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Cognitive and physical activities often interact with arousal in our daily lives. As anexample, a reaction of fear triggered by visual identification of a dangerous object or situationmay elicit a stress response that prepares the body to fight or flee. The proposed study thereforeconsiders arousal to stimuli in relation to motor actions. Specifically, it investigates whetherphysical exertion affects arousal. Physical exertion is experimentally induced with a hand griptask in which participants squeeze a hand dynamometer with exerted grip force proportional totheir physical strength. Arousal level is assessed with a pictorial assessment task in whichparticipants report the arousal level of images on a 9-point scale. It is hypothesized that arousalratings with the concurrent hand grip task will increase with exerted handgrip force. This study issignificant because it will examine whether arousal is connected to physical action in our dailylives.

Background Cerebrovascular diseases are a major cause of morbidity and mortality worldwide and are the fifth leading cause of death in the United States. Understanding demographic differences in mortality rate trends can raise awareness of demographic disparities. We sought to investigate age‐adjusted mortality trends due to cerebrovascular diseases and ischemic stroke for demographic disparities in trend from 1999 to 2020. Methods We used the publicly accessible Centers for Disease Control and Prevention Wide‐Ranging Online Data for Epidemiologic Research database to gather mortality data to determine trends in cerebrovascular diseases and cerebral infarction mortality in the United States from 1999 to 2020. Using the Joinpoint program, temporal trends for cerebrovascular diseases and cerebral infarction mortality were calculated for each demographic group and reported as both annual percentage changes (APCs) or average APC from 1999 to 2020. In addition, trends were compared between groups for significant differences. Results We found an overall decrease in mortality rate for cerebrovascular diseases with average APC −1.9%. In 2020, age‐adjusted mortality rates due to cerebrovascular diseases in the Black population was 1031 per 1 000 000 compared with 679 in the White population. Similarly in 2020, cerebral infarction for the Black population had an age‐adjusted mortality rate of 256.3 compared with the White population's 170.4. When assessing overall trends by race and ethnic group: American Indian/Alaska Native had average APC −2.5%, Asian Pacific Americans had 2.4%, White population had −1.9%, and the Black population had −1.8%. We found a statistically significant difference in trend of decline between the Black and White population cerebrovascular diseases age‐adjusted mortality rates. No significant average APCs were found for cerebral infarction. The results of this study showcase disparities in cerebrovascular diseases mortality in the United States and where additional effort, research, and care should be focused. Conclusion The results of this study showcase disparities in mortality in the United States and where additional effort, research, and care should be focused.

Introduction: Renal failure, encompassing both acute and chronic forms, stands as a formidable public health challenge with far-reaching consequences for individual well-being and healthcare systems. This study delves into the mortality rates of renal failure in the United States over two transformative decades, from 1999 to 2020. Renal failure’s significance arises from its escalating prevalence, substantial healthcare costs, and the imperative to understand the multifaceted factors that influence its outcomes. Objectives: The primary objectives of this research are to analyze temporal trends in renal failure mortality rates, explore the impact of the Affordable Care Act (ACA) and advancements in renal care practices on mortality rates, and assess demographic disparities in mortality outcomes. Methods: Utilizing CDC WONDER’s multi-cause mortality data, we assessed mortality due to renal failure (ICD-10 Codes: N17–N19). Age-adjusted mortality rates (AAMRs) were collected and stratified by sex and race. The Joinpoint Regression Program analyzed trends, calculating annual percent change (APC) and significant average annual percent change (AAPC) from 1999 to 2020. Segmented line regression models were employed for parallel pairwise comparisons. Results: Renal failure mortality rates decreased for both sexes during the late 2000s. The ACA’s enactment in 2010 coincided with improved access to healthcare, possibly contributing to the decline. Demographic disparities highlighted variations in mortality rates across racial and gender groups. Advancements in renal care practices were evident, which were driven by innovations in treatment modalities and disease management. Significant temporal trends were observed by race, with varying periods of decrease or uptrend. Conclusions: The decline in renal failure mortality rates during the late 2000s was potentially influenced by the ACA and advances in renal care practices. Demographic disparities emphasize the need for equitable healthcare access and interventions. These findings underscore the significance of healthcare policies and medical advancements in reducing renal failure mortality rates and addressing disparities. Persistent efforts to mitigate challenges such as healthcare access, cost barriers, and disparities remain crucial to enhancing renal failure outcomes.

