Search results for: optimization intelligence strategy

Authors: Andre Peres Ramos, Franklin Cesar Flores

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Colorization is the process of add colors to a monochromatic image or video. Usually, the process involves to segment the image in regions of interest and then apply colors to each one, for videos, this process is repeated for each frame, which makes it a tedious and time-consuming job. We propose a new assisted method for video colorization; the user only has to colorize one frame, and then the colors are propagated to following frames. The user can intervene at any time to correct eventual errors in color assignment. The method consists of to extract intensity and texture descriptors from the frames and then perform a feature matching to determine the best color for each segment. To reduce computation time and give a better spatial coherence we narrow the area of search and give weights for each feature to emphasize texture descriptors. To give a more natural result, we use an optimization algorithm to make the color propagation. Experimental results in several image sequences, compared to others existing methods, demonstrates that the proposed method perform a better colorization with less time and user interference.

Keywords: colorization, feature matching, texture descriptors, video segmentation

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Authors: Provides Ng

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Platform economy is a peer-to-peer model of distributing resources facilitated by community-based digital platforms. In recent years, digital platforms are rapidly reconfiguring the public realm using hyper-personalisation techniques. This paper aims at investigating how urban planning can leapfrog into the digital age to help relieve the rising tension of the global issue of labour flow; it discusses the means to transfer techniques of hyper-personalisation into urban planning for plasticity using platform technologies. This research first denotes the limitations of the current system of urban residency, where the system maintains itself on the circulation of documents, which are data on paper. Then, this paper tabulates how some of the institutions around the world, both public and private, digitise data, and streamline communications between a network of systems and citizens using platform technologies. Subsequently, this paper proposes ways in which hyper-personalisation can be utilised to form a digital planning platform. Finally, this paper concludes by reviewing how the proposed strategy may help to open up new ways of thinking about how we affiliate ourselves with cities.

Keywords: platform urbanism, hyper-personalisation, digital inventory, urban accessibility

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Authors: Jamal Abdi

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This paper offers a critique of Samuel P. Huntington's Clash of Civilizations thesis. The overriding argument is that Huntington's thesis is characterized by failure to distinguish between 'groups' and 'categories'. Multinational civilizations overcoming their internal collective action problems, which would enable them to pursue a unified strategy vis-à-vis the West, is a rather foundational assumption in his theory. Without assigning sufficient intellectual attention to the processes through which multinational civilizations may gain the capacity for concerted action, i.e., become a group, he contended that the post-cold-war world would be shaped in large measure by interactions among seven or eight major civilizations. Thus, failure in providing a convincing analysis of multi-national civilizations' transition from categories to groups is a significant weakness in Huntington's clash theory. It is also suggested that so-called Islamic terrorism and the war on terror is not to be taken as an expression of the presence of clash between a Western and an Islamic civilization, as terrorist organizations would be superfluous in a world characterized by clash of civilizations. Consequences of multinational civilizations becoming a group are discussed in relation to contemporary Western superiority.

Keywords: clash of civilizations, groups, categories, groupism

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Authors: Aishwarya Rai

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This study explores the transformative influence of generative AI on startups, businesses, and industries. We will explore how large businesses can benefit in the area of customer operations, where AI-powered chatbots can improve self-service and agent effectiveness, greatly increasing efficiency. In marketing and sales, generative AI could transform businesses by automating content development, data utilization, and personalization, resulting in a substantial increase in marketing and sales productivity. In software engineering-focused startups, generative AI can streamline activities, significantly impacting coding processes and work experiences. It can be extremely useful in product R&D for market analysis, virtual design, simulations, and test preparation, altering old workflows and increasing efficiency. Zooming into the retail and CPG industry, industry findings suggest a 1-2% increase in annual revenues, equating to $400 billion to $660 billion. By automating customer service, marketing, sales, and supply chain management, generative AI can streamline operations, optimizing personalized offerings and presenting itself as a disruptive force. While celebrating economic potential, we acknowledge challenges like external inference and adversarial attacks. Human involvement remains crucial for quality control and security in the era of generative AI-driven transformative innovation. This talk provides a comprehensive exploration of generative AI's pivotal role in reshaping businesses, recognizing its strategic impact on customer interactions, productivity, and operational efficiency.

Keywords: generative AI, digital transformation, LLM, artificial intelligence, startups, businesses

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Authors: Francesco Coraci, Abdul-Hadi G. Abulrub

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Digital technology is transforming the landscape of the industrial sector at a precedential level by connecting people, processes, and machines in real-time. It represents the means for a new pathway to achieve innovative, dynamic competitive advantages, deliver unique customers’ values, and sustain critical relationships. Thus, success in a constantly changing environment is governed by the ability of an organization to revolutionize their business models, deliver innovative solutions, and capture values from big data analytics and insights. Businesses need to re-strategize operations and develop extra capabilities to cope with the necessity for additional flexibility and agility. The traditional “command and control” leadership style is structurally and operationally incompatible with the digital era. In this paper, the authors discuss how transformational leaders can act as a glue in the social, organizational context, which is crucial to enable the workforce and develop a psychological attachment to the digital vision.

