Short-term Time Research Articles

Behind the Loss of Salinity Resistance during Domestication: Alternative Eco-Physiological Strategies Are Revealed in Tomato Clade

Salinity stress impairs growth and physiological performance in tomato, which is one of the most economically important vegetables and is widely cultivated in arid and semi-arid areas of the world. Plant landraces, which are heterogeneous, local adaptations of domesticated species, offer a unique opportunity to valorize available germplasm, underpinning the productivity, resilience, and adaptive capacity of staple crops in vulnerable environments. Here, we investigated the response of fully mature tomato plants from a commercial variety, an ancestral wild relative, and a landrace under short-term salinity exposure, as well as their ability to recover upon cessation of stress. The heterogeneous panel evaluated in this study revealed different adaptative strategies to cope the stress. Our data highlighted the ability of the tomato clade to handle low and intermediate salinity stress for short-term exposure time, as well as its capacity to recover after the cessation of stress, although inter- and intraspecific variations in morphological and physiological responses to salinity were observed. Overall, the landrace and the wild type performed similarly to control conditions under low salinity, demonstrating an improved ability to maintain ionic balance. In contrast, the commercial genotype showed susceptibility and severe symptoms even under low salinity, with pronounced reductions in K+/Na+ ratio, PSII photochemical efficiency, and photosynthetic pigments. This research confirmed that improved salt tolerant genotypes can lead to substantial, positive impacts on horticultural production. While the salt tolerance mechanism of domesticated tomato was efficient under mild stress conditions, it failed at higher salinity levels.

Road congestion and air pollution -Analysis of spatial and temporal congestion effects

Urban traffic congestion has resulted in several adverse outcomes, including reduced traffic efficiency, increased noise pollution, and heightened exhaust emissions. It has also emerged as a significant indicator of urban health concerns. This article primarily delves into an examination of the pollution stemming from congestion. To accomplish this, the study focuses on two specific aspects of congestion measurement: long-term spatial constraints (limited travel routes) and short-term time delays (time wasted due to congestion). Expanding on this, the article explores the potential solutions to mitigate pollution effects through measures such as optimizing space utilization through public transportation systems like subways and strategically scheduling travel during holidays. These considerations are incorporated within the article's scope. Additionally, in order to address endogeneity concerns, the research conducts instrumental variable effectiveness tests from both temporal and spatial perspectives. The outcomes highlight the degradation of air quality and the increase in total traffic congestion in both the long and short term, while also indicating the presence of genuine methods to alleviate these issues. Consequently, effective collaborative efforts for prevention and control are imperative to combat environmental and traffic pollution. Moreover, optimizing sustainable urban development plans to enhance land utilization plays a pivotal role in minimizing the external costs associated with long-distance commuting.

Analisis Biaya Produksi Dalam Penganggaran UMKM Bakso Zaky Di Pancur Batu

The Bakso Zaky stall business located in Deli Serdang district, North Sumatra has been established since 2019. Mr. Zaky started his business with capital of IDR 12,500,000. The location of this business stall is very strategic because it is on the edge of the market and many people pass by it. This research method is a qualitative and secondary method, namely research that uses short-term time or with supporting data and qualitatively by conducting direct interviews with sources. The subject of this research is a meatball trader named Zaky Darius who is 31 years old. The research results obtained were that the maximum profit from chicken noodles was IDR 842,000, regular meatballs IDR 1,154,000, and large meatballs IDR 920,000, so that within a week Zaky's Bakso business experienced a net profit/profit of IDR 2,916,000.

Short-Term and Long-Term Travel Time Prediction Using Transformer-Based Techniques

