Search results for: heatmap visualization techniques

Authors: Olajumoke Adetoun Ojeleye

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The study assessed the safety culture, mindfulness and safe behaviors of students residing in the halls of residence of Obafemi Awolowo University (OAU), Ile Ife, Nigeria. The objectives of the study were to assess the level of safety mindfulness of students residing in the halls of residence of OAU, examine their safety culture and establish whether these students are involved in unsafe practices. The study employed a cross-sectional research design and instrument used for data collection was a self-structured, self-administered questionnaire. The questionnaire was tested for validity and reliability with its reliability coefficient at 0.71 before being used for data collection. Respondents were selected by multi-stage sampling technique and the sample size was 530. Data collection took 2 weeks and analysed using descriptive statistical techniques. Results showed that about half of the respondents’ population (49.8%) was between the ages of 20-24 years. There were more males (56.2%) than females (43.8%). Although data demonstrated that majority (91.7%) of the respondents are highly safety minded and the safety culture of an equally high proportion (83.4%) was adjudged fair, a lot of improvement is needed in the area of alerting or informing management of impending dangers and studying the hall handbook to internalize its contents. The study further showed that only 43.6% of respondents had good safety practices and behaviors and majority (56.4%) had fair safety practices and behaviors. One accidental discovery of the study is the finding that not a few of the students squat their counterparts. The study recommended the establishment of clearly written out complaint procedure that is accessible and available to all hall residents, building more hostels with adequate facilities to address the issue of overcrowding and also putting systems in place in order to encourage residents to report incidences/accidents.

Keywords: safe behaviours, safety culture, safety mindfulness, student

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Authors: Flora Tajiki

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This study investigates the effectiveness of non-surgical and non-medical interventions in treating infertile women with severely diminished ovarian reserve (below one), low Anti-Müllerian Hormone (AMH) levels, and symptoms of early menopause. The intervention included yoga, sunlight exposure, vitamin and mineral supplementation, relaxation techniques, and daily prayers performed both before sleep and upon waking. These methods were applied to women who had shown poor response to high-dose fertility treatments, such as IVF and microinjection cycles, leading to low-quality egg production. The focus was on women with severely reduced ovarian reserve and early menopause symptoms, some of whom continued to experience relatively regular menstrual cycles despite the onset of these symptoms. This treatment was aimed at women for whom conventional fertility methods had been ineffective. The study sample consisted of 120 married women, aged 25 to 45, from the provinces of Tehran, Alborz, and western Iran, with 35 participants completing the intervention. Individual factors such as residence, education, employment status, marriage duration, family infertility history, and previous infertility treatments were examined, with income considered as a contextual variable. The results indicate that AMH may not be a definitive marker of ovarian reserve, as lifestyle modifications, such as those implemented in this study, were associated with increased AMH levels, the return of regular menstrual cycles, and successful pregnancies. No short- or long-term complications were reported during the two-year follow-up, highlighting the potential benefits of non-surgical interventions for women with early menopause symptoms and diminished ovarian reserve.

Keywords: anti-müllerian hormone, infertility, ovarian reserve, early menopause, fertility, women’s health, lifestyle modification, pregnancy

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Authors: Jakub Zembrzuski, Bartosz Sobczyk, Mikołaj MIśkiewicz

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Besides designing new constructions, engineers all over the world must face another problem – maintenance, repairs, and assessment of the technical condition of existing bridges. To solve more complex issues, it is necessary to be familiar with the theory of finite element method and to have access to the software that provides sufficient tools which to enable create of sometimes significantly advanced numerical models. The paper includes a brief assessment of the technical condition, a description of the in situ non-destructive testing carried out and the FEM models created for global and local analysis. In situ testing was performed using strain gauges and displacement sensors. Numerical models were created using various software and numerical modeling techniques. Particularly noteworthy is the method of modeling riveted joints of the crossbeam of the viaduct. It is a simplified method that consists of the use of only basic numerical tools such as beam and shell finite elements, constraints, and simplified boundary conditions (fixed support and symmetry). The results of the numerical analyses were presented and discussed. It is clearly explained why the structure did not fail, despite the fact that the weld of the deck plate completely failed. A further research problem that was solved was to determine the cause of the rapid increase in values on the stress diagram in the cross-section of the transverse section. The problems were solved using the solely mentioned, simplified method of modeling riveted joints, which demonstrates that it is possible to solve such problems without access to sophisticated software that enables to performance of the advanced nonlinear analysis. Moreover, the obtained results are of great importance in the field of assessing the operation of bridge structures with an orthotropic plate.

Keywords: bridge, diagnostics, FEM simulations, failure, NDT, in situ testing

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Authors: Ahmed Ismail, Mustafa Baysal

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Although the energy source in the world is mainly based on fossil fuels today, there is a need for alternative energy generation systems, which are more economic and environmentally friendly, due to continuously increasing demand of electric energy and lacking power resources and networks. Distributed Energy Resources (DERs) such as fuel cells, wind and solar power have recently become widespread as alternative generation. In order to solve several problems that might be encountered when integrating DERs to power system, the microgrid concept has been proposed. A microgrid can operate both grid connected and island mode to benefit both utility and customers. For most distributed energy resources (DER) which are connected in parallel in LV-grid like micro-turbines, wind plants, fuel cells and PV cells electrical power is generated as a direct current (DC) and converted to an alternative currents (AC) by inverters. So the inverters are assumed to be primary components in a microgrid. There are many control techniques of parallel inverters to manage active and reactive sharing of the loads. Some of them are based on droop method. In literature, the studies are usually focused on improving the transient performance of inverters. In this study, the performance of two different controllers based on droop control method is compared for the inverters operated in parallel without any communication feedback. For this aim, a microgrid in which inverters are controlled by conventional droop controller and modified droop controller is designed. Modified controller is obtained by adding PID into conventional droop control. Active and reactive power sharing performance, voltage and frequency responses of those control methods are measured in several operational cases. Study cases have been simulated by MATLAB-SIMULINK.

