Search results for: wireless sensors networks

Authors: Vladimir A. Basiuk, Laura J. Flores-Sanchez, Victor Meza-Laguna, Jose O. Flores-Flores, Lauro Bucio-Galindo, Elena V. Basiuk

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Noncovalent nanohybrid materials combining carbon nanotubes (CNTs) with phthalocyanines (Pcs) is a subject of increasing research effort, with a particular emphasis on the design of new heterogeneous catalysts, efficient organic photovoltaic cells, lithium batteries, gas sensors, field effect transistors, among other possible applications. The possibility of using unsubstituted Pcs for CNT functionalization is very attractive due to their very moderate cost and easy commercial availability. However, unfortunately, the deposition of unsubstituted Pcs onto nanotube sidewalls through the traditional liquid-phase protocols turns to be very problematic due to extremely poor solubility of Pcs. On the other hand, unsubstituted free-base H₂Pc phthalocyanine ligand, as well as many of its transition metal complexes, exhibit very high thermal stability and considerable volatility under reduced pressure, which opens the possibility for their physical vapor deposition onto solid surfaces, including nanotube sidewalls. In the present work, we show the possibility of simple, fast and efficient noncovalent functionalization of single-walled carbon nanotubes (SWNTs) with a series of 3d metal(II) phthalocyanines Me(II)Pc, where Me= Co, Ni, Cu, and Zn. The functionalization can be performed in a temperature range of 400-500 °C under moderate vacuum and requires about 2-3 h only. The functionalized materials obtained were characterized by means of Fourier-transform infrared (FTIR), Raman, UV-visible and energy-dispersive X-ray spectroscopy (EDS), scanning and transmission electron microscopy (SEM and TEM, respectively) and thermogravimetric analysis (TGA). TGA suggested that Me(II)Pc weight content is 30%, 17% and 35% for NiPc, CuPc, and ZnPc, respectively (CoPc exhibited anomalous thermal decomposition behavior). The above values are consistent with those estimated from EDS spectra, namely, of 24-39%, 27-36% and 27-44% for CoPc, CuPc, and ZnPc, respectively. A strong increase in intensity of D band in the Raman spectra of SWNT‒Me(II)Pc hybrids, as compared to that of pristine nanotubes, implies very strong interactions between Pc molecules and SWNT sidewalls. Very high absolute values of binding energies of 32.46-37.12 kcal/mol and the highest occupied and lowest unoccupied molecular orbital (HOMO and LUMO, respectively) distribution patterns, calculated with density functional theory by using Perdew-Burke-Ernzerhof general gradient approximation correlation functional in combination with the Grimme’s empirical dispersion correction (PBE-D) and the double numerical basis set (DNP), also suggested that the interactions between Me(II) phthalocyanines and nanotube sidewalls are very strong. The authors thank the National Autonomous University of Mexico (grant DGAPA-IN200516) and the National Council of Science and Technology of Mexico (CONACYT, grant 250655) for financial support. The authors are also grateful to Dr. Natalia Alzate-Carvajal (CCADET of UNAM), Eréndira Martínez (IF of UNAM) and Iván Puente-Lee (Faculty of Chemistry of UNAM) for technical assistance with FTIR, TGA measurements, and TEM imaging, respectively.

Keywords: carbon nanotubes, functionalization, gas-phase, metal(II) phthalocyanines

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Authors: Chiao-Yi Chen, Dung-Ying Lin

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With the growing awareness of environmental protection and the implementation of government carbon reduction policies, the number of electric vehicles (EVs) has rapidly increased, leading to a surge in charging demand and imposing significant challenges on the existing power grid’s capacity. Traditional urban power grid planning has not adequately accounted for the additional load generated by EV charging, which often strains the infrastructure. This study aims to optimize grid operation and load management by dynamically adjusting EV charging prices based on real-time electricity supply and demand, leveraging consumer demand elasticity to enhance system efficiency. This study uniquely addresses the intricate interplay between urban traffic patterns and power grid dynamics in the context of electric vehicle (EV) adoption. By integrating Hsinchu City's road network with the IEEE 33-bus system, the research creates a comprehensive model that captures both the spatial and temporal aspects of EV charging demand. This approach allows for a nuanced analysis of how traffic flow directly influences the load distribution across the power grid. The strategic placement of charging stations at key nodes within the IEEE 33-bus system, informed by actual road traffic data, enables a realistic simulation of the dynamic relationship between vehicle movement and energy consumption. This integration of transportation and energy systems provides a holistic view of the challenges and opportunities in urban EV infrastructure planning, highlighting the critical need for solutions that can adapt to the ever-changing interplay between traffic patterns and grid capacity. The proposed dynamic pricing strategy effectively reduces peak charging loads, enhances the operational efficiency of charging stations, and maximizes operator profits, all while ensuring grid stability. These findings provide practical insights and a valuable framework for optimizing EV charging infrastructure and policies in future smart cities, contributing to more resilient and sustainable urban energy systems.

Keywords: dynamic pricing, demand elasticity, EV charging, grid load balancing, optimization

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Authors: Zahra Hamidi

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Promoting cycling, as an affordable environmentally friendly mode of transport to replace private car use has been central to sustainable transport policies. Cycling is faster than walking and combined with public transport has the potential to extend the opportunities that people can access. In other words, cycling, besides direct positive health impacts, can improve people mobility and ultimately their quality of life. Transport literature well supports the close relationship between mobility, quality of life, and, well being. At the same time inequity in the distribution of access and mobility has been associated with the key aspects of injustice and social exclusion. The pattern of social exclusion and inequality in access are also often related to population characteristics such as age, gender, income, health, and ethnic background. Therefore, while investing in transport infrastructure it is important to consider the equity of provided access for different population groups. This paper proposes a method to evaluate the equity of cycling in a city from Rawls egalitarian perspective. Since this perspective is concerned with the difference between individuals and social groups, this method combines accessibility measures and Theil index of inequality that allows capturing the inequalities ‘within’ and ‘between’ groups. The paper specifically focuses on two population characteristics as gender and ethnic background. Following Rawls equity principles, this paper measures accessibility by bikes to a selection of urban activities that can be linked to the concept of the social primary goods. Moreover, as growing number of cities around the world have launched bike-sharing systems (BSS) this paper incorporates both private and public bikes networks in the estimation of accessibility levels. Additionally, the typology of bike lanes (separated from or shared with roads), the presence of a bike sharing system in the network, as well as bike facilities (e.g. parking racks) have been included in the developed accessibility measures. Application of this proposed method to a real case study, the city of Malmö, Sweden, shows its effectiveness and efficiency. Although the accessibility levels were estimated only based on gender and ethnic background characteristics of the population, the author suggests that the analysis can be applied to other contexts and further developed using other properties, such as age, income, or health.

Keywords: accessibility, cycling, equity, gender

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Authors: Shreya Goyal

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The study aims to build user-friendly software to understand patient arrival patterns and compute the number of potential patients who will visit a particular health facility for a given period by using a machine learning algorithm. The underlying machine learning algorithm used in this study is the Support Vector Machine (SVM). Accurate prediction of patient arrival allows hospitals to operate more effectively, providing timely and efficient care while optimizing resources and improving patient experience. It allows for better allocation of staff, equipment, and other resources. If there's a projected surge in patients, additional staff or resources can be allocated to handle the influx, preventing bottlenecks or delays in care. Understanding patient arrival patterns can also help streamline processes to minimize waiting times for patients and ensure timely access to care for patients in need. Another big advantage of using this software is adhering to strict data protection regulations such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States as the hospital will not have to share the data with any third party or upload it to the cloud because the software can read data locally from the machine. The data needs to be arranged in. a particular format and the software will be able to read the data and provide meaningful output. Using software that operates locally can facilitate compliance with these regulations by minimizing data exposure. Keeping patient data within the hospital's local systems reduces the risk of unauthorized access or breaches associated with transmitting data over networks or storing it in external servers. This can help maintain the confidentiality and integrity of sensitive patient information. Historical patient data is used in this study. The input variables used to train the model include patient age, time of day, day of the week, seasonal variations, and local events. The algorithm uses a Supervised learning method to optimize the objective function and find the global minima. The algorithm stores the values of the local minima after each iteration and at the end compares all the local minima to find the global minima. The strength of this study is the transfer function used to calculate the number of patients. The model has an output accuracy of >95%. The method proposed in this study could be used for better management planning of personnel and medical resources.

