Search results for: root uptake models

Authors: Nicolae Daniel Stoica, Ion Mierlus Mazilu

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This work is one of numerical research and aims to address the issue of the design of new buildings in a 3D location of existing buildings. In today's continuous development and congestion of urban centers is a big question about the influence of the new buildings on an already existent vicinity site. Thus, in this study, we tried to focus on how existent buildings may be affected by any newly constructed buildings and in how far this influence is really decreased. The problem of modeling the influence of interaction between buildings is not simple in any area in the world, and neither in Romania. Unfortunately, most often the designers not done calculations that can determine how close to reality these 3D influences nor the simplified method and the more superior methods. In the most literature making a "shield" (the pilots or molded walls) is absolutely sufficient to stop the influence between the buildings, and so often the soil under the structure is ignored in the calculation models. The main causes for which the soil is neglected in the analysis are related to the complexity modeling of interaction between soil and structure. In this paper, based on a new simple but efficient methodology we tried to determine for a lot of study cases the influence, in terms of assessing the interaction land structure on the behavior of structures that influence a new building on an existing one. The study covers additional subsidence that may occur during the execution of new works and after its completion. It also highlighted the efforts diagrams and deflections in the soil for both the original case and the final stage. This is necessary to see to what extent the expected impact of the new building on existing areas.

Keywords: soil, structure, interaction, piles, earthquakes

Procedia PDF Downloads 291

Authors: Indika Fernando, Jiangang Fei, Roger Stanley, Hossein Enshaei

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Delicate produce such as fresh fruits are increasingly susceptible to physiological damage during the essential post-harvest operations such as transport and handling. Vibration and shock during the distribution are identified factors for produce damage within post-harvest supply chains. Mechanical damages caused during transit may significantly diminish the quality of fresh produce which may also result in a substantial wastage. Bananas are one of the staple fruit crops and the most sold supermarket produce in Australia. It is also the largest horticultural industry in the state of Queensland where 95% of the total production of bananas are cultivated. This results in significantly lengthy interstate supply chains where fruits are exposed to prolonged vibration and shocks. This paper is focused on determining the shock and vibration levels experienced by packaged bananas during transit from the farm gate to the retail market. Tri-axis acceleration data were captured by custom made accelerometer based data loggers which were set to a predetermined sampling rate of 400 Hz. The devices recorded data continuously for 96 Hours in the interstate journey of nearly 3000 Km from the growing fields in far north Queensland to the central distribution centre in Melbourne in Victoria. After the bananas were ripened at the ripening facility in Melbourne, the data loggers were used to capture the transport and handling conditions from the central distribution centre to three retail outlets within the outskirts of Melbourne. The quality of bananas were assessed before and after transport at each location along the supply chain. Time series vibration and shock data were used to determine the frequency and the severity of the transient shocks experienced by the packages. Frequency spectrogram was generated to determine the dominant frequencies within each segment of the post-harvest supply chain. Root Mean Square (RMS) acceleration levels were calculated to characterise the vibration intensity during transport. Data were further analysed by Fast Fourier Transform (FFT) and the Power Spectral Density (PSD) profiles were generated to determine the critical frequency ranges. It revealed the frequency range in which the escalated energy levels were transferred to the packages. It was found that the vertical vibration was the highest and the acceleration levels mostly oscillated between ± 1g during transport. Several shock responses were recorded exceeding this range which were mostly attributed to package handling. These detrimental high impact shocks may eventually lead to mechanical damages in bananas such as impact bruising, compression bruising and neck injuries which affect their freshness and visual quality. It was revealed that the frequency range between 0-5 Hz and 15-20 Hz exert an escalated level of vibration energy to the packaged bananas which may result in abrasion damages such as scuffing, fruit rub and blackened rub. Further research is indicated specially in the identified critical frequency ranges to minimise exposure of fruits to the harmful effects of vibration. Improving the handling conditions and also further study on package failure mechanisms when exposed to transient shock excitation will be crucial to improve the visual quality of bananas within the post-harvest supply chain in Australia.

Keywords: bananas, handling, post-harvest, supply chain, shocks, transport, vibration

Procedia PDF Downloads 190

Authors: Hang Yang, Jiahao Liu, Jichao Li, Kewei Yang, Minghao Li, Bingfeng Ge

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The system-of-systems (SoS) are utilized to characterize networks formed by integrating individual complex systems that demonstrate interdependence and interconnectedness. Research on the disintegration issue in SoS is significant in improving network survivability, maintaining network security, and optimizing SoS architecture. Accordingly, this study proposes an integrated framework called resilience-oriented disintegration in SoS (SoSRD), for modeling and solving the issue of SoS disintegration. Firstly, a SoS disintegration index (SoSDI) is presented to evaluate the disintegration effect of SoS. This index provides a practical description of the disintegration process and is the first integration of the network disintegration model and resilience models. Subsequently, we propose a resilience-oriented disintegration method based on reinforcement learning (RDRL) to enhance the efficiency of SoS disintegration. This method is not restricted by the problem scenario as well as considering the coexistence of disintegration (node/link removal) and recovery (node/link addition) during the process of SoS disintegration. Finally, the effectiveness and superiority of the proposed SoSRD are demonstrated through a case study. We demonstrate that our proposed framework outperforms existing indexes and methods in both node and link disintegration scenarios, providing a fresh perspective on network disintegration. The findings provide crucial insights into dismantling harmful SoS and designing a more resilient SoS.

