Search results for: continuous data

Authors: Uchenna Cosmas Ugwu, Osmond Chukwuemeka Ene

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Globally, older adults are considered the most vulnerable groups to age-related diseases including diabetes mellitus, obesity, cardiovascular diseases, cancer and osteoporosis. With improved access to quality healthcare services, these complications can be prevented and the incidence rates reduced to the least occurrence. The aim of this study is to validate appropriate measures for improving quality healthcare services utilization among elderly persons in Nigeria and also to determine the significant association within demographic variables. A cross-sectional survey research design was adopted. Using a convenient sampling technique, a total of 400 experts (150 registered nurses and 250 public health professionals) with minimum of doctoral degree qualification were sampled and studied. A structured instrument titled “Expert-Based Healthcare Services Utilization Questionnaire (EBHSUQ) with .83 reliability index was used for data collection. All the statistical data analysis was completed using frequency counts, percentage scores and chi-square statistics. The results were significant at p≤0.05. It was found that quality healthcare services utilization by elderly persons in Nigeria would be improved if the services are: available (83%), affordable (82%), accessible (79%), suitable (77%), acceptable (77%), continuous (75%) and stress-free (75%). Statistically, significant association existed on quality healthcare services utilization with gender (p=.03<.05) and age (p=.01<.05) while none was observed on work experience (p=.23>.05), marital status (p=.11>.05) and employment category (p=.09>.05). To improve quality healthcare services utilization for elderly persons in Nigeria, the adoption of appropriate measures by Nigerian government and professionals in healthcare sectors are paramount. Therefore, there is need for collaborative efforts by the Nigerian government and healthcare professionals geared towards educating the general public through mass sensitization, awareness campaign, conferences, seminars and workshops for the importance of accessing healthcare services.

Keywords: elderly persons, healthcare services, cross-sectional survey research design, utilization.

Procedia PDF Downloads 64

Authors: Xie Xiaobin, Zhang Yan, Shi Yipeng, Sun Wenying, Chen Shuang, Cai Zhipeng, Zhang Hong, Zhang Lixia, Xie Like

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Objective: The objective of this research is to compare the efficacy of the late in-situ surgical revision augmented with continuous infusion and needle revision on failed trabeculectomy blebs. Methods From December 2018 to December 2021, a prospective randomized controlled trial was performed on 44 glaucoma patients with failed bleb ≥ 6months with medically uncontrolled in Eye Hospital, China Academy of Chinese Medical Sciences. They were randomly divided into two groups. 22 eyes of 22 patients underwent the late in-situ surgical revision with continuous anterior chamber infusion in the study group, and 22 of 22 patients were treated with needle revision in the control group. Main outcome measures include preoperative and postoperative intraocular pressure (IOP), the number of anti-glaucoma medicines, the operation success rate, and the postoperative complications. Results The postoperative IOP values decreased significantly from the baseline in both groups (both P<0.05). IOP was significantly lower in the study group than in the control group at one week, 1, and 3 months postoperatively (all P<0.05). IOP reductions in the study group were substantially more prominent than in the control group at all postoperative time points (all P<0.05). The complete success rate in the study group was significantly higher than in the control group (71.4% vs. 33.3%, P<0.05), while the complete failure rate was significantly lower in the study group (0% vs. 28.5%, P<0.05). According to Cox’s proportional hazards regression analysis, high IOP at baseline was independently associated with increased risks of complete failure (adjusted hazard ratio=1.141, 95% confidence interval=1.021-1.276, P<0.05). There was no significant difference in the incidence of postoperative complications between the two groups (P>0.05). Conclusion: Both in-situ surgical and needle revision have acceptable success rates and safety for the late failed trabeculectomy blebs, while the former is likely to have a higher level of efficacy over the latter. Needle revision may be insufficient for eyes with low target IOP.

Keywords: glaucoma, trabeculectomy blebs, in-situ surgical revision, needle revision

