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By studying the edge-workpiece engagement (EWE), and taking two inclination (lead and tilt) angles into account, the time-varying cutting areas and boundaries are determined, the instantaneous numerical chip thickness (NCT) within different EWEs at each point of the edge curve during the milling process of the ball end milling tool is solved, and the milling force and stability are calculated. The milling force and SLD of the model in this paper (NCT-EWE model) and the model in the literature (Altintas and Otzurk model) are compared. It is verified that the algorithm adopted in this paper has higher accuracy, and the algorithm is simple and fast. In addition, it is found that increasing the lead and tilt angles can reduce the milling force and improve the stability, and the free vibration in the stable region decreases gradually, while in the non-stable region, it increases. The algorithm in this paper has certain theoretical and application value for five-axis milling. [ABSTRACT FROM AUTHOR]

Hip-related pain is a common issue in active adults affecting their quality of life, mobility, and overall function, and it can lead to persistent disability. However, diagnosing hip-related pain is challenging due to the many potential sources and causes, including intra-articular and extra-articular pathology, and referred pain from other areas (lumbar or groin related pain). To address this, there is a need for a clinical algorithm based on the best available evidence and expert consensus. This algorithm could guide healthcare professionals in assessing and managing patients with hip-related pain, during the diagnosis, test selection, intervention, monitoring, and promoting collaboration among various healthcare providers. This clinical algorithm for hip-related pain is a comprehensive, flexible, adaptable to different settings, and regularly updated to incorporate new research findings. This literature review aims to establish a clinical algorithm specifically for prescribing exercise treatment to patients with hip-related pain, addressing their individual needs and enhancing their overall care. [ABSTRACT FROM AUTHOR]

The rise in AI‐based assessments in hiring contexts has led to significant media speculation regarding their role in exacerbating or mitigating employment inequities. In this study, we examined 46,214 ratings from 4947 interviews to ascertain if gender differences in ratings were related to interactions among content (stereotype‐relevant competencies), context (occupational gender composition), and rater type (human vs. algorithm). Contrary to the hypothesized effects of smaller gender differences in algorithmic scoring than with human raters, we found that both human and algorithmic ratings of men on agentic competencies were higher than those given to women. Also unexpected, the algorithmic scoring evidenced greater gender differences in communal ratings than humans (with women rated higher than men) and similar differences in non‐stereotypic competency ratings that were in the opposite direction (humans rated men higher than women, while algorithms rated women higher than men). In more female‐dominated occupations, humans tended to rate applicants as generally less competent overall relative to the algorithms, but algorithms rated men more highly in these occupations. Implications for auditing for group differences in selection contexts are discussed. Practitioner points: Patterns of gender differences in ratings made by humans and algorithms varied across competency types.Gender difference patterns also varied by the jobs' gender composition.Investigations of rating differences between humans and algorithms should consider interview content and context. [ABSTRACT FROM AUTHOR]

Machine‐learning algorithms have the potential to revolutionise diagnostic and prognostic tasks in health care, yet algorithmic performance levels can be materially worse for subgroups that have been underrepresented in algorithmic training data. Given this epistemic deficit, the inclusion of underrepresented groups in algorithmic processes can result in harm. Yet delaying the deployment of algorithmic systems until more equitable results can be achieved would avoidably and foreseeably lead to a significant number of unnecessary deaths in well‐represented populations. Faced with this dilemma between equity and utility, we draw on two case studies involving breast cancer and melanoma to argue for the selective deployment of diagnostic and prognostic tools for some well‐represented groups, even if this results in the temporary exclusion of underrepresented patients from algorithmic approaches. We argue that this approach is justifiable when the inclusion of underrepresented patients would cause them to be harmed. While the context of historic injustice poses a considerable challenge for the ethical acceptability of selective algorithmic deployment strategies, we argue that, at least for the case studies addressed in this article, the issue of historic injustice is better addressed through nonalgorithmic measures, including being transparent with patients about the nature of the current epistemic deficits, providing additional services to algorithmically excluded populations, and through urgent commitments to gather additional algorithmic training data from excluded populations, paving the way for universal algorithmic deployment that is accurate for all patient groups. These commitments should be supported by regulation and, where necessary, government funding to ensure that any delays for excluded groups are kept to the minimum. We offer an ethical algorithm for algorithms—showing when to ethically delay, expedite, or selectively deploy algorithmic systems in healthcare settings. [ABSTRACT FROM AUTHOR]

In recent years, Australia has embarked on a digital transformation of its social services, with the primary goal of creating user‐centric services that are more attentive to the needs of citizens. This article examines operational and technological changes within Australia's National Disability Insurance Scheme (NDIS) as a result of this comprehensive government digital transformation strategy. It discusses the effectiveness of these changes in enhancing outcomes for users of the scheme. Specifically, the focus is on the National Disability Insurance Agency's (NDIA) use of algorithmic decision support systems to aid in the development of personalised support plans. This administrative process, we show, incorporates several automated elements that raise concerns about substantive fairness, accountability, transparency and participation in decision making. The conclusion drawn is that algorithmic systems exercise various forms of state power, but in this case, their subterranean administrative character positions them as “algorithmic grey holes”—spaces effectively beyond recourse to legal remedies and more suited to redress by holistic and systemic accountability reforms advocated by algorithmic justice scholarship. [ABSTRACT FROM AUTHOR]

The development of the Internet, though it brings many benefits, it also brings threats to rights and freedoms and the right to privacy is usually its ‘prey’. Internet search engines are one of the most important factors that shape the web. The establishment of the right to be forgotten as an element allowing to profile one’s image on the web was a milestone in granting protection in the on-line world. The problem of the autocomplete function stays on the margin of mainstream discussions. This function, seemingly only technical, in practice affects web traffic directions. This article presents the impact of the autocomplete function on privacy and a discussion on whether this impact is significant enough for relevant protection mechanisms to be necessary. The research aim is to answer the following question: should the right to control the autocomplete function be protected and if yes, on what terms? [ABSTRACT FROM AUTHOR]

Background Objectives Methods Results and Conclusions This study aims to assess how well several machine learning (ML) algorithms predict the physical, social, and mental health condition of university students.The physical health measurements used in the study include BMI (Body Mass Index), %BF (percentage of Body Fat), BSC (Blood Serum Cholesterol), SBP (Systolic Blood Pressure), and DBP (Diastolic Blood Pressure).The mental health evaluation relied on the following methods: PHQ‐9 (Patient Health Questionnaire‐9), ISI (Insomnia Severity Index), GAD‐7 (Generalized Anxiety Disorder Scale), and SBQ‐R (Suicidal Behaviors Questionnaire‐Revised). The study assessed KEYES, the comprehensive social health indicator. The study uses a famous methodology for training and testing four well‐known ML algorithms, namely the K‐nearest neighbors algorithm, decision trees, Naïve Bayes, and the random forest algorithm.The recall value of the RF algorithm is higher by 2.0%, 4.15%, and 11.25%, respectively. The F‐score value of the RF algorithm is also the highest. The differences amount to 4.56% (Naïve Bayes), 2.50% (DT), and 11.20% (K‐NN). Accuracy, Precision, Recall, and F‐score were used to assess the researched ML algorithms' prediction ability. With a 99.40% prediction accuracy, a 97.60% precision, a 99.30% recall, and an F‐score value of 98.70%, the Random Forest method performed the best. ML algorithms can serve as tools for the prediction of physical, mental, and social health state of patients, including students, but they have a rather narrow scope of application and do not cover all aspects of health. [ABSTRACT FROM AUTHOR]

