Hydrochemical characterization and assessment of health risks of trace elements in the Huai River Basin of China.

Basin water pollution is a global problem, especially in the densely populated areas. The Huai River Basin (abbreviated as HRB), including the Huai River system and the Yishu River system, is the sixth-largest and most densely populated watershed in China. However, there is a lack of comprehensive studies of river and well water throughout the Huai River basin, including hydrochemistry characterization and assessment of health risks. This study investigated water quality and pollution sources of river and well water in the HRB based on the hydrochemistry analysis and different water quality indices. The water body in the HRB showed weak alkalinity (pH = 8.4 ± 0.7) and had high TDS values (TDS = 339 ± 186 mg/L) with water types of HCO3-Ca-Mg and SO4-Cl-Ca-Mg in the Huai River system, and SO4-Cl-Ca-Mg in the Yishu River system. Atmospheric input and evaporation had less impact on hydrochemistry. Evaporite dissolution and carbonate weathering had a greater impact on hydrochemistry. Carbonate precipitation and cation exchange also influenced the dissolved solutes, especially Ca2+ and Na+. Samples had low to medium salinity hazards and sodium absorption ratios and were suitable for irrigation. For drinking purposes, samples were fresh water and have good or excellent according to the Water Quality Index (WQI). Land use types influenced water quality with the worst river water quality from cropland. Combining different assessment indices, the water quality of the Yishu River system performed better than the Huai River system. Absolute principal component analysis-multiple linear regression and the positive matrix factorization models identified the main pollutants as As, Ba, Cr, Ni, and Mn, with natural sources of As, Ba, and Ni and anthropogenic inputs of Cr, and Mn. Although the water quality of the HRB has improved in recent years, high potential risk from As, Cr, Mn, Ba, and Ni should be noted. This study provided vital information for basin hydrochemistry analysis and water quality assessment.

Health risk assessment and potential sources of metals in riparian soils of the Wujiang River, China.

In order to understand the pollution status of metals in the riparian soils along the Wujiang River, 26 sampling sites in the mainstream and tributary streams were selected for investigation. The geo-accumulation index (Igeo), Nemerow integrated pollution index, and potential ecological risk index were applied to evaluate the contamination status and ecological risks of metals. Results revealed that the average concentrations of As, Cd, Cr, Cu, Mn, Ni, Pb, and Zn were 12.20, 0.51, 84.01, 57.42, 922.57, 38.37, 38.06, and 127.82 mg/kg, respectively. The metal contamination degree and ecological risks in the upper reaches were significantly higher than those in the middle and lower reaches of the Wujiang River. Cd was the dominant contamination metal. Significant non-carcinogenic and carcinogenic risks of metals were found in children based on the hazard index and carcinogenic risk. As was the main non-carcinogenic and carcinogenic pollutant metal in both adults and children. According to principal component analysis, hierarchical clustering analysis, and absolute principal component scores-multiple linear regression, anthropogenic sources (mining and agricultural activities) contributed most to Zn, Pb, Cr, Cd, Cu, and Ni, with contribution rates of 89.14, 82.32, 74.46, 72.12, 68.52, and 61.02%, respectively. Natural sources contributed most to Mn, with a contribution rate of 83.07%. Unidentified sources contributed most to As, with a contribution rate of 47.27%.

Spatial distribution and ecological risk assessment of short and medium chain chlorinated paraffins in water and sediments of river Ravi, Pakistan.

Short (SCCPs) and medium (MCCPs) chain chlorinated paraffins being the emerging organic pollutants have raised serious concerns due to their widespread use and related human health risks. However, their occurrence in aquatic bodies like rivers and associated damage to ecological integrity is yet unknown in some regions of the world. The current study is the first ever assessment of SCCPs and MCCPs in sediment and water of river Ravi, Pakistan. Spatial occurrence and associated ecological risks were investigated from sediments (n = 16) and composite water samples (n = 8) collected at eight locations along the stretch of river Ravi. The concentrations of SCCPs and MCCPs varied from below limit of detection (

RPCA-based techniques for pattern extraction, hotspot identification and signal correction using data from a dense network of low-cost NO2 sensors in London.

