Competitive Network Research Articles

Genomic Alterations Affecting Competitive Endogenous RNAs (ceRNAs) and Regulatory Networks (ceRNETs) with Clinical Implications in Triple-Negative Breast Cancer (TNBC).

The concept of competitive endogenous RNA regulation has brought on a change in the way we think about transcriptional regulation by miRNA-mRNA interactions. Rather than the relatively simple idea of miRNAs negatively regulating mRNA transcripts, mRNAs and other non-coding RNAs can regulate miRNAs and, therefore, broad networks of gene products through competitive interactions. While this concept is not new, its significant roles in and implications on cancer have just recently come to light. The field is now ripe for the extrapolation of technologies with a substantial clinical impact on cancer. With the majority of the genome consisting of non-coding regions encoding regulatory RNAs, genomic alterations in cancer have considerable effects on these networks that have been previously unappreciated. Triple-negative breast cancer (TNBC) is characterized by high mutational burden, genomic instability and heterogeneity, making this aggressive breast cancer subtype particularly relevant to these changes. In the past few years, much has been learned about the roles of competitive endogenous RNA network regulation in tumorigenesis, disease progression and drug response in triple-negative breast cancer. In this review, we present a comprehensive view of the new knowledge and future perspectives on competitive endogenous RNA networks affected by genomic alterations in triple-negative breast cancer. An overview of the competitive endogenous RNA (ceRNA) hypothesis and its bearing on cellular function and disease is provided, followed by a thorough review of the literature surrounding key competitive endogenous RNAs in triple-negative breast cancer, the genomic alterations affecting them, key disease-relevant molecular and functional pathways regulated by them and the clinical implications and significance of their dysregulation. New knowledge of the roles of these regulatory mechanisms and the current acceleration of research in the field promises to generate insights into the diagnosis, classification and treatment of triple-negative breast cancer through the elucidation of new molecular mechanisms, therapeutic targets and biomarkers.

What shapes the formation of interstate benchmarking networks?

AbstractMost studies on public sector benchmarking focus on performance indicators, processes, and outcomes of managed benchmarking. This article, instead, explores the formation of spontaneous interstate benchmarking networks among U.S. state agency leaders. Informed by social comparison theory, we first recategorize benchmarking into best practice benchmarking and competitive benchmarking. Then, we quantify two benchmarking networks with a survey dataset and employ the Exponential Random Graph Model to analyze both endogenous and exogenous factors in the formation of both types of benchmarking networks. We find that the best practice benchmarking network has a popularity effect, while the competitive benchmarking network has mutuality and transitivity effects. Both types of benchmarking networks are more likely to form among states with historical policy diffusion ties and similar economic and geographic characteristics. This study contributes to the literature on public sector benchmarking and network research by exploring the factors that influence the formation of benchmarking networks.

Improved Competitive Neural Network for Classification of Human Postures Based on Data fom RGB-D Sensors

The cognitive goal of this paper is to assess whether marker‐less motion capture systems provide sufficient data to recognize human postures in the side view. The research goal is to develop a new posture classification method that allows for analysing human activities using data recorded by RGB‐D sensors. The method is insensi tive to recorded activity duration and gives satisfactory results for the sagittal plane. An improved competitive Neural Network (cNN) was used. The method of preprocessing the data is first discussed. Then, a method for classifying human postures is presented. Finally, classification quality using various distance metrics is assessed.The data sets covering the selection of human activities have been created. Postures typical for these activities have been identified using the classifying neural network. The classification quality obtained using the proposed cNN network and two other popular neural networks were compared. The results confirmed the advantage of cNN network. The developed method makes it possible to recognize human postures by observing movement in the sagittal plane.

Projective synchronization of fractional order quaternion valued uncertain competitive neural networks

In this manuscript, we investigate the projective synchronization (PS) of fractional-order quaternion-valued uncertain competitive neural networks (FQVUCNNs). First, we construct quaternion-valued adaptive control and use this control to ensure the realization of asymptotic adaptive projective synchronization (AAPS) for FQVUCNNs. Also, we examine finite-time projective synchronization (FTPS) of the FQVUCNNs using quaternion-valued feedback control. Instead of using the usual decomposition technique, we treat the quaternion-valued state as a single entity and establish the synchronization criteria for FQVUCNNs using the Lyapunov direct method and the properties of fractional derivatives. Further, the practical viability and effectiveness of the theoretical findings are demonstrated through an illustrative example, underlying the significance of this research.

