Different Levels Of Performance Research Articles

Demetriou’s tests and levels of algebraic abilities and proportional reasoning in seventh, eighth, and ninth grades

Developing algebraic thinking is a key factor in learning mathematics. Despite its importance, many students still struggle with algebraic concepts. This research investigates students’ achievements in algebraic thinking using Demetriou’s test across 7<sup>th</sup> (approximately 12-13 years old), 8<sup>th</sup> (approximately 13-14 years old), and 9<sup>th</sup> (approximately 14-15 years old) grades. The study analyzes performance in different levels of algebraic tasks (i.e., [1] extrapolation of relationships, [2] coordinating simple structures, [3] operating with undefined symbolic structures, and [4] coordination with undefined structures), revealing a significant developmental leap in algebraic abilities during the 9<sup>th</sup> grade. While no statistically significant differences were found in the first level, 9<sup>th</sup> grade students demonstrated superior performance in levels 2, 3, and 4, suggesting cognitive readiness for abstract algebraic concepts around the age of 14. The research unveils a disjointed development in algebraic abilities, indicating a progression from basic arithmetic operations to proportional reasoning before the full integration of algebraic thinking. Notably, tasks involving variables in the third level pose persistent challenges for students. The findings contribute to understanding the optimal age for introducing algebraic concepts and underscore the importance of considering cognitive development in mathematics education. The study proposes implications for educators, such as emphasizing proportional reasoning in earlier grades and employing differentiated instruction based on individual students’ abilities.

Implementation of a realistic artificial data generator for crash data generation

In this paper, a framework is outlined to generate realistic artificial data (RAD) as a tool for comparing different models developed for safety analysis. The primary focus of transportation safety analysis is on identifying and quantifying the influence of factors contributing to traffic crash occurrence and its consequences. The current framework of comparing model structures using only observed data has limitations. With observed data, it is not possible to know how well the models mimic the true relationship between the dependent and independent variables. Further, real datasets do not allow researchers to evaluate the model performance for different levels of complexity of the dataset. RAD offers an innovative framework to address these limitations. Hence, we propose a RAD generation framework embedded with heterogeneous causal structures that generates crash data by considering crash occurrence as a trip level event impacted by trip level factors, demographics, roadway and vehicle attributes. Within our RAD generator we employ three specific modules: (a) disaggregate trip information generation, (b) crash data generation and (c) crash data aggregation. For disaggregate trip information generation, we employ a daily activity-travel realization for an urban region generated from an established activity-based model for the Chicago region. We use this data of more than 2 million daily trips to generate a subset of trips with crash data. For trips with crashes crash location, crash type, driver/vehicle characteristics, and crash severity. The daily RAD generation process is repeated for generating crash records at yearly or multi-year resolution. The crash databases generated can be employed to compare frequency models, severity models, crash type and various other dimensions by facility type – possibly establishing a universal benchmarking system for alternative model frameworks in safety literature.

Should radiographs of the thoracolumbar spine remain part of the pre‐purchase examination?

SummaryThe inclusion of thoracolumbar radiographs in a pre‐purchase examination (PPE) is a topic of current and intense debate, subsequent to a statement from the Federation of European Equine Veterinary Associations (FEEVA) advocating the exclusion of spinous process radiography from the PPE. This review will consider the reasons behind the addition of such radiographs into PPEs, the limitations of radiography in ambulatory practice and therefore, the diagnostic limitations to the practitioner, the diagnostic benefit of a complete radiographic examination, and discussion of common and less‐frequent thoracolumbar pathologies. We will focus on laterolateral radiographs of the spinous processes as these are most commonly acquired during the PPE. The lack of consistent and repeatable grading systems highlights the subjective nature and variability in assessing this region radiographically. Coupled with the sparse scientific evidence of correlation between clinical signs of primary back pain, future performance in different levels and disciplines of equine athletes and radiographic abnormalities of the thoracolumbar spine leads us to conclude that taking radiographs of the back during PPEs should not be performed unless the clinical examination indicates otherwise.

