Empirical Measures Research Articles

Evaluation of a rapid multi-attribute combinatorial high-throughput UV-Vis/DLS/SLS analytical platform for rAAV quantification and characterization

Recombinant adeno-associated virus (rAAV)-based gene therapies are expanding in their application. Despite progress in manufacturing, current analytical methods for product quantification and characterization remain largely unchanged. Although critical for product and process development, in-process testing, and batch release, current analytical methods are labor-intensive, costly, and hampered by extended turnaround times and low throughput. The field requires more efficient, cost-effective analytical techniques capable of handling large sample quantities to accelerate product and process development. Here, we evaluated Stunner from Unchained Labs for quantifying and characterizing rAAVs and compared it with established analytical methods. Stunner is a combinatorial analytic technology platform that interpolates ultraviolet-visible (UV-Vis) absorption with static and dynamic light scattering (SLS/DLS) analysis to determine capsid and genomic titer, empty and full capsid ratio, and assess vector size and polydispersity. The platform offers empirical measurements with minimal sample requirements. Upon testing hundreds of rAAV vectors, comprising various serotypes and transgenes, the data show a strong correlation with established analytical methods and exhibit high reproducibility and repeatability. Some analyses can be applied to in-process samples from different purification stages and processes, fulfilling the demand for rapid, high-throughput analysis during development. In sum, the pipeline presented streamlines small- and large-batch analytics.

Machine learning assisted designing of polymers and refractive index prediction: Easy and fast screening of polymers from chemical space

For the selection of efficient materials capable of optical applicability, the property of refractive index (RI) of the material is considered extremely significant. Acquisition of RI values via empirical framework is difficult and prolonged task. Consequently, data-driven method provides a rapid alternative for estimating RI values. This study presents a fast framework that is based on machine learning (ML) approach to design novel polymers capable of performing in optical applications. The framework involves training of ML models to predict the RI values of polymers. Subsequently, 10,000 new polymers were generated utilizing the Breaking Retrosynthetically Interesting Chemical Substructures (BRICS) methodology and fast ML model is used to predict the RI values of newly generated polymers. Polymers exhibiting higher RI values were retained. The synthetic accessibility of these selected polymers was also assessed in order to facilitate the future empirical measurements. Furthermore, chemical similarity among the chosen polymers was also investigated and structural diversity was revealed among the selected polymers. This study is introducing fast and easy framework for the designing and screening of efficient materials. This framework can be modified to study other materials and properties.

Monetary tightening and U.S. bank fragility in 2023: Mark-to-market losses and uninsured depositor runs?

We develop a conceptual framework and an empirical methodology to analyze the effect of rising interest rates on the value of U.S. bank assets and bank stability. We mark-to-market the value of banks’ assets due to interest rate increases from Q1 2022 to Q1 2023, revealing an average decline of 10 %, totaling about $2 trillion in aggregate. We present a model illustrating how asset value declines due to higher rates can lead to self-fulfilling solvency runs even when banks’ assets are fully liquid. Banks with high asset losses, low capital, and, critically, high uninsured leverage are most fragile. A case study of the failed Silicon Valley Bank confirms the model insights. Our empirical measures of bank fragility suggest that, in the absence of regulatory intervention, many U.S. banks would have been at risk of self-fulfilling solvency runs.

Evaluation of grouting effectiveness for Semi-Rigid pavement base layer cracks based on Time-Frequency domain signal characteristics of 3D GPR

This study aims to address the deficiency of rapid, comprehensive, and non-destructive methods for evaluating the effectiveness of grouting treatment on semi-rigid pavement base layer cracks. By integrating radar simulation with empirical measurements, we establish a comprehensive evaluation method for the grouting treatment effectiveness of pavement base layer cracks. Initially, we propose a GprMax simulation of crack models with varying grout-filled volumes, analyzing the time–frequency domain signal characteristics before and after grouting. Based on data sensitivity, thirteen time–frequency domain signal characteristics are selected as grading evaluation indicators. Subsequently, relying on an engineering case study, we conduct a graded evaluation of the grouting effect on base layer cracks, analyzing the correlation and weight of evaluation indicators to derive a comprehensive evaluation method. Field coring results confirm the accuracy of the comprehensive graded evaluation method.

