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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]

BACKGROUND: About one-third of older adults aged 65 years and older often have mild cognitive impairment or dementia. Acoustic and psycho-linguistic features derived from conversation may be of great diagnostic value because speech involves verbal memory and cognitive and neuromuscular processes. The relative decline in these processes, however, may not be linear and remains understudied. OBJECTIVE: This study aims to establish associations between cognitive abilities and various attributes of speech and natural language production. To date, the majority of research has been cross-sectional, relying mostly on data from structured interactions and restricted to textual versus acoustic analyses. METHODS: In a sample of 71 older (mean age 83.3, SD 7.0 years) community-dwelling adults who completed qualitative interviews and cognitive testing, we investigated the performance of both acoustic and psycholinguistic features associated with cognitive deficits contemporaneously and at a 1-2 years follow up (mean follow-up time 512.3, SD 84.5 days). RESULTS: Combined acoustic and psycholinguistic features achieved high performance (F1-scores 0.73-0.86) and sensitivity (up to 0.90) in estimating cognitive deficits across multiple domains. Performance remained high when acoustic and psycholinguistic features were used to predict follow-up cognitive performance. The psycholinguistic features that were most successful at classifying high cognitive impairment reflected vocabulary richness, the quantity of speech produced, and the fragmentation of speech, whereas the analogous top-ranked acoustic features reflected breathing and nonverbal vocalizations such as giggles or laughter. CONCLUSIONS: These results suggest that both acoustic and psycholinguistic features extracted from qualitative interviews may be reliable markers of cognitive deficits in late life.

OBJECTIVE: To determine the reliability and agreement of manual and automated morphological measurements, and agreement in morphological diagnoses. METHODS: Thirty pelvic radiographs were randomly selected from the World COACH consortium. Manual and automated measurements of acetabular depth-width ratio (ADR), modified acetabular index (mAI), alpha angle (AA), Wiberg center edge angle (WCEA), lateral center edge angle (LCEA), extrusion index (EI), neck-shaft angle (NSA), and triangular index ratio (TIR) were performed. Bland-Altman plots and intraclass correlation coefficients (ICCs) were used to test reliability. Agreement in diagnosing acetabular dysplasia, pincer and cam morphology by manual and automated measurements was assessed using percentage agreement. Visualizations of all measurements were scored by a radiologist. RESULTS: The Bland-Altman plots showed no to small mean differences between automated and manual measurements for all measurements except for ADR. Intraobserver ICCs of manual measurements ranged from 0.26 (95%-CI 0-0.57) for TIR to 0.95 (95%-CI 0.87-0.98) for LCEA. Interobserver ICCs of manual measurements ranged from 0.43 (95%-CI 0.10-0.68) for AA to 0.95 (95%-CI 0.86-0.98) for LCEA. Intermethod ICCs ranged from 0.46 (95%-CI 0.12-0.70) for AA to 0.89 (95%-CI 0.78-0.94) for LCEA. Radiographic diagnostic agreement ranged from 47% to 100% for the manual observers and 63%-96% for the automated method as assessed by the radiologist. CONCLUSION: The automated algorithm performed equally well compared to manual measurement by trained observers, attesting to its reliability and efficiency in rapidly computing morphological measurements. This validated method can aid clinical practice and accelerate hip osteoarthritis research.

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]

Aerosols affect Earth's climate both directly and indirectly, which is the largest uncertainty in the assessment of radiative forcings affecting anthropogenic climate change. The standard Aerosol Robotic Network (AERONET) aerosol products have been widely used for more than 30 years. Currently, there is strong community interest in the possibility of determining aerosol composition directly from remote sensing observations. This work presents the results of applying such a recently developed approach by Li et al. to extended datasets of the directional sky radiances and spectral aerosol optical depth (AOD) measured by AERONET for the retrievals of aerosol components. First, the validation of aerosol optical properties retrieved by this component approach with AERONET standard products shows good agreement. Then, spatiotemporal variations of the obtained aerosol component concentration are characterized globally, especially the absorbing aerosol species (black carbon, brown carbon, and iron oxides) and scattering aerosol species (organic carbon, quartz, and inorganic salts). Finally, we compared the black carbon (BC) and dust column concentration retrievals to the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), products in several regions of interest (Amazon zone, Indo-China Peninsula, North India, southern Africa, sub-Sahel, Gobi Desert, Middle East, Sahara Desert, and Taklamakan Desert) for new insights on the quantitative assessment of MERRA-2 aerosol composition products (R = 0.60-0.85 for BC; R = 0.75-0.90 for dust). The new value-added and long-term aerosol composition product globally is available online (https://doi.org/10.6084/ m9.figshare.25415239.v1), which provides important measurements for the improvement and optimization of aerosol modeling to enhance estimation of the aerosol radiative forcing. SIGNIFICANCE STATEMENT: In the assessment of climate change, the uncertainty associated with aerosol radiative forcing is the largest one. The purpose of this study is to provide a new value-added and long-term aerosol composition (including absorbing and scattering aerosol species) inversion dataset derived from Aerosol Robotic Network (AERONET) measurements for characterizing their spatiotemporal variations at global scale. We find some new insights on the quantitative assessment of black carbon and dust column concentration products in the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). Our results and aerosol composition inversion dataset will provide robust support for the overall improvement and optimization of aerosol modeling to better understand the aerosol radiative forcing. KEYWORDS: Algorithms; Remote sensing; Aerosol optical properties; Air pollution; Atmospheric composition, 1. Introduction Atmospheric aerosols are considered to be an important climate forcing factor that plays a key role in the global climate system (Charlson et al. 1992; IPCC 2021). For [...]

