Field Area Research Articles

Geotechnical Evaluation of Landslide in Nanggerang Village

Landslides are significant geological events that can cause extensive damage to infrastructure, disrupt communities, and pose serious safety hazards. Understanding the mechanisms behind slope failures is crucial for effective risk mitigation and the development of engineering solutions to improve slope stability. According to data from the National Disaster Management Agency (BNPB), Indonesia experienced 83 landslide events from January to February 2024. A notable landslide occurred in Nanggerang Village, Sukasari Sub-district, Sumedang Regency, West Java Province, on February 3, 2024. This landslide happened in a terraced rice field area following heavy rainfall earlier in the day. This study focuses on evaluating the failed slope to understand its condition just before failure and the material properties that influence the landslide event. The research methodology includes field data collection, soil testing, and slope stability analysis using the Limit Equilibrium Method (LEM) with a probabilistic approach via Slide 2 software. The analysis revealed that the failed slope had an average safety factor (FS) of 0.968 and a landslide probability of 58.897%. Sensitivity analysis showed that the cohesion parameter in the soil layer (CWZ) significantly impacts the safety factor of the slope. The study concludes that the reduction in soil cohesion and internal friction angle due to excessive moisture was the primary cause of the landslide, and the cohesion parameter of the soil layer is the most sensitive factor affecting slope stability.

Sága, a Deep Learning Spectral Analysis Tool for Fungal Detection in Grains-A Case Study to Detect Fusarium in Winter Wheat.

Fusarium head blight (FHB) is a plant disease caused by various species of the Fusarium fungus. One of the major concerns associated with Fusarium spp. is their ability to produce mycotoxins. Mycotoxin contamination in small grain cereals is a risk to human and animal health and leads to major economic losses. A reliable site-specific precise Fusarium spp. infection early warning model is, therefore, needed to ensure food and feed safety by the early detection of contamination hotspots, enabling effective and efficient fungicide applications, and providing FHB prevention management advice. Such precision farming techniques contribute to environmentally friendly production and sustainable agriculture. This study developed a predictive model, Sága, for on-site FHB detection in wheat using imaging spectroscopy and deep learning. Data were collected from an experimental field in 2021 including (1) an experimental field inoculated with Fusarium spp. (52.5 m × 3 m) and (2) a control field (52.5 m × 3 m) not inoculated with Fusarium spp. and sprayed with fungicides. Imaging spectroscopy data (hyperspectral images) were collected from both the experimental and control fields with the ground truth of Fusarium-infected ear and healthy ear, respectively. Deep learning approaches (pretrained YOLOv5 and DeepMAC on Global Wheat Head Detection (GWHD) dataset) were used to segment wheat ears and XGBoost was used to analyze the hyperspectral information related to the wheat ears and make predictions of Fusarium-infected wheat ear and healthy wheat ear. The results showed that deep learning methods can automatically detect and segment the ears of wheat by applying pretrained models. The predictive model can accurately detect infected areas in a wheat field, achieving mean accuracy and F1 scores exceeding 89%. The proposed model, Sága, could facilitate the early detection of Fusarium spp. to increase the fungicide use efficiency and limit mycotoxin contamination.

The link between gas extraction and shallow seismicity around the Dalan gas field of Zagros Mountains, Iran

We analyze seismicity from 2013 to 2023 near Bushkan village in the Fars arc of the Zagros folded belt, colocated with the Dalan natural gas field. A previous study suggested unusual seismic behavior in the 2014–15 cluster, linking larger events to shallow depths (∼5 km) on steep reverse faults within the Dalan anticline. Nevertheless, ongoing debates persist regarding the potential correlation of seismicity with activities in the Dalan field. Our study investigates seismic activities using relocation, moment tensor inversion, stress inversion, and remote sensing techniques. We relocated 35 events above magnitude 3 and reconstructed source parameters for 12 events (M 4+) with uncertainties. Focal mechanisms were inverted, and morphology remote sensing was employed to ascertain the active stress state. Besides the 2014–15 cluster, we found a separate strike-slip cluster in 2018 at shallower depths (3–4 km), unusual for Zagros earthquakes. We suggest that pore pressure variations influence seismic sequences in the Dalan gas field area. However, distinguishing between human-induced and natural earthquakes in regions like Zagros with naturally elevated seismicity is challenging.

