Speed Metrics Research Articles

Total harmonic distortion analysis of inverter fed induction motor drive using neuro fuzzy type-1 and neuro fuzzy type-2 controllers

Introduction. When the working point of the indirect vector control is constant, the conventional speed and current controllers operate effectively. The operating point, however, is always shifting. In a closed-system situation, the inverter measured reference voltages show higher harmonics. As a result, the provided pulse is uneven and contains more harmonics, which enables the inverter to create an output voltage that is higher. Aim. A space vector modulation (SVM) technique is presented in this paper for type-2 neuro fuzzy systems. The inverter’s performance is compared to that of a neuro fuzzy type-1 system, a neuro fuzzy type-2 system, and classical SVM using MATLAB simulation and experimental validation. Methodology. It trains the input-output data pattern using a hybrid-learning algorithm that combines back-propagation and least squares techniques. Input and output data for the proposed technique include information on the rotation angle and change of rotation angle as input and output of produced duty ratios. A neuro fuzzy-controlled induction motor drive’s dynamic and steady-state performance is compared to that of the conventional SVM when using neuro fuzzy type-2 SVM the induction motor, performance metrics for current, torque, and speed are compared to those of neuro fuzzy type-1 and conventional SVM. Practical value. The performance of an induction motor created by simulation results are examined using the experimental validation of a dSPACE DS-1104. For various switching frequencies, the total harmonic distortion of line-line voltage using neuro fuzzy type-2, neuro fuzzy type-1, and conventional based SVMs are provided. The 3 hp induction motor in the lab is taken into consideration in the experimental validations.

Photonic neural networks and optics-informed deep learning fundamentals

The recent explosive compute growth, mainly fueled by the boost of artificial intelligence (AI) and deep neural networks (DNNs), is currently instigating the demand for a novel computing paradigm that can overcome the insurmountable barriers imposed by conventional electronic computing architectures. Photonic neural networks (PNNs) implemented on silicon integration platforms stand out as a promising candidate to endow neural network (NN) hardware, offering the potential for energy efficient and ultra-fast computations through the utilization of the unique primitives of photonics, i.e., energy efficiency, THz bandwidth, and low-latency. Thus far, several demonstrations have revealed the huge potential of PNNs in performing both linear and non-linear NN operations at unparalleled speed and energy consumption metrics. Transforming this potential into a tangible reality for deep learning (DL) applications requires, however, a deep understanding of the basic PNN principles, requirements, and challenges across all constituent architectural, technological, and training aspects. In this Tutorial, we, initially, review the principles of DNNs along with their fundamental building blocks, analyzing also the key mathematical operations needed for their computation in photonic hardware. Then, we investigate, through an intuitive mathematical analysis, the interdependence of bit precision and energy efficiency in analog photonic circuitry, discussing the opportunities and challenges of PNNs. Followingly, a performance overview of PNN architectures, weight technologies, and activation functions is presented, summarizing their impact in speed, scalability, and power consumption. Finally, we provide a holistic overview of the optics-informed NN training framework that incorporates the physical properties of photonic building blocks into the training process in order to improve the NN classification accuracy and effectively elevate neuromorphic photonic hardware into high-performance DL computational settings.

Leveraging an optimized deep belief network based on a developed version of artificial rabbits optimization for breast tumor diagnosis

Artificial intelligence can be extremely useful for identifying the location and size of a tumor by analyzing medical breast cancer images. This can help doctors to make more accurate diagnoses and plan more effective treatment strategies. It can also be used to analyze the texture and shape of tumors, which can provide important information about their malignancy and potential for metastasis. This information can be used to guide treatment decisions and monitor the progress of therapy. This paper presents a study designed to leverage an optimized deep belief network (DBN) based on a developed version of artificial Rabbits optimization (ARO) to diagnose breast tumors. The proposed model was designed and evaluated using data from the MIAS breast cancer database in reference. The performance of the proposed model was compared with other existing models for accuracy, speed, and sensitivity metrics. Results show that the proposed model can achieve better accuracy and faster performance than existing deep-learning techniques when applied to the classification of breast tumors. Furthermore, the proposed model exhibited improved sensitivity in detecting malignant tumors compared to existing models.