Abstract Introduction: Endoscopic retrograde cholangiopancreatography (ERCP) facilitates the removal of common bile duct (CBD) calculi by endoscopy. When ERCP fails, exploration of CBD is required for the clearance of CBD calculi. The optimum way for the exploration of CBD is by choledochoscopy. Dedicated flexible or rigid choledochoscopes are expensive and available only in few places in India. Since 1991, we subjected patients with suspected CBD calculi to ERCP, followed by laparoscopic cholecystectomy (LC). Patients in whom ERCP failed to clear CBD were subjected to open exploration of CBD using any easily available, suitable, straight rigid scope for choledochoscopy. Patients and Methods: Since March 1991, out of 8866 patients with cholelithiasis, 862 underwent ERCP. Ninety-six patients in whom ERCP failed to clear CBD underwent open exploration of CBD. In each case of exploration of CBD, choledochoscopy was performed using a straight rigid scope, either a cystoscope, paediatric cystoscope, hysteroscope or 5-mm laparoscopy telescope with a 5-mm cannula. Results: The CBD clearance was complete in 95 patients, and one patient had an impacted calculus at the ampulla. CBD explorations were followed by choledochoduodenostomy, T-tube placement or suturing of choledochotomy. No residual calculi were observed after such exploration. Conclusion: From our results, we advocate the following algorithm for CBD calculi in resource-limited settings. Subject patients with CBD calculi to ERCP followed by LC. In case of failed ERCP, open exploration of CBD with choledochoscopy using any suitable rigid scope. Dedicated flexible or rigid choledochoscope is not essential. This approach is cost-effective and successful. Keywords: Choledocholithiasis, choledochoscopy, endoscopic retrograde cholangiopancreatography, stone clearance, Author(s): Vinay Gangadhar Mehendale (corresponding author) [1]; Manoj S. Kamdar [2]; Sharad Narayan Shenoy [2] INTRODUCTION The prevalence of residual or missed common bile duct (CBD) stones post-treatment is a [...]

Background: Despite a recent surge in keyhole approaches to aneurysm clipping, only few studies have compared these approaches, and none have laid any foundation or roadmap for surgeons newly venturing into these procedures. Objectives: To report a single surgeon's experience of multiple keyhole approaches to these aneurysms, and to highlight the case selection algorithm, technical nuances, and surgical results. Material and Methods: Twenty-four patients (25 aneurysms) underwent aneurysm clipping using either a mini-pterional approach (MPA), lateral supraorbital approach (LSO), or a supraorbital keyhole approach (SOKHA). Intraoperative premature rupture, adequacy of clipping, and immediate postoperative and long-term functional outcomes were recorded. Results: All but five patients presented with ruptured aneurysms (Hunt and Hess grade 1 in 12 patients, grade 2 in 5 patients, and grade 3 in 2 patients). The anterior communicating artery (A-Comm; n = 14, 56%) was most frequently involved with aneurysms. The MPA was most frequently employed (n = 11, 45.8%), followed by the LSO (n = 9, 37.5%) and the SOKHA (n = 4, 16.7%). An intraoperative rupture (total n = 5, 20.8%) was most frequently seen in the SOKHA group (50%) and the only case of inadequate aneurysm clipping resulted from inadequate clipping ergonomics during the SOKHA for A-Comm aneurysm. Major complications occurred in the supraorbital approaches (one each in the LSO and SOKHA). Irrespective of the approach used, the long-term outcomes were excellent. Conclusion: In properly selected patients, keyhole approaches--particularly the MPA and the LSO--can provide excellent surgical outcomes. The learning curve in the SOKHA is steeper. A pragmatic case selection algorithm is proposed. Keywords: Aneurysm, interhemispheric keyhole, mini craniotomy, mini-pterional approach, subarachnoid hemorrhage, supraorbital approach, Author(s): Kuntal K. Das (corresponding author) [1]; Anant Mehrotra [1]; Awadhesh K. Jaiswal [1]; Vipin Sahu [1]; Preetham Dange [1]; Priyadarshi Dikshit [1]; Sanjeev Pattankar [1]; Pawan K. Verma [1]; [...]

