Search results for: hypobaric chamber training

Authors: V. Mantey, N. Findlay, I. Maddox

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The quality and quantity of global satellite data have been increasing exponentially in recent years as spaceborne systems become more affordable and the sensors themselves become more sophisticated. This is a valuable resource for many applications, including disaster management and relief. However, while more information can be valuable, the volume of data available is impossible to manually examine. Therefore, the question becomes how to extract as much information as possible from the data with limited manpower. Buildings are a key feature of interest in satellite imagery with applications including telecommunications, population models, and disaster relief. Machine learning tools are fast becoming one of the key resources to solve this problem, and models have been developed to detect buildings in optical satellite imagery. However, by and large, most models focus on affluent regions where buildings are generally larger and constructed further apart. This work is focused on the more difficult problem of detection in populated regions. The primary challenge with detecting small buildings in densely populated regions is both the spatial and spectral resolution of the optical sensor. Densely packed buildings with similar construction materials will be difficult to separate due to a similarity in color and because the physical separation between structures is either non-existent or smaller than the spatial resolution. This study finds that training models until they are overfitting the input sample can perform better in these areas than a more robust, generalized model. An overfitted model takes less time to fine-tune from a generalized pre-trained model and requires fewer input data. The model developed for this study has also been fine-tuned using existing, open-source, building vector datasets. This is particularly valuable in the context of disaster relief, where information is required in a very short time span. Leveraging existing datasets means that little to no manpower or time is required to collect data in the region of interest. The training period itself is also shorter for smaller datasets. Requiring less data means that only a few quality areas are necessary, and so any weaknesses or underpopulated regions in the data can be skipped over in favor of areas with higher quality vectors. In this study, a landcover classification model was developed in conjunction with the building detection tool to provide a secondary source to quality check the detected buildings. This has greatly reduced the false positive rate. The proposed methodologies have been implemented and integrated into a configurable production environment and have been employed for a number of large-scale commercial projects, including continent-wide DEM production, where the extracted building footprints are being used to enhance digital elevation models. Overfitted machine learning models are often considered too specific to have any predictive capacity. However, this study demonstrates that, in cases where input data is scarce, overfitted models can be judiciously applied to solve time-sensitive problems.

Keywords: building detection, disaster relief, mask-RCNN, satellite mapping

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Authors: Mohamed Mokhtar, Mostafa F. Shaaban

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Among the challenges facing solar photovoltaic (PV) systems in the United Arab Emirates (UAE), dust accumulation on solar panels is considered the most severe problem that faces the growth of solar power plants. The accumulation of dust on the solar panels significantly degrades output from these panels. Hence, solar PV panels have to be cleaned manually or using costly automated cleaning methods. This paper focuses on initiating cleaning actions when required to reduce maintenance costs. The cleaning actions are triggered only when the dust level exceeds a threshold value. The amount of dust accumulated on the PV panels is estimated using an artificial neural network (ANN). Experiments are conducted to collect the required data, which are used in the training of the ANN model. Then, this ANN model will be fed by the output power from solar panels, ambient temperature, and solar irradiance, and thus, it will be able to estimate the amount of dust accumulated on solar panels at these conditions. The model was tested on different case studies to confirm the accuracy of the developed model.

Keywords: machine learning, dust, PV panels, renewable energy

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Authors: Oladimeji Adetona Adeyeye, Emmanuel Rotimi Sadiku, Oluwaseun Olayinka Adeyeye

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In order to resuscitate soybean production and post-harvest processing especially, in term of threshing, there is need to develop an affordable threshing machine which will reduce drudgery associated with manual soybean threshing. Soybean thresher was fabricated and evaluated at Institute of Agricultural Research and Training IAR&T Apata Ibadan. The machine component includes; hopper, threshing unit, shaker, cleaning unit and the seed outlet, all working together to achieve the main objective of threshing and cleaning. TGX1835 - 10E variety was used for evaluation because of its high resistance to pests, rust and pustules. The final moisture content of the used sample was about 15%. The sample was weighed and introduced into the machine. The parameters evaluated includes moisture content, threshing efficiency, cleaning efficiency, machine capacity and speed. The threshing efficiency and capacity are 74% and 65.9kg/hr respectively. All materials used were sourced locally which makes the cost of production of the machine extremely cheaper than the imported soybean thresher.

Keywords: efficiency, machine capacity, speed, soybean, threshing

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Authors: Ana Maghfiroh

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Learning a foreign language needs a big effort from the learner itself to make their language ability grows better day by day. Among those, they also need a support from all around them including teacher, friends, as well as activities which support them to speak the language. When those activities developed well as a habit which are done regularly, it will help improving the students’ language competence. It was a qualitative research which aimed to find out and describe some activities implemented in Pesantren Al Mawaddah, Ponorogo, in order to teach the students a foreign language. In collecting the data, the researcher used interview, questionnaire, and documentation. From the study, it was found that Pesantren Al Mawaddah had successfully built the language habit on the students to speak the target language. More than 15 hours a day students were compelled to speak foreign language, Arabic or English, in turn. It aimed to habituate the students to keep in touch with the target language. The habit was developed through daily language activities, such as dawn vocabs giving, dictionary handling, daily language use, speech training and language intensive course, daily language input, and night vocabs memorizing. That habit then developed the students awareness towards the language learned as well as promoted their language mastery.

