Performance Improvement Research Articles

Positive modulation of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors differentially alters spatial learning and memory in juvenile rats younger and older than three weeks.

Remarkable performance improvements occur at the end of the third postnatal week in rodents tested in various tasks that require navigation according to spatial context. While alterations in hippocampal function at least partially subserve this cognitive advancement, physiological explanations remain incomplete. Previously, we discovered that developmental modifications to hippocampal glutamatergic α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors in juvenile rats was related to more mature spontaneous alternation behavior in a symmetrical Y-maze. Moreover, a positive allosteric modulator of AMPA receptors enabled immature rats to alternate at rates seen in older animals, suggesting an excitatory synaptic limitation to hippocampal maturation. We then validated the Barnes maze for juvenile rats in order to test the effects of positive AMPA receptor modulation on a goal-directed spatial memory task. Here we report the effects of the AMPA receptor modulator, CX614, on spatial learning and memory in the Barnes maze. Similar to our prior report, animals just over 3 weeks of age display substantial improvements in learning and memory performance parameters compared to animals just under 3 weeks of age. A moderate dose of CX614 enabled immature animals to move more directly to the goal location, but only after 1 day of training. This performance improvement was observed on the second day of training with drug delivery or during a memory probe trial performed without drug delivery after the second day of training. Higher doses created more search errors, especially in more mature animals. Overall, CX614 provided modest performance benefits for immature rats in a goal-directed spatial memory task.

Approach of establishing a high-resolution shading occupant behavior model in the office building

Occupant behavior in buildings is becoming a critical factor in determining indoor comfort and energy consumption performance. The diversity and uncertainty of occupant behavior affect the implementation of occupant-centric building operations. However, existing shading behavior models do not account for various shading states (fully open, fully closed, or partially open, significantly deviating from real-world scenarios), accessibility of shading behavior (ASB), room configurations (single-person rooms and multi-person rooms), occupancy, and other factors, resulting in lower model accuracy. This study aims to address these limitations by proposing a high-resolution shading occupant behavior (HRSOB) model that integrates occupancy considerations, accessibility of shading behavior, and data mining techniques. This HRSOB model can classify various shading occupant behaviors and be integrated into the optimization controls of occupant-centric buildings. The utilization of Markov chain occupancy models significantly improves the accuracy of predicting stochastic shading behavior. The ASB classification method analyzes the features of the occupant shading adjustment behavior (hereafter referred to as shading behavior) and objectively reflects shading behavior patterns using spatial features, thereby enhancing the resolution of the shading occupant behavior model. Data-driven techniques, such as feature selection and soft voting, enable the analysis, training, and application of shading behavior data, and rigorous evaluation and validation of the HRSOB model are conducted. The results demonstrated a 10.92% improvement in the predictive performance of the HRSOB model compared with the typical shading occupant behavior (SOB) model.

Thermal Impact Investigation of Shaded Opaque Walls in Bangkok

The effectiveness of overhang shading devices in mitigating solar radiation on opaque walls is crucial for passive building design in tropical regions. However, opaque walls commonly are side-lined in shading design due to a preoccupation with insulation properties, appearance, and aesthetics. Unprotected walls exposed to solar radiation maximize heat gain, while shaded walls decrease external heat gain and indoor temperature, resulting in improved indoor thermal comfort and well-being. This research employs EnergyPlus and SPSS to investigate the optimal design of overhang shading devices for opaque walls in Bangkok, Thailand. The investigation is divided into two phases: (1) determination of the optimal depth-to-space ratio of overhang devices based on a parametric study of 12 overhang configurations; (2) an evaluation of the ratio in reducing solar radiation. The findings demonstrate a relationship between the device depths and the wall solar radiation and indoor temperature. The optimum depth-to-space ratio is suggested as 1:1.15, which can lower the annual average indoor temperature by 3.53% (1.18°C) compared to the benchmark design. Deeper shading device depths than the optimum depth-to-space ratio can be applied, with a slight improvement in protective performance (and commensurately increased costs). This research underscores the importance of overhang shading devices as a performance-improving strategy for buildings in the tropical climate of Thailand.

