Linear Research Articles

Quadratic Unet for seismic random noise attenuation

In the field of seismic data processing, seismic denoising is essential to improve the quality of seismic data. Various deep learning methods have been proposed, showing promising performance for seismic denoising. However, most deep learning methods are based on lin ear neurons, resulting in limited expressive ability for complex seismic signals. To enhance denoising capability using non-linear neurons, we propose a supervised quadratic U-shaped network (QUnet) for seismic random noise attenuation. The quadratic neurons in QUnet are represented by a second-order polynomial of input data and weighted parameters. Nu merical synthetic seismic data experiments show that the proposed QUnet method achieves higher signal-to-noise ratio results and preserves the continuity of reflectors more effectively. We have also tested the effectiveness of QUnet on both prestack field data and post-stack seismic data. The detailed structures and weak signals of seismic data are better preserved by our proposed QUnet method.

The Impact of Flow Channel Structural Parameters on Both the Hydraulic Performance and Anticlogging Abilities of Variable Flow Emitters

Variable flow emitters are used in subsurface drip irrigation to address challenges in soil moisture transport. This study investigates the impact of flow channel structural parameters on the hydraulic performance and anticlogging ability of emitters using computational fluid dynamics (CFD) simulations and experimental tests. The results show that the realizable k–ε turbulence model can be used to simulate the flow field inside the variable flow emitter flow channel. The nRMSE between the measured (qm) and simulated (q) values of the flow rate is 11.23%, and the relative error between the measured (xm) and simulated (x) values of the flow index is 4.66%, which gives a high simulation accuracy. A polar analysis shows that the tooth angle (A) has the smallest effect on the effluent flow rate at 0.1 MPa (q0.1), x, and particle passage rate (η) of the variable flow emitter. Flow channel depth (D), tooth spacing (B), and tooth height (E) have a different order of precedence in the influence of the three indices, which are D > B > E > A, B > E > D > A and E > B > D > A, respectively. The value of η is positively correlated with the mean flow velocity (v) and the mean turbulent kinetic energy (k) in the flow channel, and η tends to increase and then decrease with the increase of x. The retention time of the particles in the flow channel is closely related to the magnitude of v and k. Three multivariate lin ear regression equations (R2 = 0.883–0.995) were constructed for q0.1, x, and η versus the flow channel structural parameters. The optimal design combination of channel structure parameters for different scenarios was determined using the scipy.optimize.minimize function in Python 3.8.0. The research results provide a reference for the optimal design of variable flow emitters.

Marker assisted identification of improved restorer lines with yield enhancing genes adaptable for aerobic conditions

To feed the world's growing population, development and introduction of climate resilient rice varieties/hybrids with increased yield ability are the need of the hour. This can be accomplished through novel genetic approaches such as hybrid rice technology, using a diverse set of parental lines with high restoring ability and specific desirable traits. In our study, for perse genetic yield improvement in aerobic restorer line AR 9-18R, hybridization was carried out with yield enhancing donor YPK 198 possessing Gn1a and OsSPL14 during kharif, 2018 and generated F1s were fixed for its hybridity through morphological and molecular analysis. In kharif, 2019 two hundred and five F2 population obtained were screened for two yield-enhancing Gn1a and OsSPL14 genes along with two fertility restorer genes Rf3 and Rf4 at ICAR-IIRR, Hyderabad. c2 analysis revealed that the c2 value of both yield enhancing genes was non-significant as the population was segregated in a ratio of 1:2:1. whereas c2 value for fertility restorer genes was significant as population deviated from 1:2:1. Phenotypic evaluation of F2 population for yield related traits exhibited high GCV and PCV for number of productive tillers, number of grains per panicle and single plant yield. Plant height, number of productive tillers, number of grains per panicle and single plant yield displayed high heritability along with high genetic advance as per cent of mean. Correlation and path co-efficient analysis revealed that plant height, number of productive tillers and number of grains per panicle are very important traits as selection criteria for effective yield improvement.

