Search results for: disturbance automation

Authors: Arkadiusz Zurek

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The potential of using drones is visible in many areas of logistics, especially in terms of their use for monitoring and control of many processes. The technologies implemented in the last decade concern new possibilities for companies that until now have not even considered them, such as warehouse inventories. Unmanned aerial vehicles are no longer seen as a revolutionary tool for Industry 4.0, but rather as tools in the daily work of factories and logistics operators. The research problem is to develop a method for measuring the weight of goods in a selected link of the clothing supply chain by drones. However, the purpose of this article is to analyze the causes of errors in traditional measurements, and then to identify adverse events related to the use of drones for the inventory of a heap of textiles intended for production purposes. On this basis, it will be possible to develop guidelines to eliminate the causes of these events in the measurement process using drones. In a real environment, work was carried out to determine the volume and weight of textiles, including, among others, weighing a textile sample to determine the average density of the assortment, establishing a local geodetic network, terrestrial laser scanning and photogrammetric raid using an unmanned aerial vehicle. As a result of the analysis of measurement data obtained in the facility, the volume and weight of the assortment and the accuracy of their determination were determined. In this article, this work presents how such heaps are currently being tested, what adverse events occur, indicate and describes the current use of photogrammetric techniques of this type of measurements so far performed by external drones for the inventory of wind farms or construction of the station and compare them with the measurement system of the aforementioned textile heap inside a large-format facility.

Keywords: drones, unmanned aerial system, UAS, indoor system, security, process automation, cost optimization, photogrammetry, risk elimination, industry 4.0

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Authors: Namugenyi Ephrance Eunice, Julianne Sansa Otim, Marco Zennaro, Stephen D. Wolthusen

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The Internet of Things (IoT) is a network of connected data processing devices, mechanical and digital machinery, items, animals, or people that may send data across a network without requiring human-to-human or human-to-computer interaction. Each component has sensors that can pick up on specific phenomena, as well as processing software and other technologies that can link to and communicate with other systems and/or devices over the Internet or other communication networks and exchange data with them. IoT is increasingly being used in fields other than consumer electronics, such as public safety, emergency response, industrial automation, autonomous vehicles, the Internet of Medical Things (IoMT), and general environmental monitoring. Consumer-based IoT applications, like smart home gadgets and wearables, are also becoming more prevalent. This paper presents the main IoT deployment areas for environmental monitoring in developing regions and the backhaul options suitable for them. A detailed review of each of the list of papers selected for the study is included in section III of this document. The study includes an overview of existing IoT deployments, the underlying communication architectures, protocols, and technologies that support them. This overview shows that Low Power Wireless Area Networks (LPWANs), as summarized in Table 1, are very well suited for monitoring environment architectures designed for remote locations. LoRa technology, particularly the LoRaWAN protocol, has an advantage over other technologies due to its low power consumption, adaptability, and suitable communication range. The prevailing challenges of the different architectures are discussed and summarized in Table 3 of the IV section, where the main problem is the obstruction of communication paths by buildings, trees, hills, etc.

Keywords: communication technologies, environmental monitoring, Internet of Things, IoT deployment challenges

Procedia PDF Downloads 85

Authors: Evans V. Mauda, Stefan H. Foord, Thinandavha C. Munyai

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The greatest threat to biodiversity is a loss of habitat through landscape fragmentation and attrition. Land use changes are therefore among the most immediate drivers of species diversity. Urbanization and agriculture are the main drivers of habitat loss and transformation in the Savanna biomes of South Africa. Agricultural expansion and the intensification in particular, take place at the expense of biodiversity and will probably be the primary driver of biodiversity loss in this century. Arthropods show measurable behavioural responses to changing land mosaics at the smallest scale and heterogeneous environments are therefore predicted to support more complex and diverse biological assemblages. Ants are premier soil turners, channelers of energy and dominate insect fauna, while spiders are a mega-diverse group that can regulate other invertebrate populations. This study aims to quantify the response of these two taxa in a rural-urban mosaic of a rapidly developing communal area. The study took place in and around two villages in the north-eastern corner of South Africa. Two replicates for each of the dominant land use categories, viz. urban settlements, dryland cultivation and cattle rangelands, were set out in each of the villages and sampled during the dry and wet seasons for a total of 2 villages × 3 land use categories × 2 seasons = 24 assemblages. Local scale variables measured included vertical and horizontal habitat structure as well as structural and chemical composition of the soil. Ant richness was not affected by land use but local scale variables such as vertical vegetation structure (+) and leaf litter cover (+), although vegetation complexity at lower levels was negatively associated with ant richness. However, ant richness was largely shaped by regional and temporal processes invoking the importance of dispersal and historical processes. Spider species richness was mostly affected by land use and local conditions highlighting their landscape elements. Spider richness did not vary much between villages and across seasons and seems to be less dependent on context or history. There was a considerable amount of variation in spider richness that was not explained and this could be related to factors which were not measured in this study such as temperature and competition. For both ant and spider assemblages the constrained ordination explained 18 % of variation in these taxa. Three environmental variables (leaf litter cover, active carbon and rock cover) were important in explaining ant assemblage structure, while two (sand and leaf litter cover) were important for spider assemblage structure. This study highlights the importance of disturbance (land use activities) and leaf litter with the associated effects on ant and spider assemblages across the study area.

Keywords: ants, assemblages, biosphere, diversity, land use, spiders, urbanization

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Authors: Faris Elghaish, Sepehr Abrishami, Mark Gaterell, Richard Wise

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The collaboration and integration between all building information management (BIM) processes and tasks are necessary to ensure that all project objectives can be delivered. The literature review has been used to explore the state of the art BIM technologies to manage construction materials as well as the challenges which have faced the construction process using traditional methods. Thus, this paper aims to articulate a framework to integrate traditional material planning methods such as ABC analysis theory (Pareto principle) to analyse and categorise the project materials, as well as using independent material planning methods such as Economic Order Quantity (EOQ) and Fixed Order Point (FOP) into the BIM 4D, and 5D capabilities in order to articulate a dependent material planning cycle into BIM, which relies on the constructability method. Moreover, we build a model to connect between the material planning outputs and the BIM 4D and 5D data to ensure that all project information will be accurately presented throughout integrated and complementary BIM reporting formats. Furthermore, this paper will present a method to integrate between the risk management output and the material management process to ensure that all critical materials are monitored and managed under the all project stages. The paper includes browsers which are proposed to be embedded in any 4D BIM platform in order to predict the EOQ as well as FOP and alarm the user during the construction stage. This enables the planner to check the status of the materials on the site as well as to get alarm when the new order will be requested. Therefore, this will lead to manage all the project information in a single context and avoid missing any information at early design stage. Subsequently, the planner will be capable of building a more reliable 4D schedule by allocating the categorised material with the required EOQ to check the optimum locations for inventory and the temporary construction facilitates.

