Search results for: wireless performance analysis

Authors: Rana Mahmoud

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This study represents a systematic study of the Operational Transconductance Amplifiers capacitance (OTA-C) filters or as it is often called Gm-C filters. OTA-C filters have been paid a great attention for the last decades. As Gm-C filters operate in an open loop topology, this makes them flexible to perform in low and high frequencies. As such, Gm-C filters can be used in various wireless communication applications. Another property of Gm-C filters is its electronic tunability, thus different filter frequency characteristics can be obtained without changing the inductance and resistance values. This can be achieved by an OTA (Operational Transconductance Amplifier) and a capacitor. By tuning the OTA transconductance, the cut-off frequency will be tuned and different frequency responses are achieved. Different high-order analog filters can be design using Gm-C filters including low pass, high pass and band pass filters. 1st and 2nd order low pass, high pass and band pass filters are presented in this paper.

Keywords: Gm-C, filters, low-pass, high-pass, band-pass

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Authors: K. Volkonitskaya, S. Lyapina

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According to the National Monitoring Centre of innovation infrastructure, scientific and technical activities and regional innovation systems by December 2014. 166 technology parks were established in Russia. Comparative analysis of technological parks performance in Russia, the USA, Israel and the European Union countries revealed significant reduction of key performance indicators in Russian innovation infrastructure institutes. The largest deviations were determined in the following indicators: new products and services launched, number of companies and jobs, amount of venture capital invested. Lower performance indicators of Russian technology parks can be partly explained by slack demand for national high-tech products and services, lack of qualified specialists in the sphere of innovation management and insufficient cooperation between different innovation infrastructure institutes. In spite of all constraints in innovation segment of Russian economy in 2010-2012 private investors for the first time proceeded to finance building of technological parks. The general purpose of the research is to answer two questions: why despite the significant investment risks private investors continue to implement such comprehensive infrastructure projects in Russia and is business model of private technological park more efficient than strategies of state innovation infrastructure institutes? The goal of the research was achieved by analyzing business models of private technological parks in Moscow, Kaliningrad, Astrakhan and Kazan. The research was conducted in two stages: the on-line survey of key performance indicators of private and state Russian technological parks and in-depth interviews with top managers and investors, who have already build private technological parks in by 2014 or are going to complete investment stage in 2014-2016. The results anticipated are intended to identify the reasons of efficient and inefficient technological parks performance. Furthermore, recommendations for improving the efficiency of state technological and industrial parks were formulated. Particularly, the recommendations affect the following issues: networking with other infrastructural institutes, services and infrastructure provided, mechanisms of public-private partnership and investment attraction. In general intensive study of private technological parks performance and development of effective mechanisms of state support can have a positive impact on the growth rates of the number of Russian technological, industrial and science parks.

Keywords: innovation development, innovation infrastructure, private technology park, public-private partnership

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Authors: Krishan P. Sharma, T. P. Sharma

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Wireless sensor nodes are resource constrained battery powered devices usually deployed in hostile and ill-disposed areas to cooperatively monitor physical or environmental conditions. Due to their limited power supply, the major challenge for researchers is to utilize their battery power for enhancing the lifetime of whole network. Communication and sensing are two major sources of energy consumption in sensor networks. In this paper, we propose a deployment strategy for enhancing the average lifetime of a sensor network by effectively utilizing communication and sensing energy to provide full coverage. The proposed scheme is based on the fact that due to heavy relaying load, sensor nodes near to the sink drain energy at much faster rate than other nodes in the network and consequently die much earlier. To cover this imbalance, proposed scheme finds optimal communication and sensing ranges according to effective load at each node and uses a non-uniform deployment strategy where there is a comparatively high density of nodes near to the sink. Probable relaying load factor at particular node is calculated and accordingly optimal communication distance and sensing range for each sensor node is adjusted. Thus, sensor nodes are placed at locations that optimize energy during network operation. Formal mathematical analysis for calculating optimized locations is reported in present work.

Keywords: load factor, network lifetime, non-uniform deployment, sensing range

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Authors: Deepak Kumar, Debasish Deb, Reena Mamgain

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Radar signal processing requires high number crunching capability. Most often this is achieved using multiprocessor platform. Though multiprocessor platform provides the capability of meeting the real time computational challenges, the architecture of the same along with mapping of the algorithm on the architecture plays a vital role in efficiently using the platform. Towards this, along with standard performance metrics, few additional metrics are defined which helps in evaluating the multiprocessor platform along with the algorithm mapping. A generic multiprocessor architecture can not suit all the processing requirements. Depending on the system requirement and type of algorithms used, the most suitable architecture for the given problem is decided. In the paper, we study different architectures and quantify the different performance metrics which enables comparison of different architectures for their merit. We also carried out case study of different architectures and their efficiency depending on parallelism exploited on algorithm or data or both.

