Novelty Approach Research Articles

Structural Damage Detection using the Circle-Inspired Optimization Algorithm

This paper presents a novelty approach to vibration-based damage detection using matrix updating with the Circle-Inspired Optimization Algorithm (CIOA). The methodology is evaluated through numerical simulations of three structures: a 10-bar truss, a cantilever beam, and a Warren truss. In all cases, the systems are subject to ambient vibrations with varying noise levels to replicate inaccuracies in the acceleration signals. Furthermore, different analysis scenarios were considered, including single and multiple damages. The Data-driven Stochastic Subspace Identification (SSI-DATA) technique is employed to determine the modal parameters of these signals. Natural frequencies and mode shapes are compared under healthy and damaged conditions to identify the damage state through the methodology. The parameters of the optimization algorithm, CIOA, were set to θ = 97º and GlobIt = 0.90. In addition, a factor specific to the algorithm, the radius reduction factor, was reduced from 0.99 to 0.98 to accelerate convergence. The number of search agents and iterations varied according to the complexity of each structure. Considering all analyses for each scenario and noise level, the highest percentage errors in damage detection obtained were 0.163% in a multiple damage scenario with noise of 3% for the 10-bar truss, 1.453% in a multiple damage scenario with 5% noise for the cantilever beam, 3.600% in a multi damage scenario with 5% noise for the Warren truss. Therefore, results demonstrate the proposed approach’s promise in identifying, locating, and quantifying single and multiple damages.

Materials and Products Development Based on a Novelty Approach to Quality and Life Cycle Assessment (QLCA).

The development of materials and the products made from them should respond to new challenges posed by market changes and also by climate change. Therefore, the objective of this investigation was to develop a method that supports the sustainable development of materials and the products made from them based on an aggregated indicator of quality and environmental load in the life cycle (QLCA). The testing and illustration of the QLCA method included a passenger car tyre and nine prototypes. These prototypes were described using eight quality criteria: season, class, size of the load index, speed index, rolling, adhesion, and external noise. Then, customer expectations regarding the importance of the criteria and satisfaction with the indicators in the current and modified states were obtained. Based on the customer assessment, the quality indicators of the prototypes were assessed. This assessment was supported by the weighted sum model (WSM) and the entropy method. Then, life cycle assessment for the reference tyre was performed using the Ecoinvent database in the OpenLCA program. LCA indicators were modelled for other prototypes, taking into account quality changes. As a result of the verification of the method, an aggregated QLCA indicator was estimated, based on which it was possible to select the most favourable (qualitatively and environmentally) prototype out of nine. This was the P4 prototype (QLCA = 0.57). The next position in the ranking was taken by P7 (QLCA = 0.43). The QLCA method can be used to determine the direction of development of materials and products in terms of their sustainable development.

Electric vehicle and photovoltaic advanced roles in enhancing the financial performance of a manufacturing and commercial setup

Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network.

An Open-Source Software Approach to Multimodal Medical Imaging: Combination of Anatomical and Thermal 3D Models

As the three-dimensional (3D) reconstruction and visualisation of medical imaging systems, such as computed tomography (CT) and magnetic resonance imaging (MRI), become common approaches for assisting physicians with a visual representation of anatomy structures, a novelty approach adding functional elements of the human body started being explored. So, medical thermography has been used as a complementary imaging technique for diagnosis and monitoring of pathologies, based on the identification of surface temperature changes. Therefore, this paper presents a new open-source software approach to multimodal medical imaging. The proposed methodology involves the 3D reconstruction of an anatomical model and thermal model, being the latter based on a Structure from Motion (SfM) approach, and the combination of both models, providing a complete 3D multimodal model that contains inner information regarding the anatomy of the body, and outer physiological information. It showed efficient results regarding the visualisation of the multimodal model in a single coordinate system in different views (axial, coronal, and sagittal), allowing it to be inspected by physicians, whilst improving pathology assessment. The 3D multimodal models have potential to be expanded to medical applications, such as diagnosis, surgery, and follow-up care, whilst being cost effective by resorting to available open-source software.

