Search results for: performance and quality

Authors: V. Howard, R. Maguire, S. Corrigan

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Background: Type 1 Diabetes (T1D) is a chronic autoimmune disease, typically diagnosed in childhood. T1D puts an enormous strain on families; controlling blood-glucose in children is difficult and the consequences of poor control for patient health are significant. Successful illness management and better health outcomes can be dependent on quality of caregiving. On diagnosis, parent-caregivers face a steep learning curve as T1D care requires a significant level of knowledge to inform complex decision making throughout the day. The majority of illness management is carried out in the home setting, independent of clinical health providers. Parent-caregivers vary in their level of knowledge and their level of engagement in applying this knowledge in the practice of illness management. Enabling researchers to quantify these aspects of the caregiver experience is key to identifying targets for psychosocial support interventions, which are desirable for reducing stress and anxiety in this highly burdened cohort, and supporting better health outcomes in children. Currently, there are limited tools available that are designed to capture this information. Where tools do exist, they are not comprehensive and do not adequately capture the lived experience. Objectives: Development of quantitative tools, informed by lived experience, to enable researchers gather data on parent-caregiver knowledge and engagement, which accurately represents the experience/cohort and enables exploration of questions that are of real-world value to the cohort themselves. Methods: This research employed an engaged approach to address the problem of quantifying two key aspects of caregiver diabetes management: Knowledge and engagement. The research process was multi-staged and iterative. Stage 1: Working from a constructivist standpoint, literature was reviewed to identify relevant questionnaires, scales and single-item measures of T1D caregiver knowledge and engagement, and harvest candidate questionnaire items. Stage 2: Aggregated findings from the review were circulated among a PPI (patient and public involvement) expert panel of caregivers (n=6), for discussion and feedback. Stage 3: In collaboration with the expert panel, data were interpreted through the lens of lived experience to create a long-list of candidate items for novel questionnaires. Items were categorized as either ‘knowledge’ or ‘engagement’. Stage 4: A Delphi-method process (iterative surveys) was used to prioritize question items and generate novel questions that further captured the lived experience. Stage 5: Both questionnaires were piloted to refine wording of text to increase accessibility and limit socially desirable responding. Stage 6: Tools were piloted using an online survey that was deployed using an online peer-support group for caregivers for Juveniles with T1D. Ongoing Research: 123 parent-caregivers completed the survey. Data analysis is ongoing to establish face and content validity qualitatively and through exploratory factor analysis. Reliability will be established using an alternative-form method and Cronbach’s alpha will assess internal consistency. Work will be completed by early 2024. Conclusion: These tools will enable researchers to gain deeper insights into caregiving practices among parents of juveniles with T1D. Development was driven by lived experience, illustrating the value of engaged research at all levels of the research process.

Keywords: caregiving, engaged research, juvenile type 1 diabetes, quantified engagement and knowledge

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Authors: Tomohiko Utsuki, Kyoka Sato

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In emergency medicine, it is recognized that brain resuscitation is very important for the reduction of mortality rate and neurological sequelae. Especially, the control of brain temperature (BT), intracranial pressure (ICP), and cerebral blood flow (CBF) are most required for stabilizing brain’s physiological state in the treatment for such as brain injury, stroke, and encephalopathy. However, the manual control of BT, ICP, and CBF frequently requires the decision and operation of medical staff, relevant to medication and the setting of therapeutic apparatus. Thus, the integration and the automation of the control of those is very effective for not only improving therapeutic effect but also reducing staff burden and medical cost. For realizing such integration and automation, a mathematical model of brain physiological state is necessary as the controlled object in simulations, because the performance test of a prototype of the control system using patients is not ethically allowed. A model of cerebral blood circulation has already been constructed, which is the most basic part of brain physiological state. Also, a migration model of extracellular fluid in brain has been constructed, however the condition that the total volume of intracranial cavity is almost changeless due to the hardness of cranial bone has not been considered in that model. Therefore, in this research, the dynamic migration model of extracellular fluid in brain was constructed on the consideration of the changelessness of intracranial cavity’s total volume. This model is connectable to the cerebral blood circulation model. The constructed model consists of fourteen compartments, twelve of which corresponds to perfused area of bilateral anterior, middle and posterior cerebral arteries, the others corresponds to cerebral ventricles and subarachnoid space. This model enable to calculate the migration of tissue fluid from capillaries to gray matter and white matter, the flow of tissue fluid between compartments, the production and absorption of cerebrospinal fluid at choroid plexus and arachnoid granulation, and the production of metabolic water. Further, the volume, the colloid concentration, and the tissue pressure of/in each compartment are also calculable by solving 40-dimensional non-linear simultaneous differential equations. In this research, the obtained model was analyzed for its validation under the four condition of a normal adult, an adult with higher cerebral capillary pressure, an adult with lower cerebral capillary pressure, and an adult with lower colloid concentration in cerebral capillary. In the result, calculated fluid flow, tissue volume, colloid concentration, and tissue pressure were all converged to suitable value for the set condition within 60 minutes at a maximum. Also, because these results were not conflict with prior knowledge, it is certain that the model can enough represent physiological state of brain under such limited conditions at least. One of next challenges is to integrate this model and the already constructed cerebral blood circulation model. This modification enable to simulate CBF and ICP more precisely due to calculating the effect of blood pressure change to extracellular fluid migration and that of ICP change to CBF.

Keywords: dynamic model, cerebral extracellular migration, brain resuscitation, automatic control

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Authors: Ezekiel Olukayode Akintunde

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There is a high level of inadequate methods at all levels of food supply in the global food industry. The inadequacies have led to vast wastages of food. Hence there is a need to curb the wastages that can later affect natural resources, water resources, and energy to avoid negative impacts on the climate and the environment. There is a need to engage multifaceted engineering packaging approaches for a sustainable food chain to ensure active packaging, intelligent packaging, new packaging materials, and a sustainable packaging system. Packaging can be regarded as an indispensable component approach that can be applied to solve major problems of sustainable food consumption globally; this is about controlling the environmental impact of packed food. The creative innovation will ensure that packaged foods are free from food-borne diseases and food chemical pollution. This paper evaluates the key shortcomings that must be addressed by innovative food packaging to ensure a safe, natural environment that will preserve energy and sustain water resources. Certain solutions, including fabricating microbial biodegradable chemical compounds/polymers from agro-food waste remnants, appear a bright path to ensure a strong and innovative waste-based food packaging system. Over the years, depletion in the petroleum reserves has brought about the emergence of biodegradable polymers as a proper replacement for traditional plastics; moreover, the increase in the production of traditional plastics has raised serious concerns about environmental threats. Biodegradable polymers have proven to be biocompatible, which can also be processed for other useful applications. Therefore, this study will showcase a workable guiding framework for designing a sustainable food packaging system that will not constitute a danger to our present society and that will surely preserve natural water resources. Various assessment methods will be deployed at different stages of the packaging design to enhance the package's sustainability. Every decision that will be made must be facilitated with methods that will be engaged per stage to allow for corrective measures throughout the cycle of the design process. Basic performance appraisal of packaging innovations. Food wastage can result in inimical environmental impacts, and ethical practices must be carried out for food loss at home. An examination in West Africa quantified preventable food wastage over the entire food value chain at almost 180kg per person per year. That is preventable food wastage, 35% of which originated at the household level. Many food losses reported, which happened at the harvesting, storage, transportation, and processing stages, are not preventable and are without much environmental impact because such wastage can be used for feeding. Other surveys have shown that 15%-20% of household food losses can be traced to food packaging. Therefore, new innovative packaging systems can lessen the environmental effect of food wastage to extend shelf‐life to lower food loss in the process distribution chain and at the household level.

Keywords: food packaging, biodegradable polymer, intelligent packaging, shelf-life

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Authors: Christine F. Boos, Fernando M. Azevedo

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Electroencephalogram (EEG) is a record of the electrical activity of the brain that has many applications, such as monitoring alertness, coma and brain death; locating damaged areas of the brain after head injury, stroke and tumor; monitoring anesthesia depth; researching physiology and sleep disorders; researching epilepsy and localizing the seizure focus. Epilepsy is a chronic condition, or a group of diseases of high prevalence, still poorly explained by science and whose diagnosis is still predominantly clinical. The EEG recording is considered an important test for epilepsy investigation and its visual analysis is very often applied for clinical confirmation of epilepsy diagnosis. Moreover, this EEG analysis can also be used to help define the types of epileptic syndrome, determine epileptiform zone, assist in the planning of drug treatment and provide additional information about the feasibility of surgical intervention. In the context of diagnosis confirmation the analysis is made using long term EEG recordings with at least 24 hours long and acquired by a minimum of 24 electrodes in which the neurophysiologists perform a thorough visual evaluation of EEG screens in search of specific electrographic patterns called epileptiform discharges. Considering that the EEG screens usually display 10 seconds of the recording, the neurophysiologist has to evaluate 360 screens per hour of EEG or a minimum of 8,640 screens per long term EEG recording. Analyzing thousands of EEG screens in search patterns that have a maximum duration of 200 ms is a very time consuming, complex and exhaustive task. Because of this, over the years several studies have proposed automated methodologies that could facilitate the neurophysiologists’ task of identifying epileptiform discharges and a large number of methodologies used neural networks for the pattern classification. One of the differences between all of these methodologies is the type of input stimuli presented to the networks, i.e., how the EEG signal is introduced in the network. Five types of input stimuli have been commonly found in literature: raw EEG signal, morphological descriptors (i.e. parameters related to the signal’s morphology), Fast Fourier Transform (FFT) spectrum, Short-Time Fourier Transform (STFT) spectrograms and Wavelet Transform features. This study evaluates the application of these five types of input stimuli and compares the classification results of neural networks that were implemented using each of these inputs. The performance of using raw signal varied between 43 and 84% efficiency. The results of FFT spectrum and STFT spectrograms were quite similar with average efficiency being 73 and 77%, respectively. The efficiency of Wavelet Transform features varied between 57 and 81% while the descriptors presented efficiency values between 62 and 93%. After simulations we could observe that the best results were achieved when either morphological descriptors or Wavelet features were used as input stimuli.