Keywords: system-of-systems, disintegration index, resilience, reinforcement learning

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Authors: Leonardo Ramirez Lopez, Edward Guillen Pinto, Carlos Ramos Linares

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The controversy brought about by the increasing use of mHealth apps and their effectiveness for disease prevention and diagnosis calls for immediate control. Although a critical topic in research areas such as medicine, engineering, economics, among others, this issue lacks reliable implementation models. However, projects such as Open Web Application Security Project (OWASP) and various studies have helped to create useful and reliable apps. This research is conducted under a quality model to optimize two mHealth apps for older adults. Results analysis on the use of two physical activity monitoring apps - AcTiv (physical activity) and SMCa (energy expenditure) - is positive and ideal. Through a theoretical and practical analysis, precision calculations and personal information control of older adults for disease prevention and diagnosis were performed. Finally, apps are validated by a physician and, as a result, they may be used as health monitoring tools in physical performance centers or any other physical activity. The results obtained provide an effective validation model for this type of mobile apps, which, in turn, may be applied by other software developers that along with medical staff would offer digital healthcare tools for elderly people.

Keywords: model, validation, effective, healthcare, elderly people, mobile app

Procedia PDF Downloads 218

Authors: Qing Wu, Fei-Yan Li, Heng-Chang Zhang

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Multitask learning support vector machines (SVMs) have recently attracted increasing research attention. Given several related tasks, the single-task learning methods trains each task separately and ignore the inner cross-relationship among tasks. However, multitask learning can capture the correlation information among tasks and achieve better performance by training all tasks simultaneously. In addition, the asymmetric squared loss function can better improve the generalization ability of the models on the most asymmetric distributed data. In this paper, we first make two assumptions on the relatedness among tasks and propose two multitask learning proximal support vector machine algorithms, named MTL-a-PSVM and EMTL-a-PSVM, respectively. MTL-a-PSVM seeks a trade-off between the maximum expectile distance for each task model and the closeness of each task model to the general model. As an extension of the MTL-a-PSVM, EMTL-a-PSVM can select appropriate kernel functions for shared information and private information. Besides, two corresponding special cases named MTL-PSVM and EMTLPSVM are proposed by analyzing the asymmetric squared loss function, which can be easily implemented by solving linear systems. Experimental analysis of three classification datasets demonstrates the effectiveness and superiority of our proposed multitask learning algorithms.

Keywords: multitask learning, asymmetric squared loss, EMTL-a-PSVM, classification

Procedia PDF Downloads 134

Authors: Yafa Haron, Hanna Adami

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Background: World Health Organization emphasizes the contribution of nurses in planning and implementing health policies and reforms. Aim: To evaluate nursing students’ attitudes towards nurses’ involvement in health policy issues. Methods: Mixed-methods; qualitative and quantitative – a descriptive study. Participants - nursing students who were enrolled in their last year in the undergraduate program (BSN). Qualitative data included two open-ended questions: What is health policy and what is the importance of studying health policy, and 18 statements on the Likert Scale range 1-5. Results: Qualitativeanalysisrevealed that the majority of students defined health policy as a set of rules and regulations that defined procedures, borders, and proper conduct. 73% of students responded that nurses should be active in policymaking, but only 22% thought that nurses were currently involved in political issues. 28% thought that nurses do not have the knowledge and the time needed (60%) for political activity. 77% thought that the work environment did not encourage nurses to be politically active. Nursing students are aware of the importance towards nurses’ involvement in health policy issues, however, they do not have role models based on their low evaluation regarding nurses’ involvement in the health policy decision making process at the local or national level. Conclusions: Results emphasize the importance and the need of implementation the recommendation to include “advance policy changes” as core competency in nursing education and practice.

Keywords: health policy, nursing education, health systems, student perceptions

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Authors: Ria M. Caramoan, Analiza P. Rollon, Karl N. Vergel

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The Simple Interactive Models for Better Air Quality (SIM-air) is an integrated approach model that allows the available information to support the integrated urban air quality management. This study utilized the vehicular air pollution information system module of SIM-air for the assessment of vehicular emissions in Commonwealth Avenue, Quezon City, Philippines. The main objective of the study is to assess and predict the contribution of different types of vehicles to the vehicular emissions in terms of PM₁₀, SOₓ, and NOₓ at different policy and technology scenarios. For the base year 2017, the results show vehicular emissions of 735.46 tons of PM₁₀, 108.90 tons of SOₓ, and 2,101.11 tons of NOₓ. Motorcycle is the major source of particulates contributing about 52% of the PM₁₀ emissions. Meanwhile, Public Utility Jeepneys contribute 27% of SOₓ emissions and private cars using gasoline contribute 39% of NOₓ emissions. Ambient air quality monitoring was also conducted in the study area for the standard parameters of PM₁₀, S0₂, and NO₂. Results show an average of 88.11 µg/Ncm, 47.41 µg/Ncm and 22.54 µg/Ncm for PM₁₀, N0₂, and SO₂, respectively, all were within the DENR National Ambient Air Quality Guideline Values. Future emissions of PM₁₀, NOₓ, and SOₓ are estimated at different scenarios. Results show that in the year 2030, PM₁₀ emissions will be increased by 186.2%. NOₓ emissions and SOₓ emissions will also be increased by 38.9% and 5.5%, without the implementation of the scenarios.