Moving the capital of Indonesia from the Jakarta area to the island of Kalimantan is an inevitable necessity due to economic, political, socio-cultural and environmental factors, which require other areas of the archipelago to be developed. It has been decided that the green city and smart city concepts will be applied in the development of the new capital, using a state-of-the-art approach, including metaverse technology. The methodology employed in this study includes in-depth interviews, focus group discussions, direct field observations, and documentation. The findings of this research consist of an analysis of the readiness of IKN and its surrounding area for the implementation of metaverse technology in IKN. This paper examines whether metaverse technology is needed in the construction of the new capital city. If so, what are the needs for the development of the new capital's metaverse, and what are the advantages (and disadvantages) of using the metaverse. The results are expected to provide a more comprehensive view of the use of metaverse technology in the new capital.

Rapid industrial development is one of the leading causes of environmental degradation. The textile industries and the domestic activities in Majalaya District produce wastewater directly discharged into the Cikakembang River. As a result, the Cikakembang River’s water quality has decreased to the point that the water quality cannot be used for daily needs. This study modeled three main parameters in water quality modelling, namely Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), and Chemical Oxygen Demand (COD). Using MATLAB, the three-water quality governing equations originating from the Advection-Dispersion Equation were solved using the Runge Kutte-4 discretization scheme. The numerical modelling was carried out along 2.36 km of the Cikakembang River. All water quality coefficients, such as the DO Saturation (DOsat), the Reaeration Rate (ka), the Dispersion Coefficient (D), the Deoxygenation Rate (kd), and the Decomposition Rate (kc), for the Cikakembang River were estimated using equations developed by existing studies. The estimation of ka and D coefficients requires hydraulic parameters, which in this study were estimated using the HEC-RAS simulation. Meanwhile, kd and kc values were obtained from the calibration and verification process. The Relative Root Mean Square Error (RRMSE) objective function was used to evaluate the results of water quality modelling at three sampling points. In the calibration process, the results of water quality modelling produced RRMSE values for the DO, BOD, and COD parameters of 1.99%, 0.36% and 0.92%, respectively. Meanwhile, for the verification process, the RRMSE values for the DO, BOD, and COD parameters are 1.95%, 1.02% and 1.86%. All water quality parameters produce small RRMSE values in the calibration and verification processes. Hence, the water quality model created has good accuracy and stability.

Abstract Detecting pancreatic duct adenocarcinoma (PDAC) in its early stages and predicting late‐stage patient prognosis undergoing chemotherapy is challenging. This work shows that the activation of specific oncogenes leads to elevated expression of mRNAs and their corresponding proteins in extracellular vesicles (EVs) circulating in blood. Utilizing an immune lipoplex nanoparticle (ILN) biochip assay, these findings demonstrate that glypican 1 (GPC1) mRNA expression in the exosomes‐rich (Exo) EV subpopulation and GPC1 membrane protein (mProtein) expression in the microvesicles‐rich (MV) EV subpopulation, particularly the tumor associated microvesicles (tMV), served as a viable biomarker for PDAC. A combined analysis effectively discriminated early‐stage PDAC patients from benign pancreatic diseases and healthy donors in sizable clinical from multiple hospitals. Furthermore, among late‐stage PDAC patients undergoing chemotherapy, lower GPC1 tMV‐mProtein and Exo‐mRNA expression before treatment correlated significantly with prolonged overall survival. These findings underscore the potential of vesicular GPC1 expression for early PDAC screenings and chemotherapy prognosis.

Given the tubal origin of high-grade serous ovarian cancer (HGSC), we sought to investigate intrauterine lavage (IUL) as a novel method of biomarker detection. IUL and serum samples were collected from patients with HGSC or benign pathology. Although CA-125 and HE4 concentrations were significantly higher in IUL samples compared to serum, they were similar between IUL samples from patients with HGSC vs benign conditions. In contrast, CA-125 and HE4 serum concentrations differed between HGSC and benign pathology (P =.002 for both). IUL and tumor samples from patients with HGSC were subjected to targeted panel sequencing and droplet digital PCR (ddPCR). Tumor mutations were found in 75 % of matched IUL samples. Serum CA-125 and HE4 biomarker levels allowed for better differentiation of HGSC and benign pathology compared to IUL samples. We believe using IUL for early detection of HGSC requires optimization, and current strategies should focus on prevention until early detection strategies improve.

