Search results for: smart hybrid powerpack (SHP)

Authors: Mike Dee

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Urban public spaces are sutured with a range of surveillance and sensor technologies that claim to enable new forms of ‘data based citizen participation’, but also increase the tendency for ‘function-creep’, whereby vast amounts of data are gathered, stored and analysed in a broad application of urban surveillance. This kind of monitoring and capacity for surveillance connects with attempts by civic authorities to regulate, restrict, rebrand and reframe urban public spaces. A direct consequence of the increasingly security driven, policed, privatised and surveilled nature of public space is the exclusion or ‘unfavourable inclusion’ of those considered flawed and unwelcome in the ‘spectacular’ consumption spaces of many major urban centres. In the name of urban regeneration, programs of securitisation, ‘gentrification’ and ‘creative’ and ‘smart’ city initiatives refashion public space as sites of selective inclusion and exclusion. In this context of monitoring and control procedures, in particular, children and young people’s use of space in parks, neighbourhoods, shopping malls and streets is often viewed as a threat to the social order, requiring various forms of remedial action. This paper suggests that cities, places and spaces and those who seek to use them, can be resilient in working to maintain and extend democratic freedoms and processes enshrined in Marshall’s concept of citizenship, calling sensor and surveillance systems to account. Such accountability could better inform the implementation of public policy around the design, build and governance of public space and also understandings of urban citizenship in the sensor saturated urban environment.

Keywords: citizenship, public space, surveillance, young people

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Authors: Eugenija Strazdiene, Violeta Bytautaite, Daivute Krisciuniene

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The object of the investigation is the suit for chemical protection, which totally covers human body together with breathing apparatus, breathing mask and helmet (JSC Ansell Protective Solutions Lithuania). The end users of such clothing are the members of rescue team – firefighters. During the presentation, the results of 3D scanning with stationary Human Solutions scanner and portable Artec Eva scanner will be compared on the basis of the efficiency of scanning procedure and scanning accuracy. Also, the possibilities to exporting scanned bodies into specialized CAD systems for suit design development and material consumption calculation will be analyzed. The necessity to understand and to implement corresponding clothing material properties during 3D visualization of garment on CAD systems will be presented. During the presentation, the outcomes of the project ‘Smart and Safe Work Wear Clothing SWW’ will be discussed. The project is carried out under the Interreg Baltic Sea Region Program as 2014-2020 European territorial cooperation objective. Thematic priority is Capacity for Innovation. The main goal of the project is to improve competitiveness and to increase business possibilities for work wear enterprises in the Baltic Sea Region. The project focuses on mass customization of products for various end users. It engages textile and clothing manufacturing technology researchers, work wear producers, end users, as well as national textile and clothing branch organizations in Finland, Lithuania, Latvia, Estonia and Poland.

Keywords: CAD systems, mass customization, 3D scanning, safe work wear

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Authors: Ahmed. A. Hossam Eldin, Karim H. Youssef, Kareem M. AboRas

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Recent current rapid industrial development and energy shortage are essential problems, which face most of the developing countries. Moreover, increased prices of fossil fuel and advanced energy conversion technology lead to the need for renewable energy resources. A study, modelling and simulation of an outdoor micro scale stand alone wind turbine was carried out. For model validation an experimental study was applied. In this research the aim was to clarify effects of real outdoor operating conditions and the instantaneous fluctuations of both wind direction and wind speed on the actual produced power. The results were compared with manufacturer’s data. The experiments were carried out in Borg Al-Arab, Alexandria. This location is on the north Western Coast of Alexandria. The results showed a real max output power for outdoor micro scale wind turbine, which is different from manufacturer’s value. This is due to the fact that the direction of wind speed is not the same as that of the manufacturer’s data. The measured wind speed and direction by the portable metrological weather station anemometer varied with time. The blade tail response could not change the blade direction at the same instant of the wind direction variation. Therefore, designers and users of micro scale wind turbine stand alone system cannot rely on the maker’s name plate data to reach the required power.

Keywords: micro-turbine, wind turbine, inverters, renewable energy, hybrid system

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Authors: Raffaello Furlan

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In the 21st century, the sustainable urban development of GCC-cities is challenged by inhabitants’ over-dependency on private-use vehicles. In turn, this habit has generated problems of urban inefficiency, contributing to traffic congestion, pollution, urban sprawling, fragmentation of the urban fabric, and various environmental and social challenges. In the context of Doha, the capital city of the State of Qatar, the over-dependency on private-use vehicles is justified by the lack of alternative public modes of transportation that support the need to connect fragmented urban districts and provide an effective solution to urban sprawl. Therefore, the current construction of the Qatar Metro Rail is offering the potential for investigating and defining a strategy for the sustainable urban development and/or urban regeneration of transit villages (TODs) in Qatar. Namely, the aim of this research study is (i) to investigate the development of transit villages (TODs) in the cultural-heritage district of Msheireb, Downtown Doha, (ii) to explore how the introduction of the new public transport system of Doha Metro can be effectively utilized as means of urban regeneration of the cultural core of the city, (iii) to propose a masterplan for TOD suitable for the district, suiting and responding to regional cultural and societal values. The findings reveal that the strategies for the sustainable urban regeneration of Msheireb are based on (i) the integration of land-use and multimodal transportation systems, (ii) the implementation of the public realm, and (iii) conservation of culture and urban identity.