High-density low-cost air quality sensor networks are a promising technology to monitor air quality at high temporal and spatial resolution. However the collected data is high-dimensional and it is not always clear how to best leverage this information, particularly given the lower data quality coming from the sensors. Here we report on the use of robust Principal Component Analysis (RPCA) using nitrogen dioxide data obtained from a recently deployed dense network of 225 air pollution monitoring nodes based on low-cost sensors in the Borough of Camden in London. RPCA addresses the brittleness of singular value decomposition towards outliers by using a decomposition of the data into low-rank and sparse contributions, with the latter containing outliers. The modal decomposition enabled by RPCA identifies major periodic patterns including spatial and temporal bias, dominant spatial variance, and north-south bias. The five most descriptive components capture 98 % of the data's variance, achieving a compression by a factor of 1500. We present a new technique that uses the sparse part of the data to identify hotspots. The data indicates that at the locations of the top 15 % most susceptible nodes in the network, the model identifies 23 % more hotspots than in all other locations combined. Moreover, the median hotspot event at these at-risk locations exceeds the mean NO2concentration by 33µg/m3. We show the potential of RPCA for signal correction; it corrects random errors yielding a reference signal with R2>0.8. Moreover, RPCA successfully reconstructs missing data from a sensor with R2=0.72 from the rest of the sensor network, an improvement upon PCA of around 50 %, allowing air quality estimations even if a sensor is out of use temporarily.

Assessing the Negative Binomial-Lindley model for crash hotspot identification: Insights from Monte Carlo simulation analysis.

Identifying hazardous crash sites (or hotspots) is a crucial step in highway safety management. The Negative Binomial (NB) model is the most common model used in safety analyses and evaluations - including hotspot identification. The NB model, however, is not without limitations. In fact, this model does not perform well when data are highly dispersed, include excess zero observations, or have a long tail. Recently, the Negative Binomial-Lindley (NB-L) model has been proposed as an alternative to the NB. The NB-L model overcomes several limitations related to the NB, such as addressing the issue of excess zero observations in highly dispersed data. However, it is not clear how the NB-L model performs regarding the hotspot identification. In this paper, an innovative Monte Carlo simulation protocol was designed to generate a wide range of simulated data characterized by different means, dispersions, and percentage of zeros. Next, the NB-L model was written as a Full-Bayes hierarchical model and compared with the Full-Bayes NB model for hotspot identification using extensive simulation scenarios. Most previous studies focused on statistical fit, and showed that the NB-L model fits the data better than the NB. In this research, however, we investigated the performance of the NB-L model in identifying the hazardous sites. We showed that there is a trade-off between the NB-L and NB when it comes to hotspot identification. Multiple performance metrics were used for the assessment. Among those, the results show that the NB-L model provides a better specificity in identifying hotspots, while the NB model provides a better sensitivity, especially for highly dispersed data. In other words, while the NB model performs better in identifying hazardous sites, the NB-L model performs better, when budget is limited, by not selecting non-hazardous sites as hazardous.

[Overview of the Application of Machine Learning for Identification and Environmental Risk Assessment of Microplastics].

Microplastics are an emerging contaminant that can persist in the environment for extended periods， posing risks to ecological systems. Recently， microplastic pollution has emerged as a major global environmental problem. In order to ensure accurate and scientific evaluation of the ecological risks associated with microplastic pollution， it is of paramount importance to improve the simplicity and reliability of microplastic identification， systematically analyze the pollution characteristics of microplastics in various environmental media， and clarify their environmental impacts. Machine learning technology has gained widespread attention in microplastic research by learning and analyzing large volumes of data to establish result evaluation or prediction models. The use of machine learning can enhance the automation and identification efficiency of visual and spectral identification of microplastics， provide scientific support for tracing the sources of microplastic pollution， and help reveal the complex environmental effects of microplastics. This review provides a summary of the application characteristics and limitations of machine learning in the aforementioned areas by reviewing the progress made in research that employs machine learning technology in microplastic identification and environmental risk assessment. Furthermore， the findings of the review will provide suggestions and prospects for the development and application of machine learning in related areas.

Genome-based taxonomic identification and safety assessment of an Enterococcus strain isolated from a homemade dairy product.

