Fingerprinting Technique Research Articles

Simultaneous determination of eight components in Amomum villosum and its overall qualityconsistency evaluation by four-dimensional fingerprints assisted with antioxidant activity

Amomum villosum (AMV), as a 1300-year-old medicine and food homology, has been lack of comprehensive quality evaluation for a long time. This study established the fingerprints of AMV by chromatographic, spectroscopic and thermal technologies and investigated the antioxidant activity to assess holistically the quality of 22 batches of AMV samples. GC-HPLC tandem fingerprints, FTIR and DSC quantum fingerprints were first developed respectively and fused for evaluating qualitatively and quantitatively, in which the contents of eight compounds were determined simultaneously. Especially, the equal-weight method was used to fuse the different analytical techniques mentioned above and to evaluate the quality of AMV samples with the help of systematically quantified fingerprint method. As a result, all samples were well differentiated and classified into five grades. Moreover, the correlation between the chromatographic fingerprint peaks and antioxidant activity was confirmed by the gray relational analysis. In summary, the proper fusion of multidimensional fingerprint techniques and multidimensional analytical techniques provides a reliable and comprehensive method for the evaluation of AMV and their related products.

WLAN Access Points Channel Assignment Strategy for Indoor Localization Systems in Smart Sustainable Cities

The idea of smart and sustainable cities is incomplete without using information and communication technologies. Wireless Local Area Network (WLAN) plays a vital role in connecting different services, transferring data and providing indoor localization. WLAN fingerprinting technique has become the most common approach for indoor localization in smart cities. This technique utilizes received signal strength (RSS) and channel status from the installed WLAN Access Points (AP). Currently, WLAN planning relies on the site survey, which is laborious and uneconomical for large areas. The WLAN has a limited number of channels assigned in the frequency reuse strategy. An inefficient channel assignment strategy will lead towards increased interference, thus degrading the overall service. This paper proposes a WLAN channel assignment strategy for large and crowded indoor areas. The proposed scheme assigns the channels to installed APs for indoor localization in fingerprinting framework. The proposed technique ensures that the proposed AP channel assignment algorithm in our network-planning scheme mitigates interference.

Effect of historical land‐use change on soil erosion in a Mediterranean catchment by integrating 137Cs measurements and WaTEM/SEDEM model

AbstractSoil erosion is especially severe in areas affected by intermittent heavy rainfalls after dry periods, and human practices such as deforestation. Mediterranean mountain environments underwent conversion of rangelands into croplands during the previous centuries to increase agricultural production but this process was reversed after land abandonment in the middle of the twentieth century allowing the natural revegetation. To understand the effect of the past practices and the current agricultural management, we have combined the strength of empirical data and spatially distributed modelling in a medium‐sized catchment representative of agroforestry landscapes of NE Spain. We developed an ensemble technique composed of 137Cs‐derived soil redistribution rates as specific point values and as grid‐based setup calibration for the WaTEM/SEDEM. Thus, we overcame the point specific limitation of the 137Cs measurement and the need for calibration of spatially distributed models that allow a spatial interpretation of soil redistribution in the catchment. We implemented an automated routine model tool to increase the calculation speed and calibrate the model thus, allowing estimates of the main model parameters for simulating a variety of temporal scenarios. With this method a good fit between modelled and measured soil redistribution rates (R2 = 0.82) was obtained (Nash–Sutcliffe efficiency: 0.79). Estimates were also consistent with the previous apportions of sediment provenance obtained by the fingerprinting technique. An increase of 300% in sediment export was predicted for the past scenario when most of the catchment was cultivated, while a reduction of 40% occurred in the future scenario of land abandonment. A high influence of input resolution was found, wherein model efficiency was reduced when pixel‐size was increased. Our study demonstrates that spatially distributed models combined with 137Cs‐derived rates provide a powerful tool to understand the driving factors of erosion and delineate the hotspot areas that could suffer high erosion if submitted to certain management practices.

Improving the design and implementation of sediment fingerprinting studies: summary and outcomes of the TRACING 2021 Scientific School.