Keywords: active and reactive power sharing, distributed generation, droop control, microgrid

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Authors: Sungsoo Na, Chanho Park

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Lung cancer is one of the most common severe diseases driving to the death of a human. Lung cancer can be divided into two cases of small-cell lung cancer (SCLC) and non-SCLC (NSCLC), and about 80% of lung cancers belong to the case of NSCLC. From several studies, the correlation between epidermal growth factor receptor (EGFR) and NSCLCs has been investigated. Therefore, EGFR inhibitor drugs such as gefitinib and erlotinib have been used as lung cancer treatments. However, the treatments result showed low response (10~20%) in clinical trials due to EGFR mutations that cause the drug resistance. Patients with resistance to EGFR inhibitor drugs usually are positive to KRAS mutation. Therefore, assessment of EGFR and KRAS mutation is essential for target therapies of NSCLC patient. In order to overcome the limitation of conventional therapies, overall EGFR and KRAS mutations have to be monitored. In this work, the only detection of EGFR will be presented. A variety of techniques has been presented for the detection of EGFR mutations. The standard detection method of EGFR mutation in ctDNA relies on real-time polymerase chain reaction (PCR). Real-time PCR method provides high sensitive detection performance. However, as the amplification step increases cost effect and complexity increase as well. Other types of technology such as BEAMing, next generation sequencing (NGS), an electrochemical sensor and silicon nanowire field-effect transistor have been presented. However, those technologies have limitations of low sensitivity, high cost and complexity of data analyzation. In this report, we propose a label-free and high-sensitive detection method of lung cancer using quartz crystal microbalance based platform. The proposed platform is able to sense lung cancer mutant DNA with a limit of detection of 1nM.

Keywords: cancer DNA, resonance frequency, quartz crystal microbalance, lung cancer

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Authors: M. Alsharqawi, T. Zayed, S. Abu Dabous

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To ensure safety and serviceability of bridge infrastructure, accurate condition assessment and rating methods are needed to provide basis for bridge Maintenance, Repair and Replacement (MRR) decisions. In North America, the common practices to assess condition of bridges are through visual inspection. These practices are limited to detect surface defects and external flaws. Further, the thresholds that define the severity of bridge deterioration are selected arbitrarily. The current research discusses the main deteriorations and defects identified during visual inspection and Non-Destructive Evaluation (NDE). NDE techniques are becoming popular in augmenting the visual examination during inspection to detect subsurface defects. Quality inspection data and accurate condition assessment and rating are the basis for determining appropriate MRR decisions. Thus, in this paper, a novel method for bridge condition assessment using the Quality Function Deployment (QFD) theory is utilized. The QFD model is designed to provide an integrated condition by evaluating both the surface and subsurface defects for concrete bridges. Moreover, an integrated condition rating index with four thresholds is developed based on the QFD condition assessment model and using K-means clustering technique. Twenty case studies are analyzed by applying the QFD model and implementing the developed rating index. The results from the analyzed case studies show that the proposed threshold model produces robust MRR recommendations consistent with decisions and recommendations made by bridge managers on these projects. The proposed method is expected to advance the state of the art of bridges condition assessment and rating.

Keywords: concrete bridge decks, condition assessment and rating, quality function deployment, k-means clustering technique

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Authors: M. S. Wickramarachchi, L. S. Nawarathna, C. M. B. Dematawewa

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Hatchery performance is critical for the profitability of poultry breeder operations. Some extrinsic parameters of eggs and breeders cause to increase or decrease the hatchability. This study aims to identify the affecting extrinsic parameters on the commercial hatchability of local chicken's eggs and determine the most efficient classification model with a hatchability rate greater than 90%. In this study, seven extrinsic parameters were considered: egg weight, moisture loss, breeders age, number of fertilised eggs, shell width, shell length, and shell thickness. Multiple linear regression was performed to determine the most influencing variable on hatchability. First, the correlation between each parameter and hatchability were checked. Then a multiple regression model was developed, and the accuracy of the fitted model was evaluated. Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), k-Nearest Neighbors (kNN), Support Vector Machines (SVM) with a linear kernel, and Random Forest (RF) algorithms were applied to classify the hatchability. This grouping process was conducted using binary classification techniques. Hatchability was negatively correlated with egg weight, breeders' age, shell width, shell length, and positive correlations were identified with moisture loss, number of fertilised eggs, and shell thickness. Multiple linear regression models were more accurate than single linear models regarding the highest coefficient of determination (R²) with 94% and minimum AIC and BIC values. According to the classification results, RF, CART, and kNN had performed the highest accuracy values 0.99, 0.975, and 0.972, respectively, for the commercial hatchery process. Therefore, the RF is the most appropriate machine learning algorithm for classifying the breeder outcomes, which are economically profitable or not, in a commercial hatchery.

Keywords: classification models, egg weight, fertilised eggs, multiple linear regression

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Authors: Marji Umil, Miane Karyle Urolaza, Ian Winston Dale Uy, John Charle Magne Valdez, Karen Elizabeth Valdez, Ervin Charles Valencia, Cheryleen Tan-Chua

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Objective: Distraction techniques can be successful in reducing the anxiety of children during medical procedures. Dental procedures, in particular, are associated with dental anxiety which has been identified as a significant and common problem in children, however, only limited studies were conducted to address such problem. Thus, this study determined the effectiveness of computer video games on the levels of anxiety of children between 5-12 years old scheduled for tooth extraction. Methods: A pre-test post-test quasi-experimental study was conducted involving 30 randomly-assigned subjects, 15 in the experimental and 15 in the control. Subjects in the experimental group played computer video games for a maximum of 15 minutes, however, no intervention was done on the control. The modified Yale Pre-operative Anxiety Scale (m-YPAS) with a Cronbach’s alpha of 0.9 was used to assess anxiety at two different points: upon arrival in the clinic (pre-test anxiety) and 15 minutes after the first measurement (post-test anxiety). Paired t-test and ANCOVA were used to analyze the gathered data. Results: Results showed that there is a significant difference between the pre-test and post-test anxiety scores of the control group (p=0.0002) which indicates an increased anxiety. A significant difference was also noted between the pre-test and post-test anxiety scores of the experimental group (p=0.0002) which indicates decreased anxiety. Comparatively, the experimental group showed lower anxiety score (p=<0.0001) than the control. Conclusion: The use of computer video games is effective in reducing the pre-operative anxiety among children and can be an alternative non-pharmacological management in giving pre-operative care.

