Search

Previous research has shown that consumers respond differently to decisions made by humans versus algorithms. Many tasks, however, are not performed by humans anymore but entirely by algorithms. In fact, consumers increasingly encounter algorithm-controlled products, such as robotic vacuum cleaners or smart refrigerators, which are steered by different types of algorithms. Building on insights from computer science and consumer research on algorithm perception, this research investigates how consumers respond to different types of algorithms within these products. This research compares high-adaptivity algorithms, which can learn and adapt, versus low-adaptivity algorithms, which are entirely pre-programmed, and explore their impact on consumers' product preferences. Six empirical studies show that, in general, consumers prefer products with high-adaptivity algorithms. However, this preference depends on the desired level of product outcome range—the number of solutions a product is expected to provide within a task or across tasks. The findings also demonstrate that perceived algorithm creativity and predictability drive the observed effects. This research highlights the distinctive role of algorithm types in the perception of consumer goods and reveals the consequences of unveiling the mind of the machine to consumers. [ABSTRACT FROM AUTHOR]

A new Las Vegas algorithm is presented for the composition of two polynomials modulo a third one, over an arbitrary field. When the degrees of these polynomials are bounded by n, the algorithm uses O(n1.43) field operations, breaking through the 3/2 barrier in the exponent for the first time. The previous fastest algebraic algorithms, due to Brent and Kung in 1978, require O(n1.63) field operations in general, and n3/2+o(1) field operations in the special case of power series over a field of large enough characteristic. If cubic-time matrix multiplication is used, the new algorithm runs in n5/3+o(1) operations, while previous ones run in O(n2) operations. Our approach relies on the computation of a matrix of algebraic relations that is typically of small size. Randomization is used to reduce arbitrary input to this favorable situation. [ABSTRACT FROM AUTHOR]

Lyme disease, caused by infection with the bacterium Borrelia burgdorferi, is the most common vectorborne illness in the United States and is steadily affecting an expanding area of the country [...]

In the current phase of artificial intelligence development, more and more attention is being paid to technologies that make autonomous decisions. Little attention has been paid to the customers' and job seekers' perceptions of heavily technology-empowered organizations in the management and marketing literature. The purpose of this research is to fill this research gap. A between-subject experiment with fictitious brands was conducted with 239 subjects to answer four hypotheses about customers' attitudes, intention to use the offer, intention to recommend the company to others, and job seekers' intentions. The results show that an autonomous algorithm universally influences job seekers more than customer referral behaviors. People confronted with brochures from banks and hospitals that use autonomous technologies express a higher intention to work in such organizations than in analogous institutions that do not offer such a service. However, only in banks that use autonomous algorithms the customers have a more positive attitude and a higher intention to use or recommend the service. The hospital's unknown brand does not benefit from such an advertising approach. This is the first study ever conducted with fictitious brands of banks and hospitals to investigate the referral towards companies using autonomous technologies. The topic is worth exploring as key market players (such as Oracle) recognize autonomous technologies as the most ground-breaking innovation that will shape business in the coming years. KEYWORDS: autonomous systems, intelligent technologies, marketing, innovation, employer branding, customer behavior., 1. INTRODUCTION Technology can already make decisions without human intervention due to the progress in artificial intelligence. It has evolved from a human support tool into a decision-maker (Schaefer et [...]

Our study examines 'Finstagramming' as a resistance strategy from influencers trying to circumvent the prescriptive nature and restrictive algorithm of Instagram. Without ever leaving the platform, Finstagram acts as an emancipatory outlet that enables influencers to share more intimate, less-conforming and unpolished content without jeopardising the highly curated, monetizable person-brand of their main account. Through a dual-method qualitative approach of netnography and in-depth interviews, we unravel this paradox of embedded escapism, where influencers toggle between main and Finsta accounts in their pursuit of authenticity. Our findings reveal the porosity of these multiple digital personae and differentiated digital work taking place on the platform. We argue that Finstagram affords a momentary escape from the digital attention economy whilst remaining tethered to socially mediated authenticity markers. [ABSTRACT FROM AUTHOR]

* Context.--Automated prostate cancer detection using machine learning technology has led to speculation that pathologists will soon be replaced by algorithms. This review covers the development of machine learning algorithms and their reported effectiveness specific to prostate cancer detection and Gleason grading. Objective.--To examine current algorithms regarding their accuracy and classification abilities. We provide a general explanation of the technology and how it is being used in clinical practice. The challenges to the application of machine learning algorithms in clinical practice are also discussed. Data Sources.--The literature for this review was identified and collected using a systematic search. Criteria were established prior to the sorting process to effectively direct the selection of studies. A 4-point system was implemented to rank the papers according to their relevancy. For papers accepted as relevant to our metrics, all cited and citing studies were also reviewed. Studies were then categorized based on whether they implemented binary or multi-class classification methods. Data were extracted from papers that contained accuracy, area under the curve (AUC), or k values in the context of prostate cancer detection. The results were visually summarized to present accuracy trends between classification abilities. Conclusions.--It is more difficult to achieve high accuracy metrics for multiclassification tasks than for binary tasks. The clinical implementation of an algorithm that can assign a Gleason grade to clinical whole slide images (WSIs) remains elusive. Machine learning technology is currently not able to replace pathologists but can serve as an important safeguard against misdiagnosis., The adoption of whole slide image (WSI) scanners in clinical practice was accelerated by US Food and Drug Administration approval in 2017, which allowed primary pathologic diagnoses to be made [...]

