General Process Research Articles

Design Hormones: Nature-Inspired Generative Design Methodology for Additive Manufacturing

To meet sustainable goals in various engineering sectors, we urgently need innovative design strategies. This work presents a nature-inspired generative design methodology to support innovation-driven sustainable engineering design. It allows designers to create unpredicted but valuable components that can be directly integrated by modern manufacturing methods such as additive manufacturing. In this approach, the design of components is redefined as a series of elemental design instructions and emergence mechanisms that react to the design environment to generate innovative designs. Inspired by plant growth and development from nature, a set of “Design Hormones” is proposed which influence “design growth and development” through analogues of biological mechanisms. The design grows and develops from the actions of the hormones and genes, which do not know anything about the emergent shape or form of the design, they simply respond to the environment. The hormones are encoded with genes in a design seed, and different designs can emerge from the same seed in response to different environments based on their influence on the seed. A practical algorithm is then presented to implement the emergence of design from a simple seed. A range of examples are tested to demonstrate the effectiveness of the proposed approach. This work contributes foundational nature-inspired generative design theory and processes to promote innovative design for manufacturing in a sustainable future towards the next industrial revolution.

Optimized Supporting Structures for Small Artefacts: Generative Designed Prototypes

Retrievals of small and medium scale are one of the fundamental and most numerous parts of the entire museal heritage set. Their exposition represents an issue, looking for supporting structures that could be non-invasive, aesthetically pleasant, customized, and, last but not least, safe and stiff enough to avoid any damage to the artifacts. Through the usage of several engineering methods, starting from the retrieval and the exhibition requirements, optimized supporting structure can be developed.The procedure here reported and applied starts from the Reverse Engineering acquisition in order to obtain the 3D model of each retrieval, also comprehending color and texture information. After that, exhibition and preservation requirements, given by heritage experts, are translated into engineering requirements, with the usage of an immersive Virtual Reality environment. With these inputs, the developed Generative Design Method (GDM), using CAD, CAE and optimization tools synergically, can optimize the configuration of supporting interfaces with the artifact and then can generate a lightweight conceptual design through a generative design process. In this way, we can take into account all the given requirements, obtaining a lightweight support, topologically optimized, highly customized, non-invasive, possibly pleasant, able to resist in safety, and additively manufacturable.In this paper, the procedure is applied on different retrievals from Museo Civico dell’Agrocimino of Soriano nel Cimino, Italy, with different scales and materials, proving the robustness of the method.

The impact of self-assessment and surveyor assessment on site visit performance under the National General Practice Accreditation scheme.

Objective There is a need to undertake more proactive and in-depth analyses of general practice accreditation processes. Two areas that have been highlighted as areas of potential inconsistency are the self-assessment and surveyor assessment of indicators. Methods The data encompass 757 accreditation visits made between December 2020 and July 2022. A mixed-effect multilevel logistic regression model determined the association between attempt of the self-assessment and indicator conformity from the surveyor assessment. Furthermore, we present a contrast of the rate of indicator conformity between surveyors as an approximation of the inter-assessor consistency from the site visit. Results Two hundred and seventy-seven (37%) practices did not attempt or accurately report conformity to any indicators at the self-assessment. Association between attempting the self-assessment and the rate of indicator non-conformity at the site visit failed to reach statistical significance (OR = 0.90 [95% CI = 1.14-0.72], P = 0.28). A small number of surveyors (N = 9/34) demonstrated statistically significant differences in the rate of indicator conformity compared to the mean of all surveyors. Conclusions Attempt of the self-assessment did not predict indicator conformity at the site visit overall. Appropriate levels of consistency of indicator assessment between surveyors at the site visit were identified.

Stochastic Modeling of Star Formation Histories. III. Constraints from Physically Motivated Gaussian Processes