Keywords: Artificial neural network, electroencephalogram signal, pattern recognition, signal processing

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Authors: Giacoma Pace

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The research seeks to evaluate whether students with impairments can achieve enhanced academic progress by actively engaging in collaborative problem-solving activities with teachers and peers, to overcome the obstacles rooted in socio-economic disparities. Furthermore, the research underscores the significance of fostering students' self-awareness regarding their learning process and encourages teachers to adopt a more interactive teaching approach. The research also posits that reducing conventional face-to-face lessons can motivate students to explore alternative learning methods, such as collaborative teamwork and peer education within the classroom. To address socio-cultural barriers it is imperative to assess their internet access and possession of technological devices, as these factors can contribute to a digital divide. The research features a case study of a Flipped Classroom Learning Unit, administered to six third-year high school classes: Scientific Lyceum, Technical School, and Vocational School, within the city of Turin, Italy. Data are about teachers and the students involved in the case study, some impaired students in each class, level of entry, students’ performance and attitude before using Flipped Classrooms, level of motivation, family’s involvement level, teachers’ attitude towards Flipped Classroom, goal obtained, the pros and cons of such activities, technology availability. The selected schools were contacted; meetings for the English teachers to gather information about their attitude and knowledge of the Flipped Classroom approach. Questionnaires to teachers and IT staff were administered. The information gathered, was used to outline the profile of the subjects involved in the study and was further compared with the second step of the study made up of a study conducted with the classes of the selected schools. The learning unit is the same, structure and content are decided together with the English colleagues of the classes involved. The pacing and content are matched in every lesson and all the classes participate in the same labs, use the same materials, homework, same assessment by summative and formative testing. Each step follows a precise scheme, in order to be as reliable as possible. The outcome of the case study will be statistically organised. The case study is accompanied by a study on the literature concerning EFL approaches and the Flipped Classroom. Document analysis method was employed, i.e. a qualitative research method in which printed and/or electronic documents containing information about the research subject are reviewed and evaluated with a systematic procedure. Articles in the Web of Science Core Collection, Education Resources Information Center (ERIC), Scopus and Science Direct databases were searched in order to determine the documents to be examined (years considered 2000-2022).

Keywords: flipped classroom, impaired, inclusivity, peer instruction

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Authors: Eniola Akande

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Introduction: English vocabulary and reading comprehension are core to academic achievement in many school subjects. Deficiency in both accounts for dismal performance in internal and external examinations among primary school pupils in Ibadan Metropolis, Nigeria. Previous studies largely focused on factors influencing pupils’ achievement in English vocabulary and reading comprehension. In spite of what literature has shown, the problem still persists, implying the need for other kinds of intervention. This study was therefore carried out to determine the effect of two transactional strategies Picture Walk (PW) and Know-Want to Learn-Learnt (KWL) on primary four pupils’ achievement in English vocabulary and reading comprehension in Ibadan Metropolis. The moderating effects of gender and learning style were also examined. Methodology: The study was anchored on Rosenblatt’s Transactional Reading and Piaget’s Cognitive Development theories; pretest-posttest control group quasi-experimental design with 3x2x3 factorial matrix was adopted. Six public primary schools were purposively selected based on the availability of qualified English language teachers in Primary Education Studies. Six intact classes (one per school) with a total of 101 primary four pupils (48 males and 53 females) participated. The intact classes were randomly assigned to PW (27), KWL (44) and conventional (30) groups. Instruments used were English Vocabulary (r=0.83), Reading Comprehension (r=0.84) achievement tests, Pupils’ Learning Style Preference Scale (r=0.93) and instructional guides. Treatment lasted six weeks. Data were analysed using the Descriptive statistics, Analysis of Covariance and Bonferroni post-hoc test at 0.05 level of significance. The mean age was 8.86±0.84 years. Result: Treatment had a significant main effect on pupils’ reading comprehension (F(2,82)=3.17), but not on English vocabulary. Participants in KWL obtained the highest post achievement means score in reading comprehension (8.93), followed by PW (8.06) and control (7.21) groups. Pupils’ learning style had a significant main effect on pupils’ achievement in reading comprehension (F(2,82)=4.41), but not on English vocabulary. Pupils with preference for tactile learning style had the highest post achievement mean score in reading comprehension (9.40), followed by the auditory (7.43) and the visual learning style (7.37) groups. Gender had no significant main effect on English vocabulary and reading comprehension. There was no significant two-way interaction effect of treatment and gender on pupils’ achievement in English vocabulary and reading comprehension. The two-way interaction effect of treatment and learning style on pupils’ achievement in reading comprehension was significant (F(4,82)=3.37), in favour of pupils with tactile learning style in PW group. There was no significant two-way interaction effect of gender and learning style on pupils’ achievement in English vocabulary and reading comprehension. The three-way interaction effects were not significant on English vocabulary and reading comprehension. Conclusion: Picture Walk and Know-Want to learn-Learnt instructional strategies were effective in enhancing pupils’ achievement in reading comprehension but not on English vocabulary. Learning style contributed considerably to achievement in reading comprehension but not to English vocabulary. Primary school, English language teachers, should put into consideration pupils’ learning style when adopting both strategies in teaching reading comprehension for improved achievement in the subject.

Keywords: comprehension-based intervention, know-want to learn-learnt, learning style, picture walk, primary school pupils

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Authors: Soumyadeep Bhaduri, Rahul Ghosh, Rahul Shukla, Manaswini Behera

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The textile industry consumes a substantial amount of water throughout the processing and production of textile fabrics. The water eventually turns into wastewater, where it acts as an immense damaging nuisance due to its dye content. Wastewater streams contain a percentage ranging from 2.0% to 50.0% of the total weight of dye used, depending on the dye class. The management of dye effluent in textile industries presents a formidable challenge to global sustainability. The current focus is on implementing wastewater treatment technology that enable the recycling of wastewater, reduce energy usage and offset carbon emissions. Microbial fuel cell (MFC) is a device that utilizes microorganisms as a bio-catalyst to effectively treat wastewater while also producing electricity. The MFC harnesses the chemical energy present in wastewater by oxidizing organic compounds in the anodic chamber and reducing an electron acceptor in the cathodic chamber, thereby generating electricity. This research investigates the potential of MFCs to tackle this challenge of azo dye removal with simultaneously generating electricity. Although MFCs are well-established for wastewater treatment, their application in dye decolorization with concurrent electricity generation remains relatively unexplored. This study aims to address this gap by assessing the effectiveness of MFCs as a sustainable solution for treating wastewater containing azo dyes. By harnessing microorganisms as biocatalysts, MFCs offer a promising avenue for environmentally friendly dye effluent management. The performance of MFCs in treating azo dyes and generating electricity was evaluated by optimizing the Chemical Oxygen Demand (COD) and Hydraulic Retention Time (HRT) of influent. COD and HRT values ranged from 1600 mg/L to 2400 mg/L and 5 to 9 days, respectively. Results showed that the maximum open circuit voltage (OCV) reached 648 mV at a COD of 2400 mg/L and HRT of 5 days. Additionally, maximum COD removal of 98% and maximum color removal of 98.91% were achieved at a COD of 1600 mg/L and HRT of 9 days. Furthermore, the study observed a maximum power density of 19.95 W/m3 at a COD of 2400 mg/L and HRT of 5 days. Electrochemical analysis, including linear sweep voltammetry (LSV), cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) were done to find out the response current and internal resistance of the system. To optimize pH and dye concentration, pH values were varied from 4 to 10, and dye concentrations ranged from 25 mg/L to 175 mg/L. The highest voltage output of 704 mV was recorded at pH 7, while a dye concentration of 100 mg/L yielded the maximum output of 672 mV. This study demonstrates that MFCs offer an efficient and sustainable solution for treating azo dyes in textile industry wastewater, while concurrently generating electricity. These findings suggest the potential of MFCs to contribute to environmental remediation and sustainable development efforts on a global scale.

