Index Method Research Articles

Application of the Composite Performance Index (CPI) Method in Optimizing the Location Point of the Goods Terminal in Banyuwangi Regency

Application of the Composite Performance Index (CPI) Method in Optimizing the Location Point of the Goods Terminal in Banyuwangi Regency

Preference for Public Transportation Service Levels in Makassar City Towards Female Passenger Satisfaction

Basic services are government affairs that are mandatory and entitled to be obtained by every citizen, one of which is public transportation services. Although it has been regulated by Minimum Service Standards (SPM), the quality of public transportation services in Makassar City is still low and has not met expectations and satisfaction, especially for female passengers. The purpose of this study was to determine the satisfaction of female passengers with the quality of public transportation services in Makassar City. The respondents of this study were 96 female passengers of public transportation in Makassar City. This research is descriptive qualitative study that refers to the Regulation of the Minister of Transportation of the Republic of Indonesia Number PM 29 of 2015. This regulation outlines criteria such as security, safety, comfort, affordability, equality, and regularity for assessing the quality of public transportation services in Makassar City. The data analysis technique in this study uses descriptive analysis employing the Community Satisfaction Indicator (SMI) analysis method. Based on the results of calculations using the public satisfaction index method, it is known that the total number of service elements has a score of 43.86. Although this indicates that the overall quality of service is quite good, it is considered that women are not satisfied with the service provided. The relationship between the level of satisfaction with the quality of service affects women's preferences in choosing the mode they want to use.

Comparing Optical and Custom IoT Inertial Motion Capture Systems for Manual Material Handling Risk Assessment Using the NIOSH Lifting Index

Assessing musculoskeletal disorders (MSDs) in the workplace is vital for improving worker health and safety, reducing costs, and increasing productivity. Traditional hazard identification methods are often inefficient, particularly in detecting complex risks, which may compromise risk management. This study introduces a semi-automatic platform using two motion capture systems—an optical system (OptiTrack®) and a Bluetooth Low Energy (BLE)-based system with inertial measurement units (IMUs), developed at the Biomedical Engineering Laboratory, Universidad de Concepción, Chile. These systems, tested on 20 participants (10 women and 10 men, aged 30 ± 9 years without MSDs), facilitate risk assessments via the digitized NIOSH Index method. Analysis of ergonomically significant variables (H, V, A, D) and calculation of the RWL and LI showed both systems aligned with expected ergonomic standards, although significant differences were observed in vertical displacement (V), horizontal displacement (H), and trunk rotation (A), indicating areas for improvement, especially for the BLE system. The BLE Inertial MoCap system recorded mean heights of 33.87 cm (SD = 4.46) and vertical displacements of 13.17 cm (SD = 4.75), while OptiTrack® recorded mean heights of 30.12 cm (SD = 2.91) and vertical displacements of 15.67 cm (SD = 2.63). Despite the greater variability observed in BLE system measurements, both systems accurately captured vertical vertical absolute displacement (D), with means of 32.05 cm (SD = 7.36) for BLE and 31.80 cm (SD = 3.25) for OptiTrack®. Performance analysis showed high precision for both systems, with BLE and OptiTrack® achieving precision rates of 98.5%. Sensitivity, however, was lower for BLE (97.5%) compared to OptiTrack® (98.7%). The BLE system’s F1 score was 97.9%, while OptiTrack® scored 98.6%, indicating both systems can reliably assess ergonomic risk. These findings demonstrate the potential of using BLE-based IMUs for workplace ergonomics, though further improvements in measurement accuracy are needed. The user-friendly BLE-based system and semi-automatic platform significantly enhance risk assessment efficiency across various workplace environments.

