Search results for: mixed models

Authors: M. S. Mahmoud

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In this paper, the adsorption performance of a novel environmental friendly material, calcium alginate gel beads as a non-conventional technique for the successful removal of copper ions from aqueous solution are reported on. Batch equilibrium studies were carried out to evaluate the adsorption capacity and process parameters such as pH, adsorbent dosages, initial metal ion concentrations, stirring rates and contact times. It was observed that the optimum pH for maximum copper ions adsorption was at pH 5.0. For all contact times, an increase in copper ions concentration resulted in decrease in the percent of copper ions removal. Langmuir and Freundlich's isothermal models were used to describe the experimental adsorption. Adsorbent was characterization using Fourier transform-infrared (FT-IR) spectroscopy and Transmission electron microscopy (TEM).

Keywords: adsorption, alginate polymer, isothermal models, equilibrium

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Authors: Zhengyi Kong, Seung-Eock Kim

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Single angle connections, which are bolted to the beam web and the column flange, are studied to investigate moment-rotation behavior. Elastic–perfectly plastic material behavior is assumed. ABAQUS software is used to analyze the nonlinear behavior of a single angle connection. The same geometric and material conditions with Yanglin Gong’s test are used for verifying finite element models. Since Kishi and Chen’s Power model and Lee and Moon’s Log model are accurate only for a limited range, simpler and more accurate hyperbolic function models are proposed. The equation for calculating rotation at ultimate moment is first proposed.

Keywords: finite element method, moment and rotation, rotation at ultimate moment, single-web angle connections

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Authors: Nseabasi Nsikakabasi Etim

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In recent times, there has been a significant shortfall in between the production and supply of animal protein to meet the ever increasing population. To meet the increasing demand for animal protein, there is a need to focus attention on the production of livestock whose nutritional requirement does not put much strain on the limited sources of feed ingredients to which men subscribe. An example of such livestock is the rabbit. Rabbit is a pseudo-ruminant herbivore which utilizes much undigested and unabsorbed feed materials as sources of nutrient for maintenance and production. Thus, this study was conducted to investigate the effects of feeding Aspilia africana as forage on the growth rates of female pseudo-ruminant herbivores (rabbits) at different physiological states. Thirty (30) Dutch breed rabbit does of 5–6 months of age were used for the experiment which was conducted in a completely randomized design for four months. The rabbits were divided into three treatment groups, ten does per treatment group; which consisted of mixed forages (Centrosema pubescent (200g), Panicum maximum (200g) and Ipomea batatas leaves (100g) without Aspilia africana (T1; control), fresh Aspilia africana (500g/dose/day) (T2) and wilted Aspilia africana (500g/dose/day) (T3). Rabbits in all treatment groups received the same concentrate (300g/animal/day) throughout the period of the study and mixed forages from the commencement of the experiment till the does kindled. After parturition, fresh and wilted Aspilia africana were introduced in treatments 2 and three respectively, whereas the control group continued on mixed forages throughout the study. The result of the study revealed that the initial average body weight of the rabbit does was 1.74kg. At mating and gestation periods, the body weights of the does in T2 was significantly higher (P<0.05) than the rest. There were no significant differences (P<0.05) in the body weights of does at kindling between the various treatment groups. During the physiological states of lactation, weaning and re-mating, the control group (T1) had significantly lower body weight than those of the treated groups (T2 and T3). Furthermore, T2 had significantly higher body weight than T3. The study revealed that Aspilia africana; mainly the fresh leaves have greater growth stimulating effects when fed to pseudo-ruminants (rabbits), thereby enhancing body weights of does during lactation and weaning.

Keywords: Aspilia africana, herbivores, pseudo-ruminants, physiological states

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Authors: Oluwaponmile D. Alao

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In this paper, two models have been developed to ascertain the best network needed for diagnosis of breast cancer. Breast cancer has been a disease that required the attention of the medical practitioner. Experience has shown that misdiagnose of the disease has been a major challenge in the medical field. Therefore, designing a system with adequate performance for will help in making diagnosis of the disease faster and accurate. In this paper, two models: backpropagation neural network and support vector machine has been developed. The performance obtained is also compared with other previously obtained algorithms to ascertain the best algorithms.

Keywords: breast cancer, data mining, neural network, support vector machine

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Authors: Azita Ramezani, Atousa Ramezani

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In the ever-escalating landscape of windows malware the necessity for pioneering defense strategies turns into undeniable this study introduces an avant-garde approach fusing the capabilities of clustering random forests and support vector machines SVM to combat the intricate web of cyber threats our fusion model triumphs with a staggering accuracy of 98.67 and an equally formidable f1 score of 98.68 a testament to its effectiveness in the realm of windows malware defense by deciphering the intricate patterns within malicious code our model not only raises the bar for detection precision but also redefines the paradigm of cybersecurity preparedness this breakthrough underscores the potential embedded in the fusion of diverse analytical methodologies and signals a paradigm shift in fortifying against the relentless evolution of windows malicious threats as we traverse through the dynamic cybersecurity terrain this research serves as a beacon illuminating the path toward a resilient future where innovative fusion models stand at the forefront of cyber threat defense.

