Balance Method Research Articles

An improved ant colony optimization algorithm for two-sided U-type assembly line balancing problems

Aiming at the improvement of production system, a new method of two-sided U-type assembly line balance is proposed. Define the partition of a two-sided U-type assembly line and derive the formula for the task start time. The multi-objective model of two-sided U-type assembly line balance is established by minimizing a smoothing index, the number of positions and stations. Considering the dual influence of heuristic factors and pheromone information, the improved ant colony optimization algorithm is proposed. Combined with 37 examples, the improved ant colony optimization algorithm is used to solve the balancing problem of a two-sided straight line and a two-sided U-type assembly line, respectively. Compared with the results of other algorithms, it verifies that the model is reasonable and the improved ant colony optimization algorithm is effective. The balance effect of a two-sided U-type assembly line is better than that of a two-sided straight line, which means the length of the assembly line is shortened.

Analysis of company performance measurement using the balanced scorecard method and analytical hierarchy process (AHP) at PT XYZ

PT XYZ is a private company engaged in freight forwarding services in Surabaya - Waingapu. In its journey, the company experienced many challenges, which continuously improved its performance management, starting from financial issues, customers, internal business processes, and learning and growth. This study aims to determine the level of company performance and provide recommendations for improvements to the Key Performance Indicator below the target. The method used is the Balanced Scorecard (BSC) supported by the Analytical Hierarchy Process (AHP). The study results show that PT XYZ's company performance score in 2020 produced a score weight of 2.88 which is included in the good category. In contrast, in 2021, the company's performance made a weighted score of 2.78 which is included in the good category, and 2022, the company's performance had a weighted score of 2.26 which is included in the sufficient category. Internal Business Processes are the focus of improvement because the achievement score still needs to catch up to the target to improve PT XYZ's performance.

A new activity coefficient model for describing adsorbed phase nonidealities

The following study introduces a new adsorbed phase activity coefficient model, the SPLINT model, for describing nonideal multicomponent systems. The derivation is presented and simplification to the thermodynamically robust ABC model in the low‑coverage limit is shown. Both the SPLINT and ABC models are fit to 25 binary systems. Thermodynamic consistency of the data is established through multiple consistency tests including the intersection rule, closed‑loop reduced spreading pressure integration, the Gibbs‑Duhem integral equality, and alignment of pure and binary data. Regressed parameters are used in Real Adsorbed Solution Theory to predict both binary and ternary adsorption. The ABC and SPLINT models accurately describe systems with considerable nonideality when the activity coefficients are symmetric; however, the SPLINT model is required for accurate description of systems with complex, asymmetric activity coefficient behavior. The new model includes one more fitting parameter than the ABC model for a single binary system, but less across multiple data sets and higher‑order systems due to the theoretical basis. The importance of capturing activity coefficient asymmetry is demonstrated with a highly nonideal system across various pressures and temperatures measured by the present authors using the Integral Mass Balance method with HFC‑125 (pentafluoroethane), HFC‑32 (difluoromethane), and zeolite H‑ZSM‑5. The theoretical basis of the SPLINT model is further enforced by presenting two methods for predicting activity coefficients with the SPLINT model: correlating the fitting parameters individually and simultaneously regressing pure isosteric heat data to extract SPLINT fitting parameters. The study concludes with a discussion of pure and binary HFC‑125/HFC‑32 data with zeolite H‑ZSM‑5. A possible binary adsorption mechanism is proposed based on isosteric heat and excess enthalpy predictions and observed system irreversibility.

Machine Learning-Based Classification of Rock Bursts in an Active Coal Mine Dominated by Non-Destructive Tremors

Rock bursts are dynamic phenomena in underground openings, causing damage to support and infrastructure, and are one of the main natural hazards in underground coal mines. The prediction of rock bursts is important for improving safety in mine openings. The hazard of rock bursts is correlated with seismic activity, but rock bursts are rare compared to non-destructive tremors. The five machine learning classifiers (multilayer perceptron, adaptive boosting, gradient boosting, K-nearest neighbors, and Gaussian naïve Bayes), along with an ensemble hard-voting classifier composed of these classifiers, were used to recognize rock bursts among the dominant non-destructive tremors. Machine learning models were trained and tested on ten sets of randomly selected data obtained from one of the active hard coal mines in the Upper Silesian Coal Basin, Poland. For each of the 627 cases in the database, 15 features representing geological, geomechanical, mining, and technical conditions in the opening as well as tremor energy and correlated peak particle velocity were determined. Geological and geomechanical parameters of the coal seams and surrounding rocks were aggregated into a single GEO index. The share of rock bursts in the database was only about 8.5%; therefore, the ADASYN balancing method, which addresses imbalanced datasets, was used. The ensemble hard-voting classifier most effectively classified rock bursts, with an average recall of 0.74.