Keywords: textile wastewater treatment, microbial fuel cell, renewable energy, sustainable wastewater treatment

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Authors: Juergen Orasche, Vesta Kohlmeier, George C. Dragan, Gert Jakobi, Patricia Forbes, Ralf Zimmermann

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Underground mining poses a difficult environment for both man and machines. At more than 1000 meters underneath the surface of the earth, ores and other mineral resources are still gained by conventional and motorised mining. Adding to the hazards caused by blasting and stone-chipping, the working conditions are best described by the high temperatures of 35-40°C and high humidity, at low air exchange rates. Separate ventilation shafts lead fresh air into a mine and others lead expended air back to the surface. This is essential for humans and machines working deep underground. Nevertheless, mines are widely ramified. Thus the air flow rate at the far end of a tunnel is sensed to be close to zero. In recent years, conventional mining was supplemented by mining with heavy diesel machines. These very flat machines called Load Haul Dump (LHD) vehicles accelerate and ease work in areas favourable for heavy machines. On the other hand, they emit non-filtered diesel exhaust, which constitutes an occupational hazard for the miners. Combined with a low air exchange, high humidity and inorganic dust from the mining it leads to 'black smog' underneath the earth. This work focuses on the air quality in mines employing LHDs. Therefore we performed personal sampling (samplers worn by miners during their work), stationary sampling and aethalometer (Microaeth MA200, Aethlabs) measurements in a platinum mine in around 1000 meters under the earth’s surface. We compared areas of high diesel exhaust emission with areas of conventional mining where no diesel machines were operated. For a better assessment of health risks caused by air pollution we applied a separated gas-/particle-sampling tool (or system), with first denuder section collecting intermediate VOCs. These multi-channel silicone rubber denuders are able to trap IVOCs while allowing particles ranged from 10 nm to 1 µm in diameter to be transmitted with an efficiency of nearly 100%. The second section is represented by a quartz fibre filter collecting particles and adsorbed semi-volatile organic compounds (SVOC). The third part is a graphitized carbon black adsorber – collecting the SVOCs that evaporate from the filter. The compounds collected on these three sections were analyzed in our labs with different thermal desorption techniques coupled with gas chromatography and mass spectrometry (GC-MS). VOCs and IVOCs were measured with a Shimadzu Thermal Desorption Unit (TD20, Shimadzu, Japan) coupled to a GCMS-System QP 2010 Ultra with a quadrupole mass spectrometer (Shimadzu). The GC was equipped with a 30m, BP-20 wax column (0.25mm ID, 0.25µm film) from SGE (Australia). Filters were analyzed with In-situ derivatization thermal desorption gas chromatography time-of-flight-mass spectrometry (IDTD-GC-TOF-MS). The IDTD unit is a modified GL sciences Optic 3 system (GL Sciences, Netherlands). The results showed black carbon concentrations measured with the portable aethalometers up to several mg per m³. The organic chemistry was dominated by very high concentrations of alkanes. Typical diesel engine exhaust markers like alkylated polycyclic aromatic hydrocarbons were detected as well as typical lubrication oil markers like hopanes.

Keywords: diesel emission, personal sampling, aethalometer, mining

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Authors: Mikko Salminen, Simo Järvelä, Niklas Ravaja

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One of the central models of emotional intelligence (EI) is that of Mayer and Salovey’s, which includes ability to monitor own feelings and emotions and those of others, ability to discriminate different emotions, and to use this information to guide thinking and actions. There is vast amount of previous research where positive links between EI and, for example, leadership successfulness, work outcomes, work wellbeing and organizational climate have been reported. EI has also a role in the effectiveness of work teams, and the effects of EI are especially prominent in jobs requiring emotional labor. Thus, also the organizational context must be taken into account when considering the effects of EI on work outcomes. Based on previous research, it is suggested that EI can also protect managers from the negative consequences of stress. Stress may have many detrimental effects on the manager’s performance in essential work tasks. Previous studies have highlighted the effects of stress on, not only health, but also, for example, on cognitive tasks such as decision-making, which is important in managerial work. The motivation for the current study came from the notion that, unfortunately, many stressed individuals may not be aware of the circumstance; periods of stress-induced physiological arousal may be prolonged if there is not enough time for recovery. To tackle this problem, physiological stress levels of managers were collected using recording of heart rate variability (HRV). The goal was to use this data to provide the managers with feedback on their stress levels. The managers could access this feedback using a www-based learning environment. In the learning environment, in addition to the feedback on stress level and other collected data, also developmental tasks were provided. For example, those with high stress levels were sent instructions for mindfulness exercises. The current study focuses on the relation between the measured physiological stress levels and EI of the managers. In a pilot study, 33 managers from various fields wore the Firstbeat Bodyguard HRV measurement devices for three consecutive days and nights. From the collected HRV data periods (minutes) of stress and recovery were detected using dedicated software. The effects of EI on HRV-calculated stress indexes were studied using Linear Mixed Models procedure in SPSS. There was a statistically significant effect of total EI, defined as an average score of Schutte’s emotional intelligence test, on the percentage of stress minutes during the whole measurement period (p=.025). More stress minutes were detected on those managers who had lower emotional intelligence. It is suggested, that high EI provided managers with better tools to cope with stress. Managing of own emotions helps the manager in controlling possible negative emotions evoked by, e.g., critical feedback or increasing workload. High EI managers may also be more competent in detecting emotions of others, which would lead to smoother interactions and less conflicts. Given the recent trend to different quantified-self applications, it is suggested that monitoring of bio-signals would prove to be a fruitful direction to further develop new tools for managerial and leadership coaching.

Keywords: emotional intelligence, leadership, heart rate variability, personality, stress

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Authors: Ahlam Said Al Azkawi, Nallusamy Sivakumar, Saif Nasser Al Bahri

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Diverse approaches and pathways are under development with the determination to develop cellulosic biofuels and other bio-products eventually at commercial scale in “bio-refineries”; however, the key challenge is mainly the high level of complexity in processing the feedstock which is complicated and energy consuming. To overcome the complications in utilizing the naturally occurring lignocellulose biomass, using waste paper as a feedstock for bio-production may solve the problem. Besides being abundant and cheap, bioprocessing of waste paper has evolved in response to the public concern from rising landfill cost from shrinking landfill capacity. Cardboard (CB) is one of the major components of municipal solid waste and one of the most important items to recycle. Although 50-70% of cardboard constitute is known to be cellulose and hemicellulose, the presence of lignin around them cause hydrophobic cross-link which physically obstructs the hydrolysis by rendering it resistant to enzymatic cleavage. Therefore, pretreatment is required to disrupt this resistance and to enhance the exposure of the targeted carbohydrates to the hydrolytic enzymes. Several pretreatment approaches have been explored, and the best ones would be those can influence cellulose conversion rates and hydrolytic enzyme performance with minimal or less cost and downstream processes. One of the promising strategies in this field is the application of surfactants, especially non-ionic surfactants. In this study, triton-X 100 was used as surfactants to treat cardboard prior enzymatic hydrolysis and compare it with acid treatment using 0.1% H2SO4. The effect of the surfactant enhancement was evaluated through its effect on hydrolysis rate in respect to time in addition to evaluating the structural changes and modification by scanning electron microscope (SEM) and X-ray diffraction (XRD) and through compositional analysis. Further work was performed to produce ethanol from CB treated with triton-X 100 via separate hydrolysis and fermentation (SHF) and simultaneous saccharification and fermentation (SSF). The hydrolysis studies have demonstrated enhancement in saccharification by 35%. After 72 h of hydrolysis, a saccharification rate of 98% was achieved from CB enhanced with triton-X 100, while only 89 of saccharification achieved from acid pre-treated CB. At 120 h, the saccharification % exceeded 100 as reducing sugars continued to increase with time. This enhancement was not supported by any significant changes in the cardboard content as the cellulose, hemicellulose and lignin content remained same after treatment, but obvious structural changes were observed through SEM images. The cellulose fibers were clearly exposed with very less debris and deposits compared to cardboard without triton-X 100. The XRD pattern has also revealed the ability of the surfactant in removing calcium carbonate, a filler found in waste paper known to have negative effect on enzymatic hydrolysis. The cellulose crystallinity without surfactant was 73.18% and reduced to 66.68% rendering it more amorphous and susceptible to enzymatic attack. Triton-X 100 has proved to effectively enhance CB hydrolysis and eventually had positive effect on the ethanol yield via SSF. Treating cardboard with only triton-X 100 was a sufficient treatment to enhance the enzymatic hydrolysis and ethanol production.

Keywords: cardboard, enhancement, ethanol, hydrolysis, treatment, Triton-X 100

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Authors: Teng Li, Kamran Mohseni

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This paper presents an inviscid regularization technique for the incompressible two-phase flow simulations. This technique is known as observable method due to the understanding of observability that any feature smaller than the actual resolution (physical or numerical), i.e., the size of wire in hotwire anemometry or the grid size in numerical simulations, is not able to be captured or observed. Differ from most regularization techniques that applies on the numerical discretization, the observable method is employed at PDE level during the derivation of equations. Difficulties in the simulation and analysis of realistic fluid flow often result from discontinuities (or near-discontinuities) in the calculated fluid properties or state. Accurately capturing these discontinuities is especially crucial when simulating flows involving shocks, turbulence or sharp interfaces. Over the past several years, the properties of this new regularization technique have been investigated that show the capability of simultaneously regularizing shocks and turbulence. The observable method has been performed on the direct numerical simulations of shocks and turbulence where the discontinuities are successfully regularized and flow features are well captured. In the current paper, the observable method will be extended to two-phase interfacial flows. Multiphase flows share the similar features with shocks and turbulence that is the nonlinear irregularity caused by the nonlinear terms in the governing equations, namely, Euler equations. In the direct numerical simulation of two-phase flows, the interfaces are usually treated as the smooth transition of the properties from one fluid phase to the other. However, in high Reynolds number or low viscosity flows, the nonlinear terms will generate smaller scales which will sharpen the interface, causing discontinuities. Many numerical methods for two-phase flows fail at high Reynolds number case while some others depend on the numerical diffusion from spatial discretization. The observable method regularizes this nonlinear mechanism by filtering the convective terms and this process is inviscid. The filtering effect is controlled by an observable scale which is usually about a grid length. Single rising bubble and Rayleigh-Taylor instability are studied, in particular, to examine the performance of the observable method. A pseudo-spectral method is used for spatial discretization which will not introduce numerical diffusion, and a Total Variation Diminishing (TVD) Runge Kutta method is applied for time integration. The observable incompressible Euler equations are solved for these two problems. In rising bubble problem, the terminal velocity and shape of the bubble are particularly examined and compared with experiments and other numerical results. In the Rayleigh-Taylor instability, the shape of the interface are studied for different observable scale and the spike and bubble velocities, as well as positions (under a proper observable scale), are compared with other simulation results. The results indicate that this regularization technique can potentially regularize the sharp interface in the two-phase flow simulations