Road lighting standardization using different light sources

This paper investigates the optimization of LED lamps and luminaires lighting in Motorized traffic (M), Conflict (C), and Pedestrian (P) areas to enhance safety and efficiency. These areas present unique challenges regarding visual clarity, spatial awareness, and risk management. LED lamps offer a promising solution due to their energy efficiency and flexibility. This research explores the specific lighting requirements in M, P, and C areas by combining theoretical analysis, empirical measurements, and practical case studies. It proposes strategies for effectively implementing LED lamp lighting and luminaires to improve visibility, mitigate conflicts, and ensure a secure environment for all users by studies the glare in all cases.

Relating state-of-charge to impedance and equivalent circuit parameters in lithium-ion cells: An experimental study

The wide-band impedance of Lithium-ion (Li-ion) batteries has become a focal point for researchers interested in State-of-Charge (SOC) estimation and battery modeling. However, the interplay between SOC, wide-band impedance, and model parameters has not been extensively explored. This paper addresses this gap by investigating how SOC impacts the impedance and Equivalent Circuit Model (ECM) parameters of a Li-ion battery cell through empirical measurements. The study utilizes discharge tests to vary the SOC and employs the Coulomb Counting Method (CCM) to estimate the approximate SOC from the response signals. A novel measurement technique is introduced and applied to determine a cell’s wide-band impedance at different SOC levels. This innovative approach uses a Pseudo-random Impulse Sequence (PRIS) perturbation signal to measure wide-band cell impedance. The collected impedance data is then used to estimate the ECM parameters. Experimental findings reveal an inverse relationship between impedance and SOC. Additionally, the ECM parameters exhibit significant correlation with SOC, particularly in mid- to low-frequency regions. This research highlights that SOC profoundly influences ECM parameters, offering valuable insights into the performance of Li-ion batteries under various conditions.

Addressing the notion of trust around ChatGPT in the high-stakes use case of insurance

The public discourse concerning the level of (dis)trust in ChatGPT and other applications based on large language models (LLMs) is loaded with generic, dread risk terms, while the heterogeneity of relevant theoretical concepts and empirical measurements of trust further impedes in-depth analysis. Thus, a more nuanced understanding of the factors driving the trust judgment call is essential to avoid unwarranted trust. In this commentary paper, we propose that addressing the notion of trust in consumer-facing LLM-based systems across the insurance industry can confer enhanced specificity to this debate. The concept and role of trust are germane to this particular setting due to the highly intangible nature of the product coupled with elevated levels of risk, complexity, and information asymmetry. Moreover, widespread use of LLMs in this sector is to be expected, given the vast array of text documents, particularly general policy conditions or claims protocols. Insurance as a practice is highly relevant to the welfare of citizens and has numerous spillover effects on wider public policy areas. We therefore argue that a domain-specific approach to good AI governance is essential to avoid negative externalities around financial inclusion. Indeed, as a constitutive element of trust, vulnerability is particularly challenging within this high-stakes set of transactions, with the adoption of LLMs adding to the socio-ethical risks. In light of this, our commentary provides a valuable baseline to support regulators and policymakers in unravelling the profound socioeconomic consequences that may arise from adopting consumer-facing LLMs in insurance.

Influence of geometric imperfections of flange joints on the fatigue load of preloaded bolts

In an ideal flange joint configuration, the fatigue endurance of preloaded bolts is contingent upon factors such as the magnitude of preload, applied working load, and the geometric dimensions of the flange. However, real-world flanged joints often deviate from this ideal geometry, introducing imperfections that can markedly escalate the fatigue burden on bolts. This study endeavors to simulate the geometric imperfections inherent in a standard DN40 flange and ascertain their impact on bolt fatigue through a combination of empirical measurements and finite element (FE) analysis. The investigation reveals that even minor deviations in flange geometry, such as non-parallelism on the order of 0.03 mm, can exert a considerable influence on bolt fatigue, particularly when the flange gap aligns with the applied eccentric working load (same side). To mitigate the deleterious effects of these imperfections, the proposal and evaluation of employing filler material between the flanges are made. Specifically, it is demonstrated that a thin layer of liquid metal filler (KemisKit Fe21), substantially alleviates the fatigue burden on bolts. Empirical findings illustrate that this corrective measure enhances the fatigue life of bolts by a factor of more than 30 under the highest applied working load condition.