Study on prestress distribution and structural performance of heptagonal six-five-strut alternated cable dome with inner hole

The cable dome structure is a highly efficient prestressed space structure with reasonable force, attractive shape, and large spanning capacity. It is increasingly widely used in stadiums, exhibition halls, terminals, and other large-scale public buildings and the roof structure of various transport hub projects. To solve the problem of weak overall structural stability of the traditional cable dome structure, a new type of prestressed space structure is proposed - heptagonal six-five-strut alternated cable dome with an inner hole. The composition of the structural system was given and a theoretical study of the distribution of structural prestress was carried out; based on the symmetry and periodicity conditions, the initial prestress distribution of the structure was accurately calculated using the node balance method, and the formulae for the geometric parameters of the structural cable-strut were derived; the numerical model was established by using the finite element software ANSYS 2022 R1, and the force characteristics of the structure under full-span uniform load, half span uniform live load, wind load, and temperature were studied. The effects of structural prestress, overall shape, and support stiffness, totaling three categories and six parameters, on the structural mechanical performance of the internal force of the member, node displacement, and support reaction force were analysed in detail and the sensitivity of the parameters is assessed quantitatively, accordingly, the values of the main design parameters such as thick-span ratio are given as suggestions. The analysis results show that the new structural system has a high load-carrying capacity and deformation resistance, and its proposal provides a new scheme and new ideas for the selection and design of the cable dome.

Correction: Kim et al. Zero-Sequence Voltage Injection Method for DC Capacitor Voltage Balancing of Wye-Connected CHB Converter under Unbalanced Grid and Load Conditions. Energies 2021, 14, 1019

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Disaggregation of canopy photosynthesis among tree species in a mixed broadleaf forest.

Carbon dioxide sequestration from the atmosphere is commonly assessed using the eddy covariance method. Its net flux signal can be decomposed into gross primary production and ecosystem respiration components, but these have seldom been tested against independent methods. In addition, eddy covariance lacks the ability to partition carbon sequestration among individual trees or species within mixed forests. Therefore, we compared gross primary production from eddy covariance versus an independent method based on sap flow and water-use efficiency, as measured by the tissue heat balance method and δ13C of phloem contents, respectively. The latter measurements were conducted on individual trees throughout a growing season in a mixed broadleaf forest dominated by three tree species, namely English oak, narrow-leaved ash and common hornbeam (Quercus robur L., Fraxinus angustifolia Vahl, and Carpinus betulus L., respectively). In this context, we applied an alternative ecophysiological method aimed at verifying the accuracy of a state-of-the-art eddy covariance system while also offering a solution to the partitioning problem. We observed strong agreement in the ecosystem gross primary production estimates (R2=0.56; P<0.0001), with correlation being especially high and nearly on the 1:1 line in the period before the end of July (R2=0.85; P<0.0001). After this period, the estimates of gross primary production began to diverge. Possible reasons for the divergence are discussed, focusing especially on phenology and the limitation of the isotopic data. English oak showed the highest per-tree daily photosynthetic rates among tree species, but the smaller, more abundant common hornbeam contributed most to the stand-level summation, especially early in the spring. These findings provide a rigorous test of the methods and the species-level photosynthesis offers avenues for enhancing forest management aimed at carbon sequestration.

THE ENERGY BALANCE METHOD COUPLED WITH THE GALERKIN METHOD FOR LOCAL FRACTIONAL NONLINEAR OSCILLATION

The method of solving the vibration frequency of local fractional nonlinear oscillators under general initial conditions is studied by coupling the energy balance method with the Galerkin method. One test case of fractional vibration is given to prove the feasibility of this method.

The effectiveness of metaheuristic algorithms modified with Fitness Distance Balance (FDB) method on RC slab bridge superstructure optimization

The effectiveness of metaheuristic algorithms modified with Fitness Distance Balance (FDB) method on RC slab bridge superstructure optimization

Endometrial scratching in unexplained repeated implantation failure causes two competing forces, angiogenesis and anti-angiogenesis: An RCT study.

