Tag: statistics

Sensors (Basel). 2023 Jun 16;23(12):5652. doi: 10.3390/s23125652.

ABSTRACT

With the evolution in technology, communication based on the voice has gained importance in applications such as online conferencing, online meetings, voice-over internet protocol (VoIP), etc. Limiting factors such as environmental noise, encoding and decoding of the speech signal, and limitations of technology may degrade the quality of the speech signal. Therefore, there is a requirement for continuous quality assessment of the speech signal. Speech quality assessment (SQA) enables the system to automatically tune network parameters to improve speech quality. Furthermore, there are many speech transmitters and receivers that are used for voice processing including mobile devices and high-performance computers that can benefit from SQA. SQA plays a significant role in the evaluation of speech-processing systems. Non-intrusive speech quality assessment (NI-SQA) is a challenging task due to the unavailability of pristine speech signals in real-world scenarios. The success of NI-SQA techniques highly relies on the features used to assess speech quality. Various NI-SQA methods are available that extract features from speech signals in different domains, but they do not take into account the natural structure of the speech signals for assessment of speech quality. This work proposes a method for NI-SQA based on the natural structure of the speech signals that are approximated using the natural spectrogram statistical (NSS) properties derived from the speech signal spectrogram. The pristine version of the speech signal follows a structured natural pattern that is disrupted when distortion is introduced in the speech signal. The deviation of NSS properties between the pristine and distorted speech signals is utilized to predict speech quality. The proposed methodology shows better performance in comparison to state-of-the-art NI-SQA methods on the Centre for Speech Technology Voice Cloning Toolkit corpus (VCTK-Corpus) with a Spearman’s rank-ordered correlation constant (SRC) of 0.902, Pearson correlation constant (PCC) of 0.960, and root mean squared error (RMSE) of 0.206. Conversely, on the NOIZEUS-960 database, the proposed methodology shows an SRC of 0.958, PCC of 0.960, and RMSE of 0.114.

PMID:37420818 | DOI:10.3390/s23125652

Sensors (Basel). 2023 Jun 15;23(12):5628. doi: 10.3390/s23125628.

ABSTRACT

Estimating the gamma dose rate at one meter above ground level and determining the distribution of radioactive pollution from aerial radiation monitoring data are the core technical issues of unmanned aerial vehicle nuclear radiation monitoring. In this paper, a reconstruction algorithm of the ground radioactivity distribution based on spectral deconvolution was proposed for the problem of regional surface source radioactivity distribution reconstruction and dose rate estimation. The algorithm estimates unknown radioactive nuclide types and their distributions using spectrum deconvolution and introduces energy windows to improve the accuracy of the deconvolution results, achieving accurate reconstruction of multiple continuous distribution radioactive nuclides and their distributions, as well as dose rate estimation of one meter above ground level. The feasibility and effectiveness of the method were verified through cases of single-nuclide (¹³⁷Cs) and multi-nuclide (¹³⁷Cs and ⁶⁰Co) surface sources by modeling and solving them. The results showed that the cosine similarities between the estimated ground radioactivity distribution and dose rate distribution with the true value were 0.9950 and 0.9965, respectively, which could prove that the proposed reconstruction algorithm would effectively distinguish multiple radioactive nuclides and accurately restore their radioactivity distribution. Finally, the influences of statistical fluctuation levels and the number of energy windows on the deconvolution results were analyzed, showing that the lower the statistical fluctuation level and the more energy window divisions, the better the deconvolution results.

PMID:37420794 | DOI:10.3390/s23125628

Sensors (Basel). 2023 Jun 15;23(12):5626. doi: 10.3390/s23125626.