Pain is a common complaint among a spectrum of diseases. Although an ideal objective method of pain assessment is lacking, several validated tools are available for use in clinical research and practice. The tool considerations are based upon the parameters to be assessed and factors specific to patient, disease, and availability of instruments. This review classifies and brings the key aspects of currently available pain assessment tools on a single platform to ease the selection process for researchers/practitioners. The tools utilized for pain assessment were collected from articles available in PubMed and Google Scholar databases and classified into the following domains: unidimensional, multi-dimensional, investigation-based, and computerized algorithm-based tools. Their purpose of use and limitations are reviewed. The unidimensional scales are used to describe only the characteristics of pain, like intensity (e.g. numerical rating scale), type (e.g. neuropathic pain questionnaire), or pattern. In contrast, multi-dimensional tools, like Mc Gill Questionnaire, assess not only pain as an individual symptom but also its influence on physical functioning and general well-being. However, certain components like ethnicity, age, cognitive impairment, sedation, and emotion become a limiting factor in selecting the scale. In addition to these scales, a potential role of parameters such as biopotentials/markers has also been shown in pain assessment. Last, artificial intelligence is also being applied in evaluation of pain. Pain measurement is subjective in nature as assessed through questionnaires and observational tools. Currently, multi-dimensional approaches of pain assessment are available, which can lead to precision pain management. Keywords: Multi-dimensional, pain assessment tool, precision pain management, special population, unidimensional, Author(s): Sarangi S Chandra (corresponding author) [1]; Gupta Pooja [1]; Makkar T Kaur [1]; Dodamani Ramesh [2] Key Message: Pain assessment methods are evolving and it ranges from simple unidimensional [...]

This column is the continuation of our previous column (1) that describes and explains some algorithms and data transforms beyond those most commonly used. We present and discuss algorithms that [...]

BACKGROUND: The field of toxicology has witnessed substantial advancements in recent years, particularly with the adoption of new approach methodologies (NAMs) to understand and predict chemical toxicity. Class-based methods such as clustering and classification are key to NAMs development and application, aiding the understanding of hazard and risk concerns associated with groups of chemicals without additional laboratory work. Advances in computational chemistry, data generation and availability, and machine learning algorithms represent important opportunities for continued improvement of these techniques to optimize their utility for specific regulatory and research purposes. However, due to their intricacy, deep understanding and careful selection are imperative to align the adequate methods with their intended applications. OBJECTIVES: This commentary aims to deepen the understanding of class-based approaches by elucidating the pivotal role of chemical similarity (structural and biological) in clustering and classification approaches (CCAs). It addresses the dichotomy between general end point-agnostic similarity, often entailing unsupervised analysis, and end point-specific similarity necessitating supervised learning. The goal is to highlight the nuances of these approaches, their applications, and common misuses. DISCUSSION: Understanding similarity is pivotal in toxicological research involving CCAs. The effectiveness of these approaches depends on the right definition and measure of similarity, which varies based on context and objectives of the study. This choice is influenced by how chemical structures are represented and the respective labels indicating biological activity, if applicable. The distinction between unsupervised clustering and supervised classification methods is vital, requiring the use of end point-agnostic vs. end point-specific similarity definition. Separate use or combination of these methods requires careful consideration to prevent bias and ensure relevance for the goal of the study. Unsupervised methods use end pointagnostic similarity measures to uncover general structural patterns and relationships, aiding hypothesis generation and facilitating exploration of datasets without the need for predefined labels or explicit guidance. Conversely, supervised techniques demand end point-specific similarity to group chemicals into predefined classes or to train classification models, allowing accurate predictions for new chemicals. Misuse can arise when unsupervised methods are applied to end point-specific contexts, like analog selection in read-across, leading to erroneous conclusions. This commentary provides insights into the significance of similarity and its role in supervised classification and unsupervised clustering approaches. https://doi.org/10.1289/EHP14001, Introduction Chemical hazard and risk assessments aim to estimate toxicological outcomes and evaluate the potential risks that chemicals may pose to human health and the environment. However, studying chemicals one-by-one [...]

Objective: The aim of the study is to compare the accuracy of dose calculation for different dose calculation algorithms with different prescription points (air, tissue, air-tissue interface in carcinoma lung patients and bone, tissue, and bone-tissue interface in carcinoma buccal Mucosa tumors). Materials and Methods: Forty-one patients with carcinoma lung and buccal mucosa were retrospectively selected for this study. A three-dimensional conformal radiotherapy reference plan was created using the prescription point in the tissue with Monte Carlo (MC) algorithms for both the groups of patients. The reference plan was modified by changing the prescription point and algorithms in the tissue, air, air-tissue interface for lung patients and tissue, bone, and bone-tissue interface for buccal mucosa patients. The dose received by the target volume and other organs at risk (OAR) structures was compared. To find out the statistical difference between different prescription points and algorithms, the statistical tests were performed with repeated measures ANOVA. Results: The target volume receiving 95% dose coverage in lung patients decreased to -3.08%, -5.75%, and -1.87% in the dose prescription point at the air-tissue interface with the dose calculation algorithms like MC, collapsed cone (CC), and pencil beam (PB), respectively, compared to that of the MC tissue. Spinal cord dose was increased in the CC and PB algorithms in all prescription points in patients with lung and buccal mucosa. OAR dose calculated by PB in all prescription points showed a significant deviation compared to MC tissue prescription point. Conclusion: This study will help demonstrate the accuracy of dose calculation for the different dose prescription points with the different treatment algorithms in radiotherapy treatment planning. Keywords: Air-tissue interface, bone-tissue interface, dose calculation algorithms, dose prescription point, weight point, Author(s): Bharath Pandu (corresponding author) [1,2]; D. Khanna (corresponding author) [1]; Mohandass Palanisamy [3]; Saro Jacob [2]; Sherin Manichan [4] INTRODUCTION The goal of radiotherapy is to deliver an accurate [...]