A Two-Stage Approach of DEA and AHP in Selecting Optimal Wind Power Plants

Sustainable development has become a global tendency in the growth orientation of governments. Renewable energies such as wind and solar have attracted many concerns in reducing the level of fossil fuels dependence. One of the most significant stages in such a transmission process is determining the most suitable location. This article aims to propose a novelty approach combining two multiple criteria decision making models, namely data envelopment analysis (DEA) and analysis hierarchy process (AHP) to recommend the most optimal site for wind power plants. Vietnam has been one of the most successful ASEAN nations in attracting renewable energy investments; hence, the article considers it as a case study. In the first stage, there are five inputs and outputs conducted by DEA models to choose the five most potential alternatives among 12 locations for the next stage. In the second stage, the AHP model is implemented on five main criteria and 20 subcriteria that are referred to the options of experienced experts. The findings show that Ninh Thuan province, which is obtained with the highest final score (0.374), is the most suitable location for wind power plants construction. The article has contributed to renewable literature in terms of methodologies as well as to practice for both public and private stakeholders.

A large-scale last-mile consolidation model for e-commerce home delivery

E-commerce's rapid growth, combined with customer demand for fast shipping, has significantly escalated last-mile transportation, particularly home deliveries. This surge calls for novel strategies to optimize vehicle utilization while maintaining timely delivery. In response, many online retailers are incentivizing customers to embrace delayed home deliveries by offering economic incentives. Nonetheless, current transportation systems fall short in accommodating orders with extended delivery windows, primarily adhering to the First-In-First-Out rule and distance minimization via the Vehicle Routing Problem (VRP). In this paper, we introduce a new consolidation-based delivery methodology that addresses these challenges. Our approach accounts for orders with daily-based time windows and also anticipates future demand realizations (i.e., expected incoming orders). We characterize this consolidation problem using a Mixed Integer Linear Programming model and propose a custom metaheuristic approach that can tackle the problem on a large-scale setting, introducing a significant novelty approach in last-mile delivery research. We applied our methodology to one of Mexico's largest retailers and compared its performance against the company's existing transportation systems. Our approach substantially improves vehicle utilization and yields considerable reductions in distance traveled, time, and overall transportation costs, achieving cost savings of up to 52%. These savings represent tangible benefits, enabling potential revenue enhancements for businesses and cost-effective, timely deliveries for consumers. Additionally, the increased vehicle utilization implies fewer vehicles are needed for the same volume of deliveries, thereby enhancing operational efficiency. This innovative approach, therefore, presents a practical and highly effective solution for managing large-scale last-mile delivery scenarios.

Measuring efficiency of safe work environment from the perspective of the decent work Agenda

Decent Work Agenda consists of a comprehensive initiative for promoting safety at work and social protection. Over 20 years since its conceptual release, measuring the progress of its elements is still challenging even after the publication of the decent work indicators guideline by the International Labour Organization in 2012. To close this gap, we use a Directional Distance Function (DDF) to measure the efficiency of safe work environment, and propose a combined measure taking into consideration also the efficacy. To illustrate the application of DDF in a reality-based case, we conducted a longitudinal study in a multinational organization. Data were collected from 21 branches of the company over 4 years (2018–2021). In the period of analysis, 60% of the branches were efficient in average, composing an overall efficiency score of 0.91. Our results also indicated low dispersion between the efficiency scores year on year due to a small-scale interquartile range. Finally, the use of efficiency combined with efficacy resulted in a promising approach for managerial applications. This research presents some contributions. One is the novelty approach of measuring the efficiency of safe work environment using a DDF model in a real-world application. Another is the managerial benefits of identifying benchmarks, as well as revealing potential improvements as a mechanism to reduce decent work deficits. From a modeling perspective, our conclusions suggest caution in considering only efficiency to measure safe work environment due to its relative nature. Thus, further studies are recommended to explore the use of combined measures in the analysis of decent work.

