Initial Step Research Articles

Piecewise circular interface construction using height functions

AbstractA piecewise circular interface construction (PCIC) method is described, where height functions based curvature estimates are directly utilised for accurate interface reconstruction under the framework of volume of fluid method. The present work is an attempt to develop a robust and accurate higher order interface reconstruction algorithm that is capable of accurate simulation of surface tension dominated flows. The proposed hybrid method (H‐PCIC) is thus able to take advantage of merits of both PCIC and HF methods, achieving at least second order convergence with respect to both interface reconstruction and curvature computation. This is in addition to the significantly superior quality of the reconstructed interface with respect to PLIC methods. This seamless blending of the HF and PCIC quantities is enabled by c0‐correction procedures applied to base PLIC and initial PCIC steps. More recent variants of the height function method with variable stencil size are used for calculation of radius of curvature. The capability of this proposed method towards simulation of flow problems within a well‐balanced two‐phase solver is established with help of multiple complex two‐phase flow problems. This validation exercise also demonstrates the capability of PCIC class of methods towards solutions of two‐phase flows with intricate physics.

A time‐varying meshing stiffness model for gears with mixed elastohydrodynamic lubrication based on load‐sharing

AbstractIn mixed elastohydrodynamic lubrication (EHL), the load distribution between the gear meshing surfaces is shared by the oil film and the asperities of the gear's rough surface. Based on the load‐sharing concept, this paper proposes a time‐varying meshing stiffness (TVMS) model for gears with mixed EHL. The initial step involves the utilization of the Greenwood‐Williamson model to calculate the contact stiffness of surface asperities, while the lubricating film is assessed using a curve‐fitting formula to investigate the influence of gear surface morphology on TVMS. Subsequently, the incorporation of gear fillet foundation deformation and friction enables accurate TVMS determination. The proposed method is employed to examine the meshing stiffness of the gear pair under both dry lubrication and EHL conditions. Comparative analysis reveals favorable agreement between the proposed model and experimental results obtained under dry lubrication, thereby highlighting the superior performance of the proposed approach. Moreover, the time‐varying friction coefficient under EHL is computed, and the impacts of gear surface morphology parameters, temperature, speed, and load on lubrication conditions and TVMS are investigated. The findings presented in this paper contribute significantly to advancements in gear design and performance.

Establishing A Greenhouse For Melon Cultivation: A Community Service Program To A Farmer Group In Lamongan Jawa Timur

The demand for fresh and high-quality melon has been steadily growing over the years. Certainly, supply chain challenges significantly impact farmers' ability to produce and distribute their products efficiently for some reasons. Moreover, melon crops are vulnerable to changes in weather and climate. Support in the form of climate-resilient farming practices, weather forecasting, and access to drought-resistant or heat-tolerant varieties can help farmers mitigate risks. Hence, melon farmers, like farmers of any other crop, require support for various reasons due challenging industry, and farmers often face a range of factors that can impact their performance. This article aims to explore how a community service program support a farmer group in Lamongan Jawa Timur by establishing a greenhouse for melon cultivation. The primary focus of a community development report is to document the planning, implementation, progress, and outcomes of a specific community development program. Data collection for a community development report involves qualitative approaches such as surveys, interviews, focus groups, and observations. The method follows the sequential step: The initial step is gathering information about the community's needs, challenges, and aspirations by conducting online interviews, and offline focus group discussion to understand the specific areas that require improvement. The second step is stakeholder mapping, which includes community members, local leaders, local university partners, and local business partners who have a vested interest in the program. The third step entails assessing potential risks and challenges that could hinder the development process. This includes identifying economic, social, environmental, or cultural risks. Based on the information gathered in the previous steps, the design of the community development program was to set clear goals, objectives, strategies, and action plans by tailoring to address the specific needs and circumstances of the community. Last, putting the program into action involves executing the strategies and action plans outlined in the program design, which includes organizing workshops, training sessions, and infrastructure development. The results show that community development is a dynamic and iterative process. Flexibility and adaptability are key qualities as the situation on the ground may change, and new insights may emerge. Additionally, involving the community at every stage of the process fosters ownership, empowerment, and sustainability of the development initiatives.

