Initial Phase Research Articles

Compact City and Mayoral Entrepreneurship: A Study of Success and Setbacks in Two Japanese Cities

Controlling urban sprawl are the overarching challenges facing cities worldwide. This article delves into the pivotal role of policy entrepreneurship throughout the planning and implementation phases of compact city initiatives. Drawing insights from the experiences of Aomori City and Toyama City in Japan, this study scrutinizes the drivers behind the shift toward urban compactness and its consequent outcomes. The incorporation of policy studies theories into the realm of compact city development has been scant. To bridge this gap, the research leverages process-oriented theories from policy studies to dissect the decision-making processes guiding compact city development in these two pivotal municipalities. Through this investigation, it is revealed that the effective transition to compact urbanization in Japanese municipalities predominantly stems from policy entrepreneurship. The author posits that this achievement is primarily attributed to adept local mayors who possess the acumen to cater to specific local needs.

Curiosity Satisfaction Increases Event-related Potentials Sensitive to Reward.

Successful learning depends on various factors such as depth of processing, motivation, or curiosity about information. A strong drive to learn something or the expectation of receiving a reward can be crucial to enhance learning. However, the influence of curiosity on the processing of new information and its similarity with reward processing is not well understood. This study examined whether states of curiosity influence specific ERPs associated with reward processing and whether these ERPs are related with later memory benefits. In an initial screening phase, participants indicated their curiosity and confidence in prior knowledge about answers to various trivia questions. In a subsequent study phase, we targeted different time windows related to reward processing during the presentation of trivia answers containing the reward positivity (RewP; 250-350 msec), the P3 (250-500 msec), and the late-positive-potential (LPP; 600-1000 msec). In a following surprise memory test, we found that participants recalled more high- than low-curiosity answers. The RewP, P3, and LPP showed greater positive mean amplitudes for high compared with low curiosity, reflecting increased reward processing. In addition, we found that the RewP and the P3 showed more positive mean amplitudes for later recalled compared with later forgotten answers, but curiosity did not modulate this encoding-related results. These findings support the view that the satisfaction of curiosity resembles reward processing, indicated by ERPs.

Clinical efficacy of the Invisalign mandibular advancement appliance: A retrospective investigation

This retrospective investigation aimed to determine whether the achieved changes in the treatment of Class II malocclusion were the same as those planned after the prescribed wear of an initial phase of Invisalign treatment with the mandibular advancement appliance (MAA) (Align Technology, Santa Jose, Calif). All patients treated with the MAA and who satisfied inclusion/exclusion criteria were selected from a database of 16,500 patients treated with clear aligners by 16 orthodontists. The pretreatment, planned, and achieved overjet measurements were documented from data provided in Align Technology's software facility, ClinCheck. Changes in intermaxillary anteroposterior (AP) first permanent molar (FPM) relationships were evaluated using Geomagic Control X (3D systems, Rock Hill, SC) metrology software. Most of the 195 patients who satisfied the inclusion criteria were females (n= 104; 53.3%). The mean age was 12.62 ± 2.20 years. The pretreatment overjet was reduced from 6.49 ± 2.86 mm to 4.61 ± 2.22 mm after prescribed MAA wear, which was 42.5% of the planned outcome. The pretreatment AP FPM reduced from 3.14 ± 1.95 mm to 2.24 ± 2.51 mm, which was 31.3% of that planned. Thirty-eight (19.5%) patients experienced an increase in overjet where a reduction was planned. Less than half of the planned overjet reduction and less than a third of the planned AP FPM correction were achieved with the MAA. Almost 20% of patients completed the MAA phase of treatment with an increased overjet despite a reduction being planned.