Keywords: fusion models, cyber threat defense, windows malware, clustering, random forests, support vector machines (SVM), accuracy, f1-score, cybersecurity, malicious code detection

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Authors: Mette Dalgaard Nielsen

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The paper serves the purpose of providing an eco-systemic typology of fashion resale business models in Denmark while pointing to possibilities to learn from its wisdom during a time when a fundamental break with the dominant linear fashion paradigm has become inevitable. As we transgress planetary boundaries and can no longer continue the unsustainable path of over-exploiting the Earth’s resources, the global fashion industry faces a tremendous need for change. One of the preferred answers to the fashion industry’s sustainability crises lies in the circular economy, which aims to maximize the utilization of resources by keeping garments in use for longer. Thus, in the context of fashion, resale business models that allow pre-owned garments to change hands with the purpose of being reused in continuous cycles are considered to be among the most efficient forms of circularity. Methodologies: The paper is based on empirical data from an ongoing project and a series of qualitative pilot studies that have been conducted on the Danish resale market over a 2-year time period from Fall 2021 to Fall 2023. The methodological framework is comprised of (n) ethnography and fieldwork in selected resale environments, as well as semi-structured interviews and a workshop with eight business partners from the Danish fashion and textiles industry. By focusing on the real-world circulation of pre-owned garments, which is enabled by the identified resale business models, the research lets go of simplistic hypotheses to the benefit of dynamic, vibrant and non-linear processes. As such, the paper contributes to the emerging research field of circular economy and fashion, which finds itself in a critical need to move from non-verified concepts and theories to empirical evidence. Findings: Based on the empirical data and anchored in the business partners, the paper analyses and presents five distinct resale business models with different product, service and design characteristics. These are 1) branded resale, 2) trade-in resale, 3) peer-2-peer resale, 4) resale boutiques and consignment shops and 5) resale shelf/square meter stores and flea markets. Together, the five business models represent a plurality of resale-promoting business model design elements that have been found to contribute to the circulation of pre-owned garments in various ways for different garments, users and businesses in Denmark. Hence, the provided typology points to the necessity of prioritizing several rather than single resale business model designs, services and initiatives for the resale market to help reconfigure the linear fashion model and create a circular-ish future. Conclusions: The article represents a twofold research ambition by 1) presenting an original, up-to-date eco-systemic typology of resale business models in Denmark and 2) using the typology and its eco-systemic traits as a tool to understand different business model design elements and possibilities to help fashion grow out of its linear growth model. By basing the typology on eco-systemic mechanisms and actual exemplars of resale business models, it becomes possible to envision the contours of a genuine alternative to business as usual that ultimately helps bend the linear fashion model towards circularity.

Keywords: circular business models, circular economy, fashion, resale, strategic design, sustainability

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Authors: M. K. Witek-Hajduk, T. M. Napiórkowski

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A number of studies discussed the topic of benefits of retailers-manufacturers cooperation and coopetition. However, there are only few publications focused on the benefits of cooperation and coopetition between retailers and their suppliers of durable consumer goods; especially in the context of business model of cooperating partners. This paper aims to provide a clustering approach to segment retailers selling consumer durables according to the benefits they obtain from their cooperation with key manufacturers and differentiate the said retailers’ in term of the business models of cooperating partners. For the purpose of the study, a survey (with a CATI method) collected data on 603 consumer durables retailers present on the Polish market. Retailers are clustered both, with hierarchical and non-hierarchical methods. Five distinctive groups of consumer durables’ retailers are (based on the studied benefits) identified using the two-stage clustering approach. The clusters are then characterized with a set of exogenous variables, key of which are business models employed by the retailer and its partnering key manufacturer. The paper finds that the a combination of a medium sized retailer classified as an Integrator with a chiefly domestic capital and a manufacturer categorized as a Market Player will yield the highest benefits. On the other side of the spectrum is medium sized Distributor retailer with solely domestic capital – in this case, the business model of the cooperating manufactrer appears to be irreleveant. This paper is the one of the first empirical study using cluster analysis on primary data that defines the types of cooperation between consumer durables’ retailers and manufacturers – their key suppliers. The analysis integrates a perspective of both retailers’ and manufacturers’ business models and matches them with individual and joint benefits.

Keywords: benefits of cooperation, business model, cluster analysis, retailer-manufacturer cooperation

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Authors: Vishnu Nagendra, Marten Van Der Veen, Stefania Giodini

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Effective humanitarian logistics operations are a cornerstone in the success of disaster relief operations. However, for effectiveness, they need to be demand driven and supported by adequate data for prioritization. Without this data operations are carried out in an ad hoc manner and eventually become chaotic. The current availability of geospatial data helps in creating models for predictive damage and vulnerability assessment, which can be of great advantage to logisticians to gain an understanding on the nature and extent of the disaster damage. This translates into actionable information on the demand for relief goods, the state of the transport infrastructure and subsequently the priority areas for relief delivery. However, due to the unpredictable nature of disasters, the accuracy in the models need improvement which can be done using remote sensing data from UAVs (Unmanned Aerial Vehicles) or satellite imagery, which again come with certain limitations. This research addresses the need for a framework to combine data from different sources to support humanitarian logistic operations and prediction models. The focus is on developing a workflow to combine data from satellites and UAVs post a disaster strike. A three-step approach is followed: first, the data requirements for logistics activities are made explicit, which is done by carrying out semi-structured interviews with on field logistics workers. Second, the limitations in current data collection tools are analyzed to develop workaround solutions by following a systems design approach. Third, the data requirements and the developed workaround solutions are fit together towards a coherent workflow. The outcome of this research will provide a new method for logisticians to have immediately accurate and reliable data to support data-driven decision making.

