Search results for: smart beta

Authors: Septimia Sarbu

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The entropy rate of a stochastic process is a fundamental concept in information theory. It provides a limit to the amount of information that can be transmitted reliably over a communication channel, as stated by Shannon's coding theorems. Recently, researchers have focused on developing new measures of information that generalize Shannon's classical theory. The aim is to design more efficient information encoding and transmission schemes. This paper continues the study of generalized entropy rates, by deriving a closed-form solution to the Sharma-Mittal entropy rate for Gaussian processes. Using the squeeze theorem, we solve the limit in the definition of the entropy rate, for different values of alpha and beta, which are the parameters of the Sharma-Mittal entropy. In the end, we compare it with Shannon and Rényi's entropy rates for Gaussian processes.

Keywords: generalized entropies, Sharma-Mittal entropy rate, Gaussian processes, eigenvalues of the covariance matrix, squeeze theorem

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Authors: Jyoti Jha, Asim Tewari, Sushil Mishra

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The alpha-beta Titanium alloy (Ti-6Al-4V) is the most common alloy in the aerospace industry. The hot workability of Ti–6Al–4V has been investigated by means of hot compression tests carried out in the 750–950 °C temperature range and 0.001–10s-1 strain rate range. Stress-strain plot obtained from the Gleeble 3800 test results show the dynamic recrystallization at temperature 950 °C. The effect of microstructural characteristics of the deformed specimens have been studied and correlated with the test temperature, total strain and strain rate. Finite element analysis in DEFORM 2D has been carried out to see the effect of flow stress parameters in different zones of deformed sample. Dynamic recrystallization simulation based on Cellular automata has been done in DEFORM 2D to simulate the effect of hardening and recovery during DRX. Simulated results well predict the grain growth and DRX in the deformed sample.

Keywords: compression test, Cellular automata, DEFORM , DRX

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Authors: M. G. Ghanem, A. M. M. A. Allam, Diaa E. Fawzy, Mehmet Faruk Cengiz

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This paper is devoted to the design and implementation of a smartwatch textile antenna for Wi-Fi bio-medical applications in millimetric wave bands. The antenna is implemented on a leather textile-based substrate to be embedded in a smartwatch. It enables the watch to pick Wi-Fi signals without the need to be connected to a mobile through Bluetooth. It operates at 60 GHz or WiGig (Wireless Gigabit Alliance) band with a wide band for higher rate applications. It also could be implemented over many stratified layers of the body organisms to be used in the diagnosis of many diseases like diabetes and cancer. The structure is designed and simulated using CST (Studio Suite) program. The wearable patch antenna has an octagon shape, and it is implemented on leather material that acts as a flexible substrate with a size of 5.632 x 6.4 x 2 mm3, a relative permittivity of 2.95, and a loss tangent of 0.006. The feeding is carried out using differential feed (discrete port in CST). The work provides five antenna implementations; antenna without ground, a ground is added at the back of the antenna in order to increase the antenna gain, the substrate dimensions are increased to 15 x 30 mm2 to resemble the real hand watch size, layers of skin and fat are added under the ground of the antenna to study the effect of human body tissues human on the antenna performance. Finally, the whole structure is bent. It is found that the antenna can achieve a simulated peak realized gain in dB of 5.68, 7.28, 6.15, 3.03, and 4.37 for antenna without ground, antenna with the ground, antenna with larger substrate dimensions, antenna with skin and fat, and bent structure, respectively. The antenna with ground exhibits high gain; while adding the human organisms absorption, the gain is degraded because of human absorption. The bent structure contributes to higher gain.

Keywords: bio medical engineering, millimetric wave, smart watch, textile antennas, Wi-Fi