Vision transformer with masked autoencoders for referable diabetic retinopathy classification based on large-size retina image.

Computer-aided diagnosis systems based on deep learning algorithms have shown potential applications in rapid diagnosis of diabetic retinopathy (DR). Due to the superior performance of Transformer over convolutional neural networks (CNN) on natural images, we attempted to develop a new model to classify referable DR based on a limited number of large-size retinal images by using Transformer. Vision Transformer (ViT) with Masked Autoencoders (MAE) was applied in this study to improve the classification performance of referable DR. We collected over 100,000 publicly fundus retinal images larger than 224×224, and then pre-trained ViT on these retinal images using MAE. The pre-trained ViT was applied to classify referable DR, the performance was also compared with that of ViT pre-trained using ImageNet. The improvement in model classification performance by pre-training with over 100,000 retinal images using MAE is superior to that pre-trained with ImageNet. The accuracy, area under curve (AUC), highest sensitivity and highest specificity of the present model are 93.42%, 0.9853, 0.973 and 0.9539, respectively. This study shows that MAE can provide more flexibility to the input image and substantially reduce the number of images required. Meanwhile, the pretraining dataset scale in this study is much smaller than ImageNet, and the pre-trained weights from ImageNet are not required also.

Experimental investigation of generating superheated steam using a parabolic dish with a cylindrical cavity receiver: A case study

Abstract This study uses solar energy to convert steam from a saturated to a superheated state using a solar steam superheater system. This collector system comprised a parabolic dish with a cylindrical cavity. The parabolic dish is of 2 m diameter and 0.83 m focal distance, covered with a reflective surface made of 1,283 rectangular pieces of mirrors, each of 3.5 cm × 4 cm that were fixed in place with glue type (FnTai). The receiver is a stainless-steel cylindrical cavity having a diameter of 17 cm and a length of 25 cm. A helical copper coil with a diameter of 10 mm and a length of 6 m was inserted inside the cylindrical cavity. The experimental work utilized saturated steam produced separately from an auxiliary electric boiler system, which was made along with other system components. A 0.011 kg/s of saturated steam at a temperature of 112°C enters the copper coil and is heated by the solar radiation reflected by the parabolic dish onto the receiver. As a result, the temperature of the steam is increased to 169.5°C at the receiver outlet. It was found that the collector efficiency is 55.6%. In addition, the convection and radiation heat losses are 12.14 and 10.98%, respectively. Also, the heat losses of (spillage, reflection, and conduction) were estimated to be 21.18%. The mass flow rate and pressure of the saturated steam from the boiler and entering the receiver affected the superheated steam production process. The process of superheating the steam, coupled with the subsequent improvement in thermal performance, indicates an increased efficiency of the collector. This is achieved by boosting the generation of useful heat and mitigating heat losses.

Calibration of Hybrid-Maize Model for Simulation of Soil Moisture and Yield in Production Corn Fields

Model calibration is essential for acceptable model performance and applications. The Hybrid-Maize model, developed at the University of Nebraska-Lincoln, is a process-based crop simulation model that simulates maize growth as a function of crop and field management and environmental conditions. In this study, we calibrated and validated the Hybrid-Maize model using soil moisture and yield data from eight commercial production fields in two years. We used a new method for the calibration and multi-parameter optimization (MPO) based on kriging with modified criteria for selecting the parameter combinations. The soil moisture-related parameter combination (SM-PC3) improved simulations of soil water dynamics, but improvement in model performance is still required. The grain yield-related parameter combination significantly improved the yield simulation. We concluded that the calibrated model is good enough for irrigation water management at the field scale. Future studies should focus on improving the model performance in simulating total soil water (TSW) dynamics at different soil depths by including more soil water processes in a more dynamic manner.

A Capacitive Particle-Analyzing Smoke Detector for Very Early Fire Detection.