Keywords: unmanned aerial vehicles, damage prediction models, remote sensing, data driven decision making

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Authors: Xian Li, Qing-Guo Wang, Jiangshuai Huang, Jidong Liu, Ming Yu, Tan Kok Poh

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In power industry, accurate electricity demand forecasting for a certain leading time is important for system operation and control, etc. In this paper, we investigate the modeling and forecasting of Singapore’s electricity demand. Several standard models, such as HWT exponential smoothing model, the ARMA model and the ANNs model have been proposed based on historical demand data. We applied them to Singapore electricity market and proposed three refinements based on simulation to improve the modeling accuracy. Compared with existing models, our refined model can produce better forecasting accuracy. It is demonstrated in the simulation that by adding forecasting error into the forecasting equation, the modeling accuracy could be improved greatly.

Keywords: power industry, electricity demand, modeling, forecasting

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Authors: Prajakta Musale, Bhargav Parlikar, Sakshi Parkhi, Anshu Parihar, Aryan Parikh, Diksha Parasharam, Parth Jadhav

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AR technology is basically a development of VR technology that harnesses the power of computers to be able to read the surroundings and create projections of digital models in the real world for the purpose of visualization, demonstration, and education. It has been applied to education, fields of prototyping in product design, development of medical models, battle strategy in the military and many other fields. Our Engineering Design and Innovation (EDAI) project focuses on the usage of augmented reality, visual mapping, and 3d-visualization along with animation and text boxes to help students in fields of education get a rough idea of the concepts such as flow and mechanical movements that may be hard to visualize at first glance.

Keywords: spatial mapping, ARKit, depth sensing, real-time rendering

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Authors: Abbas Kamalibandpey, Alain Beland, Joseph Mukendi Kabuya

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Since a rock mass is a discontinuum medium, its behaviour is governed by discontinuities such as faults, joint sets, lithologic contact, and bedding planes. Thus, rock slope stability analysis in jointed rock masses is largely dependent upon discontinuities constitutive equations. This paper studies the difference between the Mohr-Coulomb (MC) and the Barton-Bandis (BB) joint constitutive numerical models for lithological contacts and joint sets. For the rock in these models, generalized Hoek-Brown criteria have been considered. The joint roughness coefficient (JRC) and the joint wall compressive strength (JCS) are vital parameters in the BB model. The numerical models are applied to the rock slope stability analysis in the Mont-Wright (MW) mine. The Mont-Wright mine is owned and operated by ArcelorMittal Mining Canada (AMMC), one of the largest iron-ore open pit operations in Canada. In this regard, one of the high walls of the mine has been selected to undergo slope stability analysis with RS2D software, finite element method. Three piezometers have been installed in this zone to record pore water pressure and it is monitored by radar. In this zone, the AMP-IF and QRMS-IF contacts and very persistent and altered joint sets in IF control the rock slope behaviour. The height of the slope is more than 250 m and consists of different lithologies such as AMP, IF, GN, QRMS, and QR. To apply the B-B model, the joint sets and geological contacts have been scanned by Maptek, and their JRC has been calculated by different methods. The numerical studies reveal that the JRC of geological contacts, AMP-IF and QRMS-IF, and joint sets in IF had a significant influence on the safety factor. After evaluating the results of rock slope stability analysis and the radar data, the B-B constitutive equation for discontinuities has shown acceptable results to the real condition in the mine. It should be noted that the difference in safety factors in MC and BB joint constitutive models in some cases is more than 30%.

Keywords: barton-Bandis criterion, Hoek-brown and Mohr-Coulomb criteria, open pit, slope stability

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Authors: Tefera Worku Mekonnen, Hiseh Chih Tsai