Keywords: habit, communicative competence, daily language activities, Pesantren

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Authors: Lenka Mynaříková, Hedvika Boukalová

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The study looks at the ability to detect nonverbal deception among police officers and management students in the Czech Republic. Respondents from police departments (n=197) and university students of human resources (n=161) completed a deception detection task and evaluated veracity of the statements of suspects in 21 video clips from real crime investigations. Their evaluations were based on nonverbal behavior. Voices in the video clips were modified so that words were not recognizable, yet paraverbal voice characteristics were preserved. Results suggest that respondents have a tendency to lie bias based on their profession. In the evaluation of video clips, stereotypes also played a significant role. The statements of suspects of a different ethnicity, younger age or specific visual features were considered deceitful more often. Research might be beneficial for training in professions that are in need of deception detection techniques.

Keywords: deception detection, police officers, human resources, forensic psychology, forensic studies, organizational psychology

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Authors: Anas H. Aljemely, Jianping Xuan

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Rolling bearing fault diagnosis plays a pivotal issue in the rotating machinery of modern manufacturing. In this research, a raw vibration signal and improved deep learning method for bearing fault diagnosis are proposed. The multi-dimensional scales of raw vibration signals are selected for evaluation condition monitoring system, and the deep learning process has shown its effectiveness in fault diagnosis. In the proposed method, employing an Exponential linear unit (ELU) layer in a convolutional neural network (CNN) that conducts the identical function on positive data, an exponential nonlinearity on negative inputs, and a particular convolutional operation to extract valuable features. The identification results show the improved method has achieved the highest accuracy with a 100-dimensional scale and increase the training and testing speed.

Keywords: bearing fault prognostics, developed CNN model, multiple-scale evaluation, deep learning features

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Authors: Assamen Ayalew Ejigu, Liang-Chiun Chao

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In recent semiconductor and nanotechnology, quality material synthesis, proper characterizations, and productions are the big challenges. As cuprous oxide (Cu2O) is a promising semiconductor material for photovoltaic (PV) and other optoelectronic applications, this study was aimed at to grow and characterize high quality Cu2O nanorods for the improvement of the efficiencies of thin film solar cells and other potential applications. In this study, well-structured cuprous oxide (Cu2O) nanorods were successfully fabricated using IBSD method in which the Cu2O samples were grown on silicon substrates with a substrate temperature of 400°C in an IBSD chamber of pressure of 4.5 x 10-5 torr using copper as a target material. Argon, and oxygen gases were used as a sputter and reactive gases, respectively. The characterization of the Cu2O nanorods (NRs) were done in comparison with Cu2O thin film (TF) deposited with the same method but with different Ar:O2 flow rates. With Ar:O2 ratio of 9:1 single phase pure polycrystalline Cu2O NRs with diameter of ~500 nm and length of ~4.5 µm were grow. Increasing the oxygen flow rates, pure single phase polycrystalline Cu2O thin film (TF) was found at Ar:O2 ratio of 6:1. The field emission electron microscope (FE-SEM) measurements showed that both samples have smooth morphologies. X-ray diffraction and Rama scattering measurements reveals the presence of single phase Cu2O in both samples. The differences in Raman scattering and photoluminescence (PL) bands of the two samples were also investigated and the results showed us there are differences in intensities, in number of bands and in band positions. Raman characterization shows that the Cu2O NRs sample has pronounced Raman band intensities, higher numbers of Raman bands than the Cu2O TF which has only one second overtone Raman signal at 2 (217 cm-1). The temperature dependent photoluminescence (PL) spectra measurements, showed that the defect luminescent band centered at 720 nm (1.72 eV) is the dominant one for the Cu2O NRs and the 640 nm (1.937 eV) band was the only PL band observed from the Cu2O TF. The difference in optical and structural properties of the samples comes from the oxygen flow rate change in the process window of the samples deposition. This gave us a roadmap for further investigation of the electrical and other optical properties for the tunable fabrication of the Cu2O nano/micro structured sample for the improvement of the efficiencies of thin film solar cells in addition to other potential applications. Finally, the novel morphologies, excellent structural and optical properties seen exhibits the grown Cu2O NRs sample has enough quality to be used in further research of the nano/micro structured semiconductor materials.

Keywords: defect levels, nanorods, photoluminescence, Raman modes

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Authors: Lu Si, Jie Yu, Shasha Li, Jun Ma, Lei Luo, Qingbo Wu, Yongqi Ma, Zhengji Liu

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Instance selection (IS) technique is used to reduce the data size to improve the performance of data mining methods. Recently, to process very large data set, several proposed methods divide the training set into some disjoint subsets and apply IS algorithms independently to each subset. In this paper, we analyze the limitation of these methods and give our viewpoint about how to divide and conquer in IS procedure. Then, based on fast condensed nearest neighbor (FCNN) rule, we propose a large data sets instance selection method with MapReduce framework. Besides ensuring the prediction accuracy and reduction rate, it has two desirable properties: First, it reduces the work load in the aggregation node; Second and most important, it produces the same result with the sequential version, which other parallel methods cannot achieve. We evaluate the performance of FCNN-MR on one small data set and two large data sets. The experimental results show that it is effective and practical.

