Search results for: ideal and actual coping

Authors: Margherita Tufarelli, Leonardo Giliberti, Elena Pucci

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The fashion industry's interest in virtuality is linked, on the one hand, to the emotional and immersive possibilities of digital resources and the resulting languages and, on the other, to the greater efficiency that can be achieved throughout the value chain. The interaction between digital innovation and deep-rooted manufacturing traditions today translates into a paradigm shift for the entire fashion industry where, for example, the traditional values of industrial secrecy and know-how give way to experimentation in an open as well as participatory way, and the complete emancipation of virtual reality from actual 'reality'. The contribution aims to investigate the theme of digitisation in the Italian fashion industry, analysing its opportunities and the criticalities that have hindered its diffusion. There are two reasons why the most common approach in the fashion sector is still analogue: (i) the fashion product lives in close contact with the human body, so the sensory perception of materials plays a central role in both the use and the design of the product, but current technology is not able to restore the sense of touch; (ii) volumes are obtained by stitching flat surfaces that once assembled, given the flexibility of the material, can assume almost infinite configurations. Managing the fit and styling of virtual garments involves a wide range of factors, including mechanical simulation, collision detection, and user interface techniques for garment creation. After briefly reviewing some of the salient historical milestones in the resolution of problems related to the digital simulation of deformable materials and the user interface for the procedures for the realisation of the clothing system, the paper will describe the operation and possibilities offered today by the latest generation of specialised software. Parametric avatars and digital sartorial approach; drawing tools optimised for pattern making; materials both from the point of view of simulated physical behaviour and of aesthetic performance, tools for checking wearability, renderings, but also tools and procedures useful to companies both for dialogue with prototyping software and machinery and for managing the archive and the variants to be made. The article demonstrates how developments in technology and digital procedures now make it possible to intervene in different stages of design in the fashion industry. An integrated and additive process in which the constructed 3D models are usable both in the prototyping and communication of physical products and in the possible exclusively digital uses of 3D models in the new generation of virtual spaces. Mastering such tools requires the acquisition of specific digital skills and, at the same time, traditional skills for the design of the clothing system, but the benefits are manifold and applicable to different business dimensions. We are only at the beginning of the global digital transformation: the emergence of new professional figures and design dynamics leaves room for imagination, but in addition to applying digital tools to traditional procedures, traditional fashion know-how needs to be transferred into emerging digital practices to ensure the continuity of the technical-cultural heritage beyond the transformation.

Keywords: digital fashion, digital technology and couture, digital fashion communication, 3D garment simulation

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Authors: A. Augustyn, M. Florek, M. Herezniak

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Since the public authorities are constantly pressured by the public opinion to showcase the tangible and measurable results of their efforts, the evaluation of place brand-related activities becomes a necessity. Given the political and social character of place branding process, the legitimization of the branding efforts requires the compliance of the objectives set out in the city brand strategy with the actual needs, expectations, and aspirations of various internal stakeholders. To deliver on the diverse promises, city authorities and brand managers need to translate them into the measurable indicators against which the brand strategy effectiveness will be evaluated. In concert with these observations are the findings from branding and marketing literature with a widespread consensus that places should adopt a more systematic and holistic approach in order to ensure the performance of their brands. However, the measurement of the effectiveness of place branding remains insufficiently explored in theory, even though it is considered a significant step in the process of place brand management. Therefore, the aim of the research presented in the current paper was to collect insights on the nature of effectiveness measurement of city brand strategies and to juxtapose these findings with the theoretical assumptions formed on the basis of the state-of-the-art literature review. To this end, 15 international academic experts (out of 18 initially selected) with affiliation from ten countries (five continents), were individually interviewed. The standardized set of 19 open-ended questions was used for all the interviewees, who had been selected based on their expertise and reputation in the fields of place branding/marketing. Findings were categorized into four modules: (i) conceptualizations of city brand effectiveness, (ii) methodological issues of city brand effectiveness measurement, (iii) the nature of measurement process, (iv) articulation of key performance indicators (KPIs). Within each module, the interviewees offered diverse insights into the subject based on their academic expertise and professional activity as consultants. They proposed that there should be a twofold understanding of effectiveness. The narrow one when it is conceived as the aptitude to achieve specific goals, and the broad one in which city brand effectiveness is seen as an increase in social and economic reality of a place, which in turn poses diverse challenges for the measurement concepts and processes. Moreover, the respondents offered a variety of insights into the methodological issues, particularly about the need for customization and flexibility of the measurement systems, for the employment of interdisciplinary approach to measurement and implications resulting therefrom. Considerable emphasis was put on the inward approach to measurement, namely the necessity to monitor the resident’s evaluation of brand related activities instead of benchmarking cities against the competitive set. Other findings encompass the issues of developing appropriate KPIs for the city brand, managing the measurement process and the inclusion of diverse stakeholders to produce a sound measurement system. Furthermore, the interviewees enumerated the most frequently made mistakes in measurement mainly resulting from the misunderstanding of the nature of city brands. This research was financed by the National Science Centre, Poland, research project no. 2015/19/B/HS4/00380 Towards the categorization of place brand strategy effectiveness indicators – findings from strategic documents of Polish district cities – theoretical and empirical approach.

Keywords: city branding, effectiveness, experts’ insights, measurement

Procedia PDF Downloads 144

Authors: Pierobon Enrico, Missiaggia Marta, Castelluzzo Michele, Tommasino Francesco, Ricci Leonardo, Scifoni Emanuele, Vincezo Monaco, Boscardin Maurizio, La Tessa Chiara

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Clinical outcomes collected over the past three decades have suggested that ion therapy has the potential to be a treatment modality superior to conventional radiation for several types of cancer, including recurrences, as well as for other diseases. Although the results have been encouraging, numerous treatment uncertainties remain a major obstacle to the full exploitation of particle radiotherapy. To overcome therapy uncertainties optimizing treatment outcome, the best possible radiation quality description is of paramount importance linking radiation physical dose to biological effects. Microdosimetry was developed as a tool to improve the description of radiation quality. By recording the energy deposition at the micrometric scale (the typical size of a cell nucleus), this approach takes into account the non-deterministic nature of atomic and nuclear processes and creates a direct link between the dose deposited by radiation and the biological effect induced. Microdosimeters measure the spectrum of lineal energy y, defined as the energy deposition in the detector divided by most probable track length travelled by radiation. The latter is provided by the so-called “Mean Chord Length” (MCL) approximation, and it is related to the detector geometry. To improve the characterization of the radiation field quality, we define a new quantity replacing the MCL with the actual particle track length inside the microdosimeter. In order to measure this new quantity, we propose a two-stage detector consisting of a commercial Tissue Equivalent Proportional Counter (TEPC) and 4 layers of Low Gain Avalanche Detectors (LGADs) strips. The TEPC detector records the energy deposition in a region equivalent to 2 um of tissue, while the LGADs are very suitable for particle tracking because of the thickness thinnable down to tens of micrometers and fast response to ionizing radiation. The concept of HDM has been investigated and validated with Monte Carlo simulations. Currently, a dedicated readout is under development. This two stages detector will require two different systems to join complementary information for each event: energy deposition in the TEPC and respective track length recorded by LGADs tracker. This challenge is being addressed by implementing SoC (System on Chip) technology, relying on Field Programmable Gated Arrays (FPGAs) based on the Zynq architecture. TEPC readout consists of three different signal amplification legs and is carried out thanks to 3 ADCs mounted on a FPGA board. LGADs activated strip signal is processed thanks to dedicated chips, and finally, the activated strip is stored relying again on FPGA-based solutions. In this work, we will provide a detailed description of HDM geometry and the SoC solutions that we are implementing for the readout.

Keywords: particle tracking, ion therapy, low gain avalanche diode, tissue equivalent proportional counter, microdosimetry

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Authors: Ahmed Elaksher, Islam Omar

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Accurate geometric relation algorithms are imperative in Earth and planetary satellite and aerial image processing, particularly for high-resolution images that are used for topographic mapping. Most of these satellites carry push-broom sensors. These sensors are optical scanners equipped with linear arrays of CCDs. These sensors have been deployed on most EOSs. In addition, the LROC is equipped with two push NACs that provide 0.5 meter-scale panchromatic images over a 5 km swath of the Moon. The HiRISE carried by the MRO and the HRSC carried by MEX are examples of push-broom sensor that produces images of the surface of Mars. Sensor models developed in photogrammetry relate image space coordinates in two or more images with the 3D coordinates of ground features. Rigorous sensor models use the actual interior orientation parameters and exterior orientation parameters of the camera, unlike approximate models. In this research, we generate a generic push-broom sensor model to process imageries acquired through linear array cameras and investigate its performance, advantages, and disadvantages in generating topographic models for the Earth, Mars, and the Moon. We also compare and contrast the utilization, effectiveness, and applicability of available photogrammetric techniques and softcopies with the developed model. We start by defining an image reference coordinate system to unify image coordinates from all three arrays. The transformation from an image coordinate system to a reference coordinate system involves a translation and three rotations. For any image point within the linear array, its image reference coordinates, the coordinates of the exposure center of the array in the ground coordinate system at the imaging epoch (t), and the corresponding ground point coordinates are related through the collinearity condition that states that all these three points must be on the same line. The rotation angles for each CCD array at the epoch t are defined and included in the transformation model. The exterior orientation parameters of an image line, i.e., coordinates of exposure station and rotation angles, are computed by a polynomial interpolation function in time (t). The parameter (t) is the time at a certain epoch from a certain orbit position. Depending on the types of observations, coordinates, and parameters may be treated as knowns or unknowns differently in various situations. The unknown coefficients are determined in a bundle adjustment. The orientation process starts by extracting the sensor position and, orientation and raw images from the PDS. The parameters of each image line are then estimated and imported into the push-broom sensor model. We also define tie points between image pairs to aid the bundle adjustment model, determine the refined camera parameters, and generate highly accurate topographic maps. The model was tested on different satellite images such as IKONOS, QuickBird, and WorldView-2, HiRISE. It was found that the accuracy of our model is comparable to those of commercial and open-source software, the computational efficiency of the developed model is high, the model could be used in different environments with various sensors, and the implementation process is much more cost-and effort-consuming.

