Detection Index Research Articles

The Association between Dental Caries, Periodontal Status, and Sleep Patterns in Children.

Sleep quality has a significant impact on children's overall health and is associated with oral diseases. This study aimed to investigate the relationship between dental caries, periodontal status, and sleep patterns in 8-12-year-old children. This cross-sectional study was conducted on 120 children aged 8-12 years. The children's sleep habits questionnaire (CSHQ) was employed to evaluate sleep status. Another questionnaire was used to collect data on lifestyle habits. Dental caries status was evaluated using the decayed, missing, and filled teeth (DMFT) and International Caries Detection and Assessment System (ICDAS) indices. Periodontal status was assessed through the plaque index (PI), probing depth (PD), gingival index (GI), and periodontal screening and recording (PSR). The association between oral health and sleep patterns was evaluated using Spearman's coefficient correlation, independent t-test, and the Chi-squared test. The ICDAS mean was significantly higher in children with improper sleep patterns (p = 0.032). However, there was no statistically significant relationship between DMFT and sleep patterns (p = 0.346). The GI, PD, and PSR mean were significantly higher in the inappropriate sleep pattern group. There was a significant relationship between children's sleep patterns and GI, PD, and PSR indices (p = 0.033, p = 0.020, p = 0.028, respectively). However, there was no significant association between PI and sleep patterns (p = 0.277). Children's sleep patterns were associated with dental caries and periodontal status. Adequate sleep is required to prevent oral diseases such as dental caries and periodontal diseases. According to this study, it is recommended that children should be encouraged to get adequate sleep by their parents and dentists. Mehdipour A, Abbasi R, Keykha E, et al. The Association between Dental Caries, Periodontal Status, and Sleep Patterns in Children. Int J Clin Pediatr Dent 2024;17(8):925-932.

Association between the uptake rates of countermeasure-type colorectal cancer screening, new cancer detection indices, and number of municipal public health nurses: An ecological study

Objective We conducted an ecological analysis of the structures and processes of municipalities implementing countermeasure-type colorectal cancer screening services, which are associated with high mortality and morbidity rates. We analyzed the populations' demographic characteristics, number of public health nurses (PHNs), and human base for such services. The process was evaluated using the screening uptake rates for countermeasure-type cancer screening and detection indices.Methods The data included municipal population figures, areas, and national health insurance enrolments, all sourced from a government statistics portal (e-Stat). We obtained the number of PHNs per 100,000 population from PHN activity area surveys, information on municipal colorectal cancer screening from public health centers (PHCs) and health promotion project reports, and cancer detection indices from the National Cancer Registry data. The analysis covered 1,234 municipalities with populations of ≥10,000 and ≥50,000, categorized into three groups based on the presence of PHCs. The internal structures were compared using multiple regression analysis.Results The number of PHNs per 100,000 population was categorized as follows; <50,000 population group (42.9), ≥50,000 population group (24.3), and PHC-present city group (16.4).Among these groups, the mass and individual screening rates were 96.2% and 47.7%, 69.1% and 91.5%, and 83.7% and 69.9%, respectively. The average uptake rates of countermeasure-type screenings and detailed examinations were 10.6-13.7% and 68.4-75.3%, respectively. In both cases, the <50,000 population and PHC-present city groups exhibited high and low values, respectively. However, the proportion of patients with "early cancer" detection was approximately 42% in all groups.Multiple regression analysis, using the countermeasure-type screening uptake rate and colorectal cancer detection indices as dependent variables, revealed that in the <50,000 population group, in which mass screening was prevalent, the number of PHNs was significantly positively correlated with the countermeasure-type screening uptake rate and proportion of "new cancers" detected by screening.Contrastingly, the PHC-present city group showed no correlation between the number of PHNs and countermeasure-type screening uptake rate, but a highly detailed examination uptake rate was significantly positively correlated with the proportion of "early cancer" detection.Conclusion In municipalities without PHCs, countermeasure-type screening uptake rates, particularly mass screening rates, were positively correlated with the number of PHNs and cancer detection indices. In cities with PHCs, in which individual screening was prevalent, the detailed examination uptake rate through countermeasure-type screening was correlated with detection indices.