The aim of this study was to explore the taxonomic identification and evaluate the safety of a bacterium, Enterococcus lactis IDCC 2105, isolated from homemade cheese in Korea, using whole genome sequence (WGS) analysis. It sought to identify the species level of this Enterococcus spp., assess its antibiotic resistance, and evaluate its virulence potential. WGS analysis confirmed the bacterial strain IDCC 2105 as E. lactis and identified genes responsible for resistance to erythromycin and clindamycin, specifically msrC, and eatAv, which are chromosomally located, indicating a minimal risk for horizontal gene transfer. The absence of plasmids in E. lactis IDCC 2105 further diminishes the likelihood of resistance gene dissemination. Additionally, our investigation into seven virulence factors, including hemolysis, platelet aggregation, biofilm formation, hyaluronidase, gelatinase, ammonia production, and ß-glucuronidase activity, revealed no detectable virulence traits. Although bioinformatic analysis suggested the presence of collagen adhesion genes acm and scm, these were not corroborated by phenotypic virulence assays. Based on these findings, E. lactis IDCC 2105 presents as a safe strain for potential applications, contributing valuable information on its taxonomy, antibiotic resistance profile, and lack of virulence factors, supporting its use in food products.

An IoT-based low-cost architecture for smart libraries using SDN.

In the evolving landscape of smart libraries, this research pioneers an IoT-based low-cost architecture utilizing Software-Defined Networking (SDN). The increasing demand for more efficient and economical solutions in library management, particularly in the realm of RFID-based processes such as authentication, property circulation, and book loans, underscores the significance of this study. Leveraging the collaborative potential of IoT and SDN technologies, our proposed system introduces a fresh perspective to tackle these challenges and advance intelligent library management. In response to the evolving landscape of smart libraries, our research presents an Internet of Things (IoT)-based low-cost architecture utilizing SDN. The exploration of this architectural paradigm arises from a recognized gap in the existing literature, pointing towards the necessity for more efficient and cost-effective solutions in managing library processes. Our proposed algorithm integrates IoT and SDN technologies to intelligently oversee various library activities, specifically targeting RFID-based processes such as authentication, property circulation management, and book loan management. The system's architecture, encompasses components like the data center, SDN controllers, RFID tags, tag readers, and other network sensors. By leveraging the synergy between RFID and SDN, our innovative approach reduces the need for constant operator supervision in libraries. The scalability and software-oriented nature of the architecture cater to extensive library environments. Our study includes a two-phase investigation, combining practical implementation in a small-scale library with a simulation environment using MATLAB 2021. This research not only fills a crucial gap in current knowledge but also lays the foundation for future advancements in the integration of IoT and SDN technologies for intelligent library management.

Molecular Identification and Phytochemical Analysis and Bioactivity Assessment of Catharanthus roseus Leaf Extract: Exploring Antioxidant Potential and Antimicrobial Activities.

Plants have long been at the main focus of the medical industry's attention due to their extensive list of biological and therapeutic properties and ethnobotanical applications. Catharanthus roseus, sometimes referred to as Nithyakalyani in Tamil, is an Apocynaceae family member used in traditional Indian medicine. It also examines the plant's potential antimicrobial and antioxidant activities as well as its preliminary phytochemical makeup. Leaf material from C. roseus was analyzed and found to include a variety of phytochemicals including alkaloids, terpenoids, flavonoids, tannins, phenols, saponins, glycosides, quinones, and steroids. Four of the seven secondary metabolic products discovered in C. roseus leaves showed bioactive principles: 3-methylmannoside, squalene, pentatriacontane, and 2,4,4-trimethyl-3-hydroxymethyl-5a-(3-methyl-but-2-enyl)-cyclohexene. Catharanthus roseus is rich in the anticancer compounds vinblastine and vincristine. Whole DNA was isolated from fresh leaves, then amplified, sequenced, and aligned to find prospective DNA barcode candidates. One DNA marker revealed the restricted genetic relationship among C. roseus based on genetic distance and phylogenetic analysis. The antioxidant activity of the plant extract was evaluated using the DPPH, ABTS, phosphomolybdenum, FRAP, and superoxide radical scavenging activity assays, while the antibacterial potential was evaluated using the agar well diffusion assay. The ethanol extract of C. roseus was found to have the highest reducing power. In addition, a 4- to 21-mm-wide zone of inhibition was seen when the C. roseus extract was tested against bacterial and fungal stains. In conclusion, C. roseus has the most promise as an antibacterial and antioxidant agent.