Keywords: play therapy, preoperative anxiety, tooth extraction, video games

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Authors: Farida Habib, Haya Alfozan, Eman Miligi, Najla Alotaibi

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Background: Women’s satisfaction with maternity services, especially care during labor and birth, has become highly significant to healthcare providers, administrators, and policymakers. Purpose: The aims of this study were to assess maternal satisfaction regarding the quality of care during labor and to compare the level of maternal satisfaction between women who delivered by physicians and those delivered by midwives. Methodology: A descriptive, cross-sectional, correlational design was used. A convenient sample of 180 low-risk cases of immediate postpartum women who delivered at King Abdul-Aziz medical city was recruited. Women whose babies were diagnosed with serious health problems were excluded from the study. Data were collected using a self-administered questionnaire. The validity and reliability of the questionnaire were ensured. The questionnaire included three parts, namely: demographics data, medical history, and obstetrical history, and the last part is the satisfaction assessment tool. Ethical confederations were ensured. Maternal satisfaction during labor was classified in terms of health care, health workers' communication, and the environment. Results: Regarding health care, women were highly satisfied with care received from nurse (M = 4.21 + 0.88), medical care received (M = 4.17 + 0.79), and comfort techniques (M = 4.04 + 0.91). Regarding health workers' communication, women were highly satisfied with the provider to treat with dignity and respect (M = 4.03 + 0.91) and orientation to the toilet, bathroom, washing area (M = 4.00 + 0.93). Regarding the environment, women were highly satisfied with the experience of their baby's birth (M = 4.18 + 0.98) and supplies with drugs and supplies (M = 4.09 + 0.97). There was no statistically significant difference in maternal satisfaction between women who delivered by physicians and those delivered by midwives. Conclusion: Women were generally satisfied with their labor and delivery experience. There was no difference in maternal satisfaction on the labor process between women who delivered by physicians and those delivered by midwives.

Keywords: maternity, satisfaction, labor, delivery

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Authors: Waqas Hassan Tanveer, Yoon Ho Lee, Taehyun Park, Wonjong Yu, Yaegeun Lee, Yusung Kim, Suk Won Cha

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Nickel-Gadolinium Doped Ceria (Ni-GDC) cermet anodic thin films were prepared on Scandia Stabilized Zirconia (ScSZ) electrolyte supports by radio frequency (RF) sputtering, with a range of different sputtering powers (50 – 200W) and background Ar gas pressures (30 – 90mTorr). The effects of varying sputtering power and pressure on the properties of Ni-GDC films were studied using Focused Ion Beam (FIB), X-ray Photoelectron Spectroscopy (XPS), X-ray Diffraction (XRD), Energy Dispersive X-ray (EDX), and Atomic Force Microscopy (AFM) techniques. The Ni content was found to be always higher than the Ce content, at all sputtering conditions. This increased Ni content was attributed to significantly higher energy transfer efficiency of Ni ions as compared to Ce ions with Ar background sputtering gas. The solid oxide fuel cell configuration was completed by using lanthanum strontium manganite (LSM/YSZ) cathodes on the other side of ScSZ supports. Performance comparison of cells was done by Voltage-Current-Power (VIP) curves, while the resistances of various cell components were observed by nyquist plots. Initial results showed that anode films made by higher powered RF sputtering performed better than lower powered ones for a specific Ar pressure. Interestingly, however, anodes made at highest power and pressure, were not the ones that showed the maximum power output at an intermediate solid oxide fuel cell temperature of 800°C. Finally, an optimal sputtering condition was reported for high performance Ni-GDC anodes.

Keywords: intermediate temperature solid oxide fuel cells, nickel-cermet anodic thin films, nyquist plots, radio frequency sputtering

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Authors: Romeu Bonk Mesquita

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Argentina is the South American number 1 country of destiny to intraregional migration flows. This research aims to shed light on the main trends of the Argentine immigrant policy from 2016 on, when Mauricio Marci was elected President, taking the approval of the current and fairly protective of human rights Ley de Migraciones (2003) as an analytical starting point. Foreign Policy Analysis (FPA) serves as the theoretical background, highlighting decision-making processes and institutional designs that encourage or constraint political and social actors. The analysis goes through domestic and international levels, observing how immigration policy is formulated as a public policy and is simultaneously connected to Mercosur and other international organizations, such as the International Organization for Migration (IOM) and the United Nations High Commissioner for Refugees (UNHCR). Thus, the study revolves around the Direccion Nacional de Migraciones, which is the state agency in charge of executing the country’s immigrant policy, as to comprehend how its internal processes and the connections it has with both domestic and international institutions shape Argentina’s immigrant policy formulation and execution. Also, it aims to locate the migration agenda within the country’s contemporary social and political context. The methodology is qualitative, case-based and oriented by process-tracing techniques. Empirical evidence gathered includes official documents and data, media coverage and interviews to key-informants. Recent events, such as the Decreto de Necesidad y Urgencia 70/2017 issued by President Macri, and the return of discursive association between migration and criminality, indicate a trend of nationalization and securitization of the immigration policy in contemporary Argentina.