Keywords: building information management, BIM, economic order quantity, EOQ, fixed order point, FOP, BIM 4D, BIM 5D

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Authors: Vivek Anand, Rainer Naegele

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Industry 4.0 envisages 'smart' manufacturing and services, taking the automation of the 3rd Industrial Revolution to the autonomy of the 4th Industrial Revolution. Powered by innovations in technology and business models, this disruptive transformation is revitalising industry by integrating silos across and beyond value chains. Motivated by the challenges faced by SMEs and Startups in understanding and adopting Industry 4.0, this paper aims to analyse the concept of Growth Poles and evaluate the possibility of its application to Innovation Clusters that strive to propel Industry 4.0 adoption and capacity building. The proposed paper applies qualitative research methodologies including focus groups and survey questionnaires to identify the various factors that affect formation and development of Innovation Clusters. Employing content analysis, the interaction between SMEs and other ecosystem players in such clusters is studied. A strong collaborative culture is a key driver of digital transformation and technology adoption across sectors, value chains and supply chains; and will position these cluster-based growth poles at the forefront of industrial renaissance. Motivated by this argument, and based on the results of the qualitative research, a roadmap will be proposed to position Innovation Clusters as Growth Poles and effective ecosystems to support Industry 4.0 adoption in a region in the medium to long term. This paper will contribute to the current understanding of the role of Innovation Clusters in capacity building. Relevant management and policy implications stem from the analysis. Furthermore, the findings will be helpful for academicians and policymakers alike, who can leverage an ‘innovation cluster policy’ to enable Industry 4.0 Growth Poles in their regions.

Keywords: digital transformation, fourth industrial revolution, growth poles, industry 4.0, innovation clusters, innovation policy, SMEs and startups

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Authors: Jesús Horacio, Alfredo Ollero, Víctor Bouzas-Blanco, Augusto Pérez-Alberti

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Rivers have traditionally been classified, assessed and managed in terms of hydrological, chemical and / or biological criteria. Geomorphological classifications had in the past a secondary role, although proposals like River Styles Framework, Catchment Baseline Survey or Stroud Rural Sustainable Drainage Project did incorporate geomorphology for management decision-making. In recent years many studies have been attracted to the geomorphological component. The geomorphological processes and their associated forms determine the structure of a river system. Understanding these processes and forms is a critical component of the sustainable rehabilitation of aquatic ecosystems. The fluvial auto-classification approach suggests that a river is a self-built natural system, with processes and forms designed to effectively preserve their ecological function (hydrologic, sedimentological and biological regime). Fluvial systems are formed by a wide range of elements with multiple non-linear interactions on different spatial and temporal scales. Besides, the fluvial auto-classification concept is built using data from the river itself, so that each classification developed is peculiar to the river studied. The variables used in the classification are specific stream power and mean grain size. A discriminant analysis showed that these variables are the best characterized processes and forms. The statistical technique applied allows to get an individual discriminant equation for each geomorphological type. The geomorphological classification was developed using sites with high naturalness. Each site is a control point of high ecological and geomorphological quality. The changes in the conditions of the control points will be quickly recognizable, and easy to apply a right management measures to recover the geomorphological type. The study focused on Galicia (NW Spain) and the mapping was made analyzing 122 control points (sites) distributed over eight river basins. In sum, this study provides a method for fluvial geomorphological classification that works as an open and flexible tool underlying the fluvial auto-classification concept. The hydrographic mapping is the visual expression of the results, such that each river has a particular map according to its geomorphological characteristics. Each geomorphological type is represented by a particular type of hydraulic geometry (channel width, width-depth ratio, hydraulic radius, etc.). An alteration of this geometry is indicative of a geomorphological disturbance (whether natural or anthropogenic). Hydrographic mapping is also dynamic because its meaning changes if there is a modification in the specific stream power and/or the mean grain size, that is, in the value of their equations. The researcher has to check annually some of the control points. This procedure allows to monitor the geomorphology quality of the rivers and to see if there are any alterations. The maps are useful to researchers and managers, especially for conservation work and river restoration.

Keywords: fluvial auto-classification concept, mapping, geomorphology, river

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Authors: Bamikole J. Adeyemi, Prashant Jadhawar, Lateef Akanji

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Liquid-liquid interfacial flow is an important process that has applications across many spheres. One such applications are residual oil mobilization, where crude oil and low salinity water are emulsified due to lowered interfacial tension under the condition of low shear rates. The amphiphilic components (asphaltenes and resins) in crude oil are considered to assemble at the interface between the two immiscible liquids. To justify emulsification, drag and snap-off suppression as the main effects of low salinity water, mobilization of residual oil is visualized as thickening and slip of the wetting phase at the brine/crude oil interface which results in the squeezing and drag of the non-wetting phase to the pressure sinks. Meanwhile, defining the boundary conditions for such a system can be very challenging since the interfacial dynamics do not only depend on interfacial tension but also the flow rate. Hence, understanding the flow boundary condition at the brine/crude oil interface is an important step towards defining the influence of low salinity water composition on residual oil mobilization. This work presents a numerical evaluation of three slip boundary conditions that may apply at liquid-liquid interfaces. A mathematical model was developed to describe the evolution of a viscoelastic interfacial thin liquid film. The base model is developed by the asymptotic expansion of the full Navier-Stokes equations for fluid motion due to gradients of surface tension. This model was upscaled to describe the dynamics of the film surface deformation. Subsequently, Jeffrey’s model was integrated into the formulations to account for viscoelastic stress within a long wave approximation of the Navier-Stokes equations. To study the fluid response to a prescribed disturbance, a linear stability analysis (LSA) was performed. The dispersion relation and the corresponding characteristic equation for the growth rate were obtained. Three slip (slip, 1; locking, -1; and no-slip, 0) boundary conditions were examined using the resulted characteristic equation. Also, the dynamics of the evolved interfacial thin liquid film were numerically evaluated by considering the influence of the boundary conditions. The linear stability analysis shows that the boundary conditions of such systems are greatly impacted by the presence of amphiphilic molecules when three different values of interfacial tension were tested. The results for slip and locking conditions are consistent with the fundamental solution representation of the diffusion equation where there is film decay. The interfacial films at both boundary conditions respond to exposure time in a similar manner with increasing growth rate which resulted in the formation of more droplets with time. Contrarily, no-slip boundary condition yielded an unbounded growth and it is not affected by interfacial tension.

Keywords: boundary conditions, liquid-liquid interfaces, low salinity water, residual oil mobilization

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Authors: Giulio Nisoli, Jonas Brüngger, Karin Hostettler, Nicole Stoller, Katrin Fischer

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Technical operators in the railway domain are experts responsible for the supervisory control of the railway power grid as well as of the railway tunnels. The technical systems used to master these demanding tasks are constantly increasing in their degree of automation. It becomes therefore difficult for technical operators to maintain the control over the technical systems and the processes of their job. In particular, the operators must have the necessary experience and knowledge in dealing with a malfunction situation or unexpected event. For this reason, it is of growing importance that the skills relevant for the execution of the job are maintained and further developed beyond the basic training they receive, where they are educated in respect of technical knowledge and the work with guidelines. Training methods aimed at improving the cognitive skills needed by technical operators are still missing and must be developed. Goals of the present study were to identify which are the relevant cognitive skills of technical operators in the railway domain and to define which topics should be addressed by the training of these skills. Observational interviews were conducted in order to identify the main tasks and the organization of the work of technical operators as well as the technical systems used for the execution of their job. Based on this analysis, the most demanding tasks of technical operators could be identified and described. The cognitive skills involved in the execution of these tasks are those, which need to be trained. In order to identify and analyze these cognitive skills a cognitive task analysis (CTA) was developed. CTA specifically aims at identifying the cognitive skills that employees implement when performing their own tasks. The identified cognitive skills of technical operators were summarized and grouped in training topics. For every training topic, specific goals were defined. The goals regard the three main categories; knowledge, skills and attitude to be trained in every training topic. Based on the results of this study, it is possible to develop specific training methods to train the relevant cognitive skills of the technical operators.