Assessment and solutions for vulnerability of urban rail transit network based on complex network theory: A case study of Chongqing.

As a typical complex network system, the operating environment of rail transit network (RTN) is complex and demanding. This study aims to accurate assess the weaknesses and vulnerability of RTN, which is crucial for ensuring its smooth operation. Taking Chongqing Rail Transit (CRT) as an example, this study developed a network topology model using the spatial L method and analyzed the network structure characteristics, along with the importance of key nodes under different indicators, based on complex network theory. Additionally, this study analyzed the geographical spatial distribution characteristics of nodes based on the topography and urban spatial structure of Chongqing. Then, this study classified the nodes in the RTN according to basic topological indicators, namely degree, betweenness centrality, network efficiency, and passenger flow volume (PFV). The results indicated six cluster of nodes, reflecting the variability in node vulnerability concerning overall influence (providing alternative paths, reducing path length), regional aggregation capacity, and transportation capacity. Finally, this study proposed targeted management strategies for different clusters of nodes and their respective geographical locations, providing necessary references for rational planning, safety protection, and sustainable construction of RTN.

Identification and quantification techniques of polymorphic forms - A review.

In the pharmaceutical industry, the unexpected appearance of crystalline forms could impact the therapeutic efficacy of an Active Pharmaceutical Ingredient (API). For quality control, a thorough qualitative and quantitative monitoring of pharmaceutical solid forms is essential to ensure the detection and the quantification of crystalline forms, wither different or with the same chemical composition (polymorphs) at a low detection level. The purpose of this paper was to review and highlight the importance of choosing adequate solid-state techniques for detection and quantification APIs that present polymorphism - based on limits of detection (LOD) and quantification (LOQ), pharmacopeias specifications, international guidelines and studies reported in the literature. To this study, the powder X-ray diffraction (PXRD), differential scanning calorimetry (DSC), Infrared and Raman spectroscopies and solid-state nuclear magnetic resonance (NMR) were the solid-state techniques analyzed. Additionally, the Argentine, Brazilian, British, European, International, Japanese, Mexican and the United States of America pharmacopeias were reviewed. Based on the analysis performed, the advantages and disadvantages of these techniques, as well as the LOD and LOQ values of APIs were reported. In comparison to these solid-state techniques, reference material used for identification analyses should be previously identified with the corresponding polymorph. Without this previous procedure, the patterns, the spectra, and DSC curves of the reference material can only be used to confirm the mixture of solid forms, not being able to specify which polymorphs are contained in the sample. A major advantage of PXRD is the use of the calculated diffraction patterns obtained from the Crystallographic Information Frameworks (CIFs) files which could be used as a reference pattern without any other information, assistance technique, or physical standards. Regarding the quantification aspect, different pharmacopeias suggest various methods such as the PXRD combining with Rietveld method, which can be used to obtain lower LOD values for minority phases in the mixture of different substances without the need for a calibration curve. Raman spectroscopy can detect polymorphs in small particles and solid-state NMR spectroscopy is a powerful technique for quantification not only crystalline but also crystalline-amorphous mixtures. Finally, this review intends to be a useful tool to control, with efficiency and accuracy, the polymorphism of APIs in pharmaceutical compounds.

Sources of hydrocarbons and their risk assessment in seawater and sediment samples collected from the Nile Delta coast of the Mediterranean Sea.

The aim of this work is to examine the levels, distribution, bases, and hazards of n-alkanes (n-C9 to n-C20) and PAHs in the seawater and sediments around oil production locations in the whole delta region. The variations in the levels of PAHs and n-alkanes in seawater and sediment of the Nile delta coast of the Mediterranean were investigated using GC-MS/MS. The Σn-alkanes residues ranged between 12.05 and 93.51 mg/L (mean: 50.45 ± 17.49 mg/L) and 4.70 to 84.03 µg/g (mean: 31.02 ± 27.995 µg/g) in seawater and sediments, respectively. Total PAHs concentrations ranged between 4.485 and 16.337 µg/L (average: 9.47 ± 3.69 µg/L) and 1.32 to 28.38 ng/g (average 8.61 ± 7.57 ng/g) in seawater and sediment samples, respectively. The CPI (carbon preference index) values fluctuated between 0.62 and 1.72 (seawater) and from 0.234 to 2.175 (sediment), proposing the variation sources of n-alkane in the studied area. PAHs concentrations were lower than the Effective Range Low (ERL) and Effective Range Median (ERM) levels. The Toxic Equivalent Quotient (TEQ) values oscillated between 0.002 and 6.84 ng/L and from 3.72 to 13.48 ng/g for the seawater and sediment samples, respectively. The Ant/(Ant + Phe) ratio in sediment and seawater samples indicated a pyrolytic source while the BaA/(BaA + Chry) ratio indicates petrogenic sources in most of the studied stations.