A novelty approach to solve an economic dispatch problem for a renewable integrated micro-grid using optimization techniques

Introduction. The renewable integrated microgrid has considered several distributed energy sources namely photovoltaic power plant, thermal generators, wind power plant and combined heat and power source. Economic dispatch problem is a complex operation due to large dimension of power systems. The objective function becomes non linear due to the inclusion of many constraints. Hourly demand of a commercial area is taken into consideration for performing economic dispatch and five combinations are considered to find the best optimal solution to meet the demand. The novelty of the proposed work consists of a Sparrow Search Algorithm is used to solve economic load dispatch problem to get the better convergence and accuracy in power generation with minimum cost. Purpose. Economic dispatch is performed for the renewable integrated microgrid, in order to determine the optimal output of all the distributed energy sources present in the microgrid to meet the load demand at minimum possible cost. Methods. Sparrow Search Algorithm is compared with other algorithms like Particle Swarm Optimization, Genetic Algorithm and has been proved to be more efficient than Particle Swarm Optimization, Genetic Algorithm and Conventional Lagrange method. Results. The five combinations are generation without solar power supply system and Combined Heat and Power source, generation without solar and wind power supply systems, generation including all the distributed energy sources, generation without wind power supply system and Combined Heat and Power source, generation without thermal generators. Practical value. The proposed optimization algorithm has been very supportive to determine the optimal power generation with minimal fuel to meet the large demand in commercial area.

Determination of fiber yield and quality in cotton cultivars, applied with biologically reclaimed greywater

Application of recycled municipal wastewater and industrial effluents using biological treatment methods to industrial crops is an ingenious practice to ensure the combined means of adopting sustainable industrial crops production through sustainable freshwater management. An investigation was made to study the effects of recycled greywater treated using the extended form of Effective Microorganisms in the sewage treatment plant, over the growth, biochemical contents, yield and fiber quality of MCU7 and SVPR2 cotton cultivars, grown under experimental conditions. The efficacy of the recycled greywater treatment was compared with ground well water as control and untreated greywater as on another treatment. Improved yield of lint fiber and cotton seeds were obtained with a ginning out turn (GOT) of 71.66% and 66.1% in the SVPR2 and MCU7 cultivars, respectively, when irrigated with recycled greywater. Similarly, both the cotton cultivars, supplied with the recycled greywater was found with a significantly higher (P ≤ 0.01) stable length (SL) and fiber strength, as well as significantly higher spinning consistency index (SCI) (P ≤ 0.05) and uniformity ratio, when compared with the other two treatments. The novelty approach, adopted in the experiment therefore can be concluded using the recycled greywater to the specific cotton cultivars in water-deficit agro-climatic conditions would be beneficial to sustain the cotton production, would be in turn benefiting the industries.

My Lovely Granny’s Farm: An immersive virtual reality training system for children with autism spectrum disorder

One of the biggest difficulties faced by children with Autism Spectrum Disorder during their learning process and general life, is communication and social interaction. In recent years, researchers and practitioners have invested in different approaches to improving aspects of their communication and learning. However, there is still no consolidated approach and the community is still looking for new approaches that can meet this need. Addressing this challenge, in this article we propose a novelty approach (i.e., an Adaptive Immersive Virtual Reality Training System), aiming to enrich social interaction and communication skills for children with Autism Spectrum Disorder. In this adaptive system (called My Lovely Granny’s Farm), the behavior of the virtual trainer changes depending on the mood and actions of the users (i.e., patients/learners). Additionally, we conducted an initial observational study by monitoring the behavior of children with autism in a virtual environment. In the initial study, the system was offered to users with a high degree of interactivity so that they might practice various social situations in a safe and controlled environment. The results demonstrate that the use of the system can allow patients who needed treatment to receive therapy without leaving home. Our approach is the first experience of treating children with autism in Kazakhstan and can contribute to improving the communication and social interaction of children with Autism Spectrum Disorder. We contribute to the community of educational technologies and mental health by providing a system that can improve communication among children with autism and providing insights on how to design this kind of system.