Melatonin seed priming improves growth and physio-biochemical aspects of Zinnia elegans under salt stress

Salt stress is a major abiotic aspect that inhibits plant development, growth, and productivity of ornamental plants. In recent years, there has been a lot of interest in seed priming and biological treatments for salt stress mitigation in plants. Melatonin (MLT) is a versatile signaling molecule detected in higher plants. Its function is involved in various signaling and physiological processes, particularly under severe environmental conditions. The aim of this study was to assess the performance of different levels of MLT (0, 1, 10, 20 μM) primed zinnia plants (Zinnia elegans cv. ‘Super Yoga’) under salt stress (100 mM NaCl). Salt stress exposure reduced growth and physiological variables and increased oxidative stress, recorded as a rise in stress markers. The results showed that seed priming with MLT improved morphological characteristics of zinnia, such as shoot and root length, shoot diameter, leaf length, root collar diameter, number of roots, root fresh, and dry weight. MLT based seed priming also increased photosynthesis pigments and reduced salt induced oxidative damage by enhancing the activity of antioxidant enzymes (i.e. superoxide dismutase-SOD, peroxidase-POD, and catalase-CAT). The reduction in oxidative stress was recorded as decrease in electrolyte leakage (EL), hydrogen peroxide (H2O2), and malondialdehyde (MDA). Primed plants also showed significant increase in the osmoprotectants proline and glycine betaine, as compared to the non-primed salt stress plants. Exposure of plants to salt stress lead to an increase in sodium (Na+) and potassium (K+) contents in plant tissues in the non-primed plants. However, priming with MLT reduced the uptake of Na+ and improved the uptake of K+ in the leaves of zinnia. Overall, these results provided evidence that MLT seed priming may ameliorate the ill effects of salt-induced oxidative stress in zinnia plants.

Quantitative Evaluation of Piano Performance Technique and Style Based on Continuous Discretization Method

Abstract The piano education industry occupies a huge market. However, the automatic piano performance evaluation function has shortcomings in the existing piano education. In this paper, we first design a piano performance evaluation model based on the continuous discretization method for piano performance audio and improve the piano performance feature filtering and feature extraction methods through cubic spline interpolation and support vector regression, respectively. Secondly, a piano performance evaluation system based on music theory knowledge is studied to evaluate the user’s performance ability based on the rating of rhythm, main melody, and musicality performance of different levels of score performance audio and to achieve the purpose of assisting piano teaching. The results show that the difference between the scoring based on the continuous, discrete model and the actual level value of the player is less than 0.05, and the output of the evaluation system can achieve consistency; in the comparison experiments, the accuracy of three traditional evaluation models, BP, RNN, and LSTM, is 45%, 42%, and 75%, respectively, while the accuracy of the continuous, discrete model is 78%, with an average performance improvement of 23%. The continuous, discrete piano performance evaluation model proposed in this paper is more effective and objective in evaluating users’ piano performance ability and provides a guiding reference for performance evaluation in the piano education industry.

An Iterative Implementation of the Signal Space Separation Method for Magnetoencephalography Systems with Low Channel Counts.

The signal space separation (SSS) method is routinely employed in the analysis of multichannel magnetic field recordings (such as magnetoencephalography (MEG) data). In the SSS method, signal vectors are posed as a multipole expansion of the magnetic field, allowing contributions from sources internal and external to a sensor array to be separated via computation of the pseudo-inverse of a matrix of the basis vectors. Although powerful, the standard implementation of the SSS method on MEG systems based on optically pumped magnetometers (OPMs) is unstable due to the approximate parity of the required number of dimensions of the SSS basis and the number of channels in the data. Here we exploit the hierarchical nature of the multipole expansion to perform a stable, iterative implementation of the SSS method. We describe the method and investigate its performance via a simulation study on a 192-channel OPM-MEG helmet. We assess performance for different levels of truncation of the SSS basis and a varying number of iterations. Results show that the iterative method provides stable performance, with a clear separation of internal and external sources.

