Performance Improvement Research Articles

Hierarchical core-shell structured CoZn-MOFs derived materials anode with raspberry-like shape for high-performance lithium storage

Significant progress had been made in various fields with transition metal oxide electrode materials, particularly in battery materials where structural control played a crucial role in enhancing their electrochemical properties. Therefore, this study aimed to investigate the potential of a raspberry-like hierarchical core-shell structure derived from Co–Zn MOF as anode materials by varying concentration of reaction solution in hydrothermal method. The hierarchical raspberry-like hollow shell structure was beneficial to expose more active sites and reduced the diffusion distance of Li+ to the internal electroactive sites, improving the overall electrical conductivity of the electrode by full contact with carbon. Carbon nanotubes were incorporated into more suitable core-shell structure. They were uniformly coated the hollow sphere and filled inside of that to form a carbon skeleton. Specifically, the carbon nanotubes inside of the hollow sphere supported the shell not to collapse. This mitigated volume expansion during the cycling process, maintains structure stability, and allowed for rearrangement to occur during cycling to achieve a high specific capacity. Furthermore, the carbon nanotubes inside and outside of the hollow sphere created a high-density interface that enhanced interfacial polarisation and accelerated the rate of Li+ passage during cycling. These effects had resulted in an improvement in the material's performance. It is noteworthy that the specific capacity attains 1050 mAh g-1 when exposed to a current density of 0.2 A g-1. Notably, even after conducting 500 cycles at a current density of 0.5 A g-1, the specific capacity remains stable at 652 mAh g-1.

Inverse design method of thermal devices with thermal Hall effect

Efficient thermal management is important for both performance and efficiency improvements of thermal devices. For designing reasonable materials and structures of such devices, various design methods were proposed where the material thermal conductivity tensors were positive definite and symmetric based on the physical requirements. Here, we propose an inverse design method for thermal devices considering the thermal Hall effect, which makes the material thermal conductivity tensor asymmetric. Enlarging the design space consisting of the symmetric constitutive tensors to that of the asymmetric ones, there is a possibility of improving the theoretical performance limit of thermal devices. We formulate an inverse problem based on the free material optimization formalism, parameterizing the design space so that the physically available property could be naturally satisfied. Several numerical experiments are provided to show the validity and the utility of the proposed method.

The Effect Of HR Management And Career Development On Employee Performance With Job Satisfaction As An Intervening Variable In The Education Office And The Culture Of Padang Panjang City

The Education and Culture Office of Padang Panjang City is a government agency responsible for all matters related to education in its area, tasked with carrying out government affairs in the field of education based on the principle of autonomy and assistance duties, and carrying out other tasks related to education. To improve service and employee satisfaction, agencies must have good management, which includes improved HR management and better career development programs. Effective HR management is needed in encouraging employee performance improvement but also needs to be supported by paying attention to employee job satisfaction. Therefore, the purpose of this study is to find out how the influence of HR management and career development on employee performance through job satisfaction as an intervening variable, the number of samples in this study was 41 respondents using the Smart PLS 3 analysis tool and data collection techniques in this study with the distribution of questionnaires using the Likert scale. The results of this study show that the most dominant variable, namely HR management, affects job satisfaction positively and significantly, if human resource management is done correctly, job satisfaction will also increase. In addition, job satisfaction has a positive and significant effect on employee performance, this shows that if employees feel happy and satisfied in carrying out their work, then employees can perform better. The most dominant indirect influence is that HR management has a positive and significant effect on employee performance through job satisfaction.

HRM practices, organizational learning and organizational performance: evidence from the big four financial services in France

Purpose This paper aims to examine the mediation effect of organizational learning on the link between human resource management (HRM) practices and organizational performance in some Big4 financial services companies. Design/methodology/approach The quantitative methodology was chosen for this research, using resource theory and knowledge-based approach to explain the relationship between latent variables. A sample of 403 HR employees and managers of the companies under study in France was selected in 2022. Structural equations modeling was used based on the Spss-Amos program to test the research hypotheses. Findings The results revealed that organizational learning played a mediating role between HRM practices (hiring, training, motivation and decision-making) and organizational performance and that learning enabled the performance of workers to improve and achieve competitive advantages in this field. Research limitations/implications The sample was based on four international companies working in the field of financial services and consulting and providing their services within France, which may affect the generalisability of the results and limit them to the studied sector. Practical implications The contribution of the study is to improve the awareness of administrators, decision makers and company employees of the importance of organizational learning for companies, and to stimulate motivation to learn and exchange knowledge in a constructive way that enhances organizational performance. Working on organizational culture change through HRM-practices-based learning as an effective mechanism for organizational performance improvement is one implication. These practises influence cadres' attitudes toward their work, which improves their performance. Social implications Working on organizational culture change through HRM-practices-based learning as an effective mechanism for organizational performance improvement is one implication. These practises influence cadres' attitudes toward their work, which improves their performance. Originality/value This study seeks to provide cadres and executives with an in-depth analysis of HRM and organizational learning, which, through its integration of these attributes, can contribute to the earning of knowledge-based competitive advantage and achieve superior and sustainable performance.

