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The increasing apprehension for health, safety and quality of life in modern society has resulted in the widespread use of biosensors. Biosensors are characterised by their high sensitivity, real‐time monitoring, and easy integration, making them indispensable for environmental monitoring on‐site, as well as invasive and non‐invasive health monitoring. Signal processing and analysis are crucial to biosensor applications, with an important role being played by chemometrics in this regard. This review presents a review of recent research findings in the fields of environmental and health monitoring. In addition, it investigates the role that chemometrics plays in the processing and analysis of biosensor data. The research comprises conventional statistical techniques, including principal component analysis and wavelet transform, as well as modern techniques of artificial intelligence, such as machine learning with neural networks. Through the examination of various algorithm strengths and weaknesses, significant recommendations are offered for biosensor applications. Furthermore, the assessment delivers focused proposals for surmounting signal processing difficulties in biosensors. Additionally, the review contains a concise analysis and reflection on the issue of multiple detection and analysis. The review intends to give essential guidance to future researchers in selecting efficient and sensible methods of data processing for their studies. [ABSTRACT FROM AUTHOR]

This research introduces an Artificial Intelligence (AI) based model designed to concurrently optimize energy supply management, biocide dosing, and maintenance scheduling for heat exchangers. This optimization considers energetic, technical, economic, and environmental considerations. The impact of biofilm on heat exchangers is assessed, revealing a 41% reduction in thermal efficiency and a 113% increase in flow frictional resistance of the fluid compared to the initial state. Consequently, the pump's power consumption, required to maintain hydraulic conditions, rises by 9%. The newly developed AI model detects the point at which the heat exchanger's performance begins to decline due to accumulating dirt, marking day 44 of experimentation as the threshold to commence the antifouling biocide dosing. Leveraging this AI model to monitor heat exchanger efficiency represents an innovative approach to optimizing antifouling biocide dosing and reduce the environmental impact stemming from industrial plants. [ABSTRACT FROM AUTHOR]

This work combines the Urn allocation and O'Brien and Fleming multiple testing procedure to compare two treatments in clinical trials in a novel way. It is shown that this approach overcomes the constraints that previously made it challenging to apply the original adaptive design to clinical trials. The method provides unique flexibility, enabling trials to be stopped early if one treatment shows it is superior without compromising the efficiency of the original multiple testing procedure in terms of type I error rate and power. Experimental data and simulated case examples are used to illustrate the efficacy and robustness of this original approach and its potential for usage in a variety of clinical settings. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is looked upon nowadays as the potential major catalyst for the fourth industrial revolution. In the last decade, AI use in Orthopaedics increased approximately tenfold. Artificial intelligence helps with tracking activities, evaluating diagnostic images, predicting injury risk, and several other uses. Chat Generated Pre-trained Transformer (ChatGPT), which is an AI-chatbot, represents an extremely controversial topic in the academic community. The aim of this review article is to simplify the concept of AI and study the extent of AI use in Orthopaedics and sports medicine literature. Additionally, the article will also evaluate the role of ChatGPT in scientific research and publications. Level of evidence: Level V, letter to review. [ABSTRACT FROM AUTHOR]

This work combines the Urn allocation and O’Brien and Fleming multiple testing procedure to compare two treatments in clinical trials in a novel way. It is shown that this approach overcomes the constraints that previously made it challenging to apply the original adaptive design to clinical trials. The method provides unique flexibility, enabling trials to be stopped early if one treatment shows it is superior without compromising the efficiency of the original multiple testing procedure in terms of type I error rate and power. Experimental data and simulated case examples are used to illustrate the efficacy and robustness of this original approach and its potential for usage in a variety of clinical settings.

Abstract Artificial intelligence (AI) is looked upon nowadays as the potential major catalyst for the fourth industrial revolution. In the last decade, AI use in Orthopaedics increased approximately tenfold. Artificial intelligence helps with tracking activities, evaluating diagnostic images, predicting injury risk, and several other uses. Chat Generated Pre‐trained Transformer (ChatGPT), which is an AI‐chatbot, represents an extremely controversial topic in the academic community. The aim of this review article is to simplify the concept of AI and study the extent of AI use in Orthopaedics and sports medicine literature. Additionally, the article will also evaluate the role of ChatGPT in scientific research and publications. Level of evidence: Level V, letter to review.

