Landmines Research Articles

Indigenous Knowledge on Shifting Cultivation and Sustainable Agriculture

Indigenous knowledge on shifting mountainous farm management of Arfak farmers is the original culture of Arfak people, held firm passed down to generations. This can be seen in the agricultural activities of Arfak farmers who are still applying indigenous knowledge to this day. Modernization can affect the existence and can also be a threat to the indigenous knowledge of Arfak farmers. The low level of education of farmers, the oral transfer of indigenous knowledge and the absence of written documentation of indigenous knowledge are the causes of indigenous knowledge being swept away in the flow of modernization. Apart from that, the existence of indigenous knowledge is increasingly threatened by the development of plantations and urban areas. Especially the increasing demand for land for plantations, infrastructure, settlements and mining. Based on data from the West Papua Agriculture Service, in 2017 plantation development reached 53,806 ha and mining reached 89,256 ha in the Arfak Mountains. Agricultural modernization and industrialization will certainly reduce shifting cultivation and the value of indigenous knowledge. The high biodiversity makes Arfak Mountains a conservation area and makes this region a center of highland vegetable production in West Papua. In 2021, agricultural sector contributed 33.61% to growth Arfak Mountains Gross Regional Domestic Product. This study aims to describe how Arfak farmers apply their indigenous knowledge, namely the concept of igya ser hanjob in managing land to support sustainable agricultural activities. This research uses qualitative approach with inductive methods and case study techniques to document the knowledge and experience Arfak farmers selected with the snowball technique. Data was collected through interviews and observations. This study found that sustainable land management in agricultural activities is based on the ecological concept of the Arfak people, namely igya ser hanjob. This concept regulates appropriate management areas for agriculture and ensures environment sustainability and family food. This ecological concept was socialized for generations Arfak farming family. However, in reality there is a threat of landslides in agricultural areas due to socio-economic and ecological changes in mountainous farming are of Arfak community.

Revegetation and Quality Indicators of Technosols in Restored Mine Fields with Saline Mine Spoils

Revegetation and Quality Indicators of Technosols in Restored Mine Fields with Saline Mine Spoils

Research on the Prediction Model of Coal Spontaneous Combustion Hazard Level Based on IGWO-GRNN

ABSTRACT To accurately predict the risk level of spontaneous coal combustion, we introduce an improved grey wolf optimization strategy (IGWO) to optimize the generalized regression neural network (GRNN), thereby establishing the IGWO-GRNN coal spontaneous combustion risk level prediction model. The superiority of the IGWO algorithm over the grey wolf optimization algorithm (GWO), whale optimization algorithm (WOA) and particle swarm optimization algorithm (PSO) was verified. Fifty sets of sample data of spontaneous coal combustion in the mining area were selected as the research object, and the data were randomly divided into the training set and the test set according to the ratio of 8:2. The prediction results of the IGWO-GRNN prediction model were obtained and compared with those of the GWO-GRNN, GRNN, BP (Backpropagation), and GRU (Gated Recurrent Unit) models, and the absolute value of the relative error was introduced as an evaluation index of the model prediction accuracy. The results demonstrate that IGWO outperforms GWO, WOA, and PSO in terms of optimization efficiency, with the IGWO-GRNN prediction model yielding the most accurate results. Specifically, the maximum absolute value of the relative error does not exceed 4.27%, and the average relative error is only 3.29%, indicating a substantial improvement over the other models. Therefore, this study achieves high-precision prediction under small-sample data, which provides an important reference value for the small-sample prediction problem in the mining field.

Evaluation of Water Inrush Risk in the Fault Zone of the Coal Seam Floor in Madaotou Coal Mine, Shanxi Province, China

