Short-term Data Research Articles

Judging Rainfall Intensity from Inter-Tip Times: Comparing ‘Straight-Through’ and Syphon-Equipped Tipping-Bucket Rain Gauge Performance

The inter-tip times (ITTs) of tipping-bucket rain gauges (TBRGs) potentially provide the highest-resolution intensity data that can be acquired from this type of gauge. At an intensity of 100 mm h−1, a typical gauge with a sensitivity of 0.2 mm of rainfall would be expected to tip every 7.2 s. However, TBRGs are often equipped with syphons to reduce the dynamic calibration error that results from continued (and unmeasured) inflow to a bucket as it tips. This increases the accuracy of rainfall depth recording, but the time to fill and empty the syphon can reduce the ability of a TBRG to respond to (and for the ITTs to reflect) short-term intensity fluctuations. This ability is already limited by the discretisation arising from the filling and emptying of the buckets themselves. Laboratory tests with controlled water inflow rates were performed using two high-quality TBRGs, one a ‘straight-through’ design and the other syphon-equipped. These confirmed that at all intensities at which the syphon operates, a regular sequence of fixed-duration ITTs (such as the 7.2 s mentioned above) does not occur. Rather, the ITTs are perturbed by the syphon cycling. The gauges were also co-located in the field and linked to carefully synchronised event data loggers. Data collected during several rainfall events revealed differences in the ITTs and again confirm that the ITT sequence of a syphon-equipped TBRG exhibits artefacts related to syphon operation that are not present in the ‘straight-through’ data. These artefacts can result in ITT differences of many minutes, depending on the rainfall intensity and are problematic for the use of ITTs to estimate intensity. Peaks and troughs in the intensity profile also differed between the two gauges. It is recommended that in the application of TBRGs for studies where short-term intensity data are required, ‘straight-through’ gauges should be used, and syphon-equipped gauges should be avoided.

Efficacy and safety of tildrakizumab for the treatment of moderate-to-severe plaque psoriasis of the scalp: A multicenter, randomized, double-blind, placebo-controlled, Phase 3b study

Scalp psoriasis is common and difficult to treat. To evaluate efficacy and safety of tildrakizumab for the treatment of scalp psoriasis. In this Phase 3b, randomized, double-blind, placebo (PBO)-controlled study (NCT03897088), patients with moderate-to-severe plaque psoriasis affecting the scalp (Investigator Global Assessment modified [IGA mod] 2011 [scalp] ≥3, Psoriasis Scalp Severity Index [PSSI] ≥12, ≥30% scalp surface area affected) received tildrakizumab 100mg or PBO at W0 and W4. The primary endpoint was IGA mod 2011(scalp) score of "clear" or "almost clear" with ≥2-point reduction from baseline at W16 (IGA mod2011 [scalp] response). Key secondary endpoints were PSSI 90 response at W12 and W16 and IGA mod 2011 (scalp) response at W12. Safety was assessed from adverse events. Of patients treated with tildrakizumab (n=89) vs PBO (n=82), 49.4% vs 7.3% achieved IGA mod 2011 (scalp) response at W16 (primary endpoint) and 46.1% vs 4.9% at W12; 60.7% vs 4.9% achieved PSSI 90 response at W16 and 48.3% vs 2.4% at W12 (all P<.00001). No serious treatment-related adverse events occurred. Only short-term data are presented. Tildrakizumab was efficacious for the treatment of scalp psoriasis with no new safety signals.

Short-term panel data analysis of the effect of flood risk communication on individual evacuation decisions

People-centered risk communication is important to mitigate the flood damage caused by the recent increase in heavy rainfall events in Japan. Longitudinal studies are particularly important for evaluating the effectiveness of risk communication methods; however, current research is insufficient. To address this gap, we conducted a longitudinal study, specifically through four panel surveys conducted over a short period, to investigate the effects of various risk communication methods such as running an evacuation simulation to learn about flood damage, providing information about the evacuation behavior of others, and distributing hazard maps. The results of a fixed effects analysis of the panel data suggest that the impact of risk communication depends on the initial evacuation attitude. In particular, we find that distributing hazard maps had a negative effect on the evacuation behavior of those who initially responded that they would evacuate. This suggests that residents in non-flood-prone areas may have acquired the correct hazard perception from these hazard maps. However, for those who initially chose not to evacuate, receiving the distributed content had a positive effect on their evacuation behavior 12 h before the typhoon hit. This suggests that those who initially chose not to evacuate may have reconsidered their decision. The findings of this study may help future risk communication by reducing congestion at evacuation sites due to excessive evacuation, while increasing the evacuation rate of those who should evacuate.