Keywords: cognitive skills, cognitive task analysis, technical operators in the railway domain, training topics

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Authors: Andrea Palazzo, Daniel Macek, Veronika Malinova

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Contour processing is the new technology challenge for architects and construction companies. The many advantages it promises make it one of the most interesting solutions for construction in terms of automation of building processes. The technology for 3D printing houses offers many application possibilities, from low-cost construction, to being considered by NASA for visionary projects as a good solution for building settlements on other planets. Another very important point is that clients, as architects, will no longer have many limits in design concerning ideas and creativity. The prices for real estate are constantly increasing and the lack of availability of construction materials as well as the speculation that has been created around it in 2021 is bringing prices to such a level that in the future real estate developers risk not being able to find customers for these ultra-expensive homes. Hence, this paper starts with the introduction of 3D printing, which now has the potential to gain an important position in the market, becoming a valid alternative to the classic construction process. This technology is not only beneficial from an economic point of view but it is also a great opportunity to have an impact on the environment by reducing CO2 emissions. Further on in the article we will also understand if, after the COP 26 (2021 United Nations Climate Change Conference), world governments could also push towards building technologies that reduce the waste materials that are needed to be disposed of and at the same time reduce emissions with the contribution of governmental funds. This paper will give us insight on the multiple benefits of 3D printing and emphasise the importance of finding new solutions for materials that can be used by the printer. Therefore, based on the type of material, it will be possible to understand the compatibility with current regulations and how the authorities will be inclined to support this technology. This will help to enable the rise and development of this technology in Europe and in the rest of the world on actual housing projects and not only on prototypes.

Keywords: additive manufacturing, contour crafting, development, new regulation, printing material

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Authors: Humaira Athar, Brajeshwar Singh

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Generally, the brightness of the indoor environment of buildings is entirely maintained by the artificial lighting which has consumed a large amount of resources. It is reported that lighting consumes about 19% of the total generated electricity which accounts for about 30-40% of total energy consumption. One possible way is to reduce the lighting energy by exploiting sunlight either through the use of suitable devices or energy efficient materials like translucent concrete. Translucent concrete is one such architectural concrete which allows the passage of natural light as well as artificial light through it. Several attempts have been made on different aspects of translucent concrete such as light guiding materials (glass fibers, plastic fibers, cylinder etc.), concrete mix design and manufacturing methods for use as building elements. Concerns are, however, raised on various related issues such as poor compatibility between the optical fibers and cement paste, unaesthetic appearance due to disturbance occurred in the arrangement of fibers during vibration and high shrinkage in flowable concrete due to its high water/cement ratio. Need is felt to develop translucent concrete to meet the requirement of structural safety as OPC concrete with the maximized saving in energy towards the power of illumination and thermal load in buildings. Translucent concrete was produced using pre-treated plastic optical fibers (POF, 2mm dia.) and high slump white concrete. The concrete mix was proportioned in the ratio of 1:1.9:2.1 with a w/c ratio of 0.40. The POF was varied from 0.8-9 vol.%. The mechanical properties and light transmission of this concrete were determined. Thermal conductivity of samples was measured by a transient plate source technique. Daylight illumination was measured by a lux grid method as per BIS:SP-41. It was found that the compressive strength of translucent concrete increased with decreasing optical fiber content. An increase of ~28% in the compressive strength of concrete was noticed when fiber was pre-treated. FE-SEM images showed little-debonded zone between the fibers and cement paste which was well supported with pull-out bond strength test results (~187% improvement over untreated). The light transmission of concrete was in the range of 3-7% depending on fiber spacing (5-20 mm). The average daylight illuminance (~75 lux) was nearly equivalent to the criteria specified for illumination for circulation (80 lux). The thermal conductivity of translucent concrete was reduced by 28-40% with respect to plain concrete. The thermal load calculated by heat conduction equation was ~16% more than the plain concrete. Based on Design-Builder software, the total annual illumination energy load of a room using one side translucent concrete was 162.36 kW compared with the energy load of 249.75 kW for a room without concrete. The calculated energy saving on an account of the power of illumination was ~25%. A marginal improvement towards thermal comfort was also noticed. It is concluded that the translucent concrete has the advantages of the existing concrete (load bearing) with translucency and insulation characteristics. It saves a significant amount of energy by providing natural daylight instead of artificial power consumption of illumination.

Keywords: energy saving, light transmission, microstructure, plastic optical fibers, translucent concrete

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Authors: Gihan Thilakarathna, Fernando Ishara, Rathnayake Yasith, Bandara A. M. R. Y.

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E-learning is a crucial component of intelligent education. Even in the midst of a pandemic, E-learning is becoming increasingly important in the educational system. Several e-learning programs are accessible for students. Here, we decided to create an e-learning framework for children. We've found a few issues that teachers are having with their online classes. When there are numerous students in an online classroom, how does a teacher recognize a student's focus on academics and below-the-surface behaviors? Some kids are not paying attention in class, and others are napping. The teacher is unable to keep track of each and every student. Key challenge in e-learning is online exams. Because students can cheat easily during online exams. Hence there is need of exam proctoring is occurred. In here we propose an automated online exam cheating detection method using a web camera. The purpose of this project is to present an E-learning platform for math education and include games for kids as an alternative teaching method for math students. The game will be accessible via a web browser. The imagery in the game is drawn in a cartoonish style. This will help students learn math through games. Everything in this day and age is moving towards automation. However, automatic answer evaluation is only available for MCQ-based questions. As a result, the checker has a difficult time evaluating the theory solution. The current system requires more manpower and takes a long time to evaluate responses. It's also possible to mark two identical responses differently and receive two different grades. As a result, this application employs machine learning techniques to provide an automatic evaluation of subjective responses based on the keyword provided to the computer as student input, resulting in a fair distribution of marks. In addition, it will save time and manpower. We used deep learning, machine learning, image processing and natural language technologies to develop these research components.

Keywords: math, education games, e-learning platform, artificial intelligence

Procedia PDF Downloads 156

Authors: Atakan Aral Ormancı, Tuğçe Arslantaş, Murat Özcü

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This study presents the design and implementation of an experimental chassis-level system for various control applications. Specifically, the height level of the chassis is controlled using proportional integrated derivative, fuzzy logic, and deep learning control methods. Real-time data obtained from height and pressure sensors installed in a 6x2 truck chassis, in combination with pulse-width modulation signal values, are utilized during the tests. A prototype pneumatic system of a 6x2 truck is added to the setup, which enables the Smart Pneumatic Actuators to function as if they were in a real-world setting. To obtain real-time signal data from height sensors, an Arduino Nano is utilized, while a Raspberry Pi processes the data using Matlab/Simulink and provides the correct output signals to control the Smart Pneumatic Actuator in the truck chassis. The objective of this research is to optimize the time it takes for the chassis to level down and up under various loads. To achieve this, proportional integrated derivative control, fuzzy logic control, and deep learning techniques are applied to the system. The results show that the deep learning method is superior in optimizing time for a non-linear system. Fuzzy logic control with a triangular membership function as the rule base achieves better outcomes than proportional integrated derivative control. Traditional proportional integrated derivative control improves the time it takes to level the chassis down and up compared to an uncontrolled system. The findings highlight the superiority of deep learning techniques in optimizing the time for a non-linear system, and the potential of fuzzy logic control. The proposed approach and the experimental results provide a valuable contribution to the field of control, automation, and systems engineering.

