Delta Model Research Articles

Numerical Analysis and Design of a High‐Frequency Surface Acoustic Wave Transducer: Influence of Piezoelectric Substrates and IDTs Configurations

ABSTRACTThe development of SAW transducers requires a series of steps ranging from material selection and geometry design to the selection of fabrication techniques for their characterization and validation process. Here, we use the finite element method (FEM) to present a methodology and a detailed analysis of the design of SAW transducers in a delay line configuration. First, we simulate single‐finger and double‐finger configurations with different geometries and designs of IDTs on two piezoelectric substrates, LiNbO in 64 YX and LiNbO 128 YX orientation, to compare the simulation results with the analytical delta model and thereby validate the simulation process, presenting Root Mean Square Error (RMSE) values ranging from 10.79 dB to 16.42 dB. With the above, we performed a comparative analysis to determine the influence of piezoelectric material and IDT configuration by studying a specific transducer design made to operate at a resonance frequency of 97.02 MHz. We compared identical designs on 128 YX and 64 YX orientations of LiNbO with single and double‐finger configurations and added the comparison with the SPUDT configuration. We observed the best results for the single‐finger IDT configuration in 128 YX LiNbO orientation compared to the other variants through its insertion loss level of −7.29 dB, its average sidelobe level of −18.63 dB, and its average transition band slope for the main lobe of 15.09 dB/MHz. The results guide new researchers or students in expanding the use of numerical tools in designing SAW transducers and SAW devices.

On Data Driven SIRD Model of Delta and Omicron Variants of COVID-19

The compartmental model stands as a cornerstone in quantitatively describing the transmission dynamics of diseases. Through a series of assumptions, this model can be formulated and subsequently validated against real-world conditions. Leveraging the abundance of COVID-19 data presently available, this study endeavors to reverse engineer the model construction process. Specifically, we analyse the compartmental model governing two notable variants of COVID-19: Delta and Omicron, utilizing empirical data. Employing the SINDy method, we extract parameters that define the model by effectively fitting the available data. To ensure robustness, the obtained model undergoes validation via comparison with real-world data through numerical integration. Additionally, we conduct fine-tuning in regularization techniques and input features to refine model selection. The constructed model then undergoes thorough analysis to gain qualitative insights and interpretations regarding the transmission dynamics of COVID-19.

Evaluating the comprehensive flood impact assessment on the head reach of the Chiniot dam project

The major sources of surface water available to Pakistan are perennial flows of the River Indus and its tributaries i.e. Jhelum and Chenab Rivers. The creation of a reservoir of 0.9 MAF Capacity will cause the water level to rise and reduction in flow velocity on the Chenab River upstream of Chiniot Bridge. Consequently, it will cause sediment deposition which would further increase the water level due to the raised river bed level by deposited sediments. To study this storage, the reservoir has been modelled in HEC-RAS software to assess pre-dam and post-dam scenarios to simulate different conditions of river flow for water surface profile and sediment delta modelling of the reservoir reach, using historical daily water and sediment flows (1985–2022). The results of flood routing showed that the reservoir has attenuated the peak flood volume with safe releases at downstream side with proper operation of spillway gates. The average sediment inflow in the reservoir is worked out to be 38.4 metric tons. The results of sediment modelling and reservoir flood management indicated that after every 05 years of normal reservoir operation, reservoir flushing is recommended. Without sediment flushing, the river bed would be further raised beyond 189.0 m due to incoming sediment, resulting in further rise of water level up to 192.8 m which is the existing crest level of upstream training works of Talibwala Motorway Bridge.

Sedimentary evolutionary processes, architecture, and sedimentary model of a lobate shallow-water delta: insights from flume simulation experiments