Harsh environments such as alternating wet and dry conditions and cyclic loading cause erosion in steel bars, leading to severe damage to structures. Steel bars are also prone to bending under long-term fatigue loads. This paper establishes the bending performance test of corroded reinforced concrete (RC) beam under fatigue load based on the mathematical–statistical algorithm. It investigates the influence of heavy load on the fatigue performance of damaged RC beams, the influence of corrosive environment on the fatigue performance of RC beams, the degree of corrosion of steel bars in concrete beams under fatigue loading, and the distribution of chloride ions. The study results found that the stress ratio has a significant effect on the maximum crack width of the specimen beam. The greater is the stress ratio, the longer the fatigue life. This directly affects the performance of the specimen beam under fatigue loading. For this reason, we should pay special attention to the impact of corrosion on the fatigue performance of a structure during the design of steel bars.

This paper describes a new method for testing randomized clinical trials with binary outcomes, which combines the O'Brien and Fleming (1979) multiple-testing procedure with optimal allocations and unequal weighted samples simultaneously. The O'Brien and Fleming method of group sequential testing is a simple and effective method with the same Type I error and power as a fixed one-stage chi-square test, with the option to terminate early if one treatment is clearly superior to another. This study modified the O'Brien and Fleming procedure, resulting in a more flexible new procedure, where the optimal allocation assists in allocating more subjects to the winning treatment without compromising the integrity of the study, while unequal weighting allows for different samples to be chosen for different stages of a trial. The new optimal weighted multiple-testing procedure (OWMP), based on simulation studies, is relatively robust to the added features because it showed a high preference for decreasing the Type I error and maintaining the power. In addition, the procedure was illustrated using simulated and real-life examples. The outcomes of the current study suggest that the new procedure is as effective as the original. However, it is more flexible. [ABSTRACT FROM AUTHOR]

This paper describes a new method for testing randomized clinical trials with binary outcomes, which combines the O’Brien and Fleming (1979) multiple-testing procedure with optimal allocations and unequal weighted samples simultaneously. The O’Brien and Fleming method of group sequential testing is a simple and effective method with the same Type I error and power as a fixed one-stage chi-square test, with the option to terminate early if one treatment is clearly superior to another. This study modified the O’Brien and Fleming procedure, resulting in a more flexible new procedure, where the optimal allocation assists in allocating more subjects to the winning treatment without compromising the integrity of the study, while unequal weighting allows for different samples to be chosen for different stages of a trial. The new optimal weighted multiple-testing procedure (OWMP), based on simulation studies, is relatively robust to the added features because it showed a high preference for decreasing the Type I error and maintaining the power. In addition, the procedure was illustrated using simulated and real-life examples. The outcomes of the current study suggest that the new procedure is as effective as the original. However, it is more flexible.

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This paper reports the analysis of soil radon data recorded in the seismic zone-V, located in the northeastern part of India (latitude 23.73N, longitude 92.73E). Continuous measurements of soil-gas emission along Chite fault in Mizoram (India) were carried out with the replacement of solid-state nuclear track detectors at weekly interval. The present study was done for the period from March 2013 to May 2015 using LR-115 Type II detectors, manufactured by Kodak Pathe, France. In order to reduce the influence of meteorological parameters, statistical analysis tools such as multiple linear regression and artificial neural network have been used. Decrease in radon concentration was recorded prior to some earthquakes that occurred during the observation period. Some false anomalies were also recorded which may be attributed to the ongoing crustal deformation which was not major enough to produce an earthquake. [ABSTRACT FROM AUTHOR]

Numerous studies have focused on determining whether objective statistical methods can be used to discriminate between known matches and nonmatches when comparing laboratory prepared toolmarks. This study involved an analysis of striated toolmarks made as a function of varying vertical and horizontal angles of attack. Comparisons based on experimental data show that replicate toolmarks from the same tool show high correlation values at identical vertical and horizontal angles, with the correlation decreasing as the angular difference increases, especially for horizontal angular changes. Comparisons between nonmatching samples produce low correlation values that remain unchanged as horizontal angular differences increase. While complete statistical separation was not achieved between matching and nonmatching samples, there is evidence demonstrating that toolmarks can be identified if the variation in horizontal angle is within 10°. The experiment shows that computer-aided comparison techniques could be viable for identification with the proper statistical algorithm. [ABSTRACT FROM AUTHOR]

