Balancing Strategy Research Articles

Postural control strategies with alterations in visual input conditions in a standing position.

Mediated by balance systems, postural control relies heavily on visual input. However, the precise relationship between various visual conditions and postural strategies for balance remains unclear. This study investigated alterations in the center of pressure (COP) and ankle muscle activity during standing positions in healthy individuals under different visual conditions. Fifty-three healthy university students participated in the study. Stabilometry was conducted to evaluate the total path length and outer circumferential area for the COP. Further, surface electromyography was utilized to assess the activity of the bilateral tibialis anterior (TA) and lateral head of gastrocnemius muscles. Participants were instructed to maintain an upright posture under three visual conditions: binocular (eyes open) and monocular (either closing the dominant or nondominant eye). The three conditions were compared using a one-way analysis of variance. No significant differences were observed in the COP across visual conditions. However, in the TA of the nondominant leg, the mean values for the binocular, dominant eye, and nondominant eye conditions were 0.91, 1.08, and 1.14 times that of the resting standing position. Notably, the TA of the nondominant leg was significantly lower in the binocular condition than the dominant and nondominant eye conditions. The study findings indicate that the COP remain consistent despite variations in external visual input. This stability is likely maintained through compensatory adjustments, such as increased TA muscle activity in the nondominant leg under the nondominant eye condition. These observations highlight the complex interplay between the visual perception and postural stability.

Effect of mediolateral leg perturbations on walking balance in people with chronic stroke: A randomized controlled trial.

Many people with chronic stroke (PwCS) exhibit deficits in step width modulation, an important strategy for walking balance. A single exposure to swing leg perturbations can temporarily strengthen this modulation. The objective of this parallel, double-blinded, randomized controlled trial was to investigate whether repeated perturbations cause sustained increases in step modulation (NCT02964039; funded by the VA). 54 PwCS at the Medical University of South Carolina were randomly assigned to one of three intervention groups: Control (n = 18), with minimal forces; Assistive (n = 18), pushing the swing leg toward a mechanically appropriate location; Perturbing (n = 18), pushing the swing leg away from a mechanically appropriate location. All intervention groups included 24 training sessions over 12-weeks with up to 30-minutes of treadmill walking while interfaced with a novel force-field and a 12-week follow-up period, with five interspersed assessment sessions. Our primary outcome measure was paretic step width modulation, the partial correlation between step width and pelvis displacement (ρSW). Secondarily, we quantified swing and stance leg contributions to step modulation, clinical assessments of walking balance and confidence, and real-world falls. Outcomes were analyzed for participants who completed all assessment sessions (n = 44). Only the Perturbing group exhibited significant increases in paretic ρSW, which were present after 4-weeks of training and sustained through follow-up (t = 2.42-3.17). These changes were due to improved control of paretic swing leg positioning. However, perturbation-induced changes in step modulation were not always significantly greater than those in the Control group, and clinical assessments were similar across intervention groups. Participants in the Perturbing group experienced a lower fall rate than those in the Control group (incidence rate ratio = 0.53), although our small sample size warrants caution. The present results indicate that perturbations can cause sustained modifications of targeted biomechanical characteristics of post-stroke gait, although such changes alone may be insufficient to change more complex clinical assessments.

Sustainable optimization of balancing valve settings in urban heating systems with an enhanced Jaya algorithm

With the continuous growth of global energy demand, energy conservation in heating has become a research hotspot for urban sustainable development. However, the research on energy conservation by adjusting the balance valve in the secondary network of heating system is relatively few. To address this gap, this paper solves the balancing valve opening adjustment (BVOA) problem with the goal of minimizing heat consumption and the energy usage of circulation pumps. A mixed-integer linear programming model is designed for this problem, and an effective EJaya algorithm is proposed. Initially, a random initialization method with three scrambling strategies is employed to generate high-quality solutions. Moreover, a segmented operator is proposed to enhance the algorithm’s search efficiency. Subsequently, Q-learning is utilized to select from three neighborhood operators, enhancing the algorithm’s local search capabilities. Additionally, an adaptive search balance strategy is designed to balance the methods of updating solutions, aiding in finding better solutions. Finally, extensive experiments have validated the effectiveness and efficiency of the proposed algorithm in solving the BVOA problem, contributing to the promotion of urban energy sustainability and social welfare.