High utility itemsets mining based on hybrid harris hawk optimization and beluga whale optimization algorithms

The commonly used high utility itemsets mining method for massive data is the intelligent optimization algorithm. In this paper, the WHO (Whale-Hawk Optimization) algorithm is proposed by integrating the harris hawk optimization (HHO) algorithm with the beluga whale optimization (BWO) algorithm. Additionally, a whale initialization strategy based on good point set is proposed. This strategy helps to guide the search in the initial phase and increase the diversity of the population, which in turn improve the convergence speed and algorithm performance. By applying this improved algorithm to the field of high utility itemsets mining, it provides new solutions to optimization problems and data mining problems. To evaluate the performance of the proposed WHO, a large number of experiments are conducted on six datasets, chess, connect, mushroom, accidents, foodmart, and retail, in terms of convergence, recall rates, and runtime. The experimental results show that the convergence of the proposed WHO is optimal in five datasets and has the shortest runtime in all datasets. Compared to PSO, AF, BA, and GA, the average recall rate in the six datasets increased by 32.13%, 49.95%, 12.15%, and 16.24%, respectively.

Performance enhancement of machine learning algorithm for breast cancer diagnosis using hyperparameter optimization

Breast cancer is the most fatal women’s cancer, and accurate diagnosis of this disease in the initial phase is crucial to abate death rates worldwide. The demand for computer-aided disease diagnosis technologies in healthcare is growing significantly to assist physicians in ensuring the effectual treatment of critical diseases. The vital purpose of this study is to analyze and evaluate the classification efficiency of several machine learning algorithms with hyperparameter optimization techniques using grid search and random search to reveal an efficient breast cancer diagnosis approach. Choosing the optimal combination of hyperparameters using hyperparameter optimization for machine learning models has a straight influence on the performance of models. According to the findings of several evaluation studies, the k-nearest neighbor is addressed in this study for effective diagnosis of breast cancer, which got a 100.00% recall value with hyperparameters found utilizing grid search. k-nearest neighbor, logistic regression, and multilayer perceptron obtained 99.42% accuracy after utilizing hyperparameter optimization. All machine learning models showed higher efficiency in breast cancer diagnosis with grid search-based hyperparameter optimization except for XGBoost. Therefore, the evaluation outcomes strongly validate the effectiveness and reliability of the proposed technique for breast cancer diagnosis.

Joint Effects of Winds and Tides on Near-Inertial Internal Waves in the Northern South China Sea: A Three-Dimensional Numerical Study

Abstract Joint effects of winds and tides on near-inertial internal waves (NIWs) are numerically investigated via a series of three-dimensional quasi-realistic simulations in the northern South China Sea (NSCS). Model results demonstrate that in the presence of wind-induced NIWs, more tidal energy is transferred to NIWs, while in the presence of tide-induced NIWs, the extreme wind (cyclone) would inject less near-inertial kinetic energy (NIKE). The interaction between wind-induced and tide-induced NIWs produces total NIKE more (or less) than a linear superposition of that generated by wind and tide forcing alone at different sites in the NSCS. Specifically, near the Luzon Strait, both tides and winds make positive contributions to the local near-inertial energy input, resulting in more than 30% enhancement of total NIKE (>0.5 kJ m−2). However, in some deep-water regions along the cyclone paths, energy is transferred from cyclones to NIWs and also from NIWs to internal tides. Due to this “energy pipeline” effect, tide- and wind-induced NIWs contribute to weakening of total NIKE (∼0.3 kJ m−2 or 30%). Additionally, sensitivity experiments with varying initial tidal phases indicate that the interaction between wind-induced NIKE and tide-induced NIKE is robust in most of the model domain (over 80%) under different phase alignments between wind- and tide-induced NIWs. From the perspective of cyclones, tide-induced NIKE is comparable to wind-induced NIKE in the Luzon Strait before the arrival of cyclones, while tide-induced NIKE is two orders of magnitude smaller than wind-induced NIKE in most of the NSCS after the arrival of cyclones. Overall, our results highlight the joint effects of wind and tide forcing on the local NIW dynamics in the NSCS. Significance Statement Near-inertial internal waves (NIWs) are ubiquitous phenomena in stratified water, which can significantly influence the ocean mixing, especially across the thermocline. NIWs are generated mainly by wind forcing near the sea surface, but also by internal tide breaking in the ocean interior. Hence, a question that arises is whether winds or tides play dominant roles in generating NIWs. In fact, due to the interaction between wind- and tide-induced NIWs, the total near-inertial kinetic energy (NIKE) is not merely a linear superposition of that generated by winds and tides forcing alone. By carrying out numerical experiments, we find that extreme winds can help to transfer more energy from tides to NIWs, while tides would suppress energy transferring from winds to NIWs. As a result, this fact is crucial in accurately reproducing NIW dynamics in a targeted region, thereby determining redistribution of local ocean mixing intensity.