Regional competition, spatial spillover effects, and entrepreneurship subsidy policies—evidence from incubators in Guangdong, China

ABSTRACT Using data from 2016 to 2018 for all incubators in Guangdong, China, we find that for start-up incubators, the average subsidy level of the city where the incubator is located is significantly positively correlated with the number of patents obtained per unit area of the incubator, regardless of the subsidy amount obtained by the incubator itself. However, this phenomenon is not significant for incubators that have been established for more than 1 year. The subsidy policy of the local government has a significant positive direct effect on the number of patents obtained per unit area of local incubators, but a significant negative indirect effect on incubators in neighbouring regions. Both effects are similar in magnitude, with the overall effect of the subsidy policy being almost completely offset. Therefore, owing to the spatial spillover effect, a considerable part of the effect of the local government subsidy policy only manifests as competition for high-quality enterprises between regions, and the actual improvement of the operational performance of the enterprises themselves is not obvious. Therefore, ignoring the spatial spillover effect of subsidy policies greatly reduces their actual effect.

Fluorinated Quasi 2D Perovskite Solar Cells with Improved Stability and Over 19% Efficiency

Abstract2D Ruddlesden–Popper perovskites (RPP) with excellent environmental and structural stability are emerging photovoltaic materials. Here, a benzylamine‐based spacer, namely 3,5‐difluorobenzylamine (DF‐BZA), is developed for stable and efficient quasi‐2D‐RP perovskite solar cells (PSCs). Compared to benzylamine (BZA)‐based quasi‐2D RPP, the DF‐BZA‐based perovskite film exhibited superior film quality with significantly enlarged grain size and improved charge carrier lifetime owing to the fluorine atoms in DF‐BZA. As a result, the optimized (DF‐BZA)2FA3Pb4I13 PSCs achieve a power conversion efficiency (PCE) of 19.24%, while BZA‐based PSCs ((BZA)2FA3Pb4I13) only achieve a PCE of 17.04%. This represents the champion PCE using FA as the A‐site cation in quasi‐2D RPPs with n = 4. Moreover, due to the effective insertion of fluorinated spacers into the inorganic layers, the moisture resistance stability and 85 °C thermal stability of (DF‐BZA)2FA3Pb4I13 are significantly improved. The improvement of photovoltaic performance and stability highlight the great potential of DF‐BZA‐based spacer for high‐performance quasi‐2D RP PSCs.

Photoresponse Enhancement in MAPbI3/Si Heterojunction Through P(VDF‐TrFE) Doping and Introducing Pyro‐Phototronic Effect

AbstractThe photovoltaic effect and pyro‐phototronic effect offer a promising approach to enhance self‐powered photodetectors (PDs) performance. However, further investigation is warranted to explore the performance improvement, the impact of pyroelectric effect, and the underlying coupling mechanism. In this study, heterojunction PDs consisting of ITO/CH3NH3(MA)PbI3:P(VDF‐TrFE)/Si are prepared, with P(VDF‐TrFE) concentration varying from 0 to 1 wt.%. The results reveal that the concentration of P(VDF‐TrFE) has a significant impact on the photovoltaic response, with the optimal concentration being 0.8 wt.%. The PD displays a broadband response (405–1064 nm) at zero bias, peaking at 780 nm. Furthermore, the introduction of P(VDF‐TrFE) significantly enhances the responsivity (R) and detectivity (D), resulting in a remarkable increase of 5814%. The values of R and D reach 1.93 × 104 V W−1 and 1.99 × 1012 Jones, respectively. Moreover, the PD demonstrates an ultrafast response time of 81.6/208 µs. Additionally, the pyro‐phototronic effect extends the response wavelength to 1550 nm, surpassing the spectral limit of the heterojunction. Importantly, the heterojunction exhibits improved weak light detection ability. At 6.37 µW cm−2 intensity, R and D show an impressive increase of 21 683%, reaching 8.99 × 104 V W−1 and 9.3 × 1013 Jones, respectively.