3GPP 5G V2X Error Correction Coding for Various Propagation Environments: A QoS Approach

Vehicle-to-everything (V2X) communications is an emerging branch of wireless communications and has recently gained a lot of interest not only from academia but also from the automotive and telecommunications industry. An important feature of V2X telecommunication systems is that the communication quality varies quickly in time due to the high mobility of the transceivers and due to the rapid change in the fading characteristics of the communication medium. For this reason, it is vital to maintain tolerable quality of service (QoS) in a V2X communication environment, in terms of communication reliability, power, end-to-end latency, data rate, communication range, throughput and vehicle density. A significant impact on the quality parameters of the system has been the coding scheme used by the V2X telecommunication system. For this purpose, we examine the effect that popular coding schemes have on the QoS of a V2X communication system. More specifically, the impact of fourth-generation long-term evolution (4G-LTE) turbo codes, fifth-generation new radio (5G-NR) polar codes and 5G-NR low-density parity-check codes (LDPC) on the QoS parameters of a V2X communication system is researched. Το this end, we employ stochastic V2X propagation models that simulate every possible V2X channel state and traffic environment, based on the third-generation partnership project (3GPP) Release 16 specifications. These propagation models form the basis of our study, since through them, at a first level, we analyze the frame error rate (FER) performance for different levels of signal-to-noise ratios (SNRs) for all the aforementioned coding schemes and 128-bit V2X-compatible data frames, and, at a second level, we investigate the impact of these propagation models and coding schemes on the V2X QoS parameters. Our analysis shows that turbo-based coding schemes satisfy all the QoS parameters and achieve overall communication quality comparable to polar and better than LDPC, making them suitable for small-frame 5G V2X services.

ASSESSMENT OF SST2 BEHAVIORS TO EVALUATE SEVERITY/CLINICAL IMPACT OF ACUTE PULMONARY EMBOLISM

Objective: Pulmonary embolism (PE) is a potentially life-threatening disorder. Beyond its usefulness in the prognostic stratification of heart failure, sST2 can represent a biomarker with high utility in several acute conditions. Our study was aimed to investigate whether sST2 can be used as a clinical marker of severity and prognostic outcome in acute PE. Design and method: We enrolled 72 patients with documented PE and 38 healthy subjects; we measured the plasma concentrations of sST2 to evaluate the prognostic and severity performance of different levels of sST2 according to its association with the pulmonary embolism severity index (PESI) score and several parameters of respiratory function. Results: PE patients had significantly higher levels of sST2 compared with healthy subjects (87.74 ± 17.1 vs. 17.1 ± 0.4 ng/mL, p < 0.001); we found higher PESI scores and serum lactate values in the group of patients with sST2 > 35 ng/mL compared with patients with sST2 < 35 ng/mL (138.7 ± 14.9 vs. 103.7 ± 15.1 and 2.43 ± 0.69 vs. 1.025 ± 0.05 mmol/L, respectively; p < 0.05). Patients with sST2 > 35 ng/mL showed higher radiological severity of PE compared with patients with sST2 < 35 ng/mL. Moreover, sST2 was the strongest parameter with a discriminative capacity for the development of acute respiratory failure and a PESI score >106 with respect to C reactive protein (CRP), creatinine, d-dimer, and serum lactate. Conclusions: We clearly demonstrated that sST2 significantly increased in PE and that its elevation was associated with disease severity. Therefore, sST2 may be used as a clinical marker in the evaluation of PE severity. However, further studies with larger patient populations are required to confirm these findings.

