Search results for: component mode synthesis

Authors: Anna-Maria Ramezanzadeh

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Demand for Arabic language is growing worldwide, driven by increased interest in the multifarious purposes the language serves, both for the population of heritage learners and those studying Arabic as a foreign language. The diglossic, or indeed multiglossic nature of the language as used in Arabic speaking communities however, is seldom represented in the content of classroom courses. This disjoint between the nature of provision and students’ expectations can severely impact their engagement with course material, and their motivation to either commence or continue learning the language. The nature of motivation and its relationship to multiglossia is sparsely explored in current literature on Arabic. The theoretical framework here proposed aims to address this gap by presenting a model and instruments for the measurement of Arabic learners’ motivation in relation to the multiple strands of the language. It adopts and develops the Second Language Motivation Self-System model (L2MSS), originally proposed by Zoltan Dörnyei, which measures motivation as the desire to reduce the discrepancy between leaners’ current and future self-concepts in terms of the second language (L2). The tripartite structure incorporates measures of the Current L2 Self, Future L2 Self (consisting of an Ideal L2 Self, and an Ought-To Self), and the L2 Learning Experience. The strength of the self-concepts is measured across three different domains of Arabic: Classical, Modern Standard and Colloquial. The focus on learners’ self-concepts allows for an exploration of the effect of multiple factors on motivation towards Arabic, including religion. The relationship between Islam and Arabic is often given as a prominent reason behind some students’ desire to learn the language. Exactly how and why this factor features in learners’ L2 self-concepts has not yet been explored. Specifically designed surveys and interview protocols are proposed to facilitate the exploration of these constructs. The L2 Learning Experience component of the model is operationalized as learners’ task-based engagement. Engagement is conceptualised as multi-dimensional and malleable. In this model, situation-specific measures of cognitive, behavioural, and affective components of engagement are collected via specially designed repeated post-task self-report surveys on Personal Digital Assistant over multiple Arabic lessons. Tasks are categorised according to language learning skill. Given the domain-specific uses of the different varieties of Arabic, the relationship between learners’ engagement with different types of tasks and their overall motivational profiles will be examined to determine the extent of the interaction between the two constructs. A framework for this data analysis is proposed and hypotheses discussed. The unique combination of situation-specific measures of engagement and a person-oriented approach to measuring motivation allows for a macro- and micro-analysis of the interaction between learners and the Arabic learning process. By combining cross-sectional and longitudinal elements with a mixed-methods design, the model proposed offers the potential for capturing a comprehensive and detailed picture of the motivation and engagement of Arabic learners. The application of this framework offers a number of numerous potential pedagogical and research implications which will also be discussed.

Keywords: Arabic, diglossia, engagement, motivation, multiglossia, sociolinguistics

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Authors: Nickolas A. Stewart, Donald C. Hefelfinger, Garrett V. Brittain, Timothy C. Frommeyer, Adrienne Stolfi

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Orthopedic surgery is currently considered one of the most competitive specialties that medical students can apply to for residency training. As evidenced by increasing United States Medical Licensing Examination (USMLE) scores, overall grades, and publication, presentation, and abstract numbers, this specialty is getting increasingly competitive. The recent change of USMLE Step 1 scores to pass/fail has resulted in additional challenges for medical students planning to apply for orthopedic residency. Until now, these scores have been a tool used by residency programs to screen applicants as an initial factor to determine the strength of their application. With USMLE STEP 1 converting to a pass/fail grading criterion, the question remains as to what will take its place on the ERAS application. The primary objective of this study is to determine the trends in the number of research projects, abstracts, presentations, and publications among orthopedic residency applicants. Secondly, this study seeks to determine if there is a relationship between the number of research projects, abstracts, presentations, and publications, and match rates. The researchers utilized the National Resident Matching Program's Charting Outcomes in the Match between 2007 and 2022 to identify mean publications and research project numbers by allopathic and osteopathic US orthopedic surgery senior applicants. A paired t test was performed between the mean number of publications and research projects by matched and unmatched applicants. Additionally, simple linear regressions within matched and unmatched applicants were used to determine the association between year and number of abstracts, presentations, and publications, and a number of research projects. For determining whether the increase in the number of abstracts, presentations, and publications, and a number of research projects is significantly different between matched and unmatched applicants, an analysis of covariance is used with an interaction term added to the model, which represents the test for the difference between the slopes of each group. The data shows that from 2007 to 2022, the average number of research publications increased from 3 to 16.5 for matched orthopedic surgery applicants. The paired t-test had a significant p-value of 0.006 for the number of research publications between matched and unmatched applicants. In conclusion, the average number of publications for orthopedic surgery applicants has significantly increased for matched and unmatched applicants from 2007 to 2022. Moreover, this increase has accelerated in recent years, as evidenced by an increase of only 1.5 publications from 2007 to 2001 versus 5.0 publications from 2018 to 2022. The number of abstracts, presentations, and publications is a significant factor regarding an applicant's likelihood to successfully match into an orthopedic residency program. With USMLE Step 1 being converted to pass/fail, the researchers expect students and program directors will place increased importance on additional factors that can help them stand out. This study demonstrates that research will be a primary component in stratifying future orthopedic surgery applicants. In addition, this suggests the average number of research publications will continue to accelerate. Further study is required to determine whether this growth is sustainable.

Keywords: publications, orthopedic surgery, research, residency applications

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Authors: Lena Payati, Maria Kazou, Effie Tsakalidou

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Table olives are one of the most popular fermented vegetables worldwide, which along with olive oil, have a crucial role in the world economy. They are highly appreciated by the consumers for their characteristic taste and pleasant aromas, while several health and nutritional benefits have been reported as well. Until recently, microbial biogeography, i.e., the study of microbial diversity over time and space, has been mainly associated with wine. However, nowadays, the term 'terroir' has been extended to other crops and food products so as to link the geographical origin and environmental conditions to quality aspects of fermented foods. Taking the above into consideration, the present study focuses on the microbial fingerprinting of the most important olive varieties of Greece with the state-of-the-art amplicon-based metagenomics analysis. Towards this, in 2019, 61 samples from 38 different olive varieties were collected at the final stage of ripening from 13 well spread geographical regions in Greece. For the metagenomics analysis, total DNA was extracted from the olive samples, and the 16S rRNA gene and ITS DNA region were sequenced and analyzed using bioinformatics tools for the identification of bacterial and yeasts/fungal diversity, respectively. Furthermore, principal component analysis (PCA) was also performed for data clustering based on the average microbial composition of all samples from each region of origin. According to the composition, results obtained, when samples were analyzed separately, the majority of both bacteria (such as Pantoea, Enterobacter, Roserbergiella, and Pseudomonas) and yeasts/fungi (such as Aureobasidium, Debaromyces, Candida, and Cladosporium) genera identified were found in all 61 samples. Even though interesting differences were observed at the relative abundance level of the identified genera, the bacterial genus Pantoea and the yeast/fungi genus Aureobasidium were the dominant ones in 35 and 40 samples, respectively. Of note, olive samples collected from the same region had similar fingerprint (genera identified and relative abundance level) regardless of the variety, indicating a potential association between the relative abundance of certain taxa and the geographical region. When samples were grouped by region of origin, distinct bacterial profiles per region were observed, which was also evident from the PCA analysis. This was not the case for the yeast/fungi profiles since 10 out of the 13 regions were grouped together mainly due to the dominance of the genus Aureobasidium. A second cluster was formed for the islands Crete and Rhodes, both of which are located in the Southeast Aegean Sea. These two regions clustered together mainly due to the identification of the genus Toxicocladosporium in relatively high abundances. Finally, the Agrinio region was separated from the others as it showed a completely different microbial fingerprinting. However, due to the limited number of olive samples from some regions, a subsequent PCA analysis with more samples from these regions is expected to yield in a more clear clustering. The present study is part of a bigger project, the first of its kind in Greece, with the ultimate goal to analyze a larger set of olive samples of different varieties and from different regions in Greece in order to have a reliable olives’ microbial biogeography.

Keywords: amplicon-based metagenomics analysis, bacteria, microbial biogeography, olive microbiota, yeasts/fungi

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Authors: Nir Nissim, Erez Shalom, Tomer Lancewiki, Yuval Elovici, Yuval Shahar

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Background: A Medical Device (MD) is an instrument, machine, implant, or similar device that includes a component intended for the purpose of the diagnosis, cure, treatment, or prevention of disease in humans or animals. Medical devices play increasingly important roles in health services eco-systems, including: (1) Patient Diagnostics and Monitoring; Medical Treatment and Surgery; and Patient Life Support Devices and Stabilizers. MDs are part of the medical device eco-system and are connected to the network, sending vital information to the internal medical information systems of medical centers that manage this data. Wireless components (e.g. Wi-Fi) are often embedded within medical devices, enabling doctors and technicians to control and configure them remotely. All these functionalities, roles, and uses of MDs make them attractive targets of cyber-attacks launched for many malicious goals; this trend is likely to significantly increase over the next several years, with increased awareness regarding MD vulnerabilities, the enhancement of potential attackers’ skills, and expanded use of medical devices. Significance: We propose to develop and implement Cyber-Med, a unique collaborative project of Ben-Gurion University of the Negev and the Clalit Health Services Health Maintenance Organization. Cyber-Med focuses on the development of a comprehensive detection framework that relies on a critical attack repository that we aim to create. Cyber-Med will allow researchers and companies to better understand the vulnerabilities and attacks associated with medical devices as well as providing a comprehensive platform for developing detection solutions. Methodology: The Cyber-Med detection framework will consist of two independent, but complementary detection approaches: one for known attacks, and the other for unknown attacks. These modules incorporate novel ideas and algorithms inspired by our team's domains of expertise, including cyber security, biomedical informatics, and advanced machine learning, and temporal data mining techniques. The establishment and maintenance of Cyber-Med’s up-to-date attack repository will strengthen the capabilities of Cyber-Med’s detection framework. Major Findings: Based on our initial survey, we have already found more than 15 types of vulnerabilities and possible attacks aimed at MDs and their eco-system. Many of these attacks target individual patients who use devices such pacemakers and insulin pumps. In addition, such attacks are also aimed at MDs that are widely used by medical centers such as MRIs, CTs, and dialysis engines; the information systems that store patient information; protocols such as DICOM; standards such as HL7; and medical information systems such as PACS. However, current detection tools, techniques, and solutions generally fail to detect both the known and unknown attacks launched against MDs. Very little research has been conducted in order to protect these devices from cyber-attacks, since most of the development and engineering efforts are aimed at the devices’ core medical functionality, the contribution to patients’ healthcare, and the business aspects associated with the medical device.

