Search results for: data structure

Authors: Mohamad Amin Amini, Mehdi Poursha

Abstract:

Nonlinear response history analysis (NL-RHA) is a robust analytical tool for estimating the seismic demands of structures responding in the inelastic range. However, because of its conceptual and numerical complications, the nonlinear static procedure (NSP) is being increasingly used as a suitable tool for seismic performance evaluation of structures. The conventional pushover analysis methods presented in various codes (FEMA 356; Eurocode-8; ATC-40), are limited to the first-mode-dominated structures, and cannot take higher modes effect into consideration. Therefore, since more than a decade ago, researchers developed enhanced pushover analysis procedures to take higher modes effect into account. The main objective of this study is to propose an enhanced invariant lateral displacement distribution to take higher modes effect into consideration in performing a displacement-based pushover analysis, whereby a set of laterally applied displacements, rather than forces, is monotonically applied to the structure. For this purpose, the effect of different parameters such as the spectral displacement of ground motion, the modal participation factor, and the effective modal participating mass ratio on the lateral displacement distribution is investigated to find the best distribution. The major simplification of this procedure is that the effect of higher modes is concentrated into a single invariant lateral load distribution. Therefore, only one pushover analysis is sufficient without any need to utilize a modal combination rule for combining the responses. The invariant lateral displacement distribution for pushover analysis is then calculated by combining the modal story displacements using the modal combination rules. The seismic demands resulting from the different procedures are compared to those from the more accurate nonlinear response history analysis (NL-RHA) as a benchmark solution. Two structures of different heights including 10 and 20-story special steel moment resisting frames (MRFs) were selected and evaluated. Twenty ground motion records were used to conduct the NL-RHA. The results show that more accurate responses can be obtained in comparison with the conventional lateral loads when the enhanced modal lateral displacement distributions are used.

Keywords: displacement-based pushover, enhanced lateral load distribution, higher modes effect, nonlinear response history analysis (NL-RHA)

Procedia PDF Downloads 268

Authors: Francisco Fernandez de Castro

Abstract:

The Conditional Cash Transfer (CCT) model gives monetary transfers to beneficiary families on the condition that they take specific education and health actions. According to the economic rationale of CCTs the poor need incentives to invest in their human capital because they are trapped by a lack of information and misleading beliefs. If left to their own decision, the poor will not be able to choose what is in their best interests. The basic assumption of the CCT model is that the poor need incentives to take care of their own education and health-nutrition. Due to the incentives (income cash transfers and conditionalities), beneficiary families are supposed to attend doctor visits and health talks. Children would stay in the school. These incentivized behaviors would produce outcomes such as better health and higher level of education, which in turn will reduce poverty. Based on a grounded theory approach to conduct a two-year period of qualitative data collection in northern Mexico, this study shows that this explanation is incomplete. In addition to the information failure and inadequate beliefs, there are structural barriers in everyday life of households that make health-nutrition and education investments difficult. In-depth interviews and observation work showed that the program takes for granted local conditions in which beneficiary families should fulfill their co-responsibilities. Data challenged the program’s assumptions and unveiled local obstacles not contemplated in the program’s design. These findings have policy and research implications for the CCT agenda. They bring elements for late programming due to the gap between the CCT strategy as envisioned by policy designers, and the program that beneficiary families experience on the ground. As for research consequences, these findings suggest new avenues for scholarly work regarding the causal mechanisms and social processes explaining CCT outcomes.

Keywords: conditional cash transfers, incentives, poverty, structural barriers

Procedia PDF Downloads 110

Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

Procedia PDF Downloads 114

Authors: Ezana Haddis Weldeghebrael

Abstract:

Addis Ababa City Administration is engaged in a massive facelift of the inner-city. The paper, therefore, aims to analyze the challenges of the current urban regeneration effort by paying special attention to Lideta and Basha Wolde Chilot projects. To this end, the paper has adopted a documentary research strategy to collect the data and Institutionalist perspective as well as the concept of urban regeneration to analyze the data. The sources were selected based on relevance and recency. Academic research outputs were used primarily. However, where much scholastic publications are not available institutional reports, newspaper articles, and expert presentations were used. The major findings of the research revealed that although the second generation of urban redevelopment projects have attempted to involve affected groups and succeeded in designing better neighborhoods, they are riddled with three major drawbacks. The first one is institutional constraints, i.e. absence of urban redevelopment strategy as well as housing policy, broad definition of ‘public purpose’, little regard for informal businesses, limitation on rights groups, negotiation power not devolved at sub-city level and no plan for groups that cannot afford to pay the down payment for low-cost apartments. The second one is planning limitation, i.e. absence of genuine affected group participation as well as consultative level of public engagement. The third one is implementation failure, i.e. no regard to maintaining social bond, non-participatory and ill-informed resettlement, interference from senior government officials, failure to protect the poor from speculators, corruption and disregard to heritage buildings. Based on the findings, the paper concluded that the current inner-city redevelopment has failed to be socially sustainable and calls for enactment of housing policy as well as redevelopment strategy, affected group participation, on-site resettlement, empowering the Sub-city to manage the project and allowing housing rights groups to advocate for the poor slum dwellers.

Keywords: participation, redevelopment, planning, implementation, consultation

Procedia PDF Downloads 422

Authors: O. O. Fola-Matthews, O. O. Soyinka, D. N. Bitalo

Abstract:

Nigeria is one of the major shark fishing nations in Africa, but its fisheries managers still record catch data in aggregates ‘sharks’ with no species-specific details. This is because most of the shark specimens look identical in morphology, and field identification of some closely related species is tricky. This study uses DNA barcoding as a method to identify shark species from five different landing areas off the Nigerian Coast. 100 dorsal fins were sampled in order to provide a Chondrichthyan sequence that would be matched to reference specimens in a DNA barcode database

Keywords: BOLD, DNA barcoding, nigeria, sharks

Procedia PDF Downloads 157

Authors: Maria Miranda-Medina, Sara Salopek, Andras Vernes, Martin Jech

Abstract:

Sliding lubricated surfaces induce the formation of tribofilms that reduce friction, wear and prevent large-scale damage of contact parts. Engine oils and lubricants use antiwear and antioxidant additives such as zinc dialkyldithiophosphate (ZDDP) from where protective tribofilms are formed by degradation. The ZDDP tribofilms are described as a two-layer structure composed of inorganic polymer material. On the top surface, the long chain polyphosphate is a zinc phosphate and in the bulk, the short chain polyphosphate is a mixed Fe/Zn phosphate with a gradient concentration. The polyphosphate chains are partially adherent to steel surface through a sulfide and work as anti-wear pads. In this contribution, ZDDP tribofilms formed on gray cast iron surfaces are studied. The tribofilms were generated in a reciprocating sliding tribometer with a piston ring-cylinder liner configuration. Fully formulated oil of SAE grade 5W-30 was used as lubricant during two tests at 40Hz and 50Hz. For the estimation of the tribofilm thicknesses, spectroscopic ellipsometry was used due to its high accuracy and non-destructive nature. Ellipsometry works under an optical principle where the change in polarisation of light reflected by the surface, is associated with the refractive index of the surface material or to the thickness of the layer deposited on top. Ellipsometrical responses derived from tribofilms are modelled by effective medium approximation (EMA), which includes the refractive index of involved materials, homogeneity of the film and thickness. The materials composition was obtained from x-ray photoelectron spectroscopic studies, where the presence of ZDDP, O and C was confirmed. From EMA models it was concluded that tribofilms formed at 40 Hz are thicker and more homogeneous than the ones formed at 50 Hz. In addition, the refractive index of each material is mixed to derive an effective refractive index that describes the optical composition of the tribofilm and exhibits a maximum response in the UV range, being a characteristic of glassy semitransparent films.