In the evolving field of Intelligent Transportation Systems (ITSs), accurate and reliable traffic prediction is essential in enhancing management and planning capabilities. Accurately predicting traffic conditions over both short-term and long-term intervals is vital for the practical application of ITS. The integration of deep learning into traffic prediction has proven crucial in advancing traffic prediction beyond traditional approaches, particularly in analyzing and forecasting complex traffic scenarios. Despite these advancements, the existing methods are unable to effectively handle both short-term and long-term traffic patterns given their complex nature, revealing a need for more comprehensive forecasting solutions. To address this need, we propose a new approach named the Short-Term and Long-Term Integrated Transformer (SLIT). SLIT is a Transformer-based encoder–decoder architecture, designed for the effective prediction of both short-term and long-term travel time durations. The architecture integrates the Enhanced Data Preprocessing (EDP) with the Short-Term and Long-Term Integrated Encoder–Decoder (SLIED). This harmonious combination enables SLIT to effectively capture the complexities of traffic data over varying time horizons. Extensive evaluations on a large-scale real-world traffic dataset demonstrate the excellence of SLIT compared with existing competitive methods in both short- and long-term travel time predictions across various metrics. SLIT exhibits significant improvements in prediction results, particularly in short-term forecasting. Remarkable improvements are observed in SLIT, with enhancements of up to 9.67% in terms of all evaluation metrics across various time horizons. Furthermore, SLIT demonstrates the capability to analyze traffic patterns across various road complexities, proving its adaptability and effectiveness in diverse traffic scenarios with improvements of up to 10.83% in different road conditions. The results of this study highlight the high potential of SLIT in significantly enhancing traffic prediction within ITS.

Rapid Bacterial Identification from Positive Blood Cultures by MALDI-TOF MS Following Short-Term Incubation on Solid Media.

This study aimed to assess the performance of the MALDI-TOF MS short incubation method for bacterial identification at short-term incubation times to improve the reporting of blood cultures. MALDI-TOF MS analysis was conducted at intervals of 2, 4, and 6 hours during the development of microbial biomass on solid media until successful identification was achieved, with a final assessment at 24 hours for conventional identification. Species-level identification rates at the 2nd, 4th, 6th, and 24th hours were 57.5%, 83.6%, 93.1%, and 93.1% for Gram-negative bacilli; 12.5%, 42.7%, 76.1%, 97.8% for Gram-positive cocci and 0%, 11.8%, 17.6%, 58.8% for Gram-positive bacilli, respectively. The species-level identification rate was 76.5% for all monomicrobial cultures at the 6th hour. Our results have led us to implement this method into our routine laboratory workflow, and we have started to report rapid identification results for Gram-negative bacteria on the day of blood culture positivity.

Optimization and scheduling scheme of park-integrated energy system based on multi-objective Beluga Whale Algorithm

To improve the consumption rate of renewable energy and ensure the stability of power and heat supply within the industrial park, a park-integrated energy system (PIES), which incorporates electric heating equipment, energy storage devices, and multiple micro sources, is established. Based on the established PIES, a price-based demand response (PDR) mechanism is introduced to establish a multi-objective optimization and scheduling model with two time scales: day-ahead scheduling and intra-day scheduling, aiming to minimize the impact on the main grid and ensure the flexibility of system operation and real-time scheduling. With the objectives of minimizing operating costs and maximizing environmental benefits for PIES, a hybrid algorithm named Non-Dominated Sorting Beluga Whale Optimization (NSBWO), which combines the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Beluga Whale Optimization (BWO) algorithm is proposed to solve the optimization problem of the model. Simultaneously, an adaptive penalty function is introduced in the algorithm to handle constraint problems in optimization and additional penalty term for wind and solar curtailment is included within the constraints to improve the consumption rate of sustainable energy. The proposed NSBWO algorithm exhibits more robust search capabilities and faster convergence speed than the NSGA-II and MOPSO algorithm. Simulation results show that the proposed NSBWO algorithm can achieve low-carbon economic scheduling of the system in both long-term and short-term time scales and improve renewable energy consumption. Due to its flexibility, intra-day scheduling has lower operating costs than day-ahead scheduling.

팜유 가격 예측을 위한 딥러닝 기반 단기 시계열 예측 모델링

팜유 가격 예측을 위한 딥러닝 기반 단기 시계열 예측 모델링

Association of childbirth experience with long–term psychological outcomes: a prospective cohort study