Keywords: medical device, cyber security, attack, detection, machine learning

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Authors: Behnaz Baghaei, Mikael Skrifvars

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Sustainability is today a trend that is seen everywhere, with no exception for the textiles 31 industry. However, there is a rather significant downside regarding how the textile industry currently operates, namely the huge amount of end-of-life textiles coming along with it. Approximately 73% of the 53 million tonnes of fibres used annually for textile production is landfilled or incinerated, while only 12% is recycled as secondary products. Mechanical recycling of end-of-life textile fabrics into yarns and fabrics was before very common, but due to the low costs for virgin man-made fibres, the current textile material composition diversity, the fibre material quality variations and the high recycling costs this route is not feasible. Another way to decrease the ever-growing pile of textile waste is to repurpose the textile. If a feasible methodology can be found to reuse end-of life textiles as secondary market products including a manufacturing process that requires rather low investment costs, then this can be highly beneficial to counteract the increasing textile waste volumes. In structural composites, glass fibre textiles are used as reinforcements, but today there is a growing interest in biocomposites where the reinforcement and/or the resin are from a biomass resource. All-cellulose composites (ACCs) are monocomponent or single polymer composites, and they are entirely made from cellulose, ideally leading to a homogeneous biocomposite. Since the matrix and the reinforcement are both made from cellulose, and therefore chemically identical, they are fully compatible with each other which allow efficient stress transfer and adhesion at their interface. Apart from improving the mechanical performance of the final products, the recycling of the composites will be facilitated. This paper reports the recycling of end-of-life cellulose containing textiles by fabrication of all-cellulose composites (ACCs). Composite laminates were prepared by using an ionic liquid (IL) in a hot process, involving a partial dissolving of the cellulose fibres. Discharged denim fabrics were used as the reinforcement while dissolved cellulose from two different cellulose resources was used as the matrix phase. Virgin cotton staple fibres and recovered cotton from polyester/cotton (polycotton) waste fabrics were used to form the matrix phase. The process comprises the dissolving 6 wt.% cellulose solution in the ionic liquid 1-butyl-3-methyl imidazolium acetate ([BMIM][Ac]), this solution acted as a precursor for the matrix component. The denim fabrics were embedded in the cellulose/IL solution after which laminates were formed, which also involved removal of the IL by washing. The effect of reuse of the recovered IL was also investigated. The mechanical properties of the obtained ACCs were determined regarding tensile, impact and flexural properties. Mechanical testing revealed that there are no clear differences between the values measured for mechanical strength and modulus of the manufactured ACCs from denim/cotton-fresh IL, denim/recovered cotton-fresh IL and denim/cotton-recycled IL. This could be due to the low weight fraction of the cellulose matrix in the final ACC laminates and presumably the denim as cellulose reinforcement strongly influences and dominates the mechanical properties. Fabricated ACC composite laminates were further characterized regarding scanning electron microscopy.

Keywords: all-cellulose composites, denim fabrics, ionic liquid, mechanical properties

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Authors: Gabriela R. Silveira, Claudia S. Rocha, Lais S. Vitti, Jeane L. Borges, Helen B. Durgante

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Emerging adulthood occurs after adolescence in a period that maybe be marked by experimentation, identity reconfigurations, labor life demands, and insertion in the work environment, which tends to generate stress and emotional instability. Health promotion programs for the development of strengths and virtues, based on Positive Psychology, for emerging adults are sparse in Brazil. The aim of this study was to evaluate the preliminary effects of an online multi-component Positive Psychology program for the health promotion of emerging adults based on Cognitive Behavioural Therapy and Positive Psychology. The program included six online (synchronous) weekly group sessions of approximately two hours each and homework (asynchronous) activities. The themes worked were Values and self-care/Prudence, Optimism, Empathy, Gratitude, Forgiveness, and Meaning of life and work. This study presents data from a longitudinal, pre-experimental design with pre (T1) and post-test (T2) evaluation in the intervention group. 47 individuals aged between 19-30 years old participated, mean age of 24.53 years (SD=3.13), 37 females (78.7%). 42 (89.4%) self-defined as heterosexual, four (8.5%) as homosexual, and one (2.5%) as bisexual. 33 (70.2%) had incomplete higher education, four (8.5%) completed higher education, and seven (14.9%) had a graduate level of education. 27 participants worked (57.4%), out of which 25 were health workers (53.2%). 14 (29.8%) were caregivers, 27 (57.4%) had a spiritual belief, 36 (76.6%) had access to leisure, and 38 (80.9%) had perceived social support. The instruments used were a sociodemographic questionnaire, the 10-item Perceived Stress Scale, and the 12-item General Health Questionnaire. The program was advertised on social networks and interested participants filled out the Consent Form and the evaluation protocol at T1 and T2 via Google Docs form. The main research was approved (CEP n.1,899,368; 4,143,219; CAAE: 61997516.5.0000.5334) and complied with sanitary and Ethics criteria in research with human beings. Wilcoxon statistics revealed significant improvements in indicators of perceived stress between T1 (X=22.21, SD=6.79) and T2 (X=15.10, SD=5.82); (Z=-4.353; p=0.001) as well as depression and anxiety symptoms (T1:X=26.72, SD=8.84; T2: X=19.23, SD=4.68); (Z=-3.945, p=0.001) of the emerging adults after their participation in the programme. The programme has an innovative character not only for presenting an online Positive Psychology approach but also for being based on an intervention developed, evaluated, and manualized in Brazil. By focusing on emerging adults, this study contributes to advancing research on a relatively new field in developmental studies. As a limitation, this is a pre-experimental and pilot study, requiring an increase in sample size for greater statistical robustness, also qualitative data analysis is crucial for methodological complementarity. The importance of investing efforts to accompany this age group and provide advances in longitudinal research in the area of health promotion and disease prevention is highlighted.

Keywords: emerging adults, disease prevention, health promotion, online program

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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: Anastasia Constantinou, Stephen Morris

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Background: The adoption of synchronous and asynchronous telemedicine modalities for primary care consultations has exponentially increased since the COVID-19 pandemic. However, there is limited understanding of how virtual consultations influence healthcare resource utilization and other quality measures including safety, timeliness, efficiency, patient and provider satisfaction, cost-effectiveness and environmental impact. Aim: Quantify the rate of follow-up visits, emergency department visits, hospitalizations, request for investigations and prescriptions and comment on the effect on different quality measures associated with different telemedicine modalities used for primary care services and primary care referrals to secondary care Design and setting: Systematic review in primary care Methods: A systematic search was carried out across three databases (Medline, PubMed and Scopus) between August and November 2023, using terms related to telemedicine, general practice, electronic referrals, follow-up, use and efficiency and supported by citation searching. This was followed by screening according to pre-defined criteria, data extraction and critical appraisal. Narrative synthesis and metanalysis of quantitative data was used to summarize findings. Results: The search identified 2230 studies; 50 studies are included in this review. There was a prevalence of asynchronous modalities in both primary care services (68%) and referrals from primary care to secondary care (83%), and most of the study participants were females (63.3%), with mean age of 48.2. The average follow-up for virtual consultations in primary care was 28.4% (eVisits: 36.8%, secure messages 18.7%, videoconference 23.5%) with no significant difference between them or F2F consultations. There was an average annual reduction of primary care visits by 0.09/patient, an increase in telephone visits by 0.20/patient, an increase in ED encounters by 0.011/patient, an increase in hospitalizations by 0.02/patient and an increase in out of hours visits by 0.019/patient. Laboratory testing was requested on average for 10.9% of telemedicine patients, imaging or procedures for 5.6% and prescriptions for 58.7% of patients. When looking at referrals to secondary care, on average 36.7% of virtual referrals required follow-up visit, with the average rate of follow-up for electronic referrals being higher than for videoconferencing (39.2% vs 23%, p=0.167). Technical failures were reported on average for 1.4% of virtual consultations to primary care. When using carbon footprint estimates, we calculate that the use of telemedicine in primary care services can potentially provide a net decrease in carbon footprint by 0.592kgCO2/patient/year. When follow-up rates are taken into account, we estimate that virtual consultations reduce carbon footprint for primary care services by 2.3 times, and for secondary care referrals by 2.2 times. No major concerns regarding quality of care, or patient satisfaction were identified. 5/7 studies that addressed cost-effectiveness, reported increased savings. Conclusions: Telemedicine provides quality, cost-effective, and environmentally sustainable care for patients in primary care with inconclusive evidence regarding the rates of subsequent healthcare utilization. The evidence is limited by heterogeneous, small-scale studies and lack of prospective comparative studies. Further research to identify the most appropriate telemedicine modality for different patient populations, clinical presentations, service provision (e.g. used to follow-up patients instead of initial diagnosis) as well as further education for patients and providers alike on how to make best use of this service is expected to improve outcomes and influence practice.

