Globally Research Articles

Perceptions of quality of care in Midwife-led Birth Centres (MLBCs) in Uganda: Why do women choose MLBCs over other options?

BackgroundMidwife-led birth centres (MLBCs) are associated with reduced childbirth interventions, higher satisfaction rates, and improved birth outcomes. The evidence on quality of care in MLBCs from low and middle-income countries (LMIC) is limited. AimThis study aimed to explore the perceptions of women and midwives regarding the quality of care in four MLBCs in Uganda. MethodsA qualitative study was conducted in four MLBCs in Uganda. We conducted interviews with women and midwives in the MLBCs to explore their perceptions and experiences related to care in the MLBCs. The study obtained ethical approval. Deductive thematic analysis was used for data analysis. ResultsThree key themes were identified regarding the perceptions of women and midwives about the quality of care in the MLBCs: providing respectful, and dignified care; a focus on woman-centred care; and reasons for choosing care in the MLBC. Women valued the respectful and humane care characterised by dignified and non-discriminatory care, non-abandonment, privacy, and consented care. The woman-centred care in the MLBC involved individualised holistic care, providing autonomy and empowerment, continuity of care, promoting positive birth experience, confidence in the woman’s own abilities, and responsive providers. Women chose MLBCs because the services were perceived to be available, accessible, affordable, with comprehensive and effective referral mechanisms. ConclusionWomen perceived care to be respectful, woman-centred, and of good quality. Global attention should be directed to scaling up the establishment of MLBCs, especially in LMIC, to improve the positive childbirth experience and increase access to care.

COVID-19 and its implications for the future global influence of the European Union

On January 30, 2020, the severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) that causes coronavirus disease 2019 (COVID-19) was identified by the World Health Organization (WHO) as constituting a Public Health Emergency of International Concern (PHEIC). With little information on how to diagnose, treat and safeguard people against the disease, countries around the world responded to the outbreak differently depending on the national context. As vaccines were rolled-out and economic measures were put in place, varied speculations started to emerge on the geopolitical implications of these measures for the European Union (EU). This paper examines some of the views from the growing body of literature on the future implications of COVID-19 on the EU’s integration, and more specifically, on whether the EU is likely to emerge as the most influential powerhouse in the post-COVID-19 world. The paper argues that, based on the current conjecture, one cannot confirm the possibility of the envisioned future given the many hurdles that the EU must first deal with before being able to don such a crown.

Rapid Mass Conversion for Environmental Microplastics of Diverse Shapes.

Rivers have been recognized as the primary conveyors of microplastics to the oceans, and seaward transport flux of riverine microplastics is an issue of global attention. However, there is a significant discrepancy in how microplastic concentration is expressed in field occurrence investigations (number concentration) and in mass flux (mass concentration). Of urgent need is to establish efficient conversion models to correlate these two important paradigms. Here, we first established an abundant environmental microplastic dataset and then employed a deep neural residual network (ResNet50) to successfully separate microplastics into fiber, fragment, and pellet shapes with 92.67% accuracy. We also used the circularity (C) parameter to represent the surface shape alteration of pellet-shaped microplastics, which always have a more uneven surface than other shapes. Furthermore, we added thickness information to two-dimensional images, which has been ignored by most prior research because labor-intensive processes were required. Eventually, a set of accurate models for microplastic mass conversion was developed, with absolute estimation errors of 7.1, 3.1, 0.2, and 0.9% for pellet (0.50 ≤ C < 0.75), pellet (0.75 ≤ C ≤ 1.00), fiber, and fragment microplastics, respectively; environmental samples have validated that this set is significantly faster (saves ∼2 h/100 MPs) and less biased (7-fold lower estimation errors) compared to previous empirical models.

Green leadership and competitive advantage: The role of mediation from knowledge management and talent management

This research aims to analyze the impact of green leadership on competitive advantage through the mediating roles of knowledge management and talent management. In the context of the Industrial Revolution 4.0 (IR4.0), where technology companies worldwide have international ramifications on innovation, sustainability issues, and environmental impacts such as carbon emissions, e-waste, and natural resource extraction that garner global attention, we chose technology companies in Indonesia as a model. Data was collected using an e-questionnaire, distributed to company managers, and then analyzed using SEM-AMOS. The research results demonstrate that green leadership does not have a direct influence on competitive advantage but rather exerts an indirect influence through knowledge management and talent management. These findings strengthen the view that sustainable strategy must be an essential part of an organization's efforts to achieve competitive advantage.

