Fair Comparison Research Articles

The Value of Active Management in Equity Mutual Funds: A Case of Mutual Funds Distributed in the Czech Republic

The primary purpose of this study is to evaluate the active management in a mutual fund from the perspective of a Czech retail investor and compare it against their passive alternative. In total, 50 mutual funds and three passive exchange-traded funds (ETFs) were analyzed across three regional groups (Global, American, and European) from July 2011 to June 2021. The data was analyzed through regression utilizing the Fama-French 3 factor model to determine the funds’ alpha, ensuring a fair risk-adjusted comparison of funds. The detailed analysis showed that 13 of the 50 officially active funds are not considered active and may be suspected of so-called closet indexing. Such funds are being paid for active management, which they are not performing. Thus, the client is being deceived and is essentially getting an overpriced index fund. Moreover, it was confirmed that ongoing fee is a negative performance predictor – more expensive funds deliver lower net returns; thus, they cannot justify their cost. The findings mentioned above were consistent among the analyzed groups but were less apparent and significant for the European funds. Lastly, the results were compared with previous research and discussed from theoretical and practical perspectives.

A Comparative Analysis of Modern Object Detection Algorithms: YOLO vs. SSD vs. Faster R-CNN

In recent years, object detection has become a crucial component in various computer vision applications, including autonomous driving, surveillance, and image recognition. This study provides a comprehensive comparative analysis of three prominent object detection algorithms: You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), and Faster Region-Based Convolutional Neural Networks (Faster R-CNN). The background of this research lies in the growing need for efficient and accurate object detection methods that can operate in real-time. YOLO is known for its speed, SSD for its balance between speed and accuracy, and Faster R-CNN for its high detection accuracy, albeit at a slower pace. The methodology involves implementing these algorithms on a standardized dataset and evaluating their performance based on various metrics, including detection accuracy, processing speed, and computational resource requirements. Each algorithm is tested under similar conditions to ensure a fair comparison. The results indicate that while YOLO excels in real-time applications due to its high speed, SSD offers a middle ground with respectable accuracy and speed, making it suitable for applications requiring a balance of both. Faster R-CNN demonstrates superior accuracy, making it ideal for scenarios where detection precision is paramount, despite its slower performance. This comparative analysis highlights the strengths and weaknesses of each algorithm, providing valuable insights for researchers and practitioners in selecting the appropriate object detection method for their specific needs.

Self-attention deep ConvLSTM with sparse-learned channel dependencies for wearable sensor-based human activity recognition

In this study, we propose a novel deep-learning architecture with sparse learning for human activity recognition. The proposed model contains 1D CNNs and LSTM layers with a self-attention mechanism to enhance a substantial number of time points in time-series data for human activity recognition systems. Based on the recent success of squeeze-and-excite (SE) networks, the proposed deep learning model utilizes the SE module to enhance channel-wise interdependencies, which in turn leads to a boost in performance. In addition, we utilized sparse learning to retrain only weak nodes and freeze stronger nodes in a fully connected layer prior to classification layer. Furthermore, we utilized an entropy-inspired formula to find sparsely located weaker nodes and validated our model on various datasets, including Opportunity, UCI-HAR, and WISDM. Herein, we present an extensive analysis and survey of state-of-the-art studies, in addition to our proposed research. For a fair comparison, we evaluated our deep learning architecture using various performance metrics and achieved better results; the proposed model outperformed state-of-the-art algorithms for human activity recognition.