Global exponential convergence and synchronization for exponential numerical competitive neural networks with different time scales and fuzzy logic

For nonlocal fuzzy delayed competitive neural networks, a novel difference model is first developed in this article using the Mittag-Leffler Euler difference method. Secondly, the existence of a sole globally bounded solution and globally exponential stability to the discrete-time networks are addressed based on Banach contractive mapping principle and the constant variation formula for sequences. Besides, exponential synchronization of the discrete-time networks is investigated and a feedback controller is designed from the viewpoint of the theory of controls. It is the first time to consider nonlocal fuzzy delayed competitive neural networks by using Mittag-Leffler Euler differences. In contrast to nonlocal discrete approaches, such difference competitive neural networks could preferably figure the features of continuous networks. The difference method in this paper extends and improves the traditional difference approaches, for example, Adams-Bashforth Euler differences. Moreover, this article explores some avenue for the research of discrete-time fractional-order systems and develops a set of theories and methodologies for the future investigations.

Whole-Transcriptome Profiling and Functional Prediction of Long Non-Coding RNAs Associated with Cold Tolerance in Japonica Rice Varieties.

Low-temperature chilling is a major abiotic stress leading to reduced rice yield and is a significant environmental threat to food security. Low-temperature chilling studies have focused on physiological changes or coding genes. However, the competitive endogenous RNA mechanism in rice at low temperatures has not been reported. Therefore, in this study, antioxidant physiological indices were combined with whole-transcriptome data through weighted correlation network analysis, which found that the gene modules had the highest correlation with the key antioxidant enzymes superoxide dismutase and peroxidase. The hub genes of the superoxide dismutase-related module included the UDP-glucosyltransferase family protein, sesquiterpene synthase and indole-3-glycerophosphatase gene. The hub genes of the peroxidase-related module included the WRKY transcription factor, abscisic acid signal transduction pathway-related gene plasma membrane hydrogen-ATPase and receptor-like kinase. Therefore, we selected the modular hub genes and significantly enriched the metabolic pathway genes to construct the key competitive endogenous RNA networks, resulting in three competitive endogenous RNA networks of seven long non-coding RNAs regulating three co-expressed messenger RNAs via four microRNAs. Finally, the negative regulatory function of the WRKY transcription factor OsWRKY61 was determined via subcellular localization and validation of the physiological indices in the mutant.

Structural normative of consumer textile technologies entrepreneurship and the sustainability in Southwest Rural Areas of Nigeria

The study examined the sustainability of consumer textile technology entrepreneurship amidst its structural normative in southwest rural area of Nigeria. It explicitly analyzed entrepreneurial cultural traits and economics agility needs related variables of the entrepreneurship influences on sustainability. The study was conducted in the rural southwestern region of Nigeria. Four states (Lagos, Ondo, Oyo, and Ogun) were randomly selected from the region. Multi-stage and stratified sampling techniques were used in selecting 1,649 consumer textile entrepreneurs across the states. Questionnaire, key informants and focus group discussion (FGD) guides were employed in data collection. While descriptive statistics were used in describing the data, correlation analysis was applied in establishing direction of influence between sustainability and structural normative of consumer textile technologies entrepreneurship Sustainability level of the entrepreneurship in southwest rural area of Nigeria was average. Respondents were independent entrepreneurs of favorable attitude towards the enterprise. While 52.7 percent were averagely proactive, 58.8 percent were averagely risk-averse with exhibition of low creativities skill on the entrepreneurship. The entrepreneurs possessed average knowledge of competitive advantage and networking strategies needs on the job but skills practices was poor. While 33.0 percent exhibits fairly good managerial skill on the job, 20.8 percent displayed high, and 46.2 percent was low. CTT entrepreneurial traits needs related variables {proactive aptitude (r = 0.620), autonomy value (r = 0.591), fatalism (r = -0.317), marketing system (r=0.574), disposition (r= 0.409), risk averse (r= -0.344), and economic agility needs {(competitive strategic skill (r = 0.318), networking (r = 0.501), labor accessibility (r = 0.321)} showed significant relationship with sustainability. It was established that respondent’s proficient in CTTE entrepreneurial traits related variables and economic agility were paramount in the sustainability, nevertheless there is need to establish related schools or vocational training centers for skill acquisitions and updating on the entrepreneurship in the studied area. Change agents and other related organizations that were involve in technology transfer and rural development should create awareness program on potential values and benefits of the entrepreneurship mostly in turning wastes to wealth. This would enhance enlightenment for building better positive attitudes toward CTTE. The environment must be made livable through job creation and accessible infrastructure to prevent migration and attracts youth’s influxes into the community. This would also enhance labour availability and patronage. Standard cluster market should be established in the vicinity to enhance expertise and inputs supply.