ABSTRACT

As criminal activity increasingly relies on digital devices, the field of digital forensics plays a vital role in identifying and investigating criminals. In this paper, we addressed the problem of anomaly detection in digital forensics data. Our objective was to propose an effective approach for identifying suspicious patterns and activities that could indicate criminal behavior. To achieve this, we introduce a novel method called the Novel Support Vector Neural Network (NSVNN). We evaluated the performance of the NSVNN by conducting experiments on a real-world dataset of digital forensics data. The dataset consisted of various features related to network activity, system logs, and file metadata. Through our experiments, we compared the NSVNN with several existing anomaly detection algorithms, including Support Vector Machines (SVM) and neural networks. We measured and analyzed the performance of each algorithm in terms of the accuracy, precision, recall, and F1-score. Furthermore, we provide insights into the specific features that contribute significantly to the detection of anomalies. Our results demonstrated that the NSVNN method outperformed the existing algorithms in terms of anomaly detection accuracy. We also highlight the interpretability of the NSVNN model by analyzing the feature importance and providing insights into the decision-making process. Overall, our research contributes to the field of digital forensics by proposing a novel approach, the NSVNN, for anomaly detection. We emphasize the importance of both performance evaluation and model interpretability in this context, providing practical insights for identifying criminal behavior in digital forensics investigations.

PMID:37420791 | DOI:10.3390/s23125626

Sensors (Basel). 2023 Jun 15;23(12):5623. doi: 10.3390/s23125623.

ABSTRACT

This article describes an empirical exploration on the effect of information loss affecting compressed representations of dynamic point clouds on the subjective quality of the reconstructed point clouds. The study involved compressing a set of test dynamic point clouds using the MPEG V-PCC (Video-based Point Cloud Compression) codec at 5 different levels of compression and applying simulated packet losses with three packet loss rates (0.5%, 1% and 2%) to the V-PCC sub-bitstreams prior to decoding and reconstructing the dynamic point clouds. The recovered dynamic point clouds qualities were then assessed by human observers in experiments conducted at two research laboratories in Croatia and Portugal, to collect MOS (Mean Opinion Score) values. These scores were subject to a set of statistical analyses to measure the degree of correlation of the data from the two laboratories, as well as the degree of correlation between the MOS values and a selection of objective quality measures, while taking into account compression level and packet loss rates. The subjective quality measures considered, all of the full-reference type, included point cloud specific measures, as well as others adapted from image and video quality measures. In the case of image-based quality measures, FSIM (Feature Similarity index), MSE (Mean Squared Error), and SSIM (Structural Similarity index) yielded the highest correlation with subjective scores in both laboratories, while PCQM (Point Cloud Quality Metric) showed the highest correlation among all point cloud-specific objective measures. The study showed that even 0.5% packet loss rates reduce the decoded point clouds subjective quality by more than 1 to 1.5 MOS scale units, pointing out the need to adequately protect the bitstreams against losses. The results also showed that the degradations in V-PCC occupancy and geometry sub-bitstreams have significantly higher (negative) impact on decoded point cloud subjective quality than degradations of the attribute sub-bitstream.

PMID:37420788 | DOI:10.3390/s23125623

Waste Manag. 2023 Jul 5;169:91-100. doi: 10.1016/j.wasman.2023.06.037. Online ahead of print.

ABSTRACT

Interest in the conversion of manure in biogas via anaerobic digestion (AD) is growing, but questions remain about the biosafety of digestates. For a period of one year, we monitored the impact of three mesophilic agricultural biogas plants (BPs) mainly fed with pig manure (BP1, BP3) or bovine manure (BP2) on the physicochemical parameters, the composition of the microbial community and the concentration of bacteria (E. coli, enterococci, Salmonella, Campylobacter, Listeria monocytogenes, Clostridium perfringens, Clostridium botulinum and Clostridioides difficile). The BP2 digestate differed from those of the two other BPs with a higher nitrogen content, more total solids and greater abundance of Clostridia MBA03 and Disgonomonadacea. Persistence during digestion ranked from least to most, was: Campylobacter (1.6 to >2.9 log₁₀ reduction, according to the BP) < E. coli (1.8 to 2.2 log₁₀) < Salmonella (1.1 to 1.4 log₁₀) < enterococci (0.2 to 1.2 log₁₀) and C. perfringens (0.2 to 1 log₁₀) < L. monocytogenes (-1.2 to 1.6 log₁₀) < C. difficile and C. botulinum (≤0.5 log₁₀). No statistical link was found between the reduction in the concentration of the targeted bacteria and the physicochemical and operational parameters likely to have an effect (NH₃, volatile fatty acids and total solids contents, hydraulic retention time, presence of co-substrates), underlining the fact that the fate of the bacteria during mesophilic digestion depends on many interacting factors. The reduction in concentrations varied significantly over the sampling period, underlining the need for longitudinal studies to estimate the impact of AD on pathogenic microorganisms.