A microfluidic patch for wireless wearable electrochemical detection of sweat metabolites

Wearable sweat sensors provide an innovative detection method for analyzing human physiological and biochemical parameters and play an important role in detecting the potential risks of chronic diseases. Existing commercial wearable sensors (Apple iWatch, HUAWEI smart band) have problems such as single detection index (heart rate or blood pressure), and mainly focus on the physical information. This work studied an integrated microfluidic patch to simultaneously detect several biomarkers (pH, sodium ions, and uric acid) in sweat. We achieved the detection of pH level, sodium, and uric acid in linear ranges of 4–8, 0–160 mM, and 5–160 μM, respectively, covering well the clinically relevant ranges in the healthy human sweat. Laser engraving was chosen to prepare a microfluidic layer, and the sweat collection function was realized by combining hydrophilic and hydrophobic microfluidic synergy. It could efficiently collect the required sweat sample volume (80 μL) within 30 s while avoiding the contamination caused by directly contact with the skin. In addition, a wireless signal acquisition and transmission system was designed with the signal reading, processing, and wireless transmission functions, which can display the testing results in real time on Android phone-specific application software. We combined the wireless system with a microfluidic patch for on-body testing to verify the good wearability of the sweat sensor. This research realized the wireless data processing and transmission while improving sweat collection efficiency. In addition, it could detect various sweat physiological components in real-time, and provides a multiparameter, real-time, and portable sensing platform.

Enhanced rainfall nowcasting of tropical cyclone by an interpretable deep learning model and its application in real-time flood forecasting

Reliable Tropical Cyclone (TC) rainfall and flood forecasts play an important role in disaster prevention and mitigation. Numerous studies have demonstrated the promising performance of deep learning in hydrometeorological forecasts. However, few studies have investigated the potential enhancement of advanced TC track forecasts in predicting rainfall and induced flood. In this study, a novel rainfall nowcasting model (TCRainNet) is developed by fusing TC track characteristics with antecedent rainfall in a Convolution LSTM to predict hourly rainfall with a lead time of 6 h. The nowcasts are subsequently used to drive an event-based Xin’anjiang hydrological model for real-time flood forecasting. The model performance is interpretated by the occlusion sensitivity approach, and the propagation of errors from TC track forecasts to flood forecasts is quantified. The results underscore the superiority of TC track characteristics as input features for rainfall nowcasts, as indicated by a Mutual Information value of up to 0.51. The generated nowcasts are found to have averaged Probability of Detection (POD) and Critical Success Index (CSI) greater than 0.27 and 0.2 respectively. The Mean Absolute Error (MAE) of the nowcasts falls below 2.6 mm, which is only 46 % of the ECMWF operational high-resolution forecasts. The rainfall-driven flood forecasts have NSE greater than 0.7 and PBIAS smaller than 20 % with lead time up to + 4 h. It is shown that the position error of 0.45° and intensity error of 10 hPa&7.8 m/s in TC track forecasts generally result in 0.9 mm degradation in rainfall forecasts and 10% decline in the accuracy of rainfall-driven flood forecasts. The effectiveness of our method presents favorable applicability in advancing disaster mitigation efforts.

Predictive value of neutrophil to lymphocyte ratio, platelet to lymphocyte ratio, and systemic inflammatory index for detection of recurrence of pleomorphic adenoma of the major salivary glands: a multicenter study

