Search results for: multi criteria decision making (MCDM)

Authors: Abdelhammid Chibane

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This study summarizes state-of-the-art knowledge in the seismic response of vertically irregular building frames. Criteria defining vertical irregularity as per the current building codes have been discussed. A review of studies on the seismic behaviour of vertically irregular structures along with their findings has been presented. It is observed that building codes provide criteria to classify the vertically irregular structures and suggest dynamic analysis to arrive at design lateral forces. Most of the studies agree on the increase in drift demand in the tower portion of set-back structures and on the increase in seismic demand for buildings with discontinuous distributions in mass, stiffness, and strength. The largest seismic demand is found for the combined-stiffness-and-strength irregularity.

Keywords: mass irregularity, set-back structure, stiffness irregularity, strength irregularity, vertical irregularity

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Authors: Kazuyoshi Mori

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In this paper, we consider the number of parameters for the parametrization of stabilizing controllers for RHinf systems with size 2 × 2. Fortunately, any plant of this model can admit doubly coprime factorization. Thus we can use the Youla parameterization to parametrize the stabilizing contollers . However, Youla parameterization does not give itself the minimal number of parameters. This paper shows that the minimal number of parameters is four. As a result, we show that the Youla parametrization naturally gives the parameterization of stabilizing controllers with minimal numbers.

Keywords: RHinfo, parameterization, number of parameters, multi-input, multi-output systems

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Authors: Rizki Oktariza, Sri Fauza Pratiwi, Hilza Ikhsanti

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Human depends on fossil fuels as the bigger sources of considerable energy in all sectors. Based on that cases, we are needed alternative energy to supplies needed for fuel, one of them by using energy fuel from the biodiesel. The raw materials that can be used for producing the biodiesel energy are Jatropha curcas L. In Indonesia, the availability of land for the development of the Jatropha curcas L which has very appropriate Indonesia reached 14.2 million hectares, with an area of suitable in Kalimantan around 10 million hectares. In Central Kalimantan, as one of the provinces of Kalimantan, has considerable potential planting Jatropha curcas L because of the physical condition and have a largest of the agricultural land. To support the potential of Jatropha curcas L in Central Kalimantan, spatial analysis is needed to find out the appropriate areas for Jatropha curcas L growing land. The suitability of region is influenced by several variables i.e., rainfall, the slope of the land, the surface temperature and the altitude of a region. The compliance of criteria are divided into four criteria: high suitable (S1), moderately suitable (S2), marginally suitable (S3), not suitable (N). The suitability of the region is based on these variables and made an overlay analysis of these variables by using Geographic Information System. Based on this overlay analysis will results a map of the suitability area for planting Jatropha curcas L, which is distribution criteria is high suitable (S1) of 213,245 ha, moderately suitable (S2) of 14,389,353 ha, marginally suitable (S3) 360,357 ha, not suitable (N) 0.020 ha.

Keywords: geographic information system, Jatropha curcas L., overlay, the suitable area

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Authors: Behzad Panahirad, UğUr Atikol

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The current study is based on a combined heat and power system with multi-objectives, driven by biomass. The system consists of a combustion chamber (CC), a single effect absorption cooling system (SEACS), an air conditioning unit (AC), a reheat steam Rankine cycle (RRC), an organic Rankine cycle (ORC) and an electrolyzer. The purpose of this system is to produce hydrogen, electricity, heat, cooling, and air conditioning. All the simulations had been performed by Engineering Equation Solver (EES) software. Pine sawdust is the selected biofuel for the combustion process. The overall utilization factor (εₑₙ) and exergetic efficiency (ψₑₓ) were calculated to be 2.096 and 24.03% respectively. The performed renewable and environmental impact analysis indicated a sustainability index of 1.316 (SI) and a specific CO2 emission of 353.8 kg/MWh. The parametric study is conducted based on the variation of ambient (sink) temperature, biofuel mass flow rate, and boilers outlet temperatures. The parametric simulation showed that the increase in biofuel mass flow rate has a positive effect on the sustainability of the system.

Keywords: biomass, exergy assessment, multi-objective plant, CO₂ emission, irreversibility

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Authors: Ozgu Turgut

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Water scarcity is a problem for many regions which requires immediate action, and solutions cannot be postponed for a long time. It is known that farming consumes a significant portion of usable water. Although in recent years, the efforts to make the transition to dripping or spring watering systems instead of using surface watering started to pay off. It is also known that this transition is not necessarily translated into an increase in the capacity dedicated to other water consumption channels such as city water or power usage. In order to control and allocate the water resource more purposefully, new watering systems have to be used with monitoring abilities that can limit the usage capacity for each farm. In this study, a decision support model which relies on a bi-objective stochastic linear optimization is proposed, which takes crop yield and price volatility into account. The model generates annual planting plans as well as water usage limits for each farmer in the region while taking the total value (i.e., profit) of the overall harvest. The mathematical model is solved using the L-shaped method optimally. The decision support model can be especially useful for regional administrations to plan next year's planting and water incomes and expenses. That is why not only a single optimum but also a set of representative solutions from the Pareto set is generated with the proposed approach.

