Allocation Errors Research Articles

Management of drug supply chain information based on "artificial intelligence + vendor managed inventory" in China: perspective based on a case study.

To employ a drug supply chain information system to optimize drug management practices, reducing costs and improving efficiency in financial and asset management. A digital artificial intelligence + vendor managed inventory (AI+VMI)-based system for drug supply chain information management in hospitals has been established. The system enables digitalization and intelligentization of purchasing plans, reconciliations, and consumption settlements while generating purchase, sales, inventory reports as well as various query reports. The indicators for evaluating the effectiveness before and after project implementation encompass drug loss reporting, inventory discrepancies, inter-hospital medication retrieval frequency, drug expenditure, and cloud pharmacy service utilization. The successful implementation of this system has reduced the hospital inventory rate to approximately 20% and decreased the average annual inventory error rate from 0.425‰ to 0.025‰, significantly boosting drug supply chain efficiency by 42.4%. It has also minimized errors in drug application, allocation, and distribution while increasing adverse reaction reports. Drug management across multiple hospital districts has been standardized, leading to improved access to medicines and enhanced patient satisfaction. The AI+VMI system improves drug supply chain management by ensuring security, reducing costs, enhancing efficiency and safety of drug management, and elevating the professional competence and service level of pharmaceutical personnel.

Multi-Objective Optimization for Thrust Allocation of Dynamic Positioning Ship

Thrust allocation (TA) plays a critical role in the dynamic positioning system (DPS). The task of TA is to allocate the rotational speed and angle of each thruster to generate the generalized control forces. Most studies take TA as a single-objective optimization problem; however, TA is a multi-objective optimization problem (MOP), which needs to satisfy multiple conflicting allocation objectives simultaneously. This study proposes an improved multi-objective particle swarm optimization (IMOPSO) method to deal with the non-convex MOP of TA. The objective functions of reducing the allocation error, and minimizing the power consumption and the tear-and-wear of thrusters under physical constraints, are established and solved via MOPSO. To enhance the global seeking ability, the improved mutation strategy combined with the roulette wheel mechanism is adopted. It is shown through test data that IMOPSO converges better than multi-objective algorithms such as MOPSO and nondominated sorting genetic algorithm II (NSGA-II). Simulations are conducted for a DP ship with two propeller–rudder combinations. The simulation results with the single-objective PSO algorithm show that the proposed IMOPSO algorithm reduces thrust allocation errors in the three directions of surge, sway, and yaw by 48.48%, 39.64%, and 15.02%, respectively, and reduces power consumption by 44.53%, which demonstrates the feasibility and effectiveness of the proposed method.

Hierarchical Control of AC/DC Hybrid Microgrid Based on Primary Model Predictive Optimization and Secondary Switching Control

The fluctuating characteristics of renewable energy generation in hybrid AC/DC microgrids, combined with timevarying loads, can result in high total harmonic distortion (THD) and distorted output voltage and current waveforms. To address these issues, a faster and more comprehensive primary and secondary hierarchical control method is required. In this paper, a faster model predictive optimization algorithm is introduced as a primary control method to predict operational states in advance, maintain a low THD state, and reduce the impact on power quality. Then, a secondary switching control is added to correct frequency and power allocation errors caused by primary control, recover microgrid voltage and frequency to their rated values, and ensure stable reactive power. Finally, simulation and comparison results prove the proposed method's effectiveness and applicability.

Alterations in driving ability and their relationship with morphometric magnetic resonance imaging indicators in patients with amnestic mild cognitive impairment and Alzheimer's disease.

