Measurement Of Information Research Articles

Short-term air quality prediction using point and interval deep learning systems coupled with multi-factor decomposition and data-driven tree compression

Clean air, as a symbol of high-quality air quality, is the most basic requirement for people to maintain health. Moreover, in keeping humans fit, accurate short-term air quality prediction is vital. The decomposition algorithm can better capture the local features and temporal changes of the data. However, it increases the computation time, resource consumption, and complexity of the model. On the other hand, existing forecasting systems overlook instability and uncertainty. To solve the above problems, a deterministic and uncertainty AOA-DBGRU-MDN deep learning systems is proposed, which combines arithmetic optimization algorithm (AOA), double-layer bi-directional GRUs (DBGRU), and mixture density network (MDN). The above systems consider meteorological factors and air pollutants comprehensively. It involves feature selection using maximum information coefficient (MIC), decomposition using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm, classification, and compression of decomposed components using entropy-Huffman tree compression. Firstly, the information measurement process reduces the number of components significantly. Following the incorporation of multi-factor data, the optimal DBGRU model is then obtained using AOA. Finally, the training errors are fitted using MDN to obtain interval prediction results. The experiments demonstrate that (1) Using the CEEMDAN algorithm can improve the prediction accuracy; (2) Classifying and reconstructing the data based on entropy-Huffman tree compression can not only decrease the model's training volume and improve training efficiency but also boost the model's prediction accuracy; (3) The AOA-DBGRU-MDN system performs probabilistic prediction to obtain an effective and intuitive prediction interval to improve the point prediction of air quality prediction.

Low velocity impact resistance of hybrid CFRP‐elastomer‐metal laminates: Influence of stacking sequence and impact conditions on damage mechanisms

AbstractFiber‐metal laminates (FMLs) are generally regarded as excellent lightweight materials for advanced structure design. To enhance the mechanical properties, the common FMLs can be optimized using carbon fibers. However, the combination of carbon fibers with aluminum induces interfacial challenges. Preventing galvanic corrosion with elastomeric interlayers is an effective solution. The lay‐up configuration greatly effects the impact damage resistance of hybrid CFRP‐elastomer‐metal laminates (HyCEMLs). In this work, micro‐CT scans and optical micrographic inspections on HyCEMLs are conducted after impact tests to ascertain the microstructural origins behind the mechanical performance changes. In addition, finite element models of different HyCEML configurations are built to complement the limited experimental data. The damage mechanisms of HyCEML with different configurations under various impact conditions are further compared. The numerical results suggest that impact energy is a more informative measure in terms of damage mode and size than impact velocity and momentum. Results also indicate that when the thickness for each sub‐laminate of HyCEML is maintained the same, hybrid laminates with aluminum stacked outside is beneficial for delaying the occurrence of matrix cracking and delaminations, and enhances HyCEML's resistance to global deformation. These findings will contribute to engineering hybrid laminates with desired impact performance for lightweight load‐bearing structures.Highlights The hybrid laminate with elastomeric interlayers is a forward‐looking solution in impact applications. Impact energy is a more informative measure in terms of assessing the damage mode and extent in HyCEMLs. The influence of stacking sequence on damage mechanisms of HyCEMLs is evaluated. Microstructural origins behind variations of hybrid laminates in the impact resistance are revealed.

Characterizing the nonmonotonic behavior of mutual information along biochemical reaction cascades.

Cells sense environmental signals and transmit information intracellularly through changes in the abundance of molecular components. Such molecular abundances can be measured in single cells and exhibit significant heterogeneity in clonal populations even in identical environments. Experimentally observed joint probability distributions can then be used to quantify the covariability and mutual information between molecular abundances along signaling cascades. However, because stationary state abundances along stochastic biochemical reaction cascades are not conditionally independent, their mutual information is not constrained by the data-processing inequality. Here, we report the conditions under which the mutual information between stationary state abundances increases along a cascade of biochemical reactions. This nonmonotonic behavior can be intuitively understood in terms of noise propagation and time-averaging stochastic fluctuations that are short-lived compared to an extrinsic signal. Our results reemphasize that mutual information measurements of stationary state distributions of cellular components may be of limited utility for characterizing cellular signaling processes because they do not measure information transfer.

