Balance Precision Research Articles

Updates in Immunotherapy for Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with limited effective therapeutic options. Due to a variety of cancer cell-intrinsic factors, including KRAS mutations, chemokine production, and other mechanisms that elicit a dysregulated host immune response, PDAC is often characterized by poor immune infiltration and an immune-privileged fibrotic stroma. As understanding of the tumor microenvironment (TME) evolves, novel therapies are being developed to target immunosuppressive mechanisms. Immune checkpoint inhibitors have limited efficacy when used alone or with radiation. Combinations of immune therapies, along with chemotherapy or chemoradiation, have demonstrated promise in preclinical and early clinical trials. Despite dismal response rates for immunotherapy for metastatic PDAC, response rates with neoadjuvant immunotherapy are somewhat encouraging, suggesting that incorporation of immunotherapy in the treatment of PDAC should be earlier in the disease course. Precision therapy for PDAC may be informed by advances in transcriptomic sequencing that can identify immunophenotypes, allowing for more appropriate treatment selection for each individual patient. Personalized and antigen-specific therapies are an increasing topic of interest, including adjuvant immunotherapy using personalized mRNA vaccines to prevent recurrence. Further development of personalized immune therapies will need to balance precision with generalizability and cost.

Research on the Inventory Management in the Modern Business

Inventory management plays a pivotal role in modern business operations, impacting operational efficiencies, financial security, and competitive advantages. This essay explores the multidimensional field of inventory management, delving into seminal research articles. It shows how accounting methods and strategic decision-making interact, shaping operational pathways. Operations research guides inventory management strategies' effectiveness assessment. Creating a culture of continuous improvement based on empirical observations is equally crucial. Innovations, like structured Markov Decision Processes (MDPs) with convex cost functions, bridge theory and real-world applications. The essay also examines forecasting errors' implications when relying too heavily on predictions, highlighting optimal control techniques as resilience tools in volatile environments. Contemporary inventory strategies balance precision and adaptability, focusing on supply/demand synchronization. Empirical case studies exemplify efficient responses to market changes. Variability management in inventory frameworks offers insights into resource allocation across diverse product lines. Lastly, the essay deconstructs the strategic implications of asymmetric sales changes on inventory strategies. In a globalized, technologically dynamic age with shifting market forces, prudent inventory management is imperative. This essay showcases its significance with empirical wisdom and academic rigor, illuminating quantitative precision, strategic insight, and operational excellence in modern business environments.

The Effect of Core Training on Agility, Explosive Strength and Balance in Young Female Volleyball Players

The present study aimed to determine the effect of core training on agility, explosive strength and balance in young female volleyball players. 20 young female volleyball players volunteered to participate in the study. Vertical jump, counter movement jump, pro-agility and dynamic balance tests were applied to all volleyball players before and after the core-training program. The athletes were selected randomly and divided into two groups as study group (n=10) and control group (n=10). The study group performed core exercises added to the beginning of the training sessions for 8 weeks (3days/week) in addition to volleyball training. The control group, on the other hand, continued volleyball training alone. At the end of 8 weeks, the tests applied in the pre-test were repeated and analyzed for all participants in order to determine the effect of core training on agility, explosive strength and balance parameters. In the statistical analysis of the study, Kurtosis-Skewness test was used to determine the normal distribution of the data. Mixed Anova and two-independent independent-samples t-test were used to make in-group and between-group comparisons. During the study, the level of significance was accepted as 0.05. A statistically significant difference was found in the balance precision index, static vertical jump (cm), counter movement jump (cm) and pro-agility agility test (sec) parameters when comparing the study and control groups. As a result, core exercises performed in addition to volleyball training appear to have a positive effect on agility, explosive strength and balance performances.

An efficient approach to identifying anti-government sentiment on Twitter during Michigan protests.