Keywords: sustainable urbanism, smart growth, TODs, cultural district, Msheireb Downtown Doha

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Authors: Yilei Xue, Yat-Hing Ham, Wenting Qiu, Wan Chan, Stefan Nagl

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Polydopamine (PDA) is a popular material in biological and medical applications due to its excellent biocompatibility, outstanding physicochemical properties, and facile fabrication. In this project, a new sandwich-structured PDA and silver (Ag) hybrid material named PDA-Ag-PDA was synthesized and characterized layer-by-layer, where silver nanoparticles (Ag NPs) are wrapped in PDA coatings, using SEM, AFM, 3D surface metrology, and contact angle meter. The silver loading capacity is positively proportional to the roughness value of the initial PDA film. This designed film was subsequently integrated within a digital microfluidic (DMF) platform coupling with an oxygen sensor layer for on-chip antibacterial assay. The concentration of E. coli was quantified on DMF by real-time monitoring oxygen consumption during E. coli growth with the optical oxygen sensor layer. The PDA-Ag-PDA coating shows an 99.9% reduction in E. coli population under non-nutritive condition with 1-hour treatment and has a strong growth inhibition of E. coliin nutrient LB broth as well. Furthermore, PDA-Ag-PDA film maintaining a low cytotoxicity effect to human cells. After treating with PDA-Ag-PDA film for 24 hours, 82% HEK 293 and 86% HeLa cells were viable. The SERS enhancement factor of PDA-Ag-PDA is estimated to be 1.9 × 104 using Rhodamine 6G (R6G). Multifunctional PDA-Ag-PDA coating provides an alternative platform to conjugate biomolecules and perform biological applications on DMF, in particular, for the adhesive protein and cell study.

Keywords: polydopamine, silver nanoparticles, digital microfluidic, optical sensor, antimicrobial assay, SERS

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Authors: Meziane Rachid, Boufala Seddik, Hamzi Amar, Amara Mohamed

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Today’s energy industry seeks maximum benefit with maximum reliability. In order to achieve this goal, design engineers depend on reliability optimization techniques. This work uses a harmony search algorithm (HS) meta-heuristic optimization method to solve the problem of wind-Solar-Gas power systems design optimization. We consider the case where redundant electrical components are chosen to achieve a desirable level of reliability. The electrical power components of the system are characterized by their cost, capacity and reliability. The reliability is considered in this work as the ability to satisfy the consumer demand which is represented as a piecewise cumulative load curve. This definition of the reliability index is widely used for power systems. The proposed meta-heuristic seeks for the optimal design of series-parallel power systems in which a multiple choice of wind generators, transformers and lines are allowed from a list of product available in the market. Our approach has the advantage to allow electrical power components with different parameters to be allocated in electrical power systems. To allow fast reliability estimation, a universal moment generating function (UMGF) method is applied. A computer program has been developed to implement the UMGF and the HS algorithm. An illustrative example is presented.

Keywords: reliability optimization, harmony search optimization (HSA), universal generating function (UMGF)

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Authors: Kamran Siddique, Hiroyuki Asada, Yoshifumi Ogami

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Micro gas turbine (MGT) nowadays has a wide variety of applications from drones to hybrid electric vehicles. As microfabrication technology getting better, the size of MGT is getting smaller. Overall performance of MGT is dependent on the individual components. Each component’s performance is dependent and interrelated with another component. Therefore, careful consideration needs to be given to each and every individual component of MGT. In this study, the focus is on improving the performance of the compressor in order to improve the overall performance of MGT. Computational Fluid Dynamics (CFD) is being performed using the software FLUENT to analyze the design of a micro compressor. Operating parameters like mass flow rate and RPM, and design parameters like inner blade angle (IBA), outer blade angle (OBA), blade thickness and number of blades are varied to study its effect on the performance of the compressor. Pressure ratio is used as a tool to measure the performance of the compressor. Higher the pressure ratio, better the design is. In the study, target mass flow rate is 0.2 g/s and RPM to be less than or equal to 900,000. So far, a pressure ratio of above 3 has been achieved at 0.2 g/s mass flow rate with 5 rotor blades, 0.36 mm blade thickness, 94.25 degrees OBA and 10.46 degrees IBA. The design in this study differs from a regular centrifugal compressor used in conventional gas turbines such that compressor is designed keeping in mind ease of manufacturability. So, this study proposes a compressor design which has a good pressure ratio, and at the same time, it is easy to manufacture using current microfabrication technologies.

Keywords: computational fluid dynamics, FLUENT microfabrication, RPM

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Authors: Azad Khalid, Sifa Dogan

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Because of its persistence in conventional treatment plants and broad prevalence in water bodies, the pharmaceutical chemical carbamazepine (CBZ) has been suggested as an anthropogenic marker to evaluate water quality. This study provides a thorough examination of the origins and occurrences of CBZ in water bodies, as well as the drug's toxicological effects and laws. Given CBZ's well-documented negative consequences on the human body when used medicinally, cautious monitoring in water is advised. CBZ residues in drinking water may enter embryos and newborns via intrauterine exposure or breast-feeding, causing congenital abnormalities and/or neurodevelopmental issues over time. The insufficiency of solo solutions was shown after an in-depth technical study of traditional and sophisticated treatment technologies. Nanofiltration and reverse osmosis membranes are more successful at removing CBZ than traditional activated sludge and membrane bioreactor techniques. Recent research has shown that severe chemical cleaning, which is essential to prevent membrane fouling, may lower long-term removal efficiency. Furthermore, despite the efficacy of activated carbon adsorption and advanced oxidation processes, a few issues such as chemical cost and activated carbon renewal must be carefully examined. Individual technology constraints lead to the benefits of combined and hybrid systems, namely the heterogeneous advanced oxidation process.