A significant association between endometrial vascularity and pregnancy has been shown in previous research, while poor vascularization was attributed to repeated implantation failure (RIF). One possible approach to enhance angiogenesis for successful implantation is endometrial scratching (ES). The purpose was to investigate endometrial responses to scratching by profiling angiogenesis-related gene expression in unexplained RIF participants. In this randomized controlled trial study, 20 infertile women with unexplained RIF were assigned to 2 groups by the balanced block randomization method (n = 10/each group): the intervention group (group A) (who received ES in the follicular phase) and the control group (group B). Endometrial biopsy was performed in the secretory phase. Gene expression profiling was performed using a polymerase chain reaction-array kit for human-angiogenic growth factors. The implantation and clinical pregnancy rates were also assessed. Among the angiogenesis-promoting genes, FGF1, FGF13, FGF2, TGFA, ANG, ANGPT1, and VEGFA were significantly upregulated (p 0.05). IL12A (an angiogenesis-inhibiting cytokine) was significantly upregulated (p 0.01). In contrast, 15 genes with angiogenesis-related functions, including CXCL11, CXCL13, CXCL3, CXCL5, CXCL6, EREG, FIGF, FST, IL10, LEP, PPBP, PROK1, RHOB, TNF, and TYMP, were downregulated after ES. No significant differences were observed between the intervention (group A) and control (group B) groups in terms of implantation (43.75% vs. 28.57%) or clinical pregnancy rates (75% vs. 57.1%). ES induced significant alterations in the expression of angiogenesis-related genes, with notable up/downregulation of key angiogenic/antiangiogenic factors. These findings enhance our understanding of the molecular responses triggered by ES, underscoring the potential influence of ES on the complex processes of angiogenesis crucial for implantation.

SJFO: Sail Jelly Fish Optimization enabled VM migration with DRNN-based prediction for load balancing in cloud computing

ABSTRACT The dynamic workload is evenly distributed among all nodes using balancing methods like hosts or VMs. Load Balancing as a Service (LBaaS) is another name for load balancing in the cloud. In this research work, the load is balanced by the application of Virtual Machine (VM) migration carried out by proposed Sail Jelly Fish Optimization (SJFO). The SJFO is formed by combining Sail Fish Optimizer (SFO) and Jellyfish Search (JS) optimizer. In the Cloud model, many Physical Machines (PMs) are present, where these PMs are comprised of many VMs. Each VM has many tasks, and these tasks depend on various parameters like Central Processing Unit (CPU), memory, Million Instructions per Second (MIPS), capacity, total number of processing entities, as well as bandwidth. Here, the load is predicted by Deep Recurrent Neural Network (DRNN) and this predicted load is compared with a threshold value, where VM migration is done based on predicted values. Furthermore, the performance of SJFO-VM is analysed using the metrics like capacity, load, and resource utilization. The proposed method shows better performance with a superior capacity of 0.598, an inferior load of 0.089, and an inferior resource utilization of 0.257.

Review about evaluation methods of recoverable reserves of deep water drive gas reservoirs in China

It has been widely accepted that China is one of the biggest natural gas consumers. Related to the imports of LNG, China stands in a very uncomfortable situation. Most domestic gas reservoirs fall within deep water drive gas reservoirs inordinately, which has entered the production depletion stage. Accurate estimation of SEC recoverable reserves of deep water drive gas reservoirs is of great significance for gas consumption planning and peak shaving. The existing calculation methods of recoverable reserves mainly consist of static methods and dynamic methods. In the early stage of exploration and development, the volumetric method has often been utilized to calculate the recoverable reserves. With the continuous development of gas reservoirs, the main methods for evaluation are dynamic methods, including the successive subtraction of production method, water drive curve method, prediction model method, attenuation curve method, improved virtual curve method, and material balance method for deep gas reservoirs.

A quick diagnostic method for corrosion-inhibition balance and a green slurry for copper chemical mechanical polishing

In this paper, a green inhibitor, adenine, is introduced into the Cu-CMP slurry to prevent the toxic ingestion in the CMP process. Moreover, in order to effectively optimize and control the copper chemical mechanical polishing based on the corrosion-inhibition balance, an expression is proposed to diagnostic the balance quickly via the least square fitting of the experimental data. Based on the corrosion-inhibition balance optimization, a pH-neutral slurry (pH = 6.8 ∼ 7.2) incorporating adenine with a high material removal rate (MRR > 600 nm/min), a low surface roughness (Ra < 1 nm) as well as low surface defects (no micro-scratches, number of surface corrosion pits ≤ 0.02 counts·μm−2, particle residues ≤ 0.98 counts·μm−2) is achieved. This newly developed slurry and the diagnostic method for the corrosion-inhibition balance should be promising for designing the green slurry for Cu-CMP.

