Innovative Components Research Articles

Feature ensemble network for medical image segmentation with multi‐scale atrous transformer

AbstractRecent years have witnessed notable advancements in medical image segmentation through deep convolutional neural networks. However, a notable limitation lies in the local operation of convolution, which hinders the ability to fully exploit global semantic information. To overcome the challenges prevalent in medical image segmentation, the feature ensemble network with multi‐scale atrous transformer is proposed. At the core of the approach lies the multi‐scale contextual integration module, which is based on the multi‐scale atrous transformer and facilitates contextual integration of multi‐level features. To extract discriminative fine‐grained features of the target region, a hybrid attention mechanism that synergistically combines spatial and channel attention, thereby sharpening the model's focus on crucial target information within high‐level features, is incorporated. Additionally, the channel‐aware feature reconstruction module is introduced as an innovative component engineered to tackle feature similarity issues across different categories. This module performs feature reconstruction based on channel perception, effectively widening the feature gap between categories and enhancing the segmentation capability. It is worth mentioning that our approach surpasses the state‐of‐the‐art method using three benchmark datasets in medical image segmentation.

SATISFACTION WITH THE QUALITY OF EDUCATION AMONG STUDENTS OF PEDAGOGICAL SPECIALTIES: DATA OF A REGIONAL UNIVERSITY

The article investigates students’ satisfaction with the quality of education in pedagogical training. The results of the survey of students have shown that most of the reasons for choosing a future profession are related to the interest in pedagogical activity. Among various aspects of the quality and conditions of education, the quality of teaching, provision with computer equipment, access to the Internet, and software are rated the highest. Most of the respondents are satisfied with their training in the educational program. According to the results of the study it is suggested to increase the practical component of education, to develop educational profiles, to increase the innovative component of education and emphasis on modern teaching technologies; to improve the material and information-technological base of education.

Modern distributed production (manufacturing) as an important part of the future Ukrainian nationally rooted industry

The purpose of this paper is to propose a variant for the development of a part of the national industry, which would be free from the problems of large investments and threats of unprofitability against the background of innovations. Before the full-scale war, which began in 2022, Ukrainian industry was an offspring and a powerful part of the Soviet heavy industry, mainly of the third technological order. Now, the enterprises that were powerful generators of the national economy GDP are to a greater extent located in the temporarily occupied or front-line territory. Industrial regions suffered significant damage to the production base and infrastructure facilities. The post-war restoration of the domestic industry requires large capital investments, which the national economy is not capable of. In addition, there is a limitation on the duration of investment projects due to the rapid development of modern technologies. According to experts, any project whose implementation exceeds 3 years is risky. In addition, there is a Marxian tendency of the rate of profit to decrease, due to a change in the capital structure, in particular, a decrease in labor expenses. Modern processes of mechanization of production, increase of innovative components (computerization, Internet of things, digitalization, etc.) increase the risks of unprofitability. The authors give an example of the closure of coal mining enterprises in European countries against the background of scientific and technical progress. At the same time, based on the analysis of literary sources, the trend of the spread of the DIY 4.0 phenomenon (from Do It Yourself) was revealed – the use of the most modern technological elements for the manufacture of products designed to meet the manufacturers' own needs. Especially in the USA, the Maker Movement, which is a new culture of industrial activity, is developing. The article substantiates that the technological base of DIY 4.0 and the culture of the Maker Movement are the foundation of the new distributed manufacturing, free from the "curse" of mass investment and unprofitable innovation. It is distributed manufacturing that is able to fundamentally change the quality picture and structure of industry, to ensure the national rooting of industrial production.