Structural and dynamic analysis of green investments in Russia

«Green» investments are an important tool for solving environmental problems, increasing the competitiveness of the economy and improving the quality of life. One of the key aspects in the study of «green» investments is structural and dynamic analysis, which allows you to get a description of the space-time aspect of «green» investments. The findings play an important role in shaping the targeted state investment policy. This aspect determines the relevance of this study, which purpose is to conduct a structural and dynamic analysis of green investments in Russia. The practical significance of the findings is determined by a comprehensive approach to assessing the homogeneity of the green investments’ distribution in Russia. The article studied the concentration of «green» investments in federal districts and by areas of their use in order to identify the causes of inequality in the distribution of «green» investments. Analysis of structural differences in the volume of «green» investments in dynamics for the period from 2013 to 2022 was carried out in the article. Special statistical methods are used in the article, such as typological groupings, index method, construction and analysis of structural series, analysis of dynamic series, comparison of parallel series, etc. The results obtained during the study are also significant in matters of international cooperation in the field of ecology and sustainable development of the country. Managing a green investment structure allows a country to meet international standards and requirements.

Reliability analysis and uncertainty evaluation for assessing low velocity car impacted cosmetic damage of prototyped RC bridge pier

Crashworthiness of low velocity vehicles with reinforced concrete (RC) bridge pier has become widespread scenario that warrants a continuous threat on the structural viability. Even, low velocity small car collisions creates a short duration quasi-static to dynamic effect in different damage levels from low and cosmetic to collapse, depending on energy dissipation, not generally considered in design practices, making the piers susceptible to various level of damage. Bridge piers do not always collapse upon impact, and some are kept in service without pertinent health examinations that warrant serviceability. Unfortunately, little attention has been provided to keep the post impact low to medium distressed piers in service. Medium to higher damage need a complete replacement, whereas the low to cosmetic damage needs an additional meticulous investigation. This study is an attempt to assess cosmetic damage and residual capacities of RC pier via pendulum impacts to replicate low velocity car crash scenarios. To investigate post impact performance, experimental results are captured and transformed into realistic crash scenarios. Deterministic analysis via dynamic increase factor (DIF) approach to evaluate damage index (λ) and probabilistic method via resistance reduction method (RRM) to capture the uncertainties are performed in determining residual and reduced capacity of the representative pier. To identify damage incurred from collision and identify the probability of failure (Pf), a limit state (LS) equation has been developed comprising impact load and resistance and utilized as a model to estimate reliability index (β). Both the models used are able to precisely capture reduced capacities providing a good agreement between the shear and the axial capacity which control primary resistance of the impact loads and principal serviceability respectively. This study will provide an aid to forensic structural engineers.

Spatial pattern and landscape change prediction of blowouts in sandy grassland

Blowouts are a common type of wind-eroded landform found in sandy and desertified areas. They also represent a major degradative surface process affecting grassland ecosystems. Blowouts exacerbate changes in surface morphology through their effects on other surface phenomena including vegetation. In this paper, Xilingol League sandy grassland blowouts are taken as the research object, and the U.S. Keyhole satellite data and China’s Gaofen-1 satellite data are used as the data source, and the blowouts are extracted based on the 3 S technology for a total of six periods of high-resolution remote sensing image data in the study area from 1962 to 2023. The Landscape Pattern Index method and Fuzzy Land Use Simulation (FLUS) modelling applied to changes over the last six decades provided spatial evolution parameters for predicting future blowout distributions. Results showed that blowouts affecting the Xilingol grassland area increased by 16.81% over the past 60 years. The patch density (PD) increased by 0.9 per hectare. The mean proximity index (PROX_MN) and mean Euclidean nearest neighbour distance (ENN_MN) showed a tendency to decrease and then increase indicating initial expansion and then merging of adjacent blowouts to create the present landscape. The FLUS model used ten factors to predict changes in blowout distributions from 2023 to 2033. Factors included digital elevation model (DEM), slope, aspect, normalized difference vegetation index (NDVI), mean annual temperature, mean annual precipitation, population density, real GDP, distance to water, and distance to impervious surfaces. It was found that grassland area decreased by 6217.12 hm2 and blowout area decreased by 102.91 hm2. Results of this study can expand understanding of blowout morphodynamics in ecologically sensitive areas.