This article discusses a general framework for smoothing parameter estimation for models with regular likelihoods constructed in terms of unknown smooth functions of covariates. Gaussian random effects and parametric terms may also be present. By construction the method is numerically stable and convergent, and enables smoothing parameter uncertainty to be quantified. The latter enables us to fix a well known problem with AIC for such models, thereby improving the range of model selection tools available. The smooth functions are represented by reduced rank spline like smoothers, with associated quadratic penalties measuring function smoothness. Model estimation is by penalized likelihood maximization, where the smoothing parameters controlling the extent of penalization are estimated by Laplace approximate marginal likelihood. The methods cover, for example, generalized additive models for nonexponential family responses (e.g., beta, ordered categorical, scaled t distribution, negative binomial and Tweedie distributions), generalized additive models for location scale and shape (e.g., two stage zero inflation models, and Gaussian location-scale models), Cox proportional hazards models and multivariate additive models. The framework reduces the implementation of new model classes to the coding of some standard derivatives of the log-likelihood. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Abstract Here we propose a simple statistical algorithm for rapidly scoring loci associated with disease or traits due to recessive mutations or deletions using genome‐wide single nucleotide polymorphism genotyping case–control data in unrelated individuals. This algorithm identifies loci by defining homozygous segments of the genome present at significantly different frequencies between cases and controls. We found that false positive loci could be effectively removed from the output of this procedure by applying different physical size thresholds for the homozygous segments. This procedure is then conducted iteratively using random sub‐datasets until the number of selected loci converges. We demonstrate this method in a publicly available data set for Alzheimer's disease and identify 26 candidate risk loci in the 22 autosomes. In this data set, these loci can explain 75% of the genetic risk variability of the disease.

Spatial and temporal dynamics of phytoplankton biomass and water turbidity can provide crucial information about the function, health and vulnerability of lagoon ecosystems (coral reefs, sea grasses, etc.). A statistical algorithm is proposed to estimate chlorophyll-a concentration ([chl-a]) in optically complex waters of the New Caledonian lagoon from MODIS-derived "remote-sensing" reflectance (Rrs). The algorithm is developed via supervised learning on match-ups gathered from 2002 to 2010. The best performance is obtained by combining two models, selected according to the ratio of Rrs in spectral bands centered on 488 and 555 nm: a log-linear model for low [chl-a] (AFLC) and a support vector machine (SVM) model or a classic model (OC3) for high [chl-a]. The log-linear model is developed based on SVM regression analysis. This approach outperforms the classical OC3 approach, especially in shallow waters, with a root mean squared error 30% lower. The proposed algorithm enables more accurate assessments of [chl-a] and its variability in this typical oligo- to meso-trophic tropical lagoon, from shallow coastal waters and nearby reefs to deeper waters and in the open ocean. [ABSTRACT FROM AUTHOR]

Understanding how genes are differentially expressed across tissues is key to reveal the etiology of human diseases. Genes are never expressed in isolation, but rather co-expressed in a community; thus, they co-act through intricate but well-orchestrated networks. However, existing approaches cannot coalesce the full properties of gene-gene communication and interactions into networks. In particular, the unavailability of dynamic gene expression data might impair the application of existing network models to unleash the complexity of human diseases. To address this limitation, we developed a statistical pipeline named DRDNetPro to visualize and trace how genes dynamically interact with each other across diverse tissues, to ascertain health risk from static expression data. This protocol contains detailed tutorials designed to learn a series of networks, with the illustration example from the Genotype-Tissue Expression (GTEx) project. The proposed toolbox relies on the method developed in our published paper ( Chen et al., 2022 ), coding all genes into bidirectional, signed, weighted, and feedback looped networks, which will provide profound genomic information enabling medical doctors to design precise medicine. Graphical abstract Flowchart illustrating the use of DRDNetPro. The left panel contains the summarized pipeline of DRDNetPro and the right panel contains one pseudo-illustrative example. See the Equipment and Procedure sections for detailed explanations., Competing Interests: Competing interests The authors declare that no competing interests exist., (Copyright © 2023 The Authors; exclusive licensee Bio-protocol LLC.)