Context.--Urinalysis instrument-specific dip strips offer physicians qualitative results for actionable analytes (protein, glucose, leukocyte esterase, nitrates, hemoglobin, and ketones). Objective.--To explain a strategy implemented to support clinical decision-making by providing urine quantification of protein, glucose, white blood cells (WBCs), and red blood cells because of urine strip shortages. Design.--During shortages, we implemented an automated algorithm that triggered sending urine samples to the automation line for quantification of protein and glucose and ensured that urine microscopy was performed to obtain WBC and red blood cell counts. The algorithm printed 2 labels so nursing staff would collect 2 specimens. We monitored the turnaround time from the specimen being received in the laboratory to result verification, ensured that the culture reflex order was triggered, and tracked complaints by physicians regarding not having usual urinalysis results. Prior to implementation, correlation between sample types for protein and glucose measurement was found acceptable. Results.--The algorithm was put in place twice during 2022. The turnaround time of urine microscopic study was identical to that obtained when the urinalysis was done with the strips; however, the quantification of glucose and protein took approximately 30 minutes more. Urine reflex cultures were triggered correctly with the algorithm, as they were derived entirely from a WBC count higher than 10 per high-power field. During the shortage period we had only 1 complaint, by a physician wanting to have results of nitrates. Conclusions.--During urine strip shortages, we successfully implemented a diversion algorithm that provided actionable urinalysis analytes in a timely manner with minimal provider complaints., doi: 10.5858/arpa.2023-0217-OA The COVID-19 pandemic has caused shortages of many resources required for laboratory testing, such as swabs, collection tubes, reagents, and even personnel. Mitigation strategies have been used to [...]

In the United States, an administrative agency called the Copyright Royalty Board (CRB) sets rates for various compulsory licenses. Under those licenses, qualified parties can pay a government-set fee and [...]

Antitrust enforcers now recognize the risks associated with many sellers' use of a singular hub's pricing algorithm. But what if many rivals use several different hubs for dynamic pricing? The common assumption is that in such instances, competition among the pricing hubs would support competition among the sellers. This Article, however, argues differently and instead introduces the concept of secondary algorithmic tacit collusion, which leads to anticompetitive effects independent of primary market conditions. This phenomenon may lead to the evils of price fixing but on a far wider scale. Contrary to traditional tacit collusion, this aggregated form of collusion--via algorithmic hub-and-spoke structures--can occur in markets with many competitors and with seemingly competitive dynamics. This Article outlines how the combination of hub-and-spoke frameworks on the primary market and conscious parallelism on the secondary market for algorithmic pricing services can lead to secondary tacit collusion, and what the agencies and courts can do to prevent this harm., TABLE OF CONTENTS I. INTRODUCTION 462 II. ALGORITHMIC ALIGNMENT 468 A. Tacit Algorithmic Collusion 468 B. Algorithmic Hub-and-Spoke 473 C. Difference Between Hub-and-Spoke and Tacit Algorithmic 477 Collusion III. SECONDARY [...]

We propose an approach for Bayesian inference in time-varying structural vector autoregressions (SVARs) identified with sign restrictions. The linchpin of our approach is a class of rotation-invariant time-varying SVARs in which the prior and posterior densities of any sequence of structural parameters belonging to the class are invariant to orthogonal transformations of the sequence. Our methodology is new to the literature. In contrast to existing algorithms for inference based on sign restrictions, our algorithm is the first to draw from a uniform distribution over the sequences of orthogonal matrices given the reduced-form parameters. We illustrate our procedure for inference by analyzing the role played by monetary policy during the latest inflation surge. JEL classification: C11, C51, E52, E58 Key words: time-varying parameters, structural vector autoregressions, identification, 1 Introduction Structural vector autoregressions (SVARs) featuring time-varying parameters are commonly used in empirical macroeconomics to study a wide range of classical questions such as the economic consequences of policy [...]

* Context.--The neurotrophic tropomyosin receptor kinase (NTRK) family gene rearrangements have been recently incorporated as predictive biomarkers in a 'tumor-agnostic' manner. However, the identification of these patients is extremely challenging because the overall frequency of NTRK fusions is below 1%. Academic groups and professional organizations have released recommendations on the algorithms to detect NTRK fusions. The European Society for Medical Oncology proposal encourages the use of next-generation sequencing (NGS) if available, or alternatively immunohistochemistry (IHC) could be used for screening with NGS confirmation of all positive IHC results. Other academic groups have included histologic and genomic information in the testing algorithm. Objective.--To apply some of these triaging strategies for a more efficient identification of NTRK fusions within a single institution, so pathologists can gain practical insight on how to start looking for NTRK fusions. Design.--A multiparametric strategy combining histologic (secretory carcinomas of the breast and salivary gland; papillary thyroid carcinomas; infantile fibrosarcoma) and genomic (driver-negative non-small cell lung carcinomas, microsatellite instability-high colorectal adenocarcinomas, and wild-type gastrointestinal stromal tumors) triaging was put forward. Results.--Samples from 323 tumors were stained with the VENTANA pan-TRK EPR17341 Assay as a screening method. All positive IHC cases were simultaneously studied by 2 NGS tests, Oncomine Comprehensive Assay v3 and FoundationOne CDx. With this approach, the detection rate of NTRK fusions was 20 times higher (5.57%) by only screening 323 patients than the largest cohort in the literature (0.30%) comprising several hundred thousand patients. Conclusions.--Based on our findings, we propose a multiparametric strategy (ie, 'supervised tumor-agnostic approach') when pathologists start searching for NTRK fusions., In solid tumors, actionable gene fusions can be identified in a wide range of cancer types. (1) The neurotrophic tropomyosin receptor kinase (NTRK) family gene rearrangements have been recently incorporated [...]

