Additional Parameters Research Articles

Simultaneously Optimizing Molecular Stacking and Phase Separation via Solvent‐Solid Hybrid Additives Enables Organic Solar Cells with over 19% Efficiency†

Comprehensive SummaryGiven the crucial role of film morphology in determining the photovoltaic parameters of organic solar cells (OSCs), solvent or solid additives have been widely used to realize fine‐tuned film morphological features to further improve the performance of OSCs. However, most high‐performance OSCs are processed only using single component additive, either solvent additive or solid additive. Herein, a simple molecular building block, namely thieno[3,4‐b]thiophene (TT), was utilized as the solid additive to coordinate with the widely used solvent additive, 1‐chloronaphthalene (CN), to modulate the film morphology. Systematical investigations revealed that the addition of TT could prevent the excessive aggregation to form a delicate nanoscale phase separation, leading to enhanced charge transport and suppressed charge recombination, as well as superior photovoltaic performance. Consequently, the PM6:Y6 based OSCs with the addition of hybrid additive of CN + TT demonstrated the optimal PCE of 18.52%, with a notable FF of 79.6%. More impressively, the PM6:Y6:PC71BM based ternary OSCs treated with the hybrid additives delivered a remarkable efficiency of 19.05%, which ranks among the best values of Y6‐based OSCs reported so far. This work highlights the importance of the hybrid additive strategy in regulating the active layer morphology towards significantly improved performance.

Recent Advances in Synthesis and Applications of Organic Ionic Saltsbased Sensor Arrays

Abstract: Sensor arrays contain a group of sensors, improve observations with new dimensions, provide better estimations, and additional parameters in comparison to the individual selective sensor. The array-based sensing technique provides good performance to respond to various gaseous or liquid analytes. Room temperature ionic liquids (RTILs) (melting point <25oC) and Group of uniform materials based on organic salts (GUMBOS) (melting point =25-250oC) are organic ionic salts, composed of an oppositely charged pair of bulky organic cations and bulky organic/inorganic anion and shows interesting tunable physicochemical properties. In this review article, we will discuss the sensing performance of ILs- and GUMBOS-based sensor arrays. ILs-based sensor arrays have been used in electrochemical gas sensing, solvent discrimination, colorimetric gas sensing, sensing of organic compounds, optoelectronic sensing of vapors and solutions, and vapour sensing through IL/QCM systems. GUMBOS-based sensor arrays have been employed in vapour sensing through the GUMBOS/QCM method, detection, and discrimination of proteins.

BViT: Broad Attention-Based Vision Transformer.

Recent works have demonstrated that transformer can achieve promising performance in computer vision, by exploiting the relationship among image patches with self-attention. They only consider the attention in a single feature layer, but ignore the complementarity of attention in different layers. In this article, we propose broad attention to improve the performance by incorporating the attention relationship of different layers for vision transformer (ViT), which is called BViT. The broad attention is implemented by broad connection and parameter-free attention. Broad connection of each transformer layer promotes the transmission and integration of information for BViT. Without introducing additional trainable parameters, parameter-free attention jointly focuses on the already available attention information in different layers for extracting useful information and building their relationship. Experiments on image classification tasks demonstrate that BViT delivers superior accuracy of 75.0%/81.6% top-1 accuracy on ImageNet with 5M/22M parameters. Moreover, we transfer BViT to downstream object recognition benchmarks to achieve 98.9% and 89.9% on CIFAR10 and CIFAR100, respectively, that exceed ViT with fewer parameters. For the generalization test, the broad attention in Swin Transformer, T2T-ViT and LVT also brings an improvement of more than 1%. To sum up, broad attention is promising to promote the performance of attention-based models. Code and pretrained models are available at https://github.com/DRL/BViT.

Application of the blastomere count variations "skip value" in the embryo AI assessment