DEVELOPMENT OF THE SMART ECONOMY AS THE MAIN SOURCE OF COMPETITIVENESS AND SUSTAINABLE DEVELOPMENT

The development of the smart economy is a topic of great relevance in the contemporary world, particularly in the context of rapid technological advancement and digital transformation. The smart economy is predicated upon the utilisation of sophisticated technologies, including artificial intelligence, the Internet of Things, data analytics, blockchain, and others, with the objective of optimising processes across a multitude of domains, including production, transportation, healthcare, education, energy, trade, and others. The principal advantages of the advancement of the smart economy are that the utilisation of the most recent technologies permits the automation of processes, the optimisation of resources and the enhancement of production efficiency. Those countries and companies that adopt smart technologies proactively can secure a competitive advantage in the market through innovation supported by digital solutions. The objective of this article is to conceptualise the development of the smart economy as the primary source of competitiveness and sustainability. The successful development of the smart economy requires the right management strategy, investment in innovation and education, and partnership between public authorities, the private sector and the public. The growing role of the smart economy defines it as a key factor of competitiveness and sustainability for countries and companies in the modern world. The research methodology includes the integration of different philosophical methods (axiological, systemic, synergistic) with the Agile methodology, which has made it possible to comprehensively analyse the development of the smart economy as the main source of competitiveness and sustainability. Agile methodology, as a methodology of complex adaptive systems, flexible management and innovative components, has allowed to show the practical levers of using mechanisms to ensure sustainable growth, the role of information technologies in the development of a smart economy to achieve the goals of ESG (environmental, social and corporate management) of sustainable development. Research results. The emergence and development of the smart economy as an economy of knowledge, innovation, science and progress, based on intelligent machines and information networks, platforms and tools, which is an integral part of the innovative economy, is presented; the criteria and advantages of the smart economy are defined. The directions of development of the smart economy, its potential impact on competitiveness and sustainability are identified. Among the directions of development of the smart economy, the symmetric economy is distinguished, which is based on symmetric relations between economic subjects and objects, which are the driving force of economic development. The symmetry of the economic structure is a model for the development of a smart economy, a source of economic development, a subject-centred smart economy. It turns out that the smart economy includes the development of the main trends of the latest technologies, such as artificial intelligence, the Internet of Things (IoT), blockchain and data analytics, to optimise production, manage resources and create effective business models.

Experimental study and design methodology of sacrificial-energy dissipation beam-column joint

To enable secondary energy-dissipation components to actively attract and dissipate seismic energy, a new type of sacrificial-energy dissipation beam-column joint (SEDJ) was proposed. This study validates the performance of the proposed SEDJ through full-scale quasi-static loading tests. The rotation angle of the proposed SEDJ at the beam end, corresponding to the peak strength Fu, can be controlled within 1/250 - 1/50. In addition, the beam and column components remain elastic, thereby achieving the seismic performance objectives of "no damage, sacrificial-energy dissipation, and no collapse under service-level (SLE), design-basis (DBE), and maximum-considered (MCE) earthquakes", respectively. This study enhances the design of the SEDJ by adopting a built-in sleeve connection scheme to significantly reduce the free-stroke displacement, and a double-coupling block scheme to solve the issue of stress concentration in friction plates that leads to cracking. Furthermore, the efficiency of the installation and disassembly of friction plates is improved, thus enabling the rapid replacement of bolts and friction plates after strong earthquakes. Finally, a practical engineering design methodology is proposed that decouples the bending moments of the sacrificial MR and energy-dissipation MS stages, this methodology activates the sacrificial mechanism under DBEs or MCEs to protect primary beam and column components, and will provide potential insights for the development and application of innovative structural components based on the sacrificial mechanism design philosophy.

Carbon dots as dual inhibitors of tau and amyloid-beta aggregation for the treatment of Alzheimer's disease

Alzheimer's disease (AD) is the most common form of senile dementia, presenting a significant challenge for the development of effective treatments. AD is characterized by extracellular amyloid plaques and intraneuronal neurofibrillary tangles. Therefore, targeting both hallmarks through inhibition of amyloid beta (Aβ) and tau aggregation presents a promising approach for drug development. Carbon dots (CD), with their high biocompatibility, minimal cytotoxicity, and blood-brain barrier (BBB) permeability, have emerged as promising drug nanocarriers. Congo red, an azo dye, has gathered significant attention for inhibiting amyloid-beta and tau aggregation. However, Congo red's inability to cross the BBB limits its potential to be used as a drug candidate for central nervous system (CNS) diseases. Furthermore, current studies only focus on using Congo red to target single disease hallmarks, without investigating dual inhibition capabilities. In this study, we synthesized Congo red-derived CD (CRCD) by using Congo red and citric acid as precursors, resulting in three variants, CRCD1, CRCD2 and CRCD3, based on different mass ratios of precursors. CRCD2 and CRCD3 exhibited sustained low cytotoxicity, and CRCD3 demonstrated the ability to traverse the BBB in a zebrafish model. Moreover, thioflavin T (ThT) aggregation assays and AFM imaging revealed CRCD as potent inhibitors against both tau and Aβ aggregation. Notably, CRCD1 emerged as the most robust inhibitor, displaying IC50 values of 0.2 ± 0.1 and 2.1 ± 0.5 µg/mL against tau and Aβ aggregation, respectively. Our findings underscore the dual inhibitory role of CRCD against tau and Aβ aggregation, showcasing effective BBB penetration and positioning CRCD as potential nanodrugs and nanocarriers for the CNS. Hence, CRCD-based compounds represent a promising candidate in the realm of multi-functional AD therapeutics, offering an innovative formulation component for future developments in this area. Statement of significanceThis article reports Congo red-derived carbon dots (CRCD) as dual inhibitors of tau and amyloid-beta (Aβ) aggregation for the treatment of Alzheimer's disease (AD). The CRCD are biocompatible and show strong fluorescence, high stability, the ability to cross the blood-brain barrier, and the function of addressing two major pathological features of AD.

