The purpose of this study is to predict the frequency of mortality from urban traffic injuries for the most vulnerable road users before, during and after the confinement caused by COVID-19 in Santiago de Cali, Colombia. Descriptive statistical methods were applied to the frequency of traffic crash frequency to identify vulnerable road users. Spatial georeferencing was carried out to analyze the distribution of road crashes in the three moments, before, during, and after confinement, subsequently, the behavior of the most vulnerable road users at those three moments was predicted within the framework of the probabilistic random walk. The statistical results showed that the most vulnerable road user was the cyclist, followed by motorcyclist, motorcycle passenger, and pedestrian. Spatial georeferencing between the years 2019 and 2020 showed a change in the behavior of the crash density, while in 2021 a trend like the distribution of 2019 was observed. The predictions of the daily crash frequencies of these road users in the three moments were very close to the reported crash frequency. The predictions were strengthened by considering a descriptive analysis of a range of values that may indicate the possibility of underreporting in cases registered in the city’s official agency. These results provide new elements for policy makers to develop and implement preventive measures, allocate emergency resources, analyze the establishment of policies, plans and strategies aimed at the prevention and control of crashes due to traffic injuries in the face of extraordinary situations such as the COVID-19 pandemic or other similar events.

In order to explore the application of the new integrated intelligent spore capture system developed in China in the prediction of cucumber downy mildew and cucumber powdery mildew, the main working parameters of the integrated intelligent spore capture system, such as the presence or absence of air cutting head, the height of air collection port and the time of air collection, were optimized by identifying the morphology of captured spores in the case of natural disease in the field. The relationship between the disease index of cucumber downy mildew and cucumber powdery mildew in greenhouse and the amount of spores captured was analyzed through the dynamic monitoring of disease and spores. The results show that when the air cutting head is not installed, the height of the air collection port is 70 cm, and the period of 10: 00–10: 30 was beneficial to the capture of spores. The disease index of cucumber downy mildew and cucumber powdery mildew had a strong positive correlation with the total amount of spores captured for 7 consecutive days. Continuous monitoring of cucumber downy mildew sporangia and rapid increase in the number is a predictor of the occurrence or rapid increase of cucumber downy mildew. The conidia of cucumber powdery mildew were not detected before the disease onset, and the number of conidia captured was still small at the peak of the disease. The research shows that the integrated intelligent spore capture system is suitable for the prediction of cucumber downy mildew, but there are still some problems in the prediction of cucumber powdery mildew.

Preserving roads involves regularly evaluating government policy through advanced assessments using vehicles with specialized capabilities and high-resolution scanning technology. However, the cost is often not affordable due to a limited budget. Road surface surveys are highly expected to use low-cost tools and methods capable of being carried out comprehensively. This research aims to create a road damage detection application system by identifying and qualifying precisely the type of damage that occurs using a single CNN to detect objects in real time. Especially for the type of pothole, further analysis is to measure the volume or dimensions of the hole with a LiDAR smartphone. The study area is 38 province’s representative area in Indonesia. This research resulted in the iRodd (intelligent-road damage detection) for detection and classification per type of road damage in real-time object detection. Especially for the type of pothole damage, further analysis is carried out to obtain a damage volume calculation model and 3D visualization. The resulting iRodd model contributes in terms of completion (analyzing the parameters needed to be related to the road damage detection process), accuracy (precision), reliability (the level of reliability has high precision and is still within the limits of cost-effective), correct prediction (four-fifths of all positive objects that should be identified), efficient (object detection models strike a good balance between being able to recognize objects with high precision and being able to capture most objects that would otherwise be detected-high sensitivity), meanwhile, in the calculation of pothole volume, where the precision level is established according to the volume error value, comparing the derived data to the reference data with an average error of 5.35% with an RMSE value of 6.47 mm. The advanced iRodd model with LiDAR smartphone devices can present visualization and precision in efficiently calculating the volume of asphalt damage (potholes).

Photovoltaic systems have shown significant attention in energy systems due to the recent machine learning approach to addressing photovoltaic technical failures and energy crises. A precise power production analysis is utilized for failure identification and detection. Therefore, detecting faults in photovoltaic systems produces a considerable challenge, as it needs to determine the fault type and location rapidly and economically while ensuring continuous system operation. Thus, applying an effective fault detection system becomes necessary to moderate damages caused by faulty photovoltaic devices and protect the system against possible losses. The contribution of this study is in two folds: firstly, the paper presents several categories of photovoltaic systems faults in literature, including line-to-line, degradation, partial shading effect, open/close circuits and bypass diode faults and explores fault discovery approaches with specific importance on detecting intricate faults earlier unexplored to address this issue; secondly, VOSviewer software is presented to assess and review the utilization of machine learning within the solar photovoltaic system sector. To achieve the aims, 2258 articles retrieved from Scopus, Google Scholar, and ScienceDirect were examined across different machine learning and energy-related keywords from 1990 to the most recent research papers on 14 January 2025. The results emphasise the efficiency of the established methods in attaining fault detection with a high accuracy of over 98%. It is also observed that considering their effortlessness and performance accuracy, artificial neural networks are the most promising technique in finding a central photovoltaic system fault detection. In this regard, an extensive application of machine learning to solar photovoltaic systems could thus clinch a quicker route through sustainable energy production.

Accurate prediction of US Treasury bond yields is crucial for investment strategies and economic policymaking. This paper explores the application of advanced machine learning techniques, specifically Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) models, in forecasting these yields. By integrating key economic indicators and policy changes, our approach seeks to enhance the precision of yield predictions. Our study demonstrates the superiority of LSTM models over traditional RNNs in capturing the temporal dependencies and complexities inherent in financial data. The inclusion of macroeconomic and policy variables significantly improves the models’ predictive accuracy. This research underscores a pioneering movement for the legacy banking industry to adopt artificial intelligence (AI) in financial market prediction. In addition to considering the conventional economic indicator that drives the fluctuation of the bond market, this paper also optimizes the LSTM to handle situations when rate hike expectations have already been priced-in by market sentiment.

In rural areas, land use activities around primary arterial roads influence the road section’s traffic characteristics. Regulations dictate the design of primary arterial roads to accommodate high speeds. Hence, there is a mix of traffic between high-speed vehicles and vulnerable road users (pedestrians, bicycles, and motorcycles) around the land. As a result, researchers have identified several arterial roads in Indonesia as accident-prone areas. Therefore, to improve the road user’s safety on primary arterial roads, it is necessary to develop models of the influence of various factors on road traffic accidents. This research uses binary logistic regression analysis. The independent variables are carelessness, disorderliness, high speed, horizontal alignment, road width, clear zone, road shoulder width, signs, markings, and land use. Meanwhile, the dependent variable is the frequency of accidents, where the frequency of accidents consists of multi-accident vehicles (MAV) and single-accident vehicles (SAV). This study collects data for a traffic accident prediction model based on collision frequency in accident-prone areas. The results, road shoulder width, and road sign factor all have an impact on the frequency of traffic accidents. According to a realistic risk analysis, MAV and SAV have no risk difference. After validation, this model shows a confidence level of 92%. This demonstrates that the model generates estimations that accurately reflect reality and are applicable to a wider population. This research has the potential to assist engineers in improving road safety on primary arterial roads. In addition, the model can help the government measure the impact of implemented policies and engage the public in traffic accident prevention efforts.