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Accurate ultra-short-term prediction of UT1-UTC is crucial for real-time applications in high-precision reference frame conversions. Presently, traditional LS + AR and LS + MAR hybrid methods are commonly employed for UT1-UTC prediction. However, inherent unmodeled errors in fitting residuals of these methods often compromise the prediction performance. Thus, mitigating these common unmodeled errors presents an opportunity to enhance UT1-UTC prediction performance. Consequently, we propose a novel hybrid difference method for UT1-UTC ultra-short-term prediction by integrating LOD prediction and the prediction of the sum of the LOD and the first-order-difference UT1-UTC. The evaluation demonstrated promising results: (1) The mean absolute errors (MAEs) of the proposed method range from 21 to 869 µs in 1–10-day UT1-UTC predictions. (2) Comparative analysis against zero-/first-/second-order-difference LS + AR and zero-/first-order-difference LS + MAR hybrid method reveals a substantial reduction in MAEs by an average of 54/64/44 µs, and 47/20 µs, respectively, with the proposed method. (3) Correspondingly, the proposed method achieves average improvement percentages of 17%/18%/15%, and 13%/3% in 1–10-day UT1-UTC predictions. [ABSTRACT FROM AUTHOR]

In South Korea, to systematically share and operate data related to the national level of the digital twin, a reference model standard for the National Digital Twin (NDT) concept was established in 2021. This was followed by the Korean Industrial Standards (KS) for buildings. Despite these efforts, the implementation of digital twin projects faces challenges owing to a low level of understanding of the standards and the absence of a standardized data construction process. Consequently, data construction that adheres to these standards is not being accomplished. Additionally, a lack of understanding of data models, semantic elements, and GML editing methods has led to the creation of appearance-focused data. To address these issues, this study proposes a digital twin data (building domain) construction guide that organizes geometric information, semantic information, and attribute information for building-related digital twin data components. This study aims to enable stakeholders to easily construct fundamental digital twin data in accordance with the standards, ensuring high-quality product creation that meets the requirements. Furthermore, the detailed data construction requirements defined in this guide will allow it to serve as a manual usable by both the public and private sectors. However, the proposed data model currently operates at a conceptual and logical level, which may limit its immediate applicability. Therefore, future research should include expert evaluations of the model and its transformation into a physical model that can be implemented and deployed on the relevant platform. [ABSTRACT FROM AUTHOR]

BIM (building information modeling) is widely recognized for enhancing the efficiency and precision of building energy modeling (BEM), primarily by reducing model development time and improving model accuracy. This paper presents a novel framework leveraging "openBIM" to standardize and harmonize BIM-driven solutions for energy simulations, facilitating comprehensive operational carbon impact assessments. Unlike existing approaches, our framework uniquely integrates information delivery specifications (IDS) with openBIM standards to define the minimum data requirements within the IFC schema, tailored to various levels of development (LOD). This innovation ensures consistent data exchange and interoperability across diverse energy modeling and simulation tools, addressing common challenges of data fragmentation and inaccuracy in operational carbon assessments. By advancing the current state of the art, the proposed framework empowers energy modelers, LCA analysts, and asset managers to streamline IDS implementation, fostering more efficient and reliable construction industry practices. This research thus marks a significant step towards achieving more sustainable building projects through enhanced data-driven insights. [ABSTRACT FROM AUTHOR]

Phytopathology deals with a branch of biology encompassing pathogens that infect plants. Pathogenic fungi, bacteria, viruses, viroids, and phytoplasmas are notorious and hard to control; preventive measures are important for managing disease as early as possible. Age-old management practices are time-consuming and labour-intensive processes. In the past, nucleic acid-based methods, such as hybridization, amplification, and sequencing, have been used extensively for the preliminary identification of plant pathogens. Recently, PCR-based methods have been widely used for the detection of plant pathogens. However, PCR methods are time-bound and require high-quality DNA extraction because of inhibitors' effects on PCR sensitivity. Several isothermal detection techniques are commonly used for the onsite detection of plant pathogens. Among them, loop-mediated isothermal amplification (LAMP) is a paradigm diagnostic tool for early plant pathogen detection. Hence, in this review, we discuss the rapid, reliable, sensitive method of the LAMP assay and the limit of detection (LOD) in different sectors of plant pathology. We also address the advantages and disadvantages of different LAMP approaches and future prospects. [ABSTRACT FROM AUTHOR]

Purpose: Food forensics is an emerging field and the initial part of this review showcases the toxic effects and the instrumental methods applied for the detection of the most commonly used azo dyes. Electrochemical detection has a lot of advantages and hence the significance of the most important techniques used in the electrochemical detection is discussed. The major part of this review highlights the surface modified electrodes, utilized for the detection of the most important azo dyes to achieve low detection limit (LOD). Methods: A thorough literature study was conducted using scopus, science direct and other scientific databases using specific keywords such as toxic azo dyes, electrochemical detection, modified electrodes, LOD etc. The recent references in this field have been included. Results: From the published literature, it is observed that with the growing interests in the field of electrochemical techniques, a lot of importance have been given in the area of modifying the working electrodes. The results unambiguously show that the modified electrodes outperform bare electrodes and offer a lower LOD value. Conclusion: According to the literature reports it can be concluded that, compared to other detection methods, electrochemical techniques are much dependable and reproducible. The fabrication of the electrode material with the appropriate modifications is the main factor that influences the sensitivity. Electrochemical sensors can be designed to be more sensitive, more reliable, and less expensive. These sensors can be effectively used by toxicologists to detect trace amounts of harmful dyes in food samples. [ABSTRACT FROM AUTHOR]