Optimization of the Multi-Level Spring Restrainer for Bridges by Hybrid Particle Swarm and Gravitational Search Algorithm

This paper proposes a new multi-level spring restrainer (MLSR) that exhibits multi stiffness performance in different levels of movement of bridge superstructure to prevent unseating during applied dynamic loads. The analytical model of the proposed MLSR was formulated and the fabricated prototype was tested using dynamic actuator. Based on the developed analytical mode, the function of MLSR device relied on 12 parameters that further complicated the design process to achieve the best performance. However, the conventional optimization techniques utilized only one or a few factors for simple systems. Therefore, a multi-objective optimization method is proposed in this study by introducing the hybridization of Particle Swarm Optimization and Gravitational Search algorithm (PSOGSA) to optimize the restrainer parameters, as well as to improve the seismic performance of bridges using the optimum design. The optimized MLSR was implemented in the bridge subjected to multi-directional ground motion and its multi-level action to prevent unseating of bridge deck when the applied excitation was evaluated. The optimization process revealed girder displacement in three directions and the number of plastic hinges decreased from 44 to 99% for the optimized design. The time history analysis disclosed that the use of optimized MLSR device decreased the structural seismic response, such as the 3D deck movements, from 79 to 90%. Next, the base shear and drift ratio of bridge bent reduced to 75 and 85% in longitudinal direction and to 72 and 90% in transverse direction, correspondingly. The outcomes signify that the proposed MLSR device and the optimization algorithm have successfully improved the bridge structure resistance against severe ground motions.

Polynomial filters for camera-based structural intensity analysis on curved plates

Structural intensity can be used as a measure to detect energy sources, sinks, and transfer paths in solid structures. It provides a valuable design tool for vibro-acoustic problems next to modal analysis. However, it has been proven very challenging to determine structural intensity experimentally for the general case of curved plates. This is due to the requirement of an accurate measurement of the full 3D vibrational field and the computation of spatial gradients thereof. The purpose of this paper is to propose a mesh-free, inherently smoothing, polynomial filtering approach and investigate its application for structural intensity analysis on curved plates based on a stereo camera measurement. Numerical studies are conducted to determine reasonable algorithm parameters (discretization, fitting radius, and the number of iterations) and their performance for different levels of uncertainty and various displacement fields. The results show that, depending on the uncertainty in the measurement, optimal values of the algorithm parameters exist. Furthermore, it is essential to achieve a high signal-to-noise ratio in the camera measurement. The computation of structural intensity is validated experimentally on the case of a flat plate and a curved plate (mockup oil pan). For the flat plate, good agreement with a reference finite element simulation is obtained. While it is not possible to derive quantitative results for the mockup oil pan under realistic measurement uncertainty, qualitative conclusions can be still drawn.

Assessment of sST2 Behaviors to Evaluate Severity/Clinical Impact of Acute Pulmonary Embolism

Pulmonary embolism (PE) is a potentially life-threatening disorder. Beyond its usefulness in the prognostic stratification of heart failure, sST2 can represent a biomarker with high utility in several acute conditions. Our study was aimed to investigate whether sST2 can be used as a clinical marker of severity and prognostic outcome in acute PE. We enrolled 72 patients with documented PE and 38 healthy subjects; we measured the plasma concentrations of sST2 to evaluate the prognostic and severity performance of different levels of sST2 according to its association with the pulmonary embolism severity index (PESI) score and several parameters of respiratory function. PE patients had significantly higher levels of sST2 compared with healthy subjects (87.74 ± 17.1 vs. 17.1 ± 0.4 ng/mL, p < 0.001); we found higher PESI scores and serum lactate values in the group of patients with sST2 > 35 ng/mL compared with patients with sST2 < 35 ng/mL (138.7 ± 14.9 vs. 103.7 ± 15.1 and 2.43 ± 0.69 vs. 1.025 ± 0.05 mmol/L, respectively; p < 0.05). Patients with sST2 > 35 ng/mL showed higher radiological severity of PE compared with patients with sST2 < 35 ng/mL. Moreover, sST2 was the strongest parameter with a discriminative capacity for the development of acute respiratory failure and a PESI score >106 with respect to C reactive protein (CRP), creatinine, d-dimer, and serum lactate. We clearly demonstrated that sST2 significantly increased in PE and that its elevation was associated with disease severity. Therefore, sST2 may be used as a clinical marker in the evaluation of PE severity. However, further studies with larger patient populations are required to confirm these findings.