Keywords: instance selection, data reduction, MapReduce, kNN

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Authors: Gaurav Sancheti

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This study presents an impacting approach of Artificial Neural Networks (ANNs) in determining the quantity of High Tensile Steel (HTS) strands required in post-tensioned (PT) slabs. Various PT slab configurations were generated by varying the span and depth of the slab. For each of these slab configurations, quantity of required HTS strands were recorded. ANNs with backpropagation algorithm and varying architectures were developed and their performance was evaluated in terms of Mean Square Error (MSE). The recorded data for the quantity of HTS strands was used as a feeder database for training the developed ANNs. The networks were validated using various validation techniques. The results show that the proposed ANNs have a great potential with good prediction and generalization capability.

Keywords: artificial neural networks, back propagation, conceptual design, high tensile steel strands, post tensioned slabs, validation techniques

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Authors: Mariia Karizhenskaia, Tanvi Nandani, Ali Tafazoli Moghadam

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Hospice palliative care (HPC) is one of the most complicated philosophies of care in which physical, social/cultural, and spiritual aspects of human life are intermingled with an undeniably significant role in every aspect. Among these dimensions of care, culture possesses an outstanding position in the process and goal determination of HPC. This study shows the importance of cultural elements in the establishment of effective and optimized structures of HPC in the Canadian healthcare environment. Our systematic search included Medline, Google Scholar, and St. Lawrence College Library, considering original, peer-reviewed research papers published from 1998 to 2023 to identify recent national literature connecting culture and palliative care delivery. The most frequently presented feature among the articles is the role of culture in the efficiency of the HPC. It has been shown frequently that including the culturespecific parameters of each nation in this system of care is vital for its success. On the other hand, ignorance about the exclusive cultural trends in a specific location has been accompanied by significant failure rates. Accordingly, implementing a culture-wise adaptable approach is mandatory for multicultural societies. The following outcome of research studies in this field underscores the importance of culture-oriented education for healthcare staff. Thus, all the practitioners involved in HPC will recognize the importance of traditions, religions, and social habits for processing the care requirements. Cultural competency training is a telling sample of the establishment of this strategy in health care that has come to the aid of HPC in recent years. Another complexity of the culturized HPC nowadays is the long-standing issue of racialization. Systematic and subconscious deprivation of minorities has always been an adversity of advanced levels of care. The last part of the constellation of our research outcomes is comprised of the ethical considerations of culturally driven HPC. This part is the most sophisticated aspect of our topic because almost all the analyses, arguments, and justifications are subjective. While there was no standard measure for ethical elements in clinical studies with palliative interventions, many research teams endorsed applying ethical principles for all the involved patients. Notably, interpretations and projections of ethics differ in varying cultural backgrounds. Therefore, healthcare providers should always be aware of the most respectable methodologies of HPC on a case-by-case basis. Cultural training programs have been utilized as one of the main tactics to improve the ability of healthcare providers to address the cultural needs and preferences of diverse patients and families. In this way, most of the involved health care practitioners will be equipped with cultural competence. Considerations for ethical and racial specifications of the clients of this service will boost the effectiveness and fruitfulness of the HPC. Canadian society is a colorful compilation of multiple nationalities; accordingly, healthcare clients are diverse, and this divergence is also translated into HPC patients. This fact justifies the importance of studying all the cultural aspects of HPC to provide optimal care on this enormous land.

Keywords: cultural competence, end-of-life care, hospice, palliative care

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Authors: Stephen O. Amaraihu

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This research investigated the effect of Computer Based Instruction on Students’ interest, achievement, and retention in Algebra in Abia State College of Education (Technical), Arochukwu. Three research questions and two hypotheses guided the study. Two instruments, Maths Achievement Test (MAT) and Maths Interest Inventory were employed, to test a population of three hundred and sixteen (316) NCE 1 students in algebra. It is expected that this research will lead to the improvement of students’ performance and enhance their interest and retention of basic algebraic concept. It was found that the majority of students in the college are not proficient in the use of ICT as a result of a lack of trained personnel. It was concluded that the state government was not ready to implement the usage of mathematics in Abia State College of Education. The paper recommends, amongst others, the employment of mathematics Lectures with competent skills in ICT and the training of lecturers of mathematics.

Keywords: achievement, computer based instruction, interest, retention

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Authors: Aparajita Mukherjee, D. P. Mukherjee

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This paper critically assessed pollution problems in small and medium enterprises with unique references to foundries and sponge iron industries to survey the adverse impact on human societies and the environment. The objective of this paper was to show how cleaner production concept was implemented in one foundry through improvisation of existing technology in India. Incremental advancement of existing technology minimized environmental issues and resource utilization. This study depicted that poor fiscal help, poor enforcement of government regulations, owners’ attitude and lacking specialized technical workers were the significant hindrances towards cleaner production. The paper explored the possible ways to overcome these hindrances for cleaner production. On a more general level, findings raise important questions regarding the need for a new paradigm for the implementation of cleaner production. Improvisation of existing technology in these enterprises would be cost effective towards sustainable development.