Keywords: decision support, farming, water, tactical planning, optimization, stochastic, pareto

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Authors: Marwa Qaraqe, Muhammad Ismail, Erchin Serpedin

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This paper presents a novel architecture for a patient-specific epileptic seizure onset detector using scalp electroencephalography (EEG). The proposed architecture is based on the decision fusion calculated from energy and neural synchronization related features. Specifically, one level of the detector calculates the condition number (CN) of an EEG matrix to evaluate the amount of neural synchronization present within the EEG channels. On a parallel level, the detector evaluates the energy contained in four EEG frequency subbands. The information is then fed into two independent (parallel) classification units based on support vector machines to determine the onset of a seizure event. The decisions from the two classifiers are then combined together according to two fusion techniques to determine a global decision. Experimental results demonstrate that the detector based on the AND fusion technique outperforms existing detectors with a sensitivity of 100%, detection latency of 3 seconds, while it achieves a 2:76 false alarm rate per hour. The OR fusion technique achieves a sensitivity of 100%, and significantly improves delay latency (0:17 seconds), yet it achieves 12 false alarms per hour.

Keywords: epilepsy, EEG, seizure onset, electroencephalography, neuron, detection

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Authors: K. Shiba, T. Kobayashi, T. Kaburagi, Y. Kurihara

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Elderly population in the world is increasing, and consequently, their nursing burden is also increasing. In such situations, monitoring and evaluating their daily action facilitates efficient nursing care. Especially, we focus on an unconscious activity during sleep, i.e. turnover. Monitoring turnover during sleep is essential to evaluate various conditions related to sleep. Bedsores are considered as one of the monitoring conditions. Changing patient’s posture every two hours is required for caregivers to prevent bedsore. Herein, we attempt to develop an unconstrained nocturnal monitoring system using a sensing device based on piezoelectric ceramics that can detect the vibrations owing to human body movement on the bed. In the proposed method, in order to construct a multi-points sensing, we placed two sensing devices under the right and left legs at the head-side of an ordinary bed. Using this equipment, when a subject lies on the bed, feature is calculated from the output voltages of the sensing devices. In order to evaluate our proposed method, we conducted an experiment with six healthy male subjects. Consequently, the period during which turnover occurs can be correctly classified as the turnover period with 100% accuracy.

Keywords: turnover, piezoelectric ceramics, multi-points sensing, unconstrained monitoring system

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Authors: Piyanut Sujit

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The purposes of this study were to: 1) create knowledge and develop web based instruction on the puppet show, 2) evaluate the effectiveness of the web based instruction on the puppet show by using the criteria of 80/80, and 3) compare and analyze the achievement of the students before and after learning with web based instruction on the puppet show. The population of this study included 53 students in the Program of Library and Information Sciences who registered in the subject of Reading and Reading Promotion in semester 1/2011, Suansunandha Rajabhat University. The research instruments consisted of web based instruction on the puppet show, specialist evaluation form, achievement test, and tests during the lesson. The research statistics included arithmetic mean, variable means, standard deviation, and t-test in SPSS for Windows. The results revealed that the effectiveness of the developed web based instruction was 84.67/80.47 which was higher than the set criteria at 80/80. The student achievement before and after learning showed statistically significant difference at 0.05 as in the hypothesis.

Keywords: puppet, puppet show, web based instruction, library and information sciences

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Authors: Reza Nematirad, Anil Pahwa, Balasubramaniam Natarajan

