Search results for: reduced order macro models

Authors: Htet Khaing Lin

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This study explores the relationship between a diverse set of variables, encompassing both environmental and socio-economic factors, and poverty levels in the Philippines for the years 2012, 2015, and 2018. Employing Ordinary Least Squares (OLS), Spatial Lag Models (SLM), and Spatial Error Models (SEM), this study delves into the dynamics of key indicators, including daytime and nighttime land surface temperature, cropland surface, urban land surface, rainfall, population size, normalized difference water, vegetation, and drought indices. The findings reveal consistent patterns and unexpected correlations, highlighting the need for nuanced policies that address the multifaceted challenges arising from the interplay of environmental and socio-economic factors.

Keywords: poverty analysis, OLS, spatial lag models, spatial error models, Philippines, google earth engine, satellite data, environmental dynamics, socio-economic factors

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Authors: M. Yilmaz, I. Yilmaz, M. Uysal

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The Earth system generates different phenomena that are observable at the surface of the Earth such as mass deformations and displacements leading to plate tectonics, earthquakes, and volcanism. The dynamic processes associated with the interior, surface, and atmosphere of the Earth affect the three pillars of geodesy: shape of the Earth, its gravity field, and its rotation. Geodesy establishes a characteristic structure in order to define, monitor, and predict of the whole Earth system. The traditional and new instruments, observables, and techniques in geodesy are related to the gravity field. Therefore, the geodesy monitors the gravity field and its temporal variability in order to transform the geodetic observations made on the physical surface of the Earth into the geometrical surface in which positions are mathematically defined. In this paper, the main components of the gravity field modeling, (Free-air and Bouguer) gravity anomalies are calculated via recent global models (EGM2008, EIGEN6C4, and GECO) over a selected study area. The model-based gravity anomalies are compared with the corresponding terrestrial gravity data in terms of standard deviation (SD) and root mean square error (RMSE) for determining the best fit global model in the study area at a regional scale in Turkey. The least SD (13.63 mGal) and RMSE (15.71 mGal) were obtained by EGM2008 for the Free-air gravity anomaly residuals. For the Bouguer gravity anomaly residuals, EIGEN6C4 provides the least SD (8.05 mGal) and RMSE (8.12 mGal). The results indicated that EIGEN6C4 can be a useful tool for modeling the gravity field of the Earth over the study area.

Keywords: free-air gravity anomaly, Bouguer gravity anomaly, global model, land gravity

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Authors: Susana Aragoneses Garrido

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Every day, the roads are scenery of numerous accidents involving vehicles, producing thousands of deaths and serious injuries all over the world. Investigations have revealed that Human Factors (HF) are one of the main causes of road accidents in modern societies. Distracted driving (including external or internal aspects of the vehicle), which is considered as a human factor, is a serious and emergent risk to road safety. Consequently, a further analysis regarding this issue is essential due to its transcendence on today’s society. The objectives of this investigation are the detection and assessment of the HF in order to provide solutions (including a better vehicle design), which might mitigate road accidents. The methodology of the project is divided in different phases. First, a statistical analysis of public databases is provided between Spain and The UK. Second, data is classified in order to analyse the major causes involved in road accidents. Third, a simulation between different paths and vehicles is presented. The causes related to the HF are assessed by Failure Mode and Effects Analysis (FMEA). Fourth, different car models are evaluated using the Rapid Upper Body Assessment (RULA). Additionally, the JACK SIEMENS PLM tool is used with the intention of evaluating the Human Factor causes and providing the redesign of the vehicles. Finally, improvements in the car design are proposed with the intention of reducing the implication of HF in traffic accidents. The results from the statistical analysis, the simulations and the evaluations confirm that accidents are an important issue in today’s society, especially the accidents caused by HF resembling distractions. The results explore the reduction of external and internal HF through the global analysis risk of vehicle accidents. Moreover, the evaluation of the different car models using RULA method and the JACK SIEMENS PLM prove the importance of having a good regulation of the driver’s seat in order to avoid harmful postures and therefore distractions. For this reason, a car redesign is proposed for the driver to acquire the optimum position and consequently reducing the human factors in road accidents.

Keywords: analysis vehicles, asssesment, ergonomics, car redesign

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Authors: Jianli Chang, Yusheng Zhang, Yali Yao, Diane Hildebrandt, Xinying Liu

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The influence of feed-gas ratio on the ethene hydroformylation over an Rh-Co bimetallic catalyst supported by reduced graphene oxide (RGO) has been investigated in a tubular fixed bed reactor. Argon was used as balance gas when the feed-gas ratio was changed, which can keep the partial pressure of the other two kinds of gas constant while the ratio of one component in feed-gas was changed. First, the effect of single-component gas ratio on the performance of ethene hydroformylation was studied one by one (H₂, C₂H₄ and CO). Then an optimized ratio was found to obtain a high selectivity to C₃ oxygenates. The results showed that: (1) 0.5%Rh-20%Co/RGO is a promising heterogeneous catalyst for ethene hydroformylation. (2) H₂ and CO have a more significant influence than C₂H₄ on selectivity to oxygenates. (3) A lower H₂ ratio and a higher CO ratio in feed-gas can lead to a higher selectivity to oxygenates. (4) The highest selectivity to oxygenates, 61.70%, was obtained at the feed-gas ratio CO: C₂H₄: H₂ = 4: 2: 1.