Keywords: carbamazepine, occurrence, toxicity, conventical treatment, advanced oxidation process (AOPs)

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Authors: Ryadi Adityavarman

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The study is an analysis by using the postcolonial theoretical lens on the search for a distinctive architectural identity by architect Maclaine Pont in Indonesia in the early twentieth century. Influenced by progressive architectural thinking and enlightened humanism at the time, Pont applied the fundamental principles of Structural Rationalism by using a creative combination of traditional Indonesian architectural typology and innovative structural application. The interpretive design strategy also celebrated creative use of local building materials with sensible tropical climate design response. Moreover, his holistic architectural scheme, including inclusion of local custom of building construction, represents the notion of Gesamkunstwerk. By using such hybrid strategy, Maclaine Pont intended to preserve the essential cultural identity and vernacular architecture of the indigenous. The study will chronologically investigate the evolution of Structural Rationalism architecture philosophy of Viollet-le-Duc to Hendrik Berlage’s influential design thinking in the Dutch modern architecture, and subsequently to the Maclaine Pont’s innovative design in Indonesia. Consequently, the morphology analysis on his exemplary design works of ITB campus (1923) and Pohsarang Church (1936) is to understand the evolutionary influence of Structural Rationalism theory. The postmodern analysis method is to highlight the validity of Pont’s idea in the contemporary Indonesian architecture within the culture of globalism era.

Keywords: Indonesian modern architecture, postcolonial, structural rationalism, critical regionalism

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Authors: Andreas Gohs, Reinhold Kosfeld

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This study should be of interest for practitioners who want to predict precisely the sales numbers of vehicles equipped with an innovative propulsion technology as well as for researchers interested in applied (regional) time series analysis. The study is based on the numbers of new registrations of pure electric and hybrid cars. Methods of time series analysis like ARIMA are compared with the Bass Diffusion-model concerning their forecasting performances for new registrations in Germany at the national and federal state levels. Especially it is investigated if the additional information content from regional data increases the forecasting accuracy for the national level by adding predictions for the federal states. Results of parameters of the Bass Diffusion Model estimated for Germany and its sixteen federal states are reported. While the focus of this research is on the German market, estimation results are also provided for selected European and other countries. Concerning Bass-parameters and forecasting performances, we get very different results for Germany's federal states and the member states of the European Union. This corresponds to differences across the EU-member states in the adoption process of this innovative technology. Concerning the German market, the adoption is rather proceeded in southern Germany and stays behind in Eastern Germany except for Berlin.

Keywords: bass diffusion model, electric vehicles, forecasting performance, market diffusion

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Authors: João Gago dos Santos, Paulo Pereira Almeida

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The present study intends to deepen and understand the reasons that led to the decline and disappearance of wooden construction systems in Portugal, for that reason, its use in history must be analyzed. It is observed that this material was an integral part of the construction systems in Europe and Portugal for centuries, and it is possible to conclude that its decline happens with the appearance of hybrid construction and later with the emergence and development of reinforced concrete technology. It is also verified that wood as a constructive element, and for that reason, an element of development had great importance in national construction, with its peak being the Pombaline period, after the 1755 earthquake. In this period, the great scarcity of materials in the metropolis led to the import wood from Brazil for the reconstruction of Lisbon. This period is linked to an accentuated exploitation of forests, resulting in laws and royal decrees aimed at protecting them, guaranteeing the continued existence of profitable forests, crucial to the reconstruction effort. The following period, with the gradual loss of memory of the catastrophe, resulted in a construction that was weakened structurally as a response to a time of real estate speculation and great urban expansion. This was the moment that precluded the inexistence of the use of wood in construction. At the beginning of the 20th century and in the 30s and 40s, with the appearance and development of reinforced concrete, it became part of the great structures of the state, and it is considered a versatile material capable of resolving issues throughout the national territory. It is at this point that the wood falls into disuse and practically disappears from the new works produced.

Keywords: construction history, construction in portugal, construction systems, wood construction

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Authors: Eda Cagli, Irem Erel Goktepe

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Externally triggered and effective release of therapeutics from polymer nanoplatforms is one of the key issues in cancer treatment. In this study, we aim to prepare polymer multilayer films which are stable at physiological conditions (little or no drug release) but release drug molecules at acidic pH and via application of AC magnetic field. First, novel stimuli responsive diblock copolymers composed of pH- and temperature-responsive blocks were synthesized. Then, block copolymer micelles with pH-responsive core and temperature responsive coronae will be obtained via pH-induced self-assembly of these block copolymers in aqueous environment. A model anticancer drug, e.g. Doxorubicin will be loaded in the micellar cores. Second, superparamagnetic nanoparticles will be synthesized. Magnetic nanoparticles and drug loaded block copolymer micelles will be used as building blocks to construct the multilayers. To mimic the acidic nature of the tumor tissues, Doxorubicin release from the micellar cores will be induced at acidic conditions. Moreover, Doxorubicin release from the multilayers will be facilitated via magnetothermal trigger. Application of AC magnetic field will induce the heating of magnetic nanoparticles resulting in an increase in the temperature of the polymer platform. This increase in temperature is expected to trigger conformational changes on the temperature-responsive micelle coronae and facilitate the release of Doxorubicin from the surface. Such polymer platform may find use in biomedical applications.