Keywords: radar signal processing, multiprocessor architecture, efficiency, load imbalance, buffer requirement, pipeline, parallel, hybrid, cluster of processors (COPs)

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Authors: Mathias U. Bonet, Pericles Pilidis, Georgios Doulgeris

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An aero-derivative marine Gas Turbine engine model is simulated to be installed as the main propulsion prime mover to power a cruise ship which is designed and routed to transport intending Muslim pilgrims for the annual hajj pilgrimage from Nigeria to the Islamic port city of Jeddah in Saudi Arabia. A performance assessment of the Gas Turbine engine has been conducted by examining the effect of varying aerodynamic and hydrodynamic conditions encountered at various geographical locations along the scheduled transit route during the voyage. The investigation focuses on the overall behavior of the Gas Turbine engine employed to power and propel the ship as it operates under ideal and adverse conditions to be encountered during calm and rough weather according to the different seasons of the year under which the voyage may be undertaken. The variation of engine performance under varying operating conditions has been considered as a very important economic issue by determining the time the speed by which the journey is completed as well as the quantity of fuel required for undertaking the voyage. The assessment also focuses on the increased resistance caused by the fouling of the submerged portion of the ship hull surface with its resultant effect on the power output of the engine as well as the overall performance of the propulsion system. Daily ambient temperature levels were obtained by accessing data from the UK Meteorological Office while the varying degree of turbulence along the transit route and according to the Beaufort scale were also obtained as major input variables of the investigation. By assuming the ship to be navigating the Atlantic Ocean and the Mediterranean Sea during winter, spring and summer seasons, the performance modeling and simulation was accomplished through the use of an integrated Gas Turbine performance simulation code known as ‘Turbomach’ along with a Matlab generated code named ‘Poseidon’, all of which have been developed at the Power and Propulsion Department of Cranfield University. As a case study, the results of the various assumptions have further revealed that the marine Gas Turbine is a reliable and available alternative to the conventional marine propulsion prime movers that have dominated the maritime industry before now. The techno-economic and environmental assessment of this type of propulsion prime mover has enabled the determination of the effect of changes in weather and sea conditions on the ship speed as well as trip time and the quantity of fuel required to be burned throughout the voyage.

Keywords: ambient temperature, hull fouling, marine gas turbine, performance, propulsion, voyage

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Authors: Gabriela Kloudova, Miloslav Stehlik, Peter Sos

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The quality of sleep can affect combat performance where the vigilance, accuracy and reaction time are a decisive factor. In the present study, airborne and special units are measured on duty using actigraphy fingerprint scoring algorithm and QEEG (quantitative EEG). Actigraphic variables of interest will be: mean nightly sleep duration, mean napping duration, mean 24-h sleep duration, mean sleep latency, mean sleep maintenance efficiency, mean sleep fragmentation index, mean sleep onset time, mean sleep offset time and mean midpoint time. In an attempt to determine the individual somnotype of each subject, the data like sleep pattern, chronotype (morning and evening lateness), biological need for sleep (daytime and anytime sleepability) and trototype (daytime and anytime wakeability) will be extracted. Subsequently, a series of recommendations will be included in the training plan based on daily routine, timing of the day and night activities, duration of sleep and the number of sleeping blocks in a defined time. The aim of these modifications in the training plan is to reduce day-time sleepiness, improve vigilance, attention, accuracy, speed of the conducted tasks and to optimize energy supplies. Regular improvement of the training supposed to have long-term neurobiological consequences including neuronal activity changes measured by QEEG. Subsequently, that should enhance cognitive functioning in subjects assessed by the digital cognitive test batteries and improve their overall performance.

Keywords: sleep quality, combat performance, actigraph, somnotype

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Authors: S. J. Shahidzadeh Tabatabaei, A. M. Fattahi

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Modal analysis of an FGM plate composed of Al2O3 ceramic phase and 304 stainless steel metal phases was performed in this paper by ABAQUS software with the assumption that the behavior of material is elastic and mechanical properties (Young's modulus and density) are variable in the thickness direction of the plate. Therefore, a sub-program was written in FORTRAN programming language and was linked with ABAQUS software. For modal analysis, a finite element analysis was carried out similar to the model of other researchers and the accuracy of results was evaluated after comparing the results. Comparison of natural frequencies and mode shapes reflected the compatibility of results and optimal performance of the program written in FORTRAN as well as high accuracy of finite element model used in this research. After validation of the results, it was evaluated the effect of material (n parameter) on the natural frequency. In this regard, finite element analysis was carried out for different values of n and in simply supported mode. About the effect of n parameter that indicates the effect of material on the natural frequency, it was observed that the natural frequency decreased as n increased; because by increasing n, the share of ceramic phase on FGM plate has decreased and the share of steel phase has increased and this led to reducing stiffness of FGM plate and thereby reduce in the natural frequency. That is because the Young's modulus of Al2O3 ceramic is equal to 380 GPa and Young's modulus of SUS304 steel is 207 GPa.

