Employing a Toxic Aging Coin approach to assess hexavalent chromium (Cr[VI])-induced neurotoxic effects on behavior: Heads for age differences.

We are facing a rapidly growing geriatric population (65+) that will live for multiple decades and are challenged with environmental pollution far exceeding that of previous generations. Consequently, we currently have a poor understanding of how environmental pollution will impact geriatric health distinctly from younger populations. Few toxicology studies have considered age differences with geriatric individuals. Critically, all top ten most prevalent age-related diseases are linked to metal exposures. Hexavalent chromium [Cr(VI)] is a metal of major environmental health concern that can induce aging phenotypes and neurotoxicity. However, there are many knowledge gaps for Cr(VI) neurotoxicity, including how Cr(VI) impacts behavior. To address this, we exposed male rats across three ages (3-, 7-, and 18-months old) to Cr(VI) in drinking water (0, 0.05, 0.1 mg/L) for 90 days. These levels reflect the maximum contaminant levels determined by the World Health Organization (WHO) and the U.S. Environmental Protection Agency (US EPA). Here, we report how these Cr(VI) drinking water levels impacted rat behaviors using a battery of behavior tests, including grip strength, open field assay, elevated plus maze, Y-maze, and 3-chamber assay. We observed adult rats were the most affected age group and memory assays (spatial and social) exhibited the most significant effects. Critically, the significant effects were surprising as rats should be particularly resistant to these Cr(VI) drinking water levels due to the adjustments applied in risk assessment from rodent studies to human safety, and because rats endogenously synthesize vitamin C in their livers (vitamin C is a primary reducer of Cr[VI] to Cr[III]). Our results emphasize the need to broaden the scope of toxicology research to consider multiple life stages and suggest the current regulations for Cr(VI) in drinking water need to be revisited.

Ransomware: Analysing the Impact on Windows Active Directory Domain Services.

Ransomware has become an increasingly popular type of malware across the past decade and continues to rise in popularity due to its high profitability. Organisations and enterprises have become prime targets for ransomware as they are more likely to succumb to ransom demands as part of operating expenses to counter the cost incurred from downtime. Despite the prevalence of ransomware as a threat towards organisations, there is very little information outlining how ransomware affects Windows Server environments, and particularly its proprietary domain services such as Active Directory. Hence, we aim to increase the cyber situational awareness of organisations and corporations that utilise these environments. Dynamic analysis was performed using three ransomware variants to uncover how crypto-ransomware affects Windows Server-specific services and processes. Our work outlines the practical investigation undertaken as WannaCry, TeslaCrypt, and Jigsaw were acquired and tested against several domain services. The findings showed that none of the three variants stopped the processes and decidedly left all domain services untouched. However, although the services remained operational, they became uniquely dysfunctional as ransomware encrypted the files pertaining to those services.

GLASS: A Citizen-Centric Distributed Data-Sharing Model within an e-Governance Architecture.

E-governance is a process that aims to enhance a government's ability to simplify all the processes that may involve government, citizens, businesses, and so on. The rapid evolution of digital technologies has often created the necessity for the establishment of an e-Governance model. There is often a need for an inclusive e-governance model with integrated multiactor governance services and where a single market approach can be adopted. e-Governance often aims to minimise bureaucratic processes, while at the same time including a digital-by-default approach to public services. This aims at administrative efficiency and the reduction of bureaucratic processes. It can also improve government capabilities, and enhances trust and security, which brings confidence in governmental transactions. However, solid implementations of a distributed data sharing model within an e-governance architecture is far from a reality; hence, citizens of European countries often go through the tedious process of having their confidential information verified. This paper focuses on the sinGLe sign-on e-GovernAnce Paradigm based on a distributed file-exchange network for security, transparency, cost-effectiveness and trust (GLASS) model, which aims to ensure that a citizen can control their relationship with governmental agencies. The paper thus proposes an approach that integrates a permissioned blockchain with the InterPlanetary File System (IPFS). This method demonstrates how we may encrypt and store verifiable credentials of the GLASS ecosystem, such as academic awards, ID documents and so on, within IPFS in a secure manner and thus only allow trusted users to read a blockchain record, and obtain the encryption key. This allows for the decryption of a given verifiable credential that stored on IPFS. This paper outlines the creation of a demonstrator that proves the principles of the GLASS approach.

