Search results for: forest act

Authors: Piyush Samant, Ravinder Agarwal

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Complementary and Alternative Medicine (CAM) techniques are quite popular and effective for chronic diseases. Iridology is more than 150 years old CAM technique which analyzes the patterns, tissue weakness, color, shape, structure, etc. for disease diagnosis. The objective of this paper is to validate the use of iridology for the diagnosis of the diabetes. The suggested model was applied in a systemic disease with ocular effects. 200 subject data of 100 each diabetic and non-diabetic were evaluated. Complete procedure was kept very simple and free from the involvement of any iridologist. From the normalized iris, the region of interest was cropped. All 63 features were extracted using statistical, texture analysis, and two-dimensional discrete wavelet transformation. A comparison of accuracies of six different classifiers has been presented. The result shows 89.66% accuracy by the random forest classifier.

Keywords: complementary and alternative medicine, classification, iridology, iris, feature extraction, disease prediction

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Authors: Waraporn Wimuktalop

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This research will select regions which are vulnerable to flooding in different level. Mathematical principles will be systematically and rationally utilized as a tool to solve problems of selection the regions. Therefore the method called Multiple Criteria Decision Making (MCDM) has been chosen by having two analysis standards, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and AHP (Analytic Hierarchy Process). There are three criterions that have been considered in this research. The first criterion is climate which is the rainfall. The second criterion is geography which is the height above mean sea level. The last criterion is the land utilization which both forest and agriculture use. The study found that the South has the highest risk of flooding, then the East, the Centre, the North-East, the West and the North, respectively.

Keywords: multiple criteria decision making, TOPSIS, analytic hierarchy process, flooding

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Authors: Nguyen J. M., Lucas G., Ruan S., Digonnet H., Antonioli D.

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Introduction: Deep Learning (DL) is a very powerful tool for analyzing image data. But for tabular data, it cannot compete with machine learning methods like XGBoost. The research question becomes: can tabular data be transformed into images that can be analyzed by simple CNNs (Convolutional Neuron Networks)? Will DL be the absolute tool for data classification? All current solutions consist in repositioning the variables in a 2x2 matrix using their correlation proximity. In doing so, it obtains an image whose pixels are the variables. We implement a technology, DeepNIC, that offers the possibility of obtaining an image for each variable, which can be analyzed by simple CNNs. Material and method: The 'ROP' (Regression OPtimized) model is a binary and atypical decision tree whose nodes are managed by a new artificial neuron, the Neurop. By positioning an artificial neuron in each node of the decision trees, it is possible to make an adjustment on a theoretically infinite number of variables at each node. From this new decision tree whose nodes are artificial neurons, we created the concept of a 'Random Forest of Perfect Trees' (RFPT), which disobeys Breiman's concepts by assembling very large numbers of small trees with no classification errors. From the results of the RFPT, we developed a family of 10 statistical information criteria, Nguyen Information Criterion (NICs), which evaluates in 3 dimensions the predictive quality of a variable: Performance, Complexity and Multiplicity of solution. A NIC is a probability that can be transformed into a grey level. The value of a NIC depends essentially on 2 super parameters used in Neurops. By varying these 2 super parameters, we obtain a 2x2 matrix of probabilities for each NIC. We can combine these 10 NICs with the functions AND, OR, and XOR. The total number of combinations is greater than 100,000. In total, we obtain for each variable an image of at least 1166x1167 pixels. The intensity of the pixels is proportional to the probability of the associated NIC. The color depends on the associated NIC. This image actually contains considerable information about the ability of the variable to make the prediction of Y, depending on the presence or absence of other variables. A basic CNNs model was trained for supervised classification. Results: The first results are impressive. Using the GSE22513 public data (Omic data set of markers of Taxane Sensitivity in Breast Cancer), DEEPNic outperformed other statistical methods, including XGBoost. We still need to generalize the comparison on several databases. Conclusion: The ability to transform any tabular variable into an image offers the possibility of merging image and tabular information in the same format. This opens up great perspectives in the analysis of metadata.

Keywords: tabular data, CNNs, NICs, DeepNICs, random forest of perfect trees, classification

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Authors: Faruk Ozdemir, Roy Kalawsky, Peter Hubbard

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Accurate estimation of RUL provides a basis for effective predictive maintenance, reducing unexpected downtime for industrial equipment. However, while models such as the Random Forest have effective predictive capabilities, they are the so-called ‘black box’ models, where interpretability is at a threshold to make critical diagnostic decisions involved in industries related to aviation. The purpose of this work is to present a prognostic framework that embeds Explainable Artificial Intelligence (XAI) techniques in order to provide essential transparency in Machine Learning methods' decision-making mechanisms based on sensor data, with the objective of procuring actionable insights for the aviation industry. Sensor readings have been gathered from critical equipment such as turbofan jet engine and landing gear, and the prediction of the RUL is done by a Random Forest model. It involves steps such as data gathering, feature engineering, model training, and evaluation. These critical components’ datasets are independently trained and evaluated by the models. While suitable predictions are served, their performance metrics are reasonably good; such complex models, however obscure reasoning for the predictions made by them and may even undermine the confidence of the decision-maker or the maintenance teams. This is followed by global explanations using SHAP and local explanations using LIME in the second phase to bridge the gap in reliability within industrial contexts. These tools analyze model decisions, highlighting feature importance and explaining how each input variable affects the output. This dual approach offers a general comprehension of the overall model behavior and detailed insight into specific predictions. The proposed framework, in its third component, incorporates the techniques of causal analysis in the form of Granger causality tests in order to move beyond correlation toward causation. This will not only allow the model to predict failures but also present reasons, from the key sensor features linked to possible failure mechanisms to relevant personnel. The causality between sensor behaviors and equipment failures creates much value for maintenance teams due to better root cause identification and effective preventive measures. This step contributes to the system being more explainable. Surrogate Several simple models, including Decision Trees and Linear Models, can be used in yet another stage to approximately represent the complex Random Forest model. These simpler models act as backups, replicating important jobs of the original model's behavior. If the feature explanations obtained from the surrogate model are cross-validated with the primary model, the insights derived would be more reliable and provide an intuitive sense of how the input variables affect the predictions. We then create an iterative explainable feedback loop, where the knowledge learned from the explainability methods feeds back into the training of the models. This feeds into a cycle of continuous improvement both in model accuracy and interpretability over time. By systematically integrating new findings, the model is expected to adapt to changed conditions and further develop its prognosis capability. These components are then presented to the decision-makers through the development of a fully transparent condition monitoring and alert system. The system provides a holistic tool for maintenance operations by leveraging RUL predictions, feature importance scores, persistent sensor threshold values, and autonomous alert mechanisms. Since the system will provide explanations for the predictions given, along with active alerts, the maintenance personnel can make informed decisions on their end regarding correct interventions to extend the life of the critical machinery.