Keywords: machine learning, SVM, HIPAA, data

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Authors: Antonella Falanga, Armando Cartenì

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Today, an unprecedented amount of data coming from several sources, including mobile devices, sensors, tracking systems, and online platforms, characterizes our lives. The term “big data” not only refers to the quantity of data but also to the variety and speed of data generation. These data hold valuable insights that, when extracted and analyzed, facilitate informed decision-making. The 4Vs of big data - velocity, volume, variety, and value - highlight essential aspects, showcasing the rapid generation, vast quantities, diverse sources, and potential value addition of these kinds of data. This surge of information has revolutionized many sectors, such as business for improving decision-making processes, healthcare for clinical record analysis and medical research, education for enhancing teaching methodologies, agriculture for optimizing crop management, finance for risk assessment and fraud detection, media and entertainment for personalized content recommendations, emergency for a real-time response during crisis/events, and also mobility for the urban planning and for the design/management of public and private transport services. Big data's pervasive impact enhances societal aspects, elevating the quality of life, service efficiency, and problem-solving capacities. However, during this transformative era, new challenges arise, including data quality, privacy, data security, cybersecurity, interoperability, the need for advanced infrastructures, and staff training. Within the transportation sector (the one investigated in this research), applications span planning, designing, and managing systems and mobility services. Among the most common big data applications within the transport sector are, for example, real-time traffic monitoring, bus/freight vehicle route optimization, vehicle maintenance, road safety and all the autonomous and connected vehicles applications. Benefits include a reduction in travel times, road accidents and pollutant emissions. Within these issues, the proper transport demand estimation is crucial for sustainable transportation planning. Evaluating the impact of sustainable mobility policies starts with a quantitative analysis of travel demand. Achieving transportation decarbonization goals hinges on precise estimations of demand for individual transport modes. Emerging technologies, offering substantial big data at lower costs than traditional methods, play a pivotal role in this context. Starting from these considerations, this study explores the usefulness impact of big data within transport demand estimation. This research focuses on leveraging (big) data collected during the COVID-19 pandemic to estimate the evolution of the mobility demand in Italy. Estimation results reveal in the post-COVID-19 era, more than 96 million national daily trips, about 2.6 trips per capita, with a mobile population of more than 37.6 million Italian travelers per day. Overall, this research allows us to conclude that big data better enhances rational decision-making for mobility demand estimation, which is imperative for adeptly planning and allocating investments in transportation infrastructures and services.

Keywords: big data, cloud computing, decision-making, mobility demand, transportation

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Authors: R. Nandhini, Gaurab Mudbhari

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Ranging from the field of health care to self-driving cars, machine learning and deep learning algorithms have revolutionized the field with the proper utilization of images and visual-oriented data. Segmentation, regression, classification, clustering, dimensionality reduction, etc., are some of the Machine Learning tasks that helped Machine Learning and Deep Learning models to become state-of-the-art models for the field where images are key datasets. Among these tasks, classification and clustering are essential but difficult because of the intricate and high-dimensional characteristics of image data. This finding examines and assesses advanced techniques in supervised classification and unsupervised clustering for image datasets, emphasizing the relative efficiency of Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), Deep Embedded Clustering (DEC), and self-supervised learning approaches. Due to the distinctive structural attributes present in images, conventional methods often fail to effectively capture spatial patterns, resulting in the development of models that utilize more advanced architectures and attention mechanisms. In image classification, we investigated both CNNs and ViTs. One of the most promising models, which is very much known for its ability to detect spatial hierarchies, is CNN, and it serves as a core model in our study. On the other hand, ViT is another model that also serves as a core model, reflecting a modern classification method that uses a self-attention mechanism which makes them more robust as this self-attention mechanism allows them to lean global dependencies in images without relying on convolutional layers. This paper evaluates the performance of these two architectures based on accuracy, precision, recall, and F1-score across different image datasets, analyzing their appropriateness for various categories of images. In the domain of clustering, we assess DEC, Variational Autoencoders (VAEs), and conventional clustering techniques like k-means, which are used on embeddings derived from CNN models. DEC, a prominent model in the field of clustering, has gained the attention of many ML engineers because of its ability to combine feature learning and clustering into a single framework and its main goal is to improve clustering quality through better feature representation. VAEs, on the other hand, are pretty well known for using latent embeddings for grouping similar images without requiring for prior label by utilizing the probabilistic clustering method.

Keywords: machine learning, deep learning, image classification, image clustering

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Authors: Pankaj Kumar Jha, Mahendra Singh Hada, Brijendra Singh, Sandeep Yadav

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Protective relays are commonly connected to the secondary windings of instrument transformers, i.e., current transformers (CTs) and/or capacitive voltage transformers (CVTs). The purpose of CT and CVT is to provide galvanic isolation from high voltages and reduce primary currents and voltages to a nominal quantity recognized by the protective relays. Selecting the correct instrument transformers for an application is imperative: failing to do so may compromise the relay’s performance, as the output of the instrument transformer may no longer be an accurately scaled representation of the primary quantity. Having an accurately rated instrument transformer is of no use if these devices are not properly connected. The performance of the protective relay is reliant on its programmed settings and on the current and voltage inputs from the instrument transformers secondary. This paper will help in understanding the fundamental concepts of the connections of Instrument Transformers to the protection relays and the effect of incorrect connection on the performance of protective relays. Multiple case studies of protection system mal-operations due to incorrect connections of instrument transformers will be discussed in detail in this paper. Apart from the connection issue of instrument transformers to protective relays, this paper will also discuss the effect of multiple earthing of CTs and CVTs secondary on the performance of the protection system. Case studies presented in this paper will help the readers to analyse the problem through real-world challenges in complex power system networks. This paper will also help the protection engineer in better analysis of disturbance records. CT and CVT connection errors can lead to undesired operations of protection systems. However, many of these operations can be avoided by adhering to industry standards and implementing tried-and-true field testing and commissioning practices. Understanding the effect of missing neutral of CVT, multiple earthing of CVT secondary, and multiple grounding of CT star points on the performance of the protection system through real-world case studies will help the protection engineer in better commissioning the protection system and maintenance of the protection system.

Keywords: bus reactor, current transformer, capacitive voltage transformer, distance protection, differential protection, directional earth fault, disturbance report, instrument transformer, ICT, REF protection, shunt reactor, voltage selection relay, VT fuse failure

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Authors: Sugandha Gupta, Arun Kumar Jha