Procedia PDF Downloads 84

Authors: Anubha Kaushik, Raman Preet

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Distillery wastewater in the form of spent wash is a complex and strong industrial effluent, with high load of organic pollutants that may deplete dissolved oxygen on being discharged into aquatic systems and contaminate groundwater by leaching of pollutants, while untreated spent wash disposed on land acidifies the soil. Stringent legislative measures have therefore been framed in different countries for discharge standards of distillery effluent. Utilising the organic pollutants present in various types of wastes as food by mixed microbial populations is emerging as an eco-friendly approach in the recent years, in which complex organic matter is converted into simpler forms, and simultaneously useful gases are produced as renewable and clean energy sources. In the present study, wastewater from a rice bran based distillery has been used as the substrate in a dark fermenter, and native microbial consortium from the digester sludge has been used as the inoculum to treat the wastewater and produce hydrogen. After optimising the operational conditions in batch reactors, sequential batch mode and continuous flow stirred tank reactors were used to study the best operational conditions for enhanced and sustained hydrogen production and removal of pollutants. Since the rate of hydrogen production by the microbial consortium during dark fermentation is influenced by concentration of organic matter, pH and temperature, these operational conditions were optimised in batch mode studies. Maximum hydrogen production rate (347.87ml/L/d) was attained in 32h dark fermentation while a good proportion of COD also got removed from the wastewater. Slightly acidic initial pH seemed to favor biohydrogen production. In continuous stirred tank reactor, high H₂ production from distillery wastewater was obtained from a relatively shorter substrate retention time (SRT) of 48h and a moderate organic loading rate (OLR) of 172 g/l/d COD.

Keywords: distillery wastewater, hydrogen, microbial consortium, organic pollution, sludge

Procedia PDF Downloads 277

Authors: Pradeep Singh, Shrish Verma

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Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data. Some company do not store and keep track of all artifacts which are required for software fault prediction. To construct fault prediction model for such company, the training data from the other projects can be one potential solution. The earlier we predict the fault the less cost it requires to correct. The training data consists of metrics data and related fault data at function/module level. This paper investigates fault predictions at early stage using the cross-project data focusing on the design metrics. In this study, empirical analysis is carried out to validate design metrics for cross project fault prediction. The machine learning techniques used for evaluation is Naïve Bayes. The design phase metrics of other projects can be used as initial guideline for the projects where no previous fault data is available. We analyze seven data sets from NASA Metrics Data Program which offer design as well as code metrics. Overall, the results of cross project is comparable to the within company data learning.

Keywords: software metrics, fault prediction, cross project, within project.

Procedia PDF Downloads 344

Authors: Renata Gagić-Serdar, Miroslava Markovic, Ljubinko Rakonjac, Aleksandar Lučić

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The paper present the results of the entomological experimental studies of the biological, ecological, and (bionomic) insect performances, such as seasonal adaptation of introduced monophagous false indigo and albizias weevil’s Acanthoscelides pallidipennis Motschulsky. and Bruchidius terrenus (Sharp), Coleoptera: Chrysomelidae: Bruchinae, to phenological phases of aggressive invasive host plant Amorpha fruticosa L. and Albizia julibrissin (Fabales: Fabaceae) on the territory of Republic of Serbia with special attention on assessing and monitoring of new formed and detected inter species relations between autochthons parasite wasps from fauna (Hymenoptera: Chalcidoidea) and herbaceous seed weevil beetle. During 15 years (2006-2021), on approximately 30 localities, data analyses were done for observed experimental host plants from samples with statistical significance. Status of genera from families Hymenoptera: Chalcidoidea.: Pteromalidae and Eulophidae, after intensive investigations, has been trophicly identified. Recorded seed pest species of A. fruticosa or A. julibrissin (Fabales: Fabaceae) was introduced in Serbia and planted as ornamental trees, they also were put undergo different kinds of laboratory and field research tests during this period in a goal of collecting data about lasting each of develop stage of their seed beetles. Field generations in different stages were also monitored by continuous infested seed collecting and its disection. Established host plant-seed predator linkage was observed in correlation with different environment parameters, especially water level fluctuations in bank corridor formation stands and riparian cultures.

Keywords: amorpha, albizia, chalcidoid wasp, invasiveness, weevils

Procedia PDF Downloads 94

Authors: Vesna Kirandziska, Nevena Ackovska, Ana Madevska Bogdanova

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The problem of emotion recognition is a challenging problem. It is still an open problem from the aspect of both intelligent systems and psychology. In this paper, both voice features and facial features are used for building an emotion recognition system. A Support Vector Machine classifiers are built by using raw data from video recordings. In this paper, the results obtained for the emotion recognition are given, and a discussion about the validity and the expressiveness of different emotions is presented. A comparison between the classifiers build from facial data only, voice data only and from the combination of both data is made here. The need for a better combination of the information from facial expression and voice data is argued.

Keywords: emotion recognition, facial recognition, signal processing, machine learning

Procedia PDF Downloads 315

Authors: Sartaj Singh, Amar Singh, Ashok Sharma, Sandeep Kaur

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With upcoming threats in a digital world, we need to work continuously in the area of security in all aspects, from hardware to software as well as data modelling. The rise in social media activities and hunger for data by various entities leads to cybercrime and more attack on the privacy and security of persons. Cryptography has always been employed to avoid access to important data by using many processes. Symmetric key and asymmetric key cryptography have been used for keeping data secrets at rest as well in transmission mode. Various cryptosystems have evolved from time to time to make the data more secure. In this research article, we are studying various cryptosystems in asymmetric cryptography and their application with usefulness, and much emphasis is given to Elliptic curve cryptography involving algebraic mathematics.