Taxonomic Implication of Conducting Elements in the Acrocarpous Mosses

Present study deals with the structure and development of conducting elements in the nine orders of acrocarpous mosses. The significance of conducting tissues in mosses in relation to their habitat conditions, growth forms and leaf cell patterns has been discussed. Features of cells in different portions of the stem and the laminal cell patterns and costa are taken into consideration. Although water-conducting cells are unspecialized in mosses, yet the study shows that they seem to play a vital role in the conduction and provide additional criteria for the distinction of taxa. Four categories have been determined as (i) Acrocarpous mosses with a distinct thick-walled conducting strand (6-7 layered) as hydrome which is surrounded by patches of leptoids. Cortex consists of thick walled cells (6-10 layered). Costa has stereidal cells and well developed conducting elements in leaf, example Polytrichum (ii) Acrocarps with thick-walled, narrow, elongated conducting strand (4-5 layered), cells angular in the cortex (6-8 layered). Leaf cells are rectangular, irregular and porous with incrassate walls, example Dicranum (iii) Acrocarps with conducting tissue (2-5 layered) stereidal, thick walled or thin walled varying in the course of development. Leaf cells are mutipapillate, rounded-quadrate, costa is present, examples Hyophila, Philonotis and (iv) Epiphytic pleurocarps with conducting cells rudimentary, thickened, scattered (2-4 layers) and parenchymatous, cortical cells (2-3 layered) thick walled. Leaf cells are small, rounded or linear and papillate, costa may be present or absent, example Leucodon . The study would constitute a formidable task, especially if intraspecific structural variability is considered. It serves a model system in the eco-physiological aspects.

Integration is Success Model as an Evaluation Factor of Local Government Information Systems in Indonesia

The increasing development of information and communication technology has impacted changes in all fields including the government sector, marked by the implementation of new policies, namely Minister of Home Affairs Regulation Number 70 of 2019 concerning Regional Government Information Systems (SIPD). This research evaluates the success of implementing the Regional Government Information System (SIPD) application in the Metro City Regional Government through the IS Success Model. The Grand Theory used in this research is the Information Success Model. The variables in this research use the constructs of the IS Success Model, including information quality, system quality, service quality, and user satisfaction. The sampling technique in this research used Nonprobability Sampling which used Purposive Sampling with several respondent criteria so that a sample of 93 respondents was obtained. Data analysis in this study used SPSS version 26 with multiple line ar regression analysis testing. The research results show that system quality and service quality have a positive effect on SIPD user satisfaction, while information quality does not affect SIPD user satisfaction.

Machine learning approach for predicting compressive strength in foam concrete under varying mix designs and curing periods

Efforts to reduce the weight of buildings and structures, counteract the seismic threat to human life, and cut down on construction expenses are widespread. A strategy employed to address these challenges involves the adoption of foam concrete. Unlike traditional concrete, foam concrete maintains the standard concrete composition but excludes coarse aggregates, substituting them with a foam agent. This alteration serves a dual purpose: diminishing the concrete’s overall weight, thereby achieving a lower density than regular concrete, and creating voids within the material due to the foam agent, resulting in excellent thermal conductivity. This article delves into the presentation of statistical models utilizing three different methods—linear (LR), non-linear (NLR), and artificial neural network (ANN)—to predict the compressive strength of foam concrete. These models are formulated based on a dataset of 97 sets of experimental data sourced from prior research endeavors. A comparative evaluation of the outcomes is subsequently conducted, leveraging statistical benchmarks like the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE), with the aim of identifying the most proficient model. The results underscore the remarkable effectiveness of the ANN model. This is evident in the ANN model’s R2 value, which surpasses that of the LR model by 36% and the non-linear model by 22%. Furthermore, the ANN model demonstrates significantly lower MAE and RMSE values compared to both the LR and NLR models.