The Length of Day (LOD) and the Universal Time (UT1) play crucial roles in satellite positioning, deep space exploration, and related fields. The primary method for predicting LOD and UT1 is least squares fitting combined with autoregressive (AR) models. Polynomial Curve Fitting (PCF) has greater accuracy in capturing long-term trends compared to standard least squares fitting. In this study, PCF combined with Weighted Least Squares (WLS) is employed to fit and extrapolate the periodic and trend components of the LOD series after removing tidal influences. Additionally, considering the time-varying characteristics of the LOD series, a Long Short-Term Memory (LSTM) network is utilized to predict the residuals derived from the fitting process. The 14 C04 LOD series released by the International Earth Rotation and Reference System Service (IERS) is used as the base series, with 70 LOD and UT1-UTC prediction experiments conducted during the period from 1 September 2021–31 December 2022. The results indicate that the PCF+WLS+LSTM method is well-suited for medium- and long-term (90–360 days) prediction of the LOD and UT1-UTC. Significant improvements in prediction accuracy were obtained for periods ranging from 90–360 days, particularly beyond 150 days, where the average accuracy improved by over 20% compared to IERS Bulletin A. Specifically, the largest prediction accuracy increase for LOD and UT1-UTC was 49.5% and 59.2%, respectively. [ABSTRACT FROM AUTHOR]

A fast and highly sensitive electrochemical sensor (ECS) is crucially desirable for observing synthetic dyes in foodstuffs, as excessive consumption of these colorants can pose risks to human health, including toxicity and pathogenicity. This research introduces the creation of an ECS comprising a CuO‐ZrO2 nanocomposite for detecting Sunset Yellow (SY) dye in beverage and food items. The synthesized CuO‐ZrO2 material underwent thorough characterization using various physicochemical and electroanalytical methods. The ECS deposited over glassy carbon electrode (GCE) demonstrated its best response for SY when operated using a 0.01‐M, pH 5 phosphate‐buffered saline (PBS) solution. The optimal performance for the SY detection was obtained at a scan rate of 0.1 V s−1. Under these ideal conditions, the CuO‐ZrO2 demonstrated a linear range of 0.01–50 ppm for SY. The current limited of detection was 7699 μA, while the sensitivity value was determined to be 19.43 μA μM−1 cm−2. This straightforward and highly responsive sensor offers rapid and cost‐effective detection of SY. [ABSTRACT FROM AUTHOR]

In many urban planning and visualization applications, it is crucial to have 3D window details. However, the process of acquiring and reconstructing them can be challenging. Therefore, in many 3D city models, buildings often lack 3D windows. Instead, their building façades are usually represented using 2D planar textures (i.e., LoD2 CityGML models with textures). To generate a 3D façade from 2D images, current methods often need to carefully design various grammars to achieve desired results, which can be tedious. A useful property of building, the window semantics information, is also lacking. The main contribution of this paper is proposing a semantics-aware method for addition and LoD control of 3D window details to LoD2 CityGML models with textures (namely SLOD2+WIN). A deep learning-based two-level window-pane detection is introduced, and the detection results are then processed and adjusted to generate 3D windows and add to the building models. Unlike other methods, the semantics (i.e., frames and panes) are considered for adding window details. We also propose and integrate a LoD scheme for 3D windows following the same concept as the LoD in CityGML. The tedious efforts of reconstruction or grammar creation can be reduced in our method. Only the information present in the texture itself is extracted, and the shape and pattern information of windows are obtained and adjusted from the detection results in an efficient and unsupervised manner to achieve neat window parsing. Specifically, clustering-based window/pane alignment, neatness-based window image voting, grid-based symmetry, thickness filtering, and fitting-based window-top modeling are proposed. To demonstrate the effectiveness and usefulness of SLOD2+WIN, experiments on several datasets of 3D cities are conducted to add 3D window details with the novel LoD schema, illustrative applications in architectural visualization and urban planning are also showcased. This paper extended its conference version. [ABSTRACT FROM AUTHOR]

• QTL mapping was done in bitter gourd using SSR markers. • From 630 SSRs, 35 were obtained as polymorphic by parental polymorphism screening. • Twenty eight QTLs for fourteen yield and yield attributing traits were detected. • QTL hotspot for yield per plant was detected on linkage group 1. • Higher variation was observed in the mapping population. The present study was conducted for QTL mapping for yield and other yield attributing traits in bitter gourd using Simple Sequence Repeat (SSR) markers. A total of 630 SSR markers were screened for polymorphism in two contrasting parents (DBGS-2 and Pusa Purvi), out of which only 35 were polymorphic. F 1 plants (10 individuals) in which hybridity was ascertained (using these polymorphic markers); were further employed for development of mapping population (F 2) consisting of 120 plants. Higher variation was present in the mapping population as evident from the wider range value of the characters. Continuous frequency distribution classes combined with bell-shaped, symmetrical normal distribution curve, revealed the quantitative inheritance nature of the traits studied. Higher PCV than the GCV for all the traits, along with high difference between the PCV and GCV for majority of traits indicated higher influence of environment in expression of these traits in the mapping population. Presence of transgressive segregation was also noted for majority of the traits. Amongst the various linkage groups (LG), LG 4 had the maximum number of markers, covering 171.07 cM map distance. LG4 also possessed the maximum number (nine) of QTLs while LG1 had six. QTL mapping using polymorphic SSRs resulted in detection of a total of 28 QTLs for fourteen traits viz. yield per plant (kg), earliness or flower related traits (node to first female flower, node to first male flower, days to first male flower, male to female flower ratio), fruit traits (fruit number per plant, fruit diameter (cm), fruit length/ diameter ratio, pericarp thickness (mm) and number of seed per fruit) and vegetative traits (internodal length (cm), number of primary branches, leaf width (cm), length and width ratio). The LOD score of these QTLs ranged from 3.01 to 64.47, the total phenotypic variances (PVE) ranged from 1.52 to 34.57 % and additive effects ranged from –3.69 to 17.07. Of the total, nineteen were major QTLs, having PVE >10 %. Three QTLs were detected for yield per plant while a total of seven for the traits imparting earliness viz. days to first male flower, node to first female flower and node to first male flower. Amongst all the QTLs detected, qFrtLDR-4-1 (K) had the maximum LOD (64.47) and PVE (34.57 %) value. Two hotspots were detected with multiple QTLs clustered in the LG 1. The first hotspot possessed four QTLs [qLfW-1-1, qMFR-1-1 (K), qSPF-1-1, qNFmlF-1-1 (K)] while the second had two related to yield per plant [qYldpl-1-1 (K) and qYldpl-1-1]. Many of these QTLs are also being reported for the first time in bitter gourd. The findings of the present study can be used to fasten the bitter gourd improvement by utilizing these in MAS, DNA fingerprinting, genetic mapping, genomics analysis etc. [ABSTRACT FROM AUTHOR]