Keywords: SME pollution, ecological crisis, sustainable development, cleaner production, training

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Authors: Manuel Chacon-Fuentes, Leonardo Bardehle, Marcelo Lizama, Claudio Reyes, Andres Quiroz

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The insect-plant interaction is a complex process in which the plant is able to release chemical signaling that modifies the behavior of insects. Insect herbivory can trigger mechanisms that allow the increase in the production of secondary metabolites that allow coping against the herbivores. Monoterpenes are a kind of secondary metabolites involved in direct defense acting as repellents of herbivorous or even in indirect defense acting as attractants for insect predators. In addition, an increase of the monoterpenes concentration is an effect commonly associated with the herbivory. Hence, plants subjected to damage by herbivory increase the monoterpenes production in comparison to plants without herbivory. In this framework, co-evolutionary aspects play a fundamental role in the adaptation of the herbivorous to their host and in the counter-adaptive strategies of the plants to avoid the herbivorous. In this context, Ugni molinae 'murtilla' is a native shrub from Chile characterized by its antioxidant activity mainly related to the phenolic compounds presents in its fruits. The larval stage of the red grape caterpillar Chilesia rudis Butler (Lepidoptera: Arctiidae) has been reported as an important defoliator of U. molinae. This insect is native from Chile and probably has been involved in a co-evolutionary process with murtilla. Therefore, we hypothesized that herbivory by the red grape caterpillar increases the emission of monoterpenes in Ugni molinae. Ecotypes 19-1 and 22-1 of murtilla were established and maintained at 25° C in the Laboratorio de Química Ecológica at Universidad de La Frontera. Red grape caterpillars of ⁓40 mm were collected near to Temuco (Chile) from grasses, and they were deprived of food for 24 h before performing the assays. Ten caterpillars were placed on the foliage of the ecotypes 19-1 and 22-1 and allowed to feed during 48 h. After this time, caterpillars were removed from the ecotypes and monoterpenes were collected. A glass chamber was used to enclose the ecotypes and a Porapak-Q column was used to trap the monoterpenes. After 24 h of capturing, columns were desorbed with hexane. Then, samples were injected in a gas chromatograph coupled to mass spectrometer and monoterpenes were determined according to the NIST library. All the experiments were performed in triplicate. Results showed that α-pinene, β-phellandrene, limonene, and 1,8 cineole were the main monoterpenes released by murtilla ecotypes. For the ecotype 19-1, the abundance of α-pinene was significantly higher in plants subjected to herbivory (100%) in relation to control plants (54.58%). Moreover, β-phellandrene and 1,8 cineole were observed only in control plants. For ecotype 22-1, there was no significant difference in monoterpenes abundance. In conclusion, the results suggest a trade-off of β-phellandrene and 1,8 cineole in response to herbivory damage by red grape caterpillar generating an increase in α-pinene abundance.

Keywords: Chilesia rudis, gas chromatography, monoterpenes, Ugni molinae

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Authors: Yusra A. Ibrahim

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Educational efforts to address the school-to-prison pipeline (STPP) and retain students in school require equipping teachers with evidence-based approaches to handle social-emotional behavior (SEB) needs. One aspect of these efforts involves training teachers to utilize effective and current technologies, thereby reducing SEB challenges faced by students with disabilities in their classrooms. This literature review examines eight studies conducted within the past 10 years (from 2013 to 2023) that focus on enhancing SEB needs of students with disabilities using technology. The review reveals that autism spectrum disorder (ASD), emotional behavioral disorder (EBD), and attention deficit and hyperactivity disorder (ADHD) are the predominant disabilities studied through technology interventions. Additionally, it highlights that these studies focused on examining the effectiveness of technologies in reducing disruptive behaviors, increasing on-task behaviors, reducing anxiety, and promoting social skills.

Keywords: school-to-prison pipeline, technology, evidence-based practices, EBD

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Authors: Gabriel Jinga, Madalina Dumitru

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The objective of this paper is to present the process of change in management accounting in Romania, a former communist country from Eastern Europe. In order to explain this process, we used the contingency and institutional theories. We focused on the following directions: the presentation of the scientific context and motivation of this research and the case study. We presented the state of the art in the process of change in the management accounting from the international and national perspective. We also described the evolution of management accounting in Romania in the context of economic and political changes. An important moment was the fall of communism in 1989. This represents a starting point for a new economic environment and for new management accounting. Accordingly, we developed a case study which presented this evolution. The conclusion of our research was that the changes in the management accounting system of the company analysed occurred in the same time with the institutionalization of some elements (e.g. degree of competition, training and competencies in management accounting). The management accounting system was modeled by the contingencies specific to this company (e.g. environment, industry, strategy).

Keywords: management accounting, change, Romania, contingency, institutional theory

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Authors: Asmaa Shehata

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Prior research indicates that second language (L2) learners encounter difficulty in the distinguishing novel L2 contrasting sounds that are not contrastive in their native languages. L2 orthographic information, however, is found to play a positive role in the acquisition of non-native phoneme contrasts. While most studies have mainly involved a familiar written script (i.e., the Roman script), the influence of a foreign, unfamiliar script is still unknown. Therefore, the present study asks: Does unfamiliar L2 script play a role in creating distinct phonological representations of novel contrasting phonemes? It is predicted that subjects’ performance in the unfamiliar orthography group will outperform their counterparts’ performance in the control group. Thus, training that entails orthographic inputs can yield a significant improvement in L2 adult learners’ identification and lexical encoding of novel L2 consonant contrasts. Results are discussed in terms of their implications for the type of input introduced to L2 learners to improve their language learning.