One-step multi-view clustering via deep-level semantics exploiting

Multi-view clustering (MVC) has gained promising performance improvement compared with traditional signal-view clustering due to the complementary information of multiple views. However, existing MVC methods exploit clustering structure by utilising signal-layer mapping, such that they cannot exploit the underlying deep-level semantic information in complex and interleaved multi-view data. Moreover, existing methods usually conduct multi-view fusion and clustering separately, which results in unpromising performance. To address the above problems, one-step MVC via deep-level semantics exploiting (DLSE) is proposed to exploit deep-level semantic information and learn the indicator matrix using a one-step manner. To be specific, a novel deep matrix factorisation (DMF) paradigm is designed to exploit the hierarchical semantics via a layer-wise scheme, so that samples from the same clusters are forced to be closer in the low-dimensional space layer by layer. Furthermore, to make the learned representation preserve the local geometric structure of data, DLSE introduces a local preservation regularisation to guide DMF. Meanwhile, by employing spectral rotating fusion, the cluster indicator can be obtained directly. Extensive experiments demonstrate the superiority of DLSE in contrast with some state-of-the-art methods.

Cardiothoracic operating room blood gas workflow performance improvement initiative.

To evaluate the current workflow of blood gas ordering and testing in a cardiothoracic operating room to identify opportunities to streamline the process, using performance improvement methodologies. Issues with specimen relabeling were identified that lead to delayed results and potential patient safety concerns. Blood gas specimen relabeling was evaluated for operating room cases from August 2018 to December 2022. An OpTime Epic Sidebar button for arterial blood gas and venous blood gas orders was created in January 2019 to streamline the ordering process so that laboratory barcode labels were then printed in the operating room and attached to the specimen, eliminating the need for relabeling by the technologists. This Epic Sidebar intervention led to a drastic improvement of appropriate labeling, which has been sustained. From March 2019 to January 2023, with our new workflow, over 95% of blood gas specimens arrived barcode labeled compared to less than 1% in the preintervention era. A multidisciplinary team with key stakeholders is important to address complex care issues. Performance improvement methodology is critical to develop interventions that hardwire the process. This intervention led to a sustained reduction in secondary relabeling of patient samples and improved timeliness of reporting of blood gas results.

Reduction of Lean Wastes by Using Value Stream Mapping: A Case Study of Textile Company in Pakistan

Purpose: Lean manufacturing, originating from the Toyota Production System (TPS), aims to reduce waste and optimize resources. Developed countries must adopt Lean practices for performance improvements while developing countries often use just-in-case approaches. Value Stream Mapping (VSM) is a crucial tool for diagnosing, implementing, and maintaining Lean Manufacturing, helping identify improvement opportunities and eliminating waste. The research at the company aims to eliminate Lean wastes and line unbalancing issues to improve lead time and the value-added ratio (VAR), enhancing production efficiency. The research aligns with Sustainable Development Goal 12 (Responsible Production & Consumption) by focusing on waste reduction.
 Methodology: The research uses time study, Edaw max, and Visio software to analyze tasks, create VSMs of current and future states, and control charts to examine data variations over time. Data collection involves cycle time, batch size, packet size, and the number of workers required for each activity.
 Findings: The research objectives include studying the existing scenario of production units via VSM, identifying, and eliminating Lean wastes, and comparing proposed and existing scenarios for improvement opportunities. The literature review highlights the importance of Lean manufacturing in eliminating unnecessary processes, reducing lead time, and fostering positive stakeholder relationships.
 Unique Contribution to Theory, Practice and Policy: This study suggests the better way to reduce the lean wastages and balance the line by using VSM technique. The future state VSM is created to conceptualize potential improvements and gather feedback, focusing on reducing non-value-adding tasks, work-in-process inventory, workforce, and overall process time.