YTHDF1's Regulatory Involvement in Breast Cancer Prognosis, Immunity, and the ceRNA Network.

YTH N6-methyladenosine RNA binding protein 1 (YTHDF1), an m6A reader, has a role in the development and progression of breast cancer as well as the immunological microenvironment. The networks of competing endogenous RNA in cancer have received much attention in research. In tumor gene therapy, the regulatory networks of m6A and competing endogenous RNA are increasingly emerging as a new route. We evaluated the relationship between the YTHDF1 expression, overall survival, and clinicopathology of breast cancer using TCGA, PrognoScan, and other datasets. We used Western blot to demonstrate that YTHDF1 is substantially expressed in breast cancer tissues. Furthermore, we explored YTHDF1's functions in the tumor mutational burden, microsatellite instability, and tumor microenvironment. Our findings indicate that YTHDF1 is a critical component of the m6A regulatory proteins in breast cancer and may have a particular function in the immunological microenvironment. Crucially, we investigated the relationship between YTHDF1 and the associated competitive endogenous RNA regulatory networks, innovatively creating three such networks (Dehydrogenase/Reductase 4-Antisense RNA 1-miR-378g-YTHDF1, HLA Complex Group 9-miR-378g-YTHDF1, Taurine Up-regulated 1-miR-378g-YTHDF1). Furthermore, we showed that miR-378g could inhibit the expression of YTHDF1, and that miR-378g/YTHDF1 could impact MDA-MB-231 proliferation. We speculate that YTHDF1 may serve as a biomarker for poor prognosis and differential diagnosis, impact the growth of breast cancer cells via the ceRNA network axis, and be a target for immunotherapy against breast cancer.

Establishing a carcinoembryonic antigen-associated competitive endogenous RNA network and forecasting an important regulatory axis in colon adenocarcinoma patients.

Colorectal cancer is one of the top five malignant tumors in the world in terms of morbidity and mortality. Numerous long non-coding RNAs (lncRNAs) are specifically expressed in tumours and can affect various types of human cancer by participating in competitive endogenous RNA (ceRNA) regulatory networks. However, the specific mechanisms and complex networks of ceRNA regulatory patterns in colon adenocarcinoma (COAD) remain unclear. Using The Cancer Genome Atlas (TCGA) database, we identified the differentially expressed lncRNA, microRNA (miRNA), and messenger RNA (mRNA) between colon cancer and normal tissues, as well as between groups with high and low CEACAM5 expression. Then, we constructed CEACAM5-related ceRNA networks, established the key lncRNA-miRNA-mRNA regulatory axis, and explored the biological mechanisms of this axis and its clinical significance in colon cancer from multiomic aspects. We constructed a ceRNA network of 18 lncRNAs, 177 miRNAs, and 25 mRNAs associated with CEACAM5 and finally established the key LCMT1-AS2/miR-454-3p/ribosomal protein S6 kinase A5 (RPS6KA5) axis associated with overall survival. Subsequent investigations have indicated that this regulatory axis could potentially participate in the progression of COAD and exert influence on the therapeutic outcomes of chemotherapy and immunotherapy. It may be involved in the PI3K-Akt signaling pathway and may modify the tumor immune microenvironment and influence the course of COAD. Additionally, it may be related to ferroptosis, N6-methyladenosine (m6A) methylation, and tumor stemness and interfere with the sensitivity of tumor cells to 5-fluorouracil and immunotherapy. The LCMT1-AS2/RPS6KA5 axis may be instrumental in tumor progression, potentially acting as a prognostic biomarker and therapeutic target.

The role of competing endogenous RNA network in the development of hepatocellular carcinoma: potential therapeutic targets.