Keywords: predictive maintenance, explainable artificial intelligence, prognostic, RUL, machine learning, turbofan engines, C-MAPSS dataset

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Authors: Anna Daskalaki, Andreas Ashikalis

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This is a conceptual project that suggests an alternative type of summer camp in the forest of Rouvas in the island of Crete. Taking into account some feasts that are organised by the locals or mountaineering clubs near the church of St. John, we created a network of lightweight timber structures that serve the needs of the visitor. These structures are transformable and satisfy the need for rest, food, and sleep – this means a seat, a table and a tent are embodied in each structure. These structures blend in with the environment as they are being installed according to the following parameters: (a) the local relief, (b) the clusters of trees, and (c) the existing paths. Each timber structure could be considered as a module that could be totally independent or part of a bigger construction. The design showcases the advantages of a timber structure as it can be quite adaptive to the needs of the project, but also it is a sustainable and environmentally friendly material that can be recycled. Finally, it is important to note that the basic goal of this project is the minimum alteration of the natural environment.

Keywords: lightweight structures, timber, transformable, tent

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Authors: Dirk V. H. K. Franco

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For many of us a lot of phenomena are considered a risk. Examples are: climate change, decrease of biodiversity, amount of available, clean water and the decreasing variety of living organism in the oceans. On the other hand a lot of people perceive the following trends as catastrophic: the sea level, the melting of the pole ice, the numbers of tornado’s, floods and forest fires, the national security and the potential of 192 million climate migrants in 2060. The interest for climate, health and the possible solutions is large and common. The 5th IPCC states that the last decades especially human activities (and in second order natural emissions) have caused large, mainly negative impacts on our ecological environments. Chris Stringer stated that we represent, nowadays after evolution, the only one version of the possible humanity. At this very moment we are faced with an (over) crowded planet together with global climate changes and a strong demand for energy and material resources. Let us hope that we can counter these difficulties either with better application of existing technologies or by inventing new (applications of) clean technologies together with new business models.

Keywords: clean technologies, catastrophic, climate, possible solutions

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Authors: Mahdi Bazarganigilani

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Computer-aided diagnosis (CAD) systems can play crucial roles in diagnosing crucial diseases such as breast cancer at the earliest. In this paper, a CAD system for the diagnosis of breast cancer was introduced and evaluated. This CAD system was developed by using spatio-temporal analysis of data on a set of consecutive thermographic images by employing wavelet transformation. By using this analysis, a very accurate machine learning model using random forest was obtained. The final results showed a promising accuracy of 91% in terms of the F1 measure indicator among 200 patients' sample data. The CAD system was further extended to obtain a detailed analysis of the effect of smaller sub-areas of each breast on the occurrence of cancer.

Keywords: computer-aided diagnosis systems, thermographic analysis, spatio-temporal analysis, image processing, machine learning

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Authors: Ran Goldblatt, Nicholas Jones, Jennifer Mannix, Brad Bottoms

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Accurate, complete, and up-to-date geospatial information is the foundation of successful disaster management. When the 2010 Haiti Earthquake struck, accurate and timely information on the distribution of critical infrastructure was essential for the disaster response community for effective search and rescue operations. Existing geospatial datasets such as Google Maps did not have comprehensive coverage of these features. In the days following the earthquake, many organizations released high-resolution satellite imagery, catalyzing a worldwide effort to map Haiti and support the recovery operations. Of these organizations, OpenStreetMap (OSM), a collaborative project to create a free editable map of the world, used the imagery to support volunteers to digitize roads, buildings, and other features, creating the most detailed map of Haiti in existence in just a few weeks. However, large portions of the island are still not fully covered by OSM. There is an increasing need for a tool to automatically identify which areas in Haiti, as well as in other countries vulnerable to disasters, that are not fully mapped. The objective of this project is to leverage different types of remote sensing measurements, together with machine learning approaches, in order to identify geographical areas where OSM coverage of building footprints is incomplete. Several remote sensing measures and derived products were assessed as potential predictors of OSM building footprints coverage, including: intensity of light emitted at night (based on VIIRS measurements), spectral indices derived from Sentinel-2 satellite (normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), soil-adjusted vegetation index (SAVI), urban index (UI)), surface texture (based on Sentinel-1 SAR measurements)), elevation and slope. Additional remote sensing derived products, such as Hansen Global Forest Change, DLR`s Global Urban Footprint (GUF), and World Settlement Footprint (WSF), were also evaluated as predictors, as well as OSM street and road network (including junctions). Using a supervised classification with a random forest classifier resulted in the prediction of 89% of the variation of OSM building footprint area in a given cell. These predictions allowed for the identification of cells that are predicted to be covered but are actually not mapped yet. With these results, this methodology could be adapted to any location to assist with preparing for future disastrous events and assure that essential geospatial information is available to support the response and recovery efforts during and following major disasters.