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A large number of ferroelectric materials have been intensely investigated for applications in non-volatile ferroelectric random access memories (FeRAMs), piezoelectric transducers, actuators, pyroelectric sensors, high dielectric constant capacitors, etc. Bismuth layered ferroelectric materials such as Strontium Bismuth Tantalate (SBT) has attracted a lot of attention due to low leakage current, high remnant polarization and high fatigue endurance up to 1012 switching cycles. However, pure SBT suffers from various major limitations such as high dielectric loss, low remnant polarization values, high processing temperature, bismuth volatilization, etc. Significant efforts have been made to improve the dielectric and ferroelectric properties of this compound. Firstly, it has been reported that electrical properties vary with the Sr/ Bi content ratio in the SrBi2Ta2O9 compsition i.e. non-stoichiometric compositions with Sr-deficient / Bi excess content have higher remnant polarization values than stoichiometic SBT compositions. With the objective to improve structural, dielectric, ferroelectric and piezoelectric properties of SBT compound, rare earth holmium (Ho3+) was chosen as a donor cation for substitution onto the Bi2O2 layer. Moreover, hardly any report on holmium substitution in stoichiometric SrBi2Ta2O9 and non-stoichiometric Sr0.8Bi2.2Ta2O9 compositions were available in the literature. The holmium substituted SrBi2-xHoxTa2O9 (x= 0.00-2.0) and Sr0.8Bi2.2Ta2O9 (x=0.0 and 0.01) compositions were synthesized by the solid state reaction method. The synthesized specimens were characterized for their structural and electrical properties. X-ray diffractograms reveal single phase layered perovskite structure formation for holmium content in stoichiometric SBT samples up to x ≤ 0.1. The granular morphology of the samples was investigated using scanning electron microscope (Hitachi, S-3700 N). The dielectric measurements were carried out using a precision LCR meter (Agilent 4284A) operating at oscillation amplitude of 1V. The variation of dielectric constant with temperature shows that the Curie temperature (Tc) decreases on increasing the holmium content. The specimen with x=2.0 i.e. the bismuth free specimen, has very low dielectric constant and does not show any appreciable variation with temperature. The dielectric loss reduces significantly with holmium substitution. The polarization–electric field (P–E) hysteresis loops were recorded using a P–E loop tracer based on Sawyer–Tower circuit. It is observed that the ferroelectric property improve with Ho substitution. Holmium substituted specimen exhibits enhanced value of remnant polarization (Pr= 9.22 μC/cm²) as compared to holmium free specimen (Pr= 2.55 μC/cm²). Piezoelectric co-efficient (d33 values) was measured using a piezo meter system (Piezo Test PM300). It is observed that holmium substitution enhances piezoelectric coefficient. Further, the optimized holmium content (x=0.01) in stoichiometric SrBi2-xHoxTa2O9 composition has been substituted in non-stoichiometric Sr0.8Bi2.2Ta2O9 composition to obtain further enhanced structural and electrical characteristics. It is expected that a new class of ferroelectric materials i.e. Rare Earth Layered Structured Ferroelectrics (RLSF) derived from Bismuth Layered Structured Ferroelectrics (BLSF) will generate which can be used to replace static (SRAM) and dynamic (DRAM) random access memories with ferroelectric random access memories (FeRAMS).

Keywords: dielectrics, ferroelectrics, piezoelectrics, strontium bismuth tantalate

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Authors: Denise Bianco

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Several studies on innovation and creativity in organisations emphasise the need to expand horizons and take on alternative and unexpected views to produce something new. This paper theorises the potential impact artists can have as creative catalysts, working embedded in non-artistic organisations. It begins from an understanding that in today's ever-changing scenario, organisations are increasingly seeking to open up new creative thinking through deviant behaviours to produce innovation and that art residencies need to be critically revised in this specific context in light of their disruptive potential. On the one hand, this paper builds upon recent contributions made on workplace creativity and related concepts of deviance and disruption. Research suggests that creativity is likely to be lower in work contexts where utter conformity is a cardinal value and higher in work contexts that show some tolerance for uncertainty and deviance. On the other hand, this paper draws attention to Artist-in-Residence as a vehicle for epistemic friction between divergent and convergent thinking, which allows the creation of unparalleled ways of knowing in the dailiness of situated and contextualised social processes. In order to do so, this contribution brings together insights from the most relevant theories on organisational creativity and unconventional agile methods such as Art Thinking and direct insights from ethnographic fieldwork in the context of embedded art residencies within work organisations to propose a redefinition of Artist-in-Residence and their potential impact on organisational creativity. The result is a re-definition of embedded Artist-in-Residence in organisational settings from a more comprehensive, multi-disciplinary, and relational perspective that builds on three focal points. First the notion that organisational creativity is a dynamic and synergistic process throughout which an idea is framed by recurrent activities subjected to multiple influences. Second, the definition of embedded Artist-in-Residence as an assemblage of dynamic, productive relations and unexpected possibilities for new networks of relationality that encourage the recombination of knowledge. Third, and most importantly, the acknowledgment that embedded residencies are, at the very essence, bi-cultural knowledge contexts where creativity flourishes as the result of open-to-change processes that are highly relational, constantly negotiated, and contextualised in time and space.

Keywords: artist-in-residence, convergent and divergent thinking, creativity, creative friction, deviance and creativity

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Authors: Obiamaka Nwobu, Samuel Faboyede, O. Oluseyi

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This study investigated the influence of gender on expectations and actual experiences from Industrial Work Experience, which is an aspect of the curriculum of undergraduate accounting students in selected Nigerian Universities. A survey research design was employed. Copies of a research questionnaire were made and administered to eighty (80) accounting students in selected Nigerian Universities who embarked on Students’ Industrial Work Experience Scheme (SIWES). Their expectations were juxtaposed with their actual experiences gleaned from the Industrial Work Experience. The data for the purpose of this study was analyzed using independent sample t-test. A total of fifteen (15) male and forty four (44) female students responded to the survey. This resulted in a response rate of 73.8 per cent. The results of this study indicated that there was no significant difference in the expectation of male and female undergraduate accounting students that the internship experience will be able to prepare them for an accounting career in the future, impart relevant knowledge, relate theories to work environment, enhance knowledge in financial accounting, cost accounting, accounting software, and general practice of accounting; prepare financial statements, interpret financial statements, develop problem solving skills, communication skills, and interpersonal skills; improve personal confidence and self-esteem, increase exposure to latest technology in the workplace, build rapport and networks, provide earnings, job experience, provide information and experience to choose career path. Furthermore, findings from the survey showed that there were differences in the expectations of students and their actual experiences with respect to their ability to relate theories to work environment, enhance knowledge in financial accounting, cost accounting, accounting software and exposure to latest technology in the workplace. The study only examined the perceptions of students from two Universities in South-West Nigeria. The research instrument used in this study can be administered to undergraduate accounting students in other universities in Nigeria. The Industrial Work Experience Scheme for undergraduate accounting students should be highly encouraged by tertiary institutions in Nigeria. This will ultimately make the students well prepared for a career in accounting.

Keywords: gender, expectations, actual experiences, industrial work experience

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Authors: Gevorgianiene Violeta, Sumskiene Egle

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Background. Child removal is a profound and life-altering measure that significantly impacts both children and their mothers. Unfortunately, mothers with intellectual disabilities are disproportionately affected by the removal of their children. This action is often taken due to concerns about the mother's perceived inability to care for the child, instances of abuse and neglect, or struggles with addiction. In many cases, the failure to meet society's standards of a "good mother" is seen as a deviation from conventional norms of femininity and motherhood. From an institutional perspective, separating a child from their mother is sometimes viewed as a step toward restoring justice or doing what is considered "right." In another light, this act of child removal can be seen as the removal of a mother from her child, an attempt to shield society from the complexities and fears associated with motherhood for women with disabilities. This separation can be likened to the Greek legend of the Minotaur, a fearsome beast confined within an impenetrable labyrinth. By reimagining this legend, we can see the social fears surrounding 'mothering with intellectual disability' as deeply sealed within an unreachable place. The Aim of this Presentation. Our goal with this presentation is to draw from our research and the metaphors found in the Greek legend to delve into the profound challenges faced by mothers with intellectual disabilities in raising their children. These challenges often become entangled within an insurmountable labyrinth, including navigating complex institutional bureaucracies, enduring persistent doubts cast upon their maternal competencies, battling unfavorable societal narratives, and struggling to retain custody of their children. Coupled with limited social support networks, these challenges frequently lead to situations resulting in maternal failure and, ultimately, child removal. On a broader scale, this separation of a child from their mother symbolizes society’s collective avoidance of confronting the issue of 'mothering with disability,' which can only be effectively addressed through united efforts. Conclusion. Just as in the labyrinth of the Minotaur legend, the struggles faced by mothers with disabilities in their pursuit of retaining their children reveal the need for a metaphorical 'string of Ariadne.' This string symbolizes the support offered by social service providers, communities, and the loved ones these women often dream of but rarely encounter in their lives.

Keywords: motherhood, disability, child removal, support.