REVOLUTIONIZING COGNITIVE ASSESSMENT: THE POWER OF DIGITAL BIOMARKERS AND ECOLOGICALLY VALID METHODS

Abstract Digital technologies have allowed neuropsychologists to utilize process-based approaches to assess cognitive function and change. Traditional methods of neuropsychological assessment involve obtaining single scores (often composites) to represent cognitive function. However, modern process-based techniques involve the use of digital data collection to acquire insights into both the manner and reasoning behind an individual’s performance. The present study will share insights into the task development and validation of active and passive digital cognitive assessments from two longitudinal studies of aging: (1) a study of non-dementia participants (N=322), ultra-brief mobile cognitive tests were administered up to six times per day for two weeks; and (2) a neuro-exergaming study (N=31) enrolling patients with MCI and their co-residing study partner, where participants used a study-provided tablet to complete a neuro-exergaming protocol and digital cognitive assessments (e.g., digital trailmaking and eStroop). We analyzed both traditional and process-based metrics in two studies, including mean response time and patterns of inaccuracy. Study one found significant correlations between MCI status and metrics for survey completion time, working memory, and processing speed. Study two found weak to moderate correlations between cognitive function measures from digital and paper-based neuropsychology. Our findings suggest that remote self-administered digital measures can detect subtle cognitive changes. The event-level data, which is collected through a process-based approach, has enormous untapped potential for discovering and confirming novel digital biomarkers of cognitive health and progression that can be monitored beyond clinical settings.

HACR-MDL: HANDWRITTEN ARABIC CHARACTER RECOGNITION MODEL USING DEEP LEARNING

Abstract. Despite the enormous effort and prior research, Arabic handwritten character recognition still has a deep, wide-ranging, and untapped scope for study owing to the enormous challenges faced in this research area. The reason for such challenges is that the Arabic script comprises 28 alphabets, each of which can be written in two to four different forms depending on where it appears in a word—beginning, middle, end, or isolated. The Convolutional Neural Network (CNN or ConvNet) is a subtype of neural network that is commonly used in image classification, speech recognition, video processing, object detection, and segmentation because its built-in convolutional layer reduces the high dimensionality of images without losing significant information. Hence, the scope of this study is to examine the classification performance of various deep CNN models on offline handwritten Arabic character recognition. Based on the experimental comparative studies, this research proposes a Handwritten Arabic Character Recognition Model using Deep Learning (HACR-MDL), a modified CNN model. The proposed model is trained and tested using the AHCD dataset achieving an accuracy of 98.54%. The results achieved showed that HACR outperformed the recent research offline handwritten Arabic character recognition in terms of model complexity, speed, model parameters, and performance metrics.

Evolvable Hardware Based Optimal Position Control of Quadcopter

Trading off performance metrics in control design for position tracking is unavoidable. This has severe consequences in mission-critical systems such as quadcopter applications. The controller area and propulsion energy are conflicting design parameters, whereas the reliability and tracking speed are related metrics to be optimized. In this research, a switching-based position controller was co-simulated with the quadcopter model. Performance analysis of the Field Programmable Gate Array (FPGA)-based controller validates a better scheme for tracking speed, propulsion energy, and reliability optimization under similar error performance. To improve the computation power and controller area, the dynamic partial reconfiguration(DPR) approach has been adapted and implemented on FPGA using the Vivado Integrated Development Environment (IDE), where a ranking-based approach brings into action either proportional derivative, sliding mode, or model predictive controllers for each dimension of position tracking. It is verified by analyzing the cumulative tracking speed, reliability, controller area, and propulsion energy metrics that the proposed controller can optimize all these metrics within three successive iterations of tracking either in the same direction or in any combination of directions. Concerning the implementation results of the controller with the switching-based controller, there is 6 % computation power and 30 % resource savings due to DPR .