Keywords: FGM plates, modal analysis, natural frequency, finite element method

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Authors: Bunthida Chunngam, Thanyanan Worasesthaphong

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This research had objective to develop of the website learning the local wisdom in Phra Nakhon Si Ayutthaya province and studied satisfaction of system user. This research sample was multistage sample for 100 questionnaires, analyzed data to calculated reliability value with Cronbach’s alpha coefficient method α=0.82. This system had 3 functions which were system using, system feather evaluation and system accuracy evaluation which the statistics used for data analysis was descriptive statistics to explain sample feature so these statistics were frequency, percentage, mean and standard deviation. This data analysis result found that the system using performance quality had good level satisfaction (4.44 mean), system feather function analysis had good level satisfaction (4.11 mean) and system accuracy had good level satisfaction (3.74 mean).

Keywords: website, learning, local wisdom, Phra Nakhon Si Ayutthaya province

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Authors: Ozan Yavuz Baytemir, Ender Cigeroglu, Gokhan Osman Ozgen

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Vibration is an important issue in the design of various components of aerospace, marine and vehicular applications. In order not to lose the components’ function and operational performance, vibration isolation design involving the optimum isolator properties selection and isolator positioning processes appear to be a critical study. Knowing the growing need for the vibration isolation system design, this paper aims to present two types of software capable of implementing modal analysis, response analysis for both random and harmonic types of excitations, static deflection analysis, Monte Carlo simulations in addition to study of parameter and location optimization for different types of isolation problem scenarios. Investigating the literature, there is no such study developing a software-based tool that is capable of implementing all those analysis, simulation and optimization studies in one platform simultaneously. In this paper, the theoretical system model is generated for a 6-DOF rigid body. The vibration isolation system of any mechanical structure is able to be optimized using hybrid method involving both global search and gradient-based methods. Defining the optimization design variables, different types of optimization scenarios are listed in detail. Being aware of the need for a user friendly vibration isolation problem solver, two types of graphical user interfaces (GUIs) are prepared and verified using a commercial finite element analysis program, Ansys Workbench 14.0. Using the analysis and optimization capabilities of those GUIs, a real application used in an air-platform is also presented as a case study at the end of the paper.

Keywords: hybrid optimization, Monte Carlo simulation, multi-degree-of-freedom system, parameter optimization, location optimization, passive vibration isolation analysis

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Authors: Fang Ye, Han Yu, Yibing Li

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Radio interference is one of the major concerns in using the global positioning system (GPS) for civilian and military applications. Interference signals are produced not only through all electronic systems but also illegal jammers. Among different types of interferences, continuous wave (CW) interference has strong adverse impacts on the quality of the received signal. In this paper, we make more detailed analysis for CW interference effects on GPS signal quality. Based on the C/A code spectrum lines, the influence of CW interference on the acquisition performance of GPS receivers is further analysed. This influence is supported by simulation results using GPS software receiver. As the most important user parameter of GPS receivers, the mathematical expression of bit error probability is also derived in the presence of CW interference, and the expression is consistent with the Monte Carlo simulation results. The research on CW interference provides some theoretical gist and new thoughts on monitoring the radio noise environment and improving the anti-jamming ability of GPS receivers.

Keywords: GPS, CW interference, acquisition performance, bit error probability, Monte Carlo

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Authors: P. C. Tewari, Parveen Kumar

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The paper describes the availability analysis of milling system of a rice milling plant using probabilistic approach. The subsystems under study are special purpose machines. The availability analysis of the system is carried out to determine the effect of failure and repair rates of each subsystem on overall performance (i.e. steady state availability) of system concerned. Further, on the basis of effect of repair rates on the system availability, maintenance repair priorities have been suggested. The problem is formulated using Markov Birth-Death process taking exponential distribution for probable failures and repair rates. The first order differential equations associated with transition diagram are developed by using mnemonic rule. These equations are solved using normalizing conditions and recursive method to drive out the steady state availability expression of the system. The findings of the paper are presented and discussed with the plant personnel to adopt a suitable maintenance policy to increase the productivity of the rice milling plant.