Rewarded Maze Training Increases Approach Behavior in Rats Through Neurogenesis-Dependent Growth of Ventral Hippocampus–Prelimbic Circuits

BackgroundLearning complex navigation routes increases hippocampal volume in humans, but it is not clear whether this growth impacts behaviors outside the learning situation or what cellular mechanisms are involved. MethodsWe trained rats with pharmacogenetic suppression of adult neurogenesis and littermate controls in 3 mazes over 3 weeks and tested novelty approach behavior several days after maze exposure. We then measured hippocampus and prelimbic cortex volumes using magnetic resonance imaging and assessed neuronal and astrocyte morphology. Finally, we investigated the activation and behavioral role of the ventral CA1 (vCA1)-to-prelimbic pathway using immediate-early genes and DREADDs (designer receptors exclusively activated by designer drugs). ResultsMaze training led to volume increase of both the vCA1 region of the hippocampus and the prelimbic region of the neocortex compared with rats that followed fixed paths. Growth was also apparent in individual neurons and astrocytes in these 2 regions, and behavioral testing showed increased novelty approach in maze-trained rats in 2 different tests. Suppressing adult neurogenesis prevented the effects on structure and approach behavior after maze training without affecting maze learning itself. The vCA1 neurons projecting to the prelimbic area were more activated by novelty in maze-trained animals, and suppression of this pathway decreased approach behavior. ConclusionsRewarded navigational learning experiences induce volumetric and morphologic growth in the vCA1 and prelimbic cortex and enhance activation of the circuit connecting these 2 regions. Both the structural and behavioral effects of maze training require ongoing adult neurogenesis, suggesting a role for new neurons in experience-driven increases in novelty exploration.

An Empirical Spatial Network Model Based on Human Mobility for Epidemiological Research: A Case Study

Constructing a data-driven spatial contact network model is challenging in epidemiological research. In this study, we examine the applicability of geotagged Twitter data as an instrumental data source for tackling such a challenge. Geotagged Twitter data carrying geolocations of the account users have the strength for longitudinal data collection at a massive scale. Still, the unstructured nature of the data exerts significant methodological and computational difficulties. We focus on methodological solutions and develop a novelty approach that lets a spatial contact network emerge naturally from the massive amount of geospatial tweets. We show that such a data-driven network has reflected the assumptions made by network models regarding human behaviors and has the potential of being used for epidemiological research. To this end, we investigate the network properties and study the spread of pathogens on the proposed spatial contact network by using the homogeneous and heterogeneous susceptible–infectious–recovered (SIR) network models and the event-driven Gillespie’s algorithm. Our simulation results strongly suggest that it is feasible to explicitly construct data-driven spatial models using massive longitudinal Twitter data for public health research.

Decentralizing Online Food Delivery Services: A Blockchain and IoT Model for Smart Cities

With the worldwide pandemic outbreak, the style of restaurant food consumption underwent a major shift towards online delivery services. This poses an urgent need in trust between main stakeholders of the process: requiring restaurants to correspond to the quality declared, providing high quality and safe to consume meals, and obliging delivery entities to deliver food carefully, scrupulously following the delivery conditions. In this research, we explore a novelty approach that combines IoT, Blockchain technology, and city LoRa network to create a new trusted, decentralized approach for food distribution process in the context of a Smart City. This approach allows controlling the food delivery process using sensors data to control live location, temperature, vibrations, and shakings during the transportation process. We also suggest a fresh perspective on a rating system of delivery entities, where reputation points will be provided both from the side of the restaurant and the final consumer. This will create more trust towards the delivery entity since information will be tamper-proof and immutable due to the nature of Blockchain. This novel system proposal allows rethinking the online food delivery process in the context of Smart City, using the city’s LoRa LPWAN radio frequency technology and Blockchain decentralized solution.

Segmentation of Structural Elements from 3D Point Cloud Using Spatial Dependencies for Sustainability Studies.