Poster Symposium: Leveraging Digital Technology to Capture Highly Nuanced Neuropsychological Behavior: Realizing the Vision of the Boston Process Approach to Neuropsychological Assessment

Summary AbstractNeuropsychological test scores tap a number of underlying cognitive abilities. Examining the means by which omnibus scores are achieved provides considerable information regarding brain - behavior relationships and a richer context for clinical interpretation. This examination is the core tenant of the Boston Process Approach. Nonetheless, quantification of errors and process can be time consuming. However, the development of digital assessment technology is able to meet this challenge. For example, using a digital clock drawing test, previously unappreciated behaviors are now easily quantified and can dissociate between dementia and MCI subtypes. Research presented in this paper session provides additional insight into how digital technology can be leveraged as a powerful tool to capture behavior that, until recently, was either impractical or impossible to measure.The assessment of graphomotor behavior can be challenging. In the context of a large-scale normative neuroimaging study, Colcombe and colleagues have engineered a digital Archimedes Spiral Test that includes measures of speed variability, rotational smoothness, and goodness of fit to the model. The temporal and spatial precision of these metrics is impressive. This research shows that, age predicted greater variable drawing speed, greater tracing errors, reduced rotational smoothness, and increasing drawing speed variability.MacKay-Brandt and colleagues present data using a digital version of the Trail Making Tests (TMT), one of the most commonly administered neuropsychological tests. This research provides a panel of new parameters to evaluate TMT performance, including detailed speed metrics with spatial segregation to parse circle connection time from dwell time within a circle. Interestingly, dwell time, rather than traditional total time to completion, was the strongest predictor of differences between conditions and across age. Baliga and colleagues present data on a protocol of novel cancellation tests. Memory clinic patients were classified into groups presenting with mild dementia, mild cognitive impairment, and those who were cognitively normal. Digital parameters of interest included correct responses, commissions, mean intra-response latency, and mean apple pencil touch. Using these parameters, significant between group differences were obtained. Moreover, logistic regression analyses were able to classify patients into their respective groups.It is well understood that paragraph recall tests assess a variety of underlying cognitive abilities. Andersen and colleagues studied Logical Memory recall in the Long-Life Family Study and extracted linguistic parameters that included word count, grammatical features (e.g., prepositions), and content words related to specific categories (e.g., work). Participants were classified as cognitively normal or impaired. Analyses identified distinct linguistic features of free recall that predicted cognitive status.Hershkovich and colleagues extract measured pauses and speech frequency behavior also from a paragraph recall test. A combination of paragraph recall pause duration, speech frequency parameters, and demographic variables were able to classify older adults with and without cognitive compromise. Collectively, the evidence provided in this series of papers demonstrates that digital platforms can capture and quantify highly nuanced neurocognitive behavior to enrich information available to researchers and clinicians for analysis and clinical formulations. Digital assessment technology holds promise to realize the vision of the Boston Process Approach and revolutionize neuropsychological assessment.

Traffic safety performance evaluation in a connected vehicle environment with queue warning and speed harmonization applications

With the increased adoption of connected vehicle (CV) technologies, safety information is becoming increasingly available to drivers. This study investigates three main questions (1) Do CV-based traffic management applications improve safety on roadways with existing infrastructure-based traffic management systems? (2) Can combining two CV technologies have a greater impact on safety than a single CV technology? and (3) Do geometric and traffic composition factors impact the efficiency of CV technologies? We applied a rarely-used CV dataset and conducted a comprehensive simulation analysis of varying conditions and CV penetration rates that studies have not considered. Two CV applications (queue warning and speed harmonization) implemented in the Intelligent Network Flow Optimization experiment in Seattle, WA were evaluated. Results showed that driver safety performance, based on speed metrics (standard deviation and percentage of extreme values) improved under the CV driving conditions. Combining conventional variable speed limit systems with queue warnings also improved safety for CV drivers. Furthermore, the implementation of a single CV application (queue warning) showed positive changes in the aforementioned speed metrics, congestion mitigation, and reduced conflicts. With the two CV applications combined, no significant differences were observed. Additional tests investigated the impacts of lane changes and roadway attributes on safety in the CV environment.