By using equipment such as time-lapse cameras and 3D laser scanners, this study conducted flume simulation experiments to obtain topographic elevation, the amount of sediment increment, sedimentary cross sections, and other related data and diagrams. Quantitative software was then utilized for sedimentological analysis to clarify the depositional evolution, architecture, and depositional model of lobate shallow-water deltas. The research results show that under the influence of hydraulic conditions and main distributary channels, lobate shallow-water deltas primarily undergo three evolution stages: initial channel formation, distributary channel system formation, and continuous stable evolution stage. The architecture elements of lobate shallow-water deltas can be divided into distributary channels dominated by channel deposition, proximal river mouth bars, transitional bars, and distal bars. The lateral composition and downstream deposition of the river-bar combination are the main structural units of the shallow water delta. The proximal profile distributary channels exist in the form of thick channel sand bodies with multiple repetitive intercalation cycles, and thick proximal bar deposits form on both sides of the distributary channels. The bifurcation of distributary channels in the middle profile leads to a reduction in channel scale, while large-scale proximal bars still develop on both sides of the channel. In the distal profile, a substantial river mouth bar complex is formed at the break of the delta front slope, and with the gradual progradation of the delta, the river mouth bar deposition zone becomes flattened. Based on the insights of the flume simulation experiments, a sedimentary model for lobate shallow-water delta was established.

Performance Comparison of VVC, AV1, HEVC, and AVC for High Resolutions

Over the years, there has been growing interest in multimedia services, especially in the video domain, where firms and subscribers require higher resolutions, framerates, and sampling precision. This results in a huge amount of data that needs to be processed, stored, and transmitted. As a result, researchers face the challenge of developing new compression standards that can reduce the amount of data while maintaining the same quality. In this paper, the compression performance of the latest and most commonly used video codecs, namely H.266/VVC, AV1, H265/HEVC, and H.264/AVC was examined. The test set included seven sequences of various content at 8K, Ultra HD (UHD), and Full HD (FHD) resolutions, encoded to bitrates ranging from 1 to 15 Mbps for FHD and UHD resolutions and from 5 to 50 Mbps for 8K resolution. Objective quality metrics, such as peak signal-to-noise ratio (PSNR), the structural similarity index (SSIM), and video multi-method assessment fusion (VMAF) were used to measure codec performance. The results showed that H.266/VVC outperformed all other codecs, namely H.264/AVC, H.265/HEVC, and AV1, in terms of the Bjøntegaard delta (BD) model. The average bitrate savings were approximately 78% for H.266/VVC, 63% for AV1, and 53% for H.265/HEVC relative to H.264/AVC, 59% for H.266/VVC and 22% for AV1 compared to H.264/AVC, and 46% for H.266/VVC relative to AV1 (all for 8K resolution). The results also showed that codec performance varied depending on resolution, with higher resolutions showing greater efficiency for newly developed codecs, such as H.266/VVC and AV1. This confirms the fact that the H.266/VVC and AV1 codecs were primarily developed for videos at high resolutions, such as 8K and/or UHD.

Deltas: New paradigms

AbstractDeltas are deposits directly accumulated by land‐generated gravity flows in a standing body of water. The paradigm of deltaic sedimentation has dramatically changed during recent years, from the popular very simplified ternary models of marine littoral deltas towards more realistic and comprehensive models, considering the importance of sediment‐laden river discharges. Ternary delta models were designed for clean rivers, where a stream flow drags the sediments. Depending on the basin dynamics, these littoral deposits can be modified, forming tidal‐dominated, wave‐dominated or fluvial‐dominated littoral deltas. In recent years, a new classification of delta systems was proposed, based on contrasting the salinity of the receiving water body with the bulk density of the incoming fluvial discharge. Rivers are highly dynamic systems, and their discharges can be very variable in terms of flow duration and sediment concentration. Additionally, the salinity of the receiving water body can exhibit significant variability, especially in closed lakes and epicontinental seas, ranging from freshwater to brines. This scenario allows the distinction of three major delta categories (hypopycnal, homopycnal and hyperpycnal deltas) which can be in turn subdivided, defining seven delta types. Hypopycnal deltas form when the bulk density of the incoming flow is lower than the density of the water in the basin, allowing the definition of three delta types, corresponding to hypersaline littoral deltas, marine littoral deltas and brackish littoral deltas. Homopycnal deltas form when the bulk density of the incoming flow is similar to the density of the water in the basin, defining a delta type termed homopycnal littoral deltas. Hyperpycnal deltas form when the bulk density of the incoming flow is higher than the density of the water in the basin, allowing the definition of three categories termed hyperpycnal littoral deltas, hyperpycnal subaqueous deltas and hyperpycnal fan deltas.