Keywords: ambient air quality, emissions inventory, mobile air pollution, vehicular emissions

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Authors: Bilgehan Gültekin, Tuba Gültekin

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Peace diplomacy is the most important international tool to keep peace all over the world. The study titled “peace diplomacy for international relations” is consist of three part. In the first part, peace diplomacy is going to be introduced as a tool of peace communication and peace management. And, in this part, peace communication will be explained by international communication perspective. In the second part of the study,public relations events and communication campaigns will be developed originally for peace diplomacy. In this part, it is aimed original public communication dialogue management tools for peace diplomacy. the aim of the final part of the study, is to produce original public communication model for international relations. The model includes peace modules, peace management projects, original dialogue procedures and protocols, dialogue education, dialogue management strategies, peace actors, communication models, peace team management and public diplomacy steps. The creative part of the study aims to develop a model used for international relations for all countries. Creative Peace Diplomacy Model will be developed in the case of Turkey-Turkey-France and Turkey-Greece relations. So, communication and public relations events and campaigns are going to be developed as original for only this study.

Keywords: peace diplomacy, public communication model, dialogue management, international relations

Procedia PDF Downloads 541

Authors: Victor Breux, Jérôme Boutet, Alain Goret, Viviane Cattin

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Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%.

Keywords: anomaly detection, autoencoder, data centers, deep learning

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Authors: Qudama Albu-Jasim, Majdi Kanaan

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A case study about the load rating and strengthening evaluation of the six-span of steel girders bridge in Colton city of State of California is investigated. To simulate the load rating strengthening assessment for the Colton Overhead bridge, a three-dimensional finite element model built in the CSiBridge program is simulated. Three-dimensional finite-element models of the bridge are established considering the nonlinear behavior of critical bridge components to determine the feasibility and strengthening capacity under load rating analysis. The bridge was evaluated according to Caltrans Bridge Load Rating Manual 1st edition for rating the superstructure using the Load and Resistance Factor Rating (LRFR) method. The analysis for the bridge was based on load rating to determine the largest loads that can be safely placed on existing I-girder steel members and permitted to pass over the bridge. Through extensive numerical simulations, the bridge is identified to be deficient in flexural and shear capacities, and therefore strengthening for reducing the risk is needed. An in-depth parametric study is considered to evaluate the sensitivity of the bridge’s load rating response to variations in its structural parameters. The parametric analysis has exhibited that uncertainties associated with the steel’s yield strength, the superstructure’s weight, and the diaphragm configurations should be considered during the fragility analysis of the bridge system.

Keywords: load rating, CSIBridge, strengthening, uncertainties, case study

Procedia PDF Downloads 211

Authors: Igor Filikhin, Vladimir M. Suslov, Roman Ya. Kezerashvili, Branislav Vlahivic

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The mass-polarization term of the three-body kinetic energy operator is evaluated for different systems which include two identical particles: A+A+B. The term has to be taken into account for the analysis of AB- and AA-interactions based on experimental data for two- and three-body ground state energies. In this study, we present three-body calculations within the framework of a potential model for the kaonic clusters K−K−p and ppK−, nucleus 3H and hypernucleus 6 ΛΛHe. The systems are well clustering as A+ (A+B) with a ground state energy E2 for the pair A+B. The calculations are performed using the method of the Faddeev equations in configuration space. The phenomenological pair potentials were used. We show a correlation between the mass ratio mA/mB and the value δB of the mass-polarization term. For bosonic-like systems, this value is defined as δB = 2E2 − E3, where E3 is three-body energy when VAA = 0. For the systems including three particles with spin(isospin), the models with average AB-potentials are used. In this case, the Faddeev equations become a scalar one like for the bosonic-like system αΛΛ. We show that the additional energy conected with the mass-polarization term can be decomposite to a sum of the two parts: exchenge related and reduced mass related. The state of the system can be described as the following: the particle A1 is bound within the A + B pair with the energy E2, and the second particle A2 is bound with the pair with the energy E3 − E2. Due to the identity of A particles, the particles A1 and A2 are interchangeable in the pair A + B. We shown that the mass polarization δB correlates with a type of AB potential using the system αΛΛ as an example.

Keywords: three-body systems, mass polarization, Faddeev equations, nuclear interactions