Satellite-derived bathymetry from WorldView-2 based on linear and machine learning regression in the optically complex shallow water of the coral reef ecosystem of Kemujan island

Shallow coastal water bathymetry information is required for various navigation, management, conservation, and research purposes. Bathymetry data modeled through satellite remote sensing (SDB) is considered more effective and efficient because it is cheaper, can cover a large area, and can be done routinely. In this study, we assess the linear (LR) and machine learning regression performance of bathymetry mapping in the optically shallow water of a coral reef ecosystem. We selected random forest regression (RFR) and support vector machine regression (SVR) machine learning regression models. All three regression models were trained using field bathymetry data, and different tuning parameter scenarios were applied to each model to obtain the best bathymetry map. Independent field bathymetry data were used for the assessment, which included root mean squared error (RMSE), a plot between reference and predicted depths, and an underwater topographic profile in three unique areas. Each unique area represented different water depths, underwater topography, benthic cover variations, and water clarity. This highlights and provides a comprehensive insight into how each model performs under different underwater topographic conditions. Kemujan Island, which has various coral reef geomorphic classes and underwater topographic variations, was selected as the study site. The RMSE of the SDB mapping accuracy obtained in this research ranged from 0.82 to 0.87 m for RFR, 0.96–1.17 m for SVR, and 1.00–1.23 m for LR. Overall, RFR produced SDB with higher accuracy and precision compared to SVR and LR. RFR managed to estimate the depth that follows the underwater transverse profile accurately and precisely in three sites and does not require a band ratio to produce accurate SDB in areas with various benthic covers. LR competes well with RFR in predicting water depths of up to 6 m but cannot accurately predict depths beyond 6 m. The study results provide a comprehensive reference for developing robust automated mapping models for producing SDB, increasing the availability of optically shallow water bathymetry information worldwide while reducing the cost of conducting bathymetric surveys.

Study of flexural strength of concrete containing mineral admixtures based on machine learning

In this paper, the prediction of flexural strength was investigated using machine learning methods for concrete containing supplementary cementitious materials such as silica fume. First, based on a database of suitable characteristic parameters, the flexural strength prediction was carried out using linear (LR) model, random forest (RF) model, and extreme gradient boosting (XGB) model. Subsequently, the influence of each input parameter on the flexural strength was analyzed using the SHAP model based on the optimal prediction model. The results showed that LR, RF, and XGB enhanced the accuracy of forecasting sequentially. Among the characteristic parameters, the most significant effect on the flexural strength of concrete is the water-binder ratio, and the water-binder ratio shows a negative correlation with flexural strength. The effect of maintenance age on flexural strength is second only to the water-binder ratio, and it shows a positive trend. When the amount of fly ash is less than 40% and the amount of slag or silica fume is less than 30%, the correlation between the amount of supplementary cementitious materials and flexural strength fluctuates and a positive peak in flexural strength is observed. However, at a dosage greater than the above, the supplementary cementitious materials all reduce flexural strength. The interaction interval and the degree of interaction between the supplementary cementitious materials and the cement content also differ in predicting flexural strength.

Spectral band-shifting of multispectral remote-sensing reflectance products: Insights for matchup and cross-mission consistency assessments

