Search results for: Mwekera forest reserve

Authors: Vincent Liu

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Machine learning algorithms (ML) can achieve high accuracy in predicting outcomes compared to classical models. Metaheuristic, nature-inspired algorithms can enhance traditional ML algorithms by optimizing them such as by performing feature selection. We compare ten ML algorithms to predict 30-day hospital readmission rates for diabetes patients in the US using a dataset from UCI Machine Learning Repository with feature selection performed by Greywolf nature-inspired algorithm. The baseline accuracy for the initial random forest model was 65%. After performing feature engineering, SMOTE for class balancing, and Greywolf optimization, the machine learning algorithms showed better metrics, including F1 scores, accuracy, and confusion matrix with improvements ranging in 10%-30%, and a best model of XGBoost with an accuracy of 95%. Applying machine learning this way can improve patient outcomes as unnecessary rehospitalizations can be prevented by focusing on patients that are at a higher risk of readmission.

Keywords: diabetes, machine learning, 30-day readmission, metaheuristic

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Authors: N. Moazami, R. Ranjbar, A. Ashori

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Out of hundreds of microalgal strains reported, only very few of them are capable for production of high content of lipid. Therefore, the key technical challenges include identifying the strains with the highest growth rates and oil contents with adequate composition, which were the main aims of this work. From 147 microalgae screened for high biomass and oil productivity, the Nannochloropsis sp. PTCC 6016, which attained 52% lipid content, was selected for large scale cultivation in Persian Gulf Knowledge Island. Nannochloropsis strain PTCC 6016 belongs to Eustigmatophyceae (Phylum heterokontophyta) isolated from Mangrove forest area of Qheshm Island and Persian Gulf (Iran) in 2008. The strain PTCC 6016 had an average biomass productivity of 2.83 g/L/day and 52% lipid content. The biomass productivity and the oil production potential could be projected to be more than 200 tons biomass and 100000 L oil per hectare per year, in an outdoor algal culture (300 day/year) in the Persian Gulf climate.

Keywords: biofuels, microalgae, Nannochloropsis, raceway open pond, bio-jet

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Authors: Milad Vahidi, Mahmod R. Sahebi, Mehrnoosh Omati, Reza Mohammadi

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Forest management organizations need information to perform their work effectively. Remote sensing is an effective method to acquire information from the Earth. Two datasets of remote sensing images were used to classify forested regions. Firstly, all of extractable features from hyperspectral and PolSAR images were extracted. The optical features were spectral indexes related to the chemical, water contents, structural indexes, effective bands and absorption features. Also, PolSAR features were the original data, target decomposition components, and SAR discriminators features. Secondly, the particle swarm optimization (PSO) and the genetic algorithms (GA) were applied to select optimization features. Furthermore, the support vector machine (SVM) classifier was used to classify the image. The results showed that the combination of PSO and SVM had higher overall accuracy than the other cases. This combination provided overall accuracy about 90.56%. The effective features were the spectral index, the bands in shortwave infrared (SWIR) and the visible ranges and certain PolSAR features.

Keywords: hyperspectral, PolSAR, feature selection, SVM

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Authors: Rana Parween, Rob Marchant

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With the continued loss of global biodiversity despite the application of modern conservation techniques, it has become crucial to investigate non-conventional methods. Accelerated destruction of ecosystems due to altered land use, climate change, cultural and social change, necessitates the exploration of society-biodiversity attitudes and links. While the loss of species and their extinction is a well-known and well-documented process that attracts much-needed attention from researchers, academics, government and non-governmental organizations, the loss of traditional ecological knowledge and practices is more insidious and goes unnoticed. The growing availability of 'indirect experiences' such as the internet and media are leading to a disaffection towards nature and the 'Extinction of Experience'. Exacerbated by the lack of documentation of traditional practices and skills, there is the possibility for the 'extinction' of traditional practices and skills before they are fully recognized and captured. India, as a mega-biodiverse country, is also known for its historical conservation strategies entwined in traditional beliefs. Indigenous communities hold skillsets, knowledge, and traditions that have accumulated over multiple generations and may play an important role in conserving biodiversity today. This study explores the differences in knowledge and attitudes towards conserving biodiversity, of three different stakeholder groups living around Jim Corbett National Park, based on their age, traditions, and association with the protected area. A triangulation designed multi-strategy investigation collected qualitative and quantitative data through a questionnaire survey of village elders, the general public, and forest officers. Following an inductive approach to analyzing qualitative data, the thematic content analysis was followed. All coding and analysis were completed using NVivo 11. Although the village elders and some general public had vast amounts of traditional knowledge, most of it was related to animal husbandry and the medicinal value of plants. Village elders were unfamiliar with the concept of the term ‘biodiversity’ albeit their way of life and attitudes ensured that they care for the ecosystem without having the scientific basis underpinning biodiversity conservation. Inherently, village elders were keen to conserve nature; the superimposition of governmental policies without any tangible benefit or consultation was seen as detrimental. Alienating villagers and consequently the village elders who are the reservoirs of traditional knowledge would not only be damaging to the social network of the area but would also disdain years of tried and tested techniques held by the elders. Forest officers advocated for biodiversity and conservation education for women and children. Women, across all groups, when questioned about nature conservation, showed more interest in learning and participation. Biodiversity not only has an ethical and cultural value, but also plays a role in ecosystem function and, thus, provides ecosystem services and supports livelihoods. Therefore, underpinning and using traditional knowledge and incorporating them into programs of biodiversity conservation should be explored with a sense of urgency.

Keywords: biological diversity, mega-biodiverse countries, traditional ecological knowledge, society-biodiversity links

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Authors: Raghad A. Altowairgi

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With the wide spread of social media platforms, young people tend to use them extensively as the first means of communication due to their ease and modernity. But these platforms often create a fertile ground for bullies to practice their aggressive behavior against their victims. Platform usage cannot be reduced, but intelligent mechanisms can be implemented to reduce the abuse. This is where machine learning comes in. Understanding and classifying text can be helpful in order to minimize the act of cyberbullying. Artificial intelligence techniques have expanded to formulate an applied tool to address the phenomenon of cyberbullying. In this research, machine learning models are built to classify text into two classes; cyberbullying and non-cyberbullying. After preprocessing the data in 4 stages; removing characters that do not provide meaningful information to the models, tokenization, removing stop words, and lowering text. BoW and TF-IDF are used as the main features for the five classifiers, which are; logistic regression, Naïve Bayes, Random Forest, XGboost, and Catboost classifiers. Each of them scores 92%, 90%, 92%, 91%, 86% respectively.