Keywords: ethene hydroformylation, reduced graphene oxide, rhodium cobalt bimetallic catalyst, the effect of feed-gas ratio

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Authors: Emin Z. Mahmud

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Shaking table tests are planned in order to deepen the understanding of the behavior of confined masonry structures with or without openings. The tests are realized in the laboratory of the Institute of Earthquake Engineering and Engineering Seismology (IZIIS) – Skopje. The specimens were examined separately on the shaking table, with uniaxial, in-plane excitation. After testing, samples were strengthened with GFRP (Glass Fiber Reinforced Plastic) and re-tested. This paper presents the observations from a series of shaking-table tests done on a 1:1 scaled confined masonry wall model, with opening for a window – specimens CMWuS (before strengthening) and CMWS (after strengthening). Frequency and stiffness changes before and after GFRP wall strengthening are analyzed. Definition of dynamic properties of the models was the first step of the experimental testing, which enabled acquiring important information about the achieved stiffness (natural frequencies) of the model. The natural frequency was defined in the Y direction of the model by applying resonant frequency search tests. It is important to mention that both specimens CMWuS and CMWS are subjected to the same effects. The initial frequency of the undamaged model CMWuS is 18.79 Hz, while at the end of the testing, the frequency decreased to 12.96 Hz. This emphasizes the reduction of the initial stiffness of the model due to damage, especially in the masonry and tie-beam to tie-column connection. After strengthening the damaged wall, the natural frequency increases to 14.67 Hz. This highlights the beneficial effect of strengthening. After completion of dynamic testing at CMWS, the natural frequency is reduced to 10.75 Hz.

Keywords: behaviour of masonry structures, Eurocode, frequency, masonry, shaking table test, strengthening

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Authors: Virender Singh, Mathew Rees, Simon Hampton, Sivaram Annadurai

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Plant identification is a challenging task that aims to identify the family, genus, and species according to plant morphological features. Automated deep learning-based computer vision algorithms are widely used for identifying plants and can help users narrow down the possibilities. However, numerous morphological similarities between and within species render correct classification difficult. In this paper, we tested custom convolution neural network (CNN) and vision transformer (ViT) based models using the PyTorch framework to classify plants. We used a large dataset of 88,000 provided by the Royal Horticultural Society (RHS) and a smaller dataset of 16,000 images from the PlantClef 2015 dataset for classifying plants at genus and species levels, respectively. Our results show that for classifying plants at the genus level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420 and other state-of-the-art CNN-based models suggested in previous studies on a similar dataset. ViT model achieved top accuracy of 83.3% for classifying plants at the genus level. For classifying plants at the species level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420, with a top accuracy of 92.5%. We show that the correct set of augmentation techniques plays an important role in classification success. In conclusion, these results could help end users, professionals and the general public alike in identifying plants quicker and with improved accuracy.

Keywords: plant identification, CNN, image processing, vision transformer, classification

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Authors: Asmae Tafraouti, Yasmina Layouni, Pascal Kleimann

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The growing market for Microsystems (MST) and Micro-Electromechanical Systems (MEMS) is driving the research for alternative manufacturing techniques to microelectronics-based technologies, which are generally expensive and time-consuming. Hot-embossing and micro-injection modeling of thermoplastics appear to be industrially viable processes. However, both require the use of master models, usually made in hard materials such as steel. These master models cannot be fabricated using standard microelectronics processes. Thus, other micromachining processes are used, as laser machining or micro-electrical discharge machining (µEDM). In this work, µEDM has been used. The principle of µEDM is based on the use of a thin cylindrical micro-tool that erodes the workpiece surface. The two electrodes are immersed in a dielectric with a distance of a few micrometers (gap). When an electrical voltage is applied between the two electrodes, electrical discharges are generated, which cause material machining. In order to produce master models with high resolution and smooth surfaces, it is necessary to well control the discharge mechanism. However, several problems are encountered, such as a random electrical discharge process, the fluctuation of the discharge energy, the electrodes' polarity inversion, and the wear of the micro-tool. The effect of different parameters, such as the applied voltage, the working capacitor, the micro-tool diameter, the initial gap, has been studied. This analysis helps to improve the machining performances, such: the workpiece surface condition and the lateral crater's gap.

Keywords: craters, electrical discharges, micro-electrical discharge machining (µEDM), microsystems

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Authors: Brahim-Fares Zaidi, Malika Boudraa, Sid-Ahmed Selouani

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Our work aims to improve our Automatic Recognition System for Dysarthria Speech (ARSDS) based on the Hidden Models of Markov (HMM) and the Hidden Markov Model Toolkit (HTK) to help people who are sick. With pronunciation problems, we applied two techniques of speech parameterization based on Mel Frequency Cepstral Coefficients (MFCC's) and Perceptual Linear Prediction (PLP's) and concatenated them with JITTER and SHIMMER coefficients in order to increase the recognition rate of a dysarthria speech. For our tests, we used the NEMOURS database that represents speakers with dysarthria and normal speakers.