Keywords: Argentine foreign policy, human rights, immigrant policy, Mercosur

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Authors: C. V. Nnamani, O. L. Adedeji

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The contemporary global economic meltdown has devastating effect on the Nigerian’s economy and its frantic search for alternative source of national revenue aside from oil and gas has become imperative for economic emancipation for Nigerians. Apicultural entrepreneurship could provide a source of livelihood if the basic knowledge of those plant genetic resources needed by bees is made available. A palynological evaluation of those palynotaxa which honey bees forage for pollen and nectar was carried out after standard acetolysis method. Results showed that the honey samples were highly diversified and rich in honey plants. A total of 9544.3 honey pollen, consisting of 39 honey plants belonging to 21 plant families and distributed within 38 genera were identified excluding 238 unidentified pollen grains. Data from the analysis equally revealed that Elaeis guineensis Jacq, Anacardium occidentale L, Diospyros mespiliformis Hochist xe ADC, Alchornea cordifolia Muell, Arg, Daniella oliveri (Rolfe) Hutch & Dalz, Irvingia wombolu Okafor ex Baill, Treculia africana Decne, Nauclea latifolia Smith and Crossopteryx febrifuga Afzil ex Benth were the predominant honey plants. It provided a guide to the optimal utilization of floral resources by honeybees in these regions, showing the opportunity and amazing potentials for apiculture entrepreneurship of these palytaxa. Most of these plants are rare, threatened and endangered. It calls for urgent conservation techniques and step by all players. Critical awareness creation to ensure farmers knowledge of these palynotaxa to ensure proper understanding and attendance boost from them as economic empowerment is needed.

Keywords: palynotaxa, acetolysis, enterprise, livelihood, Nigeria

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Authors: A. Clementking, C. Jothi Venkateswaran

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Mathematical and computational functionalities such as descriptive mining, optimization, and predictions are espoused to resolve natural resource planning. The water quality prediction and its attributes influence determinations are adopted optimization techniques. The water properties are tainted while merging water resource one with another. This work aimed to predict influencing water resource distribution connectivity in accordance to water quality and sediment using an innovative proposed master-slave neural network back-propagation model. The experiment results are arrived through collecting water quality attributes, computation of water quality index, design and development of neural network model to determine water quality and sediment, master–slave back propagation neural network back-propagation model to determine variations on water quality and sediment attributes between the water resources and the recommendation for connectivity. The homogeneous and parallel biochemical reactions are influences water quality and sediment while distributing water from one location to another. Therefore, an innovative master-slave neural network model [M (9:9:2)::S(9:9:2)] designed and developed to predict the attribute variations. The result of training dataset given as an input to master model and its maximum weights are assigned as an input to the slave model to predict the water quality. The developed master-slave model is predicted physicochemical attributes weight variations for 85 % to 90% of water quality as a target values.The sediment level variations also predicated from 0.01 to 0.05% of each water quality percentage. The model produced the significant variations on physiochemical attribute weights. According to the predicated experimental weight variation on training data set, effective recommendations are made to connect different resources.

Keywords: master-slave back propagation neural network model(MSBPNNM), water quality analysis, multivariate analysis, environmental mining

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Authors: Sanjar Davronov

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Linguistic research of food discourse helps to analyze gastronomic picture of the world which plays important role in cross-cultural communications. 20 hours lecture can’t provide broad knowledge about national picture of the world of native speakers whose language being studied by future translator students. This abstract analyses how to research food discourse in “Cross-cultural (or lingvo-cultural) communication” lectures for ESL students. During compare Uzbek and American national meals, we found some specific features of food names in both countries. For example: If names of food includes advertising character in USA restaurant menus like: New York strip Sirloin crowned with Fresh – squeezed orange and lemon with a hint of garlic; Uzbek meals names are too simple, short and force general afford in underlining action – preparation process like: “Dimlama” (dimla(verb-to stew)+ma(suffix of past perfect like- stew- stewed). “Qovurdoq” (qovur (verb- to fry)+ doq (suffix of adverb like “fried one”) but these are the most delicious and difficult in preparing national meals however it is heritage of national cuisine. There are also similarity between US and Uzbek food names which has geographical color - South African Lobster tail; Qashqadaryo tandiri (lamb prepared in “tandir” typical national oven with pine leafs in Qashkadarya region). Food for European people contains physical context more than spiritual but in Asian literature especially Uzbek food has some pragmatic stuff: salt and bread (associates with hospitality and humanity), don’t be faithlessness 40 for owners of house where you where a guest. We share some teaching techniques for food discourse analyzing lectures.

Keywords: cross-cultural communications, food discourse, ESL lectures, linguistic research

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Authors: Diptiman Dinda, Shyamal Kumar Saha

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In the 21st century, sensitive and selective detection of trace amounts of explosives has become a serious problem. Generally, nitro compound and its derivatives are being used worldwide to prepare different explosives. Recently, TNP (2, 4, 6 trinitrophenol) is the most commonly used constituent to prepare powerful explosives all over the world. It is even powerful than TNT or RDX. As explosives are electron deficient in nature, it is very difficult to detect one separately from a mixture. Again, due to its tremendous water solubility, detection of TNP in presence of other explosives from water is very challenging. Simple instrumentation, cost-effective, fast and high sensitivity make fluorescence based optical sensing a grand success compared to other techniques. Graphene oxide (GO), with large no of epoxy grps, incorporate localized nonradiative electron-hole centres on its surface to give very weak fluorescence. In this work, GO is functionalized with 2, 6-diamino pyridine to remove those epoxy grps. through SN2 reaction. This makes GO into a bright blue luminescent fluorophore (DAP/rGO) which shows an intense PL spectrum at ∼384 nm when excited at 309 nm wavelength. We have also characterized the material by FTIR, XPS, UV, XRD and Raman measurements. Using this as fluorophore, a large fluorescence quenching (96%) is observed after addition of only 200 µL of 1 mM TNP in water solution. Other nitro explosives give very moderate PL quenching compared to TNP. Such high selectivity is related to the operation of FRET mechanism from fluorophore to TNP during this PL quenching experiment. TCSPC measurement also reveals that the lifetime of DAP/rGO drastically decreases from 3.7 to 1.9 ns after addition of TNP. Our material is also quite sensitive to 125 ppb level of TNP. Finally, we believe that this graphene based luminescent material will emerge a new class of sensing materials to detect trace amounts of explosives from aqueous solution.