Keywords: internet of things, strategy, change leadership, dynamic competitive advantage, digital transformation

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Authors: Jonas Hinker, Lisa Zumholz, Johanna M. A. Myrzik

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Despite strong efforts by the German government to make a transition to higher quality level of building stocks, the rate of renovation continues to remain below the proclaimed level of 2%. As the mandatory standards for residential retrofits are well-balanced in such a way that strict adherence to them guarantees profit from the investment, it becomes difficult to explain the reasons why there are so many people hesitant with their investments. Risks and transaction costs can be understood as socio-technical boundaries and have to be taken into consideration to be able to understand why a worthwhile investment is postponed or rejected. This paper therefore presents a method for investment analyses that is focused on such socio-technical constraints, which helps to reveal the strongest misconceptions of home owners. By depicting sensitivities and risk factors in an integrated and impartial way, such a tool can be utilized by home owners to address reservations and misunderstandings. In the end, this leads to an exploitation of smaller energy efficiency measures that makes up a big demand reduction in the residential sector altogether.

Keywords: energy refurbishment, investment analysis, residential buildings, risk-aware investment strategy

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Authors: Faruk Ozdemir, Roy Kalawsky, Peter Hubbard

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Accurate estimation of RUL provides a basis for effective predictive maintenance, reducing unexpected downtime for industrial equipment. However, while models such as the Random Forest have effective predictive capabilities, they are the so-called ‘black box’ models, where interpretability is at a threshold to make critical diagnostic decisions involved in industries related to aviation. The purpose of this work is to present a prognostic framework that embeds Explainable Artificial Intelligence (XAI) techniques in order to provide essential transparency in Machine Learning methods' decision-making mechanisms based on sensor data, with the objective of procuring actionable insights for the aviation industry. Sensor readings have been gathered from critical equipment such as turbofan jet engine and landing gear, and the prediction of the RUL is done by a Random Forest model. It involves steps such as data gathering, feature engineering, model training, and evaluation. These critical components’ datasets are independently trained and evaluated by the models. While suitable predictions are served, their performance metrics are reasonably good; such complex models, however obscure reasoning for the predictions made by them and may even undermine the confidence of the decision-maker or the maintenance teams. This is followed by global explanations using SHAP and local explanations using LIME in the second phase to bridge the gap in reliability within industrial contexts. These tools analyze model decisions, highlighting feature importance and explaining how each input variable affects the output. This dual approach offers a general comprehension of the overall model behavior and detailed insight into specific predictions. The proposed framework, in its third component, incorporates the techniques of causal analysis in the form of Granger causality tests in order to move beyond correlation toward causation. This will not only allow the model to predict failures but also present reasons, from the key sensor features linked to possible failure mechanisms to relevant personnel. The causality between sensor behaviors and equipment failures creates much value for maintenance teams due to better root cause identification and effective preventive measures. This step contributes to the system being more explainable. Surrogate Several simple models, including Decision Trees and Linear Models, can be used in yet another stage to approximately represent the complex Random Forest model. These simpler models act as backups, replicating important jobs of the original model's behavior. If the feature explanations obtained from the surrogate model are cross-validated with the primary model, the insights derived would be more reliable and provide an intuitive sense of how the input variables affect the predictions. We then create an iterative explainable feedback loop, where the knowledge learned from the explainability methods feeds back into the training of the models. This feeds into a cycle of continuous improvement both in model accuracy and interpretability over time. By systematically integrating new findings, the model is expected to adapt to changed conditions and further develop its prognosis capability. These components are then presented to the decision-makers through the development of a fully transparent condition monitoring and alert system. The system provides a holistic tool for maintenance operations by leveraging RUL predictions, feature importance scores, persistent sensor threshold values, and autonomous alert mechanisms. Since the system will provide explanations for the predictions given, along with active alerts, the maintenance personnel can make informed decisions on their end regarding correct interventions to extend the life of the critical machinery.

Keywords: predictive maintenance, explainable artificial intelligence, prognostic, RUL, machine learning, turbofan engines, C-MAPSS dataset

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Authors: S. S. Panda, Arkesh Chouhan, Yogesh Deshpande

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The present paper deals with designing and fabricating an apparatus for the speedy and accurate manufacturing of fiber reinforced composite lamina of different orientation, thickness and stacking sequences for testing. Properties derived through an analytical approach are verified through measuring the elastic modulus, ultimate tensile strength, flexural modulus and flexural strength of the samples. The 0⁰ orientation ply looks stiffer compared to the 90⁰ ply. Similarly, the flexural strength of 0⁰ ply is higher than to the 90⁰ ply. Sample machinability has been studied by conducting numbers of drilling based on Taguchi Design experiments. Multi Responses (Delamination and Damage grading) is obtained using the desirability approach and optimum cutting condition (spindle speed, feed and drill diameter), at which responses are minimized is obtained thereafter. Delamination increases nonlinearly with the increase in spindle speed. Similarly, the influence of the drill diameter on delamination is higher than the spindle speed and feed rate.