As coal seams are mined at greater depths, the threat of high water pressure from the confined aquifer in the floor that mining operations face has become increasingly prominent. Taking the Madaotou mine field in the Datong Coalfield as the research object, in the context of mining under pressure, for the main coal seams in the mining area, first of all, an improved evaluation method for the vulnerability of floor water inrush is adopted for hazard prediction. Secondly, numerical simulation is used to conduct a simulation analysis on the fault zones in high-risk areas. By using the fuzzy C-means clustering method (FCCM) to improve the classification method for the normalized indicators in the original variable-weight vulnerability evaluation, the risk zoning for water inrush from the coal seam floor is determined. Then, through the numerical simulation method, a simulation analysis is carried out on high-risk areas to simulate the disturbance changes of different mining methods on the fault zones so as to put forward reasonable mining methods. The results show that the classification of the variable-weight intervals of water inrush from the coal seam floor is more suitable to be classified by using fuzzy clustering, thus improving the prediction accuracy. Based on the time effect of the delayed water inrush of faults, different mining methods determine the duration of the disturbance on the fault zones. Therefore, by reducing the disturbance time on the fault zones, the risk of karst water inrush from the floor of the fault zones can be reduced. Through prediction evaluation and simulation analysis, the evaluation of the risk of water inrush in coal mines has been greatly improved, which is of great significance for ensuring the safe and efficient mining of mines.

Air temperature and precipitation constraining the modelled wetland methane emissions in a boreal region in northern Europe

Abstract. Wetland methane responses to temperature and precipitation are studied in a boreal wetland-rich region in northern Europe using ecosystem process models. Six ecosystem models (JSBACH-HIMMELI, LPX-Bern, LPJ-GUESS, JULES, CLM4.5, and CLM5) are compared to multi-model means of ecosystem models and atmospheric inversions from the Global Carbon Project and upscaled eddy covariance flux results for their temperature and precipitation responses and seasonal cycles of the regional fluxes. Two models with contrasting response patterns, LPX-Bern and JSBACH-HIMMELI, are used as priors in atmospheric inversions with Carbon Tracker Europe–CH4 (CTE-CH4) in order to find out how the assimilation of atmospheric concentration data changes the flux estimates and how this alters the interpretation of the flux responses to temperature and precipitation. Inversion moves wetland emissions of both models towards co-limitation by temperature and precipitation. Between 2000 and 2018, periods of high temperature and/or high precipitation often resulted in increased emissions. However, the dry summer of 2018 did not result in increased emissions despite the high temperatures. The process models show strong temperature and strong precipitation responses for the region (51 %–91 % of the variance explained by both). The month with the highest emissions varies from May to September among the models. However, multi-model means, inversions, and upscaled eddy covariance flux observations agree on the month of maximum emissions and are co-limited by temperature and precipitation. The setup of different emission components (peatland emissions, mineral land fluxes) has an important role in building up the response patterns. Considering the significant differences among the models, it is essential to pay more attention to the regional representation of wet and dry mineral soils and periodic flooding which contribute to the seasonality and magnitude of methane fluxes. The realistic representation of temperature dependence of the peat soil fluxes is also important. Furthermore, it is important to use process-based descriptions for both mineral and peat soil fluxes to simulate the flux responses to climate drivers.

Use of spatial analysis in the mining field in the Udorka Valley. Introduction to geoarchaeological search for flint workshops at site 24 in Poręba Dzierżna (Małopolska Province, southern Poland)

The prehistoric chocolate flint mine in the Udorka Valley (site 24 in Poręba Dzierżna, Lesser Poland Voivodeship, southern Poland) has been under excavation since 2018. It is the first mine of this raw material discovered outside the Świętokrzyskie Mountains. The currently available dating indicates that the mine was used in the Mesolithic and Neolithic periods.Spatial analysis of artifacts from the fill of the mine shaft allowed for the reconstruction of individual phases of its formation. It also questioned the issue of homogeneity of the assemblage of artifacts from this mining facility and from the overlying layers. Although they are of the workshop type, their simultaneity should be doubted. In order to establish the chronological and cultural framework of flint production in the mining field, it seems necessary to determine the original location of the workshop, which was destroyed and washed out as a result of some kind of runoff. This flow created the sediment that now lies above the shafts, and which is extremely rich in flint artifacts.

