Clustering Effect Research Articles

Boosting Up Electrosynthesis of Urea with Nitrate and Carbon Dioxide via Synergistic Effect of Metallic Iron Cluster and Single-Atom.

Electrocatalytic conversion of nitrates and carbon dioxide to urea under ambient conditions shows promise as a potential substitute for traditional urea synthesis processes characterized by high consumption and pollution. In this study, a straightforward one-pot method is employed to prepare a highly efficient FeNC-Fe1N4 electrocatalyst, consisting of atomically dispersed Fe1N4 sites and metallic Fe clusters (FeNC) with particle size of 4-7nm. The FeNC-Fe1N4 catalyst exhibits remarkable electrocatalytic activity for urea synthesis from nitrate anion (NO3 -) and carbon dioxide (CO2), achieving a urea production rate of 38.2mmolgcat -1h-1 at -0.9V (vs RHE) and a Faradaic efficiency of 66.5% at -0.6V (vs RHE). Both experimental and theoretical results conclusively demonstrate that metallic Fe clusters and Fe1N4 species provide active sites for the adsorption and activation of NO3 - and CO2, respectively, and the synergistic effect between Fe1N4 and metallic Fe clusters significantly enhances the electrochemical efficiency of urea synthesis. In all, this work contributes to the rational design and comprehensive synthesis of a dual-active site iron-based electrocatalyst, facilitating efficient and sustainable urea synthesis.

An inversion-based clustering approach for complex clusters

BackgroundThe choice of an appropriate similarity measure plays a pivotal role in the effectiveness of clustering algorithms. However, many conventional measures rely solely on feature values to evaluate the similarity between objects to be clustered. Furthermore, the assumption of feature independence, while valid in certain scenarios, does not hold true for all real-world problems. Hence, considering alternative similarity measures that account for inter-dependencies among features can enhance the effectiveness of clustering in various applications.MethodsIn this paper, we present the Inv measure, a novel similarity measure founded on the concept of inversion. The Inv measure considers the significance of features, the values of all object features, and the feature values of other objects, leading to a comprehensive and precise evaluation of similarity. To assess the performance of our proposed clustering approach that incorporates the Inv measure, we evaluate it on simulated data using the adjusted Rand index.ResultsThe simulation results strongly indicate that inversion-based clustering outperforms other methods in scenarios where clusters are complex, i.e., apparently highly overlapped. This showcases the practicality and effectiveness of the proposed approach, making it a valuable choice for applications that involve complex clusters across various domains.ConclusionsThe inversion-based clustering approach may hold significant value in the healthcare industry, offering possible benefits in tasks like hospital ranking, treatment improvement, and high-risk patient identification. In social media analysis, it may prove valuable for trend detection, sentiment analysis, and user profiling. E-commerce may be able to utilize the approach for product recommendation and customer segmentation. The manufacturing sector may benefit from improved quality control, process optimization, and predictive maintenance. Additionally, the approach may be applied to traffic management and fleet optimization in the transportation domain. Its versatility and effectiveness make it a promising solution for diverse fields, providing valuable insights and optimization opportunities for complex and dynamic data analysis tasks.

Modelling Time to Recovery from Multidrug Resistant Tuberculosis in Southern Ethiopia

Introduction: Multidrug resistant tuberculosis (MDR-TB) is a global heath threat, resistant to key anti-TB drugs. It is ranked among the top 10 causes of death worldwide. Therefore, the current study investigated time to recovery from MDR-TB in southern Ethiopia. Data, Materials and Methods: Restrospective data from selected hospitals in SNNPR (January 2016 to December 2021) were analyzed. A cluster sample of 301 MDR-TB patients (131 NEMMCH, 121 BH, 49 AGH) was considered. Results: Among the 301 cases, 116 (38.5%) were censored. While 185 (61.5%) were recovered. Parametric shared frailty models were employed to account unobserved heterogeneity among the Hospitals and patients and AFT models were employed. the median recovery time of MDR-TB is 22 months. The clustering effect of frailty model was hospitals. Weibull-gamma shared frailty model was appropriate for this data. Conclusion: The final model showed that males have higher recovery rates than females. Extra pulmonary MDR-TB and Urban residency correleted with longer recovery times. The recovery rate increases with increasing baseline weight, education level, and occupation. But, the recovery rate decreases with smoking, co-morbidities, previous drug history, history of TB, and alcohol use Recommendation: All concerned bodies should be cognizant on the risk factors of MDR-TB in SNNP region By providing on early case detection and appropriate treatment of drug-susceptible MDR-TB, since it is essential to shorten the recovery time of MDR-TB patients in line with WHO guidelines