Metals and phosphorus in the sediments of streams emptying into the Çanakkale strait (Dardanelles): Spatial distribution, pollution status, risk assessment and source identification.

The Çanakkale Strait is exposed to various pollutants due to its strategic location. It is thought that stream inputs may contribute significantly to metal and phosphorus (P) accumulation in the strait. In this study, the spatial distribution, pollution status, ecological risks and possible sources of twelve metals and P in the sediments of seven important streams emptying into the strait were analyzed. The results showed that Zn (226 mg/kg), Ba (67.2 mg/kg) and Pb (10.4 mg/kg) concentrations were higher in the Umurbey Stream due to mining activities, while P concentration (295 mg/kg) was higher in the Çanakkale Stream due to both agricultural activities and domestic wastewater discharges. Modified hazard quotient (mHQ), enrichment factor (EF) and contamination factor (Cf) values revealed that Zn and Pb showed high and moderate contamination in the US3 and US4 sampling sites of the Umurbey Stream, respectively. Similarly, P showed moderate contamination in the ÇS3 site of the Çanakkale Stream. Nemerow pollution index (NPI) showed that the US3 (2.41) and US4 (4.28) sites of the Umurbey Stream were slightly and moderately polluted, respectively. Toxic risk index (TRI) values demonstrated that the sediments in only the US4 site (5.17) of the Umurbey Stream may pose a low toxic risk due to high Zn content. Similarly, based on comparison results with sediment quality guidelines (SQGs), it was found that high Zn content may lead to adverse effects on sediment-dwelling organisms in the US4 site. In addition, the PEC-quotient value in the US4 site exceeded 0.5, confirming the finding that the sediments in this site could be toxic to benthic organisms. Finally, correlation, cluster and factor analyzes were used to determine possible sources of elements. Mining activities, natural sources and mixed sources (agricultural activities and natural sources) were identified as the main sources of elements in the sediments of the streams. This study can provide an important reference for evaluating stream sediment pollution and managing marine pollution.

Riparian Soil Pollution Caused by Sediment Metal Transport: Seasonal Changes and Ecological Risk Assessment.

The accumulation of pollutants in the sediment along surface water may negatively affect riparian zones and increase ecological risk. This article investigates the effects of metal sediments on riparian soil via field monitoring and ICP-OES analysis. To this end, pollution levels, seasonal changes, and potential sources of the pollutants were determined for the Melen River watershed, Turkey. The ecological statuses (contamination factor, enrichment factor, index of geo-accumulation, pollution index, modified pollution index, and potential and modified ecological risk indexes) of the watershed were also analyzed. Although no significant seasonal differences in the metal sediments were observed, their spatial distribution in the sediments and riparian soils varied markedly. Cr (11.4 to 136), Co (7.7 to 21.52), Cu (11.4 to 76.6), and Ni (14.06 to 128.2) recorded as mg/kg significantly increased from the upstream to the downstream. The metals possessing the highest risk in the sediment and riparian soil regarding the river health were Cu, Co, and Ni. The risk values were found to be heavily polluted (PI > 3 and MPI > 10), and the risk indexes were above the "desired environment without the risk". The risk index was found to be more than 50, and the modified risk indexes exceeded 200 at many points. The transportation of pollutants in surface water became evident in the sediment, resulting in adverse effects on the riparian zone and the ecological system.

Assessment of the genetic diversity of lymnaeid (Gastropoda: Pulmonata) snails and their infection status with trematode cercariae in different regions of Thailand.