Keywords: Arabic, consonant contrasts, foreign script, lexical encoding, orthography, word learning

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Authors: Ashish James, Anusha James

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The impressive predictive performance of deep learning techniques on a wide range of tasks has led to its widespread use. Estimating the confidence of these predictions is paramount for improving the safety and reliability of such systems. However, the uncertainty estimates provided by neural networks (NNs) tend to be overconfident and unreasonable. Ensemble of NNs typically produce good predictions but uncertainty estimates tend to be inconsistent. Inspired by these, this paper presents a framework that can quantitatively estimate the uncertainties by leveraging the advances in transfer learning through slight modification to the existing training pipelines. This promising algorithm is developed with an intention of deployment in real world problems which already boast a good predictive performance by reusing those pretrained models. The idea is to capture the behavior of the trained NNs for the base task by augmenting it with the uncertainty estimates from a supplementary network. A series of experiments with known and unknown distributions show that the proposed approach produces well calibrated uncertainty estimates with high quality predictions.

Keywords: uncertainty estimation, neural networks, transfer learning, regression

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Authors: Abhishek Sharma, Arnab Banerjee

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The research is intended to study the solar electric propulsion (SEP) technology for planetary missions. The main benefits of using solar electric propulsion for such missions are shorter flight times, more frequent target accessibility and the use of a smaller launch vehicle than that required by a comparable chemical propulsion mission. Energized by electric power from on-board solar arrays, the electrically propelled system uses 10 times less propellant than conventional chemical propulsion system, yet the reduced fuel mass can provide vigorous power which is capable of propelling robotic and crewed missions beyond the Lower Earth Orbit (LEO). The various thrusters used in the SEP are gridded ion thrusters and the Hall Effect thrusters. The research is solely aimed to study the ion thrusters and investigate the complications related to it and what can be done to overcome the glitches. The ion thrusters are used because they are found to have a total lower propellant requirement and have substantially longer time. In the ion thrusters, the anode pushes or directs the incoming electrons from the cathode. But the anode is not maintained at a very high potential which leads to divergence. Divergence leads to the charges interacting against the surface of the thruster. Just as the charges ionize the xenon gases, they are capable of ionizing the surfaces and over time destroy the surface and hence contaminate it. Hence the lifetime of thruster gets limited. So a solution to this problem is using substances which are not easy to ionize as the surface material. Another approach can be to increase the potential of anode so that the electrons don’t deviate much or reduce the length of thruster such that the positive anode is more effective. The aim is to work on these aspects as to how constriction of the deviation of charges can be done by keeping the input power constant and hence increase the lifetime of the thruster. Predominantly ring cusp magnets are used in the ion thrusters. However, the study is also intended to observe the effect of using solenoid for producing micro-solenoidal magnetic field apart from using the ring cusp magnetic field which are used in the discharge chamber for prevention of interaction of electrons with the ionization walls. Another foremost area of interest is what are the ways by which power can be provided to the Solar Electric Propulsion Vehicle for lowering and boosting the orbit of the spacecraft and also provide substantial amount of power to the solenoid for producing stronger magnetic fields. This can be successfully achieved by using the concept of Electro-dynamic tether which will serve as a power source for powering both the vehicle and the solenoids in the ion thruster and hence eliminating the need for carrying extra propellant on the spacecraft which will reduce the weight and hence reduce the cost of space propulsion.

Keywords: electro-dynamic tether, ion thruster, lifetime of thruster, solar electric propulsion vehicle

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Authors: L. Arumuru

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The paper investigates computer anxiety and the use of computerized library system by university librarians in Delta State University library, Abraka, Nigeria. Some of the root causes of computer anxiety among university librarians such as lack of exposure to computers at early age, inadequate computer skills, inadequate computer training, fear at the sight of a computer, lack of understanding of how computers work, etc. were pin-pointed in the study. Also, the different services rendered in the university libraries with the aid of computers such as reference services, circulation services, acquisition services, cataloguing and classification services, etc. were identified. The study employed the descriptive survey research design through the expo-facto method, with a population of 56 librarians, while the simple percentage and frequency counts were used to analyze the data generated from the administered copies of the questionnaire. Based on the aforementioned root causes of computer anxiety and the resultant effect on computerized library system, recommendations were proffered in the study.

Keywords: computer anxiety, computerized library system, library services, university librarians

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Authors: Eltaib Elzarrouk E. E. Abdalmajeed

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This research aims at shedding the light on environmental accounting, which is considered to be one of the most important areas in accounting discipline. It also studies the reality of the application of environmental accounting in the iron and steel sector in Libya. The questionnaire of this study was used for data collection from respondents who are employed in the Libyan Iron and Steel Company, Misurata – Libya (LISC). The Statistical Package for Social Sciences (SPSS) was also used for the analysis. Several important results were revealed include that the (LISC) relatively applies environmental accounting, and it faces some obstacles in conducting its application. Furthermore, the researched company realizes the importance of applying environmental accounting as a need for quality procedures. It was suggested that training courses should be held periodically to spread the awareness of environmental accounting environment. In addition, social responsibility and sustainability should be taken into consideration in the company's strategic plan.