Objective Background Methods Results Conclusions To propose a rational basis for donor testing in cases of suspected antibody‐mediated transfusion‐related lung injury (AMT).Anti‐leukocyte antibodies in donated blood are established causes of transfusion‐related lung injury (TRALI). However, the question of whether to test donors for antibodies is not identical to whether the case meets definition criteria for TRALI. There is a balance between the potential benefits of testing and the costs of donor deferral and investigation. We propose that a decision‐making process based on optimising the balance between risk and benefit requires a subjective choice of the relative value of different outcomes of testing.We have developed a formal decision model to illustrate how these choices affect testing decisions.Using a Bayesian probability model, we show that the diagnostic benefit and TRALI prevention benefit of testing donors have a complex interrelationship with the number of implicated donors and clinical suspicion of antibody‐mediated TRALI (AMT) and that rational testing choices vary according to value assigned to outcomes.The challenges to the use of a formal decision model for clinical testing are discussed and conclude that a formal model is a useful consensus‐building tool for improving consistency and openness in decision making. [ABSTRACT FROM AUTHOR]

Study Design Objective Summary of Background Data Methods Results Conclusions Retrospective clinical study.The purpose of this study was to establish a novel classification of migrated lumbar disk herniation (LDH) based on magnetic resonance imaging and provide appropriate treatment strategies for each type through algorithms.Full‐endoscopic lumbar discectomy is a surgical technique that has been developed rapidly in recent years. For migrated LDH, few surgeons currently classify it with multiplanar positioning, and there is no consensus on the choice of treatment strategy. Therefore, we established a new multiplanar classification criteria that can localize the lesions more accurately than previous studies.A total of 263 eligible patients from March 2017 to March 2022 were included. Protrusions for each patient were located based on our classification and the surgical approach was selected according to our algorithms. The clinical symptoms of all patients before surgery, and at 1 day, 1 month, 3 months, 6 months, and 12 months after surgery were collected. Evaluations were performed using visual analogue scale (VAS), Oswestry Disability Index (ODI) and modified MacNab criteria. We used the chi‐squared test, one‐way analysis of variance (ANOVA), and t‐test to compare perioperative results and postoperative 3‐month, 6‐month, and 12‐month follow‐up results.VAS (low back pain) scores were reduced from 5.33 ± 2.67 to 0.73 ± 0.77 (p < 0.001), and VAS (leg pain) scores were reduced from 7.44 ± 2.21 to 0.37 ± 0.51 (p < 0.001). ODI scores improved from 58.46 ± 8.04 to 12.57 ± 2.51 (p < 0.001). According to the modified MacNab criteria, the excellent and good rate reached 92.78% at the 12‐month follow‐up. Twenty‐six patients developed complications, all of which improved after treatment. Recurrence occurred in 13 patients, and four of them underwent secondary surgery.This is an innovative classification method using multi‐plane positioning, and the algorithm used with it can help surgeons make appropriate choices when using endoscopic technology to treat migrated LDH. Statistical analysis of follow‐up data confirmed that this is a safe and effective strategy. [ABSTRACT FROM AUTHOR]

Laminated metal‐composite structures, known as fiber metal laminates (FMLs), are modern lightweight materials extensively utilized in diverse industries such as aerospace and automotive manufacturing. These materials offer enhanced impact and fatigue resistance, making them invaluable for various applications. However, machining FMLs poses a challenge due to the occurrence of delamination and heterogenous in nature during conventional methods. Therefore, this study aims to investigate the quality characteristics such as kerf width, surface roughness and kerf taper of Magnesium based FMLs processed with an unconventional machining process namely abrasive water jet cutting (AWJC). These FMLs comprise alternately stacked kevlar/carbon fibers adhesively bonded with epoxy resin matrix filled with varying wt% of reduced graphene oxide (r‐GO), combined with AZ31B alloy sheet as the skin material. AWJC experiments were performed by varying the process parameters including waterjet pressure (275–325 MPa), stand‐off distance (2.5–3.5 mm), and cutting speed (600–800 mm/min) based on the box–behnken experimental design. The findings from the statistical analysis underscore the significant influences of stand‐off distance and waterjet pressure on kerf characteristics, while the inclusion of r‐GO is observed to notably affect the surface quality. In pursuit of enhancing cut quality, a multi‐response optimization was conducted employing the Gorilla Troops Optimization (GTO) algorithm. Comparative analysis with established metaheuristics like the gray wolf algorithm, dragonfly algorithm, and harmony search algorithm reveals GTO's superior performance across various metrics, including rapid convergence, diversity, and spacing values. Further, the cutting‐induced damages such as matrix plowing, fiber‐metal debonding and composite delamination were observed through microstructure analysis. Highlights: Magnesium (AZ31B) fiber metal laminate comprising with various wt% of reduced graphene oxide was fabricated.Abrasive waterjet cutting of FMLs were performed based on box–behnken design experimental approach.Statistical analysis and influence of process parameters on the kerf width, surface roughness and kerf taper were investigated.Multi‐response optimization was carried out using metaheuristic‐based gorilla troops algorithm. [ABSTRACT FROM AUTHOR]