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Authors: Islam Sherif, Ahmed Ashour, Ahmed Hassan, Hatem Osman

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Anterior Cruciate Ligament (ACL) injuries are common among athletes and individuals participating in sports with sudden stops, pivots, and changes in direction. Arthroscopic surgery is the gold standard for ACL reconstruction, aiming to restore knee stability and function. Recent years have witnessed significant advancements in arthroscopic surgery techniques, graft materials, and technological innovations, revolutionizing the field of ACL reconstruction. This presentation delves into the latest advancements in arthroscopic surgery techniques for ACL reconstruction and their potential impact on patient outcomes. Traditionally, autografts from the patellar tendon, hamstring tendon, or quadriceps tendon have been commonly used for ACL reconstruction. However, recent studies have explored the use of allografts, synthetic scaffolds, and tissue-engineered grafts as viable alternatives. This abstract evaluates the benefits and potential drawbacks of each graft type, considering factors such as graft incorporation, strength, and risk of graft failure. Moreover, the application of augmented reality (AR) and virtual reality (VR) technologies in surgical planning and intraoperative navigation has gained traction. AR and VR platforms provide surgeons with detailed 3D anatomical reconstructions of the knee joint, enhancing preoperative visualization and aiding in graft tunnel placement during surgery. We discuss the integration of AR and VR in arthroscopic ACL reconstruction procedures, evaluating their accuracy, cost-effectiveness, and overall impact on surgical outcomes. Beyond graft selection and surgical navigation, patient-specific planning has gained attention in recent research. Advanced imaging techniques, such as MRI-based personalized planning, enable surgeons to tailor ACL reconstruction procedures to each patient's unique anatomy. By accounting for individual variations in the femoral and tibial insertion sites, this personalized approach aims to optimize graft placement and potentially improve postoperative knee kinematics and stability. Furthermore, rehabilitation and postoperative care play a crucial role in the success of ACL reconstruction. This abstract explores novel rehabilitation protocols, emphasizing early mobilization, neuromuscular training, and accelerated recovery strategies. Integrating technology, such as wearable sensors and mobile applications, into postoperative care can facilitate remote monitoring and timely intervention, contributing to enhanced rehabilitation outcomes. In conclusion, this presentation provides an overview of the cutting-edge advancements in arthroscopic surgery techniques for ACL reconstruction. By embracing innovative graft materials, augmented reality, patient-specific planning, and technology-driven rehabilitation, orthopedic surgeons and sports medicine specialists can achieve superior outcomes in ACL injury management. These developments hold great promise for improving the functional outcomes and long-term success rates of ACL reconstruction, benefitting athletes and patients alike.

Keywords: arthroscopic surgery, ACL, autograft, allograft, graft materials, ACL reconstruction, synthetic scaffolds, tissue-engineered graft, virtual reality, augmented reality, surgical planning, intra-operative navigation

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Authors: Ntloyalefu Justinah, Fumane Khanare

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English learning has been found as one of the prevalent areas of difficulty for Deaf learners. However, studies conducted indicated that this challenge experienced by Deaf learners is an upsetting concern globally as is blamed and hampered by various reasons such as the way English is taught at schools, lack of teachers ' skills and knowledge, therefore, impact negatively on their academic performance. Despite any difficulty in English learning, this language is considered nowadays as the key tool to an educational and occupational career especially in Lesotho. This paper, therefore, intends to contribute to the existing literature by providing the views of Lesotho English teachers, which focuses on how effectively Universal design for learning can be implemented to enhance the academic performance of Deaf learners in context of the English language classroom. The purpose of this study sought to explore the use of universal design for learning (UDL) to support Deaf learners in Lesotho Secondary schools. The present study is informed by interpretative paradigm and situated within a qualitative research approach. Ten participating English teachers from two inclusive schools were purposefully selected and telephonically interviewed to generate data for this study. The data were thematically analysed. The findings indicated that even though UDL is identified as highly proficient and promotes flexibility in teaching methods teachers reflect limited knowledge of the UDL approach. The findings further showed that UDL ensures education for all learners, including marginalised groups, such as learners with disabilities through different teaching strategies. This means that the findings signify the effective use of UDL for the better performance of the English language by Deaf learners (DLs). This aligns with literature that shows mobilizing English teachers as assets help DLs to be engaged and have control in their communities by defining and solving problems using their resources and connections to other networks for asset and exchange. The study, therefore, concludes that teachers acknowledge that even though they assume to be knowledgeable about the definition of UDL, they have a limited practice of the approach, thus they need to be equipped with some techniques and skills to apply for supporting the performance of DLs by using UDL approach in their English teaching. The researchers recommend the awareness of UDL principles by the ministry of Education and Training and teachers training Universities, as well as teachers training colleges, for them to include it in their curricula so that teachers could be properly trained on how to apply it in their teaching effectively

Keywords: deaf learners, Lesotho, support learning, universal design for learning

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Authors: Ylva Fältholm, Cathrine Norberg

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One of the major threats identified to successful future mining is that women do not find the industry attractive. Many attempts have been made, for example in Sweden and Australia, to create organizational structures and mining communities attractive to both genders. Despite such initiatives, many mining areas are developing into gender-segregated fly-in/fly out communities dominated by men with both social and economic consequences. One of the challenges facing many mining companies is thus to break traditional gender patterns and structures. To do this increased knowledge about gender in the context of mining is needed. Since language both constitutes and reproduces knowledge, increased knowledge can be gained through an exploration and description of the mining discourse from a gender perspective. The aim of this study is to explore what conceptual ideas are activated in connection to the physical/geographical mining area and to work within the mining industry. We use a combination of critical discourse analysis implying close reading of selected texts, such as policy documents, interview materials, applications and research and innovation agendas, and analyses of linguistic patterns found in large language corpora covering millions of words of contemporary language production. The quantitative corpus data serves as a point of departure for the qualitative analysis of the texts, that is, suggests what patterns to explore further. The study shows that despite technological and organizational development, one of the most persistent discourses about mining is the conception of dangerous and unfriendly areas infused with traditional notions of masculinity ideals and manual hard work. Although some of the texts analyzed highlight gender issues, and describe gender-equalizing initiatives, such as wage-mapping systems, female networks and recruitment efforts for women executives, and thereby render the discourse less straightforward, it is shown that these texts are not unambiguous examples of a counter-discourse. They rather illustrate that discourses are not stable but include opposing discourses, in dialogue with each other. For example, many texts highlight why and how women are important to mining, at the same time as they suggest that gender and diversity are all about women: why mining is a problem for them, how they should be, and what they should do to fit in. Drawing on a constitutive view of discourse, knowledge about such conflicting perceptions of women is a prerequisite for succeeding in attracting women to the mining industry and thereby contributing to the development of future mining.

Keywords: discourse, corpus linguistics, gender, mining

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Authors: Dimitra Das, Anuradha Mitra, Kalyan Kumar Chattopadhyay