Keywords: cyberbullying, machine learning, Bag-of-Words, term frequency-inverse document frequency, natural language processing, Catboost

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Authors: Hemant Kumar, R. N. K. Sharma, A. P. Krishna

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The study has been carried out on the Saranda in Jharkhand and a part of Odisha state. Geospatial data of Hyperion, a remote sensing satellite, have been used. This study has used a wide variety of patterns related to image processing to enhance and extract the mining class of Fe and Mn ores.Landsat-8, OLI sensor data have also been used to correctly explore related minerals. In this way, various processes have been applied to increase the mineralogy class and comparative evaluation with related frequency done. The Hyperion dataset for hyperspectral remote sensing has been specifically verified as an effective tool for mineral or rock information extraction within the band range of shortwave infrared used. The abundant spatial and spectral information contained in hyperspectral images enables the differentiation of different objects of any object into targeted applications for exploration such as exploration detection, mining.

Keywords: Hyperion, hyperspectral, sensor, Landsat-8

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Authors: Mariame Taibi, Marouane Aouiji, Rachid Bengueddour

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The Moroccan coastline is particularly rich in algae and constitutes a reserve of species with considerable economic, social and ecological potential. This work focuses on the research and characterization of algae bioactive compounds that can be used in pharmacology or phytopathology. The biochemical composition of the green alga Ulva lactuca (Ulvophyceae) was studied by determining the content of moisture, ash, phenols, flavonoids, total tannins, and chlorophyll. Seven solvents: distilled water, methanol, ethyl acetate, chloroform, benzene, petroleum ether, and hexane, were tested for their effectiveness in recovering chemical compounds. The identification of functional groupings, as well as the bioactive chemical compounds, was determined by FT-IR and GC-MS. The moisture content of the alga was 77%, while the ash content was 15%. Phenol content differed from one solvent studied to another, while chlorophyll a, b, and total chlorophyll were determined at 14%, 9.52%, and 25%, respectively. Carotenoid was present in a considerable amount (8.17%). The experimental results show that methanol is the most effective solvent for recovering bioactive compounds, followed by water. Moreover, the green alga Ulva lactuca is characterized by a high level of total polyphenols (45±3.24 mg GAE/gDM), average levels of total tannins and flavonoids (22.52±8.23 mg CE/gDM, 15.49±0.064 mg QE/gDM) respectively. The results of Fourier transform infrared spectroscopy (FT-IR) confirmed the presence of alcohol/phenol and amide functions in Ulva lactuca. The GC-MS analysis gave precisely the compounds contained in the various extracts, such as phenolic compounds, fatty acids, terpenoids, alcohols, alkanes, hydrocarbons, and steroids. All these results represent only a first step in the search for biologically active natural substances from seaweed. Additional tests are envisaged to confirm the bioactivity of seaweed.

Keywords: algae, Ulva lactuca, phenolic compounds, FTIR, GC-MS

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Authors: Norah Mohammed Alshahrani, Abdulaziz Almaleh

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Autism Spectrum Disorder (ASD) is a condition related to issues with brain development that affects how a person recognises and communicates with others which results in difficulties with interaction and communication socially and it is constantly growing. Early recognition of ASD allows children to lead safe and healthy lives and helps doctors with accurate diagnoses and management of conditions. Therefore, it is crucial to develop a method that will achieve good results and with high accuracy for the measurement of ASD in children. In this paper, ASD datasets of toddlers and children have been analyzed. We employed the following machine learning techniques to attempt to explore ASD and they are Random Forest (RF), Decision Tree (DT), Na¨ıve Bayes (NB) and Support Vector Machine (SVM). Then Feature selection was used to provide fewer attributes from ASD datasets while preserving model performance. As a result, we found that the best result has been provided by the Support Vector Machine (SVM), achieving 0.98% in the toddler dataset and 0.99% in the children dataset.

Keywords: autism spectrum disorder, machine learning, feature selection, support vector machine

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Authors: Ravid Yinon

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Introduction: Pelvic trauma causes high mortality, particularly among the elderly population. Pelvic injury ranges from low-energy incidents such as falls to high-energy trauma like motor vehicle accidents. The mortality rate among high-energy trauma patients is higher, as can be expected. The elderly population is more vulnerable to pelvic trauma even at low energy mechanisms due to the fragility and diminished physiological reserve of these patients. The aim of this study is to examine whether there is a higher long-term mortality in pelvic injuries in the elderly from the low-energy mechanism than those injured in high energy. Methods: A retrospective cohort study was conducted in a level 1 trauma center with injured patients aged 65 years and over with pelvic trauma. The patients were divided into two groups of low and high-energy mechanisms of injury. Multivariate analysis was conducted to characterize the differences between the groups. Results: There were 585 consecutive injured patients over the age of 65 with a documented pelvic injury who were treated at the primary trauma center between 2008-2020. The injured in the high energy group were younger (mean HE- 75.18, LE-80.73), with fewer comorbidities (mean 0.78 comorbidities at HE and 1.28 at LE), more men (52.6% at HE and 27.4% at LE), were consumed more treatments facilities such as angioembolization, ICU admission, emergency surgeries and blood products transfusion and higher mortality rate at admission (HE- 19/133, 14.28%, LE- 10/452, 2.21%) compared to the low energy group. However, in a long-term follow-up of one year after the injury, mortality in the low-energy group was significantly higher (HE- 14/114, 12.28%, LE- 155/442, 35.06%). Discussion: Although it can be expected that in the mechanism of high energy, the mortality rate in the long term would be higher, it was found that mortality at the low energy patient was higher. Apparently, low-energy pelvic injury in geriatric patients is a measure of frailty in these patients, causes injury to more frail and morbid patients, and is a predictor of mortality in this population in the long term. Conclusion: The long-term follow-up of injured elderly with pelvic trauma should be more intense, and the healthcare provider should put more emphasis on the rehabilitation of these special patient populations in an attempt to prevent long-term mortality.