Effectiveness of Surgical Deroofing and Carbon Dioxide Laser in Moderate-to-Severe Hidradenitis Suppurativa Patients.

Hidradenitis suppurativa (HS) is a chronic, inflammatory skin condition that causes pain and discomfort in various body regions. This review explores the comparative effectiveness of two surgical techniques, namely, surgical deroofing and carbon dioxide laser therapy, in managing symptomatic HS, particularly in patients with Hurley stage I-III disease. We conducted a systematic literature search on PubMed and ClinicalTrials.gov. The clinical evidence suggests that surgical deroofing and carbon dioxide laser treatment are effective strategies for managing symptomatic HS. However, a comprehensive analysis of 1,120 patients indicates a higher recurrence rate with surgical deroofing. Further investigation into short-term and long-term follow-up data revealed comparable recurrence-free rates within 12 months post-procedure. Beyond 12 months, carbon dioxide laser treatment exhibited slightly higher recurrence-free rates, which necessitate more extensive studies for validation due to the limited sample size. In addition, surgical deroofing demonstrated quicker healing times, while carbon dioxide laser therapy showcased varying timelines, with primary closure after laser excision presenting a two-week healing time. Both procedures reported high patient satisfaction, emphasizing the need for personalized treatment decisions. Therefore, further research is essential to evaluate the efficacy of each treatment modality considering individual patient profiles and disease severity. It will benefit individuals affected by HS, leading to better health outcomes.

Abstract 7125: Inhibition of IDH1 impairs homologous recombination and induces sensitivity to PARP inhibitor in pancreatic cancer

Abstract Introduction: Pancreatic ductal adenocarcinoma (PDAC) is a major cause of cancer-related deaths in the United States. While Poly (ADP-ribose) polymerase (PARP) inhibitors hold promise in treating PDAC with Homologous Recombination Deficiency (HRD). However, HRD occurs in only 10% of cases, leaving 90% with intact Homologous Recombination Proficiency (HRP). Our research highlights the key role of wild-type isocitrate dehydrogenase 1 (IDH1) in maintaining cellular function. We found that inhibiting IDH1 increases DNA damage, suppressing the DNA repair pathway crucial for genomic integrity. This makes cancer cells susceptible to PARP inhibitors, offering a new PDAC treatment approach. Methods: The effect of IDH1 inhibition (Ivosidenib) combined with PARP inhibition (Olaparib) on in vitro growth inhibition were assessed through Trypan blue and PicoGreen in drug combination assays. Additionally, we investigated the molecular mechanism underlying the synergistic effects of drug combination. To further evaluate the therapeutic potential of IDH1 inhibition in combination with PARP inhibitors, we examined tumor growth in xenograft models of PDAC in athymic nude mice. Results: Our data report that, mechanistically, IDH1 inhibition decreased expression of HR DNA repair pathway molecules, such as BRCA1 and BRCA2, and leading to decreased HR repair efficiency in pancreatic cancer cells. Short-term cell viability data demonstrated that targeting IDH1 with ivosidenib when combined with DNA-damaging agent (Olaparib) had a synergistic effect in HRP cell lines (MiaPaCa-2 and Panc-1) with a positive synergy score and Bliss score greater than 1. Additionally, we assessed long-term cell survival using colony formation assays, which yielded a dramatic reduction in cell survival for both Panc1 and MiaPaCa-2 cells when olaparib was combined with ivosidenib, as compared to single-agent controls. Consequently, this reduction increased the levels of apoptosis induced by olaparib. Furthermore, we discovered that these two compounds complemented each other in DNA damage response, leading to an increase in the DNA damage biomarker γ-H2AX. In vivo studies using xenograft models demonstrated that the combination of ivosidenib and olaparib synergistically enhanced anti-tumor activity as compared to either single-agent used alone. Conclusion: Our data provides insights into the mechanisms underlying the susceptibility of PDAC tumor growth to a dual treatment approach involving an IDH1 inhibitor and a PARP inhibitor, particularly in the context of HRP cancer. This highlights the promise of precision medicine for pancreatic cancer treatment, though further pre-clinical studies are needed for validation and refinement. Citation Format: Mehrdad Zarei, Omid Hajihassani, Hallie J. Graor, Alexander W. Loftus, Soubhi Tahhan, Faith Nakazzi, Parnian Naji, Luke D. Rothermel, Jonothan R. Brody, Jordan M. Winter. Inhibition of IDH1 impairs homologous recombination and induces sensitivity to PARP inhibitor in pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7125.