Keywords: disaster management, Haiti, machine learning, OpenStreetMap, remote sensing

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Authors: Kever Bruno Paradelo Gomes, Rosana Carvalho Martins

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The home gardens areas are production systems, which are located near the homes and are quite common in the tropics. They consist of agricultural and forest species and may also involve the raising of small animals to produce food for subsistence as well as income generation, with a special focus on the conservation of biodiversity. Home gardens are diverse Agroforestry systems with multiple uses, among many, food security, income aid, traditional medicine. The work was carried out on rural properties of the family farmers of the Ponte Alta Rural Nucleus, Gama Administrative Region, in the city of Brasília, Federal District- Brazil. The present research is characterized methodologically as a quantitative, exploratory and descriptive nature. The instruments used in this research were: bibliographic survey and semi-structured questionnaire. The data collection was performed through the application of a semi-structured questionnaire, containing questions that referred to the perception and behavior of the interviewed producer on the subject under analysis. In each question, the respondent explained his knowledge about sustainability, agroecological practices, environmental legislation, conservation methods, forest and medicinal species, ago social and socioeconomic characteristics, use and purpose of agroforestry and technical assistance. The sample represented 55.62% of the universe of the study. We interviewed 99 people aged 18-83 years, with a mean age of 49 years. The low level of education, coupled with the lack of training and guidance for small family farmers in the Ponte Alta Rural Nucleus, is one of the limitations to the development of practices oriented towards sustainable and agroecological agriculture in the nucleus. It is observed that 50.5% of the interviewed people landed with agroforestry yards less than 20 years ago, and only 16.17% of them are older than 35 years. In identifying agriculture as the main activity of most of the rural properties studied, attention is drawn to the cultivation of medicinal plants, fruits and crops as the most extracted products. However, it is verified that the crops in the backyards have the exclusive purpose of family consumption, which could be complemented with the marketing of the surplus, as well as with the aggregation of value to the cultivated products. Initiatives such as this may contribute to the increase in family income and to the motivation and value of the crop in agroecological gardens. We conclude that home gardens of Ponte Alta are highly diverse thus contributing to local biodiversity conservation of are managed by women to ensure food security and allows income generation. The tradition of existing knowledge on the use and management of the diversity of resources used in agroforestry yards is of paramount importance for the development of sustainable alternative practices.

Keywords: agriculture, agroforestry system, rural development, sustainability

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Authors: Vareesha Masood, Ammara Gul, Nabeeha Areej, Muhammad Asif Masood, Hamna Imran

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The universal use of PDF files has prompted hackers to use them for malicious intent by hiding malicious codes in their victim’s PDF machines. Machine learning has proven to be the most efficient in identifying benign files and detecting files with PDF malware. This paper has proposed an approach using a decision tree classifier with parameters. A modern, inclusive dataset CIC-Evasive-PDFMal2022, produced by Lockheed Martin’s Cyber Security wing is used. It is one of the most reliable datasets to use in this field. We designed a PDF malware detection system that achieved 99.2%. Comparing the suggested model to other cutting-edge models in the same study field, it has a great performance in detecting PDF malware. Accordingly, we provide the fastest, most reliable, and most efficient PDF Malware detection approach in this paper.

Keywords: PDF, PDF malware, decision tree classifier, random forest classifier

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Authors: Surachart Koyadun

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Introduction: Plasmodium knowlesi (P. knowlesi) malaria is a zoonotic disease that is classified as type 5 of human malaria. Commonly found in macaques (Macaca fascicularis) and (Macaca nemestrina), P. knowlesi is capable of resulting in both uncomplicated and severe malaria in humans. Situation of P. knowlesi malaria in Phang-Nga province for the past 3 years from 2020 – 2022 revealed no case report in 2020, however, a total of 14 cases had been reported in 2021 - 2022. This research aimed to 1) study the epidemiology of P. knowlesi, 2) examine the clinical manifestations of P. knowlesi patients, 3) analyze the ecology and entomology of P. knowlesi, and 4) analyze the diagnosis and treatment of P. knowlesi. Method: This research was a retrospective descriptive study/case report. The study was conducted in 14 patients with P. knowlesi malaria between 2021 and 2022 in 4 districts of Phang-Nga Province, Thailand including Thapput, Kapong, Takuapa and Khuraburi. Results: The study subjects of P. knowlesi malaria were all males. Most of them were working age groups as farmers and worked in forest or plantation areas. All had no history of blood transfusions. Most of the patients did not use mosquito nets and had a history of camping in the forest prior to the onset of fever. An analysis of all 14 sources of infection unveiled the area is home to macaques, and that area has detected Anopheles mosquito, which is the carrier of the disease. Majority of them got sick in the dry season of Thailand (December-April). The main symptoms brought to the hospital were fever, chills, headache, body aches. Laboratory findings on the first day of diagnosis were as follows: The white blood cell count was found within the normal range. In the proportion of white blood cells, eosinophils were found to be slightly higher than normal. Slight anemia was found on early examination. The platelet count was found to be below normal in all cases. Severely low platelet count (2,000 cells/mm3) was found in severe cases with multiple complications. No patient was found dead but 85.7% of complications were found, with acute renal failure being the most common. Patients with delayed diagnosis and treatment of malaria (inaccurate diagnosis or late access to the hospital) had the highest severity and complications than those who had seen the doctor since the first 3-4 days of illness or the screening of symptoms and risk history by the malaria clinic staff at vector-borne disease control unit. Conclusion and Recommendation: P. knowlesi malaria is an emerging infectious disease transmitted from animals to humans. There are challenges in epidemiology, entomology, ecology for effective surveillance, prevention and control. Early diagnosis and treatment would reduce complications and prevent death.

Keywords: malaria, plasmodium knowlesi, epidemiology, ecology, entomology, diagnosis, treatment

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Authors: Sabir Tehrankhan Hasanov, Mir Movsum Anar Dadashev

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The article provides a brief overview of irrigation systems and canals applied to slopes, landslide-prone, seismic areas, and swelling and collapsing soils. The contemporary construction of the canal used for irrigation, energy, and water supply purposes is described. In order to ensure the durability, longevity, and reliability of the channel, a damping mat made of cast material is created under its cover, and the top is covered with a waterproof screen. Dowels are placed on the bottom and sides of the channel, and the bottom dowel is riveted to the solid bedrock and connected with piles placed at certain distances. Drainage was placed next to the bottom dowel, an operation road was created on one side of the channel, and a berm road was created on the other side. A bathtub was built on the side of the road, and a forest-bush strip was built on its bank.