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Enhancement of drug efficacy is essential in cancer treatment. The immune stimulator ovalbumin (Ova)-coated citric acid (AC-)-stabilized iron oxide nanoparticles (AC-IO-Ova NPs) and enhanced permeability and retention (EPR) based tumor targeted 4.5 (4.5G) poly(amidoamine) dendrimer-cisplatin nanocomplex (4.5GDP-Cis-pt NC) were used for enhanced anticancer efficiency. The formations of 4.5GDP-Cis-pt NC, AC-IO, and AC-IO-Ova NPs have been examined by FTIR, X-ray diffraction, Raman, and X-ray photoelectron spectroscopy. The conjugation of cisplatin (Cis-pt) with 4.5GDP was confirmed using carbon NMR. The tumor-specific 4.5GDP-Cis-pt NC provided ~45% and 28% cumulative cisplatin release in 72 h at pH 6.5 and 7.4, respectively. A significant immune response with high TNF-α and IL-6 cytokine secretion was confirmed when the co-incubation of AC-IO-Ova with RAW 264.7 or HaCaT cells. AC-IO-Ova NP was biocompatible in different cell lines, even at a high concentration (200 µg mL−1). In contrast, AC-IO-Ova NPs mixed with 4.5GDP-Cis-pt NC (Cis-pt at 15 µg mL−1) significantly increased the cytotoxicity against the cancer cells, which is dose-dependent on the concentration of AC-IO-Ova NPs. The increased anticancer effects may be attributed to the generation of reactive oxygen species (ROS). Moreover, the efficiency of anticancer cells may be further assisted by induction of an innate immune response via M1 macrophage polarization due to the presence of AC-IO-Ova NPs. We provide a better synergestic chemoimmunotherapeutic strategy to enhance the efficiency of anticancer of cisplatin via chemotherapeutic agent 4.5GDP-Cis-pt NC and induction of proinflammatory cytokines to stimulate innate immunity through AC-IO-Ova NPs against tumors.

Keywords: cisplatin-release, iron oxide, ovalbumin, poly(amidoamine) dendrimer

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Authors: Anastasiia Yu. Timofeeva

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Two new algorithms for nonparametric estimation of errors-in-variables models are proposed. The first algorithm is based on penalized regression spline. The spline is represented as a piecewise-linear function and for each linear portion orthogonal regression is estimated. This algorithm is iterative. The second algorithm involves locally weighted regression estimation. When the independent variable is measured with error such estimation is a complex nonlinear optimization problem. The simulation results have shown the advantage of the second algorithm under the assumption that true smoothing parameters values are known. Nevertheless the use of some indexes of fit to smoothing parameters selection gives the similar results and has an oversmoothing effect.

Keywords: grade point average, orthogonal regression, penalized regression spline, locally weighted regression

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Authors: Lely A. Luengas, Pedro R. Vizcaya, Giovanni Sánchez

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In this paper, a methodology is presented to obtain the Static Alignment Model for any transtibial amputee person. The proposed methodology starts from experimental data collected on the Hospital Militar Central, Bogotá, Colombia. The effects of transtibial prosthesis malalignment on amputees were measured in terms of joint angles, center of pressure (COP) and weight distribution. Some statistical tools are used to obtain the model parameters. Mathematical predictive models of prosthetic alignment were created. The proposed models are validated in amputees and finding promising results for the prosthesis Static Alignment. Static alignment process is unique to each subject; nevertheless the proposed methodology can be used in each transtibial amputee.

Keywords: information theory, prediction model, prosthetic alignment, transtibial prosthesis

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Authors: Getachew Mekaa, Bezatu Mengisteb, Tena Alamirewc

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Ashebeka River is the main source of drinking water supply for Assela City and its surrounding inhabitants. Apart from seasonal water reliability disruption, the cost of treating water downstream of the river has been increasing over time due to increased pollutants and suspended sediments. Therefore, this research aimed to identify geo-location and prioritize suspended sediment sources in the Ashebeka River catchment using sediment fingerprinting. We collected 58 composite soil samples and a river water sample for suspended sediment samples from the outlet, which were then filtered using Whatman filter paper. The samples were quantified for geochemical tracers with multi-element capability, and inductively coupled plasma-optical emission spectrometry (ICP-OES). Tracers with significant p-value and that passed the Kruskal-Wallis (KW) test were analyzed for stepwise discriminant function analysis (DFA). The DFA results revealed tracers with good discrimination were subsequently used for the mixed model analysis. The relative significant sediment source contributions from sub-catchments (km2): 3, 4, 1, and 2 were estimated as 49.31% (8), 26.71% (5), 23.65% (5.6), and 0.33% (28.4) respectively. The findings of this study will help the water utilities to prioritize areas of intervention, and the approach used could be followed for catchment prioritization in water safety plan development. Moreover, the findings of this research shed light on the integration of sediment fingerprinting into water safety plans to ensure the reliability of drinking water supplies.

Keywords: disruption of drinking water reliability, ashebeka river catchment, sediment fingerprinting, sediment source contribution, mixed model

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Authors: Ahmed Elbeheri, Tarek Zayed

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Steel bridges deterioration has been one of the problems in North America for the last years. Steel bridges deterioration mainly attributed to the difficult weather conditions. Steel bridges suffer fatigue cracks and corrosion, which necessitate immediate inspection. Visual inspection is the most common technique for steel bridges inspection, but it depends on the inspector experience, conditions, and work environment. So many Non-destructive Evaluation (NDE) models have been developed use Non-destructive technologies to be more accurate, reliable and non-human dependent. Non-destructive techniques such as The Eddy Current Method, The Radiographic Method (RT), Ultra-Sonic Method (UT), Infra-red thermography and Laser technology have been used. Digital Image processing will be used for Corrosion detection as an Alternative for visual inspection. Different models had used grey-level and colored digital image for processing. However, color image proved to be better as it uses the color of the rust to distinguish it from the different backgrounds. The detection of the rust is an important process as it’s the first warning for the corrosion and a sign of coating erosion. To decide which is the steel element to be repainted and how urgent it is the percentage of rust should be calculated. In this paper, an image processing approach will be developed to detect corrosion and its severity. Two models were developed 1st to detect rust and 2nd to detect rust percentage.

