Traceability is an important tool in the harmonization and standardization of reporting laboratory results, making them comparable across measurement systems. Driven by International Standardization Organization (ISO) 15189 accreditation requirements, medical laboratories have entered the era of metrological traceability. Although calibrators are a key component in the entire metrological traceability system, there is controversy over internal quality control (IQC) materials. It has been proposed that trueness materials supplied by the system\'s manufacturer with metrological traceability can be used to confirm that the performance of the measuring system is properly unbiased. This article focuses on the implementation challenges and operational hurdles of applying traceability concepts to IQC materials for trueness verification in medical laboratories regarding the most recent 2022 edition of ISO 15189 standard requirements for IQC and metrological traceability. There are practical considerations concerning the acquiring of IQC materials. We must acknowledge the limitations and restrictions that manufacturers and laboratories face before the recommendations can be applied in routine practices.
Sljedivost je važan alat za harmonizaciju i standardizaciju izvještaja o laboratorijskim rezultatima, čineći ih uporedivim u svim sistemima mjerenja. Vođene zahtevima za akreditaciju Međunarodne organizacije za standardizaciju (ISO) 15189, medicinske laboratorije su ušle u eru metrološke sledljivosti. Iako su kalibratori ključna komponenta u celom sistemu metrološke sledljivosti, postoje kontroverze oko materijala za internu kontrolu kvaliteta (IKK). Predloženo je da se materijali za istinitost koje je isporučio proizvođač sistema sa metrološkom sledljivošću mogu koristiti za potvrdu da su performanse mernog sistema ispravno nepristrasne. Ovaj članak se fokusira na izazove implementacije i operativne prepreke primene koncepata sledljivosti na IKK materijale za verifikaciju istinitosti u medicinskim laboratorijama u vezi sa najnovijim zahtevima standarda ISO 15189 za IKK i metrološku sledljivost iz 2022. godine. Postoje praktična razmatranja u vezi sa nabavkom IKK materijala. Moramo da priznamo ograničenja sa kojima se suočavaju proizvođači i laboratorije pre nego što se preporuke mogu primeniti u rutinskoj praksi.

The extensive use of plastic products has resulted in a significant influx of microplastics into aquatic ecosystems, particularly in highly urbanized areas and their associated river environments. However, the specific pathways and quantities through which these microplastics enter the river environment are still unclear, which poses a challenge in developing effective measures to mitigate their sources. In this paper, the spatiotemporal variations of microplastics from different sources in highly urbanized rivers within the Shenzhen Bay watershed were investigated through field sampling, experimental and statistical analysis, and the measures of microplastic reduction were discussed. The observation results exhibited a negative logarithmic correlation between the abundance of microplastics in river water and monthly rainfall (R = 0.994, MSE = 0.051, p < 0.05). When the monthly rainfall was <6 mm, the abundance of microplastics was absolutely dependent on point sources. While the rainfall exceeded 470 mm, the abundance was absolutely predominantly influenced by nonpoint source microplastics. The annual load of microplastics from the watershed was 5.39 × 1012 items, of which 61.6 % originated from point sources. Among the microplastics from point sources, 92.1 % were derived from fibers generated by textile washing. Fragmented microplastics (41.9 %) were the most common type of microplastics from nonpoint sources, primarily originating from the disintegration and weathering of disposable plastics. In the future, there is an expectation to reduce the microplastic load in the watershed to 15.9 % of the total by improving sewage treatment processes and infrastructure. This study can provide scientific guidance for environmental planning and serve as a warning regarding the impact of microplastics on ecosystems in urbanized areas.

Physical unclonable functions (PUFs) have emerged as an unprecedented solution for modern information security and anticounterfeiting by virtue of their inherent unclonable nature derived from distinctive, randomly generated physical patterns that defy replication. However, the creation of traceable optical PUF tags remains a formidable challenge. Here, we demonstrate a traceable PUF system whose unclonability arises from the random distribution of diamonds and the random intensity of the narrow emission from germanium vacancies (GeV) within the diamonds. Tamper-resistant PUF labels can be manufactured on diverse and intricate structural surfaces by blending diamond particles into polydimethylsiloxane (PDMS) and strategically depositing them onto the surface of objects. The resulting PUF codes exhibit essentially perfect uniformity, uniqueness, reproducibility, and substantial encoding capacity, making them applicable as a private key to fulfill the customization demands of circulating commodities. Through integration of a digitized \"challenge-response\" protocol, a traceable and highly secure PUF system can be established, which is seamlessly compatible with contemporary digital information technology. Thus, the GeV-PUF system holds significant promise for applications in data security and blockchain anticounterfeiting, providing robust and adaptive solutions to address the dynamic demands of these domains.