Keywords: telemedicine, healthcare utilisation, digital interventions, environmental impact, sustainable healthcare

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Authors: Aritra Das, Tanmay Mahapatra, Prabir Maharana, Sridhar Srikantiah

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In the absence of adequate well-equipped neonatal-care facilities serving rural Bihar, India, the practice of essential home-based newborn-care remains critically important for reduction of neonatal and infant mortality, especially among pre-term and small-for-gestational-age (Low-birth-weight) newborns. To improve the child health parameters in Bihar, ‘Very-Low-Birth-Weight (vLBW) Tracking’ intervention is being conducted by CARE India, since 2015, targeting public facility-delivered newborns weighing ≤2000g at birth, to improve their identification and provision of immediate post-natal care. To assess the effectiveness of the intervention, 200 public health facilities were randomly selected from all functional public-sector delivery points in Bihar and various outcomes were tracked among the neonates born there. Thus far, one pre-intervention (Feb-Apr’2015-born neonates) and three post-intervention (for Sep-Oct’2015, Sep-Oct’2016 and Sep-Oct’2017-born children) follow-up studies were conducted. In each round, interviews were conducted with the mothers/caregivers of successfully-tracked children to understand outcome, service-coverage and care-seeking during the neonatal period. Data from 171 matched facilities common across all rounds were analyzed using SAS-9.4. Identification of neonates with birth-weight ≤ 2000g improved from 2% at baseline to 3.3%-4% during post-intervention. All indicators pertaining to post-natal home-visits by frontline-workers (FLWs) improved. Significant improvements between baseline and post-intervention rounds were also noted regarding mothers being informed about ‘weak’ child – at the facility (R1 = 25 to R4 = 50%) and at home by FLW (R1 = 19%, to R4 = 30%). Practice of ‘Kangaroo-Mother-Care (KMC)’– an important component of essential newborn care – showed significant improvement in postintervention period compared to baseline in both facility (R1 = 15% to R4 = 31%) and home (R1 = 10% to R4=29%). Increasing trend was noted regarding detection and birth weight-recording of the extremely low-birth-weight newborns (< 1500 g) showed an increasing trend. Moreover, there was a downward trend in mortality across rounds, in each birth-weight strata (< 1500g, 1500-1799g and >= 1800g). After adjustment for the differential distribution of birth-weights, mortality was found to decline significantly from R1 (22.11%) to R4 (11.87%). Significantly declining trend was also observed for both early and late neonatal mortality and morbidities. Multiple regression analysis identified - birth during immediate post-intervention phase as well as that during the maintenance phase, birth weight > 1500g, children of low-parity mothers, receiving visit from FLW in the first week and/or receiving advice on extra care from FLW as predictors of survival during neonatal period among vLBW newborns. vLBW tracking was found to be a successful and sustainable intervention and has already been handed over to the Government.

Keywords: weak newborn tracking, very low birth weight babies, newborn care, community response

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Authors: E. A. Krasikov

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As the service life of an operating nuclear power plant (NPP) increases, the potential misunderstanding of the degradation of aging components must receive more attention. Integrity assurance analysis contributes to the effective maintenance of adequate plant safety margins. In essence, the reactor pressure vessel (RPV) is the key structural component determining the NPP lifetime. Environmentally induced cracking in the stainless steel corrosion-preventing cladding of RPV’s has been recognized to be one of the technical problems in the maintenance and development of light-water reactors. Extensive cracking leading to failure of the cladding was found after 13000 net hours of operation in JPDR (Japan Power Demonstration Reactor). Some of the cracks have reached the base metal and further penetrated into the RPV in the form of localized corrosion. Failures of reactor internal components in both boiling water reactors and pressurized water reactors have increased after the accumulation of relatively high neutron fluences (5´1020 cm–2, E>0,5MeV). Therefore, in the case of cladding failure, the problem arises of hydrogen (as a corrosion product) embrittlement of irradiated RPV steel because of exposure to the coolant. At present when notable progress in plasma physics has been obtained practical energy utilization from fusion reactors (FR) is determined by the state of material science problems. The last includes not only the routine problems of nuclear engineering but also a number of entirely new problems connected with extreme conditions of materials operation – irradiation environment, hydrogenation, thermocycling, etc. Limiting data suggest that the combined effect of these factors is more severe than any one of them alone. To clarify the possible influence of the in-service synergistic phenomena on the FR structural materials properties we have studied hydrogen-irradiated steel interaction including alternating hydrogenation and heat treatment (annealing). Available information indicates that the life of the first wall could be expanded by means of periodic in-place annealing. The effects of neutron fluence and irradiation temperature on steel/hydrogen interactions (adsorption, desorption, diffusion, mechanical properties at different loading velocities, post-irradiation annealing) were studied. Experiments clearly reveal that the higher the neutron fluence and the lower the irradiation temperature, the more hydrogen-radiation defects occur, with corresponding effects on the steel mechanical properties. Hydrogen accumulation analyses and thermal desorption investigations were performed to prove the evidence of hydrogen trapping at irradiation defects. Extremely high susceptibility to hydrogen embrittlement was observed with specimens which had been irradiated at relatively low temperature. However, the susceptibility decreases with increasing irradiation temperature. To evaluate methods for the RPV’s residual lifetime evaluation and prediction, more work should be done on the irradiated metal–hydrogen interaction in order to monitor more reliably the status of irradiated materials.

Keywords: hydrogen, radiation, stability, structural steel

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Authors: Payam Mousavi, Andrew L. Stewart, Huiwen You, Aryeh F. G. Fayerman

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We present a decision-support tool to assist an operator in the detection and tracking of a suspect vehicle traveling to an unknown target destination. Multiple data sources, such as traffic cameras, traffic information, weather, etc., are integrated and processed in real-time to infer a suspect’s intended destination chosen from a list of pre-determined high-value targets. Previously, we presented our work in the detection and tracking of vehicles using traffic and airborne cameras. Here, we focus on the fusion and processing of that information to predict a suspect’s behavior. The network of cameras is represented by a directional graph, where the edges correspond to direct road connections between the nodes and the edge weights are proportional to the average time it takes to travel from one node to another. For our experiments, we construct our graph based on the greater Los Angeles subset of the Caltrans’s “Performance Measurement System” (PeMS) dataset. We propose a Bayesian approach where a posterior probability for each target is continuously updated based on detections of the suspect in the live video feeds. Additionally, we introduce the concept of ‘soft interventions’, inspired by the field of Causal Inference. Soft interventions are herein defined as interventions that do not immediately interfere with the suspect’s movements; rather, a soft intervention may induce the suspect into making a new decision, ultimately making their intent more transparent. For example, a soft intervention could be temporarily closing a road a few blocks from the suspect’s current location, which may require the suspect to change their current course. The objective of these interventions is to gain the maximum amount of information about the suspect’s intent in the shortest possible time. Our system currently operates in a human-on-the-loop mode where at each step, a set of recommendations are presented to the operator to aid in decision-making. In principle, the system could operate autonomously, only prompting the operator for critical decisions, allowing the system to significantly scale up to larger areas and multiple suspects. Once the intended target is identified with sufficient confidence, the vehicle is reported to the authorities to take further action. Other recommendations include a selection of road closures, i.e., soft interventions, or to continue monitoring. We evaluate the performance of the proposed system using simulated scenarios where the suspect, starting at random locations, takes a noisy shortest path to their intended target. In all scenarios, the suspect’s intended target is unknown to our system. The decision thresholds are selected to maximize the chances of determining the suspect’s intended target in the minimum amount of time and with the smallest number of interventions. We conclude by discussing the limitations of our current approach to motivate a machine learning approach, based on reinforcement learning in order to relax some of the current limiting assumptions.

Keywords: autonomous surveillance, Bayesian reasoning, decision support, interventions, patterns of life, predictive analytics, predictive insights

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Authors: Noura Al Akkari, Aurélie Foucquier, Sylvain Lespinats

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Load monitoring is a management process for energy consumption towards energy savings and energy efficiency. Non Intrusive Load Monitoring (NILM) is one method of load monitoring used for disaggregation purposes. NILM is a technique for identifying individual appliances based on the analysis of the whole residence data retrieved from the main power meter of the house. Our NILM framework starts with data acquisition, followed by data preprocessing, then event detection, feature extraction, then general appliance modeling and identification at the final stage. The event detection stage is a core component of NILM process since event detection techniques lead to the extraction of appliance features. Appliance features are required for the accurate identification of the household devices. In this research work, we aim at developing a new event detection methodology with accurate load disaggregation to extract appliance features. Time-domain features extracted are used for tuning general appliance models for appliance identification and classification steps. We use unsupervised algorithms such as Dynamic Time Warping (DTW). The proposed method relies on detecting areas of operation of each residential appliance based on the power demand. Then, detecting the time at which each selected appliance changes its states. In order to fit with practical existing smart meters capabilities, we work on low sampling data with a frequency of (1/60) Hz. The data is simulated on Load Profile Generator software (LPG), which was not previously taken into consideration for NILM purposes in the literature. LPG is a numerical software that uses behaviour simulation of people inside the house to generate residential energy consumption data. The proposed event detection method targets low consumption loads that are difficult to detect. Also, it facilitates the extraction of specific features used for general appliance modeling. In addition to this, the identification process includes unsupervised techniques such as DTW. To our best knowledge, there exist few unsupervised techniques employed with low sampling data in comparison to the many supervised techniques used for such cases. We extract a power interval at which falls the operation of the selected appliance along with a time vector for the values delimiting the state transitions of the appliance. After this, appliance signatures are formed from extracted power, geometrical and statistical features. Afterwards, those formed signatures are used to tune general model types for appliances identification using unsupervised algorithms. This method is evaluated using both simulated data on LPG and real-time Reference Energy Disaggregation Dataset (REDD). For that, we compute performance metrics using confusion matrix based metrics, considering accuracy, precision, recall and error-rate. The performance analysis of our methodology is then compared with other detection techniques previously used in the literature review, such as detection techniques based on statistical variations and abrupt changes (Variance Sliding Window and Cumulative Sum).