HDL-IDS: A Hybrid Deep Learning Architecture for Intrusion Detection in the Internet of Vehicles.

Internet of Vehicles (IoV) is an application of the Internet of Things (IoT) network that connects smart vehicles to the internet, and vehicles with each other. With the emergence of IoV technology, customers have placed great attention on smart vehicles. However, the rapid growth of IoV has also caused many security and privacy challenges that can lead to fatal accidents. To reduce smart vehicle accidents and detect malicious attacks in vehicular networks, several researchers have presented machine learning (ML)-based models for intrusion detection in IoT networks. However, a proficient and real-time faster algorithm is needed to detect malicious attacks in IoV. This article proposes a hybrid deep learning (DL) model for cyber attack detection in IoV. The proposed model is based on long short-term memory (LSTM) and gated recurrent unit (GRU). The performance of the proposed model is analyzed by using two datasets-a combined DDoS dataset that contains CIC DoS, CI-CIDS 2017, and CSE-CIC-IDS 2018, and a car-hacking dataset. The experimental results demonstrate that the proposed algorithm achieves higher attack detection accuracy of 99.5% and 99.9% for DDoS and car hacks, respectively. The other performance scores, precision, recall, and F1-score, also verify the superior performance of the proposed framework.

Comparison of Entropy Calculation Methods for Ransomware Encrypted File Identification.

Ransomware is a malicious class of software that utilises encryption to implement an attack on system availability. The target's data remains encrypted and is held captive by the attacker until a ransom demand is met. A common approach used by many crypto-ransomware detection techniques is to monitor file system activity and attempt to identify encrypted files being written to disk, often using a file's entropy as an indicator of encryption. However, often in the description of these techniques, little or no discussion is made as to why a particular entropy calculation technique is selected or any justification given as to why one technique is selected over the alternatives. The Shannon method of entropy calculation is the most commonly-used technique when it comes to file encryption identification in crypto-ransomware detection techniques. Overall, correctly encrypted data should be indistinguishable from random data, so apart from the standard mathematical entropy calculations such as Chi-Square (χ2), Shannon Entropy and Serial Correlation, the test suites used to validate the output from pseudo-random number generators would also be suited to perform this analysis. The hypothesis being that there is a fundamental difference between different entropy methods and that the best methods may be used to better detect ransomware encrypted files. The paper compares the accuracy of 53 distinct tests in being able to differentiate between encrypted data and other file types. The testing is broken down into two phases, the first phase is used to identify potential candidate tests, and a second phase where these candidates are thoroughly evaluated. To ensure that the tests were sufficiently robust, the NapierOne dataset is used. This dataset contains thousands of examples of the most commonly used file types, as well as examples of files that have been encrypted by crypto-ransomware. During the second phase of testing, 11 candidate entropy calculation techniques were tested against more than 270,000 individual files-resulting in nearly three million separate calculations. The overall accuracy of each of the individual test's ability to differentiate between files encrypted using crypto-ransomware and other file types is then evaluated and each test is compared using this metric in an attempt to identify the entropy method most suited for encrypted file identification. An investigation was also undertaken to determine if a hybrid approach, where the results of multiple tests are combined, to discover if an improvement in accuracy could be achieved.

Trusted Threat Intelligence Sharing in Practice and Performance Benchmarking through the Hyperledger Fabric Platform.