Keywords: layer-by-layer films, magnetothermal trigger, smart polymers, stimuli responsive

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Authors: Sergey Kopysov, Nikita Nedozhogin, Leonid Tonkov

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The article presents a parallel iterative solver for large sparse linear systems which can be used on a heterogeneous platform. Traditionally, the problem of solving linear systems does not scale well on multi-CPU/multi-GPUs clusters. For example, most of the attempts to implement the classical conjugate gradient method were at best counted in the same amount of time as the problem was enlarged. The paper proposes the pipelined variant of the conjugate gradient method (PCG), a formulation that is potentially better suited for hybrid CPU/GPU computing since it requires only one synchronization point per one iteration instead of two for standard CG. The standard and pipelined CG methods need the vector entries generated by the current GPU and other GPUs for matrix-vector products. So the communication between GPUs becomes a major performance bottleneck on multi GPU cluster. The article presents an approach to minimize the communications between parallel parts of algorithms. Additionally, computation and communication can be overlapped to reduce the impact of data exchange. Using the pipelined version of the CG method with one synchronization point, the possibility of asynchronous calculations and communications, load balancing between the CPU and GPU for solving the large linear systems allows for scalability. The algorithm is implemented with the combined use of technologies: MPI, OpenMP, and CUDA. We show that almost optimum speed up on 8-CPU/2GPU may be reached (relatively to a one GPU execution). The parallelized solver achieves a speedup of up to 5.49 times on 16 NVIDIA Tesla GPUs, as compared to one GPU.

Keywords: conjugate gradient, GPU, parallel programming, pipelined algorithm

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Authors: Lucia Rozumová, Ivo Šafařík, Jana Seidlerová, Pavel Kůs

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Biosorption by biological waste materials from agriculture industry could be a cost-effective technique for removing metal ions from wastewater. The performance of new biosorbent systems, consisting of the waste matrixes which were magnetically modified by iron oxide nanoparticles, for the removal of lead ions from an aqueous solution was tested. The use of low-cost and eco-friendly adsorbents has been investigated as an ideal alternative to the current expensive methods. This article deals with the removal of metal ions from aqueous solutions by modified waste products - orange peels, sawdust, peanuts husks, used tea leaves and ground coffee sediment. Magnetically modified waste materials were suspended in methanol and then was added ferrofluid (magnetic iron oxide nanoparticles). This modification process gives the predictions for the formation of the smart materials with new properties. Prepared material was characterized by using scanning electron microscopy, specific surface area and pore size analyzer. Studies were focused on the sorption and desorption properties. The changes of iron content in magnetically modified materials after treatment were observed as well. Adsorption process has been modelled by adsorption isotherms. The results show that magnetically modified materials during the dynamic sorption and desorption are stable at the high adsorbed amount of lead ions. The results of this study indicate that the biological waste materials as sorbent with new properties are highly effective for the treatment of wastewater.

Keywords: biological waste, sorption, metal ions, ferrofluid

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Authors: Zory Marantz

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Hybrid flexible (HyFlex) is a pedagogical methodology whereby an instructor delivers content in three modalities, i.e. live in-person (LIP), live online synchronous (LOS), and non-live online asynchronous (nLOaS). HyFlex is focused on providing the largest level of flexibility needed to achieve a cohesive environment across all modalities and incorporating four basic principles – learner’s choice, reusability, accessibility, and equivalency. Much literature has focused on the advantages of this methodology in providing students with the flexibility to choose their learning modality as best suits their schedules and learning styles. Initially geared toward graduate-level students, the concept has been applied to undergraduate studies, particularly during our national pedagogical response to the COVID19 pandemic. There is still little literature about the practicality and feasibility of HyFlex for hardware laboratory intensive engineering technology programs, particularly in dense, urban commuter institutions of higher learning. During a semester of engineering, a lab-based course was taught in the HyFlex modality, and students were asked to complete a survey about their experience. The data demonstrated that there is no single mode that is preferred by a majority of students and the usefulness of any modality is limited to how familiar the student and instructor are with the technology being applied. The technology is only as effective as our understanding and comfort with its functionality. For HyFlex to succeed in its implementation in an engineering technology environment within an urban commuter institution, faculty and students must be properly introduced to the technology being used.

Keywords: education, HyFlex, technology, urban, commuter, pedagogy

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Authors: Zahra Tork

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Before the spread of the Internet and the use of mobile phones, women were generally far from society and many restrictions were applied to them, but after the spread of the Internet and the cultural and social changes brought about by it, women in society got a new one and many restrictions regarding them disappeared; As we can see today, many women, like men, have a high social base, they earn for themselves, they can travel alone, stay out late at night, take personal and family photos or videos on virtual pages. Publish themselves (while before this, showing or publishing photos of women was considered dishonorable or indecent). In this article, an attempt is made to examine the effect of the internet on mobile phones and virtual social networks in changing beliefs, norms and social values and their relationship with the social mobility of women and the effect of these factors on social and human development be paid. For this reason, social and human development is discussed first, and then the role of the media in development is explained, and finally, the social mobility of women is discussed. Since the purpose of this study is to better understand the social mobility of Iranian women through the development of the Internet in mobile phones, a qualitative study using focus groups has been adopted. The results of this research indicated that the Internet has caused changes in the value and cultural system of the Iranian people, and women have also redefined their roles and identity. In this new definition, many of the past restrictions have disappeared and women have gained the same freedoms as men. Finally, these factors (change in values and norms and redefinition of the role of women) joined hands and caused the social mobility of women in Iran.