Smoke detectors face the challenges of increasing accuracy, sensitivity, and high reliability in complex use environments to ensure the timeliness, accuracy, and reliability of very early fire detection. The improvement in and innovation of the principle and algorithm of smoke particle concentration detection provide an opportunity for the performance improvement in the detector. This study is a new refinement of the smoke concentration detection principle based on capacitive detection of cell structures, and detection signals are processed by a multiscale smoke particle concentration detection algorithm to calculate particle concentration. Through experiments, it is found that the detector provides effective detection of smoke particle concentrations ranging from 0 to 10% obs/m; moreover, the detector can detect smoke particles at parts per million (PPM) concentration levels (at 2 and 5 PPM), and the accuracy of the detector can reach at least the 0.5 PPM level. Furthermore, the detector can detect smoke particle concentrations at better than 1 PPM accuracy even in an environment with 6% obs/m oil gas particles, 7% obs/m large dust interference particles, or 8% obs/m small dust interference particles.

Insight into the Improvement Strategies of Aqueous Electrolyte for Aqueous Rechargeable Batteries

AbstractAqueous rechargeable batteries (ARBs) have attracted wide attention due to their rich resources, environmental friendliness, and safety. The high ionic conductivity, safety and interfacial wettability of the aqueous electrolyte are essential for energy storage devices. However, the narrow electrochemical stability window (ESW) of the aqueous electrolyte and the side reactions of the electrode/electrolyte lead to the low energy density of the ARBs. In the past studies, the above problems were generally solved by improving the ion transport characteristics of the electrolyte and the electrode/electrolyte interface. Herein, we discuss the background, limitations, and key concepts for performance improvement of aqueous electrolytes, aiming to provide a comprehensive perspective and inspiration for further research on aqueous electrolytes and promote their development and application in the field of energy storage.

Ultralow Thermal Conductivity of a Chalcogenide System Pt3Bi4Q9 (Q = S, Se) Driven by the Hierarchy of Rigid [Pt6Q12]12- Clusters Embedded in Soft Bi-Q Sublattice.

Knowledge of structure-property relationships in solids with intrinsic low thermal conductivity is crucial for fields such as thermoelectrics, thermal barrier coatings, and refractories. Herein, we propose a new "rigidness in softness" structural scheme for intrinsic low lattice thermal conductivity (κL), which embeds rigid clusters into the soft matrix to induce large lattice anharmonicity, and accordingly discover a new series of chalcogenides Pt3Bi4Q9 (Q = S, Se). Pt3Bi4S9-xSex (x = 3, 6) achieved an intrinsic ultralow κL down to 0.39 W/(m K) at 773 K, which is considerably low among the Bi chalcogenide thermoelectric materials. Pt3Bi4Q9 contains the rigid cubic [Pt6Q12]12- clusters embedded in the soft Bi-Q sublattice, involving multiple bonding interactions and vibration hierarchy. The hierarchical structure yields a large lattice anharmonicity with high Grüneisen parameters (γ) 1.97 of Pt3Bi4Q9, as verified by the effective scatter of low-lying optical phonons toward heat-carrying acoustic phonons. Consequently, the rigid-soft coupling significantly inhibits heat propagation, exhibiting low acoustic phonon frequencies (∼25 cm-1) and Debye temperatures (ΘD = 170.4 K) in Pt3Bi4Se9. Owing to the suppressed κL and considerable power factor (PF), the ZT value of Pt3Bi4S6Se3 can reach 0.56 at 773 K without heavy carrier doping, which is competitive among the pristine Bi chalcogenides. Theoretical calculations predicted a large potential for performance improvement via proper doping, indicating the great potential of this structure type for promising thermoelectric materials.

How Outreach Training and Supportive Supervision (OTSS) Affect Health Facility Readiness and Health-Care Worker Competency to Prevent and Treat Malaria in Niger: A Secondary Analysis of OTSS Data.