Keywords: Euler equations, incompressible flow simulation, inviscid regularization technique, two-phase flow

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Authors: Nan Xu

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In airlines, the crew scheduling problem is usually decomposed into two stages: crew pairing and crew rostering. In the crew pairing stage, pairings are generated such that each flight is covered by exactly one pairing and the overall cost is minimized. In the crew rostering stage, the pairings generated in the crew pairing stage are combined with off days, training and other breaks to create individual work schedules. The paper focuses on cabin crew rostering problem, which is challenging due to the extremely large size and the complex working rules involved. In our approach, the objective of rostering consists of two major components. The first is to minimize the number of unassigned pairings and the second is to ensure the fairness to crew members. There are two measures of fairness to crew members, the number of overnight duties and the total fly-hour over a given period. Pairings should be assigned to each crew member so that their actual overnight duties and fly hours are as close to the expected average as possible. Deviations from the expected average are penalized in the objective function. Since several small deviations are preferred than a large deviation, the penalization is quadratic. Our model of the airline crew rostering problem is based on column generation. The problem is decomposed into a master problem and subproblems. The mater problem is modeled as a set partition problem and exactly one roster for each crew is picked up such that the pairings are covered. The restricted linear master problem (RLMP) is considered. The current subproblem tries to find columns with negative reduced costs and add them to the RLMP for the next iteration. When no column with negative reduced cost can be found or a stop criteria is met, the procedure ends. The subproblem is to generate feasible crew rosters for each crew member. A separate acyclic weighted graph is constructed for each crew member and the subproblem is modeled as resource constrained shortest path problems in the graph. Labeling algorithm is used to solve it. Since the penalization is quadratic, a method to deal with non-additive shortest path problem using labeling algorithm is proposed and corresponding domination condition is defined. The major contribution of our model is: 1) We propose a method to deal with non-additive shortest path problem; 2) Operation to allow relaxing some soft rules is allowed in our algorithm, which can improve the coverage rate; 3) Multi-thread techniques are used to improve the efficiency of the algorithm when generating Line-of-Work for crew members. Here a column generation based algorithm for the airline cabin crew rostering problem is proposed. The objective is to assign a personalized roster to crew member which minimize the number of unassigned pairings and ensure the fairness to crew members. The algorithm we propose in this paper has been put into production in a major airline in China and numerical experiments show that it has a good performance.

Keywords: aircrew rostering, aircrew scheduling, column generation, SPPRC

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Authors: Daniel R. dos Santos, Mateus S. Maldonado, Estevão J. R. Batista

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The military commanders increasingly dependent on spatial awareness, as knowing where enemy are, understanding how war battle scenarios change over time, and visualizing these trends in ways that offer insights for decision-making. Thanks to advancements in geospatial technologies and artificial intelligence algorithms, the commanders are now able to modernize military operations on a universal scale. Thus, geovisualisation has become an essential asset in the defense sector. It has become indispensable for better decisionmaking in dynamic/temporal scenarios, operation planning and management for the war field, situational awareness, effective planning, monitoring, and others. For example, a 3D visualization of war field data contributes to intelligence analysis, evaluation of postmission outcomes, and creation of predictive models to enhance decision-making and strategic planning capabilities. However, old-school visualization methods are slow, expensive, and unscalable. Despite modern technologies in generating 3D point clouds, such as LIDAR and stereo sensors, monocular depth values based on deep learning can offer a faster and more detailed view of the environment, transforming single images into visual information for valuable insights. We propose a dedicated monocular depth reconstruction approach via deep learning techniques for 3D geovisualisation of satellite images. It introduces scalability in terrain reconstruction and data visualization. First, a dataset with more than 7,000 satellite images and associated digital elevation model (DEM) is created. It is based on high resolution optical and radar imageries collected from Planet and Copernicus, on which we fuse highresolution topographic data obtained using technologies such as LiDAR and the associated geographic coordinates. Second, we developed an imagery-DEM fusion strategy that combine feature maps from two encoder-decoder networks. One network is trained with radar and optical bands, while the other is trained with DEM features to compute dense 3D depth. Finally, we constructed a benchmark with sparse depth annotations to facilitate future research. To demonstrate the proposed method's versatility, we evaluated its performance on no annotated satellite images and implemented an enclosed environment useful for Geovisualisation applications. The algorithms were developed in Python 3.0, employing open-source computing libraries, i.e., Open3D, TensorFlow, and Pythorch3D. The proposed method provides fast and accurate decision-making with GIS for localization of troops, position of the enemy, terrain and climate conditions. This analysis enhances situational consciousness, enabling commanders to fine-tune the strategies and distribute the resources proficiently.

Keywords: depth, deep learning, geovisualisation, satellite images

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Authors: Mindaugas Liaudanskas, Darius Kviklys, Kristina Zymonė, Raimondas Raudonis, Jonas Viškelis, Norbertas Uselis, Pranas Viškelis, Valdimaras Janulis

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Apples are among the most commonly consumed fruits in the world. Based on data from the year 2014, approximately 84.63 million tons of apples are grown per annum. Apples are widely used in food industry to produce various products and drinks (juice, wine, and cider); they are also used unprocessed. Apples in human diet are an important source of different groups of biological active compounds that can positively contribute to the prevention of various diseases. They are a source of various biologically active substances – especially vitamins, organic acids, micro- and macro-elements, pectins, and phenolic, triterpenic, and other compounds. Triterpenic compounds, which are characterized by versatile biological activity, are the biologically active compounds found in apples that are among the most promising and most significant for human health. A specific analytical procedure including sample preparation and High Performance Liquid Chromatography (HPLC) analysis was developed, optimized, and validated for the detection of triterpenic compounds in the samples of different apples, their peels, and flesh from widespread apple cultivars 'Aldas', 'Auksis', 'Connel Red', 'Ligol', 'Lodel', and 'Rajka' grown in Lithuanian climatic conditions. The conditions for triterpenic compound extraction were optimized: the solvent of the extraction was 100% (v/v) acetone, and the extraction was performed in an ultrasound bath for 10 min. Isocratic elution (the eluents ratio being 88% (solvent A) and 12% (solvent B)) for a rapid separation of triterpenic compounds was performed. The validation of the methodology was performed on the basis of the ICH recommendations. The following characteristics of validation were evaluated: the selectivity of the method (specificity), precision, the detection and quantitation limits of the analytes, and linearity. The obtained parameters values confirm suitability of methodology to perform analysis of triterpenic compounds. Using the optimised and validated HPLC technique, four triterpenic compounds were separated and identified, and their specificity was confirmed. These compounds were corosolic acid, betulinic acid, oleanolic acid, and ursolic acid. Ursolic acid was the dominant compound in all the tested apple samples. The detected amount of betulinic acid was the lowest of all the identified triterpenic compounds. The greatest amounts of triterpenic compounds were detected in whole apple and apple peel samples of the 'Lodel' cultivar, and thus apples and apple extracts of this cultivar are potentially valuable for use in medical practice, for the prevention of various diseases, for adjunct therapy, for the isolation of individual compounds with a specific biological effect, and for the development and production of dietary supplements and functional food enriched in biologically active compounds. Acknowledgements. This work was supported by a grant from the Research Council of Lithuania, project No. MIP-17-8.

Keywords: apples, HPLC, triterpenic compounds, validation

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Authors: Mohammad Karimi Moridani, Mohammad Abdi Zadeh, Zahra Shahiazar Mazraeh

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In recent years, the use of intelligent systems in biomedical engineering has increased dramatically, especially in the diagnosis of various diseases. Also, due to the relatively simple recording of the electrocardiogram signal (ECG), this signal is a good tool to show the function of the heart and diseases associated with it. The aim of this paper is to design an intelligent system for automatically detecting a normal electrocardiogram signal from abnormal one. Using this diagnostic system, it is possible to identify a person's heart condition in a very short time and with high accuracy. The data used in this article are from the Physionet database, available in 2016 for use by researchers to provide the best method for detecting normal signals from abnormalities. Data is of both genders and the data recording time varies between several seconds to several minutes. All data is also labeled normal or abnormal. Due to the low positional accuracy and ECG signal time limit and the similarity of the signal in some diseases with the normal signal, the heart rate variability (HRV) signal was used. Measuring and analyzing the heart rate variability with time to evaluate the activity of the heart and differentiating different types of heart failure from one another is of interest to the experts. In the preprocessing stage, after noise cancelation by the adaptive Kalman filter and extracting the R wave by the Pan and Tampkinz algorithm, R-R intervals were extracted and the HRV signal was generated. In the process of processing this paper, a new idea was presented that, in addition to using the statistical characteristics of the signal to create a return map and extraction of nonlinear characteristics of the HRV signal due to the nonlinear nature of the signal. Finally, the artificial neural networks widely used in the field of ECG signal processing as well as distinctive features were used to classify the normal signals from abnormal ones. To evaluate the efficiency of proposed classifiers in this paper, the area under curve ROC was used. The results of the simulation in the MATLAB environment showed that the AUC of the MLP and SVM neural network was 0.893 and 0.947, respectively. As well as, the results of the proposed algorithm in this paper indicated that the more use of nonlinear characteristics in normal signal classification of the patient showed better performance. Today, research is aimed at quantitatively analyzing the linear and non-linear or descriptive and random nature of the heart rate variability signal, because it has been shown that the amount of these properties can be used to indicate the health status of the individual's heart. The study of nonlinear behavior and dynamics of the heart's neural control system in the short and long-term provides new information on how the cardiovascular system functions, and has led to the development of research in this field. Given that the ECG signal contains important information and is one of the common tools used by physicians to diagnose heart disease, but due to the limited accuracy of time and the fact that some information about this signal is hidden from the viewpoint of physicians, the design of the intelligent system proposed in this paper can help physicians with greater speed and accuracy in the diagnosis of normal and patient individuals and can be used as a complementary system in the treatment centers.