Characterization of heterogeneities in the sandy facies of the Opalinus Clay (Mont Terri underground rock laboratory, Switzerland)

SUMMARY This contribution is presenting a multidisciplinary investigation of heterogeneities in a clay rock formation, based on seismic tomography, logging and core analysis, as a reconnaissance study for a diffusion experiment. Diffusion experiments in clay rock formations provide crucial experimental data on diffusive transport of radionuclides (RN) in extremely low hydraulic conductivity media. Previous diffusion experiments, conducted, for example, in the Mont Terri underground rock laboratory within the relatively homogeneous shaly facies of Opalinus Clay, and modelling studies of these experiments have demonstrated that the clay rock could sufficiently well be described as a homogeneous anisotropic medium. For other lithofacies, characterized by larger heterogeneity, such simplification may be unsuitable, and the description of heterogeneity over a range of scales will be important. The sandy facies of the Opalinus Clay exhibits a significantly more pronounced heterogeneity compared to the shaly facies, and a combined characterization and RN diffusion study has been initiated to investigate various approaches of heterogeneity characterization and subsequent diffusion in a heterogeneous environment. As an initial step, two inclined exploratory boreholes have been drilled to access the margins of the experiment location. These boreholes have been used to acquire a cross-hole tomographic seismic data set. Optical, natural gamma and backscattering logging were applied and rock cores were analysed. The integrated results of these investigations allowed the identification of an anomalous brighter layer within the investigated area of the sandy facies of approximately 1 m thickness and with its upper bound at roughly 10 m depth within the inclined exploratory wells. Mineralogical analyses revealed only slight variations throughout the rock cores and indicated that the anomalous layer exhibited a slightly higher quartz content, and locally significantly higher calcite contents, accompanied by a lower content of clay minerals. The anomalous layer was characterized by reduced natural gamma emissions, due to the lower clay content, and increased neutron backscattering likely indicating an increased porosity. Seismic P-wave velocities, derived from anisotropic tomography, exhibited a maximal gradient near the top of this layer. The transition from the overlaying darker rock matrix into this layer has been identified as an appropriate location for the setup of a tracer diffusion experiment in a heterogeneous environment.

Importance of Elemental Chemical Speciation Studies in Enriched Food: Nutritional Quality, Toxicity, and Economic Improvement

For several reasons, mainly cost and local productivity, the world population does not have access to a balanced diet that contains all the macro and micronutrients necessary to maintain physiological functions for a healthy life. Nutritional education, supplementation, and consuming enriched (or fortified) foods appear as alternatives to supply daily demands and minimize malnutrition. Adding essential elements as salts (e.g., iron, calcium, and zinc) to ready-to-eat processed foods, such as milk, flour, and juices is already adopted in several countries. The choice of the compound to be added, as well as the transport vehicle (foods), must be very well evaluated since the cost, long-term consumption, and bioavailability of the added chemical species are imperative to ensure the nutritional quality of enriched food.1 Another alternative to produce enriched foods is cultivating an enriched medium (Figure 1), adding essential elements to soil or in nutritive solution (hydroponic procedure), irrigating leaves, or immersing seeds.2 In this case, the chemical species used to the enrich food must be absorbed, translocated, and accumulated in the edible part.2 Studies have shown that the iron enrichment of adzuki beans using iron nitrate or iron chloride was unsuccessful since iron inorganic species interact strongly with the antinutrients (tannins or phytates) present in the roots, forming insoluble complexes and preventing their translocation.3 Alternatives found to overcome this obstacle were enrichment by applying iron complexes with EDTA (ethylenediaminetetra-acetic acid)3 or iron nanoparticles, mainly encapsulated.4 The nanoparticle application has been gaining prominence in agriculture, aiming to carry fertilizer, pesticides, and nutrients to stimulate plant growth and increase macro and micronutrient availability and absorption efficiency.5,6 Besides the interaction between essential elements with antinutrients, evaluating the competition between elemental species is important, because synergistic or antagonistic effects can be observed. In both cases, chemical species must interact with other components present in food or cultivation medium, altering its chemical composition when compared to food cultivated in conventional conditions. The antagonistic effect between selenium and mercury was observed in edible mushrooms, while the synergistic effect was observed with lead and selenium.7,8 Finally, it must be evaluated if the enrichment promotes the production of non-bioavailable or toxic species. Regardless of the food enrichment strategy, it is important to highlight the need to identify and quantify the elemental chemical species in the enriched foods by chemical speciation analysis. In the Figure 1 is shown examples of elemental chemical species; they can differ according to their oxidation states, inorganic forms, and organometallic or isotopic composition.9 For chemical speciation studies, initial fractionation steps (e.g., extraction procedures) are carried out; subsequently, chromatographic, or non-chromatographic methods can be used to identify/determine the chemical species. The hyphenation (Figure 1) between separation techniques, mainly chromatography, with high sensitivity detectors, such as inductively coupled plasma mass spectrometry (ICP-MS), is commonly applied.9-11 Nonetheless, extremely creative procedures used in non-chromatographic chemical speciation, working only with chemical reactions and solubility differences, can also be applied to determine chemical species. Non-chromatographic strategies were applied for iron (reaction with hydroxylamine and precipitation with trichloroacetic acid and HCl) and selenium (cloud point extraction) speciation in enriched adzuki sprouts.3 In summary, food enrichment success is closely associated with chemical speciation studies since there are chemical species that will be absorbed in a cultivation medium, as well as the chemical species that are formed during translocation and accumulation, which must be in bioavailable forms, in order to act on the different metabolic systems of the human body, including remedying prevalences. In this scenario, it is imperative to go beyond determining the total concentration of essential elements, since quantifying their species will provide information regarding essentiality and toxicity. Finally, the formation of bioavailable chemical species will add nutritional quality and, consequently, economic benefits that are so essential for countries with specific prevalences to combat in a predominantly agricultural economy.

