Longer Time Range Research Articles

Availability bias in road safety systematic reviews and its impact on the meta-analysis findings.

Meta-analyses, which present the best source of information on the effectiveness of interventions, are influenced by several biases. One category relates to the convenience of selective inclusion of those primary studies, which are more easily available than others. This availability bias includes bias from excluding the grey literature, bias from excluding non-English literature, and bias from excluding older studies. Existing studies are not conclusive about the impacts of this bias; in addition, none of them focus on road safety meta-analyses. To fill this gap, the present paper consisted of two studies: (1) exploring the presence of availability bias in road safety meta-analyses, and (2) demonstrating the impact of availability bias in several example meta-analyses. Based on an analysis of 80 existing meta-analyses, the first study concluded that compared to the medicine meta-analyses, the road safety meta-analyses use a longer time range, are more often restricted in terms of language, and less often involve the grey literature. The second study utilized selected unrestricted data samples to demonstrate the impact of availability bias in seven meta-analyses. The differences in intervention effectiveness in terms of crash frequency changes between unrestricted and restricted scenarios were identified. This shows that the search restrictions clearly lead to availability bias, which influences the differences in meta-analysis results.

Hybrid model based on similar power extraction and improved temporal convolutional network for probabilistic wind power forecasting

Accurate wind power generation forecasting is of great significance to improve the operation of power system. Probabilistic forecasting has a higher application value in power grid because it can provide more abundant forecasting information than deterministic forecasting. In addition, multi-step forecasting can provide forecasting results in a longer time range, so that decision makers can make longer-term planning and strategic arrangements. In this paper, we propose a novel multi-step improved temporal convolutional network based on quadratic spline quantile function (MITCN-QSQF) for probabilistic wind power forecasting. First, we combine maximum information coefficient, Gaussian similarity and adaptive resample to propose an effective similar power generation feature extraction method (MGR) for power generation. Then the temporal convolutional network is improved to construct the multi-step time series forecasting model MITCN. By combining the proposed model and the powerful probabilistic forecasting method quadratic spline quantile function (QSQF), high-quality probabilistic forecasting of wind power is achieved. Through comprehensive simulations on an open-source dataset, the superiority and efficiency of the proposed method are verified. Compared with some advanced benchmarks, the proposed model can obtain more accurate deterministic and probabilistic forecasting results.

Collective behavior of self-steering active particles with velocity alignment and visual perception

The formation and dynamics of swarms is widespread in living systems, from bacterial biofilms to schools of fish and flocks of birds. We study this emergent collective behavior via agent-based simulations in a model of active Brownian particles with visual-perception-based steering and alignment interactions. The dynamics, shape, and internal structure of the emergent aggregates, clusters, and swarms of these intelligent active Brownian particles are determined by the maneuverabilities Ωv and Ωa, quantifying the steering based on the visual signal and polar alignment, respectively, the propulsion velocity, characterized by the Péclet number Pe, the vision angle θ, and the orientational noise. Various nonequilibrium dynamical aggregates—like motile wormlike swarms and milling, and close-packed or dispersed clusters—are obtained. Small vision angles imply the formation of small clusters, while large vision angles lead to more complex clusters. In particular, a strong polar-alignment maneuverability Ωa favors elongated wormlike swarms, which display superdiffusive motion over a much longer time range than individual ABPs, whereas a strong vision-based maneuverability Ωv favors compact, nearly immobile aggregates. Swarm trajectories show long persistent directed motion, interrupted by sharp turns. Milling rings, where a wormlike swarm bites its own tail, emerge for an intermediate regime of Pe and vision angles. Our results offer insights into the behavior of animal swarms and provide design criteria for swarming microbots. Published by the American Physical Society 2024

Isimila Prehistoric Site, Tanzania: Comparative Faunal Datings and ESR, with a Reassessment