Keywords: automotive, chassis level control, control systems, pneumatic system control

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Authors: Denchai Woradechjumroen, Haorong Li, Yuebin Yu

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Supermarkets are the most electricity-intensive type of commercial buildings. The unsuitable indoor environment of a supermarket provided by abnormal HVAC operations incurs waste energy consumption in refrigeration systems. This current study briefly describes significantly solid backgrounds and proposes easy-to-use analysis terminology for investigating the impact of HVAC operations on refrigeration power consumption using the field-test data obtained from building automation system (BAS). With solid backgrounds and prior knowledge, expected energy interactions between HVAC and refrigeration systems are proposed through Pearson’s correlation analysis (R value) by considering correlations between equipment power consumption and dominantly independent variables (driving force conditions). The R value can be conveniently utilized to evaluate how strong relations between equipment operations and driving force parameters are. The calculated R values obtained from field data are compared to expected ranges of R values computed by energy interaction methodology. The comparisons can separate the operational conditions of equipment into faulty and normal conditions. This analysis can simply investigate the condition of equipment operations or building sensors because equipment could be abnormal conditions due to routine operations or faulty commissioning processes in field tests. With systematically solid and easy-to-use backgrounds of interactions provided in the present article, the procedures can be utilized as a tool to evaluate the proper commissioning and routine operations of HVAC and refrigeration systems to detect simple faults (e.g. sensors and driving force environment of refrigeration systems and equipment set-point) and optimize power consumption in supermarket buildings. Moreover, the analysis will be used to further study FDD research for supermarkets in future.

Keywords: energy interaction, HVAC, R-value, supermarket buildings

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Authors: A. S. Ibrahim Mamane, S. Giljean, M.-J. Pac, G. L’Hostis

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Fiber reinforced composite laminates are particularly subject to residual stresses due to their heterogeneity and the complex chemical, mechanical and thermal mechanisms that occur during their processing. Residual stresses are now well known to cause damage accumulation, shape instability, and behavior disturbance in composite parts. Many works exist in the literature on techniques for minimizing residual stresses in thermosetting and thermoplastic composites mainly. To study in-depth the influence of processing mechanisms on the formation of residual stresses and to minimize them by establishing a reliable correlation, it is essential to be able to measure very precisely the profile of residual stresses in the composite. Residual stresses are important data to consider when sizing composite parts and predicting their behavior. The incremental hole drilling is very effective in measuring the gradient of residual stresses in composite laminates. This method is semi-destructive and consists of drilling incrementally a hole through the thickness of the material and measuring relaxation strains around the hole for each increment using three strain gauges. These strains are then converted into residual stresses using a matrix of coefficients. These coefficients, called calibration coefficients, depending on the diameter of the hole and the dimensions of the gauges used. The reliability of the incremental hole drilling depends on the accuracy with which the calibration coefficients are determined. These coefficients are calculated using a finite element model. The samples’ features and the experimental conditions must be considered in the simulation. Any mismatch can lead to inadequate calibration coefficients, thus introducing errors on residual stresses. Several calibration coefficient correction methods exist for isotropic material, but there is a lack of information on this subject concerning composite laminates. In this work, a Python program was developed to automatically generate the adequate finite element model. This model allowed us to perform a parametric study to assess the influence of experimental errors on the calibration coefficients. The results highlighted the sensitivity of the calibration coefficients to the considered errors and gave an order of magnitude of the precisions required on the experimental device to have reliable measurements. On the basis of these results, improvements were proposed on the experimental device. Furthermore, a numerical method was proposed to correct the calibration coefficients for different types of materials, including thick composite parts for which the analytical approach is too complex. This method consists of taking into account the experimental errors in the simulation. Accurate measurement of the experimental errors (such as eccentricity of the hole, angular deviation of the gauges from their theoretical position, or errors on increment depth) is therefore necessary. The aim is to determine more precisely the residual stresses and to expand the validity domain of the incremental hole drilling technique.

Keywords: fiber reinforced composites, finite element simulation, incremental hole drilling method, numerical correction of the calibration coefficients, residual stresses

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Authors: M. Towhidur Rahman, Nasim Zaman Piyas, M. Sadiqul Baree, Shahnewaz Ahmed

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The design of high-speed craft has recently become one of the most active areas of naval architecture. Speed increase makes these vehicles more efficient and useful for military, economic or leisure purpose. The planing hull is designed specifically to achieve relatively high speed on the surface of the water. Speed on the water surface is closely related to the size of the vessel and the installed power. The Savitsky method was first presented in 1964 for application to non-monohedric hulls and for application to stepped hulls. This method is well known as a reliable comparative to CFD analysis of hull resistance. A computer program based on Savitsky’s method has been developed using MATLAB. The power of high-speed vessels has been computed in this research. At first, the program reads some principal parameters such as displacement, LCG, Speed, Deadrise angle, inclination of thrust line with respect to keel line etc. and calculates the resistance of the hull using empirical planning equations of Savitsky. However, some functions used in the empirical equations are available only in the graphical form, which is not suitable for the automatic computation. We use digital plotting system to extract data from nomogram. As a result, value of wetted length-beam ratio and trim angle can be determined directly from the input of initial variables, which makes the power calculation automated without manually plotting of secondary variables such as p/b and other coefficients and the regression equations of those functions are derived by using data from different charts. Finally, the trim angle, mean wetted length-beam ratio, frictional coefficient, resistance, and power are computed and compared with the results of Savitsky and good agreement has been observed.

Keywords: nomogram, planing hull, principal parameters, regression

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Authors: Krupali Mukeshkumar, Jinesh Shah

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Background: Bilateral Sagittal Split Osteotomy (BSSO), used for the correction of mandibular deformities, is a common oral and maxillofacial surgical procedure. Inferior alveolar nerve dysfunction is commonly reported post-operatively by patients as paresthesia or anesthesia. The current literature lacks a consensus on the incidence of inferior alveolar nerve dysfunction as patients are not routinely assessed pre and post-operatively with an objective assessment. The range of incidence varies from 9% to 85% of patients, with some authors arguing that 100% of patients experience nerve dysfunction immediately post-surgery. Systematic reviews have shown a difference between incidence rates at different follow-up periods using objective and subjective methods. Aim: To identify the incidence of inferior alveolar nerve dysfunction following BSSO. Gold standard: Nerve dysfunction incidence rates similar or lower than current literature of 83% day one post-operatively and 18.4% at one year follow up. Setting: A retrospective cross-sectional audit of patients treated between 2017-2019 at the Royal Stoke University Hospital, Maxillofacial and Orthodontic departments. Sample: All patients who underwent a BSSO (with or without le fort one osteotomy) between 2017–2019 were identified from the database. Patients with pre-existing neurosensory disturbance, those who had a genioplasty at the same time and those with no follow-up were excluded. The sample consisted of 121 patients, 37 males and 84 females between the ages of 17-50 years at the time of surgery. Methods: Clinical records of 121 cases were reviewed to assess the age, sex, type of mandibular osteotomy, status of the nerve during the surgical procedure, type of bony split and incidence of nerve dysfunction at follow-up appointments. The surgical procedure was carried out by three Maxillo-facial surgeons and follow-up appointments were carried out in the Orthodontic and Oral and Maxillo-facial departments. Results: 120 patients were treated to correct the mandibular facial deformity and 1 patient was treated for sleep apnoea. Seventeen patients had a mandibular setback and 104 patients had mandibular advancement. 68 patients reported inferior alveolar nerve dysfunction at one week following their surgery. Seventy-six patients had temporary paresthesia present between 2 weeks and 12 months post-surgery. 13 patients had persistent nerve dysfunction at 12 months, of which 1 had a bad bony split during the BSSO. The incidence of nerve dysfunction postoperatively was 6.6% after 1 day, 56.1% at 1 week, 62.8% at 2 weeks, 59.5% between 3-6 weeks, 43.0% between 8-16 weeks and 10.7% at 1 year. Conclusions: The results of this audit show a similar incidence rate to the research gold standard at the one-year follow-up. Future Recommendations: No changes to surgical procedure or technique are indicated, but a need for improved documentation and a standardized approach for assessment of post-operative nerve dysfunction would be beneficial.