The WPR-LQ-7 is a UHF (1.3575 GHz) wind profiler radar used for routine measurements of the lower troposphere at Shigaraki MU Observatory (34.85∘ N, 136.10∘ E; Japan) at a vertical resolution of 100 m and a time resolution of 10 min. Following studies carried out with the 46.5 MHz middle and upper atmosphere (MU) radar (Luce et al., 2018), we tested models used to estimate the rate of turbulence kinetic energy (TKE) dissipation ε from the Doppler spectral width in the altitude range ∼ 0.7 to 4.0 km above sea level (a.s.l.). For this purpose, we compared LQ-7-derived ε using processed data available online (http://www.rish.kyoto-u.ac.jp/radar-group/blr/shigaraki/data/, last access: 24 July 2023) with direct estimates of ε (εU) from DataHawk UAVs. The statistical results reveal the same trends as reported by Luce et al. (2018) with the MU radar, namely (1) the simple formulation based on dimensional analysis εLout=σ3/Lout, with Lout∼70 m, provides the best statistical agreement with εU. (2) The model εN predicting a σ2N law (N is Brunt–Vaïsälä frequency) for stably stratified conditions tends to overestimate for εU≲5×10-4 m2 s−3 and to underestimate for εU≳5×10-4 m2 s−3. We also tested a model εS predicting a σ2S law (S is the vertical shear of horizontal wind) supposed to be valid for low Richardson numbers (Ri=N2/S2). From the case study of a turbulent layer produced by a Kelvin–Helmholtz (K–H) instability, we found that εS and εLout are both very consistent with εU, while εN underestimates εU in the core of the turbulent layer where N is minimum. We also applied the Thorpe method from data collected from a nearly simultaneous radiosonde and tested an alternative interpretation of the Thorpe length in terms of the Corrsin length scale defined for weakly stratified turbulence. A statistical analysis showed that εS also provides better statistical agreement with εU and is much less biased than εN. Combining estimates of N and shear from DataHawk and radar data, respectively, a rough estimate of the Richardson number at a vertical resolution of 100 m (Ri100) was obtained. We performed a statistical analysis on the Ri dependence of the models. The main outcome is that εS compares well with εU for low Ri100 (Ri100≲1), while εN fails. εLout varies as εS with Ri100, so that εLout remains the best (and simplest) model in the absence of information on Ri. Also, σ appears to vary as Ri100-1/2 when Ri100≳0.4 and shows a degree of dependence on S100 (vertical shear at a vertical resolution of 100 m) otherwise.

This study analyzes turbulent energy fluxes in the Arctic atmospheric boundary layer (ABL) using measurements with a small uncrewed aircraft system (sUAS). Turbulent fluxes constitute a major part of the atmospheric energy budget and influence the surface heat balance by distributing energy vertically in the atmosphere. However, only few in situ measurements of the vertical profile of turbulent fluxes in the Arctic ABL exist. The study presents a method to derive turbulent heat fluxes from DataHawk2 sUAS turbulence measurements, based on the flux gradient method with a parameterization of the turbulent exchange coefficient. This parameterization is derived from high-resolution horizontal wind speed measurements in combination with formulations for the turbulent Prandtl number and anisotropy depending on stability. Measurements were taken during the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition in the Arctic sea ice during the melt season of 2020. For three example cases from this campaign, vertical profiles of turbulence parameters and turbulent heat fluxes are presented and compared to balloon-borne, radar, and near-surface measurements. The combination of all measurements draws a consistent picture of ABL conditions and demonstrates the unique potential of the presented method for studying turbulent exchange processes in the vertical ABL profile with sUAS measurements.