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Organic polymers, constructed from light elements like carbon, hydrogen, nitrogen, oxygen, sulphur, and boron atoms, are the emergent class of non-toxic, metal-free, environmental benign advanced materials. Covalent triazine-based polymers with a functional triazine group are significant class of organic materials due to their remarkable stability arising out of strong covalent bonds. They can conventionally form hydrogen bonds, favour π–π contacts, and they were recently revealed to be involved in interesting anion–π interactions. The present work mainly focuses upon the development of a single-crystalline, highly cross-linked triazine-based nitrogen-rich organic polymer with nanodendritic morphology and significant thermal stability. The polymer has been synthesized through hydrothermal treatment of melamine and ethylene glycol resulting in cross-polymerization via condensation-polymerization reaction. The crystal structure of the polymer has been evaluated by employing Rietveld whole profile fitting method. The polymer has been found to be composed of monoclinic melamine having space group P21/a. A detailed insight into the chemical structure of the as synthesized polymer has been elucidated by Fourier Transform Infrared Spectroscopy (FTIR) and Raman spectroscopic analysis. X-Ray Photoelectron Spectroscopic (XPS) analysis has also been carried out for further understanding of the different types of linkages required to create the backbone of the polymer. The unique rod-like morphology of the triazine based polymer has been revealed from the images obtained from Field Emission Scanning Electron Microscopy (FESEM) and Transmission Electron Microscopy (TEM). Interestingly, this polymer has been found to selectively detect mercury (Hg²⁺) ions at an extremely low concentration through fluorescent quenching with detection limit as low as 0.03 ppb. The high toxicity of mercury ions (Hg²⁺) arise from its strong affinity towards the sulphur atoms of biological building blocks. Even a trace quantity of this metal is dangerous for human health. Furthermore, owing to its small ionic radius and high solvation energy, Hg²⁺ ions remain encapsulated by water molecules making its detection a challenging task. There are some existing reports on fluorescent-based heavy metal ion sensors using covalent organic frameworks (COFs) but reports on mercury sensing using triazine based polymers are rather undeveloped. Thus, the importance of ultra-trace detection of Hg²⁺ ions with high level of selectivity and sensitivity has contemporary significance. A plausible sensing phenomenon by the polymer has been proposed to understand the applicability of the material as a potential sensor. The impressive sensitivity of the polymer sample towards Hg²⁺ is the very first report in the field of highly crystalline triazine based polymers (without the introduction of any sulphur groups or functionalization) towards mercury ion detection through photoluminescence quenching technique. This crystalline metal-free organic polymer being cheap, non-toxic and scalable has current relevance and could be a promising candidate for Hg²⁺ ion sensing at commercial level.

Keywords: fluorescence quenching , mercury ion sensing, single-crystalline, triazine-based polymer

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Authors: Victoria L. Chester, Usha Kuruganti

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The forest industry is a major economic sector of Canada and also one of the most dangerous industries for workers. The use of mechanized mobile forestry harvesting machines has successfully reduced the incidence of injuries in forest workers related to manual labor. However, these machines have also created additional concerns, including a high machine operation learning curve, increased the length of the workday, repetitive strain injury, cognitive load, physical and mental fatigue, and increased postural loads due to sitting in a confined space. It is critical to obtain objective performance data for employers to develop appropriate work practices for this industry, however ergonomic field studies of this industry are lacking mainly due to the difficulties in obtaining comprehensive data while operators are cutting trees in the woods. The purpose of this study was to establish a measurement and experimental protocol to examine the effects of worker skill level and movement training speed (joystick crane speed) on harvesting performance using a forestry simulator. A custom wrist angle measurement device was developed as part of the study to monitor Euler angles during operation of the simulator. The device of the system consisted of two accelerometers, a Bluetooth module, three 3V coin cells, a microcontroller, a voltage regulator and an application software. Harvesting performance and crane data was provided by the simulator software and included tree to frame collisions, crane to tree collisions, boom tip distance, number of trees cut, etc. A pilot study of 3 operators with various skill levels was tested to identify factors that distinguish highly skilled operators from novice or intermediate operators. Dependent variables such as reaction time, math skill, past work experience, training movement speed (e.g. joystick control speeds), harvesting experience level, muscle activity, and wrist biomechanics were measured and analyzed. A 10-channel wireless surface EMG system was used to monitor the amplitude and mean frequency of 10 upper extremity muscles during pre and postperformance on the forestry harvest stimulator. The results of the pilot study showed inconsistent changes in median frequency pre-and postoperation, but there was the increase in the activity of the flexor carpi radialis, anterior deltoid and upper trapezius of both arms. The wrist sensor results indicated that wrist supination and pronation occurred more than flexion and extension with radial-ulnar rotation demonstrating the least movement. Overall, wrist angular motion increased as the crane speed increased from slow to fast. Further data collection is needed and will help industry partners determine those factors that separate skill levels of operators, identify optimal training speeds, and determine the length of training required to bring new operators to an efficient skill level effectively. In addition to effective and employment training programs, results of this work will be used for selective employee recruitment strategies to improve employee retention after training. Further, improved training procedures and knowledge of the physical and mental demands on workers will lead to highly trained and efficient personnel, reduced risk of injury, and optimal work protocols.

Keywords: EMG, forestry, human factors, wrist biomechanics

Procedia PDF Downloads 147

Authors: Antonella Falanga, Armando Cartenì

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"Big data" refers to extremely vast and complex sets of data, encompassing extraordinarily large and intricate datasets that require specific tools for meaningful analysis and processing. These datasets can stem from diverse origins like sensors, mobile devices, online transactions, social media platforms, and more. The utilization of big data is pivotal, offering the chance to leverage vast information for substantial advantages across diverse fields, thereby enhancing comprehension, decision-making, efficiency, and fostering innovation in various domains. Big data, distinguished by its remarkable attributes of enormous volume, high velocity, diverse variety, and significant value, represent a transformative force reshaping the industry worldwide. Their pervasive impact continues to unlock new possibilities, driving innovation and advancements in technology, decision-making processes, and societal progress in an increasingly data-centric world. The use of these technologies is becoming more widespread, facilitating and accelerating operations that were once much more complicated. In particular, big data impacts across multiple sectors such as business and commerce, healthcare and science, finance, education, geography, agriculture, media and entertainment and also mobility and logistics. Within the transportation sector, which is the focus of this study, big data applications encompass a wide variety, spanning across optimization in vehicle routing, real-time traffic management and monitoring, logistics efficiency, reduction of travel times and congestion, enhancement of the overall transportation systems, but also mitigation of pollutant emissions contributing to environmental sustainability. Meanwhile, in public administration and the development of smart cities, big data aids in improving public services, urban planning, and decision-making processes, leading to more efficient and sustainable urban environments. Access to vast data reservoirs enables deeper insights, revealing hidden patterns and facilitating more precise and timely decision-making. Additionally, advancements in cloud computing and artificial intelligence (AI) have further amplified the potential of big data, enabling more sophisticated and comprehensive analyses. Certainly, utilizing big data presents various advantages but also entails several challenges regarding data privacy and security, ensuring data quality, managing and storing large volumes of data effectively, integrating data from diverse sources, the need for specialized skills to interpret analysis results, ethical considerations in data use, and evaluating costs against benefits. Addressing these difficulties requires well-structured strategies and policies to balance the benefits of big data with privacy, security, and efficient data management concerns. Building upon these premises, the current research investigates the efficacy and influence of big data by conducting an overview of the primary and recent implementations of big data in transportation systems. Overall, this research allows us to conclude that big data better provide to enhance rational decision-making for mobility choices and is imperative for adeptly planning and allocating investments in transportation infrastructures and services.

Keywords: big data, public transport, sustainable mobility, transport demand, transportation planning

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Authors: Baptiste Brodard

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In Switzerland, as well as in other European countries, specific social issues related to Muslims have recently emerged in public debates. In addition to the question of terrorism and radicalization, Muslim migrant populations are highly affected by social problems such as crime, poverty, marginalization, and overrepresentation in prisons. This situation has drawn the state’s attention to the need for implementing new responses to the challenges of religious extremism, crime, and social exclusion particularly involving Muslims. While local authorities have begun to implement trainings and projects to tackle these new social issues, Muslim grassroots associations have developed some initiatives to address the needs of the population, mainly focusing on problems related to Islam and Muslims but also addressing the rest of the population. Finally, some local authorities have acknowledged the need for these alternative initiatives as well as their positive contributions to society. The study is based on a Ph.D. research grounded on a case study of three Islamic networks in Switzerland, including various local organizations tackling social exclusion and religious radicalization through innovative grassroots projects. Using an ethnographic approach, it highlights, on the one hand, the specificities of such organizations by exploring the role of Islamic norms within the social work practices. On the other hand, it focuses on the inclusion of such faith-based projects within the mainstream society, observing the relationships between Islamic organisations and both the state and other civil society organizations. Finally, the research study aims to identify some innovative ways and trends of social work involving the inclusion of community key actors within the process. Results showed similar trends with Islamic social work developed in other European countries such as France and the United Kingdom, but also indicate a range of specificities linked to the Swiss socio-political context, which shapes the involvement of religious actors in different ways. By exploring faith-based commitment to addressing concrete social issues, the study finally contributes to shedding light on the link between Islam, social work and activism within the European context.

