Search results for: linear predictive coding (LPC)

Authors: Kun Chen, Yan Wang, Yangyu Zhao, Shufang Li, Lian Chen, Xiaoyue Guo, Jue Zhang, Jing Fang

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The coupling strength between uterine electromyography (UEMG) and Fetal heart rate (FHR) signals during peripartum reflects the fetal biophysical activities. Therefore, UEMG-FHR coupling characterization is instructive in assessing placenta function. This study introduced a physiological marker named elevated frequency of UEMG-FHR coupling (E-UFC) and explored its predictive value for pregnancies complicated by pre-eclampsia and small for gestational age (SGA). Placental insufficiency patients (n=12) and healthy volunteers (n=24) were recruited and participated. UEMG and FHR were recorded non-invasively by a trans-abdominal device in women at term with singleton pregnancy (32-37 weeks) from 10:00 pm to 8:00 am. The product of the wavelet coherence and the wavelet cross-spectral power between UEMG and FHR was used to weight these two effects in order to quantify the degree of the UEMG-FHR coupling. E-UFC was exacted from the resultant spectrogram by calculating the mean value of the high-coherence (r > 0.5) frequency band. Results showed the high-coherence between UEMG and FHR was observed in the frequency band (1/512-1/16Hz). In addition, E-UFC in placental insufficiency patients was weaker compared to healthy controls (p < 0.001) at group level. These findings suggested the proposed approach could be used to quantitatively characterize the fetal biophysical activities, which is beneficial for early detection of placental insufficiency and reduces the occurrence of adverse pregnancy.

Keywords: uterine electromyography, fetal heart rate, coupling analysis, wavelet analysis

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Authors: Tas Yong Koh, Steve Rowlinson, Yuzhong Shen

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In construction safety management, safety climate has long been linked to workers' safety behaviors and performance. For this reason, safety climate concept and tools have been used as heuristics to diagnose a range of safety-related issues by some progressive contractors in Hong Kong and elsewhere. However, as a diagnostic tool, safety climate tends to treat the different components of the climate construct in a linear fashion. Safety management in construction projects, in reality, is a multi-faceted and multilevel phenomenon that resembles a complex system. Hence, understanding safety management in construction projects requires not only the understanding of safety climate but also the organizational-systemic nature of the phenomenon. Our involvement, diagnoses, and interpretations of a range of safety climate-related issues which culminated in the project’s sub-optimum safety performance in an infrastructure construction project have brought about such revelation. In this study, a range of data types had been collected from various hierarchies of the project site organization. These include the frontline workers and supervisors from the main and sub-contractors, and the client supervisory personnel. Data collection was performed through the administration of safety climate questionnaire, interviews, observation, and document study. The findings collectively indicate that what had emerged in parallel of the seemingly linear climate-based exploration is the exposition of the organization-systemic nature of the phenomenon. The results indicate the negative impacts of climate perceptions mismatch, insufficient work planning, and risk management, mixed safety leadership, workforce negative attributes, lapsed safety enforcement and resources shortages collectively give rise to the project sub-optimum safety performance. From the dynamic causation and multilevel perspective, the analyses show that the individual, group, and organizational levels issues are interrelated and these interrelationships are linked to negative safety climate. Hence the adoption of both perspectives has enabled a fuller understanding of the phenomenon of safety management that point to the need for an organizational-systemic intervention strategy. The core message points to the fact that intervention at an individual level will only meet with limited success if the risks embedded in the higher levels in group and project organization are not addressed. The findings can be used to guide the effective development of safety infrastructure by linking different levels of systems in a construction project organization.

Keywords: construction safety management, dynamic causation, multilevel analysis, safety climate

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Authors: Tina Beyene

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In this paper examines the state of grassroots feminist activism in the backdrop of state feminism in Ethiopia. Specifically, I examine the impact of the Charities and Societies Proclamation (aka CSO law), a 2009 law that banned so-called foreign NGOs—i.e., those receiving more than 10% of its operating budget from non-local sources— from working in the areas of human rights, democracy, governance, and gender equality. Viewed as government retribution for the NGO opposition to the government in the 2005 elections, the law aimed to halt the work groups such as the Ethiopian Women Lawyers Association (EWLA), who were defined as a “foreign” NGO. Based on interviews with prominent Ethiopian women’s rights leaders in Addis Ababa, Ethiopia, I assess how grassroots feminist organizing adapts to state suppression on the one hand, and the aggressive entry of the state into women’s rights work on the other hand. While the 2009 law has slowed down the work of women’s rights activism, displaced feminists view feminist advocacy as cyclical and the state as neither fully adversarial nor an ally but rather as an instable entity that at times provides political openings to push ambitious feminist agendas. Grassroots activists are regrouping and developing new political responses strategies such as coding rights issues to fit state mandate; dissembling rights work in permissible social provision language; rechanneling political work into informal spaces and unregistered social clubs; innovating new funding partnerships, and reassembling as privately held research and advocacy companies. my study reveals how grassroots feminist politics operates in the shadow of a hostile state and within the confines of local politics.

