Control Model Research Articles

A Qualitative Scoping Review of Community Firearm Violence in Low-Income Settings.

Firearm violence is a public health crisis in the United States that disproportionately impacts community members in low-income areas who witness and experience violence and violent victimization at elevated rates compared to other socioeconomic groups, often as result of community disinvestment and systemic racism (Smith et al., Soc Sci Med 246:112587, 2020). While quantitative reviews of firearm violence and related factors exist, a review of qualitative methods and findings regarding exposure to firearm violence has not yet been conducted. This scoping review sought to address a gap in the literature by summarizing the findings of qualitative studies on community firearm violence in low-income settings in the U.S. EBSCO databases, Criminal Justice Abstracts, National Criminal Justice Reference Service Abstracts, ProQuest, and PsycINFO were searched for studies that described the firearm related experiences of individuals and families in low-income communities. Thirty studies met the criteria for review. Findings were situated within the Centers for Disease Control and Prevention's (CDC) Social-Ecological Model as a framework for prevention (CDC, The social-ecological model: a framework for prevention, https://www.cdc.gov/violenceprevention/about/social-ecologicalmodel.html , 2018; Dahlberg and Krug, World Report on violence and health, World Health Organization, Geneva, 2002). A critique of the literature, as well as implications and future directions of findings, are discussed. This study may inform future research questions and programs that center the voices of those most impacted by firearm violence.

Status of infection prevention and control in Cameroon healthcare facilities: lessons learned from the WHO COVID-19 scorecard tool under the hierarchy of control model

BackgroundInfection prevention and control (IPC) helps prevent disease transmission in healthcare facilities. There is a dearth of information on the implementation of IPC during the COVID-19 outbreak in Cameroon using the recommended WHO COVID-19 IPC scorecard tool. The present study assessed healthcare facilities’ compliance to IPC by continuous assessments, with an evaluation of the tool using the hierarchy of control theory. MethodsThis cross-sectional study was conducted in the 10 administrative regions of Cameroon by evaluating healthcare facilities prioritized by the Ministry of Public Health as high-risk facilities between March 2020 and November 2023. Comparisons were made regarding the facilities’ ownership, level and status. Results2,188 assessments from 1,358 healthcare facilities were collected. The median IPC scores at each evaluation were between the intermediate and advanced level, with a bias linked with decreasing selection of facilities. However, only 172 (13%) healthcare facilities achieved advanced IPC score (≥75%). Higher IPC scores were found in hospitals (p<0.001) and in private facilities (p=0.02). Predictors of good IPC compliance were hospital (OR=3.7, CI: 1.4-9.8) and private facility (OR=2.3, CI: 1.6-3.3). The tool met the five domains of the hierarchy of control model. ConclusionRepeated IPC assessments using recommended tools contribute to a better compliance of IPC by healthcare facilities in resources constrained settings.

Mechanism and kinetics of efficient removal of as from a high arsenic-bearing ZnSO4 solution using ultrasound enhanced oxygen

The traditional lime method for arsenic (As) removal from high arsenic-bearing ZnSO4 solution has disadvantages such as high cost, large amount of wet residue, significant secondary hazards, and long process flow. This study developed a new technology for efficient As removal using ultrasound enhanced oxygen. The effects of zinc roasting dust (ZRD) addition, reaction temperature, oxygen flow rate, and ultrasound power on As removal efficiency were investigated. Under the optimal conditions, the removal rate of As reached 97.90 %. The1O2, O2∙- and •OH radicals generated during the ultrasound-enhanced oxygen process significantly enhance the oxidation efficiency of As(III) and Fe2+. Ultrasound can also refine particle size and improve the removal efficiency of As. Compared with traditional lime method, the reaction time and wet residue amount have been reduced by 40 % and 70.37 %, respectively. Field emission scanning electron microscopy (FE-SEM-EDS), Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS) analyses indicate that As in ZnSO4 solution was primarily removed through by Fe(OH)3 colloidal adsorption and amorphous FeAsO4. Kinetic analysis revealed that the neutralization and As removal process using ZRD under ultrasound and oxygen action conforms to the internal diffusion control model. Ultrasound-enhanced oxygen As removal can reduce the reaction activation energy from 26.94 to 21.02 kJ/mol, effectively lowering the reaction barrier for As removal and demonstrating faster reaction rates under ultrasound conditions. Ultrasound-enhanced oxygen removal of As from high arsenic-bearing ZnSO4 solution provides an efficient and low-cost potential solution for enterprises.