Studying Degradation of Micro- and Nano-Scale Silicon in Si/ Rgo-Anode Materials for Lithium-Ion Batteries – Towards a Fair Comparison

An attractive approach to increase the Li+-storage capacity of anode materials in Lithium-ion batteries (LiBs), is to replace or combine the routinely-applied graphite (372 mAh g-1) with higher-capacity materials. Among others, silicon (Si) with a theoretical capacity of 3579 mAh g-1 is a promising candidate [1]. Silicon is not yet been used as an anode material in its pure form, due to its low conductivity and volume increase of up to 300 % during lithiation. This two-step expansion (1st: Si + 2 Li+ + 2 e- → SiLi2.0; 2nd: SiLi2.0 + 1.5 Li+ + 1.5 e- → SiLi3.5) is a source of mechanical stress that can ultimately lead to battery failure. Furthermore, the initial lithiation of crystalline silicon (c-Si + 3.5 Li+ + 3.5 e- → SiLi3.5) causes an amorphization of the pristine crystalline lattice. This structural change causes further degradation of the material, which can hardly be avoided [2].The main degradation mechanisms resulting from expansion and amorphization are I) The uncontrolled formation of a passivation layer due to the cyclic volume change taking place during (de-)lithiation, which leads to a continuous break-up and reformation of the solid electrolyte interphase (SEI). II) The pulverization of particles resulting in mechanical stress and the electrical isolation of particles or particle fragments delaminating from the electrode [3]. The first mechanism discussed is more dominant for nanometer scaled particles (nm-Si), while the second mechanism occurs mostly for particles in the micrometer range (µm-Si).Our approach combines, nm- or µm-sized Si particles with different matrix materials. By incorporating the particles into an either graphitic or soft carbon-like support, we aim to increase the conductivity of the electrode material and gain additional buffer space thereby reducing interparticle mechanical stress [4]. On the one hand, the systems of carbon-based anode materials are already well understood in terms of electrode composition, processing parameters, electrolyte compatibility, formation of a stable SEI and the resulting high cycling stability. On the other hand, the observed degradation mechanisms will occur with different predominance in different Si/C composites depending on the Si particle size and the chosen carbon matrix. However, the latter is not completely understood. We will address these issues by comparing different Si/C-composites consisting of nm-Si-particles or µm-Si particles in a reduced graphite oxide (rGO) matrix and in a graphite-matrix.Electrochemical methods, such as galvanostatic cycling, impedance spectroscopy and dq/dU-analysis in 2- and 3-electrode setups, were coupled with optical and spectroscopical techniques (e.g. XRD, FTIR, SEM) to trace back the electrochemically measurable degradation of the electrode to structural changes during formation and cycling. Particular attention was paid to the localization and analysis of defect areas with increased degradation by correlating the optical and spectroscopical results data with the lithiation and delithiation processes. Considering the different SoC levels, we can identify the ongoing aging of the different electrode materials. Based on this, we propose the use of soft carbon-like structures such as rGO as a matrix material for Si/C composites due to its higher cycling stability under the investigated conditions.

Harmonization of Testing Procedures for All Solid State Batteries

All Solid State Batteries (ASSBs) with lithium-ion based conducting solid state electrolytes are considered the next generation high performance batteries. They enable high power densities due to their single ion conducting solid electrolyte, eliminating salt concentration gradients and related polarization losses in the cell, and ensuring an unrivalled level of safety due to their non-combustibility. Currently, a variety of ASSBs based on different solid state electrolytes such as polymers, thiophosphates, oxides and combinations thereof are being developed.One general problem with ASSBs is establishing and maintaining contact between the solid electrolyte and the active material phase during production and cycling, respectively. In conventional lithium-ion batteries (LiBs), this contact is ensured by the liquid state of the electrolyte, but in ASSBs, chemical expansion and contraction of the active material during lithiation and delithiation can detach this contact, resulting in decreased capacity due to the loss of active material. As a consequence, ASSBs are often operated under pressurized conditions, applying pressures significantly exceeding those in conventional LiBs. The same holds for the operating temperature window. Especially for polymer electrolyte-based ASSBs, they are often operated at higher temperatures to compensate for the low ionic conductivity of polymers at room temperature. With respect to cell testing, such operating requirements must be considered, and testing protocols are designed according to the individual requirements of the tested cell.This contribution aims to provide an overview of testing protocols for various types of ASSBs applied to different cells with polymer-, thiophosphate-, oxide-, and hybrid-electrolytes. These protocols will be compared with standardized testing routines for conventional LiBs. Based on this compilation, a harmonized testing procedure that covers the special requirements of the individual cell types and enables a fair comparison of different ASSBs is suggested. Additionally, examples of ASSB testing results will be discussed, taking into consideration the harmonization of different testing parameters.