Keywords: pelvic trauma, elderly trauma, high energy trauma, low energy trauma

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Authors: Mutwakil Nafi, Abed Elaziz El Amein, Muna El Dawi, Khalafala Salih, Osma Elbahi, Abed Elhalim Abou

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Recently a large deposit of oolitic iron ore of Late Carboniferous-Permotriassic-Lower Jurassic age was discovered in Wadi Halfa and Argein areas, North Sudan. It seems that the iron ore mineralization exists in the west and east bank of the River Nile of the study area that are found on the Egyptian-Sudanese border. The Carboniferous-Lower Jurassic age strata were covered by 67 sections and each section has been examined and carefully described. The iron-ore in Wadi Halfa occurs as oolitic ironstone and contained two horizons: (A) horizon and (B) horizon. Only horizon (A) was observed in southern Argein area. The texture of the ore is variable depending on the volume of the component. In thin sections the average of the ooids were ranged between 90% - 80%. The matrix varies between 10%-20% by volume and detritus quartz in other component my reach up to 30% by volume in sandy massive ore. Ooids size ranges from 0.2mm-1.00 mm on average in very coarse ooids may attend up to 1 mm in size. The matrix around the ooids is dominated by iron hydroxide, carbonate, fine and amorphous silica. The probable ore reserve estimate of 1.234 billion at a head grade of 41.29% Fe for the Wadi Halfa Oolitic Ironstone Formation. The iron ore shows higher content of phosphorus ranges from 6.15% to 0.16%, with mean 1.45%. The new technology Hatch–Ironstone Chloride Segregation (HICS) can be used to produce commercial-quality of iron and reduce phosphorus and silica to acceptable levels for steel industry. The development of infra structures and presence huge quantity of iron ore would make exploitation of the iron ore economic.

Keywords: HICS, Late Carboniferous age, oolitic iron ore, phosphorus

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Authors: Kamyar Tolouei, Ehsan Moosavi

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In open-pit mines, the long-term production scheduling optimization problem (LTPSOP) is a complicated problem that contains constraints, large datasets, and uncertainties. Uncertainty in the output is caused by several geological, economic, or technical factors. Due to its dimensions and NP-hard nature, it is usually difficult to find an ideal solution to the LTPSOP. The optimal schedule generally restricts the ore, metal, and waste tonnages, average grades, and cash flows of each period. Past decades have witnessed important measurements of long-term production scheduling and optimal algorithms since researchers have become highly cognizant of the issue. In fact, it is not possible to consider LTPSOP as a well-solved problem. Traditional production scheduling methods in open-pit mines apply an estimated orebody model to produce optimal schedules. The smoothing result of some geostatistical estimation procedures causes most of the mine schedules and production predictions to be unrealistic and imperfect. With the expansion of simulation procedures, the risks from grade uncertainty in ore reserves can be evaluated and organized through a set of equally probable orebody realizations. In this paper, to synthesize grade uncertainty into the strategic mine schedule, a stochastic integer programming framework is presented to LTPSOP. The objective function of the model is to maximize the net present value and minimize the risk of deviation from the production targets considering grade uncertainty simultaneously while satisfying all technical constraints and operational requirements. Instead of applying one estimated orebody model as input to optimize the production schedule, a set of equally probable orebody realizations are applied to synthesize grade uncertainty in the strategic mine schedule and to produce a more profitable and risk-based production schedule. A mixture of metaheuristic procedures and mathematical methods paves the way to achieve an appropriate solution. This paper introduced a hybrid model between the augmented Lagrangian relaxation (ALR) method and the metaheuristic algorithm, the Harris Hawks optimization (HHO), to solve the LTPSOP under grade uncertainty conditions. In this study, the HHO is experienced to update Lagrange coefficients. Besides, a machine learning method called Random Forest is applied to estimate gold grade in a mineral deposit. The Monte Carlo method is used as the simulation method with 20 realizations. The results specify that the progressive versions have been considerably developed in comparison with the traditional methods. The outcomes were also compared with the ALR-genetic algorithm and ALR-sub-gradient. To indicate the applicability of the model, a case study on an open-pit gold mining operation is implemented. The framework displays the capability to minimize risk and improvement in the expected net present value and financial profitability for LTPSOP. The framework could control geological risk more effectively than the traditional procedure considering grade uncertainty in the hybrid model framework.

Keywords: grade uncertainty, metaheuristic algorithms, open-pit mine, production scheduling optimization

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Authors: Bo-Jian Wu, Si-Heng Wu

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Objectives: Frailty is a condition of a person who has chronic health problems complicated by a loss of physiological reserve and deteriorating functional abilities. The frailty syndrome was defined by Fried and colleagues, i.e., weight loss, fatigue, decreased grip strength, slow gait speed, and low physical activity. However, to our best knowledge, there have been rare studies exploring the prevalence of frailty and its association with falls in patients with schizophrenia. Methods: A total of 559 hospitalized patients were recruited from a public psychiatric hospital in 2013. The majority of the subjects were males (361, 64.6%). The average age was 53.5 years. All patients received the assessment of frailty status defined by Fried and colleagues. The status of a fall within one year after the assessment of frailty, clinical and demographic data was collected from medical records. Logistic regression was used to calculate the odds ratio of associated factors. Results : A total of 9.2% of the participants met the criteria of frailty. The percentage of patients having a fall was 7.2%. Age were significantly associated with frailty (odds ratio = 1.057, 95% confidence interval = 1.025-1.091); however, sex was not associated with frailty (p = 0.17). After adjustment for age and sex, frailty status was associated with a fall (odds ratio = 3.62, 95% confidence interval = 1.58-8.28). Concerning the components of frailty, decreased grip strength (odds ratio = 2.44, 95% confidence interval = 1.16-5.14), slow gait speed (odds ratio = 2.82, 95% confidence interval = 1.21-6.53), and low physical activity (odds ratio = 2.64, 95% confidence interval = 1.21-5.78) were found to be associated with a fall. Conclusions: Our findings suggest the prevalence of frailty was about 10% in hospitalized patients with chronic patients with schizophrenia, and frailty status was significant with a fall in this group. By using the status of frailty, it may be beneficial to potential target candidates having fallen in the future as early as possible. The effective intervention of prevention of further falls may be given in advance. Our results bridge this gap and open a potential avenue for the prevention of falls in patients with schizophrenia. Frailty is certainly an important factor for maintaining wellbeing among these patients.