Keywords: hidden Markov model toolkit (HTK), hidden models of Markov (HMM), Mel-frequency cepstral coefficients (MFCC), perceptual linear prediction (PLP’s)

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Authors: Anat Keller

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Given that macro-prudential supervision is a relatively new policy area and its empirical and analytical research are still in their infancy, its theoretical foundations are also lagging behind. This paper contributes to the developing discussion on effective legal and institutional macroprudential supervision frameworks. In the first part of the paper, it is argued that effectiveness as a key benchmark poses some challenges in the context of macroprudential supervision such as the difficulty in proving causality between supervisory actions and the achievement of the supervisor’s mission. The paper suggests that effectiveness in the macroprudential context should, therefore, be assessed at the supervisory decision-making process (to be differentiated from the supervisory outcomes). The second part of the essay examines whether insights from behavioural economics can point to biases in the macroprudential decision-making process. These biases include, inter alia, preference bias, groupthink bias and inaction bias. It is argued that these biases are exacerbated in the multilateral setting of the macroprudential supervision framework in the EU. The paper then examines how legal and institutional frameworks should be designed to acknowledge and perhaps contain these identified biases. The paper suggests that the effectiveness of macroprudential policy will largely depend on the existence of clear and robust transparency and accountability arrangements. Accountability arrangements can be used as a vehicle for identifying and addressing potential biases in the macro-prudential framework, in particular, inaction bias. Inclusiveness of the public in the supervisory process in the form of transparency and awareness of the logic behind policy decisions may assist in minimising their potential unpopularity thus promoting their effectiveness. Furthermore, a governance structure which facilitates coordination of the macroprudential supervisor with other policymakers and incorporates outside perspectives and opinions could ‘break-down’ groupthink bias as well as inaction bias.

Keywords: behavioural economics and biases, effectiveness of macroprudential supervision, legal and institutional macroprudential frameworks, macroprudential decision-making process

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Authors: A. B. M. Rezaul Islam, Ernur Karadogan

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Shape memory alloys (SMAs) are known for their shape memory effect and pseudoelasticity behavior. Their thermomechanical behaviors are modeled by numerous researchers using microscopic thermodynamic and macroscopic phenomenological point of view. Tanaka, Liang-Rogers and Ivshin-Pence models are some of the most popular SMA macroscopic phenomenological constitutive models. They describe SMA behavior in terms of stress, strain and temperature. These models involve material parameters and they have associated uncertainty present in them. At different operating temperatures, the uncertainty propagates to the output when the material is subjected to loading followed by unloading. The propagation of uncertainty while utilizing these models in real-life application can result in performance discrepancies or failure at extreme conditions. To resolve this, we used probabilistic approach to perform the sensitivity and uncertainty analysis of Tanaka, Liang-Rogers, and Ivshin-Pence models. Sobol and extended Fourier Amplitude Sensitivity Testing (eFAST) methods have been used to perform the sensitivity analysis for simulated isothermal loading/unloading at various operating temperatures. As per the results, it is evident that the models vary due to the change in operating temperature and loading condition. The average and stress-dependent sensitivity indices present the most significant parameters at several temperatures. This work highlights the sensitivity and uncertainty analysis results and shows comparison of them at different temperatures and loading conditions for all these models. The analysis presented will aid in designing engineering applications by eliminating the probability of model failure due to the uncertainty in the input parameters. Thus, it is recommended to have a proper understanding of sensitive parameters and the uncertainty propagation at several operating temperatures and loading conditions as per Tanaka, Liang-Rogers, and Ivshin-Pence model.

Keywords: constitutive models, FAST sensitivity analysis, sensitivity analysis, sobol, shape memory alloy, uncertainty analysis

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Authors: Seyed Reza Mirdehghan, Mohammad Reza Haeri Yazdi

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In this paper, the stability and the performance of a two-degree-of-freedom robot under an interaction with a unknown environment has been investigated. The time when the robot returns to its initial position after an interaction and the primary resistance of the robot against the impact must be reduced. Thus, the applied torque on the motor will be reduced. The impedance control is an appropriate method for robot control in these conditions. The stability of the robot at interaction moment was transformed to be a robust stability problem. The dynamic of the unknown environment was modeled as a weight function and the stability of the robot under an interaction with the environment has been investigated using the robust control concept. To improve the performance of the system, a force controller has been designed which the normalized impedance after interaction has been reduced. The resistance of the robot has been considered as a normalized cost function and its value was 0.593. The results has showed reduction of resistance of the robot against impact and the reduction of convergence time by lower than one second.