Keywords: photogrammetry, push-broom sensors, IKONOS, HiRISE, collinearity condition

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Authors: Vitaliy Petrov, Natalia Shusharina, Vitaliy Kasymov, Maksim Patrushev, Evgeny Bogdanov

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The leading worldwide death reasons are ischemic heart disease and other cardiovascular illnesses. Generally, the common symptom is high blood pressure. Long-time blood pressure control is very important for the prophylaxis, correct diagnosis and timely therapy. Non-invasive methods which are based on Korotkoff sounds are impossible to apply often and for a long time. Implantable devices can combine longtime monitoring with high accuracy of measurements. The main purpose of this work is to create a real-time monitoring system for decreasing the death rate from cardiovascular diseases. These days implantable electronic devices began to play an important role in medicine. Usually implantable devices consist of a transmitter, powering which could be wireless with a special made battery and measurement circuit. Common problems in making implantable devices are short lifetime of the battery, big size and biocompatibility. In these work, blood pressure measure will be the focus because it’s one of the main symptoms of cardiovascular diseases. Our device will consist of three parts: the implantable pressure sensor, external transmitter and automated workstation in a hospital. The Implantable part of pressure sensors could be based on piezoresistive or capacitive technologies. Both sensors have some advantages and some limitations. The Developed circuit is based on a small capacitive sensor which is made of the technology of microelectromechanical systems (MEMS). The Capacitive sensor can provide high sensitivity, low power consumption and minimum hysteresis compared to the piezoresistive sensor. For this device, it was selected the oscillator-based circuit where frequency depends from the capacitance of sensor hence from capacitance one can calculate pressure. The external device (transmitter) used for wireless charging and signal transmission. Some implant devices for these applications are passive, the external device sends radio wave signal on internal LC circuit device. The external device gets reflected the signal from the implant and from a change of frequency is possible to calculate changing of capacitance and then blood pressure. However, this method has some disadvantages, such as the patient position dependence and static using. Developed implantable device doesn’t have these disadvantages and sends blood pressure data to the external part in real-time. The external device continuously sends information about blood pressure to hospital cloud service for analysis by a physician. Doctor’s automated workstation at the hospital also acts as a dashboard, which displays actual medical data of patients (which require attention) and stores it in cloud service. Usually, critical heart conditions occur few hours before heart attack but the device is able to send an alarm signal to the hospital for an early action of medical service. The system was tested with wireless charging and data transmission. These results can be used for ASIC design for MEMS pressure sensor.

Keywords: MEMS sensor, RF power, wireless data, oscillator-based circuit

Procedia PDF Downloads 589

Authors: Nodar Kekelidze, David Kekelidze, Elza Khutsishvili, Bela Kvirkvelia

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The semiconductor devices are irreplaceable elements for investigations in Space (artificial Earth satellite, interplanetary space craft, probes, rockets) and for investigation of elementary particles on accelerators, for atomic power stations, nuclear reactors, robots operating on heavily radiation contaminated territories (Chernobyl, Fukushima). Unfortunately, the most important parameters of semiconductors dramatically worsen under irradiation. So creation of radiation-resistant semiconductor materials for opto and microelectronic devices is actual problem, as well as investigation of complicated processes developed in irradiated solid states. Homogeneous single crystals of InP-InAs solid solutions were grown with zone melting method. There has been studied the dependence of the optical absorption coefficient vs photon energy near fundamental absorption edge. This dependence changes dramatically with irradiation. The experiments were performed on InP, InAs and InP-InAs solid solutions before and after irradiation with electrons and fast neutrons. The investigations of optical properties were carried out on infrared spectrophotometer in temperature range of 10K-300K and 1mkm-50mkm spectral area. Radiation fluencies of fast neutrons was equal to 2·1018neutron/cm2 and electrons with 3MeV, 50MeV up to fluxes of 6·1017electron/cm2. Under irradiation, there has been revealed the exponential type of the dependence of the optical absorption coefficient vs photon energy with energy deficiency. The indicated phenomenon takes place at high and low temperatures as well at impurity different concentration and practically in all cases of irradiation by various energy electrons and fast neutrons. We have developed the common mechanism of this phenomenon for unirradiated materials and implemented the quantitative calculations of distinctive parameter; this is in a satisfactory agreement with experimental data. For the irradiated crystals picture get complicated. In the work, the corresponding analysis is carried out. It has been shown, that in the case of InP, irradiated with electrons (Ф=1·1017el/cm2), the curve of optical absorption is shifted to lower energies. This is caused by appearance of the tails of density of states in forbidden band due to local fluctuations of ionized impurity (defect) concentration. Situation is more complicated in the case of InAs and for solid solutions with composition near to InAs when besides noticeable phenomenon there takes place Burstein effect caused by increase of electrons concentration as a result of irradiation. We have shown, that in certain conditions it is possible the prevalence of Burstein effect. This causes the opposite effect: the shift of the optical absorption edge to higher energies. So in given solid solutions there take place two different opposite directed processes. By selection of solid solutions composition and doping impurity we obtained such InP-InAs, solid solution in which under radiation mutual compensation of optical absorption curves displacement occurs. Obtained result let create on the base of InP-InAs, solid solution radiation-resistant optical materials. Conclusion: It was established the nature of optical absorption near fundamental edge in semiconductor materials and it was created radiation-resistant optical material.

Keywords: InAs-InP, electrons concentration, irradiation, solid solutions

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Authors: Natalie Hoyer

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Bridges in both roadway and railway systems depend on bearings to ensure extended service life and functionality. These bearings enable proper load distribution from the superstructure to the substructure while permitting controlled movement of the superstructure. The design of bridge bearings, according to Eurocode DIN EN 1337 and the relevant sections of DIN EN 1993, increasingly requires the use of thick plates, especially for long-span bridges. However, these plate thicknesses exceed the limits specified in the national appendix of DIN EN 1993-2. Furthermore, compliance with DIN EN 1993-1-10 regulations regarding material toughness and through-thickness properties necessitates further modifications. Consequently, these standards cannot be directly applied to the selection of bearing materials without supplementary guidance and design rules. In this context, a recommendation was developed in 2011 to regulate the selection of appropriate steel grades for bearing components. Prior to the initiation of the research project underlying this contribution, this recommendation had only been available as a technical bulletin. Since July 2023, it has been integrated into guideline 804 of the German railway. However, recent findings indicate that certain bridge-bearing components are exposed to high fatigue loads, which necessitate consideration in structural design, material selection, and calculations. Therefore, the German Centre for Rail Traffic Research called a research project with the objective of defining a proposal to expand the current standards in order to implement a sufficient choice of steel material for bridge bearings to avoid brittle fracture, even for thick plates and components subjected to specific fatigue loads. The results obtained from theoretical considerations, such as finite element simulations and analytical calculations, are validated through large-scale component tests. Additionally, experimental observations are used to calibrate the calculation models and modify the input parameters of the design concept. Within the large-scale component tests, a brittle failure is artificially induced in a bearing component. For this purpose, an artificially generated initial defect is introduced at the previously defined hotspot into the specimen using spark erosion. Then, a dynamic load is applied until the crack initiation process occurs to achieve realistic conditions in the form of a sharp notch similar to a fatigue crack. This initiation process continues until the crack length reaches a predetermined size. Afterward, the actual test begins, which requires cooling the specimen with liquid nitrogen until a temperature is reached where brittle fracture failure is expected. In the next step, the component is subjected to a quasi-static tensile test until failure occurs in the form of a brittle failure. The proposed paper will present the latest research findings, including the results of the conducted component tests and the derived definition of the initial crack size in bridge bearings.