Alternative protein-based foods must contribute to micronutrient adequacy

ABSTRACT Sustainable diets must consider health, economic, environmental, and social outcomes. The development and production of alternative protein foods should also make these considerations. We examined the nutritional role of these foods, with New Zealand (NZ) as a case study. We used the DELTA Model® to assess 2020 NZ nutrient supply. We then simulated the substitution of 50% of meat supply (by mass) with various plant protein sources (soy, peas, beans, and mushroom), and observed the impact on nutrient supply. NZ had an undersupply of calcium (38%), vitamin E (34% deficit), dietary fibre (20%), potassium (13%), and vitamin C (10%) compared to population requirements. Contrastingly, there was sufficient protein and amino acid supply for an additional 2.4 million people. Halving of meat supply resulted in decreased availability for potassium (21% deficit), zinc (17%), folate (10%), and iron (9%). Soy proved the best nutritional replacement for meat, with reduced deficits for all undersupplied nutrients compared to 2020. Replacement with other modelled protein sources resulted in greater nutrient deficits. The nutritional implications beyond protein must be considered when making dietary substitutions. There are clear public health reasons to fortify alternative proteins with both the nutrients lost in substitution and those already in deficit.

Analysis of global nutrient gaps and their potential to be closed through redistribution and increased supply.

Global food systems are crucial for sustaining life on Earth. Although estimates suggest that the current production system can provide enough food and nutrients for everyone, equitable distribution remains challenging. Understanding global nutrient distribution is vital for addressing disparities and creating effective solutions for the present and future. This study analyzes global nutrient supply changes to address inadequacies in certain populations using the existing DELTA Model®, which uses aggregates of global food production to estimate nutrient adequacy. By examining the 2020 global food commodity and nutrient distribution, we project future food production in 2050 needs to ensure global adequate nutrition. Our findings reveal that while some nutrients appear to be adequately supplied on a global scale, many countries face national insufficiencies (% supply below the population reference intake) in essential vitamins and minerals, such as vitamins A, B12, B2, potassium, and iron. Closing these gaps will require significant increases in nutrient supply. For example, despite global protein supply surpassing basic needs for the 2050 population, significant shortages persist in many countries due to distribution variations. A 1% increase in global protein supply, specifically targeting countries with insufficiencies, could address the observed 2020 gaps. However, without consumption pattern changes, a 26% increase in global protein production is required by 2050 due to population growth. In this study, a methodology was developed, applying multi-decade linear convergence to sufficiency values at the country level. This approach facilitates a more realistic assessment of future needs within global food system models, such as the DELTA Model®, transitioning from idealized production scenarios to realistic projections. In summary, our study emphasizes understanding global nutrient distribution and adjusting minimum global nutrient supply targets to tackle country-level inequality. Incorporating these insights into global food balance models can improve projections and guide policy decisions for sustainable, healthy diets worldwide.

The Impact of Primary Care Physician Capacity on Preventable Hospitalizations: Identifying Bright Spots in the Appalachian & Mississippi Delta Regions.

Several studies have documented that higher rates of primary care physicians are associated with lower rates of preventable hospitalizations. Counties with higher rates of preventable hospitalizations are found in the Appalachian and Mississippi (MS) Delta Regions. (1) To determine if the association of primary care capacity with preventable hospitalizations is different in the Appalachian and MS Delta regions compared to the rest of the U.S., and (2) to explore primary care capacity in counties with lower-than-expected preventable hospitalization rates. This study modeled preventable hospitalizations with primary care physicians (PCP) per 100,000 (PCP capacity) while controlling for several factors. A spatial regime variable was also included, which modeled Appalachian and MS Delta regions separately. Next, PCP capacity was removed from the model and a geospatial residual analysis was performed to identify geographic clusters of counties with lower-than-expected rates of preventable hospitalizations (bright spots). PCP capacity in bright spots was then compared to that in counties with higher-than-expected rates (cold spots). Higher PCP capacity was significantly associated with lower rates of preventable hospitalizations in the rest of U.S. model, though was not significant for the Appalachian or MS Delta models. The residual analysis showed that compared to counties with higher-than-expected rates (cold spots), counties with lower-than-expected rates (bright spots) had significantly higher PCP capacity, though not in the MS Delta region. Consistent with previous literature, it was found that the factors associated with preventable hospitalizations vary by region, though the results are mixed when looking at the Appalachian and MS Delta regions separately. Future research should explore characteristics of bright spots within the Appalachian and MS Delta regions.