Keywords: electrical disaggregation, DTW, general appliance modeling, event detection

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Authors: Maria Luiza Fulga, Georgeta Truca, Mihaela Alexandru, Andriescu Mariana, Crin Marcean

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The significance of nursing as a subject in the post-secondary healthcare curriculum is a major. We aimed to enable our students to assess the patient's risk, to establish prevention measures and to adapt to a specific learning context, in order to acquire the skills and abilities necessary for the nursing profession. In order to achieve these objectives, during the three years of study, teachers put an emphasis on acquiring communication skills, because in our country after the first cycle of hospital accreditation concluded in 2016, the National Authority for Quality of Health Management has introduced the criteria for the implementation and application of the nursing process according to the accreditation standards. According to these requirements, the nurse has to carry out the nursing assessment, based on communication as a distinct component, so that they can identify nursing diagnoses and implement the nursing plan. In this respect, we, the teachers, have refocused, by approaching various teaching strategies and preparing students for the real context of learning and applying what they learn. In the educational process, the tutors in the hospitals have an important role to play in acquiring professional skills. Students perform their activity in the hospital in accordance with the curriculum, in order to verify the practical applicability of the theoretical knowledge acquired in the school classes and also have the opportunity to acquire their skills in a real learning context. In clinical education, the student nurse learns in the middle of a guidance team which includes a practice tutor, who is a nurse that takes responsibility for the practical/clinical learning of the students in their field of activity. In achieving this objective, the tutor's abilities involve pedagogical knowledge, knowledge for the good of the individual and nursing theory, in order to be able to guide clinical practice in accordance with current requirements. The aim of this study is to find out the students’ confidence level in practice tutors in hospitals, the students’ degree of satisfaction in the pedagogical skills of the tutors and the practical applicability of the theoretical knowledge. In this study, we used as a method of investigation a student satisfaction questionnaire regarding the clinical practice in the hospital and the sample of the survey consisted of 100 students aged between 20 and 50 years, from the first, second and third year groups, with the General Nurse specialty (nurses responsible for general care), from 'Fundeni' Healthcare Post-Secondary School, Bucharest, Romania. Following the analysis of the data provided, we arrived the conclusion that the hospital tutor needs to improve his/her pedagogical skills, the knowledge of nursing diagnostics, and the implementation of the nursing plan, so that the applicability of the theoretical notions would be increased. Future plans include the pedagogical training of the medical staff, as well as updating the knowledge needed to implement the nursing process in order to meet current requirements.

Keywords: clinical training, nursing process, pedagogical skills, tutor

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Authors: Muhammad Ahnaf Zahin, Yaw Adu-Gyamfi

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Bus transit is a crucial component of transportation networks, especially in urban areas. Any intelligent transportation system must have accurate real-time information on bus travel times since it minimizes waiting times for passengers at different stations along a route, improves service reliability, and significantly optimizes travel patterns. Bus agencies must enhance the quality of their information service to serve their passengers better and draw in more travelers since people waiting at bus stops are frequently anxious about when the bus will arrive at their starting point and when it will reach their destination. For solving this issue, different models have been developed for predicting bus travel times recently, but most of them are focused on smaller road networks due to their relatively subpar performance in high-density urban areas on a vast network. This paper develops a deep learning-based architecture using a single-step multi-station forecasting approach to predict average bus travel times for numerous routes, stops, and trips on a large-scale network using heterogeneous bus transit data collected from the GTFS database. Over one week, data was gathered from multiple bus routes in Saint Louis, Missouri. In this study, Gated Recurrent Unit (GRU) neural network was followed to predict the mean vehicle travel times for different hours of the day for multiple stations along multiple routes. Historical time steps and prediction horizon were set up to 5 and 1, respectively, which means that five hours of historical average travel time data were used to predict average travel time for the following hour. The spatial and temporal information and the historical average travel times were captured from the dataset for model input parameters. As adjacency matrices for the spatial input parameters, the station distances and sequence numbers were used, and the time of day (hour) was considered for the temporal inputs. Other inputs, including volatility information such as standard deviation and variance of journey durations, were also included in the model to make it more robust. The model's performance was evaluated based on a metric called mean absolute percentage error (MAPE). The observed prediction errors for various routes, trips, and stations remained consistent throughout the day. The results showed that the developed model could predict travel times more accurately during peak traffic hours, having a MAPE of around 14%, and performed less accurately during the latter part of the day. In the context of a complicated transportation network in high-density urban areas, the model showed its applicability for real-time travel time prediction of public transportation and ensured the high quality of the predictions generated by the model.

Keywords: gated recurrent unit, mean absolute percentage error, single-step forecasting, travel time prediction.

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Authors: Mei Xuan Tan, Gim-Yang Maggie Pee, Mei Chee Tan

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Academic performance, in the form of scoring high grades in enrolled subjects, is not the only significant trait in achieving success. Engineering graduates with experience in working on hands-on projects in a team setting are highly sought after in industry upon graduation. Such projects are typically real world problems that require the integration and application of knowledge and skills from several disciplines. In a traditional university setting, subjects are taught in a silo manner with no cross participation from other departments or disciplines. This may lead to knowledge compartmentalization and students are unable to understand and connect the relevance and applicability of the subject. University instructors thus see this integration across disciplines as a challenging task as they aim to better prepare students in understanding and solving problems for work or future studies. To improve students’ academic performance and to cultivate various skills such as critical thinking, there has been a gradual uptake in the use of an active learning approach in introductory science and engineering courses, where lecturing is traditionally the main mode of instruction. This study aims to discuss the implementation and experience of a hands-on, interdisciplinary project that involves all the four core subjects taught during the term at the Singapore University of Technology Design (SUTD). At SUTD, an interdisciplinary design activity, named 2D, is integrated into the curriculum to help students reinforce the concepts learnt. A student enrolled in SUTD experiences his or her first 2D in Term 1. This activity. which spans over one week in Week 10 of Term 1, highlights the application of chemistry, physics, mathematics, humanities, arts and social sciences (HASS) in designing an engineering product solution. The activity theme for Term 1 2D revolved around “work and play”. Students, in teams of 4 or 5, used a scaled-down model of a chemical launcher to launch a projectile across the room. It involved the use of a small chemical combustion reaction between ethanol (a highly volatile fuel) and oxygen. This reaction generated a sudden and large increase in gas pressure built up in a closed chamber, resulting in rapid gas expansion and ejection of the projectile out of the launcher. Students discussed and explored the meaning of play in their lives in HASS class while the engineering aspects of a combustion system to launch an object using underlying principles of energy conversion and projectile motion were revisited during the chemistry and physics classes, respectively. Numerical solutions on the distance travelled by the projectile launched by the chemical launcher, taking into account drag forces, was developed during the mathematics classes. At the end of the activity, students developed skills in report writing, data collection and analysis. Specific to this 2D activity, students gained an understanding and appreciation on the application and interdisciplinary nature of science, engineering and HASS. More importantly, students were exposed to design and problem solving, where human interaction and discussion are important yet challenging in a team setting.

Keywords: active learning, collaborative learning, first year undergraduate, interdisciplinary, STEAM

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Authors: Filip Varga, Nina Jeran, Martina Biosic, Zlatko Satovic, Martina Grdisa

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Dalmatian pyrethrum (Tanacetum cinerariifolium /Trevir./ Sch. Bip.), a species endemic to the eastern Adriatic coastline, is the source of natural insecticide pyrethrin. Pyrethrin is a mixture of six compounds (pyrethrin I and II, cinerin I and II, jasmolin I and II) that exhibits high insecticidal activity with no detrimental effects to the environment. A recently optimized matrix-solid phase dispersion method (MSPD), using florisil as the sorbent, acetone-ethyl acetate (1:1, v/v) as the elution solvent, and sodium sulfate anhydrous as the drying agent was utilized to extract the pyrethrins from 10 wild populations (20 individuals per population) distributed along the Croatian coast. All six components in the extracts were qualitatively and quantitatively determined by high-performance liquid chromatography with a diode array detector (HPLC-DAD). Pearson’s correlation index was calculated between pyrethrin compounds, and differences between the populations using the analysis of variance were tested. Additionally, the correlation of each pyrethrin component with spatio-ecological variables (bioclimate, soil properties, elevation, solar radiation, and distance from the coastline) was calculated. Total pyrethrin content ranged from 0.10% to 1.35% of dry flower weight, averaging 0.58% across all individuals. Analysis of variance revealed significant differences between populations based on all six pyrethrin compounds and total pyrethrin content. On average, the lowest total pyrethrin content was found in the population from Pelješac peninsula (0.22% of dry flower weight) in which total pyrethrin content lower than 0.18% was detected in 55% of the individuals. The highest average total pyrethrin content was observed in the population from island Zlarin (0.87% of dry flower weight), in which total pyrethrin content higher than 1.00% was recorded in only 30% of the individuals. Pyrethrin I/pyrethrin II ratio as a measure of extract quality ranged from 0.21 (population from the island Čiovo) to 5.88 (population from island Mali Lošinj) with an average of 1.77 across all individuals. By far, the lowest quality of extracts was found in the population from Mt. Biokovo (pyrethrin I/II ratio lower than 0.72 in 40% of individuals) due to the high pyrethrin II content typical for this population. Pearson’s correlation index revealed a highly significant positive correlation between pyrethrin I content and total pyrethrin content and a strong negative correlation between pyrethrin I and pyrethrin II. The results of this research clearly indicate high intra- and interpopulation diversity of Dalmatian pyrethrum with regards to pyrethrin content and composition. The information obtained has potential use in plant genetic resources conservation and biodiversity monitoring. Possibly the largest potential lies in designing breeding programs aimed at increasing pyrethrin content in commercial breeding lines and reintroduction in agriculture in Croatia. Acknowledgment: This work has been fully supported by the Croatian Science Foundation under the project ‘Genetic background of Dalmatian pyrethrum (Tanacetum cinerariifolium /Trevir/ Sch. Bip.) insecticidal potential’ - (PyrDiv) (IP-06-2016-9034).