PurposeIdentifying best practices for sediment fingerprinting or tracing is important to allow the quantification of sediment contributions from catchment sources. Although sediment fingerprinting has been applied with reasonable success, the deployment of this method remains associated with many issues and limitations.MethodsSeminars and debates were organised during a 4-day Thematic School in October 2021 to come up with concrete suggestions to improve the design and implementation of tracing methods.ResultsFirst, we suggest a better use of geomorphological information to improve study design. Researchers are invited to scrutinise all the knowledge available on the catchment of interest, and to obtain multiple lines of evidence regarding sediment source contributions. Second, we think that scientific knowledge could be improved with local knowledge and we propose a scale of participation describing different levels of involvement of locals in research. Third, we recommend the use of state-of-the-art sediment tracing protocols to conduct sampling, deal with particle size, and examine data before modelling and accounting for the hydro-meteorological context under investigation. Fourth, we promote best practices in modelling, including the importance of running multiple models, selecting appropriate tracers, and reporting on model errors and uncertainty. Fifth, we suggest best practices to share tracing data and samples, which will increase the visibility of the fingerprinting technique in geoscience. Sixth, we suggest that a better formulation of hypotheses could improve our knowledge about erosion and sediment transport processes in a more unified way.ConclusionWith the suggested improvements, sediment fingerprinting, which is interdisciplinary in nature, could play a major role to meet the current and future challenges associated with global change.Supplementary informationThe online version contains supplementary material available at 10.1007/s11368-022-03203-1.

Robust satellite antenna fingerprinting under degradation using recurrent neural network

Antenna fingerprinting is critical for a range of physical-layer wireless security protocols to prevent eavesdropping. The fingerprinting process exploits manufacturing defects in the antenna that cause small imperfections in signal waveform, which are unique to each antenna and hence device identity. It is an established process for physical-layer wireless authentication with proven usage systems in terrestrial systems. The premise relies on accurate signal feature discovery from a large set of similar antennas and stable fingerprint patterns over the operational life of the antenna. However, in space, many low-cost satellite antennas suffer degradation from atomic oxygen (AO). This is particularly a problem for nano-satellites or impromptu temporary space antennas to establish an emergency link, both of which are designed to operate for a short time span and are currently not always afforded protective coating. Current antenna fingerprinting techniques only use Support Vector Machine (SVM) and Convolutional Neural Networks (CNNs) to take a snap-shot fingerprint before degradation, and hence fail to capture temporal variations due to degradation. Here, we show how we can perform robust antenna fingerprinting (99.34% accuracy) for up to 198 days under intense AO degradation damage using Recurrent Neural Networks (RNNs). We compare our RNN results with CNNs and SVM techniques using different signal features and for different Low-Earth Orbit (LEO) satellite scenarios. We believe this initial research can be further improved and has real-world impact on physical-layer security of short-term nano-satellite antennas in space.

Nickel mine soil is a potential source for soybean plant growth promoting and heavy metal tolerant rhizobia.

Mine soil is not only barren but also contaminated by some heavy metals. It is unclear whether some rhizobia survived under extreme conditions in the nickel mine soil. Therefore, this study tries to isolate some effective soybean plant growth promoting and heavy metal resistant rhizobia from nickel mine soil, and to analyze their diversity. Soybean plants were used to trap rhizobia from the nickel mine soil. A total of 21 isolates were preliminarily identified as rhizobia, which were clustered into eight groups at 87% similarity level using BOXA1R-PCR fingerprinting technique. Four out of the eight representative isolates formed nodules on soybean roots with effectively symbiotic nitrogen-fixing and plant growth promoting abilities in the soybean pot experiment. Phylogenetic analysis of 16S rRNA, four housekeeping genes (atpD-recA-glnII-rpoB) and nifH genes assigned the symbiotic isolates YN5, YN8 and YN10 into Ensifer xinjiangense and YN11 into Rhizobium radiobacter, respectively. They also showed different tolerance levels to the heavy metals including cadmium, chromium, copper, nickel, and zinc. It was concluded that there were some plant growth promoting and heavy metal resistant rhizobia with the potential to facilitate phytoremediation and alleviate the effects of heavy metals on soybean cultivation in nickel mine soil, indicating a novel evidence for further exploring more functional microbes from the nickel mine soil.