Fuzzy Multi-Agent Simulation for Collective Energy Management of Autonomous Industrial Vehicle Fleets

This paper presents a multi-agent simulation implemented in Python, using fuzzy logic to explore collective battery recharge management for autonomous industrial vehicles (AIVs) in an airport environment. This approach offers adaptability and resilience through a distributed system, taking into account variations in AIV battery capacity. Simulation scenarios were based on a proposed charging/discharging model for an AIV battery. The results highlight the effectiveness of adaptive fuzzy multi-agent models in optimizing charging strategies, improving operational efficiency, and reducing energy consumption. Dynamic factors such as workload variations and AIV-infrastructure communication are taken into account in the form of heuristics, underlining the importance of flexible and collaborative approaches in autonomous systems. In particular, an infrastructure capable of optimizing charging according to energy tariffs can significantly reduce consumption during peak hours, highlighting the importance of such strategies in dynamic environments. An optimal control model is established to improve the energy consumption of each AIV during its mission. The energy consumption depends on the speed, as demonstrated via numerical simulations using realistic data. The speed profile of each AIV is adjusted according to the various constraints within an airport. Overall, the study highlights the potential of incorporating adaptive fuzzy multi-agent models for AIV energy management to boost efficiency and sustainability in industrial operations.

Cost-effective and optimal control analysis for mitigation strategy to chocolate spot disease of faba bean

Faba bean is one of the most important grown plants worldwide for human and animal. Despite its various importance, the productivity of faba bean has been constrained by several biotic and abiotic factors. Many faba bean pathogens have been reported so far, of which the most important yield limiting disease is Chocolate Spot Disease (Botrytis fabae). The dynamics of disease transmission and decision-making processes for intervention programs for disease control are now better understood through the use of mathematical modeling. In this paper a deterministic mathematical model for Chocolate Spot disease (CSD) on faba bean plant with an optimal control model was developed and analyzed to examine the best strategy in controlling CSD. The optimal control model is developed with three control interventions, namely prevention (u1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$u_{1}$$\\end{document}), quarantine (u2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$u_{2}$$\\end{document}) and chemical control (u3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$u_{3}$$\\end{document}). The Pontryagin’€™s maximum principle isused to derive the Hamiltonian, the adjoint variables, the characterization of the controls and the optimality system. A cost-effective approach is chosen from a set of possible integrated strategies using the incremental cost-effectiveness ratio (ICER). The forward-backward sweep iterative approach is used to run numerical simulations. We obtained the Hamiltonian, the adjoint variables, the characterization of the controls and the optimality system. The numerical results demonstrate that each integrated strategy can reduce the diseases within the specified period. However due to limited resources, an integrated strategy prevention and uprooting was found to be a best cost-effective strategy to combat CSD. Therefore, attention should be given for the integrated cost-effective and environmentally eco-friendly strategy by stake holders and policy makers to control CSD and disseminate the integrated intervention to the farmers in order to fight the spread of CSD in the Faba bean population and produce the expected yield from the field.

