Innovative Mechanisms Research Articles

Modernization of Intellectual Property Discipline Governance Based on Multivariate Statistical Analysis Towards the Construction of a Powerful Intellectual Property Country

Abstract In this paper, after using the TF-IDF algorithm to calculate the weights of intellectual property keywords of digital content, the weights of fused word multi-features are improved to obtain the proportion of intellectual property keywords. Based on intellectual property keywords, combining adversarial fingerprint generation and intellectual property authentication, an intellectual property protection model based on RCNN is constructed. From the perspective of strong nation-building, the research on intellectual property affecting the artificial intelligence industry is designed, and multivariate statistical analysis of scientific governance of intellectual property in the context of strong nation-building is carried out. The results show that the SSIM values obtained by the method of this paper are much better than those obtained by the comparison method in terms of model analysis. Except for the anti-quantization attack, the rest of the indicators are all greater than 0.9. It confirms that the method presented in this paper has excellent robustness in protecting intellectual property. In the empirical analysis of the impact of intellectual property rights on the artificial intelligence industry, except for RDF, which is significant at the 5% level, the regression coefficients of the other explanatory variables are all significant at the 1% level, 2 R amounting to 0.860771 and with a DW value of 1.667387 in the range of 1.5-2.5, that is, it indicates that the level of China’s intellectual property rights protection and the competitiveness of the AI industry are positively correlated. It is positively correlated. This study promotes the improvement of China’s innovation ability and the formation of innovation mechanisms from the aspect of intellectual property protection so as to realize the comprehensive catching up of technology and economy of late-developed countries.

Empowering CO2 Eco-Refrigeration With Colossal Breathing-Caloric-Like Effects in MOF-508b.

Today, ≈20% of the electric consumption is devoted to refrigeration; while, ≈50% of the final energy is dedicated to heating applications. In this scenario, many cooling devices and heat-pumps are transitioning toward the use of CO2 as an eco-friendly refrigerant, favoring carbon circular economy. Nevertheless, CO2 still has some limitations, such as large operating pressures (70-150bar) and a critical point at 31 °C, which compromises efficiency and increases technological complexity. Very recently, an innovative breathing-caloric mechanism in the MIL-53(Al) compound is reported, which implies gas adsorption under CO2 pressurization boosted by structural transitions and which overcomes the limitations of stand-alone CO2. Here, the breathing-caloric-like effects of MOF-508b are reported, surpassing by 40% those of MIL-53(Al). Moreover, the first thermometry device operating at room temperature and under the application of only 26bar of CO2 is presented. Under those conditions, this material presents values of ΔT ≈ 30K, reaching heating temperatures of 56 °C and cooling temperatures of -10 °C, which are already useful for space heating, air-conditioning, food refrigeration, and freezing applications.

Innovative Mechanisms of Shaping the Migration Attractiveness of Ukraine Regions

"The article examines the concept of migration attractiveness of the regions in its correlation with investment attractiveness and its corresponding factors of influence. It is stressed that the example of Ukraine is a unique in its kind, not experienced critical deteriorative influence of either hybrid war or active hostilities on the migration attractiveness of the regions. As a result of literature research and studies of Ukrainian data, it is proposed to use methodologies for assessment of investment attractiveness when designing mechanisms of shaping migration attractiveness of the country’ regions, and apply matrix systemic approach, taking into account, among other factors, sustainability (ESG) patterns."

A hybrid lightweight breast cancer classification framework using the histopathological images

A crucial element in the diagnosis of breast cancer is the utilization of a classification method that is efficient, lightweight, and precise. Convolutional neural networks (CNNs) have garnered attention as a viable approach for classifying histopathological images. However, deeper and wider models tend to rely on first-order statistics, demanding substantial computational resources and struggling with fixed kernel dimensions that limit encompassing diverse resolution data, thereby degrading the model’s performance during testing. This study introduces BCHI-CovNet, a novel lightweight artificial intelligence (AI) model for histopathological breast image classification. Firstly, a novel multiscale depth-wise separable convolution is proposed. It is introduced to split input tensors into distinct tensor fragments, each subject to unique kernel sizes integrating various kernel sizes within one depth-wise convolution to capture both low- and high-resolution patterns. Secondly, an additional pooling module is introduced to capture extensive second-order statistical information across the channels and spatial dimensions. This module works in tandem with an innovative multi-head self-attention mechanism to capture the long-range pixels contributing significantly to the learning process, yielding distinctive and discriminative features that further enrich representation and introduce pixel diversity during training. These novel designs substantially reduce computational complexities regarding model parameters and FLOPs, which is crucial for resource-constrained medical devices. The outcomes achieved by employing the suggested model on two openly accessible datasets for breast cancer histopathological images reveal noteworthy performance. Specifically, the proposed approach attains high levels of accuracy: 99.15 % at 40× magnification, 99.08 % at 100× magnification, 99.22 % at 200× magnification, and 98.87 % at 400× magnification on the BreaKHis dataset. Additionally, it achieves an accuracy of 99.38 % on the BACH dataset. These results highlight the exceptional effectiveness and practical promise of BCHI-CovNet for the classification of breast cancer histopathological images.

