Dynamic Operation Research Articles

Decoupling of external and internal dynamics in driven two-level systems

We show how a laser driven two-level system including quantized external degrees of freedom for each state can be decoupled into a set of oscillator equations acting only on the external degrees of freedom with operator valued damping representing the detuning. We give a way of characterizing the solvability of this family of problems by appealing to a classical oscillator with time-dependent damping. As a consequence of this classification we (i) obtain analytic and representation-free expressions for Rabi oscillations including external degrees of freedom with and without an external linear potential, (ii) show that whenever the detuning operator can be diagonalized (analytically or numerically) the problem decomposes into a set of classical equations, and (iii) we can use the oscillator equations as a perturbative basis to describe Rabi oscillations in weak but otherwise arbitrary external potentials. Moreover, chirping of the driving fields phase emerges naturally as a means of compensating the Ehrenfest/mean-value part of the detuning operator's dynamics while in the presence of driving phase noise leads to a stochastic evolution equation of Langevin type. Lastly, our approach is representation free with respect to the external degrees of freedom and as consequence a suitable representation or basis expansion can be chosen a posteriori depending on the desired application at hand. Published by the American Physical Society 2024

Impact of Negative Gate Stress on the Reliability of p-GaN Gate HEMT Devices under Dynamic Switching Operation

Abstract In this study, we investigated the impact of single stress (negative gate bias stress) and combined stress (both negative gate bias stress and drain voltage stress) on the instability of the threshold voltage (VTH) on p-GaN gate HEMT devices during dynamic switching operation. The dynamic VTH shift during pulsed negative gate bias stress was investigated for the first time. The VTH exhibits an initial rapid increase upon the initiation of stress and a slight decrease with increasing stress time. For the applied stresses of VGS = -1/-2/-3 V, the VTH shift values were, respectively, 0.12/0.16/0.19 V at the stress time of 100 s. Under the stress of VGS = -3 V and VDS = 100 V, the shift value of VTH was 0.61 V. After the combined stress, the saturation and off-state leakage currents exhibited a significant reduction, with the latter decreasing by one order of magnitude after the reverse voltage stress. When only a negative gate bias stress is applied, the degradation of the device can recover quickly after the stress is removed. In contrast, the positive shift in VTH caused by the drain-voltage stress is difficult to recover. Furthermore, the mechanisms underlying the VTH shift mentioned above were explored. Under single-stress conditions, the shift in VTH is associated with shallow traps that trap electrons. Meanwhile, under the combined stress, the degradation of Vth was also affected by the deep-level traps.

Thermal analysis of a flat-plate solar collector filled with water under the dynamic operation via a multiparameter sensitivity analysis utilizing the Monte-Carlo method

Thermal analysis of a flat-plate solar collector filled with water under the dynamic operation via a multiparameter sensitivity analysis utilizing the Monte-Carlo method

Application of Central Composite Design for Commercial Laundry Wastewater Treatment by Packed bed Electrocoagulation using sacrificial iron electrodes.

This research paper deals with a novel method utilizing packed bed electrocoagulation (PBEC) comprising of sacrificial iron electrodes and coupled with extracellular polymeric substances (EPS) used as flocculent agents for the treatment of commercial laundry wastewater (LWW). The study employs stainless steel cathodes, graphite anodes, and scrap iron pieces as sacrificial electrodes, ensuring efficient treatment in dynamic batch mode operation with enhanced contact time facilitated by serpentine flow. The initial characteristics of LWW were COD 579 ± 30 mg/L, TSS of 60 ± 10 mg/L, TS of 622 ± 20 mg/L, turbidity of 110 ± 5 NTU, pH of 9 ± 0.5, NPEOs of 570 ±150 μg/L and conductivity of 494 ± 20 mS/cm. The results demonstrate effective removal of turbidity (98 ± 2%), TS (95 ± 3%), COD (89 ± 5%), and NPEOs (53 ± 2%) under optimized current intensity: 2.99A, treatment time: 58.8 minutes and enhanced EPS dose from 5.8 mg/L to 8.0 mg/L. The economic feasibility analysis reveals energy consumption as the primary expenditure, with a treatment cost of 1.20$CAN/m3. This research introduces sustainable treatment for commercial LWW, meeting Quebec's reuse standards, implying reuse potential and responsible wastewater management.