The current manuscript focuses on the experimental setup to determine/identify the presence of heavy elements in water samples collected from a tropical River in Prayagraj (UP), India. This report explores the potential of the laser-induced breakdown spectroscopy (LIBS) technique to analyze liquid samples from various rivers (Ganga, Yamuna, and Sangam) in Prayagraj. We presented a detailed description of the equipment used to record the LIBS spectra of the liquid samples. To get the best signal-to-noise ratio and the signal-to-background ratio of LIBS spectra of the liquid samples, several parameters, such as laser energy, the repetition rate of the laser, pumping velocity (revolutions per minute) of the liquid jet, the focal length of the focusing lens, and the average number of laser shots, are optimized. We draw a calibration curve (a graph between the spectral intensity of the elements in the LIBS spectra and their concentration in the liquid) for a specified concentration of solutions. This calibration curve is used to determine the limit of detection (LOD) of an element in the liquid solution. Finally, the water from the Ganga, Yamuna, and Sangam basins is collected, and the LIBS spectra in the liquid phase are recorded using the optimized parameters to determine the presence of heavy metals. The experimental results revealed the presence of heavy elements such as chromium (Cr), barium (Ba), and mercury (Hg) in these samples. The LOD of Ba in water has been determined using the calibration curve method and does not exceed the safe limit of around 12 ppm. This is the first report on river water for toxicity assessment using the LIBS technique. For the classification/identification of the waters from various rivers based on their toxicity, we employed multivariate techniques such as principal component analysis and hierarchy component analysis. [ABSTRACT FROM AUTHOR]

In this work, a gas sensing system based on chalcogenide (ChG) glass platform in the mid-infrared (Mid-IR) region is modeled. The proposed gas sensing system composed of a linear tapers waveguide, ridge waveguide, a multimode interferometer (MMI) coupler and transducer arms. The components of the sensing system were simulated using FimmWave from Photon design. First, we determined the structural key parameters of the ridge waveguide that allow for single-mode propagation while maximizing the evanescent confinement field factor. The obtained results show that at the gas absorption wavelengths, λ CO 2 = 4.26 µm and λ CO = 4.6 µm, the evanescent confinement field factor, reached 3.12% and 3.24%, respectively. For these operating wavelengths, a maximum transmission of 99.8% was achieved with a taper length of 450 µm. The footprint of the MMI coupler is 32 × 9975 µm2. A Contrast of 16.6 dB and insertion losses of 2 dB and 2.87 dB were obtained at λ CO 2 = 4.26 µm and λ CO = 4.6 µm respectively. The sensor performance was validated at 4.26 µm and 4.6 µm, respectively, giving a detection limit of 10.73 ppm for carbon dioxide (CO2) at 4.26 µm and 138 ppm for carbon monoxide (CO) at 4.6 µm. A sensitivity of 3.02 mW.L/mol and 0.12 mW. L/mol, was achieved at the wavelenghts of interset. The obtained results of the sensor by the optimizations of its components serve to enhance a gas sensing system based on chalcogenide (ChG) glass platform. [ABSTRACT FROM AUTHOR]

Abstract Precise, fast, and reliable identification and quantification of microplastic contamination are essential for determining their environmental concentrations for risk assessments. This study investigates the use of nuclear magnetic resonance (NMR) spectroscopy to quantify microplastics by analysing dilution series of polystyrene (PS), polyisoprene-cis (PI), polybutadiene-cis (PB), polylactic acid (PLA), polyvinyl chloride (PVC) and polyurethane (PU). Each polymer type was dissolved in a suitable solvent and an internal standard was utilized for quantification. Detection and quantification limits for each polymer type were established in two ways: (1) by using an equation based on proton signals and an internal standard with known concentration and (2) by using the LOQ based on the signal-to-noise ratio. Both data sets were compared and showed that using the internal standard (method 1) results in more accurate and lower concentration limits in the range of 0.2–8 µg mL−1 for all six polymer types, while the LOQ based on the SNR (method 2) gives consistently higher concentration limits (1–10 µg mL−1). The research shows the accuracy, efficacy, and reliability of quantitative NMR spectroscopy for polymer analysis in these concentration ranges compared to established quantifying methods, such as, PyGC/MS, FTIR, or Raman spectroscopy.