Keywords: steel bridge, bridge inspection, steel corrosion, image processing

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Authors: Jyh-Cherng Chen, Yi-Jie Lin

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This study tries to reuse the fly ash of municipal solid waste incinerator (MSWI) for the synthesis of zeolites. The fly ashes were treated with NaOH alkali fusion at different temperatures for 40 mins and then synthesized the zeolites with hydrothermal method at 105oC for different operation times. The effects of different operation conditions and the optimum synthesis parameters were explored. The specific surface area, surface morphology, species identification, adsorption capacity, and the reuse potentials of the synthesized zeolites were analyzed and evaluated. Experimental results showed that the optimum operation conditions for the synthesis of zeolite from the mixed fly ash were Si/Al=20, alkali/ash=1.5, alkali fusion reaction with NaOH at 800oC for 40 mins, hydrolysis with L/S=200 at 105oC for 24 hr, and hydrothermal synthesis at 105oC for 48 hr. The largest specific surface area of synthesized zeolite could be increased to 943.05m2/g. The influence of different operation parameters on the synthesis of zeolite from mixed fly ash followed the sequence of Si/Al > hydrolysis L/S> hydrothermal time > alkali fusion temperature > alkali/ash ratio. The XRD patterns of synthesized zeolites were identified to be similar with the ZSM-23 zeolite. The adsorption capacities of synthesized zeolite for pollutants were increased as rising the specific surface area of synthesized zeolite. In summary, MSWI fly ash can be treated and reused to synthesize the zeolite with high specific surface area by the alkali fusion and hydrothermal method. The zeolite can be reuse for the adsorption of various pollutants. They have great potential for development.

Keywords: alkali fusion, hydrothermal, fly ash, zeolite

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Authors: Anita Vidacs, Csaba Vagvolgyi, Judit Krisch

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The attachment of microorganisms to different surfaces and the development of biofilms can lead to outbreaks of food-borne diseases and economic losses due to perished food. In food processing environments, bacterial communities are generally formed by mixed cultures of different species. Plants are sources of several antimicrobial substances that may be potential candidates for the development of new disinfectants. We aimed to investigate cinnamon (Cinnamomum zeylanicum), marjoram (Origanum majorana), and thyme (Thymus vulgaris). Essential oils and their major components (cinnamaldehyde, terpinene-4-ol, and thymol) on four-species biofilms of E. coli, L. monocytogenes, P. putida, and S. aureus. Experiments had three parts: (i) determination of minimum bactericide concentration and the killing time with microdilution methods; (ii) elimination of the four-species 24– and 168-hours old biofilm from stainless steel, polypropylene, tile and wood surfaces; and (iii) comparing the disinfectant effect with industrial used per-acetic based sanitizer (HC-DPE). E. coli and P. putida were more resistant to investigated essential oils and their main components in biofilm, than L. monocytogenes and S. aureus. These Gram-negative bacteria were detected on the surfaces, where the natural based disinfectant had not total biofilm elimination effect. Most promoted solutions were the cinnamon essential oil and the terpinene-4-ol that could eradicate the biofilm from stainless steel, polypropylene and even from tile, too. They have a better disinfectant effect than HC-DPE. These natural agents can be used as alternative solutions in the battle against bacterial biofilms.

Keywords: biofilm, essential oils, surfaces, terpinene-4-ol

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Authors: Magdi Kamil Shafik Zekry

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The variety of available virtual equipment, strategies and activities may confuse and disorient even an experienced marketer. this is applicable mainly to B2B organizations, which might be generally less flexible in uptaking of virtual era than B2C businesses. B2B groups are lacking a framework that corresponds to the specifics of the B2B enterprise, and which facilitates to assess a business enterprise’s talents and to select the precise course. A B2B digital marketing adulthood model facilitates to fill this gap. however, modern-day advertising gives no widely permitted digital marketing adulthood model, and consequently, some marketing establishments offer their personal tools. The cause of this paper is constructing an optimized B2B virtual advertising and marketing maturity version based totally on a SWOT (strengths, weaknesses, opportunities, and threats) analysis of present models. The contemporary have a look at gives an analytical assessment of the existing virtual advertising adulthood models with open get entry to. The effects of the studies are twofold. First, the furnished SWOT analysis outlines the principle blessings and disadvantages of current fashions. Secondly, the strengths of existing virtual advertising maturity models, enables to discover the primary traits and the structure of an optimized B2B virtual advertising adulthood version. The research findings imply that most effective one out of three analyzed fashions may be used as a separate tool. This look at is a number of the first inspecting the usage of maturity models in virtual marketing. It helps businesses to select among the present virtual advertising fashions, the best one. furthermore, it creates a base for destiny research on virtual advertising adulthood fashions. This study contributes to the emerging B2B virtual advertising and marketing literature by using offering a SWOT analysis of the present virtual marketing maturity fashions and suggesting a structure and fundamental characteristics of an optimized B2B digital advertising adulthood model.