Image quality improvement of a one-step spectral CT reconstruction on a prototype photon-counting scanner

Objective. X-ray spectral computed tomography (CT) allows for material decomposition (MD). This study compared a one-step material decomposition MD algorithm with a two-step reconstruction MD algorithm using acquisitions of a prototype CT scanner with a photon-counting detector (PCD). Approach. MD and CT reconstruction may be done in two successive steps, i.e. decompose the data in material sinograms which are then reconstructed in material CT images, or jointly in a one-step algorithm. The one-step algorithm reconstructed material CT images by maximizing their Poisson log-likelihood in the projection domain with a spatial regularization in the image domain. The two-step algorithm maximized first the Poisson log-likelihood without regularization to decompose the data in material sinograms. These sinograms were then reconstructed into material CT images by least squares minimization, with the same spatial regularization as the one step algorithm. A phantom simulating the CT angiography clinical task was scanned and the data used to measure noise and spatial resolution properties. Low dose carotid CT angiographies of 4 patients were also reconstructed with both algorithms and analyzed by a radiologist. The image quality and diagnostic clinical task were evaluated with a clinical score. Main results. The phantom data processing demonstrated that the one-step algorithm had a better spatial resolution at the same noise level or a decreased noise value at matching spatial resolution. Regularization parameters leading to a fair comparison were selected for the patient data reconstruction. On the patient images, the one-step images received higher scores compared to the two-step algorithm for image quality and diagnostic. Significance. Both phantom and patient data demonstrated how a one-step algorithm improves spectral CT image quality over the implemented two-step algorithm but requires a longer computation time. At a low radiation dose, the one-step algorithm presented good to excellent clinical scores for all the spectral CT images.

Comparison of CNN Architecture of Image Classification Using CIFAR10 Datasets

This paper demonstrates image classification using deep learning. Deep learning has the inherent ability to automatically discover and extract meaningful features for specific applications. Among the popular techniques in deep learning, the convolutional neural network (CNN) stands out. CNN consists of an input layer, hidden layers, and an output layer, where meaningful features are automatically extracted from input images.
 This paper presents the performance and identifies the most effective CNN architectures for accurately classifying images in the CIFAR-10 dataset. Five CNN architectures were implemented, namely [Architecture 1], [Architecture 2], [Architecture 3], [Architecture 4], and [Architecture 5] using the CIFAR-10 dataset. The architectures were selected based on the need to explore variations in convolutional filter sizes, dense layers, and batch normalization to assess their impact on CIFAR-10 image classification performance. Each architecture was trained on a standard training set and evaluated on a validation set. We used specific details on data preprocessing and training settings for a consistent and fair comparison. After training and evaluation, we have obtained the following results for each architecture.
 Architecture 1 has a training accuracy of 74.7% and validation accuracy of 76.6%, Architecture 2 has 96.09% and 86.08%, Architecture 3 has 77% and 78.1%, Architecture 4 has 67.91% and 69.54%, and Architecture 5 has 94.64% and 87.34% of training accuracy and validation accuracy respectively. After conducting a comparative analysis, we found that Architecture 5 has achieved the highest validation accuracy in classifying images in the CIFAR-10 dataset. These findings suggest that Architecture 5 is a promising choice for image classification tasks involving the CIFAR-10 dataset.