Measuring Multidimensional Poverty of the Population of the Regions of Russia: A Hybrid Approach

The research paper is devoted to the measurement of poverty (and its depth) of the population of Russian regions on the basis of a hybrid approach. This approach involves both an objective assessment of the phenomenon under study (according to Rosstat data) and taking into account the subjective perception of poverty by the population. In the first case, the index method is used in combination with the method of self-organising maps by T. Kohonen and an ensemble of multilayer perseptrons of various configurations. Artificial intelligence allows not only to deepen the results of retrospective assessment of multidimensional poverty through cluster analysis, but also to realise the predictive function. In the second case, a sociological survey is conducted on the example of a certain region of Russia (the Republic of Bashkortostan). According to the results of the cluster analysis, taking into account the earlier retrospective assessment, we can conclude that there is a high degree of polarisation of Russian regions based on the actual level of ‘multidimensional poverty’. ‘Oases’ (St. Petersburg, Moscow and the Republic of Tatarstan) contrast with “centres” (the Republic of Ingushetia, the Republic of Tyva, the Karachay-Cherkess Republic and the Republic of Altai) of multidimensional poverty. At the same time, the Republic of Bashkortostan (one of the old-industrial regions with a rich mineral and raw material base) in the formed rating regularly occupied the median position among other constituent entities of the Russian Federation. The main (key) problems in the sphere of poverty for this region of Russia are specified based on the results of sociological research. Such a study also makes it possible to deepen the retrospective assessment of ‘multidimensional poverty’, since not all the necessary data for a comprehensive analysis of the phenomenon under study are generated by Rosstat on a regular basis. The results obtained in the framework of the study can be used in the actualisation of the most important direction of the state social policy of modern Russia – the fight against poverty. In the long term, the top management of the country needs to ensure a significant reduction in the poverty level of the population regardless of the territorial factor (constituent entity of the Russian Federation).

Development of Kopasanda Leaves Extract (Chromolaena odorata (L.)) Phytosome Using Solvent Evaporation Method.

Kopasanda (Chromolaena odorata (L.) R.M.King &H.Rob) or siam weed exhibited various pharmacological activities such as anticancer, antidiabetic, and wound healing activities. The phenolic content of kopasanda leaf extract was high in the range of 56.15 mg GAE/g-242.3 mg GAE/g. But, it reported that phenolic compounds exhibited poor bioavailability because of their physicochemical properties. The phytosomes system improves the effectiveness of phytoconstituents. Aims of the research as prepare and evaluate the phytosome from kopasanda leaf extract. Kopasanda dried leaves were extracted by the sonication method. The solvent evaporation method was used in the phytosome preparation with various concentrations between extract and phospholipid ( 2:1; 1:1; 1:2; and 1:3). Furthermore, phytosome formation was analyzed for its entrapment efficiency, Polydispersity Index (PDI), particle size, spectroscopy infra-red (IR), and Transmission Electron Microscope (TEM) method. The total phenolic content (TPC) of crude was 110.969 ± 0.752 mgGAE/g. The optimum phytosome was gained with a concentration between extract and phospholipid (1:3). It resulted in the entrapment efficiency of 99.930 ± 0.001%, particle size, and polydispersity index equal to 206.5 ± 8.613nm and 0.528 ± 0.038. The morphology of the phytosome was spherical and the phytosome formation was verified by the IR spectrum by comparing the phytosome and the physical mixture of extract and phospholipid. The results obtained showed that phytosome containing kopasanda leaf extract was formed using the solvent evaporation method.

Cells Total Antioxidant Capacity (TAC) and External Expression of Enzymatic Browning for the Commercially Important Penaeids Prawns Parapenaeus longirostris and Penaeus kerathurus

The enzymatic browning process, known also as melanosis, is a significant indicator of oxidative damage in commercially important shrimps and prawns. Total Antioxidant Capacity (TAC) serves as a vital measure of molecular defense against melanosis, a crucial mechanism for organisms to combat detrimental factors such as free radicals. This contribution delves into the assessment of this process for the penaeid species, Parapenaeus longirostris and Penaeus kerathurus. Samples were stored at refrigerator temperature (4 °C) for 0, 1, 2, 4, and 8 days for P. longirostris, and for 0, 2, 4, 8, and 16 days for P. kerathurus. Each day’s samples consisted of groups of ten individuals (5 males, and 5 females). TAC was determined using the CrO5 method. Results revealed an initial TAC increase followed by a gradual decrease over time. Additionally, exterior changes in both species were visually documented daily to capture the macroscopic manifestation of enzymatic browning, aligning with the molecular profile of the samples. Furthermore, Quality Index Method was conducted, where five evaluators graded the organoleptic qualities (texture, odor, color) of the prawn individuals, daily. This assessment aimed to scrutinize the external alterations of the shrimps under refrigeration conditions, revealing changes via sensory evaluation and the Quality Index Method.