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Time series data consist of successive data points collected over a period of time. Accurate prediction of future values is essential for informed decision-making in several real-world applications, including electricity load demand forecasting, lifetime estimation of industrial machinery, traffic planning, weather prediction, and the stock market. Due to their critical relevance and wide application, there has been considerable interest in time series forecasting in recent years. However, the proliferation of sensors and IoT devices, real-time monitoring systems, and high-frequency trading data introduce significant intricate temporal variations, rapid changes, noise, and non-linearities, making time series forecasting more challenging. Classical methods such as Autoregressive integrated moving average (ARIMA) and Exponential Smoothing aim to extract pre-defined temporal variations, such as trends and seasonality. While these methods are effective for capturing well-defined seasonal patterns and trends, they often struggle with more complex, non-linear patterns present in real-world time series data. In recent years, deep learning has made significant contributions to time series forecasting. Recurrent Neural Networks (RNNs) and their variants, such as Long short-term memory (LSTMs) and Gated Recurrent Units (GRUs), have been widely adopted for modeling sequential data. However, they often suffer from the locality, making it difficult to capture local trends and rapid fluctuations. Convolutional Neural Networks (CNNs), particularly Temporal Convolutional Networks (TCNs), leverage convolutional layers to capture temporal dependencies by applying convolutional filters along the temporal dimension. Despite their advantages, TCNs struggle with capturing relationships between distant time points due to the locality of one-dimensional convolution kernels. Transformers have revolutionized time series forecasting with their powerful attention mechanisms, effectively capturing long-term dependencies and relationships between distant time points. However, the attention mechanism may struggle to discern dependencies directly from scattered time points due to intricate temporal patterns. Lastly, Multi-Layer Perceptrons (MLPs) have also been employed, with models like N-BEATS and LightTS demonstrating success. Despite this, MLPs often face high volatility and computational complexity challenges in long-horizon forecasting. To address intricate temporal variations in time series data, this study introduces Times2D, a novel framework that parallelly integrates 2D spectrogram and derivative heatmap techniques. The spectrogram focuses on the frequency domain, capturing periodicity, while the derivative patterns emphasize the time domain, highlighting sharp fluctuations and turning points. This 2D transformation enables the utilization of powerful computer vision techniques to capture various intricate temporal variations. To evaluate the performance of Times2D, extensive experiments were conducted on standard time series datasets and compared with various state-of-the-art algorithms, including DLinear (2023), TimesNet (2023), Non-stationary Transformer (2022), PatchTST (2023), N-HiTS (2023), Crossformer (2023), MICN (2023), LightTS (2022), FEDformer (2022), FiLM (2022), SCINet (2022a), Autoformer (2021), and Informer (2021) under the same modeling conditions. The initial results demonstrated that Times2D achieves consistent state-of-the-art performance in both short-term and long-term forecasting tasks. Furthermore, the generality of the Times2D framework allows it to be applied to various tasks such as time series imputation, clustering, classification, and anomaly detection, offering potential benefits in any domain that involves sequential data analysis.

Keywords: derivative patterns, spectrogram, time series forecasting, times2D, 2D representation

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Authors: Abdelmanie Suleimat, Nagham Abu Shaqra, Sawsan Majali, Issam Adawi, Heba Abo Shindi, Anas Al Mohtaseb

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The Government of Jordan recognizes that maternal mortality constitutes a grave public health problem. Over the past two decades, there has been significant progress in improving the quality of maternal health services, resulting in improved maternal and child health outcomes. Despite these efforts, measurement and analysis of maternal mortality remained a challenge, with significant discrepancies from previous national surveys that inhibited accuracy. In response with support from USAID, the Jordan Maternal Mortality Surveillance Response (JMMSR) System was established to collect, analyze, and equip policymakers with data for decision-making guided by interdisciplinary multi-levelled advisory groups aiming to eliminate preventable maternal deaths, A 2016 Public Health Bylaw required the notification of deaths among women of reproductive age. The JMMSR system was launched in 2018 and continues annually, analyzing data received from health facilities, to guide policy to prevent avoidable deaths. To date, there have been four annual national maternal mortality reports (2018-2021). Data is collected, reviewed by advisory groups, and then consolidated in an annual report to inform and guide the Ministry of Health (MOH); JMMSR collects the necessary information to calculate an accurate maternal mortality ratio and assists in identifying leading causes and contributing factors for each maternal death. Based on this data, national response plans are created. A monitoring and evaluation plan was designed to define, track, and improve implementation through indicators. Over the past four years, one of these indicators, ‘percent of facilities notifying respective health directorates of all deaths of women of reproductive age,’ increased annually from 82.16%, 92.95%, and 92.50% to 97.02%, respectively. The Government of Jordan demonstrated commitment to the JMMSR system by designating the MOH to primarily host the system and lead the development and dissemination of policies and procedures to standardize implementation. The data was translated into practical and evidence-based recommendations. The successful impact of results deepened the understanding of maternal mortality in Jordan, which convinced the MOH to amend the Bylaw now mandating electronic reporting of all births and neonatal deaths from health facilities to empower the JMMSR system, by developing a stillbirths and neonatal mortality surveillance and response system.

Keywords: maternal health, maternal mortality, preventable maternal deaths, maternal morbidity

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Authors: U. Yavas, M. Gokasan

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Control of diesel engine’s air path has drawn a lot of attention due to its multi input-multi output, closed coupled, non-linear relation. Today, precise control of amount of air to be combusted is a must in order to meet with tight emission limits and performance targets. In this study, passenger car size diesel engine is modeled by AVL Boost RT, and then simulated with standard, industry level PID controllers. Finally, linear model predictive control is designed and simulated. This study shows the importance of modeling and control of diesel engines with flexible algorithm development in computer based systems.