Incidence of Postpartum Hemorrhage in Induced Versus Normal Labour

Aim: Postpartum hemorrhage (PPH), defined as blood loss exceeding 500 mL within 24 hours of childbirth, remains a major cause of maternal morbidity and mortality worldwide. PPH can result in severe complications, including anemia, transfusion requirements, organ dysfunction, and even maternal death. Prompt recognition, effective management, and risk reduction strategies are essential for improving maternal outcomes. Objective: To examine the cause and frequency of Postpartum Hemorrhage (“PPH”) in induced versus normal labour. Methods: This research was conducted in the Obstetrics and Gynecology Department, Unit no.2 at the Central Park Teaching Hospital. The research was conducted on three hundred and ten (310) patients, who were classified into two equal groups for the purposes of fair comparison. Group 1 consisted of one hundred and fifty-five (155) women who were either induced or had a cesarian section whereas Group 2 had one hundred and fifty-five (155) women who underwent a normal labour. The study was conducted in a six (6) month time duration from May 2023 to July 2023, the reason for PPH was observed and recorded in both groups. The patients and the hospital were informed beforehand of the research and written, and at times oral, consents were taken from both for recording the information below. Results: After examining both Group 1 and Group 2, it was observed that induced labour was associated with 5.8% higher risk of PPH as compared to normal labour. It was further observed that frequency of PPH was highest in women induced by oxytocin as compared to women induced by prostaglandin. Practical Implications: It will help in early and prompt management of labour and it also suggest that labour shouldn’t be induced until unless it’s indicated otherwise it can lead to post-partum hemorrhage. Conclusions: The risk of PPH increases with inducing the labour as compared to normal labour which is why a patient should only be induced in case of utmost emergency. Moreover, if such an emergency does arise, one should use prostaglandin as they are safer than oxytocin. Keywords: Postpartum Hemorrhage, Induction, Induced Labour, Labour, Spontaneous Labour, Normal Labour

Comparative study on the dynamic responses of monopile and jacket-supported offshore wind turbines considering the pile-soil interaction in transitional waters

In this study, simplified finite element models for monopile and jacket offshore wind turbines, referred to as MOWT and JOWT, respectively, were established for dynamic response analysis using the X-SEA program, based on the National Renewable Energy Laboratory's 5 MW OWT model. First, using five design constraints, an optimization design was conducted for the substructures and foundations of both MOWT and JOWT located in transitional waters to ensure a fair comparison of the dynamic performances of the two structures. Next, putting special attention to the natural frequency, tower top displacement and member stress, two kinds of OWT models with fixed and flexible bases subjected to combined wind, wave and current loads were analyzed using the pile-soil interaction module of X-SEA to further explore the influence of the foundation flexibility on dynamic responses. Subsequently, a parametric study was conducted to evaluate the sensitivity of the dynamic responses to the changes in environmental conditions, soil properties and pile embedded lengths. Finally, a comparison of the dynamic performances, parameter sensitivities, and total masses of the MOWT and JOWT was performed to determine the optimal fixed OWT support structure for the 50 m water depth of the southwest sea area of South Korea.

Exploring the dependence of gas cooling and heating functions on the incident radiation field with machine learning

ABSTRACT Gas cooling and heating functions play a crucial role in galaxy formation. But, it is computationally expensive to exactly compute these functions in the presence of an incident radiation field. These computations can be greatly sped up by using interpolation tables of pre-computed values, at the expense of making significant and sometimes even unjustified approximations. Here, we explore the capacity of machine learning to approximate cooling and heating functions with a generalized radiation field. Specifically, we use the machine learning algorithm XGBoost to predict cooling and heating functions calculated with the photoionization code cloudy at fixed metallicity, using different combinations of photoionization rates as features. We perform a constrained quadratic fit in metallicity to enable a fair comparison with traditional interpolation methods at arbitrary metallicity. We consider the relative importance of various photoionization rates through both a principal component analysis (PCA) and calculation of SHapley Additive exPlanation (shap) values for our XGBoost models. We use feature importance information to select different subsets of rates to use in model training. Our XGBoost models outperform a traditional interpolation approach at each fixed metallicity, regardless of feature selection. At arbitrary metallicity, we are able to reduce the frequency of the largest cooling and heating function errors compared to an interpolation table. We find that the primary bottleneck to increasing accuracy lies in accurately capturing the metallicity dependence. This study demonstrates the potential of machine learning methods such as XGBoost to capture the non-linear behaviour of cooling and heating functions.