Renewable energy is undergoing significant advancements through the utilization of new technologies. Among these technologies, fixed solar panels serve as a fundamental type of solar photovoltaic energy generator. Monitoring and optimizing the performance of these panels is achieved through the implementation of controller algorithms. However, there is a growing emphasis on axis tracking systems for solar panels, as that has the feasible to extract more power compared to fixed panels. In this context, the integration of the Internet of Things (IoT), big data analytics, deep learning, and cloud computing presents a contemporary solution for addressing various challenges associated with monitoring renewable solar energy systems. By employing sensors and actuators grouped within an IoT framework, real-time data can be extracted and stored in a cloud server, allowing remote access from anywhere. Furthermore, the use of meta-heuristic optimization algorithms, such as the Whale Optimization Algorithm, has become commonplace in solving engineering issues. In this proposed system, the performance of horizontal axis trackers is analyzed and compared with fixed panels using the WOA-based axis tracking analysis. Through the application of a control algorithm, optimal values can be obtained under typical weather conditions. The system's simulation and performance are thoroughly assessed and evaluated. [ABSTRACT FROM AUTHOR]

In this study, an online monitoring system was implemented for the operational control of an industrial-scale steam generation system. The proposed system allows the migration from measuring boiler efficiency under stationary conditions to measuring it under seasonal conditions, providing greater opportunities for improvement in energy performance. For this purpose, a baseline and goal line model of energy consumption as a function of steam production for an operational base period was constructed. The study shows that the steam generation process has a consumption rate of 0.0885 m³/kg, which is associated with boiler technology, fuel quality, insulation condition, heat recovery equipment, among others. In addition, there is evidence of non-production consumption of 41.225 m³/h, which is mainly due to average consumption in non-productive maneuvers such as starting, stopping, and searching for an operating regime in response to changes in steam demand. The correlation between gas consumption and steam generation was 86.4 %, which represents a 13.6 % variability of consumption associated with controllable operational variables with opportunity for adjustment. The monitoring and control of the main operating parameters of the boiler allows an economic savings of 3.13 % in the boiler, which implies a reduction in operating costs of 92,843 USD per year. The analysis of the operational parameters indicates that it is necessary to maintain a feed water temperature higher than 65 °C and a flue gas temperature lower than 214 °C to ensure boiler efficiency higher than 80 %. With the implementation of activities focused on reducing the combustion gas temperature, increasing the feed water temperature, and controlling operational variables, a 9.01 % reduction in the boiler's natural gas consumption was achieved because of the implementation of operational control. [ABSTRACT FROM AUTHOR]

IMPRINTS-CETSA (Integrated Modulation of Protein Interaction States—Cellular Thermal Shift Assay) provides a highly resolved means to systematically study the interactions of proteins with other cellular components, including metabolites, nucleic acids and other proteins, at the proteome level, but no freely available and user-friendly data analysis software has been reported. Here, we report IMPRINTS.CETSA, an R package that provides the basic data processing framework for robust analysis of the IMPRINTS-CETSA data format, from preprocessing and normalization to visualization. We also report an accompanying R package, IMPRINTS.CETSA.app, which offers a user-friendly Shiny interface for analysis and interpretation of IMPRINTS-CETSA results, with seamless features such as functional enrichment and mapping to other databases at a single site. For the hit generation part, the diverse behaviors of protein modulations have been typically segregated with a two-measure scoring method, i.e. the abundance and thermal stability changes. We present a new algorithm to classify modulated proteins in IMPRINTS-CETSA experiments by a robust single-measure scoring. In this way, both the numerical changes and the statistical significances of the IMPRINTS information can be visualized on a single plot. The IMPRINTS.CETSA and IMPRINTS.CETSA.app R packages are freely available on GitHub at https://github.com/nkdailingyun/IMPRINTS.CETSA and https://github.com/mgerault/IMPRINTS.CETSA.app , respectively. IMPRINTS.CETSA.app is also available as an executable program at https://zenodo.org/records/10636134. [ABSTRACT FROM AUTHOR]

A non-linear finite-element macro-element-based multipurpose algorithm to simulate reinforced concrete (RC) structural members (SMs) such as columns, beams, beam–columns and shear walls under cyclic combined loading is proposed, which can be applied for several different types of loading. In the proposed algorithm, the SMs are divided into an appropriate number of macro-elements and the surfaces of the critical sections are discretised into a large number of fixed rectangular finite fibres. The proposed algorithm has been validated by comparing the simulated results to the results of experimental testing on full-scale RC SMs. To illustrate the influence of the mechanical properties of materials, the influences of reinforcement percentage and lateral force orientation angle and axial force on the behaviour of the SM, a parametric study has been performed. The parametric study allows optimisation of the choices in compliance with the safety and economic criteria in building construction projects. Furthermore, the application of the proposed algorithm illustrates how a heavy axial force makes SMs brittle, reduces the ductility, imposes a large amount of material loss and causes quick failure of the SM. [ABSTRACT FROM AUTHOR]

During the implementation of the Internet of Things (IoT), the performance of communication and sensing antennas that are embedded in smart surfaces or smart devices can be affected by objects in their reactive near field due to detuning and antenna mismatch. Matching networks have been proposed to re-establish impedance matching when antennas become detuned due to environmental factors. In this work, the change in the reflection coefficient at the antenna, due to the presence of objects, is first characterized as a function of the frequency and object distance by applying Gaussian process regression on experimental data. Based on this characterization, for random object positions, it is shown through simulation that a dynamic environment can lower the reliability of a matching network by up to 90%, depending on the type of object, the probability distribution of the object distance, and the required bandwidth. As an alternative to complex and power-consuming real-time adaptive matching, a new, resilient network tuning strategy is proposed that takes into account these random variations. This new approach increases the reliability of the system by 10% to 40% in these dynamic environment scenarios. [ABSTRACT FROM AUTHOR]

The intelligent Liver Function Testing (iLFT) pathway is a novel, algorithm-based system which provides automated laboratory investigations and clinical feedback on abnormal liver function test (LFT) results from primary care. iLFT was introduced to NHS Tayside, Scotland, in August 2018 in response to vast numbers of abnormal LFTs, many of which were not appropriately investigated, coupled with rising mortality from chronic liver disease. Here, we outline the development and implementation of the iLFT pathway, considering the implications for the diagnostic laboratories, primary care services and specialist hepatology clinics. Additionally, we describe the utility, outcomes and evolution of iLFT, which was used over 11,000 times in its first three years alone. Finally, we will consider the future of iLFT and propose areas where similar 'intelligent' approaches could be used to add value to laboratory investigations. [ABSTRACT FROM AUTHOR]