Proposed Multi-ST Model for Collaborating Multiple Robots in Dynamic Environments

Coverage path planning describes the process of finding an effective path robots can take to traverse a defined dynamic operating environment where there are static (fixed) and dynamic (mobile) obstacles that must be located and avoided in coverage path planning. However, most coverage path planning methods are limited in their ability to effectively manage the coordination of multiple robots operating in concert. In this paper, we propose a novel coverage path planning model (termed Multi-ST) which utilizes the spiral-spanning tree coverage algorithm with intelligent reasoning and knowledge-based methods to achieve optimal coverage, obstacle avoidance, and robot coordination. In experimental testing, we have evaluated the proposed model with a comparative analysis of alternative current approaches under the same conditions. The reported results show that the proposed model enables the avoidance of static and moving obstacles by multiple robots operating in concert in a dynamic operating environment. Moreover, the results demonstrate that the proposed model outperforms existing coverage path planning methods in terms of coverage quality, robustness, scalability, and efficiency. In this paper, the assumptions, limitations, and constraints applicable to this study are set out along with related challenges, open research questions, and proposed directions for future research. We posit that our proposed approach can provide an effective basis upon which multiple robots can operate in concert in a range of ‘real-world’ domains and systems where coverage path planning and the avoidance of static and dynamic obstacles encountered in completing tasks is a systemic requirement.

Perforated plate ventilation system and dynamics of infectious respiratory particle transmission

AbstractWith the removal of indoor pollutants and the assurance of air quality emerging as critical research topics, the optimization of the internal environment in offices, where people stay for extended periods, is essential for controlling the spread of infectious respiratory particles. Frequent movements of personnel and the operation of doors and windows within offices significantly impact the mechanisms of droplet transmission, warranting further investigation. This study employs computational fluid dynamics simulations to explore the droplet dispersion characteristics and pollutant removal efficiency of the simplified model of perforated plate ventilation system (PPVS) (the diameter of the air supply openings has been reasonably simplified and uniformly set to 0.02 m) in office settings, as well as the impact of dynamic door operation scenarios on droplet spread and concentrations in breathing zones. To optimize the ventilation system's pollutant removal efficiency, airflow velocities (2.86, 3.18, and 5.00 m/s) are varied, with simulations conducted at the optimal velocity of 3.18 m/s. The effects of continuous door operations, door‐opening directions (towards the office and towards the isolation room), and opening speeds (π/4, π/6, π/8, and π/10 rad/s) are also examined, revealing significant impacts on droplet spread. Results indicate that PPVS effectively reduces indoor pollutant concentrations at all tested airflow velocities, with the optimal speed identified as 3.18 m/s. Additionally, door‐opening direction and speed can significantly influence droplet spread. Opening doors towards isolation rooms at smaller angles (less than 30°) effectively reduces droplet concentrations in personnel breathing zones, thereby mitigating the risk of droplet transmission. Faster door‐opening speeds also contribute to lower droplet concentrations in these zones. This innovative study explores the impacts of PPVS and dynamic door operation dynamics on droplet transmission during respiratory disease outbreaks, providing valuable theoretical insights and technical support for disease prevention and indoor air quality improvement.

Effective practice of innovative leadership with constant changes in social and cultural norms in China

ABSTRACT Maintaining competitiveness is crucial for firms to survive with increasing global competition. One of the key elements for maintaining competitiveness is the development of innovative leadership. The dynamic business operations of firms require leadership capabilities in general, and innovative leadership capabilities in particular, for achieving long-term survivability. This research adopts a holistic approach to investigate multiple factors that influence firm innovation through the effective practice of innovative leadership with constant changes in social and cultural norms in China. We have based our research on observations of three case studies in China and in-depth interviews conducted with key informants. This study contributes to redefining innovative leaders and innovative leadership. We illustrate the contextual factors that influence the adoption and practices of innovative leadership. We also identify crucial aspects of the quality of leaders and the engagement between leaders and subordinates that determine the outcomes of implementing innovative leadership. These aspects influence firms’ innovative capabilities and the effective practice of innovative leadership in the transitional economy of contemporary China.