Keywords: impedance control, control system, robots, interaction

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Authors: Mahbub C. Mishu, Venktesh N. Dubey, Tamas Hickish, Jonathan Cole

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Pressure ulcer is a common problem for today's healthcare industry. It occurs due to external load applied to the skin. Also when the subject is immobile for a longer period of time and there is continuous load applied to a particular area of human body,blood flow gets reduced and as a result pressure ulcer develops. Body support surface has a significant role in preventing ulceration so it is important to know the characteristics of support surface under loading conditions. In this paper we have presented mathematical models of different types of viscoelastic materials and also we have shown the validation of our simulation results with experiments.

Keywords: pressure ulcer, viscoelastic material, mathematical model, experimental validation

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Authors: Bahram Fathi, Seyedhossein Sajadifar, Naser Khiabani

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Data envelopment analysis (DEA) has recently gained popularity in energy efficiency analysis. A common feature of the previously proposed DEA models for measuring energy efficiency performance is that they treat energy consumption as an input within a production framework without considering undesirable outputs. However, energy use results in the generation of undesirable outputs as byproducts of producing desirable outputs. Within a joint production framework of both desirable and undesirable outputs, this paper presents several DEA-type linear programming models for measuring energy efficiency performance. In addition to considering undesirable outputs, our models treat different energy sources as different inputs so that changes in energy mix could be accounted for in evaluating energy efficiency. The proposed models are applied to measure the energy efficiency performances of 12 OPEC countries and the results obtained are presented.

Keywords: energy efficiency, undesirable outputs, data envelopment analysis

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Authors: Mimi Hafizah Abdullah, Hanani Farhah Harun, Nik Ruzni Nik Idris

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With the implied volatility as an important factor in financial decision-making, in particular in option pricing valuation, and also the given fact that the pricing biases of Leland option pricing models and the implied volatility structure for the options are related, this study considers examining the implied adjusted volatility smile patterns and term structures in the S&P/ASX 200 index options using the different Leland option pricing models. The examination of the implied adjusted volatility smiles and term structures in the Australian index options market covers the global financial crisis in the mid-2007. The implied adjusted volatility was found to escalate approximately triple the rate prior the crisis.

Keywords: implied adjusted volatility, financial crisis, Leland option pricing models, Australian index options

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Authors: Melissa de Zeeuw, Peter Smolianov, Arnold Bohl

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This study was designed to help and improve international performance as well increase swimming participation in the Netherlands. Over 200 sources of literature on sport delivery systems from 28 Australasian, North and South American, Western and Eastern European countries were analyzed to construct a globally applicable model of high performance swimming integrated with mass participation, comprising of the following seven elements and three levels: Micro level (operations, processes, and methodologies for development of individual athletes): 1. Talent search and development, 2. Advanced athlete support. Meso level (infrastructures, personnel, and services enabling sport programs): 3. Training centers, 4. Competition systems, 5. Intellectual services. Macro level (socio-economic, cultural, legislative, and organizational): 6. Partnerships with supporting agencies, 7. Balanced and integrated funding and structures of mass and elite sport. This model emerged from the integration of instruments that have been used to analyse and compare national sport systems. The model has received scholarly validation and showed to be a framework for program analysis that is not culturally bound. It has recently been accepted as a model for further understanding North American sport systems, including (in chronological order of publications) US rugby, tennis, soccer, swimming and volleyball. The above model was used to design a questionnaire of 42 statements reflecting desired practices. The statements were validated by 12 international experts, including executives from sport governing bodies, academics who published on high performance and sport development, and swimming coaches and administrators. In this study both a highly structured and open ended qualitative analysis tools were used. This included a survey of swim coaches where open responses accompanied structured questions. After collection of the surveys, semi-structured discussions with Federation coaches were conducted to add triangulation to the findings. Lastly, a content analysis of Dutch Swimming’s website and organizational documentation was conducted. A representative sample of 1,600 Dutch Swim coaches and administrators was collected via email addresses from Royal Dutch Swimming Federation' database. Fully completed questionnaires were returned by 122 coaches from all key country’s regions for a response rate of 7,63% - higher than the response rate of the previously mentioned US studies which used the same model and method. Results suggest possible enhancements at macro level (e.g., greater public and corporate support to prepare and hire more coaches and to address the lack of facilities, monies and publicity at mass participation level in order to make swimming affordable for all), at meso level (e.g., comprehensive education for all coaches and full spectrum of swimming pools particularly 50 meters long), and at micro level (e.g., better preparation of athletes for a future outside swimming and better use of swimmers to stimulate swimming development). Best Dutch swimming management practices (e.g., comprehensive support to most talented swimmers who win Olympic medals) as well as relevant international practices available for transfer to the Netherlands (e.g., high school competitions) are discussed.

Keywords: sport development, high performance, mass participation, swimming

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Authors: Fouzia Amireche-Ziar, Ilham Mokbel, Jacques Jose

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The vapour pressures of the three binary mixtures: 1, 2- dichloroethane + 1,3-dioxolane, + 1,4-dioxane or + tetrahydropyrane, are carried out at ten temperatures ranging from 273 to 353.15 K. An accurate static device was employed for these measurements. The VLE data were reduced using the Redlich-Kister equation by taking into consideration the vapour pressure non-ideality in terms of the second molar virial coefficient. The experimental data were compared to the results predicted with the DISQUAC and Dortmund UNIFAC group contribution models for the total pressures P and the excess molar Gibbs energies GE.