Feature Selection and Spectral Indices for Identifying Maize Stress Types

This study aims to identify different types of stress on maize leaves using feature selection and spectral index methods. Spectral data were collected from leaves under heavy metal, water, fertilizer stress, as well as under normal healthy conditions. Preprocessing steps such as continuum removal (CR), standard normal variable (SNV) transformation, multiple scattering correction (MSC), detrend correction (DT), and first-order derivative (FOD) were applied to the raw spectra. Various feature selection methods including ReliefF, chi-square test, recursive feature elimination (FRE), mutual information (MI), random forest (RF), and gradient boosting tree (GBT) were employed to determine the importance scores of different spectral bands, thus identifying sensitive spectral features capable of distinguishing various stress types. Spectral indices for stress type differentiation were constructed using label correlation method. Classification models were built using support vector machine (SVM), K-nearest neighbors (KNN), Gaussian naive Bayes (GNB), extreme gradient boosting (XGBoost), RF, and adaptive boosting (AdaBoost) algorithms. Results indicate that the characteristic spectral bands for differentiating stress types are primarily distributed around the red edge (near 700–800 nm) and water absorption valley (near 1900 nm). Spectral indices constructed using combinations of spectral bands around the near-infrared plateau absorption valley (near 1185 nm) and water absorption valley (near 1460 nm) effectively differentiate maize stress types. Among the modeling classification algorithms, RF and AdaBoost algorithms exhibited optimal performance, demonstrating high classification accuracy on both training and validation sets. These findings hold promise for providing new technical support for maize stress monitoring and diagnosis in agricultural production.

Microwave self-healing characteristics of the internal voids in steel slag asphalt mixture subjected to salt-freeze-thaw cycles

Microwave heating technology can be used to repair freeze-thaw (F-T) cycle damage and cracks caused by the use of snow melting salts and de-icers in winter. This study utilized three different gradations of asphalt mixtures: AC-13, SMA-13, and OGFC-13, and enhanced their microwave absorption performance with steel slag (SS). The heating uniformity was evaluated through surface and internal temperature tests. Additionally, the mechanisms of damage and healing of the asphalt mixtures under salt freeze-thaw (S-F-T) damage conditions were explored using S-F-T cycles test and X-ray computed tomography scans (CT). The results showed that OGFC-13 exhibits a significantly higher rate of temperature increase compared to the other two types due to its stronger wave absorption capacity. The thermal hysteresis caused by steel slag leads to uneven spatial temperature distribution. The study recommends an optimal microwave heating time of 40 s for SS asphalt mixtures, which has minimal impact on the mixture structure, by defining an evaluation method for the uniformity index and local overheat ratio. After undergoing S-F-T cycles, the porosity of all three asphalt mixtures increased, particularly at the top and bottom of the specimens. Among them, OGFC-13 exhibited the poorest durability under S-F-T conditions, with the S-F-T cycles having a significant impact on medium-sized voids (0.15–0.6 mm). After microwave heating, AC-13 and SMA-13 showed good repair effects, with porosity levels returning to their initial states. For large voids (>2.36 mm) in OGFC, microwave heating may not effectively reduce the number of voids. Additionally, the healing rate of mixture specimens at certain mid-height ranges is higher due to elevated surface temperatures in these areas, making it especially effective in repairing F-T damage. This study shows that microwave heating of SS asphalt mixtures can improve the durability of pavement in cold regions. However, implementation challenges include the need for specialized equipment and potential uneven heating. Future research should explore optimizing microwave heating techniques to minimize local overheating and investigate the long-term performance of microwave-repaired asphalt pavements under various environmental conditions.