Common Colonization Genes Profiling and BOX-PCR Based Genotyping of Streptococcus agalactiae from Pregnant Women in Tehran, Iran

Background: Streptococcus agalactiae or group B Streptococcus (GBS) is a prominent cause of severe neonatal infections. Group B Streptococcus is a part of the intestinal and vaginal normal flora. Maternal colonization is recognized as the main path of GBS transmission. Group B Streptococcus is a pathobiont that changes from a non-symptomatic mucosal carriage state to a significant bacterial pathogen, causing major infections. Objectives: This study aimed to investigate the concomitant presence of major colonization genes, including ftsA, ftsB, lmb, and sfbA, and to determine the genetic relatedness of clinical GBS isolates. Methods: The GBS isolates were obtained from urinary and placental samples of pregnant women with a urinary tract infection, who were admitted to a hospital in Tehran, Iran. The presence of some major colonization factors was investigated via multiplex PCR assay. Genotyping of the isolates was performed using the BOX-PCR fingerprint technique with a BOX-A1R primer. Next, the data were analyzed using the UPGMA method and the coefficient of Jaccard in NTSYS software. Results: A total of 60 GBS isolates were examined in this study. The concomitant presence of target colonization genes was observed in all isolates. The BOX-PCR discriminated GBS isolates into six different genetic clusters at a 60% cutoff point. The majority of isolates (80%) from both clinical samples were clustered into genotypes 2, 6, and 4, while the rest (20%) were distributed equally into three different genotypes. Conclusions: Determining the colonization associated genes and genetic polymorphism in a different geographical area provides the epidemiological basis for the prevention of GBS infections in pregnant women and infants.

Artificial intelligence informed toxicity screening of amine chemistries used in the synthesis of hybrid organic–inorganic perovskites

AbstractThis article describes a machine learning guided framework for screening the potential toxicity impact of amine chemistries used in the synthesis of hybrid organic–inorganic perovskites. Using a combination of a probabilistic molecular fingerprint technique that encodes bond connectivity (MinHash) coupled to non‐linear data dimensionality reduction methods (Uniform Manifold Approximation and Projection), we develop an “Amine Atlas.” We show how the Amine Atlas can be used to rapidly screen the relative toxicity levels of amine molecules used in the synthesis of 2D and 3D perovskites and help identify safer alternatives. Our work also serves as a framework for rapidly identifying molecular similarity guided, structure–function relationships for safer materials chemistries that also incorporate sustainability/toxicity concerns.

Accurate indoor positioning system based on modify nearest point technique

Wireless fidelity (Wi-Fi) is common technology for indoor environments that use to estimate required distances, to be used for indoor localization. Due to multiple source of noise and interference with other signal, the receive signal strength (RSS) measurements unstable. The impression about targets environments should be available to estimate accurate targets location. The Wi-Fi fingerprint technique is widely implemented to build database matching with real data, but the challenges are the way of collect accurate data to be the reference and the impact of different environments on signals measurements. In this paper, optimum system proposed based on modify nearest point (MNP). To implement the proposal, 78 points measured to be the reference points recorded in each environment around the targets. Also, the case study building is separated to 7 areas, where the segmentation of environments leads to ability of dynamic parameters assignments. Moreover, database based on optimum data collected at each time using 63 samples in each point and the average will be final measurements. Then, the nearest point into specific environment has been determined by compared with at least four points. The results show that the errors of indoor localization were less than (0.102 m).

A Robust and Accurate Indoor Localization Using Learning-Based Fusion of Wi-Fi RTT and RSSI.