Background and Purpose: Size-specific dose estimates (SSDE) have been introduced into computed tomography (CT) dosimetry to tailor patients' unique sizes to facilitate accurate CT radiation dose quantification and optimization. The purpose of this study was to develop and validate an automated algorithm for the determination of patient size (effective diameter) and SSDE. Materials and Methods: A MATLAB platform was used to develop software of algorithms based on image segmentation techniques to automate the calculation of patient size and SSDE. The algorithm was used to automatically estimate the individual size and SSDE of four CT dose index phantoms and 80 CT images of pediatric patients comprising head, thorax, and abdomen scans. For validation, the American Association of Physicists in Medicine (AAPM) manual methods were used to determine the patient's size and SSDE for the same subjects. The accuracy of the proposed algorithm in size and SSDE calculation was evaluated for agreement with the AAPM's estimations (manual) using Bland-Altman's agreement and Pearson's correlation coefficient. The normalized error, system bias, and limits of agreement (LOA) between methods were derived. Results: The results demonstrated good agreement and accuracy between the automated and AAPM's patient size estimations with an error rate of 1.9% and 0.27% on the patient and phantoms study, respectively. A 1% percentage difference was found between the automated and manual (AAPM) SSDE estimates. A strong degree of correlation was seen with a narrow LOA between methods for clinical study (r > 0.9771) and phantom study (r > 0.9999). Conclusion: The proposed automated algorithm provides an accurate estimation of patient size and SSDE with negligible error after validation. Keywords: Algorithm, automation, body size, computed tomography scan, image segmentation, pediatric, radiation dosage, size-specific dose estimate, Author(s): Muhammad Kabir Abdulkadir (corresponding author) [1,2]; Noor Diyana Osman [1]; Anusha Achuthan [3]; Radin A. Nasirudin [1]; Muhammad Zabidi Ahmad [1]; Noor Hasyima Mat Zain [1]; Ibrahim Lutfi Shuaib [...]

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 [...]

In the fingermark detection field, background illustrations may negatively impact the visibility of the marks in presence. They can indeed locally reduce the contrast or mask ridge details. When conducting a research project, the choice for plain or illustrated substrates is mostly driven by the research objectives as well as the development level of the investigated technique (e.g., new reagent, comparison between two techniques, semi‐operational test). Also, it is not clear how the human assessors in charge of grading the quality of the detected fingermarks actually cope with fingermarks standing astride background prints. In recent publications, latent quality metrics (LQM) and improved latent fingerprint quality metrics (ILFQM) algorithms have been identified as potential alternatives to traditional human assessment. The authors of the present study were therefore interested in assessing the performance of these algorithms with regards to illustrated backgrounds. To reach that goal, 480 fingermarks were deposited on porous (240) and non‐porous (240) items presenting various kinds of illustrations (e.g., colored areas, writings, geometric shapes, lines). Several detection techniques were then applied in sequence: 1,2‐indanedione/zinc + ninhydrin + physical developer for porous items, and superglue fuming followed by dye‐staining or black magnetic powder for non‐porous items. A total of 884 pictures were obtained and their quality assessed by LQM and ILFQM. The results were then analyzed to determine if background prints may influence the metrics provided both algorithms, and if yes, to which extent. [ABSTRACT FROM AUTHOR]

Large-scale regular nucleic acid testing (NAT) such as Regular-1/7, played a crucial role in the successful and sustained containment of COVID-19 in China, enabling rapid case detection in low-risk areas. However, Regular-1/7 strategies are extremely costly and time-consuming. To address these challenges and facilitate the broader implementation of large-scale NAT during outbreaks of highly contagious diseases with long incubation periods, we propose a novel sampling strategy called Weighted Hub and Time Sampling (WHTS). This strategy considers the intensity of social activity, the degree of social overlap, and the time interval since the last NAT for each individual. Based on a scale-free contact network model, we simulated the spread of scenario in a community and compared the performance of the WHTS strategy to the Regular-1/7 strategy in terms of cost reduction and shorter alert time. The results show that WHTS-1/14 strategy can save half of the cost, and WHTS-1/7 strategy can alert 1.0 days in advance, compared to Regular-1/7 strategy. Additionally, The WHTS-1/14 strategy also achieves promising results under various scenarios, even though it samples half as many people as the Regular-1/7 strategy. Our study offers a valuable reference for large-scale testing in future efforts to manage emerging infectious diseases. [ABSTRACT FROM AUTHOR]