Background: Surgical site infection (SSI) is an important cause of disease burden and healthcare costs. Fully manual surveillance is time-consuming and prone to subjectivity and inter-individual variability, which can be partly overcome by semi-automated surveillance. Algorithms used in orthopaedic SSI semi-automated surveillance have reported high sensitivity and important workload reduction. This study aimed to design and validate different algorithms to identify patients at high risk of SSI after hip or knee arthroplasty. Methods: Retrospective data from manual SSI surveillance between May 2015 and December 2017 were used as gold standard for validation. Knee and hip arthroplasty were included, patients were followed up for 90 days and European Centre for Disease Prevention and Control SSI classification was applied. Electronic health records data was used to generate different algorithms, considering combinations of the following variables: ≥1 positive culture, ≥ 3 microbiological requests, antimicrobial therapy ≥ 7 days, length of hospital stay ≥ 14 days, orthopaedics readmission, orthopaedics surgery and emergency department attendance. Sensitivity, specificity, negative and predictive value, and workload reduction were calculated. Results: In total 1631 surgical procedures were included, of which 67.5% (n = 1101) in women; patients' median age was 69 years (IQR 62 to 77) and median Charlson index 2 (IQR 1 to 3). Most surgeries were elective (92.5%; n = 1508) and half were hip arthroplasty (52.8%; n = 861). SSI incidence was 3.8% (n = 62), of which 64.5% were deep or organ/space infections. Positive culture was the single variable with highest sensitivity (64.5%), followed by orthopaedic reintervention (59.7%). Twenty-four algorithms presented 90.3% sensitivity for all SSI types and 100% for deep and organ/space SSI. Workload reduction ranged from 59.7 to 67.7%. The algorithm including ≥ 3 microbiological requests, length of hospital stay ≥ 14 days and emergency department attendance, was one of the best options in terms of sensitivity, workload reduction and feasibility for implementation. Conclusions: Different algorithms with high sensitivity to detect all types of SSI can be used in real life, tailored to clinical practice and data availability. Emergency department attendance can be an important variable to identify superficial SSI in semi-automated surveillance. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) technology can reshape music education to enable students to learn musical instruments and compose music in a fun and easy way. This article examines the effectiveness of AI-enabled music teaching. The study seeks to incorporate AI-powered chatbots into piano classes in seven music schools and evaluate their effect on student performance. The results show that students who used the given app achieved better results than those enrolled in conventional classes. Overall, exploiting AI resulted in a 15% increase in academic performance, but the intensity of training led to variation in achievements. With this app, the scores increased as follows: piano playing by 6.51%; solfeggio and music literature by 4%; and vocal signing by 0.56%. The integration of chatbots in music education opens new developmental prospects. Based on the present findings, AI can be successfully integrated into music education. [ABSTRACT FROM AUTHOR]

AbstractStatistical comparison of multiple time series in their underlying frequency patterns has many real applications. However, existing methods are only applicable to a small number of mutually independent time series, and empirical results for dependent time series are only limited to comparing two time series. We propose scalable methods based on a new algorithm that enables us to compare the spectral density of a large number of time series. The new algorithm helps us efficiently obtain all pairwise feature differences in frequency patterns between M time series, which plays an essential role in our methods. When all M time series are independent of each other, we derive the joint asymptotic distribution of their pairwise feature differences. The asymptotic dependence structure between the feature differences motivates our proposed test for multiple mutually independent time series. We then adapt this test to the case of multiple dependent time series by partially accounting for the underlying dependence structure. Additionally, we introduce a global test to further enhance the approach. To examine the finite sample performance of our proposed methods, we conduct simulation studies. The new approaches demonstrate the ability to compare a large number of time series, whether independent or dependent, while exhibiting competitive power. Finally, we apply our methods to compare multiple mechanical vibrational time series. [ABSTRACT FROM AUTHOR]

Objective: To construct algorithms with a sequential decision analysis pathway for monitoring of the fetal heart rate and managing fetal heart rate bradycardia, late decelerations and tachycardia during labour. Population: Low‐risk pregnant women in labour with singleton cephalic term pregnancies. Setting: Institutional births in low‐ and middle‐income countries. Search strategy: We sought relevant published clinical algorithms, guidelines and randomised trials/reviews by searching the Cochrane Library, PubMed and Google on the terms: "fetal AND heart AND rate AND algorithm AND (labour OR intrapartum)", up to March 2020. Case scenarios: The two scenarios included were fetal heart rate bradycardia or late decelerations (potentially related to uterine rupture, placental abruption, cord prolapse, maternal hypotension, uterine hyperstimulation or unexplained) and fetal heart rate tachycardia (potentially related to maternal hyperthermia, infection, dehydration or unexplained). The algorithms provide pathways for definition, assessment, diagnosis, interventions to correct the abnormalities and ongoing monitoring leading to mode of birth, and linking to other algorithms in the series. Conclusions: The algorithms provide a framework for monitoring and managing fetal heart rate bradycardia, late decelerations and tachycardia during labour. We emphasise the inherent diagnostic inaccuracy of fetal heart rate monitoring, the tendency to over‐diagnose fetal compromise, the need to consider fetal heart rate information in the context of other clinical features and the need to engage in informed, shared, family‐centred decision‐making. We note the need for further research on methods of fetal assessment during labour including clinical fetal arousal testing and the rapid biophysical profile test. Decision analysis algorithms for fetal bradycardia, late decelerations and tachycardia highlight diagnostic limitations. Decision analysis algorithms for fetal bradycardia, late decelerations and tachycardia highlight diagnostic limitations. [ABSTRACT FROM AUTHOR]