Great attention has been paid to indoor localization due to its wide range of associated applications and services. Fingerprinting and time-based localization techniques are among the most popular approaches in the field due to their promising performance. However, fingerprinting techniques usually suffer from signal fluctuations and interference, which yields unstable localization performance. On the other hand, the accuracy of time-based techniques is highly affected by multipath propagation errors and non-line-of-sight transmissions. To combat these challenges, this paper presents a hybrid deep-learning-based indoor localization system called RRLoc which fuses fingerprinting and time-based techniques with a view of combining their advantages. RRLoc leverages a novel approach for fusing received signal strength indication (RSSI) and round-trip time (RTT) measurements and extracting high-level features using deep canonical correlation analysis. The extracted features are then used in training a localization model for facilitating the location estimation process. Different modules are incorporated to improve the deep model’s generalization against overtraining and noise. The experimental results obtained at two different indoor environments show that RRLoc improves localization accuracy by at least 267% and 496% compared to the state-of-the-art fingerprinting and ranging-based-multilateration techniques, respectively.

Using magnetic resonance fingerprinting to characterize periventricular nodular heterotopias in pharmacoresistant epilepsy.

We aimed to use a novel magnetic resonance fingerprinting (MRF) technique to examine in vivo tissue property characteristics of periventricular nodular heterotopia (PVNH). These characteristics were further correlated with stereotactic-electroencephalographic (SEEG) ictal onset findings. We included five patients with PVNH who had SEEG-guided surgery and at least 1 year of seizure freedom or substantial seizure reduction. High-resolution MRF scans were acquired at 3 T, generating three-dimensional quantitative T1 and T2 maps. We assessed the differences between T1 and T2 values from the voxels in the nodules located in the SEEG-defined seizure onset zone (SOZ) and non-SOZ, on -individual and group levels. Receiver operating characteristic analyses were performed to obtain the optimal classification performance. Quantification of SEEG ictal onset signals from the nodules was performed by calculating power spectrum density (PSD). The association between PSD and T1 /T2 values was further assessed at different frequency bands. Individual-level analysis showed T1 was significantly higher in SOZ voxels than non-SOZ voxels (p<.05), with an average 73% classification accuracy. Group-level analysis also showed higher T1 was significantly associated with SOZ voxels (p<.001). At the optimal cutoff (normalized T1 of 1.1), a 76% accuracy for classifying SOZ nodules from non-SOZ nodules was achieved. T1 values were significantly associated with ictal onset PSD at the ultraslow, θ, β, γ, and ripple bands (p<.05). T2 values were significantly associated with PSD only at the ultraslow band (p<.05). Quantitative MRF measures, especially T1 , can provide additional noninvasive information to separate nodules in SOZ and non-SOZ. The T1 and T2 tissue property changes carry electrophysiological underpinnings relevant to the epilepsy, as shown by their significant positive associations with power changes during the SEEG seizure onset. The use of MRF as a supplementary noninvasive tool may improve presurgical evaluation for patients with PVNH and pharmacoresistant epilepsy.

3D MR fingerprinting using Seiffert spirals

PurposeSeiffert spirals were recently explored as an efficient way to traverse 3D k‐space compared to traditional 3D techniques. Several studies have shown the ability of 3D MR fingerprinting (MRF) techniques to acquire T1 and T2 relaxation maps in a short period of time. However, these sequences do not sample across a large region of 3D k‐space every TR, especially in the way that Seiffert trajectories can.MethodsA 3D MRF sequence was designed using 8 Seiffert spirals rotated in 3D k‐space, with flip angle modulation for T1 and T2 sensitivity. The sequence was compared to an MRF sequence using a 2D spiral rotated in 3D k‐space using the tiny golden angle acquisition with similar resolution/readout duration. Both sequences were evaluated using simulations, phantom validation, and in vivo imaging.ResultsIn all experiments, the Seiffert spiral MRF sequence performed similar to if not better than the multi‐axis 2D spiral MRF sequence. Strong intraclass correlation coefficients (&gt; 0.9) were found between conventional and MRF sequences in phantoms, whereas the in vivo results showed slightly less aliasing artifact with the Seiffert trajectory.ConclusionIn this study, Seiffert spirals were used within the MRF framework to acquire high‐resolution T1 and T2 relaxation time maps in less than 2.5 min. The reduced aliasing artifacts seen with the Seiffert sequence suggests that sampling over 3D k‐space evenly each TR can improve quantification or shorten scan times.