5G V2X Performance Comparison for Different Channel Coding Schemes and Propagation Models

Channel coding is a fundamental procedure in wireless telecommunication systems and has a strong impact on the data transmission quality. This effect becomes more important when the transmission must be characterised by low latency and low bit error rate, as in the case of vehicle-to-everything (V2X) services. Thus, V2X services must use powerful and efficient coding schemes. In this paper, we thoroughly examine the performance of the most important channel coding schemes in V2X services. More specifically, the impact of use of 4th-Generation Long-Term Evolution (4G-LTE) turbo codes, 5th-Generation New Radio (5G-NR) polar codes and low-density parity-check codes (LDPC) in V2X communication systems is researched. For this purpose, we employ stochastic propagation models that simulate the cases of line of sight (LOS), non-line of sight (NLOS) and line of sight with vehicle blockage (NLOSv) communication. Different communication scenarios are investigated in urban and highway environments using the 3rd-Generation Partnership Project (3GPP) parameters for the stochastic models. Based on these propagation models, we investigate the performance of the communication channels in terms of bit error rate (BER) and frame error rate (FER) performance for different levels of signal to noise ratio (SNR) for all the aforementioned coding schemes and three small V2X-compatible data frames. Our analysis shows that turbo-based coding schemes have superior BER and FER performance than 5G coding schemes for the vast majority of the considered simulation scenarios. This fact, combined with the low-complexity requirements of turbo schemes for small data frames, makes them more suitable for small-frame 5G V2X services.

Concurrent validity and reliability of at-home teleneuropsychological evaluations among people with and without HIV.

To determine the reliability of teleneuropsychological (TNP) compared to in-person assessments (IPA) in people with HIV (PWH) and without HIV (HIV-). Participants included 80 PWH (Mage = 58.7, SDage = 11.0) and 23 HIV- (Mage = 61.9, SDage = 16.7). Participants completed two comprehensive neuropsychological IPA before one TNP during the COVID-19 pandemic (March-December 2020). The neuropsychological tests included: Hopkins Verbal Learning Test-Revised (HVLT-R Total and Delayed Recall), Controlled Oral Word Association Test (COWAT; FAS-English or PMR-Spanish), Animal Fluency, Action (Verb) Fluency, Wechsler Adult Intelligence Scale 3rd Edition (WAIS-III) Symbol Search and Letter Number Sequencing, Stroop Color and Word Test, Paced Auditory Serial Addition Test (Channel 1), and Boston Naming Test. Total raw scores and sub-scores were used in analyses. In the total sample and by HIV status, test-retest reliability and performance-level differences were evaluated between the two consecutive IPA (i.e., IPA1 and IPA2), and mean in-person scores (IPA-M), and TNP. There were statistically significant test-retest correlations between IPA1 and IPA2 (r or ρ = .603-.883, ps < .001), and between IPA-M and TNP (r or ρ = .622-.958, ps < .001). In the total sample, significantly lower test-retest scores were found between IPA-M and TNP on the COWAT (PMR), Stroop Color and Word Test, WAIS-III Letter Number Sequencing, and HVLT-R Total Recall (ps < .05). Results were similar in PWH only. This study demonstrates reliability of TNP in PWH and HIV-. TNP assessments are a promising way to improve access to traditional neuropsychological services and maintain ongoing clinical research studies during the COVID-19 pandemic.