Numerical analysis of running resistance of a completion string with packers and sliding sleeves in horizontal wells

The installation of a completion string in small-diameter horizontal wells often presents challenges such as instability, bending, and high frictional resistance. To accurately estimate the running resistance during deployment and ensure safe descent to the bottom of the well, a three-dimensional numerical model has been developed incorporates real-time contact interaction between the completion string and the wellbore, while maintaining precision and field applicability. The analysis evaluates several factors, including buckling behavior, average and dynamics transient value of contact force, axial forces, and frictional forces. Results reveal that the upper pipe string exerting thrust on the lower pipe string causes bending and spiral buckling along with resulting contact with wellbore. Maximum hook load and maximum negative axial force occur at a inclination angle of 65°. Contact strength is more pronounced in inclined and horizontal section compared to vertical section. Leveraging extensive data, the study has yielded a quick prediction formula for quickly calculating the resistance experienced by a completion string of field area. The numerical results offer valuable insights into comprehending the resistance encountered during the descent of a completion string in horizontal wells, and provide robust theoretical support for predicting descent resistance.

Systematic Modeling and Optimization for High‐Efficiency Interdigitated Back‐Contact Crystalline Silicon Solar Cells

This study utilizes Quokka3, an advanced solar cell simulation program, specifically tailored for interdigitated back‐contact (IBC) crystalline silicon (c‐Si) solar cells. Through meticulous Quokka3 simulations, the influence of several geometric and wafer characteristics of the solar cell backside on current–voltage (I–V) performance has been scientifically explored for IBC c‐Si solar cells. The investigation encompasses parameters such as wafer thickness, bulk lifetime, resistivity, emitter and back surface field area fraction, and front‐ and rear‐surface passivation. Optimal values for these parameters have been proposed to enhance the efficiency of IBC solar cells. These recommendations contain an emitter percentage of 70%, a wafer thickness ranging from 200 μm, a wafer resistivity of 1 Ω cm, and a wafer bulk lifetime of at least 10 ms. Moreover, under conditions where the cell is not short‐circuited, the potential for achieving higher cell efficiency, up to 26.64%, has been shown.

PIV investigation of stalled flow field near the blade rim region of mixed-flow pump under different tip clearances

In order to further explore the physical mechanism of rotating stall induced by different rim leakage flow intensification, the velocity distribution of the stall flow field of the mixed flow pump under different rim clearance scales was obtained by Particle Image Velocimetry technology. By comparing the flow structure under different shooting sections, different phases and different working conditions, the influence of rim clearance scale on the flow field near the wall of the mixed flow pump was revealed. The results show that under the design flow conditions, there is an obvious reflux phenomenon near the hub of the guide vane inlet of the mixed flow pump under the four kinds of rim clearance, but the intensity is weak, and it does not affect the main flow field in the impeller channel. With the increase of rim clearance scale, the leakage flow intensity also increases, and the main flow area affected by the TLV structure also increases. In the deep stall condition, the reflux vortex at the inlet of the guide vane of the mixed flow pump still exists at each clearance, but the unsteady flow structure begins to appear inside, especially the large secondary vortex structure formed at the guide vane inlet, which seriously blocks the main flow. With the increase of the rim clearance, the flow field at the inlet of the guide vane is also obviously affected, and the secondary vortex core gradually moves towards the end wall area, resulting in an increase in the blocked area in the flow field. At the same time, the unsteady flow intensity of the flow field shows a nonlinear increasing trend. The unsteady strength of flow field increases rapidly with small gap impellers, while the unsteady strength changes slowly with large gap impellers. This study provides a reference for optimizing the rim clearance of mixed-flow pump and improving the efficiency of mixed-flow pump.