Optical properties of Ba-impurity defect clusters in KH2PO4 crystals

Potassium dihydrogen phosphate (KDP) is widely used in electro-optical switches and inverter crystals in inertial confinement fusion (ICF). The early studies suggested that defect clusters are one of the main reasons why the laser-induced damage threshold (LIDT) is far lower than the theoretical value. However, it is not clear what type of defect clusters cause this phenomenon. The BaK defect cluster models have been determined by simulation using General Utility Lattice Programs (GULP). We found that BaK+VH and BaK+VK cause a dramatic distortion on H-O bonds with maximum values of 29.5 % and 27.12 %, respectively. By analyzing the configuration coordinate diagram and optical spectra, we believe that both defect clusters can promote ionization through defect-assisted "2+1" photon absorption, which leads to free electrons more easily exceeding the critical value for electron avalanche breakdown. Our study will offer insight into the effects of defect clusters in KDP crystals.

Exploring the reliability and validity of multiple mini-interviews in admission to teacher education

ABSTRACT This study addressed the need to explore the reliability and validity of the recently renewed aptitude test based on the multiple mini-interview (MMI) format in Finnish teacher education programmes. Utilising a nationwide dataset (N = 3306), we explored the reliability of the format using intraclass correlations and multilevel modelling. Furthermore, we validated the intended structure of the MMI with confirmatory factor analysis and contrasted the scores of the MMI with other admission scores, i.e. cognitive measures. The results showed mostly small effects of clustering of the applicants according to the different interviewers and circuits. The largest variance component for the MMI total score was the applicant (73.3%), followed by measurement error (15.09%). The intended structure was validated, and the MMI seemed to measure different attributes from other admission measures. Thus, the study demonstrated satisfactory reliability and validity of MMI in admission to teacher education, yet the stations need further development.

Improved (related-key) differential cryptanalysis on [formula omitted]

LBlock presented at ACNS 2011 is a lightweight block cipher. The length of the block and master key for LBlock is 64 and 80 bits, respectively. LBlock adopts the Feistel-SP network and has effective hardware implementation. The 18-round attack on LBlock is the most well-known single-key truncated differential attack to date. While using the MILP model in ASIACRYPT 2014, a 14-round related-key characteristic with probability 2−49 was discovered. In this paper, we investigate the (related-key) differential behavior of LBlock and implement the first 20-round differential attack against it under the single-key scenario. First, the related-key characteristics of LBlock are sought using the SAT (Boolean Satisfiability Problem) model with Matsui’s bounds constraints. Then, in order to increase the probabilities of distinguishers, a new algorithm is provided to search for characteristics with a clustering effect. Following that, the probability of a 17-round related-key differential which is the longest distinguisher is increased from 2−63 to 2−59.05. Additionally, several valid 15-round differentials found by the algorithm can be used to perform a 20-round differential attack. Finally, by expanding the 15-round distinguisher, we are able to perform the key recovery attack on 20-round LBlock. Data, time, and memory complexity are 260.98, 269.84, and 260.98, respectively. Compared to the existing single-key differential attack, our result covers two more rounds. The new algorithm can not only design block ciphers but also evaluate their security.

Robust deep image clustering using convolutional autoencoder with separable discrete Krawtchouk and Hahn orthogonal moments

By cooperatively learning features and assigning clusters, deep clustering is superior to conventional clustering algorithms. Numerous deep clustering algorithms have been developed for a variety of application levels; however, the majority are still incapable of learning robust noise-resistant latent features, which limits the clustering performance. To address this open research challenge, we introduce, for the first time, a new approach called: Robust Deep Embedded Image Clustering algorithm with Separable Krawtchouk and Hahn Moments (RDEICSKHM). Our approach leverages the advantages of Krawtchouk and Hahn moments, such as local feature extraction, discrete orthogonality, and noise tolerance, to obtain a meaningful and robust image representation. Moreover, we employ LayerNormalization to further improve the latent space quality and facilitate the clustering process. We evaluate our approach on four image datasets: MNIST, MNIST-test, USPS, and Fashion-MNIST. We compare our method with several deep clustering methods based on two metrics: clustering accuracy (ACC) and normalized mutual information (NMI). The experimental results show that our method achieves superior or competitive performance on all datasets, demonstrating its effectiveness and robustness for deep image clustering.