Keywords: fall, frailty, schizophrenia, Taiwan

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Authors: E. Marei, A. Hammad, S. Ismail, A. El-Gindy

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This study aims to investigate the ability of different formula of mixed bacteria as a biological treatments of wastewater after primary treatment as a bio-treatment and bio-removal and bio-adsorbent of different heavy metals in natural circumstances. The wastewater was collected from Sarpium forest site-Ismailia Governorate, Egypt. These treatments were mixture of free cells and mixture of immobilized cells of different bacteria. These different formulas of mixed bacteria were prepared under Lab. condition. The obtained data indicated that, as a result of wastewater bio-treatment, the removal rate was found to be 76.92 and 76.70% for biological oxygen demand, 79.78 and 71.07% for chemical oxygen demand, 32.45 and 36.84 % for ammonia nitrogen as well as 91.67 and 50.0% for phosphate after 24 and 28 hrs with mixed free cells and mixed immobilized cells, respectively. Moreover, the bio-removals of different heavy metals were found to reach 90.0 and 50. 0% for Cu ion, 98.0 and 98.5% for Fe ion, 97.0 and 99.3% for Mn ion, 90.0 and 90.0% Pb, 80.0% and 75.0% for Zn ion after 24 and 28 hrs with mixed free cells and mixed immobilized cells, respectively. The results indicated that 13.86 and 17.43% of removal efficiency and reduction of total dissolved solids were achieved after 24 and 28 hrs with mixed free cells and mixed immobilized cells, respectively.

Keywords: wastewater bio-treatment , bio-sorption heavy metals, biological desalination, immobilized bacteria, free cell bacteria

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Authors: Geetika Richa, M. L. Sharma

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Bamboo as an ancient herbal medicine has been used for thousands of years in Asia and goes by many names such as tabashir, banslochan etc. It is often used for its tonic and astringent properties. Modern analysis of bamboos show high amount of vitamins and minerals which makes them valuable as a curative. Bamboo leaf decoction and young shoots are known as remedy for intestinal worms, healing of ulcers and stomach disorders. Bamboos are known to be propagated by large scale plantations but propagation through seeds occurs very limited as they have very short viability of few months. Seeds loses viability over a period of time even under controlled conditions and important factors that affect seed viability is the decline in reserve food material, decrease in membrane integrity and fall in endogenous level of growth hormones. Invigoration treatments that include hydration, dehydration, incorporation of bioactive chemicals such as growth regulators, nutrients and antioxidants etc. improve the seed performance. Our studies were aimed to determine the most effective invigoration treatments to enhance vigour and viability of seeds by following invigoration treatments, i.e., hardening. Treated seeds were stored at controlled temperature and humidity (in desiccators at 4°C). In hardening, chemicals were applied in 3 different concentrations to three replicates of 10 seeds. Hardening was done withGA3, IAA, (each with concentrations of 10 ppm, 20 ppm and 50 ppm), calcium oxychloride, neem leaf powder and clay (each with concentrations of 2%, 5% and 10%). Statistically all the hardening materials were effective but GA3 50 ppm was the most effective one in maintaining germination percentage and vigour index. Hardening treatments increased the germination percentage of seeds, i.e. 86.2%, over control which showed germination percentage of 80.2%. It was concluded that in order to maintain seed viability during storage for longer period of time, invigoration treatments have been found to be very effective.

Keywords: invigoration, seed quality, viability, hardening, membrane integrity, decoction

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Authors: Anudeep Appe, Bhanu Poluparthi, Lakshmi Kasivajjula, Udai Mv, Sobha Bagadi, Punya Modi, Aditya Singh, Hemanth Gunupudi, Spenser Troiano, Jeff Paul, Justin Stovall, Justin Yamamoto

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The necessity of data-driven decisions in healthcare strategy formulation is rapidly increasing. A reliable framework which helps identify factors impacting a healthcare provider facility or a hospital (from here on termed as facility) market share is of key importance. This pilot study aims at developing a data-driven machine learning-regression framework which aids strategists in formulating key decisions to improve the facility’s market share which in turn impacts in improving the quality of healthcare services. The US (United States) healthcare business is chosen for the study, and the data spanning 60 key facilities in Washington State and about 3 years of historical data is considered. In the current analysis, market share is termed as the ratio of the facility’s encounters to the total encounters among the group of potential competitor facilities. The current study proposes a two-pronged approach of competitor identification and regression approach to evaluate and predict market share, respectively. Leveraged model agnostic technique, SHAP, to quantify the relative importance of features impacting the market share. Typical techniques in literature to quantify the degree of competitiveness among facilities use an empirical method to calculate a competitive factor to interpret the severity of competition. The proposed method identifies a pool of competitors, develops Directed Acyclic Graphs (DAGs) and feature level word vectors, and evaluates the key connected components at the facility level. This technique is robust since its data-driven, which minimizes the bias from empirical techniques. The DAGs factor in partial correlations at various segregations and key demographics of facilities along with a placeholder to factor in various business rules (for ex. quantifying the patient exchanges, provider references, and sister facilities). Identified are the multiple groups of competitors among facilities. Leveraging the competitors' identified developed and fine-tuned Random Forest Regression model to predict the market share. To identify key drivers of market share at an overall level, permutation feature importance of the attributes was calculated. For relative quantification of features at a facility level, incorporated SHAP (SHapley Additive exPlanations), a model agnostic explainer. This helped to identify and rank the attributes at each facility which impacts the market share. This approach proposes an amalgamation of the two popular and efficient modeling practices, viz., machine learning with graphs and tree-based regression techniques to reduce the bias. With these, we helped to drive strategic business decisions.