BackgroundThe aim of this study was to investigate the correlation between inflammatory biomarkers such as the systemic immune-inflammation index (SII), the neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR) and the recurrence of pleomorphic adenomas (PAs). MethodsRetrospective multicenter study. 608 patients diagnosed with PA and treated by tumor enucleation were included in the analyses. The patients. were extracted from a twenty-year database of two referral centers (January 2000 to January 2020). Data about age, sex, voluntary habits, tumor characteristics, tumor treatment and values of NLR, PLR and SII were collected. A receiver operating characteristic (ROC) curve was used to calculate the optimal cut-off values for SII, PLR and NLR. A linear regression model (LRM) and a non-linear logistic regression model (NLRM) were implemented for NLR, PLR and SII. ResultsAll three inflammatory parameters were statistically significant correlated to the recurrence of the tumor. The best performance was achieved by NLR (cut-off 2.960) with AUC = 0.91, accuracy of 96.1%, sensitivity of 78.4% and specificity of 97.2%, PPV = 64.4% and NPV = 98.6%. LRM and NLRM including all hematological parameters did not improve performance in terms of recurrence detection. ConclusionThe use of three inflammatory biomarkers could be a useful tool to predict recurrence of tumor in case of PA ≤ 3 cm. NLR in particular could be sensitive with a cut-off of 2.960. The use of NLR, PLR and SII could be a good reference point for surgical decision making and follow-up in clinical practice.

Two-Dimensional Mammography Imaging Techniques for Screening Women with Silicone Breast Implants: A Pilot Phantom Study.

This study aimed to evaluate the impact of three two-dimensional (2D) mammographic acquisition techniques on image quality and radiation dose in the presence of silicone breast implants (BIs). Then, we propose and validate a new International Atomic Energy Agency (IAEA) phantom to reproduce these techniques. Images were acquired on a single Hologic Selenia Dimensions® unit. The mammography of the left breast of a single clinical case was included. Three methods of image acquisition were identified. They were based on misused, recommended, and reference settings. In the clinical case, image criteria scoring and the signal-to-noise ratio on breast tissue (SNRBT) were determined for two 2D projections and compared between the three techniques. The phantom study first compared the reference and misused settings by varying the AEC sensor position and, second, the recommended settings with a reduced current-time product (mAs) setting that was 13% lower. The signal-difference-to-noise ratio (SDNR) and detectability indexes at 0.1 mm (d' 0.1 mm) and 0.25 mm (d' 0.25 mm) were automatically quantified using ATIA software. Average glandular dose (AGD) values were collected for each acquisition. A statistical analysis was performed using Kruskal-Wallis and corrected Dunn tests (p < 0.05). The SNRBT was 2.6 times lower and the AGD was -18% lower with the reference settings compared to the recommended settings. The SNRBT values increased by +98% with the misused compared to the recommended settings. The AGD increased by +79% with the misused settings versus the recommended settings. The median values of the reference settings were 5.8 (IQR 5.7-5.9), 1.2 (IQR 0.0), 7.0 (IQR 6.8-7.2) and 1.2 (IQR 0.0) mGy and were significantly lower than those of the misused settings (p < 0.03): 7.9 (IQR 6.1-9.7), 1.6 (IQR 1.3-1.9), 9.2 (IQR 7.5-10.9) and 2.2 (IQR 1.4-3.0) mGy for the SDNR, d' 0.1 mm, d' 0.25 mm and the AGD, respectively. A comparison of the recommended and reduced settings showed a reduction of -6.1 ± 0.6% (p = 0.83), -7.7 ± 0.0% (p = 0.18), -6.4 ± 0.6% (p = 0.19) and -13.3 ± 1.1% (p = 0.53) for the SDNR, d' 0.1 mm, d' 0.25 mm and the AGD, respectively. This study showed that the IAEA phantom could be used to reproduce the three techniques for acquiring 2D mammography images in the presence of breast implants for raising awareness and for educational purposes. It could also be used to evaluate and optimize the manufacturer's recommended settings.

