Single Model Research Articles

Cascaded Interleaved DC–DC Converter for a Bidirectional Electric Vehicle Charging Station

The dc-dc bidirectional step-up interleaved converter coupled by a central capacitor in a cascaded topology is used as a Bidirectional Electric Vehicle Charging Station (BEVCS) for charging and discharging the battery of an Electric Vehicle (EV). In this paper, a unified (coupled) model is proposed combining all switching intervals in a single model, allowing an accurate analysis and a better design for the controllers. In addition, this solution enables not only stage 1 to regulate the dc-link voltage on the terminals of stage 2, but also to use a classical Proportional-Integral ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$PI$</tex-math></inline-formula> ) controller or an adaptive <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{SoC}$</tex-math></inline-formula> -sharing function as a control structure. Finally, stability analysis, simulations, and experimental results are accomplished to evaluate the effectiveness of the proposed approach.

Optimized parameter extraction techniques for enhanced performance evaluation of organic solar cells

The global energy landscape is in the midst of a transformative shift, compelled by the urgent need to reduce our reliance on fossil fuels and embrace eco-friendly alternatives. Organic photovoltaics (OPVs) have emerged as a promising alternative, offering the distinct advantage of performing well in low-light conditions, including indoor environments. Extensive research and development efforts are dedicated to realizing the full potential of OPVs as adaptable, cost-effective, and environmentally friendly solar energy solutions. This paper conducts a thorough examination of the intricate characterization of organic solar cells, with a specific emphasis on crucial parameters like power conversion efficiency, open-circuit voltage, and fill factor. The study utilizes a single diode model to simulate these cells' behavior, employing a meticulous process for parameter extraction. This method leverages Origin software and Python programming, incorporating open-source packages to ensure robust validation. This systematic and rigorous approach significantly enhances our comprehension of OPVs and plays a substantial role in optimizing their performance. In essence, this research represents a significant step forward in advancing sustainable energy technologies, laying a foundation for a greener and more environmentally conscious future.

Exploring the potentials of artificial intelligence towards carbon neutrality: Technological convergence forecasting through link prediction and community detection

To explore future opportunities for artificial intelligence (AI) in achieving carbon neutrality goals, this study introduces a forecasting framework that combines link prediction and community detection. The proposed framework begins by utilizing patent information and the International Patent Classification to construct a co-occurrence network. Subsequently, it is computed and validated using two emerging technology datasets: one related to AI towards carbon neutrality and the other to Virtual Reality. A feature selection method and classification model are employed in this process. Finally, predicted convergence links are clustered using network modularity detection to unveil future trends. Through a comprehensive comparison of various similarity indicators in link prediction, this study identifies local indices (Resource Allocation, Adam-Adar) and global indices (Rooted Pagerank, SimRank) as the most featured prediction indicators. These research findings address a gap in the existing literature by providing insights into the rationale behind the selection of link similarity indicators, thereby resolving the issue of randomness in indicator selection. This study also finds that among all single and ensemble models, tree-based models, particularly Random Forest, emerge as the best-performing classifiers. The predicted results suggest that AI technologies towards carbon neutrality can be categorized into two future trends: theoretical methodological foundations and application sectors. This enables the identification and recommendation of future technological potentials on both micro and macro levels. Specifically, it facilitates the discovery of the most promising subdivision technology fields in the future, operating at the microlevel of patent main grouping, often characterized by significant cross-disciplinary links, and at the macrolevel, by visualizing potential technological convergence networks and clustering the trends. The analytical processes and quantitative outcomes of this study can support strategic decision-making for AI, offering valuable insights for both policy-makers and practitioners in their pursuit of green innovation solutions towards carbon neutrality.

Multifunctional Optical Tomography System With High-Fidelity Surface Extraction Based on a Single Programmable Scanner and Unified Pinhole Modeling.