Galaxy formation and evolution involve a variety of effectively stochastic processes that operate over different timescales. The extended regulator model provides an analytic framework for the resulting variability (or “burstiness”) in galaxy-wide star formation due to these processes. It does this by relating the variability in Fourier space to the effective timescales of stochastic gas inflow, equilibrium, and dynamical processes influencing giant molecular clouds' creation and destruction using the power spectral density (PSD) formalism. We use the connection between the PSD and autocovariance function for general stochastic processes to reformulate this model as an autocovariance function, which we use to model variability in galaxy star formation histories (SFHs) using physically motivated Gaussian processes in log star formation rate (SFR) space. Using stellar population synthesis models, we then explore how changes in model stochasticity can affect spectral signatures across galaxy populations with properties similar to the Milky Way and present-day dwarfs, as well as at higher redshifts. We find that, even at fixed scatter, perturbations to the stochasticity model (changing timescales vs. overall variability) leave unique spectral signatures across both idealized and more realistic galaxy populations. Distributions of spectral features including Hα and UV-based SFR indicators, Hδ and Ca H and K absorption-line strengths, D n (4000), and broadband colors provide testable predictions for galaxy populations from present and upcoming surveys with the Hubble Space Telescope, James Webb Space Telescope, and Nancy Grace Roman Space Telescope. The Gaussian process SFH framework provides a fast, flexible implementation of physical covariance models for the next generation of spectral energy distribution modeling tools. Code to reproduce our results can be found at https://github.com/kartheikiyer/GP-SFH.

A New Multi-resolution Analysis Method for Electrooculography Signals.

Electrooculography (EOG) signals indicate the degree and direction of eye movements. Hence, EOG signals have been useful in eye movement controlled rehabilitation systems. Denoising and accurate identification of the type of eye movement in EOG signals are the major challenges in their analysis. The state-of-the-art techniques for EOG signal analysis concerning denoising and eye movement extraction are based on multi-resolution analysis using wavelet bases, such as Haar or Daubechies. However, these wavelets are designed for general purpose signal processing applications and hence are not optimized for the EOG signal structures. In this paper, we propose a new multi-resolution basis specific to the analysis of EOG signals. The scaling and wavelet functions for the basis are derived from the signatures of blinks and saccades respectively, and hence we name them as blinklets and saclets accordingly, thereby forming a new multi-resolution basis. These descriptors are found to be more effective than standard wavelets for EOG signals, signal denoising, and for identifying the different eye movement signatures such as saccades, blinks, smooth pursuits, and fixations, as tested on the Physiosig and Centre for Biomedical Cybernetics Eye Movement (CBC-EM) EOG Databases.

A checklist of European butterfly larval foodplants.

Butterflies are charismatic insects and have been well studied, particularly in Europe. They are disproportionately used in generating and testing hypotheses; on everything from general evolutionary processes, such as speciation or host association dynamics; to conservation-related studies, such as climate change or habitat loss. Accurate lists of the larval foodplants for European butterflies are not readily available. Mistakes are propagated and information cannot be checked for accuracy. The level of evidence is unknown, and how usage varies between countries is poorly understood. The study consulted 1119 references to produce 19,488 records of larval foodplants for European butterflies. This resulted in 5589 larval host plant records for 464 European butterfly species, with multiple references, enabling information to be checked. Information was unavailable for 59 species. The level of evidence for each relationship shows the current state of knowledge. Significant issues were identified for 3.9% of records extracted from references due to mistakes, ambiguous or unknown plant names, distribution issues, resulting in information being lost. Plants with questionable distributions suggest either mis-identification or species that have been split. Little is known about plant usage in Eastern Europe. The larval foodplants of many monophagous and Satyrinae butterflies are poorly studied. Only 63% of threatened 2010 Red Listed butterflies have reliable host plant records. The study has provided ecologists with a valuable resource, of a more accurate checklist of the larval foodplants for each European country. Why plant usage varies over a butterfly's distribution opens up some interesting research questions.

Improving Molecule Generation and Drug Discovery with a Knowledge-enhanced Generative Model.

Recent advancements in generative models have established state-of-the-art benchmarks in the generation of molecules and novel drug candidates. Despite these successes, a significant gap persists between generative models and the utilization of extensive biomedical knowledge, often systematized within knowledge graphs, whose potential to inform and enhance generative processes has not been realized. In this paper, we present a novel approach that bridges this divide by developing a framework for knowledge-enhanced generative models called KARL. We develop a scalable methodology to extend the functionality of knowledge graphs while preserving semantic integrity, and incorporate this contextual information into a generative framework to guide a diffusion-based model. The integration of knowledge graph embeddings with our generative model furnishes a robust mechanism for producing novel drug candidates possessing specific characteristics while ensuring validity and synthesizability. KARL outperforms state-of-the-art generative models on both unconditional and targeted generation tasks.