The current situation of hepatocellular carcinoma (HCC) management is challenging due to its high incidence, mortality, recurrence and metastasis. Recent advances in gene genetic and expression regulation have unveiled the significant role of non-coding RNA (ncRNA) in various cancers. This led to the formulation of the competing endogenous RNA (ceRNA) hypothesis, which posits that both coding RNA and ncRNA, containing miRNA response elements (MRE), can share the same miRNA sequence. This results in a competitive network between ncRNAs, such as lncRNA and mRNA, allowing them to regulate each other. Extensive research has highlighted the crucial role of the ceRNA network in HCC development, impacting various cellular processes including proliferation, metastasis, cell death, angiogenesis, tumor microenvironment, organismal immunity, and chemotherapy resistance. Additionally, the ceRNA network, mediated by lncRNA or circRNA, offers potential in early diagnosis and prevention of HCC. Consequently, ceRNAs are emerging as therapeutic targets for HCC. The complexity of these gene networks aligns with the multi-target approach of traditional Chinese medicine (TCM), presenting a novel perspective for TCM in combating HCC. Research is beginning to show that TCM compounds and prescriptions can affect HCC progression through the ceRNA network, inhibiting proliferation and metastasis, and inducing apoptosis. Currently, the lncRNAs TUG1, NEAT1, and CCAT1, along with their associated ceRNA networks, are among the most promising ncRNAs for HCC research. However, this field is still in its infancy, necessitating advanced technology and extensive basic research to fully understand the ceRNA network mechanisms of TCM in HCC treatment.

NcRNAs-mediated TIMELESS overexpression in lung adenocarcinoma correlates with reduced tumor immune cell infiltration and poor prognosis.

Lung adenocarcinoma (LUAD) has a poor prognosis. Circadian genes such as TIMELESS have been associated with several pathologies, including cancer. The expression of TIMELESS and the relationship between TIMELESS, infiltration of tumors and prognosis in LUAD requires further investigation. In this study, we investigated the expression of TIMELESS and its association with survival across several types of human cancer using data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression Program. Noncoding RNAs (ncRNAs) regulating overexpression of TIMELESS in lung adenocarcinoma (LUAD) were explored with expression, correlation, and survival analyses. Immune cell infiltration and biomarkers were analyzed between different TIMELESS expression levels. The relationship between TIMELESS expression and immunophenoscores, which were used to predict response to immunotherapy, was evaluated. TIMELESS was identified as a potential oncogene in LUAD. NcRNA analysis showed MIR4435-2HG/hsa-miR-1-3p may interact with TIMELESS in a competitive endogenous RNA network in LUAD tumor tissues. Most immune cells were significantly decreased in TCGA LUAD tumor tissues with high TIMELESS expression except for CD4+T cells and Th2 cells. TIMELESS expression in LUAD tumor tissues was significantly negatively correlated with neutrophil biomarkers, dendritic cell biomarkers (HLA-DPB1, HLA-DQB1, HLA-DRA, HLA-DPA1, CD1C) and an immunophenoscore that predicted outcomes associated with the use of immune checkpoint inhibitors. These findings imply that ncRNAs-mediated TIMELESS overexpression in LUAD tumor tissues correlated with poor prognosis, reduced immune cell infiltration in the tumor microenvironment, and poor response to immune checkpoint inhibitors.

Constructing a competitive endogenous RNA network of EndMT-related atherosclerosis through weighted gene co-expression network analysis.

Atherosclerosis is a chronic inflammatory disease characterized by endothelial dysfunction and plaque formation. Under pro-inflammatory conditions, endothelial cells can undergo endothelial-to-mesenchymal transition (EndMT), contributing to atherosclerosis development. However, the specific regulatory mechanisms by which EndMT contributes to atherosclerosis remain unclear and require further investigation. Dan-Shen-Yin (DSY), a traditional Chinese herbal formula, is commonly used for cardiovascular diseases, but its molecular mechanisms remain elusive. Emerging evidence indicates that competing endogenous RNA (ceRNA) networks play critical roles in atherosclerosis pathogenesis. In this study, we constructed an EndMT-associated ceRNA network during atherosclerosis progression by integrating gene expression profiles from the Gene Expression Omnibus (GEO) database and weighted gene co-expression network analysis. Functional enrichment analysis revealed this EndMT-related ceRNA network is predominantly involved in inflammatory responses. ROC curve analysis showed the identified hub genes can effectively distinguish between normal vasculature and atherosclerotic lesions. Furthermore, Kaplan-Meier analysis demonstrated that high expression of IL1B significantly predicts ischemic events in atherosclerosis. Molecular docking revealed most DSY bioactive components can bind key EndMT-related lncRNAs, including AC003092.1, MIR181A1HG, MIR155HG, WEE2-AS1, and MIR137HG, suggesting DSY may mitigate EndMT in atherosclerosis by modulating the ceRNA network.