Keywords: neart rate variability, signal processing, linear and non-linear features, classification methods, ROC Curve

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Authors: Taher Abofol, Eldad Yechiam, Thorsten Pachur

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In four studies, we examined whether seller and buyers differ not only in subjective price levels for objects (i.e., the endowment effect) but also in their relative accuracy given objects varying in expected value. If, as has been proposed, sellers stand to accrue a more substantial loss than buyers do, then their pricing decisions should be more sensitive to expected-value differences between objects. This is implied by loss aversion due to the steeper slope of prospect theory’s value function for losses than for gains, as well as by loss attention account, which posits that losses increase the attention invested in a task. Both accounts suggest that losses increased sensitivity to relative values of different objects, which should result in better alignment of pricing decisions to the objective value of objects on the part of sellers. Under loss attention, this characteristic should only emerge under certain boundary conditions. In Study 1 a published dataset was reanalyzed, in which 152 participants indicated buying or selling prices for monetary lotteries with different expected values. Relative EV sensitivity was calculated for participants as the Spearman rank correlation between their pricing decisions for each of the lotteries and the lotteries' expected values. An ANOVA revealed a main effect of perspective (sellers versus buyers), F(1,150) = 85.3, p < .0001 with greater EV sensitivity for sellers. Study 2 examined the prediction (implied by loss attention) that the positive effect of losses on performance emerges particularly under conditions of time constraints. A published dataset was reanalyzed, where 84 participants were asked to provide selling and buying prices for monetary lotteries in three deliberations time conditions (5, 10, 15 seconds). As in Study 1, an ANOVA revealed greater EV sensitivity for sellers than for buyers, F(1,82) = 9.34, p = .003. Importantly, there was also an interaction of perspective by deliberation time. Post-hoc tests revealed that there were main effects of perspective both in the condition with 5s deliberation time, and in the condition with 10s deliberation time, but not in the 15s condition. Thus, sellers’ EV-sensitivity advantage disappeared with extended deliberation. Study 3 replicated the design of study 1 but administered the task three times to test if the effect decays with repeated presentation. The results showed that the difference between buyers and sellers’ EV sensitivity was replicated in repeated task presentations. Study 4 examined the loss attention prediction that EV-sensitivity differences can be eliminated by manipulations that reduce the differential attention investment of sellers and buyers. This was carried out by randomly mixing selling and buying trials for each participant. The results revealed no differences in EV sensitivity between selling and buying trials. The pattern of results is consistent with an attentional resource-based account of the differences between sellers and buyers. Thus, asking people to price, an object from a seller's perspective rather than the buyer's improves the relative accuracy of pricing decisions; subtle changes in the framing of one’s perspective in a trading negotiation may improve price accuracy.

Keywords: decision making, endowment effect, pricing, loss aversion, loss attention

Procedia PDF Downloads 347

Authors: Manon Frédout, Laëtitia Bucari, Mathias Aloui, Gaëtan Duhamel, Olivier Rovellotti, Javier Blanco

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Ecosystems must be resilient to ensure cleaner air, better water and soil quality, and thus healthier citizens. Technology can be an excellent tool to support urban ecosystem restoration projects, especially when based on Open Source and promoting Open Data. This is the goal of the ecoTeka application: one single digital tool for tree management which allows decision-makers to improve their urban forestry practices, enabling more responsible urban planning and climate change adaptation. EcoTeka provides city councils with three main functionalities tackling three of their challenges: easier biodiversity inventories, better green space management, and more efficient planning. To answer the cities’ need for reliable tree inventories, the application has been first built with open data coming from the websites OpenStreetMap and OpenTrees, but it will also include very soon the possibility of creating new data. To achieve this, a multi-source algorithm will be elaborated, based on existing artificial intelligence Deep Forest, integrating open-source satellite images, 3D representations from LiDAR, and street views from Mapillary. This data processing will permit identifying individual trees' position, height, crown diameter, and taxonomic genus. To support urban forestry management, ecoTeka offers a dashboard for monitoring the city’s tree inventory and trigger alerts to inform about upcoming due interventions. This tool was co-constructed with the green space departments of the French cities of Alès, Marseille, and Rouen. The third functionality of the application is a decision-making tool for urban planning, promoting biodiversity and landscape connectivity metrics to drive ecosystem restoration roadmap. Based on landscape graph theory, we are currently experimenting with new methodological approaches to scale down regional ecological connectivity principles to local biodiversity conservation and urban planning policies. This methodological framework will couple graph theoretic approach and biological data, mainly biodiversity occurrences (presence/absence) data available on both international (e.g., GBIF), national (e.g., Système d’Information Nature et Paysage) and local (e.g., Atlas de la Biodiversté Communale) biodiversity data sharing platforms in order to help reasoning new decisions for ecological networks conservation and restoration in urban areas. An experiment on this subject is currently ongoing with Montpellier Mediterranee Metropole. These projects and studies have shown that only 26% of tree inventory data is currently geo-localized in France - the rest is still being done on paper or Excel sheets. It seems that technology is not yet used enough to enrich the knowledge city councils have about biodiversity in their city and that existing biodiversity open data (e.g., occurrences, telemetry, or genetic data), species distribution models, landscape graph connectivity metrics are still underexploited to make rational decisions for landscape and urban planning projects. This is the goal of ecoTeka: to support easier inventories of urban biodiversity and better management of urban spaces through rational planning and decisions relying on open databases. Future studies and projects will focus on the development of tools for reducing the artificialization of soils, selecting plant species adapted to climate change, and highlighting the need for ecosystem and biodiversity services in cities.

Keywords: digital software, ecological design of urban landscapes, sustainable urban development, urban ecological corridor, urban forestry, urban planning

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Authors: Lina Psarra, Manogj Sundaresan, Purjeet Sutar

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In response to the global imperative for sustainable solutions, this article proposes the development of a comprehensive circularity index applicable to a wide range of products across various industries. The absence of a consensus on using a universal metric to assess circularity performance presents a significant challenge in prioritizing and effectively managing sustainable initiatives. This circularity index serves as a quantitative measure to evaluate the adherence of products, processes, and systems to the principles of a circular economy. Unlike traditional distinct metrics such as recycling rates or material efficiency, this index considers the entire lifecycle of a product in one single metric, also incorporating additional factors such as reusability, scarcity of materials, reparability, and recyclability. Through a systematic approach and by reviewing existing metrics and past methodologies, this work aims to address this gap by formulating a circularity index that can be applied to diverse product portfolio and assist in comparing the circularity of products on a scale of 0%-100%. Project objectives include developing a formula, designing and implementing a pilot tool based on the developed Product Circularity Index (PCI), evaluating the effectiveness of the formula and tool using real product data, and assessing the feasibility of integration into various sustainability initiatives. The research methodology involves an iterative process of comprehensive research, analysis, and refinement where key steps include defining circularity parameters, collecting relevant product data, applying the developed formula, and testing the tool in a pilot phase to gather insights and make necessary adjustments. Major findings of the study indicate that the PCI provides a robust framework for evaluating product circularity across various dimensions. The Excel-based pilot tool demonstrated high accuracy and reliability in measuring circularity, and the database proved instrumental in supporting comprehensive assessments. The PCI facilitated the identification of key areas for improvement, enabling more informed decision-making towards circularity and benchmarking across different products, essentially assisting towards better resource management. In conclusion, the development of the Product Circularity Index represents a significant advancement in global sustainability efforts. By providing a standardized metric, the PCI empowers companies and stakeholders to systematically assess product circularity, track progress, identify improvement areas, and make informed decisions about resource management. This project contributes to the broader discourse on sustainable development by offering a practical approach to enhance circularity within industrial systems, thus paving the way towards a more resilient and sustainable future.

Keywords: circular economy, circular metrics, circularity assessment, circularity tool, sustainable product design, product circularity index

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Authors: James G. Matthew, Seonmin Huh, Frank X. Bennett

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English for Specific Purposes, ESP, aims to equip learners with necessary English language skills. Many ESP programs address language skills for job performance, including reading job related documents and oral proficiency. Within ESP is English for occupational purposes, EOP, which centers around developing communicative competence for the globalized workplace. Many ESP and EOP courses lack the content needed to assist students to progress at work, resulting in the need to create lexical compilation for different professions. It is important to teach communicative competence and soft skills for real job-related problem situations and address the complexities of the real world to help students to be successful in their professions. ESP and EOP research is therefore trying to balance both profession-specific educational contents as well as international multi-disciplinary language skills for the globalized workforce. The current study will build upon the existing discussion by developing pedagogy to assist students in their career through developing a strong practical command of relevant English vocabulary. Our research question focuses on the pedagogy two professors incorporated in their English for employment courses. The current study is a qualitative case study on the modes of teaching delivery for EOP in South Korea. Two foreign professors teaching at two different universities in South Korea volunteered for the study to explore their teaching practices. Both professors’ curriculums included the components of employment-related concept vocabulary, business presentations, CV/resume and cover letter preparation, and job interview preparation. All the pre-made recorded video lectures, live online class sessions with students, teachers’ lesson plans, teachers’ class materials, students’ assignments, and midterm and finals video conferences were collected for data analysis. The study then focused on unpacking representative patterns in their teaching methods. The professors used their strengths as native speakers to extend the class discussion from narrow and restricted conversations to giving students broader opportunities to practice authentic English conversation. The methods of teaching utilized three main steps to extend the conversation. Firstly, students were taught concept vocabulary. Secondly, the vocabulary was then combined in speaking activities where students had to solve scenarios, and the students were required to expand on the given forms of words and language expressions. Lastly, the students had conversations in English, using the language learnt. The conversations observed in both classes were those of authentic, expanded English communication and this way of expanding concept vocabulary lessons into extended conversation is one representative pedagogical approach that both professors took. Extended English conversation, therefore, is crucial for EOP education.