Drivers with dementia are at a higher risk of motor vehicle accidents. The characteristics of driving behaviour of patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) have not been fully elucidated. We investigated driving ability and its relationship with cognitive function and magnetic resonance imaging (MRI) morphometry indicators. The driving abilities of 19 patients with AD and 11 with amnestic MCI (aMCI) were evaluated using a driving simulator. The association between each driving ability parameter and the Mini-Mental State Examination (MMSE) score or voxel-based specific regional analysis system for AD (VSRAD) was assessed. Patients with AD made a significantly higher number of operational errors than those with aMCI in attention allocation in the complex task test (P = 0.0008). The number of operational errors in attention allocation in the complex task test significantly and negatively correlated with MMSE scores in all participants (r = -0.4354, P = 0.0162). The decision time in the selective reaction test significantly and positively correlated with the severity and extent of medial temporal structural atrophy (r = 0.4807, P = 0.0372; r = 0.4862, P = 0.0348; respectively). An increase in the operational errors for attention allocation in the complex task test could be a potential indicator of progression from aMCI to AD. Atrophy of the medial temporal structures could be a potential predictor of impaired judgement in driving performance in aMCI and AD. A driving simulator could be useful for evaluating the driving abilities of individuals with aMCI and AD.

Efficiency of green and low-carbon coordinated development for mega urban agglomerations: an empirical study

As a critical engine for national economic growth, mega urban agglomerations have significant scale effects and economic and environmental spillover effects. This paper aims to study the green and low-carbon coordinated development of mega urban agglomerations to evaluate the country’s level of ecological civilization and its green and low-carbon development. The traditional research on green and low-carbon urban development tends to homogenize the redistribution theme, leading to significant errors in spatial allocation. This results in a lower accuracy of spatial distribution calculations for green development. Additionally, the research is constrained by data precision and methodology, making it challenging to measure the spatial differences in green and low-carbon development within urban clusters at the block level. This limitation hinders the ability to conduct detailed studies on the efficiency variations of green and low-carbon development in urban clusters. To achieve this aim, the study adopts the DPSR framework of the economic, resource, social, and ecological environment complex system and focuses on the Guangdong-Hong Kong-Macao Greater Bay Area in China. The study uses the entropy method, Gini coefficient method, and non-expected output super-efficiency SBM model to analyze the spatial effects and development efficiency of green and low-carbon development in this region from 2006 to 2020. The study results indicate that: (1) the overall level of green and low-carbon development in the Greater Bay Area is on the rise, with Shenzhen, Guangzhou, Foshan, and Zhuhai showing more stable development than other cities. Foreign direct investment and fixed asset investment in science and technology have significantly promoted green and low-carbon development. (2) The spatial differences in the region’s level of green and low-carbon development have narrowed trends, mainly due to differences between regions. However, well-developed cities such as Guangzhou and Shenzhen have taken the initiative to lead the development of other cities, fully leveraging their advantages in science and technology, geographical location, and other resources to promote the improvement of the external orientation of other cities. (3) The overall development efficiency of green and low-carbon in the Greater Bay Area is on the rise, with Guangzhou region showing overall stability, and Shenzhen region and Zhuhai region experiencing multiple ups and downs in their development. The three sub-regions show significant differences, but the balance and coordination of development have significantly improved. Finally, this study provides theoretical support for the future green and low-carbon development of urban clusters. It is advantageous for integrating the mainstream policy analysis framework of environmental economics with the complex adaptive systems of urban clusters. The research expands the boundaries of existing theoretical studies and offers new methodological approaches for interdisciplinary research. The study achieves a balance between the opportunity effects of green and low-carbon development and environmental policy constraints in super large urban clusters, effectively enhancing resource utilization efficiency in these clusters.

Technical note: Comparing three different methods for allocating river points to coarse-resolution hydrological modelling grid cells

Abstract. The allocation of points in a river network to pixels of a coarse-resolution hydrological modelling grid is a well-known issue, especially for hydrologists who use measurements at gauging stations to calibrate and validate distributed hydrological models. To address this issue, the traditional approach involves examining grid cells surrounding the considered river point and selecting the best candidate, based on distance and upstream drainage area as decision criteria. However, recent studies have suggested that focusing on basin boundaries rather than basin areas could prevent many allocation errors, even though the performance gain is rarely assessed. This paper compares different allocation methods and examines their relative performances. Three methods representing various families of methods have been designed: area-based, topology-based and contour-based methods. These methods are implemented to allocate 2580 river points to a 1 km hydrological modelling grid. These points are distributed along the entire hydrographic network of the French southeastern Mediterranean region, covering upstream drainage areas ranging from 5 to 3000 km2. The results indicate that the differences between the methods can be significant, especially for small upstream catchment areas.