Historically, threat information sharing has relied on manual modelling and centralised network systems, which can be inefficient, insecure, and prone to errors. Alternatively, private blockchains are now widely used to address these issues and improve overall organisational security. An organisation's vulnerabilities to attacks might change over time. It is utterly important to find a balance among a current threat, the potential countermeasures, their consequences and costs, and the estimation of the overall risk that this provides to the organisation. For enhancing organisational security and automation, applying threat intelligence technology is critical for detecting, classifying, analysing, and sharing new cyberattack tactics. Trusted partner organisations can then share newly identified threats to improve their defensive capabilities against unknown attacks. On this basis, organisations can help reduce the risk of a cyberattack by providing access to past and current cybersecurity events through blockchain smart contracts and the Interplanetary File System (IPFS). The suggested combination of technologies can make organisational systems more reliable and secure, improving system automation and data quality. This paper outlines a privacy-preserving mechanism for threat information sharing in a trusted way. It proposes a reliable and secure architecture for data automation, quality, and traceability based on the Hyperledger Fabric private-permissioned distributed ledger technology and the MITRE ATT&CK threat intelligence framework. This methodology can also be applied to combat intellectual property theft and industrial espionage.

A Comparative Analysis of Honeypots on Different Cloud Platforms.

In 2019, the majority of companies used at least one cloud computing service and it is expected that by the end of 2021, cloud data centres will process 94% of workloads. The financial and operational advantages of moving IT infrastructure to specialised cloud providers are clearly compelling. However, with such volumes of private and personal data being stored in cloud computing infrastructures, security concerns have risen. Motivated to monitor and analyze adversarial activities, we deploy multiple honeypots on the popular cloud providers, namely Amazon Web Services (AWS), Google Cloud Platform (GCP) and Microsoft Azure, and operate them in multiple regions. Logs were collected over a period of three weeks in May 2020 and then comparatively analysed, evaluated and visualised. Our work revealed heterogeneous attackers' activity on each cloud provider, both when one considers the volume and origin of attacks, as well as the targeted services and vulnerabilities. Our results highlight the attempt of threat actors to abuse popular services, which were widely used during the COVID-19 pandemic for remote working, such as remote desktop sharing. Furthermore, the attacks seem to exit not only from countries that are commonly found to be the source of attacks, such as China, Russia and the United States, but also from uncommon ones such as Vietnam, India and Venezuela. Our results provide insights on the adversarial activity during our experiments, which can be used to inform the Situational Awareness operations of an organisation.

A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT.

A large number of smart devices in Internet of Things (IoT) environments communicate via different messaging protocols. Message Queuing Telemetry Transport (MQTT) is a widely used publish-subscribe-based protocol for the communication of sensor or event data. The publish-subscribe strategy makes it more attractive for intruders and thus increases the number of possible attacks over MQTT. In this paper, we proposed a Deep Neural Network (DNN) for intrusion detection in the MQTT-based protocol and also compared its performance with other traditional machine learning (ML) algorithms, such as a Naive Bayes (NB), Random Forest (RF), k-Nearest Neighbour (kNN), Decision Tree (DT), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs). The performance is proved using two different publicly available datasets, including (1) MQTT-IoT-IDS2020 and (2) a dataset with three different types of attacks, such as Man in the Middle (MitM), Intrusion in the network, and Denial of Services (DoS). The MQTT-IoT-IDS2020 contains three abstract-level features, including Uni-Flow, Bi-Flow, and Packet-Flow. The results for the first dataset and binary classification show that the DNN-based model achieved 99.92%, 99.75%, and 94.94% accuracies for Uni-flow, Bi-flow, and Packet-flow, respectively. However, in the case of multi-label classification, these accuracies reduced to 97.08%, 98.12%, and 90.79%, respectively. On the other hand, the proposed DNN model attains the highest accuracy of 97.13% against LSTM and GRUs for the second dataset.

An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks.

