Tag: statistics

Sensors (Basel). 2023 May 5;23(9):4487. doi: 10.3390/s23094487.

ABSTRACT

Background: Near-infrared spectroscopy (NIRS) relative concentration signals contain ‘noise’ from physiological processes such as respiration and heart rate. Simultaneous assessment of NIRS and respiratory rate (RR) using a single sensor would facilitate a perfectly time-synced assessment of (cerebral) physiology. Our aim was to extract respiratory rate from cerebral NIRS intensity signals in neonates admitted to a neonatal intensive care unit (NICU). Methods: A novel algorithm, NRR (NIRS RR), is developed for extracting RR from NIRS signals recorded from critically ill neonates. In total, 19 measurements were recorded from ten neonates admitted to the NICU with a gestational age and birth weight of 38 ± 5 weeks and 3092 ± 990 g, respectively. We synchronously recorded NIRS and reference RR signals sampled at 100 Hz and 0.5 Hz, respectively. The performance of the NRR algorithm is assessed in terms of the agreement and linear correlation between the reference and extracted RRs, and it is compared statistically with that of two existing methods. Results: The NRR algorithm showed a mean error of 1.1 breaths per minute (BPM), a root mean square error of 3.8 BPM, and Bland-Altman limits of agreement of 6.7 BPM averaged over all measurements. In addition, a linear correlation of 84.5% (p < 0.01) was achieved between the reference and extracted RRs. The statistical analyses confirmed the significant (p < 0.05) outperformance of the NRR algorithm with respect to the existing methods. Conclusions: We showed the possibility of extracting RR from neonatal NIRS in an intensive care environment, which showed high correspondence with the reference RR recorded. Adding the NRR algorithm to a NIRS system provides the opportunity to record synchronously different physiological sources of information about cerebral perfusion and respiration by a single monitoring system. This allows for a concurrent integrated analysis of the impact of breathing (including apnea) on cerebral hemodynamics.

PMID:37177691 | DOI:10.3390/s23094487

Sensors (Basel). 2023 Apr 30;23(9):4422. doi: 10.3390/s23094422.

ABSTRACT

Gait and balance are an intricate interplay between the brain, nervous system, sensory organs, and musculoskeletal system. They are greatly influenced by the type of footwear, walking patterns, and surface. This exploratory study examines the effects of the Infinity Walk, pronation, and footwear conditions on brain effective connectivity patterns. A continuous-wave functional near-infrared spectroscopy device collected data from five healthy participants. A highly computationally efficient connectivity model based on the Grange causal relationship between the channels was applied to data to find the effective relationship between inter- and intra-hemispheric brain connectivity. Brain regions of interest (ROI) were less connected during the barefoot condition than during other complex walks. Conversely, the highest interconnectedness between ROI was observed while wearing flat insoles and medially wedged sandals, which is a relatively difficult type of footwear to walk in. No statistically significant (p-value <0.05) effect on connectivity patterns was observed during the corrected pronated posture. The regions designated as motoric, sensorimotor, and temporal became increasingly connected with difficult walking patterns and footwear conditions. The Infinity Walk causes effective bidirectional connections between ROI across all conditions and both hemispheres. Due to its repetitive pattern, the Infinity Walk is a good test method, particularly for neuro-rehabilitation and motoric learning experiments.

PMID:37177624 | DOI:10.3390/s23094422

Sensors (Basel). 2023 Apr 30;23(9):4404. doi: 10.3390/s23094404.

ABSTRACT

This work investigates the effectiveness of deep neural networks within the realm of battery charging. This is done by introducing an innovative control methodology that not only ensures safety and optimizes the charging current, but also substantially reduces the computational complexity with respect to traditional model-based approaches. In addition to their high computational costs, model-based approaches are also hindered by their need to accurately know the model parameters and the internal states of the battery, which are typically unmeasurable in a realistic scenario. In this regard, the deep learning-based methodology described in this work was been applied for the first time to the best of the authors’ knowledge, to scenarios where the battery’s internal states cannot be measured and an estimate of the battery’s parameters is unavailable. The reported results from the statistical validation of such a methodology underline the efficacy of this approach in approximating the optimal charging policy.

PMID:37177604 | DOI:10.3390/s23094404

Sensors (Basel). 2023 Apr 28;23(9):4357. doi: 10.3390/s23094357.