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Authors: Andrew Kirabira

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This study is an investigation into the effectiveness of climate-smart agriculture (CSA) technologies in improving productivity through managing biotic and abiotic stresses in the coffee agroecological zones of Uganda. The motive is to enhance farmer livelihoods. The study was initiated as a result of the decreasing productivity of the crop in Uganda caused by the increasing prevalence of pests, diseases and abiotic stresses. Despite 9 years of farmers’ application of CSA, productivity has stagnated between 700kg -800kg/ha/yr which is only 26% of the 3-5tn/ha/yr that CSA is capable of delivering if properly applied. This has negatively affected the incomes of the 10.6 million people along the crop value chain which has in essence affected the country’s national income. In 2019/20 FY for example, Uganda suffered a deficit of $40m out of singularly the increasing incidence of one pest; BCTB. The amalgamation of such trends cripples the realization of SDG #1 and #13 which are the eradication of poverty and mitigation of climate change, respectively. In probing CSA’s effectiveness in curbing such a trend, this study is guided by the objectives of; determining the existing farmers’ knowledge and perceptions of CSA amongst the coffee farmers in the diverse coffee agro-ecological zones of Uganda; examining the relationship between the use of CSA and prevalence of selected coffee pests, diseases and abiotic stresses; ascertaining the difference in the market organization and pricing between conventionally and CSA produced coffee; and analyzing the prevailing policy environment concerning the use of CSA in coffee production. The data collection research design is descriptive in nature; collecting data from farmers and agricultural extension workers in the districts of Ntungamo, Iganga and Luweero; each of these districts representing a distinct coffee agroecological zone. Policy custodian officers at district, cooperatives and at the crop’s overseeing national authority were also interviewed.

Keywords: climate change, food security, field stresses, Productivity

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.

Keywords: big data, machine learning, smart city, social cost, transportation network

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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 semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. It 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 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

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

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Authors: Anil Kumar Gupta, Dronadula Venkata Sai Praneeth, Brajesh Dubey, Arundhuti Devi, Suravi Kalita, Khanindra Sharma

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Increase in urbanization and industrialization have led to improve in the standard of living. However, at the same time, the challenges due to improper solid waste management are also increasing. Municipal Solid Waste management is considered as a vital step in the development of urban infrastructure. The present study focuses on developing a solid waste management plan for an urban area in a developing country. The current scenario of solid waste management practices at various urban bodies in India is summarized. Guwahati city in the northeastern part of the country and is also one of the targeted smart cities (under the governments Smart Cities program) was chosen as case study to develop and implement the solid waste management plan. The whole city was divided into various divisions and waste samples were collected according to American Society for Testing and Materials (ASTM) - D5231-92 - 2016 for each division in the city and a composite sample prepared to represent the waste from the entire city. The solid waste characterization in terms of physical and chemical which includes mainly proximate and ultimate analysis were carried out. Existing primary and secondary collection systems were studied and possibilities of enhancing the collection systems were discussed. The composition of solid waste for the overall city was found to be as: organic matters 38%, plastic 27%, paper + cardboard 15%, Textile 9%, inert 7% and others 4%. During the conference presentation, further characterization results in terms of Thermal gravimetric analysis (TGA), pH and water holding capacity will be discussed. The waste management options optimizing activities such as recycling, recovery, reuse and reduce will be presented and discussed.

Keywords: proximate, recycling, thermal gravimetric analysis (TGA), solid waste management

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Authors: Belouadah Rabah, Guyomar Daneil, Guiffard Benoit

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The magnetoelectric (ME) materials has dielectric polarization induced by the magnetic field or induced magnetization under an electric field. A strong ME effect requires the simultaneous presence of magnetic moments and electric dipoles. In the last decades, extensive research has been conducted on the ME effect in single phase and composite materials. This article reported the results obtained with two samples, the first is mono layer of PVDF bi-stretched and the second is the multi layer PVDF bi-stretched with the Polyurethane filled with micro particles magnetic Fe3O4 (PU+2% Fe3O4). Compare with non ME material like Alumine, a large ME polarization coefficient for the two samples was obtained. The piezoelectric properties of the PVDF and elastic proprieties of Pu+2% Fe3O4 give a big linear ME coefficient of the multi layer PVDF/(Pu+2% Fe3O4) than in the monolayer of PVDF.

Keywords: magnetoelectric effect, polymers, magnetic particles, composites, films