In recent years, there has been a massive increase in the amount of Internet of Things (IoT) devices as well as the data generated by such devices. The participating devices in IoT networks can be problematic due to their resource-constrained nature, and integrating security on these devices is often overlooked. This has resulted in attackers having an increased incentive to target IoT devices. As the number of attacks possible on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to cope with these attacks efficiently. In this paper, we highlight several machine learning (ML) methods such as k-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) that can be used in IDS. In this work, ML algorithms are compared for both binary and multi-class classification on Bot-IoT dataset. Based on several parameters such as accuracy, precision, recall, F1 score, and log loss, we experimentally compared the aforementioned ML algorithms. In the case of HTTP distributed denial-of-service (DDoS) attack, the accuracy of RF is 99%. Furthermore, other simulation results-based precision, recall, F1 score, and log loss metric reveal that RF outperforms on all types of attacks in binary classification. However, in multi-class classification, KNN outperforms other ML algorithms with an accuracy of 99%, which is 4% higher than RF.

A Privacy-Preserving Healthcare Framework Using Hyperledger Fabric.

Electronic health record (EHR) management systems require the adoption of effective technologies when health information is being exchanged. Current management approaches often face risks that may expose medical record storage solutions to common security attack vectors. However, healthcare-oriented blockchain solutions can provide a decentralized, anonymous and secure EHR handling approach. This paper presents PREHEALTH, a privacy-preserving EHR management solution that uses distributed ledger technology and an Identity Mixer (Idemix). The paper describes a proof-of-concept implementation that uses the Hyperledger Fabric's permissioned blockchain framework. The proposed solution is able to store patient records effectively whilst providing anonymity and unlinkability. Experimental performance evaluation results demonstrate the scheme's efficiency and feasibility for real-world scale deployment.

Treatment outcomes with the use of maxillomandibular fixation screws in the management of mandible fractures.

PURPOSE: The purpose of this prospective randomized study was to assess whether uncomplicated mandible fractures could be treated successfully in an open or closed fashion using maxillomandibular fixation (MMF) screws. MATERIALS AND METHODS: This was a prospective institutional review board-approved study involving 20 adult patients who presented to the university emergency department or oral and maxillofacial surgical clinic with uncomplicated mandible fractures. Patients who met the exclusion criteria consented to enter the study in the open reduction internal fixation (ORIF) or the closed (MMF) study group. Six to 8 MMF screws were used to obtain intermaxillary fixation (IMF) in the 2 groups. Screw failure was documented. All screws were removed at 5 to 6 weeks postoperatively. Insertional torque (IT) was measured at time of screw placement to assess primary stability. Clinical and photographic documentation was performed to assess fracture healing, occlusion, and gingival health. Ten-centimeter visual analog scales were used to assess patient-centered outcomes. Cone-beam computed tomography was performed to assess the long-term effects on the periodontium and roots. A cost comparison was performed to determine whether the use of screws was cost effective compared with arch bars. RESULTS: Fifteen men and 5 women (mean age, 25.2 yr) entered the study. All patients displayed adequate fracture healing based on clinical examination. All patients had acceptable occlusion at 5 to 6 weeks postoperatively. Total screw failure was 27 of 106 screws (25.5%). Forty percent of screws placed in the MMF group failed compared with only 6% in the ORIF group. Gingival health scores were favorable. Factors that had a significant effect on screw failure included a lower IT (P = .002), use in closed (MMF) treatment (P < .001), and use in the posterior jaw (P = .012). Minimal pain was associated with the MMF screws and pre-existing occlusion was re-established based on patients' subjective responses. The MMF group reported a statistically significant lower quality of life (P < .001) compared with the ORIF group. There was only 1 screw site that had a facial cortical bone defect noted at 6-month follow-up CBCT examination. There were no discernible long-term root defects. Cost analysis showed that the use of MMF screws saved around $600 per patient in operating room usage cost alone compared with the estimated use of arch bars. CONCLUSIONS: Uncomplicated mandible fractures were successfully treated using MMF screws in open and closed treatments. However, the utility in closed treatment was decreased because of significant screw failure and patient noncompliance. The screws were well tolerated by the patients. There was minimal long-term damage to the periodontium and dental roots. The cost of screws was more than offset by time savings.