An Improved Whale Optimization Algorithm with Adaptive Fitness‐Distance Balance

AbstractWhale optimization algorithm (WOA) is a new bio‐meta‐heuristic algorithm presented to simulate the predatory humpback whales' behavior in the ocean. In previous studies, WOA has been observed to exhibit lower accuracy and slower convergence rates. In this paper, we propose an improved the WOA by innovatively incorporating an adaptive fitness‐distance balance strategy, namely AFWOA. AFWOA can continuously and efficiently identify the maximum potential candidate solutions from the population within the search process, thus improving the accuracy rate and convergence speed of the algorithm. Through various experiments in IEEE CEC2017 and an ill‐conditional problem, AFWOA is proven to be more competitive than the original WOA, several other state‐of‐the‐art WOA variants and other four classic meta‐heuristic algorithms. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Effect of Sulphur Application in Oilseed Crops: A Review

Oilseeds play a pivotal role in India’s agricultural economy, with oil derived from these crops serving as a fundamental raw material for various industries. Sulphur (S) plays a crucial role in augmenting both the yield and quality of oilseed crops. Achieving a balanced fertilization strategy that incorporates S is imperative for fostering the high-quality growth of oilseed crops. Several researches indicate that S application in the range of 20-60 kg ha-1 substantially influences crop growth, yield, nutrient uptake and the economic aspects of oilseed cultivation. This review offers valuable insights into the latest research endeavors conducted in the realm of oilseed crops, specifically concerning sulphur application.

A Study on Performance Evaluation of Balanced Scheme with Reference to Top 5 Asset Management Companies

A Study on Performance Evaluation of Balanced Scheme with Reference to Top 5 Asset Management Companies

Low-power adaptive sampling electronic nose system with a Radon transform-based convolutional neural network for optimized gas recognition

The electronic nose (E-nose) systems provide great promise in capturing information about the identity of gaseous chemical analytes as well as their temporal variation. However, the regular and dense sampling events of the E-nose often lead to a substantial amount of redundancy in the temporal structure, which in turn limits their application in efficiency-sensitive scenarios such as advanced robotics and the Internet of Things. Herein, we introduce a novel operating mechanism for E-noses, which dynamically adjusts the time interval of operation to reduce power consumption and information content through an adaptive sampling mechanism. Three strategic schemes with different operating principles are presented to implement this mechanism: the accurate scheme targets the optimized discrimination accuracy, the vague scheme is oriented toward power-sensitive scenarios, and the balanced scheme aims to balance cost and benefit. In order to ensure that the adaptively sampled inhomogeneous data can be compatible with each other, we further propose an algorithm based on the Radon transform that can convert non-uniform time series with timestamp information into the same size tensor. Finally, we designed a convolutional neural network-based classification model for gaseous chemical analytes, providing guidance on scheme and parameter selection with an accuracy of up to 99 % and power savings of up to 96 %. Overall, this work provides a novel solution to optimize temporal redundancy in E-nose systems and a generalized approach to the corresponding deep-learning data processing.