Keywords: slope, channel, landslide, collapse, swell, soil, structure

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Authors: Karina Zaccari, Ernesto Cordeiro Marujo

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This paper presents a Machine Learning (ML) approach to support Meningitis diagnosis in patients at a children’s hospital in Sao Paulo, Brazil. The aim is to use ML techniques to reduce the use of invasive procedures, such as cerebrospinal fluid (CSF) collection, as much as possible. In this study, we focus on predicting the probability of Meningitis given the results of a blood and urine laboratory tests, together with the analysis of pain or other complaints from the patient. We tested a number of different ML algorithms, including: Adaptative Boosting (AdaBoost), Decision Tree, Gradient Boosting, K-Nearest Neighbors (KNN), Logistic Regression, Random Forest and Support Vector Machines (SVM). Decision Tree algorithm performed best, with 94.56% and 96.18% accuracy for training and testing data, respectively. These results represent a significant aid to doctors in diagnosing Meningitis as early as possible and in preventing expensive and painful procedures on some children.

Keywords: machine learning, medical diagnosis, meningitis detection, pediatric research

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Authors: Khadija Bouzaachane, El Mahdi El Guarmah

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The classification of low-resolution satellite images has been a worthwhile and fertile field that attracts plenty of researchers due to its importance in monitoring geographical areas. It could be used for several purposes such as disaster management, military surveillance, agricultural monitoring. The main objective of this work is to classify efficiently and accurately low-resolution satellite images by using novel technics of deep learning and reinforcement learning. The images include roads, residential areas, industrial areas, rivers, sea lakes, and vegetation. To achieve that goal, we carried out experiments on the sentinel-2 images considering both high accuracy and efficiency classification. Our proposed model achieved a 91% accuracy on the testing dataset besides a good classification for land cover. Focus on the parameter precision; we have obtained 93% for the river, 92% for residential, 97% for residential, 96% for the forest, 87% for annual crop, 84% for herbaceous vegetation, 85% for pasture, 78% highway and 100% for Sea Lake.

Keywords: classification, deep learning, reinforcement learning, satellite imagery

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Authors: Bin Liu

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Prokaryotic promoter, consisted of two short DNA sequences located at in -35 and -10 positions, is responsible for controlling the initiation and expression of gene expression. Different types of promoters have different functions, and their consensus sequences are similar. In addition, their consensus sequences may be different for the same type of promoter, which poses difficulties for promoter identification. Unfortunately, all existing computational methods treat promoter identification as a binary classification task and can only identify whether a query sequence belongs to a specific promoter type. It is desired to develop computational methods for effectively identifying promoters and their types. Here, a two-layer predictor is proposed to try to deal with the problem. The first layer is designed to predict whether a given sequence is a promoter and the second layer predicts the type of promoter that is judged as a promoter. Meanwhile, we also analyze the importance of feature and sequence conversation in two aspects: promoter identification and promoter type identification. To the best knowledge of ours, it is the first computational predictor to detect promoters and their types.

Keywords: promoter, promoter type, random forest, sequence information

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Authors: P. Halder, A. Zaman

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It is estimated that heart disease accounts for one in ten deaths worldwide. United States deaths due to heart disease are among the leading causes of death according to the World Health Organization. Cardiovascular diseases (CVDs) account for one in four U.S. deaths, according to the Centers for Disease Control and Prevention (CDC). According to statistics, women are more likely than men to die from heart disease as a result of strokes. A 50% increase in men's mortality was reported by the World Health Organization in 2009. The consequences of cardiovascular disease are severe. The causes of heart disease include diabetes, high blood pressure, high cholesterol, abnormal pulse rates, etc. Machine learning (ML) can be used to make predictions and decisions in the healthcare industry. Thus, scientists have turned to modern technologies like Machine Learning and Data Mining to predict diseases. The disease prediction is based on four algorithms. Compared to other boosts, the Ada boost is much more accurate.

Keywords: heart disease, cardiovascular disease, coronary artery disease, feature selection, random forest, AdaBoost, SVM, decision tree

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Authors: Krzysztof Gwozdzinski, Janusz Mazur

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Bory Tucholskie (Tucholskie Forest) is one of the largest pine forest complexes in Poland. It occupies approx. 3,000 square kilometers of Sandr in the Brda and Wda basin and the Tuchola Plain and the Charzykowskie Plain. Since 2010 it has transformed into The Bory Tucholskie Biosphere Reserve, according to the UNESCO decision. The area of the Bory Tucholskie National Park (BTNP), the park area, has been designated in 1996. There is little data on the presence of heavy metals in the Park's lakes. Concentration of heavy metals in the water of 19 lakes in the BTNP was examined. The lakes were divided into two groups: subglacial channel lakes of Struga Siedmiu Jezior (the Seven Lakes Stream) and other lakes. Heavy metals (transition metals) belong to d-block of elements. The part of these metals plays an important role in the function of living organisms as metalloproteins (enzymes, hemoproteins, vitamins, etc.). However, heavy metals are also typical; heavy metals are typical anthropogenic pollutants. Water samples were collected at the deepest points of lakes during spring and during summer stagnation. The analysis of metals was performed in an atomic absorption spectrophotometer Varian Spectra A300/400 in electric atomizer (GTA 96) in graphite cuvette. In the waters of the Seven Lakes Stream (Ostrowite, Zielone, Jelen, Belczak, Glowka, Plesno, Skrzynka, Mielnica) the increase in the concentration of the manganese and iron from outflow to inflow of Charzykowskie lake was found, while the concentration of copper (approx. 4 μg dm⁻³) and cadmium ( < 0.5 μg dm⁻³) was similar in all lakes. The concentration of the lead also varied within 2.1-3.6 μg dm⁻³. The concentration of nickel was approx. 3-fold higher in Ostrowite lake than other lakes of Struga. In turn the waters of the lakes Ostrowite, Jelen and Belczak were rich in zinc. The lowest level of heavy metals was observed in Zielone lake. In the second group of lakes, i.e., Krzywce Wielkie and Krzywce Male the heavy metal concentrations were lower than in the waters of Struga but higher than in oligotrophic lakes, i.e., Nierybno, Gluche, Kociol, Gacno Wielkie, Gacno Mae, Dlugie, Zabionek, and Sosnowek. The concentration of cadmium was below 0.5 μg dm⁻³ in all the studied lakes from this group. In the group of oligotrophic lakes the highest concentrations of metals such as manganese, iron, zinc and nickel in Gacno Male and Gacno Wielkie were observed. The high level of manganese in Sosnowek and Gacno Wielkie lakes was found. The lead level was also high in Nierybno lake and nickel in Gacno Wielkie lake. The lower level of heavy metals was in oligotrophic lakes such as Kociol, Dlugie, Zabionek and α-mesotrophic lake, Krzywce Wielkie. Generally, the level of heavy metals in studied lakes situated in Bory Tucholskie National Park was lower than in other lakes of Bory Tucholskie Biosphere Reserve.

