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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 [...]

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]

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]

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]

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]

Parkinson's disease (PD) is a serious neurological disease that is threatening the whole world population. The devolution of the neurons located in the substanstia nigra of the brain causes, bradykinesia, rigidity and resting tremor, which are characteristic motor symptoms, occuring in advanced stages. Currently, there is not an effective treatment for PD, it is just controlled by some prescribtions. Early detection of this disease affects the choice of treatment. Recent studies on early diagnosis by analyzing electroencephalography (EEG) recordings have provided a glimmer of hope. Therefore, in this study, an efficient PD detection method from EEG data by using a new set of features is searched. An opensource resting state data of 28 subjects divided as Parkinson and control gorups were anlyzed. PSDs of the EEG frequency bands that are delta, theta, alpha, beta and gamma and Median Spectral Frequency (MSF), Spectral Entropy (SE), Kolmogorov Algorithmic Complexity (KAC) and Weighted Symbolic Mutual Information (wSMI) were extracted as features. The performance of the PD and control group was evaluated with Gradient Boosting (GB), Gaussian Naive Bayes (GNB), and K-nearest Neighbor (KNN), Support Vector Machines (SVM), Logistic Regression (LR), Categorical Boosting (CatBoost) and Extreme Gradient Boosting (XGBoost) Algorithms. A 85% accuracy was achieved with the XGBoost algorithm, using 31 channels and 13 features which outperforms the results of previous studies using this dataset in the literature. [ABSTRACT FROM AUTHOR]

Primary angle closure glaucoma (PACG) is a major cause of visual impairment, particularly in Asia. Although effective screening tools are necessary, the current gold standard is complex and time-consuming, requiring extensive expertise. Artificial intelligence has introduced new opportunities for innovation in ophthalmic imaging. Anterior chamber depth (ACD) is a key risk factor for angle closure and has been suggested as a quick screening parameter for PACG. This study aims to develop an AI algorithm to quantitatively predict ACD from anterior segment photographs captured using a portable smartphone slit-lamp microscope. We retrospectively collected 204,639 frames from 1586 eyes, with ACD values obtained by anterior-segment OCT. We developed two models, (Model 1) diagnosable frame extraction and (Model 2) ACD estimation, using SWSL ResNet as the machine learning model. Model 1 achieved an accuracy of 0.994. Model 2 achieved an MAE of 0.093 ± 0.082 mm, an MSE of 0.123 ± 0.170 mm, and a correlation of R = 0.953. Furthermore, our model's estimation of the risk for angle closure showed a sensitivity of 0.943, specificity of 0.902, and an area under the curve (AUC) of 0.923 (95%CI: 0.878–0.968). We successfully developed a high-performance ACD estimation model, laying the groundwork for predicting other quantitative measurements relevant to PACG screening. [ABSTRACT FROM AUTHOR]

Objective(s): Pediatric epistaxis is a common, often non‐operative condition encountered by Otolaryngologists. The present study seeks to (1) describe our outcomes of epistaxis management, (2) estimate the associated healthcare burden, and (3) propose a clinical model to optimize care coordination with primary care and advanced practice providers. Methods: Retrospective case series of pediatric patients treated outpatient for epistaxis by a single otolaryngologist from 2021 to 2022. The primary outcome after treatment with nasal lubricants was defined as (1) refractory epistaxis, (2) improvement, or (3) complete resolution. Cost data for office versus operative nasal cautery were analyzed. Results: In total, 122 patients were included for analysis. Over a follow‐up duration of 20.5 months (IQR 8–36), 24.6% of patients experienced refractory epistaxis, 41.8% of patients found improvement, and 33.6% had complete resolution (n = 122). Refractory epistaxis was associated with a family history of coagulopathy (p =.007), daily epistaxis episodes (p =.043), and anemia (p <.001). Average direct hospital costs associated with nasal cautery were $187 for in‐office cauterization and $2179 for intraoperative cauterization. Estimated patient/third party payors savings were $1617 and $15,412 for in‐office and intraoperative procedures, respectively, and $541.59 for specialty office visits alone. The average charge for laboratory work‐up was $576. Conclusion: Approximately 75% of patients with epistaxis experienced improvement or resolution of symptoms with nasal lubrication alone. Refractory epistaxis was associated with a family history of coagulopathy, daily epistaxis episodes, and anemia. Otolaryngology visits for epistaxis were associated with a direct healthcare expense burden. Adaptation of our clinical model may mitigate these costs while improving patient care. Level of Evidence: 4. [ABSTRACT FROM AUTHOR]