Keywords: environment, environmental accounting, environmental accounting disclosure, The Libyan Iron and Steel Company, Misurata- Libya (LISC)

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Authors: Bandhan Dey, Muhsina Bintoon Yiasha, Gulam Sulaman Choudhury

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Chest disease is one of the most problematic ailments in our regular life. There are many known chest diseases out there. Diagnosing them correctly plays a vital role in the process of treatment. There are many methods available explicitly developed for different chest diseases. But the most common approach for diagnosing these diseases is through X-ray. In this paper, we proposed a multi-classification deep learning model for diagnosing COVID-19, lung cancer, pneumonia, tuberculosis, and atelectasis from chest X-rays. In the present work, we used the transfer learning method for better accuracy and fast training phase. The performance of three architectures is considered: InceptionV3, VGG-16, and VGG-19. We evaluated these deep learning architectures using public digital chest x-ray datasets with six classes (i.e., COVID-19, lung cancer, pneumonia, tuberculosis, atelectasis, and normal). The experiments are conducted on six-classification, and we found that VGG16 outperforms other proposed models with an accuracy of 95%.

Keywords: deep learning, image classification, X-ray images, Tensorflow, Keras, chest diseases, convolutional neural networks, multi-classification

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Authors: Philip Symonds, Jon Taylor, Zaid Chalabi, Michael Davies

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With the world climate projected to warm and major cities in developing countries becoming increasingly populated and polluted, governments are tasked with the problem of overheating and air quality in residential buildings. This paper presents the development of an adaptable model of these risks. Simulations are performed using the EnergyPlus building physics software. An accurate metamodel is formed by randomly sampling building input parameters and training on the outputs of EnergyPlus simulations. Metamodels are used to vastly reduce the amount of computation time required when performing optimisation and sensitivity analyses. Neural Networks (NNs) are compared to a Radial Basis Function (RBF) algorithm when forming a metamodel. These techniques were implemented using the PyBrain and scikit-learn python libraries, respectively. NNs are shown to perform around 15% better than RBFs when estimating overheating and air pollution metrics modelled by EnergyPlus.

Keywords: neural networks, radial basis functions, metamodelling, python machine learning libraries

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Authors: Ardalan Tofighi Soleimandarabi

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Artificial intelligence has revolutionized the concrete construction industry and improved processes by increasing efficiency, accuracy, and sustainability. This article examines the applications of artificial intelligence in predicting the compressive strength of concrete, optimizing mixing plans, and improving structural health monitoring systems. Artificial intelligence-based models, such as artificial neural networks (ANN) and combined machine learning techniques, have shown better performance than traditional methods in predicting concrete properties. In addition, artificial intelligence systems have made it possible to improve quality control and real-time monitoring of structures, which helps in preventive maintenance and increases the life of infrastructure. Also, the use of artificial intelligence plays an effective role in sustainable construction by optimizing material consumption and reducing waste. Although the implementation of artificial intelligence is associated with challenges such as high initial costs and the need for specialized training, it will create a smarter, more sustainable, and more affordable future for concrete structures.

Keywords: artificial intelligence, concrete construction, compressive strength prediction, structural health monitoring, stability

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Authors: Ugbor Raphael Oluchukwu

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The survey research design was adopted to examine whether liquidity and cash management practices of owner-managed firms in South East Nigeria influence their profitability, growth and survival. Four independent variables (accounting systems, working capital management, budgetary control, and managerial planning) were used in the evaluation which was restricted to eight small firms. Results indicate that one variable, working capital management alone dominate the liquidity perception of owner managers. As a result, owner managers find it difficult to meet maturing business obligations as growth sets in. The study also reveals that the four independent variables have significant impact on the profitability, growth and survival of owner managed firms. Owner managers are therefore advised to undertake regular entrepreneurship training in order to upgrade their liquidity and cash management knowledge and practices to enhance their overall performance.

Keywords: liquidity management, owner-managed firm, profitability, survival

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Authors: Evelyn Fabian

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The focus of this discussion centres on the emerging issues in Early Childhood Care and development in Nigeria. Early childhood care is the bedrock of Nigeria’s educational system. However, there are critical issues that had not been addressed and it is frustrating the entire educational process. Thus, this paper will show the inter-connectedness between these issues such as poor funding, trained skillful teachers that would supervise the learning process of the kids, unconducive learning environment and lack of relevant facilities. For a clear grasp of these issues, the researcher visited 36 early childhood centres distributed across the 36 spates of Nigeria. The findings which were expressed in simple percentages revealed a near total absence or government neglect of these critical areas. The findings equally showed a misplaced priority in the government allocation of funds to early child care education and development. The study concludes that this mismatch in the training of these categories of pupils, government should expedite action in addressing these emerging issues in early childhood care and development in Nigeria.