A FEA-Based Fast AC Steady-State Algorithm for a Voltage-Driven PMSM by a Novel Voltage-Flux-Driven Model

Finite element analysis (FEA) has been used more frequently than ever before in electrical machine design covering different disciplines such as electromagnetics, rotor dynamics, thermodynamics etc. The accuracy and speed of FEA are in high demands, especially when the time-stepping FEA is employed in the electromagnetic design of the machine. In this paper, to accelerate numerical transient in FEA of a voltage-driven permanent magnet synchronous machine (PMSM), a fast AC steady-state algorithm has been proposed, in which the voltage supply is converted into the stator flux linkage and the machine can be regarded as driven by the flux linkage directly. The electromagnetic transient is eliminated by injecting additional voltage signals to compensate the oscillating stator flux linkage in transient and the time-stepping error caused by the backward Euler in FEA. And the additional voltage signals will finally disappear after the steady state is achieved within about a half electrical time period. In addition, the maximum torque per ampere (MTPA) control logic can also be realized by adjusting the initial phase angle of the voltage supply based on the vector diagram. The simulation speed of the proposed method in this paper was compared to other traditional models and the accuracy was verified by experimental results.

Floral Initiation, Organogenesis, and Flowering of Mature Paphiopedilum Clair de Lune ‘Edgard Van Belle’ Award of Merit/American Orchid Society Plants Derived from Divisions

Paphiopedilum Clair de Lune ‘Edgard Van Belle’, an excellent Maudiae-type hybrid that has been propagated by artificial division for a long time. We studied its flower bud initiation, development of floral organs, and flowering habits with a view to providing information for flowering control and efficient commercial production. According to our research, the flower bud initiation phase of this cultivar begins in February every year, and 80% of the plants completed sepal primordium differentiation in March, The flower bud differentiation lasts for 6 to 7 months, until flowering in August. Within 1 to 3 months after flower bud differentiation, all tested plants differentiated lateral buds. After 5 to 6 months, the new, aboveground vegetative shoots reached their maximum growth, with an average plant height of 20 cm, five leaves, and a shoot dry weight of more than 3 g. From February to April of the following year, a new cycle of flower development and vegetative growth began. In addition, this cultivar was notably sensitivity to high ambient temperature during the late phase of flower development, with a flower bud drop rate as high as 33.3% under average day/night temperatures of 29.0/26.5 °C.

Understanding government social media communication strategies and public engagement during the COVID-19 crisis in Lebanon

The COVID-19 pandemic has prompted governments to adopt various online communication strategies, including the use of social media platforms. Drawing on the crisis and emergency risk communication model, this study investigates the Facebook communication strategies employed by the Ministry of Public Health (MoPH) during the COVID-19 crisis in Lebanon and examines the public engagement with these efforts. A content analysis was conducted on 2,001 posts related to COVID-19 from January 2020 to June 2021. The posts were classified into categories and the frequency and median were used to measure government posting activity and engagement. The study analyzed additional post features such as hashtags, mentions, captions, and language to provide further context to the findings. Trend analysis was also conducted to examine how the metrics varied with key events. The results show that MoPH predominantly used photos (72.00%) and the majority of its posts were published during the initial phase of the outbreak (74.20%). The most prevalent categories of posts were uncertainty reduction (54.57%), self-efficacy (30.33%), and vaccines and immunization (16.89%). MoPH’s Facebook page witnessed a surge in followers during the post-COVID-19 period, increasing by over 300.00% from the pre-COVID-19 years. Posts content, media type, and crisis stage influenced the level of public engagement. The study reveals that Facebook is effective for strategic health crisis communication. Furthermore, it provides practical insights for public sector emergency managers responsible for online communication during outbreaks.