The quality of health services is key to the goal of averting morbidity and mortality from malaria. From July 2020 to August 2021, PMI Impact Malaria supported the implementation of four rounds of Outreach Training and Supportive Supervision (OTSS) in 12 health districts in the two regions of Niger: Dosso and Tahoua. Through OTSS, trained supervisors conducted onsite visits to observe an average of 174 healthcare workers (HCWs) per round in 96 public primary health facilities, managing persons with fever or conducting antenatal care (ANC) consultations, and then provided instant and individualized feedback and onsite training. Data from health facility readiness, case management, and malaria in pregnancy (MiP) checklists across the four rounds were analyzed using Wilcoxon's and the χ2 tests. These analyses highlighted improved facility readiness, including an increased likelihood that HCWs had received classroom training, and facilities had increased availability of guidelines and algorithms by round 4 compared with round 1. Median HCW performance scores showed an improvement in the correct performance and interpretation of malaria rapid diagnostic tests, in classification of malaria as uncomplicated or severe, and in the management of uncomplicated malaria across the four rounds. For MiP services, malaria prevention and the management of pregnant women with malaria also improved from round 1 to round 4. These findings provide further evidence that OTSS can achieve rapid improvements in health facility readiness and HCW competency in managing outpatients and ANC clients.

A mixture of Si3N4 powder and SiO2 sand as a novel DC arc quenching medium for improvement of the current-limiting performance of a model electric fuse

A current-limiting and consequent direct current (DC) arc interruption requires an increase in arc resistance rarc for a DC fuse. Flattery silica (SiO2) sand has been used as an arc quenching medium in fuses for decades. However, there are few reports about a novel arc quenching medium which enables more rarc increases compared to flattery SiO2 sand. This paper uniquely selected a novel arc quenching medium based on the following material properties: (1) electrical resistivity and thermal conductivity in the solid phase, (2) the possibility of rarc increasing in the gas phase, (3) the possibility of electrical insulation after re-solidification, and (4) the cost of materials. As a result, we originally selected a powder mixture of silicon-nitride (Si3N4) and flattery SiO2 sand as a promising novel arc quenching medium. Therefore, DC interruption experiments were performed using a Si3N4/flattery SiO2 sand powder mixture under different Si3N4 concentration conditions. As a result of 1000 A DC arc quenching experiments, the utilization of Si3N4 powder with the flattery SiO2 sand successfully increased the arc resistance rarc during the arc quenching process. The increasing Si3N4 concentration exhibited a further increase in rarc during the arc quenching process. As a result of the rarc increase, the arc quenching time was significantly shortened to 30 ms for 30 wt.% Si3N4 conditions from 54 ms for the SiO2 sand conditions. In addition, utilization of Si3N4 powder achieved high insulation resistance over few MΩ after DC arc interruption. The above results clearly indicate that utilization of Si3N4 powder with flattery SiO2 sand can increase the DC interruption performance of the fuse.

Chemical Mechanical Polishing for Monocrystal SiC Assisted by the Heterogeneous Sono-Fenton Oxidation

The efficient chemical mechanical polishing (CMP) ability of monocrystal SiC determines the development of advanced semiconductor device. In order to improve the ultra-precision polishing efficiency for SiC, a CMP method based on the heterogeneous sono-Fenton oxidation was proposed. The effects of heterogeneous sono-Fenton on SiC surface softening and polishing based on hydroxyl radical oxidation were studied by means of the corrosion and CMP experiments. Utilizing the indentation tester, the ultra depth of field microscope and optical profilometer, the synergistic effects of heterogeneous sono-Fenton method on the improvement of SiC surface softening and polishing performance were revealed. The research results showed that the change rates of SiC hardness and elastic modulus after heterogeneous sono-Fenton corrosion were the highest, which were 0.12 and 0.6, respectively. The depth variation of indentation on SiC after heterogeneous sono-Fenton corrosion was the highest, which was 1.741 μm. Correspondingly, the improvement degree of SiC surface quality after polishing based on the heterogeneous sono-Fenton corrosion was the greatest and the material removal rate was the highest, which were Sa2.94 nm and 0.469 μm h−1, respectively.

EEG-based finger movement classification with intrinsic time-scale decomposition.