Keywords: early childhood, ECCE, education, emerging issues

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Authors: Nelomi Anandagoda, Leanne J. Eveson

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During the COVID-19 pandemic, the surge in in-patient admissions across the medical directorate of a district general hospital necessitated the implementation of an incident rota. Conscious of the impact on training and professional development, the idea of developing a virtual teaching programme was conceived. The programme initially aimed to provide junior doctors, specialist nurses, pharmacists, and allied healthcare professionals from medical specialties and those re-deployed from other specialties (e.g., ophthalmology, GP, surgery, psychiatry) the knowledge and skills to manage the deteriorating patient with COVID-19. The programme was later developed to incorporate the general internal medicine curriculum. To facilitate continuing medical education whilst maintaining social distancing during this period, a virtual platform was used to deliver teaching to junior doctors across two large district general hospitals and two community hospitals. Teaching sessions were recorded and uploaded to a common platform, providing a resource for participants to catch up on and re-watch teaching sessions, making strides towards reducing discrimination against the professional development of less than full-time trainees. Thus, creating a learning environment, which is inclusive and accessible to adult learners in a self-directed manner. The negative impact of the pandemic on the well-being of healthcare professionals is well documented. To support the multi-disciplinary team, the virtual teaching programme evolved to included sessions on well-being, resilience, and work-life balance. Providing teaching for learners across the multi-disciplinary team (MDT) has been an eye-opening experience. By challenging the concept that learners should only be taught within their own peer groups, the authors have fostered a greater appreciation of the strengths of the MDT and showcased the immense wealth of expertise available within the trust. The inclusive nature of the teaching and the ease of joining a virtual teaching session has facilitated the dissemination of knowledge across the MDT, thus improving patient care on the frontline. The weekly teaching programme has been running for over eight months, with ongoing engagement, interest, and participation. As described above, the teaching programme has evolved to accommodate the needs of its learners. It has received excellent feedback with an appreciation of its inclusive, multi-disciplinary, and holistic nature. The COVID-19 pandemic provided a catalyst to rapidly develop novel methods of working and training and widened access/exposure to the virtual technologies available to large organisations. By merging pedagogical expertise and technology, the authors have created an effective online learning environment. Although the authors do not propose to replace face-to-face teaching altogether, this model of virtual multidisciplinary team, cross-site teaching has proven to be a great leveler. It has made high-quality teaching accessible to learners of different confidence levels, grades, specialties, and working patterns.

Keywords: cross-site, cross-speciality, inter-disciplinary, multidisciplinary, virtual teaching

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Authors: Qurat Ul Ain Saleem, Kanwal Ameen

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The library and information science discipline is universally known as a feminist profession. It is considered a suitable field for females in Pakistan like a few other fields such as teaching and healthcare. It is also reflected through the uneven enrollment at graduate levels in library schools across the country as there are more females as compared to males. However, that uneven ratio does not really translate in the profession after passing out. There are more males in the professional as compared to females, as well as males can be seen on managerial and administrative posts majorly. A few females who joined the perception remain underrated and are hardly seen at managerial or administrative positions in the academic libraries. Therefore, this study was designed to highlight the perceptions of those females who have joined the profession to identify the issues related to equality faced by them as a professional. A qualitative research design based on a semi-structured interview was selected as an appropriate method to achieve the objectives of this study. Female librarians working in the higher education commission’s recognized public and private sector universities of Punjab, Pakistan, were selected as the population for this study. Female librarians shared that inequalities and discrimination based on face value, experience, communication, and relationship with the manager are common at their workplaces. They added that managers prefer male professionals to deal with delegation or presentations though we both can do that. Female professionals from the private sector believed that library managers make final hiring and selection decisions based on job duties and gender. However, the one with strong references will be preferred for the job. Also, private-sector employees suffered more prejudice due to the non-availability of proper patterns of promotions and increments. The government personnel said there is always a proper board/procedure for hiring and promotions; therefore, it is difficult for them to identify any inequality. Participants were dissatisfied with their managers for not allowing them to attend training and conferences. The majority of participants from the private sector said they wouldn't speak up to prejudice because they are afraid of losing their jobs and their voice is lost in a male-dominated society where males hold numerous authoritative positions and females are considered less competent. Nonetheless, the discrimination and inequalities affected the work motivation and enthusiasm of employees. Therefore, organizations should not discriminate against the staff in terms of facilities and benefits. The sample may not represent the true picture of gender equality in university libraries of Pakistan due to less number of participants and limited geographical boundaries. It is also assumed that some females may refrain from disclosing factual information or some may exaggerate the facts as a large number of participants requested to become part of the study. Equal opportunities should be offered to female library professionals to uplift and involve them to mitigate the perception of gender dominance. The organizations or immediate authorities should allow their staff to participate in training opportunities to learn modern practices to better serve the community.

Keywords: equality-workplace, libraries as workplace, female professionals, librarians-Pakistan

Procedia PDF Downloads 117

Authors: A. Karasek

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Rapid growth of Information Technologies (IT) has had huge influence on enterprises, and it has contributed to its promotion and increasingly extensive use in enterprises. Information Technologies have to a large extent determined the processes taking place in a enterprise; what is more, IT development has brought the need to adopt a brand new approach to human resources management in an enterprise. The use of IT in Human Resource Management (HRM) is of high importance due to the growing role of information and information technologies. The aim of this paper is to evaluate the use of information technologies in human resources management in enterprises. These practices will be presented in the following areas: Recruitment and selection, development and training, employee assessment, motivation, talent management, personnel service. Results of conducted survey show diversity of solutions applied in particular areas of human resource management. In the future, further development in this area should be expected, as well as integration of individual HRM areas, growing mobile-enabled HR processes and their transfer into the cloud. Presented IT solutions applied in HRM are highly innovative, which is of great significance due to their possible implementation in other enterprises.