Objective: To explore the correlation between blastomere count variations "skip value" which extracted from by time-lapse technology (TLT) combined with artificial intelligence (AI) and morphological features of in vitro fertilization (IVF) embryo, and to test its feasibility in clinical applications. Methods: This study was a diagnostic experiment (AI reassessment of embryo transferred patients), a total of 6 545 embryos from 1 226 patients who underwent IVF at the Women and Children's Hospital of Chongqing Medical University from December 2020 to December 2021 were retrospectively analyzed, of which 2 869 embryos were attempted to cultured to blastocyst stage by TLT. The embryo dynamic map (EDM) was drawn by Embryo Viewer, a TLT recording software, based on embryo developmental kinetics. The self-developed AI embryo evaluation software identified and recorded the number of cleavages in real time during embryonic development, and compared with the EDM, the correlation between the skip value formed by the change of cleavage sphere counts and the outcomes of the embryos was analyzed. The correlation among skip value, morphological score of embryo, implantation rate and live birth rate were performed by Spearman and step-up logistic regression. The receiver operating characteristic (ROC) curve was selected for reporting there relationship of skip value and morphology. Finally, predicting power of skip value for implantation and live birth rate were performed by ROC analysis. Results: The total skip values extracted from the blastomere count of embryos (72 hours post-fertilization) were negatively correlated with abnormal cleavage, blastocyst formation rate, day 3 (D3)-cell score, uneven size and fragmentation (the β values were -0.268, -0.116, -0.213, -0.159 and -0.222, respectively; all P<0.001); positively correlated with D3-cell number (β=0.034; P<0.001); negatively correlated with blastocyst formation rate and implantation rate (OR=0.97, 95%CI: 0.93-0.99, P=0.034; OR=0.96, 95%CI: 0.93-0.98, P=0.044). The power of predicting implantation were similar between the order selection of skip values and traditional morphology criteria [area under curve (AUC): 0.679 vs 0.620]. Live birth rate were negatively correlated with female age (OR=0.91, 95%CI: 0.88-0.93; P<0.001), D3 general score (OR=0.77, 95%CI: 0.59-0.99; P=0.045) and order selection of skip values (OR=0.98, 95%CI: 0.96-0.99; P=0.038), while positively correlated with retrieved oocyte number and endometrial thickness in embryo transferred (OR=1.08, 95%CI:1.05-1.11, P<0.001; OR=1.09, 95%CI:1.06-0.12, P<0.001, respectively) from multivariate regression analysis, and the power of predicting live birth was 0.666 for AUC. Conclusions: The skip value and its order form is a systematic quantification of embryo development, correlated with embryo developmental quality and clinical outcome. It could be an addition parameter for embryo culture and selection.

Physiological and radiological parameters predicting outcome from penetrating traumatic brain injury treated in the deployed military setting

IntroductionPenetrating traumatic brain injury (TBI) is the most common cause of death in current military conflicts, and results in significant morbidity in survivors. Identifying those physiological and radiological parameters associated...

Optimisation of Exhaust Emissions, Vibration, and Noise of Unmodified Diesel Engine Fuelled with Canola Biodiesel-Diesel Blends with Natural Gas Addition by Using Response Surface Methodology

The paper presents methods to determine the optimum input parameters of CNG addition, biodiesel blend ratio, and engine speed to improve engine responses in terms of exhaust emissions, vibration, and noise of CNG-biodiesel-diesel fuelled engine. Box-Behnken based on response surface methodology was used to predict and optimise input parameters. Variance analysis was applied to determine the significant relationship between the input parameters and engine responses. At optimum input parameters (CNG addition=9.24 L/min, biodiesel blend ratio=40%, engine speed=1524.24 rpm), the optimum engine responses of NOx, CO, CO2, O2, engine vibration acceleration, and noise were 93.77 ppm, 438.05 ppm, 1.47%, 18.59%, 37.17 m/s2 and 91.34 dB[A], respectively. In terms of coefficient determination of R2, the values were 99.11%, 99.22%, 99.41%, 99.70%, 98.65%, and 98.60% respectively. The correlation between the optimised result and the engine test result showed an acceptable error limit for NOx, CO, CO2, O2, engine vibration acceleration, and noise as 4.2%, 3.8%, 4.9%, 0.25%, 4.12%, and 0.17%, respectively.

A New Global Gas Dynamics Performance Optimization Method for Refrigeration Centrifugal Compressor Stage Based on the Immune Algorithm

Abstract This study proposes a new global gas dynamics optimization method, which was applied to a multi-objective optimization task of centrifugal compressor performance with the aim of determining the improvement probability for achieving high efficiency across a wide operating range. Initially, the original nondominated neighbor immune algorithm (NNIA) was extended to solve constrained multi-objective optimization problems for the first time, which mainly incorporated a procedure for handling inequality and equality constraints without additional parameters. Subsequently, an adaptive topological back-propagation multilayer feed-forward artificial neural network (BP-MLFANN) was trained using the modified NNIA to quickly evaluate the fitness value of the centrifugal compressor stage performance during the optimization. The feasibility of the method was validated using the first stage of a refrigeration centrifugal compressor. The results indicated a substantial enhancement in the stage efficiency of the optimized impeller at the Near-stall, Design, and Near-choke operating points, with increasement of 1.8%, 1.9%, and 4%, respectively, as compared to the baseline stage. The flow field analysis shows that the impact loss at impeller leading edge and flow separation in the passage reduced greatly, the mixing process between the leakage flow and mainstream in the channel is significantly weakened, thus the flow field becomes more uniform after optimization. The new global gas dynamics optimization method provides a reference for the development of efficient and rapid optimization techniques for centrifugal compressor.