LCDFormer: Long-term correlations dual-graph transformer for traffic forecasting

Traffic forecasting has always been a critical component of intelligent transportation systems. Due to the complexity of traffic prediction models, most research just only consider short-term historical data in the temporal dimension. However, learning temporal patterns necessitates the involvement of long-term historical data. Additionally, many models are limited in capturing spatial features by only considering short-distance spatial information connected to the target node. To solve these problems, we propose a dual-graph transformer, namely Long-term Correlations Dual-graph transFormer (LCDFormer), designed to capture long-term correlations and long-distance spatial correlations. It is entirely based on attention mechanisms, and as far as we know, there is limited research adopting this approach. Our work addresses this gap in the literature. In particular, we have devised a time aggregation method capable of consolidating long-term historical time series, concurrently addressing the impact of long-term temporal correlations while minimizing the influence of redundant data. Subsequently, we have introduced a novel spatio-temporal attention module that compresses spatial information to generate short-term input sequences while modeling dynamic long-range spatial correlations. We conducted extensive experiments with LCDFormer on five real-world traffic datasets. The results indicate that LCDFormer, considering long-term spatio-temporal correlations, is better able to learn the spatio-temporal patterns of traffic data. Compared to the current state-of-the-art baseline, our model has demonstrated outstanding predictive performance with a maximum improvement of 5.02% in mean absolute error, 4.33% in root mean square error and 7.32% in mean absolute percentage error. The source codes are available at: https://github.com/NanakiC/LCDFormer.

Analyzing the Challenges of Diversifying the Future Economy of Ukraine

Ukraine encounters a multitude of obstacles that necessitate resolution in order to enhance its economy. Key factors such as GDP growth, foreign direct investment flows, government efficiency, interest rates, research and development investment, and trade openness have a significant impact on diversification processes. Managing these characteristics can be highly demanding when striving to develop a resilient and diverse economic structure. The analysis illustrates the influence of several economic conditions on Ukraine's diversity from Q1-1996 to Q4-2022. The autoregressive distributed lag (ARDL) method is employed to examine the relationship between series. The ZA unit root test determines the stationarity of data using a mixed order of integration. The Gregory-Hansen and the bound ARDL co-integration tests confirm the presence of co-integration among series. The findings indicate that GDP, RIR, and TO have a detrimental influence on DIV, whereas FDI, GOVEF, and R&amp;D have a notable beneficial impact on it. The short-term data produced comparable results, albeit in smaller amounts. The inclusion of the co-integrating element in the model facilitates the convergence towards a long-term equilibrium state with an annual growth rate of 18.39%. The utilization of the wavelet method is applied for evaluating diversification because it can identify both short-term variations and long-term patterns in non-stationary data. Overall, the research suggests that it is advisable to use thorough and flexible diversification techniques to increase the variety of traded goods and develop a strong institutional framework to minimize the effects of unforeseen disruptions and inefficient resources Ukraine.

Physics-informed machine learning for battery degradation diagnostics: A comparison of state-of-the-art methods

Monitoring the health of lithium-ion batteries’ internal components as they age is crucial for optimizing cell design and usage control strategies. However, quantifying component-level degradation typically involves aging many cells and destructively analyzing them throughout the aging test, limiting the scope of quantifiable degradation to the test conditions and duration. Fortunately, recent advances in physics-informed machine learning (PIML) for modeling and predicting the battery state of health demonstrate the feasibility of building models to predict the long-term degradation of a lithium-ion battery cell’s major components using only short-term aging test data by leveraging physics. In this paper, we present four approaches for building physics-informed machine learning models and comprehensively compare them, considering accuracy, complexity, ease-of-implementation, and their ability to extrapolate to untested conditions. We delve into the details of each physics-informed machine learning method, providing insights specific to implementing them on small battery aging datasets. Our study utilizes long-term cycle aging data from 24 implantable-grade lithium-ion cells subjected to varying temperatures and C-rates over four years. This paper aims to facilitate the selection of an appropriate physics-informed machine learning method for predicting long-term degradation in lithium-ion batteries, using short-term aging data while also providing insights about when to choose which method for general predictive purposes.