Integrating techniques of social network analysis and word embedding for word sense disambiguation

PurposeThis research addresses the challenge of polysemous words in word embedding techniques, which are commonly used in text mining. It aims to resolve word sense ambiguity by introducing a social network sense disambiguation (SNSD) model based on social network analysis (SNA).Design/methodology/approachThe SNSD model treats words as members of a social network and their co-occurrence relationships as interactions. By analyzing these interactions, the model identifies words with high betweenness centrality, which may act as bridges between different word sense communities, indicating polysemy. This unsupervised method does not rely on pre-tagged resources and is validated using the IMDb dataset.FindingsThe SNSD model effectively resolves word sense ambiguity in word embeddings, proving to be a cost-effective and adaptable solution to this issue. The experimental results demonstrate that the model enhances the accuracy of word embeddings by accurately identifying the correct meanings of polysemous words.Originality/valueThis study is the first to apply SNA to word sense disambiguation (WSD). The SNSD model offers a novel, unsupervised approach that overcomes the limitations of traditional supervised or knowledge-based methods, providing a valuable contribution to the field of text mining.

Psychological Analysis and Career Decision-Making of College Students Based on Cognitive Algorithms on Distributed Platforms

This research uses cognitive analytics on a distributed computer system to investigate how students’ decision-making behaviors relate to their personality traits. The goal of the research is to improve our knowledge of the variables affecting students’ professional choices and to offer a solid, expandable framework for individualized career counseling. The investigation examines a range of psychological characteristics, such as personality characteristics, cognitive capacities, passions, and socioeconomic status, by utilizing cognitive algorithms. Massive databases may be processed more easily thanks to the distributed system, which also ensures effective computing and analyses in real time. The approach entails gathering information from a heterogeneous student body, then analyzing it and extracting features to find meaningful trends. Neural networks and combination techniques are examples of sophisticated algorithms for machine learning that are used to create models of prediction that help with career suggestions. The results show how well cognitive computers capture the intricate interactions between variables that influence career choices. The flexibility of the distributed platform guarantees the system’s suitability for big colleges and universities, offering students individualized career counseling on a broad scale. This study offers a fresh method for comprehending and assisting students in their professional choices, which makes a significant contribution to the fields of data mining for education and career planning.

Environmental and human health risk assessment of potentially toxic elements in rehabilitating iron mine lands in the Brazilian Amazon.

Waste pile substrates from Fe mining may carry potentially toxic elements (PTE). Rehabilitation efforts must maintain soil vegetation cover effectively, avoiding the dispersion of particulate matter and reducing the risk to the environment and human health. Therefore, this study aims to evaluate the pseudo-total and extractable contents, perform chemical fractionation, and assess the bioaccessibility and risk of PTE in waste piles of Fe mining in the Eastern Amazon. Soils were sampled from waste piles in different stages of environmental rehabilitation and from non-rehabilitated and native forest areas. The waste materials exhibit mean pseudo-total concentrations of Zn, Ni, Cr, and Cu that exceed the Brazilian soil quality threshold. However, they do not surpass reference values for human health safety. In addition, these elements are predominantly associated with the residual fraction, suggesting low availability. Among the 11 PTE evaluated, only Al, Cu, Fe, Mn, and Zn presented concentrations that were bioaccessible to the gastrointestinal tract. At the same time, Al, Fe, and Mn showed lung bioaccessibility. Soil properties limiting PTE extractability and bioaccessibility include pH and base saturation. Considering only elements above threshold levels, no environmental risk was observed, and the human health risk was considered insignificant for adult oral and inhalation exposure routes. Finally, the results show that high pseudo-total PTE contents in the analyzed Fe waste piles do not necessarily indicate high risks. However, substrate properties should be monitored over time to better understand their potential impacts and the main factors influencing their bioavailability.

UAV-MSSH: A novel UAV photogrammetry-based framework for mining surface three-dimensional movement basin monitoring