Procedia PDF Downloads 377

Authors: Deepanwita Deka, Dhruva Kumar Jha

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Endophytes are the microbes that colonize living, internal tissues of plants without causing any immediate, overt negative effects. Endophytes are rich source of therapeutic substances like antimicrobial, anticancerous, herbicidal, insecticidal, immunomodulatory compounds. Brucea mollis, commonly known as Quinine in Assam, belonging to the family Simaroubaceae, is a shrub or small tree, recorded as endangered species in North East India by CAMP survey in 2003. It is traditionally being used as antimalarial and antimicrobial agent and has antiplasmodial, cytotoxic, anticancer, diuretic, cardiovascular effect etc. Being endangered and medicinal; this plant may host certain noble endophytes which need to be studied in depth. The aim of the present study was isolation and identification of potent endophytic fungi from Brucea mollis, an endangered medicinal plant, to protect it from extinction due to over use for medicinal purposes. Aseptically collected leaves, barks and roots samples of healthy plants were washed and cut into a total of 648 segments of about 2 cm long and 0.5 cm broad with sterile knife, comprising 216 segments each from leaves, barks and roots. These segments were surface sterilized using ethanol, mercuric chloride (HgCl2) and aqueous solution of sodium hypochlorite (NaClO). Different media viz., Czapeck-Dox-Agar (CDA, Himedia), Potato-Dextrose-Agar (PDA, Himedia), Malt Extract Agar (MEA, Himedia), Sabourad Dextrose Agar (SDA, Himedia), V8 juice agar, nutrient agar and water agar media and media amended with plant extracts were used separately for the isolation of the endophytic fungi. A total of 11 fungal species were recovered from leaf, bark and root tissues of B. mollis. The isolates were screened for antimicrobial, antioxidant and enzymatic activities using certain protocols. Cochliobolus geniculatus was identified as the most dominant species. The mycelia sterilia (creamy white) showing highest inhibitory activity against Candida albicans (MTCC 183) was induced to sporulate using modified PDA media. The isolate was identified as Geosmithia pallida. The internal transcribed spacer of rDNA was sequenced for confirmation of the taxonomic identity of the sterile mycelia (creamy white). The internal transcribed spacer r-DNA sequence was submitted to the NCBI (KU693285) for the first time from India. G. pallida and Penicillium showed highest antioxidant activity among all the isolates. The antioxidant activity of G. pallida and Penicillium didn’t show statistically significant difference (P˃0.05). G. pallida, Cochliobolus geniculatus and P. purpurogenum respectively showed highest cellulase, amylase and protease activities. Thus, endopytic fungal isolates may be used as potential natural resource of pharmaceutical importance. The endophytic fungi, Geosmithia pallida, may be used for synthesis of pharmaceutically important natural products and consequently can replace plants hitherto used for the same purpose. This study suggests that endophytes should be investigated more aggressively to better understand the endophyte biology of B. mollis.

Keywords: Antimicrobial activity, antioxidant activity, Brucea mollis, endophytic fungi, enzyme activity, Geosmithia pallida

Procedia PDF Downloads 187

Authors: Nasim Fallahi, Erasmo Carrera, Alfonso Pagani

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Variable angle tow describes the fibres which are curvilinearly steered in a composite lamina. Significantly, stiffness tailoring freedom of VAT composite laminate can be enlarged and enabled. Composite structures with curvilinear fibres have been shown to improve the buckling load carrying capability in contrast with the straight laminate composites. However, the optimal design and analysis of VAT are faced with high computational efforts due to the increasing number of variables. In this article, an efficient optimum solution has been used in combination with 1D Carrera’s Unified Formulation (CUF) to investigate the optimum fibre orientation angles for buckling analysis. The particular emphasis is on the LE-based CUF models, which provide a Lagrange Expansions to address a layerwise description of the problem unknowns. The first critical buckling load has been considered under simply supported boundary conditions. Special attention is lead to the sensitivity of buckling load corresponding to the fibre orientation angle in comparison with the results which obtain through the Genetic Algorithm (GA) optimization frame and then Artificial Neural Network (ANN) is applied to investigate the accuracy of the optimized model. As a result, numerical CUF approach with an optimal solution demonstrates the robustness and computational efficiency of proposed optimum methodology.

Keywords: beam structures, layerwise, optimization, variable stiffness

Procedia PDF Downloads 142

Authors: Pelin Kinay, Andy P. Morse, Elmer V. Villanueva, Karyn Morrissey, Philip L Staddon, Shanzheng Zhang, Jingjing Liu

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The present study aims to evaluate the climate change and health related knowledge, attitudes and practices (KAP) of the elderly population (60 years plus) in Hefei and Suzhou cities of China (n=300). This cross-sectional study includes 150 participants in each city. Data regarding demographic characteristics, KAP, and climate change perceptions were collected using a semi-structured questionnaire. When asked about the potential impacts of climate change over 79% of participants stated that climate change affected their lifestyle. Participants were most concerned about storms (51.7%), food shortage (33.3%) and drought (26%). The main health risks cited included water contamination (32%), air pollution related diseases (38.3%) and lung disease (43%). Finally, a majority (68.3%) did not report receiving government assistance on climate change issues. Logistic regression models were used to analyse the data in order to understand the links between socio-demographical factors and KAP of the participants. These findings provide insights for potential adaptation strategies targeting the elderly. It is recommended that government should take responsibility in creating awareness strategies to improve the coping capacity of elderly in China to climate change and its health impacts and develop climate change adaptation strategies.

Keywords: China, climate change, elderly, KAP

Procedia PDF Downloads 267

Authors: Qinghua Xing, Noha M. Mesbah, Haisheng Wang, Jun Li, Baisuo Zhao

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Droplet digital PCR (ddPCR) is being increasingly adopted for gene detection and quantification because of its higher sensitivity and specificity. According to previous observations and our lab data, it is essential to use endogenous reference genes (RGs) when investigating gene expression at the mRNA level under salt stress. This study aimed to select and validate suitable RGs for gene expression under salt stress using ddPCR. Six candidate RGs were selected based on the tandem mass tag (TMT)-labeled quantitative proteomics of Alkalicoccus halolimnae at four salinities. The expression stability of these candidate genes was evaluated using statistical algorithms (geNorm, NormFinder, BestKeeper and RefFinder). There was a small fluctuation in cycle threshold (Ct) value and copy number of the pdp gene. Its expression stability was ranked in the vanguard of all algorithms, and was the most suitable RG for quantification of expression by both qPCR and ddPCR of A. halolimnae under salt stress. Single RG pdp and RG combinations were used to normalize the expression of ectA, ectB, ectC, and ectD under four salinities. The present study constitutes the first systematic analysis of endogenous RG selection for halophiles responding to salt stress. This work provides a valuable theory and an approach reference of internal control identification for ddPCR-based stress response models.