Keywords: exclusion, Islam, Muslims, social work, Switzerland

Procedia PDF Downloads 128

Authors: Magdalena Smoktunowicz, Renata Wawrzyniak, Malgorzata Waleron, Krzysztof Waleron

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Pectobacterium spp. were previously classified into the Erwinia genus founded in 1917 to unite at that time all Gram-negative, fermentative, nonsporulating and peritrichous flagellated plant pathogenic bacteria. After work of Waldee (1945), on Approved Lists of Bacterial Names and bacteriology manuals in 1980, they were described either under the species named Erwinia or Pectobacterium. The Pectobacterium genus was formally described in 1998 of 265 Pectobacterium strains. Currently, there are 21 species of Pectobacterium bacteria, including Pectobacterium betavasculorum since 2003, which caused soft rot on sugar beet tubers. Based on the biochemical experiments carried out for this, it is known that these bacteria are gram-negative, catalase-positive, oxidase-negative, facultatively anaerobic, using gelatin and causing symptoms of soft rot on potato and sugar beet tubers. The mere fact of growing on sugar beet may indicate a metabolism characteristic only for this species. Metabolomics, broadly defined as the biology of the metabolic systems, which allows to make comprehensive measurements of metabolites. Metabolomics, in combination with genomics, are complementary tools for the identification of metabolites and their reactions, and thus for the reconstruction of metabolic networks. The aim of this study was to apply the GC-MS-based untargeted metabolomics to study the metabolism of P. betavasculorum in different growing conditions. The metabolomic profiles of biomass and biomass media were determined. For sample preparation the following protocol was used: extraction with 900 µl of methanol: chloroform: water mixture (10: 3: 1, v: v) were added to 900 µl of biomass from the bottom of the tube and up to 900 µl of nutrient medium from the bacterial biomass. After centrifugation (13,000 x g, 15 min, 4oC), 300µL of the obtained supernatants were concentrated by rotary vacuum and evaporated to dryness. Afterwards, two-step derivatization procedure was performed before GC-MS analyses. The obtained results were subjected to statistical calculations with the use of both uni- and multivariate tests. The obtained results were evaluated using KEGG database, to asses which metabolic pathways are activated and which genes are responsible for it, during the metabolism of given substrates contained in the growing environment. The observed metabolic changes, combined with biochemical and physiological tests, may enable pathway discovery, regulatory inference and understanding of the homeostatic abilities of P. betavasculorum.

Keywords: GC-MS chromatograpfy, metabolomics, metabolism, pectobacterium strains, pectobacterium betavasculorum

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Authors: Liza Ireni-Saban

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The 6.6 magnitude quake event that occurred in 2003 (Bam, Iran) made it impossible for the Iranian government to handle disaster relief efforts domestically. In this extreme event, the Iranian government reached out to the international community, and this created a momentum that had to be carried out by trust-building efforts on all sides, often termed ‘Disaster Diplomacy’. Indeed, the circumstances were even more critical when one considers the increasing political and economic isolation of Iran within the international community. The potential for transformative political space to be opened by disaster has been recognized by dominant international political actors. Despite the fact that Bam 2003 post-disaster relief efforts did not catalyze any diplomatic activities on all sides, it is suggested that few international aid agencies have successfully used disaster recovery to enhance their popular legitimacy and reputation among the international community. In terms of disaster diplomacy, an actor’s reputational capital may affect his ability to build coalitions and alliances to achieve international political ends, to negotiate and build understanding and trust with foreign publics. This study suggests that the post-disaster setting may benefit from using the ecology of games framework to evaluate the role of bridging actors and mediators in facilitating collaborative governance networks. Recent developments in network theory and analysis provide means of structural embeddedness to explore how reputational capital can be built through brokerage roles of actors engaged in a disaster management network. This paper then aims to structure the relations among actors that participated in the post-disaster relief efforts in the 2003 Bam earthquake (Iran) in order to assess under which conditions actors may be strategically utilized to serve as mediating organizations for future disaster events experienced by isolated nations or nations in conflict. The results indicate the strategic use of reputational capital by the Iranian Ministry of Foreign Affairs as key broker to build a successful coordinative system for reducing disaster vulnerabilities. International aid agencies rarely played brokerage roles to coordinate peripheral actors. U.S. foreign assistance (USAID), despite coordination capacities, was prevented from serving brokerage roles in the system.

Keywords: coordination, disaster diplomacy, international aid organizations, Iran

Procedia PDF Downloads 155

Authors: Christos Karolemeas, Avgi Vassi, Georgia Christodoulopoulou

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Although the past decades, planning a cycle network became an inseparable part of all transportation plans, there is still a lot of room for improvement in the way planning is made, in order to create safe and direct cycling networks that gather the parameters that positively influence one's decision to cycle. The aim of this article is to study, evaluate and visualize the bikeability of cities. This term is often used as the 'the ability of a person to bike' but this study, however, adopts the term in the sense of bikeability as 'the ability of the urban landscape to be biked'. The methodology used included assessing cities' accessibility by cycling, based on international literature and corresponding walkability methods and the creation of a 'bikeability index'. Initially, a literature review was made to identify the factors that positively affect the use of bicycle infrastructure. Those factors were used in order to create the spatial index and quantitatively compare the city network. Finally, the bikeability index was applied in two case studies: two Greek municipalities that, although, they have similarities in terms of land uses, population density and traffic congestion, they are totally different in terms of geomorphology. The factors suggested by international literature were (a) safety, (b) directness, (c) comfort and (d) the quality of the urban environment. Those factors were quantified through the following parameters: slope, junction density, traffic density, traffic speed, natural environment, built environment, activities coverage, centrality and accessibility to public transport stations. Each road section was graded for the above-mentioned parameters, and the overall grade shows the level of bicycle accessibility (low, medium, high). Each parameter, as well as the overall accessibility levels, were analyzed and visualized through Geographic Information Systems. This paper presents the bikeability index, its' results, the problems that have arisen and the conclusions from its' implementation through Strengths-Weaknesses-Opportunities-Threats analysis. The purpose of this index is to make it easy for researchers, practitioners, politicians, and stakeholders to quantify, visualize and understand which parts of the urban fabric are suitable for cycling.

Keywords: accessibility, cycling, green spaces, spatial data, urban environment

Procedia PDF Downloads 111

Authors: Mohammad R. Jafari, Francois G. Bernardeau

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With its arid climate, Qatar experiences low annual rainfall, intense storms, and high evaporation rates. However, the fast-paced rate of infrastructure development in the capital city of Doha has led to recurring instances of surface water flooding as well as rising groundwater levels. Public Work Authority (PWA/ASHGHAL) has implemented an approach to collect and discharge the flood water into a) positive gravity systems; b) Emergency Flooding Area (EFA) – Evaporation, Infiltration or Storage off-site using tankers; and c) Discharge to deep injection wells. As part of the flood prevention scheme, 21 deep injection wells have been constructed to discharge the collected surface and groundwater table in Doha city. These injection wells function as an alternative in localities that do not possess either positive gravity systems or downstream networks that can accommodate additional loads. These injection wells are 400-m deep and are constructed in a complex karstic subsurface condition with large cavities. The injection well system will discharge collected groundwater and storm surface runoff into the permeable Umm Er Radhuma Formation, which is an aquifer present throughout the Persian Gulf Region. The Umm Er Radhuma formation contains saline water that is not being used for water supply. The injection zone is separated by an impervious gypsum formation which acts as a barrier between upper and lower aquifer. State of the art drilling, grouting, and geophysical techniques have been implemented in construction of the wells to assure that the shallow aquifer would not be contaminated and impacted by injected water. Injection and pumping tests were performed to evaluate injection well functionality (injectability). The results of these tests indicated that majority of the wells can accept injection rate of 200 to 300 m³ /h (56 to 83 l/s) under gravity with average value of 250 m³ /h (70 l/s) compared to design value of 50 l/s. This paper presents design and construction process and issues associated with these injection wells, performing injection/pumping tests to determine capacity and effectiveness of the injection wells, the detailed design of collection system and conveying system into the injection wells, and the operation and maintenance process. This system is completed now and is under operation, and therefore, construction of injection wells is an effective option for flood control.