Keywords: availability modeling, Markov process, milling system, rice milling plant

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Authors: Jisun Lee, Jay Hyoun Kwon

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As an alternative way to compensate the INS (inertial navigation system) error in non-GNSS (Global Navigation Satellite System) environment, geophysical database referenced navigation is being studied. In this study, both gravity gradient and terrain data were combined to complement the weakness of sole geophysical data as well as to improve the stability of the positioning. The main process to compensate the INS error using geophysical database was constructed on the basis of the EKF (Extended Kalman Filter). In detail, two type of combination method, centralized and decentralized filter, were applied to check the pros and cons of its algorithm and to find more robust results. The performance of each navigation algorithm was evaluated based on the simulation by supposing that the aircraft flies with precise geophysical DB and sensors above nine different trajectories. Especially, the results were compared to the ones from sole geophysical database referenced navigation to check the improvement due to a combination of the heterogeneous geophysical database. It was found that the overall navigation performance was improved, but not all trajectories generated better navigation result by the combination of gravity gradient with terrain data. Also, it was found that the centralized filter generally showed more stable results. It is because that the way to allocate the weight for the decentralized filter could not be optimized due to the local inconsistency of geophysical data. In the future, switching of geophysical data or combining different navigation algorithm are necessary to obtain more robust navigation results.

Keywords: Extended Kalman Filter, geophysical database referenced navigation, gravity gradient, terrain

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Authors: Ririt Aprilin Sumarsono

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Composite member (concrete and steel) has been widely advanced for structural utilization due to its best performance in resisting load, reducing the total weight of the structure, increasing stiffness, and other available advantages. On the other hand, the environment load such as corrosion (e.g. chloride ingress) creates significant time-dependent degradation for steel. Analysis performed in this paper is mainly considered uniform corrosion for evaluating the composite beam without examining the pit corrosion as the initial corrosion formed. Corrosion level in terms of weight loss is modified in yield stress and modulus elasticity of steel. Those two mechanical properties are utilized in this paper for observing the stresses due to corrosion attacked. As corrosion level increases, the effective width of the composite beam in the concrete section will be wider. The position of a neutral axis of composite section will indicate the composite action due to corrosion of composite beam so that numerous shear connectors provided must be reconsidered. Flexure capacity quantification provides stresses, and shear capacity calculation derives connectors needed in overcoming the shear problem for composite beam under corrosion. A model of simply supported composite beam examined in this paper under uniform corrosion where the stresses as the focus of the evaluation. Principal stress at the first stage of composite construction decline as the corrosion level incline, parallel for the second stage stress analysis where the tension region held by the steel undergoes lower capacity due to corrosion. Total stresses of the composite section for steel to be born significantly decreases particularly in the outermost fiber of tension side. Whereas, the available compression side is smaller as the corrosion level increases so that the stress occurs on the compression side shows reduction as well. As a conclusion, the increment of corrosion level will degrade both compression and tension side of stresses.

Keywords: composite beam, modulus of elasticity, stress analysis, yield strength, uniform corrosion

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Authors: Thilivhali E. Rasimphi, David Tinarwo

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Access to energy is crucial in poverty alleviation, economic growth, education, and agricultural improvement. The best renewable energy source is one which is locally available, affordable, and can easily be used and managed by local communities. The usage of renewable energy technology has the potential to alleviate many of the current problems facing rural areas. To address energy poverty, biogas technology has become an important part of resolving such. This study, therefore, examines the performance of digesters in Gawula village; it also identifies the contributing factors to the adoption and use of the technology. Data was collected using an open-ended questionnaire from biogas users. To evaluate the performance of the digesters, a data envelopment analysis (DEA) non-parametric technique was used, and to identify key factors affecting adoption, a logit model was applied. The reviewed critical barriers to biogas development in the area seem to be a poor institutional framework, poor infrastructure, a lack of technical support, user training on maintenance and operation, and as such, the implemented plants have failed to make the desired impact. Thus most digesters were abandoned. To create awareness amongst rural communities, government involvement is key, and there is a need for national programs. Biogas technology does what few other renewable energy technologies do, which is to integrate waste management and energy. This creates a substantial opportunity for biogas generation and penetration. That is, a promising pathway towards achieving sustainable development through biogas technology.

Keywords: domestic biogas technology, economic, sustainable, social, rural development

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Authors: E. Akpinar, A. Erol, M.F. Cakir

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Precast reinforced concrete (RC) structures are excellent alternatives for construction world all over the globe, thanks to their rapid erection phase, ease mounting process, better quality and reasonable prices. Such structures are rather popular for industrial buildings. For the sake of economic importance of such industrial buildings as well as significance of safety, like every other type of structures, performance assessment and structural risk analysis are important. Fragility curves are powerful tools for damage projection and assessment for any sort of building as well as precast structures. In this study, a comparative review of current knowledge on fragility analysis of industrial precast RC structures were presented and findings in previous studies were compiled. Effects of different structural variables, parameters and building geometries as well as soil conditions on fragility analysis of precast structures are reviewed. It was aimed to briefly present the information in the literature about the procedure of damage probability prediction including fragility curves for such industrial facilities. It is found that determination of the aforementioned structural parameters as well as selecting analysis procedure are critically important for damage prediction of industrial precast RC structures using fragility curves.