MRI-Based Radiomics and Delta-Radiomics Models of the Patella Predict the Radiographic Progression of Osteoarthritis: Data From the FNIH OA Biomarkers Consortium

To analyse the MRI-based radiomics and delta-radiomics features to establish radiomics models for predicting the radiographic progression of osteoarthritis (OA). The data used in this research come from the dataset of the FNIH Biomarker Consortium Project within the Osteoarthritis Initiative (OAI). 565 participants randomly divided into training and validation groups at a 7:3 ratio. The training cohort consisted of 395 participants and included 202 cases. The validation cohort consisted of 170 participants and included 87 cases. Least absolute shrinkage and selection operator (LASSO) was used for feature selection. Support vector machine (SVM) was used to establish radiomics models and clinical and biomarker models for predicting the radiographic progression of OA. The predictive ability of the model was evaluated by the area under the curve (AUC). The baseline, 24M, Delta, and two combination radiomics models (Baseline and Delta, 24M and Delta) all showed good predictive performance in the training and validation cohorts, with the combination model exhibiting the best performance. In the training cohort, the AUCs were 0.851 (95% CI: 0.812-0.890), 0.825 (95% CI: 0.784-0.865), 0.804 (95% CI: 0.761-0.847), 0.892 (95% CI: 0.860-0.924) and 0.884 (95% CI: 0.851-0.917), respectively. The AUCs in the validation cohort were 0.741 (95% CI: 0.667-0.814), 0.786 (95% CI: 0.716-0.856), 0.745 (95% CI: 0.671-0.819), 0.781 (95% CI: 0.711-0.851) and 0.802 (95% CI: 0.736-0.869), respectively. As compared, the clinical and biomarker models have AUC <0.74. The DeLong test showed that the predictive performance of the radiomics models in the training and validation cohorts was significantly better than that of the clinical and biomarker models (P<0.001). The MRI-based radiomics models of the patella all showed good predictive performance performed better than the clinical and biomarker models in predicting the radiographic progression of OA. Delta radiomics can improve the predictive performance of the single time model, the combined model of 24M and Delta provided the best predictive performance.

Early acquired resistance to EGFR-TKIs in lung adenocarcinomas before radiographic advanced identified by CT radiomic delta model based on two central studies

Early acquired resistance (EAR) to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) in lung adenocarcinomas before radiographic advance cannot be perceived by the naked eye. This study aimed to discover and validate a CT radiomic model to precisely identify the EAR. Training cohort (n = 67) and internal test cohort (n = 29) were from the First Affiliated Hospital of Fujian Medical University, and external test cohort (n = 29) was from the Second Affiliated Hospital of Xiamen Medical College. Follow-up CT images at three different times of each patient were collected: (1) baseline images before EGFR-TKIs therapy; (2) first follow-up images after EGFR-TKIs therapy (FFT); (3) EAR images, which were the last follow-up images before radiographic advance. The features extracted from FFT and EAR were used to construct the classic radiomic model. The delta features which were calculated by subtracting the baseline from either FFT or EAR were used to construct the delta radiomic model. The classic radiomic model achieved AUC 0.682 and 0.641 in training and internal test cohorts, respectively. The delta radiomic model achieved AUC 0.730 and 0.704 in training and internal test cohorts, respectively. Over the external test cohort, the delta radiomic model achieved AUC 0.661. The decision curve analysis showed that when threshold of the probability of the EAR to the EGFR-TKIs was between 0.3 and 0.82, the proposed model was more benefit than treating all patients. Based on two central studies, the delta radiomic model derived from the follow-up non-enhanced CT images can help clinicians to identify the EAR to EGFR-TKIs in lung adenocarcinomas before radiographic advance and optimize clinical outcomes.