Spatial and temporal dynamics of phytoplankton biomass and water turbidity can provide crucial information about the function, health and vulnerability of lagoon ecosystems (coral reefs, sea grasses, etc.). A statistical algorithm is proposed to estimate chlorophyll-a concentration ([chl-a]) in optically complex waters of the New Caledonian lagoon from MODIS-derived “remote-sensing” reflectance (Rrs). The algorithm is developed via supervised learning on match-ups gathered from 2002 to 2010. The best performance is obtained by combining two models, selected according to the ratio of Rrs in spectral bands centered on 488 and 555 nm: a log-linear model for low [chl-a] (AFLC) and a support vector machine (SVM) model or a classic model (OC3) for high [chl-a]. The log-linear model is developed based on SVM regression analysis. This approach outperforms the classical OC3 approach, especially in shallow waters, with a root mean squared error 30% lower. The proposed algorithm enables more accurate assessments of [chl-a] and its variability in this typical oligo- to meso-trophic tropical lagoon, from shallow coastal waters and nearby reefs to deeper waters and in the open ocean.

ABSTRACT Objectives Non-invasive prenatal testing for fetal trisomy 21 ( T21) by massively parallel shotgun sequencing ( MPSS) is available for clinical use but its efficacy is limited by several factors, e.g. the proportion of cell-free fetal DNA in maternal plasma and sequencing depth. Existing algorithms discard DNA reads from the chromosomes for which testing is not being performed (i.e. those other than chromosome 21) and are thus more susceptible to diluted fetal DNA and limited sequencing depth. We aimed to describe and evaluate a novel algorithm for aneuploidy detection (genome-wide normalized score ( GWNS)), which normalizes read counts by the proportions of DNA fragments from chromosome 21 in normal controls. Methods We assessed the GWNS approach by comparison with two existing algorithms, i.e. Z-score and normalized chromosome value ( NCV), using theoretical approximations and computer simulations in a set of 86 cases (64 euploid and 22 T21 cases). We then validated GWNS by studying an expanded set of clinical samples ( n = 208). Finally, dilution experiments were undertaken to compare performance of the three algorithms ( Z-score, NCV, GWNS) when fetal DNA concentration was low. Results At fixed levels of significance and power, GWNS required a smaller fetal DNA proportion and fewer total MPSS reads compared to Z-score or NCV. In dilution experiments, GWNS also outperformed the other two methods by reaching the correct diagnosis with the lowest range of fetal DNA concentrations ( GWNS, 3.83-4.75%; Z-score, 4.75-5.22%; NCV, 6.47-8.58%). Conclusion Our results demonstrate that GWNS is comparable to Z-score and NCV methods regarding the performance of detecting fetal T21. Dilution experiments suggest that GWNS may perform better than the other methods when fetal fraction is low. Copyright © 2014 ISUOG. Published by John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

The problem of detecting structural damage by exploiting vibration signal measurements produced from earthquake excitation is addressed in this work. Following the Wenchuan earthquake of 12 May 2008, a residential masonry building was selected for instrumentation with accelerometers by the Harbin Institute of Technology. This building had been damaged in the Wenchuan earthquake. It represents a rare case of an instrumented building that has been previously damaged, thus serving as a full-scale benchmark model to evaluate structural identification and damage detection methods. More than 20 earthquakes were recorded by the system after the building was instrumented. For detecting the structural damage, a vibration-based damage detection algorithm for structures under earthquake excitation is introduced in this paper. This method is based on a residual associated with subspace-based modal identification, with global χ2-tests built on this residual. This method makes effective use of non-stationary and limited duration earthquake excitation, handles the uncertainties, and further detects the structural damage. The results obtained from these structural responses and earthquake excitations are reported using both identification and damage detection methods. [ABSTRACT FROM AUTHOR]

Abstract: For more exact navigation and positioning on the navigation systems, this paper discusses the improvement of statistical algorithms based on fuzzy knowledge with a brand-new theory. The improved fusion algorithms based on Bayesian theory and fuzzy methods on the track recognition are also given, respectively. However, this paper mainly makes a comparison of the improved new algorithms and two old algorithms for track recognition. The computer simulation shows the validity and feasibility of two improved algorithms. At the same time, the simulation results show that the overall performance of two improved algorithms for the track recognition all is better than that of the probabilistic algorithms. Based on the experiments in the dense target environment, the correct average recognition rate of statistical algorithms is 66.96% at the highest; that of the improved new algorithm based on fuzzy knowledge is 77.98%, that of the Bayesian fusion algorithm is 86.75%, however, that of the fuzzy fusion algorithm can be 90.14%. These results support the usefulness of the two improved algorithms. © 2002 Elsevier Science. All rights reserved. [Copyright &y& Elsevier]