Keywords: Dalmatian pyrethrum, HPLC, MSPD, pyrethrin

Procedia PDF Downloads 115

Authors: Rabindranath Bag, Surjeet Singh

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Materials in which the electrons are strongly correlated provide some of the most challenging and exciting problems in condensed matter physics today. After the discovery of high critical temperature superconductivity in layered or two-dimensional copper oxides, many physicists got attention in cuprates and it led to an upsurge of interest in the synthesis and physical properties of copper-oxide based material. The quest to understand superconducting mechanism in high-temperature cuprates, drew physicist’s attention to somewhat simpler compounds consisting of spin-chains or one-dimensional lattice of coupled spins. Low-dimensional quantum magnets are of huge contemporary interest in basic sciences as well emerging technologies such as quantum computing and quantum information theory, and heat management in microelectronic devices. Spin ladder is an example of quasi one-dimensional quantum magnets which provides a bridge between one and two dimensional materials. One of the examples of quasi one-dimensional spin-ladder compounds is Sr14Cu24O41, which exhibits a lot of interesting and exciting physical phenomena in low dimensional systems. Very recently, the ladder compound Sr14Cu24O41 was shown to exhibit long-distance quantum entanglement crucial to quantum information theory. Also, it is well known that hole-compensation in this material results in very high (metal-like) anisotropic thermal conductivity at room temperature. These observations suggest that Sr14Cu24O41 is a potential multifunctional material which invites further detailed investigations. To investigate these properties one must needs a large and high quality of single crystal. But these systems are showing incongruently melting behavior, which brings many difficulties to grow a large and quality of single crystals. Hence, we are using TSFZ (Travelling Solvent Floating Zone) method to grow the high quality of single crystals of the low dimensional magnets. Apart from this, it has unique crystal structure (alternating stacks of plane containing edge-sharing CuO2 chains, and the plane containing two-leg Cu2O3 ladder with intermediate Sr layers along the b- axis), which is also incommensurate in nature. It exhibits abundant physical phenomenon such as spin dimerization, crystallization of charge holes and charge density wave. The maximum focus of research so far involved in introducing defects on A-site (Sr). However, apart from the A-site (Sr) doping, there are only few studies in which the B-site (Cu) doping of polycrystalline Sr14Cu24O41 have been discussed and the reason behind this is the possibility of two doping sites for Cu (CuO2 chain and Cu2O3 ladder). Therefore, in our present work, the crystals (pristine and Cu-site doped) were grown by using TSFZ method by tuning the growth parameters. The Laue diffraction images, optical polarized microscopy and Scanning Electron Microscopy (SEM) images confirm the quality of the grown crystals. Here, we report the single crystal growth, magnetic and transport properties of Sr14Cu24O41 and its lightly doped variants (magnetic and non-magnetic) containing less than 1% of Co, Ni, Al and Zn impurities. Since, any real system will have some amount of weak disorder, our studies on these ladder compounds with controlled dilute disorder would be significant in the present context.

Keywords: low-dimensional quantum magnets, single crystal, spin-ladder, TSFZ technique

Procedia PDF Downloads 253

Authors: Abdelkader Soumaya, Aymen Arfaoui, Noureddine Ben Ayed, Ali Kadri

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The Chotts belt represents the southernmost folded structure in the Tunisian Atlas domain. It is dominated by inherited deep extensional E-W trending fault zones, which are reactivated as strike-slip faults during the Cenozoic compression. By examining the geological maps at different scales and based on the fieldwork data, we propose new structural interpretations for the geometries and fault kinematics in the Chotts chain. A set of ENE-WSW right-lateral en echelon folds, with curved shapes and steeply inclined southern limbs, is visible in the map view of this belt. These asymmetric tight anticlines are affected by E-W trending fault segments linked by local bends and stepovers. The revealed kinematic indicators along one of these E-W striated faults (Tafferna segment), such as breccias and gently inclined slickenlines (N094, 80N, 15°W pitch angles), show direct evidence of dextral strike-slip movement. The calculated stress tensors from corresponding faults slip data reveal an overall strike-slip tectonic regime with reverse component and NW-trending sub-horizontal σ1 axis ranking between N130 to N150. From west to east, we distinguished several types of structures along the segmented dextral fault system of the Chotts Range. The NE-SW striking fold-thrust belt (~25 km-long) between two continuously linked E-W fault segments (NW of Tozeur town) has been suggested as a local restraining bend. The central part of the Chotts chain is occupied by the ENE-striking Ksar Asker anticlines (Taferna, Torrich, and Sif Laham), which are truncated by a set of E-W strike-slip fault segments. Further east, the fault segments of Hachichina and Sif Laham connected across the NW-verging asymmetric fold-thrust system of Bir Oum Ali, which can be interpreted as a left-stepping contractional bend (~20 km-long). The oriental part of the Chotts belt corresponds to an array of subparallel E-W oriented fault segments (i.e., Beidha, Bouloufa, El Haidoudi-Zemlet El Beidha) with similar lengths (around 10 km). Each of these individual separated segments is associated with curved ENE-trending en echelon right-stepping anticlines. These folds are affected by a set of conjugate R and R′ shear-type faults indicating a dextral strike-lip motion. In addition, the relay zones between these E-W overstepping fault segments define local releasing stepovers dominated by NW-SE subsidiary faults. Finally, the Chotts chain provides well-exposed examples of strike-slip tectonics along E-W distributed fault segments. Each fault zone shows a typical strike-slip architecture, including parallel fault segments connecting via local stepovers or bends. Our new structural interpretations for this region reveal a great influence of the E-W deep fault segments on regional tectonic deformations and stress field during the Cenozoic shortening.

Keywords: chotts belt, tunisian atlas, strike-slip fault, stepovers, fault segments

Procedia PDF Downloads 51

Authors: John Lachapelle

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Introduction: Neural stimulation is increasingly targeted toward treatment of back pain, PTSD, Parkinson’s disease, and for sensory perception. Sensory recording during stimulation is important in order to examine neural response to stimulation. Most neural amplifiers (headstages) focus on noise efficiency factor (NEF). Conversely, neural headstages need to handle artifacts from several sources including power lines, movement (EMG), and neural stimulation itself. In this work a layered approach to artifact rejection is used to reduce corruption of the neural ENG signal by 60dBv, resulting in recovery of sensory signals in rats and primates that would previously not be possible. Methods: The approach combines analog techniques to reduce and handle unwanted signal amplitudes. The methods include optimized (1) sensory electrode placement, (2) amplifier configuration, and (3) artifact blanking when necessary. The techniques together are like concentric moats protecting a castle; only the wanted neural signal can penetrate. There are two conditions in which the headstage operates: unwanted artifact < 50mV, linear operation, and artifact > 50mV, fast-settle gain reduction signal limiting (covered in more detail in a separate paper). Unwanted Signals at the headstage input: Consider: (a) EMG signals are by nature < 10mV. (b) 60 Hz power line signals may be > 50mV with poor electrode cable conditions; with careful routing much of the signal is common to both reference and active electrode and rejected in the differential amplifier with <50mV remaining. (c) An unwanted (to the neural recorder) stimulation signal is attenuated from stimulation to sensory electrode. The voltage seen at the sensory electrode can be modeled Φ_m=I_o/4πσr. For a 1 mA stimulation signal, with 1 cm spacing between electrodes, the signal is <20mV at the headstage. Headstage ASIC design: The front end ASIC design is designed to produce < 1% THD at 50mV input; 50 times higher than typical headstage ASICs, with no increase in noise floor. This requires careful balance of amplifier stages in the headstage ASIC, as well as consideration of the electrodes effect on noise. The ASIC is designed to allow extremely small signal extraction on low impedance (< 10kohm) electrodes with configuration of the headstage ASIC noise floor to < 700nV/rt-Hz. Smaller high impedance electrodes (> 100kohm) are typically located closer to neural sources and transduce higher amplitude signals (> 10uV); the ASIC low-power mode conserves power with 2uV/rt-Hz noise. Findings: The enhanced neural processing ASIC has been compared with a commercial neural recording amplifier IC. Chronically implanted primates at MGH demonstrated the presence of commercial neural amplifier saturation as a result of large environmental artifacts. The enhanced artifact suppression headstage ASIC, in the same setup, was able to recover and process the wanted neural signal separately from the suppressed unwanted artifacts. Separately, the enhanced artifact suppression headstage ASIC was able to separate sensory neural signals from unwanted artifacts in mouse-implanted peripheral intrafascicular electrodes. Conclusion: Optimizing headstage ASICs allow observation of neural signals in the presence of large artifacts that will be present in real-life implanted applications, and are targeted toward human implantation in the DARPA HAPTIX program.