Keywords: damage prediction, fragility curve, industrial buildings, precast reinforced concrete structures

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Authors: Waheed O. Majekodunmi

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Climate change is a real global phenomenon and an unquestionable threat to our quest for a healthy and livable planet. It is now regarded as potentially the most monumental environmental challenge people and the planet will be confronted with over the next centuries. Expectedly, climate change mitigation was one of the central themes of COP 28. Despite contributing the least to climate change, Africa is and remains the hardest hit by the negative consequences of climate change including poor growth performance. Currently, it is being hypothesized that the high level of vulnerability and exposure to climate-related disasters, low adaptive capacity against global warming and high mitigation costs of climate change across the continent could be linked to the recent abysmal economic performance of African countries, especially in oil-producing countries where greenhouse gas emissions, is potentially more prevalent. This paper examines the impact of climate change on the economic performance of selected oil-producing countries in Africa using evidence from Nigeria, Algeria and Angola. The objective of the study is to determine whether or not climate change influences the economic performance of oil-producing countries in Africa by examining the nexus between economic growth and climate-related variables. The study seeks to investigate the effect of climate change on the pace of economic growth in African oil-producing countries. To achieve the research objectives, this study utilizes a quantitative approach by using historical and current secondary data sets to determine the relationship between climate-related variables and economic growth variables in the selected countries. The study employed numbers, percentages, tables and trend graphs to explain the trends or common patterns between climate change, economic growth and determinants of economic growth: governance effectiveness, infrastructure, macroeconomic stability and regulatory efficiency. Results from the empirical analysis of data show that the trends of economic growth and climate-related variables in the selected oil-producing countries are in the opposite directions as the increasing share of renewable energy sources in total energy consumption and the reduction in greenhouse gas emissions per capita in the oil-producing countries did not translate to higher economic growth. Further findings show that annual surface temperatures in the selected countries do not share similar trends with the food imports ratio and GDP per capita annual growth rate suggesting that climate change does not impact significantly agricultural productivity and economic growth in oil-producing countries in Africa. Annual surface temperature was also found to not share a similar pattern with governance effectiveness, macroeconomic stability and regulatory efficiency reinforcing the claim that some economic growth variables are independent of climate change. The policy implication of this research is that oil-producing African countries need to focus more on improving the macroeconomic environment and streamlining governance and institutional processes to boost their economic performance before considering the adoption of climate change adaptation and mitigation strategies.

Keywords: climate change, climate vulnerability, economic growth, greenhouse gas emissions per capita, oil-producing countries, share of renewable energy in total energy consumption

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Authors: Fally Titikpina, Abderafi Charki, Antoine Caucheteux, David Bigaud

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The building sector is one of the largest energy consumer with about 40% of the final energy consumption in the European Union. Ensuring building energy performance is of scientific, technological and sociological matter. To assess a building energy performance, the consumption being predicted or estimated during the design stage is compared with the measured consumption when the building is operational. When valuing this performance, many buildings show significant differences between the calculated and measured consumption. In order to assess the performance accurately and ensure the thermal efficiency of the building, it is necessary to evaluate the uncertainties involved not only in measurement but also those induced by the propagation of dynamic and static input data in the model being used. The evaluation of measurement uncertainty is based on both the knowledge about the measurement process and the input quantities which influence the result of measurement. Measurement uncertainty can be evaluated within the framework of conventional statistics presented in the \textit{Guide to the Expression of Measurement Uncertainty (GUM)} as well as by Bayesian Statistical Theory (BST). Another choice is the use of numerical methods like Monte Carlo Simulation (MCS). In this paper, we proposed to evaluate the uncertainty associated to the use of a simplified model for the estimation of the energy consumption of a given building. A detailed review and discussion of these three approaches (GUM, MCS and BST) is given. Therefore, an office building has been monitored and multiple sensors have been mounted on candidate locations to get required data. The monitored zone is composed of six offices and has an overall surface of 102 $m^2$. Temperature data, electrical and heating consumption, windows opening and occupancy rate are the features for our research work.

Keywords: building energy performance, uncertainty evaluation, GUM, bayesian approach, monte carlo method

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Authors: Yassine Jadi, Lin Jie

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In order to serve the society in an electronic manner, many developing countries have launched tremendous e-government projects. The strategies of development and implementation e-government system have reached different levels, and to ensure consistency of development, the governments need to evaluate e-government performance. The United nation has design e-government development ranking index (EGDI) that rely on three indexes, Online service index (OSI), Telecommunication Infrastructure index (TII), and human capital index( HCI) which are not reflecting the interaction between a government and their citizens. Based on data envelopment analyses (DEA) technique, we are using E-participating index (EPI) as an output of government effort to evaluate the performance of e-government system. Therefore, the ranking index can be achieved in efficiency manner.