PMID:37418788 | DOI:10.1016/j.wasman.2023.06.037

Int J Hyg Environ Health. 2023 Jul 5;252:114215. doi: 10.1016/j.ijheh.2023.114215. Online ahead of print.

ABSTRACT

INTRODUCTION: Firefighting is one of the most hazardous occupations due to exposure to per- and polyfluoroalkyl substances (PFAS) and polycyclic aromatic hydrocarbons (PAHs). Such exposure is suspected to affect the cardiometabolic profile, e.g., liver function and serum lipids. However, only a few studies have investigated the impact of this specific exposure among firefighters.

METHODS: Men included in the CELSPAC-FIREexpo study were professional firefighters (n = 52), newly recruited firefighters in training (n = 58), and controls (n = 54). They completed exposure questionnaires and provided 1-3 samples of urine and blood during the 11-week study period to allow assessment of their exposure to PFAS (6 compounds) and PAHs (6 compounds), and to determine biomarkers of liver function (alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT), aspartate aminotransferase (AST), alkaline phosphatase (ALP) and total bilirubin (BIL)) and levels of serum lipids (total cholesterol (CHOL), low-density lipoprotein cholesterol (LDL) and triglycerides (TG)). The associations between biomarkers were investigated both cross-sectionally using multiple linear regression (MLR) and Bayesian weighted quantile sum (BWQS) regression and prospectively using MLR. The models were adjusted for potential confounders and false discovery rate correction was applied to account for multiplicity.

RESULTS: A positive association between exposure to PFAS and PAH mixture and BIL (β = 28.6%, 95% CrI = 14.6-45.7%) was observed by the BWQS model. When the study population was stratified, in professional firefighters and controls the mixture showed a positive association with CHOL (β = 29.5%, CrI = 10.3-53.6%) and LDL (β = 26.7%, CrI = 8.3-48.5%). No statistically significant associations with individual compounds were detected using MLR.

CONCLUSIONS: This study investigated the associations between exposure to PFAS and PAHs and biomarkers of cardiometabolic health in the Czech men, including firefighters. The results suggest that higher exposure to a mixture of these compounds is associated with an increase in BIL and the alteration of serum lipids, which can result in an unfavourable cardiometabolic profile.

PMID:37418783 | DOI:10.1016/j.ijheh.2023.114215

Psychiatry Res. 2023 Jul 4;326:115330. doi: 10.1016/j.psychres.2023.115330. Online ahead of print.

ABSTRACT

Posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) frequently co-occur and can cause significant impairment. Data are lacking as to whether interventions targeting both PTSD and MDD may improve treatment outcomes among individuals with this comorbidity compared with existing evidence-based PTSD treatments alone. This randomized trial compared the effectiveness of cognitive processing therapy (CPT) enhanced with behavioral activation (BA+CPT) versus CPT among 94 service members (52 women and 42 men; age M = 28.5 years) with comorbid PTSD and MDD. The primary outcome was clinician-administered depression symptom severity on the Montgomery-Åsberg Depression Rating Scale (MADRS) from pretreatment through 3-month follow-up. Intent-to-treat analyses using multilevel models showed statistically and clinically significant decreases in MADRS scores for both conditions over time, with no significant differences between BA+CPT and CPT. Secondary depression and PTSD symptom outcomes followed a similar pattern of results. For diagnostic MDD and PTSD outcomes using available data, no statistically significant differences between treatments emerged at posttreatment or 3-month follow-up. Sessions attended, dropout rate, and treatment satisfaction did not significantly differ between treatments. Outcomes were comparable for both treatments, suggesting that BA+CPT and CPT were similarly effective psychotherapy options for comorbid PTSD and MDD.

PMID:37418778 | DOI:10.1016/j.psychres.2023.115330

Psychiatry Res. 2023 Jun 17;326:115302. doi: 10.1016/j.psychres.2023.115302. Online ahead of print.