Design and Analysis of Variable-Stiffness Fast-Steering Mirror

The non-variable stiffness of the flexible hinge in the fast-steering mirror (FSM) cannot adapt to varying load demands. To address this issue, this paper presents an innovative variable-stiffness rotational mechanism designed for use with FSMs. Firstly, the working principle of the variable-stiffness mechanism is introduced, and the influence of the length of each structure on the stiffness and the nonlinear influence are analyzed. Then, the variable-stiffness mechanism is applied to the FSM for the variable-stiffness experiment and variable-load experiment. The experimental results show that the variable-stiffness mechanism designed in this paper can realize the change in stiffness. The errors between the experimental value and the theoretical value of the three sets of experiments are +5.72%, +7.57%, and +6.57%. The FSM’s stiffness nonlinearity is very small, and the resonance frequency of the FSM before and after increasing the load can be consistent. The variable-stiffness mechanism can change the frequency characteristics by changing the rotational stiffness of the FSM.

Farmers Knowledge on Indigenous Soil Types: A Review

This review explains the significance of smallholder farmers’ understanding of soil types as a dependable basis for subsistence crop production. The data presented in this review was sourced from literature on subsistence farming, indigenous soil nomenclature, and the suitability of various soils for different crop varieties. The review findings revealed four primary soil classifications, specifically loam, clay, sandy, and rocky, each exhibiting distinct variations in texture and colour. Farmers utilise soil texture and colour as indicators of the suitability of soil for certain crops. For example, black clay soil has high moisture retention and low susceptibility to erosion that mitigates the risk of crop failure. Farmers utilise this knowledge to mitigate the impacts of increasing temperatures and unpredictable precipitation patterns on the planting and management of crops. This type of adaptation mechanism could be incorporated into climate change adaptation policy to foster the use of community-based and innovative mechanisms to mitigate and cope with the negative impacts of climate change on subsistence food production. Keywords: Indigenous knowledge, ethnopedology, soil taxonomy, climate change, sustainable development, climate adaptation

CARE FOR OLDER ADULTS: WHAT SKILLS DO HEALTH CARE EMPLOYERS VALUE IN COMMUNITY COLLEGE STUDENTS?

Abstract Community colleges (CCs) play an important role in educating healthcare workers engaged in caring for aging populations with greater healthcare and long-term care needs. Healthcare employers and nursing home leaders are obligated to ensure that care provided by their employees meets quality standards, is patient-centered and free from psychological and physical harm. To increase the employability of CC students and understand the skills valued by employers in improving care for older adults, we explored the role literacy, numeracy, and problem-solving skills play in CC student success in the workplace. Using a qualitative descriptive design, we conducted semi-structured interviews with CC administrators (n=9), faculty (n=6), and employers (n=10) who hire healthcare workers (certified nursing assistants, licensed practical nurses (LPNs), and registered nurses (RNs)) educated at CCs. Our findings show that communication skills are highly valued, especially the ability to communicate on the level of the nursing home resident or hospital patient, along with their family members. Numeracy skills, such as measuring medication dosages, critical thinking skills, and problem-solving skills, including conflict resolution, emotional intelligence, and compassionate care are valued by employers. Employers reported that LPNs and RNs have limited leadership and supervisory skills and there is a need to use innovative mechanisms (e.g., case studies, online courses) to improve those skills. Serving on CC advisory councils, clinical rotations and employers’ input into program development were reported as beneficial strategies. Findings from this project will inform both CC administrators and employers regarding skills that are valued in caring for older adults.

An assessment of bilateral debt swap financing indispensable for economic growth and environment sustainability: a policy implication for heavily indebted countries.