Keywords: bridge bearings, brittle fracture, fatigue, initial crack size, large-scale tests

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Authors: Prashant Gupta

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Throughout India, temples have served as cultural centers, commerce hubs, art galleries, educational institutions, and social centers in addition to being places of worship since centuries. Across the country, there are over two million temples, which are crucial economic hubs, attracting devotees and tourists worldwide. In India, we have 53 temples per each 100,000 Indians. As per NSSO survey, the temple economy is worth about $40 billion and 2.32 per cent of GDP based on major temple’s survey, which only includes formal sector. It could be much larger as an actual estimation has not been done yet. In India, 43.1% of total economy represents informal sector. Over 10 billion domestic tourists visit to new destinations every year within India. Even 20 per cent of the 90 million foreign tourists visited Madurai and Mahabalipuram temples which became the most visited tourist spot in 2022. Recently the current central government in power have started revitalizing the ancient Indian civilization by reconstructing and beautifying the major temples of India i.e., Kashi Vishwanath Corridor, Mahakaleshwara Temple, Kedarnath, Ayodhya etc. The reason researcher chose Kashi as a case study because it is known as a Spiritual Capital of India, which is also the abode for the spread of Hinduism, Buddhism, Jainism and Sikkism, which are core Sanatan Dharmic practices. 17,800 Million INR Amount was spend to redevelop Kashi Vishwanath Corridor since 2019. RESEARCH OBJECTIVES 1. To assess historical contribution of temples in socio economic development and revival of Indic Civilization. 2. To examine the role of temples redevelopment for informal sector businesses. 3. To identify the sub-sectors of informal sector businesses 4. To identify products and services of informal businesses for investigation of marketing strategies and business development. PROPOSED METHODS AND PROCEDURES This study will follow a mixed approach, employing both qualitative and quantitative methods of research. To conduct the study, data will be collected from 500 informal business owners through structured questionnaire and interview instruments. The informal business owners will be selected using a systematic random sampling technique. In addition, documents from government offices of the last 10 years of tax collection will be reviewed to substantiate the study. To analyze the study, descriptive and econometric analysis techniques will be employed. EXPECTED CONTRIBUTION OF THE PROPOSED STUDY By studying the contribution of temple re-development on informal business creation and growth, the study will be beneficial to the informal business owners and the government. For the government, scientific and empirical evidence on the contribution of temple re-development for informal business creation and growth to give evidence the study will give based infrastructural development and boosting tax collection. For informal businesses, the study will give them a detailed insight on the nature of their business and the possible future growth potential of their business, and the alternative products and services supplying to their customers in the future. Studying informal businesses will help to identify the key products and services which are majorly profitable and possess potential to multiply and grow through correct product marketing strategies and business development.

Keywords: business development, informal sector businesses, services and products marketing, temple economics

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Authors: Benjamin Kaufmann, Michael Hofstätter, Nadine Raidl, Peter Supancic

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ZnO varistors are the leading overvoltage protection elements in today’s electronic industry. Their highly non-linear current-voltage characteristics, very fast response times, good reliability and attractive cost of production are unique in this field. There are challenges and questions unsolved. Especially, the urge to create even smaller, versatile and reliable parts, that fit industry’s demands, brings manufacturers to the limits of their abilities. Although, the varistor effect of sintered ZnO is known since the 1960’s, and a lot of work was done on this field to explain the sudden exponential increase of conductivity, the strict dependency on sinter parameters, as well as the influence of the complex microstructure, is not sufficiently understood. For further enhancement and down-scaling of varistors, a better understanding of the microscopic processes is needed. This work attempts a microscopic approach to investigate ZnO varistor performance. In order to cope with the polycrystalline varistor ceramic and in order to account for all possible current paths through the material, a preferably realistic model of the microstructure was set up in the form of three-dimensional networks where every grain has a constant electric potential, and voltage drop occurs only at the grain boundaries. The electro-thermal workload, depending on different grain size distributions, was investigated as well as the influence of the metal-semiconductor contact between the electrodes and the ZnO grains. A number of experimental methods are used, firstly, to feed the simulations with realistic parameters and, secondly, to verify the obtained results. These methods are: a micro 4-point probes method system (M4PPS) to investigate the current-voltage characteristics between single ZnO grains and between ZnO grains and the metal electrode inside the varistor, micro lock-in infrared thermography (MLIRT) to detect current paths, electron back scattering diffraction and piezoresponse force microscopy to determine grain orientations, atom probe to determine atomic substituents, Kelvin probe force microscopy for investigating grain surface potentials. The simulations showed that, within a critical voltage range, the current flow is localized along paths which represent only a tiny part of the available volume. This effect could be observed via MLIRT. Furthermore, the simulations exhibit that the electric power density, which is inversely proportional to the number of active current paths, since this number determines the electrical active volume, is dependent on the grain size distribution. M4PPS measurements showed that the electrode-grain contacts behave like Schottky diodes and are crucial for asymmetric current path development. Furthermore, evaluation of actual data suggests that current flow is influenced by grain orientations. The present results deepen the knowledge of influencing microscopic factors on ZnO varistor performance and can give some recommendations on fabrication for obtaining more reliable ZnO varistors.

Keywords: metal-semiconductor contact, Schottky diode, varistor, zinc oxide

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Authors: Xingyu Gao, Qiang Wu

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Patents play a crucial role in protecting innovation and intellectual property. Early prediction of the impact of artificial intelligence (AI) patents helps researchers and companies allocate resources and make better decisions. Understanding the key factors that influence patent impact can assist researchers in gaining a better understanding of the evolution of AI technology and innovation trends. Therefore, identifying highly impactful patents early and providing support for them holds immeasurable value in accelerating technological progress, reducing research and development costs, and mitigating market positioning risks. Despite the extensive research on AI patents, accurately predicting their early impact remains a challenge. Traditional methods often consider only single factors or simple combinations, failing to comprehensively and accurately reflect the actual impact of patents. This paper utilized the artificial intelligence patent database from the United States Patent and Trademark Office and the Len.org patent retrieval platform to obtain specific information on 35,708 AI patents. Using six machine learning models, namely Multiple Linear Regression, Random Forest Regression, XGBoost Regression, LightGBM Regression, Support Vector Machine Regression, and K-Nearest Neighbors Regression, and using early indicators of patents as features, the paper comprehensively predicted the impact of patents from three aspects: technical, social, and economic. These aspects include the technical leadership of patents, the number of citations they receive, and their shared value. The SHAP (Shapley Additive exPlanations) metric was used to explain the predictions of the best model, quantifying the contribution of each feature to the model's predictions. The experimental results on the AI patent dataset indicate that, for all three target variables, LightGBM regression shows the best predictive performance. Specifically, patent novelty has the greatest impact on predicting the technical impact of patents and has a positive effect. Additionally, the number of owners, the number of backward citations, and the number of independent claims are all crucial and have a positive influence on predicting technical impact. In predicting the social impact of patents, the number of applicants is considered the most critical input variable, but it has a negative impact on social impact. At the same time, the number of independent claims, the number of owners, and the number of backward citations are also important predictive factors, and they have a positive effect on social impact. For predicting the economic impact of patents, the number of independent claims is considered the most important factor and has a positive impact on economic impact. The number of owners, the number of sibling countries or regions, and the size of the extended patent family also have a positive influence on economic impact. The study primarily relies on data from the United States Patent and Trademark Office for artificial intelligence patents. Future research could consider more comprehensive data sources, including artificial intelligence patent data, from a global perspective. While the study takes into account various factors, there may still be other important features not considered. In the future, factors such as patent implementation and market applications may be considered as they could have an impact on the influence of patents.

Keywords: patent influence, interpretable machine learning, predictive models, SHAP

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Authors: Omarova Gulnara, Assubayeva Saltanat, Tugambay Symbat, Bulegenov Kanat

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The article discusses the problem of developing a methodology for studying thin and nanoscale gold in ores and placers of primary deposits, which will allow us to develop schemes for revealing dispersed gold inclusions and thus improve its recovery rate to increase the gold reserves of the Republic of Kazakhstan. The type of studied gold, is characterized by a number of features. In connection with this, the conditions of its concentration and distribution in ore bodies and formations, as well as the possibility of reliably determining it by "traditional" methods, differ significantly from that of fine gold (less than 0.25 microns) and even more so from that of larger grains. The mineral composition of rocks (metasomatites) and gold ore and the mineralization associated with them were studied in detail on the Kalba ore field in Kazakhstan. Mineralized zones were identified, and samples were taken from them for analytical studies. The research revealed paragenetic relationships of newly formed mineral formations at the nanoscale, which makes it possible to clarify the conditions for the formation of deposits with a particular type of mineralization. This will provide significant assistance in developing a scheme for study. Typomorphic features of gold were revealed, and mechanisms of formation and aggregation of gold nanoparticles were proposed. The presence of a large number of particles isolated at the laboratory stage from concentrates of gravitational enrichment can serve as an indicator of the presence of even smaller particles in the object. Even the most advanced devices based on gravitational methods for gold concentration provide extraction of metal at a level of around 50%, while pulverized metal is extracted much worse, and gold of less than 1 micron size is extracted at only a few percent. Therefore, when particles of gold smaller than 10 microns are detected, their actual numbers may be significantly higher than expected. In particular, at the studied sites, enrichment of slurry and samples with volumes up to 1 m³ was carried out using a screw lock or separator to produce a final concentrate weighing up to several kilograms. Free gold particles were extracted from the concentrates in the laboratory using a number of processes (magnetic and electromagnetic separation, washing with bromoform in a cup to obtain an ultracontentrate, etc.) and examined under electron microscopes to investigate the nature of their surface and chemical composition. The main result of the study was the detection of gold nanoparticles located on the surface of loose metal grains. The most characteristic forms of gold secretions are individual nanoparticles and aggregates of different configurations. Sometimes, aggregates form solid dense films, deposits, and crusts, all of which are confined to the negative forms of the nano- and microrelief on the surfaces of golden. The results will provide significant knowledge about the prevalence and conditions for the distribution of fine and nanoscale gold in Kazakhstan deposits, as well as the development of methods for studying it, which will minimize losses of this type of gold during extraction. Acknowledgments: This publication has been produced within the framework of the Grant "Development of methodology for studying fine and nanoscale gold in ores of primary deposits, placers and products of their processing" (АР23485052, №235/GF24-26).