Keywords: delamination, FRP composite, Taguchi design, multi response optimization

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Authors: Narong Anurak

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The objectives of the study were to determine the factors influencing tourists’ destination decision making for cultural tourism in the southern provinces, to examine the potential for developing cultural tourism and to guideline for marketing strategy for cultural tourism in Nakhon Si Thammarat. Both quantitative and qualitative data were applied in this study. The samples of 400 cases for quantitative analysis were tourists who were interested in cultural tourism in the southern provinces, and traveled to cultural sites in Nakhon Si Thammarat, Surat Thani, and Phuket, and 14 representatives from provincial tourism committee of Nakhon Si Thammarat. The study found that Thai and foreign tourists are influenced by different important marketing mix factors (7Ps) when making decisions for cultural tourism in southern provinces. The important factors for Thai respondents were physical evidence, price, people, and place at high importance level, whereas, product, process, and promotion were moderate importance level as well.

Keywords: marketing mix factors, Nakhon Si Thammarat province, sustainable cultural tourism, tourists decision making

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Authors: Elahe Moradi, Hoseinali A. Khonakdar

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The escalating interest in renewable polymers is undeniable, albeit accompanied by inherent challenges. In our study, we endeavored to make a significant contribution to environmental conservation by introducing an eco-friendly structure, developed through an innovative approach. Specifically, we enhanced the compatibility between two immiscible polymers, namely poly (lactic acid) (PLA) and poly (butylene adipate-co-terephthalate) (PBAT). Our strategy involved the use of polyhedral oligomeric silsesquioxanes (POSS) nanoparticles, equipped with an epoxy functional group (Epoxy-POSS), to accomplish this objective with solution casting method. The incorporation of 1% nanoparticles into the PLA blend resulted in a decrease in its cold crystallization temperature. Furthermore, these nanoparticles possess the requisite capability to enhance molecular mobility, facilitated by the induction of a lubrication effect. The emergence of a PLA-CO-POSS-CO-PBAT structure at the interface between PLA and PBAT led to a significant amplification of the interactions at the interface of the matrix and the dispersed phase.

Keywords: compatibilization, thermal behavior, structure-properties, nanocomposite, PLA, PBAT

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Authors: Bokku Kang, Changsoo Chon, Han Sung Kim

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We developed new design of rod of pedicle screw system that is beneficial in maintaining the spacing between the vertebrae and assessed the performance of the posterior fixation screw systems by numerical analysis according to the range of motion (flexion, extension, lateral bending, and axial rotation) of the vertebral column after inserting the pedicle screws. The simulation results showed that the conventional rod was the most low equivalent stress value among implant units in the case of flexion, extension and lateral bending of the vertebrae. In all cases except the torsional rotation, the results showed that the stress level of the single and double rounded rod exceeded about 30% to 70% compare to the conventional rod. Therefore, this product is not suitable for actual application in the field yet and it seems that product design optimization is necessary. Acknowledgement: This research was supported by the Ministry of Trade, Industry & Energy (MOTIE), Korea Institute for Advancement of Technology (KIAT) through the Encouragement Program for The Industries of Economic Cooperation Region.

Keywords: lumber spine, internal fixation device, finite element method, biomechanics

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Authors: S. P. Jordan, G. Christian, S. P. Jeffs

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Modern composite materials are increasingly being chosen in replacement of heavier metallic material systems within many engineering fields including aerospace and automotive industries. The increasing push towards satisfying environmental targets are fuelling new material technologies and manufacturing processes. This paper will introduce materials and manufacturing processes using metal matrix composites along with manufacturing processes optimized at Alvant Ltd., based in Basingstoke in the UK which offers modern, cost effective, selectively reinforced composites for light-weighting applications within engineering. An overview and introduction into modern optimized manufacturing methods capable of producing viable replacements for heavier metallic and lower temperature capable polymer composites are offered. A review of the capabilities and future applications of this viable material is discussed to highlight the potential involved in further optimization of old manufacturing techniques, to fully realize the potential to lightweight material using cost-effective methods.

Keywords: aluminium matrix composites, light-weighting, hybrid squeeze casting, strategically placed reinforcements

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Authors: Luis F. Caballero-castro, Dirsa Feliciano, Daniela Novoa, Orlando Arroyo, Jesús D. Villalba-morales