Bilateral debt swap is an innovative global financing mechanism designed to support heavily indebted countries (HICs). It is a debt-restructuring process involving donor countries forgiving debt owed by HICs in exchange for commitments to undertake projects on environment and socio-economic development. It is a unique approach designed to help heavily indebted countries get back on their feet. Effective debt swap financing can lead to both economic growth and environment sustainability, but they are challenging to implement. This study examines the impact of bilateral debt swap financing on economic growth and environment sustainability. For the purpose, we have used debt swap index developed with Kaiser-Meyer-Olkin (KMO) methodology. KMO widely used approach of Principle Component Analysis (PCA) to solve the problem of "over-identification" and make strong correlation among endogenous variables of interest. In order to validate the nexus empirically between bilateral debt swap financing with economic growth and environment sustainability, we have employed the Two-Step System Generalized Method of Moments (SYS-GMM) approach in 25 countries for the period of 2002 to 2021. This modern econometric method addresses endogeneity issues and controls for unobserved heterogeneity in panel data. At the same time, the technique addresses the simultaneity problem, reverse causality, and remove selection bias. Findings of the study shows that effective bilateral debt swap financing can boost economic growth and environment sustainability by investing domestic resources for targeted activities along with reduced debt burden. Empirical results reveal that 1% change in debt swap financing can lead to a maximum of 0.23% growth in the economy and 0.28% improvement in environment sustainability. However, it is important to note that in most empirical specifications, results are inconclusive. One possible reason for this is often ineffective debt swap practices coupled with inadequate monitoring and evaluation in HICs. Policymakers should focus on enhancing debt swap policies to promote economic growth and environment sustainability.

Enforcing Sustainability

After the Lisbon Treaty, the European Union (EU) has prioritized sustainable development as a key aspect of its external trade and investment policy. This emphasis on sustainability has been reinforced through treaty- making and has received support from the Court of Justice of the European Union (cjeu). However, there are notable variations and nuances in the understanding and implementation of sustainability in the EU’s treaties. Moreover, these treaties introduce innovative dispute resolution mechanisms, such as arbitration and alternative dispute resolution methods, which can significantly impact international dispute settlement practices. Opinion 2/ 15 on the EU- Singapore Free Trade Agreement marked a crucial moment in clarifying the scope and nature of sustainable development chapters within the EU’s trade and investment policy. Different agreements concluded after the Lisbon Treaty reflect diverse approaches to sustainability. In the context of environmental protection, these agreements encompass a range of provisions, from generic to more specific clauses addressing particular aspects. This article critically analyzes the effectiveness and scope of “green conditionality” within the Sustainable Development Chapters of EU-established Trade and Investment Agreements. It examines the incorporation of environmental provisions in these agreements, evaluates their role in promoting sustainable development objectives, and explores the implications of dispute settlement and international arbitration mechanisms. This analysis aims to provide a cutting- edge perspective on the topic.

Inclusiveness of value chains as an innovative mechanism for ensuring economic security

Inclusiveness of value chains as an innovative mechanism for ensuring economic security

Enhanced visual SLAM for construction robots by efficient integration of dynamic object segmentation and scene semantics

With the increasing adoption of autonomous mobile robots in the construction industry, accurate localization and mapping in dynamic construction environments have become paramount. This is typically tackled via Simultaneous Localization and Mapping (SLAM) techniques. Primarily designed for static environments, traditional SLAM systems struggle to maintain robustness and accuracy in dynamic settings. To address this challenge, this study presents an enhanced visual SLAM system specifically tailored for dynamic construction environments. The proposed system, named vSLAM-Con, introduces an adaptive dynamic object segmentation method, utilizing an innovative AD-keyframes selection mechanism grounded on optical flow magnitude to diminish computational overhead while preserving competitive tracking accuracy. Additionally, a semantic-based feature update process is developed, leveraging scene understanding and continuous observation to augment the reliability of tracking features. This system's performance, evaluated on both an established public benchmark and a custom construction dataset, shows substantial improvements over the baseline and competitive results with the state-of-the-art algorithms. More importantly, it largely reduces the processing time compared to state-of-the-arts, demonstrating robust tracking performance even under highly dynamic conditions. The findings highlight the system's potential to contribute significantly to autonomous robotics in construction, offering more accurate navigation and interaction capabilities in complex, ever-changing environments.