DCG-GAN: design concept generation with generative adversarial networks

Abstract Generative adversarial networks (GANs) have recently been proposed as a potentially disruptive approach to generative design due to their remarkable ability to generate visually appealing and realistic samples. Yet, we show that the current generator-discriminator architecture inherently limits the ability of GANs as a design concept generation (DCG) tool. Specifically, we conduct a DCG study on a large-scale dataset based on a GAN architecture to advance the understanding of the performance of these generative models in generating novel and diverse samples. Our findings, derived from a series of comprehensive and objective assessments, reveal that while the traditional GAN architecture can generate realistic samples, the generated and style-mixed samples closely resemble the training dataset, exhibiting significantly low creativity. We propose a new generic architecture for DCG with GANs (DCG-GAN) that enables GAN-based generative processes to be guided by geometric conditions and criteria such as novelty, diversity and desirability. We validate the performance of the DCG-GAN model through a rigorous quantitative assessment procedure and an extensive qualitative assessment involving 89 participants. We conclude by providing several future research directions and insights for the engineering design community to realize the untapped potential of GANs for DCG.

Generative AI: Exploring the Applications of Generative Models in Creative Industries

Generative artificial intelligence, enabled through sophisticated machine learning frameworks, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), is disrupting the creative landscape within many of the creative industries, opening a new set of tools for content creation. Therefore, this article investigates the disruptions generative models present across multiple aspects of creative production, focusing on the influences of art, music, literature, and design. In the visual arts, generative AI produces original artworks that blend aesthetics and styles that extend conventional notions of creativity; in music, AI-composition tools allow musicians to compose original musical works and experiment with entirely new genres. In literature and storytelling, the same generative processes offer AI systems the ability to propose narrative frameworks and other textual elements that may instigate human writers to take these concepts further with a narrative or may stand on their own as original content. In design, generative algorithms support product development all the way down to quickly prototyping new products. The paper includes specific case studies or outlines of the application of generative AI tools to proposed projects, a discussion of effectiveness with the respective generative model, and the potential limitations generative models present for each creative area. The paper will also reflect on the threats generative AI may pose, the potential to redefine human creativity, originality, and ownership, implications for the potential decision-making capabilities of these systems, and if there may be consequences for society. In all these considerations, there are opportunities for and more significant implications of the potential of generative AI to assist and expand human creative possibilities throughout the entire creative process.

FR-MIL: Distribution Re-calibration based Multiple Instance Learning with Transformer for Whole Slide Image Classification.

In digital pathology, whole slide images (WSI) are crucial for cancer prognostication and treatment planning. WSI classification is generally addressed using multiple instance learning (MIL), alleviating the challenge of processing billions of pixels and curating rich annotations. Though recent MIL approaches leverage variants of the attention mechanism to learn better representations, they scarcely study the properties of the data distribution itself i.e., different staining and acquisition protocols resulting in intra-patch and inter-slide variations. In this work, we first introduce a distribution re-calibration strategy to shift the feature distribution of a WSI bag (instances) using the statistics of the max-instance (critical) feature. Second, we enforce class (bag) separation via a metric loss assuming that positive bags exhibit larger magnitudes than negatives. We also introduce a generative process leveraging Vector Quantization (VQ) for improved instance discrimination i.e., VQ helps model bag latent factors for improved classification. To model spatial and context information, a position encoding module (PEM) is employed with transformer-based pooling by multi-head self-attention (PMSA). Evaluation of popular WSI benchmark datasets reveals our approach improves over state-of-the-art MIL methods. Further, we validate the general applicability of our method on classic MIL benchmark tasks and for point cloud classification with limited points https://github.com/PhilipChicco/FRMIL.

Influence of Processing Oil Content on Rubber-Filler Interactions in Silica/Carbon Black-Filled Natural Rubber Compounding