More than sixty years ago, 1957-58 University of Chicago excavations at the Isimila Prehistoric Site in the Southern Highlands of Tanzania exposed a sequence of Pleistocene deposits that included Earlier Stone Age aggregates. Now, the available correlations with extinct fauna from other dated East African localities and preliminary electron spin resonance (ESR) estimates indicate that the Isimila Formation was deposited over a much longer time range than originally proposed. As originally noted, Earlier Stone Age ‘large cutting tools' or LCT's found in the upper Lisalamagasi Member are more refined than are those found in the Sands units of the lower Lukingi Member. This suggests a correlation with Kalambo Falls Prehistoric Site for the upper member, with new OSL dating at ca. 400–500 ka. The new faunal correlations and ESR estimates indicate that the lower member was deposited between 500 and 900 ka, during the later Early Pleistocene to the early Middle Pleistocene. A reassessment of Isimila including unpublished excavation data raises questions about the Pleistocene human occupations and their palaeoenvironments on the Highlands Plateau.

Spatio-temporal graph convolutional networks driven by data-physical fusion for parameter prediction of natural gas dehydration system

Triethylene glycol dehydration unit is a piece of essential device for removing moisture from raw natural gas during natural gas production. However, the existing station equipment management systems are mostly collection-oriented with little analysis, lack the effective methods of parameter prediction and fault warning, and the strong coupling between the monitoring parameters is a problem should be study. To solve these problems, this paper analyzes the time dependence and spatial correlation of these parameters. Also, a spatio-temporal graph convolutional networks prediction model driven by data-physical fusion (SG-STGCN) is proposed for constructing the graph structure. Firstly, the signed directed graph model is established based on the physical process, and the weight of each edge is obtained by using the grey relational analysis (GRA). Secondly, by stacking spatio-temporal convolutional modules, the temporal and spatial dependencies over a long range of time are captured to realize multivariate parameter prediction. Then, the real-time monitoring data of a dehydration station are used for analysis. The experimental results showed that the proposed method can achieves the best predict result compared with other methods, and can be used in the fault early warning to maintain high reliability of equipment. Finally, the SG-STGCN has been integrated and tested successfully on the real-time monitoring platform of a dehydration unit.

High-Resolution Load Forecasting on Multiple Time Scales Using Long Short-Term Memory and Support Vector Machine

Electricity load prediction is an essential tool for power system planning, operation and management. The critical information it provides can be used by energy providers to maximise power system operation efficiency and minimise system operation costs. Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) are two suitable methods that have been successfully used for analysing time series problems. In this paper, the two algorithms are explored further for load prediction; two load prediction algorithms are developed and verified by using the half-hourly load data from the University of Warwick campus energy centre with four different prediction time horizons. The novelty lies in comparing and analysing the prediction accuracy of two intelligent algorithms with multiple time scales and in exploring better scenarios for their prediction applications. High-resolution load forecasting over a long range of time is also conducted in this paper. The MAPE values for the LSTM are 2.501%, 3.577%, 25.073% and 69.947% for four prediction time horizons delineated. For the SVM, the MAPE values are 2.531%, 5.039%, 7.819% and 10.841%, respectively. It is found that both methods are suitable for shorter time horizon predictions. The results show that LSTM is more capable of ultra-short and short-term forecasting, while SVM has a higher prediction accuracy in medium-term and long-term forecasts. Further investigation is performed via blind tests and the test results are consistent.

Penggunaan Metode Rolling Mosaic Untuk Mendukung Pengembangan Peta Prakiraan Daerah Penangkapan Ikan Wilayah Pesisir

Predicted fishing ground maps (PPDPI) which made using satellite image data is often constrained by clouds, causing its production not too optimal. Rolling mosaic methods examined here is expected to reduce cloud cover so the information about oceanographic conditions can be more visible and can increasing PPDPI production. In July, the percentage of sea surface temperature data can increase from 15.3%-30.29% using 1-day mosaic data, to 40.46%-56.75% using 3-day mosaic, it increases to 72.24%-77.88% using 7-day mosaic data and increase to 84.19%-89.07% using 14-day mosaic. While the percentage of sea surface temperature data in December can be increased from around 4.93%-13.03% to 41.48%- 51.60%. In general, at July and December, the relationship between 1-day mosaic and 3-days mosaic data, 7-days and 14-days are very strong, but the strength of the relationship will decrease (the correlation coefficient gets smaller) along with the increasing of the time range used to mosaicking the data. The RMSE shows that the RMSE between the 1-day mosaic with 3-days mosaic is 0.288 (July), 0.263 (December); RMSE between 1-day mosaic and 7-days mosaic is 0.388 (July), 0.387 (December) and RMSE between 1-day mosaic and 14-days mosaic is 0.471 (July), 0.477 (December). This RMSE values shows that the longer time range used to construct the mosaic, the error value will also increase. Scoring analysis using percentage of data, correlation coefficient and RMSE as a parameters indicate that the 7-days mosaic method has the highest score so it is considered as the best method to be used to predict sea surface temperature with minimum cloud cover.