Keywords: e-HR, human resources management, HRM practices, HRMS, information technologies

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Authors: Maryam Fallahpoor, Biswajeet Pradhan

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Background: Artificial intelligence (AI) serves as a valuable tool in mitigating the scarcity of human resources required for the evaluation and categorization of vast quantities of medical imaging data. When AI operates with optimal precision, it minimizes the demand for human interpretations and, thereby, reduces the burden on radiologists. Among various AI approaches, deep learning (DL) stands out as it obviates the need for feature extraction, a process that can impede classification, especially with intricate datasets. The advent of DL models has ushered in a new era in medical imaging, particularly in the context of COVID-19 detection. Traditional 2D imaging techniques exhibit limitations when applied to volumetric data, such as Computed Tomography (CT) scans. Medical images predominantly exist in one of two formats: neuroimaging informatics technology initiative (NIfTI) and digital imaging and communications in medicine (DICOM). Purpose: This study aims to employ DL for the classification of COVID-19-infected pulmonary patients and normal cases based on 3D CT scans while investigating the impact of image format. Material and Methods: The dataset used for model training and testing consisted of 1245 patients from IranMehr Hospital. All scans shared a matrix size of 512 × 512, although they exhibited varying slice numbers. Consequently, after loading the DICOM CT scans, image resampling and interpolation were performed to standardize the slice count. All images underwent cropping and resampling, resulting in uniform dimensions of 128 × 128 × 60. Resolution uniformity was achieved through resampling to 1 mm × 1 mm × 1 mm, and image intensities were confined to the range of (−1000, 400) Hounsfield units (HU). For classification purposes, positive pulmonary COVID-19 involvement was designated as 1, while normal images were assigned a value of 0. Subsequently, a U-net-based lung segmentation module was applied to obtain 3D segmented lung regions. The pre-processing stage included normalization, zero-centering, and shuffling. Four distinct 3D CNN models (ResNet152, ResNet50, DensNet169, and DensNet201) were employed in this study. Results: The findings revealed that the segmentation technique yielded superior results for DICOM images, which could be attributed to the potential loss of information during the conversion of original DICOM images to NIFTI format. Notably, ResNet152 and ResNet50 exhibited the highest accuracy at 90.0%, and the same models achieved the best F1 score at 87%. ResNet152 also secured the highest Area under the Curve (AUC) at 0.932. Regarding sensitivity and specificity, DensNet201 achieved the highest values at 93% and 96%, respectively. Conclusion: This study underscores the capacity of deep learning to classify COVID-19 pulmonary involvement using real 3D hospital data. The results underscore the significance of employing DICOM format 3D CT images alongside appropriate pre-processing techniques when training DL models for COVID-19 detection. This approach enhances the accuracy and reliability of diagnostic systems for COVID-19 detection.

Keywords: deep learning, COVID-19 detection, NIFTI format, DICOM format

Procedia PDF Downloads 94

Authors: B. Shaw, K. Doherty

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The Cultural Communication and Computing Research Institute (C3RI) is a diverse multidisciplinary research institute including art, design, media production, communication studies, computing and engineering. Across these disciplines it can seem like there are enormous differences of research practice and convention, including differing positions on objectivity and subjectivity, certainty and evidence, and different political and ethical parameters. These differences sit within, often unacknowledged, histories, codes, and communication styles of specific disciplines, and it is all these aspects that can make understanding of research practice across disciplines difficult. To explore this, a one day event was orchestrated, testing how a PhD community might communicate and share research in progress in a multi-disciplinary context. Instead of presenting results at a conference, research students were tasked to articulate their method of inquiry. A working party of students from across disciplines had to design a conference call, visual identity and an event framework that would work for students across all disciplines. The process of establishing the shape and identity of the conference was revealing. Even finding a linguistic frame that would meet the expectations of different disciplines for the conference call was challenging. The first abstracts submitted either resorted to reporting findings, or only described method briefly. It took several weeks of supported intervention for research students to get ‘inside’ their method and to understand their research practice as a process rich with philosophical and practical decisions and implications. In response to the abstracts the conference committee generated key methodological categories for conference sessions, including sampling, capturing ‘experience’, ‘making models’, researcher identities, and ‘constructing data’. Each session involved presentations by visual artists, communications students and computing researchers with inter-disciplinary dialogue, facilitated by alumni Chairs. The apparently simple focus on method illuminated research process as a site of creativity, innovation and discovery, and also built epistemological awareness, drawing attention to what is being researched and how it can be known. It was surprisingly difficult to limit students to discussing method, and it was apparent that the vocabulary available for method is sometimes limited. However, by focusing on method rather than results, the genuine process of research, rather than one constructed for approval, could be captured. In unlocking the twists and turns of planning and implementing research, and the impact of circumstance and contingency, students had to reflect frankly on successes and failures. This level of self – and public- critique emphasised the degree of critical thinking and rigour required in executing research and demonstrated that honest reportage of research, faults and all, is good valid research. The process also revealed the degree that disciplines can learn from each other- the computing students gained insights from the sensitive social contextualizing generated by communications and art and design students, and art and design students gained understanding from the greater ‘distance’ and emphasis on application that computing students applied to their subjects. Finding the means to develop dialogue across disciplines makes researchers better equipped to devise and tackle research problems across disciplines, potentially laying the ground for more effective collaboration.

Keywords: interdisciplinary, method, research student, training

Procedia PDF Downloads 209