Compound marine cold spells and hypoxic events in a nearshore upwelling system

Prolonged periods of extreme warm and cold seawater temperature anomalies, known as marine heatwaves (MHWs) and marine cold spells (MCSs), respectively, can have significant impacts on coastal marine ecosystems. Prior research has examined the co-occurrence of multiple extremes [low dissolved oxygen (DO) and/or low pH] during MHWs, with the impact of these compound events potentially exceeding that of a single stressor due to synergistic effects. However, we are not aware of any studies that have examined compound extreme events during MCSs, despite the ecosystem implications. Along Eastern Boundary Current Upwelling Systems (EBUS), strong-wind driven upwelling has led to reduced warming trends and persistence of MCSs, as well as an increased seasonal risk for coastal hypoxia via the cross-shelf advection of cold, low DO subthermocline waters. In this short communication, we investigated for the first time compound MCS-hypoxic events at a nearshore site in central California using several decades of nearshore water temperature data, short-term nearshore DO data, and several decades of DO data from the California Cooperative Oceanic Fisheries Investigations program. From 1988 to 2020, we identified 55 MCS events, with 50 events (∼90.9%) initiated during anomalously strong upwelling. Using long-term temperature-DO relationships, we identified 20 of the 55 MCS events (∼34.6%) as potential compound MCS-hypoxic events, with an average duration of 11.7 days. These compound events occurred almost exclusively during the major upwelling season (March to June) when there is a high propensity for the advection of cold, low DO subthermocline waters from the adjacent shelf into the nearshore. This work provides the first known investigation into the co-occurrence of MCS and hypoxic events in a nearshore upwelling system and provides a baseline for assessing future changes to these compound events in a changing climate, with important implications for ecosystem health in EBUS globally.

Alcohol consumption questionnaire: Scale development in a sample of Mexican American young adults and association with ADH7

BackgroundTo understand why some individuals who develop alcohol use disorders (AUD) first begin to drink heavily, a number of scales have been developed that index aspects of alcohol craving and restraint from drinking. We developed a new measure called the Alcohol Consumption Questionnaire (ACQ), based in part on items modified from scales used to index binge eating, because there are data to suggest that binge eating and binge drinking may share common antecedents. We present an initial validity study using data from a sample of Mexican Americans. MethodsData were from 699 Mexican American young adults in San Diego County, CA. A subsample (n = 60) had short-term test-retest data. Factor analysis and reliability assessment guided item reduction. Item response theory (IRT) analyses quantified item severity and identified questions with differential item functioning (DIF). Logistic regression assessed associations of mean scale scores with AUD, adjusting for key demographics, alcohol expectancies and subjective response to alcohol. We also examined associations with a protective genetic variant downstream from the alcohol dehydrogenase 7 (ADH7) gene. ResultsThe scale was reduced from 20 to 14 questions, which can be summarized by a single overall score (Cronbach's alpha = 0.896) or by two sub-scores (Consumption: 12 items, Cronbach's alpha = 0.896; Enjoyment: 2 items, Cronbach's alpha = 0.780). Test-retest reliability was very high (0.80–0.98) in this sample. The overall ACQ score and each subdomain score were strongly associated with AUD (ORs = 5.95 mild; 11.41 moderate; 48.56 severe) and family history of AUD. Respondents with the protective genetic variant had significantly lower overall ACQ scores (p < 0.001). ConclusionThe ACQ is a novel measure of alcohol consumption with strong relationships with both the AUD phenotype and ADH7 gene variants in a sample of Mexican American young adults.

