Decision Algorithm Research Articles

Designing transport scheme of 3D naked-eye system

3D naked-eye is constructed from multi-stream as a typical representation of stereoscopic video. Its enormous data volume and stringent low-delay transport requirements pose significant challenges for high-quality real-time transport. Through analysis and experimental verification that current streaming media transport frameworks using the server–client or peer-to-peer scheme face difficulties when transmitting 3D naked-eye in a one-to-one format. Besides, the existing bandwidth estimation algorithms cannot achieve the expected performance when dealing with delay-sensitive traffic. This results in low bandwidth utilization and slow bandwidth estimation, rendering it unfeasible to deliver multi-stream on time. We propose an effective transport framework with different modules for real-time multi-stream and introduce an Agent-to-Agent transport scheme that provides many-to-one connection as the main implementation way of 3D naked-eye transport framework. Additionally, we propose a direct bandwidth estimation algorithm to quickly match network bandwidth for low-delay transport. The Agent terminal centrally processes consolidates transports, and provides macro-level management of multiple video streams. The algorithm directly detects the available bandwidth using packet interval and packet rate models. Finally, using rate decision algorithm arbitrates the results to directly measure the maximum available bandwidth of the link. The Agent-to-Agent achieves 99% bandwidth utilization, addressing the limitations of existing streaming schemes in handling concurrent data streams. Our algorithm provides precise bandwidth estimates with minimal time overhead, meeting the requirements of a delay-sensitive 3D naked-eye system across diverse environments.

Latissimus dorsi transfer or lower trapezius transfer: a treatment algorithm for irreparable posterosuperior rotator cuff tears muscles transfers in posterosuperior rotator cuff tears

IntroductionTendon transfers of the latissimus dorsi (LDT) or the lower trapezius (LTT) are treatment options for irreparable posterosuperior rotator cuff tears (PSIRCT). There is still no consensus on which type of tendon transfer is superior in the treatment of PSIRCT. Due to the differences in the anatomy and biomechanics, we hypothesize that there are different clinical situations in which either LDT or LTT should be preferred. The aim of this study was to evaluate the clinical and radiological outcomes of LDT and LTT in patients with PSIRCT to establish a clinical algorithm for the treatment decision. Materials and MethodsThis is a retrospective, single center observational study. Included were patients who underwent arthroscopically assisted LDT (aaLDT) or arthroscopically assisted LTT (aaLTT) for PSIRCT. In all patients, range of motion (ROM), external rotation strength, Visual Analogue Scale of Pain (VAS) and Subjective Shoulder Value (SSV) were determined pre- and postoperatively. Constant-Murley Score (CS) was evaluated at final follow-up. Complication rate, failure of the tendon transfer and revision rate were analyzed. ResultsIn total, 29 aaLDT (age 64 years, median follow-up time 45 months) and 8 aaLTT (age 54 years, median follow-up time 34 months) were included. Active ROM, VAS and SSV was significantly improved in both cohorts. At follow-up, the median CS was 73 (aaLDT) and 77 (aaLTT), respectively. Failure rate including revision surgery was 14% (aaLDT) and 13% (aaLTT), respectively. Low functional findings preoperatively were correlated to a lower functional outcome at follow-up in both groups. Painful loss of anterior elevation (PLEA) and loss of ER had no significant impact on functional outcomes in aaLDT. ConclusionFollowing the treatment algorithm based on the clinical examination, clinical outcome parameters, active ROM and pain could be significantly improved. Good preoperative function was associated with a good clinical outcome in both transfers. A low failure and revision rate supports the good decision-making of the algorithm presented.

Current Concepts in Brachial Plexus Birth Injuries: A Comprehensive Narrative Review.

Brachial plexus birth injury (BPBI) encompasses a spectrum of upper extremity paralysis cases following childbirth. The etiology of BPBI is multifactorial, involving maternal, obstetric, and neonatal associative factors. Despite opportunities for spontaneous recovery, recent literature demonstrates that a significant proportion of infants experience residual deficits and functional limitations as they age. Understanding the complex anatomy of the brachial plexus, clinical presentations of the pathology, diagnostic workup, current treatment options, and common secondary sequelae is instrumental for appropriate management of BPBI. Following a comprehensive search strategy used by the authors to identify relevant literature relating to the progression, patho-anatomy, clinical presentation, management, and treatment of BPBI, this comprehensive narrative review outlines current approaches to assess, manage, and advance BPBI care. We advocate for prompt referral to specialized multicenter brachial plexus clinics for accurate diagnosis, timely intervention, and individualized patient-centered assessment. Further research is needed to elucidate mechanisms of injury, refine diagnostic protocols, and optimize long-term outcomes. Collaboration between healthcare providers and families is paramount in providing comprehensive care for infants with BPBI. This review offers insights into the current understanding and management of BPBI, highlighting the importance of tailored approaches and intraoperative decision-making algorithms to optimize functional outcomes.