Noble metal clusters substitution in porous Ni substrate renders high mass-specific activities toward oxygen evolution reaction and methanol oxidation reaction

The sluggish reaction kinetics of the oxygen evolution reaction (OER) and methanol oxidation reaction (MOR) remain obstacles to the commercial promotion of water splitting and direct methanol fuel cells. Considering the vital role of noble metals in electrocatalytic activity, this work focuses on the rational synthesis of Ni-noble metal composite nanocatalysts for overcoming the drawbacks of high cost and susceptible oxidized surfaces of noble metals. The inherent catalytic activity is improved by the altered electronic structure and effective active sites of the catalyst induced by the size effect of noble metal clusters. In particular, a series of Ni-noble metal nanocomposites are successfully synthesized by partially introducing noble metal into Ni with porous interfacial defects derived from Ni-Al layered double hydroxide (LDH). The Ni10Pd1 nanocomposite exhibits high OER catalytic activity with an overpotential of 0.279 V at 10 mA/cm2, surpassing Ni10Ag1 and Ni10Au1 counterparts. Furthermore, the average diameter of Pd clusters gradually increases from 5.57 nm to 44.44 nm with the increased proportion of doped Pd, leading to the passivation of catalytic activity due to the exacerbated surface oxidation of Pd in the form of Pd2+. After optimization, Ni10Pd1 delivers significantly enhanced OER and MOR electroactivities and long-term stability compared to that of Ni2Pd1, Ni1Pd1 and Ni1Pd2, which is conducive to the effective utilization of Pd and alleviation of surface oxidation.

Radiation Damage Simulation Using Molecular Dynamics in Ni-Based Alloy

In the present study, we investigated the irradiation-induced induction in Ni80Cr20, Ni80Fe20, Ni50Fe50, Ni40Fe40Cr20 by molecular dynamics (MD) simulation. A previously published modified potential is used to provide a detailed account of the process involved in the production and evolution of defects. Ni50Fe50 and Ni40Fe40Cr20 alloys exhibit comparable damage level and better radiation response compared to Ni80Cr20 and Ni80Fe20. The inhibition effect of interstitial clusters increases with the complexity of alloying elements. The alloying of Cr has resulted in Ni80Fe20 and Ni50Fe50 tend to form 1/3<111> dislocation loops while at the same time making Ni40Fe40Cr20 and Ni80Cr20 more susceptible to stacking fault tetrahedra formation than the remaining two alloys.

Investigation of the few-mode ytterbium-erbium doped fiber amplifier including effects of ion clustering and secondary energy transfer

Investigation of the few-mode ytterbium-erbium doped fiber amplifier including effects of ion clustering and secondary energy transfer

Clustering of passive tracers in a random acoustic velocity field

We consider the effects of passive tracer clustering (e.g., the magnetic field energy in stellar atmospheres) in a random acoustic velocity field. A method for numerical modeling of a two-dimensional random acoustic field is proposed. The field is described by a correlation tensor defined by traveling isotropic waves, taking into account dissipation. Two metrics for measuring the clustering effects are used: concentration and density. Using numerical modeling, we show that the tracer concentration is almost always clustered. The situation with the density is different; as the dissipation tends to zero, the time to reach the clustered states increases significantly. In addition, due to the tracer transport out of the density clustering regions, only a part of the tracer is clustered. For the presented analyses, we considered ensembles of Lagrangian particles and introduced and applied the statistical topography methodology.

Graph-Driven deep Multi-View Clustering with self-paced learning

Multi-view clustering has attracted widespread attention because it can improve clustering performance by integrating information from various views of samples. However, many existing methods either neglect graph information entirely or only partially incorporate it, leading to information loss and non-comprehensive representation. Besides, they usually make use of graph information by determining a fixed number of neighbors based on prior knowledge, which limits the exploration of graph information contained in data. To address these issues, we propose a novel method, termed Graph-Driven deep Multi-View Clustering with self-paced learning (GDMVC), which integrates both feature information and graph information to better explore information within the data. Additionally, based on the idea of self-paced learning, this method gradually increases the number of neighbors and updates the similarity matrix, progressively providing more graph information to guide representation learning. By this way, we avoid issues associated with a fixed number of neighbors and ensure a thorough exploration of graph information contained in the original data. Furthermore, this method not only ensures the consistency among views but also leverages graph information to further enhance the unified representation, aiming to obtain more separable cluster structures. Extensive experiments on real datasets demonstrate its effectiveness for multi-view clustering. Our code will be released on https://github.com/yff-java/GDMVC/.

