Search Results (9)

The decrease in employee performance that occurs during organizational change is one of the main problems that this study attempts to address. This phenomenon, which is known as resistance to change, has been directly linked to the failure or abandonment of change initiatives when performance drops to critical levels. This study proposes an innovative approach to organizational change management based on a model that integrates real-time performance monitoring and employee reassignment to tasks. This approach contributes to improving overall system performance and stabilizing costs by achieving a reduction in resistance to change through staff training and dynamic reallocation of human resources. The method utilizes Evolutionary Dynamic Multi-Objective Optimization with the aim of both maximizing performance and minimizing costs. It incorporates the performance of employees in each task and the associated costs, enabling continuous adjustment of task assignments in accordance with temporal variability in the factors that affect the success of organizational change. Experimental simulations show that the proposed method leads to a considerable enhancement in overall system performance, cost stabilization, and a significant reduction in the risk of change abandonment. More specifically, the proposed method demonstrates an improvement in total performance from 55% to over 200% in comparison to three reference methods. Furthermore, it achieves faster recovery and a lower performance drop, especially in critical stages, providing optimized decision-making during the change process and leading to the new desired and improved state being achieved in a time that is up to 27% shorter, consequently reducing the risk of abandonment. The proposed method operates as both an optimization tool and a real-time decision support system. The continuous analysis of employee performance and cost provides actionable indications of the current state of change, allowing for timely detection and intervention. Full article

Exploring the action mechanisms and enhancement pathways of the resilience of agricultural product green supply chains is conducive to strengthening the system’s risk resistance capacity and providing decision support for achieving the “dual carbon” goals. Based on theories such as dynamic capability theory and complex adaptive systems, this paper constructs a resilience framework covering the three stages of “steady-state maintenance–dynamic adjustment–continuous evolution” from both single and multiple perspectives. Combined with 768 units of multi-agent questionnaire data, it adopts Structural Equation Modeling (SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to analyze the influencing factors of resilience and reveal the nonlinear mechanisms of resilience formation. Secondly, by integrating configurational analysis with machine learning, it innovatively constructs a resilience level prediction model based on fsQCA-XGBoost. The research findings are as follows: (1) fsQCA identifies a total of four high-resilience pathways, verifying the core proposition of “multiple conjunctural causality” in complex adaptive system theory; (2) compared with single algorithms such as Random Forest, Decision Tree, AdaBoost, ExtraTrees, and XGBoost, the fsQCA-XGBoost prediction method proposed in this paper achieves an optimization of 66% and over 150% in recall rate and positive sample identification, respectively. It reduces false negative risk omission by 50% and improves the ability to capture high-risk samples by three times, which verifies the feasibility and applicability of the fsQCA-XGBoost prediction method in the field of resilience prediction for agricultural product green supply chains. This research provides a risk prevention and control paradigm with both theoretical explanatory power and practical operability for agricultural product green supply chains, and promotes collaborative realization of the “carbon reduction–supply stability–efficiency improvement” goals, transforming them from policy vision to operational reality. Full article

This academic paper proposes a terahertz (THz) device featuring dynamic adjustability. This device relies on composite metamaterials made of graphene and vanadium dioxide (VO₂). By integrating the electrically adjustable traits of graphene with the phase transition attributes of VO₂, the suggested metamaterial device can achieve both broadband absorption and dual-band linear-to-circular polarization conversion (LCPC) in the terahertz frequency range. When VO₂ is in its metallic state and the Fermi level of graphene is set to zero electron volts (eV), the device shows remarkable broadband absorption. Specifically, it attains an absorption rate exceeding 90% within the frequency span of 2.28–3.73 terahertz (THz). Moreover, the device displays notable polarization insensitivity and high resistance to changes in the incident angle. Conversely, when VO₂ shifts to its insulating state and the Fermi level of graphene stays at 0 eV, the device operates as a highly effective polarization converter. It attains the best dual-band linear-to-circular polarization conversion within the frequency ranges of 4.31–5.82 THz and 6.77–7.93 THz. Following the alteration of the Fermi level of graphene, the device demonstrated outstanding adjustability. The designed multi-functional device features a simple structure and holds significant application potential in terahertz technologies, including cloaking technology, reflectors, and spatial modulators. Full article

With the rapid advancement of the marine economy, conventional salvage equipment has become increasingly inadequate in meeting the operational demands of complex aquatic environments and deep-sea salvage operations. This study presents the preliminary design of a novel salvage catamaran and proposes a multi-level fuzzy comprehensive evaluation framework for hydrodynamic performance under multi-sea-state and multi-operational conditions. A hydrodynamic performance evaluation indicator system was established, integrating resistance and seakeeping criteria. Computational fluid dynamics (CFDs) simulations with overset grids were employed to calculate the resistance characteristics. Potential flow-theory-based analysis quantified motion responses under irregular waves. The framework effectively distinguishes performance variations across five sea states and two sets of loading conditions through composite scoring. Key findings demonstrate that wave-added resistance coefficients increase proportionally with a significant wave height (Hs) and spectral peak period (Tp), while payload variations predominantly influence heave amplitudes. A fuzzy mathematics-driven model assigned entropy–Analytic Hierarchy Process (AHP) hybrid weights, revealing operational trade-offs: Case1-Design achieved optimal seakeeping and resistance, whereas Case5-Light exhibited critical motion thresholds. Adaptive evaluation strategies were proposed, including dynamic weight adjustments for long/short-wave-dominated regions via sliding window entropy updates. This work advances the systematic evaluation of catamarans, offering a validated methodology for balancing hydrodynamic efficiency and operational safety in salvage operations. Full article