Keywords: competition, DAGs, facility, healthcare, machine learning, market share, random forest, SHAP

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Authors: Abdulkarim Yusuf

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Motivation: The Nigerian economy has seen significant macroeconomic instability, fuelled mostly by an overreliance on fluctuating oil revenues. The rising disparity between tax receipts and government spending in Nigeria necessitates government borrowing to fund the anticipated pace of economic growth. Rising public debt and fiscal sustainability are limiting the government's ability to invest in key infrastructure that promotes private investment and growth in Nigeria. Objective: This paper fills an empirical research vacuum by examining the impact of public debt on fiscal sustainability in Nigeria, given the significance of fiscal stability in decreasing poverty and the constraints that an unsustainable debt burden imposes on it. Data and method: Annual time series data covering the period 1980 to 2022 exposed to conventional and structural breaks stationarity tests and the Autoregressive Distributed Lag estimation approach were adopted for this study. Results: The results reveal that domestic debt stock, debt service payment, foreign reserve stock, exchange rate, and private investment all had a major adverse effect on fiscal stability in the long and short run, corroborating the debt overhang and crowding-out hypothesis. External debt stock, prime lending rate, and degree of trade openness, which boosted fiscal stability in the long run, had a major detrimental effect on fiscal stability in the short run, whereas foreign direct investment inflows had an important beneficial impact on fiscal stability in both the long and short run. Implications: The results indicate that fiscal measures that inspire domestic resource mobilization, sustainable debt management techniques, and dependence on external debt to boost deficit financing will improve fiscal stability and drive growth.

Keywords: ARDL co-integration, debt overhang, debt servicing, fiscal stability, public debt

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Authors: Edwin Lerch, Dirk Audring, Cuong Nguyen Mau, Duc Ninh Nguyen, The Cuong Nguyen, The Van Nguyen

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The National Load Dispatch Centre of Electricity Vietnam (EVNNLDC) and Siemens PTI investigated the stability of the electrical 500/220 kV transportation system of Vietnam. The general scope of the investigations is improving the stability of the Vietnam power system and giving the EVNNLDC staff the capability to decide how to deal with expected stability challenges in the future, which are related to the very fast growth of the system. Rapid system growth leads to a very high demand of power transmission from North to South. This was investigated by stability studies of interconnected power system with neighboring countries. These investigations are performed in close cooperation and coordination with the EVNNLDC project team. This important project includes data collection, measurement, model validation and investigation of relevant stability phenomena as well as training of the EVNNLDC staff. Generally, the power system of Vietnam has good voltage and dynamic stability. The main problems are related to the longitudinal system with more power generation in the North and Center, especially hydro power, and load centers in the South of Vietnam. Faults on the power transmission system from North to South risks the stability of the entire system due to a high power transfer from North to South and high loading of the 500 kV backbone. An additional problem is the weak connection to Cambodia power system which leads to interarea oscillations mode. Therefore, strengthening the power transfer capability by new 500kV lines or HVDC connection and balancing the power generation across the country will solve many challenges. Other countermeasures, such as wide area load shedding, PSS tuning and correct SVC placement will improve and stabilize the power system as well. Primary frequency reserve should be increased.

Keywords: dynamic power transmission system studies, blackout prevention, power system interconnection, stability

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Authors: Jing Jiang, Wenhuan XU, Lei Zhang, Shiyi Zhang, Tongli Wang

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Climate data quality significantly affects the reliability of ecological modeling. In the Asia Pacific (AP) region, low-quality climate data hinders ecological modeling. ClimateAP, a software developed in 2017, generates high-quality climate data for the AP region, benefiting researchers in forestry and agriculture. However, its adoption remains limited. This study aims to confirm the validity of biologically relevant variable data generated by ClimateAP during the normal climate period through comparison with the currently available gridded data. Climate data from 2,366 weather stations were used to evaluate the prediction accuracy of ClimateAP in comparison with the commonly used gridded data from WorldClim1.4. Univariate regressions were applied to 48 monthly biologically relevant variables, and the relationship between the observational data and the predictions made by ClimateAP and WorldClim was evaluated using Adjusted R-Squared and Root Mean Squared Error (RMSE). Locations were categorized into mountainous and flat landforms, considering elevation, slope, ruggedness, and Topographic Position Index. Univariate regressions were then applied to all biologically relevant variables for each landform category. Random Forest (RF) models were implemented for the climatic niche modeling of Cunninghamia lanceolata. A comparative analysis of the prediction accuracies of RF models constructed with distinct climate data sources was conducted to evaluate their relative effectiveness. Biologically relevant variables were obtained from three unpublished Chinese meteorological datasets. ClimateAPv3.0 and WorldClim predictions were obtained from weather station coordinates and WorldClim1.4 rasters, respectively, for the normal climate period of 1961-1990. Occurrence data for Cunninghamia lanceolata came from integrated biodiversity databases with 3,745 unique points. ClimateAP explains a minimum of 94.74%, 97.77%, 96.89%, and 94.40% of monthly maximum, minimum, average temperature, and precipitation variances, respectively. It outperforms WorldClim in 37 biologically relevant variables with lower RMSE values. ClimateAP achieves higher R-squared values for the 12 monthly minimum temperature variables and consistently higher Adjusted R-squared values across all landforms for precipitation. ClimateAP's temperature data yields lower Adjusted R-squared values than gridded data in high-elevation, rugged, and mountainous areas but achieves higher values in mid-slope drainages, plains, open slopes, and upper slopes. Using ClimateAP improves the prediction accuracy of tree occurrence from 77.90% to 82.77%. The biologically relevant climate data produced by ClimateAP is validated based on evaluations using observations from weather stations. The use of ClimateAP leads to an improvement in data quality, especially in non-mountainous regions. The results also suggest that using biologically relevant variables generated by ClimateAP can slightly enhance climatic niche modeling for tree species, offering a better understanding of tree species adaptation and resilience compared to using gridded data.