Keywords: endogenous reference gene, salt stress, ddPCR, RT-qPCR, Alkalicoccus halolimnae

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Authors: A. I. Augie, Y. Alhassan, U. Z. Magawata

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Geophysical investigation was carried out at wacot rice factory Argungu north-western Nigeria, using the 2D electrical resistivity method. The area falls between latitude 12˚44′23ʺN to 12˚44′50ʺN and longitude 4032′18′′E to 4032′39′′E covering a total area of about 1.85 km. Two profiles were carried out with Wenner configuration using resistivity meter (Ohmega). The data obtained from the study area were modeled using RES2DIVN software which gave an automatic interpretation of the apparent resistivity data. The inverse resistivity models of the profiles show the high resistivity values ranging from 208 Ωm to 651 Ωm. These high resistivity values in the overburden were due to dryness and compactness of the strata that lead to consolidation, which is an indication that the area is free from leachate contaminations. However, from the inverse model, there are regions of low resistivity values (1 Ωm to 18 Ωm), these zones were observed and identified as clayey and the most contaminated zones. The regions of low resistivity thereby indicated the leachate plume or the highly leachate concentrated zones due to similar resistivity values in both clayey and leachate. The regions of leachate are mainly from the factory into the surrounding area and its groundwater. The maximum leachate infiltration was found at depths 1 m to 15.9 m (P1) and 6 m to 15.9 m (P2) vertically, as well as distance along the profiles from 67 m to 75 m (P1), 155 m to 180 m (P1), and 115 m to 192 m (P2) laterally.

Keywords: contaminant, leachate, soil, groundwater, electrical, resistivity

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Authors: Asal Pournaghshband, Roham Maher

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This study presents a numerical analysis of the cyclic behavior of H-shaped steel columns, focusing on different steel grades, including austenitic, ferritic, duplex stainless steel, and carbon steel. Finite Element (FE) models were developed and validated against experimental data, demonstrating a predictive accuracy of up to 6.5%. The study examined key parameters such as energy dissipation and failure modes. Results indicate that duplex stainless steel offers the highest strength, with superior energy dissipation but a tendency for brittle failure at maximum strains of 0.149. Austenitic stainless steel demonstrated balanced performance with excellent ductility and energy dissipation, showing a maximum strain of 0.122, making it highly suitable for seismic applications. Ferritic stainless steel, while stronger than carbon steel, exhibited reduced ductility and energy absorption. Carbon steel displayed the lowest performance in terms of energy dissipation and ductility, with significant strain concentrations leading to earlier failure. These findings provide critical insights into optimizing material selection for earthquake-resistant structures, balancing strength, ductility, and energy dissipation under seismic conditions.

Keywords: energy dissipation, finite element analysis, H-shaped columns, seismic performance, stainless steel grades

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Authors: Lars Rödel, Jonas Krebs, Gregor Müller

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The growing price pressure due to the increasing number of global suppliers, the growing individualization of products and ever-shorter delivery times are upcoming challenges in the industry. In this context, Mass Personalization stands for the individualized production of customer products in batch size 1 at the price of standardized products. The possibilities of digitalization and automation of technical order processing open up the opportunity for companies to significantly reduce their cost of complexity and lead times and thus enhance their competitiveness. Many companies already use a range of CAx tools and configuration solutions today. Often, the expert knowledge of employees is hidden in "knowledge silos" and is rarely networked across processes. DesignChain describes the automated digital process from the recording of individual customer requirements, through design and technical preparation, to production. Configurators offer the possibility of mapping variant-rich products within the Design Chain. This transformation of customer requirements into product features makes it possible to generate even complex CAD models, such as those for large-scale plants, on a rule-based basis. With the aid of an automated CAx chain, production-relevant documents are thus transferred digitally to production. This process, which can be fully automated, allows variants to always be generated on the basis of current version statuses.

Keywords: automation, design, CAD, CAx

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Authors: Mounir Kehal, Adel Al Araifi

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The post-globalisation epoch has placed businesses everywhere in new and different competitive situations where knowledgeable, effective and efficient behaviour has come to provide the competitive and comparative edge. Enterprises have turned to explicit- and even conceptualising on tacit- Knowledge Management to elaborate a systematic approach to develop and sustain the Intellectual Capital needed to succeed. To be able to do that, you have to be able to visualize your organization as consisting of nothing but knowledge and knowledge flows, whilst being presented in a graphical and visual framework, referred to as automated organizational cartography. Hence, creating the ability of further actively classifying existing organizational content evolving from and within data feeds, in an algorithmic manner, potentially giving insightful schemes and dynamics by which organizational know-how is visualised. It is discussed and elaborated on most recent and applicable definitions and classifications of knowledge management, representing a wide range of views from mechanistic (systematic, data driven) to a more socially (psychologically, cognitive/metadata driven) orientated. More elaborate continuum models, for knowledge acquisition and reasoning purposes, are being used for effectively representing the domain of information that an end user may contain in their decision making process for utilization of available organizational intellectual resources (i.e. Autocart). In this paper we present likewise an empirical research study conducted previously to try and explore knowledge diffusion in a specialist knowledge domain.