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Authors: Amira Abdelrasoul

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This study aimed to ascertain the controlled permeability of biomimetic cellulose triacetate (CTA) membranes by investigating the electrical oscillatory behavior across impregnated membranes (IM). The biomimetic CTA membranes were infused with a fatty acid to induce electrical oscillatory behavior and, hence, to ensure controlled permeability. In situ synchrotron radiation micro-computed tomography (SR-μCT) at the BioMedical Imaging and Therapy (BMIT) Beamline at the Canadian Light Source (CLS) was used to evaluate the main morphology of IMs compared to neat CTA membranes to ensure fatty acid impregnation inside the pores of the membrane matrices. A monochromatic beam at 20 keV was used for the visualization of the morphology of the membrane. The X-ray radiographs were recorded by means of a beam monitor AA-40 (500 μm LuAG scintillator, Hamamatsu, Japan) coupled with a high-resolution camera, providing a pixel size of 5.5 μm and a field of view (FOV) of 4.4 mm × 2.2 mm. Changes were evident in the phase transition temperatures of the impregnated CTA membrane at the melting temperature of the fatty acid. The pulsations of measured voltages were related to changes in the salt concentration of KCl in the vicinity of the electrode. Amplitudes and frequencies of voltage pulsations were dependent on the temperature and concentration of the KCl solution, which controlled the permeability of the biomimetic membranes. The presented smart biomimetic membrane successfully combined porous polymer support and impregnating liquid not only imitate the main barrier properties of the biological membranes but could be easily modified to achieve some new properties, such as facilitated and active transport, regulation by chemical, physical and pharmaceutical factors. These results open new frontiers for the facilitation and regulation of active transport and permeability through biomimetic smart membranes for a variety of biomedical and drug delivery applications.

Keywords: biomimetic, membrane, synchrotron, permeability, morphology

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Authors: Samira Kalantary, Sara Taghipour, Mansoure Ghias Abadi

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The Internet of Things (IOT) is a technological revolution that represents the future of computing and communications. IOT is the convergence of Internet with RFID, NFC, Sensor, and smart objects. Fog Computing is the natural platform for IOT. At present, the IOT as a new network communication technology has rapidly shifted from concept to application under fog computing virtual storage computing platform. In this paper, we describe everything about IOT and difference between cloud computing and fog computing.

Keywords: cloud computing, fog computing, Internet of Things (IoT), IOT application

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Authors: Sakshi Singh, Shampa Chakraverty

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One of the essential characteristics of Blockchain is the ability to validate the integrity of new transactions added to the Blockchain. Moreover, one of the essential consensus algorithms, Proof of Work, performs this job. In this work, we implemented the Proof of Work consensus method on the block formed by performing the transaction using Ganache. The primary goal of this implementation is to understand the process and record how Proof of Work works in reality on newly created blocks.

Keywords: proof of work, blockchain, ganache, smart contract

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Authors: Yael Neumann

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Attention deficits are common in people with aphasia (PWA). Two treatment approaches address these deficits: domain-general methods like Play Attention, which focus on cognitive functioning, and domain-specific methods like Language-Specific Attention Treatment (L-SAT), which use linguistically based tasks. Research indicates that L-SAT can improve both attentional deficits and functional language skills, while Play Attention has shown success in enhancing attentional capabilities among school-aged children with attention issues compared to standard cognitive training. This study employed a randomized controlled cross-over single-subject design to evaluate the effectiveness of these two attention treatments over 25 weeks. Four PWA participated, undergoing a battery of eight standardized tests measuring language and cognitive skills. The treatments were counterbalanced. Play Attention used EEG sensors to detect brainwaves, enabling participants to manipulate items in a computer game while learning to suppress theta activity and increase beta activity. An algorithm tracked changes in the theta-to-beta ratio, allowing points to be earned during the games. L-SAT, on the other hand, involved hierarchical language tasks that increased in complexity, requiring greater attention from participants. Results showed that for language tests, Participant 1 (moderate aphasia) aligned with existing literature, showing L-SAT was more effective than Play Attention. However, Participants 2 (very severe) and 3 and 4 (mild) did not conform to this pattern; both treatments yielded similar outcomes. This may be due to the extremes of aphasia severity: the very severe participant faced significant overall deficits, making both approaches equally challenging, while the mild participant performed well initially, leaving limited room for improvement. In attention tests, Participants 1 and 4 exhibited results consistent with prior research, indicating Play Attention was superior to L-SAT. Participant 2, however, showed no significant improvement with either program, although L-SAT had a slight edge on the Visual Elevator task, measuring switching and mental flexibility. This advantage was not sustained at the one-month follow-up, likely due to the participant’s struggles with complex attention tasks. Participant 3's results similarly did not align with prior studies, revealing no difference between the two treatments, possibly due to the challenging nature of the attention measures used. Regarding participation and ecological tests, all participants showed similar mild improvements with both treatments. This limited progress could stem from the short study duration, with only five weeks allocated for each treatment, which may not have been enough time to achieve meaningful changes affecting life participation. In conclusion, the performance of participants appeared influenced by their level of aphasia severity. The moderate PWA’s results were most aligned with existing literature, indicating better attention improvement from the domain-general approach (Play Attention) and better language improvement from the domain-specific approach (L-SAT).