Trust in the government is an important dimension of happiness according to the World Happiness Report (Skelton, 2022). Recently, social media platforms have been exploited to erode this trust by spreading hate-filled, violent, anti-government sentiment. This trend was amplified during the COVID-19 pandemic to protest the government-imposed, unpopular public health and safety measures to curb the spread of the coronavirus. Detection and demotion of anti-government rhetoric, especially during turbulent times such as the COVID-19 pandemic, can prevent the escalation of such sentiment into social unrest, physical violence, and turmoil. This article presents a classification framework to identify anti-government sentiment on Twitter during politically motivated, anti-lockdown protests that occurred in the capital of Michigan. From the tweets collected and labeled during the pair of protests, a rich set of features was computed from both structured and unstructured data. Employing feature engineering grounded in statistical, importance, and principal components analysis, subsets of these features are selected to train popular machine learning classifiers. The classifiers can efficiently detect tweets that promote an anti-government view with around 85% accuracy. With an F1-score of 0.82, the classifiers balance precision against recall, optimizing between false positives and false negatives. The classifiers thus demonstrate the feasibility of separating anti-government content from social media dialogue in a chaotic, emotionally charged real-life situation, and open opportunities for future research.

Machine learned daily life history classification using low frequency tracking data and automated modelling pipelines: application to North American waterfowl

BackgroundIdentifying animal behaviors, life history states, and movement patterns is a prerequisite for many animal behavior analyses and effective management of wildlife and habitats. Most approaches classify short-term movement patterns with high frequency location or accelerometry data. However, patterns reflecting life history across longer time scales can have greater relevance to species biology or management needs, especially when available in near real-time. Given limitations in collecting and using such data to accurately classify complex behaviors in the long-term, we used hourly GPS data from 5 waterfowl species to produce daily activity classifications with machine-learned models using “automated modelling pipelines”.MethodsAutomated pipelines are computer-generated code that complete many tasks including feature engineering, multi-framework model development, training, validation, and hyperparameter tuning to produce daily classifications from eight activity patterns reflecting waterfowl life history or movement states. We developed several input features for modeling grouped into three broad categories, hereafter “feature sets”: GPS locations, habitat information, and movement history. Each feature set used different data sources or data collected across different time intervals to develop the “features” (independent variables) used in models.ResultsAutomated modelling pipelines rapidly developed easily reproducible data preprocessing and analysis steps, identification and optimization of the best performing model and provided outputs for interpreting feature importance. Unequal expression of life history states caused unbalanced classes, so we evaluated feature set importance using a weighted F1-score to balance model recall and precision among individual classes. Although the best model using the least restrictive feature set (only 24 hourly relocations in a day) produced effective classifications (weighted F1 = 0.887), models using all feature sets performed substantially better (weighted F1 = 0.95), particularly for rarer but demographically more impactful life history states (i.e., nesting).ConclusionsAutomated pipelines generated models producing highly accurate classifications of complex daily activity patterns using relatively low frequency GPS and incorporating more classes than previous GPS studies. Near real-time classification is possible which is ideal for time-sensitive needs such as identifying reproduction. Including habitat and longer sequences of spatial information produced more accurate classifications but incurred slight delays in processing.

Optimization method based on genetic algorithm for the mass compensation of the rotor on-line dynamic balance system

Abstract The rotor is the key component of the rotary drilling rig, it can realize the rotation and feed movement of the drill. the rotor unit of the rotary drilling rig should take on-line dynamic. Because unbalanced faults would lead to vibration. In order to further improve the pricision of on-line dynamic balance, an optimization strategy based on Genetic Algorithm for the mass compensation of the rotor on-line dynamic balancing system is proposed. A mass compensation optimization model is established. By the way of taking the phase of counterweight movement as the optimization variable. At the same time,taking the residual stress under dynamic balance as the optimization objective. According to the principle of Genetic Algorithm, the movement phase of the counterweight can be calculated when the residual force reaches the minimum value. By performing simulation analysis, the residual force can be calculated under different rotating speeds and different amount of unbalance, the residual force after optimizing balance can be reduced greatly. The simulation results showed that the residual force of the rotor after optimizing balance can decrease by 95.74% on average. Therefore, an optimization strategy based on Genetic Algorithm for the mass compensation of the rotor can effectively improve dynamic balance precision and operating performance of rotary drilling rig.