Integrating Sustainability and Digitalization in Business Model Innovation. A Bibliometric Study

Abstract The aim of the study is to get a comprehensive understanding of the research landscape at the nexus between business model innovation, sustainability and/or digitalization, in order to determine key research patterns and trends. Accordingly, the following objectives have guided the development of the paper: (a) to examine the dynamics and structure of the research output (articles and citations) on the topic; (b) to explore the network of the most impactful articles; (c) to trace the origins of the research on the topic; (d) to decipher current developments in the research on the topic; and (e) to search for emerging trends in the research on the topic. An advanced search of the SCOPUS database was conducted to identify the relevant literature available; 1195 articles (published between 2014 and 2023) were selected and exported in VOSviewer for bibliometric analysis. The results suggest the emergence of a niche research area in the broad strategic management field, bringing together four related (sub)topics: (1) circular economy and sustainable business models; (2) tools, frameworks and processes for sustainable business model innovation; (3) digitalization and business model transformation; (4) business models for societal and sustainability transitions. For this research area to crystallize, the identified emerging research trends – on digital innovation and technology; strategic management and analytical methods; supply chain and manufacturing; policy, governance and sustainability – need to be further explored, both individually (in search of refinement and clarity) and collectively (in search of consistency and synergy).

Exploring the impact of galactic interactions and mergers on the central oxygen abundance of APEX/EDGE–CALIFA galaxies

ABSTRACT In this study, we explore the impact of the galactic interaction/mergers on the central oxygen abundance. We analyse 234 star-forming galaxies included in the Calar Alto Legacy Integral Field Area survey with integrated molecular gas observations from the Atacama Pathfinder EXperiment millimeter telescope and the CARMA interferometer. This database has the most optical integral field spectroscopy data with CO data for yet, with integrated measurements within $\sim 1~{R_{\rm{eff}}}$. Our sample includes 125 isolated galaxies (control sample) and 109 galaxies in different merging stages. We find that despite whether the merging galaxies show an increase or decrease in their molecular gas fraction, the oxygen abundance does not vary significantly, in comparison to our control sample. Therefore, the enhancement and suppression of oxygen abundance are similar in both isolated galaxies and interacting/merging galaxies. On the contrary, regardless of the merger stage (including isolated sample), galaxies that present an increase in their specific star formation rate present a metallicity dilution. We suggest that both internal and external events affect the chemical composition of merging galaxies.

Exploring galaxy properties of eCALIFA with contrastive learning

Contrastive learning (CL) has emerged as a potent tool for building meaningful latent representations of galaxy properties across a broad spectrum of wavelengths, ranging from optical and infrared to radio frequencies. These latent representations facilitate a variety of downstream tasks, including galaxy classification, similarity searches in extensive datasets, and parameter estimation, which is why they are often referred to as foundation models for galaxies. In this study, we employ CL on the latest extended data release from the Calar Alto Legacy Integral Field Area (CALIFA) survey, which encompasses a total of 895 galaxies with enhanced spatial resolution that reaches the limits imposed by natural seeing (FWHMPSF ∼ 1.5). We demonstrate that CL can be effectively applied to Integral Field Unit (IFU) surveys, even with relatively small training sets, to construct meaningful embedding where galaxies are well separated based on their physical properties. We discover that the strongest correlations in the embedding space are observed with the equivalent width of Hα, galaxy morphology, stellar metallicity, luminosity-weighted age, stellar surface mass density, the [NII]/Hα ratio, and stellar mass, in descending order of correlation strength. Additionally, we illustrate the feasibility of unsupervised separation of galaxy populations along the star formation main sequence, successfully identifying the blue cloud and the red sequence in a two-cluster scenario, and the green valley population in a three-cluster scenario. Our findings indicate that galaxy luminosity profiles have minimal impact on the construction of the embedding space, suggesting that morphology and spectral features play a more significant role in distinguishing between galaxy populations. Moreover, we explore the use of CL for detecting variations in galaxy population distributions across different large-scale structures, including voids, clusters, and filaments and walls. Nonetheless, we acknowledge the limitations of the CL framework and our specific training set in detecting subtle differences in galaxy properties, such as the presence of an AGN or other minor scale variations that exceed the scope of primary parameters such as the stellar mass or morphology. Conclusively, we propose that CL can serve as an embedding function for the development of larger models capable of integrating data from multiple datasets, thereby advancing the construction of more comprehensive foundation models for galaxies.