The rapid advancement of artificial intelligence (AI) technology has catalyzed unprecedented innovation in the healthcare industry, transforming medical practices and patient care. However, this progress brings significant ethical challenges, highlighting the need for a comprehensive exploration of algorethics—the intersection of algorithm design and ethical considerations. This study aimed to conduct a narrative review of reviews in the field of algorethics with specific key questions. The review utilized a standardized checklist for narrative reviews, including the ANDJ Narrative Checklist, to ensure thoroughness and consistency. Searches were performed on PubMed, Scopus, and Google Scholar. The review revealed a growing emphasis on integrating fairness, transparency, and accountability into AI systems, alongside significant progress in ethical AI development. The importance of collaboration between different domains of scientific production, such as social sciences and standardization (like the IEEE), and the development of guidelines is significantly emphasized, with demonstrated direct impact in the health domain. However, gaps persist, particularly in the lack of standardized evaluation methods and the challenges posed by complex sectors like healthcare. The findings underscore the need and importance for robust data governance to prevent biases and highlight the importance of cross-disciplinary collaboration in creating comprehensive ethical frameworks for AI. The field of algorethics has important applications in the health domain, and there is a significant increase in attention, with a focus on addressing issues and seeking both practical and theoretical solutions. Future research should prioritize establishing standardized evaluation practices for AI, fostering interdisciplinary collaboration, developing sector-specific ethical guidelines, exploring AI's long-term societal impacts, and enhancing ethical training for developers. Continued attention to emerging ethical standards is also crucial for aligning AI technologies with evolving ethical principles. [ABSTRACT FROM AUTHOR]

With increasing urbanization and the demand for efficient, flexible transportation solutions, demand-responsive transportation services (DTRS) has emerged as a viable alternative to traditional public transit. However, determining the optimal fleet size to balance the investment and operational revenue remains a significant challenge for service providers. In this article, we address the optimization of fleet size in point-to-point shared demand DRTS, which widely operates within many cities. To capture the uncertain passenger demands in the future when planning the fleet size currently, we model this problem with a framework of two-stage stochastic programming with recourse. Fleet sizing decisions are made in the first stage before the uncertain demands are revealed. After the uncertainty is revealed, the second stage involves making additional decisions to maximize operational revenue. The objective is to optimize the total revenue of the first-stage decisions and the expected revenue of the recourse actions. To solve this practical problem, we resort to the Model Predictive Control method (MPC) and propose a network decomposition approach that first converts the transportation network to a nodal tree structure and then develops a Nodal Tree Recourse with Dependent Arc Capacities (NTRDAC) algorithm to obtain the exact value of the expected recourse functions. In the experiments, NTRDAC is able to produce results within seconds for transportation networks with over 30 nodes. In contrast, a commercial solver is only capable of solving networks with up to five nodes. The stability tests show that NTRDAC remains robust as the problem size varies. Lastly, the value of the stochastic solution (VSS) was evaluated, and the results indicate that it consistently outperforms the expected value solutions. Numerical experiments show that the performance of the NTRDAC algorithm is quite encouraging and fit for large-scale practical problems. [ABSTRACT FROM AUTHOR]