The accurate acquisition and prediction of the chlorophyll content in peanuts can provide reference for scientifically sound planting and management of peanut crops. In this study, peanut canopy leaves analysed during the flowering and pegging period were taken as the research object, and the spectral data collected by a portable hyperspectral spectrometer were used as the data source to study the inversion model of peanut leaf soil and plant analyser development (SPAD). To address the issues of randomness, the need for multiple iterations, and strong subjectivity in threshold selection in the initial variable set of the random frog (RF) algorithm, an improved RF algorithm was developed based on the sparse group lasso tool. By analysing and comparing the results of variable extraction of the improved RF algorithm and six characteristic wavelength screening algorithms, three algorithms were selected for pairwise coupling. These six algorithms were the correlation coefficient method (CC), successive projections algorithm, iteratively retains informative variables (IRIV), uninformative variable elimination (UVE), competitive adaptive reweighed sampling, and a genetic algorithm. The results show that: (1) the results of CC, UVE, and IRIV algorithms are better in a single algorithm experiment; and (2) in the coupled algorithms experiment, CC-UVE, CC-IRIV, and UVE-IRIV methods can effectively reduce dimensions, and the model has a certain level of stability. Among them, the UVE-IRIV-Extreme gradient boosting model had the highest accuracy, R2 = 0.652, RMSE = 1.867, and the variable compression rate was as high as 97.84%. The research results can provide a theoretical basis for rapid and accurate monitoring of the SPAD values of peanut leaves. [ABSTRACT FROM AUTHOR]

ABSTRACT Object Methods Results Conclusion With the development of new technologies, the surgical algorithm for ossification of posterior longitudinal ligament (OPLL) in the cervical spine also needs to be updated. The aim of this study is to elucidate a new surgical classification algorithm to guide the choice of surgical approaches for cervical OPLL based on its location and extent. In this algorithm, anterior controllable antedisplacement and fusion (ACAF) will be used as a new surgical option.This is a single‐centered, retrospective, cohort study utilizing a novel algorithm based on the following three criteria: (1) the positional relationship between ossification and uncinate process (UP), (2) the K‐line, and (3) the ossification segment for surgical decision‐making. Patients diagnosed with cervical OPLL who received surgical intervention guided by the algorithm were included. Patient demographics, Japanese Orthopedic Association (JOA) scores, surgical time, imaging data before and 2 years after surgery, and the occurrence of complications were extracted from the database. Paired t‐test was used for intragroup comparison, and one‐way ANOVA test was used for the intergroup analyses.Based on this novel algorithm, 15 patients with Type I, 8 patients with Type II a, 2 patients with Type II b1, 5 patients with Type II b2 were included. The decision‐making for the surgical techniques used in each patient followed the recommendation of the novel algorithm. The postoperative JOA scores of all types of patients improved significantly (p < 0.05), and the invasion rates of vertebral canal had also been significantly reduced (p < 0.05). In terms of restoring cervical curvature, patients with Type I (receiving ACAF) and Type II b2 (receiving laminectomy with instrumented fusion) benefited more from surgery (p < 0.05).A new algorithm guiding the choice of surgical approach for cervical OPLL was validated in a series of 30 patients. Through this analysis, we obtained on their clinical outcomes and complications. ACAF surgery is an ideal choice for Type I patients, with ossification located between UPs, while for patients with ossification exceeding UPs, it is better to perform anterior cervical corpectomy and fusion or posterior surgery. [ABSTRACT FROM AUTHOR]

Nowadays, the progress of technology covers, among other things, the development of modern techniques and high technologies used in land surveys. Unmanned aerial vehicles (UAVs), as a good alternative to conventional land survey techniques, have currently played an increasing role. The advantages of using unmanned aerial vehicles in photogrammetric measurements include a relatively short mission time for large-area surveys. In addition, photogrammetric products have a wider range of applications compared with conventional geodetic surveys. Many scientific publications delve into the quality of photogrammetric products, but the accuracy of UAVs in the context of geodetic standards has not been investigated in full. In this paper, the authors attempt to fill the observed research gap. The research has analysed the position of objects recorded in geodetic databases referring to their counterparts based on an accurate "true" orthophotomap from a photogrammetry campaign employing an unmanned aerial vehicle. The outcomes were referenced with land survey accuracy standards set out by relevant legislation. To ensure a smooth assessment of the result's accuracy a computing algorithm with a module for selecting comparable points and verifying the results was designed. The tool can be implemented in surveys carried out in any area thanks to open-source GIS software. The analysis showed that a detailed orthophotomap delivered using UAVs can be a valuable data source on objects recorded in geodetic databases covering selected cadastral and topographic objects and land development components. A general verification of the accuracy and validity of a geodetic numerical map and preliminary detection of areas for potential updates can be a particularly useful application of photogrammetry. [ABSTRACT FROM AUTHOR]

Conformational ensemble generation and the search for the global minimum conformation are important problems in computational chemistry. In this work, a variant on the conformer‐rotamer ensemble sampling tool (CREST) iterative metadynamics (iMTD) algorithm designed for determining structural ensembles and energetics of noncovalent clusters of flexible molecules is presented. We term this new algorithm a low‐energy diversity‐enhanced variant on CREST, or LEDE‐CREST. As with CREST, the energies are evaluated using the semiempirical GFN2‐xTB extended tight binding approach. The utility of the algorithm is highlighted by generating ensembles for a variety of noncovalent clusters of flexible or rigid monomers using both CREST and LEDE‐CREST. [ABSTRACT FROM AUTHOR]