The Relevance of Creating an Integral Pedagogical System of Career Guidance Work with Students of Different Levels of Education

Introduction. The article reveals the issues of organizing career guidance and mentoring activities at a university through a description of holistic career guidance work with students of different levels of education in the “school – university” system; areas of career guidance work are considered; passive and active methods used in the implementation of career guidance activities are described. Methodology. The methodological basis of the study was based on the principles of the system-activity approach and the competence approach. To achieve the research goal, theoretical (analysis, synthesis, generalization and systematization of scientific publications data) and empirical (observation, survey) methods were used. Results. The authors have revealed an integrated pedagogical system of career guidance work with students of different levels of education, described the main directions of career guidance through the implementation of activities implemented at the university with school-age students and students, characterized the advantages of implementing an integrated pedagogical system of career guidance work with students. Conclusions. An integrated pedagogical system of career guidance is an important condition for the functioning of a university in a competitive environment. The innovative component of the presented holistic pedagogical system of career guidance work consists in the implementation of a continuous system of career guidance (“school – university”, “student – entrant – student – young specialist”), as well as in conducting career guidance and educational activities with students of different levels of education.

Innovative components of Smart Cities with a special focus of Water Distribution Systems Challenges and Opportunities: A Review

Innovative components of Smart Cities with a special focus of Water Distribution Systems Challenges and Opportunities: A Review

On the implementation of hydraulic-interconnected-suspensions at the primary suspension stage of high-speed rail vehicles

In recent years, huge investments have been made to improve the dynamic performance of high-speed trains. Research into innovative suspension components has been part of the development of this transport system for decades. Innovative devices can allow rail vehicles to deal with the constantly increasing speed required by the global market. Among the most innovative suspension layouts proposed in railway dynamics in past years, limited attention has been given to Hydraulic Interconnected Suspensions (HIS). This layout is composed of two hydraulic cylinders with external hydraulic connections. Hydraulic Interconnected Suspensions allow promising tuning capabilities due to their ability to offer different responses based on the specific inputs given to the cylinders. This layout is rarely considered for rail vehicles, and the few previous works related to this topic considered the HIS layout to be applied at the secondary suspension stage. In this context, this paper proposes applying an HIS layout to the primary suspension stage of rail vehicles, in order to overcome the trade-offs between ride comfort, running safety and maximum car body displacement that need to be considered by bogie manufacturers when designing and optimising these mechanical systems. A nonlinear physical model of the HIS is proposed for co-simulation with a Multi-body (MB) model of a high-speed train. The improvement provided implementing an HIS at the primary suspension stage is then compared to similar enhancements that could be made when tuning and varying the standard suspension components of a bogie.

Edge Machine Learning-Enabled Predictive Fault Detection System for Conveyor Belt Maintenance Optimization in Industrial Settings

Digital transformation is essential for industrial and manufacturing sectors, especially in conveyor belt systems. On the other hand, Innovative electronic components and software applications offer opportunities for advanced fault prediction, minimizing operational disruptions, reducing inefficiencies, and enhancing overall industrial efficiency. Efficient conveyor belt systems are crucial for industrial operations, requiring early fault detection to prevent costly downtime and ensure safety. The utilization of embedded machine learning via the Edge Impulse platform enhances data collection and spectral feature extraction using wavelet approximation. This article presents a novel methodology for predictive fault detection in conveyor belt systems employing edge machine learning. Leveraging the ESP32 microcontroller and accelerometer sensor for real-time vibration data capture, a four-layer Artificial Neural Network (ANN) model, trained on 336 feature samples, is deployed via Edge Impulse and TensorFlow scripts. TensorFlow Lite compresses the model for microcontroller integration. The ESP32 microcontroller, acting as an edge device, achieves real-time predictions with 97.5% accuracy and 0.20 minimal loss. This work promises significant strides in predictive maintenance, offering cost savings, operational reliability, and efficiency gains in industrial settings, revolutionizing maintenance strategies.