Keywords: effective medium approximation, reciprocating sliding tribometer, spectroscopic ellipsometry, zinc dialkyldithiophosphate

Procedia PDF Downloads 245

Authors: Shammael Selorfia

Abstract:

The Quality improvement project aimed to improve junior doctors and nurses’ knowledge and confidence in diagnosing and managing delirium on inpatient surgical wards in a tertiary hospital. The study aimed to develop a standardised assessment and management checklist for all staff working with patients who were presenting with signs of delirium. The aim of the study was to increase confidence of staff at dealing with delirium and improve the quality of referrals that were being sent to the Mental Health Liaison team over a 6-month period. A significant proportion of time was being spent by the Mental Health Liaison triage nurses on referrals for delirium. Data showed 28% of all delirium referrals from surgical teams were being closed at triage reflecting a poor standard of quality of those referrals. A qualitative survey of junior doctors in 6 surgical specialties in a UK tertiary hospital was conducted. These specialties include general surgery, vascular, plastic, urology, neurosurgery, and orthopaedics. The standardised checklist was distributed to all surgical wards. A comparison was made between the Mental health team caseload of delirium before intervention was compared and after. A Qualitative survey at end of 3-month cycle and compare overall caseload on Mental Health Liaison team to pre-QIP data with aim to improve quality of referrals and reduce workload on Mental Health Liaison team. At the end of the project cycle, we demonstrated an improvement in the quality of referrals with a decrease in the percentage of referrals being closed at triage by 8%. Our surveys also indicated an increase in the knowledge of official trust delirium guidelines and confidence at managing the patients. This project highlights that a new approach to delirium using multi-component interventions is needed, where the diagnosis of delirium is shared amongst medical and nursing staff, and everyone plays role in management. The key is improving awareness of delirium and encouraging the use of recognized diagnostic tools and official guidelines. Recommendations were made to the trust on how to implement a long-lasting change.

Keywords: delirium, surgery, quality, improvement

Procedia PDF Downloads 72

Authors: Leonore Robieux, Franck Zenasni, Marc Pocard, Clarisse Eveno

Abstract:

Empirical data in health psychology studies show the necessity to consider the doctor-patient communication and its positive impact on outcomes such as patients’ satisfaction, treatment adherence, physical and psychological wellbeing. In this line, the present research aims to define the role and determinants of an effective doctor–patient communication during the treatment of patients with serious illness (peritoneal carcinomatosis). We carried out a prospective longitudinal study including patients treated for peritoneal carcinomatosis of various origins. From November 2016, to date, data were collected using validated questionnaires at two times of evaluation: one month before the surgery (T0) and one month after (T1). Thus, patients reported their (a) anxiety and depression levels, (b) standardized and individualized quality of life and (c) how they perceived communication, attitude and empathy of the surgeon. 105 volunteer patients (Mean age = 58.18 years, SD = 10.24, 62.2% female) participated to the study. PC arose from rare diseases (14%), colorectal (38%), eso-gastric (24%) and ovarian (8%) cancer. Three groups are defined according to the severity of their pathology and the treatment offered to them: (1) important surgical treatment with the goal of healing (53%), (2) repeated palliative surgical treatment (17%), and (3) the patients recused for surgical treatment, only palliative approach (30%). Results are presented according to Baron and Kenny recommendations. The regressions analyses show that only depression and anxiety are sensitive to the communication and empathy of surgeon. The main results show that a good communication and high level of empathy at T0 and T1 limit depression and anxiety of the patients in T1. Results also indicate that the severity of the disease modulates this positive impact of communication: better is the communication the less are the level of depression and anxiety of the patients. This effect is higher for patients treated for the more severe disease. These results confirm that, even in the case severe disease a good communication between patient and physician remains a significant factor in promoting the well-being of patients. More specific training need to be developed to promote empathic care.

Keywords: clinical empathy, determinants, healthcare, psychological wellbeing

Procedia PDF Downloads 117

Authors: Adriana Salgado, Sandra Castaneda, Ivan Perez

Abstract:

Learning to read and write is a complex process involving several cognitive skills, contextual, and cultural environments. The basis of this development is linguistic skills, such as the ability to name and understand vocabulary, retell a story, phonological awareness, letter knowledge, among others. In children with autism spectrum disorder (ASD), one of the main concerns is related to language disorders. Nevertheless, most of the children with ASD are able to decode written information but have difficulties in reading comprehension. The research of these processes in the Spanish-speaking population is limited. However, the increasing prevalence of this diagnosis (1 in 115 children) in Mexico has implications at different levels. Educational research is an important area of interest in ASD children, such as emergent literacy. Reading and writing expand the possibilities of academic, cultural, and social information access. Taking this information into account, the objective of this research was to identify the relationship between language skills, alphabet knowledge, phonological awareness, and early reading and writing in ASD Spanish-speaking children. The method used for this research was based on tasks that were selected, adapted and in some cases designed to measure initial reading and writing, as well as language skills (naming, receptive vocabulary, and narrative skills), phonological awareness (similar phonological word pairs, beginning sound awareness and spelling) and letter knowledge, in a sample of 45 children (38 boys and 7 girls) with prior diagnosis of ASD. Descriptive analyses, as well as bivariate correlations, cluster analysis, and canonical correspondence, were obtained for the data results. Results showed that variability was large; however, it was possible to characterize the sample in low, medium, and high score groups regarding children performance. The low score group (46.7% of the sample), had a null or deficient performance in language skills and phonological awareness, some could identify up to five letters of the alphabet, showed no early reading skills but they could scribble. The middle score group was characterized by a highly variable performance in different tasks, with better language skills in receptive and naming vocabulary, some narrative, letter knowledge, and phonological awareness (beginning sound awareness) skills. The high score group, (24.4% of the sample) had the best performance in language skills in relation to the sample data, as well as in the rest of the measured skills. Finally, scores were canonically correlated between naming, receptive vocabulary, narrative, phonological awareness, letter knowledge and initial learning of reading and writing skills for the high score group and letter knowledge, naming and receptive vocabulary for the lower score group, which is consistent with previous research in typical and ASD children. In conclusion, the obtained data is consistent with previous studies. Despite large variability, it was possible to identify performance profiles and relations based on linguistic, phonological awareness, and letter knowledge skills. These skills were predictor variables of the initial development of reading and writing. The above has implications for a future program and strategies development that may benefit the acquisition of reading and writing in ASD children.