Insights into dynamic switching behavior and electric field distribution of depletion-mode GaN HEMT with bonding pad over active layout

Bonding pad over active (BPOA) layout, which stacks traditional horizontal structure pad electrodes vertically above the active area, is an area-effective device architecture and packaging-enabled solution for GaN-based high electron mobility transistors (HEMTs). In this work, the dynamic switching and electric field distribution of such layout, as well as associated capacitance–voltage and trapping characteristics, are comprehensively studied on D-mode GaN-on-sapphire HEMT by performing numerical simulations. In terms of different pad electrodes covering the active area, BPOA is composed of various structures such as gate-related and drain-related BPOAs (i.e. G-BPOA and D-BPOA), among which G-BPOA exhibits inferior switching characteristics due to the additional introduction of Miller capacitance that prolongs the device switching, while breakdown voltage of D-BPOA is 400–900 V lower than other BPOA counterparts due to the interplay between D-BPOA and the drain electrode. Furthermore, the effects of trap capture cross section and trap density on switching characteristics are evaluated. These results highlight the differences in electrical characteristics of various structures within the BPOA layout, and provide valuable insights into BPOA device design and performance improvement.

Influence of Anodic Aluminum Oxide Nanostructures on Resistive Humidity Sensing

Humidity nanosensors play a vital role in modern technology industries, including weather forecasts, industrial manufacturing, agriculture, food and chemistry storage. In recent years, research on humidity sensors has focused on different materials such as ceramics, polymers, carbon-based materials, semiconductors, MXenes or triboelectric nanogenerators, each with their own advantages and disadvantages. Among them, anodic aluminum oxide (AAO) is a well-known ceramic humidity sensor material with a long history of research and development. AAO humidity sensors offer advantages such as simple manufacturing processes, controllable nanostructures, high thermal stability and biocompatibility. However, traditional AAO fabrication still has disadvantages like high costs and longer process times. Hence, finding a low-cost and efficient method to fabricate AAO for controlling different nanostructures to meet the requirements is consistently a major research topic. From our previous studies, we have studied the relationship between the AAO capacitive humidity sensor and its nanostructures. In this paper, we explore the effect of an AAO nanoporous structure controlled by an anodization voltage of 20–40 V on the resistive-type humidity sensor performance instead of a capacitive one. We efficiently apply one-step hybrid pulse anodization at 25 °C to significantly reduce the processing time compared to the traditional two-step process under 0–10 °C. The AAO nanostructures and their impact on sensor measurements of humidity at 20–80 RH% will be discussed in detail. An electrical resistive sensing mechanism is established for further performance improvement by controlling anodization voltage.

The Impact of Mobile Applications on Quran Education: A Survey of Student Performance and Satisfaction.

In the field of Quranic education, mobile applications have become cutting edge instruments that offer chances to improve instruction and successfully involve learners. With a thorough survey-based analysis, this study seeks to evaluate how mobile applications affect Quranic teaching. The research methodology comprised creating a structured questionnaire to collect information on a range of topics, such as application usage trends, efficacy evaluations, contributions to bettering education, perceived effects on students' comprehension and retention of Quranic knowledge, improvement of student performance, and contrasts between traditional and mobile application-based learning approaches. Students enrolled in online university programs with an emphasis on Quranic education made up the population for this study. A sample size of 60 respondents provided insightful information. Descriptive statistics and inferential techniques were included in quantitative data analysis, which allowed for a detailed investigation of survey responses and the correlations between variables. The results show that respondents place a high value on mobile applications for improving Quranic education, and the usage patterns of these applications vary widely. Participants generally held favorable opinions about the usefulness of mobile applications in raising student achievement and comprehension of Quranic knowledge. Furthermore, it became evident that mobile application-based learning was preferred over conventional techniques. The study identifies opportunities for further research in this area and emphasizes the significance of using mobile technology to enhance Quranic instruction. In general, the study advances our understanding of how mobile applications fit into Quranic education and influences pedagogical strategies used in online learning environments.

End-to-End Light Field Image Compression with Multi-Domain Feature Learning

Recently, end-to-end light field image compression methods have been explored to improve compression efficiency. However, these methods have difficulty in efficiently utilizing multi-domain features and their correlation, resulting in limited improvement in compression performance. To address this problem, a novel multi-domain feature learning-based light field image compression network (MFLFIC-Net) is proposed to improve compression efficiency. Specifically, an EPI-based angle completion module (E-ACM) is developed to obtain a complete angle feature by fully exploring the angle information with a large disparity contained in the epipolar plane image (EPI) domain. Furthermore, in order to effectively reduce redundant information in the light field image, a spatial-angle joint transform module (SAJTM) is proposed to reduce redundancy by modeling the intrinsic correlation between spatial and angle features. Experimental results demonstrate that MFLFIC-Net achieves superior performance on MS-SSIM and PSNR metrics compared to public state-of-the-art methods.