Innovation and Optimization of Physical Education Senior High School Examination Based on Digital Transformation: Path, Mechanism and Practice

With the rapid development of digital technology, physical education is ushering in an unprecedented digital transformation. In the physical education high school examination, digital transformation can not only improve the efficiency and accuracy of the examination, but also provide a more fair and just evaluation environment for students. From the perspective of digital transformation, this paper discusses the path, mechanism and practice of innovation and optimization in the evaluation of middle school physical education examination. It aims to provide theoretical and practical support for digital transformation in physical education assessment, and promote the effective integration of modern technology and physical education.

A lightweight network based on local–global feature fusion for real-time industrial invisible gas detection with infrared thermography

The detection of industrial invisible gas plays a vital role in preventing environmental pollution and fire accidents. Optical gas imaging (OGI) with infrared thermography is widely used in the field of gas leak monitoring and treatment by visualizing gases to quickly and accurately detect and locate the gas leak sources. However, this method still relies on manual visual inspection. Existing automatic visual gas detection methods suffer from insufficient gas feature extraction and high computational cost due to the indistinct gas features in thermal images. To address these problems, we propose a new lightweight network specialized for thermal gas feature extraction, namely GasViT, sufficiently extracting gas features at very low computational cost by local–global feature fusion. Specifically, two new feature extraction modules Multi-scale Fusion Feature Attention (MsFFA) and Multi-head Linear Self-attention (MhLSa) are proposed for GasViT. MsFFA enhances the gas local feature extraction ability by constructing multi-scale channel and spatial feature fusion maps, enabling the network to focus on more valid local information. MhLSa complements the gas global features with very low computational cost by efficiently encoding the global information of the image in terms of the innovative linear self-attention mechanism. Our experimental results on the self-made Industrial Invisible Gas (IIG) Dataset show GasViT achieves 82.7% mAP50, significantly outperforming the state-of-the-art lightweight networks. Moreover, GasViT achieves 33 FPS real-time detection with a running memory footprint of only 47.2 MB on edge computing devices, making it extremely suitable for portable and embedded detection devices than existing methods to cover gas leakage detection in complex and hazardous industrial scenarios.

Structure-based design, and development of amidinyl, amidoximyl and hydroxamic acid based organic molecules as novel antimalarial drug candidates

Malaria remains a significant global health concern causing numerous fatalities and the emergence of antimalarial drug resistance highlights the urgent need for novel therapeutic options with innovative mechanisms of action and targets. This study aimed to design potential inhibitors of Plasmodium falciparum 6-pyruvoyltetrahydropterin synthase (PfPTPS), synthesize them, and experimentally validate their efficacy as antimalarial agents. A structure-based approach was employed to design a series of novel derivatives, including amidinyl, amidoximyl and hydroxamic acid analogs (1c, 1d, 2b, and 3b), with a focus on their ability to bind to the Zn2+ present in the active site of PfPTPS. The syntheses of these compounds were accomplished through various multi-step synthetic pathways and their structural identities were confirmed using 1H and 13C NMR spectra, mass spectra, and elemental analysis. The compounds were screened for their antiplasmodial activity against the NF54 strain of P. falciparum and in vitro cytotoxicity testing was performed using L-6 cells. The in vivo acute toxicity of the compounds was evaluated in mice. Docking studies of the compounds with the 3D structure of PfPTPS revealed their strong binding affinities, with compound 3b exhibiting notable metal–acceptor interaction with the Zn2+ in the protein binding pocket thereby positioning it as a lead compound for PfPTPS inhibition. The in vitro antiplasmodial studies revealed moderate efficacies against the Pf NF54 strain, particularly compounds 1d and 3b which displayed IC50 < 0.2 μM. No significant cytotoxicity was noted on the L-6 rat cell line. Moreover, in vivo studies suggested that compound 3b exhibited both safety and efficacy in treating rodent malaria. The identified lead compound in this study represents a possible candidate for antimalarial drug development and can be further explored in the search for alternative antifolate drugs to combat the malaria menace.

Targeting KRAS-Mutated Gastrointestinal Malignancies with Small-Molecule Inhibitors: A New Generation of Breakthrough Therapies.