Background: Thyroid nodules are challenging to accurately characterize on ultrasound (US), though the emergence of risk stratification systems and more recently artificial intelligence (AI) algorithms has improved nodule classification. The purpose of this study was to evaluate the performance of a recent Food and Drug Administration (FDA)-cleared AI tool for detection of malignancy in thyroid nodules on US. Methods: One year of consecutive thyroid US with ≥1 nodule from Duke University Hospital and its affiliate community hospital (649 nodules from 347 patients) were retrospectively evaluated. Included nodules had ground truth diagnoses by surgical pathology, fine needle aspiration (FNA), or three-year follow-up US showing stability. An FDA-cleared AI tool (Koios DS Thyroid) analyzed each nodule to generate (i) American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) descriptors, scores, and follow-up recommendations and (ii) an AI-adapter score to further adjust risk assessments and recommendations. Four groups were then compared: (i) Koios with AI-adapter, (ii) Koios without AI-adapter, (iii) clinical radiology report, and (iv) radiology report combined with AI-adapter. Performance of the final recommendations (FNA or no FNA) was determined based on ground truth, and comparison between the four groups was made using sensitivity, specificity, and receiver-operating-curve analysis. Results: Of 649 nodules, 32 were malignant and 617 were benign. Performance of Koios with AI-adapter enabled was similar to radiologists (area under the curve [AUC] 0.70 for both, [CI 0.60–0.81] and [0.60–0.79], respectively). Koios with AI-adapter had improved specificity compared to radiologists (0.63 [CI: 0.59–0.67] versus 0.43 [CI: 0.38–0.48]) but decreased sensitivity (0.69 [CI: 0.50–0.83) versus 0.81 [CI: 0.61, 0.92]). Highest performance was seen when the radiology interpretation was combined with the AI-adapter (AUC 0.76, [CI: 0.67–0.85]). Combined with the AI-adapter, radiologist specificity improved from 0.43 ([CI: 0.38–0.48]) to 0.53 ([CI: 0.49–0.58]) (McNemar's test p < 0.001), resulting in 17% fewer FNA recommendations, with unchanged sensitivity (0.81, p = 1). Conclusion: Koios DS demonstrated standalone performance similar to radiologists, though with lower sensitivity and higher specificity. Performance was best when radiologist interpretations were combined with the AI-adapter component, with improved specificity and reduced unnecessary FNA recommendations. [ABSTRACT FROM AUTHOR]

Satellite remote sensing is the best way to derive sea surface skin temperature (SSTskin) in the Arctic. However, as surface temperature retrieval algorithms in the infrared (IR) part of the electromagnetic spectrum are designed to compensate for atmospheric effects mainly due to water vapor, MODIS SSTskin retrievals have larger uncertainties at high latitudes where the atmosphere is very dry and cold, which is an extreme in the distribution of global conditions. MODIS R2019 SSTskin fields are currently derived using latitudinally and monthly dependent algorithm coefficients, including an additional band above 60°N to better represent the effects of Arctic atmospheres. However, the R2019 processing of MODIS SSTskin still has some unrevealed error characteristics. This study uses 21 years (2002–2022) of collocated, simultaneous satellite brightness temperature (BT) data from Aqua MODIS and in situ buoy-measured subsurface temperature data from iQuam for validation. Unlike elsewhere over the oceans, the 11 μm and 12 μm BT differences are poorly related to the column water vapor at high latitudes, resulting in poor atmospheric water vapor correction. Anomalous BT difference signals are identified, caused by the temperature and humidity inversions in the lower troposphere, which are especially significant during the summer. Although the existence of negative BT differences is physically reasonable, this makes the retrieval algorithm lose its effectiveness. Moreover, the statistics of the MODIS SSTskin data when compared with the iQuam buoy temperature data show large differences (in terms of mean and standard deviation) for the matchups at the Northern Atlantic and Pacific sides of the Arctic due to the disparity of in situ measurements and distinct surface and vertical atmospheric conditions. Therefore, it is necessary to further improve the retrieval algorithms to obtain more accurate MODIS SSTskin data to study surface ocean processes and climate change in the Arctic. [ABSTRACT FROM AUTHOR]

Study Design: Retrospective clinical study. Objective: 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. Summary of Background Data: 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. Methods: 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. 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. Conclusions: 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]

Regional and nationwide geophysical merges consisting of magnetic and radiometric datasets have become standard practice within Australia and some parts of the world. The best practice for geophysical merges is to ensure the highest quality survey data is incorporated in all given areas. However, the traditional means of accomplishing this determination is quite qualitative without a well-defined methodology. Here, a quantitative algorithmic approach is provided, reducing user "guesswork" regarding which survey supersedes others when occupying the same space within geophysical merges. This is accomplished by assessing a wide range of survey specifications and applying weighted scores to their relative importance. This algorithm is applied to 889 datasets flown in New South Wales between 1957 and 2023 and provides a clear efficacy in determination of data quality based on survey design. Results from this algorithm have demonstrated proficiency in ranking surveys from highest to lowest quality and have formed the basis of all statewide geophysical merges in NSW since 2020. [ABSTRACT FROM AUTHOR]