Stabilized quantum-enhanced SIEM architecture and speed-up through Hoeffding tree algorithms enable quantum cybersecurity analytics in botnet detection.

For the first time, we enable the execution of hybrid quantum machine learning (HQML) methods on real quantum computers with 100 data samples and real-device-based simulations with 5000 data samples, thereby outperforming the current state of research of Suryotrisongko and Musashi from 2022 who were dealing with 1000 data samples and quantum simulators (pure software-based emulators) only. Additionally, we beat their reported accuracy of 76.8% by an average accuracy of 91.2%, all within a total execution time of 1687 s. We achieve this significant progress through two-step strategy: Firstly, we establish a stable quantum architecture that enables us to execute HQML algorithms on real quantum devices. Secondly, we introduce new hybrid quantum binary classifiers (HQBCs) based on Hoeffding decision tree algorithms. These algorithms speed up the process via batch-wise execution, reducing the number of shots required on real quantum devices compared to conventional loop-based optimizers. Their incremental nature serves the purpose of online large-scale data streaming for domain generation algorithm (DGA) botnet detection, and allows us to apply HQML to the field of cybersecurity analytics. We conduct our experiments using the Qiskit library with the Aer quantum simulator, and on three different real quantum devices from Azure Quantum: IonQ, Rigetti, and Quantinuum. This is the first time these tools are combined in this manner.

Chromium Selectively Accumulates in the Rat Hippocampus after 90 Days of Exposure to Cr(VI) in Drinking Water and Induces Age- and Sex-Dependent Metal Dyshomeostasis.

Hexavalent chromium (Cr[VI]) is a widespread environmental pollutant in air and water that is primarily attributed to industrial pollution. The current maximum contaminant levels (MCLs) for drinking water from the World Health Organization and the U.S. Environmental Protection Agency (0.05 and 0.1 mg/L, respectively) were set based on contact dermatitis and warrant further toxicological investigation. While Cr(VI) is neurotoxic and accumulates in the brain, most animal studies only report whole-brain Cr, leaving large knowledge gaps. Few studies consider differences between ages or sexes, and fewer consider essential metal dyshomeostasis. We sought to investigate where Cr accumulates in the brain, considering sex and age differences, following a 90-day drinking water exposure to current MCLs. Here, we report Cr levels in six brain regions of rats exposed to drinking water Cr(VI). We observed Cr only accumulated in the hippocampus, and only in older females. We further assessed changes to essential metals in the hippocampus, observing opposite effects across sexes and between young rats compared to older rats. In sum, our data indicate drinking water Cr(VI) selectively targeted the hippocampus, with geriatric females accumulating the most Cr, and induced significant essential metal dyshomeostasis even in tissues lacking evident Cr accumulation.

M-F interactions and heterobimetallics: furthering the understanding of heterobimetallic stabilization.

Heterobimetallic complexes containing alkali, alkaline-earth, and divalent europium metals utilizing the perfluoro-tert-butoxide (PFTB) ligand following the general formula, [AM(PFTB)3(co-ligand)x] (A=Na, K; M=Mg, Sr, Ba, Eu; co-ligand=THF, toluene), have been isolated. These compounds sublime at low temperatures with low residual weight indicating their potential as metal-organic chemical-vapor deposition (MOCVD) precursors. The complexes have unique molecular architectures that are strongly influenced by M-F interactions, as was verified in the solid state by using X-ray crystallography. The significance of these interactions were further reinforced by bond-valence sums analysis and (19) F VT-NMR spectroscopy, in which rotational energies of 18.75 and 19.08 kcal mol(-1) were measured.

MicroRNA regulation and its effects on cellular transcriptome in human immunodeficiency virus-1 (HIV-1) infected individuals with distinct viral load and CD4 cell counts.