Response of groundnut to phosphorus and bio-organics in Alfisols of Red and Laterite zone of India

Present study was carried out for three consecutive kharif season of 2018, 2019 and 2020 to evaluate the performance of different levels of chemical phosphatic fertilizers and FYM in combination with PSB inoculant on groundnut growth, yield, quality and nutrient uptake in an acidic Red and Laterite soil. Application of higher dose of phosphatic fertilizer along with PSB inoculant yielded better results with respect to groundnut plant height, nodulation, pegs and pods plant−1, weight of mature pods plant−1, kernel and haulm yield, protein and oil content, and nutrient uptake. Treatment combination T7 (60 kg ha−1 of P2O5 + PSB inoculant) was observed as the best treatment combination for deriving maximum growth, yield and nutrients uptake of kharif groundnut crop. Treatment T6 (application of 40 kg ha−1 of P2O5 + PSB inoculant) reported similar results. However, maximum profit ($862), benefit cost ratio (2.7) and net return per unit cost of input was significantly higher in T7 compared to T6. Thus, we recommend application of 60 kg ha−1 of P2O5 with PSB inoculant (T7) for the light textured acid soils of Red and Laterite zone of India for higher production and profitability.

Evaluation Strategy of the Piano Performance by the Deep Learning Long Short-Term Memory Network

With the development of society and the progress of technology, the piano education industry has a large market. In view of the problem of high payment fees in the piano education industry, the scientific and automatic nature of piano performance evaluation has attracted people’s attention. However, since most of the piano performance evaluation schemes are based on rules, the continuity of the piano music and the accuracy of playing are ignored. Therefore, the purpose is to design a scientific piano performance evaluation scheme that can play a certain role in the sustainable development of the piano education industry. Firstly, long short-term memory in deep learning is explored. Secondly, the musical characteristics of piano performance are analyzed according to the musical instrument digital interface. The piano music features are extracted, and a long short-term memory-based musical instrument digital interface piano performance evaluation model is constructed. Finally, it analyzes the number of hidden layers implemented in the long short-term memory model for piano performance evaluation. The accuracy of piano performance evaluation under different models is analyzed. Under the bidirectional long short-term memory network model, different piano performance levels are evaluated to realize the study of piano performance evaluation strategies. Compared with the accuracy of the recurrent neural network and the long short-term memory model with different hidden layers, the bidirectional long short-term memory model has the highest test accuracy, with an average of 69.78%. When the hidden layer of the bidirectional long short-term memory model is 3, the loss function L value is the smallest, which is 0.11. Different levels of piano skills are evaluated, and the results of the systematic evaluation are consistent with the performance of different levels. This shows that the BLSM model is feasible for the piano performance evaluation strategy system. This study not only conducts an in-depth analysis of the deep learning long short-term memory model but also proposes a long short-term memory-based musical instrument digital interface piano performance evaluation model. Additionally, the flaws such as the incomplete consideration of musical continuity and expressiveness when evaluating piano performance pieces have been compensated. Finally, through different model validations, the bidirectional long short-term memory model is concluded with good accuracy in piano performance evaluation. These conclusions provide theoretical research and practical significance for the accuracy of piano performance evaluation.

Shaking table testing of two storey As-Built and retrofitted scaled URM buildings

In November 2016 a Mw 7.8 earthquake struck the upper South Island in New Zealand causing landslides and significant damage to buildings and transportation infrastructures. The earthquake was followed by numerous large aftershocks leading the Ministry for Business, Innovation and Employment to rapidly release a policy that required securing of street-facing parapets and façade of unreinforced masonry buildings with a particular focus on heritage precincts with high pedestrian traffic. While implementation of the policy was ongoing, the experimental campaign described herein aimed to provide building owners and practicing engineers with a better understanding of the performance of different levels of building strengthening and also to propose simple and cost-effective retrofit solutions. The earthquake behaviour of scaled two-storey unreinforced clay-brick masonry buildings was realistically replicated using a shake-table. Two different levels of strengthening were investigated: (i) the use of commonly adopted wall-to-diaphragm connections was used as benchmark, (ii) the addition of vertical strong-backs installed on the upper storey wall and moment-resisting frames placed at the ground floor. Building construction details and material characteristics as well as the obtained results during the shaking-table tests in terms of response of the structure, damage mechanisms and performance achieved are reported herein.