Abstract Over the years, prediction techniques for the highly variable angular velocity of the Earth represented by Earth's rotation (UT1-UTC) and length-of-day (LOD) have been continuously improved. This is because many applications like navigation, astronomy, space exploration, climate studies, timekeeping, disaster monitoring, and geodynamic studies, all rely on predictions of these Earth rotation parameters. They provide early warning of changes in the Earth's rotation, allowing various industries and scientific fields to operate more precisely and efficiently. Thus, in our study, we focused on short-term prediction for UT1-UTC (dUT1) and LOD. Our prediction approach is to combine machine learning (ML) technique with efficient evolutionary computation (EC) algorithms to achieve reliable and improved predictions. Gaussian process regression (GPR) is used as the ML technique with genetic algorithm (GA) as the EC algorithm. GA is used for hyperparameter optimization of GPR model as selecting appropriate values for hyperparameter are essential to ensure that the prediction model can accurately capture the underlying patterns in the data. We conducted some experiments with our prediction approach to thoroughly test its capabilities. Moreover, two forecasting strategies were used to assess the performance in both hindcast and operational settings. In most of the experiments, the data used are the multi-technique combinations (C04) generated by International Earth Rotation and Reference Systems Service (IERS). In one of the experiments, we also investigated the performance of our prediction model on dUT1 and LOD from four different products obtained from IERS EOP 20 C04, DTRF20, JTRF20 and USNO. The prediction products are evaluated with real estimates of the EOP product with which the model is trained. The combined excitations of the atmosphere, oceans, hydrology, and sea level (AAM + OAM + HAM + SLAM) are used as predictors because they are highly correlated to the input data. The results depict the highest performance of 0.412 ms in dUT1 and 0.092 ms/day in LOD, on day 10 of predictions. It is worth noting that the later predictions were obtained by incorporating the uncertainty of the input data as weights in the prediction model, which was a novel approach tested in this study. Graphical Abstract

Xanthomonas arboricola pv. pruni (Xap) is the causal agent of bacterial spot of stone fruits and almond (Prunus spp). Detection of Xap is typically carried out using quantitative real-time PCR (qPCR) combined with culture-based isolation. However, qPCR does not differentiate between viable and dead cells, potentially leading to an overestimation of the infective population in a sample. Such overestimation could result in unnecessary phytosanitary measures. The present study aims to develop a specific protocol ideally targeting to detection of only live Xap bacterial cells. To address this challenge, the viable quantitative PCR (v-qPCR) method was evaluated using three nucleic acid-binding dyes: propidium monoazide (PMA), a combination of PMA and ethidium monoazide (EMA), and PMAxx™, an improved version of PMA. PMAxx™ proved to be the most suitable dye for the detection and quantification of living bacterial cells. This methodology was also evaluated in infected plant material over time and can be considered a rapid and reliable alternative to PCR methods for detecting only those putative infective Xap that may pose a risk for Prunus crops. Key points: • Protocol to detect biofilm and planktonic viable X. arboricola pv. pruni cells. • Host validated protocol. • Benefits, reduction of chemicals in disease control. [ABSTRACT FROM AUTHOR]

This article proposes that Resource Description Framework (RDF) technology is well suited for representing and storing inherently connected epistolary data. We justify this proposition by creating an RDF-based digital edition of the correspondence of members of the Bernoulli dynasty and Leonhard Euler and depicting the benefits this type of edition provides, which are hardly achievable otherwise. We describe the ontologies defined to fully represent letters and illustrate how we formulated letters' historical, scientific content, rich with mathematical formulae, markups, figures, and references, as RDF statements to facilitate complex queries common in the humanities field. Next, we outline the tools we developed for presenting these editions in an efficient and user-friendly form, hiding the complexity of the data structure from non-technical users while offering sophisticated analysis features to explore atoms of knowledge and their relations. Additionally, since the citability and durability of the studied digital sources play a crucial role for scholars who publish research on the editions, we explain the steps we undertook to ensure the sustainability of the RDF-based editions and to offer persistent citation possibility. [ABSTRACT FROM AUTHOR]

Over the years, prediction techniques for the highly variable angular velocity of the Earth represented by Earth's rotation (UT1-UTC) and length-of-day (LOD) have been continuously improved. This is because many applications like navigation, astronomy, space exploration, climate studies, timekeeping, disaster monitoring, and geodynamic studies, all rely on predictions of these Earth rotation parameters. They provide early warning of changes in the Earth's rotation, allowing various industries and scientific fields to operate more precisely and efficiently. Thus, in our study, we focused on short-term prediction for UT1-UTC (dUT1) and LOD. Our prediction approach is to combine machine learning (ML) technique with efficient evolutionary computation (EC) algorithms to achieve reliable and improved predictions. Gaussian process regression (GPR) is used as the ML technique with genetic algorithm (GA) as the EC algorithm. GA is used for hyperparameter optimization of GPR model as selecting appropriate values for hyperparameter are essential to ensure that the prediction model can accurately capture the underlying patterns in the data. We conducted some experiments with our prediction approach to thoroughly test its capabilities. Moreover, two forecasting strategies were used to assess the performance in both hindcast and operational settings. In most of the experiments, the data used are the multi-technique combinations (C04) generated by International Earth Rotation and Reference Systems Service (IERS). In one of the experiments, we also investigated the performance of our prediction model on dUT1 and LOD from four different products obtained from IERS EOP 20 C04, DTRF20, JTRF20 and USNO. The prediction products are evaluated with real estimates of the EOP product with which the model is trained. The combined excitations of the atmosphere, oceans, hydrology, and sea level (AAM + OAM + HAM + SLAM) are used as predictors because they are highly correlated to the input data. The results depict the highest performance of 0.412 ms in dUT1 and 0.092 ms/day in LOD, on day 10 of predictions. It is worth noting that the later predictions were obtained by incorporating the uncertainty of the input data as weights in the prediction model, which was a novel approach tested in this study. [ABSTRACT FROM AUTHOR]

This study investigates the detection of organic compounds using a surface plasmon resonance (SPR) sensor comprising Zinc oxide (ZnO), Silver (Ag), and graphene oxide (GO) layers. Graphene oxide is chosen as topmost layer due to its impressive attributes, including a large surface area, robust light absorption, thermal stability, and biocompatibility. By optimizing the thickness of the ZnO and Ag, the performance of the sensor is enhanced. The proposed sensor configuration significantly improved the sensitivity and obtained the maximal value of 215°/RIU at minimum reflectance (Rmin). At a ZnO thickness of 20 nm and Ag thickness of 35 nm, the sensor achieves maximal sensitivity of 222.79/RIU for detecting the n-octane organic compound. This improved sensor design shows efficacy in detecting analytes with refractive index (RI) variations ranging from 1.33 to 1.39. Additionally, the proposed sensor demonstrated a limit of detection (LoD) of 5.65 RIU and figure of merit (FoM) of around 50.75/RIU, highlighting its high sensitivity, and detection accuracy. Furthermore, substantial penetration depths (PDs) of 219.71 nm for water and 211.11 nm for n-octane are obtained. This analysis highlights the exceptional performance of the proposed sensor, rendering it highly suitable for sensing applications in both industrial and biomedical domains. [ABSTRACT FROM AUTHOR]