Keywords: graphene, functionalization, fluorescence quenching, FRET, nitroexplosive detection

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Authors: Hadjer Sadoune, Liza Lamini, Scherazade Krim, Amel Djouadi, Rachida Rihani

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Deep neural networks are highly regarded for their accuracy in predicting intricate fermentation processes. Their ability to learn from a large amount of datasets through artificial intelligence makes them particularly effective models. The primary obstacle in improving the performance of these models is to carefully choose the suitable hyperparameters, including the neural network architecture (number of hidden layers and hidden units), activation function, optimizer, learning rate, and other relevant factors. This study predicts biogas production from real wastewater treatment plant data using a sophisticated approach: hybrid Bayesian optimization with a tree-structured Parzen estimator (BO-TPE) for an optimised deep neural network (DNN) model. The plant utilizes an Upflow Anaerobic Sludge Blanket (UASB) digester that treats industrial wastewater from soft drinks and breweries. The digester has a working volume of 1574 m3 and a total volume of 1914 m3. Its internal diameter and height were 19 and 7.14 m, respectively. The data preprocessing was conducted with meticulous attention to preserving data quality while avoiding data reduction. Three normalization techniques were applied to the pre-processed data (MinMaxScaler, RobustScaler and StandardScaler) and compared with the Non-Normalized data. The RobustScaler approach has strong predictive ability for estimating the volume of biogas produced. The highest predicted biogas volume was 2236.105 Nm³/d, with coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) values of 0.712, 164.610, and 223.429, respectively.

Keywords: anaerobic digestion, biogas production, deep neural network, hybrid bo-tpe, hyperparameters tuning

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Authors: Galal Omer, Onisimo Mutanga, Elfatih M. Abdel-Rahman, Elhadi Adam

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Threatened tree species (TTS) play a significant role in ecosystem functioning and services, land use dynamics, and other socio-economic aspects. Such aspects include ecological, economic, livelihood, security-based, and well-being benefits. The development of techniques for mapping and monitoring TTS is thus critical for understanding the functioning of ecosystems. The advent of advanced imaging systems and supervised learning algorithms has provided an opportunity to classify TTS over fragmenting landscape. Recently, vegetation maps have been produced using advanced imaging systems such as WorldView-2 (WV-2) and robust classification algorithms such as support vectors machines (SVM) and artificial neural network (ANN). However, delineation of TTS in a fragmenting landscape using high resolution imagery has widely remained elusive due to the complexity of the species structure and their distribution. Therefore, the objective of the current study was to examine the utility of the advanced WV-2 data for mapping TTS in the fragmenting Dukuduku indigenous forest of South Africa using SVM and ANN classification algorithms. The results showed the robustness of the two machine learning algorithms with an overall accuracy (OA) of 77.00% (total disagreement = 23.00%) for SVM and 75.00% (total disagreement = 25.00%) for ANN using all eight bands of WV-2 (8B). This study concludes that SVM and ANN classification algorithms with WV-2 8B have the potential to classify TTS in the Dukuduku indigenous forest. This study offers relatively accurate information that is important for forest managers to make informed decisions regarding management and conservation protocols of TTS.

Keywords: artificial neural network, threatened tree species, indigenous forest, support vector machines

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Authors: Jean-François Pouilly

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This study addresses the growing complexity of embedded systems and the critical need for secure, reliable software. Traditional cybersecurity testing methods, often conducted late in the development cycle, struggle to keep pace. This talk explores how formal methods, integrated with advanced analysis tools, empower C/C++ developers to 1) Proactively address vulnerabilities and bugs, which includes formal methods and abstract interpretation techniques to identify potential weaknesses early in the development process, reducing the reliance on penetration and fuzz testing in later stages. 2) Streamline development by focusing on bugs that matter, with close to no false positives and catching flaws earlier, the need for rework and retesting is minimized, leading to faster development cycles, improved efficiency and cost savings. 3) Enhance software dependability which includes combining static analysis using abstract interpretation with full context sensitivity, with hardware memory awareness allows for a more comprehensive understanding of potential vulnerabilities, leading to more dependable and secure software. This approach aligns with industry best practices (ISO2626 or ISO 21434) and empowers C/C++ developers to deliver robust, secure embedded systems that meet the demands of today's and tomorrow's applications. We will illustrate this approach with the TrustInSoft analyzer to show how it accelerates verification for complex cases, reduces user fatigue, and improves developer efficiency, cost-effectiveness, and software cybersecurity. In summary, integrating formal methods and sound Analyzers enhances software reliability and cybersecurity, streamlining development in an increasingly complex environment.

Keywords: safety, cybersecurity, ISO26262, ISO24434, formal methods

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Authors: Sreejani Barua, P. P. Srivastav

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Formulation of new functional food products for diabetes patients and obsessed people is a challenge for food industries till date. Starch is a certainly happening, ecological, reasonable and profusely obtainable polysaccharide in plant material. In the present scenario, there is a great interest in modifying starch functional properties without destroying its granular structure using different modification techniques. Resistant starch (RS) contains almost zero calories and can control blood glucose level to prevent diabetes. The current study focused on modification of mung bean starch which is a good source of legumes carbohydrate for the production of functional food. Heat moisture treatment (HMT) of mung starch was conducted at moisture content of 10-30%, temperature of 80-120 °C and time of 8-24 h.The content of resistant starch after modification was significantly increased from native starches containing RS 7.6%. The design combinations of HMT had been completed through Central Composite Rotatable Design (CCRD). The effects of HMT process variables on the yield of resistant starch was studied through Rapid Surface Methodology (RSM). The highest increase of resistant starch was found up to 34.39% when treated the native starch with 30% m.c at 120 °C temperature for 24 h.The functional properties of both native and modified mung bean starches showed that there was a reduction in the swelling power and swelling volume of HMT starches. However, the solubility of the HMT starches was higher than that of untreated native starch and also observed change in structural (scanning electron microscopy), X-Ray diffraction (XRD) pattern, blue value and thermal (differential scanning calorimetry) properties. Therefore, replacing native mung bean starch with heat-moisture treated mung bean starch leads to the development of new products with higher resistant starch levels and functional properties.