Keywords: cryptography, symmetric key cryptography, asymmetric key cryptography

Procedia PDF Downloads 124

Authors: Celeste Labuschagne, Sam Ramaila, Thasmai Dhurumraj

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Although formative assessment is acknowledged as crucial for teachers to gauge students’ understanding of subject content, applying formative assessment in an online context is more challenging than in a traditional Physical Sciences classroom. This study examines challenges experienced by Grade 10 Physical Sciences teachers when enacting online formative assessment. The empirical investigation adopted a generic qualitative design and involved three purposively selected Grade 10 Physical Sciences teachers from three different schools and quintiles within the Tshwane North District in South Africa. Data were collected through individual and focus group interviews. Technological, pedagogical, and content knowledge (TPACK) was utilised as a theoretical framework underpinning the study. The study identified a myriad of challenges experienced by Grade 10 Physical Sciences teachers when enacting online formative assessment. These challenges include the utilisation of Annual Teaching Plans, lack of technological knowledge, and internet connectivity. The Department of Basic Education faces the key imperative to provide continuous teacher professional development and concomitant online learning materials that can facilitate meaningful enactment of online formative assessment in various educational settings.

Keywords: COVID-19, challenges, online formative assessment, physical sciences, TPACK

Procedia PDF Downloads 66

Authors: Rong-Terng Juang

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Autonomous vehicles offer the possibility of significant benefits to social welfare. However, fully automated cars might not be going to happen in the near further. To speed the adoption of the self-driving technologies, many governments worldwide are passing laws requiring data recorders for the testing of autonomous vehicles. Currently, the self-driving vehicle, (e.g., shuttle bus) has to be monitored from a remote control center. When an autonomous vehicle encounters an unexpected driving environment, such as road construction or an obstruction, it should request assistance from a remote operator. Nevertheless, large amounts of data, including images, radar and lidar data, etc., have to be transmitted from the vehicle to the remote center. Therefore, this paper proposes a data compression method of in-vehicle networks for remote monitoring of autonomous vehicles. Firstly, the time-series data are rearranged into a multi-dimensional signal space. Upon the arrival, for controller area networks (CAN), the new data are mapped onto a time-data two-dimensional space associated with the specific CAN identity. Secondly, the data are sampled based on differential sampling. Finally, the whole set of data are encoded using existing algorithms such as Huffman, arithmetic and codebook encoding methods. To evaluate system performance, the proposed method was deployed on an in-house built autonomous vehicle. The testing results show that the amount of data can be reduced as much as 1/7 compared to the raw data.

Keywords: autonomous vehicle, data compression, remote monitoring, controller area networks (CAN), Lidar

Procedia PDF Downloads 163

Authors: Lun Lou, Jintu Fan

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While personal protective equipment (PPE) prevents the healthcare personnel from exposing to harmful surroundings, it creates a barrier to the dissipation of body heat and perspiration, leading to severe heat stress during prolonged exposure, especially in hot environments. It has been found that most of the existed personal cooling strategies have limitations in achieving effective cooling performance with long duration and lightweight. This work aimed to develop a lightweight (<1.0 kg) and less expensive wearable air cooling and dehumidifying system (WCDS) that can be applied underneath the protective clothing and provide 50W mean cooling power for more than 5 hours at 35°C environmental temperature without compromising the protection of PPE. For the WCDS, blowers will be used to activate an internal air circulation inside the clothing microclimate, which doesn't interfere with the protection of PPE. An air cooling and dehumidifying chamber (ACMR) with a specific design will be developed to reduce the air temperature and humidity inside the protective clothing. Then the cooled and dried air will be supplied to upper chest and back areas through a branching tubing system for personal cooling. A detachable ice cooling unit will be applied from the outside of the PPE to extract heat from the clothing microclimate. This combination allows for convenient replacement of the cooling unit to refresh the cooling effect, which can realize a continuous cooling function without taking off the PPE or adding too much weight. A preliminary thermal manikin test showed that the WCDS was able to reduce the microclimate temperature inside the PPE averagely by about 8°C for 60 minutes when the environmental temperature was 28.0 °C and 33.5 °C, respectively. Replacing the ice cooling unit every hour can maintain this cooling effect, while the longest operation duration is determined by the battery of the blowers, which can last for about 6 hours. This unique design is especially helpful for the PPE users, such as health care workers in infectious and hot environments when continuous cooling and dehumidifying are needed, but the change of protective clothing may increase the risk of infection. The new WCDS will not only improve the thermal comfort of PPE users but can also extend their safe working duration.