Soybean is scientifically known as Glycine max. It belongs to the Fabaceae family. It consists of a lot of bioactive phytochemicals like saponin, phenolic acid, flavonoid, sphingolipids and phytosterols. It also owns excellent immune‐active effects in the physiological system. Soy and its phytochemicals have been found to have pharmacological properties that include anticancer, antioxidant, anti‐hypercholesterolaemic, anti‐diabetic, oestrogenic, anti‐hyperlipidaemic, anti‐inflammatory, anti‐obesity, anti‐hypertensive, anti‐mutagenic, immunomodulatory, anti‐osteoporotic, antiviral, hepatoprotective, antimicrobial, goitrogenic anti‐skin ageing, wound healing, neuroprotective and anti‐photoageing activities. Present study has been designed to set standard pharmacognostical extraction method, complexation of compounds, qualitative evaluation through phytochemical screening, identification by TLC, physicochemical properties, solubility profile, total phenolic, flavonoid content as well as analytical evaluation or characterisation like UV and FT‐IR of methanolic extract of G. max. The final observations like physicochemical properties such as total ash value, LOD and pH were recorded. Phytochemical screenings show the presence of flavonoid, alkaloid, saponin, carbohydrate, tannins, protein, gums and mucilage, fixed oils and fats. The results were found significant. Further in silico studies proved creatinine and euparin to be potent wound healing agents. [ABSTRACT FROM AUTHOR]

Edible Swiftlet Nest (ESN) is a swiftlet's saliva product that contains many beneficial compounds. However, improper washing leaves behind toxic nitrite content. To ensure its absence, sensitive and accurate methods are needed, such as UV-Vis spectrophotometry. However, long analysis time and complicated maintenance, limit its further use. In this research, cyclic voltammetry was developed as an alternative analysis method for nitrite content in ESN. The results show that the LOD and LOQ of this method are 0.658529 ppm and 1.995544 ppm, respectively. Compared to this value, analysis using UV-visible spectrophotometry shows lower LOD and LOQ values, respectively 0.061579 ppm and 0.186602 ppm. [ABSTRACT FROM AUTHOR]

UV Spectrophotometric method has been developed to determine Gliclazide in bulk pure and tablet forms. It is a simple, accurate, reproducible, rapid and less time-consuming method. The maximum wavelength of the drug was found to be 232nm. Beer Lamberts' law was obeyed in the concentration range of 2-20 µg/ml. (LOD)The limit of detection and limit of qualification (LOQ) was found to be 0.16 µg/ml and 0.50 µg/ml from this method per cent recovery of the drug was found to be 99.30% which indicates no interaction of the excipients. This method was found accurate, simple, precise and rapid for determination of tablet dosage form. [ABSTRACT FROM AUTHOR]

Abstract Metal phthalocyanines (MPcs) are promising materials for electrochemical sensing due to their physicochemical properties, including redox activity, structural versatility, and chemical stability. These materials can incorporate various metals into their central core, ensuring tunable catalytic activity and enhanced sensitivity and selectivity. This makes MPcs valuable for designing advanced electrochemical sensors, which require precise and reliable performance for applications ranging from environmental monitoring to biomedical diagnostics. This review discusses the advancements in MPc‐based catalysts for electrochemical sensors, focusing on their superior catalytic properties, stability under diverse operating conditions, and high functionalization potential. The unique redox behavior of the metal center in MPcs ensures improved detection capabilities of analytes like biomolecules, heavy metal ions, and environmental pollutants, positioning MPc materials as a cornerstone in future sensor technology. MPc‐based sensors have diverse applications across various fields, including environmental sensing, medical diagnostics, and industrial process monitoring. Recent reports highlight the practical relevance and growing importance of MPcs in real‐world applications. Challenges associated with MPc‐based sensors include scalability, environmental stability, and integration into practical devices. The review concludes with a discussion on the future outlook on MPcs in the design and development of next‐generation electrochemical sensors, paving the way for more efficient, cost‐effective, and reliable detection technologies.

In South Korea, to systematically share and operate data related to the national level of the digital twin, a reference model standard for the National Digital Twin (NDT) concept was established in 2021. This was followed by the Korean Industrial Standards (KS) for buildings. Despite these efforts, the implementation of digital twin projects faces challenges owing to a low level of understanding of the standards and the absence of a standardized data construction process. Consequently, data construction that adheres to these standards is not being accomplished. Additionally, a lack of understanding of data models, semantic elements, and GML editing methods has led to the creation of appearance-focused data. To address these issues, this study proposes a digital twin data (building domain) construction guide that organizes geometric information, semantic information, and attribute information for building-related digital twin data components. This study aims to enable stakeholders to easily construct fundamental digital twin data in accordance with the standards, ensuring high-quality product creation that meets the requirements. Furthermore, the detailed data construction requirements defined in this guide will allow it to serve as a manual usable by both the public and private sectors. However, the proposed data model currently operates at a conceptual and logical level, which may limit its immediate applicability. Therefore, future research should include expert evaluations of the model and its transformation into a physical model that can be implemented and deployed on the relevant platform.

