Early Anticipation Research Articles

Multi-modal transformer with language modality distillation for early pedestrian action anticipation

Language-vision integration has become an increasingly popular research direction within the computer vision field. In recent years, there has been a growing recognition of the importance of incorporating linguistic information into visual tasks, particularly in domains such as action anticipation. This integration allows anticipation models to leverage textual descriptions to gain deeper contextual understanding, leading to more accurate predictions. In this work, we focus on pedestrian action anticipation, where the objective is the early prediction of pedestrians’ future actions in urban environments. Our method relies on a multi-modal transformer model that encodes past observations and produces predictions at different anticipation times, employing a learned mask technique to filter out redundancy in the observed frames. Instead of relying solely on visual cues extracted from images or videos, we explore the impact of integrating textual information in enriching the input modalities of our pedestrian action anticipation model. We investigate various techniques for generating descriptive captions corresponding to input images, aiming to enhance the anticipation performance. Evaluation results on available public benchmarks demonstrate the effectiveness of our method in improving the prediction performance at different anticipation times compared to previous works. Additionally, incorporating the language modality in our anticipation model proved significant improvement, reaching a 29.5% increase in the F1 score at 1-second anticipation and a 16.66% increase at 4-second anticipation. These results underscore the potential of language-vision integration in advancing pedestrian action anticipation in complex urban environments.

Time to recovery and predictors among admitted preterm neonates in the neonatal intensive care units of public hospitals of Addis Ababa, Ethiopia, 2021

IntroductionEthiopia implemented measures to reduce preterm mortality, and much is currently being done to avoid preterm death, yet preterm death remains the top cause of infant death. As a result, evaluating median time of recovery and determinants will provide information to planners and policymakers to design strategies to improve preterm survival.MethodsHospital-based retrospective follow-up study was conducted in four selected public hospitals of Addis Ababa from September 2018 to August 2021. Data were collected using a pretested structured questionnaire. Epi-data 4.6 and STATA Version 16 were used for data entry and analysis. Kaplan-Meier survival curve, log-rank test, and median time were computed. To find predictors of time to recovery, a multivariable Cox proportional hazards regression model was fitted, and variables with a p-value less than 0.05 were considered statistically significant.ResultsA total of 466 preterm babies were included in the study of which 261 (56.1%) preterm neonates survived and were discharged from NICUs. The median time to recovery was 10 days (95% CI: 9–12). Low birth weight (Adjusted hazard-ratio [AHR]: 1.91, 95% CI: 1.2–3.06), normal birth weight (AHR: 2.09, 95% CI: 1.16–3.76), late preterm (AHR: 1.91, 95% CI: 1.02–3.55), no hospital-acquired infection (AHR: 2.19, 95% CI: 1.36–3.5), no thrombocytopenia (AHR: 1.96, 95% CI: 1.27–3.02), continuous positive airway pressure (AHR: 0.66, 95% CI: 0.48–0.91), and kangaroo mother care (AHR: 2.04, 95% CI: 1.48–2.81) were found to be independent predictors of time to recovery of preterm babies.Discussion/ConclusionThe recovery rate was found relatively low. Several predictors of preterm recovery time were discovered in the study. The majority of predictors were preventable or treatable. Therefore, emphasis should be given towards prevention and early anticipation, and management of these predictors. Studies to assess the quality of care and cause of low survival rate of preterm infants are recommended.