Role of tin clustering in band structure and thermodynamic stability of GeSn by atomistic modeling

Synthesis of device-quality GeSn materials with higher Sn compositions is hindered by various factors, such as Sn segregation, clustering, and short-range ordering effects. In the present work, the impact of the clustering of Sn atoms in a GeSn semiconductor alloy was studied by density functional theory using SG15 pseudopotentials in a Synopsys QuantumATK tool, where the thermodynamic stability, effective band structure, indirect and direct bandgaps, and density of states (DOS) were computed to highlight the difference between a cluster-free random GeSn alloy and a GeSn alloy with Sn–Sn clusters. A 54-atom bulk Ge1–xSnx (x = 3.71%–27.77%) supercell was constructed with cluster-free and a first nearest neighbor Sn–Sn clustered GeSn alloy at each composition for this work. Computation using the generalized gradient approximation exchange-correlation functional showed that the thermodynamic stability of GeSn was reduced due to the clustering of Sn, which increased the formation energy of the GeSn alloys by increasing the Hartree potential energy and exchange-correlation energy. Moreover, with the effective band structure of the GeSn material at a Sn composition of ∼22%, both direct (Eg,Γ) and indirect (Eg,L) bandgaps decreased by a large margin of 40.76 and 120.17 meV, respectively, due to Sn–Sn clustering. On the other hand, Eg,Γ and Eg,L decrease is limited to 0.5 and 12.8 meV, respectively, for Sn composition of ∼5.6%. Similar impacts were observed on DOS, in an independent computation without deducing from the electronic band structure, where the width of the forbidden band reduces due to the clustering of Sn atoms in GeSn. Moreover, using the energy bandgaps of GeSn computed with the assumption of it being a random alloy having well-dispersed Sn atoms needs revision by incorporating clustering to align with the experimentally determined bandgap. This necessitates incorporating the effect of Sn atoms clustered together at varying distributions based on experimental characterization techniques such as atom probe tomography or extended x-ray absorption fine structure to substantiate the energy bandgap of the GeSn alloy at a particular composition with precision. Hence, considering the effect of Sn clusters during material characterization, beginning with the accurate energy bandgap characterization of GeSn would help in mitigating the effect of process variations on the performance characteristics of GeSn-based group IV electronic and photonic devices such as varying leakage currents in transistors and photodiodes as well as the deviation from the targeted wavelength of operation in lasers and photodetectors.

Unique solvation structure induced by anionic Cl in aqueous zinc ion batteries

Aqueous zinc ion batteries (AZIBs) have garnered significant attention in large-scale static energy storage battery systems due to their low cost, high safety and environmental friendliness. However, it has some inherent problems during operation, such as the occurrence of side reactions (hydrogen evolution reaction, HER) and anode corrosion, formation of by-products and growth of metal dendrites. To analyze the mechanism of generation from aspect of the electrolyte solvation structure and make cell efficiency further improvements based on it, so we use DFT calculations to find the most stable solvation structure in AZIBs with ZnCl2 as the electrolyte and analyze it. We define the relative concentration Cr, and calculate different groups metal cation cluster structures such as [Zn(H2O)n]2+, [ZnCl(H2O)n]+, [ZnCl2(H2O)n] and [ZnCl3(H2O)n]‐ that exist at different Cr. We discuss the effect of different clusters formed due to the Cr variations on the battery performance in terms of three aspects: the structural conformation, the cluster characteristics (including the hydrogen bonding network, bond lengths, bond angles, as well as the electrostatic potential ESP) and the cluster performance (including the adsorption energy Ea, binding energy Eb, and desolvation energy Edes). The results shows that the electrolyte metal cation Zn2+ can be coordinated with up to six H2O molecules in first shell, and this metal cation solvation structure contributes to the occurrence and formation of side reactions and by-products, which reduces the battery efficiency. Increasing the electrolyte anion Cl− concentration by appropriately increasing the Cr helps to desolvate the metal cation cluster structure, which greatly improves the battery efficiency and suppresses the side reactions and by-products. Yet the improvement effect was not obviously further improved by further increasing the Cl− concentration.