IntroductionThe use of illicitly manufactured synthetic opioids, specifically fentanyl and its analogs, has escalated exponentially in the United States over the last decade. Due to the targeted nature of drug detection methods in clinical laboratories and the ever-evolving list of synthetic opioids of concern, alternative analytical approaches are needed.MethodsUsing the fentanyl analog screening (FAS) kit produced by the Centers for Disease Control and Prevention (CDC), we developed a liquid chromatography-high resolution mass spectrometry (LC-HRMS) synthetic opioid spectral library and data acquisition method using information dependent acquisition of product ion spectra. Chromatographic retention times, limits of detection and matrix effects, in urine and serum, for the synthetic opioids in the FAS kit (n = 150) were established. All urine and serum specimens sent to a clinical toxicology laboratory for comprehensive drug testing in 2019 (n = 856) and 2021 (n = 878) were analyzed with the FAS LC-HRMS library to determine the prevalence of fentanyl analogs and other synthetic opioids, retrospectively (2019) and prospectively (2021).ResultsThe limit of detection (LOD) of each opioid ranged from 1 to 10 ng/mL (median, 2.5 ng/mL) in urine and 0.25-2.5 ng/mL (median, 0.5 ng/mL) in serum. Matrix effects ranged from -79 % to 86 % (median, -37 %) for urine, following dilution and direct analysis, and -80 % to 400 % (median, 0 %) for serum, following protein precipitation. The prevalence of fentanyl/fentanyl analogs in serum samples increased slightly from 2019 to 2021 while it remained the same in urine. There were only 2 samples identified that contained a fentanyl analog without the co-occurrence of fentanyl or fentanyl metabolites. Analysis of the established MS/MS spectral library revealed characteristic fragmentation patterns in most fentanyl analogs, which can be used for structure elucidation and drug identification of future analogs.ConclusionsThe LC-HRMS method was capable of detecting fentanyl analogs in routine samples sent for comprehensive drug testing. The method can be adapted to accommodate testing needs for the evolving opioid epidemic.

This paper explores enhancing metadata usability across domains using RDF graphs, known for their flexible expressiveness and global connectivity. For user's 'find' tasks, effective methods leverage common properties and controlled URIs linking to Linked Open Data (LOD). N-ary structured descriptions prove instrumental for 'identify' and 'select' tasks. The proposed dual-layer model adeptly balances detailed descriptions with ease of use, offering applicability to a diverse range of information expressions. Examples, including the Japan Search RDF Schema, showcase the versatility of this approach. [ABSTRACT FROM AUTHOR]

In the Japanese official detection method for unauthorized genetically modified (GM) papayas, one of two types of real-time PCR reagents with DNA polymerase (TaqMan Gene Master Mix [Taq-Man Gene] or FastGene QPCR Probe Mastermix w/ROX [FastGene]) is primarily used for measurement. In 2022, we conducted a laboratory performance study on the unauthorized GM papaya line PRSV-YK, and the results revealed that high threshold cycle (Cq) values for the PRSV-YK detection test were obtained using TaqMan Gene with the 7500 Fast & 7500 Real-Time PCR System (ABI7500) and QuantStudio 12K Flex (QS12K), indicating the possibility of false negatives. The possibility of similar problems with all unauthorized GM papaya lines detection tests needs to be evaluated. In this study, we performed detection tests on unauthorized GM papaya lines (PRSV-YK, PRSV-SC, and PRSV-HN), the cauliflower mosaic virus 35S promotor (CaM), and a papaya positive control (Chy), and examined how the limits of detection (LOD) for each test are affected by two types of DNA polymerases (TaqMan Gene and FastGene) and three types of real-time PCR instruments (ABI7500, QS12K, and LightCycler 480 Instrument II [LC480]). In the PRSV-YK and PRSV-SC detection tests using ABI7500 and QS12K, measurement with TaqMan Gene showed a higher LOD than FastGene. In this case, an exponential amplification curve was confirmed on the amplification plot; however, the amplification curve did not cross the ARn threshold line and the correct Cq value was not obtained with a threshold line = 0.2. The other tests (PRSV-HN, CaM, and Chy with ABI7500 and QS12K, and all detection tests with LC480) showed no important differences in the LOD for each test using either DNA polymerase. Therefore, when performing PRSV-YK and PRSV-SC detection tests with the ABI7500 or QS12K, FastGene should be used to avoid false negatives for foods containing GM papaya lines PRSV-YK and PRSV-SC at low mixing levels. [ABSTRACT FROM AUTHOR]

The growing adoption of Building Information Modeling (BIM) within the architectural, engineering, and construction (AEC) sector raises questions about the quality of BIM data deliverables for project owners. Therefore, assessment and evaluation of such BIM data against relevant documents such as the BIM Execution Plan (BEP), the Level of Definition (LOD)/Level of Information (LOI) matrix, and quality control customized checklists become critical, especially in large construction projects. This study primarily aims to create an automated system for assessing the quality of 3D BIM model data, utilizing a proposed project quality control checklist. The automated system consists of four key elements: a BIM-based model, a Data Extraction and Analysis Module, a Data Storage Module, and a Data Visualization Module. The Data Extraction and Analysis Module extracts relevant information and parameters from BIM models to evaluate their quality against predefined checklists. Then, it transfers the information and stores the results in a database. The database is connected to an engineering project collaboration tool, ProjectWise, to automatically update and store the data in the cloud. The database is then connected to an interactive data visualization platform, Power BI, to enable automatic visualization of the generated quality assessment results of the BIM models' data. This system was applied to a Canadian infrastructure construction project by its BIM department during the preliminary and detailed design phases. It demonstrated an average quality score (AQS) of 87.6% for the BIM models and significantly reduced failing items by around 30%. This study concludes that the system offers a robust, practical solution for enhancing the quality control process in BIM model data management, thereby aiding engineers in timely model adjustments to meet project requirements. [ABSTRACT FROM AUTHOR]