Modelling permafrost distribution using the temperature at top of permafrost model in the boreal forest environment of Whatì, NT

Current permafrost models in Canadian boreal forests are generally of low spatial resolution as they cover regional or continental scales. This study aims to understand the viability of creating a temperature at the top of permafrost (TTOP) model on a local scale in the boreal wetland environment of Whatì, Northwest Territories from short-term field-collected temperature data. The model utilizes independent variables of vegetation, topographic position index, and elevation, with the dependent variables being ground surface temperature collected from 60 ground temperature nodes and 1.5 m air temperature collected from 10 temperature stations. In doing this, the study investigates the relationship vegetation and disturbance have on ground temperature and permafrost distribution. The model predicts that 31% of the ground is underlain by permafrost, based on a mean annual temperature at TTOP of &lt;0 °C. This model shows an accuracy of 62.5% when compared to cryotic assessment sites (CAS). Most inaccuracies, showing the limitations of the TTOP model, came from peat plateaus that had been burned in the most recent forest fire in 2014. These resulted in out-of-equilibrium permafrost and climatic conditions that TTOP cannot handle well. Commonly, permafrost mapping places Whatì in the extensive discontinuous zone, estimating that between 50% and 90% of the ground is underlain by permafrost. The study shows that a climatically driven TTOP model calibrated with CAS can be used to illustrate ground temperature heterogeneity from short-term data in boreal forest wetland environments. However, this approach likely underestimates permafrost extent and is perhaps not the best-suited modelling choice for near-surface permafrost, which is currently out of equilibrium with the current climate.

A data-driven data-augmentation method based on Slim-generative Adversarial Imputation Networks for short-term ship-motion attitude prediction

The performance of data-driven complex ship-motion attitude prediction models not only depends on the structure and algorithm design of the model but is also affected by the quantity and quality of training samples. Insufficient available data, resulting from factors such as low sampling rates and high costs, could inhibit the achievement of optimal prediction accuracy. This limitation in turn increases the model’s expected generalization error of the model and causes overfitting in regression models. Nevertheless, by artificially expanding the training dataset, data-augmentation methods enhance the resistance of the model to overfitting, thereby improving the prediction performance. This study introduces a novel data-driven model for ship-motion prediction based on the principles of Slim Generative Adversarial Imputation Networks (SGAINs). The model not only increases the amount of data but also considers temporal correlation features in the enhancement process, thus uniquely addressing the issue of poor accuracy of prediction models caused by insufficient training datasets. The simulation results demonstrate the effectiveness and superiority of the proposed model for short-term ship-motion attitude data augmentation.

Fault Diagnosis and Detection for Battery System in Real-World Electric Vehicles Based on Long-Term Feature Outlier Analysis

Accurate detection and diagnosis battery faults are increasingly important to guarantee safety and reliability of battery systems. Developed methods for battery early fault diagnosis concentrate on short-term data to analyze the deviation of external features without considering the long-term latent period of faults. This work proposes a novel data-driven method to detect long-term latent fault and abnormality for electric vehicles based on real-world operation data. Specifically, the battery fault features are extracted from the incremental capacity curves, which are smoothed by advanced filter algorithms. Secondly, principal component analysis algorithm is utilized to reduce dimensionality and the cumulative percent variance is to determine the number of significant features. Based on the features, a cluster algorithm is employed to capture the battery potential failure information. Moreover, the cumulative root-mean-square-deviation is introduced to quantificationally analyze the degree of the battery failures using large-scale battery data to avoid the missing fault reports using short-term data. In addition, a battery system failure index is proposed to evaluate battery fault conditions. The results indicate that the proposed long-term feature analysis method can effectively detect and diagnose faults.

Informer Model based Wind Power Forecast with Tropical Storms Present

When severe tropical storms pass through, regional wind speeds fluctuate greatly, and the volatility of wind farm output also increases significantly. At the same time, the duration of tropical storms is long, and it is difficult for short-term time series data prediction models to be effective, in which case the unstable output of wind turbines will have a greater impact on power system dispatching. This paper first examines the characteristics of tropical storm movement, namely the change in wind speed, and then uses the Informer long time series data prediction model to predict the total change in wind turbine output in the next 10 days after the storm has passed. The actual case proves that the Informer model is ideal in predicting the long time series of wind power output during a tropical storm.