Keywords: Mung bean starch, heat moisture treatment, functional properties, resistant starch

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Authors: Ruqaiya Khalil, Saman Usmani, Zaheer Ul-Haq

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The accurate characterization of ligand binding affinity is indispensable for designing molecules with optimized binding affinity. Computational tools help in many directions to predict quantitative correlations between protein-ligand structure and their binding affinities. Molecular dynamics (MD) simulation is a modern state-of-the-art technique to evaluate the underlying basis of ligand-protein interactions by characterizing dynamic and energetic properties during the event. Autoimmune diseases arise from an abnormal immune response of the body against own tissues. The current regimen for the described condition is limited to immune-modulators having compromised pharmacodynamics and pharmacokinetics profiles. One of the key player mediating immunity and tolerance, thus invoking autoimmunity is Interleukin-2; a cytokine influencing the growth of T cells. Molecular dynamics simulation techniques are applied to seek insight into the inhibitory mechanisms of newly synthesized compounds that manifested immunosuppressant potentials during in silico pipeline. In addition to estimation of free energies associated with ligand binding, MD simulation yielded us a great deal of information about ligand-macromolecule interactions to evaluate the pattern of interactions and the molecular basis of inhibition. The present study is a continuum of our efforts to identify interleukin-2 inhibitors of both natural and synthetic origin. Herein, we report molecular dynamics simulation studies of Interluekin-2 complexed with different antagonists previously reported by our group. The study of protein-ligand dynamics enabled us to gain a better understanding of the contribution of different active site residues in ligand binding. The results of the study will be used as the guide to rationalize the fragment based synthesis of drug-like interleukin-2 inhibitors as immune-modulators.

Keywords: immuno-modulators, MD simulation, protein-ligand interaction, structure-based drug design

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Authors: Layan Moussa, Darine Salam, Samir Mustapha

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Heavy metal contamination in agricultural soils in Lebanon poses serious environmental and health problems. Intensive efforts are employed to improve existing quantification methods of heavy metals in contaminated environments since conventional detection techniques have shown to be time-consuming, tedious, and costly. The implication of hyperspectral remote sensing in this field is possible and promising. However, factors impacting the efficiency of hyperspectral imaging in detecting and quantifying heavy metals in agricultural soils were not thoroughly studied. This study proposes to assess the use of hyperspectral imaging for the detection of Ni in agricultural clay soil collected from the Bekaa Valley, a major agricultural area in Lebanon, under different contamination levels and soil moisture content. Soil samples were contaminated with Ni, with concentrations ranging from 150 mg/kg to 4000 mg/kg. On the other hand, soil with background contamination was subjected to increased moisture levels varying from 5 to 75%. Hyperspectral imaging was used to detect and quantify Ni contamination in the soil at different contamination levels and moisture content. IBM SPSS statistical software was used to develop models that predict the concentration of Ni and moisture content in agricultural soil. The models were constructed using linear regression algorithms. The spectral curves obtained reflected an inverse correlation between both Ni concentration and moisture content with respect to reflectance. On the other hand, the models developed resulted in high values of predicted R2 of 0.763 for Ni concentration and 0.854 for moisture content. Those predictions stated that Ni presence was well expressed near 2200 nm and that of moisture was at 1900 nm. The results from this study would allow us to define the potential of using the hyperspectral imaging (HSI) technique as a reliable and cost-effective alternative for heavy metal pollution detection in contaminated soils and soil moisture prediction.

Keywords: heavy metals, hyperspectral imaging, moisture content, soil contamination

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Authors: Arkadiusz Zurek

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The potential of using drones is visible in many areas of logistics, especially in terms of their use for monitoring and control of many processes. The technologies implemented in the last decade concern new possibilities for companies that until now have not even considered them, such as warehouse inventories. Unmanned aerial vehicles are no longer seen as a revolutionary tool for Industry 4.0, but rather as tools in the daily work of factories and logistics operators. The research problem is to develop a method for measuring the weight of goods in a selected link of the clothing supply chain by drones. However, the purpose of this article is to analyze the causes of errors in traditional measurements, and then to identify adverse events related to the use of drones for the inventory of a heap of textiles intended for production purposes. On this basis, it will be possible to develop guidelines to eliminate the causes of these events in the measurement process using drones. In a real environment, work was carried out to determine the volume and weight of textiles, including, among others, weighing a textile sample to determine the average density of the assortment, establishing a local geodetic network, terrestrial laser scanning and photogrammetric raid using an unmanned aerial vehicle. As a result of the analysis of measurement data obtained in the facility, the volume and weight of the assortment and the accuracy of their determination were determined. In this article, this work presents how such heaps are currently being tested, what adverse events occur, indicate and describes the current use of photogrammetric techniques of this type of measurements so far performed by external drones for the inventory of wind farms or construction of the station and compare them with the measurement system of the aforementioned textile heap inside a large-format facility.

Keywords: drones, unmanned aerial system, UAS, indoor system, security, process automation, cost optimization, photogrammetry, risk elimination, industry 4.0

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Authors: Ainouna Bouziane

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The ability of Electron Tomography to recover the 3D structure of catalysts, with spatial resolution in the subnanometer scale, has been widely explored and reviewed in the last decades. A variety of experimental techniques, based either on Transmission Electron Microscopy (TEM) or Scanning Transmission Electron Microscopy (STEM) have been used to reveal different features of nanostructured catalysts in 3D, but High Angle Annular Dark Field imaging in STEM mode (HAADF-STEM) stands out as the most frequently used, given its chemical sensitivity and avoidance of imaging artifacts related to diffraction phenomena when dealing with crystalline materials. In this regard, our group has developed a methodology that combines image denoising by undecimated wavelet transforms (UWT) with automated, advanced segmentation procedures and parameter selection methods using CS-TVM (Compressed Sensing-total variation minimization) algorithms to reveal more reliable quantitative information out of the 3D characterization studies. However, evaluating the accuracy of the magnitudes estimated from the segmented volumes is also an important issue that has not been properly addressed yet, because a perfectly known reference is needed. The problem particularly complicates in the case of multicomponent material systems. To tackle this key question, we have developed a methodology that incorporates volume reconstruction/segmentation methods. In particular, we have established an approach to evaluate, in quantitative terms, the accuracy of TVM reconstructions, which considers the influence of relevant experimental parameters like the range of tilt angles, image noise level or object orientation. The approach is based on the analysis of material-realistic, 3D phantoms, which include the most relevant features of the system under analysis.