Research on paging enhancements for 5G-A downlink transmission energy saving

5G-Advanced (5G-A), an evolutionary iteration of 5G, effectively enhances 5G services. The increasing complexity in downlink services scenarios stresses the necessity for research into the integration of efficient communication with low-carbon solutions. Historically, there has been an emphasis on reliability and precision, at the expense of power consumption. Although energy-saving technologies like Idle mode-Discontinuous Reception (IDRX) and Paging Early Indication (PEI) have been introduced to reduce power consumption in UE, they have not been fully tailored to the paging characteristics of 5G-A downlink services. In this paper, we take full account of the impact of paging message density on energy saving measures and propose an enhanced paging technology, termed Predictive-PEI (PPEI), which is designed to reduce UE overhead while minimizing latency whenever possible. Towards this end, we design a dual threshold decision framework founded on machine learning, mainly involving two steps. We first use the LSTM-FNN neural network to forecast the arrival moment of upcoming paging messages based on past real information. Then, the output of the initial prediction is as the input of the next dual threshold decision algorithm, to determine the optimal moment for transmitting the PEI. The restrictive factors, encompass average delay threshold and cache capacity threshold, playing a role in decisions regarding paging message caching and decoding. Compared to the existing schemes, our PPEI scheme flexibly sends efficient PEI according to the actual paging characteristics by introducing machine learning, resulting in substantial power savings of up to 38.89% while concurrently ensuring effective latency control.

Anesthetic key points in a patient with a terminal ileum neuroendocrine tumor and a rare carcinoid left heart disease presented for non-cardiac surgery: case report

BackgroundCarcinoid tumors are rare neuroendocrine malignancies presenting in an increasing number in our center. The incidence of carcinoid tumors is approximatively between 2.5 and 5 cases per 100,000 people of whom about 50% develop carcinoid syndrome. Once the carcinoid syndrome has developed, a carcinoid cardiomyopathy can occur. Carcinoid heart disease (CaHD) remains a serious and rare complication associated with a significant increase in morbidity and mortality. Although carcinoid tumors have been known and studied for several years, there are still scarce data on the anesthetic management and the peri operative period.Case presentationWe describe a case of a Caucasian 44-year-old woman with an unusual presentation of left CaHD with an ileal neuroendocrine tumor and liver metastases. Our preoperative somatostatin administration protocol, limit the cardiac damage. The maintenance of stable hemodynamics, the use of balanced anesthetic technique, all along with a good understanding of the pathology, played a major role in the successful management of anesthesia. This case report allows us to introduce our decision algorithm for the management of this type of pathology in our tertiary hospital, Cliniques Universitaires Saint-Luc.ConclusionDespite the paucity of data, anesthetic management of patients with carcinoid tumor can be safely performed with effective hemodynamic monitoring and a good understanding of the pathophysiology. Knowledge and application of a clear institutional algorithm for octreotide administration and multidisciplinary consultation at a referral center are essential for the management of these patients.

IMPROVED DECISION-MAKING ALGORITHM FOR THE REDEVELOPMENT OF INDUSTRIAL AREAS

Raising of problem. As a result of the financial and economic crisis, many industrial enterprises have ceased operations or significantly reduced production volumes due to the shrinking markets for their products, which is accompanied by destructive processes and phenomena. In a dynamic competitive environment, some enterprises have outdated and worn-out fixed assets, their products are not in demand, their premises are leased, large areas are covered with waste and harmful substances that are environmentally hazardous, low technological level of production, weak scientific and technical sphere, and reduced supply of labour resources. Therefore, ensuring sustainable development of Ukraine is impossible without solving the problems of restructuring and qualitative renewal of industrial production, reconstruction of degraded urban areas occupied by abandoned buildings and structures of industrial enterprises. Thus, there is an objective need to increase the level of profitability of land development by justifying alternative land use options: demolition of degraded industrial enterprises and construction of new facilities or reconstruction of industrial buildings and structures with a change of functional purpose. However, there is currently no unified approach to building an information model of the decision-making process for the reconstruction of industrial enterprises with a change in their functional purpose. Translated with DeepL.com (free version). Purpose of the aricle. To develop an improved decision-making algorithm for the reconstruction of mining enterprises with a change in functional purpose. Conclusion. The decision-making algorithm for the reconstruction of industrial enterprises with a change in functional purpose, which ensures the receipt of substantiated values of technical and economic indicators of such projects, depending on the available initial information about the object and the conditions of production of works, and also provides the possibility of specifying the values indicators of the effectiveness of managerial decisions at different stages of the life cycle, thanks to the adjustment of the values of the duration and cost of reconstruction by varying the parameters that are part of the determining factors.