Cikakembang River, a tributary of Citarum River, is situated in the densely populated Majalaya District, renowned for textile production. Direct discharges of domestic and industrial pollutants into the river contribute to substantial pollution, making it crucial to manage pollution levels. This implies that controlling pollution is crucial, as it significantly impacts the condition of Citarum River, already infamous as one of the world most polluted rivers. A key indicator for assessing river water quality is Biological Oxygen Demand (BOD), representing the oxygen required for microorganism-mediated decomposition. This parameter is influenced by deoxygenation rate, denoted as kd. Therefore, this study aimed to analyze the most suitable kd value for Cikakembang River using various empirical methods, including Simple, Fujimoto, Sawyer, Thomas, Fair, and Hydroscience. The result showed that Thomas method provided the most accurate prediction for BOD concentration of the river. In rainy season, Root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) values were 0.542, 0.035, and 0.981, respectively, and in dry season, the values were 0.117, 0.009, and 0.999. Additionally, kd value effectively simulated the river water quality using HEC-RAS, yielding satisfactory results. RMSE, MAPE, and R2 values for BOD concentration were 3.551, 0.162, and 0.331 in rainy season and 1.071, 0.100, and 0.812 in dry season. Finally, the modeling result showed that Cikakembang River did not meet the Class 2 Water Quality Standard during both rainy and dry seasons. This finding is critical, as it underscores the severity of the pollution problem in the river and the urgent need for comprehensive and effective management strategies to improve its water quality.

Free flap procedure provides an overall success rate of 97%, which decreases to 85% in hypercoagulable states. COVID-19, as a pro-thrombotic disorder, therefore seems detrimental to free flap survival. We encountered a case of unique pattern of free flap partial failure in a young male who underwent extremity reconstruction. The patient was diagnosed as COVID-19 positive on the 3rd day post-reconstruction. The flap survived well for the first 7 days post-operatively, but gradually the skin got necrosed and the subcutaneous fat layer was preserved when debriding. To our knowledge, this is the only case in which the skin of the free flap of a COVID-19 positive patient was necrosed almost entirely subsequently, while the subcutaneous fat was relatively preserved.

An administrator is employed to identify network security breaches in their organizations by using a Network Intrusion Detection and Prevention System (NIDPS), which is presented in this paper that can detect and preventing a wide range of well-known network attacks. It is now more important than ever to recognize different cyber-attacks and network abnormalities that build an effective intrusion detection system plays a crucial role in today's security. NSL-KDD benchmark data set is extensively used in literature, although it was created over a decade ago and will not reflect current network traffic and low-footprint attacks. Canadian Institute of Cyber security introduced a new data set, the CICIDS2017 network data set, which solved the NSL-KDD problem. With our approach, we can apply a variety of machine learning techniques like linear regression, Random Forest and ID3. The efficient IDPS is indeed implemented and tested in a network environment utilizing several machine learning methods. A model that simulates an IDS-IPS system by predicting whether a stream of network data is malicious or benign is our objective. An Enhanced ID3 is proposed in this study to identify abnormalities in network activity and classify them. For benchmark purposes, we also develop an auto encoder network, PCA, and K-Means Clustering. On CICIDS2017, a standard dataset for network intrusion, we apply Self-Taught Learning (STL), which is a deep learning approach. To compare, we looked at things like memory, Recall, Accuracy, and Precision.

Citarum River is one of the biggest rivers in Java Island and the primary source of Jakarta's water supply. Named the dirtiest river globally in 2013, the Indonesian government is committed to improving the Citarum River's water quality by 2025. However, the Majalaya District, the center of the textile industry area in the upstream part of the Citarum River, is indicated to have significantly contributed to the river pollution. Hence, it is essential to conduct a study to evaluate the water quality in the Citarum River, especially in the Majalaya District. The results showed a considerable decrease in water quality from upstream to downstream of the Citarum River in 2013-2021. Concurrently, it was also found that the factory effluent far exceeded the permissible limit.