Monitoring the surface three-dimensional movement caused by underground coal mining is a critical and challenging task. Despite many methods have been proposed in previous works, they focus more on the subsidence, with insufficient discussions on the horizontal displacement and the underlying coupled mechanisms between them. To address these issues, we introduce a novel UAV photogrammetry-based framework for mining surface three-dimensional movement basin monitoring, named UAV-MSSH. This framework consists of two branches: vertical subsidence and horizontal displacements. For the subsidence branch, we propose an improved cloth simulation filtering algorithm based on slope zone indexing (CSFS) to model high-precision subsidence basins. In the horizontal displacement branch, we develop a probability integral prediction prior-guided COSI-Corr (PIM-CC) method for displacement detection by bridging the gaps between the horizontal displacement and subsidence. Employing this framework, we monitored the surface three-dimensional movement using the UAV oblique photogrammetry method at a working face in western China. The accuracy evaluation results with net station GNSS data show that the RMSE of the subsidence basin is 161 mm, the relative error is 3.1 %, and the RMSEs of the east–west and north–south components are 99 mm and 85 mm, respectively, which demonstrates the effectiveness and practicality of our proposed UAV-MSSH framework in mining surface monitoring tasks. This study establishes an innovative and proven methodology for UAV monitoring in the mining field, which is valuable for safe production, disaster warning, and ecological restoration.

Variscan Gornjane granitoid as an alternative cold-water reservoir in the ore-baring and mining area of eastern Serbia: Quantitative-qualitative characterization (Carpathian-Balkan belt, Getic unit)

The diminishing high-quality groundwater reservoirs have sparked significant interest in hard-rock aquifers, especially in active mining and raw material exploration areas. This paper aims to forecast the quantity and quality of alternative water resources in the area, thus facilitating the planning and design of the existing water resource systems. The focal point is the groundwater accommodated within basement-type alternative igneous aquifers nestled within an active mining and exploration province belonging to the Carpathian-Balkan fold-and-thrust belt (Banat-Timok Province/Banatitic Belt, sector in eastern Serbia). Despite their lower water-bearing capacity, we underscore the significant hydrogeological potential of natural water igneous-type aquifers, such as the Variscan Gornjane massif.For the first time, this research identifies different reservoirs across the granite massif, providing a fresh perspective on the regional water resource systems. By categorizing reservoirs based on porosity type, flow rates, depth of reservoir rocks (in the depth range of 50 m), and distribution, this study significantly enhances the forecasting of the new water resource system, underlining the importance of this research in the field of water resources and mining. In addition to faulted sections of granite, aquifers are formed in the area characterized by weathered and decomposed granite fragments, often referred to as gruss deposits. The gruss layer has a flow rate reaching up to 0.01 l/s. In terms of the water quality, the groundwaters of the Gornjane granite massif mostly do not contain elements that are above the maximum permitted concentrations for drinking water. However, the occurrence of the elements Fe Mn in some water samples and the presence of Al Pb in one sample, as well as Se, As, Cu, Zn, Ag, Cd, Ga, and Bi, suggest the contact of water with sulfide mineralization detected in granite rocks of Rudna Glava-Tanda-Luka area, raising concerns about potential water quality issues.

Human health risk assessment from potentially toxic elements in the soils of Sudan: A meta-analysis.

Potentially toxic elements (PTEs) in soils threaten human health through several exposure pathways. However, health risks posed by PTEs in soils in developing countries have not yet been comprehensively investigated. Thus, such countries lack important information that is needed to implement sustainable solutions. In this study, we assessed the human health risks for 10 PTEs, including arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), mercury (Hg), manganese (Mn), nickel (Ni), lead (Pb), and zinc (Zn), in Sudan focusing on soils affected by anthropogenic activities, such as industrial processes, gold mining, tannery, waste dumping, traffic (affecting roadsides), urban/rural agriculture, river, and coastal sediment land uses (LUs). For this purpose, we did a meta-analysis using 3430 PTE concentrations reported from 981 anthropogenically-affected soils in 76 publications between 1996 and 2024. Ingestion was the most common exposure pathway for both carcinogenic and non-carcinogenic PTEs. Hg and Mn were the main non-carcinogenic PTEs leading to adverse health effects in children in industrially-affected soils and coastal sediments, with average hazard quotient (HQ) and hazard index (HI) values >1. Cr and Ni caused the highest carcinogenic risk to children in all anthropogenically-affected soils as indicated by average incremental lifetime cancer risk values (ILCR >1×10-4). Further major cancer risks for children were caused by As in urban agricultural and industrial LUs, Cd in >70% of all LUs, and Pb at industrial and waste dump sites. Adults were under high cancer risks due to Cd in waste dump (ILCR=9.09×10-4) and Cr and Ni (ILCR >1×10-4) in >75% of all LUs. Cd contributed >50% and >70% to ILCR for children and adults along roadsides, respectively. Our findings can guide future research on the links between soil PTEs and human health risks in developing countries. We recommend establishing effective management strategies to reduce risks to human health based on the presence of PTEs in Sudanese soils.