BackgroundThere has been limited research on the lasting impact of giving birth on both mothers and infants. This study aimed to investigate women’s memories of their childbirth experience 4 months and 4 years after giving birth. Additionally, it aimed to examine how the childbirth experience is linked to women’s mental health, sexual satisfaction, exclusive breastfeeding, and the type of subsequent birth.MethodsIn this prospective cohort study, a total of 580 women giving birth in Tabriz hospitals in 2018 were followed up for 4 years. The data were collected using a childbirth experience questionnaire, a mental health inventory, and a sexual satisfaction scale for women, and were analyzed by a Pearson correlation test, an independent samples t-test, and a general linear model.ResultsThe total scores of the childbirth experience in two short-term (4 months) and long-term (4 years) time points following the birth had a significant and strong correlation with each other (r = .51; p < .001). After adjusting for the effects of socio-demographic and obstetric characteristics, sexual satisfaction had significant relationships with childbirth experience (p < .001) and postpartum complications (p < .001). In addition, mental health had significant relationships with childbirth experience (p < .001), postpartum complications (p < .001), and low income (p = .004).ConclusionsEven 4 years after giving birth, women have a clear recall of their childbirth experience. This experience has a significant association with long-term outcomes such as sexual satisfaction, mental health, exclusive breastfeeding, and subsequent birth type.

Traffic Flow Prediction with Random Walks on Graph and Spatiotemporal Bidirectional Attention Transformer

Traffic flow prediction is crucial in intelligent transportation systems. Considering the severe disruptions caused by traffic accidents or congestion, a time series model is developed for traffic flow prediction based on multiple random walks on graphs (MRWG) and the bidirectional spatiotemporal attention mechanism (BSAM), which can adapt to both normal and exceptional situations. The MRWG mechanism is applied to capture spatial features of urban areas during traffic accidents and congestion, especially the spatial dependencies among neighboring regions. Further, a local position attention module is applied to acquire the spatial correlations between different regions to investigate their impact on the global area, while a local temporal attention module is adopted to extract short-term periodic time correlations from traffic flow data. Finally, a spatiotemporal bidirectional attention module is applied to simultaneously extract both the temporal and spatial correlations of the historical traffic flow data in order to generate the output prediction. Experiments have been conducted on NYCTaxi and NYCBike datasets with abnormal events, and the results indicate that the developed model can efficiently predict traffic flow in abnormal events, especially short-term traffic disruptions, outperforming the baseline methods under both abnormal and normal conditions.

Solar power forecasting using domain knowledge

Integrating renewable energy into the existing energy market is crucial. Solar power forecasting is essential since it depends on weather parameters and must integrate with the central grid to use the produced solar power effectively. Contemporary studies indicate that machine learning has the potential to predict the future generation of solar energy based on past data. This research demonstrates a broad range of solar power forecasting, combining the one-year time series solar power generation data, solar panel physical features, and weather information with the help of machine learning and deep learning tools with domain knowledge. The dataset is curated and preprocessed. We propose a deep learning ensemble model based on BI-LSTM. The proposed model can forecast well regardless of geographical position and is able to predict both short-term and long-term time horizons. We compared the results of the proposed model with the existing dataset and multiple standard deep learning models and found that our model produced better performance than traditional models. We also validated our model using different solar plants in Durgapur, India. For long-term forecasting, our model also outperformed the base model.

Academic Performance of Excellence: The Impact of Self-Regulated Learning and Academic Time Management Planning

The Program for International Student Assessment highlights the persistent lack of commitment and motivation among students worldwide in their school activities, which are resulting in decreased proficiency levels in reading, mathematics, and science. The magnitude of this phenomenon, with its clear social implications, suggests that we are facing a concerning quest for immediate answers and results. This research focuses on the impact of the relationships between self-regulated learning processes and the planning of time management that is dedicated to school activities on student performance, specifically in the subjects of the Mother Tongue and Mathematics. The instruments used for analysis included the Inventory of Self-Regulated Learning Processes, the Inventory of Time Management Planning, a personal data sheet, and a school data sheet. The sample in this study consisted of 688 students from primary schools in northern Portugal. The results reveal that self-regulated learning has a positive influence on how students plan time management, both in the short and long term. Additionally, a positive and statistically significant relationship is observed between short-term and long-term time management planning and students’ academic performance. This study provides an in-depth perspective on the dynamics between these elements, shedding light on the crucial nuances that shape students’ academic journeys.

Explaining empirical dynamic modelling using verbal, graphical and mathematical approaches.