The segmentation of point clouds obtained from existing buildings provides the ability to perform a detailed structural analysis and overall life-cycle assessment of buildings. The major challenge in dealing with existing buildings is the presence of diverse and large amounts of occluding objects, which limits the segmentation process. In this study, we use unsupervised methods that integrate knowledge about the structural forms of buildings and their spatial dependencies to segment points into common structural classes. We first develop a novelty approach of joining remotely disconnected patches that happened due to missing data from occluding objects using pairs of detected planar patches. Afterward, segmentation approaches are introduced to classify the pairs of refined planes into floor slabs, floor beams, walls, and columns. Finally, we test our approach using a large dataset with high levels of occlusions. We also compare our approach to recent segmentation methods. Compared to many other segmentation methods the study shows good results in segmenting structural elements by their constituent surfaces. Potential areas of improvement, particularly in segmenting walls and beam classes, are highlighted for further studies.

In situ quantitative yttrium and trace elements imaging analysis of Y-doped BaF2 crystals by LA-ICP-MS

In this study, a laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) method for in-situ determination of yttrium and trace elements in yttrium-doped barium fluoride (BaF2: Y) crystals was proposed. A facile, micro-damage procedure for quantifying the segregation coefficient of doping elements was investigated, and it was found that the actual yttrium doping concentration increases from the seed end to the tail end in BaF2: Y crystals. In micro-area analysis, this method has higher mass sensitivity which was applied to quantify the impurity content and distribution during the growth of BaF2: Y crystals. Regression coefficient of calibration curve for each element ranged from 0.9918 to 0.9995. Detection limits (DLs) were 0.05, 0.03, 0.01 and 0.01 μg g−1 for Mg, Zn, Sr and Pb, respectively. The accuracy of the proposed method was verified by inductively coupled plasma mass spectrometry/atomic emission spectroscopy (ICP-MS/AES) with wet-chemical pretreatment. The objective of the presented work was to provide a less damaging and more novelty approach for crystal sample analysis.

Sustainable Business Model Innovation: From Value Uncaptured to Value Opportunities

COVID-19 pandemic crisis threatened the stability of economy and the survival of many firms, but it has been also the chance to challenge the current economic development path and to rethink firms’ business models according to a more sustainable approach. Academic literature states that business model (BM) innovation is a driver for the transition to sustainability. Sustainable business models (SBMs) incorporate sustainability vision in the main components of business model, which are value proposition, value creation and value capturing. Nevertheless, the usual approach to business models, based on a positive concept of value, can underestimate some areas of potential opportunities to catch. For this reason, in this paper we suggest to adopt a novelty approach that emphasizes the negative concept of value (value uncaptured) to identify unexploited value opportunities. This approach can help firms innovating their BMs towards SBMs.

Effects of wildfire ashes on aquatic invertebrates: First molecular approach on Chironomus riparius larvae

The wildfire magnification in recent years has raised increasing concern about their adverse impacts on the environment. Wildfires are recognized as an important source of diffuse pollution for the nearby aquatic systems being potentially toxic to aquatic life. Albeit previous studies with wildfire runoff/ashes observed effects in aquatic organisms, to date, different severity origins of ashes and their impact at the sub-organismal level on aquatic biota have not been assessed. In this work, the molecular response of Chironomus riparius exposed to wildfire with low (LS) and high (HS) severity ashes from burnt Pine plantations was evaluated by employing an array of 42 genes related to crucial metabolic pathways by Real time-PCR. IV instar larvae were exposed for 72 h to aqueous extract of ashes (12.5 %, 25 %, 50 %, 75 % and 100 %) prepared from LS and HS ashes. Mn, Zn, and Pb were the metals found at highest concentration in both ash extracts, for HS notable Cd, Mn and Cr presence. From the 42 genes studied only 4 were not altered (22 genes modulated their response by LS and 38 genes in the case of HS) showing the opposite response at 100% with downregulated by LS and upregulated by HS. The 12.5 %, 25 %, 100 % HS and 25 % LS were the main modulators, confirmed by the integrative biomarkers response (IBR). Remarkable genotoxicity was generated by ashes even activating the apoptosis response, and endocrine disruption observed could modify the development. Moreover, detoxification and stress response were strongly activated, limiting the organism's future response to external aggressions. The employment of this novelty approach with molecular tools act as early alarm signal preventing greater damages. Overall, wildfire ashes showed to be a significant environmental disruptor to C. riparius even at lower concentration and the short exposure time employed, emphasizing the strong impact of wildfires on aquatic systems.