Analysis of land cover change in Magelang Regency And Magelang City over 15 years

Background: Land cover change is an ongoing phenomenon with significant impacts on the environment and human quality of life. An analysis of land cover change was conducted in the Magelang area, which includes both Magelang Regency and Magelang City, over a period of fifteen years from 2007 to 2023. This study aims to analyze these changes using spatial methods based on land cover maps from 2007 and 2023. Method: In this analysis, land cover was classified into five categories: settlements, dry fields, paddy fields, forests, and plantations, and then compared between the two years. Result: The analysis results demonstrate a significant increase in the area of settlements and dry fields, reflecting urban and rural development. On the other hand, there has been a decrease in the area of paddy fields and forests, which may be related to land conversion for industrial or infrastructure purposes. The change in the area of plantations was not significant, indicating continuity in the management of these lands. Conclusion: The findings of this study provide valuable insights into the dynamics of land cover change in the region and can serve as a basis for further actions to maintain a balance between development and environmental conservation. Novelty/Originality of this study: This study presents the first longitudinal spatial analysis of land cover changes over 15 years (2007–2023), revealing significant trends in urban and rural land use dynamics. This study provides a scientific basis for sustainable spatial planning by highlighting the urgency to balance development infrastructure with preserving farmland and forests.

Green Synthesis of Silver Nanoparticles from Syzygium cumini (L.) Skeels Seed Extract and their Potential Medicinal Applications.

Nanotechnology is considered as one of the fastest-developing areas in the biomedicine field. Hence, the green synthesis of silver nanoparticles from Syzygium cumini seed extract was carried out in this study. The synthesized nanoparticles were characterized by UV-Vis spectroscopy, FTIR (Fourier transform infrared), FE-SEM (Field Emission scanning electron microscopic), AFM (Atomic Force Microscope), XRD (X-ray diffraction), and EDX (Energy dispersive X-Ray). Their antioxidant and anti-inflammatory activity were evaluated by DPPH (2,2-diphenyl-1- picrylhydrazyl), PM (Phosphomolybdenum) assay, and albumin denaturation assay. Further, the antibacterial activity of the nanoparticles was studied against Gram-positive and Gram-negative bacteria using the agar well diffusion method. In addition, the antidiabetic activity of nanoparticles was studied by α-amylase and α-glucosidase inhibition assays. The surface plasmon resonance at 430 nm confirmed the formation of silver nanoparticles. They were stable and spherical in shape, with sizes ranging from 30 to 90 nm. The DPPH inhibition % of silver nanoparticles varied from 7.91±0.39% to 68.35±0.76%. The % inhibition of albumin denaturation was comparable to the diclofenac. Further, the results of antibacterial activity revealed that the zone of inhibition for all the test bacteria varied from 14.33±0.58 to 25.33±0.58 mm, where B. cereus was more susceptible. In addition, the % inhibition of α-amylase and α-glucosidase varied from 19.91±0.15% to 61.43±0.31% and 15.26±0.11% to 55.38±0.20%, respectively. This study is the first attempt of utilizing the silver nanoparticles synthesized from S. cumini seed extract for antidiabetic activity. The study suggests that these nanoparticles could be well utilized in pharmaceutical industries as an efficient antioxidant, anti-inflammatory, antibacterial, and antidiabetic drug.

A Comprehensive Survey On Machine Learning-Based Intrusion Detection System for Vehicular Area Network Architectures

Today, the development of communication technologies causes changes in many different areas. One of these areas is VANET (Vehicular Area Network) application area. With the increase in usage areas in the VANET field, ensuring VANET network security has become more critical. Many different systems have been developed to detect attacks on VANET networks. Machine learning-based systems are one of the most widely used methods in developing these intrusion detection systems (IDS). In this article, research on machine learning-based VANET IDS, which has been done recently in the literature, has been carried out. First, VANET architecture and security requirements are presented, then a comprehensive literature summary is given, and comparisons are made on different parameters. As a result, it has been determined that many different machine learning models are used in IDSs and perform high-performance detection. In addition to the machine learning algorithm used in the performance of IDSs, it has been shown that many different parameters play a critical role in the performance. The paper aims to guide new studies in this field with the gains that will increase the performance of intrusion detection systems because of the literature comparison (considering criteria such as machine learning model, simulation tools, dataset, machine learning algorithm, and performance criteria).