Kirsten rat sarcoma virus (KRAS) is one of the most important and frequently mutated oncogenes in cancer and the mutational prevalence is especially high in many gastrointestinal malignancies, including colorectal cancer and pancreatic ductal adenocarcinoma. The KRAS protein is a small GTPase that functions as an "on/off" switch to activate downstream signaling, mainly through the mitogen-activated protein kinase pathway. KRAS was previously considered undruggable because of biochemical constraints; however, recent breakthroughs have enabled the development of small-molecule inhibitors of KRAS G12C. These drugs were initially approved in lung cancer and have now shown substantial clinical activity in KRAS G12C-mutated pancreatic ductal adenocarcinoma as well as colorectal cancer when combined with anti-EGFR monoclonal antibodies. Early data are encouraging for other gastrointestinal cancers as well and many other combination strategies are being investigated. Several new KRAS G12C inhibitors and novel inhibitors of other KRAS alterations have recently entered the clinic. These molecules employ a variety of innovative mechanisms and have generated intense interest. These novel drugs are especially important as KRAS G12C is rare in gastrointestinal malignancies compared with other KRAS alterations, representing potentially groundbreaking advances. Soon, the rapidly evolving landscape of novel KRAS inhibitors may substantially shift the therapeutic landscape for gastrointestinal cancers and offer meaningful survival improvements.

Research on the Cultivation Mechanism of Innovation and Entrepreneurship Ability of Excellent Engineers

Abstract In this paper, after using PSO algorithm to encode the information on innovation and entrepreneurship education resources in universities, N particle is randomly generated to form the initial population, and it is also necessary to continuously update the particles to carry out the adjustment of the initial population size. The mathematical model of hard constraints of all courses of innovation and entrepreneurship teaching is obtained through the fitness function, on the basis of which, according to the objective function of innovation and entrepreneurship education resources in universities, each kind of resource is effectively allocated to the most adapted aspects, so as to realize the optimal allocation of innovation and entrepreneurship education resources in universities. Based on the current situation of innovation and entrepreneurship education at the Polytechnic Institute of Zhejiang University, the research hypothesis and research design are proposed, and the utilization efficiency and allocation efficiency of innovation and entrepreneurship education resources are confirmed and analyzed. The results show that before the experiment, there was a large difference in resource allocation efficiency between groups; the minimum value was 0.123, and the maximum value was 0.187. After optimization, the allocation efficiency is 0.188, with an average growth rate of 19.55%. That is, the PSO algorithm can achieve the optimization of innovation and entrepreneurship education resource allocation at the Polytechnic Institute of Zhejiang University. The four dimensions of innovation and entrepreneurship education: theoretical education, practical education, teacher quality, educational support and initiative personality of the three dimensions, motivation, change and resilience are in the presence of each other in the middle of the work, and the correlation coefficient is concentrated in the role of 0.5 on average. This study further stimulates the students passion for innovation and entrepreneurship, which is of great significance to the cultivation of students innovation and entrepreneurship abilities.

Feature detection of mineral zoning in spiral slope flow under complex conditions based on improved YOLOv5 algorithm

In actual processing plants, the quality and efficiency of the traditional spiral slope flow concentrator still rely on workers to observe the changes in the mineral belt. However, in realistic complex working conditions, the formation of mineral separation zones is subject to large uncertainties, and coupled with the limited efforts, experience, and responsibility of workers, it becomes important to free up labour and improve the efficiency and profitability of the beneficiation plant. Therefore, to solve the problem of difficult detection of fuzzy small target mineral separation point features in real scenes, an improved YOLOv5-based algorithm is proposed. Firstly, the dataset quality is well improved by image enhancement and pre-processing techniques, after that an innovative CASM attention mechanism is added to the backbone of the YOLOv5 model, followed by a multi-scale feature output and prediction enhancement in the neck part of the model, and an optimized loss function is designed to optimize the whole feature learning process. The improved effect of the model and the specific detection performance were tested using real mine belt image datasets, the ablation experiment verified the comprehensive effectiveness of the proposed improved method and finally compared it with the existing high-level attention mechanism and target detection algorithms. The experimental results show that the improved YOLOv5 algorithm proposed in this study has the best overall detection performance carrying a MAP of 0.954, which is over 20% better than YOLOv5. It is worth mentioning that the improvement to achieve this performance only increases the parameter values by 0.8M and GFLOPs by 1.8, moreover, in terms of the inference speed, it also achieves a respectable 63 FPS, implying that the proposed improved method achieves a better balance between the performance enhancement and the computational complexity of the model, the overall detection results fully satisfy the industrial requirements.

A small-scale ‘Development Impact Bond’ for hepatitis C diagnosis and treatment financing in Cameroon: the way to elimination?