The solar photovoltaic system suffers from varied environmental condition and causes power losses. This paper presents a Modified Artificial Rabbit Optimization based algorithm for MPPT. The performance of the Artificial Rabbit Optimization algorithm is improved by replacing the hidden parameter in the random hiding stage with levy flight strategy. The performance of the proposed algorithm is compared with particle swarm optimization, slap swarm optimization and artificial rabbit optimization under varying environmental conditions like change in uniform irradiance to partial shaded condition to uneven shaded condition and load variation. The simulation and experimental result gives a promising result by outperforming the particle swarm optimization, slap swarm optimization and artificial rabbit optimization algorithm in terms of quick convergence and decreasing oscillation towards maximum power points. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new vectorized MATLAB-based algorithm for efficient serial computation of global matrix/force arising from finite element method (FEM) for meshes of any type and approximation order in linear elasticity. Because for-loops in MATLAB are very slow, we propose a modified process that takes advantage of vectorization and sparse assembly to achieve good performance while using the same memory as the standard algorithm. For this purpose, by using good programming practices, the implementation of this scheme is succinctly described and can be integrated into any MATLAB package dealing with FEM. Specifically, attention is paid to the calculation of the triplet (row index, column index, matrix components) as well as the assembly of the global stiffness matrix, mass matrix and force vector. Additionally, an extension of the proposed approach for Mindlin plate theory and functionally graded materials is outlined. Finally, the accuracy of this strategy is verified on selected numerical tests after comparing the obtained results with those of ABAQUS. In terms of performance, the study conducted on a set of meshes considering the standard algorithm and two other well-known MATLAB vectorized algorithms revealed that: (i) for a 2D beam problem meshed with P 1 -triangle elements, a speedup of about 8 and 15 is achieved with sparse and fsparse, respectively. (ii) for a 3D plate problem meshed with P 1 -tetrahedral elements, a speedup of about 4 and 8 is achieved with sparse and fsparse, respectively. When compared to ABAQUS performance, the proposed scheme results in a computational time that is about five times smaller. [ABSTRACT FROM AUTHOR]

We consider polynomials f(x1, ..., xn) over a finite filed that satisfy the following condition: the set of solutions of the equation f(x1, ..., xn) = b, where b is some element of the field, coincides with the set of solutions of some system of linear equations over this field. Such polynomials are said to be multiaffine with the right-hand side b (or with respect to b). We describe a number of properties of multiaffine polynomials. Then on the basis of these properties we propose a polynomial algorithm that takes a polynomial over a finite field and an element of the field as an input and decides whether the polynomial is multiaffine with respect to this element. In case of the positive answer the algorithm also outputs a system of linear equations that corresponds to this polynomial. The complexity of the proposed algorithm is the smallest in comparison with other known algorithms that solve this problem. [ABSTRACT FROM AUTHOR]

There are high hopes that clinical decisions can be improved by adopting algorithms trained to estimate the likelihood that a patient suffers a condition C. Introducing work on the epistemic value of purely statistical evidence in legal epistemology I show that a certain type of AI devices for making medical decisions about persons rely on purely statistical evidence and that it raises an important question about the appropriateness of relying on such devices for allocating health resources. If the argument I present is sound, then it suggests a radical rethinking of the use of prevalent types of AI devices as well as the use of statistical evidence in medical practice more generally. [ABSTRACT FROM AUTHOR]

The elliptic Fourier transform is a common method of describing the shape of objects by an unique sequence of coefficients that allow comparing the shapes by mathematical methods. However, the raw coefficients contain unnecessary data unrelated to the shape, which does not provide a correct comparison. For this reason, the coefficients are normalized. This removes some of the superfluous data, but leaves information about mirror symmetry and the order in which the contour vertices are declared, that are encoded in the signs of the coefficients. This also interferes with shape comparison. The paper describes an algorithm for harmonizing the coefficients, leveling the influence of the information mentioned. Based on the example of attachment organs of monogeneans, the advantages of using harmonized coefficients for comparing the shapes of biological structures are shown. [ABSTRACT FROM AUTHOR]

Based on the differential idea, this article establishes two segmented algorithms which are the linear equations method and iterative method for solving the one-dimensional digital twin of the temperature field and pressure field of plate heat exchanger (PHE). The segmented algorithms are to consider the change of the physical properties of the fluid along the flow direction, which makes the solution of the temperature field and pressure field more accurate than that without segmentation. It can still get other important parameters besides temperature and is easy to converge with a few iterative steps. When the boundary conditions are known, it can accurately solve the heat transfer, wall temperature, convective heat transfer coefficient, temperature field and pressure field of fluid. Under six groups of test conditions, the heat exchange, temperature and pressure calculated by the two algorithms are compared with the commercial software Flownex, when the number of segments is 100, the average maximum error of each segment is not more than −0.39%, −0.97%, and 0.18%, respectively. To balance accuracy and calculation speed, this article also explores the impact of the number of segments on accuracy. It is found that when the number of segments increases to 10, the impact on the calculation accuracy can be ignored. Compared with Flownex, these two algorithms have smaller errors, and the solution speed of solving linear equations method is faster than that of iteration. It provides application value for emerging digital twin technology, data center chilled water system and other projects and industries that need to accurately control the outlet temperature and wall temperature of PHE. [ABSTRACT FROM AUTHOR]