종단자료를 활용한 초등학생의 핵심역량 성장궤적 및 경상남도교육청의 혁신교육 정책 효과성 분석

This study attempted to analyze the growth of core competencies of elementary school students in Gyeongnam and verify the effectiveness of Gyeongnam Innovation Education Policies that affect them by using short-term three-year longitudinal data from the Gyeongnam Education Longitudinal Study. The research questions are as follows. First, what is the growth Track of core competencies of elementary school students? Second, does the Gyeongnam Innovation Education Policy (class innovation and school democracy) affect the growth of core competencies of elementary school students? For the study, panel data of elementary school students who responded to the first to third surveys of the Gyeongnam Education Longitudinal Study were used. Data from 3,413 students who responded to all three-year surveys were used. The conclusion of the study is as follows. First, as a result of comparing the average value of core competencies of elementary school students, an increase, decrease, or decrease after increase pattern was found. Second, as a result of the potential growth model for core competencies, the linear change model was found to be more suitable than the non-change model. In addition, as a result of putting demographic and sociological variables such as gender, region, and school division into the linear change model, it was found that it had an effect on the initial value and rate of change of core competencies. Third, it was found that both class innovation and school democracy variables invested to analyze the effectiveness of innovative education policies affect the core competencies of elementary school students. In addition, the innovative education policy set up for research analysis was not a one-way structure that affects core competencies, but a mutually compatible structure.

Influence of neoadjuvant treatment strategy on perioperative outcomes in locally advanced rectal cancer.

Neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer facilitates tumour downstaging and complete pathological response (pCR). The goal of neoadjuvant systemic chemotherapy (total neoadjuvant chemotherapy, TNT) is to further improve local and systemic control. While some patients forgo surgery, total mesorectal excision (TME) remains the standard of care. While TNT appears to be noninferior to nCRT with respect to short-term oncological outcomes few data exist on perioperative outcomes. Perioperative morbidity including anastomotic leaks is associated with a negative effect on oncological outcomes, probably due to a delay in proceeding to adjuvant therapy. Thus, we aimed to compare conversion rates, rates of sphincter-preserving surgery and anastomosis formation rates in patients undergoing rectal resection after either TNT or standard nCRT. An institutional colorectal oncology database was searched from January 2018 to July 2023. Inclusion criteria comprised patients with histologically confirmed rectal cancer who had undergone neoadjuvant therapy and TME. Exclusion criteria comprised patients with a noncolorectal primary, those operated on emergently or who had local excision only. Outcomes evaluated included rates of conversion to open, sphincter-preserving surgery, anastomosis formation and anastomotic leak. A total of 119 patients were eligible for inclusion (60 with standard nCRT, 59 with TNT). There were no differences in rates of sphincter preservation or primary anastomosis formation between the groups. However, a significant increase in conversion to open (p = 0.03) and anastomotic leak (p = 0.03) was observed in the TNT cohort. In this series TNT appears to be associated with higher rates of conversion to open surgery and higher anastomotic leak rates. While larger studies will be required to confirm these findings, these factors should be considered alongside oncological benefits when selecting treatment strategies.

Regression modeling of subjective probability of river inundation with epistemic uncertainty: a short-term panel data analysis

Lack of sufficient knowledge about flood risk can lead to “don’t know” responses or non-response in risk perception surveys, and incorrect treatment of these responses can lead to bias in the results. In this study, we focus on the possibility that the “50%” response actually means “fifty-fifty” and thus reflects epistemic uncertainty in the subjective probability of river flooding. We conduct an analysis that introduces a concomitant-variable latent class model as a method to adjust for this epistemic uncertainty. The results of the analysis accounting for epistemic uncertainty suggest that risk communication, such as simulated evacuation experiences and flood-related information distribution, increases subjective probability. Moreover, the proportion of latent classes with epistemic uncertainty decreased with each successive wave of the survey, thereby suggesting that knowledge acquisition and learning through the demonstration experiment led to a reduction in epistemic uncertainty. The analysis using the evacuation decision-making model also suggested that the introduction of subjective probability contributed to improving the likelihood of the model. These results suggest that knowledge acquisition through risk communication and short-term panel survey can lead to correct risk perception estimations and influence evacuation decisions.