Detecting Cryptojacking Web Threats: An Approach with Autoencoders and Deep Dense Neural Networks

With the growing popularity of cryptocurrencies, which are an important part of day-to-day transactions over the Internet, the interest in being part of the so-called cryptomining service has attracted the attention of investors who wish to quickly earn profits by computing powerful transactional records towards the blockchain network. Since most users cannot afford the cost of specialized or standardized hardware for mining purposes, new techniques have been developed to make the latter easier, minimizing the computational cost required. Developers of large cryptocurrency houses have made available executable binaries and mainly browser-side scripts in order to authoritatively tap into users’ collective resources and effectively complete the calculation of puzzles to complete a proof of work. However, malicious actors have taken advantage of this capability to insert malicious scripts and illegally mine data without the user’s knowledge. This cyber-attack, also known as cryptojacking, is stealthy and difficult to analyze, whereby, solutions based on anti-malware extensions, blocklists, JavaScript disabling, among others, are not sufficient for accurate detection, creating a gap in multi-layer security mechanisms. Although in the state-of-the-art there are alternative solutions, mainly using machine learning techniques, one of the important issues to be solved is still the correct characterization of network and host samples, in the face of the increasing escalation of new tampering or obfuscation techniques. This paper develops a method that performs a fingerprinting technique to detect possible malicious sites, which are then characterized by an autoencoding algorithm that preserves the best information of the infection traces, thus, maximizing the classification power by means of a deep dense neural network.

Implementation of FPS Based Electronic Voting Machine Using GSM and Hex Key Pad

The base of democracy is voting. Each vote is important thus recording and tallying of each vote with accuracy and impartiality is important. Voting is one of the electoral routes that ensure the sustenance of democratic system in any civilization. But rigging is one of the biggest problems during the election using EVMs. The possible rate of rigging in the EVMs is five votes per minute. So, to set right the current problems in Indian voting system the aim of electronic voting system using fingerprint technique is to design an Electronic Voting Machine (EVM) system by using embedded system to eliminate the chances of rigging in an election. In this proposed system fingerprint technique is used. The vote will be confirmed in the favor of a candidate only when the fingerprint of the voter will be matched otherwise the vote will be rejected. The problem of booth capturing and multiple votes casted by single person is being addressed here. User would be able to get the message in his/her cell phone in order to ensure and acknowledging the successful casting of vote like VVPAT do in EVM. We can do on spot registration of the voter as well if candidate does not have finger print data available in EVM.

Fast GC E-Nose and Chemometrics for the Rapid Assessment of Basil Aroma

The aim of this work is to assess the potentialities of the synergistic combination of an ultra-fast chromatography-based electronic nose as a fingerprinting technique and multivariate data analysis in the context of food quality control and to investigate the influence of some factors, i.e., basil variety, cut, and year of crop, in the final aroma of the samples. A low = level data fusion approach coupled with Principal Component Analysis (PCA) and ANOVA—Simultaneous Component Analysis (ASCA) was used in order to analyze the chromatographic signals acquired with two different columns (MXT-5 and MXT-1701). While the PCA analysis results highlighted the peculiarity of some basil varieties, differing either by a higher concentration of some of the detected chemical compounds or by the presence of different compounds, the ASCA analysis pointed out that variety and year are the most relevant effects, and also confirmed the results of previous investigations.