Price Prediction and Determination of the Affecting Variables of the Real Estate by Using X-Means Clustering and CART Decision Trees

The use of machine learning in real estate is quite new. When the working area is large, the factors affecting the price may vary according to the geographical regions and socioeconomic factors. It is thought that the price prediction performance of a model that will reflect these differences will be more successful than a general model. Unsupervised learning methods can be used both to increase performance and to show the variation of different factors affecting the price according to regions. With this aim, a hybrid model of X-Means clustering and CART decision trees was established in this study. &amp;nbsp;This model successfully learned the geographical and physical variables that affect the price. The prediction performance of the model was compared with the direct capitalization method, which is the gold standard in the domain. The hybrid model has a superior performance over direct capitalization in terms of mean square error, root mean square error and adjusted R-Squared metrics. The scores were 72.86, 0.0057 and 0.978, respectively. The effect of clustering was also examined. Clustering increased the prediction performance by 36%.&amp;nbsp;

Investigating the catalytic activity of Mgn (n = 4–8) clusters for the hydrogen evolution reaction using density functional theory

AbstractTo efficiently desorb H2, pure Mgn (n = 4–8) clusters were chosen for the hydrogen evolution reaction with H2O. At the PBE0/def2‐TZVP level and the PBE0‐D3/def2‐TZVP level, the lowest energy structures of Mgn (n = 4–8) clusters and the most stable structures of Mgn@H2O (n = 4–8) complexes were searched in the local region. The transition state was predicted, and then the hydrogen evolution reaction channel was obtained by using the intrinsic reaction coordinate (IRC) to confirm the transition state. To better analyze the hydrogen reaction mechanism, the character of Mgn@H2O (n = 4–8) complexes and MgnO (n = 4–8) clusters, as well as the atomic charge change trend, were investigated using interaction region indicator function analysis (IRI) and natural population analysis (NPA). The reaction effect of Mg4 cluster and H2O is the worst. The energy barrier does, however, progressively lower as the cluster atom count rises, improving the reaction effect.

The Size and Charge Effect of Pt Cluster on the Electrocatalytic Activity Toward the First Step of Dehydrogenation of Methanol

Abstract The O–H/C–H scission of methanol on Pt clusters is a crucial step in direct methanol fuel cells applications. The first dehydrogenation process of methanol on Ptnq clusters (n = 5, 13, 19; q = 0, +1, −1) in various charge states is studied. Our findings indicate that methanol adsorbs more easily on cationic Ptn+ than on neutral Ptn or anionic Ptn−. However, the adsorption capacity of methanol on Ptnq gradually decreases with increasing cluster size, especially for CH3OH on Ptn+, which decreases significantly (from −57.61 kcal/mol to −16.41 kcal/mol). Compared with Ptn and Ptn+, the energy barrier of O–H/C–H bond cleavage is significantly reduced by injecting an electron into Ptn to form Ptn−, and the activity of the catalyst is improved. However, the energy barrier of O–H/C–H cleavage increases gradually with cluster size, leading to a decrease in catalytic activity. The effect of charge weakens as cluster size increases, and small clusters with injected electrons exhibit better catalytic activity.

Resolvent analysis of turbulent flow laden with low-inertia particles

We extend the resolvent framework to two-phase flows with low-inertia particles. The particle velocities are modelled using the equilibrium Eulerian model. We analyse the turbulent flow in a vertical pipe with Reynolds number of $5300$ (based on diameter and bulk velocity), for Stokes numbers $St^+=0-1$ , Froude numbers $Fr_z=-4,-0.4,0.4,4$ and $1/Fr_z = 0$ (gravity omitted). The governing equations are written in input–output form and a singular value decomposition is performed on the resolvent operator. As for single-phase flows, the operator is low rank around the critical layer, and the true response can be approximated using one singular vector. Even with a crude forcing model, the formulation can predict physical phenomena observed in Lagrangian simulations, such as particle clustering and gravitational effects. Increasing the Stokes number shifts the predicted concentration spectra to lower wavelengths; this shift also appears in the direct numerical simulation spectra and is due to particle clustering. When gravity is present, there are two critical layers, one for the concentration field, and one for the velocity field. For upward flow, the peak of concentration fluctuations shifts closer to the wall, in agreement with the literature. We explain this with the aid of the different locations of the two critical layers. Finally, the model correctly predicts the interaction of near-wall vortices with particle clusters. Overall, the resolvent operator provides a useful framework to explain and interpret many features observed in Lagrangian simulations. The application of the resolvent framework to higher $St^+$ flows in combination with Lagrangian simulations is also discussed.