Introduction: Hidradenitis suppurativa (HS) is a painful, inflammatory skin disease associated with a high disease burden and long diagnostic delay. Prevalence estimates of HS vary widely in the literature owing to differing estimation methodologies. This study aimed to apply stepwise algorithms to estimate the prevalence of possible/diagnosed cases of HS in the US. Methods: This was a retrospective cohort study in adult and pediatric patients with HS which utilized data from four US databases (MarketScan [Medicare and Medicaid] and Optum [electronic health record (EHR) and Clinformatics Data Mart (CDM)]). Patients with possible/diagnosed HS were identified using two algorithms (termed Algorithm 1 and Algorithm 2), which assessed symptoms such as multiple skin boils in site-specific areas based on international classification of disease (ICD) codes. Patients with diagnosed HS were defined as having ≥ 2 outpatient or ≥ 1 inpatient diagnosis codes of HS. In each database, patients with continuous medical and pharmacy benefits in the 365 days pre-index and 0–365 days post-index periods were eligible for inclusion. Results: Across all databases, Algorithm 2 (MarketScan Medicare [N = 309,916]; MarketScan Medicaid [N = 188,783]; Optum EHR [N = 366,158]; Optum CDM [N = 173,812]) identified more patients with possible/diagnosed HS than Algorithm 1 (MarketScan Medicare [N = 194,353]; MarketScan Medicaid [N = 99,276]; Optum EHR [N = 177,957]; Optum CDM [N = 112,244]). Based on ICD-9/10 codes, the 5-year period prevalence of HS ranged from 0.06% to 0.12% across all databases, while for Algorithm 1 and Algorithm 2, this ranged from 0.27% to 0.41% and 0.49% to 0.78%, respectively. Adults and females generally had a higher 5-year period prevalence versus pediatric patients and males, respectively. Conclusion: This real-world study highlights that HS diagnosis codes alone may be insufficient to estimate the prevalence of HS, demonstrating the value of employing algorithms in practice which assess for parameters such as multiple skin boils in site-specific areas. Integrating robust methods to identify the prevalence of HS may improve the diagnostic delay observed in HS and improve treatment outcomes. [ABSTRACT FROM AUTHOR]

Traditional healthcare systems have suffered from different data communication, security, data processing, and compliance issues. The traditional systems are also not well equipped to handle the new technologies like Artificial Intelligence (AI) by enabling more accurate diagnostics, personalized treatment plans, and improved patient outcomes. The existing data communication and security protocols and compliance are also not fully implemented to tackle the system's challenges. This article proposes a Tri‐Tier architecture by using data communication, AI data generative, and regulation and compliance tiers. The data communication tier is based on advanced sensing and monitoring technologies like cloud and edge‐based systems integrated with security detection mechanisms. The edge and cloud layer provides the all functions of the perception layer like smart sensing, visual sensing, and monitoring services, and can control the device's perception and behaviour. The second tier provides the AI data generative functionalities to handle real‐time synthetic medical images for predictive analytics to enhance patient care. This tier also automates routine tasks, such as administrative work and data analysis, which can free up healthcare professionals to focus on more complex tasks. The last regulation and compliance tier is responsible for handling the standards and compliance for healthcare systems. Experiments are conducted to test the data communication and security level of the proposed architecture. The results showed the suitability of existing solutions and synchronization with the proposed architecture. [ABSTRACT FROM AUTHOR]

Nowadays, commercial-off-the-shelf components (COTS) are fabricated and readily available on the market. These components come with dimensions that include limit deviations, as listed in manufacturers' catalogs. However, the existing algorithm used to solve dimension chains using the complete interchangeability method has a significant limitation: it cannot solve dimension chains where the COTS have predefined limit deviations. To overcome this limitation, an improved algorithm based on the complete interchangeability method has been developed. This improved algorithm outlines three specific cases to address all practical issues for solving the linear dimension chains. The accompanying examples help designers understand the algorithm's core principles, enabling them to apply the algorithm to their practical situations. [ABSTRACT FROM AUTHOR]