Background: Shortcomings in current analytical methods characterized by time-consuming procedures, limited sensitivity, and high costs associated with reagents and instruments, influence the research background. Objectives: The goal of this research was to find a simple, fast, and accurate way to discern and quantify substances in bulk drugs and finished products at the utmost minimal levels of detection and quantification. Materials and Methods: The methodology involved using a C18 column then a solvent mixture comprising 0.1% orthophosphoric acid and acetonitrile in an 80:20 proportion delivered at 1 mL/min-1 and observed at 247 nm with a UV detector. Results: Following rigorous verification in line with ICH guidelines, the technique revealed several notable features. It revealed rapidity with a retention time of 2.53 min, accurate with average recoveries ranging from 99.20% to 100.43%, precision as demonstrated by a relative standard deviation below 2%, specificity, and a highly linear correlation coefficient of 0.999 between 0.0025 to 6 µg mL-1. Additionally, the method displayed remarkable sensitivity, detecting concentrations as low as 56.91 ng mL-1 and a quantification limit of 172.44 ng mL-1. Conclusion: This tested RP-HPLC method has a unique mix of speed, accuracy, and high sensitivity that makes it possible to accurately measure Vardenafil hydrochloride trihydrate, even at nanogram levels. This surpasses established techniques and offers a cost-effective solution for pharmaceutical quality control and analysis. [ABSTRACT FROM AUTHOR]

A novel, sensible, rapid, reliable and economical analytical hyphenated LC-MS/MS method has been developed as a key for the safety surveillance in chronic leukemia patients, and as a part of therapeutic drug monitoring of imatinib mesylate in human saliva. Imatinib mesylate or imatinib methane sulfonate is a tyrosine kinase inhibitor, apoptosis inducer and also known to be an anticoronaviral agent. Imatinib mesylate is a monomesylate salt of imatinib used for the treatment of gastrointestinal tumors and chronic myelogenous leukemia, and also in other complex malignancies. The lmax of imatinib mesylate was observed at 258 nm by UV spectrometry, establishing a very good linearity along with sensitivity. The detection limit (LOD) =0.2925 µg mL-1 and quantitation limit (LOQ)= 0.8977 µg mL-1 were obtained from the linear concentrations taken in the range of 2-12 µg mL-1. The correlation coefficient (r2) found was 0.999. The method validation parameters according to ICH Q2 (R1) were performed. The developed method described here, UPLC-MS/MS, was found to be novel, sensitive and rapid with improved results when successfully tested for human saliva samples without significant differences in the steady state imatinib mesylate concentrations. Current method could overcome the safety issues during therapeutic drug monitoring and pharmacokinetic behavior of the drug when tested clinically. [ABSTRACT FROM AUTHOR]

Simple Summary: Soil-transmitted helminth parasites are associated with a neglected tropical disease affecting both humans and animals. Proper management of the infection requires an accurate diagnostic technique. Therefore, in an era of STH prevention and control, a new lab-on-a-disc (LoD)-based diagnostic technique called single-image parasite quantification (SIMPAQ) was developed with the ability to focus all STH eggs in an imaging zone, where a digital image can be captured for further processing. This report evaluates the performance of SIMPAQ in terms of prevalence, sensitivity, specificity and predictive values. In this study, a total of 518 animal faeces samples were examined using McMaster, test tube simple flotation, and the LoD technique. The highest prevalence was demonstrated by the LoD technique, which also demonstrated high sensitivity in the detection of STH eggs. Soil-transmitted helminth (STH) infections are caused by roundworms, hookworms, whipworms, and thread worms. Accurate diagnosis is essential for effective treatment, prevention, and control of these infections. This study evaluates a new diagnostic method called Single-image Parasite Quantification (SIMPAQ), which uses a lab-on-a-disc (LoD) technique to isolate STH eggs into a single imaging zone for digital analysis. The study evaluates the purification performance of the SIMPAQ technique for detecting STH eggs in animal samples. This was a cross-sectional study conducted among 237 pigs and 281 dogs in the Morogoro region in Tanzania. Faecal samples were collected and processed with the LoD technique, as well as flotation and McMaster (McM) methods for comparison purposes. The overall prevalence of STH infections was high as per the LoD technique (74%), followed by McM (65.44%) and flotation (65.04%). Moreover, the overall performance of the LoD technique, using McM as the gold standard, was 93.51% (sensitivity), 60.89% (specificity), 81.91% (PPV), and 83.21% (NPV). The LoD technique exhibited high prevalence, sensitivity, and NPV, which demonstrates its value for STH egg detection and its crucial role in the era of accurate STH diagnosis, promoting proper management of the infection. [ABSTRACT FROM AUTHOR]

A fast method for microplastic detection is thermal extraction desorption‐gas chromatography/mass spectrometry (TED‐GC/MS), which uses polymer‐specific thermal decomposition products as marker compounds to determine polymer mass contents in environmental samples. So far, matrix impacts of different environmental matrices on TED‐GC/MS performance had not yet been assessed systematically. Therefore, three solid freshwater matrices representing different aquatic bodies with varying organic matter contents were spiked with a total of eight polymers. Additionally, for the first time, the two biodegradable polymers polybutylene adipate terephthalate (PBAT) and polylactide (PLA) were analysed using TED‐GC/MS. The methodological focus of this work was on detectability, quality of signal formation as well as realisation of quantification procedures and determination of the limit of detection (LOD) values. Overall, TED‐GC/MS allowed the unambiguous detection of the environmentally most relevant polymers analysed, even at low mass contents: 0.02 wt% for polystyrene (PS), 0.04 wt% for the tyre component styrene butadiene rubber (SBR) and 0.2 wt% for polypropylene (PP), polyethylene (PE) and PBAT. Further, all obtained LOD values were increased in all matrices compared to the neat polymer without matrix. The LOD of the standard polymers were increased similarly (PS: 0.21–0.34 µg, SBR: 0.27–0.38 µg, PP: 0.32–0.36 µg, PMMA: 0.64–1.30 µg, PET: 0.90–1.37 µg, PE: 3.80–6.99 µg) and their decompositions by radical scission processes were not significantly influenced by the matrices. In contrast, matrix‐specific LOD increases of both biodegradable polymers PBAT (LOD: 1.41–7.18 µg) and PLA (0.84–20.46 µg) were observed, probably due to their hetero‐functional character and interactions with the matrices. In conclusion, the TED‐GC/MS performance is not solely determined by the type of the polymers but also by the composition of the matrix. [ABSTRACT FROM AUTHOR]