Algorithmic decision-making systems are ubiquitous in digital media, but the public has been largely unable to negotiate the role of algorithms in society. Building from the concept of attitude-behavior consistency for political behavior, we develop a framework for fostering algorithmic literacy to develop well-informed attitudes toward algorithms. As algorithms are increasingly relevant to broad societal effects, an integrative approach is needed for a full account of how the public makes sense of algorithms and their role in society. We designed and tested a novel intervention that combines algorithmic literacy with personalized user experiences to see how each component influenced attitudes toward algorithms. We found these methods jointly informed attitudes, but the intervention's efficacy was dependent on participants' individual differences in technology use. [ABSTRACT FROM AUTHOR]

This article asks what impact temporality and timing have on the ways in which memories are felt and made to matter on social media. Drawing on Taina Bucher's theorisation of the 'kairologic' of algorithmic media, I explore how digital memories are resurfaced or made visible to people at the 'right time' in the present. The article proposes the notion of 'right-time memories' to examine the ways in which social media platforms and timing performatively shape people's engagement with the past. Drawing on interview and focus group data, I explore four ways that right-time memories are sociotechnically produced and felt in everyday life: through an anniversary logic, personalisation, rhythms, and tensions. Ultimately, it is argued that when memories are made to matter in the present is a crucial way to further examine the temporal politics of social media platforms and algorithms. [ABSTRACT FROM AUTHOR]

Background: With over 7000 Mendelian disorders, identifying children with a specific rare genetic disorder diagnosis through structured electronic medical record data is challenging given incompleteness of records, inaccurate medical diagnosis coding, as well as heterogeneity in clinical symptoms and procedures for specific disorders. We sought to develop a digital phenotyping algorithm (PheIndex) using electronic medical records to identify children aged 0–3 diagnosed with genetic disorders or who present with illness with an increased risk for genetic disorders. Results: Through expert opinion, we established 13 criteria for the algorithm and derived a score and a classification. The performance of each criterion and the classification were validated by chart review. PheIndex identified 1,088 children out of 93,154 live births who may be at an increased risk for genetic disorders. Chart review demonstrated that the algorithm achieved 90% sensitivity, 97% specificity, and 94% accuracy. Conclusions: The PheIndex algorithm can help identify when a rare genetic disorder may be present, alerting providers to consider ordering a diagnostic genetic test and/or referring a patient to a medical geneticist. [ABSTRACT FROM AUTHOR]

Background: The national breast screening programme in the United Kingdom is under pressure due to workforce shortages and having been paused during the COVID-19 pandemic. Artificial intelligence has the potential to transform how healthcare is delivered by improving care processes and patient outcomes. Research on the clinical and organisational benefits of artificial intelligence is still at an early stage, and numerous concerns have been raised around its implications, including patient safety, acceptance, and accountability for decisions. Reforming the breast screening programme to include artificial intelligence is a complex endeavour because numerous stakeholders influence it. Therefore, a stakeholder analysis was conducted to identify relevant stakeholders, explore their views on the proposed reform (i.e., integrating artificial intelligence algorithms into the Scottish National Breast Screening Service for breast cancer detection) and develop strategies for managing 'important' stakeholders. Methods: A qualitative study (i.e., focus groups and interviews, March-November 2021) was conducted using the stakeholder analysis guide provided by the World Health Organisation and involving three Scottish health boards: NHS Greater Glasgow & Clyde, NHS Grampian and NHS Lothian. The objectives included: (A) Identify possible stakeholders (B) Explore stakeholders' perspectives and describe their characteristics (C) Prioritise stakeholders in terms of importance and (D) Develop strategies to manage 'important' stakeholders. Seven stakeholder characteristics were assessed: their knowledge of the targeted reform, position, interest, alliances, resources, power and leadership. Results: Thirty-two participants took part from 14 (out of 17 identified) sub-groups of stakeholders. While they were generally supportive of using artificial intelligence in breast screening programmes, some concerns were raised. Stakeholder knowledge, influence and interests in the reform varied. Key advantages mentioned include service efficiency, quicker results and reduced work pressure. Disadvantages included overdiagnosis or misdiagnosis of cancer, inequalities in detection and the self-learning capacity of the algorithms. Five strategies (with considerations suggested by stakeholders) were developed to maintain and improve the support of 'important' stakeholders. Conclusions: Health services worldwide face similar challenges of workforce issues to provide patient care. The findings of this study will help others to learn from Scottish experiences and provide guidance to conduct similar studies targeting healthcare reform. Study registration: researchregistry6579, date of registration: 16/02/2021. [ABSTRACT FROM AUTHOR]

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

Aims: Although therapeutic drug monitoring (TDM) of voriconazole is performed in outpatients to prevent treatment failure and toxicity, whether TDM should be performed in all or only selected patients remains controversial. This study evaluated the association between voriconazole trough concentrations and clinical events. Methods: We investigated the aggravation of clinical symptoms, incidence of hepatotoxicity and visual disturbances, change in co‐medications and interaction between voriconazole and co‐medications in outpatients receiving voriconazole between 2017 and 2021 in three facilities. Abnormal trough concentrations were defined as <1.0 mg/L (low group) and >4.0 mg/L (high group). Results: A total of 141 outpatients (578 concentration measurements) met the inclusion criteria (treatment, 37 patients, 131 values; prophylaxis, 104 patients, 447 values). The percentages of patients with abnormal concentrations were 29.0% and 31.5% in the treatment and prophylaxis groups, respectively. Abnormal concentrations showed 50% of the concentrations at the first measurement in both therapies. Aggravation of clinical symptoms was most frequently observed in the low treatment group (18.2%). Adverse events were most common in the high group for both therapies (treatment, hepatotoxicity 6.3%, visual disturbance 18.8%; prophylaxis, hepatotoxicity 27.9%). No differences were found in changes to co‐medications and drug interactions. In the prophylaxis group, prescription duration in the presence of clinical events tended to be longer than in their absence (47.4 ± 23.4 days vs 39.7 ± 21.9 days, P =.1132). Conclusions: We developed an algorithm based on clinical events for appropriate implementation of TDM in outpatients. However, future interventions based on this algorithm should be validated. [ABSTRACT FROM AUTHOR]

Algorithmic recommendation systems (ARM) on dating apps serve users with a personalised feed of profiles from other users based on the inferred preferences of the user being served. Despite concerns linking ARM to problematic dating app use and negative social outcomes, it has been suggested that critical awareness of ARM's limitations, such as that ARM restrict user choice (i.e. a 'confining' perception of ARM, or CP-ARM), can mitigate problematic usage and reduce negative social outcomes. This study tested such a prediction with semi-structured interviews (N = 20) and a subsequent survey (N = 349), which yielded surprising results – while CP-ARM can indirectly decrease compulsive use of dating apps by lowering the perceived usefulness of dating apps, it can directly increase compulsive use, which can be attributed to a sense of helplessness in controlling digital media use. Consequently, compulsive use can decrease the intention to commit in Internet-initiated romantic relationships. The finding suggests that researchers should not assume that critical awareness of algorithms leads to less problematic usage and better social outcomes but situate the inquiries in a broader socio-cultural context where everyday life is increasingly mediatised by various social platforms and individuals find it difficult to opt out. [ABSTRACT FROM AUTHOR]