Keywords: knowledge management, knowledge maps, knowledge diffusion, organizational cartography

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Authors: Aragorn Joaquin Pineda Dela Cruz

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Road traffic congestion is continually deteriorating in Hong Kong. The largest contributing factor is the increase in vehicle fleet size, resulting in higher competition over the utilisation of road space. This study proposes a project that can process closed-circuit television images and videos to provide real-time traffic detection and prediction capabilities. Specifically, a deep-learning model involving computer vision techniques for video and image-based vehicle counting, then a separate model to detect and predict traffic congestion levels based on said data. State-of-the-art object detection models such as You Only Look Once and Faster Region-based Convolutional Neural Networks are tested and compared on closed-circuit television data from various major roads in Hong Kong. It is then used for training in long short-term memory networks to be able to predict traffic conditions in the near future, in an effort to provide more precise and quicker overviews of current and future traffic conditions relative to current solutions such as navigation apps.

Keywords: intelligent transportation system, vehicle detection, traffic analysis, deep learning, machine learning, computer vision, traffic prediction

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Authors: Philipp Sommer, Amgad Agoub

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The construction sector significantly impacts global CO₂ emissions, particularly through the energy usage of residential buildings. To address this, various governments, including Germany's, are focusing on reducing emissions via sustainable refurbishment initiatives. This study examines the application of machine learning (ML) to estimate energy demands dynamically in residential buildings and enhance the potential for large-scale sustainable refurbishment. A major challenge in Germany is the lack of extensive publicly labeled datasets for energy performance, as energy performance certificates, which provide critical data on building-specific energy requirements and consumption, are not available for all buildings or require on-site inspections. Conversely, England and other countries in the European Union (EU) have rich public datasets, providing a viable alternative for analysis. This research adapts insights from these English datasets to the German context by developing a comprehensive data schema and calibration dataset capable of predicting building energy demand effectively. The study proposes a minimal feature set, determined through feature importance analysis, to optimize the ML model. Findings indicate that ML significantly improves the scalability and accuracy of energy demand forecasts, supporting more effective emissions reduction strategies in the construction industry. Integrating energy performance certificates into municipal heat planning in Germany highlights the transformative impact of data-driven approaches on environmental sustainability. The goal is to identify and utilize key features from open data sources that significantly influence energy demand, creating an efficient forecasting model. Using Extreme Gradient Boosting (XGB) and data from energy performance certificates, effective features such as building type, year of construction, living space, insulation level, and building materials were incorporated. These were supplemented by data derived from descriptions of roofs, walls, windows, and floors, integrated into three datasets. The emphasis was on features accessible via remote sensing, which, along with other correlated characteristics, greatly improved the model's accuracy. The model was further validated using SHapley Additive exPlanations (SHAP) values and aggregated feature importance, which quantified the effects of individual features on the predictions. The refined model using remote sensing data showed a coefficient of determination (R²) of 0.64 and a mean absolute error (MAE) of 4.12, indicating predictions based on efficiency class 1-100 (G-A) may deviate by 4.12 points. This R² increased to 0.84 with the inclusion of more samples, with wall type emerging as the most predictive feature. After optimizing and incorporating related features like estimated primary energy consumption, the R² score for the training and test set reached 0.94, demonstrating good generalization. The study concludes that ML models significantly improve prediction accuracy over traditional methods, illustrating the potential of ML in enhancing energy efficiency analysis and planning. This supports better decision-making for energy optimization and highlights the benefits of developing and refining data schemas using open data to bolster sustainability in the building sector. The study underscores the importance of supporting open data initiatives to collect similar features and support the creation of comparable models in Germany, enhancing the outlook for environmental sustainability.

Keywords: machine learning, remote sensing, residential building, energy performance certificates, data-driven, heat planning

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Authors: Bahman Ghaderi, Hedayat Hosseini, Parviz Kafche

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The role of knowledge management processes in achieving the strategic goals of organizations is crucial. To this end, understanding the relationship between knowledge management processes and different aspects of strategic thinking (followed by long-term organizational planning) should be considered. This research examines the relationship between each of the five knowledge management processes (creation, storage, transfer, audit, and deployment) with each dimension of strategic thinking (vision, creativity, thinking, communication and analysis) in one of the major sectors of the food industry in Iran. In this research, knowledge management and its dimensions (knowledge acquisition, knowledge storage, knowledge transfer, knowledge auditing, and finally knowledge utilization) as independent variables and strategic thinking and its dimensions (creativity, systematic thinking, vision, strategic analysis, and strategic communication) are considered as the dependent variable. The statistical population of this study consisted of 245 managers and employees of Minoo Food Industrial Group in Tehran. In this study, a simple random sampling method was used, and data were collected by a questionnaire designed by the research team. Data were analyzed using SPSS 21 software. LISERL software is also used for calculating and drawing models and graphs. Among the factors investigated in the present study, knowledge storage with 0.78 had the most effect, and knowledge transfer with 0.62 had the least effect on knowledge management and thus on strategic thinking.