Composing MPC With LQR and Neural Network for Amortized Efficiency and Stable Control

Model predictive control (MPC) is a powerful control method that handles dynamical systems with constraints. However, solving MPC iteratively in real time, i.e., implicit MPC, remains a computational challenge. To address this, common solutions include explicit MPC and function approximation. Both methods, whenever applicable, may improve the computational efficiency of the implicit MPC by several orders of magnitude. Nevertheless, explicit MPC often requires expensive pre-computation and does not easily apply to higher-dimensional problems. Meanwhile, function approximation, although scales better with dimension, still requires pre-training on a large dataset and generally cannot guarantee to find an accurate surrogate policy, the failure of which often leads to closed-loop instability. To address these issues, we propose a triple-mode hybrid control scheme, named Memory-Augmented MPC, by combining a linear quadratic regulator, a neural network, and an MPC. From its standard form, we derive two variants of such hybrid control scheme: one customized for chaotic systems and the other for slow systems. The proposed scheme does not require pre-computation and is capable of improving the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">amortized</i> running time of the composed MPC with a well-trained neural network. In addition, the scheme maintains closed-loop stability with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">any</i> neural networks of proper input and output dimensions, alleviating the need for certifying optimality of the neural network in safety-critical applications. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article was motivated by the need to reduce the amortized cost of MPC in repetitive industrial robotic applications, where long-term operational cost is important and safety is critical. Examples of such applications include factory robotic arm manipulation and fixed-route quadcopter payload transport. Unlike explicit MPC or function approximation, our approach does not require any pre-computation or pre-training. Rather, it attains task proficiency over time by learning a surrogate neural network on the spot and by gradually replacing the costly MPC with the more efficient surrogate model so long as safety permits. Consequently, the proposed scheme incurs a learning cost during the initial phase of the deployment but usually becomes more adept on the task afterwards, leading to amortized efficiency.

Digitization Process of Archives: A Case Study of the Punjab Archives, Lahore

The digitization process of archives involves the conversion of physical documents, records, and artefacts into digital formats, enabling easier access, preservation, and dissemination of valuable historical information.The present study aimed to explore the digitization and preservation of archives: a case study of the Punjab Archives, Lahore. This study uses a qualitative research methodology in order to achieve the study's goals. An inductive approach was used to collect the data. Data was collected from the staff of the Punjab archives team, the project team and primary sources. Data was converted into written transcripts with the help of NVivo software. The benefits of digitization, such as improved accessibility, enhanced searchability, increased security, efficiency of accessing resources, and the protection of cultural heritage, are discussed, along with potential benefits, challenges, and solutions. The findings of the study revealed that the Punjab archives are the richest and oldest archives in the region. Seven million archives are available in different forms (documents, manuscripts, government records, coins, stamps, lithography, books, pictures, maps, etc.) in the Punjab Archives, and 114,592 documents have been selected for the initial phase of digitization. The purpose of the project was to preserve damaged, forged, and torn records in digital form.

Artificial intelligence and feature based transfer learning framework for diagnosis of chest diseases using radiology images

The subject of artificial intelligence-assisted diagnosis and design in the medical industry is very exciting due to considerable developments in medical imaging. In real-world applications, previous manual feature extraction strategies were inefficient in achieving the required results. The number of medical image databases is quickly increasing to accommodate hospital-based diseases as a result of the numerous uses of medical images in healthcare facilities, pathology, and medical diagnostic fields. The primary objective of this study is to create a computerized Artificial intelligence system that can accurately diagnose different diseases and reduce mistakes in the testing process. The study has two primary aspects. In the initial phase, we utilized the deep transfer learning method to extract the pertinent and crucial features from the image x-ray. Subsequently, the support vector machine employs these crucial extracted features to diagnose diseases from the x-ray14 dataset. The imbalanced dataset problem was also addressed with the utilization of the Synthetic Minority Oversampling Technique (SMOTE). The authors conduct a comparative analysis of the findings from this study in relation to other cutting-edge studies and employ cross-dataset experiments to evaluate its efficacy. The results demonstrate that the proposed approach has a detection accuracy of 95.2% for the disease. The VGG-16 model achieved 78.4% accuracy and an AUC of 90%. The proposed model can be applied to other diseases for further experiments.