Type 2 diabetes mellitus increases the risk of circumcision among men aged between 30 and 69 years using a nationwide population-based dataset in Taiwan: a five-year follow-up study

BackgroundDiabetes is an important factor in the development of penile inflammation. We studied whether type 2 diabetes (DM), with/without hypertension and hyperlipidemia increased the risk of circumcision among men aged between 30 and 69 using a population-based dataset in Taiwan during a 5-year follow-up period.MethodsThe research data in this study were obtained from Taiwan’s National Health Insurance Research Database between 1997 and 2010. We identified 23,197 patients who had a new diagnosis of DM and randomly matched 115,985 subjects as controls. We observed whether circumcision was the treatment after a new DM diagnosis. The initial step involved analyzing the data using Poisson regression analysis. To address potential confounding factors, this study employed propensity score matching based on three variables. Additionally, a Cox regression with a Gamma frailty was utilized to compare outcomes between different groups.ResultsPoisson regression analysis showed that DM (RR = 1.75, 95CI = 0.10 ~ 1.22), but not hypertension (RR = 1.14, 95CI=-0.44 ~ 0.70), hyperlipidemia (RR = 0.94, 95CI=-0.66 ~ 0.53), or age (RR = 0.83, 95CI=-0.43 ~ 0.62), had an impact on circumcision treatment. Cox regression with a frailty model found that DM was a risk factor associated with circumcision (HR = 2.31, 95% CI = 1.74 ~ 3.06, p-value < 0.01), whereas no significant difference was noted between circumcision and hypertension (HR = 1.10, 95% CI = 0.80 ~ 1.51), hyperlipidemia (HR = 1.05, 95% CI = 0.79 ~ 1.40), or age (HR = 1.00, 95% CI = 0.99 ~ 1.02).ConclusionsType 2 diabetes mellitus, but not hypertension, hyperlipidemia or age increases the risk of circumcision in men aged between 30 and 69 years.