Keywords: disquac model, dortmund UNIFAC model, excess molar Gibbs energies GE, VLE

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Authors: Lara Bruno, Ilaria Tipà, Franco Delogu

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Simultaneous interpreting (SI) is a demanding language task which includes the contemporary activation of different cognitive processes. This complex activity requires interpreters not only to be proficient in their working languages; but also to have a great ability in focusing attention and controlling anxiety during their performance. Effects of Qigong meditation techniques have a positive impact on several cognitive functions, including attention and anxiety control. This study aims at exploring the influence of Qigong meditation on the quality of simultaneous interpreting. 20 interpreting students, divided into two groups, were trained for 8 days in Qigong meditation practice. Before and after training, a brief simultaneous interpreting task was performed. Language combinations of group A and group B were respectively English-Italian and Chinese-Italian. Students’ performances were recorded and rated by independent evaluators. Assessments were based on 12 different parameters, divided into 4 macro-categories: content, form, delivery and anxiety control. To determine if there was any significant variation between the pre-training and post-training SI performance, ANOVA analyses were conducted on the ratings provided by the independent evaluators. Main results indicate a significant improvement of the interpreting performance after the meditation training intervention for both groups. However, group A registered a higher improvement compared to Group B. Nonetheless, positive effects of meditation have been found in all the observed macro-categories. Meditation was not only beneficial for speech delivery and anxiety control but also for cognitive and attention abilities. From a cognitive and pedagogical point of view, present results open new paths of research on the practice of meditation as a tool to improve SI performances.

Keywords: cognitive science, interpreting studies, Qigong meditation, simultaneous interpreting, training

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Authors: Adonis Menezes, J. C. Passos

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The current panorama of technology in heat transfer and the scarcity of information about the convective boiling of CO₂ and hydrocarbon in small diameter channels motivated the development of this work. Among non-halogenated refrigerants, CO₂/ R744 has distinct thermodynamic properties compared to other fluids. The R744 presents significant differences in operating pressures and temperatures, operating at higher values compared to other refrigerants, and this represents a challenge for the design of new evaporators, as the original systems must normally be resized to meet the specific characteristics of the R744, which creates the need for a new design and optimization criteria. To carry out the convective boiling tests of CO₂, an experimental apparatus capable of storing (m= 10kg) of saturated CO₂ at (T = -30 ° C) in an accumulator tank was used, later this fluid was pumped using a positive displacement pump with three pistons, and the outlet pressure was controlled and could reach up to (P = 110bar). This high-pressure saturated fluid passed through a Coriolis type flow meter, and the mass velocities varied between (G = 20 kg/m².s) up to (G = 1000 kg/m².s). After that, the fluid was sent to the first test section of circular cross-section in diameter (D = 4.57mm), where the inlet and outlet temperatures and pressures, were controlled and the heating was promoted by the Joule effect using a source of direct current with a maximum heat flow of (q = 100 kW/m²). The second test section used a cross-section with multi-channels (seven parallel channels) with a square cross-section of (D = 2mm) each; this second test section has also control of temperature and pressure at the inlet and outlet as well as for heating a direct current source was used, with a maximum heat flow of (q = 20 kW/m²). The fluid in a biphasic situation was directed to a parallel plate heat exchanger so that it returns to the liquid state, thus being able to return to the accumulator tank, continuing the cycle. The multi-channel test section has a viewing section; a high-speed CMOS camera was used for image acquisition, where it was possible to view the flow patterns. The experiments carried out and presented in this report were conducted in a rigorous manner, enabling the development of a database on the convective boiling of the R744 in macro and micro channels. The analysis prioritized the processes from the beginning of the convective boiling until the drying of the wall in a subcritical regime. The R744 resurfaces as an excellent alternative to chlorofluorocarbon refrigerants due to its negligible ODP (Ozone Depletion Potential) and GWP (Global Warming Potential) rates, among other advantages. The results found in the experimental tests were very promising for the use of CO₂ in micro-channels in convective boiling and served as a basis for determining the flow pattern map and correlation for determining the heat transfer coefficient in the convective boiling of CO₂.

Keywords: convective boiling, CO₂/R744, macro-channels, micro-channels

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Authors: Touila Ahmed, Elie Louis, Hamza Gharbi

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State of the art question answering models have recently shown impressive performance especially in a zero-shot setting. This approach is particularly useful when confronted with a highly diverse domain such as the legal field, in which it is increasingly difficult to have a dataset covering every notion and concept. In this work, we propose a flexible generative question answering approach to contract analysis as well as a weakly supervised procedure to leverage unannotated data and boost our models’ performance in general, and their zero-shot performance in particular.