Keywords: attention, language, cognitive rehabilitation, neurofeedback

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Authors: Moshmi Sangavarapu

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The US healthcare industry has undergone tremendous digital transformation in recent years, but critical care access to lower-income ethnicities is still in its nascency. This population has historically showcased substantial hesitation to seek any medical assistance. While the lack of sufficient financial resources plays a critical role, the existing cultural and knowledge barriers also contribute significantly to widening the access gap. It is imperative to break these barriers to ensure timely access to therapeutic procedures that can save important lives! Based on ongoing research, healthcare access barriers can be best addressed by tapping the untapped potential of caregiver communities first. They play a critical role in patients’ diagnoses, building healthcare knowledge and instilling confidence in required therapeutic procedures. Recent technological advancements have opened many avenues by developing smart ways of reaching the large caregiver community. A digitized go-to-market strategy featuring connected media coupled with smart IoT devices and geo-location targeting can be collectively leveraged to reach this key audience group. AI/ML algorithms can be thoroughly trained to identify relevant data signals from users' location and browsing behavior and determine useful marketing touchpoints. The web behavior can be further assimilated with natural language processing to identify contextually relevant interest topics and decipher potential caregivers on digital avenues to serve that brand message. In conclusion, grasping the true health access journey of any lower-income ethnic group is important to design beneficial touchpoints that can alleviate patients’ concerns and allow them to break their own access barriers and opt for timely and quality healthcare.

Keywords: healthcare access, market access, diversity barriers, patient journey

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Authors: Pratap Chandra Pati, Prabina Rajib, Parama Barai

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The traditional finance theory neglects the role of sentiment factor in asset pricing. However, the behavioral approach to asset-pricing based on noise trader model and limit to arbitrage includes investor sentiment as a priced risk factor in the assist pricing model. Investor sentiment affects stock more that are vulnerable to speculation, hard to value and risky to arbitrage. It includes small stocks, high volatility stocks, growth stocks, distressed stocks, young stocks and non-dividend-paying stocks. Since the introduction of Chicago Board Options Exchange (CBOE) volatility index (VIX) in 1993, it is used as a measure of future volatility in the stock market and also as a measure of investor sentiment. CBOE VIX index, in particular, is often referred to as the ‘investors’ fear gauge’ by public media and prior literature. The upward spikes in the volatility index are associated with bouts of market turmoil and uncertainty. High levels of the volatility index indicate fear, anxiety and pessimistic expectations of investors about the stock market. On the contrary, low levels of the volatility index reflect confident and optimistic attitude of investors. Based on the above discussions, we investigate whether market-wide fear levels measured volatility index is priced factor in the standard asset pricing model for the Indian stock market. First, we investigate the performance and validity of Fama and French three-factor model and Carhart four-factor model in the Indian stock market. Second, we explore whether India volatility index as a proxy for fearful market-based sentiment indicators affect the cross section of stock returns after controlling for well-established risk factors such as market excess return, size, book-to-market, and momentum. Asset pricing tests are performed using monthly data on CNX 500 index constituent stocks listed on the National stock exchange of India Limited (NSE) over the sample period that extends from January 2008 to March 2017. To examine whether India volatility index, as an indicator of fear sentiment, is a priced risk factor, changes in India VIX is included as an explanatory variable in the Fama-French three-factor model as well as Carhart four-factor model. For the empirical testing, we use three different sets of test portfolios used as the dependent variable in the in asset pricing regressions. The first portfolio set is the 4x4 sorts on the size and B/M ratio. The second portfolio set is the 4x4 sort on the size and sensitivity beta of change in IVIX. The third portfolio set is the 2x3x2 independent triple-sorting on size, B/M and sensitivity beta of change in IVIX. We find evidence that size, value and momentum factors continue to exist in Indian stock market. However, VIX index does not constitute a priced risk factor in the cross-section of returns. The inseparability of volatility and jump risk in the VIX is a possible explanation of the current findings in the study.