ABSTRACT

Fifth Generation (5G) signals using the millimeter wave (mmWave) spectrums are highly vulnerable to blockage due to rapid variations in channel link quality. This can cause the devices or User Equipment (UE) to suffer from connection failure. In a dual connectivity (DC) network, the channel’s intermittency issues were partially solved by maintaining the UE’s connectivity to primary (LTE advanced stations) and secondary (5G mmWave stations) simultaneously. Even though the dual-connected network performs excellently in maintaining connectivity, its performance drops significantly due to the inefficient handover from one 5G mmWave station to another. The situation worsens when UE travels a long distance in a highly dense obstacle environment, which requires multiple ineffective handovers that eventually lead to performance degradation. This research aimed to propose an Adaptive TTT Handover (ATH) mechanism that deals with unpredictable 5G mmWave wireless channel behaviors that are highly intermittent. An adaptive algorithm was developed to automatically adjust the handover control parameters, such as Time-to-Trigger (TTT), based on the current state of channel condition measured by the Signal-to-Interference-Noise Ratio (SINR). The developed algorithm was tested under a 5G mmWave statistical channel model to represent a time-varying channel matrix that includes fading and the Doppler effect. The performance of the proposed handover mechanism was analyzed and evaluated in terms of handover probability, latency, and throughput by using the Network Simulator 3 tool. The comparative simulation result shows that the proposed adaptive handover mechanism performs excellently compared to conventional handovers and other enhancement techniques.

PMID:37177560 | DOI:10.3390/s23094357

Sensors (Basel). 2023 Apr 27;23(9):4346. doi: 10.3390/s23094346.

ABSTRACT

The use of artificial intelligence (AI) to detect phishing emails is primarily dependent on large-scale centralized datasets, which has opened it up to a myriad of privacy, trust, and legal issues. Moreover, organizations have been loath to share emails, given the risk of leaking commercially sensitive information. Consequently, it has been difficult to obtain sufficient emails to train a global AI model efficiently. Accordingly, privacy-preserving distributed and collaborative machine learning, particularly federated learning (FL), is a desideratum. As it is already prevalent in the healthcare sector, questions remain regarding the effectiveness and efficacy of FL-based phishing detection within the context of multi-organization collaborations. To the best of our knowledge, the work herein was the first to investigate the use of FL in phishing email detection. This study focused on building upon a deep neural network model, particularly recurrent convolutional neural network (RNN) and bidirectional encoder representations from transformers (BERT), for phishing email detection. We analyzed the FL-entangled learning performance in various settings, including (i) a balanced and asymmetrical data distribution among organizations and (ii) scalability. Our results corroborated the comparable performance statistics of FL in phishing email detection to centralized learning for balanced datasets and low organizational counts. Moreover, we observed a variation in performance when increasing the organizational counts. For a fixed total email dataset, the global RNN-based model had a 1.8% accuracy decrease when the organizational counts were increased from 2 to 10. In contrast, BERT accuracy increased by 0.6% when increasing organizational counts from 2 to 5. However, if we increased the overall email dataset by introducing new organizations in the FL framework, the organizational level performance improved by achieving a faster convergence speed. In addition, FL suffered in its overall global model performance due to highly unstable outputs if the email dataset distribution was highly asymmetric.

PMID:37177549 | DOI:10.3390/s23094346

Sensors (Basel). 2023 Apr 26;23(9):4303. doi: 10.3390/s23094303.