Currency Hegemony as a Tool of US Global Dominance

Subject. The formation of new centers of global influence predetermines corresponding changes in the global balance of power. At the same time, the lack of real reforms of the global monetary system allows the United States to continue to set the rules of the game in the global economy, despite its increasing crisis potential. In this regard, there is a need to identify the driving forces capable of producing constructive changes in the global monetary order. Objective. A summary of the practice of using the US dollar to suppress global competition and maintain American superiority in international economic relations. Results. The control of the international monetary sphere by the country — hegemon of the world economy can be traced at all stages of the development of international economic relations (IER). Currently, the hegemonic country’s self-interest from issuing international liquidity, with non-resistance to such a policy on the part of all other IER participants, hinders the development of effective anti-crisis measures and worsens the general condition of the world economy. Scattered attempts to overcome the failures of global currency regulation at the local level (including through the use of digital technologies) seem to be ineffective, since they are not systemic in nature. Conclusions. A qualitative reform of the global monetary system is impossible without combining the efforts of the world’s largest economies to counter the destructive policies of the country that issues key international liquidity. Otherwise, the costs of financial crises become an insurmountable obstacle to the global economy entering a path of sustainable development.

The role of retroelements of the human genome in the development of type 1 diabetes mellitus

Retroelements (retrotransposons and endogenous retroviruses) are a class of mobile genetic elements that move in the genome by inserting their own reverse transcribed transcripts. They serve as drivers of epigenetic regulation; therefore, individual characteristics of the distribution of retroelements in the genome influence the development of multifactorial diseases. Type 1 diabetes mellitus is a multifactorial disease with an immune response against pancreatic β cells. The role of heredity in the development of the disease is estimated at 88%, and the role of allelic variants of various genes is determined. There are also other specific types of diabetes mellitus, which account for more than 2% of cases of diabetes mellitus and are monogenic diseases with an autosomal dominant mode of inheritance due to germline mutations in the MODY genes, including HNF4A, GCK, HNF1A, HNF1B. Most patients with type 1 diabetes have the protein product and ribonucleic acid (RNA) of the insulin inhibitor HERV-W-Env, which is caused by abnormal expression of the human endogenous retrovirus (HERV). An assumption has been made about the role of retroelements in the development of type 1 diabetes mellitus. This is due to their involvement in the phylogenetic formation of the endocrine system, since in evolution retroelements turned out to be sources of regulatory sequences of hormone genes, nuclear hormone receptors and binding sites for them. Type 1 diabetes mellitus is associated with the integration of HERV into the HLA-DQ gene region, with allelic variants and sizes of VNTR variable tandem repeats (part of the SVA retroelements), which regulate the expression of the insulin gene and other hormones. For this reason, it is likely that the development of type 1 diabetes mellitus may be based on individual characteristics of the distribution of HERVs in the human genome and their dynamic changes in ontogenesis. HERVs also play a role in the etiopathogenesis of diabetes mellitus through the activation of an autoimmune response, the triggering factors of which are exogenous viral infections and stress. Thus, retroelements are involved in various mechanisms of the development of type 1 diabetes mellitus, which reflects their global regulatory influence on endocrine regulation.

Underwater Coherent Source Direction-of-Arrival Estimation Method Based on PGR-SubspaceNet

In the field of underwater acoustics, the signal-to-noise ratio (SNR) is generally low, and the underwater environment is complex and variable, making target azimuth estimation highly challenging. Traditional model-based subspace methods exhibit significant performance degradation when dealing with coherent sources, low SNR, and small snapshot data. To overcome these limitations, an improved model based on SubspaceNet, called PConv-GAM Residual SubspaceNet (PGR-SubspaceNet), is proposed. This model embeds the global attention mechanism (GAM) into residual blocks that fuse PConv convolution, making it possible to capture richer cross-channel and positional information. This enhancement helps the model learn signal features in complex underwater conditions. Simulation results demonstrate that the underwater target azimuth estimation method based on PGR-SubspaceNet exhibits lower root mean square periodic error (RMSPE) values when handling different numbers of narrowband coherent sources. Under low SNR and limited snapshot conditions, its RMSPE values are significantly better than those of traditional methods and SubspaceNet-based enhanced subspace methods. PGR-SubspaceNet extracts more features, further improving the accuracy of direction-of-arrival estimation. Preliminary experiments in a pool validate the effectiveness and feasibility of the underwater target azimuth estimation method based on PGR-SubspaceNet.