Keywords: electron microscopy, microminerology, placers, thin and nanoscale gold

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Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo Ferraioli

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In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring. SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

Keywords: SAR image, polarimetric SAR image, convolutional neural network, deep learnig, deep neural network

Procedia PDF Downloads 67

Authors: Nuno Biga

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Artificial Intelligence (AI) has the potential to transform management into several impactful ways. It allows machines to interpret information to find patterns in big data and learn from context analysis, optimize operations, make predictions sensitive to each specific situation and support data-driven decision making. The introduction of an 'artificial brain' in organization also enables learning through complex information and data provided by those who train it, namely its users. The "Assisted-BIGAMES" version of the Accident & Emergency (A&E) simulator introduces the concept of a "Virtual Assistant" (VA) sensitive to context, that provides users useful suggestions to pursue the following operations such as: a) to relocate workstations in order to shorten travelled distances and minimize the stress of those involved; b) to identify in real time existing bottleneck(s) in the operations system so that it is possible to quickly act upon them; c) to identify resources that should be polyvalent so that the system can be more efficient; d) to identify in which specific processes it may be advantageous to establish partnership with other teams; and e) to assess possible solutions based on the suggested KPIs allowing action monitoring to guide the (re)definition of future strategies. This paper is built on the BIGAMES© simulator and presents the conceptual AI model developed and demonstrated through a pilot project (BIG-AI). Each Virtual Assisted BIGAME is a management simulator developed by the author that guides operational and strategic decision making, providing users with useful information in the form of management recommendations that make it possible to predict the actual outcome of different alternative management strategic actions. The pilot project developed incorporates results from 12 editions of the BIGAME A&E that took place between 2017 and 2022 at AESE Business School, based on the compilation of data that allows establishing causal relationships between decisions taken and results obtained. The systemic analysis and interpretation of data is powered in the Assisted-BIGAMES through a computer application called "BIGAMES Virtual Assistant" (VA) that players can use during the Game. Each participant in the VA permanently asks himself about the decisions he should make during the game to win the competition. To this end, the role of the VA of each team consists in guiding the players to be more effective in their decision making, through presenting recommendations based on AI methods. It is important to note that the VA's suggestions for action can be accepted or rejected by the managers of each team, as they gain a better understanding of the issues along time, reflect on good practice and rely on their own experience, capability and knowledge to support their own decisions. Preliminary results show that the introduction of the VA provides a faster learning of the decision-making process. The facilitator designated as “Serious Game Controller” (SGC) is responsible for supporting the players with further analysis. The recommended actions by the SGC may differ or be similar to the ones previously provided by the VA, ensuring a higher degree of robustness in decision-making. Additionally, all the information should be jointly analyzed and assessed by each player, who are expected to add “Emotional Intelligence”, an essential component absent from the machine learning process.

Keywords: artificial intelligence, gamification, key performance indicators, machine learning, management simulators, serious games, virtual assistant

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Authors: Yogesh Kumar Singh, T. S. Murugesh Prabhu, Upasana Dutta, Girishchandra Yendargaye, Rahul Yadav, Rohini Gopinath Kale, Binay Kumar, Manoj Khare

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The Indian subcontinent is severely affected by floods that cause intense irreversible devastation to crops and livelihoods. With increased incidences of floods and their related catastrophes, an Early Warning System for Flood Prediction and an efficient Flood Management System for the river basins of India is a must. Accurately modeled hydrological conditions and a web-based early warning system may significantly reduce economic losses incurred due to floods and enable end users to issue advisories with better lead time. This study describes the design and development of an EWS-FP using advanced computational tools/methods, viz. High-Performance Computing (HPC), Remote Sensing, GIS technologies, and open-source tools for the Mahanadi River Basin of India. The flood prediction is based on a robust 2D hydrodynamic model, which solves shallow water equations using the finite volume method. Considering the complexity of the hydrological modeling and the size of the basins in India, it is always a tug of war between better forecast lead time and optimal resolution at which the simulations are to be run. High-performance computing technology provides a good computational means to overcome this issue for the construction of national-level or basin-level flash flood warning systems having a high resolution at local-level warning analysis with a better lead time. High-performance computers with capacities at the order of teraflops and petaflops prove useful while running simulations on such big areas at optimum resolutions. In this study, a free and open-source, HPC-based 2-D hydrodynamic model, with the capability to simulate rainfall run-off, river routing, and tidal forcing, is used. The model was tested for a part of the Mahanadi River Basin (Mahanadi Delta) with actual and predicted discharge, rainfall, and tide data. The simulation time was reduced from 8 hrs to 3 hrs by increasing CPU nodes from 45 to 135, which shows good scalability and performance enhancement. The simulated flood inundation spread and stage were compared with SAR data and CWC Observed Gauge data, respectively. The system shows good accuracy and better lead time suitable for flood forecasting in near-real-time. To disseminate warning to the end user, a network-enabled solution is developed using open-source software. The system has query-based flood damage assessment modules with outputs in the form of spatial maps and statistical databases. System effectively facilitates the management of post-disaster activities caused due to floods, like displaying spatial maps of the area affected, inundated roads, etc., and maintains a steady flow of information at all levels with different access rights depending upon the criticality of the information. It is designed to facilitate users in managing information related to flooding during critical flood seasons and analyzing the extent of the damage.

Keywords: flood, modeling, HPC, FOSS

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Authors: Zofija Tupikovskaja-Omovie, David Tyler, Sam Dhanapala, Steve Hayes

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The UK is leading in online retail and mobile adoption. However, there is a dearth of information relating to mobile apparel retail, and developing an understanding about consumer browsing and purchase behavior in m-retail channel would provide apparel marketers, mobile website and app developers with the necessary understanding of consumers’ needs. Despite the rapid growth of mobile retail businesses, no published study has examined shopping behaviour on fashion mobile websites and apps. A mixed method approach helped to understand why fashion consumers prefer websites on mobile devices, when mobile apps are also available. The following research methods were employed: survey, eye-tracking experiments, observation, and interview with retrospective think aloud. The mobile gaze tracking device by SensoMotoric Instruments was used to understand frustrations in navigation and other issues facing consumers in mobile channel. This method helped to validate and compliment other traditional user-testing approaches in order to optimize user experience and enhance the development of mobile retail channel. The study involved eight participants - females aged 18 to 35 years old, who are existing mobile shoppers. The participants used the Topshop mobile app and website on a smart phone to complete a task according to a specified scenario leading to a purchase. The comparative study was based on: duration and time spent at different stages of the shopping journey, number of steps involved and product pages visited, search approaches used, layout and visual clues, as well as consumer perceptions and expectations. The results from the data analysis show significant differences in consumer behaviour when using a mobile app or website on a smart phone. Moreover, two types of problems were identified, namely technical issues and human errors. Having a mobile app does not guarantee success in satisfying mobile fashion consumers. The differences in the layout and visual clues seem to influence the overall shopping experience on a smart phone. The layout of search results on the website was different from the mobile app. Therefore, participants, in most cases, behaved differently on different platforms. The number of product pages visited on the mobile app was triple the number visited on the website due to a limited visibility of products in the search results. Although, the data on traffic trends held by retailers to date, including retail sector breakdowns for visits and views, data on device splits and duration, might seem a valuable source of information, it cannot explain why consumers visit many product pages, stay longer on the website or mobile app, or abandon the basket. A comprehensive list of pros and cons was developed by highlighting issues for website and mobile app, and recommendations provided. The findings suggest that fashion retailers need to be aware of actual consumers’ behaviour on the mobile channel and their expectations in order to offer a seamless shopping experience. Added to which is the challenge of retaining existing and acquiring new customers. There seem to be differences in the way fashion consumers search and shop on mobile, which need to be explored in further studies.