Wind resistance calculation of large rotary mesh antenna and its influence on dynamic balance precision

Due to the wind resistance which acts on the main reflector of large rotary mesh antenna, the correct balancing result of satellite antenna is difficult to be gotten in the ground dynamic balancing test. In order to solve this problem, the dynamic balance method of large rotary mesh antenna which is under the influence of wind resistance in both low pressure environment and standard atmospheric pressure environment on the ground is studied. Based on the theoretical analysis and the experimental data of two-dimensional flow around circular cylinder, a new method of the large rotary mesh antenna wind resistance calculation is proposed, according to the CFD analysis of the three dimensional flow field. Through the dynamic equivalent method, the distributed wind resistance acted on the main reflector of the mesh antenna in the rotating state is equivalent to the principal vector and principal moment of the action point in each quadrant, and then transformed into the eccentric mass on the distribution plane. It provides a feasible and innovative way to estimate the influence of wind resistance on the dynamic balance accuracy of large mesh antenna, so as to compensate the wind resistance effect. Combined with the ground dynamic balancing requirements of a certain type of satellite mesh antenna, the whole finite element model of the mesh antenna is established, the simulation of ground dynamic balancing test is carried out, and the influence of wind resistance on the ground dynamic balancing results of the antenna is analyzed in this study, which provides important data for compensating the influence of wind resistance and ensuring the on-orbit balancing accuracy of the antenna.

RealHePoNet: a robust single-stage ConvNet for head pose estimation in the wild

Human head pose estimation in images has applications in many fields such as human–computer interaction or video surveillance tasks. In this work, we address this problem, defined here as the estimation of both vertical (tilt/pitch) and horizontal (pan/yaw) angles, through the use of a single Convolutional Neural Network (ConvNet) model, trying to balance precision and inference speed in order to maximize its usability in real-world applications. Our model is trained over the combination of two datasets: ‘Pointing’04’ (aiming at covering a wide range of poses) and ‘Annotated Facial Landmarks in the Wild’ (in order to improve robustness of our model for its use on real-world images). Three different partitions of the combined dataset are defined and used for training, validation and testing purposes. As a result of this work, we have obtained a trained ConvNet model, coined RealHePoNet, that given a low-resolution grayscale input image, and without the need of using facial landmarks, is able to estimate with low error both tilt and pan angles ( $$~4.4^{\circ }$$ average error on the test partition). Also, given its low inference time (6 ms per head), we consider our model usable even when paired with medium-spec hardware (i.e. GTX 1060 GPU). Code available at: https://github.com/rafabs97/headpose_final Demo video at: https://www.youtube.com/watch?v=2UeuXh5DjAE .

A two-stage real-time YOLOv2-based road marking detector with lightweight spatial transformation-invariant classification

In recent years, Autonomous Driving Systems (ADS) become more and more popular and reliable. Road markings are important for drivers and advanced driver assistance systems by better understanding the road environment. While the detection of road markings may suffer a lot from various illuminations, weather conditions and angles of view, most traditional road marking detection methods use fixed threshold to detect road markings, which is not robust enough to handle various situations in the real world. To deal with this problem, some deep learning-based real-time detection frameworks such as Single Shot Detector (SSD) and You Only Look Once (YOLO) are suitable for this task. However, these deep learning-based methods are data-driven even while there is no public road marking dataset. Besides, these detection frameworks usually struggle with distorted road markings and balancing between the precision and recall. We propose a two-stage YOLOv2-based network to tackle distorted road marking detection as well as to balance precision and recall. The proposed spatial transformer layer is able to handle the distorted road markings in the second stage, so as to achieve the improvement of precision. Our network is able to run at 58 FPS in a single GTX 1070 under diverse circumstances. Furthermore, we present a dataset for the public use of road marking detection tasks, which consists of 11,800 high-resolution images captured under different weather conditions. Specifically, the images are manually annotated into 13 classes with bounding boxes. We empirically demonstrate both mean average precision (mAP) and detection speed of our system over several baseline models.