Keywords: grassroots feminism, ethiopian feminism, civil society and gender, state feminism

Procedia PDF Downloads 175

Authors: Roberto Machado

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We conducted a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology, analyzing 2,996 studies and synthesizing 41 of them to explore the evolution of data science and its integration into geography. By employing optimization algorithms, we accelerated the review process, significantly enhancing the efficiency and precision of literature selection. Our findings indicate that data science has developed over five decades, facing challenges such as the diversified integration of data and the need for advanced statistical and computational skills. In geography, the integration of data science underscores the importance of interdisciplinary collaboration and methodological innovation. Techniques like large-scale spatial data analysis and predictive algorithms show promise in natural disaster management and transportation route optimization, enabling faster and more effective responses. These advancements highlight the transformative potential of data science in geography, providing tools and methodologies to address complex spatial problems. The relevance of this study lies in the use of optimization algorithms in systematic reviews and the demonstrated need for deeper integration of data science into geography. Key contributions include identifying specific challenges in combining diverse spatial data and the necessity for advanced computational skills. Examples of connections between these two fields encompass significant improvements in natural disaster management and transportation efficiency, promoting more effective and sustainable environmental solutions with a positive societal impact.

Keywords: data science, geography, systematic review, optimization algorithms, supervised learning

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Authors: Lu Gan, Songmin Shang, Marcus Chun Wah Yuen

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A conductive and stretchable cotton fabric was prepared in this study through coating the graphene nanoribbon onto the cotton fabric. The mechanical and electrical properties of the prepared cotton fabric were then investigated. As shown in the results, the graphene nanoribbon coated cotton fabric had an improvement in both mechanical strength and electrical conductivity. Moreover, the resistance of the cotton fabric had a linear dependence on the strain applied to it. The prepared graphene nanoribbon coated cotton fabric has great application potentials in smart textile industry.

Keywords: conductive fabric, graphene nanoribbon, coating, enhanced properties

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Authors: P. Tej, P. Kněž, M. Blank

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The paper presents design and production of thin-walled U-profile footbridge made of UHPFRC. The main structure of the bridge is one prefabricated shell structure made of UHPFRC with dispersed steel fibers without any conventional reinforcement. The span of the bridge structure is 10 m and the clear width of 1.5 m. The thickness of the UHPFRC shell structure oscillated in an interval of 30-45 mm. Several calculations were made during the bridge design and compared with the experiments. For the purpose of verifying the calculations, a segment of 1.5 m was first produced, followed by the whole footbridge for testing. After the load tests were done, the design was optimized to cast the final footbridge.

Keywords: footbridge, non-linear analysis, shell structure, UHPFRC, Ultra-High Performance Fibre Reinforced Concrete

Procedia PDF Downloads 237

Authors: Janne Engblom, Elias Oikarinen

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A panel dataset is one that follows a given sample of individuals over time, and thus provides multiple observations on each individual in the sample. Panel data models include a variety of fixed and random effects models which form a wide range of linear models. A special case of panel data models are dynamic in nature. A complication regarding a dynamic panel data model that includes the lagged dependent variable is endogeneity bias of estimates. Several approaches have been developed to account for this problem. In this paper, the panel models were estimated using the Arellano-Bover/Blundell-Bond Generalized method of moments (GMM) estimator which is an extension of the Arellano-Bond model where past values and different transformations of past values of the potentially problematic independent variable are used as instruments together with other instrumental variables. The Arellano–Bover/Blundell–Bond estimator augments Arellano–Bond by making an additional assumption that first differences of instrument variables are uncorrelated with the fixed effects. This allows the introduction of more instruments and can dramatically improve efficiency. It builds a system of two equations—the original equation and the transformed one—and is also known as system GMM. In this study, Finnish housing price dynamics were examined empirically by using the Arellano–Bover/Blundell–Bond estimation technique together with ordinary OLS. The aim of the analysis was to provide a comparison between conventional fixed-effects panel data models and dynamic panel data models. The Arellano–Bover/Blundell–Bond estimator is suitable for this analysis for a number of reasons: It is a general estimator designed for situations with 1) a linear functional relationship; 2) one left-hand-side variable that is dynamic, depending on its own past realizations; 3) independent variables that are not strictly exogenous, meaning they are correlated with past and possibly current realizations of the error; 4) fixed individual effects; and 5) heteroskedasticity and autocorrelation within individuals but not across them. Based on data of 14 Finnish cities over 1988-2012 differences of short-run housing price dynamics estimates were considerable when different models and instrumenting were used. Especially, the use of different instrumental variables caused variation of model estimates together with their statistical significance. This was particularly clear when comparing estimates of OLS with different dynamic panel data models. Estimates provided by dynamic panel data models were more in line with theory of housing price dynamics.