Keywords: India VIX, Fama-French model, Carhart four-factor model, asset pricing

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Authors: Zahra Mohajer, Afsaneh Soltani

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Doping is a major issue in competitive sports and is favored by vast groups of athletes. The feeling of being higher-ranking than others and gaining fame has caused many athletes to misuse drugs. The definition of doping is to use prohibited substances and/or methods that help physical or mental performances or both. Doping counts as the illegal use of chemical substances or drugs, excessive amounts of physiological substances to increase the performance at or out of competition or even the use of inappropriate medications to treat an injury to gain the ability to participate in a competition. The International Olympic Committee (IOC) and World Anti-Doping Agency (WADA) have forbidden these substances to ensure fair and equal competition and also the health of the competitors. As of 2004 WADA has published an international list of illegal substances used for doping, which is updated annually. In the process of the Genome Project scientists have gained the ability to treat numerous diseases by gene therapy, which may result in bodily performance increase and therefore a potential opportunity to misuse by some athletes. Gene doping is defined as the non-therapeutic direct and indirect genetic modifications using genetic materials that can improve the performances in sports events. Biosynthetic drugs are a form of indirect genetic engineering. The method can be performed in three ways such as injecting the DNA directly into the muscle, inserting the genetically engineered cells, or transferring the DNA using a virus as a vector. Erythropoietin is a hormone majorly released by the kidney and in small amounts by the liver. Its function is to stimulate the erythropoiesis and therefore the more production of red blood cells (RBC) which causes an increase in Hemoglobin (Hb). During this process, the oxygen delivery to muscles will increase, which will improve athletic performance and postpone exhaustion. There are ways to increase the oxygen transferred to muscles such as blood transfusion, stimulating the production of red blood cells by using Erythropoietin (EPO), and also using allosteric effectors of Hemoglobin. EPO can either be injected as a protein or can be inserted into the cells as the gene which encodes EPO. Adeno-associated viruses have been employed to deliver the EPO gene to the cells. Employing the genes that naturally exist in the human body such as the EPO gene can reduce the risk of detecting gene doping. The first research about blood doping was conducted in 1947. The study has shown that an increase in hematocrit (HCT) up to 55% following homologous transfusion makes it more unchallenging for the body to perform the exercise at the altitude. Thereafter athletes’ attraction to blood infusion escalated. Also, a study has demonstrated that by reinfusing their own blood 4 weeks after being drawn, three men have shown a rise in Hb level which improved the oxygen uptake, and a delay in exhaustion. The list of performance-enhancing drugs is published by WADA annually and includes the following drugs: anabolic agents, hormones, Beta-2 agonists, Beta-blockers, Diuretics, Stimulants, narcotics, cannabinoids, and corticosteroids.

Keywords: doping, PEDs, sports, WADA

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Authors: Laura Çami (Vorpsi), Xhon Skënderi

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In today's rapidly evolving technological landscape, the traditional principles of contract law are facing significant challenges. The emergence of new technologies, such as electronic signatures, smart contracts, and online dispute resolution mechanisms, is transforming the way contracts are formed, interpreted, and enforced. This paper examines the implications of these technological advancements on the constitutional principles of contract law. One of the fundamental principles of contract law is freedom of contract, which ensures that parties have the autonomy to negotiate and enter into contracts as they see fit. However, the use of technology in the contracting process has the potential to disrupt this principle. For example, online platforms and marketplaces often offer standard-form contracts, which may not reflect the specific needs or interests of individual parties. This raises questions about the equality of bargaining power between parties and the extent to which parties are truly free to negotiate the terms of their contracts. Another important principle of contract law is the requirement of consideration, which requires that each party receives something of value in exchange for their promise. The use of digital assets, such as cryptocurrencies, has created new challenges in determining what constitutes valuable consideration in a contract. Due to the ambiguity in this area, disagreements about the legality and enforceability of such contracts may arise. Furthermore, the use of technology in dispute resolution mechanisms, such as online arbitration and mediation, may raise concerns about due process and access to justice. The use of algorithms and artificial intelligence to determine the outcome of disputes may also raise questions about the impartiality and fairness of the process. Finally, it should be noted that there are many different and complex effects of technical improvements on the fundamental constitutional foundations of contract law. As technology continues to evolve, it will be important for policymakers and legal practitioners to consider the potential impacts on contract law and to ensure that the principles of fairness, equality, and access to justice are preserved in the contracting process.