To ensure the production of high-quality rubber products, establishing effective compatibility and robusting interactions between rubber and fillers is crucial. Since the rubber production industry faces significant environmental challenges, sustainability is becoming a necessity in the rubber business. Various materials, such as the presence of processing aids, might influence rubber-filler and filler-filler interactions. Petroleum oil as general processing oil has environmental issues, thus it is urgent to replace it with bio-based oil. This study addresses these concerns by investigating the impact of paraffinic oil and bio-based oil content on the interactions between silica/carbon black and natural rubber chains. Ranging from 0 to 10 phr, different dosages of these oils were employed. The rubber compound underwent milling using two rolled laboratory open mills post-blending with a laboratory internal mixer. Subsequently, mechanical properties were assessed. A Rubber Processing Analyzer was utilized to characterize interactions between uncured and cured silica/carbon black-filled natural rubber blends. The results revealed that all variations were distributed uniformly. Tan Delta results confirm that a chemical with a high bio-based oil concentration is easier to process. The carbon black with the largest peak of modulus values at low strain has the highest reinforcing or the poorest dispersion. Compounding with paraffinic oil has a high peak modulus value, resulting in poor dispersion when compared to bio-based oil. This research sheds light on the potential of bio-based oils as an eco-friendly alternative in rubber processing, contributing to sustainability efforts in the industry.

Русская эмиграция первой волны о советской литературе и социалистическом реализме

The paper presents an overview of the critical opinions of prominent representatives of literature, criticism and philosophical thought of the Russian European Diaspora (the “first wave” of Russian emigration). The main attention is paid to the literary process in the USSR in the 1920s–30s, in some cases Soviet post-war literature is also touched upon. The spectrum of assessments ranges from recognizing the complete failure of Soviet literature, which, moreover, has no prospects for positive development as long as Soviet power exists, — to recognizing certain successes of a number of young authors. It is quite widely believed that the most valuable period is the first post-revolutionary decade, which produced a number of good works by L. Leonov, B. Pilnyak, K. Fedin, while the general decline begins with the Decree on the restructuring of literary and artistic organizations (1932) and the introduction so-called method of socialist realism, perceived by critics as something absurd from an aesthetic point of view. However, some representatives of the emigration do not accept early Soviet literature for naturalism, coarse morals, and a manifestation of atheism. As typical negative features of Soviet literature, since the 1930s, are mentioned ideologization, artificially collisions, falseness and lies in the reflection of reality, canonicity and lack of creative freedom due to party dictates. At the same time, several writers stand out who were not affected by the general process of degradation of Russian literature in the USSR, those Yu. Olesha, K. Paustovsky, B. Pasternak are named. The work of B. Pilnyak, I. Babel, V. Kataev receives an ambiguous assessment.

Determining the optimal generalization operators for building footprints using an improved graph neural network model

Determining the optimal generalization operators of city buildings is a crucial step during the building generalization process and an important aspect of realizing cross-scale updating of map data. It is a decision-making behavior of the cartographer that can be learned and simulated using artificial intelligence algorithms. Multi-scale data can provide rich generalization samples to train the determination process. However, previous studies have focused primarily on the intelligent use of each generalization operator separately, neglecting the intelligent scheduling issue between multiple operators when they are used simultaneously. Herein, we propose an improved graph neural network (GNN) called self-neighborhood merged GNN (SNGNN) that selects the optimal generalization operators for different buildings. In SNGNN, node and edge information are passed with different weights through two modules to simulate the effects of a building on itself and the neighborhood on either side. SNGNN has been experimentally validated using sample datasets for Ningbo, China, at 1:10,000 and 1:25,000. The F1-score of the testing dataset was 94.19%, and the classification precision of each operator was ≥87%. Compared with other popular intelligent algorithms, the experimental results for SNGNN revealed better performance in determining the optimal generalization operators.

Внедрение передовых производственных технологий в России и оценка компаниями их влияния на рост производительности труда и эффективность производственного процесса

The urgent need for the transition of the Russian economy to a new technological structure under the influence of the current geopolitical and economic-political situation, as well as the strengthening of import substitution processes, poses the task of a qualitative breakthrough in the introduction of modern processes in industrial production and in the economy as a whole. The gen-eral process of the fourth industrial revolution involves the widespread introduction of robotic, au-tomated systems, and artificial intelligence, linked into single complexes interconnected by commu-nications and cloud storage systems. On the one hand, this process can lead to a massive release of labor resources from traditional blue-collar professions. From another point of view, it creates a large number of new professions to serve new directions and solve new problems. However, the answer to the question of the economic feasibility of introducing such technologies is not clearly positive. And here there can be quite unexpected results when the seemingly obvious benefits from the imple-mentation of certain modern solutions of the sixth technological order for specific organizations or industries are not confirmed. In our work, we use surveys of Russian companies for 2022 to assess the current stage of implementation of production and digital technologies. It has been established that the introduction of modern solutions that lead to the transition to a new technological structure is, in some cases, characterized by a negative effect and lack of improvements in labor productivity. Our observation contradicts the generally accepted idea that automation, digitalization and robotiza-tion will lead to the release of a significant number of workers. Moreover, among the key reasons hindering the implementation of advanced technologies, companies cite the lack of qualified per-sonnel and the inability to hire employees with the required level of knowledge and skills. In other words, we are observing a shortage of highly qualified employees against the backdrop of a rather contradictory effect of innovation in terms of freeing up labor. The introduction of technologies such as artificial intelligence or the Internet of things in most cases is characterized by the absence of a positive impact on the efficiency of internal processes and labor productivity. Our observations lead to the conclusion that there is no prospect of a labor surplus in the labor market due to the ongoing processes of the fourth industrial revolution according to Schwab.