Keywords: personal thermal management, heat stress, ppe, health care workers, wearable device

Procedia PDF Downloads 79

Authors: Samar M. Alqhtani, Suhuai Luo, Brian Regan

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Data fusion technology can be the best way to extract useful information from multiple sources of data. It has been widely applied in various applications. This paper presents a data fusion approach in multimedia data for event detection in twitter by using Dempster-Shafer evidence theory. The methodology applies a mining algorithm to detect the event. There are two types of data in the fusion. The first is features extracted from text by using the bag-ofwords method which is calculated using the term frequency-inverse document frequency (TF-IDF). The second is the visual features extracted by applying scale-invariant feature transform (SIFT). The Dempster - Shafer theory of evidence is applied in order to fuse the information from these two sources. Our experiments have indicated that comparing to the approaches using individual data source, the proposed data fusion approach can increase the prediction accuracy for event detection. The experimental result showed that the proposed method achieved a high accuracy of 0.97, comparing with 0.93 with texts only, and 0.86 with images only.

Keywords: data fusion, Dempster-Shafer theory, data mining, event detection

Procedia PDF Downloads 410

Authors: Ioannis Iglezakis, Theodoros D. Trokanas, Panagiota Kiortsi

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This paper aims to explore major legal issues arising from the collection and processing of Health Big Data in the light of the new European secondary legislation for the protection of personal data of natural persons, placing emphasis on the General Data Protection Regulation 679/2016. Whether Big Health Data can be characterised as ‘personal data’ or not is really the crux of the matter. The legal ambiguity is compounded by the fact that, even though the processing of Big Health Data is premised on the de-identification of the data subject, the possibility of a combination of Big Health Data with other data circulating freely on the web or from other data files cannot be excluded. Another key point is that the application of some provisions of GPDR to Big Health Data may both absolve the data controller of his legal obligations and deprive the data subject of his rights (e.g., the right to be informed), ultimately undermining the fundamental right to the protection of personal data of natural persons. Moreover, data subject’s rights (e.g., the right not to be subject to a decision based solely on automated processing) are heavily impacted by the use of AI, algorithms, and technologies that reclaim health data for further use, resulting in sometimes ambiguous results that have a substantial impact on individuals. On the other hand, as the COVID-19 pandemic has revealed, Big Data analytics can offer crucial sources of information. In this respect, this paper identifies and systematises the legal provisions concerned, offering interpretative solutions that tackle dangers concerning data subject’s rights while embracing the opportunities that Big Health Data has to offer. In addition, particular attention is attached to the scope of ‘consent’ as a legal basis in the collection and processing of Big Health Data, as the application of data analytics in Big Health Data signals the construction of new data and subject’s profiles. Finally, the paper addresses the knotty problem of role assignment (i.e., distinguishing between controller and processor/joint controllers and joint processors) in an era of extensive Big Health data sharing. The findings are the fruit of a current research project conducted by a three-member research team at the Faculty of Law of the Aristotle University of Thessaloniki and funded by the Greek Ministry of Education and Religious Affairs.

Keywords: big health data, data subject rights, GDPR, pandemic

Procedia PDF Downloads 129

Authors: Yong-Kyu Jung

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The fast growth in information technology has led to de-mands to access/process data. CPSs heavily depend on the time of hardware/software operations and communication over the network (i.e., real-time/parallel operations in CPSs (e.g., autonomous vehicles). Since data processing is an im-portant means to overcome the issue confronting data management, reducing the gap between the technological-growth and the data-complexity and channel-bandwidth. An adaptive perpetual data approximation method is intro-duced to manage the actual entropy of the digital spectrum. An ADAC implemented as an accelerator and/or apps for servers/smart-connected devices adaptively rescales digital contents (avg.62.8%), data processing/access time/energy, encryption/decryption overheads in AI/ML applications (facial ID/recognition).