Keywords: e-government, DEA, efficiency measurement, EGDI

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Authors: Mary Ann Cristine R. Olgado

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Diversity in the classroom might make it difficult to promote individualized learning, but differentiated instruction that caters to students' various learning preferences may prove to be beneficial. Hence, this study examined the effectiveness of Hybrid-Differentiated Mathematics Instruction (HDMI) in improving the students’ academic performance in Mathematics. It employed the quasi-experimental research design by using a comparative analysis of the two variables: the experimental and control groups. The learning styles of the students were identified using the Grasha-Riechmann Student Learning Style Scale (GRSLSS), which served as the basis for designing differentiated action plans in Mathematics. In addition, adapted survey questionnaires, pre-tests, and post-tests were used to gather information and were analyzed using descriptive and correlational statistics to find the relationship between variables. The experimental group received differentiated instruction for a month, while the control group received traditional teaching instruction. The study found that Hybrid-Differentiated Mathematics Instruction (HDMI) improved the academic performance of Grade 11-TVL students, with the experimental group performing better than the control group. This program has effectively tailored the teaching methods to meet the diverse learning needs of the students, fostering and enhancing a deeper understanding of mathematical concepts in Statistics & Probability, both within and beyond the classroom.

Keywords: differentiated instruction, hybrid, learning styles, academic performance

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Authors: Umarov Jamshid, Ju-Su Kim, Hak-Jun Lee, Man-Kyo Han, Ryum-Duck Oh

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Recently, it becomes convenient to identify the location information of cargos by using GPS and wireless communication technologies. The development of IoT technologies and tracking system allows us to confirm site situation on an ad hoc basis in all the industries and social environments. Moreover, it allows us to apply IT technologies to a manageable extent. However, there have been many limitations for using the system due to the difficulty of identifying location information in real time and also due to the simple features. To globalize the logistics related tracking system, it is required to conduct a study to resolve the aforementioned problem. On that account, this paper designed and developed the IoT and RTLS based intelligent multi tracking agent system for more secure, accurate and reliable transportation in relation to logistics.

Keywords: GPS, tracking agent system, IoT, RTLS, Logistics

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Authors: Ruchika Malhotra, Megha Khanna

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The development of change prediction models can help the software practitioners in planning testing and inspection resources at early phases of software development. However, a major challenge faced during the training process of any classification model is the imbalanced nature of the software quality data. A data with very few minority outcome categories leads to inefficient learning process and a classification model developed from the imbalanced data generally does not predict these minority categories correctly. Thus, for a given dataset, a minority of classes may be change prone whereas a majority of classes may be non-change prone. This study explores various alternatives for adeptly handling the imbalanced software quality data using different sampling methods and effective MetaCost learners. The study also analyzes and justifies the use of different performance metrics while dealing with the imbalanced data. In order to empirically validate different alternatives, the study uses change data from three application packages of open-source Android data set and evaluates the performance of six different machine learning techniques. The results of the study indicate extensive improvement in the performance of the classification models when using resampling method and robust performance measures.

Keywords: change proneness, empirical validation, imbalanced learning, machine learning techniques, object-oriented metrics

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Authors: V. Ravindra, P. A. Saikiran, M. Ramgopal

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This paper presents an energy analysis of a solar assisted trigeneration system using an Ejector for dairy applications. The working fluid in the trigeneration loop is Supercritical CO₂. The trigeneration system is a combination of Brayton cycle and ejector based vapor compression refrigeration cycle. The heating and cooling outputs are used for simultaneous pasteurization and chilling of the milk. The electrical power is used to drive the auxiliary equipment in the dairy plant. A numerical simulation is done with Engineering Equation Solver (EES), and a parametric analysis is performed by varying the operating variables over a meaningful range. The results show that the overall performance index decreases with increase in ambient temperature. For an ejector based system, the compressor work and cooling output are significant output quantities. An increase in total mass flow rate of the refrigerant (primary + secondary) results in an increase in the compressor work and cooling output.