Background Many governments in low- and middle-income countries (LMICs) have difficulties paying healthcare costs upfront leading to high out-of-pocket payments for patients. A Development Impact Bond (DIB) is an innovative financing mechanism in which pr ivate investors provide pre-payment of development program expenses. At the same time, public agencies or donors repay the investor’s investment with a reasonable interest rate if the program succeeds in delivering independently measurable results that are contractually agreed upon. This study assessed quantitatively and qualitatively the feasibility of a DIB for hepatitis C Virus (HCV) diagnosis and treatment in Cameroon. Methods A revolving fund of up to €230,000 was made available by the investor. The outcome payor reimbursed the investor only in case of good performance, defined as cured patients (HCV-RNA negative). HCV carriers who were identified were referred for treatment and tested for cure 12 weeks after completion of treatment, the outcome being validated by an independent assessor. The evaluation was guided by the six-agents model, involving interviews with relevant stakeholders (N= 22). Results In total, 253 (98%) patients completed treatment, of which 244 (96%) are cured at week 24. We estimated that the average per-patient outcome payment for HCV diagnosis and treatment is €1,542, and the average costs per treated patient is €1,858. The investor was fully repaid, including the agreed interest and bonus. Themes or findings from the interviews confirmed the feasibility of a DIB in a low-resource setting. Conclusions This study demonstrates that a DIB can be a suitable financing mechanism for HCV services, supporting the path towards elimination. When governments in LMICs do not have sufficient resources to fund such elimination programs upfront, such public-private partnerships can offer a solution.

Implementation of visual mind mapping strategy to improve students’ language competence

The opportunities of the educational process can be expanded through the use of innovative mechanisms. The aim of the work is to determine the effectiveness of teaching as a result of the implementation of the Visual Mind Mapping strategy to improve students’ foreign language competence. The aim was achieved using the general theoretical method of comparison, calculation of the concordance coefficient, the efficiency coefficient, the Whitney-Wilk test. Learning was ensured through the development of educational mechanisms, which consisted in the visualization of information and the development of intellectual activity. The study of new words, the use of digital technologies (Writer, Rosetta Stone, Busuu), the development of communication skills, and the development of professional skills were also provided. It was found among students that the use of Visual Mind Mapping contributes to obtaining a high level of knowledge in a shorter time. Focusing on Kirkpatrick model made it possible to establish that the majority of students acquired a high level of foreign language competence after testing their knowledge. The students have a positive attitude to learning using Visual Mind Mapping. The practical significance of the article is the possibility of involving innovative technologies for learning a foreign language. The prospects of further research are to compare the effectiveness of learning with the use of Visual Mind Mapping for junior and senior year students.

Digital technology and manufacturing industrial change: Evidence from the Chinese manufacturing industry

This study utilized data from 2011 to 2020 across 29 manufacturing industries in China to comprehensively analyze the macro-level impact of digital technology applications on industrial transformation. The main findings are as follows: Firstly, a significant positive correlation between digital technology application and industrial change was observed, with an average effect of 1.2128 and statistical significance (α = 1 %). In specific sectors such as textile manufacturing, resource processing, and machinery equipment manufacturing, the positive influence of digital technology on industrial transformation is evident, with respective effects of 1.2088, 1.0934, and 1.1737-all statistically significant (α = 1 %). Secondly, examining heterogeneity among different manufacturing industries revealed that process manufacturing and labor-intensive sectors exhibited more pronounced responses to digital technology, with impact coefficients of 1.2189 and 1.2784, both significant (α = 1 %). Robustness tests confirmed the reliability of these results, demonstrating that the positive relationship between digital technology application and industrial change remained robust even after addressing endogeneity concerns. Lastly, a mechanism analysis uncovered the pivotal roles of innovation mechanisms, talent effects, and government institutional arrangements in the digital technology-driven industrial transformation. Notably, the machinery and equipment manufacturing industry displayed a significant positive correlation with the innovation mechanism, with a coefficient of 3.8344. The resource processing industry showed a positive impact associated with the talent effect, with a coefficient of 0.0002. However, in the textile manufacturing and machinery and equipment manufacturing sectors, government institutional arrangements were negatively correlated with industry performance, with coefficients of −0.0056 and −0.0096, respectively. Overall, these findings offer valuable insights into how digital technology shapes the transformation of the manufacturing industry, holding great theoretical and practical significance for guiding the industry's digital transformation efforts.