Brain-computer interfaces (BCIs) are systems that acquire the brain's electrical activity and provide control of external devices. Since electroencephalography (EEG) is the simplest non-invasive method to capture the brain's electrical activity, EEG-based BCIs are very popular designs. Aside from classifying the extremity movements, recent BCI studies have focused on the accurate coding of the finger movements on the same hand through their classification by employing machine learning techniques. State-of-the-art studies were interested in coding five finger movements by neglecting the brain's idle case (i.e., the state that brain is not performing any mental tasks). This may easily cause more false positives and degrade the classification performances dramatically, thus, the performance of BCIs. This study aims to propose a more realistic system to decode the movements of five fingers and the no mental task (NoMT) case from EEG signals. In this study, a novel praxis for feature extraction is utilized. Using Proper Rotational Components (PRCs) computed through Intrinsic Time Scale Decomposition (ITD), which has been successfully applied in different biomedical signals recently, features for classification are extracted. Subsequently, these features were applied to the inputs of well-known classifiers and their different implementations to discriminate between these six classes. The highest classifier performances obtained in both subject-independent and subject-dependent cases were reported. In addition, the ANOVA-based feature selection was examined to determine whether statistically significant features have an impact on the classifier performances or not. As a result, the Ensemble Learning classifier achieved the highest accuracy of 55.0% among the tested classifiers, and ANOVA-based feature selection increases the performance of classifiers on five-finger movement determination in EEG-based BCI systems. When compared with similar studies, proposed praxis achieved a modest yet significant improvement in classification performance although the number of classes was incremented by one (i.e., NoMT).

Instrumentation for measurement: A case study on conceptual versus problem-solving knowledge

The problem under analysis looks at the overall comparison of about 1300 students’ performance considering separately their conceptual and problem-solving knowledge over 7 academic years of the course of Instrumentation for Measurement within the Mechanical Engineering Department, Faculty of Engineering, University of Porto . This course gives the relevant background for later content courses on Automation, Control, Mechatronics, Smart Factories, etc., in that Mechanical Engineering master's degree. It is important to note that such content deals with basic concepts in different science and technology topics, incorporating today's cyber systems and the Internet of Things (IoT). This content includes fundamental principles spanning traditional topics as physics, statistics, mechanical, dynamics, design, and electrical, foundational, technical, and technological knowledge, and informatics. Together, they permit access to very small changes in physical quantities making possible their measurement, interpreting them, and providing data in the cloud. Also, aspects related to a variety of specific prepared questions to motivate students to improve and enhance their comprehension instead of a pure sequence of memorization activities for a perfect “how-to-respond” are explained. The work describes details on the instructional course design, and the actions used to improve the difference in both performances, from the point of view of pedagogical strategies and their sequence of results. The main outcomes of the analysis performed on data from those 7-course editions indicate large differences between the scores of conceptual (/theoretical) and problem-solving components; steady improvement in student performance in conceptual knowledge over the years; stable performance concerning the problem-solving component; clear disruption in conceptual performance in the academic year disturbed by the pandemic situation, followed by great recovery in the following academic year. The results are discussed in the framework of the regulations and conditions in which the instruction takes place and the pedagogical strategies employed to balance conceptual and problem-solving knowledge. Finally, some suggestions are put forward to investigate further possible reasons behind the large gap in theoretical and problem-solving results. The authors hope to contribute with some helpful discussion and outcomes and to foster later feedback from the community.

Physics-Guided Dual Self-Supervised Learning for Structure-Based Material Property Prediction.

Deep learning models have been widely used for high-performance material property prediction. However, training such models usually requires a large amount of labeled data, which are usually unavailable. Self-supervised learning (SSL) methods have been proposed to address this data scarcity issue. Herein, we present DSSL, a physics-guided dual SSL framework, for graph neural network-based material property prediction, which combines node masking-based generative SSL with atomic coordinate perturbation-based contrastive SSL strategies to capture local and global information about input crystals. Moreover, we achieve physics-guided pretraining by using the macroproperty (e.g., elasticity)-related microproperty prediction of atomic stiffness as an additional pretext task. We pretrain our DSSL model on the Materials Project database and fine-tune it with 10 material property data sets. The experimental results demonstrate that teaching neural networks some physics using the SSL strategy can afford ≤26.89% performance improvement compared to that of the baseline models.