MetaVRadar: Measuring Metaverse Virtual Reality Network Activity

The ''metaverse'', wherein users can immerse in virtual worlds through their VR headsets to work, study, play, shop, socialize, and entertain, is fast becoming a reality. However, little is known about the network dynamics of metaverse VR applications, which are needed to make telecommunications network infrastructure ''metaverse ready'' to support superlative user experience. This work is an empirical measurement study of metaverse VR network behavior. By analyzing metaverse sessions on the Oculus VR headset, we first develop a categorization of user activity into distinct states ranging from login home to streetwalking and event attendance to asset trading, characterizing network traffic per state, thereby highlighting the vastly more complex nature of a metaverse session compared to streaming video or gaming. Our second contribution develops a real-time method MetaVRadar to detect metaverse session and classify the user activity state leveraging formalized flow signatures and volumetric attributes. Our third contribution practically implements MetaVRadar in a large university campus network to demonstrate its usability.

Business cycle synchronization and asymmetry in the European Union

Measuring output gaps and assessing business cycle synchronization across countries can be an important research theme for an economic union. However, different empirical models can lead to very different inference about output gaps. We analyze business cycle movements and synchronization for all European Union members for the period 1975Q1–pre-COVID and 1975Q1–2022Q4. We account for model uncertainty by averaging across a set of output gap models both by using a Phillips Curve motivated weighted average and a simple average. Similarly, we compare several alternative synchronization measures. We find that, in contrast to the previous literature, both the estimated output gap and the level of synchronization depend on the individual empirical measure used. Our averaged gaps match recession dates well and suggest that many of the countries exhibit a high degree of business cycle nonlinearities. Finally, the core Eurozone countries are highly synchronized, but there are differences in synchronization outside the core.

Irruption Theory in Phase Transitions: A Proof of Concept With the Haken-Kelso-Bunz Model

Many theoretical studies defend the existence of ongoing phase transitions in the brain dynamics that could explain its enormous plasticity to cope with the environment. However, tackling the ever-changing landscapes of brain dynamics seems a hopeless task with complex models. This paper uses a simple Haken-Kelso-Bunz (HKB) model to illustrate how phase transitions that change the number of attractors in the landscape for the relative phase between two neural assemblies can occur, helping to explain a qualitative agreement with empirical decision-making measures. Additionally, the paper discusses the possibility of interpreting this agreement with the aid of Irruption Theory (IT). Being the effect of symmetry breakings and the emergence of non-linearities in the fundamental equations, the order parameter governing phase transitions may not have a complete microscopic determination. Hence, many requirements of IT, particularly the Participation Criterion, could be fulfilled by the HKB model and its extensions. Briefly stated, triggering phase transitions in the brain activity could thus be conceived of as a consequence of actual motivations or free will participating in decision-making processes.

Behind the Door: Practical Parameterization of Propagation Parameters for IEEE 802.11ad Use Cases