Keywords: concept vocabulary, english as a foreign language, english for employment, extended conversation

Procedia PDF Downloads 92

Authors: Getaneh Berie Tarekegn, Li-Chia Tai

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With the rapid advancement of artificial intelligence, low-power built-in sensors on Internet of Things devices, and communication technologies, location-aware services have become increasingly popular and have permeated every aspect of people’s lives. Global navigation satellite systems (GNSSs) are the default method of providing continuous positioning services for ground and aerial vehicles, as well as consumer devices (smartphones, watches, notepads, etc.). However, the environment affects satellite positioning systems, particularly indoors, in dense urban and suburban cities enclosed by skyscrapers, or when deep shadows obscure satellite signals. This is because (1) indoor environments are more complicated due to the presence of many objects surrounding them; (2) reflection within the building is highly dependent on the surrounding environment, including the positions of objects and human activity; and (3) satellite signals cannot be reached in an indoor environment, and GNSS doesn't have enough power to penetrate building walls. GPS is also highly power-hungry, which poses a severe challenge for battery-powered IoT devices. Due to these challenges, IoT applications are limited. Consequently, precise, seamless, and ubiquitous Positioning, Navigation and Timing (PNT) systems are crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarms, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Arinze Ezieke, Antonio Serrano, William Clarke, Denys Villa-Gomez

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Berry cultivation results in discharge of high organic strength putrescible solid waste which potentially contributes to environmental degradation, making it imperative to assess options for its complete management. Anaerobic digestion (AD) could be an ideal option when the target is energy generation; however, due to berry fruit characteristics high carbohydrate composition, the technology could be limited by its high alkalinity requirement which suggests dosing of additives such as buffers and trace elements supplement. Overcoming this limitation in an economically viable way could entail replacement of synthetic additives with recycled by-product waste. Consequently, ash from co-smouldering of high COD characteristic AD digestate and coco-coir could be a promising material to be used to enhance the AD of berry fruit waste, given its characteristic high pH, alkalinity and metal concentrations which is typical of synthetic additives. Therefore, the aim of the research was to evaluate the stability and process performance from the AD of BFW when ash from co-smoldered digestate and coir are supplemented as alkalinity and trace elements (TEs) source. Series of batch experiments were performed to ascertain the necessity for alkalinity addition and to see whether the alkalinity and metals in the co-smouldered digestate ash can provide the necessary buffer and TEs for AD of berry fruit waste. Triplicate assays were performed in batch systems following I/S of 2 (in VS), using serum bottles (160 mL) sealed and placed in a heated room (35±0.5 °C), after creating anaerobic conditions. Control experiment contained inoculum and substrates only, and inoculum, substrate and NaHCO3 for optimal total alkalinity concentration and TEs assays, respectively. Total alkalinity concentration refers to alkalinity of inoculum and the additives. The alkalinity and TE potential of the ash were evaluated by supplementing ash (22.574 g/kg) of equivalent total alkalinity concentration to that of the pre-determined optimal from NaHCO3, and by dosing ash (0.012 – 7.574 g/kg) of varying concentrations of specific essential TEs (Co, Fe, Ni, Se), respectively. The result showed a stable process at all examined conditions. Supplementation of 745 mg/L CaCO3 NaHCO3 resulted to an optimum TAC of 2000 mg/L CaCO3. Equivalent ash supplementation of 22.574 g/kg allowed the achievement of this pre-determined optimum total alkalinity concentration, resulting to a stable process with a 92% increase in the methane production rate (323 versus 168 mL CH4/ (gVS.d)), but a 36% reduction in the cumulative methane production (103 versus 161 mL CH4/gVS). Addition of ashes at incremental dosage as TEs source resulted to a reduction in the Cumulative methane production, with the highest dosage of 7.574 g/kg having the highest effect of -23.5%; however, the seemingly immediate bioavailability of TE at this high dosage allowed for a +15% increase in the methane production rate. With an increased methane production rate, the results demonstrated that the ash at high dosages could be an effective supplementary material for either a buffered or none buffered berry fruit waste AD system.

Keywords: anaerobic digestion, alkalinity, co-smoldered digestate ash, trace elements

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Authors: Alexander Huang

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Online learning sessions have become an indispensable complement to in-classroom-learning sessions in the past two years due to the emergence of Covid-19. Due to social distance requirements, many courses and interaction-intensive sessions, ranging from music classes to debate camps, are online. However, online learning imposes a significant challenge for engaging students effectively during the learning sessions. To resolve this problem, Project PWR, a non-profit organization formed by high school students, developed an online kid-teach-kid learning environment to boost students' learning interests and further improve students’ engagement during online learning. Fundamentally, the kid-teach-kid learning model creates an affinity space to form learning groups, where like-minded peers can learn and teach their interests. The role of the teacher can also help a kid identify the instructional task and set the rules and procedures for the activities. The approach also structures initial discussions to reveal a range of ideas, similar experiences, thinking processes, language use, and lower student-to-teacher ratio, which become enriched online learning experiences for upcoming lessons. In such a manner, a kid can practice both the teacher role and the student role to accumulate experiences on how to convey ideas and questions over the online session more efficiently and effectively. In this research work, we conducted two case studies involving a 3D-Design course and a Speech and Debate course taught by high-school kids. Through Project PWR, a kid first needs to design the course syllabus based on a provided template to become a student-teacher. Then, the Project PWR academic committee evaluates the syllabus and offers comments and suggestions for changes. Upon the approval of a syllabus, an experienced and voluntarily adult mentor is assigned to interview the student-teacher and monitor the lectures' progress. Student-teachers construct a comprehensive final evaluation for their students, which they grade at the end of the course. Moreover, each course requires conducting midterm and final evaluations through a set of surveyed replies provided by students to assess the student-teacher’s performance. The uniqueness of Project PWR lies in its established kid-teach-kids affinity space. Our research results showed that Project PWR could create a closed-loop system where a student can help a teacher improve and vice versa, thus improving the overall students’ engagement. As a result, Project PWR’s approach can train teachers and students to become better online learners and give them a solid understanding of what to prepare for and what to expect from future online classes. The kid-teach-kid learning model can significantly improve students' engagement in the online courses through the Project PWR to effectively supplement the traditional teacher-centric model that the Covid-19 pandemic has impacted substantially. Project PWR enables kids to share their interests and bond with one another, making the online learning environment effective and promoting positive and effective personal online one-on-one interactions.

Keywords: kid-teach-kid, affinity space, online learning, engagement, student-teacher

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Authors: C. Mazilu, D. P. Georgescu, A. Apostu, R. Deju

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Cement mortars and concretes are some of the most used construction materials in the world, global cement production being expected to grow to approx. 5 billion tons, until 2030. But, cement is an energy intensive material, the cement industry being responsible for cca. 7% of the world's CO2 emissions. Also, natural aggregates represent non-renewable resources, exhaustible, which must be used efficiently. A way to reduce the negative impact on the environment is the use of additional hydraulically active materials, as a partial substitute for cement in mortars and concretes and/or the use of recycled concrete aggregates (RCA) for the recovery of construction waste, according to EU Directive 2018/851. One of the most effective active hydraulic admixtures is microsilica and more recently, with the technological development on a nanometric scale, nanosilica. Studies carried out in recent years have shown that the introduction of SiO2 nanoparticles into cement matrix improves the properties, even compared to microsilica. This is due to the very small size of the nanosilica particles (<100nm) and the very large specific surface, which helps to accelerate cement hydration and acts as a nucleating agent to generate even more calcium hydrosilicate which densifies and compacts the structure. The cementitious compositions containing recycled concrete aggregates (RCA) present, in generally, inferior properties compared to those obtained with natural aggregates. Depending on the degree of replacement of natural aggregate, decreases the workability of mortars and concretes with RAC, decrease mechanical resistances and increase drying shrinkage; all being determined, in particular, by the presence to the old mortar attached to the original aggregate from the RAC, which makes its porosity high and the mixture of components to require more water for preparation. The present study aims to use micro and nanosilica for increase the performance of some mortars and concretes obtained with RCA. The research focused on two types of cementitious systems: a special mortar composition used for encapsulating Low Level radioactive Waste (LLW); a composition of structural concrete, class C30/37, with the combination of exposure classes XC4+XF1 and settlement class S4. The mortar was made with 100% recycled aggregate, 0-5 mm sort and in the case of concrete, 30% recycled aggregate was used for 4-8 and 8-16 sorts, according to EN 206, Annex E. The recycled aggregate was obtained from a specially made concrete for this study, which after 28 days was crushed with the help of a Retsch jaw crusher and further separated by sieving on granulometric sorters. The partial replacement of cement was done progressively, in the case of the mortar composition, with microsilica (3, 6, 9, 12, 15% wt.), nanosilica (0.75, 1.5, 2.25% wt.), respectively mixtures of micro and nanosilica. The optimal combination of silica, from the point of view of mechanical resistance, was later used also in the case of the concrete composition. For the chosen cementitious compositions, the influence of micro and/or nanosilica on the properties in the fresh state (workability, rheological characteristics) and hardened state (mechanical resistance, water absorption, freeze-thaw resistance, etc.) is highlighted.