Deep Learning-Based Occlusion Handling of Overlapped Plants for Robotic Grasping

Instance segmentation of overlapping plants to detect their grasps for possible robotic grasping presents a challenging task due to the need to address the problem of occlusion. We addressed the problem of occlusion using a powerful convolutional neural network for segmenting objects with complex forms and occlusions. The network was trained with a novel dataset named the “occluded plants” dataset, containing real and synthetic images of plant cuttings on flat surfaces with differing degrees of occlusion. The synthetic images were created using the novel framework for synthesizing 2D images by using all plant cutting instances of available real images. In addition to the method for occlusion handling for overlapped plants, we present a novel method for determining the grasps of segmented plant cuttings that is based on conventional image processing. The result of the employed instance segmentation network on our plant dataset shows that it can accurately segment the overlapped plants, and it has a robust performance for different levels of occlusions. The presented plants’ grasp detection method achieved 94% on the rectangle metric which had an angular deviation of 30 degrees and an IoU of 0.50. The achieved results show the viability of our approach on plant species with an irregular shape and provide confidence that the presented method can provide a basis for various applications in the food and agricultural industries.

Deep Learning Skin Disease Classifiers: Current Status and Future Prospects

Background Most studies on deep learning skin disease classifiers are done with binary classifications (ie, classifying lesions into malignant and benign). However, dermatology practice involves a large number of inflammatory and infective conditions that are not easily diagnosed by nondermatologist physicians. Objective The aim of this study is to develop a machine learning–based smartphone app for multiclass skin disease classification and evaluate its performance in different levels of dermatology practice. We will also explore similar studies in the literature. Methods We developed an artificial intelligence–driven smartphone app for 40 common skin diseases and tested it in primary care, tertiary care, and private practice settings. Results In the clinical study, the overall top-1 accuracy was 75.07% (95% CI 73.75%-76.36%), top-3 accuracy was 89.62% (95% CI 88.67%-90.52%), and the mean area under the curve was 0.90 (SD 0.07). Multimedia Appendix 1 shows the top-1 positive predictive values and negative predictive values from a clinical study of 35 diseases using the developed mobile health app on patients. In the literature, there are very few studies on image-based deep learning multiclass classification of common skin diseases and none of them included evaluations in actual clinical settings. Conclusions An artificial intelligence–driven smartphone app has the potential to improve the diagnosis and management of skin diseases in patients with skin of color. Nondermatologist, primary care physicians are likely to benefit from having access to such an app. Acknowledgments Nurithm Labs collaborated with the All India Institute Of Medical Science to develop this smartphone app. Other collaborators in this work include Rashi Pangti, Jyoti Mathur, Vikas Chouhan, Sharad Kumar, Lavina Rajput, Sandesh Shah, Atula Gupta, Ambika Dixit, Dhwani Dholakia, Sanjeev Gupta, Savera Gupta, Mariam George, and Vinod Kumar Sharma. Conflicts of Interest None declared.

Testing density functional theory in a quantum Ising chain

By using the quantum Ising chain as a test bed and treating the spin polarization along the external transverse field as the "generalized density", we examine the performance of different levels of density functional approximations parallel to those widely used for interacting electrons, such as local density approximation (LDA) and generalized gradient approximation (GGA). We show that by adding the lowest-order and nearest-neighbor density variation correction to the simple LDA, a semi-local energy functional in the spirit of GGA is almost exact over a wide range of inhomogeneous density distribution. In addition, the LDA and GGA error structures bear a high level of resemblance to the quantum phase diagram of the system. These results provide insights into the triumph and failure of these approximations in a general context.

Power-over-fiber in a 10 km long multicore fiber link within a 5G fronthaul scenario.

We evaluate the impact of Power-over-Fiber (PoF) technology on the fronthaul of a 5G-NR network with an Analog-Radio-over-Fiber at 25.5 GHz on a 10 km long multicore fiber. The study in this Letter analyzes the bit error rate (BER) performance for different levels of energy transmitted by the PoF system. 133 mW of maximum optical power at reception is demonstrated showing negligible BER impact or data transmission BER improvement in a dedicated and shared scenario.