In Dialogue: Initial Steps Toward Inquiry, Meaning-Making, and Democracy

In Dialogue: Initial Steps Toward Inquiry, Meaning-Making, and Democracy

Pengaplikasian Prosedur Perhitungan untuk Menentukan Produksi Batik Tulis Madura Menggunakan Metode Fuzzy Database Model Tahani

One of the subdivisions of fuzzy logic is the study of the fuzzy database with Tahani model which is described as a model used to process data searches based on operations in fuzzy set theory to obtain appropriate information so that the data search process becomes accurate. The research aims to implement the Fuzzy Database with Tahani Model in determining the appropriate variant of batik tulis with the specified criteria. The initial step of the research is fuzzification, namely determining the domain of each complete variables with intervals of numerical variables and linguistic variables. In this research some variables were used: the price of the cloth, the number of colors and the work durations. Each variable is divided based on the interval between the lowest data and the highest data so that the variable price of the cloth is divided into 4, the number of colors is 3, and the work durations is 5. Then determine the membership function, the variable price of cloth, the number of colors, and the work duration using a linear model and triangle in determining the degree of its membership. Data tabulation from strict sets to fuzzy sets with numerical variables and linguistic variables. In preparing the criteria, 60 rule bases are based on the theory of consumer needs for batik tulis. Not all rule bases occur, because the data collects only a few criteria. Determining the value of fire strength, using the AND rule means selecting the minimum degree of membership from the available. The recommendation results determine to sort the washing machine data that has the highest fire strength value to the smallest fire strength value from each criterion.

A novel active learning Gaussian process modeling-based method for time-dependent reliability analysis considering mixed variables

The purpose of time-dependent reliability analysis is evaluating the reliability of a system or component within a specified timeframe. Several approaches have been suggested for addressing time-dependent reliability challenges, including analytical methods and methods based on response surrogate models. As a representative of Machine Learning methods, the Active Learning Gaussian process model has gained more and more popularity in time-dependent reliability analysis. In practical applications, certain uncertainties may not be adequately represented by random variables following deterministic distributions. In contrast, these uncertainties can be easily described by interval variables. This article introduces an innovative method based on Active Learning Gaussian process modeling for time-dependent reliability analysis, encompassing both random and interval variables. The initial step involves transforming stochastic processes into random parameters through spectral decomposition. Moreover, a new sampling strategy to manage mixed uncertainties is proposed, where the candidate samples of mixed variables with positive maximum extreme responses and negative minimum extreme responses are identified. These samples are utilized to construct and renew an extreme Gaussian process model. Lastly, we present an improved confidence-based learning function to accelerate the learning process, which is designed to locate the optimal random input and interval input for updating the extreme Gaussian process model. One mathematical example and two engineering examples are conducted to verify the performance of the proposed method. Results illustrate that our method offers high accuracy while reducing the evaluations of true performance functions in addressing time-dependent reliability issues under mixed uncertainties.

Fluence-dependent degradation of fibrillar type I collagen by 222 nm far-UVC radiation.

For more than 100 years, germicidal lamps emitting 254 nm ultraviolet (UV) radiation have been used for drinking-water disinfection and surface sterilization. However, due to the carcinogenic nature of 254 nm UV, these lamps have been unable to be used for clinical procedures such as wound or surgical site sterilization. Recently, technical advances have facilitated a new generation of germicidal lamp whose emissions centre at 222 nm. These novel 222 nm lamps have commensurate antimicrobial properties to 254 nm lamps while producing few short- or long-term health effects in humans upon external skin exposure. However, to realize the full clinical potential of 222 nm UV, its safety upon internal tissue exposure must also be considered. Type I collagen is the most abundant structural protein in the body, where it self-assembles into fibrils which play a crucial role in connective tissue structure and function. In this work, we investigate the effect of 222 nm UV radiation on type I collagen fibrils in vitro. We show that collagen's response to irradiation with 222 nm UV is fluence-dependent, ranging from no detectable fibril damage at low fluences to complete fibril degradation and polypeptide chain scission at high fluences. However, we also show that fibril degradation is significantly attenuated by increasing collagen sample thickness. Given the low fluence threshold for bacterial inactivation and the macroscopic thickness of collagenous tissues in vivo, our results suggest a range of 222 nm UV fluences which may inactivate pathogenic bacteria without causing significant damage to fibrillar collagen. This presents an initial step toward the validation of 222 nm UV radiation for internal tissue disinfection.