Keywords: Bory Tucholskie Biosphere Reserve, Bory Tucholskie National Park, heavy metals, lakes

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Authors: Zermina Q., Wasif Y., Naeem S., Urooj S., Sajid R. A.

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Land degradation, a leading environemtnal problem and a decrease in the quality of land has become a major global issue, caused by human activities. By land degradation, more than half of the world’s drylands are affected. The worldwide scope of main saline soils is approximately 955 M ha, whereas inferior salinization affected approximately 77 M ha. In irrigated areas, a total of 58% of these soils is found. As most of the vegetation types requires fertile soil for their growth and quality production, salinity causes serious problem to the production of these vegetation types and agriculture demands. This research aims to identify the salt affected areas in the selected part of Indus Delta, Sindh province, Pakistan. This particular mangroves dominating coastal belt is important to the local community for their crop growth. Object based image analysis approach has been adopted on Landsat TM imagery of year 2011 by incorporating different mathematical band ratios, thermal radiance and salinity index. Accuracy assessment of developed salinity landcover map was performed using Erdas Imagine Accuracy Assessment Utility. Rain factor was also considered before acquiring satellite imagery and conducting field survey, as wet soil can greatly affect the condition of saline soil of the area. Dry season considered best for the remote sensing based observation and monitoring of the saline soil. These areas were trained with the ground truth data w.r.t pH and electric condutivity of the soil samples. The results were obtained from the object based image analysis of Keti bunder and Kharo chan shows most of the region under low saline soil.Total salt affected soil was measured to be 46,581.7 ha in Keti Bunder, which represents 57.81 % of the total area of 80,566.49 ha. High Saline Area was about 7,944.68 ha (9.86%). Medium Saline Area was about 17,937.26 ha (22.26 %) and low Saline Area was about 20,699.77 ha (25.69%). Where as total salt affected soil was measured to be 52,821.87 ha in Kharo Chann, which represents 55.87 % of the total area of 94,543.54 ha. High Saline Area was about 5,486.55 ha (5.80 %). Medium Saline Area was about 13,354.72 ha (14.13 %) and low Saline Area was about 33980.61 ha (35.94 %). These results show that the area is low to medium saline in nature. Accuracy of the soil salinity map was found to be 83 % with the Kappa co-efficient of 0.77. From this research, it was evident that this area as a whole falls under the category of low to medium saline area and being close to coastal area, mangrove forest can flourish. As Mangroves are salt tolerant plant so this area is consider heaven for mangrove plantation. It would ultimately benefit both the local community and the environment. Increase in mangrove forest control the problem of soil salinity and prevent sea water to intrude more into coastal area. So deforestation of mangrove should be regularly monitored.

Keywords: indus delta, object based image analysis, soil salinity, thematic mapper

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Authors: Manuel Alves, Angelica Reis, Armindo Lobo, Valdemar Leiras

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In industry 4.0, it is common to have a lot of sensor data. In this deluge of data, hints of possible problems are difficult to spot. The digital twin concept aims to help answer this problem, but it is mainly used as a monitoring tool to handle the visualisation of data. Failure detection is of paramount importance in any industry, and it consumes a lot of resources. Any improvement in this regard is of tangible value to the organisation. The aim of this paper is to add the ability to forecast test failures, curtailing detection times. To achieve this, several anomaly detection algorithms were compared with a symbolic regression approach. To this end, Isolation Forest, One-Class SVM and an auto-encoder have been explored. For the symbolic regression PySR library was used. The first results show that this approach is valid and can be added to the tools available in this context as a low resource anomaly detection method since, after training, the only requirement is the calculation of a polynomial, a useful feature in the digital twin context.

Keywords: anomaly detection, digital twin, industry 4.0, symbolic regression

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Authors: Ayeni Leye Samuel

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Laboratory incubation study was carried out at Adeyemi College of Education, Ondo Southwestern Nigeria to determine the rate of NO3-N, NH4-N, total N, OC and available P released to the soil samples collected from Okitipupa mangrove forest. The soil samples were incubated with organic (OG), organomineral (OMF) and NPK 15:15:15 (NPKF) fertilizers. Organic and organomineral fertilizers were separately applied at the rate of 0, 0.25 and 0.5mg/100 g soil while NPKF was applied at the rate of 0.002g/100g soil. The treatments were replicated three times and arranged on CRD. The treatments were incubated for 90 days. Compared with control, OG and NPKF at all rates significantly increased (p<0.05) soil NH4-N, NO3-N, total N and available P. The order of increase in NH4-N were 10t/ha OMF> 5t/ha OMF> 5t/ha OG>10t/ha OG>control>400 kg/ha while the order of increase in NO3-N were 5t/ha OMF>10t/ha OMF>10t/ha OG>5t/ha OG>control>400 kg/ha NPKF. 5t/ha OMF had the highest, 5t/ha OMF recorded the highest pH, 5t/ha OG had the highest OC while 10t/ha OG had the highest available P.