Scientists are always interested in improving the efficiency of geophysical methods. In particular, one of the critical stages is the processing of data measured in geophysical surveys. This study aims to develop a new data processing algorithm of the improved multi-electrode electrical exploration method, and then apply it to investigate groundwater in Vientiane province, Laos as a case study. Our new proposed algorithm has two data processing options (option 1 and option 2) depending on the object to be studied. The results show that both options can determine the boundary of groundwater table. The proposed algorithm can provide the better results compared to the traditional algorithm. The results obtained at profile 1, site 1 in the study area suggest the presence of a potentially high-quality groundwater layer at depths ranging from 22 to 100 m. This finding aligns with the data from Borehole 1 in Phonhong District, situated directly above Profile 1, which similarly indicates a groundwater level at a depth of approximately 22 m. However, with option 2, the groundwater table has a wide variation, not as flat as the treatment results under option 1. The use of the improved Petrovski parameter ρ pm ct (option 2) helps to bring the results with high resolution, which in turn helps to show point/block objects more clearly. Thus, depending on the research object and purpose, it is possible to use only one of the two options and still obtain good results. [ABSTRACT FROM AUTHOR]

Cadence thresholds have been widely used to categorize physical activity intensity in health-related research. We examined the convergent validity of two cadence-based intensity classification approaches against a machine-learning-based intensity schema in 84,315 participants (≥ 40 years) with wrist-worn accelerometers. Validity study. Both cadence-based methods (one-level cadence, two-level cadence) calculated intensity-specific time based on cadence-thresholds while the two-level cadence identified stepping behaviors first. We used an overlapping plot, mean absolute error, and Spearman's correlation coefficient to examine agreements between the cadence-based and machine-learning methods. We also evaluated agreements between methods based on practically-important-difference (moderate-to-vigorous-physical activity: ± 20 min/day, moderate-physical activity: ± 15, vigorous-physical activity: ± 2.5, light-physical activity: ± 30). The group-level (median) minutes of moderate-to-vigorous- and moderate-physical activity estimated by one-level cadence were within the range of practically-important-difference compared to the machine-learning method (bias of median: moderate-to-vigorous-physical activity, − 3.5, interquartile range [− 15.8, 12.2]; moderate-physical activity, − 6.0 [− 17.2, 4.1]). The group-level vigorous- and light-physical activity minutes derived by two-level cadence were within practically-important-difference range (vigorous-physical activity: − 0.9 [− 3.1, 0.5]; light-physical activity, − 1.3 [− 28.2, 28.9]). The individual-level differences between the cadence-based and machine learning methods were high across intensities (e.g., moderate-to-vigorous-physical activity: mean absolute error [one-level cadence: 24.2 min/day; two-level cadence: 26.2]), with the proportion of participants within the practically-important-difference ranging from 8.4 % to 61.6 %. One-level cadence showed acceptable group-level estimates of moderate-to-vigorous and moderate-physical activity while two-level cadence showed acceptable group-level estimates of vigorous- and light-physical activity. The cadence-based methods might not be appropriate for individual-level intensity-specific time estimation. [ABSTRACT FROM AUTHOR]

Packet classification is a core function of network devices for providing advanced services, with the key challenge being to optimize classification speed while maintaining low memory usage. So far, many have proposed software-based packet classification solutions, with most of them adopting a multi-classifier architectures to accommodate the distribution of rule sets. Unfortunately, the need to perform lookups on each classifier during the packet classification stage significantly increases overhead, severely limiting classification speed. To address this shortfall, an efficient packet classification framework based on decision tree algorithms named MultiSplit is proposed. By leveraging the relationships of coverage and priority within the rule set, a new attribute can be abstracted for each rule, termed equivalent priority. Through this preprocessing, MultiSplit significantly reduces redundant lookup overhead while supporting the multi-classifier framework. Additionally, MultiSplit introduces a novel decision tree algorithm that combines multiple splits and intra-level binary search, significantly improving rule separation efficiency. The experimental results show that MultiSplit reduces memory consumption by 49% and decreases memory access by 63%, on average, compared with state-of-the-art packet classification algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central China Normal University is the property of Huazhong Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article discusses the semantic aspects of Kazakh sign language and its characteristics. Semantics, a field within linguistics, focuses on examining the meanings conveyed by expressions and combinations of signs. The author delves into the inquiry of the degree of similarity between verbal and sign languages, highlighting their fundamental distinctions. The primary objective of the research is to scrutinize the characteristics of parts of speech in the Kazakh language when expressed gesturally, along with the principles governing the translation of verbs and adverbial tenses. The article explains in detail the formulas for translating the text into sign language, based on the subject-object-predicate. Examples are given that illustrate the subject-object relationship and determine who acts as the speaker, "object" or "subject" of the utterance. It is necessary to note that for successful translation it is necessary first to understand the meaning of the sentence. The article concludes by emphasizing the importance of understanding both structural elements and contextual nuances in the fascinating world of the semantics of the Kazakh sign language. It inspires further research aimed at uncovering the complexities and exceptions that contribute to a deep understanding of linguistic nuances in this unique form of communication. [ABSTRACT FROM AUTHOR]