Development and validation of a clinical predictive model for high-volume lymph node metastasis of papillary thyroid carcinoma

The central lymph node metastasis (CLNM) status in the cervical region serves as a pivotal determinant for the extent of surgical intervention and prognosis in papillary thyroid carcinoma (PTC). This paper seeks to devise and validate a predictive model based on clinical parameters for the early anticipation of high-volume CLNM (hv-CLNM, > 5 nodes) in high-risk patients. A retrospective analysis of the pathological and clinical data of patients with PTC who underwent surgical treatment at Medical Centers A and B was conducted. The data from Center A was randomly divided into training and validation sets in an 8:2 ratio, with those from Center B serving as the test set. Multifactor logistic regression was harnessed in the training set to select variables and construct a predictive model. The generalization ability of the model was assessed in the validation and test sets. The model was evaluated through the receiver operating characteristic area under the curve (AUC) to predict the efficiency of hv-CLNM. The goodness of fit of the model was examined via the Brier verification technique. The incidence of hv-CLNM in 5897 PTC patients attained 4.8%. The occurrence rates in males and females were 9.4% (128/1365) and 3.4% (156/4532), respectively. Multifactor logistic regression unraveled male gender (OR = 2.17, p < .001), multifocality (OR = 4.06, p < .001), and lesion size (OR = 1.08 per increase of 1 mm, p < .001) as risk factors, while age emerged as a protective factor (OR = 0.95 per an increase of 1 year, p < .001). The model constructed with four predictive variables within the training set exhibited an AUC of 0.847 ([95%CI] 0.815–0.878). In the validation and test sets, the AUCs were 0.831 (0.783–0.879) and 0.845 (0.789–0.901), respectively, with Brier scores of 0.037, 0.041, and 0.056. Subgroup analysis unveiled AUCs for the prediction model in PTC lesion size groups (≤ 10 mm and > 10 mm) as 0.803 (0.757–0.85) and 0.747 (0.709–0.785), age groups (≤ 31 years and > 31 years) as 0.778 (0.720–0.881) and 0.837 (0.806–0.867), multifocal and solitary cases as 0.803 (0.767–0.838) and 0.809 (0.769–0.849), and Hashimoto’s thyroiditis (HT) and non-HT cases as 0.845 (0.793–0.897) and 0.845 (0.819–0.871). Male gender, multifocality, and larger lesion size are risk factors for hv-CLNM in PTC patients, whereas age serves as a protective factor. The clinical predictive model developed in this research facilitates the early identification of high-risk patients for hv-CLNM, thereby assisting physicians in more efficacious risk stratification management for PTC patients.

An Early Anticipation of Market Socialism? Liberalism, Heresy, and Knowledge in John Stuart Mill's Political Economy of Socialism

John Stuart Mill is considered one of the most important representatives of the classical school of political economy. His intellectual development exhibited a gradual transition toward more socialistic views. This transition was partly the result of his interaction with French utopian socialists, which led Mill to theoretically construct an economic system lying between what is now called market capitalism and revolutionary socialism. For Mill, socialism would be a new organic period after the transitory and critical period of the “stationary state.” This paper delineates the core tenets of Mill's stationary state and presents it as an early anticipation of what from the 1920s on is called “market socialism.” Mill's optimistic vision of the stationary state was based on the spread of associations, the socialization of knowledge among all people, competition, and the importance of individuality. These elements are connected with Mill's idiosyncratic, liberal and utilitarianist vision of (market) socialism which prepares the ground for his socialist utopia based on the ideal “from each according to his capacities; to each according to his needs.”

The role of prediction and visual tracking strategies during manual interception: An exploration of individual differences.

The interception (or avoidance) of moving objects is a common component of various daily living tasks; however, it remains unclear whether precise alignment of foveal vision with a target is important for motor performance. Furthermore, there has also been little examination of individual differences in visual tracking strategy and the use of anticipatory gaze adjustments. We examined the importance of in-flight tracking and predictive visual behaviors using a virtual reality environment that required participants (n = 41) to intercept tennis balls projected from one of two possible locations. Here, we explored whether different tracking strategies spontaneously arose during the task, and which were most effective. Although indices of closer in-flight tracking (pursuit gain, tracking coherence, tracking lag, and saccades) were predictive of better interception performance, these relationships were rather weak. Anticipatory gaze shifts toward the correct release location of the ball provided no benefit for subsequent interception. Nonetheless, two interceptive strategies were evident: 1) early anticipation of the ball's onset location followed by attempts to closely track the ball in flight (i.e., predictive strategy); or 2) positioning gaze between possible onset locations and then using peripheral vision to locate the moving ball (i.e., a visual pivot strategy). Despite showing much poorer in-flight foveal tracking of the ball, participants adopting a visual pivot strategy performed slightly better in the task. Overall, these results indicate that precise alignment of the fovea with the target may not be critical for interception tasks, but that observers can adopt quite varied visual guidance approaches.