The integration of the 60 GHz band into the IEEE 802.11 standard has revolutionized indoor wireless services. However, this band presents unique challenges to indoor wireless communication infrastructure, originally designed to handle data traffic in residential and office environments. Estimating 60 GHz signal propagation in indoor settings is particularly complicated due to dynamic contextual factors, making it essential to ensure adequate coverage for all connected devices. Consequently, empirical channel modeling plays a pivotal role in understanding real-world behavior, which is characterized by a complex interplay of stationary and mobile elements. Given the highly directional nature of 60 GHz propagation, this study addresses a seemingly simple but important question: what is the impact of employing highly directive antennas when deviating from the line of sight? To address this question, we conducted an empirical measurement campaign of wireless channels within an office environment. Our assessment focused on power losses and distribution within an angular range while an indoor base station served indoor users, simulating the operation of an IEEE 802.11ad high-speed WLAN at 60 GHz. Additionally, we explored scenarios with and without pedestrian movement in the vicinity of wireless terminals. Our observations reveal the presence of significant antenna lobes even in obstructed links, indicating potential opportunities to use angular combiners or beamformers to enhance link availability and the data rate. This empirical study provides valuable information and channel parameters to simulate 60 GHz millimeter wave (mm-wave) links in indoor environments, paving the way for more efficient and robust wireless communication systems.

Clogauite, PbBi4Te4S3, a new member of the aleksite series

AbstractClogauite, ideally PbBi4Te4S3 is the new n = 1 member of the aleksite series, PbnBi4Te4Sn+2, where n is the homologue number. Clogauite is named from the type locality, the Clogau gold mine, Dolgellau Gold belt, Gwynedd, North Wales, United Kingdom. The mineral and name have been approved by the Commission on New Minerals, Nomenclature and Classification of the International Mineralogical Association (IMA2023–062). The aleksite series is an accretional homologous series in which each member is derived from the same 5-atom tetradymite archetype. Clogauite crystallises in the trigonal crystal system (space group: P$\bar{3}$m1, #164). Three distinct polytypes of clogauite are recognised, corresponding to identical chemistry but different layer sequences, expressed as (57), (5559) and (557.559), respectively, in reference to the number of atoms in individual layer sequences. These are clogauite-12H, a = 4.277(4) Å, c = 23.46(14) Å, V = 371.598 Å3 and Z = 1; clogauite-24H, a = 4.278(4) Å, c = 46.88(31) Å, V = 743.053 Å3 and Z = 2; and clogauite-36H, a = 4.278(4) Å, c = 70.36(32) Å, V = 1115.283 Å3 and Z = 3. Clogauite is opaque, with a pale grey colour in reflected light. Reflectance is higher than tetradymite or galena. Bireflectance and anisotropy are strong. Structural data were determined from measurement of atomic-scale HAADF STEM imaging showing the internal arrangement of component atoms and characteristic selected area electron diffraction patterns for each polytype. The structures were then further constrained from ab initio total energy calculations and structure relaxation using density functional theory (DFT) using the measured parameters as input data. The relaxed crystal structure for each polytype was modelled to generate crystallographic information files (cif). STEM and electron diffraction simulations based on the crystallographic information data obtained from the DFT calculations show an excellent match to the empirical measurements.

Green Innovation and Firm Performance: The Role of Environmental Management Practices

The relationship between environmental management practices (EMPs), green innovation, and company performance is investigated in this study. Although prior studies have demonstrated the beneficial effects of implementing green management practices on financial performance, the objective of this study is to offer more comprehensive insights by integrating empirical measurements of green innovations. The study looks into how green innovations affect the connection between business performance and EMPs. EMPs are crucial policies that businesses implement to protect the environment and abide by environmental laws. According to the study, financial performance and environmental performance can reinforce one another when investments are made in green innovations. The study adds to the body of knowledge already available on the connection between business environmental sustainability and financial performance by expanding on earlier empirical claims. Keywords: Green innovation, environmental management practices, firm performance, financial performance, environmental performance, green products, technology.

Data Independent Acquisition to Inform the Development of Targeted Proteomics Assays Using a Triple Quadrupole Mass Spectrometer.