Designing a Constructive Leadership Implementation Model in the Higher Education System Using an Interpretive Structural Modeling (ISM) Approach

The present study designed a model for the implementation of constructive leadership in the higher education system using an interpretive structural modeling approach. This study was applied in terms of aim and qualitative type of descriptive in terms of method of implementation. The statistical population of the study included experts in educational and constructive leadership in higher education at Urmia University. Based on the principle of random sampling, 7 of them were selected as a sample using convenience and snowball sampling methods. A semi-structured interview with experts whose questions were designed based on the theoretical foundations of constructive leadership in higher education with the help of professors was used as the study tool. The validity of the results was examined and confirmed by the triangulation method. Also, its face validity was confirmed by the opinions of experts who were not members of the study, and its reliability was calculated at 0.83 using the inter-coder coefficient of the agreement method. The data obtained from semi-structured interviews were analyzed with open, axial, and selective coding based on grounded theory in MAXQDA software. The results showed that the model of constructive leadership in higher education based on grounded theory included 12 components and 9 dimensions. The category of causal conditions included two dimensions of the necessity of constructive management (with two components of knowledge management and organizational innovation) and organizational culture (with two components of collaborative management and innovative atmosphere). The category of contextual conditions included two dimensions of management maturity (with the component of evaluating and applying policies for constructive management) and the system of fostering change and transformation (with the component of establishing relationships with aligned people and organizations). The category of intervening conditions included the dimension of competition with the surrounding environment and increasing progress (with the component of planning and paying attention to facilities). The category of strategies included two dimensions having facilities and requirements (with the component of financing and equipment) and responsibility and cooperation (with the components of empathy and public desire). Finally, the category of implications included two dimensions of academic development (with 2 components of individual competence development and university development) and social development (with the component of gaining public trust).

Breast cancer detection based on cancer antigen 15-3; emphasis on optical and electrochemical methods: A review

Cancer antigen 15–3 (CA 15-3) is a crucial marker used in the diagnosis and monitoring of breast cancer (BC). The demand for early and precise cancer detection has grown, making the creation of biosensors that are highly sensitive and specific essential. This review paper provides a thorough examination of the progress made in optical and electrochemical biosensors for detecting the cancer biomarker CA 15-3. We focus on explaining their fundamental principles, sensitivity, specificity, and potential for point-of-care applications. The performance attributes of these biosensors are assessed by considering their limits of detection, reaction times, and operational stability, while also making comparisons to conventional methods of CA 15-3 detection. In addition, we explore the incorporation of nanomaterials and innovative transducer components to improve the performance of biosensors. This paper conducts a thorough examination of recent studies to identify the existing obstacles. It also suggests potential areas for future research in this fast progressing field.The paper provides insights into their advancement and utilization to enhance patient outcomes. Both categories of biosensors provide significant promise for the detection of CA 15-3 and offer distinct advantages compared to conventional analytical approaches.

The Linkage Between Digital Transformation and Organizational Culture: Novel Machine Learning Literature Review Based on Latent Dirichlet Allocation

AbstractOrganizational culture is a crucial component of innovation in company success, particularly in the setting of the information economy. The purpose of this research is to conduct a bibliometric analysis in order to identify dominant research topics, their potential shifts, and recent developments in the fields of organizational culture and digital transformation. It demonstrates a machine learning–supported method for identifying and segmenting the current state of this research field. The literature was identified from the Scopus database through a search query. The analyzed amount of papers (3065) was published in 1619 sources (journals, proceedings, books, etc.) with various research impacts. Identifying the dominant research topics resulted in eight topics: Social Media Connectivity; Digital Innovation Ecosystems; Socio-economic Sustainability; Digital Workforce Transformation; Digital Competence and Cultural Transformation; Knowledge, Culture, and Innovation; Data and Resource Management; and Digital Transformation Maturity. The results showed a shift in the research field on organizational culture related to digital transformation towards the subject area of business, management, and accounting, with increasing research interest and impact for the Digital Workforce Transformation as well as for the Knowledge, Culture, and Innovation topics.