Macroscopic optical tomography is a non-invasive method that can visualize the 3D distribution of intrinsic optical properties or exogenous fluorophores, making it highly attractive for small animal imaging. However, reconstructing the images requires prior knowledge of surface information. To address this, existing systems often use additional hardware components or integrate multimodal information, which is expensive and introduces new issues such as image registration. Our goal is to develop a multifunctional optical tomography system that can extract surface information using a concise hardware design. Our proposed system uses a single programmable scanner to implement both surface extraction and optical tomography functions. A unified pinhole model is used to describe both the illumination and detection procedures for capturing 3D point cloud. Line-shaped scanning is adopted to improve both spatial resolution and speed of surface extraction. Finally, we integrate the extracted surface information into the optical tomographic reconstruction to more accurately map the fluorescence distribution. Comprehensive phantom experiments with different levels of complexity were designed to evaluate the performance of surface extraction and fluorescence tomography. We also imaged the axillary lymph nodes in living mice after injection of fluorophore, demonstrating the proposed system facilitates more reliable fluorescence tomography. We have successfully developed a versatile optical tomography system by leveraging concise hardware design and unified pinhole modeling. Phantom validation demonstrates that our system provides high-precision surface information with a maximum error of 0.1 mm, while the surface-guided FMT reconstruction is more reliable than the blind reconstruction using simplified surface geometry, elevating several quantitative metrics including RMSE, CNR, and Dice. Our work explores the feasibility of obtaining additional surface information using existing components of standalone optical tomography. This makes the optical tomographic technique more accurate and more accessible to biomedical researchers.

Tracing the evolution of short-period binaries with super-synchronous fast rotators

Context. The initial distribution of rotational velocities of stars is still poorly known, and how the stellar spin evolves from birth to the various end points of stellar evolution is an actively debated topic. Binary interactions are often invoked to explain the existence of extremely fast-rotating stars (vsin i ≳ 200 km s−1). The primary mechanisms through which binaries can spin up stars are tidal interactions, mass transfer, and possibly mergers. However, fast rotation could also be primordial, that is, a result of the star formation process. To evaluate these scenarios, we investigated in detail the evolution of three known fast-rotating stars in short-period spectroscopic and eclipsing binaries, namely HD 25631, HD 191495, and HD 46485, with primaries of masses of 7, 15, and 24 M⊙, respectively, with companions of ∼1 M⊙ and orbital periods of less than 7 days. These systems belong to a recently identified class of binaries with extreme mass ratios, whose evolutionary origin is still poorly understood. Aims. We evaluated in detail three scenarios that could explain the fast rotation observed in these binaries: it could be primordial, a product of mass transfer, or the result of a merger within an originally triple system. We also discuss the future evolution of these systems to shed light on the impact of fast rotation on binary products. Methods. We computed grids of single and binary MESA models varying tidal forces and initial binary architectures to investigate the evolution and reproduce observational properties of these systems. When considering the triple scenario, we determined the region of parameter space compatible with the observed binaries and used a publicly available machine-learning model to determine the dynamical stability of the triple system. Results. We find that, because of the extreme mass-ratio between binary components, tides have a limited impact, regardless of the prescription used, and that the observed short orbital periods are at odds with post-mass-transfer scenarios. We also find that the overwhelming majority of triple systems compatible with the observed binaries are dynamically unstable and would be disrupted within years of formation, forcing a hypothetical merger to happen so close to a zero-age main-sequence that it could be considered part of the star formation process. Conclusions. The most likely scenario to form such young, rapidly rotating, and short-period binaries is primordial rotation, implying that the observed binaries are pre-interaction ones. Our simulations further indicate that such systems will subsequently go through a common envelope and likely merge. These binaries show that the initial spin distribution of massive stars can have a wide range of rotational velocities.