The backfire of mutual funds balancing financial objectives in ESG investments: Evidence from China

In response to ESG (Environment, Social, and Governance) shock, mutual funds seem to be moving toward balancing the ESG objectives with traditional financial objectives. We observe such efforts in a sample of the Chinese active mutual funds from 2016 to 2022. These funds fulfill the Principles for Responsible Investment (PRI) requirements by investing in assets that combine superior ESG performance with excellent returns. However, the balance strategy falls short of its intended goals compared to normal ESG investments but rather leads to additional underperformance. Although our work reveals potential opportunities for balancing ESG and financial objectives, the lack of learning process and the limited attention make mutual funds miss such opportunities when they switch to balance strategy to speculatively cope with PRI constraints. This outcome suggests that in the face of the new investment trends driven by ESG shock, funds would pay a price for a superficial understanding of balance strategy and hasty actions.

Coordination of energy loss and fish friendliness for a low-head tubular-pump blade based on a multi-objective optimization model

Tubular pumps and turbines are the water-to-electricity conversion devices widely used in pumped hydro storage systems for low heads and high flow rates. An ecological concern is the high proportion of fish injuries caused by the rotating blades. In this study, a tubular pump blade is retrofitted to balance the energy loss and fish friendliness through multi-objective optimization, combining computational fluid dynamics with a mathematical blade strike model. Sensitivity analysis identifies nine geometric variables out of thirteen for optimizing blade design. An approximation model is developed to implement optimization algorithm and achieve the objective function. By artificially assigning weights to targeted performance metrics, three scenarios are obtained: the optimal efficiency scheme (A), the optimal fish friendliness scheme (C) and the balanced scheme (B). Scheme C achieves a fish survival ratio of 100 % at multiple flow rates (for fish lengths of Lf/D=1/12), but results in a 6.4 % decrease in efficiency compared to scheme A. Additionally, a negative blade attack angle can improve flow pattern. Considerable reduction in fish mortality can be obtained by equipping the rotor with a larger size but lower shaft speed, while also limiting the fish size to pass through the pump running at a reduced flow rate.

A Modified Carrier-Based PWM Strategy for Common Mode Voltage Elimination and Neutral Point Voltage Balance in a Unidirectional Three-Level Converter for AC Motor Drives

A Modified Carrier-Based PWM Strategy for Common Mode Voltage Elimination and Neutral Point Voltage Balance in a Unidirectional Three-Level Converter for AC Motor Drives

Resolution of the bidimensional shallow water equations with horizontal temperature gradients by a well Balanced Roe scheme

This work presents a numerical resolution of the two-dimensional Ripa system using Roe scheme. A discretization of the source term satisfying the conservation property is presented. The mathematical model is based on the ordinary shallow water equations in merging the horizontal temperature gradient. The numerical approach employs unstructured grids and integrates Runge–Kutta method and the minmod limiter to achieve second order accuracy in both time and space domains. A strategy of mesh adaptation based on temperature gradient is implemented to refine the study domain and gain in computational cost. Various numerical tests are conducted to verify the effeciency of the numerical method.

Self Awareness During the Baby Blues Condition: Support, Emotional Balance, and Coping Strategies

Self Awareness During the Baby Blues Condition: Support, Emotional Balance, and Coping Strategies

Balanced Distribution Strategy for the Number of Recharging Requests Based on Dynamic Dual Thresholds in WRSNs

Balanced Distribution Strategy for the Number of Recharging Requests Based on Dynamic Dual Thresholds in WRSNs

Towards fine-grained load balancing with dynamical flowlet timeout in datacenter networks

In modern datacenter networks (DCNs), load balancing mechanisms are widely deployed to enhance link utilization and alleviate congestion. Recently, a large number of load balancing algorithms have been proposed to spread traffic among the multiple parallel paths. The existing solutions make rerouting decisions for all flows once they experience congestion on a path. They are unable to distinguish between the flows that really need to be rerouted and the flows that potentially have negative effects due to rerouting, resulting in frequently ineffective rerouting. Fine-grained rerouting will also cause severe packet reordering, especially in asymmetric topology scenarios. To address the above issues, we present a fine-grained traffic-differentiated load balancing (TDLB) mechanism, which aims to distinguish flows that are necessarily to be rerouted and reroute traffic in fine-grained without packet reodering. Specifically, TDLB distinguishes the traffic that must be rerouted through the host pair information in the packet header, and selects an optimal path for rerouting. To prevent severe packet reodering caused by excessive path delay differences, TDLB dynamically adjusts the flowlet timeout to segment the traffic and select the optimal path for rerouting. The NS-2 simulation results show that TDLB effectively reduces tail latency and average flow completion time (FCT) for short flows by up to 49% and 46%, respectively, compared to the state-of-the-art load balancing schemes.