Due to the reduction in fossil fuel abundance and the harmful environmental effects of burning them, the renewable resource potentials of microgrid (MG) structures have become highly highly. However, the uncertainty and variability of MGs leads to system frequency deviations in islanded or stand-alone mode. Usually, battery energy storage systems (BESSs) reduce this frequency deviation, despite limitations such as reducing efficiency in the long term and increasing expenses. A suitable solution is to use electric vehicles (EVs) besides BESSs in systems with different energy sources in the microgrid structure. In this field, due to the fast charging and discharging of EVs and the fluctuating character of renewable energy sources, controllers based on the traditional model cannot ensure the stability of MGs. For this purpose, in this research, an ultra-local model (ULM) controller with an extended state observer (ESO) for load frequency control (LFC) of a multi-microgrid (MMG) has been systematically developed. Specifically, a compensating controller based on the single-input interval type fuzzy logic controller (FLC) was used to remove the ESO error and improve the LFC performance. Since the performance of the ULM controller based on SIT2-FLC depends on specific parameters, all of these coefficients were adjusted by an improved harmony search algorithm (IHSA). Simulation and statistical analysis results show that the proposed controller performs well in reducing the frequency fluctuations and power of the system load line and offers a higher level of resistance than conventional controllers in different MG scenarios. Full article

This study focuses on InGaZnO-based synaptic devices fabricated using reactive radiofrequency sputtering deposition with highly uniform and reliable multilevel memory states. Electron trapping and trap generation behaviors were examined based on current compliance adjustments and constant voltage stressing on the ITO/InGaZnO/ITO memristor. Using O₂ + N₂ plasma treatment resulted in stable and consistent cycle-to-cycle memory switching with an average memory window of ~95.3. Multilevel resistance states ranging from 0.68 to 140.7 kΩ were achieved by controlling the V_RESET within the range of −1.4 to −1.8 V. The modulation of synaptic weight for short-term plasticity was simulated by applying voltage pulses with increasing amplitudes after the formation of a weak conductive filament. To emulate several synaptic behaviors in InGaZnO-based memristors, variations in the pulse interval were used for paired-pulse facilitation and pulse frequency-dependent spike rate-dependent plasticity. Long-term potentiation and depression are also observed after strong conductive filaments form at higher current compliance in the switching layer. Hence, the ITO/InGaZnO/ITO memristor holds promise for high-performance synaptic device applications. Full article

Resistive random-access memory (RRAM) with the ability to store and process information has been considered to be one of the most promising emerging devices to emulate synaptic behavior and accelerate the computation of intelligent algorithms. However, variation and limited resistance levels impede RRAM as a synapse for weight storage in neural network mapping. In this work, we investigate a TaO_x-based RRAM with Al ion local doping. Compared with a device without doping, the device with locally doped Al ion exhibits excellent uniformity and analog characteristics. The operating voltage and resistance states show tighter distributions. Over 150 adjustable resistance states can be achieved through tuning compliance current (CC) and reset stop voltage. Moreover, incremental resistance switching is available under optimized identical pulses. The improved uniformity and analog characteristics can be attributed to the collective effects of reduced oxygen vacancy (Vo) formation energy and weak conductive filaments induced by the local Al ion dopants. Full article

In this paper, a tuneable multilevel data storage bioresistive memory device is prepared from a composite of multiwalled carbon nanotubes (MWCNTs) and egg albumen (EA). By changing the concentration of MWCNTs incorporated into the egg albumen film, the switching current ratio of aluminium/egg albumen:multiwalled carbon nanotubes/indium tin oxide (Al/EA:MWCNT/ITO) for resistive random access memory increases as the concentration of MWCNTs decreases. The device can achieve continuous bipolar switching that is repeated 100 times per cell with stable resistance for 10⁴ s and a clear storage window under 2.5 × 10⁴ continuous pulses. Changing the current limit of the device to obtain low-state resistance values of different states achieves multivalue storage. The mechanism of conduction can be explained by the oxygen vacancies and the smaller number of iron atoms that are working together to form and fracture conductive filaments. The device is nonvolatile and stable for use in rewritable memory due to the adjustable switch ratio, adjustable voltage, and nanometre size, and it can be integrated into circuits with different power consumption requirements. Therefore, it has broad application prospects in the fields of data storage and neural networks. Full article

This study evaluated the memristive switching characteristics of a biomaterial solid polymer electrolyte (SPE) chitosan-based memristor and confirmed its artificial synaptic behavior with analog switching. Despite the potential advantages of organic memristors for high-end electronics, the unstable multilevel states and poor reliability of organic devices must be overcome. The fabricated Ti/SPE-chitosan/Pt-structured memristor has stable bipolar resistive switching (BRS) behavior due to a cation-based electrochemical reaction between a polymeric electrolyte and metal ions and exhibits excellent endurance in 5 × 10² DC cycles. In addition, we achieved multilevel per cell (MLC) BRS I-V characteristics by adjusting the set compliance current (I_cc) for analog switching. The multilevel states demonstrated uniform resistance distributions and nonvolatile retention characteristics over 10⁴ s. These stable MLC properties are explained by the laterally intensified conductive filaments in SPE-chitosan, based on the linear relationship between operating voltage margin (ΔV_switching) and I_cc. In addition, the multilevel resistance dependence on I_cc suggests the capability of continuous analog resistance switching. Chitosan-based SPE artificial synapses ensure the emulation of short- and long-term plasticity of biological synapses, including excitatory postsynaptic current, inhibitory postsynaptic current, paired-pulse facilitation, and paired-pulse depression. Furthermore, the gradual conductance modulations upon repeated stimulation by 10⁴ electric pulses were evaluated in high stability. Full article