Developing Dashboard Analytics and Visualization Tools for Effective Performance Management and Continuous Process Improvement

Performance management and continuous process improvement are essential for businesses to monitor how well their operations are running, find ways to improve them, and bring about good change. However, making decisions based on data in these areas can be challenging because performance data is complicated. This research looks into how panel analytics and data visualization tools can help manage performance well and make processes better all the time. A literature study is done on performance management frameworks, process improvement methods, data visualization techniques, and dashboard solutions that are already out there. The Lean, Six Sigma, and balanced scorecard methods are looked at for success indicators and metrics. It also looks into data visualization tools' design principles and methods like dashboards, tables, charts, and interactive features. Interviews, observations, document reviews, and internal files are ways a case organization gathers both primary and secondary data. Key performance indicators for quality, delivery, productivity, safety, and customer happiness are found in several different business roles and processes. To look at performance trends and relationships, SPSS is used for descriptive statistics and hypothesis testing. Interactive dashboard examples that use different data visualization methods are created by thinking about how the user will experience it, how it will work with other systems, and how it will help them make decisions. Dashboards let you dig deeper into processes, determine why problems happen, and compare actuals to goals. Usability testing checks how easy the tools are and how well decision-makers can learn from them. The results show that dashboard analytics can make handling performance and improvement projects easier based on data. Insights made it possible to keep track of KPIs, find methods that were not working well, and work together across teams. There are also talks about problems with data quality and change management.

Short-term results of soft tissue balance obtained with 'manual gap balancing' in varus knees; a prospective study supported by gait analysis

Aim: The aim of this study was to investigate the contribution of medial ligament release using the manual gap balance method in patients with varus deformity to alignment and functional outcomes. Materials and Methods: The study included 14 female and 8 male patients who underwent total knee arthroplasty. Telemetric radiographs of the lower extremities and valgus stress radiographs of the knees to be operated on were taken for each patient one day before and 1.5 months after the operation. The functional evaluation of patients was conducted using the HSS (Hospital for Special Surgery, 1970) scoring system. Simultaneously, gait analyses were conducted. The patients were divided into groups based on their stress radiographs. Results: The mean valgus angle in postoperative static radiographs was 2.5 degrees. A statistically significant change in rotational deformity was observed in both swing and stance phases after surgery compared to preoperative values. Improvement in coronal plane varus knees was statistically significant. No significant change was found in the flexion-extension angles of sagittal plane kinematics. It was found that there was a statistically significant difference in the data for single support, double support, and stance percentage (p

Predicting stroke events with a proactive fusion system: a comprehensive study on imbalance class handling in computational biomechanics

Stroke, as a critical global health concern and the second leading cause of death, occurs when blood flow to the brain is interrupted. Although machine learning has advanced in medical safety, there is limited research on stroke prediction using information fusion systems. This study presents a fusion framework that combines multiple base classifiers and a Meta classifier to improve stroke prediction performance. The research utilizes Grid Search optimized models, such as Random Forest, Support Vector Machine, K Nearest Neighbors, AdaBoost, Gradient Boosting, Light Gradient Boosting, Categorical Boosting, and eXtreme Gradient Boosting for in-depth stroke analysis. Since stroke events are rare and unpredictable, classification outcomes can be deceptive due to imbalanced data. This article examines stroke probability by comparing three data balancing methods: over-sampling, under-sampling, and tomek-link synthetic minority over-sampling (SMOTE-TL) to enhance prediction accuracy. The findings reveal that AdaBoost as a meta-classifier attains the highest performance in the fusion framework, with a peak of 88.09% Recall and 83.66% F1 score. This innovative approach provides crucial insights into stroke prediction and can be a valuable resource for strengthening intervention efforts in advanced healthcare systems.