A Modified TCP BBR to Enable High Fairness in High-Speed Wireless Networks

Wireless networks, especially 5G and WiFi networks, have made great strides in increasing network bandwidth and coverage over the past decades. However, the mobility and channel conditions inherent to wireless networks have the potential to impair the performance of traditional Transmission Control Protocol (TCP) congestion control algorithms (CCAs). Google proposed a novel TCP CCA based on Bottleneck Bandwidth and Round-Trip propagation time (BBR), which is capable of achieving high transmission rates and low latency through the estimation of the available bottleneck capacity. Nevertheless, some studies have revealed that BBR exhibits deficiencies in fairness among flows with disparate Round-Trip Times (RTTs) and also displays inter-protocol unfairness. In high-speed wireless networks, ensuring fairness is of paramount importance to guarantee equitable bandwidth allocation among diverse traffic types and to enhance overall network utilization. To address this issue, this paper proposes a BBR–Pacing Gain (BBR–PG) algorithm. By deriving the pacing rate control model, the impact of pacing gain on BBR fairness is revealed. Adjusting the pacing gain according to the RTT can improve BBR’s performance. Simulations and real network experiments have shown that the BBR–PG algorithm retains the throughput advantages of the original BBR algorithm while significantly enhancing fairness. In our simulation experiments, RTT fairness and intra-protocol fairness were improved by 50% and 46%, respectively.

Research on the Risk of Drilling Phases Based on the Development Model of Shallow-Water Subsea Trees

China is actively advancing offshore oil and gas exploration and development, focusing on addressing the technical challenges associated with resource extraction in shallow waters. The shallow-water subsea tree development model has gradually been applied in such environments, alleviating some construction difficulties. However, it still poses well control risks that require systematic analysis and quantitative evaluation. Given that the blowout preventer (BOP) is located on the platform and the shallow-water subsea tree is only used during certain drilling stages, this study divided the drilling process into two phases: the first three sections and the fourth section. Based on the “man–machine–material–environment” analytical framework and an improved system-theoretic process analysis (STPA), a control model for the construction phases was developed. Fault tree analysis (FTA) was then employed to identify comprehensively the potential risks from the platform to the wellbore in both phases. Subsequently, the decision-making trial and evaluation laboratory (DEMATEL) method were used to assess quantitatively the well control risks. Using the average weight as the evaluation criterion, high-risk factors exceeding the average weight in each phase were identified. The results indicate that in the shallow-water subsea tree development model, well control risks in the first three drilling sections primarily stem from human errors and equipment failures, while risks in the fourth section are mainly caused by damage to the subsea tree itself. The identified risk factors provide a theoretical basis for enhancing well control safety management in the shallow-water subsea tree development model.

Closed-loop workload input–output control of production systems: A hybrid simulation study

Workload Control (WLC) is a method of adjusting the overall workload in the production system by controlling the input of production orders and the output of produced items. It contributes to the predictability of lead times and more accurate delivery date commitments in make-to-order manufacturing. Input control relates to order release and output control to capacity adjustment. In a recent previous work, a novel approach to integrated input–output control has been proposed: a dynamic closed-loop model where automatic feedback control is added and where order release and capacity decisions are based on the observed shop floor state. In this paper, this approach is further investigated in a more realistic hybrid simulation setting, combining discrete-time controllers and a discrete-event model of a manufacturing shop. The hybrid model was applied to two different systems: an elementary 4-machine shop floor (as a proof of concept) and a more complex real system, a manufacturing cell of cylinder liners. The results show that simultaneous input–output control is effective and enables the systems to maintain WIP balance, absorb demand fluctuations, and effectively reject disturbances. The simulations of the complex system showed that it might be advantageous to provide more local or more global information to the controllers, depending on the variability of the processing times and on the managerial focus. Closed-loop discrete-event models for WLC production control are unprecedented. Also, a simultaneous input and output control setting is much less reported in WLC literature than settings with input control only (i.e., order release only). This paper contributes in both ways.