The present research work discusses the development and validation of a UV spectrophotometric method for butorphanol tartrate. Simple, accurate and cost efficient spectrophotometric method has been developed for the estimation of butorphanol tartrate in tablet dosage form. The optimum conditions for the analysis of the drug were established. The maximum wavelength (λ max) was found to be 278nm. The percentage recovery of butorphanol tartrate was in the 100.016±0.68. Beers law was obeyed in the concentration range of 2-12µg/ml. Calibration curves shows a linear relationship between the absorbance and concentration. The line equation y = 0.0674x - 0.0057 with r2 of 0.9998was obtained. Validation was performed according to ICH guidelines for linearity, accuracy, precision, LOD and LOQ. The sample solution was stable up to 36 hours. The proposed method may be suitable for the analysis of butorphanol tartrate in tablet formulation for quality control purposes. [ABSTRACT FROM AUTHOR]

The validation of chemical measurement for Phosphorus determination in fertilizers followed a chain of experimental procedures to ascertain the performance characteristic (PC), connecting Precision, accuracy, reproducibility, recovery, linearity, limit of detection and of quantification. Performance characteristics are accomplished to ensure that analytical method fits the purpose of particular testing. This method was conducted in three stages those were preparation, testing and data processing. The standard test method for determination of P2O5 content in fertilizers used for regulatory purpose is somewhat lengthy and time taking. A modification was done to the standard method to create a more efficient approach that meets validation criteria and is suitable for analyzing advisory fertilizer samples. During the year 2023-24, comprehensive research and development activity were carried out at Soil and Water Testing Laboratory (SWTL) for Research, Faisalabad and analytical data was generated. The obtained results were processed statistically and compared with the acceptance requirements. The coefficient of determination (R²), precision and accuracy/recovery percentage respectively were 0.9996, 0.3-1.10% and 98.01-102.34%, while LOD and LOQ were 0.84 and 2.25%. Based on the results the study concluded that the modified test method applied to determine P2O5was appropriate for its intended use based on the acceptance criteria of performance characteristics set forth in the validation protocol and can be applied as routine method at laboratory. [ABSTRACT FROM AUTHOR]

Biofertilizers are microbial based products, and their quality control is very much tricky job due to their biological nature and susceptibility to environmental factors. There are certain parameters that needs to be satisfied for the fitness of product. The principal parameter that showed the presence of microbes in the desired number is Total Viable Count besides other parameters etc. The series of lab studies were carried out at Biofertilizer Testing Laboratory (BTL-FSD), Soil Bacteriology Section, Faisalabad to evaluate the method development and validation characters mentioned in ISO/IEC 17025: 2017 standard for testing laboratories. The method developed keeping in view of (PSQCA standard) PS: 5330/2014 and amended according to the lab conditions and validated. Different techniques mentioned in clauses 7.2.2 and 7.2.2.3 were carried out in the lab environment. The bias expressed as LOG 10 were within the range of -0.024 to 0.034 and relative standard deviation (RSD) was <2%. The RSD of repeatability was 0.607% and reproducibility was 0.656 and 0.744%. The significance determined as t-stat was 0.280 well below the t-critical one and two tail i.e., 1.86 and 2.31, respectively and accuracy of method using robust mean was 99.8%. The measurement of uncertainty (MoU) was ±0.05 at 95% confidence interval and expanded uncertainty of the method was 0.1145. The LOD and LOQ of the method was evaluated as 0.090897 and 0.275445 expressed as LOG 10. On the basis of techniques and characters under assessment, it was concluded that method of total viable count satisfied the requirements of ISO/IEC 17025:2017 standard and validated. [ABSTRACT FROM AUTHOR]

The healthcare industry is constantly changing because of technological breakthroughs that spur new methods of diagnosing and treating illnesses. This study investigates the development of Ion Sensitive Field Effect Transistor (ISFET) sensors for DNA-based blood cancer diagnosis. This work presents the design of a two-dimensional ion-sensitive field-effect transistor. Concentration fluctuations and transfer characteristics with different oxides are studied using blood from two electrolyte solutions. It is possible to evaluate how the modeled device can be utilized as a pH sensor or a biosensor in healthcare applications by looking at how the pH changes for different oxides. Additionally, several oxides were examined in the simulated ISFET devices' output characteristics. Blood is the electrolyte to study the device's sensitivity for different oxides. When pH 7.4 is considered, SiO2 oxide is significantly more sensitive than other oxides. The resulting 2D-ISFET exhibits remarkable blood electrolyte sensitivity and holds potential as a quick detection tool for blood cancer. The results show that the ISFET possesses drain-induced barrier lowering (DIBL), greater ON-current (ION) and switching ratio (ION/IOFF), and decreased subthreshold swing (SS). The pH sensor's sensitivity and the suggested equipment can detect up to 30 fg/mL of blood cancer biomarkers. An important development in technology-driven healthcare is the emergence of DNA-based blood cancer detection utilizing ISFET sensors. This opens up new avenues for improving cancer diagnosis and patient outcomes.

This article details a correction to: Autiero, S., Elwert, F., Moscatelli, C., & Pons, J. (2023). The Seven Steps: Building the DiGA Thesaurus. Journal of Open Humanities Data, 9. https://doi.org/10.5334/johd.111.