Adaptive Remaining Capacity Estimator of Lithium-Ion Battery Using Genetic Algorithm-Tuned Random Forest Regressor Under Dynamic Thermal and Operational Environments

The increasing interests and recent advancements in artificial intelligence and machine learning have significantly accelerated the development of novel techniques for the state estimation of batteries in electrified vehicles’ battery management systems (BMSs). Determining the remaining capacity among the several BMS states is crucial for ensuring the safe and stable functioning of an electric vehicle. This paper proposes an adaptive estimator for the remaining capacity of lithium-ion batteries, leveraging a Genetic Algorithm (GA)-tuned random forest (RF) regressor. The estimator is designed to function effectively under varying thermal conditions. The optimization of critical parameters, namely, the number of estimators (n-estimators) and the minimum number of samples per leaf (min-samples-leaf), is a focal point of this study to enhance model accuracy and robustness. The model effectively captures the battery’s dynamic behavior and inherent non-linearity. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) achieved during testing demonstrate promising accuracy and superior prediction. The results demonstrated significant improvements in state of charge (SOC) estimation accuracy. The proposed GA-optimized RF model achieved an MAE of 0.0026 at 25 °C and 0.0102 at −20 °C, showing a 41.37% to 50% reduction in the MAE compared to traditional random forest models without GA optimization. The RMSE was also reduced by 18.57% to 31.01% across the tested temperature range. These improvements highlight the model’s ability to accurately estimate the SOC in varying thermal conditions.

The technique of transforming symptom's symbol into emptiness: A mind-body therapy in the Chinese context.

The technique of transforming symptom's symbol into emptiness (TSSE) is a new mind-body treatment method proposed by Tianjun Liu in 2008. It integrates Qigong and concrete object-image thinking rooted in traditional Chinese culture into modern psychotherapy and proposes that mental and physical problems can be alleviated or eliminated in the process of movement. Accordingly, the therapist needs to guide the client with various symptoms to psychological nothingness where the client cannot see or feel these symptoms, and the purpose of healing can be achieved through the experience of emptiness. TSSE is divided into static and dynamic operations and consists of 10 steps. The static operation includes trio relaxation exercises (the body, breath, and mind), identifying the target symptom, visualizing the target symptom as an object-image, visualizing a symbolic carrier, and filling out record sheet A. The dynamic operation includes trio relaxation exercises again, moving the symbolic object into the carrier, moving the carrier with the symbolic object into psychological nothingness, moving back and assessment, and filling out record sheet B. The effectiveness of TSSE can be evaluated by the therapist's judgment based on the client's performance and by the difference between the symptom impact scores recorded in sheets A and B. TSSE has been proven to be an effective psychosomatic treatment solution by some empirical studies conducted in China. Future research can combine other technologies, such as fMRI and fNIRS, to further explore the potential effective mechanisms of TSSE.

Enhancing the performance of solar-powered EV charging stations using the TOSSI-based CTF technique

In this paper, the design and analysis of a novel solar-powered EV-charging system employing a third-order sinusoidal signal integrator (TOSSI) based-CTF (character of triangular function) is proposed. The TOSSI-based CTF is used to extract fundamental active components by eliminating harmonic distortions from the load currents. This control structure has the unique capability of active current separation, employing simple mathematical operations. Moreover, the response is further refined with the help of optimized gain parameters. The designed system is capable of handling various power quality issues, including harmonic mitigation, current balancing, and power factor improvement. The EV battery is primarily charged by solar-PV power employing a bidirectional DC-DC converter. Alternatively, the EV battery may be charged by the grid supply during the unavailability of sunlight by taking the power quality issues into due consideration. The suggested control topology is used to enhance the dynamic operation of solar-powered EV charging stations experiencing solar power intermittency and variation of load. Using MATLAB, the efficacy of the proposed control topology is tested for different operating scenarios. The suggested control topology is also verified and validated using prototype hardware developed in the laboratory, where the suggested controller has proven its utility over and above existing state-of-the-art controllers in the domain.