Keywords: trigeneration, solar thermal, supercritical CO₂, ejector

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Authors: Getaneh Berie Tarekegn

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A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Emilia A. Campayo-Munoz, Alberto Cabedo-Mas

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Music students have to face the challenges of musical practice -such as discipline in study, competitiveness, or performance anxiety- that require good emotional management to enable successful performance. However, few rigorous implementations focused on studying the influence of emotional skills in student's musical performance. Responding to this gap in the literature, this study aims to explore the relationship between emotional skills and musical performance in the context of elementary music education in conservatoires. Given the individual nature of the instrumental studies and the difficult availability of teachers to be trained in emotional education, it was decided to conduct a multiple case study in a Spanish music conservatoire. Author 1 carried out the implementation of the research with three 10-year-old students who were selected from her piano class. All of them attended the third year of their piano studies. The research processes consisted of the implementation of a set of specific and cross-sectional activities designed 'ad hoc' to be articulated in the subjects of individual instrument -piano- and ensemble in parallel to the contents of musical nature. The CE-360º questionnaire was used to measure different aspects of the students' emotional skills from a multi-angle perspective, each of the questionnaires being responded by oneself, three teachers and three peers, before and after the implementation. The data from the questionnaire were compared with the grades that the students obtained during the first and last quarter of the school year in the attended subjects. Acknowledging the complexity of emotional development, the results indicate possible relations between emotional skills and musical performance in music education in conservatoires. The results show that for the cases explored; there exists a relationship between emotional skills and musical performance. Although generalizations cannot be made, this study reinforces the need to further explore emotional development in instrumental teaching and suggest the importance of inviting teachers to reflect on the pedagogical practices extended in the conservatoires and to develop and implement those that promote the work of the students' emotions.

Keywords: conservatoires, emotional skills, music education, musical performance

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Authors: Yifan Hou, Haibo Liu, Le Jiang, Wandong Su, Binqing Wang

Abstract:

Road defect detection is crucial for modern urban management and infrastructure maintenance. Traditional road defect detection methods mostly rely on manual labor, which is not only inefficient but also difficult to ensure their reliability. However, existing deep learning-based road defect detection models have poor detection performance in complex environments and lack robustness to multi-scale targets. To address this challenge, this paper proposes a distinct detection framework based on the one stage algorithm network structure. This article designs a deep feature extraction network based on RCSDarknet, which applies channel shuffling to enhance information fusion between tensors. Through repeated stacking of RCS modules, the information flow between different channels of adjacent layer features is enhanced to improve the model's ability to capture target spatial features. In addition, a multi-scale feature fusion mechanism with weighted dual flow paths was adopted to fuse spatial features of different scales, thereby further improving the detection performance of the model at different scales. To validate the performance of the proposed algorithm, we tested it using the RDD2022 dataset. The experimental results show that the enhancement algorithm achieved 84.14% mAP, which is 1.06% higher than the currently advanced YOLOv8 algorithm. Through visualization analysis of the results, it can also be seen that our proposed algorithm has good performance in detecting targets of different scales in complex scenes. The above experimental results demonstrate the effectiveness and superiority of the proposed algorithm, providing valuable insights for advancing real-time road defect detection methods.

Keywords: roads, defect detection, visualization, deep learning

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Authors: Kumar Gular, Viswanathan S., Mastour Saeed Alshahrani, Ravi Shankar Reddy, Jaya Shanker Tedla, Snehil Dixit, Ajay Prasad Gautam, Venkata Nagaraj Kakaraparthi, Devika Rani Sangadala

Abstract:

Background: Despite trunk control’s significant contribution to improving various functional activity components, the independent effect of trunk performance on quality of life is yet to be estimated in stroke survivors. Ascertaining the correlation between trunk control and self-reported quality of life while evaluating the effect of demographic and clinical characteristics on their relationship will guide concerned healthcare professionals in designing ideal rehabilitation protocols during the late sub-acute stroke stage of recovery. The aims of the present research were to (1) investigate the associations of trunk performance with self-rated quality of life and (2) evaluate if age, body mass index (BMI), and clinical characteristics mediate the relationship between trunk motor performance and perceived quality of life in the sub-acute stroke population. Methods: Trunk motor functions and quality of life among the late sub-acute stroke population aged 57.53 ± 6.42 years were evaluated through the trunk Impairment Scale (TIS) and Stroke specific quality of life (SSQOL) questionnaire, respectively. Pearson correlation coefficients and mediation analysis were performed to elucidate the relationship of trunk motor function with quality of life and determine the mediation impact of demographic and clinical characteristics on their association, respectively. Results: The current study observed significant correlations between trunk motor functions (TIS) and quality of life (SSQOL) with r=0.68 (p<0.001). Age, BMI, and type of stroke were detected as potential mediating factors in the association between trunk performance and quality of life. Conclusion: Validated associations between trunk motor functions and perceived quality of life among the late sub-acute stroke population emphasize the importance of comprehensive evaluation of trunk control. Rehabilitation specialists should focus on appropriate strategies to enhance trunk performance anticipating the potential effects of age, BMI, and type of stroke to improve health-related quality of life in stroke survivors.