Keywords: c/n ratio, immobilization, incubation study, organomineral fertilizer

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Authors: Andreas Schomburg, Claire Le Bayon, Claire Guenat, Philip Brunner

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Numerous river restoration projects have been established in Switzerland in recent years after decades of human activity in floodplains. The success of restoration projects in terms of biodiversity and ecosystem functions largely depend on the development of the floodplain soil system. Plants and earthworms as ecosystem engineers are known to be able to build up a stable soil structure by incorporating soil organic matter into the soil matrix that creates water stable soil aggregates. Their engineering efficiency however largely depends on changing soil properties and frequent floods along an evolutive floodplain transect. This study, therefore, aims to quantify the effect of flood frequency and duration as well as of physico-chemical soil parameters on plants’ and earthworms’ engineering efficiency. It is furthermore predicted that these influences may have a different impact on one of the engineers that leads to a varying contribution to aggregate formation within the floodplain transect. Ecosystem engineers were sampled and described in three different floodplain habitats differentiated according to the evolutionary stages of the vegetation ranging from pioneer to forest vegetation in a floodplain restored 15 years ago. In addition, the same analyses were performed in an embanked adjacent pasture as a reference for the pre-restored state. Soil aggregates were collected and analyzed for their organic matter quantity and quality using Rock Eval pyrolysis. Water level and discharge measurements dating back until 2008 were used to quantify the return period of major floods. Our results show an increasing amount of water stable aggregates in soil with increasing distance to the river and show largest values in the reference site. A decreasing flood frequency and the proportion of silt and clay in the soil texture explain these findings according to F values from one way ANOVA of a fitted mixed effect model. Significantly larger amounts of labile organic matter signatures were found in soil aggregates in the forest habitat and in the reference site that indicates a larger contribution of plants to soil aggregation in these habitats compared to the pioneer vegetation zone. Earthworms’ contribution to soil aggregation does not show significant differences in the floodplain transect, but their effect could be identified even in the pioneer vegetation with its large proportion of coarse sand in the soil texture and frequent inundations. These findings indicate that ecosystem engineers seem to be able to create soil aggregates even under unfavorable soil conditions and under frequent floods. A restoration success can therefore be expected even in ecosystems with harsh soil properties and frequent external disturbances.

Keywords: ecosystem engineers, flood frequency, floodplains, river restoration, rock eval pyrolysis, soil organic matter incorporation, soil structuration

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Authors: Krešimir Begović, Miloš Rydval, Jan Tumajer, Kristyna Svobodová, Thomas Langbehn, Yumei Jiang, Vojtech Čada, Vaclav Treml, Ryszard Kaczka, Miroslav Svoboda

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Environmental changes have increased the frequency and intensity of climatic extremes, particularly hotter droughts, leading to altered tree growth patterns and multi-year lags in tree recovery. The effects of shifting climatic conditions on tree growth are inhomogeneous across species’ natural distribution ranges, with large spatial heterogeneity and inter-population variability, but generally have significant consequences for contemporary forest dynamics and future ecosystem functioning. Despite numerous studies on the impacts of regional drought effects, large uncertainties remain regarding the mechanistic basis of drought legacy effects on wood formation and the ability of individual species to cope with increasingly drier growing conditions and rising year-to-year climatic variability. To unravel the complexity of climate-growth interactions and assess species-specific responses to severe droughts, we combined forward modeling of tree growth (VS-lite model) with correlation analyses against climate (temperature, precipitation, and the SPEI-3 moisture index) and growth responses to extreme drought events from multiple tree-ring parameters (tree-width and blue intensity parameters). We used an extensive dataset with over 1000 tree-ring samples from 23 nature forest reserves across an altitudinal range in Czechia and Slovakia. Our results revealed substantial spatiotemporal variability in growth responses to summer season temperature and moisture availability across species and tree-ring parameters. However, a general trend of increasing spring moisture-growth sensitivity in recent decades was observed in the Scots pine mountain forests and lowland forests of both species. The VS-lite model effectively captured nonstationary climate-growth relationships and accurately estimated high-frequency growth variability, indicating a significant incidence of regional drought events and growth reductions. Notably, growth reductions during extreme drought years and discrete legacy effects identified in individual wood components were most pronounced in the lowland forests. Together with the observed growth declines in recent decades, these findings suggest an increasing vulnerability of Norway spruce and Scots pine in dry lowlands under intensifying climatic constraints.

Keywords: dendroclimatology, Vaganova–Shashkin lite, conifers, central Europe, drought, blue intensity

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Authors: S. K. M. Basha

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Nallamalas one of the centres of Plant Diversity (CPD) (WWF&IUCN,1995) is located in the central eastern Ghats between latitudes 15.20’-16.30’N and Longitude 78.30-80.10E in Andhra Pradesh, extended to an area of 7640 Sq.Km. No Comprehensive work available for RET Plants in the study area, therefore the objective of the present paper is to document the RET Medicinal Plants of Nallamalias and their uses by the local people of the area. In India, one of the major resources to know about the number of plant species and their medicinal values is the groups who are habituated in near and deep forests. The most common groups in south Indian forests are Yanadis and Yerukulas. These two groups of people are residing in the forest, which is located very far from the modern society, towns and cities. They are following traditional methods obtained from their forefathers in all respects, including medication. They are the only source to know many medicinal plants in the areas where they reside and are also important to record the medicinal properties of various plant species which are not reported. The new reports may help in drug industry in order to develop pharmaceutical herbal medicine for human health. In the present study, nearly 150 rare species have been found to be used for various ailments. Out of these 23 species are critically endangered, over 25 are vulnerable and around 22 comes under the category of near threatened. Some important species like Christella dentate, Careya arborea are used for curing cough and cold. Piper attnuatum, piper nigrum are used for curing skin disease. Ipomoea mauritiana is used against male impotency.Glycosmis cochinensis, Entada perseatha are used as contraceptives. The roots of Andrographis nallamalayana and Acrocephalus indicus are used for leucorrhoea. While the stem barks of Gyrocarpus americanus is given orally for spider bite. Piper hymenophyllum leaves mixed with turmeric and gingerly oil is used externally for mouth ulcers in cattle. Piper nigrum fruits are used for skin diseases. Vernonia anthelmentica seeds are used for indigestion. It was widely distributed in this hills. Due to over exploitation this species was in declined condition. Sterculia urens which is a sorce of gum for tribal, due to over exploitation this species declaimed in these hills. Hence, there is an urgent need to conserve the medicinal plants and prevent their exploitation and extinction with the help of tribals. There is a need to adopt sustainable utilization, cultivation and micro propagation techniques. Medicinal plants are as potent and effective today as they were thousands of years ago. They are natures wonderful gift to mankind and are involved in India as a very rich ancient heritage of traditional systems medicine i.e., ayurveda, siddha and unani. Unfortunately, these traditions have been largely eroded because of lack of support and recognition as well as rapid destruction of natural habitats which has led to shrinkage of medicinal plants therefore the conservation of medicinal plants and the revitalization of local health traditions has been taken up on priority basis.