A Survey to Assess Job Satisfaction of Doctors in Bangladesh

Job satisfaction can be defined as the positive and negative emotions an employee associates with their job or the degree of happiness derived from their job (Singh, J.K., and Jain, M., 2013). Consequently, job satisfaction stands as one of the most extensively subjects in the field of organizational psychology (Spector, P.E., 1997). According to Locke (Locke, E., 1976), job satisfaction is the positive and enjoyable emotional state that results from assessing one’s job or job-related experiences. Previous studies have revealed that when an employee is content with their job, they are more likely to perform at their best to fulfill the organization’s objectives (Jalagat, R., 2016). Highly satisfied employees tend to exhibit regular attendance and punctuality, increased productivity, stronger commitment, and an overall sense of satisfaction in their lives (Lease, S.H., 1998). In addition to a great deal of responsibility, doctors frequently deal with difficult and complex situations (World Medical Association, Inc., 2015; European Junior Doctors Association, 2016). Over the past thirty years, stress in the medical industry has been intensively examined (Cooper et al.). Occupational stress is particularly high in the dental, medical, aviation, law enforcement, mining, and social work fields (Cooper et al., 1988). The strain in medicine comes from having to care for people instead of things and from knowing how much their actions affect other people’s lives (Caplan et al., 2017). Given that mistakes can have major consequences for patients as well as doctors, doctors’ competency is routinely assessed (Payne & Firth-Cozens, 1987). Medical professionals frequently deal with emotionally draining circumstances including patient suffering, mishaps, and mortality. When dealing with complicated events like death, doctors may find these situations extremely difficult to handle if they are not well prepared. Notably, a large percentage of physicians suffer from stress and burnout, particularly in their early training years (European Junior Doctors Association, 2016; World Medical Association, Inc., 2015). Compared to other professionals, doctors are more likely to experience burnout, particularly those in frontline care specialties (Shanafelt et al., 2012). Doctors at hospitals are more likely to become burned out than those in private practices or research institutions, according to Olkinuora’s research (Olkinuora et al., 2018). While extensive research has explored job satisfaction across various sectors in Bangladesh, there exists a significant gap in the literature concerning physicians’ job satisfaction within the hospital setting (Chen et al., 2004). Job satisfaction among physicians is a critical aspect that directly impacts the quality of healthcare delivery and overall healthcare outcomes (Franco et al., 2002). Despite its importance, there is limited research focusing specifically on physicians’ job satisfaction within the context of hospitals in Bangladesh. Existing studies on job satisfaction among healthcare workers in Bangladesh have primarily focused on the public sector, with limited attention given to physicians working in hospital settings, especially within the private sector. Moreover, while different studies have highlighted factors such as working hours, administrative tasks, rewards, recognition, and remuneration influencing physicians’ job satisfaction, it remains unclear whether similar factors hold true for physicians in Bangladesh (Syed Ghazaili & Daud, 2016). Given the unique healthcare landscape in Bangladesh and the potential differences in healthcare systems compared to high-income countries, there is a need for context-specific research to understand the factors influencing physicians’ job satisfaction in Bangladesh hospitals. This research proposal aims to address this critical knowledge gap by conducting a rigorous examination of the different factors contributing to job satisfaction among physicians working in hospitals across Bangladesh. By focusing on the unique context of healthcare in Bangladesh, this study endeavors to shed light on the specific factors that drive job satisfaction among physicians. The findings of this research will not only contribute to the limited body of knowledge on physician job satisfaction in Bangladesh but also have practical implications for healthcare management and policy development aimed at enhancing the quality of healthcare delivery in the country.