A nonlinear hyperbolic optimized grey model for market clearing price prediction: Analysis and case study

Market Clearing Price (MCP) of an energy market is an operating equilibrium attained, when consumer demand and supply bid curves intersect with each other. Early anticipation of MCP provides an upper hand to any generating company to exercise the market power in its own favour. MCP prediction technologies are evolved in recent years and emphasis is given on developing a robust mechanism for anticipating dynamic conditions. This work presents an application of a nonlinear whitening term enabled grey model for prediction of the MCP. Detailed mathematical analysis and development of the model is presented. The proposed model is named as Nonlinear Hyperbolic Optimized Grey Model (NHOGM). For optimizing the whitening equation parameters, Linear Population Size Reduction (LPSR) Success History Based Parameter Adaption Differential Evolution (L-SHADE) algorithm has been applied. Results reveal that proposed nonlinear hyperbolic grey model yields satisfactory results as compared with the state of art grey methods and other well-known optimization based methods.

Evaluation of the Predictors of Mortality in Very Low Birth Weight Neonates in Neonatal Intensive Care Unit – A Hospital-based Prospective Observational Study

Abstract Introduction: Very low birth weight (VLBW) neonates are defined by birth weight ≤1500 g. They constitute 4%–7% of all neonates but contribute &gt;30% of all neonatal deaths. Assessment of factors predicting mortality in such infants, both maternal and neonatal, can help us prioritize our resources and improve our health structure. Aim: The aim was to study factors affecting neonatal mortality in VLBW neonates in the neonatal intensive care unit (NICU). Materials and Methods: This hospital-based prospective observational study was conducted on neonates ≥26 weeks and birth weight ≤1500 g admitted in the NICU. Predesign structured pro forma was made for history and data collection. Detailed antenatal and natal history was taken, neonates were examined along the course of treatment, and the outcome was recorded in the form of discharge and death. All tests were performed at a 5% level of significance; thus, an association was significant if P &lt; 0.05. Univariate analysis and logistic regression analysis were done to determine the predictors of mortality. Results: One hundred newborns were enrolled, of which 32 expired. Respiratory distress syndrome followed by sepsis was the major cause of death. Univariate analysis showed that primigravida, history of premature rupture of membrane, anemia, meconium-stained liquor, maternal fever, use of antenatal steroids, fetal distress, resuscitation requirement, gestational age, Apgar score, need for surfactant, and delayed capillary refill time were found to be directly linked with neonatal mortality. On multivariate analysis, requirement of resuscitation (P = 0.001), gestational age ≤30 weeks (P = 0.032), the need for surfactant (P = 0.036), anemia (P = 0.019), maternal fever (P = 0.003), use of antenatal steroid (P = 0.031), and fetal distress (P = 0.02) were found to be significant predictors of neonatal mortality. Conclusions: Most of the predictors can be managed by following proper treatment protocols and implementing newer policies. Therefore, emphasis should be given to prevention, early anticipation, and management of these predictors and building better health infrastructure.