ABSTRACT

Temperature-controlled closed-loop systems are vital to the transportation of produce. By maintaining specific transportation temperatures and adjusting to environmental factors, these systems delay decomposition. Wireless sensor networks (WSN) can be used to monitor the temperature levels at different locations within these transportation containers and provide feedback to these systems. However, there are a range of unique challenges in WSN implementations, such as the cost of the hardware, implementation difficulties, and the general ruggedness of the environment. This paper presents the novel results of a real-life application, where a sensor network was implemented to monitor the environmental temperatures at different locations inside commercial temperature-controlled shipping containers. The possibility of predicting one or more locations inside the container in the absence or breakdown of a logger placed in that location is explored using combinatorial input-output settings. A total of 1016 machine learning (ML) models are exhaustively trained, tested, and validated in search of the best model and the best combinations to produce a higher prediction result. The statistical correlations between different loggers and logger combinations are studied to identify a systematic approach to finding the optimal setting and placement of loggers under a cost constraint. Our findings suggest that even under different and incrementally higher cost constraints, one can use empirical approaches such as neural networks to predict temperature variations in a location with an absent or failed logger, within a margin of error comparable to the manufacturer-specified sensor accuracy. In fact, the median test accuracy is 1.02 degrees Fahrenheit when using only a single sensor to predict the remaining locations under the assumptions of critical system failure, and drops to as little as 0.8 and 0.65 degrees Fahrenheit when using one or three more sensors in the prediction algorithm. We also demonstrate that, by using correlation coefficients and time series similarity measurements, one can identify the optimal input-output pairs for the prediction algorithm reliably under most instances. For example, discrete time warping can be used to select the best location to place the sensors with a 92% match between the lowest prediction error and the highest similarity sensor with the rest of the group. The findings of this research can be used for power management in sensor batteries, especially for long transportation routes, by alternating standby modes where the temperature data for the OFF sensors are predicted by the ON sensors.

PMID:37177507 | DOI:10.3390/s23094303

Mult Scler Relat Disord. 2023 Apr 16;75:104719. doi: 10.1016/j.msard.2023.104719. Online ahead of print.

ABSTRACT

BACKGROUND: Teriflunomide (TER) (Aubagio™) is an FDA-approved disease-modifying therapy (DMT) for relapsing-remitting multiple sclerosis (RRMS). The mechanism of action of TER is thought to be related to the inhibition of dihydroorotate dehydrogenase (DHODH), a key mitochondrial enzyme in the de novo pyrimidine synthesis pathway required by rapidly dividing lymphocytes. Several large pivotal studies have established the efficacy and safety of TER in patients with RRMS. Despite this, little is known about how the adaptive and innate immune cell subsets are affected by the treatment in patients with MS.

METHODS: We recruited 20 patients with RRMS who were newly started on TER and performed multicolor flow cytometry and functional assays on peripheral blood samples. A paired t-test was used for the statistical analysis and comparison.

RESULTS: Our data showed that TER promoted a tolerogenic environment by shifting the balance between activated pathogenic and naïve or immunosuppressive immune cell subsets. In our cohort, TER increased the expression of the immunosuppressive marker CD39 on regulatory T cells (Tregs) while it decreased the expression of the activation marker CXCR3 on CD4⁺ T helper cells. TER treatment also reduced switched memory (sm) B cells while it increased naïve B cells and downregulated the expression of co-stimulatory molecules CD80 and CD86. Additionally, TER reduced the percentage and absolute numbers of natural killer T (NKT) cells, as well as the percentage of natural killer (NK) cells and showed a trend toward reducing the CD56^dim NK pathogenic subset.

CONCLUSION: TER promotes the tolerogenic immune response and suppresses the pathogenic immune response in patients with RRMS.

PMID:37172367 | DOI:10.1016/j.msard.2023.104719

J Environ Manage. 2023 May 10;341:118029. doi: 10.1016/j.jenvman.2023.118029. Online ahead of print.

ABSTRACT

Over the last two decades, forest cover has experienced significant impacts from fires and deforestation worldwide due to direct human activities and climate change. This paper assesses trends in forest cover loss and land use and land cover changes in northern Algeria between 2000 and 2020 using datasets extracted from Google Earth Engine (GEE), such as the Hanssen Global Forest Change and MODIS Land Cover Type products (MCD12Q1). Classification was performed using the pixel-based supervised machine-learning algorithm called Random Forest (RF). Trends were analyzed using methods such as Mann-Kendall and Sen. The study area comprises 17 basins with high rainfall variability. The results indicated that the forest area decreased by 64.96%, from 3718 to 1266 km², during the 2000-2020 period, while the barren area increased by 40%, from 134,777 to 188,748 km². The findings revealed that the Constantinois-Seybousse-Mellegue hydrographic basin was the most affected by deforestation and cover loss, exceeding 50% (with an area of 1018 km²), while the Seybouse River basin experienced the highest percentage of cover loss at 40%. Nonparametric tests showed that seven river basins (41%) had significantly increasing trends of forest cover loss. According to the obtained results, the forest loss situation in Algeria, especially in the northeastern part, is very alarming and requires an exceptional and urgent plan to protect forests and the ecological system against wildfires and climate change. The study provides a diagnosis that should encourage better protection and management of forest cover in Algeria.