Keywords: dynamic model, fixed effects, panel data, price dynamics

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Authors: Kebede Mairie, Getahun Belete, Mitike Abeba

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Background: Leishmania aethiopica is a peculiar parasite causing cutaneous leishmaniasis in Ethiopia and its mainstay treatment is Sodium Stibogluconate. However, its treatment outcome in Ethiopia is not well documented. Objectives: To determine the treatment outcome of admitted cutaneous leishmaniasis patients and its associated factors in Addis Ababa, Ethiopia. Methods: A retrospective study was conducted from 1st November 2021 to 30th March 2022. Medical records of all cutaneous leishmaniasis-diagnosed and admitted patients who received parenteral sodium stibogluconate at All Africa Leprosy Rehabilitation and Training Center (ALERT) hospital, the main Leishmania treatment center in Ethiopia from July 2011 to September 2021 were reviewed. Results: A total of 827 charts of admitted cases from July 2011 to September 2021 were retrieved, but 667 (80.65%) were reviewed. Improvement in the treatment outcome was recorded in 93.36 % in the first course of SSG treatment and 96.23%, 94.62%, and 96.97% subsequently in the second, third and fourth treatment courses, respectively. Female gender and diffuse cutaneous leishmaniasis were the two predictive determinants in the treatment of cutaneous leishmaniasis. Conclusion: The study shows that parenteral sodium stibogluconate therapy treats hospitalized cutaneous leishmaniasis patients well, with female gender and diffuse cutaneous leishmaniasis having poor outcomes suggesting the need for a different approach for diffuse cutaneous leishmaniasis patients.

Keywords: cutaneous leishmaniasis, leishmania aethiopica, sodium stibogluconate, diffuse cutaneous leishmaniasis, pentostam

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Authors: Philippe Dillien, Patrice Forget, Harald Engel, Olivier Cornu, Marc De Kock, Jean Cyr Yombi

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Introduction: Hip fracture precedes commonly death in elderly people. Identification of high-risk patients may contribute to target patients in whom optimal management, resource allocation and trials efficiency is needed. The aim of this study is to construct a predictive score of mortality after hip fracture on the basis of the objective prognostic factors available: Neutrophil-to-lymphocyte ratio (NLR), age, and sex. C-Reactive Protein (CRP), is also considered as an alternative to the NLR. Patients and methods: After the IRB approval, we analyzed our prospective database including 286 consecutive patients with hip fracture. A score was constructed combining age (1 point per decade above 74 years), sex (1 point for males), and NLR at postoperative day+5 (1 point if >5). A receiver-operating curve (ROC) curve analysis was performed. Results: From the 286 patients included, 235 were analyzed (72 males and 163 females, 30.6%/69.4%), with a median age of 84 (range: 65 to 102) years, mean NLR values of 6.47+/-6.07. At one year, 82/280 patients died (29.3%). Graphical analysis and log-rank test confirm a highly statistically significant difference (P<0.001). Performance analysis shows an AUC of 0.72 [95%CI 0.65-0.79]. CRP shows no advantage on NLR. Conclusion: We have developed a score based on age, sex and the NLR to predict the risk of mortality at one year in elderly patients after surgery for a hip fracture. After external validation, it may be included in clinical practice as in clinical research to stratify the risk of postoperative mortality.