Assessment and Prediction of Hydrometeorological Drought in Corong River Basin, Indonesia

Hydrological drought analysis in a river basin is crucial because it impacts water resource management, agriculture, drinking water supply, industry, ecology, and disaster risk mitigation. It plays a key role in water usage planning, safeguarding agricultural yields, and ensuring a stable drinking water supply. In the context of this research, the Corong River basin is used as a case study. This study aims to determine the level of hydrological drought (deficit) using the Threshold Level Method (TLM) and predict hydrological drought using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. This model understands the characteristics of drought and predicts drought in the Corong River basin for early mitigation and anticipation of drought in the region. The results of this research indicate a strong relationship between the Hydrological Drought Index (HDI) and streamflow and Precipitation (PRCP), which can be used to forecast future droughts. This model is highly robust and accurate in observing the level of hydrological drought (deficit, duration, and sharpness) and predicting drought events in the Corong River basin. These findings have significant practical implications for water management and disaster risk mitigation in this river basin while also advancing the scientific understanding of hydrological drought.

PROSES PEMBUATAN DAN EDUKASI ALAT PENDETEKSI KETINGGIAN AIR DI DESA LENGGAHSARI KECAMATAN CABANGBUNGIN

Flooding is one of the natural disasters that often occurs around the neighborhood, one of which is in Lenggahsari Village which is located in the lowlands and the location of the area is between the banks of the river, namely the Citarum river and the Ciherang river. lowlands or areas close to river flow. This natural disaster cannot be predicted when flooding will occur, in this situation there are many losses caused by flooding, including losses in plantations, loss of property and even loss of life. From the disasters faced by the people of Lenggahsari village who live near the swift river flow that lasts a long time, residents or local officials must go back and forth to see the condition of the river water level, because they are worried that if the water rises quickly it can cause losses. Meanwhile, the community and officers cannot be informed when the river water rises at any time. The purpose of this research is to be able to provide a warning of the status of the water level with the sound generated by the water level detector that we will make. Making it easier for residents or officers to monitor or know the condition of the water is potentially flooding or not so that residents can take early anticipation in order to avoid casualties due to flood disasters. This flood early detection system or tool has succeeded in providing information about flooding in real time.

Deep Q-Network (DQN): Reinforcement Learning Based Approach for Secure Social Distancing Adherence with SARS-CoV-2 in Public Places

The estimates taken far and wide to deal with the SARS-CoV-2 pandemic, limiting travel, shuttering superfluous organizations and implementing all social separating arrangements, are having serious monetary consequences. a noteworthy decrease in economic action spread over the economy the world, lasting in excess of a few months, typically clear in genuine GDP. Where it is formally announced a downturn. To quicken a strong expected recuperation with rising protectionism and unilateralism. There is a requirement for individuals to come out and face the circumstance. Despite the fact that it is established that separating individuals and investigating their contacts would be inadequate to control the SARS-CoV-2 pandemic, in light of the fact that there would be an excess of deferral between the beginning of indications and seclusion. Consequently, in these sorts of conditions it is to keep people groups from infection influence and early anticipation of these tainted individuals may prompt re development the economy too. We built up a numerical model utilizing profound Deep reinforcement learning (DRL) which is poised to revolutionize the field of artificial intelligence and the use of central algorithms in deep RL, specifically the deep Q-network (DQN), trust region policy optimization (TRPO). The proposed astute checking framework can be utilized as a reciprocal apparatus to be introduced at better places and consequently screen individuals receiving the security rules. With these prudent estimations, people will have the option to win this battle against SARS-CoV-2.

Eksperimen Permainan Memancing Ikan Warna Terhadap Kemampuan Mengenal Warna Pada Anak Usia 4-5 Tahun