Keywords: ASIC, biosensors, biomedical signal processing, biomedical sensors

Procedia PDF Downloads 305

Authors: Stanislavs Gendelis, Maris Sinka, Andris Jakovics

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Insulation materials made from natural sources have become more popular for the ecologisation of buildings, meaning wide use of such renewable materials. Such natural materials replace synthetic products which consume a large quantity of energy. The most common and the cheapest natural materials in Latvia are cellulose-based (wood and agricultural plants). The ecological aspects of such materials are well known, but experimental data about physical properties remains lacking. In this study, six different samples of wood wool panels and a mixture of hemp shives and lime (hempcrete) are analysed. Thermal conductivity and heat capacity measurements were carried out for wood wool and cement panels using the calibrated hot plate device. Water vapor permeability was tested for hempcrete material by using the gravimetric dry cup method. Studied wood wool panels are eco-friendly and harmless material, which is widely used in the interior design of public and residential buildings, where noise absorption and sound insulation is of importance. They are also suitable for high humidity facilities (e.g., swimming pools). The difference in panels was the width of used wood wool, which is linked to their density. The results of measured thermal conductivity are in a wide range, showing the worsening of properties with the increasing of the wool width (for the least dense 0.066, for the densest 0.091 W/(m·K)). Comparison with mineral insulation materials shows that thermal conductivity for such materials are 2-3 times higher and are comparable to plywood and fibreboard. Measured heat capacity was in a narrower range; here, the dependence on the wool width was not so strong due to the fact that heat capacity value is related to mass, not volume. The resulting heat capacity is a combination of two main components. A comparison of results for different panels allows to select the most suitable sample for a specific application because the dependencies of the thermal insulation and heat capacity properties on the wool width are not the same. Hempcrete is a much denser material compared to conventional thermal insulating materials. Therefore, its use helps to reinforce the structural capacity of the constructional framework, at the same time, it is lightweight. By altering the proportions of the ingredients, hempcrete can be produced as a structural, thermal, or moisture absorbent component. The water absorption and water vapor permeability are the most important properties of these materials. Information about absorption can be found in the literature, but there are no data about water vapor transmission properties. Water vapor permeability was tested for a sample of locally made hempcrete using different air humidity values to evaluate the possible difference. The results show only the slight influence of the air humidity on the water vapor permeability value. The absolute ‘sd value’ measured is similar to mineral wool and wood fiberboard, meaning that due to very low resistance, water vapor passes easily through the material. At the same time, other properties – structural and thermal of the hempcrete is totally different. As a result, an experimentally-based knowledge of thermal and water vapor transmission properties for cellulose-based materials was significantly improved.

Keywords: heat capacity, hemp concrete, thermal conductivity, water vapor transmission, wood wool

Procedia PDF Downloads 205

Authors: Jessica Raven Gates, Gemma Wilson-Menzfeld, Professor Alison Steven

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In 2019, the Primary Care system in the UK National Health Service (NHS) was subject to reform and restructuring. Primary Care Networks (PCNs) were established, which aligned with a trend towards integrated care both within the NHS and internationally. The introduction of PCNs brought groups of GP practices in a locality together, to operate as a network, build on existing services and collaborate at a larger scale. PCNs were expected to bring a range of benefits to patients and address some of the workforce pressures in the NHS, through an expanded and collaborative workforce. The early establishment of PCNs was disrupted by the emerging COVID-19 pandemic. This study, set in the context of the pandemic, aimed to explore experiences of the PCN workforce, and their perceptions of the establishment of PCNs. Specific objectives focussed on examining factors perceived as enabling or hindering the success of a PCN, the impact on day-to-day work, the approach to implementing change, and the influence of the COVID-19 pandemic upon PCN development. This study is part of a three-phase PhD project that utilized qualitative approaches and was underpinned by social constructionist philosophy. Phase 1: a systematic narrative review explored the provision of preventative healthcare services in UK primary settings and examined facilitators and barriers to delivery as experienced by the workforce. Phase 2: informed by the findings of phase 1, semi-structured interviews were conducted with fifteen participants (PCN workforce). Phase 3: follow-up interviews were conducted with original participants to examine any changes to their experiences and perceptions of PCNs. Three main themes span across phases 2 and 3 and were generated through a Framework Analysis approach: 1) working together at scale, 2) network infrastructure, and 3) PCN leadership. Findings suggest that through efforts to work together at scale and collaborate as a network, participants have broadly accepted the concept of PCNs. However, the workforce has been hampered by system design and system complexity. Operating against such barriers has led to a negative psychological impact on some PCN leaders and others in the PCN workforce. While the pandemic undeniably increased pressure on healthcare systems around the world, it also acted as a disruptor, offering a glimpse into how collaboration in primary care can work well. Through the integration of findings from all phases, a new theoretical model has been developed, which conceptualises the findings from this Ph.D. study and demonstrates how the workforce has experienced change associated with the establishment of PCNs. The model includes a contextual component of the COVID-19 pandemic and has been informed by concepts from Complex Adaptive Systems theory. This model is the original contribution to knowledge of the PhD project, alongside recommendations for practice, policy and future research. This study is significant in the realm of health services research, and while the setting for this study is the UK NHS, the findings will be of interest to an international audience as the research provides insight into how the healthcare workforce may experience imposed policy and service changes.

Keywords: health services research, qualitative research, NHS workforce, primary care

Procedia PDF Downloads 39

Authors: Hannah M. Russell

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‘The Attach Project’ (TAP), is an educational intervention aimed at improving educational and socio-emotional outcomes for children who are looked after. TAP is underpinned by Attachment Theory and is adapted from Dyadic Developmental Psychotherapy (DDP), which is a treatment for children and young people impacted by complex trauma and disorders of attachment. TAP has been implemented in primary schools in Northern Ireland throughout the 2018/19 academic year. During this time, a design and implementation study has been conducted to assess the promise of effectiveness for the future dissemination and ‘scaling-up’ of the programme for a larger, randomised control trial. TAP has been designed specifically for implementation in a school setting and is comprised of a whole school element and a more individualised Key Adult-Key Child pairing. This design and implementation study utilises a mixed-methods research design consisting of quantitative, qualitative, and observational measures with stakeholder input and involvement being considered an integral component. The use of quantitative measures, such as self-report questionnaires prior to and eight months following the implementation of TAP, enabled the analysis of the strengths and direction of relations between the various components of the programme, as well as the influence of implementation factors. The use of qualitative measures, incorporating semi-structured interviews and focus groups, enabled the assessment of implementation factors, identification of implementation barriers, and potential methods of addressing these issues. Observational measures facilitated the continual development and improvement of ‘TAP training’ for school staff. Preliminary findings have provided evidence of promise for the effectiveness of TAP and indicate the potential benefits of introducing this type of attachment-based intervention across other educational settings. This type of intervention could benefit not only children who are looked after but all children who may be impacted by complex trauma or disorders of attachment. Furthermore, findings from this study demonstrate that it is possible for children to form a secondary attachment relationship with a significant adult in school. However, various implementation factors which should be addressed were identified throughout the study, such as the necessity of protected time being introduced to facilitate the development of a positive Key Adult- Key Child relationship. Furthermore, additional ‘re-cap’ training is required in future dissemination of the programme, to maximise ‘attachment friendly practice’ in the whole staff team. Qualitative findings have also indicated that there is a general opinion across school staff that this type of Key Adult- Key Child pairing could be more effective if it was introduced as soon as children begin primary school. This research has provided ample evidence for the need to introduce relationally based interventions in schools, to help to ensure that children who are looked after, or who are impacted by complex trauma or disorders of attachment, can thrive in the school environment. In addition, this research has facilitated the identification of important implementation factors and barriers to implementation, which can be addressed prior to the ‘scaling-up’ of TAP for a robust, randomised controlled trial.

Keywords: attachment, complex trauma, educational interventions, implementation

Procedia PDF Downloads 151

Authors: Suman Chaudhary, Rupinder K. Kanwar, Jagat R. Kanwar

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Azadirachta indica, also known as Neem belonging to the mahogany family is actively gaining interest in the era of modern day medicine due to its extensive applications in homeopathic medicine such as Ayurveda and Unani. More than 140 phytochemicals have been extracted from neem leaves, seed, bark and flowers for agro-medicinal applications. Among the various components, neem leaf protein (NLP) is currently the most investigated active ingredient, due to its immunomodulatory activities against tumor growth. However, these therapeutic ingredients of neem are susceptible to degradation and cannot withstand the drastic pH changes under physiological environment, and therefore, there is an urgent need of an alternative strategy such as a nano-delivery system to exploit its medicinal benefits. This study hypothesizes that guar gum (GG) derived biodegradable nano-carrier based encapsulation of NLP will improve its stability, specificity and sensitivity, thus facilitating targeted anti-cancer therapeutics. GG is a galactomannan derived from the endosperm of the guar beans seeds. Synthesis of guar nanocapsules (NCs) was performed using nanoprecipitation technique where the GG was encapsulated with NLP. Preliminary experiments conducted to characterize the NCs confirmed spherical morphology with a narrow size distribution of 30-40 nm. Differential scanning colorimetric analysis (DSC) validated the stability of these NCs even at a temperature range of 50-60°C which was well within the physiological and storage conditions. Thermogravimetric (TGA) analysis indicated high decomposition temperature of these NCs ranging upto 350°C. Additionally, Fourier Transform Infrared spectroscopy (FTIR) and the SDS-PAGE data acquired confirmed the successful encapsulation of NLP in the NCs. The anti-cancerous therapeutic property of this NC was tested on colon cancer cells (caco-2) as they are one of the most prevalent form of cancer. These NCs (both NLP loaded and void) were also tested on human intestinal epithelial cells (FHs 74) cells to evaluate their effect on normal cells. Cytotoxicity evaluation of the NCs in the cell lines confirmed that the IC50 for NLP in FHs 74 cells was ~2 fold higher than in caco-2 cells, indicating that this nanoformulation system possessed biocompatible anti-cancerous properties Immunoconfocal microscopy analysis confirmed the time dependent internalization of the NCs within 6h. Recent findings performed using Annexin V and PI staining indicated a significant increase (p ≤ 0.001) in the early and late apoptotic cell population when treated with the NCs signifying the role of NLP in inducing apoptosis in caco-2 cells. This was further validated using Western blot, Polymerase chain reaction (PCR) and Fluorescence activated cell sorter (FACS) aided protein expressional analysis which presented a downregulation of survivin, an anti-apoptotic cell marker and upregulation of Bax/Bcl-2 ratio (pro-apoptotic indicator). Further, both the NLP NC and unencapsulated NLP treatment destabilized the mitochondrial membrane potential subsequently facilitating the release of the pro-apoptotic caspase cascade initiator, cytochrome-c. Future studies will be focused towards granting specificity to these NCs towards cancer cells, along with a comprehensive analysis of the anti-cancer potential of this naturally occurring compound in different cancer and in vivo animal models, will validate the clinical application of this unprecedented protein therapeutic.