The effects of genetic and modifiable risk factors on brain regions vulnerable to ageing and disease

We have previously identified a network of higher-order brain regions particularly vulnerable to the ageing process, schizophrenia and Alzheimer’s disease. However, it remains unknown what the genetic influences on this fragile brain network are, and whether it can be altered by the most common modifiable risk factors for dementia. Here, in ~40,000 UK Biobank participants, we first show significant genome-wide associations between this brain network and seven genetic clusters implicated in cardiovascular deaths, schizophrenia, Alzheimer’s and Parkinson’s disease, and with the two antigens of the XG blood group located in the pseudoautosomal region of the sex chromosomes. We further reveal that the most deleterious modifiable risk factors for this vulnerable brain network are diabetes, nitrogen dioxide – a proxy for traffic-related air pollution – and alcohol intake frequency. The extent of these associations was uncovered by examining these modifiable risk factors in a single model to assess the unique contribution of each on the vulnerable brain network, above and beyond the dominating effects of age and sex. These results provide a comprehensive picture of the role played by genetic and modifiable risk factors on these fragile parts of the brain.

UniGen: A Unified Generative Framework for Retrieval and Question Answering with Large Language Models

Generative information retrieval, encompassing two major tasks of Generative Document Retrieval (GDR) and Grounded Answer Generation (GAR), has gained significant attention in natural language processing. Existing methods for GDR and GAR rely on separate retrieval and reader modules, which hinder simultaneous optimization. To overcome this, we present UniGen, a Unified Generative framework for retrieval and question answering that integrates both tasks into a single generative model leveraging the capabilities of large language models. UniGen employs a shared encoder and two distinct decoders for generative retrieval and question answering. To facilitate the learning of both tasks, we introduce connectors, generated by large language models, to bridge the gaps between query inputs and generation targets, as well as between document identifiers and answers. Furthermore, we propose an iterative enhancement strategy that leverages generated answers and retrieved documents to iteratively improve both tasks. Through extensive experiments on the MS MARCO and NQ datasets, we demonstrate the effectiveness of UniGen, showcasing its superior performance in both retrieval and question answering tasks.

Pathologic burden goes with the flow: MRI perfusion and pathologic burden in frontotemporal lobar degeneration due to tau

Abstract Regional cerebral blood flow (CBF) changes quantified using arterial spin labeling (ASL) are altered in neurodegenerative disorders such as frontotemporal lobar degeneration due to tau (FTLD-tau), but the relationship between ASL CBF and pathologic burden has not been assessed. Our objective was to determine whether regional ASL CBF acquired antemortem in patients with FTLD-tau is related to pathologic burden measured at autopsy in those same regions in the same patients to directly test the imaging-pathology relationship. In this case-control study, data were acquired between 3/4/2010 and 12/16/2018. Data processing and analysis were completed in 2023. Twenty-one participants with autopsy-confirmed FTLD-tau (N = 10 women, mean[SD] age 67.9[7.56] years) along with 25 control participants (N = 15 women, age 64.7[7.53]) were recruited through the cognitive neurology clinic at the University of Pennsylvania. All participants had ASL and T1-weighted images collected antemortem. ASL images were processed to estimate CBF and T1-weighted images were processed to estimate gray matter (GM) volumes in regions corresponding to regions sampled postmortem. Digital quantification of pathologic burden was performed to find the percent area occupied (%AO) of pathologic FTLD-tau at autopsy. Regional CBF and GM volumes were both related to pathologic burden in the same regions from the same participants. Strengths of model fits of imaging measures to pathologic burden were compared. CBF in FTLD-tau and controls were compared, with results considered significant at p &amp;lt; 0.05 after Bonferroni correction. We found that relative to controls, FTLD-tau displayed hypoperfusion in anterior cingulate, orbitofrontal, middle frontal, and superior temporal regions, as well as angular gyrus. For patients with FTLD-tau regional CBF was significantly associated with pathologic burden (beta = -1.07, t = -4.80, p &amp;lt; 0.005). Models including both GM volume and CBF provided significantly better fits to pathologic burden data than single modality models (p &amp;lt; 0.05, Bonferroni-corrected). Our results indicate that reduced CBF measured using ASL MRI is associated with increased pathologic burden in FTLD-tau and adds complementary predictive value of pathologic burden to structural MRI.