Keywords: knowledge management, strategic thinking, knowledge management processes, food industry

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Authors: Zhiyan Meng, Dan Liu, Jintao Meng

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Dependable encrypted traffic classification is crucial for improving cybersecurity and handling the growing amount of data. Large language models have shown that learning from large datasets can be effective, making pre-trained methods for encrypted traffic classification popular. However, attention-based pre-trained methods face two main issues: their large neural parameters are not suitable for low-computation environments like mobile devices and real-time applications, and they often overfit by getting stuck in local minima. To address these issues, we developed a lightweight transformer model, which reduces the computational parameters through lightweight vocabulary construction and Squeeze-and-Excitation Block. We use sharpness-aware optimization to avoid local minima during pre-training and capture temporal features with relative positional embeddings. Our approach keeps the model's classification accuracy high for downstream tasks. We conducted experiments on four datasets -USTC-TFC2016, VPN 2016, Tor 2016, and CICIOT 2022. Even with fewer than 18 million parameters, our method achieves classification results similar to methods with ten times as many parameters.

Keywords: sharpness-aware optimization, encrypted traffic classification, squeeze-and-excitation block, pretrained model

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Authors: Samson Ehindero, Oluwole Akinola

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Substantial evidence has shown an association between type 2 diabetes (T2D) and cognitive decline, Trans Cinnamaldehyde (TCA) has been shown to have many potent pharmacological properties. In this present study, we are currently investigating the effects of TCA on type II diabetes-induced neurodegeneration. Neurodegeneration was induced in forty (40) adult wistar rats using high fat diet (HFD) for 4 months followed by low dose of streptozotocin (STZ) (40 mg/kg, i.p.) administration. TCA was administered orally for 30 days at the doses of 40mg/kg and 60mg/kg body weight. Animals were randomized and divided into following groups; A- control group, B- diabetic group, C- TCA (high dose), D- diabetic + TCA (high dose), E- diabetic + TCA (high dose) with high fat diet, F- TCA Low dose, G- diabetic + TCA (low dose) and H- diabetic + TCA (low dose) with high fat diet. Animals were subjected to behavioral tests followed by histological studies of the hippocampus. Demented rats showed impaired behavior in Y- Maze test compared to treated and control groups. Trans Cinnamaldehyde restores the histo architecture of the hippocampus of demented rats. This present study demonstrates that treatment with trans- cinnamaldehyde improves behavioral deficits, restores cellular histo architecture in rat models of neurodegeneration.

Keywords: neurodegeneration, trans cinnamaldehyde, high fat diet, streptozotocin

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Authors: Jer Ping Ooi, Shah Rizal Bin Kasim, Nor Aini Saidin

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The objective of this study was to synthesize nano-sized β-tricalcium phosphate (β-TCP) powder and assess its cytotoxic effects on human osteoblast (hFOB1.19) by using four cytotoxicity assays, namely, lactose dehydrogenase (LDHe), tetrazolium hydroxide (XTT), neutral red (NR), and sulforhodamine B (SRB) assays. β-tricalcium phosphate (β-TCP) is a calcium phosphate compound commonly used as an implant material. To date, bulk-sized β-TCP is reported to be readily tolerated by the osteogenic cells and body based on in vitro, in vivo experiments and clinical studies. However, to what extent of nano-sized β-TCP will react in models as compared to bulk β-TCP is yet to be investigated. Thus, in this project, the cells were treated with nano β-TCP powder within a range of concentrations from 0 to 1000 μg/mL for 24, 48, and 72 h. The cytotoxicity tests showed that loss of cell viability ( > 50%) was high for hFOB1.19 cells in all assays. Cell cycle and apoptosis analysis of hFOB1.19 cells revealed that 50 μg/mL of the compound led to 30.5% of cells being apoptotic after 72 h of incubation, and the percentage was increased to 58.6% when the concentration was increased to 200 μg/mL. When the incubation time was increased from 24 to 72 h, the percentage of apoptotic cells increased from 17.3% to 58.6% when the hFOB1.19 were exposed with 200 μg/mL of nano β-TCP powder. Thus, both concentration and exposure duration affected the cytotoxicity effects of the nano β-TCP powder on hFOB1.19. We hypothesize that these cytotoxic effects on hFOB1.19 are related to the nano-scale size of the β-TCP.

Keywords: β-tricalcium phosphate, hFOB1.19, adipose-derived mesenchymal stem cells, cytotoxicity

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Authors: Yun-Tao Zhang, Jong-Yeop Bae, Whoi-Yul Kim

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Background modeling and subtraction in video analysis has been widely proved to be an effective method for moving objects detection in many computer vision applications. Over the past years, a large number of approaches have been developed to tackle different types of challenges in this field. However, the dynamic background and illumination variations are two of the most frequently occurring issues in the practical situation. This paper presents a new two-layer model based on codebook algorithm incorporated with local binary pattern (LBP) texture measure, targeted for handling dynamic background and illumination variation problems. More specifically, the first layer is designed by block-based codebook combining with LBP histogram and mean values of RGB color channels. Because of the invariance of the LBP features with respect to monotonic gray-scale changes, this layer can produce block-wise detection results with considerable tolerance of illumination variations. The pixel-based codebook is employed to reinforce the precision from the outputs of the first layer which is to eliminate false positives further. As a result, the proposed approach can greatly promote the accuracy under the circumstances of dynamic background and illumination changes. Experimental results on several popular background subtraction datasets demonstrate a very competitive performance compared to previous models.