Mass spectrometry based targeted proteomics methods provide sensitive and high-throughput analysis of selected proteins. To develop a targeted bottom-up proteomics assay, peptides must be evaluated as proxies for the measurement of a protein or proteoform in a biological matrix. Candidate peptide selection typically relies on predetermined biochemical properties, data from semi-stochastic sampling, or by empirical measurements. These strategies require extensive testing and method refinement due to the difficulties associated with prediction of peptide response in the biological matrix of interest. Gas-phase fractionated (GPF) narrow window data-independent acquisition (DIA) aids in the development of reproducible selected reaction monitoring (SRM) assays by providing matrix-specific information on peptide detectability and quantification by mass spectrometry. To demonstrate the suitability of DIA data for selecting peptide targets, we reimplement a portion of an existing assay to measure 98 Alzheimer's disease proteins in cerebrospinal fluid (CSF). Peptides were selected from GPF-DIA based on signal intensity and reproducibility. The resulting SRM assay exhibits similar quantitative precision to published data, despite the inclusion of different peptides between the assays. This workflow enables development of new assays without additional up-front data acquisition, demonstrated here through generation of a separate assay for an unrelated set of proteins in CSF from the same dataset.

Two risk assessments: Evaluating the use of indicator HF183 Bacteroides versus pathogen measurements for modelling recreational illness risks in an urban watershed

The purpose of this study was to evaluate the performance of HF183 Bacteroides for estimating pathogen exposures during recreational water activities. We compared the use of Bacteroides-based exposure assessment to exposure assessment that relied on pathogen measurements. We considered two types of recreational water sites: those impacted by combined sewer overflows (CSOs) and those not impacted by CSOs. Samples from CSO-impacted and non-CSO-impacted urban creeks were analysed by quantitative polymerase chain reaction (qPCR) for HF183 Bacteroides and eight human gastrointestinal pathogens. Exposure assessment was conducted two ways for each type of site (CSO-impacted vs. non-CSO impacted): 1) by estimating pathogen concentrations from HF183 Bacteroides concentrations using published ratios of HF183 to pathogens in sewage and 2) by estimating pathogen concentrations from qPCR measurements. QMRA (quantitative microbial risk assessment) was then conducted for swimming, wading, and fishing exposures. Overall, mean risk estimates varied from 0.27 to 53 illnesses per 1,000 recreators depending on exposure assessment, site, activity, and norovirus dose-response model. HF183-based exposure assessment identified CSO-impacted sites as higher risk, and the recommended HF183 risk-based threshold of 525 genomic copies per 100 mL was generally protective of public health at the CSO-impacted sites but was not as protective at the non-CSO-impacted sites. In the context of our urban watershed, HF183-based exposure assessment over- and under-estimated risk relative to exposure assessment based on pathogen measurements, and the etiology of predicted pathogen-specific illnesses differed significantly. Across all sites, the HF183 model overestimated risk for norovirus, adenovirus, and Campylobacter jejuni, and it underestimated risk for E. coli and Cryptosporidium. To our knowledge, this study is the first to directly compare health risk estimates using HF183 and empirical pathogen measurements from the same waterways. Our work highlights the importance of site-specific hazard identification and exposure assessment to decide whether HF183 is applicable for monitoring risk.

In situ measurements and simulations of a net cage in currents

Accurate predictions of net cages’ deformation under current loads are vitally important for the welfare of stocked fish and the safety of cage structures. In this study, a scaled cage model was deployed at a fish farm. The incoming flow profile was measured through an Acoustic Doppler Current Profiler (ADCP) with high resolution in the depth direction, and the flow inside the cage was measured through an Acoustic Doppler Vector (ADV). Pressure tags were applied to capture the lifting of the cage under varying current conditions. The incoming flow velocity shows a good correlation with the lifting of the cage, and in general the rate of the cage being lifted by the flow increases with flow speed. However, the flow speed measured inside the cage is much less correlated to the upstream flow and cage deformation. Numerical cage models consisting of truss elements were developed; two flow reduction factors r = 0.9 and 0.8 derived from empirical formulas and measurements inside cages in previous studies were applied. The numerical model with r = 0.8 can predict the cage deformation well, with most relative deviations in the depth directions being less than 15%. The study indicates the feasibility of applying pressure tags with high precision to estimate current-induced cage deformation in situ, especially when a cage is experiencing obvious deformation.