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Colombia faces a critical challenge in seismic resilience due to the prevalence of informal housing, which constitutes approximately 70% of residential structures. More than 10 million Colombians (20% of the population), live in homes susceptible to collapse in the event of an earthquake. This, combined with the fact that 83% of the population is in intermediate and high seismic hazard areas, has brought serious consequences to the country. These consequences became evident during the 1999 Armenia earthquake, which affected nearly 100,000 properties and represented economic losses equivalent to 1.88% of that year's Gross Domestic Product (GDP). Despite previous efforts to reinforce informal housing through methods like externally reinforced masonry walls, alternatives related to seismic protection systems (SPDs), such as Buckling-Restrained Braces (BRB), have not yet been explored in the country. BRBs are reinforcement elements capable of withstanding both compression and tension, making them effective in enhancing the lateral stiffness of structures. In this study, the use of low-cost and easily installable BRBs for the retrofit of informal housing in Colombia was evaluated, considering the economic limitations of the communities. For this purpose, a case study was selected involving an informally constructed dwelling in the country, from which field information on its structural characteristics and construction materials was collected. Based on the gathered information, nonlinear models with and without BRBs were created, and their seismic performance was analyzed and compared through incremental static (pushover) and nonlinear dynamic analyses. In the first analysis, the capacity curve was identified, showcasing the sequence of failure events occurring from initial yielding to structural collapse. In the second case, the model underwent nonlinear dynamic analyses using a set of seismic records consistent with the country's seismic hazard. Based on the results, fragility curves were calculated to evaluate the probability of failure of the informal housings before and after the intervention with BRBs, providing essential information about their effectiveness in reducing seismic vulnerability. The results indicate that low-cost BRBs can significantly increase the capacity of informal housing to withstand earthquakes. The dynamic analysis revealed that retrofit structures experienced lower displacements and deformations, enhancing the safety of residents and the seismic performance of informally constructed houses. In other words, the use of low-cost BRBs in the retrofit of informal housing in Colombia is a promising strategy for improving structural safety in seismic-prone areas. This study emphasizes the importance of seeking affordable and practical solutions to address seismic risk in vulnerable communities in earthquake-prone regions in Colombia and serves as a model for addressing similar challenges of informal housing worldwide.

Keywords: buckling-restrained braces, fragility curves, informal housing, incremental dynamic analysis, seismic retrofit

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Authors: Joseph Chen, N. Hundal

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Conventional machining is a form of subtractive manufacturing, in which a collection of material-working processes utilizing power-driven machine tools are used to remove undesired material to achieve a desired geometry. This paper presents an approach for comparison between turning center and vertical machining center by optimization of cutting parameters at cylindrical workpieces leading to minimum surface roughness by using taguchi methodology. Aluminum alloy was taken to conduct experiments due to its unique high strength-weight ratio that is maintained at elevated temperatures and their exceptional corrosion resistance. During testing, the effects of the cutting parameters on the surface roughness were investigated. Additionally, by using taguchi methodology for each of the cutting parameters (spindle speed, depth of cut, insert diameter, and feed rate) minimum surface roughness for the process of turn-milling was determined according to the cutting parameters. A confirmation experiment demonstrates the effectiveness of taguchi method.

Keywords: surface roughness, Taguchi parameter design, turning center, turn-milling operations, vertical machining center

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Authors: Sumanpreet Kaur, Harjit Pal Singh, Vikas Khullar

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In Wireless Sensor Network (WSN), the sensor containing motes (nodes) incorporate batteries that can lament at some extent. To upgrade the energy utilization, clustering is one of the prototypical approaches for split sensor motes into a number of clusters where one mote (also called as node) proceeds as a Cluster Head (CH). CH selection is one of the optimization techniques for enlarging stability and network lifespan. Deterministic Stable Election Protocol (DSEP) is an effectual clustering protocol that makes use of three kinds of nodes with dissimilar residual energy for CH election. Fuzzy Logic technology is used to expand energy level of DSEP protocol by using fuzzy inference system. This paper presents protocol DSEP using Fuzzy Logic (DSEP-FL) CH by taking into account four linguistic variables such as energy, concentration, centrality and distance to base station. Simulation results show that our proposed method gives more effective results in term of a lifespan of network and stability as compared to the performance of other clustering protocols.

Keywords: DSEP, fuzzy logic, energy model, WSN

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Authors: Rahbel Rahman, Rogério Meireles Pinto, Margareth Santos Zanchetta

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Background: Existing shortcomings of current health-service delivery include poor teamwork, competencies that do not address consumer needs, and episodic rather than continuous care. Brazil’s Sistema Único de Saúde (Unified Health System, UHS) is acknowledged worldwide as a model for delivering community-based care through Estratégia Saúde da Família (FHS; Family Health Strategy) interdisciplinary teams, comprised of Community Health Agents (in Portuguese, Agentes Comunitário de Saude, ACS), nurses, and physicians. FHS teams are mandated to collectively offer clinical care, disease prevention services, vector control, health surveillance and social services. Our study compares medical providers (nurses and physicians) and community-based providers (ACS) on their perceptions of work environment, professional skills, cognitive capacities and job context. Global health administrators and policy makers can leverage on similarities and differences across care providers to develop interprofessional training for community-based primary care. Methods: Cross-sectional data were collected from 168 ACS, 62 nurses and 32 physicians in Brazil. We compared providers’ demographic characteristics (age, race, and gender) and job context variables (caseload, work experience, work proximity to community, the length of commute, and familiarity with the community). Providers perceptions were compared to their work environment (work conditions and work resources), professional skills (consumer-input, interdisciplinary collaboration, efficacy of FHS teams, work-methods and decision-making autonomy), and cognitive capacities (knowledge and skills, skill variety, confidence and perseverance). Descriptive and bi-variate analysis, such as Pearson Chi-square and Analysis of Variance (ANOVA) F-tests, were performed to draw comparisons across providers. Results: Majority of participants were ACS (64%); 24% nurses; and 12% physicians. Majority of nurses and ACS identified as mixed races (ACS, n=85; nurses, n=27); most physicians identified as males (n=16; 52%), and white (n=18; 58%). Physicians were less likely to incorporate consumer-input and demonstrated greater decision-making autonomy than nurses and ACS. ACS reported the highest levels of knowledge and skills but the least confidence compared to nurses and physicians. ACS, nurses, and physicians were efficacious that FHS teams improved the quality of health in their catchment areas, though nurses tend to disagree that interdisciplinary collaboration facilitated their work. Conclusion: To our knowledge, there has been no study comparing key demographic and cognitive variables across ACS, nurses and physicians in the context of their work environment and professional training. We suggest that global health systems can leverage upon the diverse perspectives of providers to implement a community-based primary care model grounded in interprofessional training. Our study underscores the need for in-service trainings to instill reflective skills of providers, improve communication skills of medical providers and curative skills of ACS. Greater autonomy needs to be extended to community based providers to offer care integral to addressing consumer and community needs.