Keywords: neutrophil-to-lymphocyte ratio, hip fracture, postoperative mortality, medical and health sciences

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Authors: Koami West Togbetse

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The global shift towards clean and sustainable energy sources, known as the energy transition, is compelling numerous countries to transition from polluting energy systems to cleaner alternatives, commonly referred to as green energies. In this context, cobalt holds significant importance as a crucial mineral in facilitating this energy transition due to its pivotal role in electric batteries. Considering the Democratic Republic of Congo’s reputation for political instability and its position as the largest producer of cobalt, possessing over 50% of the world’s reserves, we have assessed the potential conflicts that may arise as a result of the rapid increase in cobalt demand. The results show that cobalt does not appear to be a determinant contributing to all past conflicts over the study period in the Democratic Republic of Congo (DRC). Gold, on the other hand, stands out as one of the coveted metals for rebel groups engaged in rampant exploitation, increasing the likelihood of conflicts occurring. However, a more in-depth analysis reveals a shift in the relationship between cobalt production and conflict events around 2006. Prior to 2006, increased cobalt production was significantly associated with a reduction in conflict events. However, after 2006, this relationship became positive, indicating that higher cobalt production is now linked to a slight increase in conflict events. This suggests a change in the dynamics affecting conflicts related to cobalt production before and after 2006. According to our predictive model, cobalt has the potential to emerge increasingly as a contributing factor, just like gold.

Keywords: conflicts, natural resources, energy transition, geopolitics

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Authors: J. J. Cetina-Denis, R. M. López-Gutiérrez, R. Ramírez-Ramírez, C. Cruz-Hernández

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This work addresses the problem of designing an algorithm capable of generating chaotic trajectories for mobile robots. Particularly, the chaotic behavior is induced in the linear and angular velocities of a Khepera III differential mobile robot by infusing them with the states of the H´enon chaotic map. A possible application, using the properties of chaotic systems, is patrolling a work area. In this work, numerical and experimental results are reported and analyzed. In addition, two quantitative numerical tests are applied in order to measure how chaotic the generated trajectories really are.

Keywords: chaos, chaotic trajectories, differential mobile robot, Henon map, Khepera III robot, patrolling applications

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Authors: Tatyana Rybitskaya, Alexandr Starostenko, Kseniya Ryabchenko

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Collector ring (CR) of FAIR project is a large acceptance storage ring and field quality plays a major role in the magnet design. The CR will use normal conducting dipole magnets. There will be 24 H-type sector magnets with a maximum field value of 1.6 T. The integrated over the length of the magnet field quality as a function of radius is ∆B.l/B.l = ±1x10⁻⁴. Below 1.6 T the value ∆B.l/B.l can be higher with a linear approximation up to ±2.5x10⁻⁴ at the field level of 0.8 T. An iron-dominated magnet with required field quality is produced with standard technology as the quality is dominated by the yoke geometry.

Keywords: conventional magnet, iron yoke dipole, harmonic terms, particle accelerators

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Authors: Deva Kanta Phukan

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An attempt is made where an electrically conducting fluid is injected from a fixed outer cylindrical casing onto an inner moving cylindrical rod. A magnetic field is applied parallel to the axis of the cylindrical rod. The basic governing set of partial differential equations for conservation of mass and momentum are reduced to a set of non-linear ordinary differential equation by introducing similarity transformation, which are integrated numerically. A perturbation solution for the case of large magnetic parameter is derived for constant Reynolds number.

Keywords: annular axi-symmetric stagnation flow, conducting fluid, magnetic field, moving cylinder

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Authors: Maria Sonto Maputle, Rhulani C. Shihundla, Rachel T. Lebese

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Background: Patients’ information must be complete and accurately documented in order to foster quality and continuity of care. The multidisciplinary health care members use patients’ documentation to communicate about health status, preventive health services, treatment, planning and delivery of care. The purpose of this study was to determine the practice of nursing documentation of patients’ information at selected Primary Health Care (PHC) facilities in Vhembe District, Limpopo Province, South Africa. Methods: The research approach adopted was qualitative while exploratory and descriptive design was used. The study was conducted at selected PHC facilities. Population included twelve professional nurses. Non-probability purposive sampling method was used to sample professional nurses who were willing to participate in the study. The criteria included participants’ whose daily work and activities, involved creating, keeping and updating nursing documentation of patients’ information. Qualitative data collection was through unstructured in-depth interviews until no new information emerged. Data were analysed through open–coding of, Tesch’s eight steps method. Results: Following data analysis, it was found that professional nurses’ had knowledge deficit related to insufficient training on updates and rendering multiple services daily had negative impact on accurate documentation of patients’ information. Conclusion: The study recommended standardization of registers, books and forms used at PHC facilities, and reorganization of PHC services into open day system.