A Design Method for Redundant Microthruster Layout With Allocation Error

The microthruster is generally chosen as the actuator of the drag‐free satellite control system, and the layout directly affects the stability of the satellite control. Due to the extremely rigorous objectives of drag‐free control, a design method for the redundant thruster layout is proposed in this paper to improve the control stability of the satellite and reduce the allocation error of the control system. First, the factors corresponding to the allocation errors of redundant thruster layouts are analyzed. And the minimum number of thrusters required for satellite control is determined through the calculation method of thruster redundancy. Then, the design criterion of reducing the allocation error and increasing the satellite control stability is put forward. To demonstrate the effectiveness and feasibility of the design method proposed in this paper, the space gravitational wave detection satellite is taken as a simulation case. Thrust distribution models corresponding to the designed thruster layouts are established, and the results show that a proper layout can not only improve stability but also reduce fuel consumption. The research is looking forward to providing a reference for designing the microthruster layout of the gravitational wave detection satellite in the future.

Three-way evidence theory-based density peak clustering with the principle of justifiable granularity

Clustering by fast search and find of density peaks (DPC) is an effective clustering approach that can find all the cluster centers at once with just one parameter and without iterative processing. However, the cutoff distance, a key parameter of density measurement in the DPC approach, affects the quality of the final clustering results. Its selection relies on experimental experience and lacks of a semantic explanation. Furthermore, the allocation strategy of the traditional DPC approach may cause several points to be assigned incorrectly, leading to subsequent points being assigned incorrectly and ultimately forming continuous allocation errors. To overcome the deficiencies, this paper proposes a novel three-way evidence theory-based density peak clustering with the principle of justifiable granularity (3 W-PEDP). First, the computation of the cutoff distance is converted into the search for nearest neighbors. From the perspective of granular computing, 3 W-PEDP transforms the neighbor selection issue into the construction of justifiable granularity. And the optimal neighbors can be achieved with the construction of coverage and specificity criteria. Second, inspired by three-way clustering, we adopt a two-stage method for sample allocation. On the one hand, for core point allocation, a two-layer nearest neighbor is constructed based on the achieved optimal neighbors. On the other hand, we designed a new evidence mass function to guide us in assigning the remaining points. In this novel evidence mass function, not only the labels of the assigned samples are considered, but also the information of the neighborhoods around the unassigned samples is fused. Finally, we assess the effectiveness of 3 W-PEDP on numerous public synthetic datasets and UCI real-world datasets. Then, detail comparing results with several popular clustering methods are presented. In addition, experimental studies verify the effectiveness of constructing justifiable granularity in selecting the optimal neighbors. The experimental results demonstrate 3 W-PEDP has good adaptability and robustness, which can achieve better clustering performance. Our source code is available at https://github.com/Luyangabc/3W-PEDP.

Reliability Evaluation and Optimization Configuration Method of Network Distribution Side Energy Storage System Based on Machine Vision

To reduce the motor power allocation error and improve voltage weak node conditions, this paper designed a reliability evaluation and optimization configuration method for network distribution side energy storage system based on machine vision. Firstly, the abnormal operation parameters of the energy storage system are collected and transmitted on-site. The operational reliability monitoring data of the distribution network side serves as the foundation for designing the operational reliability evaluation algorithm for the energy storage system on the same distribution network side. This algorithm further enables the optimal configuration of the energy storage system. The experimental comparison results demonstrate that upon implementation of the methods of this paper, the motor power allocation error is less, and the lowest is only 0.17 kW. Meanwhile, the fluctuation is relatively flat, and the range is between 0 kW and 0.46 kW. The improvement of weak voltage nodes is good, the voltage load is significantly decreased, and the minimum voltage load is only 5 kW, which proves that the design method of this paper has a certain application value.