Keywords: climate data validation, data quality, Asia pacific climate, climatic niche modeling, random forest models, tree species

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Authors: Medjadj Tarek, Ghribi Hayet

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This study aims to develop a flood sensitivity assessment tool using machine learning (ML) techniques and geographic information system (GIS). The importance of this study is integrating the geographic information systems (GIS) and machine learning (ML) techniques for mapping flood risks, which help decision-makers to identify the most vulnerable areas and take the necessary precautions to face this type of natural disaster. To reach this goal, we will study the case of the city of M'sila, which is among the areas most vulnerable to floods. This study drew a map of flood-prone areas based on the methodology where we have made a comparison between 3 machine learning algorithms: the xGboost model, the Random Forest algorithm and the K Nearest Neighbour algorithm. Each of them gave an accuracy respectively of 97.92 - 95 - 93.75. In the process of mapping flood-prone areas, the first model was relied upon, which gave the greatest accuracy (xGboost).

Keywords: Geographic information systems (GIS), machine learning (ML), emergency mapping, flood disaster management

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Authors: Sunday Nathaniel Obasi, Chiamak Chinasa Obasi

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Soil characterization and classification provide the basic information necessary to create a functional evaluation and soil classification schemes. Fertility capability classification (FCC) on the other hand is a technical system that groups the soils according to kinds of problems they present for management of soil physical and chemical properties. This research was carried out in Ebonyi state, southeastern Nigeria, which is an agrarian state and a leading rice producing part of southeastern Nigeria. In order to maximize the soil and enhance the productivity of rice in Ebonyi soils, soil classification, and fertility classification information need to be supplied. The state was grouped into three locations according to their agricultural zones namely; Ebonyi north, Ebonyi central and Ebonyi south representing Abakaliki, Ikwo and Ivo locations respectively. Major rice growing areas of the soils were located and two profile pits were sunk in each of the studied zones from which soils were characterized, classified and fertility capability classification (FCC) developed. Soil classification was done using United State Department of Agriculture (USDA) Soil Taxonomy and correlated with World Reference Base for soil resources. Results obtained classified Abakaliki 1 and Abakaliki 2 as Typic Fluvaquents (Ochric Fluvisols). Ikwo 1 was classified as Vertic Eutrudepts (Eutric Vertisols) while Ikwo 2 was classified as Typic Eutrudepts (Eutric Cambisols). Ivo 1 and Ivo 2 were both classified as Aquic Eutrudepts (Gleyic Leptosols). Fertility capability classification (FCC) revealed that all studied soils had mostly loamy topsoils and subsoils except Ikwo 1 with clayey topsoil. Limitations encountered in the studied soils include; dryness (d), low ECEC (e), low nutrient capital reserve (k) and water logging/ anaerobic condition (gley). Thus, FCC classifications were Ldek for Abakaliki 1 and 2, Ckv for Ikwo 1, LCk for Ikwo 2 while Ivo 1 and 2 were Legk and Lgk respectively.

Keywords: soil classification, soil fertility, limitations, modifiers, Southeastern Nigeria

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Authors: K. Sangeeth, P. Rekha, P. Preeja, P. Divya, R. Arya, R. Maneesha

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Wireless sensor networks (WSN) are small or tiny devices that consists of different sensors to sense physical parameters like air pressure, temperature, vibrations, movement etc., process these data and sends it to the central data center to take decisions. The WSN domain, has wide range of applications such as monitoring and detecting natural hazards like landslides, forest fire, avalanche, flood monitoring and also in healthcare applications. With such different applications, it is being taught in undergraduate/post graduate level in many universities under department of computer science. But the cost and infrastructure required to purchase WSN nodes for having the students getting hands on expertise on these devices is expensive. This paper gives overview about the remote triggered lab that consists of more than 100 WSN nodes that helps the students to remotely login from anywhere in the world using the World Wide Web, configure the nodes and learn the WSN concepts in intuitive way. It proposes new way called over the air programming (OTAP) and its internals that program the 100 nodes simultaneously and view the results without the nodes being physical connected to the computer system, thereby allowing for sparse deployment.

Keywords: WSN, over the air programming, virtual lab, AT45DB

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Authors: Hairin Taha, Aditya Arya, M. A. Hapipah, A. M. Mustafa

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Plants have been an important source of medicine since ancient times. Pseuduvaria monticola is a rare montane forest species from the Annonaceae family. Traditionally, the plant was used to cure symptoms of fever, inflammation, stomach-ache and also to reduce the elevated levels of blood glucose. Scientifically, we have evaluated the antidiabetic potential of the Pseuduvaria monticola bark methanolic extract on certain in vitro cell based assays, followed by in vivo study. Results from in vitro models displayed PMm upregulated glucose uptake and insulin secretion in mouse pancreatic β-cells. In vivo study demonstrated the PMm down-regulated hyperglycaemia, oxidative stress and elevated levels of pro-inflammatory cytokines in type 2 diabetic rat models. Altogether, the study revealed that Pseuduvaria monticola might be used as a potential candidate for the management of type 2 diabetes and its related complications.

Keywords: type 2 diabetes, Pseuduvaria monticola, insulin secretion, glucose uptake

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Authors: Meng-Li Lin, Shang-Hsuan Chiu

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Taiwan is a country of diverse ethnic groups, the historical background of each ethnic group is different, and the conflict between them influence on each other, result in Taiwan's multi-culture. The Changhua County in Taiwan is the largest county of Hoklo-Hakka. Hakka people get along with Hoklo people for a long time. There are integration and conflict during that time and makes Hakka people gradually assimilated Hoklo-Hakka people. Today in Changhua Plain area, many Hoklo-Hakka people do not speak Hakka language. Therefore, it has been difficult to find information of Hakka from the Hakka language in the group of Hoklo-Hakka. But in the living space or culture to find relevant historical traces of life could be confirmed in Hakka Culture. In this paper, through the investigation of descent, life field, religion, language and other investigations of the Dacun, Changhua County residents to carry out the analysis of the process of assimilating Hoklo in living cultural landscape. First is through the local literature, the elderly and other oral history stories, to investigate the changes in Dacun field historical. Second, the comparison of collected traditional Hakka culture and the living cultural landscape of Hoklo-Haka are done to explore the differences between the living cultural landscape and the traditional Hakka culture. After analysis Hoklo-Hakka living cultural landscape, the significant differences, we proposed preservation strategy to provide recommendations to save the cultural life of Hoklo-Hakka landscape in future. Changhua Dacun traditional Hakka landscape is disappearing, in this study, we explore and investigate the data of Changhua Dacun Hoklo-Hakka living cultural landscape to analyze and to provide strategic advice to save. Here we have three study purposes. 1. Discuss the Hoklo-Hakka living cultural landscape of Changhua Dacun. 2. Investigate and record the Hoklo-Hakka living cultural landscape. 3. Propose a reserve strategy of the Hoklo-Hakka living cultural landscape in future.