Keywords: documentation, knowledge, patient care, patient’s information, training

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Authors: Rita Greco, Giuseppe Carlo Marano

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Chloride induced reinforcement corrosion is widely accepted to be the most frequent mechanism causing premature degradation of reinforced concrete members, whose economic and social consequences are growing up continuously. Prevention of these phenomena has a great importance in structural design, and modern Codes and Standard impose prescriptions concerning design details and concrete mix proportion for structures exposed to different external aggressive conditions, grouped in environmental classes. This paper focuses on reinforced concrete columns load carrying capacity degradation over time due to chloride induced steel pitting corrosion. The structural element is considered to be exposed to marine environment and the effects of corrosion are described by the time degradation of the axial-bending interaction diagram. Because chlorides ingress and consequent pitting corrosion propagation are both time-dependent mechanisms, the study adopts a time-variant predictive approach to evaluate the residual strength of corroded reinforced concrete columns at different lifetimes. Corrosion initiation and propagation process is modelled by taking into account all the parameters, such as external environmental conditions, concrete mix proportion, concrete cover and so on, which influence the time evolution of the corrosion phenomenon and its effects on the residual strength of RC columns.

Keywords: pitting corrosion, strength deterioration, diffusion coefficient, surface chloride concentration, concrete structures, marine environment

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Authors: Amar Omeiri

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This paper presents a three-phase shunt active power filter for current harmonics suppression and reactive power compensation using the supply current as reference. The proposed APF has a simple control circuit; it consists of detecting the supply current instead of the load current. The advantages of this APF are simplicity of control circuits and low implementation cost. The simulation results show that the proposed APF can compensate the reactive power and suppress current harmonics with two types of non-linear loads.

Keywords: active power filter, current harmonics and reactive power compensation, PWM inverter, Total Harmonic Distortion, power quality

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Authors: F. D. Konstandakopoulou, G. A. Papagiannopoulos, N. G. Pnevmatikos, G. D. Hatzigeorgiou

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This paper examines the effects of pile-soil-structure interaction on the dynamic response of offshore platforms under the action of near-fault earthquakes. Two offshore platforms models are investigated, one with completely fixed supports and one with piles which are clamped into deformable layered soil. The soil deformability for the second model is simulated using non-linear springs. These platform models are subjected to near-fault seismic ground motions. The role of fault mechanism on platforms’ response is additionally investigated, while the study also examines the effects of different angles of incidence of seismic records on the maximum response of each platform.

Keywords: hazard analysis, offshore platforms, earthquakes, safety

Procedia PDF Downloads 156

Authors: M. A. Effati, M. R. Hojjati, M. Razmdideh

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Nowadays, engine developments have focused on internal combustion engine design call for increased engine power, reduced engine size and improved fuel economy, simultaneously. In this paper, a novel design for combustion engine is proposed. Two combustion chambers were designed in two sides of cylinder. Piston was designed in a way that two sides of piston would transfer heat energy due to combustion to linear motion. This motion would convert to rotary motion through the designed mechanism connected to connecting rod. Connecting rod operation was analyzed to evaluate applied stress in 3000, 4500 and 6000 rpm. Boundary conditions including generated pressure in each side of cylinder in these 3 situations was calculated.

Keywords: combustion engine, design, finite element method, modeling

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Authors: Anne Schattel, Mitja Echim, Christof Büskens

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Trajectory planning for deep space missions has become a recent topic of great interest. Flying to space objects like asteroids provides two main challenges. One is to find rare earth elements, the other to gain scientific knowledge of the origin of the world. Due to the enormous spatial distances such explorer missions have to be performed unmanned and autonomously. The mathematical field of optimization and optimal control can be used to realize autonomous missions while protecting recourses and making them safer. The resulting algorithms may be applied to other, earth-bound applications like e.g. deep sea navigation and autonomous driving as well. The project KaNaRiA ('Kognitionsbasierte, autonome Navigation am Beispiel des Ressourcenabbaus im All') investigates the possibilities of cognitive autonomous navigation on the example of an asteroid mining mission, including the cruise phase and approach as well as the asteroid rendezvous, landing and surface exploration. To verify and test all methods an interactive, real-time capable simulation using virtual reality is developed under KaNaRiA. This paper focuses on the specific challenge of the guidance during the cruise phase of the spacecraft, i.e. trajectory optimization and optimal control, including first solutions and results. In principle there exist two ways to solve optimal control problems (OCPs), the so called indirect and direct methods. The indirect methods are being studied since several decades and their usage needs advanced skills regarding optimal control theory. The main idea of direct approaches, also known as transcription techniques, is to transform the infinite-dimensional OCP into a finite-dimensional non-linear optimization problem (NLP) via discretization of states and controls. These direct methods are applied in this paper. The resulting high dimensional NLP with constraints can be solved efficiently by special NLP methods, e.g. sequential quadratic programming (SQP) or interior point methods (IP). The movement of the spacecraft due to gravitational influences of the sun and other planets, as well as the thrust commands, is described through ordinary differential equations (ODEs). The competitive mission aims like short flight times and low energy consumption are considered by using a multi-criteria objective function. The resulting non-linear high-dimensional optimization problems are solved by using the software package WORHP ('We Optimize Really Huge Problems'), a software routine combining SQP at an outer level and IP to solve underlying quadratic subproblems. An application-adapted model of impulsive thrusting, as well as a model of an electrically powered spacecraft propulsion system, is introduced. Different priorities and possibilities of a space mission regarding energy cost and flight time duration are investigated by choosing different weighting factors for the multi-criteria objective function. Varying mission trajectories are analyzed and compared, both aiming at different destination asteroids and using different propulsion systems. For the transcription, the robust method of full discretization is used. The results strengthen the need for trajectory optimization as a foundation for autonomous decision making during deep space missions. Simultaneously they show the enormous increase in possibilities for flight maneuvers by being able to consider different and opposite mission objectives.