Keywords: predictive control, engine control, engine modeling, PID control, feedforward compensation

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Authors: Rajaian Hoonejani Mohammad, Eshraghi Pegah, Zomorodian Zahra Sadat, Tahsildoost Mohammad

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Over the past decade, energy consumption in educational buildings has steadily increased. The purpose of this research is to provide a method to quickly predict the energy consumption of buildings using separate evaluation of zones and decomposing the building to eliminate the complexity of geometry at the early design stage. To produce this framework, machine learning algorithms such as Support vector regression (SVR) and Artificial neural network (ANN) are used to predict energy consumption and thermal comfort metrics in a school as a case. The database consists of more than 55000 samples in three climates of Iran. Cross-validation evaluation and unseen data have been used for validation. In a specific label, cooling energy, it can be said the accuracy of prediction is at least 84% and 89% in SVR and ANN, respectively. The results show that the SVR performed much better than the ANN.

Keywords: early stage of design, energy, thermal comfort, validation, machine learning

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Authors: Abdelhadi Adel, Kadri Ouahab

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This paper proposes a hybrid algorithm for the integration of systematic preventive maintenance policies in hybrid flow shop scheduling to minimize makespan. We have implemented a problem-solving approach for optimizing the processing time, methods based on metaheuristics. The proposed approach is inspired by the behavior of the human body. This hybridization is between a multi-agent system and inspirations of the human body, especially genetics. The effectiveness of our approach has been demonstrated repeatedly in this paper. To solve such a complex problem, we proposed an approach which we have used advanced operators such as uniform crossover set and single point mutation. The proposed approach is applied to three preventive maintenance policies. These policies are intended to maximize the availability or to maintain a minimum level of reliability during the production chain. The results show that our algorithm outperforms existing algorithms. We assumed that the machines might be unavailable periodically during the production scheduling.

Keywords: multi-agent systems, emergence, genetic algorithm, makespan, systematic maintenance, scheduling, hybrid flow shop scheduling

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Authors: Sahar Elsaleh, Ghada Farhat, Shaikha Al-Derham, Fasih Alam

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Background: Cesarean section (CS) delivery is one of the major concerns both in developing and developed countries. The rate of CS deliveries are on the rise globally, and especially in Qatar. Many socio-economic, demographic, clinical and institutional factors play an important role for cesarean sections. This study aims to investigate factors affecting the prevalence of CS among women in Qatar using the UNICEF’s Multiple Indicator Cluster Survey (MICS) 2012 database. Methods: The study has focused on the women’s questionnaire of the MICS, which was successfully distributed to 5699 participants. Following study inclusion and exclusion criteria, a final sample of 761 women aged 19- 49 years who had at least one delivery of giving birth in their lifetime before the survey were included. A number of socio-economic, demographic, clinical and institutional factors, identified through literature review and available in the data, were considered for the analyses. Bivariate and multivariate logistic regression models, along with a multi-level modeling to investigate clustering effect, were undertaken to identify the factors that affect CS prevalence in Qatar. Results: From the bivariate analyses the study has shown that, a number of categorical factors are statistically significantly associated with the dependent variable (CS). When identifying the factors from a multivariate logistic regression, the study found that only three categorical factors -‘age of women’, ‘place at delivery’ and ‘baby weight’ appeared to be significantly affecting the CS among women in Qatar. Although the MICS dataset is based on a cluster survey, an exploratory multi-level analysis did not show any clustering effect, i.e. no significant variation in results at higher level (households), suggesting that all analyses at lower level (individual respondent) are valid without any significant bias in results. Conclusion: The study found a statistically significant association between the dependent variable (CS delivery) and age of women, frequency of TV watching, assistance at birth and place of birth. These results need to be interpreted cautiously; however, it can be used as evidence-base for further research on cesarean section delivery in Qatar.

Keywords: cesarean section, factors, multiple indicator cluster survey, MICS database, Qatar

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Authors: Elke Meex, Elke Knapen, Griet Verbeeck

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The focus of sustainable building is gradually shifting from energy efficiency towards the more global environmental impact of building design during all life-cycle stages. In this context, many tools have been developed that use a LCA-approach to assess the environmental impact on a whole building level. Since the building design strongly influences the final environmental performance and the architect plays a key role in the design process, it is important that these tools are adapted to his work method and support the decision making from the early design phase on. Therefore, a comparative evaluation of the degree of architect-friendliness of some LCA tools on building level is made, based on an evaluation framework specifically developed for the architect’s viewpoint. In order to allow comparison of the results, a reference building has been designed, documented for different design phases and entered in all software tools. The evaluation according to the framework shows that the existing tools are not very architect-friendly. Suggestions for improvement are formulated.