Effects of modelling studies on controlled drainage in agricultural land on reduction of outflow and nitrate losses–a meta-analysis

A review with meta-analysis of outflow and nitrate loss reduction in controlled drainage (CD) vs conventional, free drainage (FD) was carried out in the study. Since the results of experimental field studies usually cover short periods of data collection, hence in this paper, meta-analyses were based on model studies that usually cover a longer time range. The databases Web of Science and Scopus were searched for eligible English articles, published until December 2020, that describe the quantity and quality of drainage water. The meta-analysis of outflow and nitrate loss reduction in CD vs FD using the mean difference (MD) with a confidence interval (CI) of 95%. The influence of each study was measured through heterogeneity, sensitivity analyses and publication bias using STATISTICA (version 13.3) for all analyses. Of the 107 works identified, 18 were finally included in the analysis based on established criteria required for an appropriate meta-analysis. In general the results indicate a reduction in average drainage outflow of 30.5% (MD = -71.26 mm; 95% CI, -103.49 –-39.04; p = 0.000) in arable land with CD in comparison to FD practice. In the case of nitrate load the reduction was 33.61% and in the drainage water there was lower content in CD practice by an average of 8.36 kg NO3 ha-1year-1 (95% CI, -9.93 –-6.79; p = 0.000). Subgroup analysis of two meta-analyses indicates that the results concerning these associations may vary with the calculated weight for each article, in which the number of years of study had the most significant impact.

54P Overall survival in patients with metastatic lung cancer from 2000 to 2020: Implementation of innovative strategies in a real-world setting

Lung cancer burden on cancer related mortality is prominent. Over the past 20 years, innovative treatments have changed the outcome of patients. When global survival datas are reported through clinical trials, and national or international epidemiological registries, limited real-life data exists with regards to the actual evolution of survival focusing on patients with metastatic lung cancer over a long range of time.

Shade induces contrasting light photosynthetic performance between Signal and Guinea Grasses

Signal grass (Urochloa decumbens) and guinea grass (Megathyrsus maximus) are African grasses that are well established in the Brazilian Savannah and we tested their adaptation to different light intensity. Plants were grown for 45 days under 0% shade (full sun) and 25, 40, and 80% induced shade to evaluate their photosynthetic performance. Light curves showed higher values of electron transport rate, photochemical quenching, and effective quantum yield in plants subjected to 0 and 25% shade for signal grass and in 25 and 40% shade for guinea grass. The potential quantum yield evaluations revealed that signal grass felt the effects of excessive light around 11:30 am on plants subjected to 0 and 25% shade. Conversely, guinea grass showed these photoinhibition effects at the same shade level but in a longer time range (9:30 am to 1:30 pm). As shade increased, there was a reduction tendency of the pigment content in signal grass and the opposite was observed for guinea grass. Stomatal conductance showed different values during the day and among different shade levels and there were no differences in relative water content between treatments and species. Results indicated better photosynthetic performance for signal grass under high intensity and better photosynthetic performance for guinea grass subjected to intermediate and higher levels of shade. Altogether, the results indicate that guinea grass seems to be a more appropriate choice for silvopasture systems.

The Relationship between Free Volume and Cooperative Rearrangement: From the Temperature-Dependent Neutron Total Scattering Experiment of Polystyrene.