Deep predictive data representation model control for photovoltaic maximum power point tracking under partial shading conditions

As deep learning progresses in recent years, deep neural networks (DNNs) have penetrated applications in various industries. The global maximum power point (GMPP) tracking issue exists in photovoltaic (PV) systems. This study proposes a deep predictive data representation model control (PDRC)-based PV GMPP tracking method. The PDRC approach establishes a data representation control model centered on deep prediction by extracting the data features such as the current, voltage, and duty cycle of PV systems controllers on a time series basis. The proposed PDRC method can receive signals of voltage and current, then output duty cycle control signals. Thereby PDRC method can control PV output power to track GMPP efficiently. Finally, the proposed PDRC approach is evaluated with the perturbation observation method (P&O), the improved particle swarm algorithm (PSO-P&O), and the modified cuckoo algorithm (CS-P&O) to compare the PV GMPP tracking performance. In the simulation experiments, the PDRC approach increases the average tracking efficiency over the comparison algorithms by at least 7.729 % and decreases the average tracking time by at least 0.0155 s under partial shading conditions (PSCs). In the physical simulation validation, the proposed PDRC approach possesses minimal power perturbation and increases the average tracking efficiency over the comparison algorithms by at least 0.407 %.

Using Biochar to Improve Soil Fertility and Productivity of Drought-Tolerant Sweet Potatoes in Thanh Hoa Coastal Sandy Soil

The coastal area of Thanh Hoa province has a large proportion of sandy soil and is suffering negative impacts from climate change that affect crop productivity. The aim of this study is to investigate the use of biochar to improve the soil fertility and productivity of drought-tolerant sweet potatoes. The effect of biochar contents on the productivity of three sweet potatoes VC 04-24, KL C3, and VC 68-2 in the coastal sandy soil area of Thanh Hoa province was examined. The results indicated that the yield of sweet potatoes was the highest when applying five tons of biochar per hectare, equivalent to using 10 tons of manure. With the same biochar content, the productivity and other factors related to the product quality of these sweet potatoes such as the number of tubers per plant and the average mass of tuber were the highest. In detail, the estimated commercial productivity of VC 04-24, KL C3, and VC 68-2 were 25.46, 24.92, and 21.76 ton/ha, respectively. With the least significant difference at the probability of 0.05% (LSD0.05), the VC 04-24 and KL C3 showed high productivity. The economic efficiency of the sweet potatoes-growing model at Hai An commune, Tinh Gia district, Thanh Hoa province showed that the productivity of the model was 24 tons/ha, higher than the actual productivity of the control model (20.2 ton/ha). The productivity and the economic efficiency of the model were 18.8% and 31.38% higher than those of the control model, respectively. In addition, the test results showed that using 5-6 ton/hectare of biochar can improve the physical and chemical properties of soils.

Hysteresis Compensation and Trajectory Tracking Control Model for Pneumatic Artificial Muscles

The optimum performance position control of pneumatic artificial muscles (PAM) is restricted by their in-built hysteresis and nonlinearity. The hysteresis is usually depicted by a phenomenological model, while the model mentioned above always only describes the hysteresis phenomenon under certain conditions. Thus, the universality of the compensator is due to its weakness in handling disparate outside conditions. Our research employs the FN–QUPI (feedforward neural network–quadratic unparallel Prandtl–Ishlinskii) model to depict the phenomenon of pressure-displacement hysteresis in PAMs. This model has high-precision expression and generalization ability for the PAM hysteresis phenomenon. According to this, an inverse model of the QUPI operator is established as a feedforward control while combining with the feedback control of incremental PID-type iterative learning. The results show that due to the hysteresis of PAM, the compound control of feedforward control and iterative learning has better tracking performance than the ordinary PID compound control in terms of convergence rate and stability. According to the mean absolute error (MAE) and root mean square error (RMSE) of the tracking process, it can be seen that the control model can achieve accurate nonlinear compensation, and the control system shows excellent robustness to different input signals.

Modeling and Regulation of Dynamic Temperature for Layer Houses Under Combined Positive- and Negative-Pressure Ventilation.