Keywords: sub-acute stroke, quality of life, functional independence, trunk control

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Authors: Soheir Weheida, Amal E. Shehata, Samira E. Aboalizm

Abstract:

The aim of this study was to examine the effect of surgical safety checklist on surgical team’s knowledge and performance in operating room. Subjects: A convenience sample 151 (48 head nurse, 45 nurse, 37 surgeon and 21 anesthesiologist) which available in operating room at two different hospitals was included in the study. Setting: The study was carried out at operating room in Menoufia University and Shebin Elkom Teaching Hospitals, Egypt. Tools: I: Surgical safety: Surgical team knowledge assessment structure interview schedule. II: WHO surgical safety observational Checklist. III: Post Surgery Culture Survey scale. Results: There was statistical significant improvement of knowledge mean score and performance about surgical safety especially in post and follow up than pre intervention, before patients entering the operating, before induction of anesthesia, skin incision and post skin closure and before patient leaves operating room, P values (P < 0.001). Improvement of communication post intervention than pre intervention between surgical team’s (4.74 ± 0.540). About two thirds (73.5 %) of studied sample strongly agreed on surgical safety in operating room. Conclusions: Implementation of surgical safety checklist has a positive effect on improving knowledge, performance and communication between surgical teams and these seems to have a positive effect on improve patient safety in the operating room.

Keywords: knowledge, operating room, performance, surgical safety checklist

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Authors: Tian Lan

Abstract:

Arbitrageurs participate in a variety of techniques in response to the existence of fluctuating sentiment, resulting in sparse sentiment exposures. This paper found that Commodity Trading Advisor (CTA) funds in the top decile rated by sentiment beta outperformed those in the bottom decile by 0.33% per month on a risk-adjusted basis, with the difference being larger among skilled managers. This paper also discovered that around ten percent of Commodity Trading Advisor (CTA) funds could accurately predict market sentiment, which has a positive correlation with fund sentiment beta and acts as a determinant in fund performance. Instead of betting against mispricing, this research demonstrates that a competent manager can achieve remarkable returns by forecasting and reacting to shifts in investor sentiment.

Keywords: investment sentiment, CTA fund, market timing, fund performance

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Authors: Riznaldi Akbar

Abstract:

In this study, we compared the performance of the Artificial Neural Network (ANN) model with back-propagation algorithm in correctly predicting in-sample and out-of-sample external debt crisis in Indonesia. We found that exchange rate, foreign reserves, and exports are the major determinants to experiencing external debt crisis. The ANN in-sample performance provides relatively superior results. The ANN model is able to classify correctly crisis of 89.12 per cent with reasonably low false alarms of 7.01 per cent. In out-of-sample, the prediction performance fairly deteriorates compared to their in-sample performances. It could be explained as the ANN model tends to over-fit the data in the in-sample, but it could not fit the out-of-sample very well. The 10-fold cross-validation has been used to improve the out-of-sample prediction accuracy. The results also offer policy implications. The out-of-sample performance could be very sensitive to the size of the samples, as it could yield a higher total misclassification error and lower prediction accuracy. The ANN model could be used to identify past crisis episodes with some accuracy, but predicting crisis outside the estimation sample is much more challenging because of the presence of uncertainty.

Keywords: debt crisis, external debt, artificial neural network, ANN

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Authors: Anna Paola Micheli, Anna Maria Calce, Loris Di Nallo

Abstract:

Sustainable finance identifies the process that leads, in the adoption of investment decisions, to take into account environmental and social factors, with the aim of orienting investments towards sustainable and long-term activities. Considering that the topic is at the center of the interest of national agendas, long-term investments will no longer be analyzed only by looking at financial data, but environmental, social, and governance (ESG) factors will be increasingly important and will play a fundamental role in determining the risk and return of an investment. Although this perspective does not deny the orientation to profit, ESG mutual funds represent sustainable finance applied to the world of mutual funds. So the goal of this paper is to verify this attitude, in particular in the energy sector. The choice of the sector is not casual: ESG is the acronym for environmental, social, and governance, and energy companies are strictly related to the environmental theme. The methodology adopted leads to a comparison between a sample of ESG funds and a sample of ESG funds with similar characteristics, using the most important indicators of literature: yield, standard deviation, and Sharpe index. The analysis is focused on equity funds. Results that are partial, due to the lack of historicity, show a good performance of ESG funds, testifying how a sustainable approach does not necessarily mean lower profits. It is clear that these first findings do not involve an absolute preference for ESG funds in terms of performance because the persistence of results is requested. Furthermore, these findings are to be verified in other sectors and in bond funds.

Keywords: mutual funds, ESG, performance, energy

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