Lymnaeid snails are some of the most widespread snails and are the first intermediate host of trematode parasites that affect human and livestock health. A full understanding of the genetic relationship of hosts and parasites is of paramount importance for effective parasite management. The present study assessed the prevalence of trematode larvae in lymnaeid snails and examined the genetic diversity of these snails collected across Thailand. We collected 672 lymnaeid snails from 39 locations in 22 provinces of six regions in Thailand. Subsequently, cercarial infection in the snails was observed by using the shedding method. Lymnaeid snails released 5 types of trematode cercariae, namely, xiphidiocercariae, echinostome cercariae I, echinostome cercariae II, furcocercous cercariae, and strigea cercariae. The phylogenetic analysis based on ITS2 and 28S rDNA sequences revealed 5 cercaria types assigned to four trematode families, of which two belong to the group of human intestinal flukes. Combination of shell morphology and sequence analysis of the mitochondrial COI and 16S rDNA genes, the lymnaeid snails were classified into two species, Radix rubiginosa and Orientogalba viridis. Moreover, the combined dataset of mtDNA genes (COI + 16S rDNA) from R. rubiginosa and O. viridis revealed 32 and 15 different haplotypes, respectively, of which only a few haplotypes were infected with cercariae. The genetic diversity and genetic structure revealed that R. rubiginosa and O. viridis experienced a bottleneck phenomenon, and showed limited gene flow between populations. Population demographic history analyses revealed that R. rubiginosa and O. viridis experienced population reductions followed by recent population expansion. These findings may improve our understanding of parasite-lymnaeid evolutionary relationships, as well as the underlying molecular genetic basis, which is information that can be used for further effective control of the spread of trematode disease.

Harnessing the power of regional baselines for broad-scale genetic stock identification: A multistage, integrated, and cost-effective approach.

In mixed-stock fishery analyses, genetic stock identification (GSI) estimates the contribution of each population to a mixture and is typically conducted at a regional scale using genetic baselines specific to the stocks expected in that region. Often these regional baselines cannot be combined to produce broader geographical baselines due to non-overlapping populations and genetic markers. In cases where the mixture contains stocks spanning across a wide area, a broad-scale baseline is created, but often at the cost of resolution. Here, we introduce a new GSI method to harness the resolution capabilities of baselines developed for regional applications in the analysis of mixtures containing individuals from a broad geographic range. This method employs a multistage framework that allows disparate baselines to be used in a single integrated process that produces estimates along with the propagated errors from each stage. All individuals in the mixture sample are required to be genotyped for all genetic markers in the baselines used by this model, but the baselines do not require overlap in genetic markers or populations representing the broad-scale or regional baselines. We demonstrate the utility of our integrated multistage model using a synthesized data set made up of Chinook salmon, Oncorhynchus tshawytscha, from the North Bering Sea of Alaska. The results show an improved accuracy for estimates using an integrated multistage framework, compared to the conventional framework of using separate hierarchical steps. The integrated multistage framework allows GSI of a wide geographic area without first developing a large scale, high-resolution genetic baseline or dividing a mixture sample into smaller regions beforehand. This approach is more cost-effective than updating range-wide baselines with all regionally important markers.

Seismic assessment of bridges through structural health monitoring: a state-of-the-art review.

The present work offers a comprehensive overview of methods related to condition assessment of bridges through Structural Health Monitoring (SHM) procedures, with a particular interest on aspects of seismic assessment. Established techniques pertaining to different levels of the SHM hierarchy, reflecting increasing detail and complexity, are first outlined. A significant portion of this review work is then devoted to the overview of computational intelligence schemes across various aspects of bridge condition assessment, including sensor placement and health tracking. The paper concludes with illustrative examples of two long-span suspension bridges, in which several instrumentation aspects and assessments of seismic response issues are discussed.

Short-term Gini coefficient estimation using nonlinear autoregressive multilayer perceptron model.