Temporal Monitoring of Simulated Burials in an Arid Environment Using RGB/Multispectral Sensor Unmanned Aerial Vehicles

For the first time, RGB and multispectral sensors deployed on UAVs were used to facilitate grave detection in a desert location. The research sought to monitor surface anomalies caused by burials using manual and enhanced detection methods, which was possible up to 18 months. Near-IR (NIR) and Red-Edge bands were the most suitable for manual detection, with a 69% and 31% success rate, respectively. Meanwhile, the enhanced method results varied depending on the sensor. The standard Reed–Xiaoli Detector (RXD) algorithm and Uniform Target Detector (UTD) algorithm were the most suitable for RGB data, with 56% and 43% detection rates, respectively. For the multispectral data, the percentages varied between the algorithms with a hybrid of the RXD and UTD algorithms yielding a 56% detection rate, the UTD algorithm 31%, and the RXD algorithm 13%. Moreover, the research explored identifying grave mounds using the normalized digital surface model (nDSM) and evaluated using the normalized difference vegetation index (NDVI) in grave detection. nDSM successfully located grave mounds at heights as low as 1 cm. A noticeable difference in NDVI values was observed between the graves and their surroundings, regardless of the extreme weather conditions. The results support the potential of using RGB and multispectral sensors mounted on UAVs for detecting burial sites in an arid environment.

How can the failure mode and effect analysis improve the working processes in the ART center?

Objective(s)Assisted reproductive technology (ART) Centers and laboratories perform complex tasks with patients and their gametes/embryos daily. The degree of problems/non-conformances in such surroundings must be minimized at zero point. This study aimed to establish the proper risk management system with well-defined process steps to prevent and eliminate problems/non-conformances. Study designFailure mode and effect analysis (FMEA) was introduced in the ART Center and the IVF laboratory. ART Center working processes were grouped into the categories of Clinical procedures, Forms, Identification, Personnel, Patients, and Facility. Working processes in the IVF laboratory were grouped into the categories of Consumables, Media, Equipment, Personnel, Working space, and Procedures. The traceability and safety of the working processes were evaluated before and after corrective measures regarding risk priority number (RPN). The severity (S), occurrence (O), and detection (D) index of problems/non-conformance were scaled from 1 to 5. The RPN was calculated by multiplying the SOD index, and the cut-off value for RPN was ≥12. ResultsThe increased RPN was found in the following working processes of the ART Center: Embryo transfer and pregnancy (RPN = 18) in the category of Clinical procedures; Informed consents and agreements (RPN = 16) in the category Forms; Continuous education of the knowledge and skills (RPN = 12) in Personnel category; Space conditions (RPN = 24) and Equipment (RPN = 12) in the category Facility.In the IVF laboratory, increased RPN was found in the following working processes: Production of the plasticware (RPN = 12), Transport (RPN = 12) and Storage (RPN = 12) in the category Consumables; Media production (RPN = 16) in category Media; Alarm notification system for the critical equipment (RPN = 12) in category Equipment; Personnel number and qualifications (RPN = 12), Standard operative procedures (SOP) (RPN = 12) and Continuous education of the knowledge and skills (RPN = 12) in category Personnel; Working conditions (RPN = 18) and Security (RPN = 20) in Working space; Patient identification (RPN = 20), Biological samples identification (RPN = 20), Records (RPN = 12), ART procedure (RPN = 30), Embryo transfer (RPN = 30) and Cryopreservation and thawing of biological samples (RPN = 30) in category Procedures.According to the RPN score, corrective measures were implemented. Most RPN scores were reduced after the implementation of the electronic witnessing system in patient/sample tracing steps. Phases Patient identification and Biological sample identification showed a double reduction of RPN scores, from 20 to 10. Also, for critical steps in ART procedures, Embryo transfer, Cryopreservation, and thawing of biological samples, the RPN score was reduced from 30 to 10. Proper education of personnel was another corrective measure that significantly contributed to a reduction of RPN scores in most of the categories. Conclusion(s)The FMEA analysis is useful in recognizing the critical steps of an ART Center. The RPN scores for patient traceability were successfully reduced using the electronic witnessing system. Nonetheless, the study has certain limitations, as FMEA is highly dependent on the specific healthcare organization, adherence to national guidelines, and the subjective nature of SOD and RPN evaluations.