Comparing or integrating remote-sensing reflectance, Rrsλsr−1, data from multispectral sensors is hindered by differences in their nominal bands and relative spectral-response functions. Similarly, assessing satellite-to-in situ-matchup assessments of Rrs using autonomous ground radiometers, such as the Ocean Color component of the Aerosol Robotic Network (AERONET-OC), faces the same challenges. Therefore, spectral band-shifting techniques are required to approximate Rrs in spectral bands that are mutually unavailable. We present a spectral-matching technique (SMTH) using a novel look-up-table (LUT) generated from Rrs obtained from the Hyperspectral Imager for the Coastal Ocean (HICO) sensor. This LUT consists of nearly 6.3 million unique Rrs spectra, processed from 8893 Level-2 HICO scenes. An extensive in situ hyperspectral dataset (n = 8289) was used to compare the performance of SMTH with three commonly used techniques, namely Melin and Sclep (2015) “MS-QAA” model, linear (LNR) and cubic spline (CS) interpolations. The comprehensive evaluation of eight sensors using in situ dataset revealed that the SMTH has significantly better overall accuracy with respect to spectral band-shifting than the existing approaches. The Bias was improved 7-fold, whereas the root-mean-square difference (RMSD) and mean absolute difference (MAD) were ∼ 15–60% lower. Even so, each model exhibits outstanding performance in certain bands. For instance, the LNR model excels in the 380 nm band, while the MS-QAA emerges as the optimal choice for the 400 and 412 nm bands. SMTH outperforms other models in the green-red region, notably above 600 nm, where most existing ocean-color sensors have a limited number of spectral bands. Despite this variability, all models, showcase satisfactory performance in the 443–560 nm region. Significantly, both SMTH and LNR demonstrated a 100% retrieval rate, the ratio of band-shifted samples to the input samples. In contrast, MS-QAA and CS returned invalid values for 7% and 39% of the input samples, respectively. Further evaluation of regional, global, and composite satellite data suggests that the trophic state significantly influences the model performance. The MS-QAA model exhibited the smallest differences within the oligotrophic waters, characterized by low chlorophyll-a concentrations. In contrast, the SMTH model demonstrates the lowest differences in mesotrophic/eutrophic waters and regions subject to harmful algal blooms. While HICO primarily sampled coastal waters, estuaries, and shallow regions from mid- to low latitudes, there is an opportunity to enhance the representativeness of our LUT by incorporating open-ocean and high-latitude Rrs spectra from the upcoming hyperspectral missions, such as NASA's Plankton, Aerosol, Clouds, ocean Ecosystem (PACE).

Optimization Techniques for Semi-Automated 3D Rigid Registration in Multimodal Image-Guided Deep Brain Stimulation

Abstract Multimodal image registration is vital in Deep Brain Stimulation (DBS) surgery. DBS treats movement disorders by implanting a neurostimulator device in the brain to deliver electrical impulses. Image registration between computed tomography (CT) and cone beam computed tomography (CBCT) involves fusing images with a specific field of view (FOV) to visualize individual electrode contacts. This contains important information about the location of segmented contacts that can reduce the time required for electrode programming. We performed a semi-automated multimodal image registration with different FOV between CT and CBCT images due to the tiny structures of segmented electrode contacts that necessitate high accuracy in the registration. In this work, we present an optimization workflow for multi-modal image registration using a combination of different similarity metrics, interpolators, and optimizers. Optimization-based rigid image registration (RIR) is a common method for registering images. The selection of appropriate interpolators and similarity metrics is crucial for the success of this optimization-based image registration process.We rely on quantitative measures to compare their performance. Registration was performed on CT and CBCT images for DBS datasets with an image registration algorithm written in Python using the Insight Segmentation and Registration Toolkit (ITK). Several combinations of similarity metrics and interpolators were used, including mean square difference (MSD), mutual information (MI), correlation and nearest neighbors (NN), linear (LI), and B-Spline (SPI), respectively. The combination of a correlation as similarity metric, B-Spline interpolation, and GD optimizer performs the best in optimizing the 3D RIR algorithm, enhancing the visualization of segmented electrode contacts. Patients undergoing DBS therapy may ultimately benefit from this.

Switching controller synthesis for discrete-time switched linear systems with average dwell time

Switching controller synthesis for discrete-time switched linear systems with average dwell time

The Influence of Internships on the Development of Soft Skills of Office Administration Education Students

This research was conducted to determine the effect of internships on the development of soft skills of Office Administration Education Students, Faculty of Social Sciences and Law, Makassar State University. The population in this study was 95 people and the sample was 95 people. While the data analysis techniques used are descriptive statistics using percentages and for inferential statistical analysis techniques using data normality tests, moment product correlation analysis, simple line ar regression analysis andpartial t tests. From the test results show that the indicators of the internship variable are in the very good category while the learning motivation variable is in the very high category. Based on the results of the product moment correlation analysis, there is a relationship between the two variables. So it can be concluded that there is a significant influence between internships on the development of soft skilss Office Administration Education Students of the Faculty of Social Sciences and Law, Makassar State University

Analytical separation of plasmid DNA isoforms using anion exchanging chromatographic monoliths with 6µm channels.