Keywords: autism, autism spectrum disorders, early literacy, emergent literacy

Procedia PDF Downloads 132

Authors: Huseyin Arasli, Maryam Abdullahi, Tugrul Gunay

Abstract:

One of the prominence elements used by the destination marketing organization (DMO) as a marketing strategy is the application of Social media tools. During the current spread of coronavirus disease (COVID-19), travel restriction was placed in most countries of the world, leading to the closure of borders movement. It should be noted that most tourism travelers depend on social media to obtain and exchange different kinds of information about COVID-19 in an unprecedented scale. The situational information people received is valued, which calls for the response of the tourism industry on the epidemic. Therefore, it is highly important to recognize such situational information and to understand how people spread this propaganda on social media platforms so that suitable information that relates the COVID-19 epidemic is available in a manner that will not tarnish the marketing strategies, festival planners. Data for this research study was collected from the desk review, which is a secondary source data, online blogs, and interview through social media chat. The results of this research show that the widespread of COVID-19 pandemics led to rapid lockdown in states and cities all over Nigeria, causing declining demands in hotels, airlines, recreation, and tourism centers. Additionally, billions of dollars lost has been recorded in the high increase of hotels and travel bookings cancellations which caused hundreds and thousands of job loss in the country. The result of this research also revealed that COVID-19 is causing more havoc on the unemployment rate indices of the country. Similarly, the over-dependence of government on petroleum has further caused considerable revenue loss, thereby raising a high poverty rate among less privileged Nigerians. Based on this result, the study suggested that there is an urgent need for the government to diversify its economy by looking at other different sectors such as tourism and agricultural farm produce to harmonize other commercial trades sectors in the country.

Keywords: social media, destination marketing organizations, DMOs, cultural COVID-19, coronavirus, hospitality, travel tour, tourism

Procedia PDF Downloads 94

Authors: Igor Jerkovic

Abstract:

Owing to the attractive sensory properties and low odour thresholds, norisoprenoids (degraded carotenoid-like structures with 3,5,5-trimethylcyclohex-2-enoic unit) have been identified as aroma contributors in a number of different matrices. C₁₃-Norisoprenoids have been found among volatile organic compounds of various honey types as well as C₉//C₁₀-norisoprenoids or C₁₄/C₁₅-norisoprenoids. Besides degradation of abscisic acid (which produces, e.g., dehydrovomifoliol, vomifoliol, others), the cleavage of the C(9)=C(10) bond of other carotenoid precursors directly generates nonspecific C₁₃-norisoprenoids such as trans-β-damascenone, 3-hydroxy-trans-β-damascone, 3-oxo-α-ionol, 3-oxo-α-ionone, β-ionone found in various honey types. β-Damascenone and β-ionone smelling like honey, exhibit the lowest odour threshold values of all C₁₃-norisoprenoids. The presentation is targeted on two uncommon C₁₃-norisoprenoids in the honey flavor that could be used as specific or nonspecific chemical markers of the botanical origin. Namely, after screening of different honey types, the focus was directed on Centaruea cyanus L. and Allium ursinum L. honey. The samples were extracted by headspace solid-phase microextraction (HS-SPME) and ultrasonic solvent extraction (USE) and the extracts were analysed by gas chromatography and mass spectrometry (GC-MS). SPME fiber with divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) coating was applied for the research of C. cyanus honey headspace and predominant identified compound was 3,4-dihydro-3-oxoedulan (2,5,5,8a-tetramethyl-2,3,5,6,8,8a-hexahydro-7H-chromen-7-one also known as 2,3,5,6,8,8a-hexahydro-2,5,5,8a-tetramethyl-7H-1-benzo-pyran-7-one). The oxoedulan structure contains epoxide and it is more volatile in comparison with its hydroxylated precursors. This compound has not been found in other honey types and can be considered specific for C. cyanus honey. The dichloromethane extract of A. ursinum honey contained abundant (E)-4-(r-1',t-2',c-4'-trihydroxy-3',6',6'-trimethylcyclohexyl)-but-3-en-2-one that was previously isolated as dominant substance from the ether extracts of New Zealand thyme honey. Although a wide variety of degraded carotenoid-like substances have been identified from different honey types, this appears to be rare situation where 3,4-dihydro-3-oxoedulan and (E)-4-(r-1',t-2',c-4'-trihydroxy-3',6',6'-trimethylcyclohexyl)-but-3-en-2-one have been found that is of great importance for chemical fingerprinting and identification of the chemical biomarkers that can complement the pollen analysis as the major method for the honey classification.

Keywords: 3, 4-dihydro-3-oxoedulan, (E)-4-(r-1', t-2', c-4'-trihydroxy-3', 6', 6'-trimethylcyclohexyl)-but-3-en-2-one, honey flavour, C₁₃-norisoprenoids

Procedia PDF Downloads 319

Authors: Radin Siti Aishah Radin A Rahman, Norasmah Othman, Zaidatol Akmaliah Lope Pihie, Hariyaty Ab. Wahid

Abstract:

The recent instability in economy was found to be influencing the situation in Malaysia whether directly or indirectly. Taking that into consideration, the government needs to find the best approach to balance its citizen’s socio-economic strata level urgently. Through education platform is among the efforts planned and acted upon for the purpose of balancing the effects of the influence, through the exposure of social entrepreneurial activity towards youth especially those in higher institution level. Armed with knowledge and skills that they gained, with the support by entrepreneurial culture and environment while in campus; indirectly, the students will lean more on making social entrepreneurship as a career option when they graduate. Following the issues of marketability and workability of current graduates that are becoming dire, research involving how far the willingness of student to create social innovation that contribute to the society without focusing solely on personal gain is relevant enough to be conducted. With that, this research is conducted with the purpose of identifying the level of entrepreneurial intention and social entrepreneurship among higher institution students in Malaysia. Stratified random sampling involves 355 undergraduate students from five public universities had been made as research respondents and data were collected through surveys. The data was then analyzed descriptively using min score and standard deviation. The study found that the entrepreneurial intention of higher education students are on moderate level, however it is the contrary for social entrepreneurship activities, where it was shown on a high level. This means that while the students only have moderate level of willingness to be a social entrepreneur, they are very committed to created social innovation through the social entrepreneurship activities conducted. The implication from this study can be contributed towards the higher institution authorities in prediction the tendency of student in becoming social entrepreneurs. Thus, the opportunities and facilities for realizing the courses related to social entrepreneurship must be created expansively so that the vision of creating as many social entrepreneurs as possible can be achieved.