Keywords: adaptive codec, AI, ML, HPC, cyber-physical, cybersecurity

Procedia PDF Downloads 78

Authors: Mindaugas Zavalis

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Analysis of experimental tests data of bending one-way reinforced concrete slabs from various articles of science revealed that voided slabs with a grid of hollow plastic inserts inside have smaller mechani-cal and physical parameters compared to continuous cross-section slabs (solid slabs). The negative influence of a reinforced concrete slab is impacted by hollow plastic inserts, which make a grid of voids in the middle of the cross-sectional area of the reinforced concrete slab. A formed grid of voids reduces the slab’s stiffness, which influences the slab’s parameters of serviceability, like deflection and cracking. Prima-ry investigation of data established during experiments illustrates that cracks occur faster in the tensile surface of the voided slab under bend-ing compared to bending solid slab. It means that the crack bending moment force for the voided slab is smaller than the solid slab and the reduction can variate in the range of 14 – 40 %. Reduce of resistance to cracking can be controlled by changing a lot of factors: the shape of the plastic hallow insert, plastic insert height, steps between plastic in-serts, usage of prestressed reinforcement, the diameter of reinforcement bar, slab effective depth, the bottom cover thickness of concrete, effec-tive cross-section of the concrete area about reinforcement and etc. Mentioned parameters are used to evaluate crack width and step of cracking, but existing analytical calculation methods for cracking eval-uation of voided slab with plastic inserts are not so exact and the re-sults of cracking evaluation in this paper are higher than the results of analyzed experiments. Therefore, it was made analytically calculations according to experimental bending tests of voided reinforced concrete slabs with hollow plastic inserts to find and propose corrections for the evaluation of cracking for reinforced concrete voided slabs with hollow plastic inserts.

Keywords: voided slab, cracking, hallow plastic insert, bending, one-way reinforced concrete, serviceability

Procedia PDF Downloads 68

Authors: Zahra Tolou-Ghamari

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Objective: Health care associated with multiresistant pathogens is rising globally. It is well known that nosocomial infections increase hospital stay, morbidity, mortality, and disability. Therefore, the aim of this study was to define the occurrence of nosocomial infections in a tertiary hospital in Isfahan/Iran. Materials and Methods: The data were extracted from the official database of hospital nosocomial infections records that included 9152 vertical rows. For each patient, the reported infections were coded by number as UTI-SUTI; Code 55, VAE-PVAP; Code 56, BSI-LCBI Code 19, SSI-DIP; Code 14, and so on. For continuous variables, mean ± standard deviation and for categorical variables, the frequency was used. Results: The study population was 5542 patients, comprised of males (n=3282) and females (n=2260). With a minimum of 15 and a maximum of 99, the mean age in 5313 patients was 58.5 ± 19.1 years old. The highest reported nosocomial infections (n= 77%) were associated with the ages 30-80 years old. Sites of nosocomial infections in 87% were as: VAE-PVAP; 27.3%, VAE-IVAC; 7.7, UTI-SUTI; 29.5%, BSI-LCBI; 12.9%, SSI-DIP; 9.5% and other individual infection (13%) with the main pathogens klebsiella pneumonia, acinetobacter baumannii and staphylococcus. Conclusions: For an efficient surveillance system, adopting pharmacotherapy used antibiotics in terms of monotherapy or polypharmacy control policy, in addition to advanced infection control programs at regional and national levels in Iran recommended.

Keywords: infection, nosocomial, ventilator, blood stream, Isfahan, Iran

Procedia PDF Downloads 78

Authors: Ya-Ping Chang

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Starting from the early stages of pregnancy, parents begin to form hopes and dreams about the future of their child. They will think about the appearance and personality of their child and may even develop many expectations. The patient in this study experienced a successful pregnancy following multiple attempts at artificial insemination. However, due to arrested embryonic development, and based on the physician’s evaluation, a caesarean section was performed at week 25. However, the baby suffered from infections and subsequently died from multiple organ failures. This study collected and analyzed objective and subjective data through observation, interviews, recording, and interactions with the patient. The following nursing issues of the patient were identified: anxiety, anticipatory grief, and adjustment disorder. The psychology of caring as proposed in Watson’s theory was applied to address these nursing issues. Comprehensive and continuous care was provided to the patient on the basis of mutual trust and individual nursing guidelines in order to alleviate the patient’s anxiety, help her to cope with grief, and prepare her for the eventual death of her child. The author helped the patient to say goodbye to her child and accept the child’s death calmly, such that she had no regrets about the experience. This nursing experience may serve as a reference to nurses managing similar cases in the future.

Keywords: dying baby, mother, grief, Watson’s theory

Procedia PDF Downloads 172

Authors: Nadine Ponnusamy

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Reducing risks to food security resulting from climate change is recognized as one of the major challenges of the 21st century. The risks to food security have intensified, primarily due to globalization, a growing population, rapid urbanization, and the constantly evolving urban environment. One of the key challenges facing cities is the need to supply sufficient food to households amid increasing demand, which necessitates a continuous effort to enhance food production. Given the severity of climate change, it is imperative to adopt solutions to address food insecurity. Communities and individuals must explore sustainable livelihood options that do not harm the environment. Urban agriculture represents one of the many strategies that can be employed to improve household food security. The objective of this research is to establish the extent to which community gardens can enhance urban food security, focusing on the Thulubukele Community Farm in Newlands West, Durban. The researcher utilized a qualitative case study approach to gain insight into urban agriculture and food security within this context, while also examining the long-term impacts on food security and community development. The sampling method utilized for selecting participants and gathering information included purposive sampling. Since the study centers on urban agriculture, key stakeholders were specifically targeted. Participants were selected for interviews based on their involvement in the food garden. In-depth interviews were conducted to collect and analyze data. Secondary data from the literature facilitated a comparative analysis of similar case studies through precedent studies. This study demonstrates that growing food not only improves the nutritional value of the produce but also enhances household food security, enables individuals to generate disposable income, and facilitates significant contributions to the local community and other organizations in need.