The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment

Abstract. As a genre of physics-informed machine learning, differentiable process-based hydrologic models (abbreviated as δ or delta models) with regionalized deep-network-based parameterization pipelines were recently shown to provide daily streamflow prediction performance closely approaching that of state-of-the-art long short-term memory (LSTM) deep networks. Meanwhile, δ models provide a full suite of diagnostic physical variables and guaranteed mass conservation. Here, we ran experiments to test (1) their ability to extrapolate to regions far from streamflow gauges and (2) their ability to make credible predictions of long-term (decadal-scale) change trends. We evaluated the models based on daily hydrograph metrics (Nash–Sutcliffe model efficiency coefficient, etc.) and predicted decadal streamflow trends. For prediction in ungauged basins (PUB; randomly sampled ungauged basins representing spatial interpolation), δ models either approached or surpassed the performance of LSTM in daily hydrograph metrics, depending on the meteorological forcing data used. They presented a comparable trend performance to LSTM for annual mean flow and high flow but worse trends for low flow. For prediction in ungauged regions (PUR; regional holdout test representing spatial extrapolation in a highly data-sparse scenario), δ models surpassed LSTM in daily hydrograph metrics, and their advantages in mean and high flow trends became prominent. In addition, an untrained variable, evapotranspiration, retained good seasonality even for extrapolated cases. The δ models' deep-network-based parameterization pipeline produced parameter fields that maintain remarkably stable spatial patterns even in highly data-scarce scenarios, which explains their robustness. Combined with their interpretability and ability to assimilate multi-source observations, the δ models are strong candidates for regional and global-scale hydrologic simulations and climate change impact assessment.

Prediction uncertainty and volatility for carbon price using an adaptive lower and upper bound estimation model

AbstractThe high volatility and uncertainty of carbon price have always been two major challenges in carbon price forecasting. To solve these two challenges, an adaptive lower‐and upper‐bound estimation (LUBE) model with improved variational mode decomposition (VMD) and PSO‐based interval optimization strategy is proposed for interval prediction of carbon price. To validate effectiveness and superiority, the adaptive LUBE model and several competitive models, including the bootstrap model, delta model, and Bayesian model, were utilized for interval prediction of carbon prices of Beijing and Shanghai. Compared with other models, the adaptive LUBE model not only has excellent coverage but also has the narrowest interval width in both training set and test set. Therefore, the excellent comparison results show that the proposed model can obtain a more reliable and higher‐quality prediction interval, which can be a novel and effective carbon prices forecasting tool for governments and enterprises.

Delta-radiomics-based models for toxicity prediction in radiotherapy: A systematic review and meta-analysis.

Delta-radiomics models are potentially able to improve the treatment assessment than single-time point features. The purpose of this study is to systematically synthesize the performance of delta-radiomics-based models for radiotherapy (RT)-induced toxicity. A literature search was performed following the PRISMA guidelines. Systematic searches were performed in PubMed, Scopus, Cochrane and Embase databases in October 2022. Retrospective and prospective studies on the delta-radiomics model for RT-induced toxicity were included based on predefined PICOS criteria. A random-effect meta-analysis of AUC was performed on the performance of delta-radiomics models, and a comparison with non-delta radiomics models was included. Of the 563 articles retrieved, 13 selected studies of RT-treated patients on different types of cancer (HNC = 571, NPC = 186, NSCLC = 165, oesophagus = 106, prostate = 33, OPC = 21) were eligible for inclusion in the systematic review. Included studies show that morphological and dosimetric features may improve the predictive model performance for the selected toxicity. Four studies that reported both delta and non-delta radiomics features with AUC were included in the meta-analysis. The AUC random effects estimate for delta and non-delta radiomics models were 0.80 and 0.78 with heterogeneity, I2 of 73% and 27% respectively. Delta-radiomics-based models were found to be promising predictors of predefined end points. Future studies should consider using standardized methods and radiomics features and external validation to the reviewed delta-radiomics model.

Develop and validate a radiomics space-time model to predict the pathological complete response in patients undergoing neoadjuvant treatment of rectal cancer: an artificial intelligence model study based on machine learning