Although many theories have been proposed to describe the nature of glass formation, its microscopic picture is still missing. Here, by a combination of neutron scattering and molecular dynamics simulation, we present the temperature-dependent atomic structure variation of polystyrene at the glass formation, free volume and cooperative rearrangement. When it is close to glass formation, the polymer is confined in tubes, whose diameter is the main chain–main chain distance, in a “static cage” from its neighbors. This definition can not only account for the kinetic pathway dependence of Williams-Landel-Ferry (WLF) free volume, but also be testified in a set of six polymers. However, the free volume which allows a monomer to move cannot be found in any frame of its real-space image. Monomers, thus, have to move cooperatively to be out of the cage. During glass formation, dynamic heterogeneity develops, and string-like cooperative rearrangement region (CRR) grows over a long range of time and length scales. All of these CRRs tend to walk through loose “static cages”. Our observation unifies the concepts of free volume and cooperative rearrangement. The former is a statistical average leading to a polydisperse “static cage” formation; while a loose “static cage” provides the way that CRRs move.

Massive-scale complicated human action recognition: Theory and applications

Recognizing various human actions in video is a challenging task, which is also one of the key tasks in computer vision. It has received extensive attention from AI researchers Bakker et al. (2003), Bruderlin and Williams (1995), Cardle et al. (2003), Carlsson (1996, 1999), Clausen and Kurth (2004). It has important applications in human behavior analysis, artificial intelligence, and video surveillance. Compared with still image classification, the time component of video provides important clues for recognition, so multiple human actions can be recognized based on motion information. In addition, video provides natural data enhancement for individual images. For motion recognition from videos, appearance and temporal dynamics are two key and complementary cues. In this work, we formulate a human motion recognition framework based on 2D spatial feature fusion of Kinect bone data. This method utilizes the human body structure and spatial geometry to represent human body structure and extract features. Meanwhile, it combines the active action and auxiliary action features of two spatial dimensions by layering, and leverages the pervasively used support vector machine and hidden Markov model to classify human body structure. Noticeably, it is difficult to extract its information due to the limitations of background clutter, viewpoint change, scale change, different lighting conditions and camera movement. In this way, while learning the classification information of human behavioral categories, designing effective representations is the key to deal with these challenges. In this work, a method for human motion recognition in video based on ResNext network is proposed. Based on our proposed ResNeXt network, using the data of RGB and optical flow, we can extract more appearance characteristics and temporal features of human actions, so as to better achieve the classification of actions. The utilization of video time segmentation method can capture the long range of time in video, in order to make better use of the longer range of time information in video. Comprehensive experimental results have shown that the proposed method improves the performance of UCF101 and HMDB51 motion recognition data sets to a certain extent.

Assessment of the EUMETSAT Multi Decadal Land Surface Albedo Data Record from Meteosat Observations

Surface albedo, defined as the ratio of the surface-reflected irradiance to the incident irradiance, is one of the parameters driving the Earth energy budget and it is for this reason an essential variable in climate studies. Instruments on geostationary satellites provide suitable observations allowing long-term monitoring of surface albedo from space. In 2012, EUMETSAT published Release 1 of the Meteosat Surface Albedo (MSA) data record. The main limitation effecting the quality of this release was non-removed clouds by the incorporated cloud screening procedure that caused too high albedo values, in particular for regions with permanent cloud coverage. For the generation of Release 2, the MSA algorithm has been replaced with the Geostationary Surface Albedo (GSA) one, able to process imagery from any geostationary imager. The GSA algorithm exploits a new, improved, cloud mask allowing better cloud screening, and thus fixing the major limitation of Release 1. Furthermore, the data record has an extended temporal and spatial coverage compared to the previous release. Both Black-Sky Albedo (BSA) and White-Sky Albedo (WSA) are estimated, together with their associated uncertainties. A direct comparison between Release 1 and Release 2 clearly shows that the quality of the retrieval improved significantly with the new cloud mask. For Release 2 the decadal trend is less than 1% over stable desert sites. The validation against Moderate Resolution Imaging Spectroradiometer (MODIS) and the Southern African Regional Science Initiative (SAFARI) surface albedo shows a good agreement for bright desert sites and a slightly worse agreement for urban and rain forest locations. In conclusion, compared with MSA Release 1, GSA Release 2 provides the users with a significantly more longer time range, reliable and robust surface albedo data record.

Bridging the Synthesis Gap: Ionic Liquids Enable Solvent-Mediated Reaction in Vapor-Phase Deposition.