Test mass 6-DOF control allocation based on the null space for space gravitational wave mission

This paper proposes a novel 6-DOF control allocation based on the null space for the capture control of the drag-free spacecraft's test mass. Considering the unconstrained case firstly, an unconstrained time-varying weighted least square control allocation method is presented to eliminate the error of the test mass along every DOF synchronously to reach stable state quickly. Subsequently, an improved null-space-based method is proposed to modify the unconstrained solution of the weighted least square control allocation to meet the physical constraints of actuators. Numerical simulations illustrate the efficiency of the proposed control allocation scheme, which realizes coordinated control allocation of 6-DOF generalized forces, reduces allocation errors and computational complexity, and improves the efficiency of capture control of the test mass.

Uncertainty quantification of additive manufacturing post-fabrication tuning of resonator-based microwave sensors

Reconfigurability, especially in terms of the ability of adjusting the operating frequency, has become an important prerequisite in the design of modern microwave components and systems. It is also pertinent to microwave sensors developed for a variety of applications such as characterization of material properties of solids or liquids. This paper discusses uncertainty quantification of additive-manufacturing-based post-fabrication tuning of resonator-based sensors implemented using a microstrip technology. Therein, the operating frequency is altered by adding metallic patches of a specific size determined by the full-wave electromagnetic (EM) model of the system. The reliability of setting up the center frequency depends on both the accuracy of the patch size (manually cut out of the copper tape), and its allocation with respect to the resonator. A rigorous statistical analysis of the patch size and its allocation errors is carried out, including a quantification of their joint effects on the sensor operating frequency. Furthermore, the analysis of a possibility of compensating the patch size inaccuracies through its appropriate positioning is conducted. The details of the proposed approach are explained using a complementary symmetric split ring resonator (CSSRR)-based sensor designed to operate in X and Ku bands with the tuning range between 10 GHz and 20 GHz. The optimized sensor's fundamental resonant frequency is 9.4 GHz, its exterior size is 25 × 30 mm2, the quality factor of the fabricated sensor is 29, and the sensitivity of the considered design is 1.1 GHz/mm with the measurement error is 0.1 percent. The obtained measurement data are indicative of a practical utility of the additive-manufacturing-based tuning technique, in particular, a possibility of reliable center frequency tuning under mild assumptions on the accuracy of manual preparation of the tuning patches. Furthermore, a practical tuning scheme has been developed and experimentally validated, which allows for a precise allocation of the operating frequency with the error not exceeding 0.01 GHz (or 0.1% in relative terms), all under assumptions of a manual preparation and placement of the tuning patch.

Robust trajectory tracking and control allocation of X-rudder AUV with actuator uncertainty

This paper proposes a robust trajectory tracking control method for an X-rudder autonomous underwater vehicle subject to imprecise model parameters, unknown disturbances, and actuator uncertainty. The control scheme consists of a kinematics controller, dynamics controller, and rudder allocator constructed using backstepping techniques. The kinematics controller employs a line-of-sight guidance law, while the dynamics controller uses integral sliding mode and a Radial Basis Function Neural Network to handle modeling errors and unknown disturbances. Finally, a Recursive Least Square Control Allocation method is developed to minimize the allocation error considering the uncertainty in the control effectiveness matrix by solving a dual second-order cone programming problem. Simulation results demonstrate that the RBFNN-based trajectory tracking method achieves satisfactory performance in the presence of time-varying environmental disturbances, and the Recursive Least Square Control Allocation method effectively deals with actuator uncertainty.

Robust Control Allocation for Space Inertial Sensor under Test Mass Release Phase with Overcritical Conditions

This paper proposes a robust control allocation for the capture control of the space inertial sensor's test mass under overcritical conditions. Uncertainty factors of the test mass control system under the overcritical condition are analyzed first, and a 6-DOF test mass dynamics model with system uncertainty is established. Subsequently, a time-varying weight function is designed to coordinate the allocation of 6-DOF generalized forces. Moreover, a robust control allocation method is proposed to distribute the commanded forces and torques into individual electrodes in an optimal manner, which takes into account the system uncertainties. This method transforms the robust control allocation problem into a second-order cone optimization problem, and its dual problem is introduced to simplify the computational complexity and improve the solving efficiency. Numerical simulation results are presented to illustrate and highlight the fine performance benefits obtained using the proposed robust control allocation method, which improves capture efficiency, increases the security margin and reduces allocation errors.