ABSTRACT

Research has shown that individuals with psychiatric disorders such as bipolar disorder (BD) and attention deficit and hyperactivity disorder (ADHD) have a higher likelihood of violent behavior. This study investigated the frequency of comorbid BD and ADHD in adultpatients and the relationship between this comorbidity and violent behavior. We assessed 105 remitted patients diagnosed with BD I (n = 91) or BD II (n = 14). The patients were administered the Sociodemographic Data Scale, the Wender-Utah Rating Scale (WURS), the Adult ADHD Self-Report Scale (ASRS), the Buss-Perry Aggression Questionnaire (BPAQ), and theViolence Tendency Scale (VTS) as self-reports. The same clinician administered the Diagnostic Interview for ADHD in adults (DIVA 2.0) to patients who scored≥36 on the WURS. Comorbid ADHD was diagnosed in 15.2% of patients according to the DIVA 2.0. In the multiple linear regression analysis, there was a statistically significant positive effect of the ASRS total score on the VTS and the BPAQ total score. Furthermore, it was found that male gender had a statistically significant positive effect on VTS total score and young age had a statistically significant positive effect on BPQA total score. These findings demonstrate an association between BD, comorbid ADHD, and violent behavior.

PMID:37418777 | DOI:10.1016/j.psychres.2023.115302

Schizophr Bull. 2023 Jul 7:sbad100. doi: 10.1093/schbul/sbad100. Online ahead of print.

ABSTRACT

BACKGROUND AND HYPOTHESIS: Psychiatric disorders impose a huge health and economic burden on modern society. However, there is currently no proven completely effective treatment available, partly owing to the inefficiency of drug target identification and validation. We aim to identify therapeutic targets relevant to psychiatric disorders by conducting Mendelian randomization (MR) analysis.

STUDY DESIGN: We performed genome-wide MR analysis by integrating expression quantitative trait loci (eQTL) of 4479 actionable genes that encode druggable proteins and genetic summary statistics from genome-wide association studies of psychiatric disorders. After conducting colocalization analysis on the brain MR findings, we employed protein quantitative trait loci (pQTL) data as genetic proposed instruments for intersecting the colocalized genes to provide further genetic evidence.

STUDY RESULTS: By performing MR and colocalization analysis with eQTL genetic instruments, we obtained 31 promising drug targets for psychiatric disorders, including 21 significant genes for schizophrenia, 7 for bipolar disorder, 2 for depression, 1 for attention deficit and hyperactivity (ADHD) and none for autism spectrum disorder. Combining MR results using pQTL genetic instruments, we finally proposed 8 drug-targeting genes supported by the strongest MR evidence, including gene ACE, BTN3A3, HAPLN4, MAPK3 and NEK4 for schizophrenia, gene NEK4 and HAPLN4 for bipolar disorder, and gene TIE1 for ADHD.

CONCLUSIONS: Our findings with genetic support were more likely to be to succeed in clinical trials. In addition, our study prioritizes approved drug targets for the development of new therapies and provides critical drug reuse opportunities for psychiatric disorders.

PMID:37418754 | DOI:10.1093/schbul/sbad100

Phys Rev Lett. 2023 Jun 23;130(25):251902. doi: 10.1103/PhysRevLett.130.251902.

ABSTRACT

Using (1.0087±0.0044)×10^{10} J/ψ events collected with the BESIII detector at the BEPCII storage ring, the process Ξ^{0}n→Ξ^{-}p is studied, where the Ξ^{0} baryon is produced in the process J/ψ→Ξ^{0}Ξ[over ¯]^{0} and the neutron is a component of the ^{9}Be, ^{12}C, and ^{197}Au nuclei in the beam pipe. A clear signal is observed with a statistical significance of 7.1σ. The cross section of the reaction Ξ^{0}+^{9}Be→Ξ^{-}+p+^{8}Be is determined to be σ(Ξ^{0}+^{9}Be→Ξ^{-}+p+^{8}Be)=(22.1±5.3_{stat}±4.5_{sys}) mb at the Ξ^{0} momentum of 0.818 GeV/c, where the first uncertainty is statistical and the second is systematic. No significant H-dibaryon signal is observed in the Ξ^{-}p final state. This is the first study of hyperon-nucleon interactions in electron-positron collisions and opens up a new direction for such research.

PMID:37418739 | DOI:10.1103/PhysRevLett.130.251902