OSTOD: One-Step Task-Oriented Dialogue with activated state and retelling response

As of present, the progress of conversational AI research has been greatly propelled by large-scale pre-trained language models. In particular, task-oriented dialogue systems have gained widespread attention owing to their immense potential in helping individuals accomplish diverse objectives, such as booking hotels, making restaurant reservations, and purchasing train tickets. In the past, task-oriented dialogue systems were typically viewed as a multi-step process that included spoken language understanding, dialogue state tracking, dialogue policy learning, and natural language generation. More recently, large-scale pre-trained language models enables the development of end-to-end neural pipeline task-oriented dialogue systems, which combine multiple steps into a single model, allowing for joint optimization and preventing error propagation. However, in order to explicitly retrieve information from databases to ensure the interpretability of the system, almost all end-to-end neural pipeline methods inevitably require predicting dialogue state as an intermediate result specialized for the domain or task, which results in significant challenges for generalization. To solve the problem above, we propose One-Step Task-Oriented Dialogue (OSTOD) in this paper, which models task-oriented dialogue by synchronously generating activated states and retelling responses, where activated states refer to slot values that contribute to database access, and retelling responses are system responses that contain activated state information. Specifically, first, automatic methods are designed to build data containing activated states and retelling responses. Then, a joint generation model that synchronously predicts activated states and retelling responses in a single step is proposed for task-oriented dialogue modelling. Based on empirical results obtained from the MultiWOZ 2.0 and MultiWOZ 2.1 datasets, our OSTOD model demonstrates comparable performance to state-of-the-art baselines. Moreover, our model exhibits exceptional generalization capabilities in few-shot learning and domain transfer scenarios.

A combination prediction model based on Theil coefficient and induced continuous aggregation operator for the prediction of Shanghai composite index

This paper proposes an interval combination prediction model for Shanghai composite index, utilizing the Theil coefficient and the induced continuous generalized ordered weighted logarithmic harmonic averaging (ICGOWLHA) operator. The effectiveness of the proposed model under specific weight conditions and the existence of its analytical solution are demonstrated. Shanghai composite index's case analysis demonstrates that, in terms of interval root mean squared error (IRMSE), interval mean absolute error (IMAE), interval mean absolute percentage error (IMAPE), and interval mean squared percentage error (IMSPE), the proposed model's predictive performance improvements over the best-performing single prediction model are 29.33%, 25.72%, 26.10%, and 28.86%, respectively. At the same time, the theoretical properties of the model are verified in the results of the case analysis, and the model's convergence is reflected in sensitivity analysis. Through extensive model comparisons, it is observed that the model proposed in this paper exhibits strong generalization, without specific limitations on data size or feature count. It demonstrates good aggregation prediction performance for interval data. Moreover, it is applicable to various fields, including finance, environment, and others.

End-to-end digital twin creation of the archaeological landscape in Uruk-Warka (Iraq)

ABSTRACT This article demonstrates recent technological advancements enabling the creation of digital twins for expansive real-world terrains. Focusing on the archaeological site Uruk-Warka in southern Iraq the German Archaeological Institute deployed the Delta Quad Pro, a winged UAV equipped with vertical take-off and landing capabilities. This UAV captured 32,000 aerial images of the ancient city and its immediate environment. Each image was precisely geotagged using an integrated DGNSS receiver. Utilizing advanced 3D photogrammetry software, we synthesized these images into a single georeferenced model. The outcome was a detailed triangulated mesh, comprising of one billion triangles and 1024 8k-resolution texture files, representing a 40 square kilometers terrain. When rendered in a game-engine and applying the new technologies Nanite and Streaming Virtual Texture, this massive dataset can be visualized in real-time. The result is ‘Uruk-VR,’ a digital twin of the Uruk-Warka archaeological site, most of which has never been investigated. Basic tools have been implemented to annotate features and measure distances within the Uruk-VR. The methodologies showcased here are scalable for creating digital twins of diverse terrains. Uruk-VR's potential extends to research, education and conservation, exemplifying how game engines can seamlessly integrate vast and diverse geospatial data in 3D space.