Investigation of the Effect of Vertimax V8 Elastic Resistance Platform and Classic Strength Training on Dynamic Balance in Basketball Players

This study aimed to investigate the effects on the dynamic balance of two different resistance training in male basketball players. 25 male basketball players who were over 18 participated in the study voluntarily. Participants were divided into two groups by using random sampling method as VertiMax strength training group and traditional strength training group (VertiMax, n=13; classic, n=12). Both training programs were administered 3 times a day for 6 weeks. In standing position, the dynamic balance values recorded on the right and left foot for 60 seconds were assessed by using EasyTech Libra Oscillating balance board. The scores used in the assessments were IBP (index of balance precision), recovery time right (return time of the visual helix balance from right side), and recovery time left (return time of the visual helix balance from left side). All tests were performed with visual feedback. Statistical analysis used three-factor mixed ANOVA. At the end of the training period, IBP (index of balance precision), recovery time right (return time of the visual helix balance from right side), recovery time left (return time of the visual helix balance from left side) showed that dynamic balance scores were similar in the VertiMax and classic training groups for right and left feet. VertiMax group in dynamic balance, the changes in IBP and recovery time right scores were found to be better. In both training groups, when the recovery time right values of the right and left feet were compared at the end of the process, it was seen that the rotation time from the right side of the visual helix motion area of the left leg was shorter (p<0.05). The results show that the VertiMax strength training included in the pre-season exercises positively affects participants’ balance performance.

Lateralidad de miembro inferior y su relación con la distribución de las presiones plantares en el equilibrio estático (Laterality of lower limbs and relationship with the plantar pressure distribution in the static balance)

La distribución de las cargas durante la marcha se ha valorado en muchos estudios, en algunos de los cuales se ha observado la influencia de la lateralidad del individuo en esta distribución. Sin embargo, en estático se han realizado pocos estudios que valoran las posibles causas de una distribución no equitativa de las cargas, algunos según la visión o no del espacio colindante y según la influencia de la lateralidad visual. En nuestro estudio vamos a observar si influye la preferencia lateral del miembro inferior en la distribución de la carga en la planta del pie, utilizando tres pruebas para valorar la dominancia lateral: chute con precisión, equilibrio dinámico y equilibrio estático (Maupas y cols., 2002). Según nuestros resultados, la preferencia lateral de miembro inferior no influye de forma significativa en la distribución de las cargas en la planta del pie, sin embargo, observamos una tendencia a que aumente la carga del miembro izquierdo en los diestros (según las pruebas de equilibrio dinámico y chute con precisión) y en el miembro derecho en los zurdos.Palabra clave: lateralidad, presiones plantares, equilibrio estático, test lateralidad.Abstract: The distribution of loads during gait has been evaluated in many studies, some of which have been seen to influence the handedness of the individual in this distribution. However, in the static pool, several studies have been carried out which assess the possible causes of inequitable distribution of the charges, some according to the vision or adjoining space and according to influence of visual laterality. In our study we will observe the influence of the lateral lower limb preference in the distribution of the load on the sole of the foot, using three tests to assess the lateral dominance: shoot with precision, dynamic balance and static balance (Maupas et al. 2002). According to our results, lower limb lateral preference does not influence significantly the distribution of charges in the sole of the foot, however, we observe a tendency to increase the load/weight on the left limb in righthanded people (according to tests of balance dynamic and precision shot) and on the right limb in left handed people.Key words: laterality, plantar pressures, static balance, laterality test.

A new double-face online dynamic balance device and its control system for high speed machine tool spindle

Dynamic balance technology is one of the basic questions that must be considered during the high speed, high-precision machine tool’s design and production course, and its function has a great impact on the working performance, efficiency and life of the machine tool. A new double-face online dynamic balance device with a pneumatic driving system is designed in the paper. Compared with similar balance devices, the dynamic balance device has many advantages: the mechanism structure is simple, the body dimension (the axial dimension in particular) is small, the practical application in the machine tool is easy to realize etc. In order to accurately separate the unbalance vibration component from the vibration measuring signal, the least square algorithm is adopted to fit the fundamental frequency signal. The method has a theoretical advantage compared with the traditional fast Fourier transform algorithm. Based on the influence coefficient principle, the implementation steps of the double-face online dynamic balance are designed. Based on these steps, the location experiments are done to test the position correcting error of the balance discs, the test weight experiments are done to estimate the influence coefficient, and the balance correction experiments are done to examine the online dynamic balance effect. The experimental results indicate that the online dynamic balance device and its control system have the advantage that the system is stable and reliable, the location of the balance disc is accurate and the dynamic balance precision is high.