Keywords: electronics engineering, marketing, sales, E-commercedigital marketing, digital maturity, innovation, SMEsB2B digital marketing strategy, digital marketing, digital marketing maturity model, SWOT analysis

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Authors: Pranav Gunhal

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This paper presents an analysis of sentiment on Twitter towards the Karnataka elections held in 2023, utilizing transformer-based models specifically designed for sentiment analysis in Indic languages. Through an innovative data collection approach involving a combination of novel methods of data augmentation, online data preceding the election was analyzed. The study focuses on sentiment classification, effectively distinguishing between positive, negative, and neutral posts while specifically targeting the sentiment regarding the loss of the Bharatiya Janata Party (BJP) or the win of the Indian National Congress (INC). Leveraging high-performing transformer architectures, specifically IndicBERT, coupled with specifically fine-tuned hyperparameters, the AI models employed in this study achieved remarkable accuracy in predicting the INC’s victory in the election. The findings shed new light on the potential of cutting-edge transformer-based models in capturing and analyzing sentiment dynamics within the Indian political landscape. The implications of this research are far-reaching, providing invaluable insights to political parties for informed decision-making and strategic planning in preparation for the forthcoming 2024 Lok Sabha elections in the nation.

Keywords: sentiment analysis, twitter, Karnataka elections, congress, BJP, transformers, Indic languages, AI, novel architectures, IndicBERT, lok sabha elections

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Authors: Woo Young Jung, Min Ho Kwon, Bu Seog Ju

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A failure of the non-structural component can cause significant damages in critical facilities such as nuclear power plants and hospitals. Historically, it was reported that the damage from the leakage of sprinkler systems, resulted in the shutdown of hospitals for several weeks by the 1971 San Fernando and 1994 North Ridge earthquakes. In most cases, water leakages were observed at the cross joints, sprinkler heads, and T-joint connections in piping systems during and after the seismic events. Hence, the primary objective of this study was to understand the seismic performance of T-joint connections and to develop an analytical Finite Element (FE) model for the T-joint systems of 2-inch fire protection piping system in hospitals subjected to seismic ground motions. In order to evaluate the FE models of the piping systems using OpenSees, two types of materials were used: 1) Steel 02 materials and 2) Pinching 4 materials. Results of the current study revealed that the nonlinear moment-rotation FE models for the threaded T-joint reconciled well with the experimental results in both FE material models. However, the system-level fragility determined from multiple nonlinear time history analyses at the threaded T-joint was slightly different. The system-level fragility at the T-joint, determined by Pinching 4 material was more conservative than that of using Steel 02 material in the piping system.

Keywords: fragility, t-joint, piping, leakage, sprinkler

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Authors: Jiraporn Prommaha

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The purpose of this research is to study the development of Thailand Brand Identity and the perception of its image in order to find any guidelines for the identity development and the image perception of Thailand Brand in Health and Wellness Tourism. The paper is conducted through mixed methods research, both the qualitative and quantitative researches. The qualitative focuses on the in-depth interview of executive administrations from public and private sectors involved scholars and experts in identity and image issue, main 11 people. The quantitative research was done by the questionnaires to collect data from foreign tourists 800; Chinese tourists 400 and UK tourists 400. The technique used for this was the Exploratory Factor Analysis (EFA), this was to determine the relation between the structures of the variables by categorizing the variables into group by applying the Varimax rotation technique. This technique showed recognition the Thailand brand image related to the 2 countries, China and UK. The results found that guidelines for brand identity development and image perception of health and wellness tourism in Thailand; as following (1) Develop communication in order to understanding of the meaning of the word 'Health and beauty tourism' throughout the country, (2) Develop human resources as a national agenda, (3) Develop awareness rising in the conservation and preservation of natural resources of the country, (4) Develop the cooperation of all stakeholders in Health and Wellness Businesses, (5) Develop digital communication throughout the country and (6) Develop safety in Tourism.

Keywords: brand identity, image perception, nation branding, health and wellness tourism, mixed methods research

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Authors: Phakamani Xaba, Robert Huberts, Bilainu Oboirien

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The present study was aimed at developing & comparing two neural-network-based predictive models to predict shelf-life/product age of South African margarine using free fatty acid (FFA), water droplet size (D3.3), water droplet distribution (e-sigma), moisture content, peroxide value (PV), anisidine valve (AnV) and total oxidation (totox) value as input variables to the model. Brick margarine products which had varying ages ranging from fresh i.e. week 0 to week 47 were sourced. The brick margarine products which had been stored at 10 & 25 °C and were characterized. JMP and MATLAB models to predict shelf-life/ margarine age were developed and their performances were compared. The key performance indicators to evaluate the model performances were correlation coefficient (CC), root mean square error (RMSE), and mean absolute percentage error (MAPE) relative to the actual data. The MATLAB-developed model showed a better performance in all three performance indicators. The correlation coefficient of the MATLAB model was 99.86% versus 99.74% for the JMP model, the RMSE was 0.720 compared to 1.005 and the MAPE was 7.4% compared to 8.571%. The MATLAB model was selected to be the most accurate, and then, the number of hidden neurons/ nodes was optimized to develop a single predictive model. The optimized MATLAB with 10 neurons showed a better performance compared to the models with 1 & 5 hidden neurons. The developed models can be used by margarine manufacturers, food research institutions, researchers etc, to predict shelf-life/ margarine product age, optimize addition of antioxidants, extend shelf-life of products and proactively troubleshoot for problems related to changes which have an impact on shelf-life of margarine without conducting expensive trials.