Relative Yield of Thermal and Nonthermal Emission during Weak Flares Observed by STIX during 2021 September 20–25

The disparate nature of the thermal–nonthermal energy partition during flares, particularly during weak flares, is still an open issue. Following the Neupert effect, quantifying the relative yield of X-ray emission in different energy bands can enable the inferring of the underlying energy release mechanism. During 2021 September 20–25, the Solar Orbiter mission—being closer to the Sun (∼0.6 au) and having a moderate separation angle (<40°) from the Sun–Earth line—offered a unique opportunity to analyze multiwavelength emission from ∼200 (mostly weak) flares, commonly observed by the Spectrometer Telescope for Imaging X-rays (STIX), STEREO-A, GOES, and the Solar Dynamics Observatory. Associating the quotient (q f ) of hard X-ray fluence (12–20 keV) and soft X-ray flux (4–10 keV) with the peak soft X-ray flux enabled us to identify strongly nonthermal flares. A multiwavelength investigation of spectral and imaging-mode observations of the 20 strongly nonthermal weak flares reveals an inverse relationship of q f with the emission measure (and density), and a positive relationship with the flare plasma temperature. This indicates that the plasma in tenuous loops attains higher temperatures compared to that in the denser loops, in response to nonthermal energy deposition. This is in agreement with the plasma parameters of the coronal loops, as derived by applying the one-dimensional Palermo–Harvard hydrodynamical code to coronal loop plasma with different initial coronal loop base pressures when subjected to similar heating inputs. Our investigation, therefore, indicates that the plasma parameters of the flaring loop in the initial phase have a decisive role in the thermal–nonthermal energy partitioning.

Unveiling success determinants for AMB-assisted phase expansion of fusion proteins in ARP/wARP

Fusion proteins (FPs) are frequently utilized as a biotechnological tool in the determination of macromolecular structures using X-ray methods. Here, we explore the use of different protein tags in various FP, to obtain initial phases by using them in a partial molecular replacement (MR) and constructing the remaining FP structure with ARP/wARP. Usually, the tag is removed prior to crystallization, however leaving the tag on may facilitate crystal formation, and structural determination by expanding phases from known to unknown segments of the complex. In this study, the Protein Data Bank was mined for an up-to-date list of FPs with the most used protein tags, Maltose Binding Protein (MBP), Green Fluorescent Protein (GFP), Thioredoxin (TRX), Glutathione transferase (GST) and the Small Ubiquitin-like Modifier Protein (SUMO). Partial MR using the protein tag, followed by automatic model building, was tested on a subset of 116 FP. The efficiency of this method was analyzed and factors that influence the coordinate construction of a substantial portions of the fused protein were identified. Using MBP, GFP, and SUMO as phase generators it was possible to build at least 75 % of the protein of interest in 36 of the 116 cases tested. Our results reveal that tag selection has a significant impact; tags with greater structural stability, such as GFP, increase the success rate. Further statistical analysis identifies that resolution, Wilson B factor, solvent percentage, completeness, multiplicity, protein tag percentage in the FP (considering amino acids), and the linker length play pivotal roles using our approach. In cases where a structural homologous is absent, this method merits inclusion in the toolkit of protein crystallographers.

Inspiratory muscle endurance is similarly reduced in the early and late stages of chronic Chagas heart disease.