Empirical dynamic modelling (EDM) is becoming an increasingly popular method for understanding the dynamics of ecosystems. It has been applied to laboratory, terrestrial, freshwater and marine systems, used to forecast natural populations and has addressed fundamental ecological questions. Despite its increasing use, we have not found full explanations of EDM in the ecological literature, limiting understanding and reproducibility. Here we expand upon existing work by providing a detailed introduction to EDM. We use three progressively more complex approaches. A short verbal explanation of EDM is then explicitly demonstrated by graphically working through a simple example. We then introduce a full mathematical description of the steps involved. Conceptually, EDM translates a time series of data into a path through a multi-dimensional space, whose axes are lagged values of the time series. A time step is chosen from which to make a prediction. The state of the system at that time step corresponds to a 'focal point' in the multi-dimensional space. The set (called the library) of candidate nearest neighbours to the focal point is constructed, to determine the nearest neighbours that are then used to make the prediction. Our mathematical explanation explicitly documents which points in the multi-dimensional space should not be considered as focal points. We suggest a new option for excluding points from the library that may be useful for short-term time series that are often found in ecology. We focus on the core simplex and S-map algorithms of EDM. Our new R package, pbsEDM, enhances understanding (by outputting intermediate calculations), reproduces our results and can be applied to new data. Our work improves the clarity of the inner workings of EDM, a prerequisite for EDM to reach its full potential in ecology and have wide uptake in the provision of advice to managers of natural resources.

Use of one-dimensional CNN for input data size reduction in LSTM for improved computational efficiency and accuracy in hourly rainfall-runoff modeling

A deep learning architecture, denoted as CNNsLSTM, is proposed for hourly rainfall–runoff modeling in this study. The architecture involves a serial coupling of the one-dimensional convolutional neural network (1D-CNN) and the long short-term memory (LSTM) network. In the proposed framework, multiple layers of the CNN component process long-term hourly meteorological time series data, while the LSTM component handles short-term meteorological time series data and utilizes the extracted features from the 1D-CNN. In order to demonstrate the effectiveness of the proposed approach, it was implemented for hourly rainfall–runoff modeling in the Ishikari River watershed, Japan. A meteorological dataset, including precipitation, air temperature, evapotranspiration, longwave radiation, and shortwave radiation, was utilized as input. The results of the proposed approach (CNNsLSTM) were compared with those of previously proposed deep learning approaches used in hydrologic modeling, such as 1D-CNN, LSTM with only hourly inputs (LSTMwHour), a parallel architecture of 1D-CNN and LSTM (CNNpLSTM), and the LSTM architecture, which uses both daily and hourly input data (LSTMwDpH). Meteorological and runoff datasets were separated into training, validation, and test periods to train the deep learning model without overfitting, and evaluate the model with an independent dataset. The proposed approach clearly improved estimation accuracy compared to previously utilized deep learning approaches in rainfall = runoff modeling. In comparison with the observed flows, the median values of the Nash–Sutcliffe efficiency for the test period were 0.455–0.469 for 1D-CNN, 0.639–0.656 for CNNpLSTM, 0.745 for LSTMwHour, 0.831 for LSTMwDpH, and 0.865–0.873 for the proposed CNNsLSTM. Furthermore, the proposed CNNsLSTM reduced the median root mean square error (RMSE) of 1D-CNN by 50.2%–51.4%, CNNpLSTM by 37.4%–40.8%, LSTMwHour by 27.3%–29.5%, and LSTMwDpH by 10.6%–13.4%. Particularly, the proposed CNNsLSTM improved the estimations for high flows (≧75th percentile) and peak flows (≧95th percentile). The computational speed of LSTMwDpH is the fastest among the five architectures. Although the computation speed of CNNsLSTM is slower than LSTMwDpH's, it is still 6.9–7.9 times faster than that of LSTMwHour. Therefore, the proposed CNNsLSTM would be an effective approach for flood management and hydraulic structure design, mainly under climate change conditions that require estimating hourly river flows using meteorological datasets.