GM-DETR: Research on a Defect Detection Method Based on Improved DETR.

Defect detection is an indispensable part of the industrial intelligence process. The introduction of the DETR model marked the successful application of a transformer for defect detection, achieving true end-to-end detection. However, due to the complexity of defective backgrounds, low resolutions can lead to a lack of image detail control and slow convergence of the DETR model. To address these issues, we proposed a defect detection method based on an improved DETR model, called the GM-DETR. We optimized the DETR model by integrating GAM global attention with CNN feature extraction and matching features. This optimization process reduces the defect information diffusion and enhances the global feature interaction, improving the neural network's performance and ability to recognize target defects in complex backgrounds. Next, to filter out unnecessary model parameters, we proposed a layer pruning strategy to optimize the decoding layer, thereby reducing the model's parameter count. In addition, to address the issue of poor sensitivity of the original loss function to small differences in defect targets, we replaced the L1 loss in the original loss function with MSE loss to accelerate the network's convergence speed and improve the model's recognition accuracy. We conducted experiments on a dataset of road pothole defects to further validate the effectiveness of the GM-DETR model. The results demonstrate that the improved model exhibits better performance, with an increase in average precision of 4.9% (mAP@0.5), while reducing the parameter count by 12.9%.

A novel highly dispersed calcium silicate hydrate nanosheets for efficient high-concentration Cu2+ adsorption

Currently, the low cost and effective purification toward heavy metal ions in wastewater has garnered global attention. Herein, we used hydrothermal method to prepare highly dispersed calcium silicate hydrate in fluorite tailings. And the stacking thickness of calcium silicate hydrate layered morphology was less than 5 nm. For high concentration Cu2+ purification investigation in wastewater, we found that the equilibrium adsorption capacity reached 797.92 mg/g via the CSH with 3:2 Ca/Si molar ratio, be 1.43–21.8 times than that of reported data. Therein, the metal-metal exchange and deposition are the primary pathways for Cu2+ adsorption, and electrostatic attraction is the secondary pathway. And the relative ∼100 % removal rate of high-concentration Ni2+ and Cr3+ ions were confirmed via CSH prepared from different tailings. This method offers a cost-effective way to utilize tailings for preparing highly efficient adsorbents toward HMIs removal in wastewater.

Occurrence of organic ultraviolet absorbers in the particle and gas samples from plastic greenhouses: Human inhalation intake risk assessment

The environmental pollution of organic ultraviolet absorbers (UVAs) has attracted global attention. However, the distribution, sources and risk assessment of UVAs in air from plastic greenhouses are rarely reported. This study was the first to investigate the concentrations of ten UVAs in the air samples from plastic greenhouses. The total concentrations of ten UVAs (∑10UVAs) in the air samples ranged from 5.7 × 103 ng/m3 to 6.3 × 103 ng/m3 (median 5.7 × 103 ng/m3) in greenhouses covered with biodegradable mulch film, 288.2 ng/m3 to 376.4 ng/m3 (median 333.9 ng/m3) in greenhouses covered with PE mulch film, and 97.9 ng/m3 to 142.6 ng/m3 (median 114.9 ng/m3) in greenhouses covered without mulch film. The concentrations of ten UVAs in 65 commercial agricultural films were simultaneously analyzed. Additionally, the potential health risks for greenhouse workers exposed to UVAs were estimated. And the migration simulations showed that the health risk in greenhouses may be higher even if only one UVA is added to the biodegradable mulch film. Therefore, the exposure risk of UVAs in plastic greenhouses needs to be highly prioritized.