Keywords: technological advancements, constitutional principles, contract law, smart contracts, online dispute resolution, freedom of contract

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Authors: Atakan Aral Ormancı, Tuğçe Arslantaş, Murat Özcü

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

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

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Authors: B. Karabuluter, O. Karaduman

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Traffic management and traffic planning are important issues, especially in big cities. Due to the increase of personal vehicles and the physical constraints of urban roads, the problem of transportation especially in crowded cities over time is revealed. This situation reduces the living standards, and it can put human life at risk because the vehicles such as ambulance, fire department are prevented from reaching their targets. Even if the city planners take these problems into account, emergency planning and traffic management are needed to avoid cases such as traffic congestion, intersections, traffic jams caused by traffic accidents or roadworks. In this study, in smart traffic management issues, proposed solutions using intelligent vehicles acting in cooperation with urban roads are examined. Traffic management is becoming more difficult due to factors such as fatigue, carelessness, sleeplessness, social behavior patterns, and lack of education. However, autonomous vehicles, which remove the problems caused by human weaknesses by providing driving control, are increasing the success of practicing the algorithms developed in city traffic management. Such intelligent vehicles have become an important solution in urban life by using 'swarm intelligence' algorithms and cooperative driving methods to provide traffic flow, prevent traffic accidents, and increase living standards. In this study, studies conducted in this area have been dealt with in terms of traffic jam, intersections, regulation of traffic flow, signaling, prevention of traffic accidents, cooperation and communication techniques of vehicles, fleet management, transportation of emergency vehicles. From these concepts, some taxonomies were made out of the way. This work helps to develop new solutions and algorithms for cities where intelligent vehicles that can perform cooperative driving can take place, and at the same time emphasize the trend in this area.

Keywords: intelligent traffic management, cooperative driving, smart driving, urban road, swarm intelligence, connected vehicles

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Authors: Tunde Puskas, Anita Andersson

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In Swedish early childhood education research, the issues related to religious identifications and practices have often been marginalized or relegated either to the realm of diversity and multiculturalism or to the realm of national traditions. This paper is part of a research project about whether religion is considered as part of Swedish cultural heritage in Swedish preschools. Our aim in this paper is to explore how a Swedish preschool balance between keeping the education non-confessional and at the same time introducing the traditions associated with advent and Christmas. Christmas was chosen because of the religious background of the holiday and because it is a tradition widely celebrated in Swedish preschools. In Swedish education system, the concept of freedom of religion is understood in the sense that education is declared to be non-confessional. Nevertheless, as the major state holidays in Sweden are tied to Lutheran Christian traditions, and according to the curriculum preschool educators, are given the task to pass on a cultural heritage, defined in terms of values, traditions, history, language, and knowledge, it is the preschool teams or individual preschool teachers who determine whether and to what extent religious considerations are/ought to be seen as part of the cultural heritage the preschool passes on. The data consists of ten video taped observations from two preschools. The video data was transcribed and the transcripts were thereafter analysed through content analysis. In the analysis, we draw on the concept of banal religiosity that has helped us to draw attention to the workings of religious considerations that are so familiar that they rarely are noticed as religious and on Ninian Smart’s theory on the dimensions of religion. The analysis shows that what the adults actually do with religion fulfils six of seven dimensions common to religious traditions as outlined by Smart. At the same time, Christmas is performed as a lived tradition within which the commercial and religious rituals intersect and result in a banal, national religiosity.

Keywords: secular advent, banal religiosity, dimensions of religion, rites

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Authors: Muhammad Isa Khan, Jalil Ur Rehman, Muhammad Afzal Khan Rao, James Chow

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Purpose: This study investigates the dose-volume variations in planning target volume (PTV) and organs-at-risk (OARs) using different collimator angles for smart arc prostate volumetric modulated arc therapy (VMAT). Awareness of the collimator angle for PTV and OARs sparing is essential for the planner because optimization contains numerous treatment constraints producing a complex, unstable and computationally challenging problem throughout its examination of an optimal plan in a rational time. Materials and Methods: Single arc VMAT plans at different collimator angles varied systematically (0°-90°) were performed on a Harold phantom and a new treatment plan is optimized for each collimator angle. We analyzed the conformity index (CI), homogeneity index (HI), gradient index (GI), monitor units (MUs), dose-volume histogram, mean and maximum doses to PTV. We also explored OARs (e.g. bladder, rectum and femoral heads), dose-volume criteria in the treatment plan (e.g. D30%, D50%, V30Gy and V38Gy of bladder and rectum; D5%,V14Gy and V22Gy of femoral heads), dose-volume histogram, mean and maximum doses for smart arc VMAT at different collimator angles. Results: There was no significance difference found in VMAT optimization at all studied collimator angles. However, if 0.5% accuracy is concerned then collimator angle = 45° provides higher CI and lower HI. Collimator angle = 15° also provides lower HI values like collimator angle 45°. It is seen that collimator angle = 75° is established as a good for rectum and right femur sparing. Collimator angle = 90° and collimator angle = 30° were found good for rectum and left femur sparing respectively. The PTV dose coverage statistics for each plan are comparatively independent of the collimator angles. Conclusion: It is concluded that this study will help the planner to have freedom to choose any collimator angle from (0°-90°) for PTV coverage and select a suitable collimator angle to spare OARs.