Evaluating gold standard corpora against gene/protein tagging solutions and lexical resources

MotivationThe identification of protein and gene names (PGNs) from the scientific literature requires semantic resources: Terminological and lexical resources deliver the term candidates into PGN tagging solutions and the gold standard corpora (GSC) train them to identify term parameters and contextual features. Ideally all three resources, i.e. corpora, lexica and taggers, cover the same domain knowledge, and thus support identification of the same types of PGNs and cover all of them. Unfortunately, none of the three serves as a predominant standard and for this reason it is worth exploring, how these three resources comply with each other. We systematically compare different PGN taggers against publicly available corpora and analyze the impact of the included lexical resource in their performance. In particular, we determine the performance gains through false positive filtering, which contributes to the disambiguation of identified PGNs.ResultsIn general, machine learning approaches (ML-Tag) for PGN tagging show higher F1-measure performance against the BioCreative-II and Jnlpba GSCs (exact matching), whereas the lexicon based approaches (LexTag) in combination with disambiguation methods show better results on FsuPrge and PennBio. The ML-Tag solutions balance precision and recall, whereas the LexTag solutions have different precision and recall profiles at the same F1-measure across all corpora. Higher recall is achieved with larger lexical resources, which also introduce more noise (false positive results). The ML-Tag solutions certainly perform best, if the test corpus is from the same GSC as the training corpus. As expected, the false negative errors characterize the test corpora and – on the other hand – the profiles of the false positive mistakes characterize the tagging solutions. Lex-Tag solutions that are based on a large terminological resource in combination with false positive filtering produce better results, which, in addition, provide concept identifiers from a knowledge source in contrast to ML-Tag solutions.ConclusionThe standard ML-Tag solutions achieve high performance, but not across all corpora, and thus should be trained using several different corpora to reduce possible biases. The LexTag solutions have different profiles for their precision and recall performance, but with similar F1-measure. This result is surprising and suggests that they cover a portion of the most common naming standards, but cope differently with the term variability across the corpora. The false positive filtering applied to LexTag solutions does improve the results by increasing their precision without compromising significantly their recall. The harmonisation of the annotation schemes in combination with standardized lexical resources in the tagging solutions will enable their comparability and will pave the way for a shared standard.

GEM-Selektor geochemical modeling package: revised algorithm and GEMS3K numerical kernel for coupled simulation codes

Reactive mass transport (RMT) simulation is a powerful numerical tool to advance our understanding of complex geochemical processes and their feedbacks in relevant subsurface systems. Thermodynamic equilibrium defines the baseline for solubility, chemical kinetics, and RMT in general. Efficient RMT simulations can be based on the operator-splitting approach, where the solver of chemical equilibria is called by the mass transport part for each control volume whose composition, temperature, or pressure has changed. Modeling of complex natural systems requires consideration of multiphase–multicomponent geochemical models that include nonideal solutions (aqueous electrolytes, fluids, gases, solid solutions, and melts). Direct Gibbs energy minimization (GEM) methods have numerous advantages for the realistic geochemical modeling of such fluid–rock systems. Substantial improvements and extensions to the revised GEM interior point method algorithm based on Karpov’s convex programming approach are described, as implemented in the GEMS3K C/C+ + code, which is also the numerical kernel of GEM-Selektor v.3 package ( http://gems.web.psi.ch ). GEMS3K is presented in the context of the essential criteria of chemical plausibility, robustness of results, mass balance accuracy, numerical stability, speed, and portability to high-performance computing systems. The stand-alone GEMS3K code can treat very complex chemical systems with many nonideal solution phases accurately. It is fast, delivering chemically plausible and accurate results with the same or better mass balance precision as that of conventional speciation codes. GEMS3K is already used in several coupled RMT codes (e.g., OpenGeoSys-GEMS) capable of high-performance computing.

Research on Air Suspension Rotation Parts’ Dynamic Equilibrium Measurements

The ideal of using air suspension is to reduce the movement friction of measurement system, since of the air suspension structure, the dynamic balance precision of rotating parts for measurement was improved. The use of air suspension measurement system to measure the deviation of the workpiece which rotating around a fixed axis is more efficient and convenient.