Reconfiguration strategies are used to reduce power losses and increase the reliability of the distribution systems. Since the optimal reconfiguration problem is a multi-objective optimization problem with non-convex functions and constraints, meta-heuristic algorithms are the most suitable choice for the problem-solving approach. One of the new meta-heuristic algorithms that exhibits excellent performance in solving multi-objective problems is the wild mice colony (WMC) algorithm, which is implemented based on aggressive and mating strategies of wild mice. In this paper, the distribution network reconfiguration problem is solved to reduce power losses, improve reliability, and increase the voltage profile of network buses using the WMC algorithm. In addition, the obtained results are compared with conventional multi-objective algorithms. The optimal reconfiguration problem is applied to the IEEE 33-bus and 69-bus test systems. The comparative study confirms the superior performance of the proposed algorithm in terms of convergence speed, execution time, and the final solution. [ABSTRACT FROM AUTHOR]

Little is known about which features of Facebook's interface appeal to users of far-right extremist groups, how such features may influence a user's interpretation of far-right extremist themes and narratives, and how this is being experienced across various nations. This paper looks at why certain 'Reactions' appealed to users in Australian and Canadian far-right groups on Facebook, and how these 'Reactions' may have characterized user decisions during their interaction with far-right extremist themes and narratives. A mixed methods approach has been used to conduct a cross-national comparative analysis of three years of 'Reaction' use across 59 Australian and Canadian far-right extremist groups on Facebook (2016–2019). The level of user engagement with administrator posts was assessed using 'Reactions' and identified themes and narratives that generated the most user engagement specific to six 'Reactions' ('Love', 'Haha', 'Wow', 'Sad', 'Angry' and 'Thankful'). This was paired with an in-depth qualitative analysis of the themes and narratives that attracted the most user engagement specific to two popular 'Reactions' used over time ('Angry' and 'Love'). Results highlight 'Angry' and 'Love' as the two most popular 'Reactions' assigned to in-group-out-group themes and narratives, with ' algorithms having propelled their partnership in these groups. [ABSTRACT FROM AUTHOR]

Background: The individualized PREdiction of DIsease Control using digital sensor Technology (iPREDICT) program was developed for asthma management using digital technology. Devices were integrated into daily lives of patients to establish a predictive model of asthma control by measuring changes from baseline health status with minimal device burden. Objectives: To establish baseline disease characteristics of the study participants, detect changes from baseline associated with asthma events, and evaluate algorithms capable of identifying triggers and predicting asthma control changes from baseline data. Patient experience and compliance with the devices were also explored. Design: This was a multicenter, observational, 24-week, proof-of-concept study conducted in the United States. Methods: Patients (⩾12 years) with severe, uncontrolled asthma engaged with a spirometer, vital sign monitor, sleep monitor, connected inhaler devices, and two mobile applications with embedded patient-reported outcome (PRO) questionnaires. Prospective data were linked to data from electronic health records and transmitted to a secure platform to develop predictive algorithms. The primary endpoint was an asthma event: symptom worsening logged by patients (PRO); peak expiratory flow (PEF) < 65% or forced expiratory volume in 1 s < 80%; increased short-acting β2-agonist (SABA) use (>8 puffs/24 h or >4 puffs/day/48 h). For each endpoint, predictive models were constructed at population, subgroup, and individual levels. Results: Overall, 108 patients were selected: 66 (61.1%) completed and 42 (38.9%) were excluded for failure to respond/missing data. Predictive accuracy depended on endpoint selection. Population-level models achieved low accuracy in predicting endpoints such as PEF < 65%. Subgroups related to specific allergies, asthma triggers, asthma types, and exacerbation treatments demonstrated high accuracy, with the most accurate, predictive endpoint being >4 SABA puffs/day/48 h. Individual models, constructed for patients with high endpoint overlap, exhibited significant predictive accuracy, especially for PEF < 65% and >4 SABA puffs/day/48 h. Conclusion: This multidimensional dataset enabled population-, subgroup-, and individual-level analyses, providing proof-of-concept evidence for development of predictive models of fluctuating asthma control. [ABSTRACT FROM AUTHOR]

Underground wastewater collection systems degrade with time, necessitating utility owners to engage in ongoing evaluations and enhancements of their asset management frameworks to preserve the performance of their assets. The inspection and condition assessment of sewer pipes are crucial for the effective operation and maintenance of sewer systems. The closed-circuit television (CCTV) is frequently employed to examine sewer pipes in the United States. This procedure is both costly and laborious because of the extensive number of pipes in a metropolis. Prioritisation of inspection for sanitary sewage pipe segments requiring repair or maintenance can be done in advance depending on their past performance. Hence, the aim of this study is to construct a predictive model for the state of sanitary sewer pipes, utilising data collected from a city located in the southcentral region of the United States. The main contribution is that this study used multiclass classification and predicted PACP scores of the pipes. Condition prediction models were developed using extensively utilised supervised machine learning algorithms including logistic regression (LR), k-nearest neighbors (k-NN), and random forest (RF). However, the bulk of the constructed models were assessed using a limited number of assessment measures, such as the receiver operator characteristic (ROC) curve and the area under the curve (AUC) value. This paper asserts that the assessment of the predictive capacity of these models cannot be determined only by relying on ROC and AUC values. Out of the three models evaluated in this study, the LR model had an AUC value of 0.76. However, this model had a higher number of misclassifications or inaccurate predictions compared to the other models. Consequently, these models were assessed using additional assessment measures, including precision, recall, and F-1 scores (which represent the harmonic mean of precision and recall). Curiously, the LR model achieved an F1-score of 0.28 on a scale ranging from 0 to 1. The RF model yielded an F1-score of 0.45 and an AUC value of 0.86. The existing model can be enhanced before it is employed by asset managers during the inspection phase to assess the state of their sanitary sewers and identify essential sewers that require immediate care. [ABSTRACT FROM AUTHOR]