Children aged 4-5 years, kindergarten education is an option to help the child's growth and development process. One of the developments of children aged 4-5 years that needs attention is the development of their sense of sight. Through color recognition, the sensitivity of his sense of sight is honed. In addition, color recognition is an early anticipation of the occurrence of color blindness in children. This study aims to determine the influence of color fish in fishing game as one of the process to improve the cognitive ability of children aged 4-5 years in recognizing colors. The type of research used is quantitative research through the One Group Pre-Test Post-Test Design method. The data collection techniques used are observation and assessment the final result. Observation results from the pre-test and post-test were then calculated using the Wilcoxon Signed Rank test. The result of this study that there is an influence on color fish fishing game on the ability to recognize colors in children aged 4-5 years in Dharma Wanita Persatuan Lampah Kindergarten. Through the Wilcoxon test, it is known that the signification value is 0.000 &lt; 0.05. The post-test showed that 89% of children were in the starting to develop (MB) category and 11% were developing as expected (BSH). Meanwhile, the post-test results show that 100% of children increased into the very well developed category (BSB). Thus, it can be concluded that the color fish of fishing game can be used to improve the ability to recognize colors in children aged 4-5 years in Dharma Wanita Persatuan Lampah Kindergarten, Gresik.

Context–aware assistive driving: an overview of techniques for mitigating the risks of driver in real-time driving environment

PurposeThis study aims to analyze driver risks in the driving environment. A complete analysis of context aware assistive driving techniques. Context awareness in assistive driving by probabilistic modeling techniques. Advanced techniques using Spatio-temporal techniques, computer vision and deep learning techniques.Design/methodology/approachAutonomous vehicles have been aimed to increase driver safety by introducing vehicle control from the driver to Advanced Driver Assistance Systems (ADAS). The core objective of these systems is to cut down on road accidents by helping the user in various ways. Early anticipation of a particular action would give a prior benefit to the driver to successfully handle the dangers on the road. In this paper, the advancements that have taken place in the use of multi-modal machine learning for assistive driving systems are surveyed. The aim is to help elucidate the recent progress and techniques in the field while also identifying the scope for further research and improvement. The authors take an overview of context-aware driver assistance systems that alert drivers in case of maneuvers by taking advantage of multi-modal human processing to better safety and drivability.FindingsThere has been a huge improvement and investment in ADAS being a key concept for road safety. In such applications, data is processed and information is extracted from multiple data sources, thus requiring training of machine learning algorithms in a multi-modal style. The domain is fast gaining traction owing to its applications across multiple disciplines with crucial gains.Research limitations/implicationsThe research is focused on deep learning and computer vision-based techniques to generate a context for assistive driving and it would definitely adopt by the ADAS manufacturers.Social implicationsAs context-aware assistive driving would work in real-time and it would save the lives of many drivers, pedestrians.Originality/valueThis paper provides an understanding of context-aware deep learning frameworks for assistive driving. The research is mainly focused on deep learning and computer vision-based techniques to generate a context for assistive driving. It incorporates the latest state-of-the-art techniques using suitable driving context and the driver is alerted. Many automobile manufacturing companies and researchers would refer to this study for their enhancements.

ANALISIS LAJU ANGKUTAN SEDIMEN UNTUK MENGANTISIPASI RESIKO BENCANA SEJAK DINI PADA SUNGAI CILEUEUR KABUPATEN CIAMIS

This study uses the Duboy's method and the Englund -Hunsen method to calculate bed load transport which aims to determine the volume of sedimentation in the Cileueur River, Ciamis Regency as a form of early anticipation of disaster risk.This research was carried out with stages starting from hydrological analysis, determining the planned rainfall, analyzing the design discharge, and analyzing the average velocity of the river.This research is based on considerations to anticipate disaster risks, especially in the Cileueur River, Ciamis Regency, this river has a length of 25,000 m and an area of 13,660.7 ha of watershed. In the Cileueur River there are several problems including silting of the river which results in frequent occurrence of large enough water overflows exceeding the cross-sectional area capacity of the river during the rainy season. Seeing the impact of the occurrence of these problems, it is necessary to conduct research in order to anticipate risks before a disaster occurs.Based on the results of the analysis and discussion, it can be concluded that the results of bed load transport calculations in the Cileueur river, Ciamis Regency using the Duboy's method obtained an average of 357.5619 tonnes/year and the Englund and Hunsen method of 317.379 tonnes/year. Then the comparison of the results of calculating the amount of bottom sediment from the two methods is 6%.