Iterative Temporal-spatial Transformer-based Cardiac T1 Mapping MRI Reconstruction

The precise reconstruction of accelerated magnetic resonance imaging (MRI) brings about notable advantages, such as enhanced diagnostic precision and decreased examination costs. In contrast, traditional cardiac MRI necessitates repetitive acquisitions across multiple heartbeats, resulting in prolonged acquisition times. Significant strides have been made in accelerating MRI through deep learning-based reconstruction methods. However, these existing methods encounter certain limitations: (1) The intricate nature of heart reconstruction involving multiple complex time-series data poses a challenge in exploring nonlinear dependencies between temporal contexts. (2) Existing research often overlooks weight sharing in iterative frameworks, impeding the effective capturing of non-local information and, consequently, limiting improvements in model performance. In order to improve cardiac MRI reconstruction, we propose a novel temporal-spatial transformer with a strategy in this study. Based on the multi-level encoder and decoder transformer architecture, we conduct multi-level spatiotemporal information feature aggregation over several adjacent views, that create nonlinear dependencies among features and efficiently learn important information among adjacent cardiac temporal frames. Additionally, in order to improve contextual awareness between neighboring views, we add cross-view attention for temporal information fusion. Furthermore, we introduce an iterative strategy for training weights during the reconstruction process, which improves feature fusion in critical locations and reduces the number of computations required to calculate global feature dependencies. Extensive experiments have demonstrated the substantial superiority of this procedure over the most advanced techniques, suggesting that it has broad potential for clinical use.

Text‐based corn futures price forecasting using improved neural basis expansion network

AbstractThe accurate forecasting of agricultural futures prices is critical for ensuring national food security. Therefore, this study proposes a text‐based deep learning forecasting model. This model first uses the ChineseBERT + a text convolution neural network to classify Weibo text and obtain a raw sentiment index. Then, complete ensemble empirical mode decomposition with adaptive noise, variational mode decomposition, correlation coefficient, and sample entropy are combined to decompose and reconstruct the raw sentiment index and obtain a denoised sentiment index. Subsequently, the neural basis expansion analysis with exogenous variables is improved by designing a weight coefficient and Optuna is used to optimize the designed weight coefficient and the hyperparameters. Finally, the SHapley Additive exPlanations value is used to increase the interpretability of prediction results. Corn futures prices for the Dalian Exchange are used in forecasting to validate the accuracy and stability of the proposed model. Experimental results show that the proposed denoising sentiment index contributes more to the improvement of predictive model performance than the raw sentiment index. The proposed text‐based deep predictive model demonstrates strong predictive ability for prediction horizons of 30 and 60 days. SHapley Additive exPlanations value analysis shows that the three features with greater effects on corn futures prices are as follows: “Corn Spot Price of Zhengzhou market,” “CBOT_corn_futures_price,” and “Pork futures price.”

Accelerating P300-based neurofeedback training for attention enhancement using iterative learning control: a randomised controlled trial

Objective. Neurofeedback (NFB) training through brain–computer interfacing has demonstrated efficacy in treating neurological deficits and diseases, and enhancing cognitive abilities in healthy individuals. It was previously shown that event-related potential (ERP)-based NFB training using a P300 speller can improve attention in healthy adults by incrementally increasing the difficulty of the spelling task. This study aims to assess the impact of task difficulty adaptation on ERP-based attention training in healthy adults. To achieve this, we introduce a novel adaptation employing iterative learning control (ILC) and compare it against an existing method and a control group with random task difficulty variation. Approach. The study involved 45 healthy participants in a single-blind, three-arm randomised controlled trial. Each group underwent one NFB training session, using different methods to adapt task difficulty in a P300 spelling task: two groups with personalised difficulty adjustments (our proposed ILC and an existing approach) and one group with random difficulty. Cognitive performance was evaluated before and after the training session using a visual spatial attention task and we gathered participant feedback through questionnaires. Main results. All groups demonstrated a significant performance improvement in the spatial attention task post-training, with an average increase of 12.63%. Notably, the group using the proposed iterative learning controller achieved a 22% increase in P300 amplitude during training and a 17% reduction in post-training alpha power, all while significantly accelerating the training process compared to other groups. Significance. Our results suggest that ERP-based NFB training using a P300 speller effectively enhances attention in healthy adults, with significant improvements observed after a single session. Personalised task difficulty adaptation using ILC not only accelerates the training but also enhances ERPs during the training. Accelerating NFB training, while maintaining its effectiveness, is vital for its acceptability by both end-users and clinicians.