Aims Several algorithms can differentiate inferior axis premature ventricular contractions (PVCs) originating from the right side and left side on 12-lead electrocardiograms (ECGs). However, it is unclear whether distinguishing the origin should rely solely on PVC or incorporate sinus rhythm (SR). We compared the dual-rhythm model (incorporating both SR and PVC) to the PVC model (using PVC alone) and quantified the contribution of each ECG lead in predicting the PVC origin for each cardiac rotation. Methods and results This multicentre study enrolled 593 patients from 11 centres—493 from Japan and Germany, and 100 from Belgium, which were used as the external validation data set. Using a hybrid approach combining a Resnet50-based convolutional neural network and a transformer model, we developed two variants—the PVC and dual-rhythm models—to predict PVC origin. In the external validation data set, the dual-rhythm model outperformed the PVC model in accuracy (0.84 vs. 0.74, respectively; P < 0.01), precision (0.73 vs. 0.55, respectively; P < 0.01), specificity (0.87 vs. 0.68, respectively; P < 0.01), area under the receiver operating characteristic curve (0.91 vs. 0.86, respectively; P = 0.03), and F1-score (0.77 vs. 0.68, respectively; P = 0.03). The contributions to PVC origin prediction were 77.3% for PVC and 22.7% for the SR. However, in patients with counterclockwise rotation, SR had a greater contribution in predicting the origin of right-sided PVC. Conclusion Our deep learning–based model, incorporating both PVC and SR morphologies, resulted in a higher prediction accuracy for PVC origin, considering SR is particularly important for predicting right-sided origin in patients with counterclockwise rotation. [ABSTRACT FROM AUTHOR]

This study contributes to the growing body of literature on traffic congestion prediction using machine learning (ML) techniques. By evaluating multiple classifiers and selecting the most appropriate one for predicting traffic congestion, this research provides valuable insights for urban planners and policymakers seeking to optimize traffic flow and reduce jamming and. Traffic jamming is a global issue that wastes time, pollutes the environment, and increases fuel usage. The purpose of this project is to forecast traffic congestion at One of the most congested areas in Amman city using multiple ML classifiers. The Naïve Bayes (NB), stochastic gradient descent (SGD) fuzzy unordered rule induction algorithm (FURIA), logistic regression (LR), decision tree (DT), random forest (RF), and multi-layer perceptron (MLP) classifiers have been chosen to predict traffic congestion at each street linked with our study area. These will be assessed by accuracy, F-measure, sensitivity, and precision evaluation metrics. The results obtained from all experiments show that FURIA is the classifier that presents the highest predictions of traffic congestion where By 100% achieved Accuracy, Precision, Sensitivity and F-measure. In the future further studies can be used more datasets and variables such as weather conditions; and drivers behavior that could integrated to predict traffic congestion accurately. [ABSTRACT FROM AUTHOR]

Background: This study aimed to develop and validate claims-based algorithms for identifying hospitalized patients with coronavirus disease 2019 (COVID-19) and disease severity. Methods: We used claims data including all patients at the National Center for Global and Medicine Hospital between January 1, 2020, and December 31, 2021. The claims-based algorithms for three statuses with COVID-19 (hospitalizations, moderate or higher status, and severe status) were developed using diagnosis codes (International Classification of Diseases, 10th revision code: U07.1, B34.2) and relevant medical procedure code. True cases were determined using the COVID-19 inpatient registry and electronic health records. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each algorithm at 6-month intervals. Results: Of the 75,711 total patients, the number of true cases was 1,192 for hospitalizations, 622 for moderate or higher status, and 55 for severe status. The diagnosis code-only algorithm for hospitalization had sensitivities 90.4% to 94.9% and PPVs 9.3% to 19.4%. Among the algorithms consisting of both diagnosis codes and procedure codes, high sensitivity and PPV were observed during the following periods: 93.9% and 97.1% for hospitalization (January-June 2021), 90.4% and 87.5% for moderate or higher status (July-December 2021), and 92.3% and 85.7% for severe status (July-December 2020), respectively. Almost all algorithms had specificities and NPVs of approximately 99%. Conclusion: The diagnosis code-only algorithm for COVID-19 hospitalization showed low validity throughout the study period. The algorithms for hospitalizations, moderate or higher status, and severe status with COVID-19, consisting of both diagnosis codes and procedure codes, showed high validity in some periods. [ABSTRACT FROM AUTHOR]