Poverty, an intricate global challenge influenced by economic, political, and social elements, is characterized by a deficiency in crucial resources, necessitating collective efforts towards its mitigation as embodied in the United Nations' Sustainable Development Goals. The Gini coefficient is a statistical instrument used by nations to measure income inequality, economic status, and social disparity, as escalated income inequality often parallels high poverty rates. Despite its standard annual computation, impeded by logistical hurdles and the gradual transformation of income inequality, we suggest that short-term forecasting of the Gini coefficient could offer instantaneous comprehension of shifts in income inequality during swift transitions, such as variances due to seasonal employment patterns in the expanding gig economy. System Identification (SI), a methodology utilized in domains like engineering and mathematical modeling to construct or refine dynamic system models from captured data, relies significantly on the Nonlinear Auto-Regressive (NAR) model due to its reliability and capability of integrating nonlinear functions, complemented by contemporary machine learning strategies and computational algorithms to approximate complex system dynamics to address these limitations. In this study, we introduce a NAR Multi-Layer Perceptron (MLP) approach for brief term estimation of the Gini coefficient. Several parameters were tested to discover the optimal model for Malaysia's Gini coefficient within 1987-2015, namely the output lag space, hidden units, and initial random seeds. The One-Step-Ahead (OSA), residual correlation, and residual histograms were used to test the validity of the model. The results demonstrate the model's efficacy over a 28-year period with superior model fit (MSE: 1.14 × 10-7) and uncorrelated residuals, thereby substantiating the model's validity and usefulness for predicting short-term variations in much smaller time steps compared to traditional manual approaches.

Topic identification, selection, and prioritization for health technology assessment in selected countries: a mixed study design.

BACKGROUND: There is limited evidence-informed guidance on TISP processes for countries where health technology assessment (HTA) is in a nascent phase. We aimed to explore the range of topic identification, selection and prioritization (TISP) processes and practices for HTA in selected countries and identify aspects relevant to emerging HTA systems. METHODS: This mixed design study included a systematic literature review, an electronic survey, and individual interviews. We conducted a systematic literature review with criteria that were developed a priori to identify countries deemed to have a recently formalized HTA system. Based on the literature review, a twenty-three item online survey was shared with the identified countries, we completed follow-up interviews with ten participants who have experience with HTA. We analyzed documents, survey responses and interview transcripts thematically to identify lessons related to TISP processes and practices. RESULTS: The literature review identified 29 nine candidate countries as having a "potential" recently formalized HTA system. Twenty-one survey responses were analyzed and supplemented with ten individual interviews. We found variation in countries' approaches to TISP - particularly between pharmaceutical and non-pharmaceutical interventions. Results indicate that TISP is heavily driven by policy makers, expert involvement, and to a lesser extent, relevant stakeholders. The use of horizon-scanning and early warning systems is uncommon. Interviewee participants provided further insight to the survey data, reporting that political awareness and an institutional framework were important to support TISP. TISP can be optimized by stronger national regulations and legislative structures, in addition to education and advocacy about HTA among politicians and decision-makers. In some settings regional networks have been useful, particularly in the development of TISP guidelines and methodologies. Additionally, the technical capacity to conduct TISP, and access to relevant local data were factors limiting TISP in national settings. Increased network collaboration and capacity building were reported as future needs. CONCLUSIONS: This study provides current insights into a topic where there is limited published peer reviewed literature. TISP is an important first step of HTA, and topics should be selected and prioritized based on local need and relevance. The limited capacity for TISP in settings where HTA is emerging may be supported by local and international collaboration to increase capacity and knowledge. To succeed, both TISP and HTA need to be embedded within national health care priority setting and decision-making. More in-depth understanding of where countries are situtated in formalizing the TISP process may help others to overcome factors that facilitate or hinder progress.

Reference-Free Vibration-Based Damage Identification Techniques for Bridge Structural Health Monitoring-A Critical Review and Perspective.

Bridges are designed and built to be safe against failure and perform satisfactorily over their service life. Bridge structural health monitoring (BSHM) systems are therefore essential to ensure the safety and serviceability of such critical transportation infrastructure. Identification of structural damage at the earliest time possible is a major goal of BSHM processes. Among many developed damage identification techniques (DITs), vibration-based techniques have shown great potential to be implemented in BSHM systems. In a vibration-based DIT, the response of a bridge is measured and analyzed in either time or space domain for the purpose of detecting damage-induced changes in the extracted dynamic properties of the bridge. This approach usually requires a comparison between two structural states of the bridge-the current state and a reference (intact/undamaged) state. In most in-situ cases, however, data on the bridge structural response in the reference state are not available. Therefore, researchers have been recently working on the development of DITs that eliminate the need for a prior knowledge of the reference state. This paper thoroughly explains why and how the reference state can be excluded from the damage identification process. It then reviews the state-of-the-art reference-free vibration-based DITs and summarizes their merits and shortcomings to give guidance on their applicability to BSHM systems. Finally, some recommendations are given for further research.