The DataHawk2 (DH2) is a small, fixed-wing, uncrewed aircraft system, or UAS, developed at the University of Colorado (CU) primarily for taking detailed thermodynamic measurements of the atmospheric boundary layer. The DH2 weighs 1.7 kg and has a wingspan of 1.3 m, with a flight endurance of approximately 60 min, depending on configuration. In the DH2's most modern form, the aircraft carries a Vaisala RSS-421 sensor for pressure, temperature, and relative humidity measurements, two CU-developed infrared temperature sensors, and a CU-developed fine-wire array, in addition to sensors required to support autopilot function (pitot tube with pressure sensor, GPS receiver, inertial measurement unit), from which wind speed and direction can also be estimated. This paper presents a description of the DH2, including information on its design and development work, and puts the DH2 into context with respect to other contemporary UASs. Data from recent field work (MOSAiC, the Multidisciplinary drifting Observatory for the Study of Arctic Climate) is presented and compared with radiosondes deployed during that campaign to provide an overview of sensor and system performance. These data show good agreement across pressure, temperature, and relative humidity as well as across wind speed and direction. Additional examples of measurements provided by the DH2 are given from a variety of previous campaigns in locations ranging from the continental United States to Japan and northern Alaska. Finally, a look toward future system improvements and upcoming research campaign participation is given.

The objective of the present study is to develop a low-cost hybrid composite using pineapple leaf fiber obtained from agro-waste as core and high modulus basalt fiber produced from volcanic rock as skin to analyze the impact of basalt fiber orientation (skin) on the tensile, flexural, and damping properties of the composite. Different composite samples were fabricated with PALF at 0º (aligned with the longitudinal direction) and with different basalt fiber orientations using the hand layup method. The findings showed that the laminate with PALF and basalt aligned in the longitudinal direction had the best flexural properties and storage modulus, while the sample with the basalt fiber oriented at 90° had the best tan delta (damping). The maximum variation in glass transition temperature due to change in fiber angle was noticed as ~3°C.

Abstract An elevated risk of myeloma precursor disease, monoclonal gammopathy of undetermined significance (MGUS), was identified among Fire Department of the City of New York (FDNY) World Trade Center (WTC)-exposed firefighters. Further investigation was needed to determine if these findings were reproducible in a more heterogeneous WTC-exposed rescue/recovery workers cohort, the Stony Brook University-General Responder Cohort GRC (SBU-GRC). MGUS risk was compared between the cohorts and to published general population estimates from Olmsted County, MN, USA. In this observational seroprevalence study, odds ratios (OR) and age-standardized risk ratios (RR) of MGUS (M-spike and light-chain-MGUS combined), M-spike, and light-chain-MGUS were estimated using logistic regression. Age-standardized prevalences were calculated for white males aged 50–79; RRs were estimated by comparing risk in the WTC-exposed cohort with the Olmsted County screened cohort. SBU-GRC had elevated odds of MGUS compared with FDNY (OR = 1.38; 95%CI = 1.00–1.89). The age-standardized prevalence of MGUS was 9.0/100 persons (95%CI = 7.5–10.6), over two-fold higher than the general population (RR = 2.08; 95%CI = 1.72–2.51); the age-standardized prevalence of light-chain-MGUS was 3.5-fold higher (RR = 3.54; 95%CI = 2.52–4.97). This study adds to mounting evidence supporting an association between WTC/environmental exposures and MGUS among rescue/recovery workers. Access to MGUS screenings for the entire WTC-exposed cohort could allow for treatment interventions that improve survival.

Historically, the occurrence of dam-break cases has been proven to cause significant loss of life and economical damage. Apart from the catastrophic nature of dam-breaks, the absence of a robust disaster prevention system exacerbates the disasters that occur. This study proposes an Early Warning System (EWS) to mitigate the impact of dam-break disasters. However, predicting the occurrence of such disasters is challenging, specifically in areas like Indonesia, where comprehensive data recording is lacking. While it may be difficult to predict the occurrence of a sunny day break, the storm-induced break is more predictable. Therefore, this study proposes a simple yet effective macro-based EWS for Earth-Filled Dam-Break Cases using a macro approach based on the Evacuation Clearance Time (ECT). By comparing the ECT value with the arrival time of the floods from the affected areas, additional evacuation time can be obtained, which will be used to determine the EWS. The proposed EWS for Cengklik Dam is given in three levels of warning indicated by the reservoir water level at +141.36 m, +141.40 m, and +141.45 m. With the proposed EWS, the results show that 100% of people are expected to reach the evacuation point safely. The case study shows that the proposed EWS can significantly reduce the risk impact of the dam-break events.