Inspiratory muscle strength (IMS) appears to be reduced in subjects with chronic Chagas heart disease (CHD), especially in the presence of heart failure (HF). However, only one study about IMS and inspiratory muscle endurance (IME) in those with CHD without heart failure is available. This study aimed to compare IMS and IME in subjects with CHD in the presence and absence of HF. This is a cross-sectional study in which 30 CHD adult patients were divided into CHD-CC group (initial phase of CHD, without HF; n = 15) and CHD-HF group (advanced phase of CHD, with HF; n = 15). We assessed IMS by maximum inspiratory pressure (MIP) and IME by incremental (Pthmax) and constant load (TLim) tests. Reduced IMS and IME were considered by predicted MIP values <70% and Pthmax/MIP <75%, respectively. Inspiratory muscle weakness (IMW) was more frequent in CHD-HF than in CHD-CC (46.7% vs. 13.3%; p = 0.05), and both groups had high frequencies of reduced IME (93.3% CHD-CC vs. 100.0% CHD-HF; p = 0.95). Age-adjusted logistic regression analysis using HF as a dependent variable showed that HF was associated with an increased chance of IMW compared with the CHD-CC group (OR = 7.47; p = 0.03; 95% CI 1.20-46.19). This study suggests that, in patients with CHD, HF is associated with IMW, and that reduction of IME is already present in the initial phase, similar to the advanced phase with HF.

A research report on the phase-domain trajectories of fault recording and their mathematical models

In currently known theories and algorithms of fault recording analysis and application, there is little literature where fault feature parameters are mined deeply from electrical physical quantities themselves. In this report the calculation method of the phase angles of the reference point and sample points is obtained, which ensures the correspondence of the time-domain waveform of digital fault recording with its phase-domain trajectory. The relationship between the initial phase angle of a sinusoid and the position of its trajectory, the characteristics of the trajectories of three-phase short-circuit currents containing dc components and the mathematical models of these trajectories are analyzed. Taking a rectangular wave current and a triangle wave current as example, the characteristics of the trajectories of non-sinusoidal and periodical waveforms containing harmonics are analyzed as well as their mathematical models. The research results show that the relationship between the initial phase angle of a sinusoid and the position of its circular trajectory is definite; the dc components have no impact on the positions of the trajectories of the short-circuit currents, but on their sizes and shapes; the harmonics have an impact on the shapes of the trajectories, and their positions are decided by the initial phase angles of the ac fundamental components in the waveforms. Subsequent study of the algorithm of fault recording analysis will be spread based on the contents in this report.

Recycling of agro-industrial waste by fabricating laminated Al-metal matrix composites: a numerical simulation and experimental study

AbstractThe utilization of agriculture waste as a recycling process in aluminium metal matrix composites (AMMCs) has gained significant attention as a novel approach in environmental toxicology. This technique aims to mitigate environmental hazards to humans by reducing the negative impact of waste materials. It has been recognized as an effective means to fabricate and enhance the properties of AMMCs. Furthermore, the use of these agro-industrial waste materials has the potential to mitigate both human and environmental risks, including pollution, while also leading to cost savings in manufacturing processes. The present investigation involves the fabrication of a novel metal matrix composite incorporating rice husk ash particles as reinforcement. The initial phase of the work involved the fabrication of novel composites. Subsequently, the focus shifted towards investigating the mechanical, physical, and wear characteristics of these composites. The forming process of the accumulative rolling was investigated by the numerical simulation using the ABAQUS program. Additionally, this study emphasizes the present utilization and prospective advancements of composites reinforced with agricultural and industrial wastes in diverse sectors, particularly in the fabrication of materials for vessels, autos, and construction industries. Furthermore, an examination of the scanning electron microscopy (SEM) of the fracture surfaces in the vicinity of layer interfaces has been conducted to investigate the quality of bonding.