An algorithmic approach to portfolio construction: A Turkish stock market case

The goal of building a diversified portfolio is to mitigate risks and manage risk-reward tradeoffs. Diversification is also crucial for ensuring the long-term success of a portfolio. Although passive investment has been on the rise in most developed markets, it has not gained wider acceptance in some emerging markets, such as Türkiye. This study proposes an approach to construct a diversified portfolio that serves as a passive investment tool in the context of the Turkish stock market. Despite the long history of equity markets in Türkiye, almost all available passive investment alternatives are prohibitively expensive, unlike those in developed markets. We design a simple methodology that addresses the essential components of building a successful portfolio while avoiding excessive fees. Additionally, we propose a dynamic balancing strategy that algorithmically adjusts the weight of each stock in the portfolio. We test this approach with historical data, demonstrating that it can achieve reasonable returns with minimal effort. Even when its performance does not beat the benchmark, the percentage deviation is still below the management fees charged for alternative investments.

Decentralized Multiagent Reinforcement Learning Based State-of-Charge Balancing Strategy for Distributed Energy Storage System

Decentralized Multiagent Reinforcement Learning Based State-of-Charge Balancing Strategy for Distributed Energy Storage System

Ethiopia and BRICS: Prospects and Challenges

This study examines Ethiopia's relationship with the BRICS group (Brazil, Russia, India, China, and South Africa), exploring both the opportunities and challenges this affiliation presents. As emerging economies gain global influence, Ethiopia's inclusion in BRICS represents a significant chance to leverage economic, political, and technological strengths. The research highlights the potential benefits for Ethiopia, including enhanced trade, investment, and technology transfer, which could drive the country's economic growth and development. However, Ethiopia also faces substantial challenges, such as infrastructure deficiencies, skill gaps, and regulatory barriers that may hinder its ability to fully align with BRICS frameworks. The study underscores Ethiopia's strategic motivations for engaging with BRICS, including accessing new markets and fostering innovation. It also addresses the risks of economic dependency and the need for a balanced diplomatic strategy with traditional Western partners. Through a mixed-method approach, the research combines quantitative and qualitative analyses to provide a comprehensive understanding of Ethiopia's position within BRICS. The study emphasizes the geopolitical significance of BRICS in reshaping global power dynamics and Ethiopia's potential role in this transformation. The study offers strategic recommendations to enhance Ethiopia's engagement with BRICS, aimed at a mutually beneficial partnership aligned with Ethiopia's development goals and regional influence. This contribution is crucial for policymakers and researchers interested in South-South cooperation and its impact on Africa's fastest-growing economies.

Optimization scheduling strategy for flexible interconnected distribution network considering transformer offline

Based on the flexible interconnection technology with intelligent soft open point (SOP), new opportunities have arisen to address issues such as complex power flow and uneven load rates in existing distribution networks. However, most existing research focuses on load balancing strategies and N-1 fault tolerance, without fully considering the impact of flexible access and offline of distribution transformers on the losses in the distribution network. Therefore, this paper analyzes the impact of structural adjustments to the distribution transformer interconnection system on the comprehensive losses of the distribution network, and concludes that under low-load conditions, the offline of lightly loaded transformers optimizes the comprehensive losses of the distribution network. However, the offline of lightly loaded transformers leads to an increase in the remaining transformer load rates in the distribution station areas. To address this shortfall, this paper proposes an optimization scheduling strategy for flexible interconnected distribution networks considering transformer offline, by simultaneously utilizing shared energy storage on the DC side to balance transformer load rates and minimize losses. Finally, case studies based on an IEEE 18-node interconnected system are conducted to validate the proposed approach under two different scenarios. The results demonstrate that the proposed control strategy outperforms existing strategies by optimizing the comprehensive losses of the system, thereby enhancing its operational economy and reliability.