Serial reversal learning in nectar-feeding bats

We explored the behavioral flexibility of Commissaris’s long-tongued bats through a spatial serial reversal foraging task. Bats kept in captivity for short periods were trained to obtain nectar rewards from two artificial flowers. At any given time, only one of the flowers provided rewards and these reward contingencies reversed in successive blocks of 50 flower visits. All bats detected and responded to reversals by making most of their visits to the currently active flower. As the bats experienced repeated reversals, their preference re-adjusted faster. Although the flower state reversals were theoretically predictable, we did not detect anticipatory behavior, that is, frequency of visits to the alternative flower did not increase within each block as the programmed reversal approached. The net balance of these changes was a progressive improvement in performance in terms of the total proportion of visits allocated to the active flower. The results are compatible with, but do not depend on, the bats displaying an ability to ‘learn to learn’ and show that the dynamics of allocation of effort between food sources can change flexibly according to circumstances.

Enhanced Transport Kinetics of Electrochromic Devices by W18O49 NW/Ti3C2Tx Composite Films

AbstractElectrochromic devices can facilitate the realization of a wide set of future applications, ranging from energy‐saving windows to smart wearables and stealth. Historically, tungsten oxides have been the most studied materials for electrochromism, albeit with bottlenecks like limited conductivity, high charge transport barrier, and low ion diffusivity. Here, inspired by the recent MXene materials, the study has engineered an electrochromic composite of MXene nanosheets (Ti3C2Tx) and W18O49 nanowires (NWs). A transparent conductive electrode is fabricated by co‐assembly of Ag and W18O49NWs, followed by depositing W18O49 NW/Ti3C2Tx layers for the fabrication of the electrochromic device. The incorporation of Ti3C2Tx nanosheets enhances the transport of electrons and ions within the electrochromic layer, leading to a significant improvement in the electrochromic performance. Noteworthily, the film structure comprising 15 layers of W18O49 NW/Ti3C2Tx composite reveals enhanced transmittance modulation (61%), rapid response time (4.5 s coloration, 6.5 s bleaching), and high coloration efficiency (139.1 cm2 C−1). Moreover, the electrode also presents a high diffusion coefficient of Li+ and good cycling stability (96.66% after 250 switching cycles). Finally, a large‐scale (15 × 20 cm2) flexible electrochromic device with a solid electrolyte is successfully fabricated and utilized as a smart window and a flexible stealth patch.

NEURAL NETWORK-BASED ADVERSE DRUG REACTION PREDICTION USING MOLECULAR SUBSTRUCTURE ANALYSES

Objective: This study aims to enhance early detection and prediction by exploiting drug molecular substructures, overcoming challenges posed by limited authentic patient data in the medical domain. Methods: The study implemented a neural network approach to optimize molecular fingerprint algorithms and employed various machine learning algorithms for predictions. Additionally, the study identified and extracted substructures associated with severe Adverse Drug Reactions (ADRs), validating their presence within drug structures through a comparison with a random set of drug structures. Predictions were made for specific molecular structures, and results were validated using clinical evidence from the literature. Results: Optimized molecular fingerprint algorithms and diverse machine-learning models yielded promising outcomes. The Area Under Curve (AUC) value for the fingerprint dataset was obtained at approximately 65%, and integrating it with patient data significantly improved the performance by about 30%. Substructure analysis pinpointed key components linked to severe ADRs, reinforcing the predictive prowess of the model. Predictions for specific molecular structures were corroborated using clinical evidence from the literature, fortifying the credibility of the proposed approach. Conclusion: In conclusion, this research effectively tackles challenges in the early detection and prediction of ADRs by leveraging machine learning algorithms, focusing on drug molecular substructures. The optimized model, incorporating both fingerprint and patient datasets, demonstrated significant improvements in predictive performance. Identifying and validating substructures linked to severe ADRs contribute to the model's reliability. The study's findings are vital for advancing drug safety and laying the groundwork for further strides in predictive modeling within the medical domain.