Petrography of terrigenous and clay rocks in the Dostlug field area

Petrography of terrigenous and clay rocks in the Dostlug field area

Multi-aspect Analysis of Rice Sustainability in the Improvement of Rice Production in North Sumatra Province, Indonesia

North Sumatra's food security highly depends on rice supply from rice fields in rice-producing districts, namely Deli Serdang, Serdang Bedagai and Simalungun. The decrease in rice field area impacts the North Sumatra region's rice supply. This research aims to analyze the sustainability status and sensitive factors that cause the sustainability of rice fields based on economic, ecological, social, technological, legal, and institutional dimensions using Multi-aspect Sustainability Analysis software. The research results show that, in aggregate, the sustainability of rice fields is in the quite good category with a score of 62. Apart from the less sustainable economic aspect with a score of 59.09, there are four other aspects, such as environmental, social, infrastructure and technology, laws and regulations, and institutions, declared sustainable with a score greater than 62. The research also found 15 sensitive key factors that can improve the sustainability status of rice fields in North Sumatra to become very sustainable

Linking sediment geochemistry with catchment processes, internal phosphorus loading and lake water quality

Research in the field of sediment geochemistry suggests potential linkages between catchment processes (land use), internal phosphorus (P) loading and lake water quality, but evidence is still poorly quantified due to a limited amount of data. Here we address the issues based on a comprehensive data set from 27 lakes in southern Finland. Specifically, we aimed at: 1) elucidating factors behind spatial variations in sediment geochemistry; 2) assessing the impact of diagenetic transformation on sediment P regeneration across lakes based on the changes in the vertical distribution of sediment components; 3) exploring the role of the sediment P forms in internal P loading (IL), and 4) determining the impact of IL on lake water quality. The relationship between sediment P concentration and field area percentage (FA%) was statistically significant in (mainly eutrophic) lakes with catchments that included more than 10 % of fields. We found that sediment iron-bound P (Fe-P) increased with increasing FA%, which agrees with the high expected losses from the cultivated areas. Additionally, populated areas increased the pool of sediment Fe-P. Internal P loading was significantly positively related to both sediment Fe-P and sediment organic P (Org-P). However, Org-P was not significant (as the third predictor) in models that had a trophic state variable as the first predictor and Fe-P as the second predictor. Further, the vertical profiles of sediment components indicated a role of diagenetic transformations in the long-term sediment P release, especially in lakes with deeper maximum depth and longer water residence time. Finally, IL was significantly positively correlated to water quality variables including phytoplankton biomass, its proportion of cyanobacteria, chlorophyll a concentration and trophic state index. Our findings suggest that reduction of P losses from the field and populated areas will decrease internal P loads and increase water quality through a reduced pool of Fe-P.

Random Forest—Based Identification of Factors Influencing Ground Deformation Due to Mining Seismicity

The goal of this study was to develop a model describing the relationship between the ground-displacement-caused tremors induced by underground mining, and mining and geological factors using the Random Forest Regression machine learning method. The Rudna mine (Poland) was selected as the research area, which is one of the largest deep copper ore mines in the world. The SAR Interferometry methods, Differential Interferometric Synthetic Aperture Radar (DInSAR) and Small Baseline Subset (SBAS), were used in the first case to detect line-of-sight (LOS) displacements, and in the second case to detect cumulative LOS displacements caused by mining tremors. The best-prediction LOS displacement model was characterized by R2 = 0.93 and RMSE = 5 mm, which proved the high effectiveness and a high degree of explanation of the variation of the dependent variable. The identified statistically significant driving variables included duration of exploitation, the area of the exploitation field, energy, goaf area, and the average depth of field exploitation. The results of the research indicate the great potential of the proposed solutions due to the availability of data (found in the resources of each mine), and the effectiveness of the methods used.