Harnessing Nanomaterials for Enhanced Energy Efficiency in Transpired Solar Collectors: A Review of Their Integration in Phase-Change Materials

The building sector plays an important role in the global climate change mitigation objectives. The reduction of CO2 emissions and energy consumption in the building sector has been intensively investigated in the last decades, with solar thermal energy considered to be one of the most promising solutions due to its abundance and accessibility. However, the discontinuity of solar energy has led to the study of thermal energy storage to improve the thermal performance of solar thermal systems. In this review paper, the integration of various types of phase-change materials (PCMs) in transpired solar collectors (TSC) is reviewed and discussed, with an emphasis on heat transfer enhancements, including nanomaterials. Thermal energy storage applied to TSC is studied in terms of design criteria, materials technologies, and its impact on thermal conductivity. This review highlights the potential of nanomaterial technology integration in terms of thermal performance improvements. The utilization of nanomaterials in solar walls holds the potential to significantly enhance their performance. The integration of diverse materials such as graphene, graphite, metal oxides, and carbon nanoparticles can pave the way for improving thermal conductivity.

A Collaborative Multidisciplinary Trauma Program Improvement Team Improves VTE Chemoprophylaxis Guideline Compliance In Non-Operative Stable TBI.

Delays in initiating venous thromboembolism (VTE) prophylaxis in patients suffering from traumatic brain injury (TBI) persist despite guidelines recommending early initiation. We hypothesized that the expansion of a Trauma Program Performance Improvement (PI) team will improve compliance of early (24-48 hour) initiation of VTE prophylaxis and will decrease VTE events in TBI patients. We performed a single-center retrospective review of all TBI patients admitted to a Level I trauma center before (2015-2016,) and after (2019-2020,) the expansion of the Trauma Performance Improvement and Patient Safety (PIPS) team and the creation of trauma process and outcome dashboards. Exclusion criteria included discharge or death within 48 hours of admission, expanding intracranial hemorrhage on CT scan, and a neurosurgical intervention (craniotomy, pressure monitor, or drains) prior to chemoprophylaxis initiation. A total of 1,112 patients met the inclusion criteria, of which 54% (n = 604) were admitted after Trauma PIPS expansion. Following the addition of a dedicated PIPS nurse in the trauma program and creation of process dashboards, the time from stable CT to VTE prophylaxis initiation decreased (52 hours to 35 hours; p < 0.001) and more patients received chemoprophylaxis at 24-48 hours (59% from 36%, p < 0.001) after stable head CT. There was no significant difference in time from first head CT to stable CT (9 vs 9 hours; p = 0.15). The Contemporary group had a lower rate of VTE events (1% vs 4%; p < 0.001) with no increase in bleeding events (2% vs 2%; p = 0.97). On multivariable analysis, being in the Early cohort was an independent predictor of VTE events (aOR: 3.74; 95%CI: 1.45-6.16). A collaborative multidisciplinary Trauma PIPS team improves guideline compliance. Initiation of VTE chemoprophylaxis within 24-48 hours of stable head CT is safe and effective. Level III, Therapeutic/Care Management.

Load Balancing with Job-Size Testing: Performance Improvement or Degradation?

In the context of decision making under explorable uncertainty, scheduling with testing is a powerful technique used in the management of computer systems to improve performance via better job-dispatching decisions. Upon job arrival, a scheduler may run some testing algorithm against the job to extract some information about its structure, e.g., its size, and properly classify it. The acquisition of such knowledge comes with a cost because the testing algorithm delays the dispatching decisions, though this is under control. In this paper, we analyze the impact of such extra cost in a load balancing setting by investigating the following questions: does it really pay off to test jobs? If so, under which conditions? Under mild assumptions connecting the information extracted by the testing algorithm in relationship with its running time, we show that whether scheduling with testing brings a performance degradation or improvement strongly depends on the traffic conditions, system size and the coefficient of variation of job sizes. Thus, the general answer to the above questions is non-trivial and some care should be considered when deploying a testing policy. Our results are achieved by proposing a load balancing model for scheduling with testing that we analyze in two limiting regimes. When the number of servers grows to infinity in proportion to the network demand, we show that job-size testing actually degrades performance unless short jobs can be predicted reliably almost instantaneously and the network load is sufficiently high. When the coefficient of variation of job sizes grows to infinity, we construct testing policies inducing an arbitrarily large performance gain with respect to running jobs untested.