Keywords: RET plants CPD, IUCN, nallamalas, yanadis, yerukulas

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Authors: P. Lalitha

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Economic and social empowerment and educational upliftment of socially disadvantaged groups and marginalized sections of society is necessary for achieving faster and more inclusive development. Programmes are being implemented through states, government’s apex corporations, and NGOs for the up-liftment of disadvantaged and marginalized sections of society. As per the primary data collected, a majority of tribal land holdings (60%) are below 2 hectare and only 5% are above 10 hectares. However, the ownership of large holdings does not give a distinct advantage unless the land is of good quality. There are areas in which even large holdings beyond 5 hectares are not sufficient to meet the food necessity of the tribal families all-round the year. Some initiatives e.g. grain-golas, jhum cultivation, wadi project, Joint Forest Management(JFM), enhancing Livelihood and Health through Traditional Knowledge Management, Associating Individual Rural Volunteers (IRVs) in SHG Bank Linkage Programme have been taken in various tribal areas of the country.

Keywords: tribals, unemployment, health, food

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Authors: Thomas E. Portegys

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An animal behavior problem is presented in the form of a nest-building task that involves two cooperating virtual birds, a male and female. The female builds a nest into which she lays an egg. The male's job is to forage in a forest for food for both himself and the female. In addition, the male must fetch stones from a nearby desert for the female to use as nesting material. The task is completed when the nest is built, and an egg is laid in it. A goal-seeking neural network and a recurrent neural network were trained and tested with little success. The goal-seeking network was then enhanced with “place cells”, allowing the birds to spatially navigate the world, building the nest while keeping themselves fed. Place cells are neurons in the hippocampus that map space.

Keywords: artificial animal intelligence, artificial life, goal-seeking neural network, nest-building, place cells, spatial navigation

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Authors: Stamatia Nasiakou, Dimitrios Chouvardas, Michael Vrahnakis, Vassiliki Kleftoyanni

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Recent research in the mountainous and semi-mountainous rural landscapes of Greece shows that they have been significantly changed over the last 80 years. These changes have the form of structural modification of land cover/use patterns, with the main characteristic being the extensive expansion of dense forests and shrubs at the expense of grasslands and extensive agricultural areas. The aim of this research was to study the 60-year changes (1960-2019) of land cover/ use units in the rural landscape of Mouzaki (Karditsa Prefecture, central Greece). Relevant cartographic material such as forest land use maps, digital maps (Corine Land Cover -2018), 1960 aerial photos from Hellenic Military Geographical Service, and satellite imagery (Google Earth Pro 2014, 2016, 2017 and 2019) was collected and processed in order to study landscape evolution. ArcGIS v 10.2.2 software was used to process the cartographic material and to produce several sets of data. Main product of the analysis was a digitized photo-mosaic of the 1960 aerial photographs, a digitized photo-mosaic of recent satellite images (2014, 2016, 2017 and 2019), and diagrams and maps of temporal transformation of the rural landscape (1960 – 2019). Maps and diagrams were produced by applying photointerpretation techniques and a suitable land cover/ use classification system on the two photo-mosaics. Demographic and socioeconomic inventory data was also collected mainly from diachronic census reports of the Hellenic Statistical Authority and local sources. Data analysis of the temporal transformation of land cover/ use units showed that they are mainly located in the central and south-eastern part of the study area, which mainly includes the mountainous part of the landscape. The most significant change is the expansion of the dense forests that currently dominate the southern and eastern part of the landscape. In conclusion, the produced diagrams and maps of the land cover/ use evolution suggest that woody vegetation in the rural landscape of Mouzaki has significantly increased over the past 60 years at the expense of the open areas, especially grasslands and agricultural areas. Demographic changes, land abandonment and the transformation of traditional farming practices (e.g. agroforestry) were recognized as the main cause of the landscape change. This study is part of a broader research project entitled “Perspective of Agroforestry in Thessaly region: A research on social, environmental and economic aspects to enhance farmer participation”. The project is funded by the General Secretariat for Research and Technology (GSRT) and the Hellenic Foundation for Research and Innovation (HFRI).

Keywords: Agroforestry, Forest expansion, Land cover/ use changes, Mountainous and semi-mountainous areas

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Authors: M. Attavanich, H. Kobayashi

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Moklen is a sub-group of ethnic minority in Thailand. In the past, they were vagabonds of the sea. Their livelihood relied on the sea but they built temporary shelters to avoid strong wind and waves during monsoon season. Recently, they have permanently settled on land along coastal area and mangrove forest in Phang Nga and Phuket Province, Southern Thailand. Moklen people have their own housing culture: the Moklen ethnic house was built from local natural materials, indicating a unique structure and design. Its wooden structure is joined by rattan ropes. The construction process is very unique because of using body-based unit of measurement for design and construction. However, there are several threats for those unique structures. One of the most important threats on Moklen ethnic house is tsunami. Especially the 2004 Indian Ocean Tsunami caused widely damage to Southern Thailand and Phang Nga province was the most affected area. In that time, Moklen villages which are located along the coastal area also affected calamitously. In order to recover the damage in affected villages, mostly new modern style houses were provided by aid agencies. This process has caused a significant impact on Moklen housing culture. Not only tsunami, but also modernization has an influence on the changing appearance of the Moklen houses and the effect of modernization has been started to experience before the tsunami. As a result, local construction knowledge is very limited nowadays because the number of elderly people in Moklen has been decreasing drastically. Last but not the least, restrictions of construction materials which are originally provided from accessible mangroves, create limitations in building a Moklen house. In particular, after the Reserved Forest Act, wood chopping without any permission has become illegal. These are some of the most important reasons for Moklen ethnic houses to disappear. Nevertheless, according to the results of field surveys done in 2013 in Phang Nga province, it is found out that some Moklen ethnic houses are still available in Tubpla Village, but only a few. Next survey in the same area in 2014 showed that number of Moklen houses in the village has been started to increase significantly. That proves that there is a high potential to conserve Moklen houses. Also the project of our research team in February 2014 contributed to continuation of Moklen ethnic house. With the cooperation of the village leader and our team, it was aimed to construct a Moklen house with the help of local participants. For the project, villagers revealed the building knowledge and techniques, and in the end, project helped community to understand the value of their houses. Also, it was a good opportunity for Moklen children to learn about their culture. In addition, NGOs recently have started to support ecotourism projects in the village. It not only helps to preserve a way of life, but also contributes to preserve indigenous knowledge and techniques of Moklen ethnic house. This kind of supporting activities are important for the conservation of Moklen ethnic houses.