Keywords: anti-tumor, guar gum, nanocapsules, neem leaf protein

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Authors: Michele Paleologo, Marta Acampora, Serena Barello, Guendalina Graffigna

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Introduction: In the face of increasing demands for higher agricultural productivity with minimal environmental impact, participatory research approaches emerge as promising means to promote innovation. However, the complexities and ambiguities surrounding these approaches in both theory and practice present challenges. This Scoping Review seeks to bridge these gaps by mapping participatory approaches in agricultural contexts, analyzing their characteristics, and identifying indicators of success. Methods: Following PRISMA guidelines, we conducted a systematic Scoping Review, searching Scopus and Web of Science databases. Our review encompassed 34 projects from diverse geographical regions and farming contexts. Thematic analysis was employed to explore the types of innovation promoted and the categories of participants involved. Results: The identified innovation types encompass technological advancements, sustainable farming practices, and market integration, forming 5 main themes: climate change, cultivar, irrigation, pest and herbicide, and technical improvement. These themes represent critical areas where participatory research drives innovation to address pressing agricultural challenges. Participants were categorized as citizens, experts, NGOs, private companies, and public bodies. Understanding their roles is vital for designing effective participatory initiatives that embrace diverse stakeholders. The review also highlighted 27 theoretical frameworks underpinning participatory projects. Clearer guidelines and reporting standards are crucial for facilitating the comparison and synthesis of findings across studies, thereby enhancing the robustness of future participatory endeavors. Furthermore, we identified three main categories of barriers and facilitators: pragmatic/behavioral, emotional/relational, and cognitive. These insights underscore the significance of participant engagement and collaborative decision-making for project success beyond theoretical considerations. Regarding participation, projects were classified as contributory (5 cases), where stakeholders contributed insights; collaborative (10 cases), with active co-designing of solutions; and co-created (19 cases), featuring deep stakeholder involvement from ideation to implementation, resulting in joint ownership of outcomes. Such diverse participation modes highlight the adaptability of participatory approaches to varying agricultural contexts. Discussion: In conclusion, this Scoping Review demonstrates the potential of participatory research in driving transformative changes in farmers' practices, fostering sustainability and innovation in agriculture. Understanding the diverse landscape of participatory approaches, theoretical frameworks, and participant engagement strategies is essential for designing effective and context-specific interventions. Collaborative efforts among researchers, practitioners, and stakeholders are pivotal in harnessing the full potential of participatory approaches and driving positive change in agricultural settings worldwide. The identified themes of innovation and participation modes provide valuable insights for future research and targeted interventions in agricultural innovation.

Keywords: participatory research, co-creation, agricultural innovation, stakeholders' engagement

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Authors: Monika Saha, A. T. M. Hasan Zobeyer, Nasreen Jahan

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El Nino-Southern Oscillation (ENSO) is the interaction between atmosphere and ocean in tropical Pacific which causes inconsistent warm/cold weather in tropical central and eastern Pacific Ocean. Due to the impact of climate change, ENSO events are becoming stronger in recent times, and therefore it is very important to study the influence of ENSO in climate studies. Bangladesh, being in the low-lying deltaic floodplain, experiences the worst consequences due to flooding every year. To reduce the catastrophe of severe flooding events, non-structural measures such as flood forecasting can be helpful in taking adequate precautions and steps. Forecasting seasonal flood with a longer lead time of several months is a key component of flood damage control and water management. The objective of this research is to identify the possible strength of teleconnection between ENSO and river flow of Surma and examine the potential possibility of long lead flood forecasting in the wet season. Surma is one of the major rivers of Bangladesh and is a part of the Surma-Meghna river system. In this research, sea surface temperature (SST) has been considered as the ENSO index and the lead time is at least a few months which is greater than the basin response time. The teleconnection has been assessed by the correlation analysis between July-August-September (JAS) flow of Surma and SST of Nino 4 region of the corresponding months. Cumulative frequency distribution of standardized JAS flow of Surma has also been determined as part of assessing the possible teleconnection. Discharge data of Surma river from 1975 to 2015 is used in this analysis, and remarkable increased value of correlation coefficient between flow and ENSO has been observed from 1985. From the cumulative frequency distribution of the standardized JAS flow, it has been marked that in any year the JAS flow has approximately 50% probability of exceeding the long-term average JAS flow. During El Nino year (warm episode of ENSO) this probability of exceedance drops to 23% and while in La Nina year (cold episode of ENSO) it increases to 78%. Discriminant analysis which is known as 'Categoric Prediction' has been performed to identify the possibilities of long lead flood forecasting. It has helped to categorize the flow data (high, average and low) based on the classification of predicted SST (warm, normal and cold). From the discriminant analysis, it has been found that for Surma river, the probability of a high flood in the cold period is 75% and the probability of a low flood in the warm period is 33%. A synoptic parameter, forecasting index (FI) has also been calculated here to judge the forecast skill and to compare different forecasts. This study will help the concerned authorities and the stakeholders to take long-term water resources decisions and formulate policies on river basin management which will reduce possible damage of life, agriculture, and property.

Keywords: El Nino-Southern Oscillation, sea surface temperature, surma river, teleconnection, cumulative frequency distribution, discriminant analysis, forecasting index

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Authors: Emily Moskwa, Delene Weber, Jacob Arnold, Guy M. Robinson, Douglas K. Bardsley

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Management strategies to reduce the risks to human life and property from wildfire are key contemporary concerns, with a growing literature exploring these issues from a social research perspective. Efforts range from narrowly focused examinations, such as comparing the level of community support for vegetation clearance with that of controlled burning, to broader considerations of what constitutes effective fire management policy and education campaigns. However, little analysis is available that integrates the social component of risk mitigation and the influence of educational materials with the biodiversity conservation strategies so often needed in fire-prone ecosystems found on the periphery of urban areas. Indeed many communities living on the fringe of Australian cities face major issues relating to an increased risk of wildfire events and a decline in local biodiversity. Inadequate policy and planning, and a lack of awareness or information, exacerbate this risk. This has brought forward an emerging governance challenge that requires the mitigation of wildfire risk while simultaneously supporting improved conservation practices in these urban-fringe areas. Focusing on the perceptions and experiences of residents of the Lower Eyre Peninsula in South Australia, this study analyses data collected from a series of semi-structured interviews with landholders (n=20) living in rural and urban-fringe areas surrounding the city of Port Lincoln, a city with a growing population and one that has faced a number of very large fires in recent years. In South Australia, new policies have assigned increased responsibility on individual landholders to manage their land and prepare themselves for a wildfire event, potentially to the detriment of the surrounding native vegetation. Our findings indicate the value of gaining a more nuanced understanding of the perceptions and behaviours of landholders living in areas of high fire risk, who often choose to live there in order to be close to the natural environment. Many interviewees demonstrated a high awareness of wildfire risk as a result of their past experience with fire, and the majority considered themselves to be well-prepared in the event of a future fire. Community interactions and educational programs were found to be effective in raising awareness of risk; however, negative trust relationships with government authorities and low exposure to information concerning biodiversity resulted in an overall misunderstanding of the relationship between risk mitigation and biodiversity protection. The study offers insights into how catastrophic fires are reframing perceptions of what constitutes effective vegetation management. It provides recommendations to assist with the development of education strategies that concurrently address wildfire management and biodiversity conservation, and contribute towards environmentally-informed and risk conscious governance.