Concerns on the Effects of Electrode Positions in Electrolyte Container for the Oxygen Evolution Reaction.

Water electrolysis is currently a major technique to produce clean hydrogen, which is regarded as a promising and sustainable energy carrier. The efficiency of water electrolysis is highly dependent on the oxygen evolution reaction (OER) on the anode. The evaluation of an OER electrocatalyst is frequently carried out on a three-electrode system in a container of electrolyte. Herein, we found that the electrode positions in the electrolyte container could significantly affect the data acquisition of OER performance. After a detailed investigation, we reveal that the difference of the OER activity of an electrocatalyst at a different position is originated from their different iRu drop and the gas diffusion resistance. For the first time, this work evokes concerns on the accurate evaluation of electrocatalysts regarding the electrode position. For fair comparisons and reliable results, it is strongly suggested to keep the electrode position unchanged in the electrochemical measurements. In addition, the establishment of a standard electrolyzer setup for electrocatalysis evaluation in the electrochemical community is also called for.

Market Integration of Small-Scale Farms: Exploring the Bambara Groundnut Markets in Nigeria

The extent of uncertainty caused by price inefficiency and instability with reference to Bambara groundnut has made the subsector a dicey one. Markets that are not integrated may convey inaccurate price signals that might distort producers’ marketing decisions and contribute to inefficient product movement. The study analyzed the market integration of Bambara groundnut in South-east, Nigeria. The study population comprised all Bambara groundnut marketers in South-east, Nigeria. Out of the five states in South-east, Nigeria, two States will be purposively selected as the study area to establish the movement of prices between the two states to establish a fair comparison of prices between the producing zone and consumption zone. The choice of the two states was based on the predominance of Bambara groundnut marketers. A two-stage sampling technique was adopted in selecting 221 respondents that were interviewed for the study. Data for this study was poised from primary sources using a well-structured questionnaire, oral interviews and direct observations of Bambara groundnut marketers and was analyzed using descriptive and inferential statistics.- The results of the VECM showed that Enugu urban retail prices had a short-run influence of -0.057 although not signicant and a long-run significant influence of -0.2391 on Abia urban retail prices. The results of the VECM estimates for both short-run and long-run relationships indicated that the Enugu rural price of Bambara groundnut had short-run influences of -0.034023 and 0.0032032 with a long-run influence of -0.203230 on Abia rural price. The study recommended that to improve pricing effectiveness and market integration, it is necessary to encourage Bambara groundnut market price information in South-east Nigeria. The flow of information regarding Bambara groundnut prices, demand, and supply will improve both market integration and pricing effacing. The government should create regulations for information services. How to Cite: Agbo Josephine Nkechi, Okpukpara Benjamin C., Ude Kingsley David, Udemba Klinsmann Uche., 2023. "Market Integration of Small-Scale Farms: Exploring the Bambara Groundnut Markets in Nigeria." Journal of Agriculture and Crops, vol. 10, pp. 11-19.

AN EVALUATION OF STEREO AND MULTIVIEW ALGORITHMS FOR 3D RECONSTRUCTION WITH SYNTHETIC DATA

Abstract. The reconstruction of 3D scenes from images has usually been addressed with two different strategies, namely stereo and multiview. The former requires rectified images and generates a disparity map, while the latter relies on the camera parameters and directly retrieves a depth map. For both cases, deep learning architectures have shown an outstanding performance. However, due to the differences between input and output data, the two strategies are difficult to compare on a common scene. Moreover, for remote sensing applications multi-view data is hard to acquire and the ground truth is either sparse or affected by outliers. Hence, in this article we evaluate the performance of stereo and multi-view architectures trained on synthetic data resembling remote sensing images. The data has been and processed and organized to be compatible with both kind of neural networks. For a fair comparison, training and testing are done only with two views. We focus on the accuracy of the reconstruction, as well as the impact of the depth range and the baseline of the stereo array. Results are presented for deep learning architectures and non-learning algorithms.