Keywords: entrepreneurial intention, higher education institutions (HEIs), social entrepreneurship, social entrepreneurial activity, gender

Procedia PDF Downloads 256

Authors: Neelam Vats, Madhvi Pathak Pillai, Rajender Lal, Indu Dabas

Abstract:

The pressure of scoring higher marks to be able to get admission in a higher ranked institution has become a social stigma for school students. It leads to various social and academic pressures on them, causing psychological anxiety. This undue stress on students sometimes may even steer to aggressive behavior or suicidal tendencies. Human mind is always surrounded by the some desires, emotions and passions, which usually disturbs our mental peace. In such a scenario, we look for a solution that helps in removing all the obstacles of mind and make us mentally peaceful and strong enough to be able to deal with all kind of pressure. Preksha meditation is one such technique which aims at bringing the positive changes for overall transformation of personality. Hence, the present study was undertaken to assess the impact of Preksha Meditation on the academic anxiety on female teenagers. The study was conducted on 120 high school students from the capital city of India. All students were in the age group of 13-15 years. They also belonged to similar social as well as economic status. The sample was equally divided into two groups i.e. experimental group (N = 60) and control group (N = 60). Subjects of the experimental group were given the intervention of Preksha Meditation practice by the trained instructor for one hour per day, six days a week, for three months for the first experimental stage and another three months for the second experimental stage. The subjects of the control group were not assigned any specific type of activity rather they continued doing their normal official activities as usual. The Academic Anxiety Scale was used to collect data during multi-level stages i.e. pre-experimental stage, post-experimental stage phase-I, and post-experimental stage phase-II. The data were statistically analyzed by computing the two-tailed-‘t’ test for inter group comparison and Sandler’s ‘A’ test with alpha = or p < 0.05 for intra-group comparisons. The study concluded that the practice for longer duration of Preksha Meditation practice brings about very significant and beneficial changes in the pattern of academic anxiety.

Keywords: academic anxiety, academic pressure, Preksha, meditation

Procedia PDF Downloads 123

Authors: Y. Saylan, F. Yılmaz, A. Denizli

Abstract:

Rheumatoid arthritis (RA) which is the most common autoimmune disorder of the body's own immune system attacking healthy cells. RA has both articular and systemic effects.Until now romatiod factor (RF) assay is used the most commonly diagnosed RA but it is not specific. Anti-cyclic citrullinated peptide (anti-CCP) antibodies are IgG autoantibodies which recognize citrullinated peptides and offer improved specificity in early diagnosis of RA compared to RF. Anti-CCP antibodies have specificity for the diagnosis of RA from 91 to 98% and the sensitivity rate of 41-68%. Molecularly imprinted polymers (MIP) are materials that are easy to prepare, less expensive, stable have a talent for molecular recognition and also can be manufactured in large quantities with good reproducibility. Molecular recognition-based adsorption techniques have received much attention in several fields because of their high selectivity for target molecules. Quartz crystal microbalance (QCM) is an effective, simple, inexpensive approach mass changes that can be converted into an electrical signal. The applications for specific determination of chemical substances or biomolecules, crystal electrodes, cover by the thin films for bind or adsorption of molecules. In this study, we have focused our attention on combining of molecular imprinting into nanofilms and QCM nanosensor approaches and producing QCM nanosensor for anti-CCP, chosen as a model protein, using anti-CCP imprinted nanofilms. For this aim, anti-CCP imprinted QCM nanosensor was characterized by Fourier transform infrared spectroscopy, atomic force microscopy, contact angle measurements and ellipsometry. The non-imprinted nanosensor was also prepared to evaluate the selectivity of the imprinted nanosensor. Anti-CCP imprinted QCM nanosensor was tested for real-time detection of anti-CCP from aqueous solution. The kinetic and affinity studies were determined by using anti-CCP solutions with different concentrations. The responses related with mass shifts (Δm) and frequency shifts (Δf) were used to evaluate adsorption properties and to calculate binding (Ka) and dissociation (Kd) constants. To show the selectivity of the anti-CCP imprinted QCM nanosensor, competitive adsorption of anti-CCP and IgM was investigated.The results indicate that anti-CCP imprinted QCM nanosensor has a higher adsorption capabilities for anti-CCP than for IgM, due to selective cavities in the polymer structure.

Keywords: anti-CCP, molecular imprinting, nanosensor, rheumatoid arthritis, QCM

Procedia PDF Downloads 357

Authors: Jose Flores, Nadia Gamboa

Abstract:

A proper water quality management requires the establishment of a monitoring network. Therefore, evaluation of the efficiency of water quality monitoring networks is needed to ensure high-quality data collection of critical quality chemical parameters. Unfortunately, in some Latin American countries water quality monitoring programs are not sustainable in terms of recording historical data or environmentally representative sites wasting time, money and valuable information. In this study, multivariate statistical techniques, such as principal components analysis (PCA) and hierarchical cluster analysis (HCA), are applied for identifying the most significant monitoring sites as well as critical water quality parameters in the monitoring network of the Jequetepeque River basin, in northern Peru. The Jequetepeque River basin, like others in Peru, shows socio-environmental conflicts due to economical activities developed in this area. Water pollution by trace elements in the upper part of the basin is mainly related with mining activity, and agricultural land lost due to salinization is caused by the extensive use of groundwater in the lower part of the basin. Since the 1980s, the water quality in the basin has been non-continuously assessed by public and private organizations, and recently the National Water Authority had established permanent water quality networks in 45 basins in Peru. Despite many countries use multivariate statistical techniques for assessing water quality monitoring networks, those instruments have never been applied for that purpose in Peru. For this reason, the main contribution of this study is to demonstrate that application of the multivariate statistical techniques could serve as an instrument that allows the optimization of monitoring networks using least number of monitoring sites as well as the most significant water quality parameters, which would reduce costs concerns and improve the water quality management in Peru. Main socio-economical activities developed and the principal stakeholders related to the water management in the basin are also identified. Finally, water quality management programs will also be discussed in terms of their efficiency and sustainability.

Keywords: PCA, HCA, Jequetepeque, multivariate statistical

Procedia PDF Downloads 348

Authors: Rose Chapman Rodriguez

Abstract:

Problem: Evidence-based practice (EBP) attributes are significantly associated with EBP implementation science, which improves patient care outcomes. Nurses influence EBP, yet little is known of the specific EBP attributes of pediatric nurses in their clinical sub-specialties. Aim: This study aims to investigate the relationship between nursing academic degree, years of experience, and clinical specialty, with mean survey scores on EBP belief, organizational culture, and implementation scales across all levels of nursing in a Children’s Hospital. Methods: A convenience sample of nurses (n=185) participated in a descriptive, cross-sectional, correlational study in May 2023. The electronic surveys comprised 11 demographic questions and nine survey items from the short-version EBP Beliefs Scale (Cronbach α = 0.81), Organizational Culture and Readiness Scale for System-wide Integration Scale (Cronbach α = 0.87), and EBP Implementation Scale (Cronbach α = 0.89). Findings: EBP belief scores were notably higher in nurses working in neonatology (m=4.33), critical care (m=4.47), and among nurse leaders (m=4.50). There was a statistically significant difference in EBP organizational culture among nurse leaders (m = 3.95, p=0.039) compared to clinical nurses (m = 3.34) and advanced practice nurses (m = 3.34). EBP implementation was favorable in neonatology (m=4.20), acute care (m=4.05), and nurse leaders (m=4.33). No significant difference or correlation was found in EBP belief, organizational culture, or implementation mean scores related to nurses' age, academic nursing degree, or years of experience in our cohort (EBP beliefs (r = -.06, p = .400), organizational readiness (r = .02, p = .770), and implementation scales (r = .01, p = .867). Conclusions: This study identified nurse’s EBP attributes in a Children’s Hospital using key variables studied in EBP social cognitive theory and learning theory. Magnet status, shared governance structure, specialty certification, and nurse leaders play a significant role in favorable EBP culture and implementation. Nurses’ unit level ‘group culture’ may vary depending on the EBP attributes and collaborative efforts of local teams. Opportunities for mentoring were identified, which may continue to enhance EBP implementation science across all nursing roles in our pediatric organization.

Keywords: evidence-based practice, peditrics, nursing roles, implementation

Procedia PDF Downloads 63

Authors: N. W. I. A. Jayawardana, W. A. T. A. Jayalath, W. M. T. Madhujith, U. Ralapanawa, R. S. Jayasekera, S. A. S. B. Alagiyawanna, A. M. K. R. Bandara, N. S. Kalupahana

Abstract:

There is mounting evidence to the effect that dietary and lifestyle changes affect the incidence of non-communicable diseases (NCDs). This study was conducted to investigate the association of diet, physical activity, smoking, alcohol consumption and duration of sleep with overweight, obesity, hypertension and diabetes in a cohort of males from the Central Province of Sri Lanka. A total of 2694 individuals aged between 17 – 68 years (Mean = 31) were included in the study. Body Mass Index cutoff values for Asians were used to categorize the participants as normal, overweight and obese. The dietary data were collected using a food frequency questionnaire [FFQ] and data on the level of physical activity, smoking, alcohol consumption and sleeping hours were obtained using a self-administered validated questionnaire. Systolic and diastolic blood pressure, random blood glucose levels were measured to determine the incidence of hypertension and diabetes. Among the individuals, the prevalence of overweight and obesity were 34% and 16.4% respectively. Approximately 37% of the participants suffered from hypertension. Overweight and obesity were associated with older age men (P<0.0001), frequency of smoking (P=0.0434), alcohol consumption level (P=0.0287) and the quantity of lipid intake (P=0.0081). Consumption of fish (P=0.6983) and salty snacks (P=0.8327), sleeping hours (P=0.6847) and the level of physical activity were not significantly (P=0.3301) associated with the incidence of overweight and obesity. Based on the fitted model, only age was significantly associated with hypertension (P < 0.001). Further, age (P < 0.0001), sleeping hours (P=0.0953) and consumption of fatty foods (P=0.0930) were significantly associated with diabetes. Age was associated with higher odds of pre diabetes (OR:1.089;95% CI:1.053,1.127) and diabetes (OR:1.077;95% CI:1.055,1.1) whereas 7-8 hrs. of sleep per day was associated with lesser odds of diabetes (OR:0.403;95% CI:0.184,0.884). High prevalence of overweight, obesity and hypertension in working-age males is a threatening sign for this area. As this population ages in the future and urbanization continues, the prevalence of above risk factors will likely to escalate.

Keywords: age, males, non-communicable diseases, obesity

Procedia PDF Downloads 332

Authors: Javier Muro, Anja Linstadter, Florian Manner, Lisa Schwarz, Stephan Wollauer, Paul Magdon, Gohar Ghazaryan, Olena Dubovyk

Abstract:

Monitoring the trade-off between biomass production and biodiversity in grasslands is critical to evaluate the effects of management practices across environmental gradients. New generations of remote sensing sensors and machine learning approaches can model grasslands’ characteristics with varying accuracies. However, studies often fail to cover a sufficiently broad range of environmental conditions, and evidence suggests that prediction models might be case specific. In this study, biomass production and biodiversity indices (species richness and Fishers’ α) are modeled in 150 grassland plots for three sites across Germany. These sites represent a North-South gradient and are characterized by distinct soil types, topographic properties, climatic conditions, and management intensities. Predictors used are derived from Sentinel-1 & 2 and a set of topoedaphic variables. The transferability of the models is tested by training and validating at different sites. The performance of feed-forward deep neural networks (DNN) is compared to a random forest algorithm. While biomass predictions across gradients and sites were acceptable (r2 0.5), predictions of biodiversity indices were poor (r2 0.14). DNN showed higher generalization capacity than random forest when predicting biomass across gradients and sites (relative root mean squared error of 0.5 for DNN vs. 0.85 for random forest). DNN also achieved high performance when using the Sentinel-2 surface reflectance data rather than different combinations of spectral indices, Sentinel-1 data, or topoedaphic variables, simplifying dimensionality. This study demonstrates the necessity of training biomass and biodiversity models using a broad range of environmental conditions and ensuring spatial independence to have realistic and transferable models where plot level information can be upscaled to landscape scale.

Keywords: ecosystem services, grassland management, machine learning, remote sensing

Procedia PDF Downloads 212

Authors: Sukru Merey, Caglar Sinayuc

Abstract:

Gas hydrate deposits which are found in deep ocean sediments and in permafrost regions are supposed to be a fossil fuel reserve for the future. The Black Sea is also considered rich in terms of gas hydrates. It abundantly contains gas hydrates as methane (CH₄~80 to 99.9%) source. In this study, by using the literature, seismic and other data of the Black Sea such as salinity, porosity of the sediments, common gas type, temperature distribution and pressure gradient, the optimum gas production method for the Black Sea gas hydrates was selected as mainly depressurization method. Numerical simulations were run to analyze gas production from gas hydrate deposited in turbidites in the Black Sea by depressurization.

Keywords: CH4 hydrate, Black Sea hydrates, gas hydrate experiments, HydrateResSim

Procedia PDF Downloads 613

Authors: Chun-Shien Lu

Abstract:

Privacy-preserving query has recently received considerable attention in the signal processing and multimedia community. It is also a critical step in wireless sensor network for retrieval of sensitive data. The purposes of privacy-preserving query in both the areas of signal processing and sensor network are the same, but the similarity and difference of the adopted technologies are not fully explored. In this paper, we first review the recently developed methods of privacy-preserving query, and then describe in a comprehensive manner what we can learn from the mutual of both areas.