Keywords: community gardens, food security, South Africa, urban agriculture

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Authors: Sašo Pečnik, Borut Žalik

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This paper presents a real-time visualization technique and filtering of classified LiDAR point clouds. The visualization is capable of displaying filtered information organized in layers by the classification attribute saved within LiDAR data sets. We explain the used data structure and data management, which enables real-time presentation of layered LiDAR data. Real-time visualization is achieved with LOD optimization based on the distance from the observer without loss of quality. The filtering process is done in two steps and is entirely executed on the GPU and implemented using programmable shaders.

Keywords: filtering, graphics, level-of-details, LiDAR, real-time visualization

Procedia PDF Downloads 308

Authors: Duane Booysen, Ashraf Kagee

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There is a high prevalence of violent crime and trauma exposure in South African society. Considering the prevalence of continuous violent crimes and traumatization in South Africa, the public mental health sector is required to combat the burgeoning effect of traumatic stress in South Africa. Trauma counselors, especially, provide important mental health services at primary health care to persons affected by traumatic events. Therefore, the evaluation and implementation of evidence-based trauma therapies is essential at a primary health care level in treating traumatic stress. A single-case design was used to evaluate the treatment effect of a Brief Trauma Treatment Programme treating persons who present with symptoms of posttraumatic stress disorder at a primary care trauma centre in Cape Town, South Africa. The sample consisted of six adult participants who presented with symptoms of posttraumatic stress and were assessed at baseline, during treatment, post-intervention and at 3-month follow. All participants received six sessions of trauma therapy. Assessment measures included the posttraumatic stress disorder symptom scale interviews for Diagnostic and Statistical Manual fifth edition (DSM5), the posttraumatic disorder checklist for DSM5, Beck Depression Inventory and Beck Anxiety Inventory. Results demonstrate that participants had noticeable reduced symptoms for traumatic stress, anxiety and depression despite living in contexts of violent crime and trauma. In conclusion, the article critically reflects on the need to evaluate and implement evidence-based treatments for the South African context, and how evidence-based treatments are used in developing socio-economic and cultural diverse contexts with continuous levels of violence and traumatization.

Keywords: psychological interventions, public mental health, traumatic stress, single-case design

Procedia PDF Downloads 155

Authors: M. Chafi, S. Akazdam, C. Asrir, L. Sebbahi, B. Gourich, N. Barka, M. Essahli

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Fixed bed adsorption has become a frequently used industrial application in wastewater treatment processes. Various low cost adsorbents have been studied for their applicability in treatment of different types of effluents. In this work, the intention of the study was to explore the efficacy and feasibility for azo dye, Acid Orange 7 (AO7) adsorption onto fixed bed column of NaOH Treated eggshell (TES). The effect of various parameters like flow rate, initial dye concentration, and bed height were exploited in this study. The studies confirmed that the breakthrough curves were dependent on flow rate, initial dye concentration solution of AO7 and bed depth. The Thomas, Yoon–Nelson, and Adams and Bohart models were analysed to evaluate the column adsorption performance. The adsorption capacity, rate constant and correlation coefficient associated to each model for column adsorption was calculated and mentioned. The column experimental data were fitted well with Thomas model with coefficients of correlation R2 ≥0.93 at different conditions but the Yoon–Nelson, BDST and Bohart–Adams model (R2=0.911), predicted poor performance of fixed-bed column. The (TES) was shown to be suitable adsorbent for adsorption of AO7 using fixed-bed adsorption column.

Keywords: adsorption models, acid orange 7, bed depth, breakthrough, dye adsorption, fixed-bed column, treated eggshell

Procedia PDF Downloads 377

Authors: Hasik Lee, Seung-Young Kho

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Nowadays, automatic fare collection (AFC) system is widely used in many countries. However, smart card data from many of cities does not contain alighting information which is necessary to build OD matrices. Therefore, in order to utilize smart card data, destinations of passengers should be estimated. In this paper, kernel density estimation was used to forecast probabilities of alighting stations of bus passengers and applied to smart card data in Seoul, Korea which contains boarding and alighting information. This method was also validated with actual data. In some cases, stochastic method was more accurate than deterministic method. Therefore, it is sufficiently accurate to be used to build OD matrices.