Keywords: margarine shelf-life, predictive modelling, neural networks, oil oxidation

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Authors: Abdul Qader Melhem, Hacene Badache

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This paper deals with the theoretical and experimental study of shear connection via simple steel reinforcement shear connectors, which are steel reinforcing bars bent into L-shapes, instead of commonly used headed studs. This suggested L-shape connectors are readily available construction material in steel reinforcement. The composite section, therefore, consists of steel inverted T-section being embedded within a lightly reinforced concrete flange at the top slab as a unit. It should be noted that the cross section of these composite models involves steel inverted T-beam, replacing the steel top flange of a standard commonly employed I-beam section. The paper concentrates on the elastic and elastic-plastic behavior of these composite models. Failure modes either by cracking of concrete or shear connection be investigated in details. Elastic and elastoplastic formulas of the composite model have been computed for different locations of NA. Deflection formula has been derived, its value was close to the test value. With a supportive designing curve, this curve is valuable for both designing engineers and researchers. Finally, suggested designing curves and valuable equations will be presented. A check is made between theoretical and experimental outcomes.

Keywords: composite, elastic-plastic, failure, inverted T-section, L-Shape connectors

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Authors: O. Poleshchuk, E. Komarov

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In this paper, semantic spaces with the properties of completeness and orthogonality (complete orthogonal semantic spaces) were chosen as models of expert evaluations. As the theoretical and practical studies have shown all the properties of complete orthogonal semantic spaces correspond to the thinking activity of experts that is why these semantic spaces were chosen for modeling. Two methods of construction such spaces were proposed. Models of comparative and fuzzy cluster analysis of expert evaluations were developed. The practical application of the developed methods has demonstrated their viability and validity.

Keywords: expert evaluation, comparative analysis, fuzzy cluster analysis, theoretical and practical studies

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Authors: Shigeyuki Haruyama, Ken Kaminishi, Junji Kaneko, Tadayuki Kyoutani, Siti Ruhana Omar, Oke Oktavianty

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This study is used as a definition method to the value and function in manufacturing sector. In concurrence of discussion about present condition of modeling method, until now definition of 1D-CAE is ambiguity and not conceptual. Across all the physics fields, those methods are defined with the formulation of differential algebraic equation which only applied time derivation and simulation. At the same time, we propose semi-acausal modeling concept and differential algebraic equation method as a newly modeling method which the efficiency has been verified through the comparison of numerical analysis result between the semi-acausal modeling calculation and FEM theory calculation.

Keywords: system model, physical models, empirical models, conservation law, differential algebraic equation, object-oriented

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Authors: Nurul Bin Ibrahim

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Advancements in neural networks have offered valuable insights into Fluid Dynamics, notably in addressing turbulence-related challenges. In this research, we introduce multiple applications of models of neural networks, namely Feed-Forward and Recurrent Neural Networks, to explore the relationship between jet formations and stratified turbulence within stochastically excited Boussinesq systems. Using machine learning tools like TensorFlow and PyTorch, the study has created models that effectively mimic and show the underlying features of the complex patterns of jet formation and stratified turbulence. These models do more than just help us understand these patterns; they also offer a faster way to solve problems in stochastic systems, improving upon traditional numerical techniques to solve stochastic differential equations such as the Euler-Maruyama method. In addition, the research includes a thorough comparison with the Statistical State Dynamics (SSD) approach, which is a well-established method for studying chaotic systems. This comparison helps evaluate how well neural networks can help us understand the complex relationship between jet formations and stratified turbulence. The results of this study underscore the potential of neural networks in computational physics and fluid dynamics, opening up new possibilities for more efficient and accurate simulations in these fields.

Keywords: neural networks, machine learning, computational fluid dynamics, stochastic systems, simulation, stratified turbulence

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Authors: Janne Engblom, Elias Oikarinen