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Measurement(s) NetCDF files include full description of all variables Technology Type(s) Uncrewed Aircraft Systems

The Instabilities, Dynamics, and Energetics accompanying Atmospheric Layering (IDEAL) program was conceived to improve understanding of the dynamics of thin strongly stratified “sheet” and deeper weakly stratified “layer” (S&L) structures in the lower troposphere under strongly stable conditions. The field portion of the IDEAL program was conducted from 24 October to 15 November 2017 at Dugway Proving Ground, Utah, to target nighttime lower troposphere S&L conditions. It employed a synergistic combination of observations by multiple simultaneous DataHawk-2 (DH2) small unmanned aircraft systems (sUASs) and concurrent ground-based profiling by the NCAR Earth Observing Laboratory Integrated Sounding System (ISS) comprising a wind profiler radar and hourly high-resolution radiosonde soundings. DH2 measurement intervals as well as vertical (∼ 2–4 km) and horizontal (∼ 5–10 km) flight trajectories were chosen based on local high-resolution weather forecasting and guided by near-real-time ISS measurements. These flights combined simultaneous vertical and slant-path profiling, and/or horizontal racetrack sampling, spanning several hours before sunrise. High-spatial- and temporal-resolution data were downlinked in real time to enable near-real-time changes in DH2 flight paths based on observed flow features. The IDEAL field program performed 70 DH2 flights on 16 d, coordinated with 93 high-resolution radiosonde soundings. In this paper, raw and derived measurements from this campaign are outlined, and preliminary analyses are briefly described. This data set, along with “quick look” figures, is available for access by other researchers, as described herein.

This paper compares the results of direct runoff obtained by a numerical model of the shallow water equations (SWE) with the synthetic unit hydrograph (SUH) methods generally used in Indonesia. It is known that such SUH methods are still empirical, thus making it difficult for users to determine a representative method. Meanwhile, the hydrodynamic approach such as the SWE numerical model can achieve more accurate results in predicting the direct runoff compared to the SUH methods, because the model itself is derived mathematically based on the physical processes. The findings indicate that the direct runoff predicted using the SWE numerical model (HEC-RAS 5.0.7) produced more accurate results for the peak discharge with an average error of -0.07%, the peak time with an average error of 19.77%, and the shape of the hydrograph with the smallest Root Mean Square Error (RMSE) from all the cases tested. Therefore, it can be concluded that in the future the use of a hydrodynamic approach is highly recommended for the direct runoff prediction. This paper was also aimed to be used as a reference in the development of new, more accurate SUH method in the future.

Diversity, equity, inclusion, and representation in various sectors have garnered increasing attention in the past two decades, including healthcare. In this report we investigate representation of females and underrepresented minorities (URM) in the field of radiology and asses for significant growth trends in representation in residency training programs in the United States. De-identified trainee demographic information for active radiology trainees from 2016 to 2021 was queried using the Accreditation Council for Graduate Medical Education (ACGME), and new radiology trainees using the National Resident Matching Program (NRMP)'s Main Residency Match Data and Reports databooks. In 2021 females represented 26.7% of DR residency trainees and 22% of IR integrated trainees. In the same year URM trainees represented 11.3% of trainees and 8.7% of IR integrated trainees. From 2017 to 2021, diagnostic radiology had a compound average growth rate (CAGR) 1% (p <0.01) of female representation and 1.12% (p<0.01) of URM representation. This study quantifies female and underrepresented minority representation among radiology trainees for diagnostic radiology and radiology subspecialities, identifying modest uptrends in representation within both demographics. [ABSTRACT FROM AUTHOR]