Keywords: global health systems, interdisciplinary health teams, community health agents, community-based care

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Authors: Francesco Nespoli

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After having posed a theoretical framework combining framing theory and new rhetoric, the paper analyze the shift in communication both adopted by UAW and FCA during the negotiations in fall 2015. The paper argues that mistakes and adjustments played a determinant role respectively in the rejection of the first tentative agreement and in the ratification of the contract. The purpose of the paper is to set a new theoretical framework for the analysis of communication in industrial relations, by describing a narrative construction of reality from the perspective of the new rhetoric. The paper thus analyze all public text, speeches, tweets and Facebook posts by the union reading them as part of the narrative set by the organization condensed by the slogan 'it’s our time'. That narrative tried to gain consensus from the members matching the expectations due to the industry recovery after more than five years of workers' sacrifices. In doing so, the analysis points out a shift in the communication strategy of the union after the first rejection of a tentative agreement in 15 years. The findings suggest that, from the communication point of view, consultation in industrial relations can be conceived as a particular kind of political communication where identification with the audience through deliberate narrative may not be effective if it is not preceded by a listening campaign.

Keywords: communication, consultation, automotive, FCA

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Authors: Hyeongbok Kim, Lingling Zhao, Xiaohong Su

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Deep reinforcement learning has been applied to address various problems in robotics, such as autonomous driving and unmanned aerial vehicle. However, because of the sparse reward penalty for a collision with obstacles during the navigation mission, the agent fails to learn the optimal policy or requires a long time for convergence. Therefore, using obstacles and enemy agents, in this paper, we present a curriculum-based boost learning method to effectively train compound skills during multi-agent reinforcement learning. First, to enable the agents to solve challenging tasks, we gradually increased learning difficulties by adjusting reward shaping instead of constructing different learning environments. Then, in a benchmark environment with static obstacles and moving enemy agents, the experimental results showed that the proposed curriculum learning strategy enhanced cooperative navigation and compound collision avoidance skills in uncertain environments while improving learning efficiency.

Keywords: curriculum learning, hard exploration, multi-agent reinforcement learning, robotic navigation, sparse reward

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Authors: Hamidreza Bagheri, Alireza Shariati

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There are many difficulties in the purification of raw components and products. However, researchers are seeking better ways for purification. One of the recent methods is extraction using supercritical fluids. In this study, the phase equilibria of benzoic acid-supercritical carbon dioxide system were investigated. Regarding the phase equilibria of this system, the modeling of solid-supercritical fluid behavior was performed using the Perturbed-Chain Statistical Association Fluid Theory (PC-SAFT) and Peng-Robinson equations of state (PR EoS). For this purpose, five PC-SAFT EoS parameters for pure benzoic acid were obtained using its experimental vapor pressure. Benzoic acid has association sites and the behavior of the benzoic acid-supercritical fluid system was well-predicted using both equations of state, while the binary interaction parameter values for PR EoS were negative. Genetic algorithm, which is one of the most accurate global optimization algorithms, was also used to optimize the pure benzoic acid parameters and the binary interaction parameters. The AAD% value for the PC-SAFT EoS, were 0.22 for the carbon dioxide-benzoic acid system.

Keywords: supercritical fluids, solubility, solid, PC-SAFT EoS, genetic algorithm

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Authors: A. Baviskar, C. Sandeep, K. Shankar

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Solving Multi-objective Optimization Problems requires faster convergence and better spread. Though existing Evolutionary Algorithms (EA's) are able to achieve this, the computation effort can further be reduced by hybridizing them with innovative strategies. This study is focuses on converging to the pareto front faster while adapting the advantages of Strength Pareto Evolutionary Algorithm-II (SPEA-II) for a better spread. Two different approaches based on optimizing the objective functions independently are implemented. In the first method, the decision variables corresponding to the optima of individual objective functions are strategically used to guide the search towards the pareto front. In the second method, boundary points of the pareto front are calculated and their decision variables are seeded to the initial population. Both the methods are applied to different constrained and unconstrained multi-objective test functions. It is observed that proposed guided search based algorithm gives better convergence and diversity than several well-known existing algorithms (such as NSGA-II and SPEA-II) in considerably less number of iterations.