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The understanding of housing price dynamics is of importance to a great number of agents: to portfolio investors, banks, real estate brokers and construction companies as well as to policy makers and households. A panel dataset is one that follows a given sample of individuals over time, and thus provides multiple observations on each individual in the sample. Panel data models include a variety of fixed and random effects models which form a wide range of linear models. A special case of panel data models is dynamic in nature. A complication regarding a dynamic panel data model that includes the lagged dependent variable is endogeneity bias of estimates. Several approaches have been developed to account for this problem. In this paper, the panel models were estimated using the Common Correlated Effects estimator (CCE) of dynamic panel data which also accounts for cross-sectional dependence which is caused by common structures of the economy. In presence of cross-sectional dependence standard OLS gives biased estimates. In this study, U.S housing price dynamics were examined empirically using the dynamic CCE estimator with first-difference of housing price as the dependent and first-differences of per capita income, interest rate, housing stock and lagged price together with deviation of housing prices from their long-run equilibrium level as independents. These deviations were also estimated from the data. The aim of the analysis was to provide estimates with comparisons of estimates between 1980-1999 and 2000-2012. Based on data of 50 U.S cities over 1980-2012 differences of short-run housing price dynamics estimates were mostly significant when two time periods were compared. Significance tests of differences were provided by the model containing interaction terms of independents and time dummy variable. Residual analysis showed very low cross-sectional correlation of the model residuals compared with the standard OLS approach. This means a good fit of CCE estimator model. Estimates of the dynamic panel data model were in line with the theory of housing price dynamics. Results also suggest that dynamics of a housing market is evolving over time.

Keywords: dynamic model, panel data, cross-sectional dependence, interaction model

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Authors: Yehjune Heo

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As biometric systems become widely deployed, the security of identification systems can be easily attacked by various spoof materials. This paper contributes to finding a reliable and practical anti-spoofing method using Convolutional Neural Networks (CNNs) based on the types of loss functions and optimizers. The types of CNNs used in this paper include AlexNet, VGGNet, and ResNet. By using various loss functions including Cross-Entropy, Center Loss, Cosine Proximity, and Hinge Loss, and various loss optimizers which include Adam, SGD, RMSProp, Adadelta, Adagrad, and Nadam, we obtained significant performance changes. We realize that choosing the correct loss function for each model is crucial since different loss functions lead to different errors on the same evaluation. By using a subset of the Livdet 2017 database, we validate our approach to compare the generalization power. It is important to note that we use a subset of LiveDet and the database is the same across all training and testing for each model. This way, we can compare the performance, in terms of generalization, for the unseen data across all different models. The best CNN (AlexNet) with the appropriate loss function and optimizers result in more than 3% of performance gain over the other CNN models with the default loss function and optimizer. In addition to the highest generalization performance, this paper also contains the models with high accuracy associated with parameters and mean average error rates to find the model that consumes the least memory and computation time for training and testing. Although AlexNet has less complexity over other CNN models, it is proven to be very efficient. For practical anti-spoofing systems, the deployed version should use a small amount of memory and should run very fast with high anti-spoofing performance. For our deployed version on smartphones, additional processing steps, such as quantization and pruning algorithms, have been applied in our final model.

Keywords: anti-spoofing, CNN, fingerprint recognition, loss function, optimizer

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Authors: Ana Claudia de Souza Costa, Beatriz de Cássia Pinheiro Goulart, Karine de Cássia Cavalari, Henrique Ceretta Oliveira, Edineis de Brito Guirardello

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Background: During the COVID-19 pandemic, nursing represents the largest category of health professionals who were on the front line. Thus, investigating the practice environment and the job satisfaction of nursing professionals during the pandemic becomes fundamental since it reflects on the quality of care and the safety climate. The aim of this study was to evaluate and compare the nursing professionals' perception of the work environment, job satisfaction, and safety climate of the different hospitals and work shifts during the COVID-19 pandemic. Method: This is a cross-sectional survey with 130 nursing professionals from public, private and mixed hospitals in Brazil. For data collection, was used an electronic form containing the personal and occupational variables, work environment, job satisfaction, and safety climate. The data were analyzed using descriptive statistics and ANOVA or Kruskal-Wallis tests according to the data distribution. The distribution was evaluated by means of the Shapiro-Wilk test. The analysis was done in the SPSS 23 software, and it was considered a significance level of 5%. Results: The mean age of the participants was 35 years (±9.8), with a mean time of 6.4 years (±6.7) of working experience in the institution. Overall, the nursing professionals evaluated the work environment as favorable; they were dissatisfied with their job in terms of pay, promotion, benefits, contingent rewards, operating procedures and satisfied with coworkers, nature of work, supervision, and communication, and had a negative perception of the safety climate. When comparing the hospitals, it was found that they did not differ in their perception of the work environment and safety climate. However, they differed with regard to job satisfaction, demonstrating that nursing professionals from public hospitals were more dissatisfied with their work with regard to promotion when compared to professionals from private (p=0.02) and mixed hospitals (p< 0.01) and nursing professionals from mixed hospitals were more satisfied than those from private hospitals (p= 0.04) with regard to supervision. Participants working in night shifts had the worst perception of the work environment related to nurse participation in hospital affairs (p= 0.02), nursing foundations for quality care (p= 0.01), nurse manager ability, leadership and support (p= 0.02), safety climate (p< 0.01), job satisfaction related to contingent rewards (p= 0.04), nature of work (p= 0.03) and supervision (p< 0.01). Conclusion: The nursing professionals had a favorable perception of the environment and safety climate but differed among hospitals regarding job satisfaction for the promotion and supervision domains. There was also a difference between the participants regarding the work shifts, being the night shifts, those with the lowest scores, except for satisfaction with operational conditions.

Keywords: health facility environment, job satisfaction, patient safety, nursing

Procedia PDF Downloads 158