Fluorescent Composite Nano‐ and Microparticles Based on Xanthene Dyes and Iron Oxides

AbstractModern industry actively uses metal structural materials to create innovative devices and components. In order to prevent emergency situations in production, nondestructive testing methods are used to detect defects on the surface of the material being developed. Among the methods of nondestructive testing, one can highlight the method of luminescent magnetic flaw detection, which allows identifying defects on the surface and under the surface of the test sample with minimal effort. It is known that in some cases, the magnetic‐powder method cannot achieve the required sensitivity due to the shape and location of defects themselves or due to certain conditions of object operation. To increase the visibility and sensitivity, magnetic nano‐ and microparticles can be conjugated with the dyes. Iron oxide nanoparticles (Fe3O4, γ‐Fe2O3) can be used as such magnetic particles, since they are quite easy to obtain and have low toxicity, high chemical stability, and a wide variety of magnetic properties. To ensure fluorescence of magnetic particles, a wide variety of fluorescent dyes associated with such particles are used. The dyes can include morin, naphthalimide derivatives, as well as semiconductor quantum dots. This article provides a review of fluorescent composite micro‐ and nanoparticles based on xanthene dyes (rhodamine, fluorescein, and pyronine). Such dyes are economically available luminophores, have good binding to the surface of magnetic particles, and therefore can be suitable for luminescent magnetic flaw detection. This review article discusses methods for producing composite nano‐ and microparticles of iron oxides with xanthene dyes and provides prospects of using composite nano‐ and microparticles in nondestructive testing methods.

Indirect effects of plasma-activated water irrigation on Tetranychus urticae populations

AbstractPlasma-activated water (PAW) is receiving increased attention as a booster of seed germination and seedling vigor, and some studies have described use of PAW to manage crop pathogens. Here, we examined physicochemical properties of two PAWs (referred to as PAW 6.0 and 9.4 min with atmospheric plasma jet) and assessed “their indirect effects” (applied as supplementary irrigation) on host suitability of tomato plants (Solanum lycopersicum L.) to two-spotted spider mites (Tetranychus urticae Koch). Exposure of water to cold plasma significantly lowered pH and increased concentrations of H2O2, NO2−, and NO3−. Supplementary PAW irrigations elicited significant increases in leaf composition of several elements (N, P, K S, Ca, and Mg), leaf reflectance, plant size, and trichome densities (except non-glandular trichomes on the adaxial surface). Preference bioassays revealed significant avoidance of settling and reduced oviposition by two-spotted spider mites on leaf discs from PAW-irrigated plants compared to those from untreated control plants. Performance bioassays showed a significant decrease in two-spotted spider mite populations on PAW-irrigated plants. Results presented in this study provide comprehensive support to the hypothesis that indirect effects of supplementary PAW irrigation significantly reduce host plant suitability to two-spotted spider mites. PAW 6.0 may be slightly better than PAW 9.4, and this difference in performance is discussed in this study. Applications of PAW as supplementary irrigation are likely highly compatible with other IPM tactics and should be considered an innovative and sustainable component in twenty-first-century pest management.

Optimizing energy storage plant discrete system dynamics analysis with graph convolutional networks

Addressing the challenges of suboptimal model performance and excessive parameters and operations in the optimization of energy storage power plants utilizing Graph Convolutional Network (GCN), this paper introduces a novel approach – the packet-switched graph convolutional network. Initially, a GCN extreme learning machine is established. Drawing inspiration from this solid foundation, we have innovatively crafted a group exchange graph convolution module. This module leverages group graph convolution techniques to amalgamate unique node feature information, tailored to diverse topology graph matrices based on various groupings. This innovative approach ensures that information flows freely and effectively among distinct groupings. Furthermore, we have designed a cutting-edge timing depth separation convolution module, comprising two innovative components. The first component introduces timing depth separation convolution, revolutionizing the original timing convolution module. The second component, the packet-switching graph convolutional network, revolutionizes the time sequence depth separation convolution process. It achieves this by employing 1 × 1 convolutional layers between different feature fusion packets, enabling seamless information exchange between distinct packets. Experimental results demonstrate the efficacy of the proposed model, with root mean square error (RMSE) metrics and root mean square error (MAE) metrics for single-step prediction reaching 46.08 and 26.22 at 60 min, respectively. In multi-step testing, the proposed model exhibits a 14.71 % reduction in RMSE error at the 15-min scale and a 9.29 % reduction at the 60-min scale compared to the benchmark model. This performance improvement enhances the operational efficiency and reliability of the energy storage plant, particularly under dynamic changes in the time series.

Development and Testing of the Relational and Structural Components of Innovativeness Across Academia and Practice for Healthcare Progress Scale.

