaii.

Coffee berry borer (Hypothenemus hampei Ferrari)(Coleoptera: Curculionidae) is the most damaging insect pest of coffee worldwide, causing significant losses in coffee yields and quality. Knowledge of vertical and temporal flight patterns in coffee berry borer could be used to optimize spray timing and precision targeting of areas within the coffee tree, which may be more susceptible. In the present study, we estimated the vertical distribution of coffee berry borer females using traps set at 1-m intervals up to 5 m in height. We also quantified coffee berry borer infestation in the low, mid, and high canopy and documented fruit availability. Temporal flight patterns were estimated using timer traps, and correlation analyses were conducted to determine the relationship between the timing of daily flight and weather variables. Across the 4 study sites, we observed that 77%-84% of the trap catch was at 1 m, 11%-20% was at 2 m, and 1%-4% was at 3-5 m in height. Fruit infestation was significantly higher in the low branches (35%) relative to the high branches (17%). Flight height remained the same year-round, regardless of fruit availability. Coffee berry borer flew in low numbers during the day and night but peaked from 12 to 4 PM. Daily flight was positively correlated with an increase in air temperature and wind speed and negatively correlated with relative humidity. Findings from this study suggest that pesticide sprays should target low- to mid-level branches at 1-2 m in height and aim to be conducted in the early afternoon when coffee berry borer are actively flying and most vulnerable to chemical controls.

Anti-inflammatory activity and structural analysis of fucoidan extracted from Sargassum fusiforme via ESI-TOF MS spectrometry

This study used Celluclast-assisted hydrolysis and DEAE-cellulose anion-exchange chromatography to extract, purify, and assess the anti-inflammatory activity of fucoidan derived from Sargassum fusiforme. The Celluclast-assisted hydrolysate was used for obtaining ethanol-precipitated S. fusiforme fucoidans (SFF) which were rich in polysaccharides and sulfates. Five fractions obtained through DEAE-cellulose chromatography, notably SFF-F5, displayed high sulfate content (29.16 ± 0.15%). The anti-inflammatory potential of these fractions was tested in lipopolysaccharide-induced RAW 264.7 macrophages. SFF-F5 was found to significantly reduce NO production at non-toxic concentrations of 12.5, 25, and 50 μg/mL. Structural analysis using Matrix assisted laser desorption/ionization time-of flight mass spectrometry (MALDI-TOF MS) revealed repetitive patterns in the fucoidan fractions. And Electrospray ionization mass spectrometry (ESI-TOF MS) spectrum of SFF fractions identified various structures of glycans bound with fucose and sulfate (Fuc1(SO3)1, Fuc2(SO3)1, Hex5Fuc1(SO3)1). In-silico evaluation based on the detected glycan structures indicated that fucoidan fractions could inhibit the dimerization of the TLR4-MD2 complex, demonstrating their potential as anti-inflammatory agents. This result confirm that the anti-inflammatory activity varies depending on the glycan composition of fucoidan. This study provides a foundation for further exploration into the relationship between the structure and anti-inflammatory activity of S. fusiforme-derived fucoidan, facilitating its effective industrial application.

Metabolic flexibility associated with flight time among combat pilots of the Brazilian air force.