Larimichthys crocea (LYC) holds significant economic value as a marine fish species. However, inaccuracies in labeling its origin can adversely affect consumer interests. Herein, a laser assisted rapid evaporative ionization mass spectrometry (LA-REIMS) and machine learning (ML) was developed for geographical authentication. When compared to iKnife, the LA demonstrated to be superior owing to reduced thermal damage to sample tissue, enhanced automation, and ease of use. Analysis of LYC from six distinct geographical origins across China revealed a total of 798 ions, which were then subjected to six classifiers to establish ML models. Following hyperparameter optimization and feature engineering, the Chi2(15%)-KNN model exhibited the highest training and testing accuracy, achieving 98.4 ± 0.9% and 98.5 ± 1.4%, respectively. This LA-REIMS/ML methodology offers a rapid, accurate, and intelligent solution for tracing the origin of LYC, thereby providing valuable technical support for the establishment of traceability systems in the aquatic product industry.

This study collected multidimensional feature data such as spectra, texture, and component contents of Polygonati Rhizoma from different origins and varieties (Polygonatum kingianum Coll. et Hemsl from Yunnan and Guizhou; Polygonatum cyrtonema Hua from Anhui and Jiangxi; Polygonatum sibiricum Red from Hunan). Multivariate statistical analysis was used to select 39 characteristic factors for distinguishing PR origins and 14 characteristic factors for discriminating PR varieties (VIP > 1 and P < 0.05). In addition, by combining multivariate statistical analysis with a deep belief network (DBN) classification algorithm, a novel artificial intelligence algorithm was developed and optimized. Compared to traditional discriminant analysis methods, the accuracy of this new approach was significantly improved, achieving a 100% discrimination rate for PR varieties and a 100% accuracy rate for tracing the origin of PR. This research provides a reference and data support for constructing intelligent algorithms based on multidimensional data fusion, to achieve food variety discrimination and origin tracing.

In 2022, the number of patients with thyroid disease in China exceeded 200 million (10 million with hyperthyroidism, 90 million with hypothyroidism, and 100 million with other thyroid disease such as goiter, thyroid nodules, and thyroid cancer). Well-established markers include FT3, FT4, TT3, TT4, and TSH tested by a number of immunoassay methods. This approach is based on the primary binding of antigen with antibody and a subsequent secondary chemical reaction that provides an indirect measure. The use of traceable standards for quantitation remains an important factor to ensure inter-assay reliability and precision. Recently, mass spectrometry (MS) has received considerable attention as an analytic tool due to high resolution and quantitative accuracy. In addition, MS allows for sensitive determination of low-abundance markers making it ideal for development of traceable standards. Furthermore, this technology will allow for the development of highly accurate thyroid biomarker assays to facilitate diagnosis, enable early treatment and improve outcomes. Herein, we provide a systematic review and summary of MS in enhancing the analysis of thyroid biomarkers.

Traditional coding methods based on graphics and digital or magnetic labels have gradually decreased their anti-counterfeiting because of market popularity. This paper presents a new magnetic anti-counterfeiting coding method. This method uses a high-performance coding material, which, along with small changes to the material itself and the particle size of the superparamagnetic nanomaterials, results in a large difference in the nonlinear magnetization response. This method, which adopts 12-site coding and establishes a screening model by measuring the voltage amplitude of 12-site variables, can code different kinds of products, establishing long-term stable coding and decoding means. Through the anti-counterfeiting experiment of wine, the experiment results show that the authenticity of the coded products can be verified using the self-developed magnetic encoding and decoding system. The new coding technology can verify the anti-counterfeiting of 9000 products, with a single detection accuracy of 97% and a detection time of less than one minute. Moreover, this coding method completely depends on the production batch of the superparamagnetic nanomaterials, which is difficult to imitate, and it provides a new coding anti-counterfeiting technology for related industries with a wide range of potential applications.