Little is known about the catastrophic bushfire from a micro‐pollution point of view, and there is also very limited understanding of the emerging contamination of microplastics and nanoplastics. Upon exposure to fire, plastic items, such as water tanks, may release a substantial quantity of microplastics and nanoplastics, as characterized in this study through the analysis of residual debris. Using Raman imaging with the scanning pixel size down to 100 nm × 100 nm, we over‐scan the sample surface to collect a hyperspectral matrix. In order to map and convert the scanning hyperspectral matrix to an image, we compare and advance the chemometrics of algorithms, including logic and principal component analysis (PCA), to extract the weak signal of microplastics and particularly nanoplastics, which enables us to directly visualize the different degrees of burning. By doing so, we can identify the microplastics and nanoplastics down to ˜100 nm, which means that we can break through the diffraction limit of the laser which is ˜296 nm (λ/2NA) to capture nanoplastics. Using statistical analysis, we estimate that 1.4–4.7 million micro‐ and nanoplastics per cm2 can be left behind by the mimicked‐bushfire‐burned plastic tank. This study suggests that bushfire can accelerate the release of micro‐ and nanoplastics in the environment. This study not only contributes essential insights into the micro‐pollution consequences of fire burning but also underscores the urgency of addressing this understudied aspect to inform environmental conservation strategies and public health measures. [ABSTRACT FROM AUTHOR]

Rhythmic transcripts play pivotal roles in driving the daily oscillations of various biological processes. Genetic or environmental disruptions can lead to alterations in the rhythmicity of transcripts, ultimately impacting downstream circadian outputs, including metabolic processes and even behavior. To statistically compare the differences in transcript rhythms between 2 or more conditions, several algorithms have been developed to analyze circadian transcriptomic data, each with distinct features. In this study, we compared the performance of 7 algorithms that were specifically designed to detect differential rhythmicity (DODR, LimoRhyde, CircaCompare, compareRhythms, diffCircadian, dryR, and RepeatedCircadian). We found that even when applying the same statistical threshold, these algorithms yielded varying numbers of differentially rhythmic transcripts, most likely because each algorithm defines rhythmic and differentially rhythmic transcripts differently. Nevertheless, the output for the differential phase and amplitude were identical between dryR and compareRhyhms, and diffCircadian and CircaCompare, while the output from LimoRhyde2 was highly correlated with that from diffCircadian and CircaCompare. Because each algorithm has unique requirements for input data and reports different information as an output, it is crucial to ensure the compatibility of input data with the chosen algorithm and assess whether the algorithm's output fits the user's needs when selecting an algorithm for analysis. [ABSTRACT FROM AUTHOR]

Recent advancements in omics and single-cell analysis highlight the necessity of numerical methods for managing the complexity of biological data. This paper introduces a simulation program for biochemical reaction systems based on the natural number simulation (NNS) method. This novel approach ensures the equitable treatment of all molecular entities, such as DNA, proteins, H2O, and hydrogen ions (H+), in biological systems. Central to NNS is its use of stoichiometric formulas, simplifying the modeling process and facilitating efficient and accurate simulations of diverse biochemical reactions. The advantage of this method is its ability to manage all molecules uniformly, ensuring a balanced representation in simulations. Detailed in Python, NNS is adept at simulating various reactions, ranging from water ionization to Michaelis-Menten kinetics and complex gene-based systems, making it an effective tool for scientific and engineering research. [ABSTRACT FROM AUTHOR]

Assessing the behaviour and physical attributes of domesticated dogs is critical for predicting the suitability of animals for companionship or specific roles such as hunting, military or service. Common methods of behavioural assessment can be time consuming, labour-intensive, and subject to bias, making large-scale and rapid implementation challenging. Objective, practical and time effective behaviour measures may be facilitated by remote and automated devices such as accelerometers. This study, therefore, aimed to validate the ActiGraph® accelerometer as a tool for behavioural classification. This study used a machine learning method that identified nine dog behaviours with an overall accuracy of 74% (range for each behaviour was 54 to 93%). In addition, overall body dynamic acceleration was found to be correlated with the amount of time spent exhibiting active behaviours (barking, locomotion, scratching, sniffing, and standing; R2 = 0.91, p < 0.001). Machine learning was an effective method to build a model to classify behaviours such as barking, defecating, drinking, eating, locomotion, resting-asleep, resting-alert, sniffing, and standing with high overall accuracy whilst maintaining a large behavioural repertoire. [ABSTRACT FROM AUTHOR]