QSAR Model based Gradient Boosting Regression of N-Arylsulfonyl-Indole-2-Carboxamide Derivatives as Inhibitors for Fructose-1,6-bisphosphatase

Background: Due to the complication caused by conventional drugs, global attention has been focused on the development of novel drugs. As a consequence, a potential theory to put T2DM under control is of great medical significance. Methods: We used the heuristic method to establish the linear model and used Gradient Boosting Regression to establish the nonlinear model of Fructose-1,6-Bisphosphatse inhibitor successively. In this study, 84 derivatives of N-Arylsulfonyl-Indole-2-Carboxamide were introduced into the models, and two outstanding QSAR models with 2 molecule descriptors were established successfully. Results: Gradient Boosting Regression rendered a good correlation with R2 of 0.943 and MSE of 0.135 for the training set, 0.916 and 0.213 for the test set, which also proves the feasibility of the implementation of the new method GBR in the field of QSAR. Meanwhile, the optimal model displayed wonderful statistical significance. Conclusion: This study makes an attempt at the application of a new method of GBR in QSAR and proves GBR as a promising tool for further study of CADD.

Recent Developments in Research on Food Waste and the Circular Economy

Food waste is a global challenge, with profound implications for food security, resource utilization, and sustainability. A circular economy represents a promising solution in addressing food waste effectively by keeping food materials and products in use and circulating them within the economy. To provide an overview of the research on food waste and the circular economy model in the past decade, this study examines 1022 documents on food waste and circular the economy to ascertain the state, themes, and knowledge structure of the field. The results showed that the number of publications has increased greatly in the past decade. “Circular economy of food”, “bioenergy”, “waste valorization”, “waste management”, “resource recovery”, and “environmental assessment” were the major research themes. Earlier studies focused on resource recovery from organic waste and the bioeconomy of bio-products; recent research interests shifted to sustainability and the valorization of agri-food waste. On the other hand, some highly cited documents and productive authors were important in the development of research on food waste and the circular economy. Furthermore, three groups of journals—“food and environmental sciences”, “open access”, and “environmental sustainability and resource management”—and five clusters of international collaboration—“European Union Group”, “advanced economy group”, “agricultural economies”, “global influencers”, and “isolated countries”—were identified. This study provides readers with an overview of the research field of food waste and the circular economy.

How Financial Risk Affects Indonesia Sovereign Sukuk Market Development

Global bond market attention is starting to witness very interesting developments in the development of Sukuk. However, the academic literature on the determinants of the development of the Sukuk market is still new and very limited. Using the Vector Autoregression (VAR)/Vector Error Correction Model (VECM) method, this study examines how the financial risk component influences the development of the Indonesian sovereign Sukuk market. Data was collected from Bank Indonesia and Indonesia’s Directorate General of Financing and Risk Management on the External Debt Statistics database for ten years from 2013Q1-2023Q1. The results of this study indicate that the foreign Debt Service Ratio (DSR) and Exchange Rate (EXC) significantly affect the development of the sovereign Sukuk Market. The foreign DSR has a negative effect, while the EXC positively affects the development of the sovereign Sukuk market. Meanwhile, the ratio of foreign debt to Gross Domestic Product (GDP) has an insignificant effect on the development of the sovereign Sukuk Market. The findings advocate policymakers in Indonesia on the importance of strengthening financial stability and regulating the issuance of Sukuk in accordance with the associated financial risks to develop the Sukuk market properly.

YOLOv8n-GAM: an improved surface defect detection network for hot-rolled strip steel

Production defects caused by irresistible factors such as process design problems or differences in steel properties in strip production affect the economic benefits of the enterprise and threaten production safety. Traditional defect detection methods are difficult to achieve real-time and high-precision detection, so developing surface defect detection methods based on deep learning is of great significance for strip production. In order to effectively improve the accuracy of the deep learning model in detecting surface defects on hot-rolled strip, in this work we propose a real-time detection model for surface defects on strip steel based on the YOLOv8n model. Firstly, the newly convolutional layer Con5v is designed to replace the original convolutional layer in the neck, and an attention mechanism is added in front of each Con5v to improve the algorithm’s ability to extract small target information. Secondly, an additional set of upsampled feature extraction units is added to the neck in order to enhance the spatial information of the feature map. Subsequently, a set of feature fusion units is incorporated and the convolutional layers in it are improved to provide better feature maps. Thirdly, the number of decoupling detection heads is increased to receive more high-quality features. The final experimental results show that YOLOv8n-GAM (YOLOv8 Nano Model with Global Attention Mechanism) achieves 81.4mAP and 82.0FPS on the NEU-DET dataset and 71.2mAP and 55.0FPS on the GC10-DET dataset, which are 5.7% and 6.9% higher than those of YOLOv8n, respectively. The model proposed in this paper achieves a comprehensive performance improvement in strip steel.