Nicotine flux and pharmacokinetics-based considerations for early assessment of nicotine delivery systems

In the past few years, technological advancements enabled the development of novel electronic nicotine delivery systems (ENDS). Several empirical measures such as “nicotine flux” are being proposed to evaluate the abuse liability potential of these products. We explored the applicability of nicotine flux for clinical nicotine pharmacokinetics (PK) and 52-week quit success from cigarettes for a wide range of existing nicotine delivery systems. We found that the differences in nicotine flux for various nicotine delivery systems are not related to changes in PK, as nicotine flux does not capture key physiological properties such as nicotine absorption rate. Further, the 52-week quit success and abuse liability potential of nicotine nasal sprays (high nicotine flux product), and nicotine inhalers (nicotine flux similar to ENDS) are low, suggesting that nicotine flux is a poor metric for the assessment of nicotine delivery systems. PK indices are more dependable for characterizing nicotine delivery systems, and a nicotine plasma CmaxTmax > 1 could improve 52-week quit success from cigarettes. However, a single metric may be inadequate to fully assess the abuse liability potential of nicotine delivery systems and needs to be further studied. A combination of in vitro and in silico approaches could potentially address the factors influencing the inhaled aerosol dosimetry and resulting PK of nicotine to provide early insights for ENDS assessments. Further research is required to understand nicotine dosimetry and PK for ad libitum product use, and abuse liability indicators of nicotine delivery systems. This commentary is intended to (1) highlight the need to think beyond a single empirical metric such as nicotine flux, (2) suggest potential PK-based metrics, (3) suggest the use of in vitro and in silico tools to obtain early insights into inhaled aerosol dosimetry for ENDS, and (4) emphasize the importance of considering comprehensive clinical pharmacology outcomes to evaluate nicotine delivery systems.

Unpacking Technical Assistance (TA) Strategies Within a State-Level Prevention Support System: A Mixed-Method Study in Determining Types and Amount of TA.

The research-practice gap between evidence-based intervention efficacy and its uptake in real-world contexts remains a central challenge for prevention and implementation science. Providing technical assistance (TA) is considered a crucial support mechanism that can help narrow the gap. However, empirical measurement of TA strategies and their variation is often lacking. The current study unpacks the black box of TA, highlighting different TA strategies, amounts, and their relation to intervention characteristics. First, we qualitatively categorized interactions between TA providers and implementers. Second, we explored how characteristics of implementing organizations and the intervention related to variations in the amount of TA delivered. Using data spanning six years, we analyzed over 10,000 encounters between TA providers and implementers. Content analysis yielded four distinct strategies: Consultation (27.2%), Coordination Logistics (24.5%), Monitoring (16.5%), and Resource Delivery (28.2%). Organizations with prior experience required less monitoring and resource delivery. Additionally, characteristics of the intervention were significantly associated with the amount of consultation, monitoring, coordination logistics, and resource delivery provided. The specific features of the intervention showed significant variation in their relation to TA strategies. These findings provide initial insights into the implications of intervention characteristics in determining how much of which TA strategies are needed to support implementations in real-world settings.

Propagation Analysis of an RFID System in the UHF Band in the Honeycomb Frame of a Beehive.

In recent years, communication systems, including RFID, have been used in intelligent beehives for beekeeping. RFID systems in the UHF frequency band offer reading distances of tens of centimetres, allowing the localisation and identification of the queen bee inside the hive. With this purpose, this work proposes an analysis of an environment of propagation that consists of a honeycomb frame, where the reader is placed within the frame, and the tag is placed in different positions over it. A honeycomb frame consists of a wooden box containing a honey wax panel, supported by metallic wires. The environment is modelled theoretically using its S-parameters and simulated in CST Studio. An analysis of these results and empirical measurements is performed. The results show that a periodicity in the received power of the tag is found with respect to the distance to the reader when the tag is located in a direction parallel to the wire, where local maximum and minimum values are found. Additionally, when the tag is placed over a wire of the frame, a higher received power is obtained compared to the case where the tag is placed between two wires. Furthermore, it has been observed that the reading range has increased with respect to free space, covering the full frame.