A time-dependent explainable radiomic analysis from the multi-omic cohort of CPTAC-Pancreatic Ductal Adenocarcinoma

Background and ObjectiveIn Pancreatic Ductal Adenocarcinoma (PDA), multi-omic models are emerging to answer unmet clinical needs to derive novel quantitative prognostic factors. We realized a pipeline that relies on survival machine-learning (SML) classifiers and explainability based on patients’ follow-up (FU) to stratify prognosis from the public-available multi-omic datasets of the CPTAC-PDA project. Materials and MethodsAnalyzed datasets included tumor-annotated radiologic images, clinical, and mutational data. A feature selection was based on univariate (UV) and multivariate (MV) survival analyses according to Overall Survival (OS) and recurrence (REC). In this study, we considered seven multi-omic datasets and compared four SML classifiers: Cox, survival random forest, generalized boosted, and support vector machines (SVM). For each classifier, we assessed the concordance (C) index on the validation set. The best classifiers for the validation set on both OS and REC underwent explainability analyses using SurvSHAP(t), which extends SHapley Additive exPlanations (SHAP). ResultsAccording to OS, after UV and MV analyses we selected 18/37 and 10/37 multi-omic features, respectively. According to REC, based on UV and MV analyses we selected 10/35 and 5/35 determinants, respectively. Generally, SML classifiers including radiomics outperformed those modelled on clinical or mutational predictors. For OS, the Cox model encompassing radiomic, clinical, and mutational features reached 75 % of C index, outperforming other classifiers. On the other hand, for REC, the SVM model including only radiomics emerged as the best-performing, with 68 % of C index. For OS, SurvSHAP(t) identified the first order Median Gray Level (GL) intensities, the gender, the tumor grade, the Joint Energy GL Co-occurrence Matrix (GLCM), and the GLCM Informational Measures of Correlations of type 1 as the most important features. For REC, the first order Median GL intensities, the GL size zone matrix Small Area Low GL Emphasis, and first order variance of GL intensities emerged as the most discriminative. ConclusionsIn this work, radiomics showed the potential for improving patients’ risk stratification in PDA. Furthermore, a deeper understanding of how radiomics can contribute to prognosis in PDA was achieved with a time-dependent explainability of the top multi-omic predictors.

Necessity of Effective Quality Control System for Bridge Construction (A Review)

As urbanization quickens and transportation systems proceed to advance, bridges, as indispensably components of urban foundation, confront progressively impressive challenges in their plan and development administration. This inquire about investigates the integration of naturally inviting, financially productive, and socially satisfactory plan standards and development administration strategies in bridge designing from the viewpoint of economical advancement. Through case investigations and information measurements, this paper presents optimized bridge plan arrangements beneath different natural conditions and examines techniques to play down asset squander and improve development effectiveness, in this way accomplishing the feasible improvement objectives of bridge building The quality parameters should easy to understand specially checklists should be easy to understand and easy for implementation In this study is carried out to analyze the scenario of Quality Control System application, to identify factors affecting the construction organization and to issue recommendations on how to improve the implementation of Quality Control System in Construction Industry. The growth in infrastructure increases in our country which leads the construction of new bridges is one most the key elements in this Initially the checklists are observed from different site the defects will identify in check lists and suggest new checklists for each items of construction. This paper finds Necessity of Effective Quality Control System for Bridge Construction by analysising the different Literature review

Transforming the Discovery of Targeted Protein Degraders: The Translational Power of Predictive PK/PD Modeling.

Targeted protein degraders (TPDs), an emerging therapeutic modality, are attracting considerable interest with the promise to address disease-related proteins that are not druggable with conventional small molecule inhibitors. Despite their novel mechanism of action, the PK/PD relationship of degraders is still approached with a mindset deeply rooted in inhibitor drugs. Here, we establish how predictive mechanistic modeling specifically tailored to TPDs can significantly enhance the value of the available information during lead generation and optimization. By integrating the results from in vitro assays with routinely collected PK data, modeling accurately predicts degradation in vivo. These predictions transform the prioritization of compounds for in vivo studies as well as the selection of optimal dose schedules and most informative measurement time points with the least number of animals. Moreover, the comprehensive modeling framework (1) identifies the PK/PD driver of targeted protein degradation and subsequent downstream pharmacodynamic effects, and (2) uncovers the fundamental difference between degrader and inhibitor PK/PD relationships. The practical utility of our predictive modeling is demonstrated with relevant use cases. This framework will allow researchers to transition from current, mostly serendipity-based approaches to more sound model-informed decision making. Going forward, the presented predictive PK/PD modeling framework lays out a rational path to incorporate inter-species differences in the pharmacology and thus promises to help with getting the dose right in clinical trials.