Between 14 and 20 July 2018, small remotely piloted aircraft systems (RPASs) were deployed to the San Luis Valley of Colorado (USA) together with a variety of surface-based remote and in situ sensors as well as radiosonde systems as part of the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE). The observations from LAPSE-RATE were aimed at improving our understanding of boundary layer structure, cloud and aerosol properties, and surface–atmosphere exchange and provide detailed information to support model evaluation and improvement work. The current paper describes the observations obtained using four different types of RPASs deployed by the University of Colorado Boulder and Black Swift Technologies. These included the DataHawk2, the Talon and the TTwistor (University of Colorado), and the S1 (Black Swift Technologies). Together, these aircraft collected over 30 h of data throughout the northern half of the San Luis Valley, sampling altitudes between the surface and 914 m a.g.l. Data from these platforms are publicly available through the Zenodo archive and are co-located with other LAPSE-RATE data as part of the Zenodo LAPSE-RATE community (https://zenodo.org/communities/lapse-rate/, last access: 27 May 2021). The primary DOIs for these datasets are https://doi.org/10.5281/zenodo.3891620 (DataHawk2, de Boer et al., 2020a, e), https://doi.org/10.5281/zenodo.4096451 (Talon, de Boer et al., 2020d), https://doi.org/10.5281/zenodo.4110626 (TTwistor, de Boer et al., 2020b), and https://doi.org/10.5281/zenodo.3861831 (S1, Elston and Stachura, 2020).

This research simulated one-dimensional wave propagation by solving the shallow water equations using the Preissman implicit numerical scheme due to its ability to maintain simplicity and stability at a larger time step value. This numerical model was fundamentally developed to satisfy the shallow water condition, where the water depth or horizontal-length scale is much smaller than the free-surface disturbance wavelength or vertical-length scale, and to comprehensively test the accuracy of the model. Consequently, three different types of waves were considered and these include (1) tidal, (2) roll, and (3) solitary. In the first case, the model was proven to be robust and accurate due to its relatively-small errors for both water-surface elevation and velocity indicating that the Preismann scheme is suitable for longwave simulations. In the second case, it was fairly accurate in capturing the periodic permanent roll waves despite showing a higher water-surface elevation than the one observed and this discrepancy is due to the neglect of the turbulent Reynold stress in the model. Meanwhile, the last case showed remarkable discrepancies in the water-surface elevation because the dispersion effect is quite significant during the wave propagation. This indicates that the Preismann scheme underestimated the wave crest along with time when the dispersion term was neglected. All simulations were performed using the tridiagonal matrix algorithm, thereby eliminating the need for iterations for the solution of the Preismann scheme. The findings of this study are beneficial to the next generation of the Preissmann-scheme models which can be designed to include turbulence and dispersion terms.

Water quality modeling is highly needed to predict and to identify the water quality alteration and distribution along the water body. Besides that, water quality modeling also can be used to predict the pollutant load if there is no data recording. The water quality analysis simulation program (WASP), will be used in this study to forecast domestic load, in particular BOD and TP loads in Duriangkang Dam. Initially, the estimated load is obtained based on the previous study that being extended by using a linear regression line. After that, the estimated load is calibrated according to the results of the water test. The WASP model is simulated for a period of 3 years from 1 January 2016 to 31 December 2018 and is divided into 2 processes, namely calibration and verification process. The calibration process is carried out in the first two years, followed by the verification process for the remainder of the year. The calibration and verification process are measured by objective functions, namely RMSE and the percentage difference. According to the simulation results, the objective functions provide satisfying findings, which means that the predicted BOD and TP loads for Duriangkang Dam during that time are accurate.

Rainfall is a major water resource with a significant role in terms of growth, environment concerns, and sustainability. Several human activities demand adequate water supply for drinking, agriculture, domestic, and commercial consumption. The accuracy of any hydrologic study depends heavily on the availability of good-quality precipitation estimates. Most countries are unable to provide sufficient climatic data, including rainfall and observed discharge statistics. This scarcity is a huge obstacle in conducting thorough hydrologic studies over a certain period. For instance, Indonesia, as an archipelagic country, has long been faced with data availability problems. For this reason, Tropical Rainfall Measuring Mission (TRMM), which was developed by NASA, became an alternative solution to rainfall data limitations. However, to be applied in hydrologic investigations, TRMM data require proper estimation and adjustment. The aim of this study was to evaluate the quality of TRMM rainfall data and its application in determining design flood and water availability. Dividing the data into several groups based on its magnitude and multiplying each unit with a correction coefficient are parts of the modification process. Subsequently, objective functions, including false alarm ratio (FAR), probability of detection (POD), and root mean square error (RMSE) were also applied. Rainfall-runoff modeling and design storm analysis at Delingan dam were used to study the TRMM correction performance. Based on the analysis, corrected TRMM showed considerable findings compared to ground station data. Model calibration and verification using corrected TRMM data provide satisfactory model parameters compared to ground station derivatives. The results also disclosed a closer fit of the corrected TRMM to catchment response translated from derived rainfall-runoff model parameters to ground station compared to control. Furthermore, design storm calculated from corrected TRMM reflects an improvement compared to uncorrected TRMM data.

