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Fulltext Illumination Intelligent Adaptation and Analysis Framework: A comprehensive solution for enhancing nighttime driving fatigue monitoring

Tian Z, Albakry NS, Du Y

PLoS One, 2024;19(8):e0308201.
PMID: 39141655 DOI: 10.1371/journal.pone.0308201

Nighttime driving presents a critical challenge to road safety due to insufficient lighting and increased risk of driver fatigue. Existing methods for monitoring driver fatigue, mainly focusing on behavioral analysis and biometric monitoring, face significant challenges under low-light conditions. Their effectiveness, especially in dynamic lighting environments, is limited by their dependency on specific environmental conditions and active driver participation, leading to reduced accuracy and practicality in real-world scenarios. This study introduces a novel 'Illumination Intelligent Adaptation and Analysis Framework (IIAAF)', aimed at addressing these limitations and enhancing the accuracy and practicality of driver fatigue monitoring under nighttime low-light conditions. The IIAAF framework employs a multidimensional technology integration, including comprehensive body posture analysis and facial fatigue feature detection, per-pixel dynamic illumination adjustment technology, and a light variation feature learning system based on Convolutional Neural Networks (CNN) and time-series analysis. Through this integrated approach, the framework is capable of accurately capturing subtle fatigue signals in nighttime driving environments and adapting in real-time to rapid changes in lighting conditions. Experimental results on two independent datasets indicate that the IIAAF framework significantly improves the accuracy of fatigue detection under nighttime low-light conditions. This breakthrough not only enhances the effectiveness of driving assistance systems but also provides reliable scientific support for reducing the risk of accidents caused by fatigued driving. These research findings have significant theoretical and practical implications for advancing intelligent driving assistance technology and improving nighttime road safety.
Fulltext Machine learning in internet financial risk management: A systematic literature review

Tian X, Tian Z, Khatib SFA, Wang Y

PLoS One, 2024;19(4):e0300195.
PMID: 38625972 DOI: 10.1371/journal.pone.0300195

Internet finance has permeated into myriad households, bringing about lifestyle convenience alongside potential risks. Presently, internet finance enterprises are progressively adopting machine learning and other artificial intelligence methods for risk alertness. What is the current status of the application of various machine learning models and algorithms across different institutions? Is there an optimal machine learning algorithm suited for the majority of internet finance platforms and application scenarios? Scholars have embarked on a series of studies addressing these questions; however, the focus predominantly lies in comparing different algorithms within specific platforms and contexts, lacking a comprehensive discourse and summary on the utilization of machine learning in this domain. Thus, based on the data from Web of Science and Scopus databases, this paper conducts a systematic literature review on all aspects of machine learning in internet finance risk in recent years, based on publications trends, geographical distribution, literature focus, machine learning models and algorithms, and evaluations. The research reveals that machine learning, as a nascent technology, whether through basic algorithms or intricate algorithmic combinations, has made significant strides compared to traditional credit scoring methods in predicting accuracy, time efficiency, and robustness in internet finance risk management. Nonetheless, there exist noticeable disparities among different algorithms, and factors such as model structure, sample data, and parameter settings also influence prediction accuracy, although generally, updated algorithms tend to achieve higher accuracy. Consequently, there is no one-size-fits-all approach applicable to all platforms; each platform should enhance its machine learning models and algorithms based on its unique characteristics, data, and the development of AI technology, starting from key evaluation indicators to mitigate internet finance risks.
Risk factors for falls in older people with pre-frailty: A systematic review and meta-analysis

Jing H, Chen Y, Liang B, Tian Z, Song F, Chen M, et al.