Past Forward: Tragedy Spurred the First Effective Land-Mine Detector: Before World War II, Soldiers Hunted for Land Mines with Pointy Sticks

Past Forward: Tragedy Spurred the First Effective Land-Mine Detector: Before World War II, Soldiers Hunted for Land Mines with Pointy Sticks

Temporal and spatial evolution law of landslide induced by overlying strata fracture in coal seam mining under slope landform: A case study of mountain land mining area in Southwest China

Temporal and spatial evolution law of landslide induced by overlying strata fracture in coal seam mining under slope landform: A case study of mountain land mining area in Southwest China

Application of Persistent Scatterer Interferometry to monitor land motion along radar line of sight in landslide-prone areas of Himachal Pradesh, India

Landslides in mountainous regions are often triggered by vertical land movements induced by tectonics or seismic activities, emphasizing the importance of monitoring such motions for early intervention. This study investigates the impact of vertical land motion on landslide-prone districts in Himachal Pradesh, India. We employ Persistent Scatterer InSAR (PS-InSAR) technology to analyse vertical land motion in the region. Utilizing a dataset spanning six years (2017–2023), we provide precise measurements of land movements with millimetre-level accuracy. Our analysis reveals a range of deformation velocities, from −10 mm/year to +5 mm/year, with observed uplift of less than 31 mm and subsidence of up to 60 mm in the study area. These movements are attributed to prolonged wet conditions, illegal land mining, and ecological vulnerability. The findings underscore the necessity of continuous monitoring to mitigate landslide risks in mountainous regions, highlighting the efficacy of PS-InSAR technology in assessing such hazards.

Assessment of Drainage Efficiency in the Backfill Disposal Area of East Air Laya Mine Field at PT. Bukit Asam; A case study from Tanjung Enim, South Sumatera, Indonesia

Assessment of Drainage Efficiency in the Backfill Disposal Area of East Air Laya Mine Field at PT. Bukit Asam; A case study from Tanjung Enim, South Sumatera, Indonesia

Identification of Tin Mining Land Cover Change in Central Bangka Regency Using Machine Learning dan Google Earth Engine (GEE)

Information on changes in the area of tin mining land is crucial as a basis for rehabilitation efforts to support environmental sustainability. Bangka Tengah Regency, located in Bangka Belitung Province, experiences high levels of tin mining activity, posing significant environmental damage risks. This is particularly concerning when mining is carried out by unconventional tin miners (TI), most of whom lack environmental permits, leaving abandoned mining pits without rehabilitation efforts. Consequently, the area of mining land expands each year, exacerbated by surges in tin prices. Mining activities may even spread to residential areas, endangering communities through metal contamination, sedimentation, and soil erosion. This study aims to analyze changes in tin mining land from 2013 to 2022 and map the distribution of tin mining areas in Bangka Tengah Regency. The analysis integrates remote sensing with Machine Learning techniques. The CART (Classification and Regression Trees) algorithm is employed for the classification of mining land changes, with analysis conducted on the Google Earth Engine (GEE) platform. The study reveals that the tin mining land area in Bangka Tengah increased from 132.19 km² in 2013 to 207.62 km² in 2022, reflecting an increase of 75.42 km² (3.58%).

A Review on Cooperative Behavior and Colony Optimization in Ants

The Ant Colony Optimization (ACO) in the data mining field can be used to extract fate-based assessors. The purpose of this article is twofold. On the one hand, we offer a review of previous ant-based approaches for rare classification tasks, comparing them with sophisticated classification techniques such as C4.5, Ripper, and Support Vector Machines (SVM). Automated Intersection Management (AIM) is an innovative concept for steering vehicles through intersections. AIM considers vehicles as bargaining to the right. This assumption is significantly different from conventionally studied intersection management problems, which mainly focus on optimizing cycle times, splits, and offsets. The main difficulty lies in defining a strategy to improve traffic efficiency.

Тіреу қабырғаларын бекіту үшін массивті модельдеу әдісімен тұрақтылықты негіздеу

The paper analyzes the existing methods of fastening retaining walls and their applicability in mountainous conditions. The purpose of the study was to substantiate the stability of the retaining wall mounting structure in mountainous conditions using the method of modeling an array of rocks. Supporting construction justification of a suitable method for modeling a rock massif in the field of mining. Mathematical models have been developed to simulate a rock mass in conditions of retaining wall mounting. Numerical methods and analytical calculations were used to conduct the study.