Single-cell RNA sequencing reveals that an imbalance in monocyte subsets rather than changes in gene expression patterns is a feature of postmenopausal osteoporosis.

The role of monocytes in postmenopausal osteoporosis is widely recognized; however, the mechanisms underlying monocyte reprogramming remain unknown. In this study, single-cell RNA sequencing (scRNA-seq) was conducted on CD14+ bone marrow monocytes obtained from three postmenopausal women with normal bone mineral density (BMD) and three women with postmenopausal osteoporosis (PMOP). Monocle2 was used to classify the monocytes into 7 distinct clusters. The proportion of Cluster 1 significantly decreased in PMOP patients, while the proportion of Cluster 7 increased. Further analysis via GSEA, transcription factor activity analysis, and sc-metabolic analysis revealed significant differences between Clusters 1 and 7. Cluster 7 exhibited upregulated pathways associated with inflammation, immunity, and osteoclast differentiation, whereas Cluster 1 demonstrated the opposite results. Monocle2, TSCAN, VECTOR and scVelo data indicated that Cluster 1 represented the initial subset and that Cluster 7 represents one of the terminal subsets. BayesPrism and ssGSEA were employed to analyze the bulk transcriptome data obtained from the GEO database. The observed alterations in the proportions of Clusters 1 and 7 were validated and found to have diagnostic significance. CD16 serves as the marker gene for Cluster 7, thus leading to an increased proportion of CD16+ monocytes in women with PMOP. Flow cytometry was used to assess the consistency of outcomes with those of the bioinformatic analysis. Subsequently, an additional scRNA-seq analysis was conducted on bone marrow mononuclear cells obtained from three patients with PMOP and three postmenopausal women with normal BMD. The differential proportions of Cluster 1 and Cluster 7 were once again confirmed, with the pathological effect of Cluster 7 may attribute to cell-cell communication. The scRNA-seq findings suggest that an imbalance in monocyte subsets is a characteristic feature of PMOP. These findings elucidate the limitations of utilizing bulk transcriptome data for detecting alterations in monocytes, which may influence novel research inquiries.

A Web-Based and Mobile Intervention Program Using a Spaced Education Approach for Workplace Mental Health Literacy: Cluster Randomized Controlled Trial.

Workplace mental health is an important global health concern. This unblinded, phase-III, wait-listed cluster randomized controlled trial aimed to examine the effectiveness of a mobile health (mHealth) psychoeducation program using a spaced education approach on mental health literacy (MHL) in the workplace. The main interest of this paper was the immediate and 3-month medium-term effect of the program on the MHL of workers. The purposely built mHealth platform was also evaluated as a health-related app. The mHealth platform was designed using the principle of spaced education as a psychoeducation intervention program, with various modules of web-based and mobile materials presented to the participant in a progressive manner. Short quizzes at the end of each module ensured adequate learning, and successful completion qualified the learner to progress to the next level. The trial recruited 456 employees of specific industries with high levels of work-related stress. Participants who were nested in different offices or units were allocated into the intervention and wait-listed control groups using a block randomization process, with the office or unit as the cluster. A separate sample of 70 individual raters were used for the evaluation of the mHealth platform. The Australian National MHL and Stigma Survey and the Mobile Apps Rating Scale were completed through a web-based self-reported survey to assess MHL and evaluate the app. The trial and follow-up data were analyzed by a generalized linear latent and mixed model with adjustments for the clustering effect of work sites and repeated measures. Of the 456 participants in the trial, 236 (51.8%) responded to the follow-up survey. Most MHL outcomes obtained significant results immediately after the intervention and across time. After adjusting for the clustering effect, the postintervention weighted mean scores were significantly higher in the intervention group than the control group for correct recognition of a mental health problem, help seeking, and stigmatization by 0.2 (SE 0.1; P=.003), 0.9 (SE 0.2; P<.001), and 1.8 (SE 0.4; P<.001), respectively. After adjusting for the clustering effect, significant differences across time were found in help-seeking intention (P=.01), stigmatization (P<.001), and social distancing (P<.001). The evaluation of the mHealth program resulted in average scores of the 4 major domains ranging from 3.8 to 4.2, with engagement having the lowest score. The mHealth psychoeducation intervention program using this platform had immediate and 3-month medium-term effects of retaining and improving MHL. The platform was evaluated to have satisfactory performance in terms of functionality, aesthetics, information content, and utility in enhancing MHL. It is anticipated that ongoing development in digital health will provide great benefits in improving the mental health of the global population.