Keywords: architect-friendliness, design supportive value, evaluation framework, tool comparison

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Authors: Sohail Ahmed, Ke Xing

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This research theoretically explored the evolution mechanism of enterprise technological innovation capability system (ETICS) from the perspective of complex adaptive systems (CAS). Starting from CAS theory, this study proposed an analytical framework for ETICS, its concepts and theory by integrating CAS methodology into the management of technological innovation capability of enterprises and discusses how to use the principles of complexity to analyze the composition, evolution and realization of the technological innovation capabilities in complex dynamic environment. This paper introduces the concept and interaction of multi-agent, the theoretical background of CAS and summarizes the sources of technological innovation, the elements of each subject and the main clusters of adaptive interactions and innovation activities. The concept of multi-agents is applied through the linkages of enterprises, research institutions and government agencies with the leading enterprises in industrial settings. The study was exploratory based on CAS theory. Theoretical model is built by considering technological and innovation literature from foundational to state of the art projects of technological enterprises. On this basis, the theoretical model is developed to measure the evolution mechanism of enterprise technological innovation capability system. This paper concludes that the main characteristics for evolution in technological systems are based on enterprise’s research and development personal, investments in technological processes and innovation resources are responsible for the evolution of enterprise technological innovation performance. The research specifically enriched the application process of technological innovation in institutional networks related to enterprises.

Keywords: complex adaptive system, echo model, enterprise knowledge system, research institutions, multi-agents.

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Authors: Obaid AlOtaibi, Salman Hussain

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Wind energy is associated with many geographical factors including wind speed, climate change, surface topography, environmental impacts, and several economic factors, most notably the advancement of wind technology and energy prices. It is the fastest-growing and least economically expensive method for generating electricity. Wind energy generation is directly related to the characteristics of spatial wind. Therefore, the feasibility study for the wind energy conversion system is based on the value of the energy obtained relative to the initial investment and the cost of operation and maintenance. In Kuwait, wind energy is an appropriate choice as a source of energy generation. It can be used in groundwater extraction in agricultural areas such as Al-Abdali in the north and Al-Wafra in the south, or in fresh and brackish groundwater fields or remote and isolated locations such as border areas and projects away from conventional power electricity services, to take advantage of alternative energy, reduce pollutants, and reduce energy production costs. The study covers the State of Kuwait with an exception of metropolitan area. Climatic data were attained through the readings of eight distributed monitoring stations affiliated with Kuwait Institute for Scientific Research (KISR). The data were used to assess the daily, monthly, quarterly, and annual available wind energy accessible for utilization. The researchers applied the Suitability Model to analyze the study by using the ArcGIS program. It is a model of spatial analysis that compares more than one location based on grading weights to choose the most suitable one. The study criteria are: the average annual wind speed, land use, topography of land, distance from the main road networks, urban areas. According to the previous criteria, the four proposed locations to establish wind farm projects are selected based on the weights of the degree of suitability (excellent, good, average, and poor). The percentage of areas that represents the most suitable locations with an excellent rank (4) is 8% of Kuwait’s area. It is relatively distributed as follows: Al-Shqaya, Al-Dabdeba, Al-Salmi (5.22%), Al-Abdali (1.22%), Umm al-Hayman (0.70%), North Wafra and Al-Shaqeeq (0.86%). The study recommends to decision-makers to consider the proposed location (No.1), (Al-Shqaya, Al-Dabdaba, and Al-Salmi) as the most suitable location for future development of wind farms in Kuwait, this location is economically feasible.

Keywords: Kuwait, renewable energy, spatial analysis, wind energy

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Authors: Temidayo Otunniyi

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This paper presents a low computational channelization algorithm for the multi-standards platform using poly phase implementation of a critically sampled hybrid Trigonometry generalized Discrete Fourier Transform, (HGDFT). An HGDFT channelization algorithm exploits the orthogonality of two trigonometry Fourier functions, together with the properties of Quadrature Mirror Filter Bank (QMFB) and Exponential Modulated filter Bank (EMFB), respectively. HGDFT shows improvement in its implementation in terms of high reconfigurability, lower filter length, parallelism, and medium computational activities. Type 1 and type 111 poly phase structures are derived for real-valued HGDFT modulation. The design specifications are decimated critically and over-sampled for both single and multi standards receiver platforms. Evaluating the performance of oversampled single standard receiver channels, the HGDFT algorithm achieved 40% complexity reduction, compared to 34% and 38% reduction in the Discrete Fourier Transform (DFT) and tree quadrature mirror filter (TQMF) algorithm. The parallel generalized discrete Fourier transform (PGDFT) and recombined generalized discrete Fourier transform (RGDFT) had 41% complexity reduction and HGDFT had a 46% reduction in oversampling multi-standards mode. While in the critically sampled multi-standard receiver channels, HGDFT had complexity reduction of 70% while both PGDFT and RGDFT had a 34% reduction.