Mitigating Imbalance of Land Cover Change Data for Deep Learning Models with Temporal and Spatiotemporal Sample Weighting Schemes

An open problem impeding the use of deep learning (DL) models for forecasting land cover (LC) changes is their bias toward persistent cells. By providing sample weights for model training, LC changes can be allocated greater influence in adjustments to model internal parameters. The main goal of this research study was to implement and evaluate temporal and spatiotemporal sample weighting schemes that manage the influence of persistent and formerly changed areas. The proposed sample weighting schemes allocate higher weights to more recently changed areas based on the inverse temporal and spatiotemporal distance from previous changes occurring at a location or within the location’s neighborhood. Four spatiotemporal DL models (CNN-LSTM, CNN-GRU, CNN-TCN, and ConvLSTM) were used to compare the sample weighting schemes to forecast the LC changes of the Columbia-Shuswap Regional District in British Columbia, Canada, using data obtained from the MODIS annual LC dataset and other auxiliary spatial variables. The results indicate that the presented weighting schemes facilitated improvement over no sample weighting and the common inverse frequency weighting scheme for multi-year LC change forecasts, lowering errors due to quantity while reducing overall allocation error severity. This research study contributes to strategies for addressing the characteristic imbalances of multitemporal LC change datasets for DL modeling endeavors.

DWDP-Stream: A Dynamic Weight and Density Peaks Clustering Algorithm for Data Stream

Identifying clusters of arbitrary shapes and constantly processing the newly arrived data points are two critical challenges in the study of clustering. This paper proposes a dynamic weight and density peaks clustering algorithm to simultaneously solve these two key issues. An online–offline framework is used, creating and maintaining micro-clusters in the online phase, and treating the micro-clusters as pseudo-points to form the final cluster in the offline phase. In the online phase, when a new data point is merged into the corresponding micro-cluster, a dynamic weight method is proposed to update the weight of the micro-cluster according to the distance between the point and the center of the micro-cluster, so as to more accurately describe the information of the micro-cluster. In the offline phase, the density peak clustering algorithm is improved, natural neighbors are introduced to adaptively obtain the local density of the data point, and the allocation process is improved to reduce the probability of allocation errors. The algorithm is evaluated on different synthetic and real-world datasets using different quality metrics. The experimental results show that the proposed algorithm improves the clustering quality in both static and streaming environments.

Fast inner-loop control of air-breathing hypersonic vehicles

This paper investigates the inner-loop attitude and velocity control problem for air-breathing hypersonic vehicles subject to practically constrained actuators. Traditional nonlinear dynamic inverse (NDI) is enhanced by an intelligent control allocation (ICA) to realise the low-complexity attitude decoupling. The ICA addresses various characteristics of aerodynamic control surfaces while providing an efficient detecting mechanism for allocation errors. Within the ICA-NDI frame, a finite-time disturbance observer-based terminal sliding mode (FTDO-based TSM) attitude controller regulates Euler angles in the fast manner. This attitude controller employs an improved integral sliding surface containing necessary information from both ICA detection and FTDO estimation, which, therefore, is capable of accommodating constrained control surfaces and rejecting mismatched disturbances. Meanwhile, an adaptive governor adjusts the velocity reference according to the current saturation level of the scramjet fuel-to-air equivalency ratio. The governor is further integrated to a baseline adaptive velocity controller to synthetically handle scramjet input saturations and uncertain factors.

Robots engage face-processing less strongly than humans.