High-performance liquid chromatography (HPLC)-based analytical assays are used to effectively monitor purity and quantity of plasmid DNA (pDNA) throughout the purification process. However, the phenomenon of physical entrapment of open circular (OC) isoforms pDNA inside narrow channels of chromatographic support decreases its accuracy and precision and the effect increases with pDNA size. The purpose of the study was to develop a chromatographic method for accurate analytical separation between isoforms of <16kbp pDNA using weak anion exchanging monolithic column with large (6µm) convective channels. Purified samples of 4.7 and 15.4kbp large pDNA with known isoform composition were prepared and their isoforms separated in ascending salt gradient. Both OC and supercoiled (SC) isoforms were baseline separated at a flow rate below 0.5mLmin-1 in a guanidinium chloride (GdnCl) gradient with a ≥95% OC pDNA elution recovery. However, these chromatographic conditions increased 2 times the peak width for linear (LIN) pDNA isoform compared to the results using monoliths with 1.4µm channel size. If other chaotropic agents, such as urea or thiocyanate (SCN), were added to Gdn ions, the elution volume for LIN isoform decreased. Optimization of combined GdnCl/GdnSCN gradient for pDNA elution resulted in a simple and robust chromatographic method, where OC-LIN and LIN-SC pDNA (up to 15kbp size) were separated with resolution above 1.0 and above 2.0, respectively. The accessibility and general acceptance of anion exchange chromatography for pDNA analytics give the newly developed method a great potential for in-process control monitoring of pDNA production processes.

Development of Estimation Models for Individual Tree Aboveground Biomass Based on TLS-Derived Parameters

Forest biomass is a foundation for evaluating the contribution to the carbon cycle of forests, and improving biomass estimation accuracy is an urgent problem to be addressed. Terrestrial laser scanning (TLS) enables the accurate restoration of the real 3D structure of forests and provides valuable information about individual trees; therefore, using TLS to accurately estimate aboveground biomass (AGB) has become a vital technical approach. In this study, we developed individual tree AGB estimation models based on TLS-derived parameters, which are not available using traditional methods. The height parameters and crown parameters were extracted from the point cloud data of 1104 trees. Then, a stepwise regression method was used to select variables for developing the models. The results showed that the inclusion of height parameters and crown parameters in the model provided an additional 3.76% improvement in model estimation accuracy compared to a DBH-only model. The optimal linear model included the following variables: diameter at breast height (DBH), minimum contact height (Hcmin), standard deviation of height (Hstd), 1% height percentile (Hp1), crown volume above the minimum contact height (CVhcmin), and crown radius at the minimum contact height (CRhcmin). Comparing the performance of the models on the test set, the ranking is as follows: artificial neural network (ANN) model &gt; random forest (RF) model &gt; linear mixed-effects (LME) model &gt; linear (LN) model. Our results suggest that TLS has substantial potential for enhancing the accuracy of individual-tree AGB estimation and can reduce the workload in the field and greatly improve the efficiency of estimation. In addition, the model developed in this paper is applicable to airborne laser scanning data and provides a novel approach for estimating forest biomass at large scales.

Comparison of Canine Forelimb Kinematic Joint Angles Collected with 2D and 3D Models.