Keywords: software defined radio, channelization, critical sample rate, over-sample rate

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Authors: Charlotte K. Lee, Henrique R. N. Aguiar, Ralph Smith, James Baldock, Sam Botchey

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Background: Femoral neck stress fractures (FNSF) are uncommon, making up 1 to 7.2% of stress fractures in healthy subjects. FNSFs are prevalent in young women, military recruits, endurance athletes, and individuals with energy deficiency syndrome or female athlete triad. Presentation is often non-specific and is often misdiagnosed following the initial examination. There is limited research addressing the return–to–activity time after FNSF. Previous studies have demonstrated prognostic time predictions based on various imaging techniques. Here, (1) OxSport clinic FNSF practice standards are retrospectively reviewed, (2) FNSF cohort demographics are examined, (3) Regression models were used to predict return–to–activity prognosis and consequently determine bone stress risk factors. Methods: Patients with a diagnosis of FNSF attending Oxsport clinic between 01/06/2020 and 01/01/2020 were selected from the Rheumatology Assessment Database Innovation in Oxford (RhADiOn) and OxSport Stress Fracture Database (n = 14). (1) Clinical practice was audited against five criteria based on local and National Institute for Health Care Excellence guidance, with a 100% standard. (2) Demographics of the FNSF cohort were examined with Student’s T-Test. (3) Lastly, linear regression and Random Forest regression models were used on this patient cohort to predict return–to–activity time. Consequently, an analysis of feature importance was conducted after fitting each model. Results: OxSport clinical practice met standard (100%) in 3/5 criteria. The criteria not met were patient waiting times and documentation of all bone stress risk factors. Importantly, analysis of patient demographics showed that of the population with complete bone stress risk factor assessments, 53% were positive for modifiable bone stress risk factors. Lastly, linear regression analysis was utilized to identify demographic factors that predicted return–to–activity time [R2 = 79.172%; average error 0.226]. This analysis identified four key variables that predicted return-to-activity time: vitamin D level, total hip DEXA T value, femoral neck DEXA T value, and history of an eating disorder/disordered eating. Furthermore, random forest regression models were employed for this task [R2 = 97.805%; average error 0.024]. Analysis of the importance of each feature again identified a set of 4 variables, 3 of which matched with the linear regression analysis (vitamin D level, total hip DEXA T value, and femoral neck DEXA T value) and the fourth: age. Conclusion: OxSport clinical practice could be improved by more comprehensively evaluating bone stress risk factors. The importance of this evaluation is demonstrated by the population found positive for these risk factors. Using this cohort, potential bone stress risk factors that significantly impacted return-to-activity prognosis were predicted using regression models.

Keywords: eating disorder, bone stress risk factor, femoral neck stress fracture, vitamin D

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Authors: Meng-Tzu Cheng, Louisa Rosenheck, Chen-Yen Lin, Eric Klopfer

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The purpose of the research is to explore some of the ways in which gameplay data can be analyzed to yield results that feedback into the learning ecosystem. Back-end data for all users as they played an MMOG, The Radix Endeavor, was collected, and this study reports the analyses on a specific genetics quest by using the data mining techniques, including the decision tree method. In the study, different reasons for quest failure between participants who eventually succeeded and who never succeeded were revealed. Regarding the in-game tools use, trait examiner was a key tool in the quest completion process. Subsequently, the results of decision tree showed that a lack of trait examiner usage can be made up with additional Punnett square uses, displaying multiple pathways to success in this quest. The methods of analysis used in this study and the resulting usage patterns indicate some useful ways that gameplay data can provide insights in two main areas. The first is for game designers to know how players are interacting with and learning from their game. The second is for players themselves as well as their teachers to get information on how they are progressing through the game, and to provide help they may need based on strategies and misconceptions identified in the data.

Keywords: MMOG, decision tree, genetics, gaming-learning interaction

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Authors: O. Fatemi

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Considering growing building construction industry in developing countries and sustainable development concept, as well as the importance of taking care of the future generations, codifying buildings scoring system based on environmental criteria, has always been a subject for discussion. The existing systems cannot be used for all the regions due to several reasons, including but not limited to variety in regional variables. In this article, the most important common LEED (Leadership in Energy and Environmental Design) and BREEAM (Building Research Establishment Environmental Assessment Method) common and Global environmental scoring systems, used in UK, USA, and Japan, respectively, have been discussed and compared with a special focus on CASBEE (Comprehensive Assessment System for Built Environment Efficiency), to credit assigning field (weighing and scores systems) as well as sustainable development criteria in each system. Then, converging and distinct fields of the foregoing systems are examined considering National Iranian Building Code. Furthermore, the common credits in the said systems not mentioned in National Iranian Building Code have been identified. These credits, which are generally included in well-known fundamental principles in sustainable development, may be considered as offered options for the Iranian building environmental scoring system. It is suggested that one of the globally and commonly accepted systems is chosen considering national priorities in order to offer an effective method for buildings environmental scoring, and then, a part of credits is added and/or removed, or a certain credit score is changed, and eventually, a new scoring system with a new title is developed for the country. Evidently, building construction industry highly affects the environment, economy, efficiency, and health of the relevant occupants. Considering the growing trend of cities and construction, achieving building scoring systems based on environmental criteria has always been a matter of discussion. The existing systems cannot be used for all the regions due to several reasons, including but not limited to variety in regional variables.