Optimal charging of Li-ion batteries using sparse identification of nonlinear dynamics

Optimal charging of Li-ion batteries requires careful management of charge rates, as high rates can lead to accelerated degradation, while low rates significantly extend charging times. Traditional methods for determining charge rates often rely on rule-based approaches, which typically fail to effectively balance battery performance with charging duration. To address this, we introduce a novel optimization approach that directly integrates the dual objectives of minimizing charge time and maximizing battery lifetime into the optimization process. Unlike most existing charge optimization methods that do not directly track battery lifetime and charge time simultaneously, our method employs a data-driven model that facilitates direct and dynamic estimation of both battery lifetime and charge time at each step of the optimization process. Specifically, we utilize the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm to predict battery capacity and voltage dynamics, which informs the calculations of lifetime and charge time required to solve the optimization problem. This approach provides a balanced optimization strategy that enhances the effectiveness of battery’s performance while maintaining the efficiency of the charging process. We applied this method to a novel next-generation NMC811 battery, featuring a cathode comprised of 80 % nickel, 10 % manganese, and 10 % cobalt, and a lithium metal foil anode - a combination not extensively studied previously. Experimental validation demonstrated that when optimized charge rates are applied every 10 cycles in a 100-cycle operation, the method leads to more stable cycling and improved capacity retention of approximately 7.4 % over the nominal charge rate, demonstrating the potential of the developed approach.

Exploring aquaculture related traits of the grooved carpet shell (Ruditapes decussatus) in relation to other bivalve species using Dynamic Energy Budget theory

To assess the potential of the grooved carpet shell (Ruditapes decussatus) for aquaculture in Europe, we used Dynamic Energy Budget (DEB) theory to perform extensive parametrization on the species and compared its energy allocation strategy with those of commonly farmed bivalve species: mussels (Mytilus edulis and Mytilus galloprovincialis), oysters (Ostrea edulis and Magallana gigas), the common cockle (Cerastoderma edule), and great scallop (Pecten maximus). The comparison was based on DEB primary parameters relevant to aquaculture production, such as maximum assimilation rate and kappa, which represents the fraction of energy allocated to maintenance and growth, and compound parameters like the von Bertalanffy growth coefficient and maximum storage density. Furthermore, we evaluated the production efficiency at the population level, which represents the ratio of assimilated energy converted into biomass. Our results revealed notable differences in energy utilization strategies among species. However, uncertainties in parameter estimation and environmental factors challenge the direct translation of these parameters to real-world aquaculture, therefore our interpretation focuses on how these parameters might influence a species’ potential for aquaculture. The grooved carpet shell exhibits a balanced energy allocation strategy with a low growth coefficient and low maintenance costs, leading to high production efficiency. Similarly, the common mussel focuses on growth with significant biomass investment over reproduction, while the Pacific oyster and Mediterranean mussel prioritize reproductive development. The flat oyster and scallop demonstrate rapid growth at the cost of the low production efficiencies. The grooved carpet shell and mussels face constraints such as limited reserves, making them comparatively more susceptible to low food quality and quantity. In contrast, high storage densities in species like the common cockle, scallop, and Pacific oyster suggest resilience to fluctuating food conditions. These findings, along with both agreements and discrepancies with existing literature, highlight the need for further experimental research to refine DEB parameters and enhance their application in aquaculture. Overall, the DEB framework proves effective for exploring aquaculture traits across species and underscores the need for additional work on temperature-related processes, life-history events, and morphological variation.