Software-Defined Networking is today a mature technology, which is developed in many networks and also embedded in novel architectures like 5G and 6G. The SDN control centralization concept brings significant advantages for management and control in SDN together with the programmability of the data plane. SDN represents a paradigm shift towards agile, efficient, and secure network infrastructures, moving away from traditional, hardware-centric models to embrace dynamic, software-driven paradigms. SDN is compliant also with the virtualization architecture defined in the Network Function Virtualization framework. However, SDN should cooperate seamlessly for some years with the distributed TCP/IP control developed during the years all over the world. Among others, the traditional tasks of routing, forwarding, load balancing, QoS assurance, security, and privacy should be solved. The SDN native centralization brings also some new challenges and problems which are different from the traditional distributed control IP networks. The algorithms and protocols usable in SDN should meet requirements like scalability, convergence, redundancy assurance, sustainability, and good real-time response, and allow orchestrated automation in enhancing network resilience and adaptability. This work presents a theoretical review of state-of-the-art SDN optimization techniques, offering a critical and comparative discussion of various algorithms having tasks such as routing (including dynamic ones), forwarding, load balancing and traffic optimization, and forwarding delay minimization. Attention is pointed to general algorithms which can offer pragmatic solutions for large systems or multiple metric routing. [ABSTRACT FROM AUTHOR]

Background: Fabry disease (FD), an X-linked lysosomal storage disorder, is caused by mutations in the gene encoding α-galactosidase A, resulting in lysosomal accumulation of globotriaosylceramide and other glycosphingolipids. Early detection of FD is challenging, accounting for delayed diagnosis and treatment initiation. This study aimed to develop an algorithm using a logistic regression model to facilitate early identification of patients based on ICD-10-GM coding using a German Sickness Fund Database. Methods: The logistic regression model was fitted on a binary outcome variable based on either a treated FD cohort or a control cohort (without FD). Comorbidities specific to the involved organs were used as covariates to identify potential FD patients with ICD-10-GM E75.2 diagnosis but without any FD-specific medication. Specificity and sensitivity of the model were optimized to determine a likely threshold. The cut-point with the largest values for the Youden index and concordance probability method and the lowest value for closest to (0,1) was identified as 0.08 for each respective value. The sensitivity and specificity for this cut-point were 80.4% and 79.8%, respectively. Additionally, a sensitivity analysis of the potential FD patients with at least two codes of E75.2 diagnoses was performed. Results: A total of 284 patients were identified in the potential FD cohort using the logistic regression model. Most potential FD patients were < 30 years old and female. The identification and incidence rates of FD in the potential FD cohort were markedly higher than those of the treated FD cohort. Conclusions: This model serves as a tool to identify potential FD patients using German insurance claims data. [ABSTRACT FROM AUTHOR]

This document is a compilation of various studies and reports related to the management of Type 2 diabetes. It includes consensus reports from different diabetes societies and clinical practice guidelines from Japan. The document covers topics such as pharmacologic approaches to glycemic treatment, trends in antidiabetic prescription patterns, and the efficacy and safety of different diabetes medications. It also discusses the impact of diabetes on specific populations and provides information on the association between diabetes medications and cardiovascular outcomes. The document provides valuable information for researchers and healthcare professionals interested in the pharmacotherapy of type 2 diabetes. [Extracted from the article]

As ultra‐high‐definition displays have gained popularity, mitigating the horizontal crosstalk effect in 8K LCD panels is crucial. High display resolution requires narrower signal line integration, intensifying the coupling effect. Traditional methods like Vcom feedback and increasing analog voltage drain‐drain (AVDD) load are slower and less accurate, leading to increased power consumption. In response, we propose an advanced digital signal compensation method. In this study, we developed a predictive model and investigated the intricate relationships among AVDD, Vcom, and storage capacity (Cst) ripples on horizontal crosstalk. Optimizing the ripple change (∆G) by varying the compensation coefficient (a) and decay ratio (τ) significantly reduces crosstalk effects. The digital compensation method allows rapid and precise compensation without delays, reducing horizontal crosstalk in 8K LCD panels from 4% to below 0.9%. This surpasses the requirement of minimizing crosstalk to less than 2%, substantially enhancing the image quality of high‐resolution displays. [ABSTRACT FROM AUTHOR]

Starting from the notion of algebraic hidden processes introduced in Ref.9, we propose a definition of quantum hidden Markov process which extends the notion of quantum Markov chain (QMC) in the same sense in which classical hidden Markov processes (HMPs) extend classical Markov chains. We give several structure theorems for this new family of quantum states which shares with the QMCs the unique property that the finite dimensional joint expectations are explicitly given without these states being linearly equivalent to product states (as the Gaussian ones). The special feature of this new class of quantum states is best exemplified by looking at their restrictions to diagonal sub-algebras: we prove that they lead to a new family of classical stochastic processes that generalize in a non-trivial way the classical HMPs. [ABSTRACT FROM AUTHOR]