Keywords: question answering, contract analysis, zero-shot, natural language processing, generative models, self-supervision

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Authors: Rachel Y. Zhang, Christopher K. Anderson

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A critical aspect of revenue management is a firm’s ability to predict demand as a function of price. Historically hotels have used simple time series models (regression and/or pick-up based models) owing to the complexities of trying to build casual models of demands. Machine learning approaches are slowly attracting attention owing to their flexibility in modeling relationships. This study provides an overview of approaches to forecasting hospitality demand – focusing on the opportunities created by machine learning approaches, including K-Nearest-Neighbors, Support vector machine, Regression Tree, and Artificial Neural Network algorithms. The out-of-sample performances of above approaches to forecasting hotel demand are illustrated by using a proprietary sample of the market level (24 properties) transactional data for Las Vegas NV. Causal predictive models can be built and evaluated owing to the availability of market level (versus firm level) data. This research also compares and contrast model accuracy of firm-level models (i.e. predictive models for hotel A only using hotel A’s data) to models using market level data (prices, review scores, location, chain scale, etc… for all hotels within the market). The prospected models will be valuable for hotel revenue prediction given the basic characters of a hotel property or can be applied in performance evaluation for an existed hotel. The findings will unveil the features that play key roles in a hotel’s revenue performance, which would have considerable potential usefulness in both revenue prediction and evaluation.

Keywords: hotel revenue, k-nearest-neighbors, machine learning, neural network, prediction model, regression tree, support vector machine

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Authors: Shama Urooj

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The present work investigates the relationship among financial inclusion, modernization, and energy performance in SCO member countries during the years 2011–2021. PCA is used to create composite indexes of financial inclusion, modernization, and energy performance. We used panel regression models that are both reliable and heteroscedasticity-consistent to look at the relationship among variables. The findings indicate that financial inclusion (FI) and modernization, along with the increased FDI, all appear to contribute to the energy performance in the SCO member countries. However, per capita GDP has a negative impact on energy performance. These results are unbiased and consistent with the robust results obtained by applying different econometric models. Feasible Generalized Least Square (FGLS) estimation is also used for checking the uniformity of the main model results. This research work concludes that there has been no policy coherence in SCO member countries regarding the coordination of growing financial inclusion and modernization for energy sustainability in recent years. In order to improve energy performance with modern development, policies regarding financial inclusion and modernization need be integrated both at national as well as international levels.

Keywords: financial inclusion, energy performance, modernization, technological development, SCO.

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Authors: Omid Shahmirzadi, Adam Lugowski, Kenneth Younge

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Automatic measurement of semantic text similarity is an important task in natural language processing. In this paper, we evaluate the performance of different vector space models to perform this task. We address the real-world problem of modeling patent-to-patent similarity and compare TFIDF (and related extensions), topic models (e.g., latent semantic indexing), and neural models (e.g., paragraph vectors). Contrary to expectations, the added computational cost of text embedding methods is justified only when: 1) the target text is condensed; and 2) the similarity comparison is trivial. Otherwise, TFIDF performs surprisingly well in other cases: in particular for longer and more technical texts or for making finer-grained distinctions between nearest neighbors. Unexpectedly, extensions to the TFIDF method, such as adding noun phrases or calculating term weights incrementally, were not helpful in our context.

Keywords: big data, patent, text embedding, text similarity, vector space model

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Authors: Ali H. Daraji, Ye Jianqiao

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The micro electrical devices of the wireless sensor network are continuously developed and become very small and compact with low electric power requirements using limited period life conventional batteries. The low power requirement for these devices, cost of conventional batteries and its replacement have encouraged researcher to find alternative power supply represented by energy harvesting system to provide an electric power supply with infinite period life. In the last few years, the investigation of energy harvesting for structure health monitoring has increased to powering wireless sensor network by converting waste mechanical vibration into electricity using piezoelectric sensors. Optimisation of energy harvesting is an important research topic to ensure a flowing of efficient electric power from structural vibration. The harvesting power is mainly based on the properties of piezoelectric material, dimensions of piezoelectric sensor, its position on a structure and value of an external electric load connected between sensor electrodes. Larger surface area of sensor is not granted larger power harvesting when the sensor area is covered positive and negative mechanical strain at the same time. Thus lead to reduction or cancellation of piezoelectric output power. Optimisation of energy harvesting is achieved by locating these sensors precisely and efficiently on the structure. Limited published work has investigated the energy harvesting for aircraft wing. However, most of the published studies have simplified the aircraft wing structure by a cantilever flat plate or beam. In these studies, the optimisation of energy harvesting was investigated by determination optimal value of an external electric load connected between sensor electrode terminals or by an external electric circuit or by randomly splitting piezoelectric sensor to two segments. However, the aircraft wing structures are complex than beam or flat plate and mostly constructed from flat and curved skins stiffened by stringers and ribs with more complex mechanical strain induced on the wing surfaces. This aircraft wing structure bonded with discrete macro fibre composite sensors was modelled using multiphysics finite element to optimise the energy harvesting by determination of the optimal number of sensors, location and the output resistance load. The optimal number and location of macro fibre sensors were determined based on the maximization of the open and close loop sensor output voltage using frequency response analysis. It was found different optimal distribution, locations and number of sensors bounded on the top and the bottom surfaces of the aircraft wing.