The data recovery strategy on machine learning against false data injection attacks in power cyber physical systems

During the transmission of power measurement data through communication networks from remote terminal unit (RTU) to the state estimator in Supervisory Control and Data Acquisition (SCADA), power cyber-physical systems (PCPSs) are more susceptible to cyber-attacks. To mitigate that threat, this paper is concerned with a new data recovery strategy on machine learning against false data injection attacks (FDIAs) in PCPSs. Firstly, in view of the limited resources (such as limited energy) of adversaries and system protections, a sparse target false data injection attack (FDIA) is constructed. Then, the FDIA detection problem is transformed into a tripartite separation problem, and the alternating direction method of multipliers on proximal exchange (ADMM-PE) is adopted to complete the intrusion detection of FDIAs. In addition, with the help of reliable mask information and real incomplete measurement data provided by the FDIA detection, a similar supervised generative adversarial imputation networks (GAIN) is proposed to complete the measurement data recovery after FDIAs. Specifically, the pseudo labels generated by data analysis methods such as k-means clustering and support vector machine (SVM) to improve the accuracy of measurement data recovery. Finally, the experimental results of PCPSs show the effectiveness and superiority of the proposed data recovery strategy against FDIAs.

Satellite observed oceanographic drivers of Mobulidae fisheries catch in the Southeast Indian Ocean

Indonesian coastal waters include several marine megafauna biodiversity hotspots. Several fish populations of ecological and socio-economic importance, such as elasmobranchs (sharks and rays), have experienced rapid decline due to unsustainable human activities, primarily overfishing. Small-scale fisheries (SSF) are currently exempt from governmental fisheries management measures despite contributing a significant proportion of a total catch. The Generalised Additive Models were used to investigate the effect of variations in oceanographic parameters of the Teluk Penyu fishing ground, south of central Java, on the magnitude of Mobulidae (Mobula spp.) catch based on its landings data over ten years (2009–2018) from one of Indonesia's largest ports, Cilacap, Central Java, Indonesia. Mobulidae catch from Teluk Penyu fishing ground was generally higher from June to November when the water exhibited relatively high sea surface salinity (sal >34.1 ‰), chlorophyll (0.32–0.45 mg/m3), and nitrate (nit >0.0045 mg NO3/m3), water speed (>0.29 m/s) and eddy kinetic energy (>0.04 m3/s2) levels, and relatively low sea surface temperature (<28 °C), oxygen (<0.182 mg O2/m3) and sea surface height (<0.9 m) levels than the other months of the year. This study reveals that satellite Earth Observation (EO) data provided a preliminary relationship between oceanographic conditions and the amount of catch for developing more effective management and conservation measures for endangered species like Mobulidae. Utilizing EO data may also be applied to help inform much-needed ecosystem-based management measures, including habitat protection and bycatch reduction for conserving endangered Mobulidae species in the Southeast Indian Ocean. The in-situ onboard ocean observation and temporal species-specific catch data will greatly complement the current work.

Effects of Music Therapy on Postoperative Pain Perception in Patients Undergoing Abdominal Surgeries Under General Anesthesia: A Prospective Controlled Randomized Comparative Clinical Study.

Background Music therapy has been shown to reduce the need for sedation and analgesics, as well as lower plasma cortisol and epinephrine levels in patients undergoing regional anesthesia. This study evaluates the efficacy of perioperative music therapy in reducing pain perception and its impact on cortisol levels. Materials and methods This prospective randomized control trial was conducted at the tertiary care center in Western Maharashtra. Sixty adult patients (males/females) undergoing elective lower abdomen surgery were included and randomized equally into two groups to listen to music using headphones at a preselected volume (Group M) or to use only headphones without any music played (Group C) for 2 hours in the preoperative, intraoperative, and postoperative periods. Demographic information, anthropometric (height, weight), and biochemical (serum cortisol) measurements were performed. American Society of Anesthesiologists (ASA) grades 1 and 2, and patients aged 18 to 65 years were included. Pain perception was assessed using the Visual Analog Scale (VAS). Results The demographic data, including mean age, BMI, ASA status, average duration of anesthesia, and average duration of surgery, were comparable between the two groups. Group M showed improved control of systolic blood pressure (SBP) at 10- and 30-minutepost-induction of general anesthesia and had a significantly lower VAS score (p < 0.05). Additionally, patient satisfaction was higher in Group M (81.4%) compared to Group C (51.4%) with a p-value of < 0.05. Intraoperative awareness was non-significant between the groups. Conclusion Music therapy is a safe, cost-effective, and efficacious method for reducing pain perception and can be used in conjunction with other treatments for postoperative pain management.