Keywords: bilateral sagittal split osteotomy, inferior alveolar nerve, mandible, nerve dysfunction

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Authors: Gary Pattemore, Michael Edgar, Andrew Job, Marina Auad, Kathryn Job

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As critical commodities become more scarce, significant time and resources have been used to better understand complicated ore bodies and extract their full potential. These challenging ore bodies provide several pain points for geologists and engineers to overcome, poor handling of these issues flows downs stream to the processing plant affecting throughput rates and recovery. Many open cut mines utilise blast hole drilling to extract additional information to feed back into the modelling process. This method requires samples to be collected during or after blast hole drilling. Samples are then sent for assay with turnaround times varying from 1 to 12 days. This method is time consuming, costly, requires human exposure on the bench and collects elemental data only. To address this challenge, research has been undertaken to utilise hyperspectral imaging across a broad spectrum to scan samples, collars or take down hole measurements for minerals and moisture content and grade abundances. Automation of this process using unmanned vehicles and on-board processing reduces human in pit exposure to ensure ongoing safety. On-board processing allows data to be integrated into modelling workflows with immediacy. The preliminary results demonstrate numerous direct and indirect benefits from this new technology, including rapid and accurate grade estimates, moisture content and mineralogy. These benefits allow for faster geo modelling updates, better informed mine scheduling and improved downstream blending and processing practices. The paper presents recommendations for implementation of the technology in open cut mining environments.

Keywords: grade control, hyperspectral scanning, artificial intelligence, autonomous mining, machine learning

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Authors: Gichun Cha, Changgil Lee, Seunghee Park

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The objective of the study is to construct the shape management method contributing to the safety of the large structure. In Korea, the research of the shape management is lack because of the new attempted technology. Terrestrial Laser Scanning (TLS) is used for measurements of large structures. TLS provides an efficient way to actively acquire accurate the point clouds of object surfaces or environments. The point clouds provide a basis for rapid modeling in the industrial automation, architecture, construction or maintenance of the civil infrastructures. TLS produce a huge amount of point clouds. Registration, Extraction and Visualization of data require the processing of a massive amount of scan data. The octree can be applied to the shape management of the large structure because the scan data is reduced in the size but, the data attributes are maintained. The octree space partitioning generates the voxel of 3D space, and the voxel is recursively subdivided into eight sub-voxels. The point cloud of scan data was converted to voxel and sampled. The experimental site is located at Sungkyunkwan University. The scanned structure is the steel-frame bridge. The used TLS is Leica ScanStation C10/C5. The scan data was condensed 92%, and the octree model was constructed with 2 millimeter in resolution. This study presents octree space partitioning for handling the point clouds. The basis is created by shape management of the large structures such as double-deck tunnel, building and bridge. The research will be expected to improve the efficiency of structural health monitoring and maintenance. "This work is financially supported by 'U-City Master and Doctor Course Grant Program' and the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIP) (NRF- 2015R1D1A1A01059291)."

Keywords: 3D scan data, octree space partitioning, shape management, structural health monitoring, terrestrial laser scanning

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Authors: Rimon Rafiah

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This paper will present a combination of two seemingly unrelated models, which are the one for estimating economic development impacts as a result of transportation investment and the other for increasing CAV penetration in order to reduce congestion. Measuring economic development impacts resulting from transportation investments is becoming more recognized around the world. Examples include the UK’s Wider Economic Benefits (WEB) model, Economic Impact Assessments in the USA, various input-output models, and additional models around the world. The economic impact model is based on WEB and is based on the following premise: investments in transportation will reduce the cost of personal travel, enabling firms to be more competitive, creating additional throughput (the same road allows more people to travel), and reducing the cost of travel of workers to a new workplace. This reduction in travel costs was estimated in out-of-pocket terms in a given localized area and was then translated into additional employment based on regional labor supply elasticity. This additional employment was conservatively assumed to be at minimum wage levels, translated into GDP terms, and from there into direct taxation (i.e., an increase in tax taken by the government). The CAV model is based on economic principles such as CAV usage, supply, and demand. Usage of CAVs can increase capacity using a variety of means – increased automation (known as Level I thru Level IV) and also by increased penetration and usage, which has been predicted to go up to 50% by 2030 according to several forecasts, with possible full conversion by 2045-2050. Several countries have passed policies and/or legislation on sales of gasoline-powered vehicles (none) starting in 2030 and later. Supply was measured via increased capacity on given infrastructure as a function of both CAV penetration and implemented technologies. The CAV model, as implemented in the USA, has shown significant savings in travel time and also in vehicle operating costs, which can be translated into economic development impacts in terms of job creation, GDP growth and salaries as well. The models have policy implications as well and can be adapted for use in Japan as well.

Keywords: CAV, economic development, WEB, transport economics

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Authors: Vadanasundari Vedarethinam, Kun Qian

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The metabolic analysis is more distal over proteomics and genomics engaging in clinics and needs rationally distinct techniques, designed materials, and device for clinical diagnosis. Conventional techniques such as spectroscopic techniques, biochemical analyzers, and electrochemical have been used for metabolic diagnosis. Currently, there are four major challenges including (I) long-term process in sample pretreatment; (II) difficulties in direct metabolic analysis of biosamples due to complexity (III) low molecular weight metabolite detection with accuracy and (IV) construction of diagnostic tools by materials and device-based platforms for real case application in biomedical applications. Development of chips with nanomaterial is promising to address these critical issues. Mass spectroscopy (MS) has displayed high sensitivity and accuracy, throughput, reproducibility, and resolution for molecular analysis. Particularly laser desorption/ ionization mass spectrometry (LDI MS) combined with devices affords desirable speed for mass measurement in seconds and high sensitivity with low cost towards large scale uses. We developed a plasmonic chip for clinical metabolic fingerprinting as a hot carrier in LDI MS by series of chips with gold nanoshells on the surface through controlled particle synthesis, dip-coating, and gold sputtering for mass production. We integrated the optimized chip with microarrays for laboratory automation and nanoscaled experiments, which afforded direct high-performance metabolic fingerprinting by LDI MS using 500 nL of serum, urine, cerebrospinal fluids (CSF) and exosomes. Further, we demonstrated on-chip direct in-vitro metabolic diagnosis of early-stage lung cancer patients using serum and exosomes without any pretreatment or purifications. To our best knowledge, this work initiates a bionanotechnology based platform for advanced metabolic analysis toward large-scale diagnostic use.