Objective: To estimate prevalence and incidence rates over time in children and youth with attention deficit/hyperactivity disorder from the validation of population-based administrative data algorithms using family physicians' electronic medical records as a reference standard. Methods: A retrospective cohort study was conducted in Ontario, Canada to identify attention deficit/hyperactivity disorder among children and youth aged 1–24 years in health administrative data derived from case-finding algorithms using family physicians' electronic medical records. Multiple administrative data algorithms identifying attention deficit/hyperactivity disorder cases were developed and tested from physician-diagnosis of attention deficit/hyperactivity disorder in the electronic medical record to determine their diagnostic accuracy. We calculated algorithm performance using sensitivity, specificity, and predictive values. The most optimal algorithm was used to estimate prevalence and incidence rates of attention deficit/hyperactivity disorder from 2014 to 2021 in Ontario. Results: The optimal performing algorithm was "2 physician visits for attention deficit/hyperactivity disorder in 1 year or 1 attention deficit/hyperactivity disorder-specific prescription" with sensitivity: 83.2% (95% confidence interval [CI], 81.8% to 84.5%), specificity: 98.6% (95% CI, 98.5% to 98.7%), positive predictive value: 78.6% (95% CI, 77.1% to 80.0%) and negative predictive value: 98.9% (95% CI, 98.8% to 99.0%). From 2014, prevalence rates for attention deficit/hyperactivity disorder increased from 5.29 to 7.48 per 100 population in 2021 (N = 281,785). Males had higher prevalence rates (7.49 to 9.59 per 100 population, 1.3-fold increase) than females (2.96–5.26 per 100 population, 1.8-fold increase) from 2014 to 2021. Incidence rates increased from 2014 (0.53 per 100 population) until 2018, decreased in 2020 then rose steeply in 2021 (0.89 per 100 population, N = 34,013). Males also had higher incidence rates than females from 2014 to 2020 with females surpassing males in 2021 (0.70–0.81 per 100 male population,1.2-fold increase versus 0.36–0.97 per 100 female population, 2.7-fold increase). Conclusions: Attention deficit/hyperactivity disorder is increasing in prevalence. We developed an administrative data algorithm that can reliably identify children and youth with attention deficit/hyperactivity disorder with good diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Background: There is an increasing need to better understand the burden of amyotrophic lateral sclerosis (ALS) using real-world data (RWD). However, identifying ALS cases using RWD presents several challenges due to the rarity of ALS and the differences in database coding systems. Methods: MarketScan claims, and the UK Clinical Practice Research Datalink (CPRD) databases were searched for diagnosis codes of ALS or MND, the only drugs approved for treating ALS (riluzole and edaravone) and clinical visits with 12-month enrolment prior to 1 January 2011. The main algorithm required ≥ 1 ALS diagnosis code together with prescriptions or clinical visits. We expanded the existing algorithm to identify unspecific (possible) ALS group that had codes for motor neuron disease (MND) and the ALS drugs. The study period was from 1 January 2011 until 31 December 2020. Results: We identified 16,246 patients with ≥ 1 ALS code in Marketscan (denominator n = 85,279,619), yet only 184 were found in the UK CPRD (denominator n = 21,318,589). Using the main algorithm 9,433 ALS patients were included in MarketScan, with a prevalence ranged between 4.5 per 100,000 in 2019 and 6.2 in 2015. In MarketScan, 3,658 (4.3 per 100,000) had ≥ 1 MND code and the ALS drug codes (possible cases). In CPRD, 47.9% of 2,785 patients with ≥ 1 MND code had a prescription for riluzole (6.3 per 100,000), regarded as possible ALS cases. Conclusions: The expanded algorithm enabled the identification of a large population with ALS, or possible ALS, and the estimation of ALS prevalence in MarketScan and CPRD. [ABSTRACT FROM AUTHOR]

Introduction Aim Methods Results Conclusion Valoctocogene roxaparvovec, a gene therapy evaluated in the phase 3 GENEr8‐1 trial, supports endogenous factor VIII (FVIII) production to prevent bleeding in people with severe haemophilia A. Individuals receiving emicizumab, an antibody mimicking the function of activated FVIII, were excluded from GENEr8‐1 enrolment since emicizumab was an investigational therapy at the time of trial initiation.Utilize pharmacokinetic simulations to provide guidance on best practices for maintaining haemostatic control while transitioning from emicizumab prophylaxis to valoctocogene roxaparvovec.To estimate bleeding risk at weekly intervals following valoctocogene roxaparvovec infusion, a published emicizumab pharmacokinetic model was used to simulate emicizumab concentrations and merged with FVIII activity time‐course data for participants in GENEr8‐1. The analysis investigated three approved emicizumab dosing regimens for two transition scenarios that varied whether the last dose of emicizumab was administered on the same day or 4 weeks after valoctocogene roxaparvovec infusion.Simulations demonstrated administering the last emicizumab dose the day of valoctocogene roxaparvovec infusion and 4 weeks after offered similar levels of haemostatic control, and bleeding risk was similar for all emicizumab dosing regimens. An algorithm was developed to provide guidance for discontinuation of emicizumab. Theoretical cases based on GENEr8‐1 participants are presented to illustrate how decisions may vary among individuals.Pharmacokinetic simulations demonstrated no clinically meaningful difference in bleeding risk caused by decaying emicizumab levels and rising gene therapy‐derived endogenous FVIII for all examined emicizumab doses and dosing regimens. Therefore, multiple approaches can safely transition individuals from emicizumab prophylaxis to valoctocogene roxaparvovec. [ABSTRACT FROM AUTHOR]

Background: Primary care physicians often lack resources and training to correctly diagnose and manage chronic insomnia disorder. Tools supporting chronic insomnia diagnosis and management could fill this critical gap. A survey was conducted to understand insomnia disorder diagnosis and treatment practices among primary care physicians, and to evaluate a diagnosis and treatment algorithm on its use, to identify ways to optimize it specifically for these providers. Methods: A panel of experts developed an algorithm for diagnosing and treating chronic insomnia disorder, based on current guidelines and experience in clinical practice. An online survey was conducted with primary care physicians from France, Germany, Italy, Spain, and the United Kingdom, who treat chronic insomnia patients, between January and February 2023. A sub-sample of participants provided open-ended feedback on the algorithm and gave suggestions for improvements. Results: Overall, 106 primary care physicians completed the survey. Half (52%, 55/106) reported they did not regularly screen for insomnia and half (51%, 54/106) felt they did not have enough time to address patients' needs in relation to insomnia or trouble sleeping. The majority (87%,92/106) agreed the algorithm would help diagnose chronic insomnia patients and 82% (87/106) agreed the algorithm would help improve their clinical practice in relation to managing chronic insomnia. Suggestions for improvements were making the algorithm easier to read and use. Conclusion: The algorithm developed for, and tested by, primary care physicians to diagnose and treat chronic insomnia disorder may offer significant benefits to providers and their patients through ensuring standardization of insomnia diagnosis and management. [ABSTRACT FROM AUTHOR]