Investigating the role of artificial intelligence in predicting perceived dysphonia level.

This study aims to investigate the role of one of these models in the field of voice pathology and compare its performance in distinguishing the perceived dysphonia level. Demographic information, voice self-assessments, and acoustic measurements related to a sample of 50 adult dysphonic outpatients were presented to ChatGPT and Perplexity AI chatbots, which were interrogated for the perceived dysphonia level. The agreement between the auditory-perceptual assessment by experts and ChatGPT and Perplexity AI chatbots, as determined by Cohen's Kappa, was not statistically significant (p = 0.429). There was also a low positive correlation (rs = 0.30, p = 0.03) between the diagnosis made by ChatGPT and Perplexity AI chatbots (rs = 0.30, p = 0.03). It seems that AI could not play a vital role in helping the voice care teams determine the perceptual level of dysphonia.

Global-local state analysis and monitoring-based velocity measurement of oil–gas–water three-phase flow

The mixture fluids of oil–gas–water metamorphose through the change of phase, properties and structure. This poses a challenge to decipher their dynamic entanglement for phase velocity measurement. It is evident that progressively grasp of process states provides valuable prior information for further flow velocity measurement. As a significant flow characteristic, multiscale gives unique inspiration for flow state monitoring and velocity measurement from a global–local view. In this work, empirical wavelet transform-based Hilbert-Huang transform (EWT-based HHT) and multiscale robust empirical permutation entropy (Ms-rePE) are employed for the analysis of CWUD and conductance signals respectively, achieving the joint characterization of oil–gas–water flow structure by acoustic-electrical dual-mode information. On this basis, a data-mechanism dual-driven model for flow velocity measurement is established under a unified multiscale framework. And a novel approach of hypergraph-based global–local structure analysis (HGLSA) is proposed to simultaneously extract global–local information embodied in the complex flow process. The proposed scheme of global–local state monitoring-based flow velocity measurement is verified by the dynamic experiment of oil–gas–water three phase flow.

(Invited) Wearable Flexible Microfluidic Biosensor for Sweat Monitoring

As we enter the post-pandemic era, our lives have certainly become more convenient than before, due to the advancement and elimination of technologies that became practical or widespread during the pandemic. Among these, technologies related to non-invasive bioinstrumentation, which can be used at an individual level to access one's own health information, have become increasingly important. Non-invasive bioinstrumentation can be broadly divided into the measurement of physical information (shape, activity and so on) and the measurement of chemical information, i.e. the components of the body. For example, facial shape is widely used for personal identification, while weight and energy expenditure related to activity are easily managed by smartphones. In contrast, chemical sensing, which is often difficult to sample and requires a wet environment for measurement, still requires breakthroughs to be used by individuals with the same ease as physical sensing.In the research field of non-invasive biomedical measurements, the technology is often developed in the form of wearable systems. Wearable microfluidic devices have been intensively studied for the measurement of body components. We focused on sweat, which contains a variety of biomarkers, and have developed a wearable microfluidic biosensing system that measures sweat components in real-time. Sweat is secreted from sweat glands on the skin of the entire body, making it easily accessible and less mentally demanding to collect. On the other hand, there are problems with sampling methods for reliable measurement, such as large fluctuations in the sweat rate and evaporation from the skin surface during secretion. We overcame this problem by using a wearable microfluidic device to transport the full amount of sweat from the sweat gland to a downstream biosensor by carrier flow, and developed a wristwatch biosensing system to monitor the constituents released as sweat independent of sweat rate.The wristwatch sweat sensor consists of a PDMS sweat sampling device, a biosensor, a fluid controller and a wireless control circuit with Bluetooth Low Energy and external analogue I/O. Communication with the mobile device is via Bluetooth Low Energy. Our method has the advantage that secretions are transported by dissolution in the carrier stream and can therefore be reliably monitored even at very low sweat levels. The pump is controlled by an microcontroller unit, so the flow rate can be controlled in real time according to the sweat rate and target substances, and the sensitivity can be improved at the expense of temporal resolution by running the pump intermittently, i.e. allowing secretions to accumulate in the carrier flow for a certain time before measurement.We monitored changes in sweat lactic acid in response to exercise load using an ergometer. The results showed an increase in sweat lactate with increasing exercise intensity, with a dependence on sweat rate. By measuring sweat at rest, we found that the lactate accumulated in the sweat glands and on the skin's surface were dissolved by the biosensor in the early stages of measurement, while the signals from the sweat glands became dominant after these had been sufficiently decreased. These results suggest that our system appropriately reflects the physiological phenomena associated with sweating. The presentation will also include the behaviour of lactic acid in sweat, which was detected by our system for the first time.