Quad-PortMIMO dielectric ResonatorAntenna with filtering response for IoT application

ABSTRACT This article presents a four-port Multi-Input Multi-Output (MIMO) antenna based on Dielectric Resonators (DR), showcasing excellent filtering characteristics, high isolation between ports, and strong diversity performance. The initial step introduces a single-port filtering Dielectric Resonator Antenna (DRA) utilizing the HEM11δ mode, featuring a complementary split ring resonator (SRR) feed structure that excites the cylindrical DR. This feed structure enables the independent adjustment of one resonance frequency and two radiation nulls. The experimental results confirm that the designed radiator works in between 2.35 and 2.57 GHz with an isolation level of more than 25 dB. The value of the gain is around 5.0 dBi within the operating band, while it is less than −15 dBi outside the working band. All these features make the designed MIMO radiator applicable for IoT applications.

The critical success factors (CSF) of blockchain technology effecting excel performance of banking sector: Case of UAE Islamic Banks

The present study aims to examine the implementation of Blockchain Technology within the financial sector, with a specific emphasis on its acceptance by Islamic banks operating in the United Arab Emirates (UAE). In the context of a technologically advanced period that necessitates expeditious and safe transactions, blockchain emerges as a viable remedy by obviating the need for intermediary entities and augmenting the velocity and security of transactions. This study uses Partial Least Squares Structural Equation Modeling (PLS-SEM) to investigate the important success aspects of technology and its influence on the performance of Islamic banks. The findings of this study are of particular importance, as they demonstrate the considerable impact of Investment Willingness on Financial Resources. Additionally, the study highlights the crucial role played by Organizational Culture and Leadership Support in fostering readiness for adoption. This study serves as an initial step towards the wider use of blockchain technology in the financial industry, with a particular focus on its application within Islamic banking institutions.

Leader Signaled Knowledge Hiding and Erosion of Cocreated Value: Microfoundational Evidence From the Test Preparation Industry

This article is an initial step toward theorizing the erosion of cocreated value in knowledge-intensive workplaces. In doing so, our research study examines how knowledge hiding behavior instigated and promoted by organizational leaders can adversely impact the microfoundations of the organization. The current study explores knowledge hiding in the Indian test preparation industry. To conduct our research inquiry, we applied a qualitative inductive approach. We interviewed 25 employees working in the test preparation industry, and the responses were analyzed using the Gioia method, a variant of grounded theory. The findings of our study reveal how leader-signaled knowledge hiding in this industry hinders value cocreation for stakeholders through outcomes, such as reduced organizational commitment, moral decoupling, team stratification, information asymmetry, and leader-induced trust deficit. The current study synthesizes findings of the explorative research complemented by a literature review to propose a conceptual framework.

Applying Digital Images to Identify Pavement Damage in Support of The Road Infrastructure Development Program

Road damage can cause discomfort while driving and even lead to accidents. According to the National Road Network Condition Map Data in 2017, the level of severe and minor road damage in the East Java region had reached 288 kilometers. Based on this data, periodic road condition assessments and maintenance are essential to minimize damage. Road maintenance efforts are crucial to support road infrastructure development programs. The initial step in road maintenance is to identify road damage, determining the necessary actions to be taken. In this research, road pavement damage identification is carried out using the Yolov5, Yolov6, and Yolov7 methods. Test results indicate that the Yolov5 method performed the best with a validation mAP (mean Average Precision) score of 42%, a Precision value of 0.544, and a Recall value of 0.453. These scores indicate that the accuracy of road pavement damage detection using the YOLO algorithm for depression, corrugation, potholes, and alligator cracking is at its maximum level.

Epidermal Growth Factor Receptor Inhibition With Erlotinib in Liver: Dose De-Escalation Pilot Trial as an Initial Step in a Chemoprevention Strategy