The environmental control of layer houses with multi-tiered cage systems is influenced by factors such as the structure of the henhouses and the heat dissipation of the flock, leading to low precision and large fluctuations in temperature control. Based on a new combined positive- and negative-pressure ventilation (CPNPV) mode, a dynamic temperature model is constructed. Additionally, a temperature control method for a layer house is designed using a variable universe fuzzy PID control algorithm (VFPID). First, based on the principles of energy and mass balance, and by decoupling the relationship between positive- and negative-pressure ventilation volumes, a dynamic temperature model for layer houses under CPNPV was established. Then, the PID parameters and the proportional relationship between positive- and negative-pressure ventilation were optimized through fuzzy rules, and a proportional exponential function was introduced to adjust the scaling of the universe, enabling fine-tuned control. Finally, a temperature control model for the layer house was built using Simulink. The results show that the coefficients of determination (R2) of the constructed dynamic temperature models are between 0.79 and 0.88, respectively, indicating high accuracy. The designed VFPID method outperformed traditional on-off control and improved control precision by 20-23.53% and 10.34-22.22% compared with PID control and fuzzy PID(FPID) control methods, respectively. This study provides new insights for the development of environmental control equipment and precise environmental regulation of layer houses.

A comparative study of the established methods and evaluation of rat trauma models.

Scientific animal models are indispensable for studying trauma repair. This work aimed at establishing a more scientific rat trauma model by studying different rat trauma models caused by different trauma numbers, locations, and trauma attachment tension unloaders and rat age. A four-trauma self-upper, lower, left and right control model; a two-trauma self-trauma bare and ring control model; and a young and old rat trauma model were created to evaluate the condition of these traumas. In the four-trauma self-control model, the healing status of the upper proximal cephalic trauma was better than that of the lower proximal caudal trauma, whereas there was no significant difference between the left and right trauma. The healing rate and postwound condition of the trauma with a ring control in the two-trauma model were better than those of the bare side. The healing speed of the old rats was slower, and the amount of extracellular matrix in the subcutaneous tissue after healing was significantly lower than that of the young rats. The double trauma with a ring is a more scientific and reasonable experimental model. There is a significant difference between young and old rats in the wound healing process. Therefore, the appropriate age of the rats should be selected according to the main age range of the patients with similar conditions in the clinical setting being mimicked.

Composition and Injection Rate Co-Optimization Method of Supercritical Multicomponent Thermal Fluid Used for Offshore Heavy Oil Thermal Recovery

Supercritical multicomponent thermal fluid injection is a new technology with great potential for offshore heavy oil thermal recovery. In the process of thermal fluid generation, the reaction conditions including temperature, pressure, and the organic mass concentration in the reaction material will significantly affect its composition and injection rate and will further affect the thermal recovery and development quality of heavy oil. However, there is a lack of relevant research on the variation rules and control methods of the composition and injection rate of supercritical multicomponent thermal fluids, resulting in a lack of technical mechanisms for effective optimization. To fill this gap, a reaction molecular dynamics simulation method was used to simulate thermal fluid generation under different temperatures, pressures, and organic mass concentrations. The changes in thermal fluid composition and yield with reaction conditions were studied, and a control model of thermal fluid composition and yield was established. The proportional relationship between the thermal fluid generation scale of an offshore heavy oil platform and the simulated thermal fluid generation scale is analyzed, and a collaborative optimization method of thermal fluid composition and injection rate in field applications is proposed. The results show the following: (1) The higher the mass concentration of organic matter, the higher the content of supercritical carbon dioxide and supercritical nitrogen in thermal fluids, and the lower the content of supercritical water. (2) The higher the temperature and pressure, the higher the thermal fluid yield, and the higher the organic mass concentration, the lower the thermal fluid yield. (3) The component fitting model conforms to the power function relationship, and the coefficient of determination R2 is greater than 0.9; the yield fitting model conforms to the modified inverse linear logarithmic function relationship, the determination coefficient R2 is greater than 0.8, and the fitting degree is high. (4) The ratio between the actual injection rate of thermal fluids in the mine field and the molecular simulated thermal fluid yield is the ratio of organic matter mass in the platform thermal fluid generator and organic matter mass in the simulated box. (5) Based on the composition and yield control model, combined with the simulation of the ratio relationship between yield and injection rate in the field, a collaborative optimization method of thermal fluid composition and injection rate was established. The research results can provide an effective technical method for predicting, controlling, and optimizing the composition and injection rate of supercritical multicomponent thermal fluids.