Evaluation of a novel real-time adaptive assist-as-needed controller for robot-assisted upper extremity rehabilitation following stroke.

Rehabilitation therapy plays an essential role in assisting people with stroke regain arm function. Upper extremity robot therapy offers a number of advantages over manual therapies, but can suffer from slacking behavior, where the user lets the robot guide their movements even when they are capable of doing so by themselves, representing a major barrier to reaching the full potential of robot-assist rehabilitation. This is a pilot clinical study that investigates the use of an electromyography-based adaptive assist-as-needed controller to avoid slacking behavior during robotic rehabilitation for people with stroke. The study involved a convenience sample of five individuals with chronic stroke who underwent a robot therapy program utilizing horizontal arm tasks. The Fugl-Meyer assessment (FM) was used to document motor impairment status at baseline. Velocity, time, and position were quantified as performance parameters during the training. Arm and shoulder surface electromyography (EMG) and electroencephalography (EEG) were used to assess the controller's performance. The cross-sectional results showed strong second-order relationships between FM score and outcome measures, where performance metrics (path length and accuracy) were sensitive to change in participants with lower functional status. In comparison, speed, EMG and EEG metrics were more sensitive to change in participants with higher functional status. EEG signal amplitude increased when the robot suggested that the robot was inducing a challenge during the training tasks. This study highlights the importance of multi-sensor integration to monitor and improve upper-extremity robotic therapy.

A computerized assessment tool for the upper extremities motor performance in individuals with Parkinson's disease

ObjectiveComputerized assessment of Parkinson's disease (PD) patients' motor performance can pave the way to subject-specific design of the rehabilitative interventions. The objective is to propose a computerized assessment tool for upper extremities motor performance in PD. It is based on kinematic indices and a virtual reality exergaming system. The reliability, construct and discriminative validity of the proposed tool were investigated. MethodsA set of virtual tasks was designed and integrated to the therapeutic programs of the participants including uni/bimanual, in-phase/anti-phase hand movements on the less/more affected sides. 33 persons with PD participated in a test re-test study to determine the reliability, discriminative validity on the medication states, and validity of the kinematic indices. Clinical assessments, including Box and Block Test, 9-Hole Peg Test, Movement Disorders Society motor section of the Unified PD Rating Scale (MDS-UPDRS III), were performed for validity assessment. ResultsTask type has a significant impact on the validity of the indices. Reaching tasks were shown to be superior. Besides, it was demonstrated that a minimum of 3 repeats is needed for reliable results in the proposed setting. Jerk indices, curvature, and spectral speed metrics lack either reliability or validity. Temporal speed metrics have superior reliability (Intraclass Correlation Coefficient ICC ≥ 0.9) and validity (Pearson ≥ 0.6) in reaching tasks. ConclusionTemporal speed metrics and point reaching virtual tasks were shown to have high potentials for objective assessment of upper extremity (UE) motor performance in PD in the context of a virtual reality exergaming rehab program.

Time-series clustering for pattern recognition of speed and heart rate while driving: A magnifying lens on the seconds around harsh events

Driving pattern recognition has been applied for the purposes of driving styles identification and harsh driving events detection. However, the evolution of driving behavior around and especially before such events has not been investigated at a microscopic level. The objective of this research is to reveal existing driving patterns around harsh events at the driving ‘pulse’ level i.e. a few seconds before and after the event. For that purpose, a time-series clustering approach is applied on speed and heart rate metrics of individual drivers using data collected from a large naturalistic driving study. Results show that there are distinct speed patterns before harsh braking, harsh acceleration, and harsh cornering events. A deceleration is identified shortly before most harsh acceleration and cornering events, which possibly indicates reckless behavior, i.e. drivers not dedicating enough time to smoothly brake before cornering, or of a brief ‘decision-making’ moment before the harsh manoeuvre. On the contrary, speed seems to be steady before harsh braking events. Regarding heart rate, the analysis revealed certain patterns only after raw data were cleansed and filtered. These patterns may show increasing, decreasing or variable heart rate trends, which may correspond to different stress patterns of drivers around harsh events. Finally, we introduce the concept of driving pattern consistency, which can reveal the share of individual drivers that follow the same harsh event pattern. It is indicated that more than half of the drivers are not consistent, suggesting that driving patterns around harsh events may be more context-related than driver personality-related.