Molecular layer deposition (MLD) is an attractive, vapor-phase deposition method for applications requiring ultrathin organic materials, such as photolithography, lithium batteries, and microelectronics. By using sequential self-limiting surface reactions, MLD offers excellent control over thickness and conformality, but there are also challenges such as a limited range of possible film compositions and long deposition times. In this study, we introduce a modified technique, termed ionic liquid assisted MLD (IL-MLD), that can overcome these barriers. By performing the surface reactions inside of an ultrathin layer of a compatible ionic liquid (IL), solvent effects are replicated inside a vacuum system, broadening the possible reactions to a much wider suite of chemistries. Using this strategy, the MLD of polyetherketoneketone, an industrially and research-relevant, high-performance thermoplastic, is reported. With this proof-of-concept, we demonstrate that IL-MLD can enable the synthesis of polymers via solvent- or catalyst-mediated reactions and establish an approach that may allow solution chemistries to be accessed in other vapor deposition techniques as well.

Experimental activities on a PEFC based powertrain for a hybrid electric minibus

With the aim to follow the National and European regulations related to the transport field numerous initiatives have given rise to studies and analyses of low/zero environmental impact powertrain. Many investments have been made in the electric vehicles (BEVs-Batteries Electric Vehicles) field although these still present limits related to low range of autonomy and long charging times. The minibus realized by CNR ITAE within the “i-NEXT” project (funded by Italian Ministry of Educational, University and Research) has a hybrid electric powertrain based on fuel cell (FC) technology and lithium batteries (FCHEV - Fuel Cell Hybrid Electric Vehicle). The present work reports the tests drive in which different conditions of the operating range have been verified.

Thermal energy storage properties and lab‐scale thermal performance in cementitious plaster of composite phase change material for energy efficiency of buildings

AbstractThis study was aimed to achieve a form‐stable composite PCM (FS‐CPCM) by incorporating the eutectic mixture of octadecane (OD) and eicosane (EC) with pumice and also investigate thermo‐regulation performance of its cementitious plaster form in lab‐scale cubic unit. The scanning electron microscopy (SEM) and Fourier transform infrared (FT‐IR) analysis results proved the existence of good physicochemical compatibility among the components of FS‐CPCM. The differential scanning calorimetry (DSC) analysis results revealed that the FS‐CPCM had phase change temperature of about 22°C and latent heat of capacity of about 77–78 J/g, respectively. Thermogravimetric analysis (TGA) and thermal cycling findings demonstrated that it had excellent thermal durability and cycling reliability. Lab‐scale thermal performance of a cubic design plastered with FS‐PCM/cement mortar was also evaluated experimentally. The results showed that the plaster including FS‐CPCM had function of maintaining the interior temperature at comfortable range for a considerable long time.

ROLLING MOSAIC METHOD TO SUPPORT THE DEVELOPMENT OF POTENTIAL FISHING ZONE FORECASTING FOR COASTAL AREAS

The production of the Indonesian Institute for Marine Research and Observation’s mapping of forecast fishing areas (peta prakiraan daerah penangkapan ikan or PPDPI) based on passive satellite imagery is often constrained by high-cloud-cover issues, which lead to sub-optimal results. This study examines the use of the rolling mosaic method for providing geophysical variables, in particular, seasurface temperature (STT) together with minimum cloud cover, to enable clearer identification of oceanographic conditions. The analysis was carried out in contrasting seasons: dry season in July 2018 and rainy season in December 2018. In general, the rolling mosaic method is able to reduce cloud cover for sea-surface temperature (SST) data. A longer time range will increase the coverage percentage (CP) of SST data. In July, the CP of SST data increased significantly, from 15.3 % to 30.29% for the reference 1D mosaic and up to 84.19 % to 89.07% for the 14D mosaic. In contrast, the CP of SST data in December tended to be lower, from 4.93 % to 13.03% in the 1D mosaic to 41.48 % to 51.60% in the14D mosaic. However, the longer time range decreases the relationship between the reference SST data and rolling mosaic method data. A strong relationship lies between the 1D mosaic and 3D mosaics, with correlation coefficients of 0.984 for July and 0.945 for December. Furthermore, a longer time range will decrease root mean square error (RMSE) values. In July, RMSE decreased from 0.288°C (3D mosaic) to 0.471°C (14D mosaic). The RMSE value in December decreased from 0.387°C (3D mosaic) to 0.477°C (14D mosaic). Based on scoring analysis of CP, correlation coefficient and RMSE value, results indicate that the 7D mosaic method is useful for providing low-cloud-coverage SST data for PPDPI production in the dry season, while the 14D mosaic method is suitable for the rainy season.