Using data from 5 academic-practice sites across the United States, researchers developed and validated a scale to measure conditions that enable healthcare innovations. Academic-practice partnerships are a catalyst for innovation and healthcare development. However, limited theoretically grounded evidence exists to provide strategic direction for healthcare innovation across practice and academia. Phase 1 of the analytical strategy involved scale development using 16 subject matter experts. Phase 2 involved pilot testing the scale. The final Innovativeness Across Academia and Practice for Healthcare Progress Scale (IA-APHPS) consisted of 7 domains: 3 relational domains, 2 structural domains, and 2 impact domains. The confirmatory factor analysis model fits well with a comparative fit index of 0.92 and a root-mean-square error of approximation of 0.06 (n = 477). As the 1st validated scale of healthcare innovation, the IA-APHPS allows nurses to use a diagnostic tool to facilitate innovative processes and outputs across academic-practice partnerships.

Con-CDVAE: A method for the conditional generation of crystal structures

In recent years, progress has been made in generating new crystalline materials using generative machine learning models, though gaps remain in efficiently generating crystals based on target properties. This paper proposes the Con-CDVAE model, an extension of the Crystal Diffusion Variational Autoencoder (CDVAE), for conditional crystal generation. We introduce innovative components, design a two-step training method, and develop three unique generation strategies to enhance model performance. The effectiveness of Con-CDVAE is demonstrated through extensive testing under various conditions, including both single and combined property targets. Ablation studies further underscore the critical role of the new components in achieving our model’s performance. Additionally, we validate the physical credibility of the generated crystals through Density Functional Theory (DFT) calculations, confirming Con-CDVAE’s potential in material science research.

Just go green

Goal: This study explores the connection between green innovation components and the competitive advantage of the manufacturing sector in China through the mediating role of green organizational culture and the moderating role of access to finance. Methodology: A self-administered survey with 310 respondents was used to collect data from the industrial sector. Data were examined using SmartPLS, and a bootstrapping method was used. Results: The findings demonstrated that the suggested moderated mediation model was accepted because the associations between the constructs were statistically significant. The mediating effect of green organizational culture and the moderating effect of access to finance were performed. The results showed that the proposed moderated mediation model was accepted because the relationships between the constructs were statistically significant. The results of the data analysis supported a positive relationship between green innovation and competitive advantage as well as a mediating effect of green organizational culture. Limitations: The study is limited to the Chinese economy; hence future studies can replicate these results on developing and developed economies. Furthermore, large sample size, different industrial sector and more advance analysis techniques can also be used in future studies. Practical Implications: The study has practical implication of green innovation and green organizational culture in enhancing competitive advantage in the Chinese manufacturing sector, considering access to finance. Originality / Value: This study contributes to the current vain of literature by examining the noval connection between competitive advantage and green innovation components of manufacturing sector in China through the mediating role of green organizational culture and the moderating role of access to finance.

A Hybrid Organic‐Crystal‐Based Curved Waveguide Network for Producing, Splitting, and Transducing Multi‐Color Outputs

AbstractHybrid optical components and circuits that deal with multiple signal generation and processing are quintessential for neural networking systems. Herein, the study reports the fabrication of one such component, a hybrid directional coupler (HDC) from blue emissive (4,4′‐bis(2,6‐di(1H‐pyrazol‐1‐yl)pyridin‐4‐yl)biphenyl) (BPP) and green emissive (E)‐1‐(((5‐bromopyridin‐2‐yl)imino)methyl)naphthalen‐2‐ol (BPyIN) pseudo‐plastic molecular crystals. Initially, a BPyIN microcrystal optical waveguide (OW1) is shaped into a strained waveguide with five bends using an atomic force microscopy cantilever‐tip aided mechanophotonics approach. Later, a singly bent BPyIN waveguide (OW2) is integrated into one of the bends at the coupling region on a strained waveguide to produce a 2 × 2 monolithic directional coupler (DC). Later, this 2 × 2 DC is extended to a HDC by integrating a blue emissive BPP waveguide (OW3) at another bend on the deformed waveguide. The fabricated HDC can effectively split the incident light into three parts with different split ratios and deliver multi‐color outputs depending on the port receiving the input signal. The spontaneous generation and processing of various signals produced in the circuit helps in understanding the functioning of complex optical neural networks. The demonstration of such innovative optical components manifests their utility for diverse applications in neural networking and quantum computing systems.