Keywords: consumer behavior, eye-tracking technology, fashion retail, mobile app, m-retail, smart phones, topshop, user experience, website

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Authors: Frederick W. Gooding, Jr., Mark Beeman

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Nearly a century ago, the groundbreaking 1915 film ‘The Birth of a Nation’ popularized the way Hollywood made movies with its avant-garde, feature-length style. The movie's subjugating and demeaning depictions of African American women (and men) reflected popular racist beliefs held during the time of slavery and the early Jim Crow era. Although much has changed concerning race relations in the past century, American sociologist Patricia Hill Collins theorizes that the disparaging images of African American women originating in the era of plantation slavery are adaptable and endure as controlling images today. In this context, a comparative analysis of the successful contemporary film, ‘Bringing Down the House’ starring Queen Latifah is relevant as this 2004 film was designed to purposely defy and ridicule classic stereotypes of African American women. However, the film is still tied to the controlling images from the past, although in a modern hybrid form. Scholars of race and film have noted that the pervasive filmic imagery of the African American woman as the loyal mammy stereotype faded from the screen in the post-civil rights era in favor of more sexualized characters (i.e., the Jezebel trope). Analyzing scenes and dialogue through the lens of sociological and critical race theory, the troubling persistence of African American controlling images in film stubbornly emerge in a movie like ‘Bringing Down the House.’ Thus, these controlling images, like racism itself, can adapt to new social and economic conditions. Although the classic controlling images appeared in the first feature length film focusing on race relations a century ago, ‘The Birth of a Nation,’ this black and white rendition of the mammy figure was later updated in 1939 with the classic hit, ‘Gone with the Wind’ in living color. These popular controlling images have loomed quite large in the minds of international audiences, as ‘Gone with the Wind’ is still shown in American theaters currently, and experts at the British Film Institute in 2004 rated ‘Gone with the Wind’ as the number one movie of all time in UK movie history based upon the total number of actual viewings. Critical analysis of character patterns demonstrate that images that appear superficially benign contribute to a broader and quite persistent pattern of marginalization within the aggregate. This approach allows experts and viewers alike to detect more subtle and sophisticated strands of racial discrimination that are ‘hidden in plain sight’ despite numerous changes in the Hollywood industry that appear to be more voluminous and diverse than three or four decades ago. In contrast to white characters, non-white or minority characters are likely to be subtly compromised or marginalized relative to white characters if and when seen within mainstream movies, rather than be subjected to obvious and offensive racist tropes. The hybrid form of both the older Jezebel and Mammy stereotypes exhibited by lead actress Queen Latifah in ‘Bringing Down the House’ represents a more suave and sophisticated merging of past imagery ideas deemed problematic in the past as well as the present.

Keywords: African Americans, Hollywood film, hybrid, stereotypes

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Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo ferraioli

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In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring . SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

Keywords: SAR image, deep learning, convolutional neural network, deep neural network, SAR polarimetry

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Authors: Ksenia Volkova, Artur Petrosyan, Ignatii Dubyshkin, Alexei Ossadtchi

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Brain-computer interfaces are a growing research field producing many implementations that find use in different fields and are used for research and practical purposes. Despite the popularity of the implementations using non-invasive neuroimaging methods, radical improvement of the state channel bandwidth and, thus, decoding accuracy is only possible by using invasive techniques. Electrocorticography (ECoG) is a minimally invasive neuroimaging method that provides highly informative brain activity signals, effective analysis of which requires the use of machine learning methods that are able to learn representations of complex patterns. Deep learning is a family of machine learning algorithms that allow learning representations of data with multiple levels of abstraction. This study explores the potential of deep learning approaches for ECoG processing, decoding movement intentions and the perception of proprioceptive information. To obtain synchronous recording of kinematic movement characteristics and corresponding electrical brain activity, a series of experiments were carried out, during which subjects performed finger movements at their own pace. Finger movements were recorded with a three-axis accelerometer, while ECoG was synchronously registered from the electrode strips that were implanted over the contralateral sensorimotor cortex. Then, multichannel ECoG signals were used to track finger movement trajectory characterized by accelerometer signal. This process was carried out both causally and non-causally, using different position of the ECoG data segment with respect to the accelerometer data stream. The recorded data was split into training and testing sets, containing continuous non-overlapping fragments of the multichannel ECoG. A deep convolutional neural network was implemented and trained, using 1-second segments of ECoG data from the training dataset as input. To assess the decoding accuracy, correlation coefficient r between the output of the model and the accelerometer readings was computed. After optimization of hyperparameters and training, the deep learning model allowed reasonably accurate causal decoding of finger movement with correlation coefficient r = 0.8. In contrast, the classical Wiener-filter like approach was able to achieve only 0.56 in the causal decoding mode. In the noncausal case, the traditional approach reached the accuracy of r = 0.69, which may be due to the presence of additional proprioceptive information. This result demonstrates that the deep neural network was able to effectively find a representation of the complex top-down information related to the actual movement rather than proprioception. The sensitivity analysis shows physiologically plausible pictures of the extent to which individual features (channel, wavelet subband) are utilized during the decoding procedure. In conclusion, the results of this study have demonstrated that a combination of a minimally invasive neuroimaging technique such as ECoG and advanced machine learning approaches allows decoding motion with high accuracy. Such setup provides means for control of devices with a large number of degrees of freedom as well as exploratory studies of the complex neural processes underlying movement execution.

Keywords: brain-computer interface, deep learning, ECoG, movement decoding, sensorimotor cortex

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Authors: Monica Arnaudo, Monika Topel, Bjorn Laumert

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Within the Swedish context, the current trend of relatively low electricity prices promotes the electrification of the energy infrastructure. The residential heating sector takes part in this transition by proposing a switch from a centralized district heating system towards a distributed heat pumps-based setting. When it comes to urban environments, two issues arise. The first, seen from an electricity-sector perspective, is related to the fact that existing networks are limited with regards to their installed capacities. Additional electric loads, such as heat pumps, can cause severe overloads on crucial network elements. The second, seen from a heating-sector perspective, has to do with the fact that the indoor comfort conditions can become difficult to handle when the operation of the heat pumps is limited by a risk of overloading on the distribution grid. Furthermore, the uncertainty of the electricity market prices in the future introduces an additional variable. This study aims at assessing the extent to which distributed heat pumps can penetrate an existing heat energy network while respecting the technical limitations of the electricity grid and the thermal comfort levels in the buildings. In order to account for the multi-disciplinary nature of this research question, a cosimulation modeling approach was adopted. In this way, each energy technology is modeled in its customized simulation environment. As part of the cosimulation methodology: a steady-state power flow analysis in pandapower was used for modeling the electrical distribution grid, a thermal balance model of a reference building was implemented in EnergyPlus to account for space heating and a fluid-cycle model of a heat pump was implemented in JModelica to account for the actual heating technology. With the models set in place, different scenarios based on forecasted electricity market prices were developed both for present and future conditions of Hammarby Sjöstad, a neighborhood located in the south-east of Stockholm (Sweden). For each scenario, the technical and the comfort conditions were assessed. Additionally, the average cost of heat generation was estimated in terms of levelized cost of heat. This indicator enables a techno-economic comparison study among the different scenarios. In order to evaluate the levelized cost of heat, a yearly performance simulation of the energy infrastructure was implemented. The scenarios related to the current electricity prices show that distributed heat pumps can replace the district heating system by covering up to 30% of the heating demand. By lowering of 2°C, the minimum accepted indoor temperature of the apartments, this level of penetration can increase up to 40%. Within the future scenarios, if the electricity prices will increase, as most likely expected within the next decade, the penetration of distributed heat pumps can be limited to 15%. In terms of levelized cost of heat, a residential heat pump technology becomes competitive only within a scenario of decreasing electricity prices. In this case, a district heating system is characterized by an average cost of heat generation 7% higher compared to a distributed heat pumps option.

Keywords: cosimulation, distributed heat pumps, district heating, electrical distribution grid, integrated energy systems

Procedia PDF Downloads 150

Authors: Delia Gabriela Moisa, Demos Parapanos, Tim Heap

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The tourism sector is mainly comprised of small and medium-sized tourism enterprises (SMEs), representing approximately 80% of global businesses in this field. These entities require focused attention and support to address challenges, ensuring their competitiveness and relevance in a dynamic industry characterized by continuously changing customer preferences. To address these challenges, it becomes imperative to consider not only socio-demographic factors but also delve into the intricate interplay of psychological elements influencing consumer behavior. This study investigates the impact of personality traits and travel motivations on visitor activities in Cumbria, United Kingdom, an iconic region marked by UNESCO World Heritage Sites, including The Lake District National Park and Hadrian's Wall. With a £4.1 billion tourism industry primarily driven by SMEs, Cumbria serves as an ideal setting for examining the relationship between tourist psychology and activities. Employing the Big Five personality model and the Travel Career Pattern motivation theory, this study aims to explain the relationship between psychological factors and tourist activities. The study further explores SME perspectives on personality-based market segmentation, providing strategic insights into addressing evolving tourist preferences.This pioneering mixed-methods study integrates quantitative data from 330 visitor surveys, subsequently complemented by qualitative insights from tourism SME representatives. The findings unveil that socio-demographic factors do not exhibit statistically significant variations in the activities pursued by visitors in Cumbria. However, significant correlations emerge between personality traits and motivations with preferred visitor activities. Open-minded tourists gravitate towards events and cultural activities, while Conscientious individuals favor cultural pursuits. Extraverted tourists lean towards adventurous, recreational, and wellness activities, while Agreeable personalities opt for lake cruises. Interestingly, a contrasting trend emerges as Extraversion increases, leading to a decrease in interest in cultural activities. Similarly, heightened Agreeableness corresponds to a decrease in interest in adventurous activities. Furthermore, travel motivations, including nostalgia and building relationships, drive event participation, while self-improvement and novelty-seeking lead to adventurous activities. Additionally, qualitative insights from tourism SME representatives underscore the value of targeted messaging aligned with visitor personalities for enhancing loyalty and experiences. This study contributes significantly to scholarship through its novel framework, integrating tourist psychology with activities and industry perspectives. The proposed conceptual model holds substantial practical implications for SMEs to formulate personalized offerings, optimize marketing, and strategically allocate resources tailored to tourist personalities. While the focus is on Cumbria, the methodology's universal applicability offers valuable insights for destinations globally seeking a competitive advantage. Future research addressing scale reliability and geographic specificity limitations can further advance knowledge on this critical relationship between visitor psychology, individual preferences, and industry imperatives. Moreover, by extending the investigation to other districts, future studies could draw comparisons and contrasts in the results, providing a more nuanced understanding of the factors influencing visitor psychology and preferences.