Bahaya Penyalahgunaan Napza dan Rokok

There are numerous addictive chemicals that are extremely harmful to the body and pose a problem for humanity in various parts of the world. One of them is commonly referred to as narcotics, psychotropic, and other addictive chemicals (drugs) or drugs (narcotics and dangerous drugs). The purpose of the service is to inform the public of the health risks associated with drug abuse and cigarette smoking, to persuade the public of the importance of protecting themselves and their families from drugs and cigarettes, and to demonstrate the effectiveness of government programs in eradicating drug abuse. In partnership with the National Narcotic Board (BNN) RI, 125 participants have received socialization through online presentations, as well as information counseling for early identification and anticipation of the hazards of narcotics, cigarette, and also new psychoactive substances (NPS). Through the socialization and education on the dangers of narcotics and cigarette use for this health, it can be concluded that the participants gain confidence, knowledge, and early detection of the type of New Psychoactive Substance (NPS), narcotics, and cigarettes, as well as a heightened awareness of the dangers of drug and cigarette use. So that this action can assist the government in strengthening by soliciting the public's assistance and support for BNN's efforts to break the chain of illegal drug trafficking, it is intended.

Prediction of Infectious Disease to Reduce the Computation Stress on Medical and Health Care Facilitators

Prediction of the infectious disease is a potential research area from the decades. With the progress in medical science, early anticipation of the disease spread becomes more meaningful when the resources are limited. Also spread prediction with limited data pose a deadly challenge to the practitioners. Hence, the paper presents a case study of the Corona virus (COVID-19). COVID-19 has hit the major parts of the world and implications of this virus, is life threatening. Research community has contributed significantly to understand the spread of virus with time, along with meteorological conditions and other parameters. Several forecasting techniques have already been deployed for this. Considering the fact, the paper presents a proposal of two Rolling horizon based Cubic Grey Models (RCGMs). First, the mathematical details of Cubic Polynomial based simple grey model is presented than two models based on time series rolling are proposed. The models are developed with the time series data of different locations, considering diverse overlap period and rolling values. It is observed that the proposed models yield satisfactory results as compared with the conventional and advanced grey models. The comparison of the performance has been carried out with calculation of standard error indices. At the end, some recommendations are also framed for the authorities, that can be helpful for decision making in tough time.

First trimester screening and prediction of preeclampsia by Application of Fetal Medicine Foundation Algorithm at Tanta University