ObjectiveIn this study, we aimed to investigate the predictive efficacy of magnetic resonance imaging (MRI) radiomics features at different time points of neoadjuvant therapy for rectal cancer in patients with pathological complete response (pCR). Furthermore, we aimed to develop and validate a radiomics space–time model (RSTM) using machine learning for artificial intelligence interventions in predicting pCR in patients.MethodsClinical and imaging data of 83 rectal cancer patients were retrospectively analyzed, and the patients were classified as pCR and non-pCR patients according to their postoperative pathological results. All patients received one MRI examination before and after neoadjuvant therapy to extract radiomics features, including pre-treatment, post-treatment, and delta features. Delta features were defined by the ratio of the difference between the pre- and the post-treatment features to the pre-treatment feature. After feature dimensionality reduction based on the above three feature types, the RSTM was constructed using machine learning methods, and its performance was evaluated using the area under the curve (AUC).ResultsThe AUC values of the individual basic models constructed by pre-treatment, post-treatment, and delta features were 0.771, 0.681, and 0.871, respectively. Their sensitivity values were 0.727, 0.864, and 0.909, respectively, and their specificity values were 0.803, 0.492, and 0.656, respectively. The AUC, sensitivity, and specificity values of the combined basic model constructed by combining pre-treatment, post-treatment, and delta features were 0.901, 0.909, and 0.803, respectively. The AUC, sensitivity, and specificity values of the RSTM constructed using the K-Nearest Neighbor (KNN) classifier on the basis of the combined basic model were 0.944, 0.871, and 0.983, respectively. The Delong test showed that the performance of RSTM was significantly different from that of pre-treatment, post-treatment, and delta models (P < 0.05) but not significantly different from the combined basic model of the three (P > 0.05).ConclusionsThe RSTM constructed using the KNN classifier based on the combined features of before and after neoadjuvant therapy and delta features had the best predictive efficacy for pCR of neoadjuvant therapy. It may emerge as a new clinical tool to assist with individualized management of rectal cancer patients.

THE ARCHITECTURE OF DYNAMIC INTEGRATED INTELLIGENCE MODEL FOR MANAGING INNOVATION PROJECTS

Existing Intelligence Models for managing innovative projects and programs have been explored. The competency-based approach is considered the basis for building a Dynamic Integrated Intelligence Model for managing innovative projects and programs. The proposed architecture of two groups of competence at a high level and five groups of Competencies lower level of the Dynamic Integrated Intelligence Model for Managing Innovation Projects. The Dynamic Integrated Intelligence model has two groups of high levels – Fluid intelligence and Crystallized intelligence. The lower-level model is presented by seven groups of interrelated competencies: strategic, managerial, emotional, social, cognitive, business, and technical. Each group of competencies of the Dynamic Integrated Intelligence Model is defined. The Dynamic Integrated Intelligence Model (DIIM) is a framework that helps individuals and organizations develop and enhance their ability to adapt to change and uncertainty. It is based on the idea that intelligence is not a fixed trait but rather a dynamic set of skills and abilities that can be developed and improved over time. By developing and enhancing these four components of intelligence, individuals, and organizations can become more adaptable, innovative, and resilient in the face of change and uncertainty. The dynamic integrated intelligence model can be applied in an Agile environment to enhance the cognitive and social-emotional skills of Agile team members. Applying the dynamic integrated intelligence model in an Agile environment can lead to better teamwork, enhanced problem-solving abilities, and improved project outcomes. The organization competence-based IPMA Delta model was used to assess the project team’s competence level. As an example of the application, the Dynamic Integrated Intelligence Model to the implementation of the double degree project of the Kyiv National University of Civil Engineering and Architecture and the Dortmund University of Applied Sciences.

Design of a Quantization-Based DNN Delta Compression Framework for Model Snapshots and Federated Learning

Deep neural networks (DNNs) have achieved remarkable success in many fields. However, large-scale DNNs also bring storage costs when storing snapshots for preventing clusters’ frequent failures or incur significant communication overheads when transmitting DNNs in the Federated Learning (FL). Recently, several approaches, such as Delta-DNN and LC-Checkpoint, aim to reduce the size of DNNs’ snapshot storage by compressing the difference between two neighboring versions of the DNNs (a.k.a., delta). However, we observe that existing approaches, applying traditional global lossy quantization techniques in DNN's delta compression, can not fully exploit the data similarity since the parameters’ value ranges vary among layers. To fully explore the similarity of the delta model and improve the compression ratio, we propose a quantization-based local-sensitive delta compression approach, named QD-Compressor, by developing a layer-based local-sensitive quantization scheme and error feedback mechanism. Specifically, the quantizers and number of quantization bits are adaptive among layers based on the value distribution and weighted entropy of the delta's parameters. To avoid quantization error degrading the performance of the restored model, an alternative error feedback mechanism is designed to dynamically correct the quantization error during the training process. Experiments on multiple popular DNNs and datasets show that QD-Compressor obtains a higher 7×-40× compression ratio in the model snapshot compression scenario than the state-of-the-art approaches. Additionally, QD-Compressor achieves an 11×-15× compression ratio to the residual model of the Federated Learning compression scenario.