Keywords: personality trait, SME, tourist behaviour, tourist motivation, visitor activity

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Authors: Mingxin Li, Liya Ni

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Autonomous parking is a valuable feature applicable to many robotics applications such as tour guide robots, UV sanitizing robots, food delivery robots, and warehouse robots. With auto-parking, the robot will be able to park at the charging zone and charge itself without human intervention. As compared to self-driving vehicles, auto-parking is more challenging for a small-scale mobile robot only equipped with a front camera due to the camera view limited by the robot’s height and the narrow Field of View (FOV) of the inexpensive camera. In this research, auto-parking of a small-scale mobile robot with a front camera only was achieved in a four-step process: Firstly, transfer learning was performed on the AlexNet, a popular pre-trained convolutional neural network (CNN). It was trained with 150 pictures of empty parking slots and 150 pictures of occupied parking slots from the view angle of a small-scale robot. The dataset of images was divided into a group of 70% images for training and the remaining 30% images for validation. An average success rate of 95% was achieved. Secondly, the image of detected empty parking space was processed with edge detection followed by the computation of parametric representations of the boundary lines using the Hough Transform algorithm. Thirdly, the positions of the entrance point and center of available parking space were predicted based on the robot kinematic model as the robot was driving closer to the parking space because the boundary lines disappeared partially or completely from its camera view due to the height and FOV limitations. The robot used its wheel speeds to compute the positions of the parking space with respect to its changing local frame as it moved along, based on its kinematic model. Lastly, the predicted entrance point of the parking space was used as the reference for the motion control of the robot until it was replaced by the actual center when it became visible again by the robot. The linear and angular velocities of the robot chassis center were computed based on the error between the current chassis center and the reference point. Then the left and right wheel speeds were obtained using inverse kinematics and sent to the motor driver. The above-mentioned four subtasks were all successfully accomplished, with the transformed learning, image processing, and target prediction performed in MATLAB, while the motion control and image capture conducted on a self-built small scale differential drive mobile robot. The small-scale robot employs a Raspberry Pi board, a Pi camera, an L298N dual H-bridge motor driver, a USB power module, a power bank, four wheels, and a chassis. Future research includes three areas: the integration of all four subsystems into one hardware/software platform with the upgrade to an Nvidia Jetson Nano board that provides superior performance for deep learning and image processing; more testing and validation on the identification of available parking space and its boundary lines; improvement of performance after the hardware/software integration is completed.

Keywords: autonomous parking, convolutional neural network, image processing, kinematics-based prediction, transfer learning

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Authors: Omer Leshem, Michael F. Schober

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This paper introduces a new method for assessing how audience members and performers feel and think during live concerts, and how audience members' recognized and felt emotions are related. Two hypotheses were tested in a live concert setting: (1) that audience members’ cognitive perspective taking ability predicts their accuracy in identifying an emotion that a jazz improviser intended to express during a performance, and (2) that audience members' affective empathy predicts their likelihood of feeling the same emotions as the performer. The aim was to stage a concert with audience members who regularly attend live jazz performances, and to measure their cognitive and affective reactions during the performance as non-intrusively as possible. Pianist and Grammy nominee Andy Milne agreed, without knowing details of the method or hypotheses, to perform a full-length solo improvised concert that would include an ‘unusual’ piece. Jazz fans were recruited through typical advertising for New York City jazz performances. The event was held at the New School’s Glass Box Theater, the home of leading NYC jazz venue ‘The Stone.’ Audience members were charged typical NYC jazz club admission prices; advertisements informed them that anyone who chose to participate in the study would be reimbursed their ticket price after the concert. The concert, held in April 2018, had 30 attendees, 23 of whom participated in the study. Twenty-two minutes into the concert, the performer was handed a paper note with the instruction: ‘Perform a 3-5-minute improvised piece with the intention of conveying sadness.’ (Sadness was chosen based on previous music cognition lab studies, where solo listeners were less likely to select sadness as the musically-expressed emotion accurately from a list of basic emotions, and more likely to misinterpret sadness as tenderness). Then, audience members and the performer were invited to respond to a questionnaire from a first envelope under their seat. Participants used their own words to describe the emotion the performer had intended to express, and then to select the intended emotion from a list. They also reported the emotions they had felt while listening using Izard’s differential emotions scale. The concert then continued as usual. At the end, participants answered demographic questions and Davis’ interpersonal reactivity index (IRI), a 28-item scale designed to assess both cognitive and affective empathy. Hypothesis 1 was supported: audience members with greater cognitive empathy were more likely to accurately identify sadness as the expressed emotion. Moreover, audience members who accurately selected ‘sadness’ reported feeling marginally sadder than people who did not select sadness. Hypotheses 2 was not supported; audience members with greater affective empathy were not more likely to feel the same emotions as the performer. If anything, members with lower cognitive perspective-taking ability had marginally greater emotional overlap with the performer, which makes sense given that these participants were less likely to identify the music as sad, which corresponded with the performer’s actual feelings. Results replicate findings from solo lab studies in a concert setting and demonstrate the viability of exploring empathy and collective cognition in improvised live performance.

Keywords: audience, cognition, collective cognition, emotion, empathy, expressed emotion, felt emotion, improvisation, live performance, recognized emotion

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Authors: Krishna Raj Regmi

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A “Climate change adaptation and disaster risk management for sustainable agriculture” project was implemented in Nepal, a Himalayan country during 2008 to 2013 sponsored jointly by Food and Agriculture Organization (FAO) and United Nations Development Programme (UNDP), Nepal. The paper is based on the results and findings of this joint pilot project. The climate change events such as increased intensity of erratic rains in short spells, trend of prolonged drought, gradual rise in temperature in the higher elevations and occurrence of cold and hot waves in Terai (lower plains) has led to flash floods, massive erosion in the hills particularly in Churia range and drying of water sources. These recurring natural and climate-induced disasters are causing heavy damages through sedimentation and inundation of agricultural lands, crops, livestock, infrastructures and rural settlements in the downstream plains and thus reducing agriculture productivity and food security in the country. About 65% of the cultivated land in Nepal is rainfed with drought-prone characteristics and stabilization of agricultural production and productivity in these tracts will be possible through adoption of rainfed and drought-tolerant technologies as well as efficient soil-water management by the local communities. The adaptation and mitigation technologies and options identified by the project for soil erosion, flash floods and landslide control are on-farm watershed management, sloping land agriculture technologies (SALT), agro-forestry practices, agri-silvi-pastoral management, hedge-row contour planting, bio-engineering along slopes and river banks, plantation of multi-purpose trees and management of degraded waste land including sandy river-bed flood plains. The stress tolerant technologies with respect to drought, floods and temperature stress for efficient utilization of nutrient, soil, water and other resources for increased productivity are adoption of stress tolerant crop varieties and breeds of animals, indigenous proven technologies, mixed and inter-cropping systems, system of rice/wheat intensification (SRI), direct rice seeding, double transplanting of rice, off-season vegetable production and regular management of nurseries, orchards and animal sheds. The alternate energy use options and resource conservation practices for use by local communities are installation of bio-gas plants and clean stoves (Chulla range) for mitigation of green house gas (GHG) emissions, use of organic manures and bio-pesticides, jatropha cultivation, green manuring in rice fields and minimum/zero tillage practices for marshy lands. The efficient water management practices for increasing productivity of crops and livestock are use of micro-irrigation practices, construction of water conservation and water harvesting ponds, use of overhead water tanks and Thai jars for rain water harvesting and rehabilitation of on-farm irrigation systems. Initiation of some works on community-based early warning system, strengthening of met stations and disaster database management has made genuine efforts in providing disaster-tailored early warning, meteorological and insurance services to the local communities. Contingent planning is recommended to develop coping strategies and capacities of local communities to adopt necessary changes in the cropping patterns and practices in relation to adverse climatic and disaster risk conditions. At the end, adoption of awareness raising and capacity development activities (technical and institutional) and networking on climate-induced disaster and risks through training, visits and knowledge sharing workshops, dissemination of technical know-how and technologies, conduct of farmers' field schools, development of extension materials and their displays are being promoted. However, there is still need of strong coordination and linkage between agriculture, environment, forestry, meteorology, irrigation, climate-induced pro-active disaster preparedness and research at the ministry, department and district level for up-scaling, implementation and institutionalization of climate change and disaster risk management activities and adaptation mitigation options in agriculture for sustainable livelihoods of the communities.