Balance Board Math: Exploring the Sense of Balance as a Basis for Functions and Graphing and Number Line Concepts

Can math concepts be experienced through the sensory modality of balance? Balance Board Math (BBM) is a set of pedagogical math activities designed to instantiate mathematical concepts through stimulation to the vestibular sense: an organ in the inner ear that detects our bodily balance and orientation. BBM establishes the different ways children spontaneously rock and move as the basis for inclusively exploring mathematical concepts together across diverse sensory profiles. I describe two activity sets where students explore focal concepts by shifting their balance on rockable balance boards: “the Balance Number Line,” using analog materials to foster understandings of the number line and negative numbers, and “Balance Graphing,” using sensors and a digital display to foster exploration of functions and graphing concepts, including the parameters of trigonometric functions and function addition. I outline proposed ways that engaging with concepts through balance-activating movement can change learners’ mathematical thinking and learning.

Online state and time‐varying parameter estimation using the implicit equal‐weights particle filter

AbstractA method is proposed for resilient and efficient estimation of the states and time‐varying parameters in nonlinear high‐dimensional systems through a sequential data assimilation process. The importance of estimating time‐varying parameters lies not only in improving prediction accuracy but also in determining when model characteristics change. We propose a particle‐filter‐based method that incorporates nudging techniques inspired by optimization algorithms in machine learning by taking advantage of the flexibility of the proposal density in particle filtering. However, as the model resolution and number of observations increase, filter degeneracy tends to be the obstacle to implementing the particle filter. Therefore, this proposed method is combined with the implicit equal‐weights particle filter (IEWPF), in which all particle weights are equal. The method is validated using the 1000‐dimensional linear model with an additive parameter and the 1000‐dimensional Lorenz‐96 model, where the forcing term is parameterized. The method is shown to be capable of resilient and efficient parameter estimation for parameter changes over time in our application with a linear observation operator. This leads to the conjecture that it applies to realistic geophysical, climate, and other problems.

Efficacy of Native T1 Mapping for Patients With Non-Ischemic Cardiomyopathy and Ventricular Functional Mitral Regurgitation Undergoing Transcatheter Edge-to-Edge Repair.

We investigated the efficacy of left ventricular (LV) myocardial damage by native T1mapping obtained with cardiac magnetic resonance (CMR) for patients undergoing transcatheter edge-to-edge repair (TEER). We studied 40 symptomatic non-ischemic heart failure (HF) patients and ventricular functional mitral regurgitation (VFMR) undergoing TEER. LV myocardial damage was defined as the native T1Z-score, which was converted from native T1values obtained with CMR. The primary endpoint was defined as HF rehospitalization or cardiovascular death over 12 months after TEER. Multivariable Cox proportional hazards analysis showed that the native T1Z-score was the only independent parameter associated with cardiovascular events (hazard ratio 3.40; 95% confidential interval 1.51-7.67), and that patients with native T1Z-scores <2.41 experienced significantly fewer cardiovascular events than those with native T1Z-scores ≥2.41 (P=0.001). Moreover, the combination of a native T1Z-score <2.41 and more severe VFMR (effective regurgitant orifice area [EROA] ≥0.30 cm2) was associated with fewer cardiovascular events than a native T1Z-score ≥2.41 and less severe VFMR (EROA <0.30 cm2; P=0.002). Assessment of baseline LV myocardial damage based on native T1Z-scores obtained with CMR without gadolinium-based contrast media is a valuable additional parameter for better management of HF patients and VFMR following TEER.

The impact of the Laval nozzle shape on thrust production, using the method of characteristics

This investigation examines the influence of Laval nozzle geometry on thrust production using the method of characteristics. It explores various divergent section configurations, analyzing their impact on nozzle design and performance. Comparative analysis of software tools, study of supersonic flow phenomena, and application of the method of characteristics form the core of this research. Findings showcase strong agreement between computational tools, emphasizing the Mach number's role in divergent section shape variations and thrust force. Additionally, the study scrutinizes over-expansion and under-expansion phenomena, validated through computational simulations. Future research should be aimed at enhancing computational methodologies and investigating additional parameters affecting nozzle performance, promising advancements in rocket propulsion technology.