SOFR term structure dynamics—Discontinuous short rates and stochastic volatility forward rates

AbstractThe Secured Overnight Funding Rate (SOFR) has become the risk‐free rate benchmark in US dollars, thus term structure models should reflect key features exhibited by SOFR and forward rates implied by SOFR futures. We construct a multifactor, stochastic volatility term structure model which incorporates these features. Calibrating to options on SOFR futures, we achieve a reasonable fit to the market across maturities and strikes in a single model. This also provides novel insights into SOFR term rate behavior (and implied volatilities) within their accrual periods, and a model mechanism by which interest rate mean reversion arises from monetary policy.

Embedding social emotional learning from the bottom up in multi‐tiered services and supports frameworks

AbstractStudent mental health needs continue to rise across the United States and many students and families rely on schools to provide services to meet these needs. Yet, an overwhelming number of available frameworks and approaches to school mental health (SMH) and overlapping terminology surrounding SMH supports like trauma‐informed (TI) approaches, social and emotional learning (SEL), and others can lead to confusion and potentially less effective implementation of services and supports for students. In this paper, we aim to mitigate this confusion and offer a solution that integrates several of these approaches into a single complementary model with a special emphasis on the role of SEL. We first present an overview of commonly used SMH frameworks. Next, we present the Trauma‐Informed Multi‐Tiered Systems of Support Model (TI‐SEL MTSS)–an adaptation of the TITI‐SEL MTSS–to include and emphasize the specific role of SEL as a critical foundational layer within a multi‐tiered system of support. The proposed adapted model maintains the key structure of a MTSS while highlighting the importance of embedding SEL pedagogy in daily teaching practices and all aspects of school life. A case study is used to illustrate how the proposed model adaptations can be used in practice and in tandem with TI and SMH services without being conflated as the same service as SEL. Practical implications for implementation are discussed.

Identification of optimal CMIP6 GCMs for future typical meteorological year in major cities of Indonesia using multi-criteria decision analysis

Many studies often use a single global climate model (GCM) across multiple cities to develop future Typical Meteorological Year (TMY), without emphasizing city-specific selection of GCM. The present investigation employs the Analytical Hierarchy Process (AHP) to assess city-specific GCMs for generating future TMY datasets across 29 Indonesian cities. Six GCMs from the coupled model intercomparison project phase 6 (CMIP6) were evaluated against Modern-Era Retrospective Analysis for Research Applications (MERRA-2) to assess their performance in simulating surface air temperature, precipitation, wind speed, and relative humidity. Six statistical measures were used to recognize the systematic biases. Further, AHP was applied to integrate these statistical measures to calculate the city-specific total relative error for each meteorological parameter. Results of total relative error show that TaiESM, 6-Model Ensemble (6ME), NorMM, and MPI-HR were best for simulating surface air temperature, precipitation, wind speed, and relative humidity in most cities, respectively. TMY recognizes distinctive importance among meteorological parameters. Thus, it is essential to reflect the parameter-specific importance while selecting GCMs for future TMY. Hence, AHP was reapplied on total relative errors accounting for differing weights of each meteorological parameter. Outcomes show that TaiESM, 6ME, and MPI-HR were found suitable for generating future TMY datasets in 18, 5, and 3 cities, respectively, while MPI-LR, NorLM, and NorMM were recommended for Boven Digoel, Medan, and Bengkulu cities, respectively. Using city-specific GCMs ensures precise and cost-effective future TMY generation, assisting urban planners and policymakers in designing environmentally sustainable buildings for anticipated climatic changes.