Connectome-based Predictive Models of General and Specific Cognitive Control.

Cognitive control, the ability to adapt thoughts and actions to shifting contexts and goals, is composed primarily of three distinct yet interrelated components: Inhibition, Shifting, and Updating. While prior research has examined the nature of different cognitive components as well as their inter-relationships, fewer studies examined whole-brain connectivity to predict individual differences for the three cognitive components and associated tasks. Here, using the Connectome-based Predictive Modelling (CPM) approach and open-access data from the Human Connectome Project, we built brain network models to successfully predict individual performance differences on the Flanker task, the Dimensional Change Card Sort task, and the 2-Back task, each putatively corresponding to Inhibition, Shifting, and Updating. We focused on grayordinate fMRI data collected during the 2-Back tasks after confirming superior predictive performance over resting-state and volumetric data. High cross-task prediction accuracy as well as joint recruitment of canonical networks, such as the frontoparietal and default-mode networks, suggest the existence of a common cognitive control factor. To directly investigate the relationships among the three cognitive control components, we developed new measures to disentangle their shared and unique aspects. Our analysis confirmed that a shared control component can be well predicted from functional connectivity patterns densely located around the frontoparietal, default-mode and dorsal attention networks. In contrast, the Shifting-specific and Inhibition-specific components exhibited lower cross-prediction performance, indicating their distinct and specialized roles. Notably, the Updating-specific component showed significant cross-prediction with the general control factor, suggesting its central role in cognitive control. Given the limitation that individual behavioral measures do not purely reflect the intended cognitive constructs, our study demonstrates the need to distinguish between common and specific components of cognitive control.

Functional bias of contractile control in mouse resistance arteries

Constrictor agonists set arterial tone through two coupling processes, one tied to (electromechanical), the other independent (pharmacomechanical) of, membrane potential (VM). This dual arrangement raises an intriguing question: is the contribution of each mechanism (1) fixed and proportionate, or (2) variable and functionally biased. Examination began in mouse mesenteric arteries with a vasomotor assessment to a classic Gq/11 (phenylephrine) or Gq/11/G12/13 (U46619) agonist, in the absence and presence of nifedipine, to separate among the two coupling mechanisms. Each constrictor elicited a concentration response curve that was attenuated and rightward shifted by nifedipine, findings consistent with functional bias. Electromechanical coupling preceded pharmacomechanical, the latter’s importance rising with agonist concentration. In this regard, ensuing contractile and phosphorylation (CPI-17 & MYPT1 (T-855 & T-697)) measures revealed phenylephrine-induced pharmacomechanical coupling was tied to protein kinase C (PKC) activity, while that enabled by U46619 to PKC and Rho-kinase. A complete switch to pharmacomechanical coupling arose when agonist superfusion was replaced by pipet application to a small portion of artery. This switch was predicted, a priori, by a computer model of electromechanical control and supported by additional measures of VM and cytosolic Ca2+. We conclude that the coupling mechanisms driving agonist-induced constriction are variable and functionally biased, their relative importance set in accordance with agonist concentration and manner of application. These findings have important implications to hemodynamic control in health and disease, including hypertension and arterial vasospasm.

Stereoselectivity Control Interplay in Racemic Lactide Polymerization by Achiral Al-Salen Complexes.

The origin of stereocontrol in ring opening polymerization (ROP) of racemic lactide (rac-LA) promoted by achiral aluminium-based catalysts has been explained through DFT calculations combined with a molecular descriptor (%VBur) and the activation strain model (ASM-NEDA) analysis. The proposed chain end control (CEC) model suggests that the ligand framework adopts a chiral configuration mimicking the enantiomorphic site control (ESC) while also incorporating control of the last inserted monomer unit. It is found that the ligand wrapping mode around the aluminium centre is dictated by the monomer configuration (R,R-LA and S,S-LA). A good correlation with experimental data is achieved only when accounting for the ligand dynamic features and its steric influences, as highlighted by %VBur steric maps and ASM-NEDA analysis. Understanding the ESC and CEC interplay is an important target for obtaining stereoselective ROP polymerization for the synthesis of biodegradable materials with tailored properties.