Dynamic Analysis of Grinding Wheel Mechanism on Large Vertical Glass Edging Machine

Insulating glass is an important architecture energy saving material, large vertical glass edging machine is one of the indispensable facilities on insulating glass product line, the vibration situation of grinding wheel mechanism on large vertical glass edging machine has an important effect on grinding quality. The paper has established the dynamic equation of grinding wheel mechanism and it was simulated by using MATLAB. The simulating result indicates that the distance (l) of grinding wheel mechanism to the top bearing has an important influence on the vibration of grinding wheel mechanism, the larger l value the higher amplitude, it should be considered when designing large vertical glass edging machine. In addition, the paper puts forward some effective solutions to reduce the vibration amplitude of grinding wheel mechanism such as improving the dynamic balance precision of grinding wheel, reducing the mass of grinding wheel mechanism and enhancing the rigidity of ball leadscrew.

Research on Safety Decline of High Speed Milling Cutter

This work explored the process of safety decline based on stress field and deformation analysis of cutter, founded vibration models of centrifugal force and cutting force, and investigated the effect of dynamic balance precision and natural frequency on safety and stability of cutter. Using the model of maximal residue height on machining surface based on the vibration of cutter and experiment results, characterized safety decline behavior of high speed milling cutter. Experiment results verified that safety decline caused by the vibration of centrifugal force was much higher than the vibration of cutting force. The results indicate that the deformation of cutter and relative displacement of subassembly change mass distribution of cutter with rotational speed increasing in unimpaired state of cutter’s integrity, direct results are dynamic balance precision decline, the safety and stability state of cutter gradual deterioration because of the vibration, the safety of cutter occurs a dynamic decline in high speed milling.

Static Balance Method for a Special Gyroscope Rotor with a Spherical Cavity

Balance precision of gyroscope rotor influences its performance directly. Aiming at the special gyroscope with a spherical cavity mentioned in this paper, a static balance method is proposed. This method can determine not only radial direction but also axial direction relative position between the mass center of rotor and the centroid of rotor’s outer outline. Combined with outer outline location, which can determine the relative position and direction between the mass center of rotor and the centroid of rotor’s outer outline, the relative position and direction between the centroid of spherical cavity and the centroid of outer outline can be determined, and we will know the position of removal amount.

Investigation of Dynamic Performance of Super-High Speed CBN Grinding Wheel

The dynamic performance of the grinding wheel system is one of the key factors to affect the super-high speed grinding process The excessive centrifugal stress acted on the wheel body can make the wheel rupture due to the super-high rotary speed of the wheel. And the alternating centrifugal force caused by the wheel imbalance can not only make the spindle and bearings vibration and failure, but also lower the machining precision and the wheel life, as well as make against the safety. In this paper, the centrifugal stress of the high speed grinding wheel and its effect on machining process were analyzed by means of finite element analysis and simulation. The alternating centrifugal force and its effect on the wheel spindle system were investigated. Furthermore, the balance precision of super high speed grinding wheel and system was discussed for achieving the high precision, safety and efficiency machining process.

Dynamic Characteristics of the Coupled System of the High Pressure Rotor and the Radial Driveshaft of a Turbofan Engine

In a turbofan engine, the high pressure rotor and the radial driveshaft, which transmit the power from the internal gear-box to the external gear-box, are geared by a spiral bevel gear pair. In this paper, a reasonably simplified dynamic model of the coupled rotors system is established, and then, the coupled stiffness matrix and coupled damping matrix of the spiral bevel gear pair are deduced. A shaft element method is proposed to investigate the lateral-torsional coupled vibration equations of the gear-rotor system. Furthermore, the mode shapes and unbalance responses of this two rotors coupled system are simulated. The results indicate that the system derives many new modes and the exciting forces on a rotor of the system would be passed to the other rotor for the gears meshing. When the rotor dynamics of a turbofan engine is being analyzed, the high pressure rotor and the radial drive shaft must be viewed as a whole. The dynamic balance precision of the rotors should be qualified properly, in order to improve the dynamic quality of the turbofan engine.