LandBench 1.0: A benchmark dataset and evaluation metrics for data-driven land surface variables prediction

The advancements in deep learning methods have presented new opportunities and challenges for predicting land surface variables (LSVs) due to their similarity with computer sciences tasks. However, few researchers focus on the benchmark datasets for LSVs predictions that hampers fair comparisons of different data-driven deep learning models. Hence, we propose a LSVs benchmark dataset and prediction toolbox to boost research in data-driven LSVs modeling and improve the consistency of data-driven deep learning models for LSVs. LSVs benchmark dataset contains a large number of hydrology-related variables, such as global soil moisture, runoff, etc., which can verify the simulation of hydrological processes. Various global data from European Centre for Medium-Range Weather Forecasts reanalysis 5 (ERA5), ERA5-land, global gridded soil information (SoilGrid), soil moisture storage capacity (SMSC), and moderate-resolution imaging spectroradiometer (MODIS) datasets have been pre-processed into daily data at 0.5-, 1-, 2-, and 4-degree resolutions to facilitate their use in data-driven models. Simple statistical metrics, i.e., the root mean squared error and correlation coefficient, are chosen to evaluate the performance of different deep learning (DL) models, including convolutional neural network, long short-term memory and convolution long short-term memory models, with lead times of 1 and 5 days. A processed-based model serves as a physic baseline, soil moisture and surface sensible heat fluxes are taken as the target variables. The developed benchmark dataset and evaluation metrics for predicting LSVs using data-driven approaches, named as the LandBench toolbox, were implemented using Pytorch. This toolbox facilitates the reimplementation of existing methods, the development of novel predictive models, and the utilization of unified evaluation metrics. Additionally, the toolbox incorporates address mapping technology to enable high-resolution global predictions with constrained computing resources. We hope LandBench will not only serves as a standardized framework, fostering equitable model comparisons, but also provides indispensable data and a robust scientific foundation essential for advancing climate change research, disaster management, and sustainable development initiatives.

Future climate impacts of sodium-ion batteries

Sodium-ion batteries (SIBs) have emerged as an alternative to lithium-ion batteries (LIBs) due to their promising performance in terms of battery cycle lifetime, safety, operating in wider temperature range, as well as the abundant and low-cost of sodium resources. This study evaluated the climate impacts of three SIBs, and compared to two LIBs under four scenarios with considering potential changes in battery performance and background productions between 2020 and 2050. To ensure a fair comparison, all batteries were modeled in the 21,700 form, and a battery dimensioning model was developed to calculate the required amount of components for each battery. We found that equal to lower GHG emissions result from the use of SIBs compared to LIBs under optimal performance scenarios. From 2020 to 2050, the climate impacts of SIBs decreased by 43–57 %. The relative contribution of the battery manufacturing process decreases from 18–32 % to 2–4 % due to the increasingly share of clean energy in the electricity grid, while the relative contribution of key battery component materials increases over time, especially for cathode active materials. These results emphasize the significance of decarbonizing the electric grid, and suggest that future investment in SIBs is promising from an environmental point of view.