PMID:37172351 | DOI:10.1016/j.jenvman.2023.118029

J Environ Manage. 2023 May 10;341:118124. doi: 10.1016/j.jenvman.2023.118124. Online ahead of print.

ABSTRACT

Currently, the phenomenon of direct interspecies electron transfer (DIET) is of great interest in the technology of anaerobic digestion (AD) due to potential performance benefits. However, the conditions for the occurrence of DIET and its limits on improving AD under conditions close to real have not been studied enough. This research is concentrated on the effect of conductive carbon cloth (R3), in comparison with a dielectric fiberglass cloth (R2) and control (R1), on the AD performance in large (90 L) thermophilic reactors, fed with a mixture of simulated organic fraction of municipal solid waste and sewage sludge. While organic loading rate (OLR) was gradually increased from 2.4 to 8.66 kg VS/(m³ day), a statistically significant (p < 0.05) difference in biogas production was observed between R1 and both R2 and R3. However, at a maximum OLR of 12.12 kg VS/(m³ day) in R3, an increase in biogas production (p < 0.05) was observed both compared to R1 (by 8.97%) and R2 (by 4.24%). The content of volatile fatty acids in R3 as a whole was the lowest, especially at the maximum OLR. Biofilm on carbon cloth was rich in syntrophic microorganisms of the genera Tepidanaerobacter, as well as Defluviitoga, capable of DIET in mixed cultures with Methanothrix, which was the most abundant methanogen in biofilm. Suspended Bifidobacterium, Fervidobacterium and Anaerobaculum were negatively affected, while Defluviitoga, Methanothermobacter and Methanosarcina, on the contrary, were positively affected by the increase in OLR and showed, respectively, a negative and positive correlation (p < 0.05) with the main AD performance parameters.

PMID:37172349 | DOI:10.1016/j.jenvman.2023.118124

J Environ Manage. 2023 May 10;341:118030. doi: 10.1016/j.jenvman.2023.118030. Online ahead of print.

ABSTRACT

PM_2.5 has been a hot concern in the recent decade. Many studies have focused on metropolises or those areas with poor air quality, but the PM_2.5 of more widespread areas is less considered. Considering the challenges of rapid economic growth and environmental problems against a developing region, we took Guiyang as a study case to assess the spatiotemporal variations and mechanism of PM2.5 pollution in an urban area from 2000 to 2020 in an extended sense. Based on PM_2.5 concentration data from 14 monitoring points in Guiyang, spatiotemporal variations and formation mechanism were assessed using wavelet, moving maximal information coefficients, and spatial correlation analysis. The urban Nighttime light data was selected to evaluate the impacts of socioeconomic factors on PM2.5 concentration using spatial correlation analysis. Further, wavelet and statistical analysis were adopted to analyze multi-dimensional temporal variations of PM_2.5 hourly concentration and the relationship with pressure, temperature, vapor pressure, relative humidity, wind, and visibility. The PM_2.5 hourly concentration was obtained from the monitoring points in downtown Guiyang according to data continuity and availability. PM_2.5 had different temporal variations at daily, monthly, seasonal, and annual levels, and interannual variation was the most obvious. The temperature was the main factor leading to the interannual temporal variation of PM_2.5. Wind and pressure were more significant for the responses of a shorter period variation with -0.76 and -0.80 of the minimum of correlation coefficient, respectively. Meanwhile, human activities significantly influenced spatiotemporal variations of PM_2.5. A spatial correlation analysis between PM2.5 and the related influencing factors from 2000 to 2018 was implemented based on a geographic information system. Besides, the landcovers within a buffer zone with a radius of 1 km on 14 monitoring points were visually interpreted to analyze the relationship between PM_2.5 and landcovers. Moreover, multivariate wavelet coherence analysis revealed the PM2.5 interaction among monitoring points. The PM_2.5 concentration in Guiyang dropped from 49 μg/m³ in 2012 to about 27 μg/m³ in 2018, and the air quality greatly improved. As in most cities, Guiyang has a significant PM_2.5 pollution island effect, with traffic and building land density contributing to higher PM_2.5 concentrations. There were some typical nonlinear spatiotemporal variations between PM_2.5 and its influencing factors, and these variations varied with the selected scale.

PMID:37172348 | DOI:10.1016/j.jenvman.2023.118030