Keywords: development of communication in Iran, development of mobile phones, development of the Internet, women's social group, social mobility

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Authors: Jingting Luo, Yong Wang, Ying Wang

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Pedestrian detection in image or video data is a very important and challenging task in security surveillance. The difficulty of this task is to locate and detect pedestrians of different scales in complex scenes accurately. To solve these problems, a deep neural network (RT-YOLOv3) is proposed to realize real-time pedestrian detection at different scales in security monitoring. RT-YOLOv3 improves the traditional YOLOv3 algorithm. Firstly, the deep residual network is added to extract vehicle features. Then six convolutional neural networks with different scales are designed and fused with the corresponding scale feature maps in the residual network to form the final feature pyramid to perform pedestrian detection tasks. This method can better characterize pedestrians. In order to further improve the accuracy and generalization ability of the model, a hybrid pedestrian data set training method is used to extract pedestrian data from the VOC data set and train with the INRIA pedestrian data set. Experiments show that the proposed RT-YOLOv3 method achieves 93.57% accuracy of mAP (mean average precision) and 46.52f/s (number of frames per second). In terms of accuracy, RT-YOLOv3 performs better than Fast R-CNN, Faster R-CNN, YOLO, SSD, YOLOv2, and YOLOv3. This method reduces the missed detection rate and false detection rate, improves the positioning accuracy, and meets the requirements of real-time detection of pedestrian objects.

Keywords: pedestrian detection, feature detection, convolutional neural network, real-time detection, YOLOv3

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Authors: Yibeltal Chanie Manie, Zebider Asire Munyelet

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5G and 6G technology offers enhanced quality of service with high data transmission rates, which necessitates the implementation of the Internet of Things (IoT) in 5G/6G architecture. In this paper, we proposed the integration of free space optical communication (FSO) with fiber sensor networks for IoT applications. Recently, free-space optical communications (FSO) are gaining popularity as an effective alternative technology to the limited availability of radio frequency (RF) spectrum. FSO is gaining popularity due to flexibility, high achievable optical bandwidth, and low power consumption in several applications of communications, such as disaster recovery, last-mile connectivity, drones, surveillance, backhaul, and satellite communications. Hence, high-speed FSO is an optimal choice for wireless networks to satisfy the full potential of 5G/6G technology, offering 100 Gbit/s or more speed in IoT applications. Moreover, machine learning must be integrated into the design, planning, and optimization of future optical wireless communication networks in order to actualize this vision of intelligent processing and operation. In addition, fiber sensors are important to achieve real-time, accurate, and smart monitoring in IoT applications. Moreover, we proposed deep learning techniques to estimate the strain changes and peak wavelength of multiple Fiber Bragg grating (FBG) sensors using only the spectrum of FBGs obtained from the real experiment.

Keywords: optical sensor, artificial Intelligence, Internet of Things, free-space optics

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Authors: Milagros Jaén-Vargas, Javier García Martínez, Karla Miriam Reyes Leiva, María Fernanda Trujillo-Guerrero, Francisco Fernandes, Sérgio Barroso Gonçalves, Miguel Tavares Silva, Daniel Simões Lopes, José Javier Serrano Olmedo

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Human Activity Recognition (HAR) is having a growing impact in creating new applications and is responsible for emerging new technologies. Also, the use of wearable sensors is an important key to exploring the human body's behavior when performing activities. Hence, the use of these dispositive is less invasive and the person is more comfortable. In this study, a database that includes three activities is used. The activities were acquired from inertial measurement unit sensors (IMU) and motion capture systems (MOCAP). The main objective is differentiating the performance from four Deep Learning (DL) models: Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and hybrid model Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), when considering acceleration, velocity and position and evaluate if integrating the IMU acceleration to obtain velocity and position represent an increment in performance when it works as input to the DL models. Moreover, compared with the same type of data provided by the MOCAP system. Despite the acceleration data is cleaned when integrating, results show a minimal increase in accuracy for the integrated signals.

Keywords: HAR, IMU, MOCAP, acceleration, velocity, position, feature maps

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Authors: Yogesh C. Sharma, A. Seetharaman

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The decision-making process in humans is a complicated system influenced by a variety of intrinsic and extrinsic factors. Human decisions have a ripple effect on subsequent decisions. In this study, the scope of human decision making is limited to employees. In an organisation, a person makes a variety of decisions from the time they are hired to the time they retire. The goal of this research is to identify various elements that influence decision-making. In addition, the environment in which a decision is made is a significant aspect of the decision-making process. Employees in today's workplace use artificial intelligence (AI) systems for automation and decision augmentation. The impact of AI systems on the decision-making process is examined in this study. This research is designed based on a systematic literature review. Based on gaps in the literature, limitations and the scope of future research have been identified. Based on these findings, a research framework has been designed to identify various factors affecting employee decision making. Employee decision making is influenced by technological advancement, data-driven culture, human trust, decision automation-augmentation, and workplace motivation. Hybrid human-AI systems require the development of new skill sets and organisational design. Employee psychological safety and supportive leadership influences overall job satisfaction.