CowScreeningDB: A public benchmark database for lameness detection in dairy cows

Lameness is one of the costliest pathological problems affecting dairy animals. It is usually assessed by trained veterinary clinicians who observe features such as gait symmetry or gait parameters as step counts in real time. With the development of artificial intelligence, various modular systems have been proposed to minimize subjectivity in lameness assessment. However, the major limitation in their development is the unavailability of a public database, as most existing ones are either commercial or privately held. To tackle this limitation, we have introduced CowScreeningDB, a multi-sensor database which was built with data from 43 dairy cows. Cows were monitored using smart watches during their normal daily routine. The uniqueness of the database lies in its data collection environment, sampling methodology, detailed sensor information, and the applications used for data conversion and storage, which ensure transparency and replicability. This data transparency makes CowScreeningDB a valuable and objectively comparable resource for further development of techniques for lameness detection for dairy cows. In addition to publicly sharing the database, we present a machine learning technique which classifies cows as healthy or lame by using raw sensory data. To facilitate fair comparisons with state-of-the-art methods, we introduce a novel benchmark. Combining the database, the machine learning technique and the benchmark validate our major objective, which is to establish the relationship between sensor data and lameness. The developed technique reports an average accuracy of 77 % for the best case scenario and presents perspectives for further development. By introducing this framework which encompasses the database, the classification algorithm and the benchmark, we significantly reduce subjectively in lameness assessment. This contribution to lameness detection fosters innovation in the field and promotes transparent, reproducible research in the pursuit of more effective management of dairy cow lameness. ImplicationsLameness detection is one of the main tasks in dairy systems, given its importance in the production ambit. However, the data used during detection is generally either held privately or sold commercially. In this study, we create a multi-sensor database (CowScreeningDB), which can be used for lameness. Because we have made the database public11(CowScreeningDB can be downloaded from https://github.com/Shahid-Ismail/CowScreeningDB-A-public-database-for-lameness-detection or https://gpds.ulpgc.es/) and free of charge for research purposes, it should act as a benchmark allowing to objectively compare techniques put forth to deal with lameness. We also provide details of the sampling system used, comprised of hardware and a baseline classification algorithm.

A Recycling Training Strategy for Medical Image Segmentation with Diffusion Denoising Models

Denoising diffusion models have found applications in image segmentation by generating segmented masks conditioned on images. Existing studies predominantly focus on adjusting model architecture or improving inference, such as test-time sampling strategies. In this work, we focus on improving the training strategy and propose a novel recycling method. During each training step, a segmentation mask is first predicted given an image and a random noise. This predicted mask, which replaces the conventional ground truth mask, is used for denoising task during training. This approach can be interpreted as aligning the training strategy with inference by eliminating the dependence on ground truth masks for generating noisy samples. Our proposed method significantly outperforms standard diffusion training, self-conditioning, and existing recycling strategies across multiple medical imaging data sets: muscle ultrasound, abdominal CT, prostate MR, and brain MR. This holds for two widely adopted sampling strategies: denoising diffusion probabilistic model and denoising diffusion implicit model. Importantly, existing diffusion models often display a declining or unstable performance during inference, whereas our novel recycling consistently enhances or maintains performance. We show for the first time that, under a fair comparison with the same network architectures and computing budget, the proposed recycling-based diffusion models achieved on-par performance with non-diffusion-based supervised training. Furthermore, by ensembling the proposed diffusion model and the non-diffusion counterpart, significant improvements to the non-diffusion models have been observed across all applications, demonstrating the value of this novel training method. This paper summarizes these quantitative results and discusses their values, with a fully reproducible JAX-based implementation, released at <a href='https://github.com/mathpluscode/ImgX-DiffSeg'>https://github.com/mathpluscode/ImgX-DiffSeg</a>

Modular Approach to Selected Configuration Interaction in an Arbitrary Spin Basis: Implementation and Comparison of Approaches.

A modular selected configuration interaction (SCI) code has been developed that is based on the existing Monte-Carlo configuration interaction code (MCCI). The modularity allows various selection protocols to be implemented with ease and allows for fair comparison between wave functions built via different criteria. We have initially implemented adaptations of existing SCI theories, which are based on either energy- or coefficient-driven selection schemes. These codes have been implemented not only in the basis of Slater determinants (SDs) but also in the basis of configuration state functions (CSFs) and extended to state-averaged regimes. This allows one to take advantage of the reduced dimensionality of the wave function in the CSF basis and also the guarantee of pure spin states. All SCI methods were found to be able to predict potential energy surfaces to high accuracy, producing compact wave functions, when compared to full configuration interaction (FCI) for a variety of bond-breaking potential energy surfaces. The compactness of the error-controlled adaptive configuration interaction approach, particularly in the CSF basis, was apparent with nonparallelity errors within chemical accuracy while containing as little as 0.02% of the FCI CSF space. The size-to-accuracy was also extended to FCI spaces approaching one billion configurations.