Optimal allocation model of port emergency resources based on the improved multi-objective particle swarm algorithm and TOPSIS method

The busy maritime traffic and occurrence of ship accidents have led to a growing recognition of the necessity to maritime emergency resources allocation. The port emergency resource allocation is of significant importance for the maritime safety. This paper presents an optimized allocation model for port emergency resources based on the improved multi-objective particle swarm optimization (IMOPSO). The model introduces the crowding distance and improves the external archive update strategy. The particle inertia weight is adjusted and a dynamic mutation operator is incorporated. The entropy-weighted technique for order preference by similarity to an ideal solution method is also employed to identify the optimal solution. A comprehensive comparison with MOPSO has been presented and discussed. Three metrics of generational distance (GD), spacing (SP) and delta indicator (Δ) were employed for performance evaluation. The results demonstrated that the proposed IMOPSO algorithm exhibited superior performance and robustness, with average values of GD = 0.0386, SP = 0.0023 and Δ = 0.6468 for ZDT test functions. The model efficacy is further validated by a case study of oil spill dispersant configuration at Zhanjiang Port, China. Seven alternative schemes have been obtained, among which the optimal scheme is selected by the entropy-weighted TOPSIS method. The overall cost is potentially to be reduced by approximately 33.03 %. The present study would provide a reference for the water pollutant control and environmental management in port waters.

Temporal asynchrony analysis for dynamic operation of hydraulic-thermal-electricity multiple energy networks based on holomorphic embedding method

Analyzing the operational states of multiple energy networks (MEN) in multi-energy systems is crucial for ensuring system stability. The dynamic operational characteristics of different energy flows pose challenges for computational analysis. Traditional steady-state methods are inadequate for addressing the dynamics of MEN, especially when dealing with temporal discrepancies between hydraulic and thermal flows in thermal networks (TN) and the heterogeneity between TN and electrical networks. Therefore, this paper proposes a novel holomorphic embedding method (HEM) based on multi-stage decomposition method. The developed HEM constructs a time coefficient matrix and utilize inner-outer loop recursion to handle the time lag between thermal flow and hydraulic flow in the TN. Additionally, we reconstruct a holomorphic matrix, integrating hydraulic flow to bridge thermal and electric power flows, thereby improving the operational heterogeneity among different networks. Real-case simulations show that when the Taylor expansion order in HEM is equal to 4, the proposed method achieves a mere 1% discrepancy from actual operational data, enhancing computational efficiency by 60% compared to the Newton-Raphson method. Moreover, in this real-case, the TN exhibits a maximum delay response time of 180s compared to electrical networks. Exploiting this delay time effectively increases renewable energy generation within multi-energy systems by 961.58kW per day.

Enhanced Oxygen Evolution Reaction in Alkaline Water Electrolysis Using Bimetallic NiFe Metal-Organic Frameworks Integrated with Carbon Nanotubes

Hydrogen is considered a very clean and highly available renewable energy resource for the future. Water electrolysis (WE) is a conspicuous promising technology to produce hydrogen on a large scale, without generating carbon dioxide. In this study, we prepared bimetallic NiFe-based metal–organic frameworks (MOFs) integrated with CNTs (NiFe–BTC@CNTs) as highly efficient electrocatalysts for the oxygen evolution reaction (OER) in alkaline water electrolysis. Combining NiFe MOFs and CNTs significantly enhanced the electrochemical performance and structural integrity of the catalyst. The NiFe–BTC@CNT (30 wt.% CNTs) demonstrates a significant low overpotential of 230 mV at 10 mA·cm⁻2, superior catalytic activity with a Tafel slope of 36 mV·dec⁻1, and excellent stability under both constant and dynamic operating conditions. These unique characteristics are attributed to the high surface area and abundant active sites provided by the bimetallic MOF structure, combined with the exceptional electrical conductivity and mechanical strength of CNTs. Furthermore, the integration of CNTs improves the dispersion while preventing the agglomeration of MOF particles, ensuring a more uniform and accessible active surface area. This research highlights the potential of the NiFe–BTC@CNT catalysts as high-performance durable electrocatalysts for sustainable hydrogen production, offering a significant advance in the development of advanced materials for energy conversion and storage applications.