Keywords: electron tomography, supported catalysts, nanometrology, error assessment

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Authors: Nkechinyere Joy Olawuyi, Babajide Samuel Afolabi, Bola Ibitoye

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This study collected a preprocessed dataset of chest radiographs and formulated a deep neural network model for detecting abnormalities. It also evaluated the performance of the formulated model and implemented a prototype of the formulated model. This was with the view to developing a deep neural network model to automatically classify abnormalities in chest radiographs. In order to achieve the overall purpose of this research, a large set of chest x-ray images were sourced for and collected from the CheXpert dataset, which is an online repository of annotated chest radiographs compiled by the Machine Learning Research Group, Stanford University. The chest radiographs were preprocessed into a format that can be fed into a deep neural network. The preprocessing techniques used were standardization and normalization. The classification problem was formulated as a multi-label binary classification model, which used convolutional neural network architecture to make a decision on whether an abnormality was present or not in the chest radiographs. The classification model was evaluated using specificity, sensitivity, and Area Under Curve (AUC) score as the parameter. A prototype of the classification model was implemented using Keras Open source deep learning framework in Python Programming Language. The AUC ROC curve of the model was able to classify Atelestasis, Support devices, Pleural effusion, Pneumonia, A normal CXR (no finding), Pneumothorax, and Consolidation. However, Lung opacity and Cardiomegaly had a probability of less than 0.5 and thus were classified as absent. Precision, recall, and F1 score values were 0.78; this implies that the number of False Positive and False Negative is the same, revealing some measure of label imbalance in the dataset. The study concluded that the developed model is sufficient to classify abnormalities present in chest radiographs into present or absent.

Keywords: transfer learning, convolutional neural network, radiograph, classification, multi-label

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Authors: Huixin Wei, Shibin Wang, Linan Li, Lei Zhou, Xinhao Tu

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Facial skin is a biomedical material with complex mechanical properties of anisotropy, viscoelasticity, and hyperelasticity. The mechanical properties of facial skin are crucial for a number of applications including facial plastic surgery, animation, dermatology, cosmetic industry, and impact biomechanics. Skin is a complex multi-layered material which can be broadly divided into three main layers, the epidermis, the dermis, and the hypodermis. Collagen fibers account for 75% of the dry weight of dermal tissue, and it is these fibers which are responsible for the mechanical properties of skin. Many research on the anisotropic mechanical properties are mainly concentrated on in vitro, but there is a great difference between in vivo and in vitro for mechanical properties of the skin. In this study, we presented a method to measure the mechanical properties of facial skin in vivo. Digital image correlation (DIC) and indentation tests were used to obtain the experiment data, including the deformation of facial surface and indentation force-displacement curve. Then, the experiment was simulated using a finite element (FE) model. Application of Computed Tomography (CT) and reconstruction techniques obtained the real tissue geometry. A three-dimensional FE model of facial skin, including a bi-layer system, was obtained. As the epidermis is relatively thin, the epidermis and dermis were regarded as one layer and below it was hypodermis in this study. The upper layer was modeled as a Gasser-Ogden-Holzapfel (GOH) model to describe hyperelastic and anisotropic behaviors of the dermis. The under layer was modeled as a linear elastic model. In conclusion, the material properties of two-layer were determined by minimizing the error between the FE data and experimental data.

Keywords: facial skin, indentation test, finite element, digital image correlation, computed tomography

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Authors: M. Johnson, R. Faggian, V. Sposito

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A participatory and engaged approach is key in connecting agricultural managers to sustainable agricultural systems to support and optimize production in Victoria’s food bowl. A sustainable intensification (SI) approach is well documented globally, but participation rates amongst Victorian farmers is fragmentary, and key outcomes and implementation strategies are poorly understood. Improvement in decision-support management tools and a greater understanding of the productivity gains available upon implementation of SI is necessary. This paper reviews the current understanding and uptake of SI practices amongst farmers in one of Victoria’s premier food producing regions, the Goulburn Broken; and it spatially analyses the potential for this region to adapt to climate change and optimize food production. A Geographical Information Systems (GIS) approach is taken to develop an interactive decision-support tool that can be accessible to on-ground agricultural managers. The tool encompasses multiple criteria analysis (MCA) that identifies factors during the construction phase of the tool, using expert witnesses and regional knowledge, framed within an Analytical Hierarchy Process. Given the complexities of the interrelations between each of the key outcomes, this participatory approach, in which local realities and factors inform the key outcomes and help to strategies for a particular region, results in a robust strategy for sustainably intensifying production in key food producing regions. The creation of an interactive, locally embedded, decision-support management and education tool can help to close the gap between farmer knowledge and production, increase on-farm adoption of sustainable farming strategies and techniques, and optimize farm productivity.

Keywords: agriculture, decision-support management tool, Geographic Information System, GIS, sustainable intensification

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Authors: Nurah Saleh Alfares