Biomechanical response of soft tissues during passage of synovial fluid in compression

The present study provides a novelty approach for the computational biological model based on continuum mixture theory in combination with power-law model for incorporating an accurate governing model for the synovial fluids. We investigated the biomechanical response of a soft tissue while passage of non-Newtonian fluid during act of loading at the rigid bony interface. A special kind of multiphasic deformation has been reported in these types of problems that justify nonlinear coupling between the fluid and solid. In modeling these types of problems, general assumption of mixture constituents incompressibility is often provoked. The mixture components are considered intrinsically incompressible; however, in the derivation of governing equations, viscoelastic behavior of the solid along with interstitial fluid was developed. The nonlinear interaction between the fluid–solid is modeled using strain-dependent permeability and is experimentally determined. This manipulation of linear model with nonlinear permeability required attention for the computational point of view. A system of nonlinear coupled partial differential equations is derived for the local fluid pressure along with an equation for solid deformation. The governing system of equations is solved numerically for the case of permeability dependent flow, whereas an exact solution is given for constant permeability case. Various interesting features, such as, pressure changes within the tissue, swelling behavior of the solid matrix, and effects of power law index on the tissue deformation have been presented graphically. A good qualitative agreement has been noticed between the exact and numerical solutions for constant permeability case.

Process optimization studies of essential parameters in the organic solvent method for the recycling of waste crystalline silicon photovoltaic modules

Improper disposal of waste solar panels can raise serious concerns about the environment and human health. Therefore, it is important to recycle the waste panels to ensure that the modules do not pollute the environment. Different techniques developed globally for the recycling of waste photovoltaic modules include organic solvent dissolution, electro-thermal heating, and thermal treatment. The present study is focused particularly on the optimization of the parameters to identify i) an efficient solvent for the dissolution of the encapsulant, ii) position of the module for improved dissolution, iii) the effect of temperature variation on solubility, and iv) saturation studies of EVA. It is concluded that Trichloroethylene at 70 °C works effectively for the separation of different components with the ratio of 1:7.44 of the module to solvent in a horizontal position. The maximum percentage of a module in the solvent is 13.44% and an increase in this percentage will result in an ineffective separation. A novelty approach has been reported for the first time to experimentally determine the quantity of solvent, position of the module, and the lowest temperature required for encapsulant dissolution. • Novel approach to determine the quantity of solvent required for given module. • The lowest temperature for effective EVA dissolution has been suggested. • A suitable solvent and the position of module have been suggested.

Amidation crosslinking of polymeric carbon nitride for boosting photocatalytic hydrogen peroxide production

Hydrogen peroxide (H2O2) has been widely used as eco-friendly oxidant in many chemical industries and environmental areas. Photocatalytic H2O2 production based on polymeric carbon nitride (g-C3N4) materials is a burgeoning research direction. The primary and secondary amine groups in g-C3N4 framework are considered to be the adverse reaction active sites during photocatalytic H2O2 generation process. Besides, these amine groups can easily form hydrogen bonds with each other which will weaken the separation and transfer of photogenerated carriers in the basal plane of g-C3N4, thus greatly restricts the H2O2 production efficiency. Here, three organic molecules (acetyl chloride, oxalyl chloride and trimesoyl chloride) were successfully covalently introduced into the g-C3N4 through an amide bond synthesis method between amine group and acyl chloride group (ac-g-C3N4-n, oc-g-C3N4-n and tc-g-C3N4-n). All three composites show an enhanced performance toward photocatalytic hydrogen peroxide production compared with pristine g-C3N4. The highest reaction activity reaches 3.2 mmol L−1 in 6 h, which is 3-fold than that of the pristine g-C3N4. This study provides a novelty approach to modify g-C3N4 that focuses on the properties of the semiconductor itself and in-depth insight into the modulation of in-plane electrical conductivity at molecular scale.