A Review on current developments in early ectasia detection – the corneal biomechanical analysis

Purpose. Topographic and tomographic parameters are often not sufficient for early diagnosis of corneal changes. Pathological processes begin in the microstructure before topographic/tomographic abnormalities become apparent. Biomechanical parameters correlate strongly with microscopic structural parameters, while they can be used for early detection of ectasia. Material and Methods. This review summarizes the biomechanical properties of the cornea with regard to the detection of early ectasia and keratoconus. Air-puff tonometry is used to record the deformation behavior of the cornea and to derive corneal deformation parameters and biomechanical indices for ectasia detection. Results. The biomechanical parameters in keratoconus differ significantly from those of healthy eyes. Changes in the cornea can be detected even before topographic or tomographic changes. Artificial intelligence approaches support the merging of the numerous available data on single parameters for appropriate handling in clinical practice. Conclusion. Examination of corneal biomechanics using air-puff tonometry is a new method to detect possible early changes in the tissue. The biomechanical parameters, especially those of Scheimpflug-based air-puff tonometry, reveal changes before they are visible on topography or tomography. This is of great importance in refractive surgery, but also in the management of keratoconus.

Pothole detection and International Roughness Index (IRI) calculation using ATVs for road monitoring

The accelerated deterioration of roads is conditioned by parameters such as climate change, poor construction, and heavy vehicle traffic. Two relevant measures to monitor the condition of a road are the International Roughness Index (IRI) and the number of functional failures in a segment, mainly potholes, since they are associated with higher risks such as accidents or damage to vehicle mechanics. In the state of the art, pothole detection or International Roughness Index (IRI) calculation algorithms are proposed, but they use vehicles designed to produce less vibration and use phones that decrease the performance of the embedded sensors. In addition, some works propose complex algorithms of higher computational load that leads to use more hardware and power consumption. In this context, the present work aims to monitor the condition of a road through low-cost dedicated sensors implemented in an urban patrolling all-terrain vehicles (ATVs), where energy consumption is optimized using low-complexity signal processing techniques for noise reduction and detection algorithms. The results show an average accuracy of 90.5% in the detection of potholes, a relative error of 8.41% in the calculation of the International Roughness Index (IRI) and an average reduction of 65.4% in the monitoring time.

Rapid Response of Daphnia magna Motor Behavior to Mercury Chloride Toxicity Based on Target Tracking.

A rapid and timely response to the impacts of mercury chloride, which is indispensable to the chemical industry, on aquatic organisms is of great significance. Here, we investigated whether the YOLOX (improvements to the YOLO series, forming a new high-performance detector) observation system can be used for the rapid detection of the response of Daphnia magna targets to mercury chloride stress. Thus, we used this system for the real-time tracking and observation of the multidimensional motional behavior of D. magna. The results obtained showed that the average velocity (v¯), average acceleration (a¯), and cumulative travel (L) values of D. magna exposed to mercury chloride stress changed significantly under different exposure times and concentrations. Further, we observed that v¯, a¯ and L values of D. magna could be used as indexes of toxicity response. Analysis also showed evident D. magna inhibition at exposure concentrations of 0.08 and 0.02 mg/L after exposure for 10 and 25 min, respectively. However, under 0.06 and 0.04 mg/L toxic stress, v¯ and L showed faster toxic response than a¯, and overall, v¯ was identified as the most sensitive index for the rapid detection of D. magna response to toxicity stress. Therefore, we provide a strategy for tracking the motile behavior of D. magna in response to toxic stress and lay the foundations for the comprehensive screening of toxicity in water based on motile behavior.

Real-Time Prediction of Oil-Type Gas Emissions in Coal-Oil-Gas Coexistence Mines.