One of the challenges in hydrologic modelling in Indonesia is data limitation. Rainfall data quality is rarely evaluated, and in some cases, the data are unavailable. The Tropical Rainfall Measuring Mission (TRMM), satellite rainfall data provided by NASA, is an alternative method to solve such problems. This study aims to promote the use of TRMM data to analyze water availability and flood discharge in Duriangkang Dam, Batam City, Indonesia, in comparison with the use of available ground station data. Results show that the ground station data contain some errors; however, overall, the data show similar patterns and acceptable differences compared with the TRMM data. The NRECA and HEC-HMS models are used to analyze water availability, and both models are calibrated using the available reservoir water level data. The NRECA model generally shows a good fit of monthly discharge, although the use of TRMM results in slightly overestimated values in dry years. Similar results are obtained for daily discharge computation using the HEC-HMS model. Water availability analysis using the TRMM data shows an acceptable margin of error. When flood discharge is computed using an uncalibrated HEC-HMS model, the TRMM data somehow yield a lower maximum daily rainfall value than the ground station data. As a result, the obtained 10,000-year flood calculated using the Hang Nadim Station and TRMM data are 1,086 and 624 m3/s, respectively. Therefore, the use of corrected TRMM data in flood discharge computation is essential but increases the value up to 897 m3/s.

Our patient Mr. A is a mentally and physically disabled gentleman. He was first diagnosed with bipolar disorder as a teenager. He incurred a lumbar spinal injury due to a motor vehicle incident in his 20s which led to weakness, numbness, and frequent infection over both of his lower extremities. He also developed alcohol addiction over the course of his life. Mr. A presented to our facility with complicated neuropsychiatric symptoms. By adopting various clinical strategies, we were able to control his symptoms of agitation, self-harm, mood swings, and stereotyped behavior. However, we were not able to improve his neurocognitive functioning or speech impairment which seemed to become severe and irreversible in a period of a few months. We felt disappointed and perplexed by the mixed treatment responses. To understand Mr. A’s clinical presentation, various laboratory tests and imaging studies were performed. Different psychotropic medications were used to manage his symptoms. Gradually, we felt that we were able to understand this case better clinically and etiologically. His bipolar disorder, alcohol addiction, and physical injury had likely all contributed to his neuropsychiatric symptoms, directly or indirectly. It is highly possible that an alcohol-related progressive dementia along with his chronic bipolar disorder played a key role in the progression of his brain neurodegeneration. Also, Wernicke-Korsakoff syndrome could reasonably be considered having developed during his clinical course. Moreover, the fluctuation of the patient’s neuropsychiatric symptoms we observed during his hospitalization reflects the increased vulnerability of the human brain under sustained neurodegeneration.

Utility distribution networks, crucial commercial operations, and critical industrial loads can suffer considerable financial losses as a result of a variety of service interruptions and outages. In developing countries like India, where alterations in power frequency and numerous other factors affecting power quality are a worry, it is crucial to take appropriate action to make power networks sustainable. To control and reduce voltage disturbances, a variety of techniques are available. For their benefits, including suitable output waveforms, inverters with multilevel outputs, such as multilevel D-STATCOMs, are frequently utilized in the power system and industry (voltage and current). Multilevel D-STATCOM control can be accomplished with several controllers. A closed loop controller is one of the controllers that use a feedback system to automatically control a system that ensures a desirable state or accepted value. Among the improvements are voltage sags and swells. The performance of the suggested controller is explained by contrasting it with both multilevel D-STATCOM and traditional PWM controls. In addition to its simplicity, robustness, and flexibility, the proposed method allows for all defects to be compensated.