Combination prediction method of students' performance based on ant colony algorithm.

Students' performance is an important factor for the evaluation of teaching quality in colleges. The prediction and analysis of students' performance can guide students' learning in time. Aiming at the low accuracy problem of single model in students' performance prediction, a combination prediction method is put forward based on ant colony algorithm. First, considering the characteristics of students' learning behavior and the characteristics of the models, decision tree (DT), support vector regression (SVR) and BP neural network (BP) are selected to establish three prediction models. Then, an ant colony algorithm (ACO) is proposed to calculate the weight of each model of the combination prediction model. The combination prediction method was compared with the single Machine learning (ML) models and other methods in terms of accuracy and running time. The combination prediction model with mean square error (MSE) of 0.0089 has higher performance than DT with MSE of 0.0326, SVR with MSE of 0.0229 and BP with MSE of 0.0148. To investigate the efficacy of the combination prediction model, other prediction models are used for a comparative study. The combination prediction model with MSE of 0.0089 has higher performance than GS-XGBoost with MSE of 0.0131, PSO-SVR with MSE of 0.0117 and IDA-SVR with MSE of 0.0092. Meanwhile, the running speed of the combination prediction model is also faster than the above three methods.

Short term power load forecasting system based on improved neural network deep learning model

AbstractThe electricity load prediction is closely related to production and daily life. The electricity load prediction is also a very important task. With the widespread application of smart grids, load data shows an exponential growth trend. The huge amount of data in the load makes power prediction even more difficult. On the basis of traditional prediction algorithms, a power load prediction model based on machine learning and neural networks is designed. Because the single model prediction has the unstable results, a combined model is obtained based on the ensemble learning idea and two single model prediction method. The prediction results are detected by the load data. From the experimental results, the mean absolute percentage error (MAPE) of the AdaBoost‐GRU data fusion model is 0.066%. Compared to the AdaBoost‐GRU data fusion model, the MAPE decreases by 1.59% and 1.12%, respectively. The relative mass scores of the two groups decrease by 132.57% and 89.14%, respectively. The prediction accuracy is improved, which has advantages compared to traditional combination models. It can effectively enhance the accuracy of short‐term power grid load forecasting. It is an important scientific and practical reference for power grid decision‐making.

Research on Vehicle Frame Optimization Methods Based on the Combination of Size Optimization and Topology Optimization

The efficient development of electric vehicles is essential to drive society towards sustainable development. Designing a lightweight frame is a key strategy to improve the economy and environment, increase energy efficiency, and reduce carbon emissions. Taking an automatic loading and unloading mixer truck as the research object, a force analysis of its frame was conducted under six typical working conditions. A size optimization method based on a hybrid model of the Kriging model and the analytic hierarchy process (AHP) is proposed. An approximate model of the mass and maximum stress of the frame was established using the Kriging model, and the Kriging model was optimized by using the multi-objective genetic optimization algorithm and the AHP method. Meanwhile, topology optimization was introduced to improve the structural performance of the frame and reduce its weight. The optimization results show that the overall weight of the frame is reduced by 11.96% compared to the pre-optimization period, though it still meets the material performance specifications. By comparing the iterative curves of the single Kriging model with those of the AHP model, it can be seen that the initial optimization efficiency of the hybrid model is about twice as much as that of the AHP model, and the final optimization result is improved by about 3.6% compared with the Kriging model. This validates the hybrid model as an effective tool for the multi-objective optimization of electric vehicle frames, providing more efficient and accurate optimization results for frame design.

Impact of a College Sexual Violence Prevention Program on Condom Use: A Mediation Analysis.