Keywords: encryption, privacy-preserving, search, security

Procedia PDF Downloads 248

Authors: Alexander Musyoki, Collins Odour

Abstract:

Over the past few years, the bakery industry in Kenya has experienced significant growth largely in part to the increased adoption of technology and automation in their processes; more specifically due to the adoption of bakery management systems to help in running bakeries. While they have been largely responsible for the improved productivity and efficiency in bakeries, most of them are now outdated and pose more challenges than benefits. The proposed online bakery management system mentioned in this paper aims to address this by allowing bakery owners to track inventory, budget, job progress, and data analytics on each job and in doing so, promote the Sustainable Development Goals 3 and 12, which aim to ensure healthy lives and promote sustainable economic growth as the proposed benefits of these features include scalability, easy accessibility, reduced acquisition costs, better reliability, and improved functionality that will allow bakeries to become more competitive, reduce waste and track inventory more efficiently. To better understand the challenges, a comprehensive study has been performed to assess these traditional systems and try to understand if an online bakery management system can prove to be advantageous to bakery owners. The study conducted gathered feedback from bakery owners and employees in Nairobi County, Kenya using an online survey with a response rate of about 86% from the target population. The responses cited complex and hard to use bakery management systems (59.7%), lack of portability from one device to the other (58.1%) and high acquisition costs (51.6%) as the top challenges of traditional bakery management systems. On the other hand, some of the top benefits that most of the respondents would realize from the online bakery management system was better reliability (58.1%) and reduced acquisition costs (58.1%). Overall, the findings suggest that an online bakery management system has a lot of advantages over traditional systems and is likely to be well-received in the market. In conclusion, the proposed online bakery management system has the potential to improve the efficiency and competitiveness of small-sized bakeries in Nairobi County. Further research is recommended to expand the sample size and diversity of respondents and to conduct more in-depth analyses of the data collected.

Keywords: ICT, technology and automation, bakery management systems, food innovation

Procedia PDF Downloads 71

Authors: Christabel Ihedike, John Mooney, Monica Price

Abstract:

Summary: This study aims to critically evaluate effect of photochemical smog on respiratory health in Nigeria. Cohort of chronic obstructive pulmonary disease (COPD) patients was recruited from two large hospitals in Abuja Nigeria. Respiratory health questionnaires, daily diaries, dyspnoea scale and lung function measurement were used to obtain health data and investigate the relationship with air quality data (principally ozone, NOx and particulate pollution). Concentrations of air pollutants were higher than WHO and Nigerian air quality standard. The result suggests a correlation between measured air quality and exacerbation of respiratory illness. Introduction: Photochemical smog is a significant health challenge in most cities and its effect on respiratory health is well acknowledged. This type of pollution is most harmful to the elderly, children and those with underlying respiratory disease. This study aims to investigate impact of increasing temperature and photo-chemically generated secondary air pollutants on respiratory health in Abuja Nigeria. Method and Result: Health data was collected using spirometry to measure lung function on routine attendance at the clinic, daily diaries kept by patients and information obtained using respiratory questionnaire. Questionnaire responses (obtained using an adapted and internally validated version of St George’s Hospital Respiratory Questionnaire), shows that ‘time of wheeze’ showed an association with participants activities: 30% had worse wheeze in the morning: 10% cannot shop, 15% take long-time to get washed, 25% walk slower, 15% if hurry have to stop and 5% cannot take-bath. There was also a decrease in Forced expiratory volume in the first second and Forced Vital Capacity, and daily change in the afternoon–morning may be associated with the concentration level of pollutants. Also, dyspnoea symptoms recorded that 60% of patients were on grade 3, 25% grade 2 and 15% grade 1. Daily frequency of the number of patients in the cohort that cough /brought sputum is 78%. Air pollution in the city is higher than Nigerian and WHO standards with NOx and PM10 concentrations of 693.59ug/m-3 and 748ugm-3 being measured respectively. The result shows that air pollution may increase occurrence and exacerbation of respiratory disease. Conclusion: High temperature and local climatic conditions in urban Nigeria encourages formation of Ozone, the major constituent of photochemical smog, resulting also in the formation of secondary air pollutants associated with health challenges. In this study we confirm the likely potency of the pattern of secondary air pollution in exacerbating COPD symptoms in vulnerable patient group in urban Nigeria. There is need for better regulation and measures to reduce ozone, particularly when local climatic conditions favour development of photochemical smog in such settings. Climate change and likely increasing temperatures add impetus and urgency for better air quality standards and measures (traffic-restrictions and emissions standards) in developing world settings such as Nigeria.

Keywords: Abuja-Nigeria, effect, photochemical smog, respiratory health

Procedia PDF Downloads 218

Authors: Ali R. Alshawawreh, Francisco Liébana-Cabanillas, Francisco J. Blanco-Encomienda

Abstract:

Collaboration between marketing and business intelligence (BI) is crucial in today's ever-evolving business landscape. These two domains play pivotal roles in molding customers' experiential knowledge. Marketing insights offer valuable information regarding customer needs, preferences, and behaviors, thus refining marketing strategies and enhancing overall customer experiences. Conversely, BI facilitates data-driven decision-making, leading to heightened operational efficiency, product quality, and customer satisfaction. The analysis of customer data through BI unveils patterns and trends, informing product development, marketing campaigns, and customer service initiatives aimed at enriching experiences and knowledge. Customer experiential knowledge (CEK) encompasses customers' implicit comprehension of consumption experiences influenced by diverse factors, including social and cultural influences. This study primarily focuses on telecommunications companies in Jordan, scrutinizing how experiential customer knowledge mediates the relationship between marketing intelligence, business intelligence, and innovation in product and service offerings. Drawing on theoretical frameworks such as the resource-based view (RBV) and service-dominant logic (SDL), the research aims to comprehend how organizations utilize their resources, particularly knowledge, to foster innovation. Employing a quantitative research approach, the study collected and analyzed primary data to explore hypotheses. The chosen method was justified for its efficacy in handling large sample sizes. Structural equation modeling (SEM) facilitated by Smart PLS software evaluated the relationships between the constructs, followed by mediation analysis to assess the indirect associations in the model. The study findings offer insights into the intricate dynamics of organizational innovation, uncovering the interconnected relationships between business intelligence, customer experiential knowledge-based innovation (CEK-DI), marketing intelligence (MI), and product and service innovation (PSI), underscoring the pivotal role of advanced intelligence capabilities in developing innovative practices rooted in a profound understanding of customer experiences. Organizations equipped with cutting-edge BI tools are better positioned to devise strategies informed by precise insights into customer needs and behaviors. Furthermore, the positive impact of BI on PSI reaffirms the significance of data-driven decision-making in shaping the innovation landscape. Companies leveraging BI demonstrate adeptness in identifying market opportunities guiding the development of novel products and services. The substantial impact of CEK-DI on PSI highlights the crucial role of customer experiences in driving organizational innovation. Firms actively integrating customer insights into their innovation processes are more likely to create offerings aligned with customer expectations, fostering higher levels of product and service innovation. Additionally, the positive and significant effect of MI on CEK-DI underscores the critical role of market insights in shaping innovative strategies. While the relationship between MI and PSI is positive, a slightly weaker significance level indicates a nuanced association, suggesting that while MI contributes to innovation, other factors may also influence the innovation landscape, warranting further exploration. In conclusion, the study underscores the essential role of intelligence capabilities, particularly artificial intelligence, in driving innovation, emphasizing the necessity for organizations to leverage market and customer intelligence for effective and competitive innovation practices. Collaborative efforts between marketing and business intelligence serve as pivotal drivers of innovation, influencing experiential customer knowledge and shaping organizational strategies and practices, ultimately enhancing overall customer experiences and organizational performance.