This paper presents a modified metaheuristic algorithm named the modified Firefly algorithm. Any metaheuristic algorithm will have exploration and exploitation steps, and the goal of modification is to maintain a balance between them. The improvement relies on movement equations, alterations to the algorithm’s structure by introducing a single loop, and a selection of movement equations at random. Two movement equations are included in the improved method and are randomly selected. This guarantees both regionally and globally focused solution-finding. This prevents the algorithm from getting stuck at a local minimum. Comparing the modified version to the original Firefly method, just one for loop is used, reducing the algorithm’s complexity. The algorithm’s performance is evaluated with 35 traditional benchmark test functions and 10 CEC2019 test functions. According to the findings, the suggested method performed optimally in 24 traditional benchmark test functions and best in the six remaining benchmark test functions. The improved algorithm produced the best outcomes in seven of the 10 CEC2019 test functions. In contrast, the Firefly algorithm produced optimal results in 18 classical benchmark test functions and the best results in 6 CEC2019 test functions. The proposed algorithm is compared with other variants of the Firefly algorithm for common test functions in the literature. The results show that the proposed algorithm outperforms other variants in most test functions. [ABSTRACT FROM AUTHOR]

Objectives Methods Results Conclusions To validate the diagnostic accuracy of a stepwise algorithm to differentiate fat‐poor angiomyolipoma (fp‐AML) from renal cancer in small renal masses (SRMs).We prospectively enrolled 223 patients with solid renal masses <4 cm and no visible fat on unenhanced computed tomography (CT). Patients were assessed using an algorithm that utilized the dynamic CT and MRI findings in a stepwise manner. The diagnostic accuracy of the algorithm was evaluated in patients whose histology was confirmed through surgery or biopsy. The clinical course of the patients was further analyzed.The algorithm classified 151 (68%)/42 (19%)/30 (13%) patients into low/intermediate/high AML probability groups, respectively. Pathological diagnosis was made for 183 patients, including 10 (5.5%) with fp‐AML. Of these, 135 (74%)/36 (20%)/12 (6.6%) were classified into the low/intermediate/high AML probability groups, and each group included 1 (0.7%)/3 (8.3%)/6 (50%) fp‐AMLs, respectively, leading to the area under the curve for predicting AML of 0.889. Surgery was commonly opted in the low and intermediate AML probability groups (84% and 64%, respectively) for initial management, while surveillance was selected in the high AML probability group (63%). During the 56‐month follow‐up, 36 (82%) of 44 patients initially surveyed, including 13 of 18 (72%), 6 of 7 (86%), and 17 of 19 (89%) in the low/intermediate/high AML probability groups, respectively, continued surveillance without any progression.This study confirmed the high diagnostic accuracy for differentiating fp‐AMLs. These findings may help in the management of patients with SRMs. [ABSTRACT FROM AUTHOR]

(1) Background: Although DR screening is effective, one of its most significant problems is a lack of attendance. The aim of the present study was to demonstrate the effectiveness of our algorithm in predicting the development of any type of DR and referable DR. (2) Methods: A retrospective study with an 11-year follow-up of a population of 120,389 T2DM patients was undertaken. (3) Results: Applying the results of the algorithm showed an AUC of 0.93 (95% CI, 0.92–0.94) for any DR and 0.90 (95% CI, 0.89–0.91) for referable DR. Therefore, we achieved a promising level of agreement when applying our algorithm. (4) Conclusions: The algorithm is useful for predicting which patients may develop referable forms of DR and also any type of DR. This would allow a personalized screening plan to be drawn up for each patient. [ABSTRACT FROM AUTHOR]

Advancements in new technologies, a reduction in CO2 emissions, and the rising demand for energy are causing a growth in the share of renewable energy sources. In distribution networks, an increasing number of distributed generators (DGs) makes the utility grid's protection complex and demanding. Vector surge and rate-of-change-of-frequency are the established anti-islanding protection methods, recognizing that the standard paradigm for protection, involving distributed generation, cannot be set only once but has to be continuously updated following the requirements and changes in the system. One of the requirements is active participation in the preservation of system frequency and voltage, which can be interrupted if the DG trips and disconnects from the utility grid. Anti-islanding protection and spurious tripping can be avoided by implementing new algorithms and techniques. This paper presents a novel protection scheme based on a voltage frequency differential. The proposed algorithm employs remote and local frequency measurements in such a manner that, for the occurrence of a frequency difference, it is assumed that the DG is in an islanding state. In this article, we demonstrate the feasibility of the algorithm through numerical analysis of grid events and laboratory testing emulating real grid-measured values. The test results show that the algorithm is resilient to false tripping for non-islanding events and more reliable than conventional methods in islanding detection. The algorithm can be set to low-frequency differential values, drastically reducing the non-detection zone in any DG type, regardless of its size and voltage level at the point of common coupling. Unlike standard anti-islanding methods, the algorithm supports the ability of the DG to fault-ride through demand. [ABSTRACT FROM AUTHOR]

Algorithmic bias occurs when algorithms incorporate biases in the human decisions on which they are trained. We find that people see more of their biases (e.g., age, gender, race) in the decisions of algorithms than in their own decisions. Research participants saw more bias in the decisions of algorithms trained on their decisions than in their own decisions, even when those decisions were the same and participants were incentivized to reveal their true beliefs. By contrast, participants saw as much bias in the decisions of algorithms trained on their decisions as in the decisions of other participants and algorithms trained on the decisions of other participants. Cognitive psychological processes and motivated reasoning help explain why people see more of their biases in algorithms. Research participants most susceptible to bias blind spot were most likely to see more bias in algorithms than self. Participants were also more likely to perceive algorithms than themselves to have been influenced by irrelevant biasing attributes (e.g., race) but not by relevant attributes (e.g., user reviews). Because participants saw more of their biases in algorithms than themselves, they were more likely to make debiasing corrections to decisions attributed to an algorithm than to themselves. Our findings show that bias is more readily perceived in algorithms than in self and suggest how to use algorithms to reveal and correct biased human decisions. [ABSTRACT FROM AUTHOR]

AbstractObjectiveData sourcesStudy selectionResultsConclusionsCurrent monitoring methods of asthma, such as peak expiratory flow testing, have important limitations. The emergence of automated acoustic sound analysis, capturing cough, wheeze, and inhaler use, offers a promising avenue for improving asthma diagnosis and monitoring. This systematic review evaluated the validity of acoustic biomarkers in supporting the diagnosis of asthma and its monitoring.A search was performed using two databases (PubMed and Embase) for all relevant studies published before November 2023.27 studies were included for analysis. Eligible studies focused on acoustic signals as digital biomarkers in asthma, utilizing recording devices to register or analyze sound.Various respiratory acoustic signal types were analyzed, with cough and wheeze being predominant. Data collection methods included smartphones, custom sensors and digital stethoscopes. Across all studies, automated acoustic algorithms achieved average accuracy of cough and wheeze detection of 88.7% (range: 61.0 − 100.0%) with a median of 92.0%. The sensitivity of sound detection ranged from 54.0 to 100.0%, with a median of 90.3%; specificity ranged from 67.0 to 99.7%, with a median of 95.0%. Moreover, 70.4% (19/27) studies had a risk of bias identified.This systematic review establishes the promising role of acoustic biomarkers, particularly cough and wheeze, in supporting the diagnosis of asthma and monitoring. The evidence suggests the potential for clinical integration of acoustic biomarkers, emphasizing the need for further validation in larger, clinically-diverse populations. [ABSTRACT FROM AUTHOR]