Keywords: Hoklo-Hakka, Dacun, save policy, life Culture

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Authors: S. O. Oyamakin, A. U. Chukwu,

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We proposed a Hyperbolic Gompertz Growth Model (HGGM), which was developed by introducing a stabilizing parameter called θ using hyperbolic sine function into the classical gompertz growth equation. The resulting integral solution obtained deterministically was reprogrammed into a statistical model and used in modeling the height and diameter of Pines (Pinus caribaea). Its ability in model prediction was compared with the classical gompertz growth model, an approach which mimicked the natural variability of height/diameter increment with respect to age and therefore provides a more realistic height/diameter predictions using goodness of fit tests and model selection criteria. The Kolmogorov-Smirnov test and Shapiro-Wilk test was also used to test the compliance of the error term to normality assumptions while using testing the independence of the error term using the runs test. The mean function of top height/Dbh over age using the two models under study predicted closely the observed values of top height/Dbh in the hyperbolic gompertz growth models better than the source model (classical gompertz growth model) while the results of R2, Adj. R2, MSE, and AIC confirmed the predictive power of the Hyperbolic Monomolecular growth models over its source model.

Keywords: height, Dbh, forest, Pinus caribaea, hyperbolic, gompertz

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Authors: Said Moqeem Sadat, Saito Takahiro, Inuzuka Norikazu, Sugiyama Ikuo

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Kabul city is the capital of Afghanistan with a population of about 4.0 million in 2009 and 6.5 million in 2025. It is geographically located in a narrow plain valley along the Kabul River and is surrounded by high mountains. Due to its sharp geological condition, the city has been suffering from floods caused by storm water and snow melting water in the rainy season. Meanwhile, potable water resources are becoming a critical issue as the underground water table is decreasing falling rapidly due to domestic usage, industrial and agricultural activities usage especially in the dry season. This paper focuses on flood water management in Kabul including suburban agricultural area considering not only for flood protection but also: 1. To reserve the quality underground water for the future population growth. 2. To irrigate farming area in dry season using storm water ponds in rainy season. 3. To discharge city contaminated flood water to the downstream safely using existing channels/new pipes. Cost and benefit is considered in this study to find out a suitable flood protection method both in rural area and city center from a view point of 1 to 3 mentioned above. In this analysis, cost mainly consists of lost opportunity to develop lands due to flood ponds in addition to construction and maintenance one including connecting channels for water collecting/discharging. Benefit mainly consists of damage reduction of flood loss due to counter measures (this is corresponding cost) in addition to the contribution to agricultural crops. As far as reservation of the ground water for the future city growth is concerned, future demand and supply are compared in case that the pumping amount is limited by this irrigation system.

Keywords: cost-benefit, hydrological modeling, water management, water quality

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Authors: Jalil Badamfirooz

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Soil wind erosion has a negative impact on the environment. Mulching is one of the most efficient soil protection techniques. Bitumen emulsion has recently been utilized as a soil cover that is sprayed directly over the soil and forms a thin film. The thin coating of bitumen emulsion prevents soil erosion and keeps moisture in the soil. Besides, some compounds release into the soil and cause environmental problems. In the present study, the effect of bitumen emulsion on the release of polycyclic aromatic hydrocarbons (PAHs) into the soil is studied in an arid land located in the central part of Iran. The soil was Loamy-Sand and saline with a pH of 8.03. Bitumen emulsion was used in this study as mulch at a rate of 4 L m2. The effect of this mulch on soil properties was investigated after 6 months of mulch application. Then PAHs concentrations were determined in samples collected from different depths in bitumen emulsion sprayed and control soils. In general, bitumen emulsion application on soil led to a significant increase in some PAHs, which was higher than soil pollution standards critical level of pollution for commerce, groundwater protection, pasture forest, and park and residence uses.

Keywords: mulch, bitumen emulsion, arid land, PAH

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Authors: Behrang Ekrami, Amin Tamadon

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Caspian red deer or maral (Cervus elaphus maral) is a ruminant from the family of Cervidae. Maintenance and protection of maral requires knowing the behavioral, physiological, environmental characteristics and factors harmful to this species. In this article, reproductive and behavioral traits of this species in both sexes are presented based on observations and the available records of protected deer in Wildlife Refuge of Semeskande, Sari (one of the sites that preserve the maral in the Free Zones of Hyrcanian forest) from 2006 to 2011. Hart characteristics including sexual behavior, apparent changes during reproductive season and reproductive physiology; and hind characteristics including of ovulation, reproductive cycle, mating, pregnancy and parturition, have been evaluated. Identification of maral reproductive characteristics in Wildlife Refuge of Semeskande, Sari is one of the most important information requirements to preserve and breed this species and will open up new routes for performing new methods of reproduction of this species in Iran wildlife parks or other refuge areas.