Keywords: deep space navigation, guidance, multi-objective, non-linear optimization, optimal control, trajectory planning.

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Authors: M. Khouil, N. Saber, M. Mestari

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In this paper, a different architecture of a collision detection neural network (DCNN) is developed. This network, which has been particularly reviewed, has enabled us to solve with a new approach the problem of collision detection between two convex polyhedra in a fixed time (O (1) time). We used two types of neurons, linear and threshold logic, which simplified the actual implementation of all the networks proposed. The study of the collision detection is divided into two sections, the collision between a point and a polyhedron and then the collision between two convex polyhedra. The aim of this research is to determine through the AMAXNET network a mini maximum point in a fixed time, which allows us to detect the presence of a potential collision.

Keywords: collision identification, fixed time, convex polyhedra, neural network, AMAXNET

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Authors: Ibrahim Özbek, Erdoğan Mehmet Özkan

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In this study, we generalize (k,n) threshold secret sharing scheme on the study Ozbek and Siap to the codes over the ring Fq+ αFq. In this way, it is mentioned that the method obtained in that article can also be used on codes over rings, and new advantages to be obtained. The method of securely sharing the key in cryptography, which Shamir first systematized and Massey carried over to codes, became usable for all error-correcting codes. The firewall of this scheme is based on the hardness of the syndrome decoding problem. Also, an open study area is left for those working for other rings and code classes. All codes that correct errors with this method have been the working area of this method.

Keywords: secret sharing scheme, linear codes, algebra, finite rings

Procedia PDF Downloads 81

Authors: Ali Oveysi Sarabi

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In this paper, controller design for the attitude and altitude dynamics of an outdoor quadrotor, which is constructed with low cost actuators and drivers, is aimed. Before designing the controller, the quadrotor is modeled mathematically in Matlab-Simulink environment. To control attitude dynamics, linear quadratic regulator (LQR) based controllers are designed, simulated and applied to the system. Two different proportional-integral-derivative action (PID) controllers are designed to control yaw and altitude dynamics. During the implementation of the designed controllers, different test setups are used. Designed controllers are implemented and tuned on the real system using xPC Target. Tests show that these basic control structures are successful to control the attitude and altitude dynamics.

Keywords: helicopter balance, flight dynamics, autonomous landing, control robotics

Procedia PDF Downloads 515

Authors: Sabyasachi Mukhopadhyay, Joseph Ogutu, Gundula Bartzke, Hans-Peter Piepho

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Rainfall is a critical component of climate governing vegetation growth and production, forage availability and quality for herbivores. However, reliable rainfall measurements are not always available, making it necessary to predict rainfall values for particular locations through time. Predicting rainfall in space and time can be a complex and challenging task, especially where the rain gauge network is sparse and measurements are not recorded consistently for all rain gauges, leading to many missing values. Here, we develop a flexible Bayesian model for predicting rainfall in space and time and apply it to Narok County, situated in southwestern Kenya, using data collected at 23 rain gauges from 1965 to 2015. Narok County encompasses the Maasai Mara ecosystem, the northern-most section of the Mara-Serengeti ecosystem, famous for its diverse and abundant large mammal populations and spectacular migration of enormous herds of wildebeest, zebra and Thomson's gazelle. The model incorporates geographical and meteorological predictor variables, including elevation, distance to Lake Victoria and minimum temperature. We assess the efficiency of the model by comparing it empirically with the established Gaussian process, Kriging, simple linear and Bayesian linear models. We use the model to predict total monthly rainfall and its standard error for all 5 * 5 km grid cells in Narok County. Using the Monte Carlo integration method, we estimate seasonal and annual rainfall and their standard errors for 29 sub-regions in Narok. Finally, we use the predicted rainfall to predict large herbivore biomass in the Maasai Mara ecosystem on a 5 * 5 km grid for both the wet and dry seasons. We show that herbivore biomass increases with rainfall in both seasons. The model can handle data from a sparse network of observations with many missing values and performs at least as well as or better than four established and widely used models, on the Narok data set. The model produces rainfall predictions consistent with expectation and in good agreement with the blended station and satellite rainfall values. The predictions are precise enough for most practical purposes. The model is very general and applicable to other variables besides rainfall.