Background: Actigraphy is often used to measure sleep in pediatric populations, despite little confirmatory evidence of the accuracy of existing sleep/wake algorithms. The aim of this study was to determine the performance of 11 sleep algorithms in relation to overnight polysomnography in children and adolescents. Methods: One hundred thirty-seven participants aged 8–16 years wore two Actigraph wGT3X-BT (wrist, waist) and three Axivity AX3 (wrist, back, thigh) accelerometers over 24-h. Gold standard measures of sleep were obtained using polysomnography (PSG; Embletta MPRPG, ST + Proxy and TX Proxy) in the home environment, overnight. Epoch by epoch comparisons of the Sadeh (two algorithms), Cole-Kripke (three algorithms), Tudor-Locke (four algorithms), Count-Scaled (CS), and HDCZA algorithms were undertaken. Mean differences from PSG values were calculated for various sleep outcomes. Results: Overall, sensitivities were high (mean ± SD: 91.8%, ± 5.6%) and specificities moderate (63.8% ± 13.8%), with the HDCZA algorithm performing the best overall in terms of specificity (87.5% ± 1.3%) and accuracy (86.4% ± 0.9%). Sleep outcome measures were more accurately measured by devices worn at the wrist than the hip, thigh or lower back, with the exception of sleep efficiency where the reverse was true. The CS algorithm provided consistently accurate measures of sleep onset: the mean (95%CI) difference at the wrist with Axivity was 2 min (-6; -14,) and the offset was 10 min (5, -19). Several algorithms provided accurate measures of sleep quantity at the wrist, showing differences with PSG of just 1–18 min a night for sleep period time and 5–22 min for total sleep time. Accuracy was generally higher for sleep efficiency than for frequency of night wakings or wake after sleep onset. The CS algorithm was more accurate at assessing sleep period time, with narrower 95% limits of agreement compared to the HDCZA (CS:-165 to 172 min; HDCZA: -212 to 250 min). Conclusion: Although the performance of existing count-based sleep algorithms varies markedly, wrist-worn devices provide more accurate measures of most sleep measures compared to other sites. Overall, the HDZCA algorithm showed the greatest accuracy, although the most appropriate algorithm depends on the sleep measure of focus. [ABSTRACT FROM AUTHOR]

This research investigated and optimized the parameters of the FDM process by employing bio-inspired algorithms for determining the optimal parameter settings in terms of surface quality and mechanical performance. Four important process parameters including layer thickness (0.11–0.33mm), part orientation (0–90∘), raster width (0.2–0.56mm), and the raster angle (0–60∘) at three variation levels were selected for fabricating the specimens (ABS material P430) using the statistical Box–Behnken design. ANOVA analysis and multiple regression analysis were used to fit the experimental data to a second-order polynomial equation. Through, the RSM analysis, the layer thickness is the key important factor that accounts for all of the responses. The fracture behavior of specimens was examined using a scanning electron microscope (SEM). From the SEM analysis, a substantial amount of plastic deformation on the fracture surface indicative of craze cracking is visible from a 0∘ orientation, indicating a totally ductile fracture mechanism. Then, three swarm intelligence algorithms such as Tasmanian Devil Optimization (TDO), Remora Optimization Algorithm (ROA), Tuna Swarm Optimization (TSO) were implemented to optimize the input parameters that would lead to minimum surface roughness and maximum tensile strength. Experimental data and predicted values varied between 1.64% and 1.84%, as shown by verification experiments. [ABSTRACT FROM AUTHOR]

Aims Methods Results Conclusion We aimed to investigate antibacterial‐induced thrombocytopenia using the China Hospital Pharmacovigilance System (CHPS) in conjunction with Visual Basic for Applications (VBA).Between September 2011 and December 2022, a 2‐phase workflow was employed to identify antibacterial‐induced thrombocytopenia, including preliminary screening in phase (I) conducted by CHPS algorithms and causality assessment by trained pharmacists in phase (II) using VBA. The incidence of thrombocytopenia in each antibacterial was calculated, and comparisons were performed between paediatric and adult patients.CHPS algorithms identified 4080 cases from 485 238 admissions (including 223 735 admissions receiving at least 1 antibacterial treatment). After ruling out cases with chemotherapy and abnormal platelet count at admission, 3832 cases were available. Using VBA, pharmacists identified 1039 cases (1246 antibacterial treatments, 28 agents) as potential thrombocytopenia instances (κ = 0.89), with an incidence of 0.46%. All antibacterial treatments correlated temporally with thrombocytopenia. Carbapenems (meropenem 1.77%), glycopeptides (vancomycin 1.55%) and lincosamides (clindamycin 0.44%) were prominent causal groups. The highest incidences of thrombocytopenia in the cephalosporins and penicillins groups were ceftazidime (2.04%) and piperacillin/tazobactam (1.24%), respectively. Among all antibacterial treatments, clindamycin showed the shortest time to onset (TTO), and erythromycin showed the longest TTO. Paediatric patients exhibited a longer TTO (61 vs. 29 h), extended time to nadir (83 vs. 37 h), lower platelet nadir count values (110 vs. 92 × 109/L), and a higher severe case proportion (12.37 vs. 3.86%) when compared with adults.Different antibacterial agents exhibit varying incidences of thrombocytopenia, with notable disparities between adults and children in the characteristics of thrombocytopenia. [ABSTRACT FROM AUTHOR]

Calculation of the final infection size has become a topic of significant interest in recent years. Despite considerable progress, determining the final infection size in a heterogeneous infectious disease model with nonlinear incidence rate on short-time scales remains a challenging problem. In this paper, we investigate a heterogeneous SEIR epidemic model with nonlinear incidence rate. We establish both the existence and uniqueness of the solution regarding final size, and based on which, we are able to introduce a computational algorithm to calculate the final infection size. Furthermore, we apply our findings to study the early phase of the COVID-19 endemic in New York County and present a numerical simulation to illustrate the practical implications of our approach. [ABSTRACT FROM AUTHOR]

In this work we investigate a nonlocal problem for the Fredholm integro-differential equation involving integral condition. The main tool used in our considerations is Dzhumabaev parametrization method. We make use of the numerical implementation of the Dzhumabaev parametrization method to obtain the desired result, which is well-supported with numerical examples. [ABSTRACT FROM AUTHOR]

The purpose of this article is to present grassroots concepts and ideas about "the algorithm" in the religious context. The power and causality of algorithms are based on lines of computer code, making a society influenced by "black boxes" or "enigmatic technologies" (as they are incomprehensible to most people). On the other hand, the power of algorithms lies in the meanings that we attribute to them. The extent of the power, agency, and control that algorithms have over us depends on how much power, agency, and control we are willing to give to algorithms and artificial intelligence, which involves building the idea of their omnipotence. The key question is about the meanings and the ideas about algorithms that are circulating in society. This paper is focused on the analysis of "vernacular/folk" theories on algorithms, reconstructed based on posts made by the users of Polish Catholic forums. The qualitative analysis of online discourse makes it possible to point out several themes, i.e., according to the linguistic concept, "algorithm" is the source domain used in explanations of religious issues (God as the creator of the algorithm, the soul as the algorithm); algorithms and the effects of their work are combined with the individualization and personalization of religion; algorithms are perceived as ideological machines. [ABSTRACT FROM AUTHOR]