Keywords: background subtraction, codebook model, local binary pattern, dynamic background, illumination change

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Authors: Fazilet Alachaher

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Within the constructivist perspective of teaching, which views skilled teaching as knowing what to do in uncertain and unpredictable situations, this research essay explores the topic of reflective teaching by investigating the following questions: (1) What is reflective teaching and why is it important? (2) Why should teachers be trained to be reflective and how can they be prepared to be reflective? (3) What is the role of the teaching context in teachers’ attempts to be reflective? This paper suggests that reflective teaching is important because of the various potential benefits to teaching. Through reflection, teachers can maintain their voices and creativeness thus have authority to affect students, curriculum and school policies. The discussions also highlight the need to prepare student teachers and their professional counterparts to be reflective, so they can develop the characteristics of reflective teaching and gain the potential benefits of reflection. This can be achieved by adopting models and techniques that are based on constructivist pedagogical approaches. The paper also suggests that maintaining teachers’ attempts to be reflective in a workplace context and aligning practice with pre-service teacher education programs require the administrators or the policy makers to provide the following: sufficient time for teachers to reflect and work collaboratively to discuss challenges encountered in teaching, fewer non-classroom duties, regular in-service opportunities, more facilities and freedom in choosing suitable ways of evaluating their students’ progress and needs.

Keywords: creative teaching, reflective teaching, constructivist pedagogical approaches, teaching context, teacher’s role, curriculum and school policies, teaching context effect

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Authors: Mudavath Raju, Bhaskar Gugulothu, B. Rajendra Naik

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The rapid advances in Very Large Scale Integration (VLSI) technology has resulted in the reduction of minimum feature size to sub-quarter microns and switching time in tens of picoseconds or even less. As a result, the degradation of high-speed digital circuits due to signal integrity issues such as coupling effects, clock feedthrough, crosstalk noise and delay uncertainty noise. Crosstalk noise in VLSI interconnects is a major concern and reduction in VLSI interconnect has become more important for high-speed digital circuits. It is the most effectively considered in Deep Sub Micron (DSM) and Ultra Deep Sub Micron (UDSM) technology. Increasing spacing in-between aggressor and victim line is one of the technique to reduce the crosstalk. Guard trace or shield insertion in-between aggressor and victim is also one of the prominent options for the minimization of crosstalk. In this paper, far end crosstalk noise is estimated with mutual inductance and capacitance RLC interconnect model. Also investigated the extent of crosstalk in capacitive and inductively coupled interconnects to minimizes the same through shield insertion technique.

Keywords: VLSI, interconnects, signal integrity, crosstalk, shield insertion, guard trace, deep sub micron

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Authors: Dení Stincer Gómez, Zuraya Monroy Nasr

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From the epistemology of emotions, one of the topics of current reflection is the function that emotions fulfill in the rational processes involved in scientific activity. So far, three functions have been assigned to them: selective, heuristic, and carriers of content. In this last function, it is argued that emotions, like our perceptual organs, contribute relevant content to reasoning, which is then converted into linguistic statements or graphic representations. In this paper, of a qualitative and philosophical nature, arguments are provided for two hypotheses 1) if emotions provide content to the mind, which then translates it into language or representations, then it is important to take up the idea of the Saussurean linguistic sign to understand this process. This sign has two elements: the signified and the signifier. Emotions would provide meanings, and reasoning creates the signifier, and 2) the meanings provided by emotions are properties and qualities of phenomena generally not accessible to the sense organs. These meanings must be imagined, and the imagination is nurtured by the feeling that "maybe this is the way." One way to access the content provided by emotions can be through imagined scientific drawings. The atomic models created since Thomson, the structure of crystals by René Just, the representations of lunar eclipses by Johannes, fractal geometry, and the structure of DNA, among others, have resulted fundamentally from the imagination. These representations, not provided by the sense organs, seem to come from the emotional involvement of scientists in their desire to understand, explain and discover.

Keywords: emotions, epistemic functions of emotions, scientific drawing, linguistic sign

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Authors: Muhammad Mazhar, Yong Zhu, Likang Qin

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Foods contain endogenous components known as dietary fibers, which are classified into soluble and insoluble forms. Dietary fibers are resistant to gut digestive enzymes, modulating anaerobic intestinal microbiota (AIM) and fabricating short-chain fatty acids (SCFAs). Acetate, butyrate, and propionate dominate in the gut, and different pathways, including Wood-Ljungdahl and acrylate pathways, generate these SCFAs. In pancreatic dysfunction, the release of insulin/glucagon is impaired, which leads to hyperglycemia. SCFAs enhance insulin sensitivity or secretion, beta-cell functions, leptin release, mitochondrial functions, and intestinal gluconeogenesis in human organs, which positively affect type 2 diabetes (T2D). Research models presented that SCFAs either enhance the release of peptide YY (PYY) and glucagon-like peptide-1 (GLP-1) from L-cells (entero-endocrine) or promote the release of leptin hormone satiation in adipose tissues through G-protein receptors, i.e., GPR-41/GPR-43. Dietary fibers are the components of foods that influence AIM and produce SCFAs, which may be offering beneficial effects on T2D. This review addresses the effectiveness of SCFAs in modulating gut AIM in the fermentation of dietary fiber and their worth against T2D.

Keywords: dietary fibers, intestinal microbiota, short-chain fatty acids, fermentation, type 2 diabetes

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