BACKGROUND: Disease progression in the absence of therapy varies significantly in HIV-1 infected individuals. Both viral and host cellular molecules are implicated; however, the exact role of these factors and/or the mechanism involved remains elusive. To understand how microRNAs (miRNAs), which are regulators of transcription and translation, influence host cellular gene expression (mRNA) during HIV-1 infection, we performed a comparative miRNA and mRNA microarray analysis using PBMCs obtained from infected individuals with distinct viral load and CD4 counts. METHODS: RNA isolated from PBMCs obtained from HIV-1 seronegative and HIV-1 positive individuals with distinct viral load and CD4 counts were assessed for miRNA and mRNA profile. Selected miRNA and mRNA transcripts were validated using in vivo and in vitro infection model. RESULTS: Our results indicate that HIV-1 positive individuals with high viral load (HVL) showed a dysregulation of 191 miRNAs and 309 mRNA transcripts compared to the uninfected age and sex matched controls. The miRNAs miR-19b, 146a, 615-3p, 382, 34a, 144 and 155, that are known to target innate and inflammatory factors, were significantly upregulated in PBMCs with high viral load, as were the inflammatory molecules CXCL5, CCL2, IL6 and IL8, whereas defensin, CD4, ALDH1, and Neurogranin (NRGN) were significantly downregulated. Using the transcriptome profile and predicted target genes, we constructed the regulatory networks of miRNA-mRNA pairs that were differentially expressed between control, LVL and HVL subjects. The regulatory network revealed an inverse correlation of several miRNA-mRNA pair expression patterns, suggesting HIV-1 mediated transcriptional regulation is in part likely through miRNA regulation. CONCLUSIONS: Results from our studies indicate that gene expression is significantly altered in PBMCs in response to virus replication. It is interesting to note that the infected individuals with low or undetectable viral load exhibit a gene expression profile very similar to control or uninfected subjects. Importantly, we identified several new mRNA targets (Defensin, Neurogranin, AIF) as well as the miRNAs that could be involved in regulating their expression through the miRNA-mRNA interaction.

Antibiotics-Free Compounds for Chronic Wound Healing.

The rapid rise in the health burden associated with chronic wounds is of great concern to policymakers, academia, and industry. This could be attributed to the devastating implications of this condition, and specifically, chronic wounds which have been linked to invasive microbial infections affecting patients' quality of life. Unfortunately, antibiotics are not always helpful due to their poor penetration of bacterial biofilms and the emergence of antimicrobial resistance. Hence, there is an urgent need to explore antibiotics-free compounds/formulations with proven or potential antimicrobial, anti-inflammatory, antioxidant, and wound healing efficacy. The mechanism of antibiotics-free compounds is thought to include the disruption of the bacteria cell structure, preventing cell division, membrane porins, motility, and the formation of a biofilm. Furthermore, some of these compounds foster tissue regeneration by modulating growth factor expression. In this review article, the focus is placed on a number of non-antibiotic compounds possessing some of the aforementioned pharmacological and physiological activities. Specific interest is given to Aloevera, curcumin, cinnamaldehyde, polyhexanide, retinoids, ascorbate, tocochromanols, and chitosan. These compounds (when alone or in formulation with other biologically active molecules) could be a dependable alternative in the management or prevention of chronic wounds.

Identification and quantification of polymorphism in the pharmaceutical compound diclofenac acid by terahertz spectroscopy and solid-state density functional theory.

Polymorph detection and quantification in crystalline materials is a principle interest of the pharmaceutical industry. Terahertz (THz) spectroscopy can be used for such analytical applications since this technique is sensitive to the intermolecular interactions of molecules in the solid state. Understanding the fundamental nature of the lattice vibrational motions leading to absorptions in THz spectra is challenging, but may be achieved through computational approaches. In this study, the THz spectra of two diclofenac acid polymorphs were obtained by THz spectroscopy, and the vibrational characters of the observed absorptions were analyzed using solid-state density functional theory (DFT). The results demonstrate the quantitative capacity of THz spectroscopy and the reliability and utility of solid-state DFT in the calculation of low-frequency vibrational motions.

Application of London-type dispersion corrections to the solid-state density functional theory simulation of the terahertz spectra of crystalline pharmaceuticals.