Background: Preeclampsia (PE) is a complex illness linked to conception that affects a variety of bodily functions and is usually connected with perinatal morbidity and deaths. Early PE anticipation will reduce this linked morbidity and death by allowing for routine maternal and foetal monitoring and the use of preventative measures. Aim of the work: the goal of this study is to investigate whether the measurement of maternal serum placental growth factor (PlGF) combined with uterine artery Doppler ultrasound, are useful in early predicting PE and to examine the diagnostic accuracy of the Fetal Medicine Foundation (FMF) Algorithm for the early screening and anticipation of the high- risk PE at 11-13 weeks conception in a group of pregnant women. Patients and Methods: one hundred and twenty primigravida women with living, singleton fetus at 11-13 weeks conception without risk factors other than being primigravida attending antenatal care clinic at the Department of Obstetrics and Gynecology at Tanta University Hospitals. All cases were subjected to ; A signed informed written consent, proper history taking, full clinical examination, estimation of body mass index (BMI), blood pressures and determination of mean arterial blood pressure (MAP), ultrasound, Uterine artery doppler ultrasound examination with the determination of the mean Pulsatility index (PI) of the uterine arteries. Maternal serum levels of PlGF were also measured by specific immunoassay (ELISA). The measured values of MAP, mean uterine artery pulsatility index (PI) and PlGF were converted into multiples of the median (MoM). Then the FMF model was used for the determination of patient-specific risk of PE at 11-13 weeks conception in each case. Results: The main results of the present study revealed that; Only 11 of 120 normal pregnant women were categorized as a high PE risk with a percentage of 9.2% and the rest 109 cases were low PE risk with a percentage of 90.8% by application of FMF algorithm. There was a significant difference between the high and low risks regarding the mean uterian UtA-Pi and PIGF levels at 11–13 weeks. Receiver operating characteristic curve (ROC) demonstrate to what extent it can be depended on PlGF, mean UtA-PI and MAP as a predictor for PE denoting the significant diagnostic performance for PIGF and mean uterine artery pulsatility index (PI). The optimal cutoff value of PlGF value using the ROC curve was ≤40 leading to a sensitivity of 90.91%, a specificity of 96.33%, a PPV of 71.4, a NPV of 99.1% and an accuracy of 95.83%. While the optimal cutoff measure of mean uterine artery PI value using the ROC curve was >1.91 leading to a sensitivity of 90.91%, a specificity of 96.33%, PPV of 71.4, NPV of 99.1% and an accuracy of 95.83%. Conclusion: On the basis of these results, it could be concluded that the combined measurement of maternal serum PlGF concentrations and mean PI of the uterine arteries at 11–13 weeks of conception may help to predict the high-risk PE in primigravida when other parameters of PE anticipation are normal. And The FMF screening algorithm for the high-risk PE group performing satisfactorily. PE estimated by the competing risk model incorporated in the FMF algorithm may be applicable locally, and the earlier screening in conception would allow women at a higher risk to be monitored accordingly, participate in early intervention trials, and commence prophylactic therapy.

A Comparative Study to Evaluate Prognostic Value of Glasgow Coma Score and Full Outline of Unresponsiveness Scale to Predict the Outcome of Unconscious Child in the Paediatric Intensive Care Unit

Background: The current issue of improving mortality and care in paediatric patient is very challenging. Early anticipation and intervention will be helpful in improving the outcome in emergency and paediatric intensive care unit (PICU) by the use of Glasgow Coma Scale (GCS) and Full Outline of Unresponsiveness (FOUR) score. Decreasing in GCS and FOUR score is associated with worsening level of consciousness. Aim: The Aim of the study was to compare the prognostic value of FOUR and GCS score in unconscious children to predict the survival in unconscious children to predict the morbidity pattern. Materials and Methods: The present observational study was done from June 2019 to June 2020 at PICU of Department of Paediatrics, BRD Medical College, Gorakhpur, following Ethical Clearance from the Institutional Ethics Committee of BRD Medical College. All the admitted unconscious children in PICU were included in our study. Data entry and analysis were done using SPSS. Data were analysed using appropriate statistical test. Results: In our study, 65 (50.0%) patients were 9–12 years old and male was predominant. Seventy-eight (60%) patients had symptoms of &lt;3 days. GCS score of E, V, M response at the time of admission was assessed in which maximum patients seen in E2 i.e., 60 (46.2%), V2 50 (38.5%) patients and M5 47 (36.1%) patients, while after 12 h patients, maximum GCS were seen in E3 54 (41.5%) patients, V2 54 (41.5%) patients and M5 (42.3%) patients and after 72 h patients maximum GCS was seen in E4 80 (61.5%) patients, V 54 (41.5%) patients and M51 (39.2%) patients. FOUR score baseline eye response was maximum in E2 39 (30%), motor response in M2 76 (58.5%), brain stem function response in B4 118 (90.8%) and respiration response in R4 44 (33.8%). Similarly, after 12 h score was E3 44 (33.8%), M3 48 (36.9%), B4 74 (56.9%), R4 56 (44.4%) while after 72 h score was E3 75 (57.7%), M4 75 (57.7%), B4 88 (67.7%) and R4 88 (67.7%). On final diagnosis, maximum was of AES 40 (30.7%). Thirty-two patients expired during the study period. Conclusion: FOUR and GCS score both is the good predictor of severity of disease and of mortality and may help in prioritising the treatment of sick as well as the counselling of parents about disease severity.