Keywords: biodiversity conservation, risk, peri-urban planning, wildfire management

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Authors: Jelena Filipovic, Marija Bodroza-Solarov, Milenko Kosutic, Nebojsa Novkovic, Vladimir Filipovic, Vesna Vucurovic

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Spelt wheat is suitable raw material for extruded products such as pasta, special types of bread and other products of altered nutritional characteristics compared to conventional wheat products. During the process of extrusion, spelt is exposed to high temperature and high pressure, during which raw material is also mechanically treated by shear forces. Spelt wheat is growing without the use of pesticides in harsh ecological conditions and in marginal areas of cultivation. So it can be used for organic and health safe food. Pasta is the most popular foodstuff; its consumption has been observed to rise. Pasta quality depends mainly on the properties of flour raw materials, especially protein content and its quality but starch properties are of a lesser importance. Pasta is characterized by significant amounts of complex carbohydrates, low sodium, total fat fiber, minerals, and essential fatty acids and its nutritional value can be improved with additional functional component. Over the past few decades, wheat pasta has been successfully formulated using different ingredients in pasta to cater health-conscious consumers who prefer having a product rich in protein, healthy lipids and other health benefits. Flaxseed flour is used in the production of bakery and pasta products that have properties of functional foods. However, it should be taken into account that food products retain the technological and sensory quality despite the added flax seed. Flaxseed contains important substances in its composition such as vitamins and minerals elements, and it is also an excellent source of fiber and one of the best sources of ω-3 fatty acids and lignin. In this paper, the quality and identification of spelt extruded product with the addition of flax seed, which is positively contributing to the nutritive and technology changes of the product, is investigated. ω-3 fatty acids are polyunsaturated essential fatty acids, and they must be taken with food to satisfy the recommended daily intake. Flaxseed flour is added in the quantity of 10/100 g of sample and 20/100 g of sample on farina. It is shown that the presence of ω-3 fatty acids in pasta can be clearly distinguished from other fatty acids by gas chromatography with mass spectrometry. Addition of flax seed flour influence chemical content of pasta. The addition of flax seed flour in spelt pasta in the quantities of 20g/100 g significantly increases the share of ω-3 fatty acids, which results in improved ratio of ω-6/ω-3 1:2.4 and completely satisfies minimum daily needs of ω-3 essential fatty acids (3.8 g/100 g) recommended by FDA. Flex flour influenced the pasta quality by increasing of hardness (2377.8 ± 13.3; 2874.5 ± 7.4; 3076.3 ± 5.9) and work of shear (102.6 ± 11.4; 150.8 ± 11.3; 165.0 ± 18.9) and increasing of adhesiveness (11.8 ± 20.6; 9.,98 ± 0.12; 7.1 ± 12.5) of the final product. Presented data point at good indicators of technological quality of spelt pasta with flax seed and that GC-MS analysis can be used in the quality control for flax seed identification. Acknowledgment: The research was financed by the Ministry of Education and Science of the Republic of Serbia (Project No. III 46005).

Keywords: GC-MS analysis, ω-3 fatty acids, flex seed, spelt wheat, daily needs

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Authors: Noura Al Akkari, Aurélie Foucquier, Sylvain Lespinats

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Load monitoring is a management process for energy consumption towards energy savings and energy efficiency. Non Intrusive Load Monitoring (NILM) is one method of load monitoring used for disaggregation purposes. NILM is a technique for identifying individual appliances based on the analysis of the whole residence data retrieved from the main power meter of the house. Our NILM framework starts with data acquisition, followed by data preprocessing, then event detection, feature extraction, then general appliance modeling and identification at the final stage. The event detection stage is a core component of NILM process since event detection techniques lead to the extraction of appliance features. Appliance features are required for the accurate identification of the household devices. In this research work, we aim at developing a new event detection methodology with accurate load disaggregation to extract appliance features. Time-domain features extracted are used for tuning general appliance models for appliance identification and classification steps. We use unsupervised algorithms such as Dynamic Time Warping (DTW). The proposed method relies on detecting areas of operation of each residential appliance based on the power demand. Then, detecting the time at which each selected appliance changes its states. In order to fit with practical existing smart meters capabilities, we work on low sampling data with a frequency of (1/60) Hz. The data is simulated on Load Profile Generator software (LPG), which was not previously taken into consideration for NILM purposes in the literature. LPG is a numerical software that uses behaviour simulation of people inside the house to generate residential energy consumption data. The proposed event detection method targets low consumption loads that are difficult to detect. Also, it facilitates the extraction of specific features used for general appliance modeling. In addition to this, the identification process includes unsupervised techniques such as DTW. To our best knowledge, there exist few unsupervised techniques employed with low sampling data in comparison to the many supervised techniques used for such cases. We extract a power interval at which falls the operation of the selected appliance along with a time vector for the values delimiting the state transitions of the appliance. After this, appliance signatures are formed from extracted power, geometrical and statistical features. Afterwards, those formed signatures are used to tune general model types for appliances identification using unsupervised algorithms. This method is evaluated using both simulated data on LPG and real-time Reference Energy Disaggregation Dataset (REDD). For that, we compute performance metrics using confusion matrix based metrics, considering accuracy, precision, recall and error-rate. The performance analysis of our methodology is then compared with other detection techniques previously used in the literature review, such as detection techniques based on statistical variations and abrupt changes (Variance Sliding Window and Cumulative Sum).

Keywords: general appliance model, non intrusive load monitoring, events detection, unsupervised techniques;

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Authors: Marina Shakleina, Konstantin Shaklein, Stanislav Yakiro

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The digitalization of modern human life over the last decade has facilitated the acquisition, storage, and processing of data, which are used to detect changes in consumer preferences and to improve the internal efficiency of the production process. This emerging trend has attracted academic interest in the use of big data in research. The study focuses on modeling the social comfort of the Russian population for the period 2010-2021 using big data. Big data provides enormous opportunities for understanding human interactions at the scale of society with plenty of space and time dynamics. One of the most popular big data sources is Google Trends. The methodology for assessing social comfort using big data involves several steps: 1. 574 words were selected based on the Harvard IV-4 Dictionary adjusted to fit the reality of everyday Russian life. The set of keywords was further cleansed by excluding queries consisting of verbs and words with several lexical meanings. 2. Search queries were processed to ensure comparability of results: the transformation of data to a 10-point scale, elimination of popularity peaks, detrending, and deseasoning. The proposed methodology for keyword search and Google Trends processing was implemented in the form of a script in the Python programming language. 3. Block and summary integral indicators of social comfort were constructed using the first modified principal component resulting in weighting coefficients values of block components. According to the study, social comfort is described by 12 blocks: ‘health’, ‘education’, ‘social support’, ‘financial situation’, ‘employment’, ‘housing’, ‘ethical norms’, ‘security’, ‘political stability’, ‘leisure’, ‘environment’, ‘infrastructure’. According to the model, the summary integral indicator increased by 54% and was 4.631 points; the average annual rate was 3.6%, which is higher than the rate of economic growth by 2.7 p.p. The value of the indicator describing social comfort in Russia is determined by 26% by ‘social support’, 24% by ‘education’, 12% by ‘infrastructure’, 10% by ‘leisure’, and the remaining 28% by others. Among 25% of the most popular searches, 85% are of negative nature and are mainly related to the blocks ‘security’, ‘political stability’, ‘health’, for example, ‘crime rate’, ‘vulnerability’. Among the 25% most unpopular queries, 99% of the queries were positive and mostly related to the blocks ‘ethical norms’, ‘education’, ‘employment’, for example, ‘social package’, ‘recycling’. In conclusion, the introduction of the latent category ‘social comfort’ into the scientific vocabulary deepens the theory of the quality of life of the population in terms of the study of the involvement of an individual in the society and expanding the subjective aspect of the measurements of various indicators. Integral assessment of social comfort demonstrates the overall picture of the development of the phenomenon over time and space and quantitatively evaluates ongoing socio-economic policy. The application of big data in the assessment of latent categories gives stable results, which opens up possibilities for their practical implementation.

Keywords: big data, Google trends, integral indicator, social comfort

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Authors: Roland F. Karthaus, Rachel S. O'Brien

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Over the past decade, the authors have undertaken applied research aimed at enabling transformation within the prison service to improve conditions and outcomes for those living, working and visiting in prisons in the UK and the communities they serve. The research has taken place against a context of reducing resources and public discontent at increasing levels of violence, deteriorating conditions and persistently high levels of re-offending. Top-down governmental policies have mainly been ineffectual and in some cases counter-productive. The prison service is characterised by hierarchical organisation, and the research has applied design thinking at multiple levels to challenge and precipitate change: top-down, middle-out and bottom-up. The research employs three distinct but related approaches, system design (top-down): working at the national policy level to analyse the changing policy context, identifying opportunities and challenges; engaging with the Ministry of Justice commissioners and sector organisations to facilitate debate, introducing new evidence and provoking creative thinking, place-based design (middle-out): working with individual prison establishments as pilots to illustrate and test the potential for local empowerment, creative change, and improved architecture within place-specific contexts and organisational hierarchies, everyday design (bottom-up): working with individuals in the system to explore the potential for localised, significant, demonstrator changes; including collaborative design, capacity building and empowerment in skills, employment, communication, training, and other activities. The research spans a series of projects, through which the methodological approach has developed responsively. The projects include a place-based model for the re-purposing of Ministry of Justice land assets for the purposes of rehabilitation; an evidence-based guide to improve prison design for health and well-being; capacity-based employment, skills and self-build project as a template for future open prisons. The overarching research has enabled knowledge to be developed and disseminated through policy and academic networks. Whilst the research remains live and continuing; key findings are emerging as a basis for a new methodological approach to effecting change in the UK prison service. An interdisciplinary approach is necessary to overcome the barriers between distinct areas of the prison service. Sometimes referred to as total environments, prisons encompass entire social and physical environments which themselves are orchestrated by institutional arms of government, resulting in complex systems that cannot be meaningfully engaged through narrow disciplinary lenses. A scalar approach is necessary to connect strategic policies with individual experiences and potential, through the medium of individual prison establishments, operating as discrete entities within the system. A reflexive process is necessary to connect research with action in a responsive mode, learning to adapt as the system itself is changing. The role of individuals in the system, their latent knowledge and experience and their ability to engage and become agents of change are essential. Whilst the specific characteristics of the UK prison system are unique, the approach is internationally applicable.

Keywords: architecture, design, policy, prison, system, transformation

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