Keywords: non-stationary covariance function, gaussian process, ungulate biomass, MCMC, maasai mara ecosystem

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Authors: Naduni Ranasinghe

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E-learning environments are getting more popular than any other due to the impact of COVID19. Even though e-learning is one of the best solutions for the teaching-learning process in the academic process, it’s not without major challenges. Nowadays, machine learning approaches are utilized in the analysis of how behavioral factors lead to better adoption and how they related to better performance of the students in eLearning environments. During the pandemic, we realized the academic process in the eLearning approach had a major issue, especially for the performance of the students. Therefore, an approach that investigates student behaviors in eLearning environments using a data-intensive machine learning approach is appreciated. A hybrid approach was used to understand how each previously told variables are related to the other. A more quantitative approach was used referred to literature to understand the weights of each factor for adoption and in terms of performance. The data set was collected from previously done research to help the training and testing process in ML. Special attention was made to incorporating different dimensionality of the data to understand the dependency levels of each. Five independent variables out of twelve variables were chosen based on their impact on the dependent variable, and by considering the descriptive statistics, out of three models developed (Random Forest classifier, SVM, and Decision tree classifier), random forest Classifier (Accuracy – 0.8542) gave the highest value for accuracy. Overall, this work met its goals of improving student performance by identifying students who are at-risk and dropout, emphasizing the necessity of using both static and dynamic data.

Keywords: academic performance prediction, e learning, learning analytics, machine learning, predictive model

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Authors: H. Hosseini, B. V. Berdyshev, I. Iskopintsev

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This paper presents a rheological model for producing shape-memory thermoplastic polymers. Shape-memory occurs as a result of internal rearrangement of the structural elements of a polymer. A non-linear viscoelastic model was developed that allows qualitative and quantitative prediction of the stress-strain behavior of shape-memory polymers during heating. This research was done to develop a technique to determine the maximum possible change in size of heat-shrinkable products during heating. The rheological model used in this work was particularly suitable for defining process parameters and constructive parameters of the processing equipment.

Keywords: elastic deformation, heating, shape-memory polymers, stress-strain behavior, viscoelastic model

Procedia PDF Downloads 326

Authors: Larry Audet

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Creativity is suddenly and fortunately a new educational focus in the United Arab Emirates and around the world. Yet still today many leaders of creativity are not sure how to introduce it to their teachers. It is impossible to simultaneously introduce every aspect of creativity into a work climate and reach any degree of organizational coherence. The number of alternatives to explore is so great; the information teachers need to learn is so vast, that even an approximation to including every concept and theory of creativity into the school organization is hard to conceive. Effective leaders of creativity need evidence-based and practical guidance for introducing and stimulating creativity in others. Machine learning models reveal new findings from KEYS Survey© data about teacher perceptions of stimulants and barriers to their individual and collective creativity. Findings from predictive and causal models provide leaders with a rational for decision-making when introducing creativity into their organization. Leaders should focus on management practices first. Analyses reveal that creative outcomes are more likely to occur when teachers perceive supportive management practices: providing teachers with challenging work that calls for their best efforts; allowing freedom and autonomy in their practice of work; allowing teachers to form creative work-groups; and, recognizing them for their efforts. Once management practices are in place, leaders should focus their efforts on modeling risk-taking, providing optimal amounts of preparation time, and evaluating teachers fairly.