Keywords: marketing intelligence, business intelligence, product, customer experiential knowledge-driven innovation

Procedia PDF Downloads 55

Authors: Olawale S. Fatoba, Stephen A. Akinlabi, Esther T. Akinlabi, Rezvan Gharehbaghi

Abstract:

The performance of material surface under wear and corrosion environments cannot be fulfilled by the conventional surface modifications and coatings. Therefore, different industrial sectors need an alternative technique for enhanced surface properties. Titanium and its alloys possess poor tribological properties which limit their use in certain industries. This paper focuses on the effect of hybrid coatings Al-Cu-Fe on a grade five titanium alloy using laser metal deposition (LMD) process. Icosahedral Al-Cu-Fe as quasicrystals is a relatively new class of materials which exhibit unusual atomic structure and useful physical and chemical properties. A 3kW continuous wave ytterbium laser system (YLS) attached to a KUKA robot which controls the movement of the cladding process was utilized for the fabrication of the coatings. The titanium cladded surfaces were investigated for its hardness, corrosion and tribological behaviour at different laser processing conditions. The samples were cut to corrosion coupons, and immersed into 3.65% NaCl solution at 28oC using Electrochemical Impedance Spectroscopy (EIS) and Linear Polarization (LP) techniques. The cross-sectional view of the samples was analysed. It was found that the geometrical properties of the deposits such as width, height and the Heat Affected Zone (HAZ) of each sample remarkably increased with increasing laser power due to the laser-material interaction. It was observed that there are higher number of aluminum and titanium presented in the formation of the composite. The indentation testing reveals that for both scanning speed of 0.8 m/min and 1m/min, the mean hardness value decreases with increasing laser power. The low coefficient of friction, excellent wear resistance and high microhardness were attributed to the formation of hard intermetallic compounds (TiCu, Ti2Cu, Ti3Al, Al3Ti) produced through the in situ metallurgical reactions during the LMD process. The load-bearing capability of the substrate was improved due to the excellent wear resistance of the coatings. The cladded layer showed a uniform crack free surface due to optimized laser process parameters which led to the refinement of the coatings.

Keywords: Al-Cu-Fe coating, corrosion, intermetallics, laser metal deposition, Ti-6Al-4V alloy, wear resistance

Procedia PDF Downloads 172

Authors: Ruth Hegarty, Noel Connaughton

Abstract:

Sustainability reporting has become a priority for many global multinational companies. This is associated with ever-increasing expectations from key stakeholders for companies to be transparent about their strategies, activities and management with regard to sustainability issues. The Global Reporting Initiative (GRI) encourages reporters to only provide information on the issues that are really critical in order to achieve the organisation’s goals for sustainability and manage its impact on environment and society. A key challenge for most reporting organisations is how to identify relevant issues for sustainability reporting and prioritise those material issues in accordance with company and stakeholder needs. A recent study indicates that most of the largest companies listed on the world’s stock exchanges are failing to provide data on key sustainability indicators such as employee turnover, energy, greenhouse gas emissions (GHGs), injury rate, pay equity, waste and water. This paper takes an indepth look at the approaches used by a select number of international sustainability leader corporates to identify key sustainability issues. The research methodology involves performing a detailed analysis of the sustainability report content of up to 50 companies listed on the 2014 Dow Jones Sustainability Indices (DJSI). The most recent sustainability report content found on the GRI Sustainability Disclosure Database is then compared with 91 GRI Specific Standard Disclosures and a small number of GRI Standard Disclosures. Preliminary research indicates significant gaps in the information disclosed in corporate sustainability reports versus the indicator content specified in the GRI Content Index. The following outlines some of the key findings to date: Most companies made a partial disclosure with regard to the Economic indicators of climate change risks and infrastructure investments, but did not focus on the associated negative impacts. The top Environmental indicators disclosed were energy consumption and reductions, GHG emissions, water withdrawals, waste and compliance. The lowest rates of indicator disclosure included biodiversity, water discharge, mitigation of environmental impacts of products and services, transport, environmental investments, screening of new suppliers and supply chain impacts. The top Social indicators disclosed were new employee hires, rates of injury, freedom of association in operations, child labour and forced labour. Lesser disclosure rates were reported for employee training, composition of governance bodies and employees, political contributions, corruption and fines for non-compliance. The reporting on most other Social indicators was found to be poor. In addition, most companies give only a brief explanation on how material issues are defined, identified and ranked. Data on the identification of key stakeholders and the degree and nature of engagement for determining issues and their weightings is also lacking. Generally, little to no data is provided on the algorithms used to score an issue. Research indicates that most companies lack a rigorous and thorough methodology to systematically determine the material issues of sustainability reporting in accordance with company and stakeholder needs.

Keywords: identification of key stakeholders, material issues, sustainability reporting, transparency

Procedia PDF Downloads 300

Authors: Sahar Abbaszadeh, Sharifah Rafidah Wan Alwi, Colin Webb, Nahid Ghasemi, Ida Idayu Muhamad

Abstract:

Toxic heavy metal discharges into environment due to rapid industrialization is a serious pollution problem that has drawn global attention towards their adverse impacts on both the structure of ecological systems as well as human health. Lead as toxic and bio-accumulating elements through the food chain, is regularly entering to water bodies from discharges of industries such as plating, mining activities, battery manufacture, paint manufacture, etc. The application of conventional methods to degrease and remove Pb(II) ion from wastewater is often restricted due to technical and economic constrains. Therefore, the use of various agro-wastes as low-cost bioadsorbent is found to be attractive since they are abundantly available and cheap. In this study, activated carbon of papaya peel (AC-PP) (as locally available agricultural waste) was employed to evaluate its Pb(II) uptake capacity from single-solute solutions in sets of batch mode experiments. To assess the surface characteristics of the adsorbents, the scanning electron microscope (SEM) coupled with energy disperse X-ray (EDX), and Fourier transform infrared spectroscopy (FT-IR) analysis were utilized. The removal amount of Pb(II) was determined by atomic adsorption spectrometry (AAS). The effects of pH, contact time, the initial concentration of Pb(II) and adsorbent dosage were investigated. The pH value = 5 was observed as optimum solution pH. The optimum initial concentration of Pb(II) in the solution for AC-PP was found to be 200 mg/l where the amount of Pb(II) removed was 36.42 mg/g. At the agitating time of 2 h, the adsorption processes using 100 mg dosage of AC-PP reached equilibrium. The experimental results exhibit high capability and metal affinity of modified papaya peel waste with removal efficiency of 93.22 %. The evaluation results show that the equilibrium adsorption of Pb(II) was best expressed by Freundlich isotherm model (R2 > 0.93). The experimental results confirmed that AC-PP potentially can be employed as an alternative adsorbent for Pb(II) uptake from industrial wastewater for the design of an environmentally friendly yet economical wastewater treatment process.