Autism spectrum disorders (ASDs) pose complex challenges, characterized by atypical behaviors, sensory sensitivities, and difficulties in social interaction. Despite extensive research, their exact causes remain elusive, indicating a multifactorial interplay of genetic, environmental, and neurological factors. This complexity calls for innovative approaches to ASD understanding and management. Motivated by the need to address the nuanced and uncertain nature of ASD-related data, in this study, we introduce a novel hybrid model called rough spherical fuzzy bipolar soft sets (RSFBSSs) by integrating rough sets, spherical fuzzy sets, and bipolar soft sets, which accommodates imprecision inherent in clinical assessments. We build upon foundational concepts of RSFBSS theory, developing a comprehensive algorithm for uncertain multiple attribute decision-making (MADM). Leveraging this framework, we aim to assess ASD symptom severity in pediatric populations, considering diverse contributing factors to ASD pathogenesis. The RSFBSSs offer advantages over existing methodologies, providing a robust framework for handling complex ASD data. The algorithmic framework facilitates accurate and individualized assessments of ASD symptomatology. To validate our model's efficacy, we conduct a comparative analysis with preexisting hybrid models, employing quantitative metrics and qualitative evaluations. Through this comprehensive evaluation, we demonstrate the superior performance and versatility of RSFBSSs, offering promising avenues for advancing ASD management. [ABSTRACT FROM AUTHOR]

Variables of phytopathological interest correlated to the impact of plant diseases, such as incidence and severity, may display a spatial pattern resulting from an underlying, yet unknown gradient. Along the main direction of the gradient the variable assessed at the site level either increases, or decreases. Spatial gradients may also arise because of the movement of a front of invasion, an imaginary moving contour separating areas already infested by a plant pathogen from those still pathogen-free. Adequate geostatistical tools may shed light on gradients directional properties, as well as on the direction the front of invasion is coming from or moving to. Tools currently available for that may be impractical due to the advanced computational and programming skills required for their application. Hence, the goals of this study were: (I) to develop, test and validate a new user-friendly geostatistical tool named DirGrad (Direction of Gradient) aimed at analyzing spatial gradients resulting from the impact of plant diseases; (II) to build an algorithm able to run DirGrad on R, one the most widespread open source software for statistics; and (III) to apply DirGrad for the ex post modelling of the invasion front dynamics. The designed algorithm was successfully validated both in silico and in the field by using data from real case studies such as those of the invasive fungal pathogens Heterobasidion irregulare and Ophiostoma novo-ulmi in a forest stand of Central Italy and across the Swedish island of Gotland, respectively. The algorithm is released as a user-friendly open-source script. [ABSTRACT FROM AUTHOR]

Forest and biomass crops for bioenergy and bioproducts can promote a sustainable bioeconomy while effectively reducing greenhouse gas (GHG) emissions to mitigate global warming. One of the most concerning issues is selecting and using appropriate modeling and analytical technologies to optimize the benefits of multi-feedstock biomass supply chains, including logistics. Machine learning (ML) has been used to solve increasingly complex supply chain problems, providing powerful tools for sustainable forest management and biomass resource development. Existing research is extensive and spans many different ML techniques, but synthesis is needed to help guide the adoption of these rapidly evolving tools. This review summarizes ML applications in forest and biomass supply chain management in terms of data, algorithms, and process examples, with an emphasis on direct application to supply chain management. ML is a viable technique to support strategic, operational, and tactical planning and decision-making in this field and can enhance the environmental and economic performance of diverse forest and biomass supply chains. [ABSTRACT FROM AUTHOR]

Path planning for unmanned aerial vehicle (UAV) is the process of determining the path that travels through each location of interest within a particular area. There are numerous algorithms proposed and described in the publications to address UAV path planning problems. However, in order to handle the complex and dynamic environment with different obstacles, it is critical to utilize the proper fusion algorithms in planning the UAV path. This paper reviews some hybrid algorithms used in finding the optimal route of UAVs that developed in the last ten years as well as their advantages and disadvantages. The UAV path planning methods were classified into categories of hybrid algorithms based on traditional, heuristic, machine learning approaches. Criteria used to evaluate algorithms include execution time, total cost, energy consumption, robustness, data, computation, obstacle avoidance, and environment. The results of this study provide reference resources for researchers in finding the path for UAVs. [ABSTRACT FROM AUTHOR]

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Nonlinear signal distortions are one of the main reasons limiting the throughput and length of modern fiber-optic communication lines. One of the developed approaches to nonlinear distortion compensation is based on the application of perturbation theory methods to the nonlinear Schrödinger equation, and allows one to obtain a relationship between transmitted and received symbols. Gradient methods that minimize the standard deviation between symbols are usually used to find the perturbation coefficients. However, the main parameter characterizing the data transmission quality is the bit error rate. We propose a modification of this approach in the form of a two-stage scheme for calculating the perturbation coefficients. At the first stage, the coefficients are calculated using the least squares method by minimizing the standard deviation, and at the second stage, the obtained solution is used as an initial approximation to minimize the error coefficient using the particle swarm method. In a numerical experiment, using the algorithm for compensating for received signal distortions based on the proposed scheme, a 0.9 dB gain in the signal-to-noise ratio is obtained for a multi-span line 20 × 100 km long and a 16QAM signal with a channel rate of 267 Gbit/s. An improvement in the accuracy of the algorithm (compared to a single-stage scheme) is shown, estimates of the computational complexity of the algorithm are obtained, and the relationship between its complexity and accuracy is presented. [ABSTRACT FROM AUTHOR]