Using Simulation Models to Train Clinicians in the Use of Point-of-Care Ultrasound.

The use of point-of-care ultrasound (POCUS) has shown to be a beneficial non-invasive vascular access assessment method by clinicians, which can provide critical elements of visual and measurable information that proves to be useful in the context of vascular access cannulation, in combination with the practical skill of the clinician performing the cannulation. However, the use of POCUS in this context is to practically train and enable individuals who are novices in using this technique to become proficient in performing this task subsequently on patients in a careful and successful way. The simulation of these vascular conditions may be useful to help healthcare professionals learn, understand, apply, and establish such practical skills for vascular cannulation safely to achieve the desired outcomes. This project intended, through the attendance of a half-day workshop, to establish skills to use POCUS in connection with simulation models and perform specific tasks to enable clinicians to use this method in their clinical practice for vascular access cannulation in patients. A mixed-methods longitudinal study design was used to evaluate the effect of a point-of-care ultrasound workshop for peripheral intravenous cannula insertion, including specific tasks for the participants to be performed on simulation models. A total of 81 individuals participated in 11 half-day workshops through 2021 and 2022. Offering a workshop that uses simulation models in combination with various POCUS devices is useful in establishing this newly learned skill in clinicians, such as measurements of depth, caliper, and direction of a vein with POCUS prior to cannulation providing essential anatomical facts to the operator, which increases the likelihood of first-time success in cannulation.

Rare-earth dopant distribution measurement of optical fibers based on filtered back projection

We present a nondestructive measurement based on filtered back projection (FBP) for the distribution of rare-earth dopant in optical fibers. Based on the approach, a measurement system is established, where the single-mode pumped laser illuminates the fiber under test from the side, generating multi-wavelength fluorescence through photoluminescence (PL). The distribution of rare-earth doped particles is proportional to the distribution of fluorescence intensity. The projection images are collected by the CCD camera at 90° relative to the excitation arm. According to the computed tomography (CT), the rotation of the optical fiber enables the experimental measurements of intensity information extracted from multi-angle projections. The rare-earth dopant distribution of the fiber is reconstructed by the improved filtered back projection algorithm, which improves the quality of the reconstructed result by incorporating total variation (TV) algorithm. To evaluate the proposed approach, rare-earth dopant distribution is compared with results measured by EPMA, exhibiting a correlation coefficient above 0.99, and the relationship between the dopant distribution and the refractive index is analyzed. Measurements on doped particle distributions at various wavelengths are conducted, and the fluorescence spectrum are constructed to further analyze the fluorescence characteristics of rare earth doped fiber. Measurement of RE dopant distribution will be useful for quality evaluation and optimization of partial active optical fibers.