On seismic gradiometric wave equation inversion for density

SUMMARY Material density remains poorly constrained in seismic imaging problems, yet knowledge of density would provide important insight into physical material properties for the interpretation of subsurface structures. We test the sensitivity to subsurface density contrasts of spatial and temporal gradients of seismic ambient noise wavefields, using wave equation inversion (WEI), a form of seismic gradiometry. Synthetic results for 3-D acoustic media suggest that it is possible to estimate relative density structure with WEI by using a full acoustic formulation for wave propagation and gradiometry. We show that imposing a constant density assumption on the medium can be detrimental to subsurface seismic velocity images. By contrast, the full acoustic formulation allows us to estimate density as an additional material parameter, as well as to improve phase velocity estimates. In 3-D elastic media, severe approximations in the governing wave physics are necessary in order to invert for density using only an array of receivers on the Earth's free surface. It is then not straightforward to isolate the comparatively weak density signal from the influence of phase velocity using gradiometric WEI. However, by using receivers both at the surface and in the shallow subsurface we show that it is possible to estimate density using fully elastic volumetric WEI.

Implementation of the Projective Quantum Eigensolver on a Quantum Computer.

We study the performance of our previously proposed projective quantum eigensolver (PQE) on IBM's quantum hardware in conjunction with error mitigation techniques. For a single qubit model of H2, we find that we are able to obtain energies within 4 millihartree (2.5 kcal/mol) of the exact energy along the entire potential energy curve, with the accuracy limited by both the stochastic error and the inconsistent performance of the IBM devices. We find that an optimization algorithm using direct inversion of the iterative subspace can converge swiftly, even to excited states, but stochastic noise can prompt large parameter updates. For the 4-site transverse-field Ising model at its critical point, PQE with an appropriate application of qubit tapering can recover 99% of the correlation energy, even after discarding several parameters. The large number of CNOT gates needed for the additional parameters introduces a concomitant error that, on the IBM devices, results in a loss of accuracy despite the increased expressivity of the trial state. Error extrapolation techniques and tapering or postselection are recommended to mitigate errors in PQE hardware experiments.

Development of a multi-wear-site, deep learning-based physical activity intensity classification algorithm using raw acceleration data.

Accelerometers are widely adopted in research and consumer devices as a tool to measure physical activity. However, existing algorithms used to estimate activity intensity are wear-site-specific. Non-compliance to wear instructions may lead to misspecifications. In this study, we developed deep neural network models to classify device placement and activity intensity based on raw acceleration data. Performances of these models were evaluated by making comparisons to the ground truth and results derived from existing count-based algorithms. 54 participants (26 adults 26.9±8.7 years; 28 children, 12.1±2.3 years) completed a series of activity tasks in a laboratory with accelerometers attached to each of their hip, wrist, and chest. Their metabolic rates at rest and during activity periods were measured using the portable COSMED K5; data were then converted to metabolic equivalents, and used as the ground truth for activity intensity. Deep neutral networks using the Long Short-Term Memory approach were trained and evaluated based on raw acceleration data collected from accelerometers. Models to classify wear-site and activity intensity, respectively, were evaluated. The trained models correctly classified wear-sites and activity intensities over 90% of the time, which outperformed count-based algorithms (wear-site correctly specified: 83% to 85%; wear-site misspecified: 64% to 75%). When additional parameters of age, height and weight of participants were specified, the accuracy of some prediction models surpassed 95%. Results of the study suggest that accelerometer placement could be determined prospectively, and non-wear-site-specific algorithms had satisfactory accuracies. The performances, in terms of intensity classification, of these models also exceeded typical count-based algorithms. Without being restricted to one specific wear-site, research protocols for accelerometers wear could allow more autonomy to participants, which may in turn improve their acceptance and compliance to wear protocols, and in turn more accurate results.

Changes in food preferences and ingestive behaviors after glucagon-like peptide-1 analog treatment: techniques and opportunities.