Keywords: employee decision making, artificial intelligence (AI) environment, human trust, technology innovation, psychological safety

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Authors: Panpan Fan, Bo Ming, Niels Anten, Jochem Evers, Yaoyao Li, Shaokun Li, Ruizhi xie

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Nitrogen (N) utilization for crop production under N deficiency conditions is subject to a trade-off between maintaining specific leaf N content (SLN), important for radiation-use efficiency (RUE), versus maintaining leaf area (LA) development, important for light capture. This paper aims to explore how maize deals with this trade-off through responses in SLN, LA and their underlying traits during the vegetative and reproductive growth stages. In a ten-year N fertilization trial in Jilin province, Northeast China, three N fertilizer levels have been maintained: N-deficiency (N0), low N supply (N1), and high N supply (N2). We analyzed data from years 8 and 10 of this experiment for two common hybrids. Under N deficiency, maize plants maintained LA and decreased SLN during vegetative stages, while both LA and SLN decreased comparably during reproductive stages. Canopy-average specific leaf area (SLA) decreased sharply during vegetative stages and slightly during reproductive stages, mainly because senesced leaves in the lower canopy had a higher SLA. In the vegetative stage, maize maintained leaf area at low N by maintaining leaf biomass (albeit hence having N content/mass) and slightly increasing SLA. These responses to N deficiency were stronger in maize hybrid XY335 than in ZD958. We conclude the main strategy of maize to cope with low N is to maintain plant growth, mainly by increasing SLA throughout the plant during early growth. N was too limiting for either strategy to be followed during later growth stages.

Keywords: leaf N content per unit leaf area, N deficiency, specific leaf area, maize strateg

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Authors: Shanshan Zhu, Mohammad Nasim

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Financial fraud continues to be a major threat to financial institutions across the world, causing colossal money losses and undermining public trust. Fraud prevention techniques, based on hard rules, have become ineffective due to evolving patterns of fraud in recent times. Against such a background, the present study probes into distinct methodologies that exploit emergent AI-driven techniques to further strengthen fraud detection. We would like to compare the performance of generative adversarial networks and graph neural networks with other popular techniques, like gradient boosting, random forests, and neural networks. To this end, we would recommend integrating all these state-of-the-art models into one robust, flexible, and smart system for real-time anomaly and fraud detection. To overcome the challenge, we designed synthetic data and then conducted pattern recognition and unsupervised and supervised learning analyses on the transaction data to identify which activities were fishy. With the use of actual financial statistics, we compare the performance of our model in accuracy, speed, and adaptability versus conventional models. The results of this study illustrate a strong signal and need to integrate state-of-the-art, AI-driven fraud detection solutions into frameworks that are highly relevant to the financial domain. It alerts one to the great urgency that banks and related financial institutions must rapidly implement these most advanced technologies to continue to have a high level of security.

Keywords: AI-driven fraud detection, financial security, machine learning, anomaly detection, real-time fraud detection

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Authors: Rowan Moore, Kai Scheffler, Eugen Damoc, Jennifer Sutton, Aaron Bailey, Stephane Houel, Simon Cubbon, Jonathan Josephs

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Purpose: MS analysis of monoclonal antibodies (mAbs) at the protein and peptide levels is critical during development and production of biopharmaceuticals. The compositions of current generation therapeutic proteins are often complex due to various modifications which may affect efficacy. Intact proteins analyzed by MS are detected in higher charge states that also provide more complexity in mass spectra. Protein analysis in native or native-like conditions with zero or minimal organic solvent and neutral or weakly acidic pH decreases charge state value resulting in mAb detection at higher m/z ranges with more spatial resolution. Methods: Three commercially available mAbs were used for all experiments. Intact proteins were desalted online using size exclusion chromatography (SEC) or reversed phase chromatography coupled on-line with a mass spectrometer. For streamlined use of the LC- MS platform we used a single SEC column and alternately selected specific mobile phases to perform separations in either denaturing or native-like conditions: buffer A (20 % ACN, 0.1 % FA) with Buffer B (100 mM ammonium acetate). For peptide analysis mAbs were proteolytically digested with and without prior reduction and alkylation. The mass spectrometer used for all experiments was a commercially available Thermo Scientific™ hybrid Quadrupole-Orbitrap™ mass spectrometer, equipped with the new BioPharma option which includes a new High Mass Range (HMR) mode that allows for improved high mass transmission and mass detection up to 8000 m/z. Results: We have analyzed the profiles of three mAbs under reducing and native conditions by direct infusion with offline desalting and with on-line desalting via size exclusion and reversed phase type columns. The presence of high salt under denaturing conditions was found to influence the observed charge state envelope and impact mass accuracy after spectral deconvolution. The significantly lower charge states observed under native conditions improves the spatial resolution of protein signals and has significant benefits for the analysis of antibody mixtures, e.g. lysine variants, degradants or sequence variants. This type of analysis requires the detection of masses beyond the standard mass range ranging up to 6000 m/z requiring the extended capabilities available in the new HMR mode. We have compared each antibody sample that was analyzed individually with mixtures in various relative concentrations. For this type of analysis, we observed that apparent native structures persist and ESI is benefited by the addition of low amounts of acetonitrile and formic acid in combination with the ammonium acetate-buffered mobile phase. For analyses on the peptide level we analyzed reduced/alkylated, and non-reduced proteolytic digests of the individual antibodies separated via reversed phase chromatography aiming to retrieve as much information as possible regarding sequence coverage, disulfide bridges, post-translational modifications such as various glycans, sequence variants, and their relative quantification. All data acquired were submitted to a single software package for analysis aiming to obtain a complete picture of the molecules analyzed. Here we demonstrate the capabilities of the mass spectrometer to fully characterize homogeneous and heterogeneous therapeutic proteins on one single platform. Conclusion: Full characterization of heterogeneous intact protein mixtures by improved mass separation on a quadrupole-Orbitrap™ mass spectrometer with extended capabilities has been demonstrated.