Contemporary Management Accounting System Practices and Managerial Performance of Nepalese Commercial Banks

Contemporary management accounting systems provide managers with the necessary organizational performance in commercial banks. The study examines the adoption and influence of contemporary management accounting system practices (CMASPs) on managerial performance within commercial banks in Kathmandu Valley, Nepal. The study treats costing, budgeting, strategic analysis, decision-support information, and performance measurement as independent variables, with managerial performance as the dependent variable. Employing descriptive and causal-comparative research designs alongside convenience sampling, the study gathered primary data from selected twenty Nepalese commercial bank branch managers and deputy managers using structured survey questionnaires rated on a 5-point Likert scale. Analysis encompassed descriptive statistics, and inferential statistics including mean, standard deviation, skewness, kurtosis, and structural equation modeling (SEM) via Path Analysis. The study reveals that CMASPs function effectively, positively affecting managerial performance. These findings highlight the significance of CMASPs in achieving superior managerial performance within Nepal's commercial banking industry, offering actionable insights for bank managers and policymakers aiming to optimize performance through effective CMASPs. Future research directions could explore these management accounting system practices across different sectors and investigate advanced MAS practices and technological integration.

Hypofractionated radiotherapy plus PD-1 antibody and SOX chemotherapy as second-line therapy in metastatic pancreatic cancer: a single-arm, phase II clinical trial

PurposeTo assess the efficacy and safety of concurrent hypofractionated radiotherapy plus anti-PD-1 antibody and SOX chemotherapy in the treatment of metastatic pancreatic cancer (mPC) after failure of first-line chemotherapy.MethodsPatients with pathologically confirmed mPC who failed standard first-line chemotherapy were enrolled. The patients were treated with a regimen of hypofractionated radiotherapy, SOX chemotherapy, and immune checkpoint inhibitors at our institution. We collected the patients’ clinical information and outcome measurements. The median progression-free survival (mPFS) was the primary endpoint of the study, followed by disease control rate (DCR), objective response rate (ORR), median overall survival (mOS) and safety. Exploratory analyses included biomarkers related to the benefits.ResultsBetween February 24, 2021, and August 30, 2023, twenty-five patients were enrolled in the study, and twenty-three patients who received at least one dose of the study agent had objective efficacy evaluation. The mPFS was 5.48 months, the mOS was 6.57 months, and the DCR and ORR were 69.5% and 30.4%, respectively. Among the seven patients who achieved a PR, the median duration of the response was 7.41 months. On-treatment decreased serum CA19-9 levels were associated with better overall survival. Besides, pretreatment inflammatory markers were associated with tumor response and survival.ConclusionsClinically meaningful antitumor activity and favorable safety profiles were demonstrated after treatment with these combination therapies in patients with refractory mPC. On-treatment decreased serum CA19-9 levels and pretreatment inflammatory markers platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), lactate dehydrogenase (LDH) might be biomarkers related to clinical benefits.Clinical trial registration: https://www.chictr.org.cn/showproj.html?proj=130211, identifier: ChiCTR2100049799, date of registration: 2021–08-09.

Legal issues in the implementation of Maternal Death Surveillance and Response: a scoping review.

The Maternal Death Surveillance and Response (MDSR) system is designed to continuously identify and review all maternal deaths. It aims to help countries understand the scale and distribution of maternal deaths, identify their causes, and inform corrective measures to address the challenge. Despite the growing adoption of the MDSR by numerous low- or middle-income countries, its implementation faces various challenges, including legal ones. This scoping review was conducted to map legal issues and challenges that arise during the implementation of the MDSR. It adapted the Bain and Kongnyuy framework, categorizing legal issues into data, people, use of findings, and legal regulation. Literature was retrieved from seven databases, complemented by additional online searches. We included studies published in English between 2010 and November 2022 that report on legal issues arising during the implementation of MDSR. Out of 1174 studies screened, 31 were selected for review. The review highlighted the limited attention given to the legal dimension of the MDSR by the research community. It also documented the lack of adequate legal framework essential for the system's effective implementation. Inadequate safeguards for informational privacy and the lack of confidentiality reinforce a prevalent sense of being blamed, mainly among health workers. Consequently, widespread under-reporting and intentional misattribution of causes of maternal death, defensive referrals, and disengagement from the MDSR process were reported. We recommend that implementing countries regulate the gathering and use of MDSR data through appropriate laws and legally ensure that the MDSR data are only used for the intended purpose. Appropriate complaint-handling mechanisms are needed in health systems to prevent the misuse of the MDSR. Future studies on MDSR implementation would benefit from involving legal experts, considering the multifaceted legal dimensions of the MDSR.