A new algorithm is presented for detecting the local weak reflection symmetries in raster images. It uses contours extracted from the segmented image. A convex hull is constructed on the contours, and so-called anchor points are placed on it. The bundles of symmetry line candidates are placed in these points. Each line splits the plane into two open half-planes and arranges the contours into three sets: the first contains the contours pierced by the considered line, while the second and the third include the contours located in one or the other half-plane. The contours are then checked for the reflection symmetry. This means looking for self-symmetries in the first set, and symmetric pairs with one contour in the second set and one contour in the third set. The line which is evaluated as the best symmetry line is selected. After that, the symmetric contours are removed from sets two and three. The remaining contours are then checked again for symmetry. A multi-branch tree representing the hierarchy of the detected local symmetries is the result of the algorithm. [ABSTRACT FROM AUTHOR]

Phylogenetic network is an evolutionary model that uses a rooted directed acyclic graph (instead of a tree) to model an evolutionary history of species in which reticulate events (e.g., hybrid speciation or horizontal gene transfer) occurred. Tree-child network is a kind of phylogenetic network with structural constraints. Existing approaches for tree-child network reconstruction can be slow for large data. In this study, we present several computational approaches for bounding from below the number of reticulations in a tree-child network that displays a given set of rooted binary phylogenetic trees. In addition, we also present some theoretical results on bounding from above the number of reticulations. Through simulation, we demonstrate that the new lower bounds on the reticulation number for tree-child networks can practically be computed for large tree data. The bounds can provide estimates of reticulation for relatively large data. [ABSTRACT FROM AUTHOR]

Background: Suicidal behaviors are prevalent among college students; however, students remain reluctant to seek support. We developed a predictive algorithm to identify students at risk of suicidal behavior and used telehealth to reduce subsequent risk. Methods: Data come from s everal waves of a prospective cohort study (2016–2022) of college students (n = 5454). All first-year students were invited to participate as volunteers. (Response rates range: 16.00–19.93%). A stepped-care approach was implemented: (i) all students received a comprehensive list of services; (ii) those reporting past 12-month suicidal ideation were directed to a safety planning application; (iii) those identified as high risk of suicidal behavior by the algorithm or reporting 12-month suicide attempt were contacted via telephone within 24-h of survey completion. Intervention focused on support/safety-planning, and referral to services for this high-risk group. Results: 5454 students ranging in age from 17–36 (s.d. = 5.346) participated; 65% female. The algorithm identified 77% of students reporting subsequent suicidal behavior in the top 15% of predicted probabilities (Sensitivity = 26.26 [95% CI 17.93–36.07]; Specificity = 97.46 [95% CI 96.21–98.38], PPV = 53.06 [95% CI 40.16–65.56]; AUC range: 0.895 [95% CIs 0.872–0.917] to 0.966 [95% CIs 0.939–0.994]). High-risk students in the Intervention Cohort showed a 41.7% reduction in probability of suicidal behavior at 12-month follow-up compared to high-risk students in the Control Cohort. Conclusions: Predictive risk algorithms embedded into universal screening, coupled with telehealth intervention, offer significant potential as a suicide prevention approach for students. [ABSTRACT FROM AUTHOR]

The article presents a method for improving the control system of electric motors driving the electrodes of high-voltage arc furnaces. The developed method today in the field of metallurgy is of great importance in solving problems such as at the first stage of melting and casting metal in arc steelmaking furnaces, when the electrodes are initially lowered into the working bath, the connection point breaks as a result of contact with a metal plate, the incompatibility of the formation of an electric arc on the electrodes, holding the electrodes at a certain distance from the metal charge. Also, automatic control of the speed of movement of the electrodes, i.e. (the time of formation of an electric arc in the electrodes, which reduces the speed of its movement, the time of lifting the electrodes, lowering, increasing the speed of movement), is relevant for solving problems such as welding electrodes into a metal shackle. The main purpose of introducing this method into practice is to reduce electricity consumption and ensure reliable operation of high-voltage electric arc furnaces by reducing the melting time of metal. [ABSTRACT FROM AUTHOR]

The built environment is a significant contributor to global energy consumption and greenhouse gas emissions. Advancements in the adoption of environmentally friendly building technology have become crucial in promoting sustainable development. These advancements play a crucial role in conserving energy. The aim is to achieve an optimal design by balancing various interrelated factors. The emergence of innovative techniques to address energy conservation have been witnessed in the built environment. This review examines existing research articles that explore different metaheuristic optimization techniques (MOTs) for energy conservation in buildings. The focus is on evaluating the simplicity and stochastic nature of these optimization techniques. The findings of the review present theoretical and mathematical models for each algorithm and assess their effectiveness in problem solving. A systematic analysis of selected algorithms using MOT is conducted, considering factors that influence wellbeing, occupant health, and indoor environmental quality. The study examines the variations among swarm intelligence MOTs based on complexity, advantages, and disadvantages. The algorithms' performances are based on the concept of uncertainty in consistently providing optimal solutions. The paper highlights the application of each technique in achieving energy conservation in buildings. [ABSTRACT FROM AUTHOR]

The text diagnoses two opposing tendencies in the research on algorithms: the first abstracts and unites heterogeneous developments under the term "algorithm"; the second emphasizes specifics such as data sets, material conditions, software libraries, interfaces, and so on, thus dissolving that which apparently algorithms do into more fine-grained analyses. The text proposes a research perspective that resolves this tension by conceiving of algorithms as a relation between the abstract and the concrete that allows to capture both in their interdependence. This approach is informed by two motives: first, the necessity to connect detailed analyses of specific information technologies with general political concerns; and second, the application of recent feminist critiques of epistemology to the analysis of algorithms. The ensuing relational perspective on algorithms is connected to the genealogy of algorithmic technology before being demonstrated regarding the mutually complementing relationships: algorithms-materiality, algorithms-data, algorithms-code, and algorithms-interfaces. [ABSTRACT FROM AUTHOR]

We study the problem of perfect tiling in the plane and explore the possibility of tiling a rectangle using integral distinct squares. Assume a set of distinguishable squares (or equivalently a set of distinct natural numbers) is given, and one has to decide whether it can tile the plane or a rectangle or not. Previously, it has been proved that tiling the plane is not feasible using a set of odd numbers or an infinite sequence of natural numbers including exactly two odd numbers. The problem is open for different situations in which the number of odd numbers is arbitrary. In addition to providing a solution to this special case, we discuss some open problems to tile the plane and rectangles in this paper. [ABSTRACT FROM AUTHOR]