Global inhibition in head-direction neural circuits: a systematic comparison between connectome-based spiking neural circuit models

The recent discovery of the head-direction (HD) system in fruit flies has provided unprecedented insights into the neural mechanisms of spatial orientation. Despite the progress, the neural substance of global inhibition, an essential component of the HD circuits, remains controversial. Some studies suggested that the ring neurons provide global inhibition, while others suggested the Δ7 neurons. In the present study, we provide evaluations from the theoretical perspective by performing systematic analyses on the computational models based on the ring-neuron (R models) and Δ7-neurons (Delta models) hypotheses with modifications according to the latest connectomic data. We conducted four tests: robustness, persistency, speed, and dynamical characteristics. We discovered that the two models led to a comparable performance in general, but each excelled in different tests. The R Models were more robust, while the Delta models were better in the persistency test. We also tested a hybrid model that combines both inhibitory mechanisms. While the performances of the R and Delta models in each test are highly parameter-dependent, the Hybrid model performed well in all tests with the same set of parameters. Our results suggest the possibility of combined inhibitory mechanisms in the HD circuits of fruit flies.

Delta Radiomics Model Predicts Lesion-Level Responses to Tyrosine Kinase Inhibitors in Patients with Advanced Renal Cell Carcinoma: A Preliminary Result

This study aimed to develop and internally validate computed tomography (CT)-based radiomic models to predict the lesion-level short-term response to tyrosine kinase inhibitors (TKIs) in patients with advanced renal cell carcinoma (RCC). This retrospective study included consecutive patients with RCC that were treated using TKIs as the first-line treatment. Radiomic features were extracted from noncontrast (NC) and arterial-phase (AP) CT images. The model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). A total of 36 patients with 131 measurable lesions were enrolled (training: validation = 91: 40). The model with five delta features achieved the best discrimination capability with AUC values of 0.940 (95% CI, 0.890‒0.990) in the training cohort and 0.916 (95% CI, 0.828‒1.000) in the validation cohort. Only the delta model was well calibrated. The DCA showed that the net benefit of the delta model was greater than that of the other radiomic models, as well as that of the treat-all and treat-none criteria. Models based on CT delta radiomic features may help predict the short-term response to TKIs in patients with advanced RCC and aid in lesion stratification for potential treatments.

Sedimentary facies and stratigraphy of the Campanian-Maastrichtian Taloka Formation, southeastern Iullemmeden Basin, Nigeria

The sedimentology of the Campanian – Maastrichtian Taloka Formation of the southeastern Iullemmeden Basin in Northwest Nigeria is poorly understood. There are still major uncertainties regarding its depositional setting. This study presents a detailed facies analysis and sequence stratigraphic interpretation for the Taloka Formation of Rima Group of the southeastern Iullemmeden Basin in Nigeria based on outcrop observations, sedimentary logging and ichnological analyses. Thin sections, representative of delineated facies associations, are included in the data. Twelve lithofacies and four facies associations were delineated. The facies associations are interpreted to represent intertidal flat, lower shoreface, delta-front and tide-influenced channel environments. The facies associations are arranged vertically into a wave- and tide-influenced deltaic facies succession. Paleosols, low-high bioturbation intensity and low ichnodiversity, (e.g., Thalassinoides and Skolithos ichnofossils) were found in the deposits. Furthermore, the stacking patterns of the Taloka Formation (juxtaposed wave- and tide-influenced facies associations overlain by nearshore and restricted marine deposits) reflect an aggradational set of shallowing upward, progradational parasequences, interpreted as lowstand systems tract: deposits formed when supply balanced accommodation, under low relative sea-level rise condition. A mixed-energy delta model is proposed for the Taloka Formation. The Taloka Formation represents an example of a slowly subsiding, highly-moderately embayed coast.