Keywords: deep injection well, flood prevention scheme, geophysical tests, pumping and injection tests, wellhead assembly

Procedia PDF Downloads 119

Authors: Ezra Kavana

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This article analyses the role those East African countries play in enforcing crude oil pipeline regulations. The paper finds that countries are more likely to have responsibility for enforcing these regulations if they have larger networks of gathering and transmission lines and if their citizens are more liberal and more pro-environment., Pipeline operations, transportation costs, new pipeline construction, and environmental effects are all heavily controlled. All facets of pipeline systems and the facilities connected to them are governed by statutory bodies. In order to support the project manager on such new pipeline projects, companies building and running these pipelines typically include personnel and consultants who specialize in these permitting processes. The primary permissions that can be necessary for pipelines carrying different commodities are mentioned in this paper. National, regional, and local municipalities each have their own permits. Through their right-of-way group, the contractor's project compliance leadership is typically directly responsible for obtaining those permits, which are typically obtained through government agencies. The whole list of local permits needed for a planned pipeline can only be found after a careful field investigation. A country's government regulates pipelines that are entirely within its borders. With a few exceptions, state regulations governing ratemaking and safety have been enacted to be consistent with regulatory requirements. Countries that produce a lot of energy are typically more involved in regulating pipelines than countries that produce little to no energy. To identify the proper regulatory authority, it is important to research the several government agencies that regulate pipeline transportation. Additionally, it's crucial that the scope determination of a planned project engage with a various external professional with experience in linear facilities or the company's pipeline construction and environmental professional to identify and obtain any necessary design clearances, permits, or approvals. These professionals can offer precise estimations of the costs and length of time needed to process necessary permits. Governments with a stronger energy sector, on the other hand, are less likely to take on control. However, the performance of the pipeline or national enforcement activities are unaffected significantly by whether a government has taken on control. Financial fines are the most efficient government enforcement instrument because they greatly reduce occurrences and property damage.

Keywords: crude oil, pipeline, regulatory compliance, and construction permits

Procedia PDF Downloads 96

Authors: Josiane Yaguibou, Ngoy Kishimba, Issiaka V. Coulibaly, Sabrina Pestilli, Falinirina Razanalison, Hantanirina Andremanisa

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Background: Madagascar has one of the least developed road networks in the world with a majority of its national and local roads being earth roads and in poor condition. In addition, the country is affected by frequent natural disasters that further affect the road conditions limiting the accessibility to some parts of the country. In 2021 and 2022, 2.21 million people were affected by drought in the Grand Sud region, and by cyclones and floods in the coastal regions, with disruptions of the health system including last mile distribution of lifesaving maternal health commodities and reproductive health commodities in the health facilities. Program intervention: The intervention uses drone technology to deliver maternal health and family planning commodities in hard-to-reach health facilities in the Grand Sud and Sud-Est of Madagascar, the regions more affected by natural disasters. Methodology The intervention was developed in two phases. A first phase, conducted in the Grand Sud, used drones leased from a private company to deliver commodities in isolated health facilities. Based on the lesson learnt and encouraging results of the first phase, in the second phase (2023) the intervention has been extended to the Sud Est regions with the purchase of drones and the recruitment of pilots to reduce costs and ensure sustainability. Key findings: The drones ensure deliveries of lifesaving commodities in the Grand Sud of Madagascar. In 2023, 297 deliveries in commodities in forty hard-to-reach health facilities have been carried out. Drone technology reduced delivery times from the usual 3 - 7 days necessary by road or boat to only a few hours. Program Implications: The use of innovative drone technology demonstrated to be successful in the Madagascar context to reduce dramatically the distribution time of commodities in hard-to-reach health facilities and avoid stockouts of life-saving medicines. When the intervention reaches full scale with the completion of the second phase and the extension in the Sud-Est, 150 hard-to-reach facilities will receive drone deliveries, avoiding stockouts and improving the quality of maternal health and family planning services offered to 1,4 million people in targeted areas.

Keywords: commodities, drones, last-mile distribution, lifesaving supplies

Procedia PDF Downloads 65

Authors: Vanessa Kummer, Maik Meusel

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Real estate demand estimates are typically derived from transaction data. However, in regions with excess demand, transactions are driven by supply and therefore do not indicate what people are actually looking for. To estimate the demand for housing in Switzerland, search subscriptions from all important Swiss real estate platforms are used. These data do, however, suffer from missing information—for example, many users do not specify how many rooms they would like or what price they would be willing to pay. In economic analyses, it is often the case that only complete data is used. Usually, however, the proportion of complete data is rather small which leads to most information being neglected. Also, the data might have a strong distortion if it is complete. In addition, the reason that data is missing might itself also contain information, which is however ignored with that approach. An interesting issue is, therefore, if for economic analyses such as the one at hand, there is an added value by using the whole data set with the imputed missing values compared to using the usually small percentage of complete data (baseline). Also, it is interesting to see how different algorithms affect that result. The imputation of the missing data is done using unsupervised learning. Out of the numerous unsupervised learning approaches, the most common ones, such as clustering, principal component analysis, or neural networks techniques are applied. By training the model iteratively on the imputed data and, thereby, including the information of all data into the model, the distortion of the first training set—the complete data—vanishes. In a next step, the performances of the algorithms are measured. This is done by randomly creating missing values in subsets of the data, estimating those values with the relevant algorithms and several parameter combinations, and comparing the estimates to the actual data. After having found the optimal parameter set for each algorithm, the missing values are being imputed. Using the resulting data sets, the next step is to estimate the willingness to pay for real estate. This is done by fitting price distributions for real estate properties with certain characteristics, such as the region or the number of rooms. Based on these distributions, survival functions are computed to obtain the functional relationship between characteristics and selling probabilities. Comparing the survival functions shows that estimates which are based on imputed data sets do not differ significantly from each other; however, the demand estimate that is derived from the baseline data does. This indicates that the baseline data set does not include all available information and is therefore not representative for the entire sample. Also, demand estimates derived from the whole data set are much more accurate than the baseline estimation. Thus, in order to obtain optimal results, it is important to make use of all available data, even though it involves additional procedures such as data imputation.

Keywords: demand estimate, missing-data imputation, real estate, unsupervised learning

Procedia PDF Downloads 285

Authors: Dr. Dionysis Panos Dpt. Communication, Internet Studies Cyprus University of Technology

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The widespread use of New Media and particularly Social Media, through fixed or mobile devices, has changed in a staggering way our perception about what is “intimate" and "safe" and what is not, in interpersonal communication and social relationships. The distribution of self and identity-related information in communication now evolves under new and different conditions and contexts. Consequently, this new framework forces us to rethink processes and mechanisms, such as what "exposure" means in interpersonal communication contexts, how the distinction between the "private" and the "public" nature of information is being negotiated online, how the "audiences" we interact with are understood and constructed. Drawing from an interdisciplinary perspective that combines sociology, communication psychology, media theory, new media and social networks research, as well as from the empirical findings of a longitudinal comparative research, this work proposes an integrative model for comprehending mechanisms of personal information management in interpersonal communication, which can be applied to both types of online (Computer-Mediated) and offline (Face-To-Face) communication. The presentation is based on conclusions drawn from a longitudinal qualitative research study with 458 new media users from 24 countries for almost over a decade. Some of these main conclusions include: (1) There is a clear and evidenced shift in users’ perception about the degree of "security" and "familiarity" of the Web, between the pre- and the post- Web 2.0 era. The role of Social Media in this shift was catalytic. (2) Basic Web 2.0 applications changed dramatically the nature of the Internet itself, transforming it from a place reserved for “elite users / technical knowledge keepers" into a place of "open sociability” for anyone. (3) Web 2.0 and Social Media brought about a significant change in the concept of “audience” we address in interpersonal communication. The previous "general and unknown audience" of personal home pages, converted into an "individual & personal" audience chosen by the user under various criteria. (4) The way we negotiate the nature of 'private' and 'public' of the Personal Information, has changed in a fundamental way. (5) The different features of the mediated environment of online communication and the critical changes occurred since the Web 2.0 advance, lead to the need of reconsideration and updating the theoretical models and analysis tools we use in our effort to comprehend the mechanisms of interpersonal communication and personal information management. Therefore, is proposed here a new model for understanding the way interpersonal communication evolves, based on a revision of social penetration theory.