Keywords: climate change adaptation, disaster risk management, soil-water management practices, sustainable agriculture

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Authors: Gregor Vidmar, Rožle Repič, Boštjan Lesar, Miha Humar

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The study investigates the impact of cracks in surface coatings on moisture content (MC) and related fungal growth in window frames made of thermally modified (TM) and native Norway spruce using hygrothermal simulations for Ljubljana, Slovenia. Comprehensive validation against field test data confirmed the numerical model's predictions, demonstrating similar trends in MC changes over the investigated four years. Various established mould growth models (isopleth, VTT, bio hygrothermal) did not appropriately reflect differences between the spruce types because they do not consider material moisture content, leading to the main conclusion that TM spruce is more resistant to moisture-related issues. Wood's MC influences fungal decomposition, typically occurring above 25% - 30% MC, with some fungi growing at lower MC under conducive conditions. Surface coatings cannot wholly prevent water penetration, which becomes significant when the coating is damaged. This study investigates the detrimental effects of surface coating cracks on wood moisture absorption, comparing TM spruce and native spruce window frames. Simulations were conducted for undamaged and damaged coatings (from 1 mm to 9 mm wide cracks) on window profiles as well as for uncoated profiles. Sorption curves were also measured up to 95% of the relative humidity. MC was measured in the frames exposed to actual climatic conditions and compared to simulated data for model validation. The study utilizes a simplified model of the bottom frame part due to convergence issues with simulations of the whole frame. TM spruce showed about 4% lower MC content compared to native spruce. Simulations showed that a 3 mm wide crack in native spruce coatings for the north orientation poses significant moisture risks, while a 9 mm wide crack in TM spruce coatings remains acceptable furthermore in the case of uncoated TM spruce could be acceptable. In addition, it seems that large enough cracks may cause even worse moisture dynamics compared to uncoated native spruce profiles. The absorption curve comes out to be the far most influential parameter, and the next one is density. Existing mould growth models need to be upgraded to reflect wood material differences accurately. Due to the lower sorption curve of TM spruce, in reality, higher RH values are obtained under the same boundary conditions, which implies a more critical situation according to these mould growth models. Still, it does not reflect the difference in materials, especially under external exposure conditions. Even if different substrate categories in the isopleth and bio-hygrothermal model or different sensitivity material classes for standard and TM wood are used, it does not necessarily change the expected trends; thus, models with MC being the inherent part of the models should be introduced. Orientation plays a crucial role in moisture dynamics. Results show that for similar moisture dynamics, for Norway spruce, the crack could be about 2 mm wider on the south than on the north side. In contrast, for TM spruce, orientation isn't as important, compared to other material properties. The study confirms the enhanced suitability of TM spruce for window frames in terms of moisture resistance and crack tolerance in surface coatings.

Keywords: hygrothermal simulations, mould growth, surface coating, thermally modified wood, window frame

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Authors: Rasha Elsawy

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Aside from the rapid growth of global information technology and the Internet, another key trend is the swift proliferation of wearable technologies. The future of wearable technologies is very bright as an emerging revolution in this technological world. Beyond this, individual continuance intention toward IT is an important area that drew academics' and practitioners' attention. The literature review exhibits that continuance usage is an important concern that needs to be addressed for any technology to be advantageous and for consumers to succeed. However, consumers noticeably abandon their wearable devices soon after purchase, losing all subsequent benefits that can only be achieved through continued usage. Purpose-This thesis aims to develop an integrated model designed to explain and predict consumers' behavioural intention(BI) and continued use (CU) of wearable fitness technology (WFT) to identify the determinants of the CU of technology. Because of this, the question arises as to whether there are differences between technology adoption and post-adoption (CU) factors. Design/methodology/approach- The study employs the unified theory of acceptance and use of technology2 (UTAUT2), which has the best explanatory power, as an underpinning framework—extending it with further factors, along with user-specific personal characteristics as moderators. All items will be adapted from previous literature and slightly modified according to the WFT/SW context. A longitudinal investigation will be carried out to examine the research model, wherein a survey will include these constructs involved in the conceptual model. A quantitative approach based on a questionnaire survey will collect data from existing wearable technology users. Data will be analysed using the structural equation modelling (SEM) method based on IBM SPSS statistics and AMOS 28.0. Findings- The research findings will provide unique perspectives on user behaviour, intention, and actual continuance usage when accepting WFT. Originality/value- Unlike previous works, the current thesis comprehensively explores factors that affect consumers' decisions to continue using wearable technology. That is influenced by technological/utilitarian, affective, emotional, psychological, and social factors, along with the role of proposed moderators. That novel research framework is proposed by extending the UTAUT2 model with additional contextual variables classified into Performance Expectancy, Effort Expectancy, Social Influence (societal pressure regarding body image), Facilitating Conditions, Hedonic Motivation (to be split up into two concepts: perceived enjoyment and perceived device annoyance), Price value, and Habit-forming techniques; adding technology upgradability as determinants of consumers' behavioural intention and continuance usage of Information Technology (IT). Further, using personality traits theory and proposing relevant user-specific personal characteristics (openness to technological innovativeness, conscientiousness in health, extraversion, neuroticism, and agreeableness) to moderate the research model. Thus, the present thesis obtains a more convincing explanation expected to provide theoretical foundations for future emerging IT (such as wearable fitness devices) research from a behavioural perspective.

Keywords: wearable technology, wearable fitness devices/smartwatches, continuance use, behavioural intention, upgradability, longitudinal study

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Authors: Soheila Sadeghi

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Accurate cost prediction is essential for effective project management and decision-making in the construction industry. This study aims to develop a cost prediction model for construction projects using Machine Learning techniques and Particle Swarm Optimization (PSO). The research utilizes a comprehensive dataset containing project cost estimates, actual costs, resource details, and project performance metrics from a road reconstruction project. The methodology involves data preprocessing, feature selection, and the development of an Artificial Neural Network (ANN) model optimized using PSO. The study investigates the impact of various input features, including cost estimates, resource allocation, and project progress, on the accuracy of cost predictions. The performance of the optimized ANN model is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results demonstrate the effectiveness of the proposed approach in predicting project costs, outperforming traditional benchmark models. The feature selection process identifies the most influential variables contributing to cost variations, providing valuable insights for project managers. However, this study has several limitations. Firstly, the model's performance may be influenced by the quality and quantity of the dataset used. A larger and more diverse dataset covering different types of construction projects would enhance the model's generalizability. Secondly, the study focuses on a specific optimization technique (PSO) and a single Machine Learning algorithm (ANN). Exploring other optimization methods and comparing the performance of various ML algorithms could provide a more comprehensive understanding of the cost prediction problem. Future research should focus on several key areas. Firstly, expanding the dataset to include a wider range of construction projects, such as residential buildings, commercial complexes, and infrastructure projects, would improve the model's applicability. Secondly, investigating the integration of additional data sources, such as economic indicators, weather data, and supplier information, could enhance the predictive power of the model. Thirdly, exploring the potential of ensemble learning techniques, which combine multiple ML algorithms, may further improve cost prediction accuracy. Additionally, developing user-friendly interfaces and tools to facilitate the adoption of the proposed cost prediction model in real-world construction projects would be a valuable contribution to the industry. The findings of this study have significant implications for construction project management, enabling proactive cost estimation, resource allocation, budget planning, and risk assessment, ultimately leading to improved project performance and cost control. This research contributes to the advancement of cost prediction techniques in the construction industry and highlights the potential of Machine Learning and PSO in addressing this critical challenge. However, further research is needed to address the limitations and explore the identified future research directions to fully realize the potential of ML-based cost prediction models in the construction domain.