Damage characterisation of GFRP composites based on clustering acoustic emission events utilizing single-failure-cause tests as reference

A new method to identify causes of fracture in composites based on acoustic emission (AE) and clusterization of AE data based on reference datasets is presented within the manuscript. Acoustic Emission (AE) is a widely used non-destructive method for fracture analysis, but data due to their multidimensionality are not easy to analyze especially if the acoustic events appear simultaneously and have similar parameters even if they are an effect of different failure mechanisms. In this research, we utilize an unsupervised learning algorithm besides the simplest K-means, through fuzzy c-means to Gaussian Mixture Model (GMM) and spectral clustering to investigate the dataset obtained from the three-point bending test manufactured by us composite. The analysis is preceded by data curation, feature determination (Laplacian score) and the best number of cluster investigations (DB index, Caliński-Harabasz score, and Silhouette method) To enable interpretation of the clustering we run an additional three groups of tests covering fibre breakage (two methods), resin fracture (in tension and in compression) and delamination (DCB test) creating reference datasets. These datasets were statistically analyzed and kernel density estimators were generated for each AE feature as well as amplitude-frequency characteristics. Clusters obtained for the main dataset were then assigned to particular causes of failure by comparing them with the reference dataset. It was found that clusters generated using spectral clustering were the most realistic ones, as it was possible to assign the cause of failure to them.

Geochemical baseline establishment and accumulation characteristics of soil heavy metals in Sabaochaqu watershed at the source of Yangtze River, Qinghai-Tibet Plateau

The establishment of soil geochemical baseline and heavy metal pollution assessment in the Qinghai-Tibet Plateau is of great significance for guiding environmental management in the high-cold and high-altitude regions. A total of 126 topsoil samples (0–20 cm) were collected and the contents of Cu, Pb, Zn, Ni, Cr, Cd, As and Hg were determined in the Sabaochaqu basin of the Tuotuo River, the source of the Yangtze River, in the Tibetan Plateau. The baseline values of 8 heavy metals were determined by mathematical statistics, iterative 2times standard deviation method, cumulative frequency and reference element standardization, and the soil heavy metal pollution in the study area was assessed by enrichment factor method and pollution index method. The results showed that the average contents of As, Cd, Cr, Cu, Hg, Ni, Pb and Zn were 31.84, 0.29, 66.07, 17.35, 0.021, 27.86, 49.35 and 88.56 mg/kg, respectively. Baseline values were 22.24, 0.217, 64.16, 15.69, 0.0191, 26.46, 34.91, and 68.62 mg/kg, respectively. There is a great difference between the baseline value of soil heavy metals in study area and the Xizang soil background value, especially the baseline value of Cd was 2.68 times of its background value. The results of the pollution evaluation based on the baseline values showed that the 8 heavy metals were slightly enriched, and the overall pollution status was light pollution, and measures should be taken to control and manage them. The research results can provide a reference value for the evaluation of soil heavy metal pollution in the source region of the Yangtze River, and also provide a theoretical basis for the construction of soil heavy metal baseline values in similar high-cold and high-altitude regions.

High-spatial-resolution quasi-distributed acoustic sensing with phase noise suppression based on φ-OFDR

A quasi-distributed acoustic sensor with high-spatial-resolution based on phase-sensitive optical frequency domain reflectometry (φ-OFDR) was demonstrated. The source of noise that affects the accuracy of phase demodulation was analyzed. Two low noise linear frequency sweeps (LFSs) with different sweep ranges obtained by injection-locking method were used to achieve high-spatial-resolution and long-distance sensing. The phase noise caused by laser, intensity and fading noises was effectively suppressed by further combining a femtosecond-laser-inscribed weak reflector array (WRA) and a phase index method (PIM). Vibration signals located a long distance of over 1 km were successfully demodulated with a spatial resolution of 2 cm through the use of 12 GHz LFS and a WRA with an interval of 2 cm. In addition, the 5 mm ultra-high spatial resolution capability of the system had been demonstrated through the use of 52 GHz LFS and a WRA with an interval of 5 mm.