Keywords: caspian red deer, reproduction, behavior, Iran

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Authors: Wei Ji

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This study was conducted in the Kansas City metropolitan area of the United States, which has experienced significant urban sprawling in recent decades. The remote sensing of land cover changes in this area spanned over four decades from 1972 through 2010. The project was implemented in two stages: the first stage focused on detection of long-term trends of urban land cover change, while the second one examined how to detect the coupled effects of human impact and climate change on urban landscapes. For the first-stage study, six Landsat images were used with a time interval of about five years for the period from 1972 through 2001. Four major land cover types, built-up land, forestland, non-forest vegetation land, and surface water, were mapped using supervised image classification techniques. The study found that over the three decades the built-up lands in the study area were more than doubled, which was mainly at the expense of non-forest vegetation lands. Surprisingly and interestingly, the area also saw a significant gain in surface water coverage. This observation raised questions: How have human activities and precipitation variation jointly impacted surface water cover during recent decades? How can we detect such coupled impacts through remote sensing analysis? These questions led to the second stage of the study, in which we designed and developed approaches to detecting fine-scale surface waters and analyzing coupled effects of human impact and precipitation variation on the waters. To effectively detect urban landscape changes that might be jointly shaped by precipitation variation, our study proposed “urban wetscapes” (loosely-defined urban wetlands) as a new indicator for remote sensing detection. The study examined whether urban wetscape dynamics was a sensitive indicator of the coupled effects of the two driving forces. To better detect this indicator, a rule-based classification algorithm was developed to identify fine-scale, hidden wetlands that could not be appropriately detected based on their spectral differentiability by a traditional image classification. Three SPOT images for years 1992, 2008, and 2010, respectively were classified with this technique to generate the four types of land cover as described above. The spatial analyses of remotely-sensed wetscape changes were implemented at the scales of metropolitan, watershed, and sub-watershed, as well as based on the size of surface water bodies in order to accurately reveal urban wetscape change trends in relation to the driving forces. The study identified that urban wetscape dynamics varied in trend and magnitude from the metropolitan, watersheds, to sub-watersheds in response to human impacts at different scales. The study also found that increased precipitation in the region in the past decades swelled larger wetlands in particular while generally smaller wetlands decreased mainly due to human development activities. These results confirm that wetscape dynamics can effectively reveal the coupled effects of human impact and climate change on urban landscapes. As such, remote sensing of this indicator provides new insights into the relationships between urban land cover changes and driving forces.

Keywords: urban land cover, human impact, climate change, rule-based classification, across-scale analysis

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Authors: Ivan Poza Martínez

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The international energy landscape has been significantly affected by the Covid-19 pandemic and te conflict in Ukraine. The Vaca Muerta sedimentary formation in Argentina´s Neuquén province has become a crucial area for energy production, specifically in the shale gas ad shale oil sectors. The massive investment required for theexploitation of this reserve make it essential to understand te determinants of the investment in the upstream sector at both local ad international levels. The aim of this study is to identify the qualitative and quantitative determinants of investment in Vaca Muerta. The research methodolody employs both quantiative ( econometrics ) and qualitative approaches. A linear regression model is used to analyze the impact in non-conventional hydrocarbons. The study highlights that, in addition to quantitative factors, qualitative variables, particularly the design of a regulatory framework, significantly influence the level of the investment in Vaca Muerta. The analysis reveals the importance of attracting both domestic and foreign capital investment. This research contributes to understanding the factors influencing investment inthe Vaca Muerta regioncomapred to other published studies. It emphasizes to role of qualitative varibles, such as regulatory frameworks, in the development of the shale gas and oil sectors. The study uses a combination ofquantitative data , such a investment figures, and qualitative data, such a regulatory frameworks. The data is collected from various rpeorts and industry publications. The linear regression model is used to analyze the relationship between the variables and the investment in Vaca Muerta. The research addresses the question of what factors drive investment in the Vaca Muerta region, both from a quantitative and qualitative perspective. The study concludes that a combination of quantitative and qualitative factors, including the design of a regulatory framework, plays a significant role in attracting investment in Vaca Muerta. It highlights the importance of these determinants in the developmentof the local energy sector and the potential economic benefits for Argentina and the Southern Cone region.

Keywords: vaca muerta, FDI, shale gas, shale oil, YPF

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Authors: Syed Jamil H. Kazmi, Faiza Sarwar

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The Indus delta of Sindh Province forms the largest creeks zone of Pakistan. The Sindh coast starts from the mouth of Hab River and terminates at Sir Creek area. In this paper, we have considered the major creeks from the site of Bin Qasim Port in Karachi to Jetty of Keti Bunder in Thatta District. A general decline in the mangrove forest has been observed that within a span of last 25 years. The unprecedented human interventions damage the creeks habitat badly which includes haphazard urban development, industrial and sewage disposal, illegal cutting of mangroves forest, reduced and inconsistent fresh water flow mainly from Jhang and Indus rivers. These activities not only harm the creeks habitat but affected the fish stock substantially. Fishing is the main livelihood of coastal people but with the above-mentioned threats, it is also under enormous pressure by fish catches resulted in unchecked overutilization of the fish resources. This pressure is almost unbearable when it joins with deleterious fishing methods, uncontrolled fleet size, increase trash and by-catch of juvenile and illegal mesh size. Along with these anthropogenic interventions study area is under the red zone of tropical cyclones and active seismicity causing floods, sea intrusion, damage mangroves forests and devastation of fish stock. In order to sustain the natural resources of the Indus Creeks, this study was initiated with the support of FAO, WWF and NIO, the main purpose was to develop a Geo-Spatial dataset for fish stock assessment. The study has been spread over a year (2013-14) on monthly basis which mainly includes detailed fish stock survey, water analysis and few other environmental analyses. Environmental analysis also includes the habitat classification of study area which has done through remote sensing techniques for 22 years’ time series (1992-2014). Furthermore, out of 252 species collected, fifteen species from estuarine and marine groups were short-listed to measure the weight, health and growth of fish species at each creek under GIS data through SPSS system. Furthermore, habitat suitability analysis has been conducted by assessing the surface topographic and aspect derivation through different GIS techniques. The output variables then overlaid in GIS system to measure the creeks productivity. Which provided the results in terms of subsequent classes: extremely productive, highly productive, productive, moderately productive and less productive. This study has revealed the Geospatial tools utilization along with the evaluation of the fisheries resources and creeks habitat risk zone mapping. It has also been identified that the geo-spatial technologies are highly beneficial to identify the areas of high environmental risk in Sindh Creeks. This has been clearly discovered from this study that creeks with high rugosity are more productive than the creeks with low levels of rugosity. The study area has the immense potential to boost the economy of Pakistan in terms of fish export, if geo-spatial techniques are implemented instead of conventional techniques.

Keywords: fish stock, geo-spatial, productivity analysis, risk

Procedia PDF Downloads 225