To trace the origin of the gushing water in the riverine area of the Beijing section of The Middle Route of South-to-North Water Diversion Project, a dataset was established comprising water chemistry, three-dimensional fluorescence spectra, and stable isotopes for different water bodies. Results indicated significant differences in Electrical Conductivity (EC), Total Dissolved Solids (TDS), and Ca2+ concentration among the gushing water, river water, and the water from the Middle Route of South-to-North Water Diversion Project (MRSD). Analysis using parallel factor analysis (PARAFAC) and fluorescence index revealed that dissolved organic matter (DOM) in the MRSD mainly originated from endogenous sources, while the river water and gushing water showed influences from both endogenous and exogenous sources. Nitrate sources varied among the water bodies, with distinct contributions from domestic sewage and fertilizer sources. The evaporation lines of river water and gushing water exhibited similar intercepts and slopes, but their intercepts and slopes are much smaller than those of the MRSD, suggesting stronger kinetic evaporative fractionation. In conclusion, the gushing water in the riverine area of the MRSD was determined to originate from the river, providing a fast and efficient method for gushing water source identification.

The Podolica cattle breed is widespread in southern Italy, and its productivity is characterized by low yields and an extraordinary quality of milk and meats. Most of the milk produced is transformed into \"Caciocavallo Podolico\" cheese, which is made with 100% Podolica milk. Fourier Transform Infrared Spectroscopy (FTIR) is the technique that, in this research work, was applied together with machine learning to discriminate 100% Podolica milk from contamination of other Calabrian cattle breeds. The analysis on the test set produced a misclassification percentage of 6.7%. Among the 15 non-Podolica samples in the test set, 2 were misclassified and recognized as Podolica milk even though the milk was from other species. The correct classification rate improved to 100% when the same method was applied to the recognition of Podolica and Pezzata Rossa milk produced by the same farm. Furthermore, this technique was tested for the recognition of Podolica milk mixed with milk from other bovine species. The multivariate model and the respective confusion matrices obtained showed that all the 14 Podolica samples (test set) mixed with 40% non-Podolica milk were correctly classified. In addition, Pezzata Rossa milk produced by the same farm was detected as a contaminant in Podolica milk from the same farm down to concentrations as little as 5% with a 100% correct classification rate in the test set. The method described yielded higher accuracy values when applied to the discrimination of milks from different breeds belonging to the same farm. One of the reasons for this phenomenon could be linked to the elimination of the environmental variable. However, the results obtained in this work demonstrate the possibility of using FTIR to discriminate between milks from different breeds.

Connected Automobile Vehicles (CAVs) enable cooperative driving and traffic management by sharing traffic information between them and other vehicles and infrastructures. However, malicious vehicles create Sybil vehicles by forging multiple identities and sharing false location information with CAVs, misleading their decisions and behaviors. The existing work on defending against Sybil attacks has almost exclusively focused on detecting Sybil vehicles, ignoring the traceability of malicious vehicles. As a result, they cannot fundamentally alleviate Sybil attacks. In this work, we focus on tracking the attack source of malicious vehicles by using a novel detection mechanism that relies on vehicle broadcast beacon packets. Firstly, the roadside units (RSUs) randomly instruct vehicles to perform customized key broadcasting and listening within communication range. This allows the vehicle to prove its physical presence by broadcasting. Then, RSU analyzes the beacon packets listened to by the vehicle and constructs a neighbor graph between the vehicles based on the customized particular fields in the beacon packets. Finally, the vehicle\'s credibility is determined by calculating the edge success probability of vehicles in the neighbor graph, ultimately achieving the detection of Sybil vehicles and tracing malicious vehicles. The experimental results demonstrate that our scheme achieves the real-time detection and tracking of Sybil vehicles, with precision and recall rates of 98.53% and 95.93%, respectively, solving the challenge of existing detection schemes failing to combat Sybil attacks from the root.