Biosystematics, fingerprinting and DNA barcoding study of the genus Lallemantia based on SCoT and REMAP markers

Lallemantia is a medicinally important plant in the world. Due to interspecific hybridization and horizontal gene transfer, spices relationship and delimitation on the genus Lallemantia is difficult based on different molecular markers. Therefore, selecting the appropriate marker can be important. Fingerprinting techniques continue to be used for genomic profiling for the characterization of germplasm and the establishment of the identity of varieties/hybrids/parental sources of aromatic and medicinal plants. For this, we need to produce detailed information on genetic diversity available in Lallemantia as well as investigate spices relationship and delimitation. Therefore, the present study was performed on Lallemantia species in Iran. We used the start codon targeted and retrotransposon-microsatellite amplified polymorphism molecular marker for our genetic investigation with the following aims: 1- To reveal the species delimitation and species relationship in Lallemantia, and 2- To investigate discriminating power of the start codon targeted and retrotransposon-microsatellite amplified polymorphism markers by Gst and NM analysis. The results obtained revealed that the start codon targeted marker is the best to show the relationships between species while the retrotransposon-microsatellite amplified polymorphism marker is the best for species delimitation. we found the loci with the high value of Gst (1.00) in start codon targeted and retrotransposon-microsatellite amplified polymorphism markers that can be used in barcoding and fingerprinting of L. royleana.

Quality evaluation of the extract of aerial parts from Atractylodes lancea based on fingerprint and chemometrics

ABSTRACT The high-performance liquid chromatography (HPLC) fingerprint technique is an effective method to evaluate the quality, identify species, ensure consistency and stability of traditional Chinese medicine (TCM). In this study, determination was performed on an Agilent ZORBAX SB-C18 (4.6 × 250 mm, 5 µm) column with methanol-water as mobile phase in gradient elution at the flow rate of 0.8 mL/min, the column temperature was 30°C, the detection wavelength was set to 260 nm. The HPLC fingerprints of 10 batches of extract of the aerial parts from Atractylodes lancea were evaluated by similarity analysis and chemometrics including hierarchical cluster analysis, principal component analysis. Meanwhile, the antibacterial activities of 10 batches of extract of the aerial parts from Atractylodes lancea were estimated on Escherichia coli (E. coli). The correlation coefficient of similarity between 10 batches of extract was ≥ 0.998, and the extract of the aerial parts from Atractylodes lancea have stable effects on inhibiting the proliferation of E. coli. Besides, bivariate correlation analysis (BCA) and partial least squares regression (PLSR) were applied to construct a spectrum-effect relationship, which proved the 5 main marker compounds were more relevant to anti-bacteria. Therefore, in this work, the results were successfully built an agile and efficient method that can ensure the intra-assay stability of biological activity of the extract of the aerial parts from Atractylodes lancea.

FP-Radar: Longitudinal Measurement and Early Detection of Browser Fingerprinting

Abstract Browser fingerprinting is a stateless tracking technique that aims to combine information exposed by multiple different web APIs to create a unique identifier for tracking users across the web. Over the last decade, trackers have abused several existing and newly proposed web APIs to further enhance the browser fingerprint. Existing approaches are limited to detecting a specific fingerprinting technique(s) at a particular point in time. Thus, they are unable to systematically detect novel fingerprinting techniques that abuse different web APIs. In this paper, we propose FP-Radar, a machine learning approach that leverages longitudinal measurements of web API usage on top-100K websites over the last decade for early detection of new and evolving browser fingerprinting techniques. The results show that FP-Radar is able to early detect the abuse of newly introduced properties of already known (e.g., WebGL, Sensor) and as well as previously unknown (e.g., Gamepad, Clipboard) APIs for browser fingerprinting. To the best of our knowledge, FP-Radar is the first to detect the abuse of the Visibility API for ephemeral fingerprinting in the wild.