Background and AimsEffective approaches for prevention of hepatocellular carcinoma (HCC) will have a significant impact on HCC-related mortality. There are strong preclinical data and rationale to support targeting epidermal growth factor receptor (EGFR) for HCC chemoprevention. Small molecule inhibitors of EGFR have been FDA-approved for cancer therapy, which provides an opportunity to re-purpose one of these drugs for chemoprevention of HCC. Unfortunately, the frequency of side-effects associated with administration of these drugs at oncology doses renders them ineffective for chemoprevention. This clinical trial assesses whether lower doses of one of these inhibitors, erlotinib, still engages EGFR in the liver to block signaling (e.g. EGFR phosphorylation). The objective of this clinical trial was determination of a safe and minimum effective dose of erlorinib for which ≥50% reduction phospho-EGFR immunohistochemical staining in the liver was observed. Methods46 participants were pre-registered and 25 participants were registered in this multicenter trial. By dose de-escalation trial design, cohorts of participants received a 7-day course of erlotinib 75 mg/day, 50 mg/day or 25 mg/day with liver tissue acquisition prior to and after erlotinib. ResultsA ≥50% reduction phospho-EGFR immunohistochemical staining in the liver was observed in a minimum of 40% of participants (predetermined threshhold) at each of the dose levels. Erlotinib was very well tolerated with few side effects observed, particularly at the dose of 25 mg/day. Favorable modulation of the Prognostic Liver Signature was observed in participants that received erlotinib. ConclusionsThese data support the selection of erlotinib doses as low as 25 mg/day of for a longer intervention to assess for evidence of efficacy as an HCC chemoprevention drug. (ClinicalTrials.gov NCT02273362)

The single metal atom (Ni, Pd, Pt) anchored on defective hexagonal boron nitride for oxidative desulfurization.

Single-atom catalysts (SACs) have attracted great attention for various chemical reactions because of their strong activity, high metal utilization ratio, and low cost. Here, by using the density functional theory (DFT) method, the stability of a single VIII-group metal atom (M = Ni, Pd, Pt) anchored on the defective hexagonal boron nitride (h-BN) sheet and its possible application in oxidative desulfurization (ODS) are investigated. Calculations show that the stability of the single M atom embedded in the h-BN surface with B and N vacancies is strikingly enhanced compared to that on the perfect h-BN surface. The catalytic activities of the defective h-BN-supported single metal atom are further studied by the activation of molecular oxygen and subsequent oxidation of dibenzothiophene (DBT). O2 is activated to the super-oxo state with large interaction energies on three M/VN surfaces. However, among the three M/VB surfaces, only Pt/VB performs efficient activation of O2. The oxidation of DBT proceeds in two steps; the rate-determining step is the initial step, in which activated O2 oxidizes DBT to produce sulfoxide. By comparing the energy barrier in the first reaction step, both Ni/VN and Pt/VB are revealed as promising candidates for the ODS reaction.

A comprehensive study on automatic non‐informative frame detection in colonoscopy videos

AbstractDespite today's developing healthcare technology, conventional colonoscopy is still a gold‐standard method to detect colon abnormalities. Due to the folded structure of the intestine and visual disturbances caused by artifacts, it can be hard for specialists to detect abnormalities during the procedure. Frames that include artifacts such as specular reflection, improper contrast levels from insufficient or excessive illumination gastric juice, bubbles, or residuals should be detected to increase an accurate diagnosis rate. In this work, both conventional machine learning and transfer learning methods have been used to detect non‐informative frames in colonoscopy videos. The conventional machine learning part consists of 5 different types of texture features, which are gray level co‐occurrence matrix (GLCM), gray level run length matrix (GLRLM), neighborhood gray‐tone difference matrix (NGTDM), focus measure operators (FMOs), and first‐order statistics. In addition to these methods, we utilized 8 different transfer learning models: AlexNet, SqueezeNet, GoogleNet, ShuffleNet, ResNet50, ResNet18, NasNetMobile, and MobileNet. The results showed that FMOs and decision tree combination gave the best accuracy and f‐measure values with almost 89% and 0.79%, respectively, for the conventional machine learning part. When the transfer learning part is taken into account, AlexNet (99.85%) and SqueezeNet (98.80%) have the highest performance metric results. This study shows the potential of both transfer learning and conventional machine learning algorithms to provide fast and accurate non‐informative frame detection to be used during a colonoscopy, which may be considered the initial step in identifying and classifying colon‐related diseases automatically to help guide physicians.