Oil-type gas disasters are a frequently occurring concern in coal-oil-gas coexistence mines. In order to actively predict the volume of oil-type gas emissions from floor rocks, this study presents an investigative methodology to forecast the geological conditions of floor rocks in front of the roadway face by utilizing the Direct Current (DC) method. The assessment of electrical resistance in rock formations, which is widely employed for identifying geological characteristics, serves as the basis for proposing a geological anomaly index derived from rock resistivity. This index effectively characterizes the stability of rock strata, providing an indirect evaluation of fracture development. As a real-time geological detection index for floor rocks that are 100 m ahead of the roadway face, it enhances predictive capabilities. Moreover, when combined with parameters such as floor rock thickness and permeability, the paper presents simulations of oil-type gas emissions under varying geological conditions. Subsequently, an adaptive optimization of the Back Propagation (BP) neural network is achieved through the Genetic Algorithm Back Propagation Neural Network (GA-BP) model to evaluate the quantity of oil-type gas emissions in roadways. This advanced real-time prediction method is applied in Huangling coal mining to predict the oil-type gas emissions from the floor rocks in the excavation roadway area. The results show consistency with field monitoring outcomes, confirming the accuracy of the predictive model. In conclusion, this advanced real-time prediction technique enables continuous monitoring and real-time forecasting of oil-type gas emissions in front of roadways. This capability facilitates the implementation of specific measures for pre-extraction in gas disaster prevention and control, thereby ensuring the safety of coal mine production. Moreover, the versatility of this advanced real-time prediction method extends to early warnings of rock mass instability-related disasters. Through a comprehensive understanding of subsurface conditions, continuous monitoring of changes, and the application of predictive models, timely actions can be taken to reduce risks and uphold safety standards.

PLS-based hellinger distance method for fault detection in chemical engineering systems

Fault detection is vital in chemical engineering systems to maintain operational efficiency, product quality, and safety through timely identification and correction of deviations from expected behavior. Although partial least squares (PLS) has proven effective in monitoring due to its ability to handle highly correlated variables, traditional detection metrics of PLS may fail to identify small abnormal changes as they rely solely on recent observations. This paper integrates PLS modeling framework with Hellinger Distance (HD)-based fault detection index to overcome the limitations of conventional detection metrics. The utilization of HD is motivated by its sensitivity to quantifying any dissimilarity between distributions, which makes it well-suited for detecting small deviations in process behavior. The HD-based index will be computed between the residuals obtained from the model in the offline stage and the online stage. The HD metric involves careful inspection and comparison of the residuals, which enables it to capture the sensitive details in the data, thus, enhancing the detection of faults. For increased flexibility, kernel density estimation is employed to establish the reference threshold of the PLS-HD approach. The performance of this approach will be evaluated using data from simulated Continuous Stirred-Tank Heater (CSTH) and Continuous Stirred-Tank Reactor (CSTR) processes, by considering various fault types such as bias, freezing, and sensor drift faults. The results demonstrate the superior performance of the proposed PLS-HD approach compared to conventional PLS monitoring methods.

Constituents from Dolichos lablab L. Flowers and Their Anti-Inflammatory Effects via Inhibition of IL-1β Release.

The occurrence of inflammation is closely related to the activation of the NLRP3 inflammasome. IL-1β produced during the activation of the NLRP3 inflammasome has strong pro-inflammatory activity and can also promote the release of inflammatory factors by other immune cells, exacerbating inflammatory damage to tissues. Utilizing IL-1β as the detection index to find small-molecule inhibitors targeting NLRP3 from natural products will benefit the search for drugs for inflammation-related diseases. During the exploration of anti-inflammatory active components derived from the flowers of Dolichos lablab L., an ingredient in traditional Chinese medicine with dual applications in both medicinal treatment and dietary consumption, fourteen compounds (1-14), including seven previously unreported ones, named flosdolilabnitrogenousols A-D (1-4) and flosdolilabsaponins A-C (5-7), were found. Their structures were established through extensive NMR spectra determination, HR-ESI-MS analysis, ECD calculations, and chemical reactions. Flosdolilabsaponin A (5) stands out as an exceptionally rare tetracyclic lactone oleane-type saponin. Additionally, the inhibitory activity on IL-1β release of all compounds, without cytotoxicity, was evaluated using BMDMs stimulated with LPS/Nigericin. An Elisa assay revealed that compounds 1, 8, 9, and 11-14 exhibited significant inhibition of IL-1β release at a concentration of 30 μM. Structure-activity relationships were also discussed. This study indicates that D. lablab flowers possess anti-inflammatory activity, which might exert its effect by suppressing the activation of the NLRP3 inflammasome.