Keywords: activated carbon, bioadsorption, lead removal, papaya peel, wastewater treatment

Procedia PDF Downloads 279

Authors: Bartlomiej S. Witkowski, Lukasz Wachnicki, Sylwia Gieraltowska, Rafal Pietruszka, Marek Godlewski

Abstract:

Zinc oxide is extensively studied II-VI semiconductor with a direct energy gap of about 3.37 eV at room temperature and high transparency in visible light spectral region. Due to these properties, ZnO is an attractive material for applications in photovoltaic, electronic and optoelectronic devices. ZnO nanorods, due to a well-developed surface, have potential of applications in sensor technology and photovoltaics. In this work we present a new inexpensive method of the ultra-fast growth of ZnO nanorods from the aqueous solution. This environment friendly and fully reproducible method allows growth of nanorods in few minutes time on various substrates, without any catalyst or complexing agent. Growth temperature does not exceed 50ºC and growth can be performed at atmospheric pressure. The method is characterized by simplicity and allows regulation of size of the ZnO nanorods in a large extent. Moreover the method is also very safe, it requires organic, non-toxic and low-price precursors. The growth can be performed on almost any type of substrate through the homo-nucleation as well as hetero-nucleation. Moreover, received nanorods are characterized by a very high quality - they are monocrystalline as confirmed by XRD and transmission electron microscopy. Importantly oxygen vacancies are not found in the photoluminescence measurements. First results for obtained by us ZnO nanorods in sensor applications are very promising. Resistance UV sensor, based on ZnO nanorods grown on a quartz substrates shows high sensitivity of 20 mW/m2 (2 μW/cm2) for point contacts, especially that the results are obtained for the nanorods array, not for a single nanorod. UV light (below 400 nm of wavelength) generates electron-hole pairs, which results in a removal from the surfaces of the water vapor and hydroxyl groups. This reduces the depletion layer in nanorods, and thus lowers the resistance of the structure. The so-obtained sensor works at room temperature and does not need the annealing to reset to initial state. Details of the technology and the first sensors results will be presented. The obtained ZnO nanorods are also applied in simple-architecture photovoltaic cells (efficiency over 12%) in conjunction with low-price Si substrates and high-sensitive photoresistors. Details informations about technology and applications will be presented.

Keywords: hydrothermal method, photoresistor, photovoltaic cells, ZnO nanorods

Procedia PDF Downloads 430

Authors: Csaba Kalman

Abstract:

In spite of the existence of numerous second language (L2) motivation theories, the teacher’s role in motivating learners has remained an under-researched niche to this day. If we narrow down our focus on the teacher’s role on motivating adult learners of English in an English as a Foreign Language (EFL) context in corporate environments, empirical research is practically non-existent. This study fills the above research niche by exploring the most motivating aspects of the teacher’s personality, behaviour, and teaching practices that affect adult learners’ L2 motivation in corporate contexts in Hungary. The study was conducted in a wide range of industries in 18 organisations that employ over 250 people in Hungary. In order to triangulate the research, 21 human resources managers, 18 language teachers, and 466 adult learners of English were involved in the investigation by participating in interview studies, and quantitative questionnaire studies that measured ten scales related to the teacher’s role, as well as two criterion measure scales of intrinsic and extrinsic motivation. The qualitative data were analysed using a template organising style, while descriptive, inferential statistics, as well as multivariate statistical techniques, such as correlation and regression analyses, were used for analysing the quantitative data. The results showed that certain aspects of the teacher’s personality (thoroughness, enthusiasm, credibility, and flexibility), as well as preparedness, incorporating English for Specific Purposes (ESP) in the syllabus, and focusing on the present, proved to be the most salient aspects of the teacher’s motivating influence. The regression analyses conducted with the criterion measure scales revealed that 22% of the variance in learners’ intrinsic motivation could be explained by the teacher’s preparedness and appearance, and 23% of the variance in learners’ extrinsic motivation could be attributed to the teacher’s personal branding and incorporating ESP in the syllabus. The findings confirm the pivotal role teachers play in motivating L2 learners independent of the context they teach in; and, at the same time, call for further research so that we can better conceptualise the motivating influence of L2 teachers.

Keywords: adult learners, corporate contexts, motivation, teacher’s role

Procedia PDF Downloads 95

Authors: Somruay Apichatibutarapong

Abstract:

The purpose of this study was to address and comparison of the attitudes towards the statistics course for undergraduate students. Data were collected from 120 students in Faculty of Sciences and Technology, Suan Sunandha Rajabhat University who enrolled in the statistics course. The quantitative approach was used to investigate the assessment and comparison of attitudes towards statistics course. It was revealed that the overall attitudes somewhat agree both in pre-test and post-test. In addition, the comparison of students’ attitudes towards the statistic course (Form A) has no difference in the overall attitudes. However, there is statistical significance in all dimensions and overall attitudes towards the statistics course (Form B).

Keywords: statistics attitude, student’s attitude, statistics, attitude test

Procedia PDF Downloads 449

Authors: Jared Abuga, Wesley Too

Abstract:

Medical error is the third leading cause of death in US, with approximately 98,000 deaths occurring every year as a result of medical errors. The world financial burden of medication errors is roughly USD 42 billion. Medication errors may lead to at least one death daily and injure roughly 1.3 million people every year. Medical error reporting is essential in creating a culture of accountability in our healthcare system. Studies have shown that attitudes and practice of healthcare workers in reporting medical errors showed that the major factors in under-reporting of errors included work stress and fear of medico-legal consequences due to the disclosure of error. Further, the majority believed that increase in reporting medical errors would contribute to a better system. Most hospitals depend on nurses to discover medication errors because they are considered to be the sources of these errors, as contributors or mere observers, consequently, the nurse’s perception of medication errors and what needs to be done is a vital feature to reducing incidences of medication errors. We sought to explore knowledge among nurses on medical errors and factors affecting or hindering reporting of medical errors among nurses working at the emergency unit, KNH. Critical care nurses are faced with many barriers to completing incident reports on medication errors. One of these barriers which contribute to underreporting is a lack of education and/or knowledge regarding medication errors and the reporting process. This study, therefore, sought to determine the availability and the use of reporting systems for medical errors in critical care unity. It also sought to establish nurses’ perception regarding medical errors and reporting and document factors facilitating timely identification and reporting of medical errors in critical care settings. Methods: The study used cross-section study design to collect data from 76 critical care nurses from Kenyatta Teaching & Research National Referral Hospital, Kenya. Data analysis and results is ongoing. By October 2022, we will have analysis, results, discussions, and recommendations of the study for purposes of the conference in 2023

Keywords: errors, medical, kenya, nurses, safety

Procedia PDF Downloads 234