Integrating Sentinel-2 and PlanetScope Image with Drone-based Seagrass Data for Seagrass Percent Cover Mapping

Seagrass field data collection activities to train remote sensing images for seagrass percent cover mapping and assess its accuracy can be laborious, costly, and time-consuming, especially for vast seagrass meadows with high density variations. There is also a potential discrepancy in information between seagrass data collected in the field, which usually covers 0.25m2 or 1m2 ground area, and the spatial resolution of remote sensing image used. PlanetScope at 3m and Sentinel-2 at 10m are the currently frequently remote sensing images used to map seagrass. There is a considerable information gap between seagrass data collected in the field and their spatial resolution. The use of seagrass field data thus involves a generalization process and a set of assumptions to justify its integration with remote sensing image. An alternative is to use the drone-based aerial image (hereafter drone data), which captures seagrass meadows at very high spatial resolution, to interpret seagrass percent cover at a level of precision similar to the remote sensing data used. This research assessed the integration of drone-based seagrass data with PlanetScope and Sentinel-2 images to map seagrass percent cover. Seagrass percent cover was interpreted from drone data for each 9m2 and 100m2 ground size following the PlanetScope and Sentinel-2 grids, respectively. Stepwise, random forest, and support vector regression were employed to develop the seagrass’s percent cover mapping model. The accuracy assessment of the resulting seagrass percent cover map involves the calculation of RMS error and plot 1:1 and its derivative analyses. Our results showed that an unparalleled benefit of using drone data is the possibility to obtain SPC information that matches the spatial resolution of satellite imagery, where techniques such as photo-quadrat and photo-transect cannot match. Drone data is successfully integrated with PlanetScope and Sentinel-2 images to produce a high accuracy SPC map effectively and efficiently. Indeed, there are challenges in using drone data, mainly related to oceanographic and weather conditions, and the difficulties in interpreting SPC at the species level.

From What You See to What We Smell: Linking Human Emotions to Bio-markers in Breath

Research has shown that the composition of breath can differ based on the human's behavioral patterns and mental and physical states immediately before being collected. These breath-collection techniques have also been extended to observe the general processes occurring in groups of humans and can link them to what those groups are collectively experiencing. In this research, we applied machine learning techniques to the breath data collected from cinema audiences. These techniques included XGBOOST Regression, Hierarchical Clustering, and Item Basket analyses created using the Apriori algorithm. They were conducted to find associations between the biomarkers in the crowd's breath and the movie's audio-visual stimuli and thematic events. This analysis enabled us to directly link what the group was experiencing and their biological response to that experience. We first extracted visual and auditory features from a movie to achieve this. We compared it to the biomarkers in the crowd's breath using regression and pattern mining techniques. Our results supported the theory that a crowd's collective experience directly correlates to the biomarkers in the crowd's breath. Consequently, these findings suggest that visual and auditory experiences have predictable effects on the human body that can be monitored without requiring expensive or invasive neuroimaging techniques.

Shades of Cultural Marginalization: Cultural Survival and Autonomy Processes

The recent rise in extremism, authoritarianism, displacement and isolationism signals troubled times for the most marginalized groups in societies. In this article, our primary emphasis is on a specific aspect of marginalization within organizational theory – referred to as cultural marginalization. We argue that the existing literature lacks an adequate theoretical understanding to address this phenomenon. To theorize cultural marginalization and uncover how marginalized groups may cope with such circumstances, we build on and problematize the culture-as-toolkit perspective. We integrate this perspective with other cultural theories that consider power structures more prominently. Drawing on this theoretical base, we develop a typology of four dynamics of cultural marginalization and conceptualize the specific cultural survival and cultural autonomy processes marginalized groups may undertake to safeguard their culture. In doing so, we contribute to the ongoing debate surrounding the toolkit perspective by providing novel insights into how marginalized groups utilize their socio-culturally constrained cultural resources in distinct ways, compared with more resourceful actors and groups. Our theoretical contributions pave the way for new avenues of research to deepen our understanding of the general process of cultural marginalization and to direct further inquiry into the survival strategies of marginalized groups and how they might (re)gain autonomy.