Glucagon-like peptide-1 (GLP-1) analogs are approved for the treatment of obesity in adults and adolescents. Reports have emerged that the weight loss effect of these medications may be related to changes in food preferences and ingestive behaviors following the treatment. Understanding the mechanisms which impact ingestive behavior could expand opportunities to develop more refined and personalized treatment options for obesity. Recent studies investigating the relationship between GLP-1 analogs and ingestive behaviors were retrieved from PubMed using the search terms: "obesity," "food preference," "taste," "ingestive behavior," "weight loss medication," "anti-obesity medication," "GLP-1 analog," "tirzepatide," "liraglutide," "semaglutide." Measurement tools were studied to compare variables used to assess food intake behavior. The main outcomes from each study were analyzed to evaluate the current standing and future directions of appetitive, ingestive, and consummatory behaviors and their association with GLP-1 analogs. Thus far, studies have primarily explored the weight loss phase and report decreased short-term appetite and food intake upon treatment. However, research during the weight maintenance phase and objective measurements of food intake are notably sparse. Additionally, verbal reports have been primarily used to examine food intake, which can be susceptible to subjectivity. Elucidating the relationship between GLP-1 analogs and ingestive behavior could reveal additional parameters which contribute to their anti-obesity effects. To better understand these mechanisms, it is imperative to consider objective measurements of food intake in future studies. Several measurement tools have been adapted to measure variables of food behavior in humans, and each must be carefully considered with their strengths and limitations to develop optimal investigations.

Affective Prompt-Tuning-Based Language Model for Semantic-Based Emotional Text Generation

The large language models based on transformers have shown strong text generation ability. However, due to the need for significant computing resources, little work has been done to generate emotional text using language models such as GPT-2. To address this issue, the authors proposed an affective prompt-tuning-based language model (APT-LM) equipped with an affective decoding (AD) method, aiming to enhance emotional text generation with limited computing resources. In detail, the proposed model incorporates the emotional attributes into the soft prompt by using the NRC emotion intensity lexicon and updates the additional parameters while freezing the language model. Then, it steers the generation toward a given emotion by calculating the cosine distance between the affective soft prompt and the candidate tokens generated by the language model. Experimental results show that the proposed APT-LM model significantly improves emotional text generation and achieves competitive performance on sentence fluency compared to baseline models across automatic evaluation and human evaluation.

Parameter optimization and spatio-temporal distribution of material for variable trajectory combined equal-thickness screen

Vibrating screen is important screening equipment used extensively in coal separation and utilization at the global scale. Traditional vibrating screen usually adopts a single vibration mode, which leads to poor screening effect. In this study, the equal-thickness screening theory was applied to the combined vibrating screen, and non-uniform frequency operation of the vibrating screen was achieved. The concept of combined excitation parameter was introduced, which presented additional adjustable parameters for the screen. These made it easier for material layer on the screen to achieve equal-thickness. Subsequently, single-factor experiments were conducted for systematic exploration of the impact of various parameters on the screening efficiency. By regulating the excitation force and frequency of the four excitation motors of the variable-trajectory combined screen, the study aims to regulate the motion speed of material groups, the distribution of materials under the screen, and the thickness distribution of the material layer on the screen. The result showed that when D = 2/19, f1&f2 = 15.00 & 11.37 Hz, respectively, k = 1.2, equal-thickness screening was realized, and the screening efficiency η was 96.32%, which is 3.35% higher than those under the optimal conditions with equal amplitude and equal vibration frequency.

Effect of wire arc additive manufacturing parameters on geometric, hardness, and microstructure of 316LSi stainless steel preforms

Effect of wire arc additive manufacturing parameters on geometric, hardness, and microstructure of 316LSi stainless steel preforms

SCORING METHOD OF EVALUATING ROAD SAFETY AS PART OF MANAGEMENT SYSTEM

Known methods of assessing the level of traffic safety on highway sections are considered. Factors that can significantly affect forecasting accuracy have been identified. It is proposed to improve the existing method by introducing additional parameters, which were obtained as a result of the formalization of accident statistics at the places of traffic accidents concentration. The purpose of the work is to provide traffic safety specialists with an effective express method of evaluating the level of traffic safety with points directly on the road. The proposed scoring method of evaluating road safety also arouses interest due to the recent implementation in Ukraine of a road safety audit at the stage of operational maintenance, which is specifically designed to conduct an independent inspection of dangerous sections of highways and prescribe low-cost and effective measures to reduce accidents. This will help to make decisions in the road safety management system that will relate to effective measures.

Predicting parameters of airway dynamics generated from inspiratory and expiratory plethysmographic airway loops, differentiating subtypes of chronic obstructive diseases

BackgroundThe plethysmographic shift volume–flow loop (sRaw-loop) measured during tidal breathing allows the determination of several lung function parameters such as the effective specific airway resistance (sReff), calculated from the ratio...