Keywords: VMAT, dose-volume histogram, collimator angle, organs-at-risk

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Authors: Getaneh Berie Tarekegn

Abstract:

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

Procedia PDF Downloads 71

Authors: Metwally Abdel Aziz Ahmed, Ahmed Shaban Abdel Hay Gabr, Inas Mohamed Saleh

Abstract:

The main objective of structural design is to ensure safety and functional performance requirements of a structural system for its target reliability levels. In this study, the reliability index for the reinforcement concrete slender columns with rectangular cross section is studied. The variable parameters studied include the loads, the concrete compressive strength, the steel yield strength, the dimensions of concrete cross-section, the reinforcement ratio, and the location of steel placement. Risk analysis program was used to perform the analytical study. The effect of load eccentricity on the reliability index of reinforced concrete slender column was studied and presented. The results of this study indicate that the good quality control improve the performance of slender reinforced columns through increasing the reliability index β.

Keywords: reliability, reinforced concrete, safety, slender column

Procedia PDF Downloads 453

Authors: Getaneh Berie Tarekegn

Abstract:

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

Procedia PDF Downloads 74

Authors: Jafar Ahmadi, Nafiseh Noormohammadi, Sedegeh Fabriki Ourang

Abstract:

GRF, Growth regulating factor, genes encode a novel class of plant-specific transcription factors. The GRF proteins play a role in the regulation of cell numbers in young and growing tissues and may act as transcription activations in growth and development of plants. Identification of GRF genes and their expression are important in plants to performance of the growth and development of various organs. In this study, to better understanding the structural and functional differences of GRFs family, 45 GRF proteins sequences in A. thaliana, Z. mays, O. sativa, B. napus, B. rapa, H. vulgare, and S. bicolor, have been collected and analyzed through bioinformatics data mining. As a result, in secondary structure of GRFs, the number of alpha helices was more than beta sheets and in all of them QLQ domains were completely in the biggest alpha helix. In all GRFs, QLQ, and WRC domains were completely protected except in AtGRF9. These proteins have no trans-membrane domain and due to have nuclear localization signals act in nuclear and they are component of unstable proteins in the test tube.

Keywords: domain, gene family, GRF, motif

Procedia PDF Downloads 457

Authors: Jaheon Gu, Wooyong Chung, Mijung Koo, Seonbok Lee, Gyoutae Park, Sangguk Ahn, Hiesik Kim, Jungil Park

Abstract:

In this paper, we present a portable ammonia gas detector for performing the gas safety management efficiently. The display of the detector is separated from its body. The display module is received the data measured from the detector using ZigBee. The detector has a rechargeable li-ion battery which can be use for 11~12 hours, and a Bluetooth module for sending the data to the PC or the smart devices. The data are sent to the server and can access using the web browser or mobile application. The range of the detection concentration is 0~100ppm.

Keywords: ammonia, detector, gas, portable

Procedia PDF Downloads 415

Authors: Karen B. Ghazaryan

Abstract:

Recently, the propagation of coupled electromagnetic and elastic waves in magneto-electro-elastic (MEE) structures attracted much attention due to the wide range of application of these materials in smart structures. MEE materials are a class of new artificial composites that consist of simultaneous piezoelectric and piezomagnetic phases. Magneto-electro-elastic composites are built up by combining piezoelectric and piezomagnetic phases to obtain a smart composite that presents not only the electromechanical and magneto-mechanical coupling but also a strong magnetoelectric coupling, which makes such materials highly valuable in technological usage. In the framework of quasi-static approach shear surface and localized waves are considered in magneto-electro-elastic piezo-active structure consisting of functionally graded 6mm hexagonal symmetry group crystals. Assuming that in a functionally graded material the elastic and electromagnetic properties vary in the same proportion in direction perpendicular to the MEE polling direction, special classes of inhomogeneity functions were found, admitting exact solutions for coupled electromagnetic and elastic wave fields. Based on these exact solutions, defining the coupled shear wave field in magneto-electro-elastic composites several modal problems are considered: shear surface waves propagation along surface of a MEE half-space, interfacial wave propagation in a MEE oppositely polarized bi-layer, Love type waves in a functionally graded MEE layer overlying a homogeneous elastic half-space. For the problems under consideration corresponding dispersion equations are deduced analytically in an explicit form and for the BaTiO₃–CoFe₂O₄ crystal numerical results estimating effects of inhomogeneity and piezo effect are carried out.