Analysis of implementing the Ukrainian healthcare reform in 2023

The main research task is to study the issue of effectiveness of implementing the healthcare reform in Ukraine using the main levers of influence on the most significant items of expenditure in the medical industry itself. Also, we investigate the main items of income and expenses of medical service providers, their specific weight and management methods for more rational use. It is worth noting that the positive dynamics in the direction of effectiveness of the healthcare industry is not only the main priority. It is also a direct tool that drives the general processes in the medical industry. Increasing the level of social protection is one of the primary goals of medicine and, as a result, improving the quality of state population life. In a chain reaction, this will lead to a reduction in costs for the healthcare industry due to faster recovery of patients and reduction of time spent in a state of temporary incapacity. One of the key aspects of the medical reform efficiency remains the issue of remuneration of workers. Before carrying out reform measures, doctor salaries were officially at the minimum level. Therefore, the principle of treatment for the patient’s money was widespread in the industry. The main motivating element of the human capital efficiency is salary – reward for work results. So, if the salary directly depends on the target indicators that must be achieved in the production process, then the employee’s motivation increases. Based on the fact that the main direction of funds expenditure at medical institutions is labour costs, the main efforts and attention should be paid in the organisational and administrative plan to work with the effective use of this item of expenses. It is worth noting that the salary of any employee is the main measure of his work efficiency. It is a motivator to improve the quality and treatment efficiency. If the amount of reward depends on the employee’s performance indicators, the system will have additional motivation not only in the quantitative characteristics of the work result, but also in the qualitative parameters.

Outward foreign direct investment and GVC position of manufacturing industry: A perspective on China's general trade and processing trade structure.

Depending on the trading modes, the effect of Outward Foreign Direct Investment (OFDI) on the manufacturing industry's position within the global value chain (GVC) may differ considerably. This paper examines the GVC position of China's manufacturing industry from 2003 to 2018, specifically focusing on the general trade and processing trade. Drawing upon this premise, this paper analyzes the effect and mechanism by which OFDI influences the GVC position of China's manufacturing industry. The result demonstrates that: (1) China's processing trade manufacturing industry has a much lower GVC position than general trade manufacturing industry. The GVC position of China's general trade manufacturing industry rose from 2.76 to 2.90 from 2003 to 2018, while processing trade manufacturing industry remained around 1.93. (2) OFDI boosts the GVC position of general trade manufacturing industry through facilitating reverse technology spillover, inducing industry structure upgrading, and enabling export scale expansion. (3) OFDI hinders the GVC position growth of processing trade manufacturing industry. The research findings offer theoretical backing for China to develop OFDI strategies that are tailored to different trading modes within the new framework of dual circulation. These strategies aim to facilitate the transformation and advancement of the manufacturing industry, as well as the growth of the GVC position.

How Moroccan Film Destination Attracts More Tourists

With the different types of crises that the world was suffering from, management of a destination becomes more difficult than before, especially to develop the destination image. Many destinations are trying different ways to market their destinations, and one of those is the film industry. Film travel is considered a new fashion in visiting Morocco, and Essaouira is an attractive and sustainable destination. Motivating tourists to follow up the trend of travel with the film the DMO should use new ways of marketing, including sustainable projects, and taking care of the tourists’ perspectives. Several studies have confirmed the importance of film tourism in the destination image. The main objective of this study is to create a general process that can be used for any destination to make it an attractive and sustainable destination and to discover the importance of the film industry as a new way to market a destination and develop its image. Essaouira is the destination chosen for this study. Different research methods were used, an offline and online questionnaire with tourists and locals about their perspectives.
 Research findings indicate that film tourism is a very important tool nowadays to promote a destination and will be very helpful to use it in the case of Essaouira. It is found that a lot of people got inspired by movies to travel, and in the case of this city, it is shown that film tourism along with sustainable tourism could improve the destination's reputation and affect the tourism sector positively.