Keywords: energy harvesting, optimisation, sensor, wing

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Authors: Mahboobeh Eghtesad, Reza Mohseni

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We develop methods for detecting a target for orthogonal frequency division multiplexing (OFDM) based radars. As a preliminary step we introduce the target and Gaussian noise models in discrete–time form. Then, resorting to match filter (MF) we derive a detector for two different scenarios: a non-fluctuating target and a Rayleigh fluctuating target. It will be shown that a MF is not suitable for Rayleigh fluctuating targets. In this paper we propose a reduced-complexity method based on fast Fourier transfrom (FFT) for such a situation. The proposed method has better detection performance.

Keywords: constant false alarm rate (CFAR), match filter (MF), fast Fourier transform (FFT), OFDM radars, Rayleigh fluctuating target

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Authors: Nagasamy Venkatesh Dhandapani

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The objective of the present study was to develop sustained release oral matrix tablets of anti epileptic drug levetiracetam. The sustained release matrix tablets of levetiracetam were prepared using hydrophilic matrix hydroxypropyl methylcellulose (HPMC) as a release retarding polymer by wet granulation method. Prior to compression, FTIR studies were performed to understand the compatibility between the drug and excipients. The study revealed that there was no chemical interaction between drug and excipients used in the study. The tablets were characterized by physical and chemical parameters and results were found in acceptable limits. In vitro release study was carried out for the tablets using 0.1 N HCl for 2 hours and in phosphate buffer pH 7.4 for remaining time up to 12 hours. The effect of polymer concentration was studied. Different dissolution models were applied to drug release data in order to evaluate release mechanisms and kinetics. The drug release data fit well to zero order kinetics. Drug release mechanism was found as a complex mixture of diffusion, swelling and erosion.

Keywords: levetiracetam, sustained-release, hydrophilic matrix tablet, HPMC grade K 100 MCR, wet granulation, zero order release kinetics

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Authors: Smail Tigani, Mohamed Ouzzif

Abstract:

This work presents a discrete quantitative state prediction algorithm with intelligent behavior making it able to self-improve some performance aspects. The specificity of this algorithm is the capacity of self-rectification of the prediction strategy before the final decision. The auto-rectification mechanism is based on two parallel mathematical models. In one hand, the algorithm predicts the next state based on event transition matrix updated after each observation. In the other hand, the algorithm extracts its residues trend with a linear regression representing historical residues data-points in order to rectify the first decision if needs. For a normal distribution, the interactivity between the two models allows the algorithm to self-optimize its performance and then make better prediction. Designed key performance indicator, computed during a Monte Carlo simulation, shows the advantages of the proposed approach compared with traditional one.

Keywords: discrete state, Markov Chains, linear regression, auto-adaptive systems, decision making, Monte Carlo Simulation

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Authors: Avichal Malhotra, Jerome Frisch, Christoph van Treeck

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The utilization of semantic, cadastral and topological data from geographic information systems (GIS) has exponentially increased for building and urban-scale energy performance simulations. Urban planners, simulation scientists, and researchers use virtual 3D city models for energy analysis, algorithms and simulation tools. For dynamic energy simulations at city and district level, this paper provides an overview of the available GIS data models and their levels of detail. Adhering to different norms and standards, these models also intend to describe building and construction industry data. For further investigations, CityGML data models are considered for simulations. Though geographical information modelling has considerably many different implementations, extensions of virtual city data can also be made for domain specific applications. Highlighting the use of the extended CityGML models for energy researches, a brief introduction to the Energy Application Domain Extension (ADE) along with its significance is made. Consequently, addressing specific input simulation data, a workflow using Modelica underlining the usage of GIS information and the quantification of its significance over annual heating energy demand is presented in this paper.

Keywords: CityGML, EnergyADE, energy performance simulation, GIS

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Authors: Akram Khelaifia, Salah Guettala, Nesreddine Djafar Henni, Rachid Chebili

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Recent seismic events have underscored the significant influence of masonry infill walls on the resilience of structures. The irregular positioning of these walls exacerbates their adverse effects, resulting in substantial material and human losses. Research and post-earthquake evaluations emphasize the necessity of considering infill walls in both the design and assessment phases. This study delves into the presence of soft stories in reinforced concrete structures with infill walls. Employing an approximate method relying on pushover analysis results, fiber-section-based macro-modeling is utilized to simulate the behavior of infill walls. The findings shed light on the presence of soft first stories, revealing a notable 240% enhancement in resistance for weak column—strong beam-designed frames due to infill walls. Conversely, the effect is more moderate at 38% for strong column—weak beam-designed frames. Interestingly, the uniform distribution of infill walls throughout the structure's height does not influence soft-story emergence in the same seismic zone, irrespective of column-beam strength. In regions with low seismic intensity, infill walls dissipate energy, resulting in consistent seismic behavior regardless of column configuration. Despite column strength, structures with open-ground stories remain vulnerable to soft first-story emergence, underscoring the crucial role of infill walls in reinforced concrete structural design.