Keywords: conservation, construction project, Moklen Ethnic House, 2004 Indian Ocean tsunami

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Authors: Hatice Esra Akgül, Celaleddin Öztürk

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Lichen biodiversity in forests is controlled by environmental conditions. Epiphytic lichens have some degree of substrate specificity. Diversity and distribution of epiphytic lichens are affected by humidity, light, altitude, temperature, bark pH of the trees.This study describes the epiphytic lichen communities with comparing their preferred tree species. 34 epiphytic lichen taxa are reported on Pinus sp. L., Quercus sp. L., Fagus sp. L., Carpinus sp. L., Abies sp. Mill., Fraxinus sp. Tourn. ex L. from different altitudes around the Şeker Canyon (Karabük, Turkey). 11 of these taxa are growing on Quercus sp., 10 of them are growing on Fagus sp., 7 of them are growing on Pinus sp., 4 of them are on Carpinus sp., 2 of them are on Abies sp. and one of them is on Fraxinus sp. Evernia prunastri (L.) Ach. is growing on both of Fagus sp. and Quercus sp. Lecanora pulicaris (Pers.) Ach. is growing on both of Abies sp. and Quercus sp.

Keywords: biodiversity, epiphytic lichen, forest, Turkey

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Authors: Bernard Kumi-Boateng, Kofi Bonsu

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The clearing of vegetation in the Tarkwa Mining Area (TMA) for the purposes of mining, lumbering and development of settlement for the increasing population has caused a large scale denudation of the forest cover and erosion of the top soil thereby degrading the agriculture land. It is, therefore, essential to know the current status of land degradation in TMA so as to facilitate land conservation policy-making. The types of degradation, the extents of the degradations and their various degrees were combined to develop a composite land degradation index to assess the current status of land degradation in TMA using GIS based techniques. The assessment revealed that the most significant types of degradation in TMA were open pit and quarry mining; urbanisation and other construction projects; and surface scraping during land clearing. It was found that 21.62 % of the total area of TMA (353.07 km2) had high degradation index rating. It is recommended that decision makers use this assessment as a reference point for future initiatives that will be taken in order to develop land conservation policy.

Keywords: degradation, GIS, land, mining

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Authors: O. J. Akinsola, Yinan Zheng, Rose Anorlu, F. T. Ogunsola, Lifang Hou, Robert Leo-Murphy

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Cervical Cancer (CC) is the 2nd most common cancer among women living in low and middle-income countries, with no associated symptoms during formative periods. With the advancement and innovative medical research, there are numerous preventive measures being utilized, but the incidence of cervical cancer cannot be truncated with the application of only screening tests. The mortality associated with this invasive cervical cancer can be nipped in the bud through the important role of early-stage detection. This study research selected an array of different top features selection techniques which was aimed at developing a model that could validly diagnose the risk factors of cervical cancer. A retrospective clinic-based cohort study was conducted on 178 HIV-associated cervical cancer patients in Lagos University teaching Hospital, Nigeria (U54 data repository) in April 2022. The outcome measure was the automated prediction of the HIV-associated cervical cancer cases, while the predictor variables include: demographic information, reproductive history, birth control, sexual history, cervical cancer screening history for invasive cervical cancer. The proposed technique was assessed with R and Python programming software to produce the model by utilizing the classification algorithms for the detection and diagnosis of cervical cancer disease. Four machine learning classification algorithms used are: the machine learning model was split into training and testing dataset into ratio 80:20. The numerical features were also standardized while hyperparameter tuning was carried out on the machine learning to train and test the data. Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN). Some fitting features were selected for the detection and diagnosis of cervical cancer diseases from selected characteristics in the dataset using the contribution of various selection methods for the classification cervical cancer into healthy or diseased status. The mean age of patients was 49.7±12.1 years, mean age at pregnancy was 23.3±5.5 years, mean age at first sexual experience was 19.4±3.2 years, while the mean BMI was 27.1±5.6 kg/m2. A larger percentage of the patients are Married (62.9%), while most of them have at least two sexual partners (72.5%). Age of patients (OR=1.065, p<0.001**), marital status (OR=0.375, p=0.011**), number of pregnancy live-births (OR=1.317, p=0.007**), and use of birth control pills (OR=0.291, p=0.015**) were found to be significantly associated with HIV-associated cervical cancer. On top ten 10 features (variables) considered in the analysis, RF claims the overall model performance, which include: accuracy of (72.0%), the precision of (84.6%), a recall of (84.6%) and F1-score of (74.0%) while LR has: an accuracy of (74.0%), precision of (70.0%), recall of (70.0%) and F1-score of (70.0%). The RF model identified 10 features predictive of developing cervical cancer. The age of patients was considered as the most important risk factor, followed by the number of pregnancy livebirths, marital status, and use of birth control pills, The study shows that data mining techniques could be used to identify women living with HIV at high risk of developing cervical cancer in Nigeria and other sub-Saharan African countries.

Keywords: associated cervical cancer, data mining, random forest, logistic regression

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