New Highly Portable Simulator (SECMA) Based on Virtual Reality for Teaching Essential Skills in Minimally Invasive Surgeries

This study presents a new minimal access surgery training system, SECMA, and its constructive validation to determine its usefulness for training basic laparoscopic skills. SECMA is an affordable, highly portable, mobile virtual reality training tool for laparoscopic techniques that integrates the Oculus Quest with a mechanical interface for surgeon simulation of forceps using the hand controllers of these devices. It allows the execution of structured activities (supported by virtual scenarios simulating operating rooms developed in Unity), performance evaluation, and real-time data capture. Two experiments were carried out: 1) coordination; and 2) capture and transport, with a total of 21 individuals divided into two groups: a novice group (inexperienced) of 10 participants and an expert group (>100 endoscopic procedures) of 11 participants. Total task time score, right-hand speed, path length, and other metrics from several consecutive runs on the simulator were compared between experts and novices. Data automatically recorded by SECMA during the experiments were analyzed using hypothesis tests, linear regressions, analysis of variance, principal component analysis, and machine learning-supervised classifiers. In the experiments, the experts scored significantly better than the novices in all the parameters used. The tasks evaluated discriminated between the skills of experienced and novice surgeons, giving the first indication of construct validity for SECMA.

Variation in flight characteristics associated with entry by eagles into rotor‐swept zones of wind turbines

Automated curtailment of wind turbines can reduce fatality rates of wildlife but the resulting increased number of curtailments can reduce power generation. Tailoring curtailment criteria for each individual turbine could reduce unnecessary curtailment, yet it is unknown whether the risk to wildlife varies among turbines. We demonstrate turbine‐specific variation in the speed, altitude, approach angle and distance metrics associated with entry by eagles into rotor‐swept zones. Our results thus illustrate the potential value of turbine‐specific curtailment criteria to reduce fatality rates of wildlife at wind energy facilities.

Improving learning science: Evaluating and comparing academic interventions using measures of learning speed

AbstractMeasures of learning speed (i.e., the amount of learning/cumulative time learner spends engaged in an intervention) are rarely included in research designed to evaluate and compare academic interventions. We build a case that includes analyses of learning speed metrics in academic intervention research can provide more useful information for prevention and remediation efforts, deter researchers from drawing misleading data‐based conclusions, and promote scientific parsimony. Research is reviewed where the same learning data is analyzed across different metrics (i.e., same amount of learning, different measurement scales) including learning per event, learning speed, and growth. Patterns are analyzed and examples are provided that demonstrate how altering metrics can influence relative effectiveness conclusions and applied recommendations when researchers compare two or more interventions, conduct component analysis or optimization studies, and apply meta‐analytic techniques. Opportunity costs for learners, educators, and learning scientists that may be caused by failure to measure learning speed are identified and analyzed. Our review and analyses support the conclusion that, in most instances, researchers can better inform prevention and remediation efforts by including precise measures of learning speed as dependent variables when they evaluate and compare interventions designed to enhance academic skills. Limitations and challenges associated with measuring learning speed are described and directions for future research are provided.