Low-Loaded Catalyst Layers For Proton Exchange Membrane Fuel Cell Dynamic Operation Part 1: Experimental Study

Low-Loaded Catalyst Layers For Proton Exchange Membrane Fuel Cell Dynamic Operation Part 1: Experimental Study

Review Paper on Vibration Damping System Using Pendulum

Abstract: This paper explores the implementation and effectiveness of vibration damping systems utilizing pendulums, focusing on their ability to mitigate unwanted oscillations in various structures. Pendulum-based dampers operate on the principles of inertia and resonance, allowing them to counteract vibrations causedby dynamic loads, seismic events, and machinery operations. The study examines different configurations, including tuned mass dampers (TMDs) and nonlinear pendulum systems, highlighting their design, functionality,and specific applications in fields such as civil engineering, aerospace, and automotive industries. Through analytical modeling and experimental validation, the paper demonstrates the effectiveness of pendulum dampers in enhancing structural stability and occupant comfort. The results indicate significant reductions in vibration amplitude, showcasing the potential of these systems to improve the longevity andsafety of infrastructure. Furthermore, the discussion addresses the advantages of pendulum-based systems over conventional damping methods, emphasizing their adaptability and efficiency.In conclusion, this study underscores the importance of pendulum vibration damping systems as a viable solution for contemporary engineering challenges, paving the way for future research and development in vibration control technologies.

Generating of A Dynamic and Secure S-Box for AES Block Cipher System Based on Modified Hexadecimal Playfair Cipher

In today's digital world, the extensive use of devices, including smartphones, tablets, IoT devices, and the internet, emphasizes the necessity for strong security measures. These measures are essential to safeguard both user data and sensitive government information. As technology advances, the enhancement of cryptographic methods becomes imperative, ensuring alignment with this rapid progression. This paper introduces a novel approach to encrypting classified data using the modified AES cipher system. It employs a dynamic and secure substitution byte operation with a 4×4 matrix, replacing the static and public 16×16 S-box found in the original version of AES. To construct the presented S-box, the system makes a secret key of 56 bytes (4×14 Rounds), where each round of the AES-256 utilizes 4 bytes to generate a 4×4 Hexadecimal Playfair matrix. By altering the S-boxes in each round and block (where each block utilizes AES key expansion to generate a 56-byte secret key for the Playfair matrix), it becomes feasible to produce distinct encrypted blocks even when the original blocks remain identical. In terms of security, the effectiveness of the provided method has been verified by experimental and analytical studies including the Avalanche effect, Balanced output, Hamming distance, and Time complexity. The result shows that the Avalanche effect of the proposed method exceeds 50% and increase the time of brute force attack. Moreover, the proposed algorithm exhibits impressive execution speed, handling approximately 200 KB in just one second.

The Enhancement of Machine Learning-Based Engine Models Through the Integration of Analytical Functions

The integration of analytical functions into machine learning-based engine models represents a significant advancement in predictive performance and operational efficiency. This paper focuses on the development of hybrid approaches to model engine combustion and temperature indices and on the synergistic effects of combining traditional analytical methods with modern machine learning techniques (such as artificial neural networks) to enhance the accuracy and robustness of such models. The main innovative contribution of this paper is the integration of analytical functions to improve the extrapolation capabilities of the data-driven models. In this work, it is demonstrated that the integrated models achieve superior predictive accuracy and generalization performance across dynamic engine operating conditions, with respect to purely neural network-based models. Furthermore, the analytical corrective functions force the output of the complete model to follow a physical trend and to assume consistent values also outside the domain of values assumed by the input features during the training procedure of the neural networks. This study highlights the potential of this integrative approach based on the implementation of the effects superposition principle. Such an approach also allows us to solve one of the intrinsic issues of data-driven modeling, without increasing the complexity of the training data’s collection and pre-processing.

Physiological Predictors of Operator Performance: The Role of Mental Effort and Its Link to Task Performance.

The present study investigated how pupil size and heart rate variability (HRV) can contribute to the prediction of operator performance. We illustrate how focusing on mental effort as the conceptual link between physiological measures and task performance can align relevant empirical findings across research domains. Physiological measures are often treated as indicators of operators' mental state. Thereby, they could enable a continuous and unobtrusive assessment of operators' current ability to perform the task. Fifty participants performed a process monitoring task consisting of ten 9-minute task blocks. Blocks alternated between low and high task demands, and the last two blocks introduced a task reward manipulation. We measured response times as primary performance indicator, pupil size and HRV as physiological measures, and mental fatigue, task engagement, and perceived effort as subjective ratings. Both increased pupil size and increased HRV significantly predicted better task performance. However, the underlying associations between physiological measures and performance were influenced by task demands and time on task. Pupil size, but not HRV, results were consistent with subjective ratings. The empirical findings suggest that, by capturing variance in operators' mental effort, physiological measures, specifically pupil size, can contribute to the prediction of task performance. Their predictive value is limited by confounding effects that alter the amount of effort required to achieve a given level of performance. The outlined conceptual approach and empirical results can guide study designs and performance prediction models that examine physiological measures as the basis for dynamic operator assistance.