PV Power Forecasting in the Hexi Region of Gansu Province Based on AP Clustering and LSTNet

Accurate PV power forecasting is becoming a mandatory task to integrate the PV system into the power grid, schedule it, and ensure the safety of the power grid. In this paper, a novel model for PV power prediction using AP-LSTNet has been proposed. It consists of a combination of affinity propagation clustering and long-term and short-term time series network models. First, the affinity propagation algorithm is used to divide the regionally distributed photovoltaic station clusters into different seasons. The Pearson correlation coefficient is used to determine the strong correlation between meteorological factors of photovoltaic power, and the bilinear interpolation method is used to encrypt the meteorological data of the corresponding photovoltaic station cluster. Furthermore, LSTNet is used to mine the long-term and short-term temporal and spatial dependence of photovoltaic power, and meteorological factor series and linear components of auto-regression are superimposed to realize the simultaneous prediction of multiple photovoltaic stations in the group. Finally, PV power plants in five cities, Wuwei, Jinchang, Zhangye, Jiuquan, and Jiayuguan in the Hexi region of Gansu Province, China, will be selected to test the proposed model. The experimental comparison shows that the prediction model achieves high prediction accuracy and robustness.

Real-time early warning and the prediction of air pollutants for sustainable development in smart cities

Air quality forecasting is vital for sustainable development in smart cities and is essential to protect public health through early warning against air pollutants. This article presents a novel ensemble deep learning method able to capture spatiotemporal features on short-term time intervals and extract high data abstraction levels. This method uses a hierarchical framework with a new activation function called HT-Tanh to improve the prediction of air pollutant emissions in real time. As such, a residual neural network (ResNet) is integrated with a convolutional neural network (CNN) to deeply extract the temporal and spatial features from pollutant and meteorological data. The model network encodes the fully connected layer to fine-tune the CNN output and decodes the temporal prediction relations based on gate recurrent unit (GRU) to obtain more accurate results. The reliability of the proposed method is verified through a case study from Shantou City, China. The model performance is compared with the recently developed and traditional deep learning models. Results reveal that the proposed method has good precision in the multiscale air quality predictions. Compared with CNN-GRU, convolutional long short-term memory, and CNN models, the forecasting results of the proposed method are significantly improved. The presented method in this article can support the early warning systems in multiple regions for air pollution modeling studies.

Top-down characterization of resource use in LCA: from problem definition of resource use to operational characterization factors for resource inaccessibility of elements in a short-term time perspective

PurposeWhen resources are extracted and used by society, they are not necessarily lost for future generations. Therefore, recent publications on impact assessment of abiotic resource use in life cycle assessment focus on a decreased accessibility of resources due to dissipation, rather than depletion. In a previous study, dissipation was defined as a function of the global change in accessible stock due to human actions, and the global amount of the accessible stock, assuming a very long-term time perspective (more than 500 years). In this paper, a short-term time perspective (25 years) is adopted.MethodsThe same generic characterization model is used, but different choices are outlined to derive characterization factors for a short-term perspective (25 years). To illustrate how the short term might be approached, a preliminary set of characterization factors is developed, based on assumptions and estimates.ResultsThe problem of resource use is defined as follows: the decrease of accessibility on a global level of primary and/or secondary elements over the short term due to the net result of compromising actions (i.e., emissions, dissipation in the technosphere, occupation in use, and exploration for new stocks). Characterization factors are derived based on assumptions, like the following: the accessible stock is based on present estimates of accessible stocks in the environment and the technosphere; estimates of accessible stocks in the technosphere are based on past extractions and generic recycling rates; all flows that are presently not recycled are assumed to be inaccessible. Finally, weighting between elements and the functions they have for the present society is based on the added value of the economic sector that is affected due to the decreased accessibility.Discussion and conclusionA preliminary set of characterization factors is proposed for 55 elements. They assess the impact of the present use of resources on the decreased accessibility in the short term due to emissions and dissipation in the technosphere. However, calculation of impact category scores is still hampered by a lack of appropriate data for dissipative flows in life cycle inventory databases. The presented calculations are based on several simplifications and proxies. A more detailed distinction of dissipative flows and estimates of stocks in the technosphere may be possible based on (dynamic) SFA modelling of elements in different applications. To derive a more mature set of characterization factors, it is recommended to use the presented model as a basis and further elaborate or replace the proxies.