LDRepFM: A Real-Time End-to-End Visible and Infrared Image Fusion Model Based on Layer Decomposition and Re-Parameterization

Visible (VIS) and infrared (IR) image fusion (VIF) is a technique used to synthesize the fused image of high visual perception. Existing fusion methods typically work by discovering the commons underlying the two modalities and fusing them in the common space. However, these methods often ignore the modality differences such as fuzzy details in the IR image and their well-designed architectures also lead to slow fusion speed. To address these issues, we propose a real-time end-to-end VIF model based on layer decomposition and re-parameterization (LDRepFM). This model is composed of a Layer Decomposition Guidance Network (LDGNet) and a Re-parameterization Fusion Network (RepFNet). Firstly, the LDGNet is used to alleviate the visual quality degradation of the fused image by decomposing the IR image into structural layer and fuzzy layer. Secondly, in order to achieve a favorable trade-off between the fusion speed and evaluation metrics, the RepFNet is utilized to decouple the training-time multi-branch and inference-time plain architecture. Thirdly, the structural layer that has been decomposed by LDGNet is utilized in constructing the guidance fusion loss, which is aimed at optimizing RepFNet. Finally, experiments conducted on the publicly available TNO, RoadScene, M3FD, and RegDB datasets demonstrate the performance of the proposed method to be comparable to the state-of-the-art in terms of both visual effect and quantitative metrics. The code is publically available at https://github.com/luming1314/LDRepFM.

Assessment of condition-induced changes on the ocular surface using novel methods to assess the tear film dynamics and the lipid layer

ObjectivesTo assess the effects of different condition-induced changes on current and novel ocular surface metrics. MethodsEighty-four healthy volunteers (22.4 ± 2.6 years) participated in this study. The ocular surface and tear film response to (1) computer use, (2) contact lens insertion and (3) tear film instillation during computer use with contact lenses were assessed. Current metrics included the ocular surface disease (OSDI) questionnaire, 5-item dry eye questionnaire (DEQ-5), bulbar redness, tear meniscus height (TMH) and non-invasive keratograph break-up time (NIKBUT). Novel metrics included the lipid layer thickness obtained from the intensity of the reflected Placido disk and the speed of tear film particles post-blink. ResultsHigher dry eye symptoms, TMH and bulbar redness, and lower values in metrics related to the intensity of the Placido disk pattern and to particle speed were found after the computer reading task (p < 0.036). When a contact lens was fitted, lower TMH, NIKBUT and particle speed metrics were obtained (p < 0.044). Mixed ANOVA revealed that artificial tears significantly ameliorated the effect of computer reading on OSDI, DEQ-5, NIKBUT, metrics related to the intensity of the Placido disk pattern and metrics related to particle speed (p < 0.033). ConclusionsComputer use and contact lens wear worsened dry eye signs and symptoms, but artificial tears ameliorated this effect. Newly developed methods can serve as a tool to detect changes in the tear film triggered by different ocular surface-disturbing conditions.

Spiral Tracing in Alzheimer’s Disease: Less Precise but not Slower

AbstractBackgroundIn the search for novel Alzheimer’s disease (AD) biomarkers, motor functional outcome measures are potential candidates, as motor impairments can precede cognitive dysfunction. Whereas most motor behavioral research in Mild Cognitive Impairment (MCI) and AD has focused on gait function, fine motor skill is affected as well. Here, we use a digitized version of the Archimedes spiral test to assess a variety of speed and accuracy metrics of fine motor function in MCI and AD compared to cognitively normal (CN) individuals.Method46 CN, 23 MCI, and 17 AD participants traced an Archimedes spiral 3 times using a digital pen tablet. Spiral outcome measures included: path length, movement time, tracing speed (average and variance), radial distance (i.e., total deviation from the spiral template; average and variance), number of template crossings, and return movements.ResultLinear regression analysis adjusted for age and sex showed that those with AD had significantly larger radial distance than CN (p = .0056, eta^2 = .096). No other significant group differences were observed (all p&gt;0.14, all eta^2&lt;.028).ConclusionResults indicate that individuals with AD are less precise when tracing an Archimedes spiral, despite adequate movement speed and steadiness. These results support a wider examination of fine motor dysfunction in AD as an inexpensive and easily collectable biomarker in AD.