Determination of the polarization of Xe nuclear spin using transverse and longitudinal Rb in-situ magnetometers in NMR co-magnetometers

NMR co-magnetometers, as novel atomic sensors, can measure the angular velocity information in inertial measurements by detecting the spin precession of Xe nuclei. The Xe polarization plays a crucial role in the sensing unit of co-magnetometers, as it enhances the signal-to-noise ratio (SNR) and sensitivity of NMR sensors. Here we elucidated the Rabi dynamics of Xe nuclear spin and demonstrated two methods to determine the Xe polarization based on the classical Bloch equations. By detecting the optical rotation angle signal, we acquired the amplitude and the phase variations information in two methods, respectively. The transverse magnetometer relies on the amplitude of the free induction decay (FID) signal and applies a short pulse to flip the Xe fields into the transverse plane, enabling precise measurements. On the other hand, the longitudinal magnetometer offers an intuitive approach to measuring the demodulated phase variations by capturing the repetitive flips of longitudinal Xe fields. This paper discusses the advantages and disadvantages of both proposed methods, with the aim of enhancing the accuracy and authenticity of nuclear spin polarization measurements for noble gas atoms in atomic sensors.

Proportions of Hypertension in Stable COPD Patients at the National Respiratory Center Persahabatan Hospital

Background: Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. Most of these deaths are related to cardiovascular disease. This is due to systemic inflammation that causes increased vascular stiffness and hypertension. These comorbidities lead to poor quality of life, low exercise tolerance, and an increased risk of hospitalization. This study aims to report the proportion of hypertension among stable COPD patients in the Indonesian population.Methods: This cross-sectional study was conducted at the National Respiratory Center Persahabatan Hospital between February and March 2023. Stable COPD patients admitted to the Asthma and COPD Polyclinic who met the criteria were enrolled. Clinical information, vital signs, spirometry results, and DLCO measurements were collected.Results: There were 84 subjects participating in this study. The result of this study shows a 60.7% proportion of hypertension in stable COPD patients. Hypertension has a significant correlation with pulmonary functional values (P=0.021), severity degree of clinical COPD (P=0.004), Brinkman index (P=0.008), and age (P=0.0001). However, hypertension association with COPD duration (P=0.505) and DLCO (P=0.122) were not significant.Conclusion: The hypertension proportion in stable COPD Indonesian patients is 60.7%. Hypertension shows a significant association with pulmonary function values, severity degree of clinical COPD, Brinkman index, and age.

Hybrid Filtering and Probabilistic Techniques for Privacy-Preserving Community Detection in OSNs

Online Social Networks (OSNs) face the major challenge of protecting participant's privacy, due to the high dimensionality and volume of the data. In real-time social networks, where hundreds of personal details of people are shared every day, there remains a significant threat to privacy. Privacy preservation is challenging for a community detection problem due to the high computational complexity and memory requirements, especially in larger real-world OSN graphs. Although weighted nodes provide better results, as they allow capturing the frequencies of the values, the privacy preservation of sensitive attributes such as specific profiles becomes harder compared to these models. This problem can result in queries and subsequent learnings from social network profiles of specific individuals, which may be of personal, political or otherwise concern. Online social networks (OSNs) grapple with significant privacy challenges due to the extensive dimensions and vast quantities of data involved. This research fills the void in current privacy-preserving community detection methodologies, which face problems in computational complexity and memory usage in large-scale OSNs. The proposed framework seeks to bolster privacy preservation through a comprehensive multi-step process. Data filtering uses a blended data filter system to remove outliers and irrelevant data, thus enhancing the quality of the input data. Density-Oriented Clustering phase employs a density-oriented clustering model to identify communities, with each cluster representing a distinct community. Privacy Preservation component introduces a new privacy preservation technique for sensitive OSN attributes, surpassing existing k-anonymization methods. The developed density-based social network community detection model and its novel privacy-preserving scheme are evaluated using the datasets Yelp, Football, Zachary and Dolphin from the SNAP dataset. Experimental results on these datasets embed a comprehensive evaluation based on the order of each node and the graph-based networks, where each node is laden with weights as proximity values, indicating the semantic proximity between communities and individuals. The proposed framework employs the normalized mutual information (NMI), modularity (Q), Rand index and runtime measurements to demonstrate widespread advantages in the multi-dimensional functional space, including greater accuracy, cluster compatibility, and computational tractability over the existing prominent traditional models for OSNs.