Robot faces often differ from human faces in terms of their facial features (e.g., lack of eyebrows) and spatial relationships between these features (e.g., disproportionately large eyes), which can influence the degree to which social brain [i.e., Fusiform Face Area (FFA), Superior Temporal Sulcus (STS); Haxby et al., 2000] areas process them as social individuals that can be discriminated from other agents in terms of their perceptual features and person attributes. Of interest in this work is whether robot stimuli are processed in a less social manner than human stimuli. If true, this could undermine human-robot interactions (HRIs) because human partners could potentially fail to perceive robots as individual agents with unique features and capabilities-a phenomenon known as outgroup homogeneity-potentially leading to miscalibration of trust and errors in allocation of task responsibilities. In this experiment, we use the face inversion paradigm (as a proxy for neural activation in social brain areas) to examine whether face processing differs between human and robot face stimuli: if robot faces are perceived as less face-like than human-faces, the difference in recognition performance for faces presented upright compared to upside down (i.e., inversion effect) should be less pronounced for robot faces than human faces. The results demonstrate a reduced face inversion effect with robot vs. human faces, supporting the hypothesis that robot faces are processed in a less face-like manner. This suggests that roboticists should attend carefully to the design of robot faces and evaluate them based on their ability to engage face-typical processes. Specific design recommendations on how to accomplish this goal are provided in the discussion.

Path tracking control strategy for off-road 4WS4WD vehicle based on robust model predictive control

With the increasing requirements for vehicle adaptability and maneuverability in various road environments, four-wheel-steering four-wheel-drive (4WS4WD) vehicles have attracted wider attention. This paper presents a robust model predictive control-based strategy for the path tracking of 4WS4WD vehicles considering external disturbances. The strategy combines model predictive control (MPC) and control allocation under an upper–lower structure. The main objective of the present work is to improve the robustness and stability of path tracking by developing an MPC algorithm in the upper layer. The controller design considers general disturbances caused by allocation errors and sudden disturbances caused by an outer force in the offset model. Based on the offset model, a robust MPC control law is obtained by converting the robustness constraints into a linear matrix inequality. The control law is mathematically demonstrated to be stable in multidisturbed conditions via the Lyapunov stability theorem. Through comparison with a similar control algorithm of path tracking and applying it on different uneven ground conditions, the proposed robust algorithm is found to effectively overcome disturbances on the system.

Demystifying the role of anaesthetists in clinical coding in the Australian healthcare system.

Despite the self-evident importance of hospital funding, many anaesthetists remain unsure of exactly how their daily work relates to hospital reimbursement. A lack of awareness of the nuances of the Australian hospital activity-based funding system has the potential to affect anaesthetic department reimbursement and thus resourcing. Activity-based funding relies on clinical coders reviewing clinical documentation and quantifying the care given to a patient during an admission. Errors in funding allocation may arise when there is a disconnect between the work performed and the information coded. In anaesthesia, there are several factors impeding this process, including clinical understanding of coding, system setup and coders' understanding of anaesthesia. This article explores these factors from the clinical anaesthetist's point of view and suggests solutions, such as awareness and education, clinician-coder cooperation and redesign of documentation systems at a systems level that anaesthetic departments can incorporate.

A domain-based and reactive COLAV method with a partially COLREGs-compliant domain for ASVs operating in confined waters

This article presents a collision avoidance (COLAV) method for autonomous surface vessels operating in confined waters with other vessels, using a novel target ship (TS) domain. The domain is implemented as part of a reactive COLAV method pipeline through the use of control barrier functions (CBFs) to avoid domain violation. A geometric interpretation of a vesselto-vessel encounter is used to classify the encounter type with respect to the COLREGs, and thereby which rules apply. Subsequently, a domain is assigned to the TS, where the domain parameters are rule-specific. In the domain design, the static environment is also considered, where an estimate for the available maneuverable space is calculated and applied when determining the size of each TS domain, in order to achieve a distance between vessels that is both safe and feasible for each encounter. Additionally, domains are assigned to static obstacles based on map data and lidar data. Once all domains for target ships and static obstacles are determined, CBFs are formulated based on the range to and velocity towards each domain. The set of CBFs are applied as inequality constraints in a quadratic program minimizing the vessel’s thrust allocation error. The efficiency and completeness of the novel TS domain are demonstrated through an extensive simulation study. The COLAV method as a whole is demonstrated through both complex simulations with multiple maneuvering vessels, and full-scale experiments with a radar- and lidar-based target tracking system. The proposed COLAV method shows compliance with COLREGs section II.