Long Term Historical Precipitation and Temperature Trend Analysis for Selected Stations in Bale and East Bale Zones of Southeastern Ethiopia

Trend analysis for the long-term average temperature and precipitation of Bale and East Bale Zones during 1901 to 2016 has been performed for selected 5 stations to understand the pattern of these important meteorological features under climate change. The precipitation characterizations viz. standardized Precipitation Index has been considered for Sinana, Goro, Ginnir, Dallo menna and Agarfa stations. This study was aimed to analysis ‘’long term historical climate analysis selected stations in Bale and East Bale zones’’. The study considered station nearby kebeles was selected purposively from highland, midland and lowland districts. Standardized Precipitation Index drought index method was used to classify drought condition of the area. The findings showed that, the average temperature of the district showed an increased in the past three decades whereas the annual precipitation of the districts showed a decreasing trend with some variation from the mean in the period 1901- 2016 which was validated with 1984 – 2016 observed data. In line with this, representative respondents were considered from lowland, highland and midland agro-ecologies. Long term annual mean temperature showed increase trends by 0.17°C per decade at Sinana, Goro, Ginnir and Agarfa stations. Additionally, it was revealed more increment by 0.19°C per decade at Dallo menna station. From long term climate data analysis, it was indicated that extremely wet recorded in 1903, 1914, 1917, 1926, 1977 and 2013, while extremely dry conditions were recorded in 1919, 1938 and 1943 annual precipitation recorded for the whole period has been calculated and interpreted.

The evaluations of oxidative stress and neurotoxicity in threatened endemic fish Barbus meridionalis from Osor River (Spain)

Mediterranean barbel (Barbus meridionalis) an endemic species is currently facing habitat destruction and pollution in Osor River (Spain) due to mining runoff that has severely deteriorated the water quality by metals, primarily zinc (Zn). In order to assess the potential risk of metal contamination and hydrological changes in the Osor River by using oxidative stress and neurotoxicity biomarkers via IBR analyses in the barbel, five different stations were chosen: upstream (S1: reference site and S2: hydrologically changed), mine (S3), and downstream (S4 and S5). The highest tissue metal levels were measured particularly at S3 and following downstream sites. SOD activity and the GSH system parameters (GPX, GST, and GSH) were the most sensitive oxidative stress indicators among the antioxidant system parameters. The organs with the greatest changes in antioxidant biomarkers were the liver and gill. As a sign of neurotoxicity, AChE activities significantly raised in the brain and muscle but drastically lowered in the kidney, liver, and gill particularly in the area of mine and downstream compared to reference site. Integrated biomarker response index (IBR) method was applied to visualize the affect of metal and hydrological alterations with biomarker response according to sites in the Osor River. IBR analyzes together with correlations between metal levels and oxidative stress biomarkers, emphasized that S2 and S3 have the greatest impact on the biomarker levels due to mine activity and hydrological changes highlighting the vulnerability to extinction of native fish B. meridionalis. It is also critical to assess the current data based on the multi-biomarker approach for a range of detrimental effects on fish fitness at the individual level as well as population persistence from an ecological standpoint.

Time-varying effects of structural oil price shocks on financial market uncertainty

This study employed the structural oil price decomposition method proposed by Ready (2018) to decompose oil price shocks into oil risk shocks, demand shocks, and supply shocks. By using the Diebold and Yilmaz (DY) spillover index and rolling window methods, the static and dynamic spillovers of structural oil price shocks on financial market uncertainty were examined. The findings suggest that oil risk shocks exhibited the strongest spillover effects on financial market uncertainty (except for South Africa), followed by oil demand shocks, while oil supply shocks had virtually no impact. Second, the effects of structural oil price shocks on financial market uncertainty were time-varying. Third, significant differences in the spillover effects of structural oil price shocks on financial market uncertainty were found across countries, and the spillover effect of oil risk shocks on the financial market uncertainty in the United States was the largest in all periods. Fourth, the spillover effects of oil demand shocks on financial market uncertainty were larger in the high than in the low oil price period (except for Japan). Finally, during the COVID-19 (coronavirus disease) pandemic compared with the pre-epidemic period, the spillover effects of oil risk shocks on the financial market uncertainty in China significantly decreased, while simultaneously, the spillover effects of oil supply shocks on the financial market uncertainty in Australia substantially increased.