Keywords: creativity, leadership, KEYS survey, teaching, work climate

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Authors: Atheer Al-Nuaimi, Harry Evdorides

Abstract:

Every day many people die or get disabled or injured on roads around the world, which necessitates more specific treatments for transportation safety issues. International road assessment program (iRAP) model is one of the comprehensive road safety models which accounting for many factors that affect road safety in a cost-effective way in low and middle income countries. In iRAP model road safety has been divided into five star ratings from 1 star (the lowest level) to 5 star (the highest level). These star ratings are based on star rating score which is calculated by iRAP methodology depending on road attributes, traffic volumes and operating speeds. The outcome of iRAP methodology are the treatments that can be used to improve road safety and reduce fatalities and serious injuries (FSI) numbers. These countermeasures can be used separately as a single countermeasure or mix as multiple countermeasures for a location. There is general agreement that the adequacy of a countermeasure is liable to consistent losses when it is utilized as a part of mix with different countermeasures. That is, accident diminishment appraisals of individual countermeasures cannot be easily added together. The iRAP model philosophy makes utilization of a multiple countermeasure adjustment factors to predict diminishments in the effectiveness of road safety countermeasures when more than one countermeasure is chosen. A multiple countermeasure correction factors are figured for every 100-meter segment and for every accident type. However, restrictions of this methodology incorporate a presumable over-estimation in the predicted crash reduction. This study aims to adjust this correction factor by developing new models to calculate the effect of using multiple countermeasures on the number of fatalities for a location or an entire road. Regression models have been used to establish relationships between crash frequencies and the factors that affect their rates. Multiple linear regression, negative binomial regression, and Poisson regression techniques were used to develop models that can address the effectiveness of using multiple countermeasures. Analyses are conducted using The R Project for Statistical Computing showed that a model developed by negative binomial regression technique could give more reliable results of the predicted number of fatalities after the implementation of road safety multiple countermeasures than the results from iRAP model. The results also showed that the negative binomial regression approach gives more precise results in comparison with multiple linear and Poisson regression techniques because of the overdispersion and standard error issues.

Keywords: international road assessment program, negative binomial, road multiple countermeasures, road safety

Procedia PDF Downloads 245

Authors: Andrés I. Ávila, Patricia Aros, César San Martín, Elizabeth Kehr, Yovana Leal

Abstract:

We consider a linear programming model to help farmers to decide if it is convinient to choose among three kinds of export fruits for their future investment. We consider area, investment, water, productivitiy minimal unit, and harvest restrictions and a monthly based model to compute the average income in five years. Also, conditions on the field as area, water availability and initia investment are required. Using the Chilean costs and dollar-peso exchange rate, we can simulate several scenarios to understand the possible risks associated to this market.

Keywords: mixed integer problem, fruit production, support decision model, fruit tree farms

Procedia PDF Downloads 460

Authors: Ferah Tesfaye Admasu

Abstract:

Autism Spectrum Disorder (ASD) often presents significant communication challenges, particularly for non-verbal individuals who struggle to express their needs and emotions effectively. Assistive technologies (AT) have emerged as vital tools in enhancing communication abilities for this population. Recent advancements in artificial intelligence (AI) hold the potential to revolutionize the design and functionality of these technologies. This study explores the application of AI in developing intelligent, adaptive, and user-centered assistive technologies for non-verbal individuals with autism. Through a review of current AI-driven tools, including speech-generating devices, predictive text systems, and emotion-recognition software, this research investigates how AI can bridge communication gaps, improve engagement, and support independence. Machine learning algorithms, natural language processing (NLP), and facial recognition technologies are examined as core components in creating more personalized and responsive communication aids. The study also discusses the challenges and ethical considerations involved in deploying AI-based AT, such as data privacy and the risk of over-reliance on technology. Findings suggest that integrating AI into assistive technologies can significantly enhance the quality of life for non-verbal individuals with autism, providing them with greater opportunities for social interaction and participation in daily activities. However, continued research and development are needed to ensure these technologies are accessible, affordable, and culturally sensitive.

Keywords: artificial intelligence, autism spectrum disorder, non-verbal communication, assistive technology, machine learning

Procedia PDF Downloads 28

Authors: H. Hotait, X. Chiementin, L. Rasolofondraibe

Abstract:

This paper presents a continuous work to detect the abnormal state in the rolling bearing by studying the vibration signature analysis and calculation of the remaining useful life. To achieve these aims, two methods; the first method is the classification to detect the degradation state by the AOM-OPTICS (Acousto-Optic Modulator) method. The second one is the prediction of the degradation state using least-squares support vector regression and then compared with the linear degradation model. An experimental investigation on ball-bearing was conducted to see the effectiveness of the used method by applying the acquired vibration signals. The proposed model for predicting the state of bearing gives us accurate results with the experimental and numerical data.

Keywords: bearings, automatization, optimization, prognosis, classification, defect detection

Procedia PDF Downloads 124