Conformity-aware adoption maximization in competitive social networks

Influence maximization (IM) problem is an extensively studied problem in social networks. It aims to find a small set of users in the social network to initiate the diffusion process and maximize the expected influence spread. Existing works on conformity-aware IM focus on the interaction between influence and conformity in a single-influence setting and ignore the role of conformity in a competitive and multiple-influence setting. This paper proposes a conformity-aware independent cascade (C-IC) model that considers the competition among multiple influences as well as the role of conformity in a user’s decision-making. It is proved that the adoption of an influence under the C-IC model is monotone and submodular. Meanwhile, we formulate two adoption maximization (AM) problems, O-AM and S-AM, which are both NP-hard. Because estimating the adoption through diffusion simulations is very time-consuming, we propose a reverse adoption estimation (RAE) method based on a reverse multiple influence sampling (RMIS) technology for the C-IC model and integrate it into the D-SSA-fix (Nguyenet al., 2018) framework, DSSA for short, to compute a solution with approximation guarantee. To further boost the performance, we present a fast one-hop adoption estimation (OAE) method and develop a heuristic algorithm based on OAE, called GOAE. Extensive experiments on eight real-world social networks show that the C-IC model is superior to a non-conformity diffusion model and that RAE+DSSA and GOAE are efficient and effective. In most cases, GOAE finds comparable solutions to RAE+DSSA and CELF with less time and memory overhead. GOAE is five to six orders of magnitude faster than CELF and RAE+DSSA is up to three orders of magnitude faster than CELF on NetHEPT. GOAE runs up to four to five orders of magnitude faster than RAE+DSSA with at most two orders of magnitude less memory usage. GOAE is more scalable than RAE+DSSA in terms of the number of seeds and the size of the social network.

Exponential Synchronization of Memristor-Based Competitive Neural Networks With Reaction-Diffusions and Infinite Distributed Delays.

Taking into account the infinite distributed delays and reaction-diffusions, this article investigates the global exponential synchronization problem of a class of memristor-based competitive neural networks (MCNNs) with different time scales. Based on the Lyapunov-Krasovskii functional and inequality approach, an adaptive control approach is proposed to ensure the exponential synchronization of the addressed drive-response networks. The closed-loop system is a discontinuous and delayed partial differential system in a cascade form, involving the spatial diffusion, the infinite distributed delays, the parametric adaptive law, the state-dependent switching parameters, and the variable structure controllers. By combining the theories of nonsmooth analysis, partial differential equation (PDE) and adaptive control, we present a new analytical method for rigorously deriving the synchronization of the states of the complex system. The derived m-norm (m ≥ 2)-based synchronization criteria are easily verified and the theoretical results are easily extended to memristor-based neural networks (NNs) without different time scales and reaction-diffusions. Finally, numerical simulations are presented to verify the effectiveness of the theoretical results.

Multistability and Robustness of Competitive Neural Networks With Time-Varying Delays.

This article is devoted to analyzing the multistability and robustness of competitive neural networks (NNs) with time-varying delays. Based on the geometrical structure of activation functions, some sufficient conditions are proposed to ascertain the coexistence of ∏i=1n(2Ri+1) equilibrium points, ∏i=1n(Ri+1) of them are locally exponentially stable, where n represents a dimension of system and Ri is the parameter related to activation functions. The derived stability results not only involve exponential stability but also include power stability and logarithmical stability. In addition, the robustness of ∏i=1n(Ri+1) stable equilibrium points is discussed in the presence of perturbations. Compared with previous papers, the conclusions proposed in this article are easy to verify and enrich the existing stability theories of competitive NNs. Finally, numerical examples are provided to support theoretical results.

Constructed competitive endogenous RNA network and patterns of immune infiltration revealing the prognostic signature for cervical cancer.