Oceanic Precipitation Nowcasting Using a UNet-Based Residual and Attention Network and Real-Time Himawari-8 Images

Qualitative precipitation forecasting plays a vital role in marine operational services. However, predicting heavy precipitation over the open ocean presents a significant challenge due to the limited availability of ground-based radar observations far from coastal regions. Recent advancements in deep learning models offer potential for oceanic precipitation nowcasting using satellite images. This study implemented an enhanced UNet model with an attention mechanism and a residual architecture (RA-UNet) to predict the precipitation rate within a 90 min time frame. A comparative analysis with the standard UNet and UNet with an attention algorithm revealed that the RA-UNet method exhibited superior accuracy metrics, such as the critical ratio index and probability of detection, with fewer false alarms. Two typical cases demonstrated that RA-UNet had a better ability to forecast monsoon precipitation as well as intense precipitation in a tropical cyclone. These findings indicate the greater potential of the RA-UNet approach for nowcasting heavy precipitation over the ocean using satellite imagery.

Lameness prevalence in lactating and dry cows - expert's, student's and farmers' assessments.

The aim of this study was to evaluate differences in lameness prevalence among Austrian dairy farms for both lactating and dry cows as assessed by locomotion scoring (LCS 1-5) by an expert and a student who had received brief training in locomotion scoring and by farmers' estimates and by farmers' lameness assessments. In total, 632 cows from 11 farms were evaluated, including 553 lactating and 79 dry cows. Lameness prevalence was calculated for all farms collectively as well as for individual farms. The ratio of lameness prevalence determined by the student/expert (Student's Assessment Index: SAI), the ratio between farmers' assessments/expert assessments (Farmers' Assessment Index: FAI) and the ratio between farmers' estimates/expert assessments (Farmers' Detection Index: FDI) were calculated. For the expert, the mean lameness prevalence (LCS≥2) among all 632 cows was 63.1%, while for the student it was 60.2%, and the farmers' assessment was 37.3%, resulting in a mean difference of 25.8% between the expert and the farmers. In cows with LCS 2, the SAI was 99.6%, in cows with LCS 3, it was 84.4%, and in cows with LCS≥4, it was 88.6%. The mean FDI and FAI for all cows on the 11 farms were 35.1% and 58.2%, respectively, with wide variation across farms (6.8-79.1% and 17.8-94.7%, respectively). Overall, lactating and dry cows exhibited a high mean lameness prevalence (63.9% vs. 59.6%), which differed by only 4.3%. Farmers should actively assess their cattle for lameness on a regular basis. Moreover, they should be trained to identify lame cows (lactating and dry cows), especially those showing mild lameness (LCS 2). Since the recently trained student achieved similar high recognition rates as the expert, it is assumed that a training in locomotion scoring can contribute to higher recognition rates.

Precipitation nowcasting method based on Spatial-Temporal Dual Discriminators

Abstract The prediction of radar echo sequence images is a spatiotemporal sequence forecasting problem, which is one of the main challenges in precipitation nowcasting. Addressing issues such as poor extraction of spatiotemporal features by previous models and blurry image sequence predictions, this study proposes a Spatial-Temporal dual Discriminator Precipitation Nowcasting Model (STD-SNGAN) based on spectral normalization generative adversarial networks (SNGAN). The model utilizes multi-scale convolution modules (Inception) to extract spatial features from radar echo images and convolutional gated recurrent units (ConvGRU) to extract temporal features between feature maps. By introducing a hidden feature sampler to enhance the extraction capability of the convolutional gated recurrent units and designing spatial-temporal dual discriminators to constrain the generator’s predicted samples, the spatiotemporal forecasting ability is enhanced. Experimental results demonstrate that the proposed STD-SNGAN model outperforms other algorithms in critical success index (CSI) and probability of detection (POD) for high echo intensity and long-term regions.