Keywords: boundary points, evolutionary algorithms (EA's), guided search, strength pareto evolutionary algorithm-II (SPEA-II)

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Authors: Rotchanaphan Hengaroonprasan, Malinee Sriariyanun, Prapakorn Tantayotai, Supacharee Roddecha, Kraipat Cheenkachorn

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Lignocellolusic material is a substance that is resistant to be degraded by microorganisms or hydrolysis enzymes. To be used as materials for biofuel production, it needs pretreatment process to improve efficiency of hydrolysis. In this work, chemical pretreatments on rice straw using three diluted organic acids, including acetic acid, citric acid, oxalic acid, were optimized. Using Response Surface Methodology (RSM), the effect of three pretreatment parameters, acid concentration, treatment time, and reaction temperature, on pretreatment efficiency were statistically evaluated. The results indicated that dilute oxalic acid pretreatment led to the highest enhancement of enzymatic saccharification by commercial cellulase and yielded sugar up to 10.67 mg/ml when using 5.04% oxalic acid at 137.11 oC for 30.01 min. Compared to other acid pretreatment by acetic acid, citric acid, and hydrochloric acid, the maximum sugar yields are 7.07, 6.30, and 8.53 mg/ml, respectively. Here, it was demonstrated that organic acids can be used for pretreatment of lignocellulosic materials to enhance of hydrolysis process, which could be integrated to other applications for various biorefinery processes.

Keywords: lignocellolusic biomass, pretreatment, organic acid response surface methodology, biorefinery

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Authors: Jean-Francois Rajotte, Martin Sotir, Frank Gouineau

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The installation and management of traffic monitoring devices can be costly from both a financial and resource point of view. It is therefore important to take advantage of in-place infrastructures to extract the most information. Here we show how low-quality urban road traffic images from cameras already available in many cities (such as Montreal, Vancouver, and Toronto) can be used to estimate traffic flow. To this end, we use a pre-trained neural network, developed for object detection, to count vehicles within images. We then compare the results with human annotations gathered through crowdsourcing campaigns. We use this comparison to assess performance and calibrate the neural network annotations. As a use case, we consider six months of continuous monitoring over hundreds of cameras installed in the city of Montreal. We compare the results with city-provided manual traffic counting performed in similar conditions at the same location. The good performance of our system allows us to consider applications which can monitor the traffic conditions in near real-time, making the counting usable for traffic-related services. Furthermore, the resulting annotations pave the way for building a historical vehicle counting dataset to be used for analysing the impact of road traffic on many city-related issues, such as urban planning, security, and pollution.

Keywords: traffic monitoring, deep learning, image annotation, vehicles, roads, artificial intelligence, real-time systems

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Authors: Farzad Vakili-Farahani, Joern Lungershausen, Kilian Wasmer

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Wobbled laser beam welding, fast oscillations of a tiny laser beam within a designed path (weld geometry) during the laser pulse illumination, opens new possibilities to improve the marco-to micro-manufacturing process. The present work introduces the wobbled laser beam welding as a robust welding strategy for improving macro-to micro-fabrication process, e.g., the laser processing for gap-bridging and packaging industry. The typical requisites and relevant equipment for the development of a wobbled laser processing unit are addressed, including a suitable laser source, light delivery system, optics, proper beam deflection system and the design geometry. In addition, experiments have been carried out on titanium plate to compare the results of wobbled laser welding with conventional pulsed laser welding. As compared to the pulsed laser welding, the wobbled laser welding offers a much greater fusion area (i.e. additional molten material) while minimizing the HAZ and provides a better confinement of the material microstructural changes.

Keywords: wobbled laser beam welding, wobbling function, beam oscillation, micro welding

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Authors: Ghofrane Rehaiem, Hamza Gharsellaoui, Samir Benahmed

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In this study, Artificial Neural Networks (ANNs) were used for modeling the parameters that allow the real-time scheduling of embedded systems under resources constraints designed for real-time applications running. The objective of this work is to implement a neural networks based approach for real-time scheduling of embedded systems in order to handle real-time constraints in execution scenarios. In our proposed approach, many techniques have been proposed for both the planning of tasks and reducing energy consumption. In fact, a combination of Dynamic Voltage Scaling (DVS) and time feedback can be used to scale the frequency dynamically adjusting the operating voltage. Indeed, we present in this paper a hybrid contribution that handles the real-time scheduling of embedded systems, low power consumption depending on the combination of DVS and Neural Feedback Scheduling (NFS) with the energy Priority Earlier Deadline First (PEDF) algorithm. Experimental results illustrate the efficiency of our original proposed approach.