Current status and prospects of breast cancer imaging-based diagnosis using artificial intelligence.

Breast imaging has several modalities, each unique in terms of its imaging position, evaluation index, and imaging method. Breast diagnosis is made by combining a large number of past imaging features with the clinical course and histological findings. Artificial intelligence (AI), which extracts the features from image data and evaluates them based on comprehensive analysis, has been making rapid progress in this regard. Many previous studies have demonstrated the usefulness and development potential of AI, such as machine learning and deep learning, in breast imaging. However, despite studies showing the good performance of AI models, their overall utilization remains low, since a large amount of diverse imaging data is required, and prospective verification is necessary to prove its high reproducibility and robustness. Sharing information and collaborating with multiple institutions to collect and verify images of different conditions and backgrounds are vital. If image diagnosis using AI can indeed ensure a more detailed diagnosis, such as breast cancer subtypes or prognosis, it can help develop personalized medicine, which is urgently required. The positive results of AI research, using such image information, can make each modality more valuable than ever. The current review summarized the results of previous studies using AI in each evaluation field and discussed the related future prospects.

Innovative approach for assessing nitrogen loss risk to surface waters from crop production in a watershed scale through nitrogen surplus index method

Although various measures are applied to control farmland nitrogen loss at the field scale, effective control strategies at the watershed scale are still lacking due to poor understanding of nitrogen loss risks across different scales. Therefore, an innovative approach to assess the risk of nitrogen loss to surface waters from crop production at the watershed scale was established, with nitrogen surplus, a more accurate indicator, as a major source factor introduced to the traditional index method. Furthermore, this approach was applied in the Baiyangdian Lake Watershed, a typically agricultural-dominated watershed in China, and the control strategies for farmland nitrogen loss reduction were investigated with scenario analysis. The major results included: (1) Areas with high (2–4 for risk to river, 1.6–3.2 for risk to lake) and very high (>4 for risk to river, >3.2 for risk to lake) risks of nitrogen loss in crop production were relatively small. For example, in the wheat-maize rotation system, these areas accounted for only 2.1–3.5 % and 0.9–1.8% of the total study area, respectively. Additionally, the critical source areas of nitrogen loss were concentrated around the receiving water bodies. (2) The nitrogen surplus in wheat season increased the nitrogen loss to surface waters. Compared to the maize season, the area with high and very high risk for nitrogen loss to the nearby river in wheat-maize rotation system increased by 58.3% and 141.4%. Furthermore, the areas with high and very high risk for nitrogen loss to the lake in wheat-maize rotation system were 129.6% and 220.6% more than that during maize season. (3) Nitrogen loss risk to the nearby rivers was correlated with nitrogen surplus, but nitrogen loss risk to the lake was related to the transport coefficient. (4) The optimized scenario for nitrogen loss reduction in crop production was the combination of nitrogen surplus being less than 20 kg/ha and 500 m banning area around the river with no fertilizer application, which reduced the nitrogen loss risk to the nearby river and to the lake by 94–95% and 89–95%. The results of this study provide a scientific basis for nutrient resource management and non-point source pollution control.

Research on Comprehensive Evaluation Indicators and Methods of World-Class Open-Pit Coal Mines

Since the 20th century, with the rapid development of the global economy, human demand for fossil energy such as coal has been increasing. In the face of fierce competition, the state and various ministries and commissions have put forward the goals and requirements of building a world-class enterprise, and how to become a world-class open-pit coal mine and establish the corresponding evaluation standards has become an urgent scientific problem to be solved. According to the characteristics of open-pit coal mine production and operation, from the perspective of benchmarking world class, the key factors affecting the construction of world-class open-pit coal mines are extracted by using the fuzzy DEMATEL method, and the comprehensive evaluation index system, evaluation standards, and methods of open-pit coal mines are established, which provides benchmarks and guidelines for the construction of world-class open-pit coal mines and improves the construction level of world-class open-pit coal mines.