The effects of applying an empirical dispersion correction to solid-state density functional theory methods were evaluated in the simulation of the crystal structure and low-frequency (10 to 90 cm(-1)) terahertz spectrum of the non-steroidal anti-inflammatory drug, naproxen. The naproxen molecular crystal is bound largely by weak London force interactions, as well as by more prominent interactions such as hydrogen bonding, and thus serves as a good model for the assessment of the pair-wise dispersion correction term in systems influenced by intermolecular interactions of various strengths. Modifications to the dispersion parameters were tested in both fully optimized unit cell dimensions and those determined by X-ray crystallography, with subsequent simulations of the THz spectrum being performed. Use of the unmodified PBE density functional leads to an unrealistic expansion of the unit cell volume and the poor representation of the THz spectrum. Inclusion of a modified dispersion correction enabled a high-quality simulation of the THz spectrum and crystal structure of naproxen to be achieved without the need for artificially constraining the unit cell dimensions.

Terahertz spectroscopy and solid-state density functional theory simulations of the improvised explosive oxidizers potassium nitrate and ammonium nitrate.

Terahertz spectroscopy provides a noninvasive and nondestructive method for detecting and identifying concealed explosives. In this work, the room-temperature and cryogenic terahertz spectra of two common improvised explosive oxidizers, namely, potassium nitrate (KN) and ammonium nitrate (AN), are presented, along with detailed solid-state density functional theory (DFT) analyses of the crystalline structures and spectral features. At both 294 and 78 K, KN exhibits two terahertz absorption features below 100 cm(-1) that have been assigned through DFT simulations to arise from hindered nitrate rotations in the KN-II crystalline polymorph. The terahertz spectrum of AN exhibits a pronounced temperature dependence. The 294 K spectrum is free of any absorptions, whereas the 78 K spectrum consists of several narrow and intense peaks. The origin of this large difference is the polymorphic transition that occurs during cooling of AN, where room-temperature AN-IV is converted to AN-V at 255 K. The 78 K terahertz spectrum of AN is assigned here to various ion rotations and translations in the AN-V polymorph lattice. The analysis of the room-temperature AN-IV terahertz spectrum proved to be more complicated. The solid-state DFT simulations predicted that the room-temperature crystal structure of AN is not very well described using the standard Pmmn space-group symmetry as previously believed. The AN-IV polymorph actually belongs to the Pmn2(1) space group, and the perceived Pmmn symmetry results from vibrational averaging through nitrate rotations. This newly observed Pmn2(1) crystal symmetry for room-temperature AN is the reason for the absence of absorption features in the 294 K terahertz spectrum of AN and provides new insight into the polymorphic transitions of this ionic solid.

BeepTrace: Blockchain-Enabled Privacy-Preserving Contact Tracing for COVID-19 Pandemic and Beyond.

The outbreak of the coronavirus disease 2019 (COVID-19) pandemic has exposed an urgent need for effective contact tracing solutions through mobile phone applications to prevent the infection from spreading further. However, due to the nature of contact tracing, public concern on privacy issues has been a bottleneck to the existing solutions, which is significantly affecting the uptake of contact tracing applications across the globe. In this article, we present a blockchain-enabled privacy-preserving contact tracing scheme: BeepTrace, where we propose to adopt blockchain bridging the user/patient and the authorized solvers to desensitize the user ID and location information. Compared with recently proposed contact tracing solutions, our approach shows higher security and privacy with the additional advantages of being battery friendly and globally accessible. Results show viability in terms of the required resource at both server and mobile phone perspectives. Through breaking the privacy concerns of the public, the proposed BeepTrace solution can provide a timely framework for authorities, companies, software developers, and researchers to fast develop and deploy effective digital contact tracing applications, to conquer the COVID-19 pandemic soon. Meanwhile, the open initiative of BeepTrace allows worldwide collaborations, integrate existing tracing and positioning solutions with the help of blockchain technology.