Keywords: new media, interpersonal communication, social penetration theory, communication exposure, private information, public information

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Authors: Darshini Mahadevia, Kanika Gounder, Saumya Lathia

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Cities, an outcome of natural growth and migration, are ever-expanding due to urban sprawl. In the Global South, urban areas are experiencing a switch from public transport to private vehicles, coupled with intensified urban agglomeration, leading to frequent longer commutes by automobiles. This increase in travel distance and motorized vehicle kilometres lead to unsustainable cities. To achieve the nationally pledged GHG emission mitigation goal, the government is prioritizing a modal shift to low-carbon transport modes like mass transit and paratransit. Mixed land-use and urban densification are crucial for the economic viability of these projects. Informed by desktop assessment of mobility plans and in-person primary surveys, the paper explores the challenges around urban densification and travel patterns in two Indian cities of contrasting nature- Surat, a metropolitan industrial city with a 5.9 million population and a very compact urban form, and Udaipur, a heritage city attracting large international tourists’ footfall, with limited scope for further densification. Dense, mixed-use urban areas often improve access to basic services and economic opportunities by reducing distances and enabling people who don't own personal vehicles to reach them on foot/ cycle. But residents travelling on different modes end up contributing to similar trip lengths, highlighting the non-uniform distribution of land-uses and lack of planned transport infrastructure in the city and the urban-peri urban networks. Additionally, it is imperative to manage these densities to reduce negative externalities like congestion, air/noise pollution, lack of public spaces, loss of livelihood, etc. The study presents a comparison of the relationship between transport systems with the built form in both cities. The paper concludes with recommendations for managing densities in urban areas along with promoting low-carbon transport choices like improved non-motorized transport and public transport infrastructure and minimizing personal vehicle usage in the Global South.

Keywords: India, low-carbon transport, travel behaviour, trip length, urban densification

Procedia PDF Downloads 217

Authors: Haiqin Zhou, Robin Irons

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The introduction of hydrogen into local networks may, in many cases, require the initial operation of those systems on natural gas/hydrogen blends, either because of a lack of sufficient hydrogen to allow a 100% conversion or because existing infrastructure imposes limitations on the % hydrogen that can be burned before the end-use technologies are replaced. In many systems, the largest number of end-use technologies are small-scale but numerous appliances used for domestic and industrial heating and cooking. In such a scenario, it is important to understand exactly how much hydrogen can be introduced into these appliances before their performance becomes unacceptable and what imposes that limitation. This study seeks to explore a range of significantly higher hydrogen blends and a broad range of factors that might limit operability or environmental acceptability. We will present tests from a burner designed for space heating and optimized for natural gas as an increasing % of hydrogen blends (increasing from 25%) were burned and explore the range of parameters that might govern the acceptability of operation. These include gaseous emissions (particularly NOx and unburned carbon), temperature, flame length, stability and general operational acceptability. Results will show emissions, Temperature, and flame length as a function of thermal load and percentage of hydrogen in the blend. The relevant application and regulation will ultimately determine the acceptability of these values, so it is important to understand the full operational envelope of the burners in question through the sort of extensive parametric testing we have carried out. The present dataset should represent a useful data source for designers interested in exploring appliance operability. In addition to this, we present data on two factors that may be absolutes in determining allowable hydrogen percentages. The first of these is flame blowback. Our results show that, for our system, the threshold between acceptable and unacceptable performance lies between 60 and 65% mol% hydrogen. Another factor that may limit operation, and which would be important in domestic applications, is the acoustic performance of these burners. We will describe a range of operational conditions in which hydrogen blend burners produce a loud and invasive ‘screech’. It will be important for equipment designers and users to find ways to avoid this or mitigate it if performance is to be deemed acceptable.

Keywords: blends, operational, domestic appliances, future system operation.

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Authors: Maxence Queyrel, Edi Prifti, Alexandre Templier, Jean-Daniel Zucker

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Analysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and stored as fastq files. Conventional processing pipelines consist in multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimensionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life data-sets as well a simulated one, we demonstrated that this original approach reaches high performance, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks.

Keywords: deep learning, disease prediction, end-to-end machine learning, metagenomics, multiple instance learning, precision medicine

Procedia PDF Downloads 125

Authors: Esmael Makarian, Ayub Elyasi, Fatemeh Saberi, Olusegun Stanley Tomomewo

Abstract:

Rock mechanical investigation is one of the most crucial activities in underground operations, especially in surveys related to hydrocarbon exploration and production, geothermal reservoirs, energy storage, mining, and geotechnics. There is a wide range of traditional methods for driving, collecting, and analyzing rock mechanics data. However, these approaches may not be suitable or work perfectly in some situations, such as fractured zones. Cutting-edge technologies have been provided to solve and optimize the mentioned issues. Internet of Things (IoT), Edge, and Cloud Computing technologies (ECt & CCt, respectively) are among the most widely used and new artificial intelligence methods employed for geomechanical studies. IoT devices act as sensors and cameras for real-time monitoring and mechanical-geological data collection of rocks, such as temperature, movement, pressure, or stress levels. Structural integrity, especially for cap rocks within hydrocarbon systems, and rock mass behavior assessment, to further activities such as enhanced oil recovery (EOR) and underground gas storage (UGS), or to improve safety risk management (SRM) and potential hazards identification (P.H.I), are other benefits from IoT technologies. EC techniques can process, aggregate, and analyze data immediately collected by IoT on a real-time scale, providing detailed insights into the behavior of rocks in various situations (e.g., stress, temperature, and pressure), establishing patterns quickly, and detecting trends. Therefore, this state-of-the-art and useful technology can adopt autonomous systems in rock mechanical surveys, such as drilling and production (in hydrocarbon wells) or excavation (in mining and geotechnics industries). Besides, ECt allows all rock-related operations to be controlled remotely and enables operators to apply changes or make adjustments. It must be mentioned that this feature is very important in environmental goals. More often than not, rock mechanical studies consist of different data, such as laboratory tests, field operations, and indirect information like seismic or well-logging data. CCt provides a useful platform for storing and managing a great deal of volume and different information, which can be very useful in fractured zones. Additionally, CCt supplies powerful tools for predicting, modeling, and simulating rock mechanical information, especially in fractured zones within vast areas. Also, it is a suitable source for sharing extensive information on rock mechanics, such as the direction and size of fractures in a large oil field or mine. The comprehensive review findings demonstrate that digital transformation through integrated IoT, Edge, and Cloud solutions is revolutionizing traditional rock mechanical investigation. These advanced technologies have empowered real-time monitoring, predictive analysis, and data-driven decision-making, culminating in noteworthy enhancements in safety, efficiency, and sustainability. Therefore, by employing IoT, CCt, and ECt, underground operations have experienced a significant boost, allowing for timely and informed actions using real-time data insights. The successful implementation of IoT, CCt, and ECt has led to optimized and safer operations, optimized processes, and environmentally conscious approaches in underground geological endeavors.

Keywords: rock mechanical studies, internet of things, edge computing, cloud computing, underground surveys, geological operations

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