Keywords: scoring system, sustainability assessment, water efficiency, national Iranian building code

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

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

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

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Authors: Snigdha Sahai, Devaki Chikkavenkatappa Yellappa

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This study outlines approaches for testing and validating stochastic models used in epidemiology, focusing on the integration and functional testing of simulation code. It details methods for combining simple functions into comprehensive simulations, distinguishing between deterministic and stochastic components, and applying tests to ensure robustness. Techniques include isolating stochastic elements, utilizing large sample sizes for validation, and handling special cases. Practical examples are provided using R code to demonstrate integration testing, handling of incorrect inputs, and special cases. The study emphasizes the importance of both functional and defensive programming to enhance code reliability and user-friendliness.

Keywords: computational epidemiology, epidemiology, public health, infectious disease modeling, statistical analysis, health data analysis, disease transmission dynamics, predictive modeling in health, population health modeling, quantitative public health, random sampling simulations, randomized numerical analysis, simulation-based analysis, variance-based simulations, algorithmic disease simulation, computational public health strategies, epidemiological surveillance, disease pattern analysis, epidemic risk assessment, population-based health strategies, preventive healthcare models, infection dynamics in populations, contagion spread prediction models, survival analysis techniques, epidemiological data mining, host-pathogen interaction models, risk assessment algorithms for disease spread, decision-support systems in epidemiology, macro-level health impact simulations, socioeconomic determinants in disease spread, data-driven decision making in public health, quantitative impact assessment of health policies, biostatistical methods in population health, probability-driven health outcome predictions

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Authors: Myung-Gon Yoon, Jung-Ho Moon, Tae Kwon Ha

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This paper considers a robust stabilization problem of a single agent in a multi-agent consensus system composed of identical agents, when the network topology of the system is completely unknown. It is shown that the transfer function of an agent in a consensus system can be described as a multiplicative perturbation of the isolated agent transfer function in frequency domain. Applying known robust stabilization results, we present sufficient conditions for a robust stabilization of an agent against unknown network topology.

Keywords: single agent control, multi-agent system, transfer function, graph angle

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Authors: Sara Boulmai̇z

Abstract:

In order to study the microbiological quality of chawarma sold in Biskra, a sampling through some fastfoods of the city was done, the parameters studied are highlighted according to the criteria required by the country's trade management. Microbiological analyzes revealed different levels of contamination by microorganisms. The 10 samples were of an overall view of unsatisfactory quality, and according to the standards, no sample was satisfactory. The range of total aerobic mesophilic flora found is between 105 and 1.2 × 10 7 CFU / g, that of fecal coliforms is 104 to 2.4 × 10 5 CFU / g. The suspected pathogenic staphylococci were between 3.103 and 2.7.106 CFU / g. Salmonellae were absent in all samples, whereas sulphite-reducing anaerobes were present in a single sample. The rate of E. cloacae was between 103 and 6.104 CFU / g. As for fungi and safe mice, their rate was 103 to 107 CFU / g. The study of the sensitivity of antibiotics showed multi-resistance to all the antibiotics tested, although there is a sensitivity towards others. All strains of Staphylococcus aureus tested demonstrated resistance against erythromycin, 30% against streptomycin, and 10% against tetracycline. While the strains of E. cloacae were resistant in all strains to amoxicillin, ceftazidime, cefotaxime, and erythromycin, while they were sensitive to fosfomycin, sulfamethoxazole trimethoperine, ciprofloxacin, and tetracycline. While against chlorophenicol and ofloxacin, the sensitivity was dominant, although there was intermediate resistance. In this study demonstrates that foodborne illnesses remain a problem that arises in addition to the increasingly observed bacterial resistance and that, after all, healthy eating is a right.

Keywords: chawarma, microbiological quality, pathogens., street food

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Authors: Echo Wang, Andrew Li

Abstract:

Transparency could be defined as the voluntary release of information by institutions that is relevant to their own evaluation. Transparency based on information disclosure is recognised to be vital for the Third Sector, as civil society organisations are under pressure to become more transparent to answer the call for accountability. The growing importance of social enterprises as hybrid organisations emerging from the nexus of the public, the private and the Third Sector makes their transparency a topic worth exploring. However, transparency for social enterprises has not yet been studied: as a new form of organisation that combines non-profit missions with commercial means, it is unclear to both the practical and the academic world if the shift in operational logics from non-profit motives to for-profit pursuits has significantly altered their transparency. This is especially so in China, where informational governance and practices of information disclosure by local governments, industries and civil society are notably different from other countries. This study investigates the transparency-seeking behaviour of social enterprises in Greater China to understand what factors at the organisational level may affect their transparency, measured by their willingness to disclose financial information. We make use of the Survey on the Models and Development Status of Social Enterprises in the Greater China Region (MDSSGCR) conducted in 2015-2016. The sample consists of more than 300 social enterprises from the Mainland, Hong Kong and Taiwan. While most respondents have provided complete answers to most of the questions, there is tremendous variation in the respondents’ demonstrated level of transparency in answering those questions related to the financial aspects of their organisations, such as total revenue, net profit, source of revenue and expense. This has led to a lot of missing data on such variables. In this study, we take missing data as data. Specifically, we use missing values as a proxy for an organisation’s level of transparency. Our dependent variables are constructed from missing data on total revenue, net profit, source of revenue and cost breakdown. In addition, we also take into consideration the quality of answers in coding the dependent variables. For example, to be coded as being transparent, an organization must report the sources of at least 50% of its revenue. We have four groups of predictors of transparency, namely nature of organization, decision making body, funding channel and field of concentration. Furthermore, we control for an organisation’s stage of development, self-identity and region. The results show that social enterprises that are at their later stages of organisational development and are funded by financial means are significantly more transparent than others. There is also some evidence that social enterprises located in the Northeast region in China are less transparent than those located in other regions probably because of local political economy features. On the other hand, the nature of the organisation, the decision-making body and field of concentration do not systematically affect the level of transparency. This study provides in-depth empirical insights into the information disclosure behaviour of social enterprises under specific social context. It does not only reveal important characteristics of Third Sector development in China, but also contributes to the general understanding of hybrid institutions.