Analysis of ventilation and infiltration rates using physics-informed neural networks: Impact of space operation and meteorological factors

This study presents the development and application of a Physics-Informed Neural Network (PINN) model to estimate ventilation and infiltration rates using long-term observation data, addressing the challenge of dynamically varying space operations and meteorological conditions. A central research equestion is: How can we accurately estimate ventilation rates while accounting for these time-varying factors? Traditional tracer gas methods require numerous measurements to accurately characterize air change rates (ACR) under dynamic space operations and varying meteorological conditions. Our PINN model integrates these fluctuating factors, providing a more precise analysis of their transient effects on ACR. We employed Shapley Additive Explanations (SHAP) to interpret the sensitivity and contributions of each influencing factor. Our findings indicate that the state of windows and doors significantly affects spatial operations, while wind speed and direction are the most impactful meteorological factors. The interaction between open windows and doors results in higher ventilation rates compared to their individual effects. Wind-related factors cause ACR variations exceeding 200 %, with the wind direction relative to the office window playing a crucial role. Additionally, external temperature and indoor-outdoor temperature differences show a strong correlation with ACR. However, limitations include the lack of outdoor CO2 measurements and the assumption of uniform indoor CO2 levels, which may affect accuracy. Generalizability is also limited due to the specificity of the space studied. Future work should incorporate outdoor CO2 data and multiple spaces to enhance model applicability. This study contributes to optimizing ventilation strategies for better indoor air quality and energy efficiency.

Effect of Credit Risk Management Techniques on Financial Performance of Deposit Taking SACCOs in Kenya

Financial performance is crucial in the realm of finance. The requirement to explain why two organizations operating in the same environment perform differently is a puzzle, and various financial research works have been devoted to solving this puzzle. Co-operative societies in Kenya are recognized as a significant contributor to national development since their presence can be traced in virtually all sectors of the economy. Although significant progress has been made by the deposit taking SACCOs in Kenya, their performance and sustainability has been debatable. The objective of this research was to assess the effect of financial risk management techniques on financial performance of deposit taking SACCOs in Kenya. The specific objective was to determine the effect of credit risk management techniques on financial performance of deposit taking SACCOs in Kenya. The study was based on Credit Risk Theory. A descriptive survey design was adopted. The population of the study was the 175-deposit-taking SACCOs in Kenya as of 31st December 2022. Due to relative population nature, sampling was not conducted and therefore the study was a census. The respondent was the risk manager of each deposit-taking SACCO. The study utilized primary data. The data was collected using a structured questionnaire. Data was analyzed using descriptive statistics such as the mean and standard deviation and inferential statistics which included correlation and regression analysis. Reliability tests were conducted on the variables each meeting the 0.7reliability threshold with validity tests being confirmed by the Bartlett’s test that confirmed validity of the variables being measured. The regression analysis revealed significant insights into the relationship between financial risk management techniques and the financial performance of deposit-taking SACCOs in Kenya. Credit risk management techniques were found to have a positive impact on financial performance with a regression coefficient of β = 0.238 and a significant value of P <0.005. In conclusion, the study provides compelling evidence of the importance of comprehensive risk management frameworks in driving the financial performance of deposit-taking SACCOs in Kenya. By implementing effective risk management practices tailored to address credit risk, SACCOs can enhance their stability, profitability, and resilience in a dynamic operating environment. These findings have significant implications for policymakers, practitioners, and stakeholders in the SACCO sector, highlighting the need to prioritize investments in risk management capabilities to promote sustainable financial growth. The study recommended that SACCOs should focus on enhancing their credit risk management frameworks. This can be achieved by implementing robust credit risk assessment mechanisms, such as comprehensive credit scoring models and regular analysis of customer credit histories.