Keywords: plasmonic chip, metabolic fingerprinting, LDI MS, in-vitro diagnostics

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Authors: Junaid Hamid Bhat

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Background: Musculoskeletal disorders are one of the main causes of occupational illness. Mechanisms and the factors like repetitive work, physical effort and posture, endangering the risk of musculoskeletal disorders would now appear to have been properly identified. Teacher’s exposure to work-related musculoskeletal disorders appears to be insufficiently described in the literature. Little research has investigated the prevalence and risk factors of musculoskeletal disorders in teaching profession. Very few studies are available in this regard and there are no studies evident in India. Purpose: To determine the prevalence of musculoskeletal disorders and to identify and measure the association of such risk factors responsible for developing musculoskeletal disorders among school teachers. Methodology: An observational cross sectional study was carried out. 500 school teachers from primary, middle, high and secondary schools were selected, based on eligibility criteria. A signed consent was obtained and a self-administered, validated questionnaire was used. Descriptive statistics was used to compute the statistical mean and standard deviation, frequency and percentage to estimate the prevalence of musculoskeletal disorders among school teachers. The data analysis was done by using SPSS version 16.0. Results: Results indicated higher pain prevalence (99.6%) among school teachers during the past 12 months. Neck pain (66.1%), low back pain (61.8%) and knee pain (32.0%) were the most prevalent musculoskeletal complaints of the subjects. Prevalence of shoulder pain was also found to be high among school teachers (25.9%). 52.0% subjects reported pain as disabling in nature, causing sleep disturbance (44.8%) and pain was found to be associated with work (87.5%). A significant association was found between musculoskeletal disorders and sick leaves/absenteeism. Conclusion: Work-related musculoskeletal disorders particularly neck pain, low back pain, and knee pain, is highly prevalent and risk factors are responsible for the development of same in school teachers. There is little awareness of musculoskeletal disorders among school teachers, due to work load and prolonged/static postures. Further research should concentrate on specific risk factors like repetitive movements, psychological stress, and ergonomic factors and should be carried out all over the country and the school teachers should be studied carefully over a period of time. Also, an ergonomic investigation is needed to decrease the work-related musculoskeletal disorder problems. Implication: Recall bias and self-reporting can be considered as limitations. Also, cause and effect inferences cannot be ascertained. Based on these results, it is important to disseminate general recommendations for prevention of work-related musculoskeletal disorders with regards to the suitability of furniture, equipment and work tools, environmental conditions, work organization and rest time to school teachers. School teachers in the early stage of their careers should try to adapt the ergonomically favorable position whilst performing their work for a safe and healthy life later. Employers should be educated on practical aspects of prevention to reduce musculoskeletal disorders, since changes in workplace and work organization and physical/recreational activities are required.

Keywords: work related musculoskeletal disorders, school teachers, risk factors funding, medical and health sciences

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Authors: Alexandre D. Ferraz, Dario H. Alliprandini, Mauro Sampaio

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Companies and other institutions are constantly seeking better organizational performance and greater competitiveness. In order to fulfill this purpose, there are many tools, methodologies and models for increasing performance. However, the Lean Management approach seems to be the most effective in terms of achieving a significant improvement in productivity relatively quickly. Although Lean tools are relatively easy to understand and implement in different contexts, many organizations are not able to transform themselves into 'Lean companies'. Most of the efforts in its implementation have shown single benefits, failing to achieve the desired impact on the performance of the overall enterprise system. There is also a growing perception of the importance of management in Lean transformation, but few studies have empirically investigated and described the 'Lean Management'. In order to understand more clearly the ideas that guide Lean Management and its influence on the maturity level of the production system, the objective of this research is analyze the relationship between the Lean Management principles and the Lean maturity level in the organizations. The research also analyzes the principles of Lean Management and its relationship with the 'Lean culture' and the results obtained. The research was developed using the case study methodology. Three manufacturing units of a German multinational company from industrial automation segment, located in different countries were studied, in order to have a better comparison between the practices and the level of maturity in the implementation. The primary source of information was the application of a research questionnaire based on the theoretical review. The research showed that higher the level of Lean Management principles, higher are the Lean maturity level, the Lean culture level, and the level of Lean results obtained in the organization. The research also showed that factors such as time for application of Lean concepts and company size were not determinant for the level of Lean Management principles and, consequently, for the level of Lean maturity in the organization. The characteristics of the production system showed much more influence in different evaluated aspects. The present research also left recommendations for the managers of the plants analyzed and suggestions for future research.

Keywords: lean management, lean principles, lean maturity level, lean manufacturing

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Authors: Wenzheng Fu, Yue Fu, Zhijiang Dong, Yujian Fu

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The integration of artificial intelligence (AI) and machine learning (ML) into project management is transforming how engineering projects are executed, monitored, and controlled. This paper provides a comprehensive survey of AI and ML applications in project management, systematically categorizing their use in key areas such as project data analytics, monitoring, tracking, scheduling, and reporting. As project management becomes increasingly data-driven, AI and ML offer powerful tools for improving decision-making, optimizing resource allocation, and predicting risks, leading to enhanced project outcomes. The review highlights recent research that demonstrates the ability of AI and ML to automate routine tasks, provide predictive insights, and support dynamic decision-making, which in turn increases project efficiency and reduces the likelihood of costly delays. This paper also examines the emerging trends and future opportunities in AI-driven project management, such as the growing emphasis on transparency, ethical governance, and data privacy concerns. The research suggests that AI and ML will continue to shape the future of project management by driving further automation and offering intelligent solutions for real-time project control. Additionally, the review underscores the need for ongoing innovation and the development of governance frameworks to ensure responsible AI deployment in project management. The significance of this review lies in its comprehensive analysis of AI and ML’s current contributions to project management, providing valuable insights for both researchers and practitioners. By offering a structured overview of AI applications across various project phases, this paper serves as a guide for the adoption of intelligent systems, helping organizations achieve greater efficiency, adaptability, and resilience in an increasingly complex project management landscape.

Keywords: artificial intelligence, decision support systems, machine learning, project management, resource optimization, risk prediction

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Authors: J. Hash, J. Converse, L. Gibson