Fighter pilots must support the effects of many stressors, including physical and psychological exertion, circadian disturbance, jet lag, and environmental stress. Despite the rigorous selection of military pilots, those factors predispose to failures in physiological compensatory mechanisms and metabolic flexibility. We compared through NMR-based metabolomics the metabolic profile of Brazilian F5 fighter pilots with different flight experiences vs. the control group of non-pilots. We hypothesized that combat pilots have metabolic flexibility associated with combat flight time. We evaluated for the first time 34 Brazilian fighter pilots from Santa Cruz Air Base (Rio de Janeiro, RJ) allocated into three groups: pilots with lower total accumulated flight experience < 1,100h (PC1, n = 7); pilots with higher total accumulated flight experience ≥ 1,100h (PC2, n = 6); military non-pilots (CONT, n = 21). Data collection included anthropometric measurements, total blood count, lipidogram, markers of oxidative stress, and serum NMR-based metabolomics. In comparison with controls (p < 0.05), pilots exhibited decreased levels of white blood cells (-13%), neutrophils (-15%), lymphocytes (-20%), alfa-glucose (-13%), lactate (-26%), glutamine (-11%), histidine (-20%), and tyrosine (-11%), but higher isobutyrate (+ 10%) concentrations. Significant correlations were found between lactate vs. amino acids in CONT (r = 0.55-0.68, p < 0.001), and vs. glutamine in PC2 (r = 0.94, p = 0.01). Fighter pilots with lower experience showed a dysregulation in immune-metabolic function in comparison with controls, which seemed to be counteracted by the accumulation of flight hours. Those findings might have implications for the health preservation and operational training of fighter pilots.

MeSat Mission: Exploring Martian Environment with THz Radiometer Payload and Optimal Trajectory

Space exploration presents vast prospects for scientific, industrial, and economic progress. This paper introduces the MeSat mission as a pioneering approach to Mars exploration. The MeSat aims to deepen our understanding of Martian conditions and resources by employing an optimized Earth-to-Mars trajectory, enabling a comprehensive study of the Martian atmosphere and surface. The mission comprises a cargo microsatellite hosting three 6U CubeSats and two 3U CubeSats, deployed into four separate Mars orbits to form a constellation. Each CubeSat carries distinct payloads: a THz radiometer for Martian water vapor atmospheric observation, a high-resolution surface camera, a high-tech spectrometer, and a Fourier transform spectrometer (FTS) for wind speed readings. This paper includes the majority of the key parameters; however, we focus our discussion more on two aspects of this pioneering mission: the first aspect contains the proposal of four distinct payloads for the study of Mars’ atmosphere and the second aspect proposes an optimal mission design algorithm that analyzes a fuel-efficient low-thrust trajectory from Earth to Mars. Regarding the payloads, the THz radiometer requires a specific design; hence, we explain this payload in more depth; the rest of the payloads, we suggest utilizing commercially available elements for the cost-effective manufacture of a whole system. For mission trajectory optimization, the study employs a dual-step hybrid optimization algorithm (PSO-homotopy) to analyze fuel-efficient low-thrust trajectories from Earth to Mars, incorporating the ephemeris dynamics model to account for gravitational perturbations in the entire Solar System. In practical mission design, crucial factors like hyperbolic excess velocity, diverse opportunities for Earth launch and Mars rendezvous, varied propulsion systems, and time of flight (TOF) play vital roles in trajectory optimization. In summary, for the MeSat mission, we propose a comprehensive Mars environmental mission design. We consider all aspects of the mission from trajectory design to engineering detail design, since we would like to inspire future Mars missions with a complete report.

First measurement in a magnetic confinement fusion experiment of the H3+H3→He5+n intermediate two-body resonant reaction

We report on the first experimental measurements made at a magnetic confinement fusion device of the tritium(T)-tritium(T) reaction T+T→He4+2n indicating the presence of the intermediate two-body resonant reaction T+T→He5+n. During the second deuterium-tritium campaign (DTE2) at the Joint European Torus, measurements of fusion plasmas with high tritium concentrations, nT/(nT+nD)≈0.99, heated with tritium neutral beam injection, were performed using the neutron time-of-flight (TOF) spectrometer TOFOR. We detect a peak in the neutron emission TOF spectrum consistent with the two-body resonant reaction. The TT neutron emission energy spectrum is modeled using an R-matrix framework where the distributions of the most likely model parameters given our experimental TOF data are determined utilizing a Markov chain Monte Carlo approach. We compare our best estimate of the T+T neutron emission energy spectrum with results obtained at inertial confinement fusion experiments at the OMEGA facility and find a spectral shape that is consistent with the energy dependency in the neutron spectrum observed at OMEGA. Published by the American Physical Society 2024