Keywords: masonry infill walls, soft Storey, pushover analysis, fiber section, macro-modeling

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Authors: M. Bhattarai, B. B. Rana, M. Kamimukai, I. Takamure, T. Kawano, M. Murai

Abstract:

The sd1-d allele at the sd1 locus on chromosome 1, originating from Taiwanese variety Dee-geo-woo-gen, has been playing important role for developing short-culm and lodging-resistant indica varieties such as IR36 in rice. The dominant allele SD1 for long culm at the locus is differentiated into SD1-in and SD1-ja which are harbored in indica and japonica subspecies’s, respectively. The sd1-d of an indica variety IR36 was substituted with SD1-in or SD1-ja by recurrent backcrosses of 17 times with IR36, and two isogenic tall lines regarding the respective dominant alleles were developed by using an indica variety IR5867 and a japonica one ‘Koshihikari’ as donors, which were denoted by '5867-36' and 'Koshi-36', respectively. The present study was conducted to examine the effect of sd1-d on yield and related traits as compared with SD1-in and SD1-ja, by using the two isogenic tall lines. Seedlings of IR36 and the two isogenic lines were transplanted on an experimental field of Kochi University, by the planting distance of 30 cm × 15 cm with two seedlings per hill, on May 3, 2017. Chemical fertilizers were supplied by basal application and top-dressing at a rate of 8.00, 6.57 and 7.52 g/m², respectively, for N, P₂O₅ and K₂O in total. Yield, yield components, and other traits were measured. Culm length (cm) was in the order of 5867-36 (101.9) > Koshi-36 (80.1) > IR36 (60.0), where '>' indicates statistically significant difference at the 5% level. Accordingly, sd1-d reduced culm by 41.9 and 20.1 cm, compared with SD1-in and SD1-ja, respectively, and the effect of elongating culm was higher in the former allele than in the latter one. Total brown rice yield (g/m²), including unripened grains, was in the order of IR36 (611) ≧ 5867-36 (586) ≧ Koshi-36 (572), indicating non-significant differences among them. Yield-1.5mm sieve (g/m²) was in the order of IR36 (596) ≧ 5867-36 (575) ≧ Koshi-36 (558). Spikelet number per panicle was in the order of 5867-36 (89.2) ≧ IR36 (84.7) ≧ Koshi-36 (79.8), and 5867-36 > Koshi-36. Panicle number per m² was in the order of IR36 (428) ≧ Koshi-36 (403) ≧ 5867-36 (353), and IR36 > 5867-36, suggesting that sd1-d increased number of panicles compared with SD1-in. Ripened-grain percentage-1.5mm sieve was in the order of Koshi-36 (86.0) ≧ 5867-36 (85.0) ≧ IR36 (82.7), and Koshi-36 > IR36. Thousand brown-rice-grain weight-1.5mm sieve (g) was in the order of 5867-36 (21.5) > Koshi-36 (20.2) ≧ IR36 (19.9). Total dry weight at maturity (g/m²) was in the order of 5867-36 (1404 ) ≧ IR36 (1310) ≧ Kosihi-36 (1290). Harvest index of total brown rice (%) was in the order of IR36 (39.6) > Koshi-36 (37.7) > 5867-36 (35.5). Hence, sd1-d did not exert significant effect on yield in indica genetic background. However, lodging was observed from the late stage of maturity in 5867-36 and Koshi-36, particularly in the former, which was principally due to their long culms. Consequently, sd1-d enables higher yield with higher fertilizer application, by enhancing lodging resistance, particularly in indica subspecies.

Keywords: rice, dwarfing gene, sd1-d, SD1-in, SD1-ja, yield

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Authors: Debarag Banerjee, Pooja Singh, Arjun Avadhanam, Saksham Srivastava

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Autonomous conversational agents, i.e., chatbots, are becoming an increasingly common mechanism for enterprises to provide support to customers and partners. In order to rate chatbots, especially ones powered by Generative AI tools like Large Language Models (LLMs), we need to be able to accurately assess their performance. This is where chatbot benchmarking becomes important. In this paper, authors propose the use of a benchmark that they call the E2E (End to End) benchmark and show how the E2E benchmark can be used to evaluate the accuracy and usefulness of the answers provided by chatbots, especially ones powered by LLMs. The authors evaluate an example chatbot at different levels of sophistication based on both our E2E benchmark as well as other available metrics commonly used in the state of the art and observe that the proposed benchmark shows better results compared to others. In addition, while some metrics proved to be unpredictable, the metric associated with the E2E benchmark, which uses cosine similarity, performed well in evaluating chatbots. The performance of our best models shows that there are several benefits of using the cosine similarity score as a metric in the E2E benchmark.

Keywords: chatbot benchmarking, end-to-end (E2E) benchmarking, large language model, user centric evaluation.

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