Camouflaged Object Detection Based on Deep Learning with Attention-Guided Edge Detection and Multi-Scale Context Fusion

In nature, objects that use camouflage have features like colors and textures that closely resemble their background. This creates visual illusions that help them hide and protect themselves from predators. This similarity also makes the task of detecting camouflaged objects very challenging. Methods for camouflaged object detection (COD), which rely on deep neural networks, are increasingly gaining attention. These methods focus on improving model performance and computational efficiency by extracting edge information and using multi-layer feature fusion. Our improvement is based on researching ways to enhance efficiency in the encode–decode process. We have developed a variant model that combines Swin Transformer (Swin-T) and EfficientNet-B7. This model integrates the strengths of both Swin-T and EfficientNet-B7, and it employs an attention-guided tracking module to efficiently extract edge information and identify objects in camouflaged environments. Additionally, we have incorporated dense skip links to enhance the aggregation of deep-level feature information. A boundary-aware attention module has been incorporated into the final layer of the initial shallow information recognition phase. This module utilizes the Fourier transform to quickly relay specific edge information from the initially obtained shallow semantics to subsequent stages, thereby more effectively achieving feature recognition and edge extraction. In the latter phase, which is focused on deep semantic extraction, we employ a dense skip joint attention module to enhance the decoder’s performance and efficiency, ensuring accurate capture of deep-level information, feature recognition, and edge extraction. In the later stage of deep semantic extraction, we use a dense skip joint attention module to improve the decoder’s performance and efficiency in capturing precise deep information. This module efficiently identifies the specifics and edge information of undetected camouflaged objects across channels and spaces. Differing from previous methods, we introduce an adaptive pixel strength loss function for handling key captured information. Our proposed method shows strong competitive performance on three current benchmark datasets (CHAMELEON, CAMO, COD10K). Compared to 26 previously proposed methods using 4 measurement metrics, our approach exhibits favorable competitiveness.

Automatic Optimization of JWL‐Miller parameters of HMX‐based aluminized explosive based on genetic algorithm

AbstractThe calibration of JWL‐Miller equation of state (EOS) parameters for aluminized explosive is a cumbersome but important work in explosive evaluation. Manual calibration is usually adopted while the work may be tedious and the optimal results may be unachievable. An automatic calibrating method was established to optimize this procedure based on genetic algorithm program and finite element software. Optimal JWL‐Miller EOS parameters were achieved by iterative calculation calibrating with cylinder test results and underwater‐explosion experiment results. Cylinder test results were adopted to illustrate the initial phase of explosion, and underwater explosion experiments were conducted to calibrating the Miller term of the equation of state. The results showed that the error between cylinder test and simulation result was less than 1 %, the error of underwater explosion impulse between test and simulation results was less than 3.73 %. The optimized parameters of JWL‐Miller EOS will be useful in the numerical simulation research of aluminized explosives.

A Comprehensive Performance Evaluation of GGBS-Based Geopolymer Concrete Activated by a Rice Husk Ash-Synthesised Sodium Silicate Solution and Sodium Hydroxide

Commercial sodium hydroxide (NaOH) and sodium silicate (SS) are commonly used as alkaline activators in geopolymer concrete production despite concerns about their availability and associated CO2 emissions. This study employs an alternative alkaline activator (AA) synthesized from a sodium silicate alternative (SSA) solution derived from rice husk ash (RHA) and a 10 M sodium hydroxide solution. The initial phase established an optimal water-to-binder (W/B) ratio of 0.50, balancing workability and structural performance. Subsequent investigations explored the influence of the alkali/precursor (A/P) ratio on geopolymer concrete properties. A control mix uses ordinary Portland cement (OPC), while ground granulated blast-furnace slag (GGBS)-based geopolymer concrete—GPC mixes (GPC1, GPC2, GPC3, GPC4) vary the A/P ratios (0.2, 0.4, 0.6, 0.8) with a 1:1 ratio of sodium silicate to sodium hydroxide (SS: SH). The engineering performance was evaluated through a slump test, and unconfined compressive strength (UCS) and tensile splitting (TS) tests in accordance with the appropriate standards. The geopolymer mixes, excluding GPC3, offer suitable workability; UCS and TS, though lower than the control mix, peak at an A/P ratio of 0.4. Despite lower mechanical strength than OPC, geopolymers’ environmental benefits make them a valuable alternative. GPC2, with a 0.4 A/P ratio and 0.5 W/B (water to binder) ratio, is recommended for balanced workability and structural performance. Future research should focus on enhancing the mechanical properties of geopolymer concrete for sustainable, high-performance mixtures.