How to increase engagement on social media using the honeycomb model

PurposeThe purpose of this paper is to understand how a company could improve public engagement on social media (SM) analysing the case of Alento, which is a human resources and consulting company located in Portugal. The company’s presence on Facebook was analysed by using the honeycomb model for SM functionality, by Kietzmann et al. (2011), as a theoretical framework.Design/methodology/approachThe authors implemented some actions to change procedures on SM management. Monitoring five months of data, the authors could compare the SM engagement before and after changes on SM management. Then, a convenience sample of 205 useable questionnaires was collected. The population of the study comprised Portuguese Facebook users who liked the Alento Facebook Fan Page. An analysis of honeycomb dimensions was performed.FindingsThe engagement level on Alento’s SM was increased by changing procedures identified on survey. In five months, Alento’s number of followers was increased by just 4.5 per cent, interactions were increased by 35 per cent, views were more than twice (+122 per cent) and fans who clicked on Alento’s Facebook page’s links were increased by 146 per cent.Research limitations/implicationsThere were also some limitations related to the sample, since the authors only obtained 205 responses. Therefore, the second suggestion for future investigations would be to conduct the survey on a larger number of people. This study just considers five months after changing procedures. A longer range of time could produce different results.Practical implicationsThe most relevant contribution of the current study is the offer of some insights into the use of a simple tool such as the honeycomb model of Kietzmann et al. (2011) for the analysis of the social engagement from a firm’s perspective, regardless of their nature.Social implicationsThe importance of SM to spread good content and to reduce media costs is reinforced in this study.Originality/valueThis study is innovative, as it identifies a need of change on SM management, proposes and implements new procedures, checking its results. SM marketing is a new wagon of study, and the problem of how to increase engagement on professional SM is on top priorities of the field.

The Preservation of Bone Cell Viability in a Human Femoral Head through a Perfusion Bioreactor.

Current methods for drug development and discovery involve pre-clinical analyses that are extremely expensive and time consuming. Animal models are not the best precedent to use, when comparing to human models as they are not synonymous with the human response, thus, alternative methods for drug development are needed. One of which could be the use of an ex vivo human organ where drugs could be tested and the effects of those drugs could be observed. Finding a viable human organ to use in these preliminary ex vivo studies is difficult due to the availability, cost, and viability. Bone tissue and marrow contain a plethora of both bone and stem cells, however, these cells need constant perfusion to be viable over a longer time range. Here we maintain bone cell sustainability in an ex vivo model, through the use of human femoral heads in a novel bioreactor. This bioreactor was designed to directly perfuse cell culture media (DMEM) through the vasculature of a femoral head, providing ideal nutrients and conditions required for maintaining organ viability. We show, for the first time, that cells within a femoral head can stay alive up to 12 h. Further development could be used to determine the effects of drugs on a human organ system and could aid in the understanding of the progression of bone diseases and pathologies.

Discrimination of photo-induced isomerization and molecular reorientation processes in azobenzene derivative doped in a polymer

In order to investigate the mechanism of photo-induced processes of azo molecules doped in polymer, temporal evolution of the absorption spectrum and transmittance for two polarization components were measured under and after optical pumping. A simple model was applied to decompose the contributions from angular hole burning (AHB) due to photo-isomerization and following molecular reorientation (MRO). The result for disperse red 1 (DR1) in poly-methyl methacrylate (PMMA) indicated the dominance of AHB in the order of seconds while slow accumulation of the MRO effect became significant during a longer time range. Several extraordinary behaviors observed in preceding studies will be explained in context given in this study. Dependence on dye concentration showed that MRO was more significant in highly loaded samples. Further, quantum yield of photo-isomerization process in this system was estimated to be 8%.