Deep convolutional neural network-based Leveraging Lion Swarm Optimizer for gesture recognition and classification

&lt;abstract&gt; &lt;p&gt;Vision-based human gesture detection is the task of forecasting a gesture, namely clapping or sign language gestures, or waving hello, utilizing various video frames. One of the attractive features of gesture detection is that it makes it possible for humans to interact with devices and computers without the necessity for an external input tool like a remote control or a mouse. Gesture detection from videos has various applications, like robot learning, control of consumer electronics computer games, and mechanical systems. This study leverages the Lion Swarm optimizer with a deep convolutional neural network (LSO-DCNN) for gesture recognition and classification. The purpose of the LSO-DCNN technique lies in the proper identification and categorization of various categories of gestures that exist in the input images. The presented LSO-DCNN model follows a three-step procedure. At the initial step, the 1D-convolutional neural network (1D-CNN) method derives a collection of feature vectors. In the second step, the LSO algorithm optimally chooses the hyperparameter values of the 1D-CNN model. At the final step, the extreme gradient boosting (XGBoost) classifier allocates proper classes, i.e., it recognizes the gestures efficaciously. To demonstrate the enhanced gesture classification results of the LSO-DCNN approach, a wide range of experimental results are investigated. The brief comparative study reported the improvements in the LSO-DCNN technique in the gesture recognition process.&lt;/p&gt; &lt;/abstract&gt;

Two-Step Hyperparameter Optimization Method: Accelerating Hyperparameter Search by Using a Fraction of a Training Dataset

Abstract Hyperparameter optimization (HPO) is an important step in machine learning (ML) model development, but common practices are archaic—primarily relying on manual or grid searches. This is partly because adopting advanced HPO algorithms introduces added complexity to the workflow, leading to longer computation times. This poses a notable challenge to ML applications, as suboptimal hyperparameter selections curtail the potential of ML model performance, ultimately obstructing the full exploitation of ML techniques. In this article, we present a two-step HPO method as a strategic solution to curbing computational demands and wait times, gleaned from practical experiences in applied ML parameterization work. The initial phase involves a preliminary evaluation of hyperparameters on a small subset of the training dataset, followed by a reevaluation of the top-performing candidate models postretraining with the entire training dataset. This two-step HPO method is universally applicable across HPO search algorithms, and we argue it has attractive efficiency gains. As a case study, we present our recent application of the two-step HPO method to the development of neural network emulators for aerosol activation. Although our primary use case is a data-rich limit with many millions of samples, we also find that using up to 0.0025% of the data—a few thousand samples—in the initial step is sufficient to find optimal hyperparameter configurations from much more extensive sampling, achieving up to 135× speedup. The benefits of this method materialize through an assessment of hyperparameters and model performance, revealing the minimal model complexity required to achieve the best performance. The assortment of top-performing models harvested from the HPO process allows us to choose a high-performing model with a low inference cost for efficient use in global climate models (GCMs).

A large field-of-view angled panel detector for the development of a plant PET system

In plant research, positron emission tomography (PET) is sometimes employed for physiological studies of plants. However, due to the generally high cost of PET systems, their application in plant research centers can be challenging. Furthermore, the configurations of commercial PET systems might not always align well with the requirements of plant research. To address these issues, we have taken the initial steps towards a solution by developing a large field-of-view (LFOV) angled panel detector intended for the development of a plant PET system. This panel detector is composed of two gadolinium orthosilicate (GSO) scintillator blocks, an angled light guide, and four flat panel photomultiplier tubes (FP-PMTs). The GSO pixel size used in the scintillator block measures 2.65 mm (radial)× 2.85 mm (axial)× 15.0 mm, arranged in a configuration of 16 (radial)× 32 (axial) to form a scintillator block with overall dimensions of 44.0 mm (radial)× 94.4 mm (axial)× 15 mm (depth). By implementing an angled light guide, the two scintillator blocks were positioned at a 22.5-degree angle, resulting in the creation of a LFOV angled panel detector. This specific angle allowed for the arrangement of 16 scintillator blocks in a hexadecagonal shape using 8 panel detectors when arranged in a circular configuration. We successfully constructed a panel detector with an approximate field-of-view of 10 cm× 10 cm and conducted a comprehensive performance evaluation. Upon irradiating the panel detector with 511 keV gamma photons, nearly all 1024 GSO pixels were accurately resolved in the position histogram, displaying minimal distortion, and exhibiting an average peak-to-valley ratio of 5.0. Additionally, the energy resolution achieved was 14% full width at half maximum (FWHM). The resulting LFOV angled panel detector holds considerable promise for the use within a cost-effective, user-friendly as well as high performance plant PET system.