Does clinically measured walking capacity contribute to real-world walking performance in Parkinson's disease?

ObjectiveThe study examined how clinically measured walking capacity contributes to real-world walking performance in persons with Parkinson's disease (PD). MethodsCross-sectional baseline data (n = 82) from a PD clinical trial were analyzed. The 6-Minute Walk Test (6MWT) and 10-Meter Walk Test (10MWT) were used to generate capacity metrics of walking endurance and fast gait speed, respectively. An activity monitor worn for seven days was used to generate performance metrics of mean daily steps and weekly moderate intensity walking minutes. Univariate linear regression analyses were used to examine associations between each capacity and performance measure in the full sample and less and more active subgroups. ResultsWalking capacity significantly contributed to daily steps in the full sample (endurance: R2=.13, p < .001; fast gait speed: R2=.07, p = .017) and in the less active subgroup (endurance: R2 =.09, p = .045). Similarly, walking capacity significantly contributed to weekly moderate intensity minutes in the full sample (endurance: R2=.13, p < .001; fast gait speed: R2=.09, p = .007) and less active subgroup (endurance: R2 = .25, p < .001; fast gait speed: R2 =.21, p = .007). Walking capacity did not significantly contribute to daily steps or moderate intensity minutes in the more active subgroup. ConclusionsWalking capacity contributed to, but explained a relatively small portion of the variance in, real-world walking performance. The contribution was somewhat greater in less active individuals. The study adds support to the idea that clinically measured walking capacity may have limited benefit for understanding real-world walking performance in PD. Factors beyond walking capacity may better account for actual walking behavior.

Shuttle-Based Storage and Retrieval System: A Literature Review

While the e-commerce logistics industry is developing rapidly, its sustainable development has received certain attention. The ultimate goal of sustainable development is to achieve common, coordinated, fair, efficient, and multi-dimensional development. To promote the sustainable development of logistics, the automation technology of warehousing is undoubtedly an excellent breakthrough, since the automation technology can not only make the warehousing system efficient and with a low-error rate, but also affect the energy consumption of the warehousing system. This paper studies the Shuttle-Based Storage and Retrieval System (SBS/RS) in automated warehousing. Moreover, the paper classifies the existing literature into three categories: Physical design (including depth, configuration, and number of tiers); control strategy (including scheduling rules, storage strategies, scheduling command, and interference); and performance evaluation (including throughput, time, and energy). These categories are all factors that warehouse designers must consider when designing a system. Finally, this paper proposes future research directions for SBS/RS: Adding speed metrics, limiting buffer size, targeting space constraints, applying four-way shuttles, and increasing the carrying capacity of a single machine.

Vascular Cognitive Impairment After Mild Stroke: Connectomic Insights, Neuroimaging, and Knowledge Translation.

Contemporary stroke assessment protocols have a limited ability to detect vascular cognitive impairment (VCI), especially among those with subtle deficits. This lesser-involved categorization, termed mild stroke (MiS), can manifest compromised processing speed that negatively impacts cognition. From a neurorehabilitation perspective, research spanning neuroimaging, neuroinformatics, and cognitive neuroscience supports that processing speed is a valuable proxy for complex neurocognitive operations, insofar as inefficient neural network computation significantly affects daily task performance. This impact is particularly evident when high cognitive loads compromise network efficiency by challenging task speed, complexity, and duration. Screening for VCI using processing speed metrics can be more sensitive and specific. Further, they can inform rehabilitation approaches that enhance patient recovery, clarify the construct of MiS, support clinician-researcher symbiosis, and further clarify the occupational therapy role in targeting functional cognition. To this end, we review relationships between insult-derived connectome alterations and VCI, and discuss novel clinical approaches for identifying disruptions of neural networks and white matter connectivity. Furthermore, we will frame knowledge translation efforts to leverage insights from cutting-edge structural and functional connectomics research. Lastly, we highlight how occupational therapists can provide expertise as knowledge brokers acting within their established scope of practice to drive substantive clinical innovation.