Aim: To investigate the relationship between potential abnormal epigenetic modification and immune cell infiltration in patients with cervical carcinoma. Materials & methods: RNA expression profiles from The Cancer Genome Atlas database were used to explore the relationship between key biomarkers and tumor-infiltrating immune cells and for clinical specimen validation. Results: Two nomogram models were developed, one with specific ceRNA and the other based on biological markers of related tumor-infiltrating immune cells. Moreover, a key biomarker (RIPOR2), which was significantly relevant to CD8 T cells. Conclusion: RIPOR2 and CD8T cells play a crucial role in the development and progression of cervical carcinoma, suggesting their potential as markers for guiding future therapeutic strategies.

Multistability of Almost Periodic Solutions for Fuzzy Competitive NNs With Time-Varying Delays.

In this article, the multistability problem of almost periodic solutions of fuzzy competitive neural networks (FCNNs) with time-varying delays is investigated. Considering more general activation functions, which are nonmonotonic and nonlinear, and incorporating the almost periodic property of the parameters in FCNNs, sufficient conditions for the multistability of almost periodic solutions are given. ∏r=1n(Lr+1) stable almost periodic solutions are obtained, where Lr depends on the geometric features of the activation functions, which enriches and extends the research on multistability in fuzzy systems. Furthermore, the extended domain of attraction based on the original state space is presented. Finally, numerical simulations are provided to verify the conclusions of this article.

Study on the Influencing Factors of the Competitive Network Pattern of Cobalt Industry Chain Trade in the Context of Big Data Analysis

Abstract The article proposes a time series model to explore the influencing factors of the cobalt industry chain trade competition network pattern. By analyzing the current situation of the evolution of the cobalt industry chain trade network, the relevant variables are selected. The data are described based on the overview of the relevant algorithmic research of the time series model. Finally, the model based on the time series data is empirically tested. The unit root is verified to be in an unsteady state by first-order differencing, and the p-values all have a probability of accepting the original hypothesis greater than 0. After the second-order differencing ADF test, all the series are smooth and all of them are second-order monotonic. After the cointegration test, it was found that the residual series is smooth at the 5% critical level, and there is a cointegration relationship.

A New Perspective of Trees Competition Network Research

Forest competition is a key factor driving forest dynamics and stand structure, and is one of the core issues in ecological research. Competition is a complex ecological process with temporal changes, which is interrelated and affected with forest growth, stand structure dynamics and environmental changes. Although there have been many researches on competition, tree growth, stand structure and climate, the interactions among trees have been studied by static competition index measurement, which usually lacks temporal variation related to natural forest development and environmental conditions, and the response mechanism and related theory of competition to stand development and environmental fluctuation have been studied insufficiently.(2) Single tree competition index model;(3) Interaction between tree competition and tree growth, stand structure and environment;(4) Tree competition based on complex network. Finally, according to the complex interaction of tree competition, the research strategy of tree competition network structure with “single tree” as node and “single tree competition relationship” as edge is discussed, in order to provide reference for the study of tree interaction and forest ecosystem dynamics.

Solar photovoltaic panel production in Mexico: A novel machine learning approach

This study examines the potential for widespread solar photovoltaic panel production in Mexico and emphasizes the country's unique qualities that position it as a strong manufacturing candidate in this field. An advanced model based on artificial neural networks has been developed to predict solar photovoltaic panel plant metrics. This model integrates a state-of-the-art non-linear programming framework using Pyomo as well as an innovative optimization and machine learning toolkit library. This approach creates surrogate models for individual photovoltaic plants including production timelines. While this research, conducted through extensive simulations and meticulous computations, unveiled that Latin America has been significantly underrepresented in the production of silicon, wafers, cells, and modules within the global market; it also demonstrates the substantial potential of scaling up photovoltaic panel production in Mexico, leading to significant economic, social, and environmental benefits. By hyperparameter optimization, an outstanding and competitive artificial neural network model has been developed with a coefficient of determination values above 0.99 for all output variables. It has been found that water and energy consumption during PV panel production is remarkable. However, water consumption (33.16 × 10−4 m3/kWh) and the emissions generated (1.12 × 10−6 TonCO2/kWh) during energy production are significantly lower than those of conventional power plants. Notably, the results highlight a positive economic trend, with module production plants generating the highest profits (35.7%) among all production stages, while polycrystalline silicon production plants yield comparatively lower earnings (13.0%). Furthermore, this study underscores a critical factor in the photovoltaic panel production process which is that cell production plants contribute the most to energy consumption (39.7%) due to their intricate multi-stage processes. The blending of Machine Learning and optimization models heralds a new era in resource allocation for a more sustainable renewable energy sector, offering a brighter, greener future.