Keywords: cost prediction, construction projects, machine learning, artificial neural networks, particle swarm optimization, project management, feature selection, road reconstruction

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Authors: Keyvan Molanorouzi, Selina Khoo, Tony Morris

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In recent years, there has been a decline in physical activity among adults. Motivation has been shown to be a crucial factor in maintaining physical activity. The purpose of this study was to whether PA motives measured by the Physical Activity and Leisure Motivation Scale PALMS predicted actual amount of PA at a later time to provide evidence for the construct validity of the PALMS. A quantitative, cross-sectional descriptive research design was employed. The Demographic Form, PALMS, and International Physical Activity Questionnaire Short form (IPAQ-S) questionnaires were used to assess motives and amount for physical activity in adults on two occasions. A sample of 640 (489 male, 151 female) undergraduate students aged 18 to 25 years (mean ±SD; 22.30±8.13 years) took part in the study. Male participants were divided into three types of activities, namely exercise, racquet sport, and team sports and female participants only took part in one type of activity, namely team sports. After 14 weeks, all 640 undergraduate students who had filled in the initial questionnaire (Occasion 1) received the questionnaire via email (Occasion 2). Of the 640 students, 493 (77%; 378 males, 115 females) emailed back the completed questionnaire. The results showed that not only were pertinent sub-scales of PALMS positively related to amount of physical activity, but separate regression analyses showed the positive predictive effect of PALMS motives for amount of physical activity for each type of physical activity among participants. This study supported the construct validity of the PALMS by showing that the motives measured by PALMS did predict amount of PA. This information can be obtained to match people with specific sport or activity which in turn could potentially promote longer adherence to the specific activity.Methods: A quantitative, cross-sectional descriptive research design was employed. The Demographic Form, PALMS, and International Physical Activity Questionnaire Short form (IPAQ-S) questionnaires were used to assess motives and amount for physical activity in adults on two occasions. A sample of 640 (489 male, 151 female) undergraduate students aged 18 to 25 years (mean ±SD; 22.30±8.13 years) took part in the study. Male participants were divided into three types of activities, namely exercise, racquet sport, and team sports and female participants only took part in one type of activity, namely team sports. After 14 weeks, all 640 undergraduate students who had filled in the initial questionnaire (Occasion 1) received the questionnaire via email (Occasion 2). Of the 640 students, 493 (77%; 378 males, 115 females) emailed back the completed questionnaire. Results: The results showed that not only were pertinent sub-scales of PALMS positively related to amount of physical activity, but separate regression analyses showed the positive predictive effect of PALMS motives for amount of physical activity for each type of physical activity among participants. This study supported the construct validity of the PALMS by showing that the motives measured by PALMS did predict amount of PA. Conclusion: This information can be obtained to match people with specific sport or activity which in turn could potentially promote longer adherence to the specific activity.

Keywords: physical activity, motivation, level of physical activity, type of physical activity

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Authors: Adriana Haulica

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Powered by Machine Learning, Precise medicine is tailored by now to use genetic and molecular profiling, with the aim of optimizing the therapeutic benefits for cohorts of patients. As the majority of Machine Language algorithms come from heuristics, the outputs have contextual validity. This is not very restrictive in the sense that medicine itself is not an exact science. Meanwhile, the progress made in Molecular Biology, Bioinformatics, Computational Biology, and Precise Medicine, correlated with the huge amount of human biology data and the increase in computational power, opens new healthcare challenges. A more accurate diagnosis is needed along with real-time treatments by processing as much as possible from the available information. The purpose of this paper is to present a deeper vision for the future of Artificial Intelligence in Precise medicine. In fact, actual Machine Learning algorithms use standard mathematical knowledge, mostly Euclidian metrics and standard computation rules. The loss of information arising from the classical methods prevents obtaining 100% evidence on the diagnosis process. To overcome these problems, we introduce MEDICOMPILLS, a new architectural concept tool of information processing in Precise medicine that delivers diagnosis and therapy advice. This tool processes poly-field digital resources: global knowledge related to biomedicine in a direct or indirect manner but also technical databases, Natural Language Processing algorithms, and strong class optimization functions. As the name suggests, the heart of this tool is a compiler. The approach is completely new, tailored for omics and clinical data. Firstly, the intrinsic biological intuition is different from the well-known “a needle in a haystack” approach usually used when Machine Learning algorithms have to process differential genomic or molecular data to find biomarkers. Also, even if the input is seized from various types of data, the working engine inside the MEDICOMPILLS does not search for patterns as an integrative tool. This approach deciphers the biological meaning of input data up to the metabolic and physiologic mechanisms, based on a compiler with grammars issued from bio-algebra-inspired mathematics. It translates input data into bio-semantic units with the help of contextual information iteratively until Bio-Logical operations can be performed on the base of the “common denominator “rule. The rigorousness of MEDICOMPILLS comes from the structure of the contextual information on functions, built to be analogous to mathematical “proofs”. The major impact of this architecture is expressed by the high accuracy of the diagnosis. Detected as a multiple conditions diagnostic, constituted by some main diseases along with unhealthy biological states, this format is highly suitable for therapy proposal and disease prevention. The use of MEDICOMPILLS architecture is highly beneficial for the healthcare industry. The expectation is to generate a strategic trend in Precise medicine, making medicine more like an exact science and reducing the considerable risk of errors in diagnostics and therapies. The tool can be used by pharmaceutical laboratories for the discovery of new cures. It will also contribute to better design of clinical trials and speed them up.

Keywords: bio-semantic units, multiple conditions diagnosis, NLP, omics

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Authors: Joel Niklaus, Matthias Sturmer

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The publication of judicial proceedings is a cornerstone of many democracies. It enables the court system to be made accountable by ensuring that justice is made in accordance with the laws. Equally important is privacy, as a fundamental human right (Article 12 in the Declaration of Human Rights). Therefore, it is important that the parties (especially minors, victims, or witnesses) involved in these court decisions be anonymized securely. Today, the anonymization of court decisions in Switzerland is performed either manually or semi-automatically using primitive software. While much research has been conducted on anonymization for tabular data, the literature on anonymization for unstructured text documents is thin and virtually non-existent for court decisions. In 2019, it has been shown that manual anonymization is not secure enough. In 21 of 25 attempted Swiss federal court decisions related to pharmaceutical companies, pharmaceuticals, and legal parties involved could be manually re-identified. This was achieved by linking the decisions with external databases using regular expressions. An automated re-identification system serves as an automated test for the safety of existing anonymizations and thus promotes the right to privacy. Manual anonymization is very expensive (recurring annual costs of over CHF 20M in Switzerland alone, according to an estimation). Consequently, many Swiss courts only publish a fraction of their decisions. An automated anonymization system reduces these costs substantially, further leading to more capacity for publishing court decisions much more comprehensively. For the re-identification system, topic modeling with latent dirichlet allocation is used to cluster an amount of over 500K Swiss court decisions into meaningful related categories. A comprehensive knowledge base with publicly available data (such as social media, newspapers, government documents, geographical information systems, business registers, online address books, obituary portal, web archive, etc.) is constructed to serve as an information hub for re-identifications. For the actual re-identification, a general-purpose language model is fine-tuned on the respective part of the knowledge base for each category of court decisions separately. The input to the model is the court decision to be re-identified, and the output is a probability distribution over named entities constituting possible re-identifications. For the anonymization system, named entity recognition (NER) is used to recognize the tokens that need to be anonymized. Since the focus lies on Swiss court decisions in German, a corpus for Swiss legal texts will be built for training the NER model. The recognized named entities are replaced by the category determined by the NER model and an identifier to preserve context. This work is part of an ongoing research project conducted by an interdisciplinary research consortium. Both a legal analysis and the implementation of the proposed system design ESRA will be performed within the next three years. This study introduces the system design of ESRA, an end-to-end system for re-identification and anonymization of Swiss court decisions. Firstly, the re-identification system tests the safety of existing anonymizations and thus promotes privacy. Secondly, the anonymization system substantially reduces the costs of manual anonymization of court decisions and thus introduces a more comprehensive publication practice.

Keywords: artificial intelligence, courts, legal tech, named entity recognition, natural language processing, ·privacy, topic modeling

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Authors: Sonja Giese, Tess N. Peacock

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South African based Innovation Edge is experimenting with technology to drive positive behavior change, enable data-driven decision making, and scale quality early years services. This paper uses five case studies to illustrate how technology can be used in resource-constrained environments to first, encourage parenting practices that build early language development (using a stage-based mobile messaging pilot, ChildConnect), secondly, to improve the quality of ECD programs (using a mobile application, CareUp), thirdly, how to affordably scale services for the early detection of visual and hearing impairments (using a mobile tool, HearX), fourthly, how to build a transparent and accountable system for the registration and funding of ECD (using a blockchain enabled platform, Amply), and finally enable rapid data collection and feedback to facilitate quality enhancement of programs at scale (the Early Learning Outcomes Measure). ChildConnect and CareUp were both developed using a design based iterative research approach. The usage and uptake of ChildConnect and CareUp was evaluated with qualitative and quantitative methods. Actual child outcomes were not measured in the initial pilots. Although parents who used and engaged on either platform felt more supported and informed, parent engagement and usage remains a challenge. This is contrast to ECD practitioners whose usage and knowledge with CareUp showed both sustained engagement and knowledge improvement. HearX is an easy-to-use tool to identify hearing loss and visual impairment. The tool was tested with 10000 children in an informal settlement. The feasibility of cost-effectively decentralising screening services was demonstrated. Practical and financial barriers remain with respect to parental consent and for successful referrals. Amply uses mobile and blockchain technology to increase impact and accountability of public services. In the pilot project, Amply is being used to replace an existing paper-based system to register children for a government-funded pre-school subsidy in South Africa. Early Learning Outcomes Measure defines what it means for a child to be developmentally ‘on track’ at aged 50-69 months. ELOM administration is enabled via a tablet which allows for easy and accurate data collection, transfer, analysis, and feedback. ELOM is being used extensively to drive quality enhancement of ECD programs across multiple modalities. The nature of ECD services in South Africa is that they are in large part provided by disconnected private individuals or Non-Governmental Organizations (in contrast to basic education which is publicly provided by the government). It is a disparate sector which means that scaling successful interventions is that much harder. All five interventions show the potential of technology to support and enhance a range of ECD services, but pathways to scale are still being tested.

Keywords: assessment, behavior change, communication, data, disabilities, mobile, scale, technology, quality

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