Comparison of two propensity score-based methods for balancing covariates: the overlap weighting and fine stratification methods in real-world claims data

BackgroundTwo propensity score (PS) based balancing covariate methods, the overlap weighting method (OW) and the fine stratification method (FS), produce superb covariate balance. OW has been compared with various weighting methods while FS has been compared with the traditional stratification method and various matching methods. However, no study has yet compared OW and FS. In addition, OW has not yet been evaluated in large claims data with low prevalence exposure and with low frequency outcomes, a context in which optimal use of balancing methods is critical. In the study, we aimed to compare OW and FS using real-world data and simulations with low prevalence exposure and with low frequency outcomes.MethodsWe used the Texas State Medicaid claims data on adult beneficiaries with diabetes in 2012 as an empirical example (N = 42,628). Based on its real-world research question, we estimated an average treatment effect of health center vs. non-health center attendance in the total population. We also performed simulations to evaluate their relative performance. To preserve associations between covariates, we used the plasmode approach to simulate outcomes and/or exposures with N = 4,000. We simulated both homogeneous and heterogeneous treatment effects with various outcome risks (1-30% or observed: 27.75%) and/or exposure prevalence (2.5-30% or observed:10.55%). We used a weighted generalized linear model to estimate the exposure effect and the cluster-robust standard error (SE) method to estimate its SE.ResultsIn the empirical example, we found that OW had smaller standardized mean differences in all covariates (range: OW: 0.0–0.02 vs. FS: 0.22–3.26) and Mahalanobis balance distance (MB) (< 0.001 vs. > 0.049) than FS. In simulations, OW also achieved smaller MB (homogeneity: <0.04 vs. > 0.04; heterogeneity: 0.0-0.11 vs. 0.07–0.29), relative bias (homogeneity: 4.04–56.20 vs. 20–61.63; heterogeneity: 7.85–57.6 vs. 15.0-60.4), square root of mean squared error (homogeneity: 0.332–1.308 vs. 0.385–1.365; heterogeneity: 0.263-0.526 vs 0.313-0.620), and coverage probability (homogeneity: 0.0–80.4% vs. 0.0-69.8%; heterogeneity: 0.0-97.6% vs. 0.0-92.8%), than FS, in most cases.ConclusionsThese findings suggest that OW can yield nearly perfect covariate balance and therefore enhance the accuracy of average treatment effect estimation in the total population.

Period-1 Motions and Bifurcations of a 3D Brushless DC Motor System with Voltage Disturbance

Abstract: In this paper, the nonlinear dynamic system of a brushless DC motor with voltage disturbance is studied analytically via a generalized harmonic balance method. A truncated Fourier series with time-varying coefficients is utilized to represent the analytical variations of nonlinear currents and voltages within this dynamic system. Bifurcations of periodic currents and voltages are obtained, and their stability is discussed through eigenvalue analysis. The frequency–amplitude characteristics of periodic currents and voltages exhibit complexity in the frequency domain. Comparative illustrations are provided to contrast the analytical solutions with numerical outcomes for periodic currents and voltages. These analytical findings can be effectively employed for controlling the brushless DC motors experiencing voltage disturbances.

Physicochemical studies on prepration, structure and bonding of Ni(II) complexes with N,O and S containing ligands

Transition metal complexes of Nickel (II) with nitrogen, Oxygen, and sulphur doners such as thiocarbohydrazido, thio-cyanato, thiosemicarbazido and water are characterized based on structural, magnetic, and spectral data. These complexes have been thoroughly investigated using the physicochemical method. The present work deals mainly with the study of preparation, bonding, and structures with magnetic and spectral properties in Nickel (II) complexes. The bonding is based on the valence bond theory. The magnetic susceptibility of these complexes is observed using Gau balance methods and spectral properties are based on NMR Spectro metre. Keywords:- Nitrogen, Sulphur, complexes, Nickel chloride, Magnetic properties, Structure. Spectral.

A vertical track nonlinear energy sink

Eliminating the effects of gravity and designing nonlinear energy sinks (NESs) that suppress vibration in the vertical direction is a challenging task with numerous damping requirements. In this paper, the dynamic design of a vertical track nonlinear energy sink (VTNES) with zero linear stiffness in the vertical direction is proposed and realized for the first time. The motion differential equations of the VTNES coupled with a linear oscillator (LO) are established. With the strong nonlinearity considered of the VTNES, the steady-state response of the system is analyzed with the harmonic balance method (HBM), and the accuracy of the HBM is verified numerically. On this basis, the VTNES prototype is manufactured, and its nonlinear stiffness is identified. The damping effect and dynamic characteristics of the VTNES are studied theoretically and experimentally. The results show that the VTNES has better damping effects when strong modulation responses (SMRs) occur. Moreover, even for small-amplitude vibration, the VTNES also has a good vibration suppression effect. To sum up, in order to suppress the vertical vibration, an NES is designed and developed, which can suppress the vertical vibration within certain ranges of the resonance frequency and the vibration intensity.