The aim of this study was to compare a Joint Coordinate System (JCS) three-dimensional (3D) kinematic model of the canine forelimb with more widely used linear (LIN) and segmental (SEG) 2D models. It was an in vivo biomechanical study. Normal adult mixed breed dogs were used in this study (n = 6). Nineteen retroreflective markers were applied to the skin of dogs' right forelimbs. Dogs were trotted and walked through the calibrated testing space. The first five good trials were used to generate sagittal plane (flexion and extension angle) waveforms from 3 different models (JCS, LIN and SEG) for the shoulder, elbow and carpal joints. The JCS model also generated transverse and frontal plane joint angular data (internal/external and abduction/adduction angles) for all three joints. Minimum, maximum and total angular displacement was calculated for each joint. Comparison of sagittal plane waveforms was performed before and after waveform alignment using statistical parametric mapping. Each model produced similar sagittal plane waveforms, though the LIN model had a greater vertical shift along the y-axis for the shoulder and elbow. Before waveform alignment, differences were revealed between the LIN model when compared to JCS or SEG model at a trot. No differences were revealed at a walk. After waveform alignment, no differences were revealed between models at a walk or trot. There were no differences in angular displacement measurements between models before or after waveform alignment at a walk or trot. The 3D JCS model reported in this study produced sagittal plane waveforms comparable to conventional 2D models while also providing joint specific information from other planes of motion.

Performance prediction, pacing profile and running pattern of elite 1-h track running events

PurposeThis study aimed at comparing the predictive accuracy of the power law (PL), 2-parameter hyperbolic (HYP) and linear (LIN) models on elite 1-h track running performance, and evaluating pacing profile and running pattern of the men’s best two 1-h track running performances of all times.MethodsThe individual running speed–distance profile was obtained for nine male elite runners using the three models. Different combinations of personal bests times (3000 m-marathon) were used to predict performance. The level of absolute agreement between predicted and actual performance was evaluated using intraclass correlation coefficient (ICC), paired t test and Bland–Altman analysis. A video analysis was performed to assess pacing profile and running pattern.ResultsRegardless of the predictors used, no significant differences (p > 0.05) between predicted and actual performances were observed for the PL model. A good agreement was found for the HYP and LIN models only when the half-marathon was the longest event predictor used (ICC = 0.718–0.737, p < 0.05). Critical speed (CS) was highly dependent on the predictors used. Unlike CS, PLV20 (i.e., the running speed corresponding to a 20-min performance estimated using the PL model) was associated with 1-h track running performances (r = 0.722–0.807, p < 0.05). An even pacing profile with minimal changes of step length and frequency was observed.ConclusionsThe PL model may offer the more realistic 1-h track running performance prediction among the models investigated. An even pacing might be the best strategy for succeeding in such running events.

Relating Profile Wall Root-Length Density Estimates to Monolith Root-Length Density Measurements of Cover Crops

Different methods have been proposed for in situ root-length density (RLD) measurement. One widely employed is the time-consuming sampling of soil cores or monoliths (MO). The profile wall (PW) method is a less precise, but faster and less laborious alternative. However, depth-differentiated functions to convert PW RLD estimates to MO RLD measurements have not yet been reported. In this study, we perform a regression analysis to relate PW results to MO results and determine whether calibration is possible for distinct crop groups (grasses, brassicas and legumes) consisting of pure and mixed stands, and whether soil depth affects this calibration. The methods were applied over two years to all crop groups and their absolute and cumulative RLD were compared using a linear (LR) and multiple linear (MLR) regression. PW RLD was found to highly underestimate MO RLD in absolute values and in highly rooted areas. However, a close agreement between both methods was found for cumulative root-length (RL) when applying MLR, highlighting the influence of soil depth. The level of agreement between methods varied strongly with depth. Therefore, the application of PW as the main RLD estimation method can provide reliable estimates of cumulative root distribution traits of cover crops.

Aquatic Physical Literacy: The Effectiveness of Applied Pedagogy on Parents' and Children's Perceptions of Aquatic Motor Competence.