Keywords: destination estimation, Kernel density estimation, smart card data, validation

Procedia PDF Downloads 352

Authors: Jae Won Shin

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We develop an evaluated nuclear data based photonuclear reaction model of GEANT4 for a more accurate simulation of photon-induced neutron production. The evaluated photonuclear data libraries from the ENDF/B-VII.1 are taken as input. Incident photon energies up to 140 MeV which is the threshold energy for the pion production are considered. For checking the validity of the use of the data-based model, we calculate the photoneutron production cross-sections and yields and compared them with experimental data. The results obtained from the developed model are found to be in good agreement with the experimental data for (γ,xn) reactions.

Keywords: ENDF/B-VII.1, GEANT4, photoneutron, photonuclear reaction

Procedia PDF Downloads 275

Authors: Rashi Bhalla, Russel Pears, M. Asif Naeem

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In this 'Information Explosion Era' analyzing data 'a critical commodity' and mining knowledge from vertically distributed data stream incurs huge communication cost. However, an effort to decrease the communication in the distributed environment has an adverse influence on the classification accuracy; therefore, a research challenge lies in maintaining a balance between transmission cost and accuracy. This paper proposes a method based on Bayesian inference to reduce the communication volume in a heterogeneous distributed environment while retaining prediction accuracy. Our experimental evaluation reveals that a significant reduction in communication can be achieved across a diverse range of dataset types.

Keywords: big data, bayesian inference, distributed data stream mining, heterogeneous-distributed data

Procedia PDF Downloads 161

Authors: Benny Sand, Yotam Lurie, Shlomo Mark

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Medical Internet of Things (MIoT), AI, and data privacy are linked forever in a gordian knot. This paper explores the conflicts of interests between the stakeholders regarding data privacy in the MIoT arena. While patients are at home during healthcare hospitalization, MIoT can play a significant role in improving the health of large parts of the population by providing medical teams with tools for collecting data, monitoring patients’ health parameters, and even enabling remote treatment. While the amount of data handled by MIoT devices grows exponentially, different stakeholders have conflicting understandings and concerns regarding this data. The findings of the research indicate that medical teams are not concerned by the violation of data privacy rights of the patients' in-home healthcare, while patients are more troubled and, in many cases, are unaware that their data is being used without their consent. MIoT technology is in its early phases, and hence a mixed qualitative and quantitative research approach will be used, which will include case studies and questionnaires in order to explore this issue and provide alternative solutions.

Keywords: MIoT, data privacy, stakeholders, home healthcare, information privacy, AI

Procedia PDF Downloads 102

Authors: Kamila Mazur-Oleszczuk, Rafal Banasiuk, Dawid Krasnowski, Maciej Pek, Marcin Podgorski, Krzysztof Rykaczewski, Sabina Zoledowska, Dawid Nidzworski

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Workout safety is one of the most significant challenges of recreational sports. Loss of water and electrolytes is a consequence of thermoregulatory sweating during exercise. The rate of sweat loss and its chemical composition can fluctuate within and among individuals. That is why we propose our sportband 'Flow' as a device for monitoring these parameters. 'Flow' consists of two parts: an intelligent module and a mobile application. The application allows verifying the training progress and data archiving. The sportband intelligent module includes temperature, heart rate and pulse measurement (non-invasive, continuous methods of workout monitoring). Apart from the standard components, the device will consist of a sweat composition analyzer situated in sportband intelligent module. Sweat is a water solution of numerous compounds such as ions (sodium up to 1609 µg/ml, potassium up to 274 µg/ml), lactic acid (skin pH is between 4.5 - 6) and a small amount of glucose. Awareness of sweat composition allows personalizing electrolyte intake after training. A comprehensive workout monitoring (sweat composition, heart rate, blood oxygen level) will provide improvement in the training routine and time management, which is our goal for the development of the sweat composition analyzer.