Geriatr Nurs, 2024 Nov 08.
PMID: 39521661 DOI: 10.1016/j.gerinurse.2024.10.030

BACKGROUND: Frailty is considered highly prevalent among the elderly, and falls are a severe adverse event that occurs at a significantly higher rate in frail elderly patients, leading to serious consequences. The pre-frailty stage represents a reversible transitional state between health and frailty, and targeted interventions for pre-frail older adults can effectively reduce the incidence of falls in this population. Existing studies have not definitely identified the risk factors for falls in pre-frail older adults. This paper explores the relevant risk factors for falls in pre-frail older adults.
METHODS: PubMed, Embase, Web of Science, Cochrane Library, CBM, CNKI, Wan fang, and VIP databases were searched for studies published from inception to 2023, without language restrictions. Observational studies were included in this systematic review that analyzed risk factors for accidental falls in pre-frail older adults. The NOS scale was used to evaluate the quality of cohort studies and case-control studies, while the AHRQ scale was used to evaluate the quality of the cross-sectional study. We utilized odds ratios (OR) and their corresponding 95 % confidence intervals (CI) to describe the statistical indicators. OR and 95 % CI values were directly extracted and organized in Excel. In cases where OR and CI values were not directly available, we extracted β and p values, calculated Exp using functions, and subsequently derived OR and 95 % CI using formulas. Finally, data pertaining to each risk factor were incorporated into RevMan 5.4 software for statistical analysis and effect size synthesis. We performed tests for heterogeneity and evaluated publication bias.
RESULTS: A total of 14,370 studies were initially identified, and 26 studies were included in the systematic review. Among these studies, 14 were of high quality, while the remaining 12 were of moderate quality. A total of 16 risk factors were identified as potential risk factors for falls in pre-frail older adults. Significant risk factors were peripheral neuropathy(OR = 3.18, 95 %CI:3.02-3.35), decreased gait speed(OR = 1.90, 95 %CI:1.60-2.27), decreased ability to perform activities of daily living(OR = 1.57, 95 % CI:1.42-1.75), grip strength decreases(OR = 1.53, 95 % CI:1.17-2.00), gender (female)(OR = 1.51, 95 % CI:1.39-1.64), pain(OR = 1.47, 95 %CI:1.41-1.54), history of falls(OR = 1.20, 95 %CI:1.13-1.28) and age(OR = 1.10, 95 %CI:1.07-1.14).
CONCLUSIONS: The occurrence of falls in pre-frail older adults is associated with multiple risk factors. These risk factors can provide clinical nursing staff with specific focal points for monitoring this population and devising targeted fall prevention measures, with the aim of reducing the incidence of falls in pre-frail older adults.
REGISTRATION: The systematic review was registered on the International Prospective Register of Systematic Review (CRD42023450670).
Fulltext An fNIRS investigation of novel expressed emotion stimulations in schizophrenia

Wang C, Zhang Y, Lim LG, Cao W, Zhang W, Wan X, et al.

Sci Rep, 2023 Jul 10;13(1):11141.
PMID: 37429942 DOI: 10.1038/s41598-023-38057-1

Living in high expressed emotion (EE) environments tends to increase the relapse rate in schizophrenia (SZ). At present, the neural substrates responsible for high EE in SZ remain poorly understood. Functional near-infrared spectroscopy (fNIRS) may be of great use to quantitatively assess cortical hemodynamics and elucidate the pathophysiology of psychiatric disorders. In this study, we designed novel low- (positivity and warmth) and high-EE (criticism, negative emotion, and hostility) stimulations, in the form of audio, to investigate cortical hemodynamics. We used fNIRS to measure hemodynamic signals while participants listened to the recorded audio. Healthy controls (HCs, [Formula: see text]) showed increased hemodynamic activation in the major language centers across EE stimulations, with stronger activation in Wernicke's area during the processing of negative emotional language. Compared to HCs, people with SZ ([Formula: see text]) exhibited smaller hemodynamic activation in the major language centers across EE stimulations. In addition, people with SZ showed weaker or insignificant hemodynamic deactivation in the medial prefrontal cortex. Notably, hemodynamic activation in SZ was found to be negatively correlated with the negative syndrome scale score at high EE. Our findings suggest that the neural mechanisms in SZ are altered and disrupted, especially during negative emotional language processing. This supports the feasibility of using the designed EE stimulations to assess people who are vulnerable to high-EE environments, such as SZ. Furthermore, our findings provide preliminary evidence for future research on functional neuroimaging biomarkers for people with psychiatric disorders.