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An aim of TS in EFL is to help learners to understand how they learn, which could help them in using the target language with other learners in language classrooms, and in their social life. The early researchers have criticised the system of teaching methods in EFL applied in Saudi schools, as they claim that it does not produce students who are highly proficient in English. Some of them suggested that enhancing learners’ TS would help to improve the learners’ proficiency of using the EFL. The textbook in Saudi schools is the central material for teachers to follow in the EFL classroom. Thus, this study is investigating the main issues that could promote TS in Saudi EFL: the textbook and the teachers. The purposes of the study are: to find out the extent to which the tasks in the textbook have the potential to support teachers in promoting TS; to discover insights into the nature of classroom activities that teachers use to encourage TS from the textbook and to explore the teachers’ views on the role of the textbook in promoting TS in the English language. These aims will improve understanding of the connection between the potential of the textbook content and the participants’ theoretical knowledge and their teaching practice. The investigation employed research techniques including the following: (1) analysis of the textbook; (2) questionnaire for EFL teachers; (3) observation for EFL classroom; (4) interviews with EFL teachers. Analysis of the third intermediate grade textbook has been undertaken, and six EFL teachers from five intermediate schools were involved in the study. Data analysis revealed that 36.71 % of the tasks in the textbook could have the potential to promote TS, and 63.29 % of the tasks in the textbook could not have the potential to promote TS. Therefore, the result of the textbook analysis showed that the majority of the tasks do not have the potential to help teachers to promote TS. Although not all teachers of the observed lessons displayed behaviour helpful to promote TS, teachers, who presented potential TS tasks in their lesson encouraged learners’ interaction and students’ engagement more than teachers who presented tasks that did not have the potential to promote TS. Therefore, the result of the teachers’ data showed that having a textbook that has the potential to promote TS is not enough to develop teaching TS in Saudi EFL since teachers’ behaviour could make the task more or less productive.

Keywords: English as a Foreign Language, metacognitive skills, textbook, thinking skills

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Authors: Milton Manyangadze, Joseph Govha, T. Bala Narsaiah, Ch. Shilpa Chakra

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The availability and access to fresh water is today a serious global challenge. This has been a direct result of factors such as the current rapid industrialization and industrial growth, persistent droughts in some parts of the world, especially in the sub-Saharan Africa as well as population growth. Growth of the chemical processing industry has also seen an increase in the levels of pollutants in our water bodies which include heavy metals among others. Heavy metals are known to be dangerous to both human and aquatic life. As such, they have been linked to several diseases. This is mainly because they are highly toxic. They are also known to be bio accumulative and non-biodegradable. Lead for example, has been linked to a number of health problems which include damage of vital internal body systems like the nervous and reproductive system as well as the kidneys. From this background therefore, the removal of the toxic heavy metal, Pb2+ from waste water was investigated using nano silica hollow spheres (NSHS) as the adsorbent. Synthesis of NSHS was done using a three-stage process in which CaCO3 nanoparticles were initially prepared as a template. This was followed by treatment of the formed oxide particles with NaSiO3 to give a nanocomposite. Finally, the template was destroyed using 2.0M HCl to give NSHS. Characterization of the nanoparticles was done using analytical techniques like XRD, SEM, and TGA. For the adsorption process, both thermodynamic and equilibrium studies were carried out. Thermodynamic studies were carried out and the Gibbs free energy, Enthalpy and Entropy of the adsorption process were determined. The results revealed that the adsorption process was both endothermic and spontaneous. Equilibrium studies were also carried out in which the Langmuir and Freundlich isotherms were tested. The results showed that the Langmuir model best described the adsorption equilibrium.

Keywords: characterization, endothermic, equilibrium studies, Freundlich, Langmuir, nanoparticles, thermodynamic studies

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Authors: G. C. Joshi, M. Paul, B. K. Kalita, V. Ranga, J. S. Rawat, P. S. Rawat

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Being a habitat of the global village, every place has established connection through the strength and power of social media piercing through the political boundaries. Social media is a digital platform, where people across the world can interact as it has advantages of being universal, anonymous, easily accessible, indirect interaction, gathering and sharing information. The power of social media lies in the intensity of sharing extreme opinions or feelings, in contrast to the personal interactions which can be easily mapped in the form of Sentiment Mapping. The easy access to social networking sites such as Facebook, Twitter and blogs made unprecedented opportunities for citizens to voice their opinions loaded with dynamics of emotions. These further influence human thoughts where social media plays a very active role. A recent incident of public importance was selected as a case study to map the sentiments of people through Twitter. Understanding those dynamics through the eye of an ordinary people can be challenging. With the help of R-programming language and by the aid of GIS techniques sentiment maps has been produced. The emotions flowing worldwide in the form of tweets were extracted and analyzed. The number of tweets had diminished by 91 % from 25/08/2017 to 31/08/2017. A boom of sentiments emerged near the origin of the case, i.e., Delhi, Haryana and Punjab and the capital showed maximum influence resulting in spillover effect near Delhi. The trend of sentiments was prevailing more as neutral (45.37%), negative (28.6%) and positive (21.6%) after calculating the sentiment scores of the tweets. The result can be used to know the spatial distribution of digital penetration in India, where highest concentration lies in Mumbai and lowest in North East India and Jammu and Kashmir.

Keywords: sentiment mapping, digital literacy, GIS, R statistical language, spatio-temporal

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Authors: N. G. Teik Hiang, Fathyah Hashim

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This is a case of how strategic management techniques can be used to help resolving problems faced by an ageing Small and Medium Enterprise (SME). Strategic way of resolving problems had been proven to be possible in this case despite general thought that strategic management is useful mostly for large corporations. Small and Medium Enterprises (SMEs) can also use strategic management in managing their business and determining their future cause of action and strategies in order to survive in this ever competent world. Strategic orientation is the key to survival and development of small and medium enterprises. In order to adapt to the fierce market competition, ageing SMEs should improve competitiveness and operational efficiency. They must therefore establish a sense of strategic management to improve the strategic management skills, combined with its own unique characteristics, and work out practical strategies to develop core competitiveness of enterprises in the fierce market competition in order to be sustainable. In this case, internal strengths and weaknesses of an SME had been identified. Strategic internal factors and external factors had been classified and further utilized to formulate potential strategies to encounter various problems faced by the SME. These strategies had been further match to take advantages of the opportunities and to overcome the weaknesses and minimize the threats it is facing. Tan, a consultant who was given the opportunity to formulate a plan for the business started with the environmental scanning (internal and external environmental analysis), assessing strengths and weaknesses for the company, strategies generation, analysis and evaluation. He had numerous discussions with the owner of the business and the senior management in order to match the key internal and external factors to formulate alternative strategies for solving the problems that the company facing. Some of the recommendations or solutions are generated from the inspiration of the owner of the business who is a very enterprising and experience businessman.

Keywords: strategic orientation, strategic management, SME, core competitiveness, sustainable

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