Keywords: cement, recycled concrete aggregates, micro/nanosilica, durability

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Authors: Ines Raies, Eric Kohler, Marc Fleury, Béatrice Ledésert

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In this work, the impact of clay minerals on the formation damage of sandstone reservoirs is studied to provide a better understanding of the problem of deep geothermal reservoir permeability reduction due to fine particle dispersion and migration. In some situations, despite the presence of filters in the geothermal loop at the surface, particles smaller than the filter size (<1 µm) may surprisingly generate significant permeability reduction affecting in the long term the overall performance of the geothermal system. Our study is carried out on cores from a Triassic reservoir in the Paris Basin (Feigneux, 60 km Northeast of Paris). Our goal is to first identify the clays responsible for clogging, a mineralogical characterization of these natural samples was carried out by coupling X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDS). The results show that the studied stratigraphic interval contains mostly illite and chlorite particles. Moreover, the spatial arrangement of the clays in the rocks as well as the morphology and size of the particles, suggest that illite is more easily mobilized than chlorite by the flow in the pore network. Thus, based on these results, illite particles were prepared and used in core flooding in order to better understand the factors leading to the aggregation and deposition of this type of clay particles in geothermal reservoirs under various physicochemical and hydrodynamic conditions. First, the stability of illite suspensions under geothermal conditions has been investigated using different characterization techniques, including Dynamic Light Scattering (DLS) and Scanning Transmission Electron Microscopy (STEM). Various parameters such as the hydrodynamic radius (around 100 nm), the morphology and surface area of aggregates were measured. Then, core-flooding experiments were carried out using sand columns to mimic the permeability decline due to the injection of illite-containing fluids in sandstone reservoirs. In particular, the effects of ionic strength, temperature, particle concentration and flow rate of the injected fluid were investigated. When the ionic strength increases, a permeability decline of more than a factor of 2 could be observed for pore velocities representative of in-situ conditions. Further details of the retention of particles in the columns were obtained from Magnetic Resonance Imaging and X-ray Tomography techniques, showing that the particle deposition is nonuniform along the column. It is clearly shown that very fine particles as small as 100 nm can generate significant permeability reduction under specific conditions in high permeability porous media representative of the Triassic reservoirs of the Paris basin. These retention mechanisms are explained in the general framework of the DLVO theory

Keywords: geothermal energy, reinjection, clays, colloids, retention, porosity, permeability decline, clogging, characterization, XRD, SEM-EDS, STEM, DLS, NMR, core flooding experiments

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Authors: Pei-Jyun Hsieh, Cheng-Hsuan Li, Bor-Chen Kuo

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For hyperspectral image classification, feature reduction is an important pre-processing for avoiding the Hughes phenomena due to the difficulty for collecting training samples. Hence, lots of researches developed feature selection methods such as F-score, HSIC (Hilbert-Schmidt Independence Criterion), and etc., to improve hyperspectral image classification. However, most of them only consider the class separability in the original space, i.e., a linear class separability. In this study, we proposed a nonlinear class separability measure based on kernel trick for selecting an appropriate feature subset. The proposed nonlinear class separability was formed by a generalized RBF kernel with different bandwidths with respect to different features. Moreover, it considered the within-class separability and the between-class separability. A genetic algorithm was applied to tune these bandwidths such that the smallest with-class separability and the largest between-class separability simultaneously. This indicates the corresponding feature space is more suitable for classification. In addition, the corresponding nonlinear classification boundary can separate classes very well. These optimal bandwidths also show the importance of bands for hyperspectral image classification. The reciprocals of these bandwidths can be viewed as weights of bands. The smaller bandwidth, the larger weight of the band, and the more importance for classification. Hence, the descending order of the reciprocals of the bands gives an order for selecting the appropriate feature subsets. In the experiments, three hyperspectral image data sets, the Indian Pine Site data set, the PAVIA data set, and the Salinas A data set, were used to demonstrate the selected feature subsets by the proposed nonlinear feature selection method are more appropriate for hyperspectral image classification. Only ten percent of samples were randomly selected to form the training dataset. All non-background samples were used to form the testing dataset. The support vector machine was applied to classify these testing samples based on selected feature subsets. According to the experiments on the Indian Pine Site data set with 220 bands, the highest accuracies by applying the proposed method, F-score, and HSIC are 0.8795, 0.8795, and 0.87404, respectively. However, the proposed method selects 158 features. F-score and HSIC select 168 features and 217 features, respectively. Moreover, the classification accuracies increase dramatically only using first few features. The classification accuracies with respect to feature subsets of 10 features, 20 features, 50 features, and 110 features are 0.69587, 0.7348, 0.79217, and 0.84164, respectively. Furthermore, only using half selected features (110 features) of the proposed method, the corresponding classification accuracy (0.84168) is approximate to the highest classification accuracy, 0.8795. For other two hyperspectral image data sets, the PAVIA data set and Salinas A data set, we can obtain the similar results. These results illustrate our proposed method can efficiently find feature subsets to improve hyperspectral image classification. One can apply the proposed method to determine the suitable feature subset first according to specific purposes. Then researchers can only use the corresponding sensors to obtain the hyperspectral image and classify the samples. This can not only improve the classification performance but also reduce the cost for obtaining hyperspectral images.

Keywords: hyperspectral image classification, nonlinear feature selection, kernel trick, support vector machine

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Authors: Sara Camino, Fernando Vega, Mercedes Cano, Benito Navarrete, José A. Camino

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Carbon capture and storage (CCS) technologies should play a relevant role towards low-carbon systems in the European Union by 2030. Partial oxy-combustion emerges as a promising CCS approach to mitigate anthropogenic CO₂ emissions. Its advantages respect to other CCS technologies rely on the production of a higher CO₂ concentrated flue gas than these provided by conventional air-firing processes. The presence of more CO₂ in the flue gas increases the driving force in the separation process and hence it might lead to further reductions of the energy requirements of the overall CO₂ capture process. A higher CO₂ concentrated flue gas should enhance the CO₂ capture by chemical absorption in solvent kinetic and CO₂ cyclic capacity. They have impact on the performance of the overall CO₂ absorption process by reducing the solvent flow-rate required for a specific CO₂ removal efficiency. Lower solvent flow-rates decreases the reboiler duty during the regeneration stage and also reduces the equipment size and pumping costs. Moreover, R&D activities in this field are focused on novel solvents and blends that provide lower CO₂ absorption enthalpies and therefore lower energy penalties associated to the solvent regeneration. In this respect, sterically hindered amines are considered potential solvents for CO₂ capture. They provide a low energy requirement during the regeneration process due to its molecular structure. However, its absorption kinetics are slow and they must be promoted by blending with faster solvents such as monoethanolamine (MEA) and piperazine (PZ). In this work, the kinetic behavior of two sterically hindered amines were studied under partial oxy-combustion conditions and compared with MEA. A lab-scale semi-batch reactor was used. The CO₂ composition of the synthetic flue gas varied from 15%v/v – conventional coal combustion – to 60%v/v – maximum CO₂ concentration allowable for an optimal partial oxy-combustion operation. Firstly, 2-amino-2-methyl-1-propanol (AMP) showed a hybrid behavior with fast kinetics and a low enthalpy of CO₂ absorption. The second solvent was Isophrondiamine (IF), which has a steric hindrance in one of the amino groups. Its free amino group increases its cyclic capacity. In general, the presence of higher CO₂ concentration in the flue gas accelerated the CO₂ absorption phenomena, producing higher CO₂ absorption rates. In addition, the evolution of the CO2 loading also exhibited higher values in the experiments using higher CO₂ concentrated flue gas. The steric hindrance causes a hybrid behavior in this solvent, between both fast and slow kinetic solvents. The kinetics rates observed in all the experiments carried out using AMP were higher than MEA, but lower than the IF. The kinetic enhancement experienced by AMP at a high CO2 concentration is slightly over 60%, instead of 70% – 80% for IF. AMP also improved its CO₂ absorption capacity by 24.7%, from 15%v/v to 60%v/v, almost double the improvements achieved by MEA. In IF experiments, the CO₂ loading increased around 10% from 15%v/v to 60%v/v CO₂ and it changed from 1.10 to 1.34 mole CO₂ per mole solvent, more than 20% of increase. This hybrid kinetic behavior makes AMP and IF promising solvents for partial oxy–combustion applications.