Revolutionizing Textile Manufacturing: Sustainable and Profitable Production by Integrating Industry 4.0, Activity-Based Costing, and the Theory of Constraints

The textile industry, a cornerstone of daily life and a highly globalized sector, faces significant environmental challenges due to its high water and energy consumption and extensive chemical usage. This study proposes a comprehensive green production planning and control model integrating Industry 4.0 concepts, activity-based costing (ABC), and the theory of constraints (TOC). The model utilizes mathematical programming to optimize product mix, maximize profitability, and minimize environmental impact. It leverages real-time sensing technologies and ERP systems to facilitate waste recovery, reduce carbon emissions, and achieve energy savings. Various carbon emission cost models, including continuous and discontinuous tax functions, are explored to balance corporate profitability with environmental sustainability. The findings demonstrate the model’s potential in optimizing resource utilization, reducing the environmental footprint, and enhancing profitability.

Real-time predictive control assessment of low-water head hydropower station considering power generation and flood discharge

In the real-time operation of cascade reservoirs, when the discharge flow of the upstream power station changes frequently, the downstream power station with a low head and small storage capacity has to adjust the gate or turbine frequently to keep the water level safe. This paper proposes a real-time optimal scheduling model based on model predictive control theory(MPC), considering the interaction between power generation and flood discharge. Firstly, the correlation analysis is carried out between the outflow of the Zhentouba hydropower station(ZTB) and the inflow of the Shaping II Hydropower Station(SP), and the spatio-temporal hydraulic connection between the ZTB and SP is obtained. The fuzzy relationship between tail water level and discharge flow is accurately described using numerical simulation, considering the interaction between power generation and discharge. Secondly, based on the precise description of inflow and outflow, a high-precision water level rolling prediction model is constructed using the water balance principle. Finally, based on the MPC, the real-time control model of SP is constructed. The results show that the water level process is steadier, with fewer gate adjustments. Compared with the observed number of gate adjustments in 2020, the number of reservoir gate adjustments after model optimization is reduced by 73.26%. It improves the operation efficiency and safety of the hydropower station and provides a guidance basis for the optimal operation of the SP.

Lexical inhibition after semantic violations recruits a domain-general inhibitory control mechanism.

Language processing is incremental. As language signals-for example, words in a sentence-unfold, humans predict and activate likely upcoming input to facilitate comprehension. Prediction not only accelerates understanding but also prompts reassessment in the case of prediction error, fostering learning and refining comprehension skills. Therefore, it is paramount to understand what happens when linguistic predictions are violated-for example, when a sentence ends in an unpredicted word. One theory, which we test here, is that the originally predicted word is actively inhibited after semantic violations. Furthermore, we tested whether this purported lexical inhibition process is achieved by a domain-general mechanism-that is, one that also inhibits other processes (e.g., movement). We combined a semantic violation task, in which highly constrained sentences primed specific words but sometimes continued otherwise, with a motoric stop-signal task. Across two experiments, semantic violations significantly impaired simultaneous action-stopping. This implies that lexical and motor inhibition share the same process. In support of this view, multivariate decoding of electroencephalographic recordings showed early overlap in neural processing between action-stopping (motor inhibition) and semantic violations (lexical inhibition). Moreover, a known signature of motor inhibition (the stop-signal P3) was reduced after this initial overlap period, further suggesting the presence of a bottleneck due to shared processing. These findings show that semantic violations trigger inhibitory processing and suggest that this lexical inhibition recruits a domain-general inhibitory control mechanism. This provides a new perspective on long-standing debates in psycholinguistics, extends the range of a well-characterized cognitive control mechanism into the linguistic domain, and offers support for recent neurobiological models of domain-general inhibitory control. (PsycInfo Database Record (c) 2024 APA, all rights reserved).