Comparison of volumetric modulated arc therapy and helical tomotherapy for prostate cancer using Pareto fronts.

Studies comparing different radiotherapy treatment techniques-such as volumetric modulated arc therapy (VMAT) and helical tomotherapy (HT)-typically compare one treatment plan per technique. Often, some dose metrics favor one plan and others favor the other, so the final plan decision involves subjective preferences. Pareto front comparisons provide a more objective framework for comparing different treatment techniques. A Pareto front is the set of all treatment plans where improvement in one criterion is possible only by worsening another criterion. However, different Pareto fronts can be obtained depending on the chosen machine settings. To compare VMAT and HT using Pareto fronts and blind expert evaluation, to explain the observed differences, and to illustrate limitations of using Pareto fronts. We generated Pareto fronts for twenty-four prostate cancer patients treated at our clinic for VMAT and HT techniques using an in-house script that controlled a commercial treatment planning system. We varied the PTV under-coverage (100%-V95%) and the rectum mean dose, and fixed the mean doses to the bladder and femoral heads. In order to ensure a fair comparison, those fixed mean doses were the same for the two treatment techniques and the sets of objective functions were chosen so that the conformity indexes of the two treatment techniques were also the same. We used the same machine settings as are used in our clinic. Then, we compared the VMAT and HT Pareto fronts using a specific metric (clinical distance measure) and validated the comparison using a blinded expert evaluation of treatment plans on these fronts for all patients in the cohort. Furthermore, we investigated the observed differences between VMAT and HT and pointed out limitations of using Pareto fronts. Both clinical distance and blind treatment plan comparison showed that VMAT Pareto fronts were better than HT fronts. VMAT fronts for 10 and 6MV beam energy were almost identical. HT fronts improved with different machine settings, but were still inferior to VMAT fronts. That VMAT Pareto fronts are better than HT fronts may be explained by the fact that the linear accelerator can rapidly vary the dose rate. This is an advantage in simple geometries that might vanish in more complex geometries. Furthermore, one should be cautious when speaking about Pareto optimal plans as the best possible plans, as their calculation depends on many parameters.

Detonation onset due to the energy accumulation effect of shock wave focusing

Shock wave focusing is a highly efficient and reliable method for detonation initiation, which is crucial for the successful operation of detonation engines. To refine the reflector design, a fair comparison of the energy accumulation abilities of different reflectors is necessary. In this study, seven different reflectors, namely, a 30° wedge reflector (WR30°), a 60° wedge reflector (WR60°), a 90° wedge reflector (WR90°), a 120° wedge reflector (WR120°), a reflector designed according to the Chester-Chisnell-Whitham relation (CCW), a parabolic reflector (PARA), and a semicylindrical reflector (SEMI), are investigated numerically. This study also includes a comparison between numerical and experimental results to confirm the accuracy of the simulations. The internal energy density near the apex zones of the reflectors is used as the metric to evaluate the energy accumulation abilities of different reflectors. The results show that the reflectors can be ranked in order of energy accumulation ability as follows: CCW > WR30° > WR60° > PARA > WR90° > WR120° > SEMI. This ranking is also supported by the corresponding numerical soot foils from each of the reflectors. The aforementioned reflectors have at least 2.5 times greater energy accumulation ability than the plane reflector, demonstrating the significant effect of shock wave focusing. Moreover, reflectors with longer converging depths appear to have a better energy accumulation effect. This study provides guidance for selecting different reflectors, which may contribute to higher detonation initiation reliability in detonation-based engines.