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Plant medicines are of growing interest for addressing mental health concerns. Ayahuasca, a traditional plant-based medicine, has established itself as a powerful way of processing trauma and precipitating healing and mood stabilization. Eye Movement Desensitization and Reprocessing (EMDR) is another treatment modality that aids in the rapid processing and resolution of trauma. We investigated group EMDR therapy, G-TEP, as a preparatory practice before Ayahuasca ceremonies to determine if the combination of these modalities supports participants in their journeys of letting go of past experiences negatively impacting mental health, thereby accentuating the healing of the plant medicine. We surveyed 96 participants (51 experimental G-TEP, 45 control grounding prior to their ceremony; age M=38.6, SD=9.1; F=57, M=34; white=39, Hispanic/Latinx=23, multiracial=11, Asian/Pacific Islander=10, other=7) in a pre-post, mixed methods design. Participants were surveyed for demographic characteristics, symptoms of PTSD and cPTSD (International Trauma Questionnaire (ITQ), depression (Beck Depression Inventory, BDI), and stress (Perceived Stress Scale, PSS) before the ceremony and at the end of the ceremony weekend. Open-ended questions also inquired about their expectations of the ceremony and results at the end. No baseline differences existed between the control and experimental participants. Overall, participants reported a decrease in meeting the threshold for PTSD symptoms (p<0.01); surprisingly, the control group reported significantly fewer thresholds met for symptoms of affective dysregulation, 2(1)=6.776, p<.01, negative self-concept, 2 (1)=7.122, p<.01, and disturbance in relationships, 2 (1)=9.804, p<.01, on subscales of the ITQ as compared to the experimental group. All participants also experienced a significant decrease in scores on the BDI, t(94)=8.995, p<.001, and PSS, t(91)=6.892, p<.001. Similar to patterns of PTSD symptoms, the control group reported significantly lower scores on the BDI, t(65.115)=-2.587, p<.01, and a trend toward lower PSS, t(90)=-1.775, p=.079 (this was significant with a one-sided test at p<.05), compared to the experimental group following the ceremony. Qualitative interviews among participants revealed a potential explanation for these relatively higher levels of depression and stress in the experimental group following the ceremony. Many participants reported needing more time to process their experience to gain an understanding of the effects of the Ayahuasca medicine. Others reported a sense of hopefulness and understanding of the sources of their trauma and the necessary steps to heal moving forward. This suggests increased introspection and openness to processing trauma, therefore making them more receptive to their emotions. The integration process of an Ayahuasca ceremony is a week- to months-long process that was not accessible in this stage of research, yet it is an integral process to understanding the full effects of the Ayahuasca medicine following the closure of a ceremony. Our future research aims to assess participants weeks into their integration process to determine the effectiveness of EMDR, and if the higher levels of depression and stress indicate the initial reaction to greater awareness of trauma and receptivity to healing.

Keywords: ayahuasca, EMDR, PTSD, mental health

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Authors: Huan Ting Liao

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In a typical manufacturing environment, computer numerical control (CNC) machining is essential for automating production through precise computer-controlled tool operations, significantly enhancing efficiency and ensuring consistent product quality. However, traditional CNC production lines often rely on manual loading and unloading, limiting operational efficiency and scalability. Although automated loading systems have been developed, they frequently lack sufficient intelligence and configuration efficiency, requiring extensive setup adjustments for different products and impacting overall productivity. This research addresses the job shop scheduling problem (JSSP) in CNC machining environments, aiming to minimize total completion time (makespan) and maximize CNC machine utilization. We propose a novel approach using reinforcement learning (RL), specifically the Q-learning algorithm, to optimize scheduling decisions. The study simulates the JSSP, incorporating robotic arm operations, machine processing times, and work order demand allocation to determine optimal processing sequences. The Q-learning algorithm enhances machine utilization by dynamically balancing workloads across CNC machines, adapting to varying job demands and machine states. This approach offers robust solutions for complex manufacturing environments by automating decision-making processes for job assignments. Additionally, we evaluate various layout configurations to identify the most efficient setup. By integrating RL-based scheduling optimization with layout analysis, this research aims to provide a comprehensive solution for improving manufacturing efficiency and productivity in CNC-based job shops. The proposed method's adaptability and automation potential promise significant advancements in tackling dynamic manufacturing challenges.

Keywords: job shop scheduling problem, reinforcement learning, operations sequence, layout optimization, q-learning

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Authors: Safaa H. Sindi, Michael Roe

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This project divides supply chain (SC) models into seven Eras, according to the evolution of the market’s needs throughout time. The five earliest Eras describe the emergence of supply chains, while the last two Eras are to be created. Research objectives: The aim is to generate the two latest Eras with their respective models that focus on the consumable goods. Era Six contains the Optimal Multidimensional Matrix (OMM) that incorporates most characteristics of the SC and allocates them into four quarters (Agile, Lean, Leagile, and Basic SC). This will help companies, especially (SMEs) plan their optimal SC route. Era Seven creates an Automated Multidimensional Model (AMM) which upgrades the matrix of Era six, as it accounts for all the supply chain factors (i.e. Offshoring, sourcing, risk) into an interactive system with Heuristic Learning that helps larger companies and industries to select the best SC model for their market. Methodologies: The data collection is based on a Fuzzy-Delphi study that analyses statements using Fuzzy Logic. The first round of Delphi study will contain statements (fuzzy rules) about the matrix of Era six. The second round of Delphi contains the feedback given from the first round and so on. Preliminary findings: both models are applicable, Matrix of Era six reduces the complexity of choosing the best SC model for SMEs by helping them identify the best strategy of Basic SC, Lean, Agile and Leagile SC; that’s tailored to their needs. The interactive heuristic learning in the AMM of Era seven will help mitigate error and aid large companies to identify and re-strategize the best SC model and distribution system for their market and commodity, hence increasing efficiency. Potential contributions to the literature: The problematic issue facing many companies is to decide which SC model or strategy to incorporate, due to the many models and definitions developed over the years. This research simplifies this by putting most definition in a template and most models in the Matrix of era six. This research is original as the division of SC into Eras, the Matrix of Era six (OMM) with Fuzzy-Delphi and Heuristic Learning in the AMM of Era seven provides a synergy of tools that were not combined before in the area of SC. Additionally the OMM of Era six is unique as it combines most characteristics of the SC, which is an original concept in itself.

Keywords: Leagile, automation, heuristic learning, supply chain models

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Authors: Estelle Arbellay, Lori D. Daniels, Shawn D. Mansfield, Alice S. Chang

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Mountain pine beetle (MPB) outbreaks are currently devastating lodgepole pine forests in western North America, which are also widely disturbed by frequent wildfires. Both MPB and fire can leave scars on lodgepole pine trees, thereby diminishing their commercial value and possibly compromising their utilization in solid wood products. In order to fully exploit the affected resource, it is crucial to understand how wounding from these two disturbance agents impact wood properties. Moreover, previous research on lodgepole pine has focused solely on sound wood and stained wood resulting from the MPB-transmitted blue fungi. By means of a quantitative multi-proxy approach, we tested the hypotheses that (i) wounding (of either MPB or fire origin) caused significant changes in wood properties of lodgepole pine and that (ii) MPB-induced wound effects could differ from those induced by fire in type and magnitude. Pith-to-bark strips were extracted from 30 MPB scars and 30 fire scars. Strips were cut immediately adjacent to the wound margin and encompassed 12 rings from normal wood formed prior to wounding and 12 rings from wound wood formed after wounding. Wood properties evaluated within this 24-year window included ring width, relative wood density, cellulose crystallinity, fibre dimensions, and carbon and nitrogen concentrations. Methods used to measure these proxies at a (sub-)annual resolution included X-ray densitometry, X-ray diffraction, fibre quality analysis, and elemental analysis. Results showed a substantial growth release in wound wood compared to normal wood, as both earlywood and latewood width increased over a decade following wounding. Wound wood was also shown to have a significantly different latewood density than normal wood 4 years after wounding. Latewood density decreased in MPB scars while the opposite was true in fire scars. By contrast, earlywood density was presented only minor variations following wounding. Cellulose crystallinity decreased in wound wood compared to normal wood, being especially diminished in MPB scars the first year after wounding. Fibre dimensions also decreased following wounding. However, carbon and nitrogen concentrations did not substantially differ between wound wood and normal wood. Nevertheless, insect-feeding and fire-heating wounding were shown to significantly alter most wood properties of lodgepole pine, as demonstrated by the existence of several morphological anomalies in wound wood. MPB and fire generally elicited similar anomalies, with the major exception of latewood density. In addition to providing quantitative criteria for differentiating between biotic (MPB) and abiotic (fire) disturbances, this study provides the wood industry with fundamental information on the physiological response of lodgepole pine to wounding in order to evaluate the utilization of scarred trees in solid wood products.