A Study to Assess Women’s Autonomy in Household Decision Making Under Field Areas of Medical College Health Unit, Pangappara

A Study to Assess Women’s Autonomy in Household Decision Making Under Field Areas of Medical College Health Unit, Pangappara

All-fiber passively Q-switched Tm-doped laser with a mode field area mismatched Tm-doped saturable absorber.

We demonstrate a high peak power, all-fiber passively Q-switched Tm-doped laser operating at 1940 nm for applications in soft tissue ablation. High peak power and passive Q-switching were achieved via a clad pumped gain fiber and a smaller core Tm-doped fiber saturable absorber respectively. Clad pumping was achieved via two 30 W diodes operating at 793 nm. At 50.7 W of pump power, laser pulses with 140 ns FWHM duration and average power of 14.5 W were obtained at a repetition rate of 328 kHz which corresponded to a pulse energy of ~44 µJ and a peak power of ~316 W. The laser had a narrow 0.14 nm linewidth at the maximum output power. The laser was used to cut chicken breast as well as ovine cortical and subcortical brain tissues and average ablation efficiencies of 29, 40 and 42% were obtained, respectively. Cutting speeds of 10 mm/s for ovine brain tissue and 5 mm/s for chicken breast were achieved. Further resection experiments were performed with the target tissue placed under a thin layer of water. A resected volume with length, width and depth of 5 mm, 2.5 mm, and 2.8 mm were obtained, corresponding to a resection rate of ~0.58 mm3/s. To our best knowledge, this is the first report of an all-fiber clad-pumped passively Q-switched Tm-doped fiber laser with a core mismatched saturable absorber being used for tissue ablation experiments.

Information system for collecting data on the area of ​​irrigated rice fields at the Jombang district agricultural service

Irrigation is one of the important elements in agriculture, especially in rice fields. Irrigation makes water more available to plants and can increase yields. Therefore, irrigation efficiency and monitoring are important to support agricultural productivity. Looking at the rapid development of technology utilization, it is also needed to help irrigation managers at the Department of Agriculture to collect data systematically. One of them is creating a data collection system in a more modern way. regarding the case of irrigation managers at the Department of Agriculture who have so far been using manual methods. The purpose of this study is to design an Information System for recording the area of ​​rice field irrigation. The SDLC (System development life cycle) method is used with a Waterfall process model: analysis, design, code generation, testing, and support. This system is created using PHP and MySQL programs, Google Chrome software, XAMPP, and MySQL database. The results of the study show that the designed system can help facilitate data collection on the area of ​​rice field irrigation at the Department of Agriculture, Jombang Regency. Keywords: Information System; Data Collection; Irrigation; waterfall

A Decade Analysis (2013-2023) of Paddy's Yield Productivity by Using Landsat 8 Imagery in Sukoharjo District, Indonesia

Sukoharjo District has the highest rice productivity in Central Java. Sukoharjo has a strategic location. It makes this area prone to land use changes. It makes a severe impact on Paddy's productivity. This condition needs to be monitored continually. Remote sensing can provide an efficient and accurate method to solve this condition. Using the NDVI from Landsat 8 imagery and ubinan data, the model can be built to calculate and analyze paddy’s productivity. The steps were 1) interpretation of paddy fields area; 2) calculation of NDVI’s mean values a month before harvesting; 3) interpretation accuracy test; 4) correlation value between NDVI and ubinan data in 2022; 5) calculation of paddy productivity; and 6) analysis. Within a decade (2013-2023), there was a reduction in paddy's yield area for 981.90 Ha. During that period, there was an increase in paddy's productivity, around 38.2 x 103 tons. Almost all sub-districts in Sukoharjo’s yield had been reduced except for Tawangsari and Weru. Kartasura and Grogol have experienced an intensive change in paddy yields to non-paddy yields. Intensive land use changes affected paddy’s productivity. Multi-temporal imagery combined with ubinan data can be used to monitor paddy’s productivity. Forty-one points were calculated for the y (productivity) and x (NDVI value) equation. The equation resulting from this method (y=6.2212x+6.7444) can be used as a reference for calculating productivity in Sukoharjo District in different periods. From different calculations, the accuracy obtained from this method was 86%.Keywords: Landsat 8; NDVI; Paddy’s Productivity; Sukoharjo; Ubinan data