Keywords: optimization, neural networks, real-time scheduling, low-power consumption

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Authors: Sushma Ghogale

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With a surge in user-generated content or feedback or reviews on the internet, it has become possible and important to know consumers' opinions about products and services. This data is important for both potential customers and businesses providing the services. Data from social media is attracting significant attention and has become the most prominent channel of expressing an unregulated opinion. Prospective customers look for reviews from experienced customers before deciding to buy a product or service. Several websites provide a platform for users to post their feedback for the provider and potential customers. However, the biggest challenge in analyzing such data is in extracting latent features and providing term-level analysis of the data. This paper proposes an approach to use topic modeling to classify the reviews into topics and conduct sentiment analysis to mine the opinions. This approach can analyse and classify latent topics mentioned by reviewers on business sites or review sites, or social media using topic modeling to identify the importance of each topic. It is followed by sentiment analysis to assess the satisfaction level of each topic. This approach provides a classification of hotel reviews using multiple machine learning techniques and comparing different classifiers to mine the opinions of user reviews through sentiment analysis. This experiment concludes that Multinomial Naïve Bayes classifier produces higher accuracy than other classifiers.

Keywords: latent Dirichlet allocation, topic modeling, text classification, sentiment analysis

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Authors: Pankaj Negi

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Nowadays the demand for receiving the high quality electrical energy is being increasing as consumer wants not only reliable but also quality power. Custom power instruments are of the most well-known compensators of power quality in distributed network. This paper present a comprehensive review of compensating custom power devices mainly DSTATCOM (distribution static compensator),DVR (dynamic voltage restorer), and UPQC (unified power quality compensator) and also deals with sliding mode control and its applications to custom power devices. The sliding mode control strategy provides robustness to custom power device and enhances the dynamic response for compensating voltage sag, swell, voltage flicker, and voltage harmonics. The aim of this paper is to provide a broad perspective on the status of compensating devices in electric power distribution system and sliding mode control strategies to researchers and application engineers who are dealing with power quality and stability issues.

Keywords: active power filters(APF), custom power device(CPD), DSTATCOM, DVR, UPQC, sliding mode control (SMC), power quality

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Authors: Mohammad Bakhshandeh

Abstract:

Structural Health Monitoring (SHM) is a crucial and necessary practice that plays a vital role in ensuring the safety and integrity of critical structures, and in particular, bridges. The continuous monitoring of bridges for signs of damage or degradation through Bridge Health Monitoring (BHM) enables early detection of potential problems, allowing for prompt corrective action to be taken before significant damage occurs. Although all monitoring techniques aim to provide accurate and decisive information regarding the remaining useful life, safety, integrity, and serviceability of bridges, understanding the development and propagation of damage is vital for maintaining uninterrupted bridge operation. Over the years, extensive research has been conducted on BHM methods, and experts in the field have increasingly adopted new methodologies. In this article, we provide a comprehensive exploration of the various BHM approaches, including sensor-based, non-destructive testing (NDT), model-based, and artificial intelligence (AI)-based methods. We also discuss the challenges associated with BHM, including sensor placement and data acquisition, data analysis and interpretation, cost and complexity, and environmental effects, through an extensive review of relevant literature and research studies. Additionally, we examine potential solutions to these challenges and propose future research ideas to address critical gaps in BHM.

Keywords: structural health monitoring (SHM), bridge health monitoring (BHM), sensor-based methods, machine-learning algorithms, and model-based techniques, sensor placement, data acquisition, data analysis

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Authors: Steven Vusmuzi Mashego, Opeyeolu Timothy Laseinde

Abstract:

This paper presents a case study on applying maintenance strategies observed in a turbo-generator at a coal power plant. Turbo generators are one of the primary and critical components in energy generation. It is essential to apply correct maintenance strategies and apply operational procedures accordingly. The maintenance strategies are implemented to ensure the high reliability of the equipment. The study was carried out at a coal power station which will transit to a cleaner energy source in the nearest future. The study is relevant as lessons learned in this system will support plans and operational models implemented when cleaner energy sources replace coal-powered turbines. This paper first outlines different maintenance strategies executed on the turbo-generator modules. Secondly, the impacts of human factors on a coal power station are discussed, and the findings prompted recommendations for future actions.

Keywords: maintenance strategies, turbo generator, operational error, human factor, electricity generation

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Authors: Mohamad F. Jamiluddin, Emeline Sarry, Ana Bejanariu, Cécile Bauche

Abstract:

Introduction: Over 360 million people are chronically infected with hepatitis B virus (HBV), of whom 1 million die each year from HBV-associated liver cirrhosis or hepatocellular carcinoma. Current treatment options for chronic hepatitis B depend on interferon-α (IFNα) or nucleos(t)ide analogs, which control virus replication but rarely eliminate the virus. Treatment with PEG-IFNα leads to a sustained antiviral response in only one third of patients. After withdrawal of the drugs, the rebound of viremia is observed in the majority of patients. Furthermore, the long-term treatment is subsequently associated with the appearance of drug resistant HBV strains that is often the cause of the therapy failure. Among the new therapeutic avenues being developed, therapeutic vaccine aimed at inducing immune responses similar to those found in resolvers is of growing interest. The high prevalence of chronic hepatitis B necessitates the design of better vaccination strategies capable of eliciting broad-spectrum of cell-mediated immunity(CMI) and humoral immune response that can control chronic hepatitis B. Induction of HBV-specific T cells and B cells by therapeutic vaccination may be an innovative strategy to overcome virus persistence. Lentiviral vectors developed and optimized by THERAVECTYS, due to their ability to transduce non-dividing cells, including dendritic cells, and induce CMI response, have demonstrated their effectiveness as vaccination tools. Method: To develop a HBV therapeutic vaccine that can induce a broad but specific immune response, we generated recombinant lentiviral vector carrying IRES(Internal Ribosome Entry Site)-containing bicistronic constructs which allow the coexpression of two vaccine products, namely HBV T- cell epitope vaccine and HBV virus like particle (VLP) vaccine. HBV T-cell epitope vaccine consists of immunodominant cluster of CD4 and CD8 epitopes with spacer in between them and epitopes are derived from HBV surface protein, HBV core, HBV X and polymerase. While HBV VLP vaccine is a HBV core protein based chimeric VLP with surface protein B-cell epitopes displayed. In order to evaluate the immunogenicity, mice were immunized with lentiviral constructs by intramuscular injection. The T cell and antibody immune responses of the two vaccine products were analyzed using IFN-γ ELISpot assay and ELISA respectively to quantify the adaptive response to HBV antigens. Results: Following a single administration in mice, lentiviral construct elicited robust antigen-specific IFN-γ responses to the encoded antigens. The HBV T- cell epitope vaccine demonstrated significantly higher T cell immunogenicity than HBV VLP vaccine. Importantly, we demonstrated by ELISA that antibodies are induced against both HBV surface protein and HBV core protein when mice injected with vaccine construct (p < 0.05). Conclusion: Our results highlight that THERAVECTYS lentiviral vectors may represent a powerful platform for immunization strategy against chronic hepatitis B. Our data suggests the likely importance of Lentiviral vector based novel bicistronic construct for further study, in combination with drugs or as standalone antigens, as a therapeutic lentiviral based HBV vaccines. THERAVECTYS bicistronic HBV vaccine will be further evaluated in animal efficacy studies.

Keywords: chronic hepatitis B, lentiviral vectors, therapeutic vaccine, virus-like particle

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Authors: M. Moreno, M. Alique, D. Otero, C. Delgado, P. Lacharmoise, L. Gracia, L. Pires, A. Moya

Abstract:

Marine environments are increasingly threatened by heavy metal contamination, including mercury (Hg), lead (Pb), and cadmium (Cd), posing significant risks to ecosystems and human health. Traditional monitoring techniques often fail to provide the spatial and temporal resolution needed for real-time detection of these contaminants, especially in remote or harsh environments. SEAWIZARD addresses these challenges by leveraging the flexibility, adaptability, and cost-effectiveness of printed electronics, with the integration of microfluidics to develop a compact, portable, and reusable sensor platform designed specifically for real-time monitoring of heavy metal ions in seawater. The SEAWIZARD sensor is a multiparametric Lab-on-Chip (LoC) device, a miniaturized system that integrates several laboratory functions into a single chip, drastically reducing sample volumes and improving adaptability. This platform integrates three printed graphene electrodes for the simultaneous detection of Hg, Cd and Pb via square wave voltammetry. These electrodes share the reference and the counter electrodes to improve space efficiency. Additionally, it integrates printed pH and temperature sensors to correct environmental interferences that may impact the accuracy of metal detection. The pH sensor is based on a carbon electrode with iridium oxide electrodeposited while the temperature sensor is graphene based. A protective dielectric layer is printed on top of the sensor to safeguard it in harsh marine conditions. The use of flexible polyethylene terephthalate (PET) as the substrate enables the sensor to conform to various surfaces and operate in challenging environments. One of the key innovations of SEAWIZARD is its integrated microfluidic layer, fabricated from cyclic olefin copolymer (COC). This microfluidic component allows a controlled flow of seawater over the sensing area, allowing for significant improved detection limits compared to direct water sampling. The system’s dual-channel design separates the detection of heavy metals from the measurement of pH and temperature, ensuring that each parameter is measured under optimal conditions. In addition, the temperature sensor is finely tuned with a serpentine-shaped microfluidic channel to ensure precise thermal measurements. SEAWIZARD also incorporates custom electronics that allow for wireless data transmission via Bluetooth, facilitating rapid data collection and user interface integration. Embedded artificial intelligence further enhances the platform by providing an automated alarm system, capable of detecting predefined metal concentration thresholds and issuing warnings when limits are exceeded. This predictive feature enables early warnings of potential environmental disasters, such as industrial spills or toxic levels of heavy metal pollutants, making SEAWIZARD not just a detection tool, but a comprehensive monitoring and early intervention system. In conclusion, SEAWIZARD represents a significant advancement in printed electronics applied to environmental sensing. By combining flexible, low-cost materials with advanced microfluidics, custom electronics, and AI-driven intelligence, SEAWIZARD offers a highly adaptable and scalable solution for real-time, high-resolution monitoring of heavy metals in marine environments. Its compact and portable design makes it an accessible, user-friendly tool with the potential to transform water quality monitoring practices and provide critical data to protect marine ecosystems from contamination-related risks.

Keywords: lab-on-chip, printed electronics, real-time monitoring, microfluidics, heavy metal contamination

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