Camelina Intercropping with Pulses a Sustainable Approach for Land Competition between Food and Non-Food Crops

With increasing global attention toward the need for mitigating climate change, the transition to sustainable energy sources has become an essential priority. Introducing alternative oilseed crops, such as camelina (Camelina sativa L.), into intercropping systems with staple food crops can mitigate ILUC (indirect land use change) and their negative impact on biofuel production. The present study compared camelina + field pea intercropping (ICw + IP, winter sowing) and camelina + lentil intercropping (ICs + IL, spring sowing) with their respective single crops regarding weed control, soil coverage, yields, and camelina seed quality (1000-seed weight, oil, and fatty acid composition). The comparison between different cropping systems was conducted using a one-way ANOVA. Both intercropping improved weed control at an early stage but no differences in soil coverage were found. Camelina seed yield was negatively affected by the presence of peas, whereas the pulse was unaffected. Conversely, camelina seed yield was not affected when intercropped with lentils while lentils reduced their yield in the intercropping. Furthermore, when camelina was intercropped with lentils, a significant increase was reported in 1000-seed weight and α -linolenic acid (C18:3) compared with the sole-camelina. However, both intercropping systems had a land equivalent ratio (LER, based on total seed yield at maturity) higher than one. Defining the best combination of crops and the optimal sowing and harvesting settings remain key to increasing the adoption of intercropping systems by farmers.

Construction of multi-scale feature fusion segmentation model of MRI knee images based on dual attention mechanism weighted aggregation.

Early diagnosis of knee osteoarthritis is an important area of research in the field of clinical medicine. Due to the complexity in the MRI imaging sequences and the diverse structure of cartilage, there are many challenges in the segmentation of knee bone and cartilage. Relevant studies have conducted semantic fusion processing through splicing or summing forms, which results in reduced resolution and the accumulation of redundant information. This study was envisaged to construct an MRI image segmentation model to improve the diagnostic efficiency and accuracy of different grade knee osteoarthritis by adopting the Dual Attention and Multi-scale Feature Fusion Segmentation network (DA-MFFSnet). The feature information of different scales was fused through the Multi-scale Attention Downsample module to extract more accurate feature information, and the Global Attention Upsample module weighted lower-level feature information to reduce the loss of key information. The collected MRI knee images were screened and labeled, and the study results showed that the segmentation effect of DA-MFFSNet model was closer to that of the manually labeled images. The mean intersection over union, the dice similarity coefficient and the volumetric overlap error was 92.74%, 91.08% and 7.44%, respectively, and the accuracy of the differential diagnosis of knee osteoarthritis was 84.42%. The model exhibited better stability and classification effect. Our results indicated that the Dual Attention and Multi-scale Feature Fusion Segmentation model can improve the segmentation effect of MRI knee images in mild and medium knee osteoarthritis, thereby offering an important clinical value and improving the accuracy of the clinical diagnosis.

Role of Lactic Acid Bacteria in Insecticide Residue Degradation.

Lactic acid bacteria are gaining global attention, especially due to their role as a probiotic. They are increasingly being used as a flavoring agent and food preservative. Besides their role in food processing, lactic acid bacteria also have a significant role in degrading insecticide residues in the environment. This review paper highlights the importance of lactic acid bacteria in degrading insecticide residues of various types, such as organochlorines, organophosphorus, synthetic pyrethroids, neonicotinoids, and diamides. The paper discusses the mechanisms employed by lactic acid bacteria to degrade these insecticides, as well as their potential applications in bioremediation. The key enzymes produced by lactic acid bacteria, such as phosphatase and esterase, play a vital role in breaking down insecticide molecules. Furthermore, the paper discusses the challenges and future directions in this field. However, more research is needed to optimize the utilization of lactic acid bacteria in insecticide residue degradation and to develop practical strategies for their implementation in real-world scenarios.