IMPLEMENTATION OF RANGE-BASED AND RANGE-FREE 3D INDOOR LOCALIZATION IN MULTI-STORY BUILDING BASED ON RSSI

The real-life indoor localization implementation in a multi-story building is reasonably necessary. Multi-floor shopping centers, airports, residential areas, especially in the big cities, apply positioning schemes to ease visitors or inhabitants. However, most indoor localization researches still emphasize 2D-indoor localization, and the multi-story indoor localization implementations are still limited. One of the challenges of 3D-indoor localization implementation is the shadowing effect caused by signal propagation obstructed by objects in the room, the walls, and floors between rooms. Some researchers conducted the 3D-indoor localization to consider the elevation property of the position estimation scenario. However, there are still very few experimental results in an actual multi-story building as the authors' concerns. This paper proposes the measurement campaign of a 3D-indoor localization system in the actual multi-story building by applying the range-based and range-free method based on the Wireless-Fidelity (Wi-Fi). This research is essential since Wi-Fi is available in almost all smart devices and is installed almost in every corner globally. Compared to other approaches, we propose a relatively simple Wi-Fi-based indoor 3D localization utilizing the specific parameter, received signal strength indicator (RSSI), in a static indoor lobbies environment. Despite some of its advantages, the RSSI parameter has a disadvantage in signal fluctuation over time. In our approach, we tried to solve this issue by applying the min-max algorithm to improve the known trilateration method as the range-based method. We implemented the min-max to observe how far the range-based can still give acceptable positioning results in an actual multi-story building. On the other hand, we used the RSSI values for the range-free method to construct the fingerprint database and employed the machine-learning (ML)-based pattern matching algorithm, the random forest algorithm. We expect to solve the shadowing problem with this radio fingerprint method and to achieve minimal errors. We conducted the measurement campaign using the low-cost Wi-Fi module, the ESP-8266, to generate the RSSI. We placed three ESP-8266 nodes on each floor of a two-floor building as the access points (APs) and an ESP-8266 as a target node or a station (STA). We emphasized two performance metrics to evaluate our proposed system performance: the location estimation accuracy observed as the mean square error (MSE) and the precision shown as the standard deviation (Std Dev). The results show that the fingerprint technique yielded the MSE of 0.9m and Std Dev of 0.69 m, while the min-max method resulted in the performance of MSE of 1.79 m and Std Dev of 0.89 m. These results show that the fingerprint technique still gave better accuracy and precision in the same measurement campaign than the min-max. However, the min-max results are also acceptable since the whole multi-floor building has more than 4 m in elevation. The indoor localization system for multi-story buildings can be applied using both the fingerprint and the min-max in a relatively static environment by observing our system performance metric

Fingerprinting and iso-absorptive resolution techniques for the spectrally overlapping Dutasteride and Silodosin mixture: Content uniformity testing along with greenness profile assessment

The realm of spectrophotometric analysis has witnessed a remarkable progress in inventing faster and simpler resolution techniques for spectrally overlapping drug mixtures. Eco-friendly and progressive spectrophotometric methods were firstly developed in this work, for the simultaneous determination of Dutasteride (DUT) and Silodosin (SLD) in their newly-marketed dosage form. The proposed methods focused on the unique spectral features of this mixture including spectral extension of SLD over DUT spectrum as well as existence of iso-absorptive points. By such way, the methods were classified into two categories; the first one was “fingerprint resolution techniques” including constant extraction coupled with spectrum subtraction and ratio subtraction coupled with constant multiplication methods. The former represented a new modification to the classical constant extraction method where one divisor and lower steps were manipulated instead. The second category was “iso-absorptive resolution techniques”, such as absorptivity centering, absorbance subtraction and amplitude modulation methods. Different solvents were investigated where ethanol was found to be the optimum one regarding drugs solubility, signal sensitivity and environmental, health & safety (EHS) score. Validity of the suggested methods was assessed as per ICH-guidelines and found to be linear over concentration ranges of 5.0–90.0 µg/mL for DUT and 5.0–120.0 µg/mL for SLD. The methods were successfully applied for quantifying the cited drugs in their combined dosage form and evaluating their content uniformity. Moreover, the insignificant statistical difference between the proposed methods and official HPLC ones encourages the utilization of such spectrophotometric methods as greener and faster candidates, especially in modest quality control laboratories. Methods’ greenness profile was finally guaranteed through several assessment tools, namely; national environmental methods index (NEMI), analytical eco-scale, green analytical procedure index (GAPI) and analytical greenness (AGREE) metric.