Reward responsiveness in autism and autistic traits – Evidence from neuronal, autonomic, and behavioural levels

Autism has been linked to atypicalities in reward processing, especially in the social domain. However, results are heterogeneous, and their interpretation is hindered by the use of personally non-relevant social rewards. In this study we investigated behavioural (reaction times), neuronal (event-related potentials), and autonomic (pupil sizes) responses to personally relevant social rewards, money, and neutral outcomes in 26 autistic and 53 non-autistic subjects varying in levels of autistic traits. As hypothesised and preregistered, autism and autistic traits did not differently influence responses to social, monetary, or neutral outcomes on either response level. While groups did not differ in behaviour (reaction times), autism was linked to generally enhanced brain responses in early anticipation and larger pupil constrictions in reward reception. Together, these results suggest that when using personally relevant stimuli, autism is linked to generally preserved, although less neuronally efficient processing of rewards. Considering the role of social relevance in reward processing, we propose an interpretation of contradictory evidence from clinical practice and empirical research.

Development of Soil Moisture Content Monitoring System for Precision Measurement of Soil Moisture in Sub-Optimal Land for Palm Oil Plantation

Indonesia has 15 million hectares of palm oil plantation, making it one of the world’s greatest palm oil and CPO producers. However, despite the large area of palm oil plantations, the production is not yet optimal. Soil moisture and environmental factors are the leading cause. Thus, a technology suited to monitor soil moisture, soil temperature, and microclimates needs to be developed. This research aims to develop a Soil Moisture Content (SMC) Monitoring System that can be used to monitor soil moisture, supported by soil temperature and microclimates variable and also operates automatically and in real-time. The device is also integrated with a cloud server. The implemented SMC was installed on the palm oil plantation of PT Kerry Sawit Indonesia, Wilmar International Plantation. On top of that, the SMC device can measure soil moisture up to three variations of depth. The acquired data was saved and sent to a cloud server. Then, a user can access the data to execute early water deficit anticipation. The resulting device is capable of measuring soil moisture accurately, which can be proven with these parameters, R square value of 0.9782, RMSE of 1.2%, and MAPE of 2.6%.

Optimized Fractional Overhead Power Term Polynomial Grey Model (OFOPGM) for market clearing price prediction

Market Clearing Price (MCP) prediction methods are beneficial for framing the profit making strategies for a generating company. Early anticipation of this information can be helpful to judge the behaviour of rivals in the energy market. Companies are always striving to develop methodologies, by which early anticipation of the market clearing conditions can be emulated. The work presented in this paper is the development of an Optimized Fractional Overhead Power Term Polynomial Grey Model (OFOPGM). Mathematical aspects of this model are explained. The coefficients of polynomial grey action quantity are obtained with an aim to minimize the prediction error. An optimization routine based on L-SHADE algorithm is developed. First the application of L-SHADE and other contemporary optimizers are reported to optimize whitening equation. Then, the decisive evaluation has been made on the basis of convergence property analysis of L-SHADE and statistical attribute analysis of optimization runs. Further, OFOPGM with L-SHADE is considered for prediction of MCPs. A comparative analysis of contemporary grey models is presented on the basis of prediction error analysis on the MCP data of Indian Energy Exchange. The results reveal that proposed model yields better fitting as compared to other models. Further the evaluation of forecasting method is conducted through several analyses. • A grey model based on Optimized Whitening Equation is proposed. • Powerful optimization Algorithm L-SHADE is applied to handle the optimization routine. • The comparative analysis of some conventional and contemporary grey models are conducted with the proposed. • Error indices of forecast are reported in the work.