Algorithm for detecting abnormal electricity usage by customers based on electricity big data and deep learning

Abstract In order to optimize the effect of individuals’ aberrant power utilization detection and reinforce the recognition rate and accuracy of abnormal power consumption detection of electricity use, an aberrant power utilization identification algorithm based on electric power big data and deep learning is proposed. Taking the two-way dialogue between power users and the power company as the basic perspective, real-time monitoring and collection of power consumption dynamics of users is carried out, and the power big data collection framework is designed. The retrieval of aberrant power utilization detection indexes is completed. Based on the deep learning algorithm, the artificial neural network base operation medium is combined to quantitatively solve the aberrant power utilization indexes, completing the design of a user aberrant power utilization detection algorithm. Experimental results show that the accuracy of the designed algorithm is 0.963, the F1 value is 0.985, and the AUC value is 0.942, which can effectively identify the aberrant power utilization and has a better performance of abnormal power consumption identification and detection.

Development of modified fuzzy FMEA method in environmental risk assessment of earth dams

The investigations have shown that the construction of the dam and its related facilities have significant physical–chemical and ecological effects on the ecosystem. Failure Mode and Effects Analysis (FMEA) is a technique for ranking risks in projects to construct dams, but it has many deficiencies and ambiguities. Therefore, to prevent the shortcomings of the classical method, the modified fuzzy inference system (MFIS-FMEA) method has been used by creating a two-stage model to more accurately assess the risk of Eyvashan Dam. First, all the considered indicators are weighted using the Shannon entropy method, and the environmental risk is prioritized using the Fuzzy OWA method. In this study, two-stage fuzzy reasoning and a Max–Min combination rule are used. When severity (SEV) and occurrence (OCC) variables are combined, the critical risk index (RCI) values are predicted in the first stage. RCI and detection index (DET) input are then used to predict the MFIS-RPN in the second stage. The results of the risk priority number (RPN) in the MFIS-RPN method are much more accurate and serious than the FIS-RPN method due to the two-stage nature and the use of new language terms. The results of the proposed MFIS-RPN technique show that the highest RPN was obtained with immediate action in the dam construction phase for soil erosion and soil pollution and in the dam operation phase for aquatic and water pollution. Therefore, due to the increase in risk score, it is necessary to take immediate and more accurate monitoring during the construction and operation phases.

Exploration of the utilization of a new land degradation index in Lake Ebinur Basin in China

Land degradation significantly impacts regional economic development and food security, particularly in arid river basins where soil and water conservation is crucial. Understanding the extent and causes of land degradation is pivotal for effectively prevention and management. This study employs the soil adjusted vegetation index (SAVI), the temperature vegetation dryness index (TVDI), and the salinization detection index (SDI), combined with the analytic hierarchy process and the entropy weight method, to construct a comprehensive land degradation index (LDI). Sen’s slope trend analysis and the Mann-Kendall significance test were used to analyze land degradation trends in the Ebinur Lake watershed from 2002 to 2022. Additionally, the optimal parameters-based geographical detector was used to examine the underlying mechanisms of land degradation. The results indicate the following: (1) From 2002 to 2012, the degree of land degradation in the Ebinur Lake watershed worsened, particularly in the eastern and southeastern parts, as well as in the southern region of Toli County. From 2012 to 2022, land degradation significantly improved, with a notable reduction in degraded land area. (2) Over the period of 2002-2022, 93.08%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$93.08\\%$$\\end{document} of the land in the research region exhibited a declining LDI trend, 3.95%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3.95\\%$$\\end{document} showed no change, and only 2.96%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2.96\\%$$\\end{document} showed an increasing LDI trend. (3) Moderate, severe, and very severe degradation mainly occurred on grassland and unused land, while light degradation and non-degradation primarily occurred on forest land and cultivated land. (4) Unreasonable land use and overgrazing were identified as the primary factors influencing land degradation, with elevation being a secondary factor. The interaction between land use and other factors was found to be most significant, followed by the synergistic effects of grazing quantity with elevation, annual average temperature, gross domestic product, soil moisture, and elevation with annual average precipitation, and temperature. The results of this study offer an empirical basis and taking decisions assistance for land degradation control in the Ebinur Lake Basin, as well as examples and references for assessing land degradation in other places.