The goal of swimming school during early school age is to promote physical literacy. According to linear or non-linear pedagogy, a linear or non-linear approach teaching style can be used. The aim of our study was to investigate whether a different teaching methodology, as in using a teacher-centered approach (linear pedagogy), or a student-centered approach (non-linear pedagogy), could differently influence the perceptions of aquatic activity of children and parents. Parents of 100 children (53 females and 47 males, age 5.9 ± 0.3 years old) participating in the study were previously interviewed to ascertain their expectations regarding the swimming course. Participants were in a medium-high social context. Subsequently, considering the data of the incoming aquatic motor competence’s tests, children were divided into a linear (LI) and non-linear (NL) pedagogy group. A total of 4 instructors were enrolled. Upon completing the swimming course, the aquatic motor competence’s test was repeated, a pictorial scale of perceived motor competence was administered, and a questionnaire regarding the course was proposed to children’s parents. Physical development and learn to swim resulted two of the most important reasons leading parents to choose swimming courses. LI achieved greater progress than the NL in actual motor competence (A.M.C.), while NL perceived a higher aquatic motor competence (P.M.C.) in 7 out of 10 items of the pictorial scale. Parents of children in LI, gave greater importance to the teacher’s role, while NL’s parents pointed the acquisition of children’s abilities as pivotal. In conclusion, NL approach was more appreciated by children, while LI method was more rewarding for parents because initial expectations were satisfied.

Improving resistance training prescription through the load-velocity relationship in breast cancer survivors: The case of the leg-press exercise

ABSTRACT The aims of this study were: (i) to analyse the load-velocity relationship in the bilateral leg-press exercise in female breast cancer survivors, (ii) to assess whether mean velocity (MV) or peak velocity (PV) show stronger relationship with the relative load, and (iii) to examine whether linear (LA) or polynomic (PA) adjustment predict the velocities associated with each %1RM with greater precision. Twenty-two female breast cancer survivors (age: 50.2 ± 10.8 years, weight: 69.6 ± 15.2 kg, height: 160.51 ± 5.25 cm) completed an incremental load test until 1RM in the bilateral leg-press exercise. The MV and the PV of the concentric phase were measured in each repetition using a linear velocity transducer, and were analysed by regression models using LA and PA. A very close relationship of MV (R 2 = 0.924; p < 0.0001; SEE = 0.08m.s−1 by LA, and R² = 0.952; p < 0.0001; SEE = 0.063 m.s−1 by PA) and PV (R² = 0.928; p < 0.0001; SEE = 0.119 m.s−1 by LA and R² = 0.941; p < 0.0001; SEE = 0.108 m.s−1 by PA) with %1RM were observed. The MV of 1RM was 0.24 ± 0.03 m·s−1, whereas the PV at 1RM was 0.60 ± 0.10 m.s−1. A comprehensive analysis of the bilateral leg-press load-velocity relationship in breast cancer survivors is presented. The results suggest that MV is the most recommendable velocity variable to prescribe the relative load during resistance training, and that the PA presents better accuracy to predict velocities associated with each %1RM, although LA is sufficiently valid to use this model as an alternative to the quadratic model. The implications for resistance training in breast cancer are discussed.

Molecular Tailoring Approach for Estimating Individual Intermolecular Interaction Energies in Benzene Clusters.

There is no general method available for the estimation of individual intermolecular interaction energies in weakly bound molecular clusters, and such studies are limited only to the dimer. Recently, we proposed a molecular tailoring approach-based method for the estimation of individual O-H···O hydrogen bond energies in water clusters. In the present work, we extend the applicability of this method for estimating the individual intermolecular interaction energies in benzene clusters, which are expected to be small. The basis set superposition error (BSSE)-corrected individual intermolecular interaction energies in linear (LN) benzene clusters, LN-(Bz)n n = 3-7, were calculated to be in the range from -1.75 to -2.33 kcal/mol with the cooperativity contribution falling between 0.05 and 0.20 kcal/mol, calculated at the MP2.5/aug-cc-pVDZ level of theory. In the case of non-linear (NLN) benzene clusters, NLN-(Bz)n n = 3-5, the BSSE-corrected individual intermolecular interaction energies exhibit a wider range from -1.16 to -2.55 kcal/mol with cooperativity contribution in the range from 0.02 to -0.61 kcal/mol. The accuracy of these estimated values was validated by adding the sum of interaction energies to the sum of monomer energies. These estimated molecular energies of clusters were compared with their actual calculated values. The small difference (<0.3 kcal/mol) in these two values suggests that our estimated individual intermolecular interaction energies in benzene clusters are quite reliable.