Designing a Real-Time Implementable Optimal Adaptive Cruise Control for Improving Battery Health and Energy Consumption in EVs through V2V Communication

Battery electric vehicles (BEVs) face challenges like their limited all-electric range, the discrepancy between promised and actual energy efficiency, and battery health degradation, despite their environmental benefits. This article proposes an optimal adaptive cruise control (OACC) framework by leveraging ideal vehicle-to-vehicle communication to address these challenges. In a connected vehicle environment, where it is assumed that the Ego vehicle’s vehicle control unit (VCU) accurately knows the speed and position of the Leading vehicle, the VCU can optimally plan the acceleration trajectory for a short-term future time window through a model predictive control (MPC) framework tailored to BEVs. The primary objective of the OACC is to reduce the energy consumption and battery state-of-health degradation of a BEV. The Chevrolet Spark 2015 is chosen as the BEV platform used to validate the effectiveness of the proposed OACC. Simulations conducted under urban and highway driving conditions, as well as under communication delay and infused noise, resulted in up to a 3.7% reduction in energy consumption and a 9.7% reduction in battery state-of-health (SOH) degradation, demonstrating the effectiveness and robustness of the proposed OACC.

Nanomaterial SnS2-Based Sensor for VOC Gas Detection at Room Temperature

Real-time indoor hazardous gases monitoring has gained interest in ensuring human health. An effort to design gas sensing devices, which are compact in size, flexible, and low power consumption, providing high-performance sensing plays a crucial role in precise sensing of the indoor environment. Here, we have fabricated 2D SnS2 flakes for gas sensor. The 2D ultra-thin SnS2, including a few layers, shows fascinating sensing toward Volatile organic compounds (VOCs) under room temperature with high response and fast reaction speed. The short-term stability versus time of the SnS2 sensor is investigated. Due to the great adsorption of gaseous molecules, the SnS2-gas sensor operates at room temperature without external heating sources. The Schottky junction-based sensor is one of the key factors contributing to the higher performance of the sensor. Furthermore, its VOC-sensing mechanism is explained obviously through the energy band diagram. The SnS2-layer is considered a promising candidate in indoor VOCs monitoring and respiratory biomarker analysis in the future.

Value of information from fatigue monitoring of the monopile foundation of an offshore wind turbine

Value of information (VoI) allows quantifying benefits of adopting structural health monitoring (SHM) even before implementation by altering the prior risk of structural failure using the probabilities of damage detection from SHM data. The VoI is the reduction in the expected failure cost when SHM data becomes available. In this study, an optimized time of SHM deployment for fatigue crack growth in an offshore wind turbine (OWT) monopile foundation is achieved by maximising VoI. The prior probabilities of failure are obtained from stochastic analysis of dynamic responses to random wind and waves characteristic of North Sea environment. Short-term stress time histories are statistically processed to determine the probability distribution functions of their extrema and ranges and statistical extrapolation techniques are then implemented to analyse the long-term probability distributions of axial stresses. The long-term stress ranges and numbers of cycles are determined by the rainflow count and used in fatigue reliability analysis. The probability of detecting a fatigue crack by SHM is modelled and used for calculating the OTW failure risk after repair is conducted based on SHM outcome. The optimal deployment time of the fatigue crack monitoring system for the analysed 5-MW OWT is determined as 21 years past erection.

Fault detection of complicated processes based on an enhanced transformer network with graph attention mechanism

Recently, deep learning becomes increasingly popular in the industrial data analysis field due to its distinguished feature representation capability. As an emerging deep learning technology, transformer network has attracted extensive attention, but its application in industrial fault detection is still not sufficiently exploited. Furthermore, the traditional transformer network mainly focuses on the time series information of the data, while ignoring the spatial characteristic of the data. For this problem, an improved transformer network model with graph attention mechanism (GA-Tran) is proposed and used for complicated process fault detection. Considering the strong coupling property of process variables, the presented method performs joint spatial-temporal learning by mining the data information in views of both time-dimension and variable-dimension. On the one hand, a transformer encoder module equipped with a self-attention mechanism is utilized to capture long-term temporal dependencies, while multi-scale convolution is integrated to abstract short-term time dependencies. On the other hand, a graph attention network is introduced into the transformer network to meticulously analyze the spatial relationships among variables. Considering the topology of the industrial process variables, we use spectral clustering to infer prior topology information, and remove intelligently irrelevant spatial information. Different from the existing transformer models with the anomaly scores as the monitoring indicator, two monitoring statistics based on encoder features and reconstruction errors are constructed for process status monitoring. Studies on the Tennessee Eastman chemical process demonstrate the effectiveness of the proposed GA-Tran method.