Keywords: flow, sportband, sweat, workout monitoring

Procedia PDF Downloads 151

Authors: Atef Alaaeddine Sarraj, Brendan Jackman, Frank Walsh

Abstract:

Tomorrow’s car will be more automated and increasingly connected. Innovative and intuitive interfaces are essential to accompany this functional enrichment. For that, today the automotive companies are competing to offer an advanced driver assistance system (ADAS) which will be able to provide enhanced navigation, collision avoidance, intersection support and lane keeping. These vision-based functions require an accurately calibrated camera. To achieve such differentiation in ADAS requires sophisticated sensors and efficient algorithms. This paper explores the different calibration methods applicable to vehicle-mounted fish-eye cameras with arbitrary fields of view and defines the first steps towards a self-calibration method that adequately addresses ADAS requirements. In particular, we present a self-calibration method after comparing different camera calibration algorithms in the context of ADAS requirements. Our method gathers data from unknown scenes while the car is moving, estimates the camera intrinsic and extrinsic parameters and corrects the wide-angle distortion. Our solution enables continuous and real-time detection of objects, pedestrians, road markings and other cars. In contrast, other camera calibration algorithms for ADAS need pre-calibration, while the presented method calibrates the camera without prior knowledge of the scene and in real-time.

Keywords: advanced driver assistance system (ADAS), fish-eye, real-time, self-calibration

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Authors: Deepak Singh, Rail Kuliev

Abstract:

The abstract highlights the critical importance of optimizing data integration and management strategies in the upstream oil and gas industry. With its complex and dynamic nature generating vast volumes of data, efficient data integration and management are essential for informed decision-making, cost reduction, and maximizing operational performance. Challenges such as data silos, heterogeneity, real-time data management, and data quality issues are addressed, prompting the proposal of several strategies. These strategies include implementing a centralized data repository, adopting industry-wide data standards, employing master data management (MDM), utilizing real-time data integration technologies, and ensuring data quality assurance. Training and developing the workforce, “reskilling and upskilling” the employees and establishing robust Data Management training programs play an essential role and integral part in this strategy. The article also emphasizes the significance of data governance and best practices, as well as the role of technological advancements such as big data analytics, cloud computing, Internet of Things (IoT), and artificial intelligence (AI) and machine learning (ML). To illustrate the practicality of these strategies, real-world case studies are presented, showcasing successful implementations that improve operational efficiency and decision-making. In present study, by embracing the proposed optimization strategies, leveraging technological advancements, and adhering to best practices, upstream oil and gas companies can harness the full potential of data-driven decision-making, ultimately achieving increased profitability and a competitive edge in the ever-evolving industry.

Keywords: master data management, IoT, AI&ML, cloud Computing, data optimization

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi, Aref Ghafouri

Abstract:

Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained.

Keywords: local nonlinear estimation, LWPR algorithm, online training method, locally weighted projection regression method

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Authors: Sabina Mohsin

Abstract:

The need for grounding of teachers and the trend of using innovative ways to deal with students of various abilities in schools, colleges and universities has always been essential in any part of the world. Teacher edification programs, and qualifications standards, all too repeatedly lack enough rigidity, extensiveness and profundity, resulting in high levels of unskilled teachers and squat student performance. Accordingly, the solution, from this viewpoint, lies in making the entry and training necessities for teaching deeper and more exact. Teachers’ continuous professional development is necessary to reach all kinds of learners in class. The training provided is a direct opportunity for new teachers to interact better and motivate students in a two way discussion class. The intention of the study was to scrutinize whether the teachers’ training played an important role to fabricate lectures and classroom activities and reflected the objectives of the training provided in various schools and universities. It also aims to examine the current practices used in the various teacher training programs and if there is any other method that can be associated to enhance the effectiveness of these programs further. This research uses qualitative data collected from interviews, peer discussions, classroom observations, reviews, feedback of students and teachers to study teacher training and teaching methods used in school and universities in Bangladesh. The study finds teacher training to be effective though it has some limitations. It also includes some suggestions to make teacher training more effective.

Keywords: current practices in teacher training, enhancing effectiveness, limitation, student motivation, teacher training

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Authors: Duc V. Nguyen

Abstract:

Induction motors are widely used in different industry areas and can experience various kinds of faults in stators and rotors. In general, fault detection and diagnosis techniques for induction motors can be supervised by measuring quantities such as noise, vibration, and temperature. The installation of mechanical sensors in order to assess the health conditions of a machine is typically only done for expensive or load-critical machines, where the high cost of a continuous monitoring system can be Justified. Nevertheless, induced current monitoring can be implemented inexpensively on machines with arbitrary sizes by using current transformers. In this regard, effective and low-cost fault detection techniques can be implemented, hence reducing the maintenance and downtime costs of motors. This work proposes a method for fault detection and diagnosis of induction motors, which combines classical fast Fourier transform and modern/advanced machine learning techniques. The proposed method is validated on real-world data and achieves a precision of 99.7% for fault detection and 100% for fault classification with minimal expert knowledge requirement. In addition, this approach allows users to be able to optimize/balance risks and maintenance costs to achieve the highest benet based on their requirements. These are the key requirements of a robust prognostics and health management system.

Keywords: fault detection, FFT, induction motor, predictive maintenance

Procedia PDF Downloads 170