Keywords: elemental analysis, fibre quality analysis, lodgepole pine, wood properties, wounding, X-ray densitometry, X-ray diffraction

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Authors: Tina Naser Torabi

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Prevalence of allergy is affected by different factors according to its base and seasonal weather changes, and it also needs various treatments.Although reasons of allergy existence are not clear but generally, allergens cause reaction between antigen and antibody because of their antigenic traits. In this state, allergens cause immune system to make mistake and identify safe material as threat, therefore function of immune system impaired because of histamine secretion. There are different reasons for allergy, but herbal reasons are on top of the list, although animal causes cannot be ignored. Important point is that allergenic compounds, cause making dedicated antibody, so in general every kind of allergy is different from the other one. Therefore, most of the plants in herbal allergenic category can cause various allergies for human beings, such as respiratory allergies, nutritional allergies, injection allergies, infection allergies, touch allergies, that each of them show different symptoms based on the reason of allergy and also each of them requires different prevention and treatment. Geographical condition is another effective factor in allergy. Seasonal changes, weather condition, herbal coverage variety play important roles in different allergies. It goes without saying that humid climate and herbal coverage variety in different seasons especially spring cause most allergies in human beings in Iran and India that are discussed in this article. These two countries are good choices for allergy prevalence because of their condition, various herbal coverage, human and animal factors. Hay fever is one of the allergies, although the reasons of its prevalence are unknown yet. It is one of the most popular allergies in Iran and India because of geographical, human, animal and herbal factors. Hay fever is on top of the list in these two countries. Significant point about these two countries is that herbal factor is the most important factor in prevalence of hay fever. Variety of herbal coverage especially in spring during herbal pollination is the main reason of hay fever prevalence in these two countries. Based on the research result of Pharmacognosy and Phytochemistry, pollination of some plants in spring is major reason of hay fever prevalence in these countries. If airborne pollens in pollination season enter the human body through air, they will cause allergic reactions in eyes, nasal mucosa, lungs, and respiratory system, and if these particles enter the body of potential person through food, they will cause allergic reactions in mouth, stomach, and other digestive systems. Occasionally, chemical materials produced by human body such as Histamine cause problems like: developing of nasal polyps, nasal blockage, sleep disturbance, risk of asthma developing, blood vasodilation, sneezing, eye tears, itching and swelling of eyes and nasal mucosa, Urticaria, decrease in blood pressure, and rarely trauma, anesthesia, anaphylaxis and finally death. This article is going to study the reasons of hay fever prevalence in Iran and India and presents prevention and treatment Method from Phytochemistry and Pharmocognocy point of view by using local natural products in these two countries.

Keywords: hay fever, India, Iran, natural treatment, phytochemistry

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Authors: Abdullah S. Alghamdi, Khaled Alghamdi, Richard O. Jenkins, Parvez I. Haris

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Background: Quantity and quality of sleep have been reported to be significant risk factors for obesity and development of metabolic disorders such as type 2 diabetes mellitus (T2DM). The relationship between diabetes and sleep quantity was reported to be U-shaped, which means increased or decreased sleeping hours can increase the risk of diabetes. The plasma glucagon levels were found to continuously decrease during night-time sleep in healthy individuals, independently of blood glucose and insulin levels. The disturbance of the circadian rhythm is also important and has been linked with an increased the chance of diabetes incidence. There is a lack of research on sleep patterns on Saudis with T2DM and how this is affected by Ramadan fasting. Aim: To assess the sleeping patterns of Saudis with T2DM (before, during, and after Ramadan), using two different techniques and relate this to their HbA1c levels. Method: This study recruited 82 Saudi with T2DM, who chose to fast during Ramadan, from the Endocrine and Diabetic Centre of Al Iman General Hospital, Riyadh, Saudi Arabia. Ethical approvals for the study were obtained from De Montfort University and Saudi Ministry of Health. Their sleeping patterns were assessed by a self-administered questionnaire (before, during, and after Ramadan). The assessment included the daily total sleeping hours (DTSH), and total night-time sleeping hours (TNTSH) of the participants. In addition, sleeping patterns of 36 patients, randomly selected from the 82 participants, were further tracked during and after Ramadan by using Fitbit Flex 2™ accelerometer. Blood samples were collected in each period for measuring HbA1c. Results: Questionnaire analysis revealed that the sleeping patterns significantly changed between the periods, with shorter hours during Ramadan (P < 0.001 for DTSH, and P < 0.001 for TNTSH). These findings were confirmed by the Fitbit data, which also indicated significant shorter sleeping hours for the DTSH, and the TNTSH during Ramadan (P < 0.001 and P < 0.001, respectively). Although there were no significant correlations between the questionnaire and Fitbit data, the TNTSH were shorter among the participants in all periods by both techniques. The mean HbA1c significantly varied between periods, with lowest level during Ramadan. Although the statistical tests did not show significant variances in the mean HbA1c between the groups of participants regarding their hours of sleeping, the lowest mean HbA1c was observed in the group of participants who slept for 6-8 hours and had longer night-time sleeping hours. Conclusion: A short sleep duration, and absence of night-time sleep were significantly observed among the majority of the study population during Ramadan, which could suppress the full benefits of Ramadan fasting for diabetic patients. This study showed that there is a good agreement between the findings of the questionnaire and the Fitbit device for evaluating sleeping patterns in a Saudi population. A larger study is needed in the future to investigate the impact of Ramadan fasting on sleep quality and quantity and its relationship with health and disease.

Keywords: Diabetes, Fasting, Fitbit, HbA1c, IPAQ, Ramadan, Sleep

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Authors: K. R. Roopesh Bharatwaj, Avinash Maharana, Favour Tobi Aborisade, Roger Young

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Presently, there are few cases of complete automation of drones and its allied intelligence capabilities. In essence, the potential of the drone has not yet been fully utilized. This paper presents feasible methods to build an intelligent drone with smart capabilities such as self-driving, and obstacle avoidance. It does this through advanced Reinforcement Learning Techniques and performs object detection using latest advanced algorithms, which are capable of processing light weight models with fast training in real time instances. For the scope of this paper, after researching on the various algorithms and comparing them, we finally implemented the Deep-Q-Networks (DQN) algorithm in the AirSim Simulator. In future works, we plan to implement further advanced self-driving and object detection algorithms, we also plan to implement voice-based speech recognition for the entire drone operation which would provide an option of speech communication between users (People) and the drone in the time of unavoidable circumstances. Thus, making drones an interactive intelligent Robotic Voice Enabled Service Assistant. This proposed drone has a wide scope of usability and is applicable in scenarios such as Disaster management, Air Transport of essentials, Agriculture, Manufacturing, Monitoring people movements in public area, and Defense. Also discussed, is the entire drone communication based on the satellite broadband Internet technology for faster computation and seamless communication service for uninterrupted network during disasters and remote location operations. This paper will explain the feasible algorithms required to go about achieving this goal and is more of a reference paper for future researchers going down this path.

Keywords: convolution neural network, natural language processing, obstacle avoidance, satellite broadband technology, self-driving

Procedia PDF Downloads 251