COVID-Net L2C-ULTRA: An Explainable Linear-Convex Ultrasound Augmentation Learning Framework to Improve COVID-19 Assessment and Monitoring.

While no longer a public health emergency of international concern, COVID-19 remains an established and ongoing global health threat. As the global population continues to face significant negative impacts of the pandemic, there has been an increased usage of point-of-care ultrasound (POCUS) imaging as a low-cost, portable, and effective modality of choice in the COVID-19 clinical workflow. A major barrier to the widespread adoption of POCUS in the COVID-19 clinical workflow is the scarcity of expert clinicians who can interpret POCUS examinations, leading to considerable interest in artificial intelligence-driven clinical decision support systems to tackle this challenge. A major challenge to building deep neural networks for COVID-19 screening using POCUS is the heterogeneity in the types of probes used to capture ultrasound images (e.g., convex vs. linear probes), which can lead to very different visual appearances. In this study, we propose an analytic framework for COVID-19 assessment able to consume ultrasound images captured by linear and convex probes. We analyze the impact of leveraging extended linear-convex ultrasound augmentation learning on producing enhanced deep neural networks for COVID-19 assessment, where we conduct data augmentation on convex probe data alongside linear probe data that have been transformed to better resemble convex probe data. The proposed explainable framework, called COVID-Net L2C-ULTRA, employs an efficient deep columnar anti-aliased convolutional neural network designed via a machine-driven design exploration strategy. Our experimental results confirm that the proposed extended linear-convex ultrasound augmentation learning significantly increases performance, with a gain of 3.9% in test accuracy and 3.2% in AUC, 10.9% in recall, and 4.4% in precision. The proposed method also demonstrates a much more effective utilization of linear probe images through a 5.1% performance improvement in recall when such images are added to the training dataset, while all other methods show a decrease in recall when trained on the combined linear-convex dataset. We further verify the validity of the model by assessing what the network considers to be the critical regions of an image with our contribution clinician.

The Effect of Task-Oriented Training and Progressive Resisted Exercise on Upper Limb Function and Quality of Life in Chronic Stroke Patients

Introduction: Stroke is defined as a neurological deficit subjected to acute focal injury of the Central Nervous System (CNS) due to vascular cause. The cause may be several embracing cerebral infarctions. Stroke is the third leading cause of death and the most common cause of disability among adults. Many studies have shown that motor impairment is a common factor leading to disability in stroke survivors. Improving the deficient motor power and strategies will reduce motor disability (Task-oriented training is given mainly for improving Functional performance). Need for the Study: Studies have shown a significant effect on motor function post-stroke with several techniques like conventional exercise, Proprioceptive Neuromuscular Facilitation (PNF), and Brunnstorm techniques. The effect of the treatment was significant because the weak muscles were strengthened, and the tight muscles were relaxed. However, there is a scarcity in the literature related to the effect of task-oriented training on motor performance in subjects with chronic stroke. Hence this study is undertaken. Methods: The study was carried out in the OPD of the College of Physiotherapy- Dayananda Sagar University-Bangalore on a sample size of 40 subjects for 6 months. It was a randomised clinical trial where subjects were split into two groups with task-oriented training and Progressive Resisted Exercises (PRE). The therapy was 30 minutes/session for 5 days /week for 4 weeks. Results: Task-oriented training showed significant changes within the group from pre- and post-trial and between the group effects, thus demonstrating it to be more significant in enhancing motor performance compared to mere strengthening exercises. Conclusion: The current study demonstrates that task oriented training compared to Progressive Resisted Exercises (PRE) works efficiently in improving upper limb functions and quality of life. The improvement in motor performance varied but the Chedoke Arm and Hand Activity Inventory and Stroke Impairment Scale showed quite significant improvement post-rehabilitation with task-oriented training compared to Progressive Resisted Exercise.