College-age students are disproportionately impacted by sexually transmitted infections. Campus programs that reduce sexual violence have received recent investment, are increasingly common, and may offer a platform to increase condom use, but this has not yet been investigated. We explore this novel question through a secondary analysis of a randomized control trial of RealConsent, a web-based, sexual assault program for college women, on three college campuses. By estimating single and multiple-mediator models we examine the relationships between study assignment, the hypothesized mediators: self-efficacy to discuss safer sex, and clarity and assertiveness in sexual communication, and consistent condom use at follow-up. In the single mediator models, self-efficacy for safer sex communication (aOR: 1.11, 95% CI: 1.03-1.19, p = .004), assertiveness in sexual communication (aOR: 1.06, 95% CI: 1.02-1.11, p =.004), and clarity in sexual communication (aOR: 1.03, 95% CI: 1.00-1.05, p = .026) demonstrated significant direct effects on condom use. No statistically significant relationships between RealConsent and the mediators, nor indirect effects were found. In the multimediator model, there were no statistically significant associations identified. Self-efficacy, assertiveness, and clarity in communication about sex may have a positive impact on condom use but we did not find evidence that RealConsent impacted these mediators and thus no mediated effect was identified. Additional research is needed to develop and assess college-based sexual violence prevention programs that include an additional focus on skills specifically related to condom negotiation and use to understand if these widespread programs offer an efficient and effective platform to reduce the impact of sexually transmitted infections (STIs) among this high-risk population.

A Low-Cost Deep-Learning-Based System for Grading Cashew Nuts

Most of the cashew nuts in the world are produced in the developing countries. Hence, there is a need to have a low-cost system to automatically grade cashew nuts, especially in small-scale farms, to improve mechanization and automation in agriculture, helping reduce the price of the products. To address this issue, in this work we first propose a low-cost grading system for cashew nuts by using the off-the-shelf equipment. The most important but complicated part of the system is its “eye”, which is required to detect and classify the nuts into different grades. To this end, we propose to exploit advantages of both the YOLOv8 and Transformer models and combine them in one single model. More specifically, we develop a module called SC3T that can be employed to integrate into the backbone of the YOLOv8 architecture. In the SC3T module, a Transformer block is dexterously integrated into along with the C3TR module. More importantly, the classifier is not only efficient but also compact, which can be implemented in an embedded device of our developed cashew nut grading system. The proposed classifier, called the YOLOv8–Transformer model, can enable our developed grading system, through a low-cost camera, to correctly detect and accurately classify the cashew nuts into four quality grades. In our grading system, we also developed an actuation mechanism to efficiently sort the nuts according to the classification results, getting the products ready for packaging. To verify the effectiveness of the proposed classifier, we collected a dataset from our sorting system, and trained and tested the model. The obtained results demonstrate that our proposed approach outperforms all the baseline methods given the collected image data.

Selection of best wavelet functions and decomposition levels for coupling with soft computing methods in estimating ETo in coastal and island regions

ABSTRACT Mass transfer-based models are among the extensively applied reference evapotranspiration (ET o ) estimation approaches that have been proven to provide reliable estimates under certain climatic and geographical conditions. A major issue with such approaches is the high variations in wind speed time series, which is a governing meteorological parameter in mass transfer-based models. few studies have focused on applying wavelet transform as a robust pre-processing technique for ETo estimation. A major question with coupling wavelet transforms with soft computing methods would be the proper choice of the wavelet function, vanishing moments and the decomposition levels. The present study aims at assessing different wavelet functions coupled with boosted regression tree (BT) models, i.e. wavelet-BT (WBT), in estimating ET o values (through mass transfer parameters) in both coastal and island regions, where it is supposed that wind speed variations are affected by sea–inland interactions and so has the sharp variations (unlike ET o ). The obtained results showed that the coupled WBT model could improve the single BT model results to a great extent, and meanwhile, the Daubechies wavelet function with six vanishing moments provided the most accurate results in all the locations. Further, the best simulation outcomes with each wavelet function were obtained when the maximum possible decomposition level was used with each function.