Until 2021, studies of scientific disciplines took as their object of study papers published in journals from certain subject categories. This paper is one of the firsts to analyse research into journalism in Spain using the Clarivate InCites analytics platform algorithm that groups papers together according to their citation relations (Citation Topics, CT) rather than by the category of the journals themselves. A unique universe was obtained made up of 63,694 items on the Web of Science, this being the global output of the CT 'Journalism' (1980–2022, both included). Spain is the world's third producer by number of items (4635), but only seventh in citations (and has the lowest proportion of citations per paper, 7.151). Most of the papers (34.01%) are published in Q3 (unlike the rest of the countries analysed, which publish more in Q1 and Q2) and in national journals (61%, even more than the US or the UK, 52% and 47%, respectively), edited by small, non-commercial publishers. Only 40.6% of the items are in the WoS Flagship citation index databases (SCIE, SSCI and AHCI). Most Spanish journals publish national research in proportions around 80%. Spanish research tends to collaborate internationally to a lesser extent (16.5%) than the other European countries analysed, and no significant change has been apparent over the last years. Spanish research into journalism is very productive but has little international presence, obtains few citations per paper, and shows no clear signs of progress towards greater international collaboration. [ABSTRACT FROM AUTHOR]

Identifying epitopes, or the segments of a protein that bind to antibodies, is critical for the development of a variety of immunotherapeutics and diagnostics. In vaccine design, the intent is to identify the minimal epitope of an antigen that can elicit an immune response and avoid off-target effects. For prognostics and diagnostics, the epitope-antibody interaction is exploited to measure antigens associated with disease outcomes. Experimental methods such as X-ray crystallography, cryo-electron microscopy, and peptide arrays are used widely to map epitopes but vary in accuracy, throughput, cost, and feasibility. By comparing machine learning epitope mapping tools, we discuss the importance of data selection, feature design, and algorithm choice in determining the specificity and prediction accuracy of an algorithm. This review discusses limitations of current methods and the potential for machine learning to deepen interpretation and increase feasibility of these methods. We also propose how machine learning can be employed to refine epitope prediction to address the apparent promiscuity of polyreactive antibodies and the challenge of defining conformational epitopes. We highlight the impact of machine learning on our current understanding of epitopes and its potential to guide the design of therapeutic interventions with more predictable outcomes. [ABSTRACT FROM AUTHOR]

Background: Current digital medical databases record systematically coded diagnoses, but many legacy databases are full of hand-written, free text diagnoses, which can only be meaningfully analysed after mapping them to a coding system. While diagnoses can be extracted from full medical notes with good accuracy, no algorithm using only an unstructured free text diagnosis with no additional data has been published to date. Objectives/methods: Therefore, we sought to create an algorithm which maps hand-written German diagnoses from our clinical photography database to ICD-10 diagnosis codes, validate its output manually by dermatologists and analyse diagnosis counts over time as a proof-of-concept of its application. Results: Our rule-based algorithm mapped 50,884 unprocessed hand-written German free-text diagnoses covering five decades to ICD-10 codes, while reaching an accuracy of 82% against 817 dermatologist-validated diagnoses. Out of 41,021 data points with the highest algorithm confidence the top 3 identified diagnosis classes were psoriasis, eczema, and non-melanoma skin cancer. The number of ICD-10 codes belonging to chronic inflammatory diseases showed a seasonal pattern with peaks in July, and when analysed aggregated by year, peaks correlated to events such as new therapy classes for these diseases. Conclusion: Using the presented algorithm, it is possible to reliably match hand-written free text of German dermatological diagnoses to ICD-10 codes, thus enabling systematic analysis of legacy databases, making past medical knowledge accessible to today's patient care. [ABSTRACT FROM AUTHOR]

Despite considerable behavioral and organizational research on advice from human advisors, and despite the increasing study of artificial intelligence (AI) in organizational research, workplace-related applications, and popular discourse, an interdisciplinary review of advice from AI (vs. human) advisors has yet to be undertaken. We argue that the increasing adoption of AI to augment human decision-making would benefit from a framework that can characterize such interactions. Thus, the current research invokes judgment and decision-making research on advice from human advisors and uses a conceptual "fit"-based model to: (1) summarize how the characteristics of the AI advisor, human decisionmaker, and advice environment influence advice exchanges and outcomes (including informed speculation about the durability of such findings in light of rapid advances in AI technology), (2) delineate future research directions (along with specific predictions), and (3) provide practical implications involving the use of AI advice by human decision-makers in applied settings. [ABSTRACT FROM AUTHOR]

Let G(p,q) be a simple graph, where p is the number of vertices and q is the number of edges if there exists a one-to-one mapping f: E(G)→{1,2,...,|E|}, so that for any two vertices uv∈E(G), if d(u)=d(v), then S(u)=S(v), where ... represents the degree of vertex u, f is called the Adjacent Vertex Reducible Edge Labeling (AVREL) of G. Building on the current graph labeling algorithm, a heuristic search algorithm is designed, and this algorithm is used to label random graphs with less than 12 vertices and obtain the result set of adjacent vertex reducible edge labeling. Based on the analysis of the result set and combining it with the known theorem, the adjacent vertex reducible edge labeling law of other compound graphs is obtained, and the related proof is given. [ABSTRACT FROM AUTHOR]