Breaking barriers, cultivating sustainability: Discovering the trifecta influence of digitalization, natural resources, and globalization on eco-innovations across 27 European nations

In recent years, the convergence of digitalization, natural resources, and globalization has profoundly influenced eco-innovations in European economies, emphasizing their significance in achieving sustainable development goals (SDGs). Additionally, digitalization enables smart infrastructure and facilitates a circular economy. Furthermore, natural resources provide prospects for transitioning to renewable energy and improving resource efficiency, while globalization connects European economies to global networks, endorsing the exchange of information and best practices. This study examines the influence of digitalization, natural resources, and globalization on eco-innovations for the 27 European Union (EU-27) nations from 1990 to 2021. The estimated outcomes reveal that digitalization substantially encourages eco-innovations. After confirming the issues of cross-sectional dependence and slope heterogeneity, this study adopts the second-generation process to estimate consistent and robust outcomes. The findings from the method of moment quantile regression (MMQR) show that digitalization significantly increases eco-innovations across all quantiles. Regarding natural resources, the findings explore the asymmetric relationship, indicating that an increase in natural resources raises the level of eco-innovations in the lower and lower-middle quantiles but decreases it in the upper quantiles. This signifies the inefficient use of natural resources in EU-27 nations. Additionally, the findings report that the globalization process significantly reduces the pace of eco-innovations. The interaction between natural resources and digitalization reveals that it increases eco-innovations at lower quantiles but reduces them at higher quantiles. As for the interaction between natural resources and globalization, the findings indicate a positive association with eco-innovations in the lower and lower-middle quantiles but a negative association in higher quantiles. Based on the estimated evidences, this study suggests some feasible policy inferences to drive eco-innovations, mitigate ecological impacts, and pave the way for a greener and more resilient future in EU-27 nations.

A noise-robust CNN architecture with global attention and gated convolutional Kernels for bearing fault detection

Detecting faults in bearings is essential for the maintenance and operation of rotating machinery. However, achieving high accuracy and noise immunity is challenging due to the involvement of intricate and noisy signals. To address this issue, this paper introduces a multi-scale separable gated convolutional neural network (GCK-MSSC). In the GCK-MSSC model, the gate convolutional kernel replaces the conventional convolutional kernel. It is designed to dynamically adjust the convolution kernel’s weights based on the input features. Additionally, the one-dimensional global attention mechanism is incorporated, enhancing the model’s global awareness within the MSSC framework. The experimental results on two public bearing datasets confirm the performance of the proposed method. It demonstrates improved performance over current leading-edge methods, especially in terms of accuracy, and proves to be significantly robust against various levels of noise. Specifically, it achieves accuracies of 99.45% and 99.78% on the two datasets. Furthermore, even after the addition of noise with a signal-to-noise ratio of 0, it still maintains an accuracy as high as 85.65% (on the Politecnico di Torino dataset).

Transformer-CNN hybrid network for improving PET time of flight prediction

Objective. In positron emission tomography (PET) reconstruction, the integration of time-of-flight (TOF) information, known as TOF-PET, has been a major research focus. Compared to traditional reconstruction methods, the introduction of TOF enhances the signal-to-noise ratio of images. Precision in TOF is measured by full width at half maximum (FWHM) and the offset from ground truth, referred to as coincidence time resolution (CTR) and bias. Approach. This study proposes a network combining transformer and convolutional neural network (CNN) to utilize TOF information from detector waveforms, using event waveform pairs as inputs. This approach integrates the global self-attention mechanism of Transformer, which focuses on temporal relationships, with the local receptive field of CNN. The combination of global and local information allows the network to assign greater weight to the rising edges of waveforms, thereby extracting valuable temporal information for precise TOF predictions. Experiments were conducted using lutetium yttrium oxyorthosilicate (LYSO) scintillators and silicon photomultiplier (SiPM) detectors. The network was trained and tested using the waveform datasets after cropping. Main results. Compared to the constant fraction discriminator (CFD), CNN, CNN with attention, long short-term memory (LSTM) and Transformer, our network achieved an average CTR of 189 ps, reducing it by 82 ps (more than 30%), 13 ps (6.4%), 12 ps (6.0%), 16 ps (7.8%) and 9 ps (4.6%), respectively. Additionally, a reduction of 10.3, 8.7, 6.7 and 4 ps in average bias was achieved compared to CNN, CNN with attention, LSTM and Transformer. Significance. This work demonstrates the potential of applying the Transformer for PET TOF estimation using real experimental data. Through the integration of both CNN and Transformer with local and global attention, it achieves optimal performance, thereby presenting a novel direction for future research in this field.