Generation and application of typical meteorological year data for PV system potential assessment: A case study in China

The potential assessment of photovoltaic(PV) systems is of great significance for installing and laying out PV. Short-term meteorological data cannot reflect the local climate characteristics, so it is not suitable for the potential assessment of PV. A typical meteorological year(TMY) can reflect a region's climatic characteristics, and therefore, it is appropriate for the potential assessment of PV. Although the generation methods of TMY for building energy consumption simulation are mature, there are still limited studies on TMY for PV. Given this, this study employs a method to generate TMY data suitable for the Chinese region, while redefining the influencing factors and their respective weights. A TMY dataset was built for 90 cities based on 38 years of measured data in China and applied to BAPV potential assessment. The study assesses annual generation, CO2 reduction, levelized cost of electricity (LCOE), and net returns(NR). The results reveal the highest annual electricity generation in the Northwest region, with Shiquanhe, Golmud, and Ejina sites reaching 215.65 MWh, 201.2 MWh, and 197.5 MWh, respectively. Conversely, southern and coastal regions, notably Chongqing, exhibit the lowest generation at 75.75 MWh. BAPV in Class I tariff areas, along with some Class II and III areas, can achieve demand-side parity (DSP) in full on-grid mode. Through operational adjustments, DSP can be implemented in 23 currently ineligible areas. This study provides dataset support for calculating PV system power generation and analyzes PV potential across diverse cities. The results contribute to policy formulation and future solar energy research, facilitating precise calculations for efficient green power planning in pursuit of carbon neutrality.

Faktor Penentu Pertumbuhan Ekonomi Indonesia

This research aims to determine: the effect of inflation on Indonesia's economic growth, the effect of HDI on Indonesia's economic growth and the effect of unemployment on economic growth in Indonesia. The data used in this research is secondary data sourced from the websites of various agencies including BPS, aseanstats.org, and kidb.adb.org, from 1993-2022. This research uses the Autoregressive Distributed Lag or ARDL analysis tool model. The ARDL model is a combination of autoregressive (AR) and distributed lag (DL) methods. The AR method is a method that uses one or more past data from the dependent variable, while DL is a regression method that involves data at the present and past times from the independent variable. This model can differentiate short-term and long-term responses of the variables studied. The findings of this study indicate that there is cointegration between the variables used in this study so that the ARDL test can be carried out. The results of short-term data processing show that inflation has had a negative and significant effect on Indonesia's economic growth in the current year, two and three years previously. Meanwhile, HDI has a significant influence in the current year and the previous two years. Meanwhile, unemployment has had a significant negative influence in the current year on Indonesia's economic growth. In long-term estimates, inflation and unemployment have a negative and significant effect on economic growth, however, HDI does not have a significant effect on Indonesia's economic growth.

Efficient implementation of a wavelet neural network model for short-term traffic flow prediction: Sensitivity analysis

The concept of a smart city is emerging to address significant challenges arising from rapid urbanization, economic growth, and climate change. Innovative technologies can be used as a means to promote sustainable and inclusive urban development. These technolgies include the deployment of the internet of things (IoT), artificial intelligence (AI), energy management, and smart transportation. In a smart city, intelligent transportation systems (ITS) play a vital role in efficient traffic management. This paper explores the use of hybrid artificial intelligence techniques for predicting short-term traffic flow data from M25 motorways in the UK. Since volume traffic flow data are non-stationary, wavelet transform (WT) as a powerful signal analyzer is applied for signal decomposition for the elimination of redundant data from input matrices. The feature selection method based on Gram-Schmidt (GS) is used for the selection of more valuable features. The elimination of redundant data can speed up the learning process and improve the generalisation capability of the prediction models. After a pre-processing stage, a wavelet neural network (WNN) with a simple structure is applied as a powerful prediction tool. Two separate structures are considered for the prediction of weekday and weekend traffic volume data. The experiments explore that the debauchies-4 (db4) wavelet function with 7 decomposition levels leads to the best detection accuracy. Moreover, the range of forecasting, the type of the day, the level of decomposition, and other factors all have an impact on prediction stability. Compared with existing prediction methods, the proposed approach produces lower values of root mean square error (RMSE) and mean absolute percentage error (MAPE) for all step-horizons analyzed. These findings provide valuable implications and insights into the development of an efficient and reliable road condition monitoring system for delivering secure and sustainable transportation services.