Keywords: disulfide bond analysis, intact analysis, native analysis, mass spectrometry, monoclonal antibodies, peptide mapping, post-translational modifications, sequence variants, size exclusion chromatography, therapeutic protein analysis, UHPLC

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Authors: Ji-Seok Koo, Hee‑Yong Kwon, Hui-Young Yun, Kyung-Hui Wang, Youn-Seo Koo

Abstract:

This paper presents a forecasting system for PM2.5 levels in Seoul, South Korea, leveraging a combination of chemical transport modeling and ConvLSTM-DNN machine learning technology. Exposure to PM2.5 has known detrimental impacts on public health, making its prediction crucial for establishing preventive measures. Existing forecasting models, like the Community Multiscale Air Quality (CMAQ) and Weather Research and Forecasting (WRF), are hindered by their reliance on uncertain input data, such as anthropogenic emissions and meteorological patterns, as well as certain intrinsic model limitations. The system we've developed specifically addresses these issues by integrating machine learning and using carefully selected input features that account for local and distant sources of PM2.5. In South Korea, the PM2.5 concentration is greatly influenced by both local emissions and long-range transport from China, and our model effectively captures these spatial and temporal dynamics. Our PM2.5 prediction system combines the strengths of advanced hybrid machine learning algorithms, convLSTM and DNN, to improve upon the limitations of the traditional CMAQ model. Data used in the system include forecasted information from CMAQ and WRF models, along with actual PM2.5 concentration and weather variable data from monitoring stations in China and South Korea. The system was implemented specifically for Seoul's PM2.5 forecasting.

Keywords: PM2.5 forecast, machine learning, convLSTM, DNN

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Authors: Akingeneye Liliane

Abstract:

The value chain concept in recent times is being used by businesses and organizations to develop and implement their businesses. Chili farming has been identified as a significant contributor to the economic growth of Bugesera district. However, numerous challenges have led to a decrease in production. The primary objective of this research was to assess the current Bugesera chili value chain, identify the bottlenecks in the value chain, and come up with interventions that can help increase the output of the Bugesera chili value chain, in a climate-smart way and enhance Long-term sustainability of the value chain. The research used a case study approach to fulfill its objectives, utilizing primary and secondary data sources. Data, both qualitative and quantitative, were gathered through semi-structured interviews conducted with 22 individual farmers, five exporters, and five supporters within the Bugesera district. A focus group discussion (FGD) with seven stakeholders was also conducted to validate the research findings. The study's results underscore the challenges faced by chili farmers and other actors in the chain, the perceptions of different stakeholders to contribute to chili production, and the importance of promoting strong collaboration among stakeholders in the chili value chain to establish a sustainable framework. Based on these findings, the study puts forward recommendations that aim to address the identified challenges and improve the chili farming sector in Bugesera. The business canvas model, as a proposed recommendation, once implemented, is believed to represent the most effective approach to enhancing chili productivity in Bugesera and securing the long-term sustainability of an export-oriented chili value chain in the district.

Keywords: building, sustainable, chili, value chain

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Authors: Akingeneye Liliane

Abstract:

The value chain concept in recent times is being used by businesses and organizations to develop and implement their businesses. Chili farming has been identified as a significant contributor to the economic growth of Bugesera district. However, numerous challenges have led to a decrease in production. The primary objective of this research was to assess the current Bugesera chili value chain, identify the bottlenecks in the value chain, and come up with interventions that can help increase the output of the Bugesera chili value chain, in a climate-smart way and enhance Long-term sustainability of the value chain. The research used a case study approach to fulfill its objectives, utilizing primary and secondary data sources. Qualitative and quantitative data were gathered through semi-structured interviews with 22 individual farmers, five exporters, and five supporters within the Bugesera district. A focus group discussion (FGD) with seven stakeholders was also conducted to validate the research findings. The study's results underscore the challenges faced by chili farmers and other actors in the chain, the perceptions of different stakeholders to contribute to chili production, and the importance of promoting strong collaboration among stakeholders in the chili value chain to establish a sustainable framework. Based on these findings, the study puts forward recommendations to address the identified challenges and improve the chili farming sector in Bugesera. The business canvas model, as a proposed recommendation, once implemented, is believed to represent the most effective approach to enhancing chili productivity in Bugesera and securing the long-term sustainability of an export-oriented chili value chain in the district.