Keywords: surface shear waves, magneto-electro-elastic composites, piezoactive crystals, functionally graded elastic materials

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Authors: Maria Kralova, Michal Palecek

Abstract:

We have detected factors leading to recidivism (the Czech Republic data). The employment during imprisonment turned out to be the most significant predictor with a positive effect on reduction of a rate of recidivism. Accordingly, we mainly focus on this predictor and its economic consequences. Smart public policy can cut government costs dramatically as more than a half of prisoners in the Czech Republic are recidivists. The operating cost cut of the Czech prison service could be CZK 127,680,000 (USD 5,889,623) in 2013 if a public policy had been set smarter.

Keywords: cost-cut, effective, optimal, public policy, reducing recidivism

Procedia PDF Downloads 529

Authors: Arun Dhakal, Geoff Cockfield

Abstract:

This paper examines the emission potential of different farming practices that the farmers have adopted in Dhanusha District of Nepal and scope of these practices in climate change mitigation. Which practice is more climate-smarter is the question that this aims to address through a life cycle assessment (LCA) of greenhouse gas (GHG) emissions. The LCA was performed to assess if there is difference in emission potential of broadly two farming systems (agroforestry–based and traditional agriculture) but specifically four farming systems. The required data for this was collected through household survey of randomly selected households of 200. The sources of emissions across the farming systems were paddy cultivation, livestock, chemical fertilizer, fossil fuels and biomass (fuel-wood and crop residue) burning. However, the amount of emission from these sources varied with farming system adopted. Emissions from biomass burning appeared to be the highest while the source ‘fossil fuel’ caused the lowest emission in all systems. The emissions decreased gradually from agriculture towards the highly integrated agroforestry-based farming system (HIS), indicating that integrating trees into farming system not only sequester more carbon but also help in reducing emissions from the system. The annual emissions for HIS, Medium integrated agroforestry-based farming system (MIS), LIS (less integrated agroforestry-based farming system and subsistence agricultural system (SAS) were 6.67 t ha-1, 8.62 t ha-1, 10.75 t ha-1 and 17.85 t ha-1 respectively. In one agroforestry cycle, the HIS, MIS and LIS released 64%, 52% and 40% less GHG emission than that of SAS. Within agroforestry-based farming systems, the HIS produced 25% and 50% less emissions than those of MIS and LIS respectively. Our finding suggests that a tree-based farming system is more climate-smarter than a traditional farming. If other two benefits (carbon sequestered within the farm and in the natural forest because of agroforestry) are to be considered, a considerable amount of emissions is reduced from a climate-smart farming. Some policy intervention is required to motivate farmers towards adopting such climate-friendly farming practices in developing countries.

Keywords: life cycle assessment, greenhouse gas, climate change, farming systems, Nepal

Procedia PDF Downloads 619

Authors: Yacoub Aljaffery

Abstract:

As much as we think negatively about social media such as TV and smart phones, there are many positive benefits our society can get from it. Cartoons, for example, are made specifically for children. However, in this paper, we will prove how cartoon videos can have a positive impact on adults, especially college students. Since cartoons are meant to be a good learning tool for children, as well as adults, we will show our audience how they can use cartoon in teaching critical thinking and other language skills.

Keywords: social media, TV, teaching, learning, cartoon movies

Procedia PDF Downloads 324

Authors: Afsane Amiri

Abstract:

Electric Vehicle (EV) technology is expected to take a major share in the light-vehicle market in the coming decades. Charging of EVs will put an extra burden on the distribution grid and in some cases adjustments will need to be made. In this paper a review of different plug-in and vehicle to grid (V2G) capable vehicles are given along with their power electronics topologies. The economic implication of charging the vehicle or sending power back to the utility is described in brief.

Keywords: energy storage system, battery unit, cost, optimal sizing, plug-in electric vehicles (PEVs), smart grid

Procedia PDF Downloads 600