With the transformation of transmission and distribution grids into smart grids that are more dominated by renewable energy, power electronics-based inverters that can improve power quality are becoming more visible. In order to maximize the output voltage quality and reduce the total harmonic distortion (THD), efficient operation of inverters is required. Therefore, in this paper, the problem of harmonic elimination in multilevel inverters is solved by using an adaptive grey wolf optimizer with local search. We have performed a grid search-based landscape analysis of the seven-level inverter to understand the behaviour of the proposed algorithm. For verification, the numerical results of the proposed adaptive grey wolf optimizer are compared with those of the original grey wolf optimization algorithm, a modified version of the grey wolf optimization algorithm, the particle swarm optimization algorithm, multi-verse optimization algorithm, and salp swarm algorithm. In the simulations, we solved the optimization model for three different structures of multilevel inverters (7, 11, and 15 levels) by changing the modulation indexes. It is found that the adaptive grey wolf optimization provides lower total harmonic distortion for different modulation indexes. [ABSTRACT FROM AUTHOR]

Introduction: Chronic Myelomonocytic Leukemia (CMML) and Myelodysplastic Syndromes (MDS) are increasingly represented in the general population. We propose a screening strategy based on algorithms calculated from quantitative and analytical data from the XN analyser. Materials and Methods: We tested the performance of previously published MDS and CMML scores on an evaluation cohort of 749 individual eligible patients over 50 years of age. These patients were classified into 3 groups as follows: 713 patients without MDS or CMML, 18 patients with MDS, and finally 18 patients with CMML. In a second step, a routine cohort of 37 828 samples was studied to evaluate the impact of this approach. Results: The concordance rate between cytology and the two scores is 92.1%. The sensitivity and specificity of the CMML score are 100% and 96.2%, respectively. For the MDS score, they are 83.3% and 89.6% respectively. The ratio of platelets measured by fluorescence on board (PLT‐F) as reflex tests generated is 1.5% after 6 months. The additional smear ratio for suspected MDS is calculated at 0.6%. Conclusion: We propose a flowchart using embedded artificial intelligence to help the cytologist in an optimized smear review and thus improve guidance to the clinician and the patients in the diagnosis process. This strategy permits a more comprehensive approach to MDS and CMML detection fitting with the new definition of CMML according to the recommendations of the World Health Organization (WHO) published in 2022. [ABSTRACT FROM AUTHOR]

Background: A suicide attempt (SA) is a clinically serious action. Researchers have argued that reducing long-term SA risk may be possible, provided that at-risk individuals are identified and receive adequate treatment. Algorithms may accurately identify at-risk individuals. However, the clinical utility of algorithmically estimated long-term SA risk has never been the predominant focus of any study. Methods: The data of this report stem from CoLaus|PsyCoLaus, a prospective longitudinal study of general community adults from Lausanne, Switzerland. Participants (N = 4,097; Mage = 54 years, range: 36–86; 54% female) were assessed up to four times, starting in 2003, approximately every 4–5 years. Long-term individual SA risk was prospectively predicted, using logistic regression. This algorithm's clinical utility was assessed by net benefit (NB). Clinical utility expresses a tool's benefit after having taken this tool's potential harm into account. Net benefit is obtained, first, by weighing the false positives, e.g., 400 individuals, at the risk threshold, e.g., 1%, using its odds (odds of 1% yields 1/(100-1) = 1/99), then by subtracting the result (400*1/99 = 4.04) from the true positives, e.g., 5 individuals (5-4.04), and by dividing the result (0.96) by the sample size, e.g., 800 (0.96/800). All results are based on 100 internal cross-validations. The predictors used in this study were: lifetime SA, any lifetime mental disorder, sex, and age. Results: SA at any of the three follow-up study assessments was reported by 1.2%. For a range of seven a priori selected threshold probabilities, ranging between 0.5% and 2%, logistic regression showed highest overall NB in 97.4% of all 700 internal cross-validations (100 for each selected threshold probability). Conclusion: Despite the strong class imbalance of the outcome (98.8% no, 1.2% yes) and only four predictors, clinical utility was observed. That is, using the logistic regression model for clinical decision making provided the most true positives, without an increase of false positives, compared to all competing decision strategies. Clinical utility is one among several important prerequisites of implementing an algorithm in routine practice, and may possibly guide a clinicians' treatment decision making to reduce long-term individual SA risk. The novel metric NB may become a standard performance measure, because the a priori invested clinical considerations enable clinicians to interpret the results directly. [ABSTRACT FROM AUTHOR]

This paper is a review of the results on the real-time optimal control problem for linear systems obtained by the Minsk school by mathematical methods of optimal control. We consider optimal control problems for dynamical objects, their deterministic mathematical models and perfect measurements of states, objects with disturbances and imperfect measurements of the observed input and output signals, the problem of optimal decentralized control of groups of interconnected dynamical objects, and application of real-time optimal control problems and the control principle to solving stabilization problems. [ABSTRACT FROM AUTHOR]

Background: Scalp-associated cutaneous squamous cell carcinoma (cSCC) presents formidable treatment challenges, especially when it leads to full-thickness defects involving bone. Aggressive or recurring cases often demand a multidisciplinary approach. Leveraging our surgical experience and a literature review, we introduce a therapeutic algorithm to guide the selection of reconstruction methods, particularly for locally advanced lesions, furthermore showing the synergy between surgery and other therapies for comprehensive, multidisciplinary disease management. Methods: Our algorithm stems from a retrospective analysis of 202 patients undergoing scalp cSCC resection and reconstruction over a 7-year period, encompassing 243 malignancies. After rigorous risk assessment and documentation of surgical procedures, reconstruction methods were therefore related to malignancy extent, depth, and individual clinical status. Results: The documented reconstructions included 76 primary closures, 115 skin grafts, 7 dermal substitute reconstructions, 33 local flaps, 1 locoregional flap, and 1 microsurgical free flap. Patients unsuitable for surgery received radiotherapy or immunotherapy after histological confirmation. Precise analysis of tumor characteristics in terms of infiltration extent and depth guided the selection of appropriate reconstruction and treatment strategies Combining these insights with an extensive literature review enabled us to formulate our algorithm for managing scalp cSCCs. Conclusions: Effectively addressing scalp cSCC, especially in locally advanced or recurrent cases, demands a systematic approach integrating surgery, radiotherapy, and immunotherapy. Our multidisciplinary team's decision-making algorithm improved patient outcomes by offering a broader spectrum of therapeutic options that can synergistically achieve optimal results. [ABSTRACT FROM AUTHOR]

Strabismus, misalignment of the eyes arising from central nervous system dysregulation and extraocular muscles imbalance, commonly manifests in childhood, leading to amblyopia, binocular vision dysfunction, torticollis and other developmental and psychological disorders. This exerts a negative impact on individuals, families and society. Timely diagnosis and intervention are crucial to prevent permanent damage to vision and stereopsis. Presently, strabismus diagnosis is reliant on the ophthalmologists′ evaluations which results in a lack of efficiency and coverage. However, routine school screening proves inadequate in assessing strabismus degree with low accuracy. Therefore, how to improve the efficiency of strabismus screening is an issue of great importance. This paper delves into the present landscape of strabismus diagnosis and treatment, considering both local and global research advancements. It focuses on the evolution of artificial intelligence technology, illuminating the utilization of artificial intelligence models and algorithms in strabismus. By pinpointing and exploring their strengths and limitations, it offers valuable insights, paving the way for future investigations into artificial intelligence-assisted strabismus diagnosis and treatment. [ABSTRACT FROM AUTHOR]