Keywords: build, sustainability, chili value chain, export-oriented

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Authors: Gabriel Sanjo Aruwajoye, E. B. Gueguim Kana

Abstract:

Agricultural residues present a low-cost feedstock for bioenergy production around the world. Cassava peels waste are rich in organic molecules that can be readily converted to value added products such as biomaterials and biofuels. However, due to the presence of high proportion of structural carbohydrates and lignin, the hydrolysis of this feedstock is imperative to achieve maximum substrate utilization and energy yield. This study model and optimises the release of Fermentable Sugar (FS) from cassava peels waste using the Response Surface Methodology. The investigated pretreatment input parameters consisted of soaking temperature (oC), soaking time (hours), autoclave duration (minutes), acid concentration (% v/v), substrate solid loading (% w/v) within the range of 30 to 70, 0 to 24, 5 to 20, 0 to 5 and 2 to 10 respectively. The Box-Behnken design was used to generate 46 experimental runs which were investigated for FS release. The obtained data were used to fit a quadratic model. A coefficient of determination of 0.87 and F value of 8.73 was obtained indicating the good fitness of the model. The predicted optimum pretreatment conditions were 69.62 oC soaking temperature, 2.57 hours soaking duration, 5 minutes autoclave duration, 3.68 % v/v HCl and 9.65 % w/v solid loading corresponding to FS yield of 91.83g/l (0.92 g/g cassava peels) thus 58% improvement on the non-optimised pretreatment. Our findings demonstrate an efficient pretreatment model for fermentable sugar release from cassava peels waste for various bioprocesses.

Keywords: feedstock pretreatment, cassava peels, fermentable sugar, response surface methodology

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Authors: Ahmad Afif, Teuku Yuri M. Zagloel

Abstract:

Sustainable supply chain management is a new pattern that has emerged recently in industry and companies. The procurement process is one of the key factors for efficiency in supply chain management practices. Partnership is one of the procurement strategies for strategic items. The success factors of the partnership must be determined to avoid things that endanger the financial and operational status of the company. The current supplier partnership research focuses on the selection of general criteria and sustainable supplier selection. Currently, there is still limited research on the success factors of supplier partnerships that focus on strategic items in the coal mining industry. Meanwhile, the procurement of coal mining has its own characteristics, and there are regulations related to the procurement of goods. Therefore, this research was conducted to determine the categories of goods that are included in the strategic items and to design the success factors of supplier partnerships. The main factors studied are general, financial, production, reputation, synergies, and sustainable. The research was conducted using the Kraljic method to determine the categories of goods that are included in the strategic items. To design a supplier partnership success factor using the Hybrid Multi Criteria Decision Making method. Integrated Fuzzy AHP-Fuzzy TOPSIS is used to determine the weight of the success factors of supplier partnerships and to rank suppliers on the factors used.

Keywords: supplier, partnership, strategic item, success factors, and coal mining industry

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Authors: Huawei Bai, Jianguo Yao, Fellow, IEEE

Abstract:

Atrial fibrillation (AF) is the most common form of heart arrhythmia and is closely associated with mortality and morbidity in heart failure, stroke, and coronary artery disease. The development of single spot optical sensors enables widespread photoplethysmography (PPG) screening, especially for AF, since it represents a more convenient and noninvasive approach. To our knowledge, most existing studies based on public and unbalanced datasets can barely handle the multiple noises sources in the real world and, also, lack interpretability. In this paper, we construct a large- scale PPG dataset using measurements collected from PPG wrist- watch devices worn by volunteers and propose an attention-based two-stream heterogeneous neural network (TSHNN). The first stream is a hybrid neural network consisting of a three-layer one-dimensional convolutional neural network (1D-CNN) and two-layer attention- based bidirectional long short-term memory (Bi-LSTM) network to learn representations from temporally sampled signals. The second stream extracts latent representations from the PPG time-frequency spectrogram using a five-layer CNN. The outputs from both streams are fed into a fusion layer for the outcome. Visualization of the attention weights learned demonstrates the effectiveness of the attention mechanism against noise. The experimental results show that the TSHNN outperforms all the competitive baseline approaches and with 98.09% accuracy, achieves state-of-the-art performance.

Keywords: PPG wearables, atrial fibrillation, feature fusion, attention mechanism, hyber network

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Authors: Joao Garretto, R. J. Yarwood, Vamsi Borra, Frank Li

Abstract:

Bluetooth Low Energy (BLE) has emerged as one of the main wireless communication technologies used in low-power electronics, such as wearables, beacons, and Internet of Things (IoT) devices. BLE’s energy efficiency characteristic, smart mobiles interoperability, and Over the Air (OTA) capabilities are essential features for ultralow-power devices, which are usually designed with size and cost constraints. Most current research regarding the power analysis of BLE devices focuses on the theoretical aspects of the advertising and scanning cycles, with most results being presented in the form of mathematical models and computer software simulations. Such computer modeling and simulations are important for the comprehension of the technology, but hardware measurement is essential for the understanding of how BLE devices behave in real operation. In addition, recent literature focuses mostly on the BLE technology, leaving possible applications and its analysis out of scope. In this paper, a coin cell battery-powered BLE Data Acquisition Device, with a 4-in-1 sensor and one accelerometer, is proposed and evaluated with respect to its Power Consumption. First, evaluations of the device in advertising mode with the sensors turned off completely, followed by the power analysis when each of the sensors is individually turned on and data is being transmitted, and concluding with the power consumption evaluation when both sensors are on and respectively broadcasting the data to a mobile phone. The results presented in this paper are real-time measurements of the electrical current consumption of the BLE device, where the energy levels that are demonstrated are matched to the BLE behavior and sensor activity.

Keywords: bluetooth low energy, power analysis, BLE advertising cycle, wireless sensor node

Procedia PDF Downloads 91