Keywords: China, information transparency, organisational behaviour, social enterprise

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Authors: Muyatdinova Aigul, Aitkaliyeva Madina

Abstract:

The article deals with the evaluation of the quality of medical services on the basis of quality indicators. For this purpose allocated initially the features of the medical services market. The Features of the market directly affect on the evaluation process that takes a multi-level and multi-stakeholder nature. Unlike ordinary goods market assessment of medical services does not only market. Such an assessment is complemented by continuous internal and external evaluation, including experts and accrediting bodies. In the article highlighted the composition of indicators for a comprehensive evaluation

Keywords: health care market, quality of health services, indicators of care quality

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Authors: Arun Prasad Jaganathan

Abstract:

In today's dynamic industrial world, traditional task allocation methods often fall short in adapting to evolving operational conditions. This paper introduces an AI-enabled task assistance and management system designed to overcome the limitations of conventional approaches. By using artificial intelligence (AI) and machine learning (ML), the system intelligently interprets user instructions, analyzes tasks, and allocates resources based on real-time data and environmental factors. Additionally, geolocation tracking enables proactive identification of potential delays, ensuring timely interventions. With its transparent reporting mechanisms, the system provides stakeholders with clear insights into task progress, fostering accountability and informed decision-making. The paper presents a comprehensive overview of the system architecture, algorithm, and implementation, highlighting its potential to revolutionize task management across diverse industries.

Keywords: artificial intelligence, machine learning, task allocation, operational efficiency, resource optimization

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Authors: Kim Quy Le, Duan Fei, Jia Wei Chew, Jun Zeng, Maria Fabiola Leyva

Abstract:

In line with the sustainable development goal, molded fiber products play important roles in reducing plastic-based packaging. To fabricate molded fiber products, besides using conventional meshing tools, 3D printing is employed to manufacture the molded fiber screen. 3D printing technique allows printing molded fiber screens with complex geometry, flexible in pore size and shape. The 3D printed molded fiber screens are in the progress of investigation to improve the de-watering efficiency, fiber collection, mechanical strength, etc. In addition, the fiber distribution on the screen is also necessary to access the quality of the screen. Besides using experimental methods to capture the fiber distribution on screen, simulation also offers using tools to access the uniformity of fiber. In this study, the fiber was simulated using the multi-sphere model to simulate the fibers. The interaction of the fibers was able to mimic by employing the discrete element method. The fiber distribution was captured and compared to the experiment. The simulation results were able to reveal the fiber deposition layer upon layer and explain the formation of uneven thickness on the tilted area of molded fiber screen.

Keywords: 3D printing, multi-jet fusion, molded fiber screen, discrete element method

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Authors: Meiyun Meng

Abstract:

Urban China is now undergoing social transformation based on its rapid economic growth, developing its individualism and feminism. This paper approaches emergent relationships between female individuals’ everyday lives and urban China through internal migration, home-making practices and life-course perspectives. Focusing on Shenzhen, it explores how ten highly educated female migrants pursue aspirations of accommodating ‘non-majority’ identities, such as lesbians, divorced, or childless women, in urban China. Based on life stories and home video tours, this paper finds how these women develop non-majority lifestyles to negotiate their aspirations. On the one hand, they ‘sail’ away from past/present situations where collectivist and hetero-patriarchal norms marginalised their non-majority identities. On the other hand, they ‘anchor’ in places where ‘new’ socio-cultural contexts allow female individuals to pursue alternative opportunities and preferential lifestyles. This paper provides fresh insights to interpret the social transformation in urban China, under the collectivist culture and hetero-patriarchal norms, through the lens of individual everyday home-making practices.

Keywords: home-making practices, internal migration, highly educated women, shenzhen, transforming urban China

Procedia PDF Downloads 91