Keywords: absorption, carbon capture, partial oxy-combustion, solvent

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Authors: O. F. Elkommos

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Teaching and learning EFL speaking modules is one of the most challenging productive modules for both instructors and learners. In a student-centered interactive communicative language teaching approach, learners and instructors should be aware of the fact that the target language must be taught as/for communication. The student must be empowered by tools that will work on more than one level of their communicative competence. Communicative learning will need a teaching and learning methodology that will address the goal. Teaching turn-taking rules, pragmatic principles and speech acts will enhance students' sociolinguistic competence, strategic competence together with discourse competence. Sociolinguistic competence entails the mastering of speech act conventions and illocutionary acts of refusing, agreeing/disagreeing; emotive acts like, thanking, apologizing, inviting, offering; directives like, ordering, requesting, advising, and hinting, among others. Strategic competence includes enlightening students’ consciousness of the various particular turn-taking systemic rules of organizing techniques of opening and closing conversation, adjacency pairs, interrupting, back-channeling, asking for/giving opinion, agreeing/disagreeing, using natural fillers for pauses, gaps, speaker select, self-select, and silence among others. Students will have the tools to manage a conversation. Students are engaged in opportunities of experiencing the natural language not as a mere extra student talking time but rather an empowerment of knowing and using the strategies. They will have the component items they need to use as well as the opportunity to communicate in the target language using topics of their interest and choice. This enhances students' communicative abilities. Available websites and textbooks now use one or more of these tools of turn-taking or pragmatics. These will be students' support in self-study in their independent learning study hours. This will be their reinforcement practice on e-Learning interactive activities. The students' target is to be able to communicate the intended meaning to an addressee that is in turn able to infer that intended meaning. The combination of these tools will be assertive and encouraging to the student to beat the struggle with what to say, how to say it, and when to say it. Teaching the rules, principles and techniques is an act of awareness raising method engaging students in activities that will lead to their pragmatic discourse competence. The aim of the paper is to show how the suggested pragmatic model will empower students with tools and systems that would support their learning. Supporting students with turn taking rules, speech act theory, applying both to texts and practical analysis and using it in speaking classes empowers students’ pragmatic discourse competence and assists them to understand language and its context. They become more spontaneous and ready to learn the discourse pragmatic dimension of the speaking techniques and suitable content. Students showed a better performance and a good motivation to learn. The model is therefore suggested for speaking modules in EFL classes.

Keywords: communicative competence, EFL, empowering learners, enhance learning, speech acts, teaching speaking, turn taking, learner centred, pragmatics

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Authors: Songdi Li, Louise Spry, Tony Woodall

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Nowadays, Corporate Social responsibility (CSR) is becoming a buzz word, and more and more academics are putting efforts on CSR studies. It is believed that CSR could influence Corporate Reputation (CR), and they hold a favourable view that CSR leads to a positive CR. To be specific, the CSR related activities in the reputational context have been regarded as ways that associate to excellent financial performance, value creation, etc. Also, it is argued that CSR and CR are two sides of one coin; hence, to some extent, doing CSR is equal to establishing a good reputation. Still, there is no consensus of the CSR-CR relationship in the literature; thus, a systematic literature review is highly in need. This research conducts a systematic literature review with both bibliometric and content analysis. Data are selected from English language sources, and academic journal articles only, then, keyword combinations are applied to identify relevant sources. Data from Scopus and WoS are gathered for bibliometric analysis. Scopus search results were saved in RIS and CSV formats, and Web of Science (WoS) data were saved in TXT format and CSV formats in order to process data in the Bibexcel software for further analysis which later will be visualised by the software VOSviewer. Also, content analysis was applied to analyse the data clusters and the key articles. In terms of the topic of CSR-CR, this literature review with bibliometric analysis has made four achievements. First, this paper has developed a systematic study which quantitatively depicts the knowledge structure of CSR and CR by identifying terms closely related to CSR-CR (such as ‘corporate governance’) and clustering subtopics emerged in co-citation analysis. Second, content analysis is performed to acquire insight on the findings of bibliometric analysis in the discussion section. And it highlights some insightful implications for the future research agenda, for example, a psychological link between CSR-CR is identified from the result; also, emerging economies and qualitative research methods are new elements emerged in the CSR-CR big picture. Third, a multidisciplinary perspective presents through the whole bibliometric analysis mapping and co-word and co-citation analysis; hence, this work builds a structure of interdisciplinary perspective which potentially leads to an integrated conceptual framework in the future. Finally, Scopus and WoS are compared and contrasted in this paper; as a result, Scopus which has more depth and comprehensive data is suggested as a tool for future bibliometric analysis studies. Overall, this paper has fulfilled its initial purposes and contributed to the literature. To the author’s best knowledge, this paper conducted the first literature review of CSR-CR researches that applied both bibliometric analysis and content analysis; therefore, this paper achieves its methodological originality. And this dual approach brings advantages of carrying out a comprehensive and semantic exploration in the area of CSR-CR in a scientific and realistic method. Admittedly, its work might exist subjective bias in terms of search terms selection and paper selection; hence triangulation could reduce the subjective bias to some degree.

Keywords: corporate social responsibility, corporate reputation, bibliometric analysis, software program

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Authors: Zhaoyang Liu, Detao Qin, Darren Delai Sun

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The onshore production of oil and gas (for example, shale gas) generates large quantities of wastewater, referred to be ‘produced water’, which contains high contents of oils and salts. The direct discharge of produced water, if not appropriately treated, can be toxic to the environment and human health. Membrane filtration has been deemed as an environmental-friendly and cost-effective technology for treating oily wastewater. However, conventional polymeric membranes have their drawbacks of either low salt rejection rate or high membrane fouling tendency when treating oily wastewater. Recent years, forward osmosis (FO) membrane filtration has emerged as a promising technology with its unique advantages of low operation pressure and less membrane fouling tendency. However, until now there is still no report about FO membranes specially designed and fabricated for treating the oily and salty produced water. In this study, a novel nanocomposite FO membrane was developed specially for treating oil- and salt-polluted produced water. By leveraging the recent advance of nanomaterials and nanotechnology, this nanocomposite FO membrane was designed to be made of double layers: an underwater oleophobic selective layer on top of a nanomaterial infused polymeric support layer. Wherein, graphene oxide (GO) nanosheets were selected to add into the polymeric support layer because adding GO nanosheets can optimize the pore structures of the support layer, thus potentially leading to high water flux for FO membranes. In addition, polyvinyl alcohol (PVA) hydrogel was selected as the selective layer because hydrated and chemically-crosslinked PVA hydrogel is capable of simultaneously rejecting oil and salt. After nanocomposite FO membranes were fabricated, the membrane structures were systematically characterized with the instruments of TEM, FESEM, XRD, ATR-FTIR, surface zeta-potential and Contact angles (CA). The membrane performances for treating produced waters were tested with the instruments of TOC, COD and Ion chromatography. The working mechanism of this new membrane was also analyzed. Very promising experimental results have been obtained. The incorporation of GO nanosheets can reduce internal concentration polarization (ICP) effect in the polymeric support layer. The structural parameter (S value) of the new FO membrane is reduced by 23% from 265 ± 31 μm to 205 ± 23 μm. The membrane tortuosity (τ value) is decreased by 20% from 2.55 ± 0.19 to 2.02 ± 0.13 μm, which contributes to the decrease of S value. Moreover, the highly-hydrophilic and chemically-cross-linked hydrogel selective layer present high antifouling property under saline oil/water emulsions. Compared with commercial FO membrane, this new FO membrane possesses three times higher water flux, higher removal efficiencies for oil (>99.9%) and salts (>99.7% for multivalent ions), and significantly lower membrane fouling tendency (<10%). To our knowledge, this is the first report of a nanocomposite FO membrane with the combined merits of high salt rejection, high oil repellency and high water flux for treating onshore oil/gas produced waters. Due to its outstanding performance and ease of fabrication, this novel nanocomposite FO membrane possesses great application potential in wastewater treatment industry.

Keywords: nanocomposite, membrane, polymer, graphene oxide

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Authors: Chang Liu, Hanwen Zhang, Vikash Sharma, Don Eliseo Lucerno-Prisno III, Emmanuel Yujuico, Maulik Chokshi, Prashanthi Krishnakumar, Bach Xuan Tran, Giang Thu Vu, Kamilla Anna Pinter, Shenglan Tang

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Background and Aims: Globally, many health systems focus on hospital-based healthcare models targeting acute care and disease treatment, which are not effective in addressing the challenges of ageing populations, chronic conditions, multi-morbidities, and increasingly unhealthy lifestyles. Recently, integrated care programs on chronic diseases have been developed, piloted, and implemented to meet such challenges. However, integrated care programs in the Asia-Pacific region vary in the levels of integration from linkage to coordination to full integration. This study aims to identify and analyze existing cases of integrated care in the Asia-Pacific region and identify the facilitators and barriers in order to improve existing cases and inform future cases. Methods: The study is a comparative study, with a combination approach of desk-based research and key informant interviews. The selected countries included in this study represent a good mix of lower-middle income countries (the Philippines, India, Vietnam, and Fiji), upper-middle income country (China), and high-income country (Singapore) in the Asia-Pacific region. Existing integrated care programs were identified through the scoping review approach. Trigger, history, general design, beneficiaries, and objectors were summarized with barriers and facilitators of integrated care based on key informant interviews. Representative case(s) in each country were selected and comprehensively analyzed through deep-dive case studies. Results: A total of 87 existing integrated care programs on chronic diseases were found in all countries, with 44 in China, 21 in Singapore, 12 in India, 5 in Vietnam, 4 in the Philippines, and 1 in Fiji. 9 representative cases of integrated care were selected for in-depth description and analysis, with 2 in China, the Philippines, and Vietnam, and 1 in Singapore, India, and Fiji. Population aging and the rising chronic disease burden have been identified as key drivers for almost all the six countries. Among the six countries, Singapore has the longest history of integrated care, followed by Fiji, the Philippines, and China, while India and Vietnam have a shorter history of integrated care. Incentives, technologies, education, and performance evaluation would be crucial for developing strategies for implementing future programs and improve already existing programs. Conclusion: Integrated care is important for addressing challenges surrounding the delivery of long-term care. To date, there is an increasing trend of integrated care programs on chronic diseases in the Asia-Pacific region, and all six countries in our study set integrated care as a direction for their health systems transformation.

Keywords: integrated healthcare, integrated care delivery, chronic diseases, Asia-Pacific region

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