Innovative educational and research center for monitoring forest resources of Siberia based on laser and microwave aerospace imaging

The possibilities of creating an innovative educational and scientific center for monitoring forest resources in Siberia on the basis of the Department of Space Facilities and Technologies of the Siberian State University of Science and Technology named after Mikhail Fedorovich Reshetnev are discussed, with the aim of training highly qualified engineering personnel and conducting promising scientific research in the field of monitoring, modeling, forecasting and management of forest resources. Methodological solutions and algorithms for three-dimensional modeling of forest structure and dynamics based on laser scanning data, digital aerial and space photography are proposed. These methods contribute to operational monitoring and can significantly reduce the cost of monitoring the condition and use of forest resources over the vast territory of Siberia. Remote sensing data is presented in the form of a geotransformed database and digital photo map, compatible in formats with computer-aided design systems and with the main geographic information systems – ArcView, ArcINFO, MapINFO. The innovative monitoring center will be used for operational state control and monitoring of forest management, the state of forest lands, forest management and forest inventory, solving problems of ecology and environmental management, geoecology, formation of a forest resource inventory, aerospace methods for studying natural resources and territories, information technology. Solving these problems will allow for the training of highly qualified specialists. The center's specialists plan to create information technologies for remote sensing of natural objects with the aim of import substitution of foreign software products. The main scientific directions of the created center: development and research of methods for system analysis of large-scale multidimensional remote sensing data based on nonparametric decision-making algorithms and parallel computing technologies; testing hypotheses about the distributions of large-volume remote sensing data based on nonparametric nuclear-type pattern recognition algorithms; detection of compact groups of large-volume remote sensing data corresponding to unimodal fragments of the joint probability density of multivariate random variables.

RECONSTRUCTION OF THE SYSTEM OF GREENING TERRITORIES OF THE DNIPRO AREA IN THE MINDS OF FURTHER TRANSFORMATIONS OF EXOGENIC, ENDOGENIC AND ANTHROPOGENIC FACTORS OF THE OLD DEVELOPMENT

Raising of problem. As a result of the financial and economic crisis, many industrial enterprises have ceased operations or significantly reduced production volumes due to the shrinking markets for their products, which is accompanied by destructive processes and phenomena. In a dynamic competitive environment, some enterprises have outdated and worn-out fixed assets, their products are not in demand, their premises are leased, large areas are covered with waste and harmful substances that are environmentally hazardous, low technological level of production, weak scientific and technical sphere, and reduced supply of labour resources. Therefore, ensuring sustainable development of Ukraine is impossible without solving the problems of restructuring and qualitative renewal of industrial production, reconstruction of degraded urban areas occupied by abandoned buildings and structures of industrial enterprises. Thus, there is an objective need to increase the level of profitability of land development by justifying alternative land use options: demolition of degraded industrial enterprises and construction of new facilities or reconstruction of industrial buildings and structures with a change of functional purpose. However, there is currently no unified approach to building an information model of the decision-making process for the reconstruction of industrial enterprises with a change in their functional purpose. Translated with DeepL.com (free version). Purpose of the aricle. To develop an improved decision-making algorithm for the reconstruction of mining enterprises with a change in functional purpose. Conclusion. The decision-making algorithm for the reconstruction of industrial enterprises with a change in functional purpose, which ensures the receipt of substantiated values of technical and economic indicators of such projects, depending on the available initial information about the object and the conditions of production of works, and also provides the possibility of specifying the values indicators of the effectiveness of managerial decisions at different stages of the life cycle, thanks to the adjustment of the values of the duration and cost of reconstruction by varying the parameters that are part of the determining factors.

Highly Efficient Hybrid Reconfigurable Intelligent Surface Approach for Power Loss Reduction and Coverage Area Enhancement in 6G Networks

This paper introduces a novel efficient hybrid reconfigurable intelligent surface (RIS) approach designed to significantly reduce power loss and enhance coverage area in 6G networks. The core innovation of this approach lies in an advanced iterative algorithm introduced as the Hybrid reconfigurable intelligent surface decision-making algorithm (HRIS-DMA) that integrates precise user location data into the RIS configuration process. By dynamically adjusting RIS elements to reflect and direct signals based on real-time user positions, this method minimises signal attenuation and optimises signal propagation. The mechanism driving the performance gains includes precise beamforming and intelligent reflection, continuously refined through iterative updates. This technique ensures robust signal strength and expanded coverage, addressing the challenges of dense and diverse deployment scenarios in 6G networks. The proposed scheme’s application in 6G networks demonstrates substantial improvements in signal quality and network reliability, paving the way for enhanced user experiences and efficient communication infrastructures. This novel approach was tested using MATLAB R2023a , and its performance was evaluated using three downlink scenarios: zero to few, few to moderate, and moderate to many obstacles. The three scenarios show higher coverages than conventional simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) and base station (BS) handover. Based on the evaluation metrics, the analysis results of the novel HRIS-DMA show 70% less signal power loss, 0.17 μs less system delay, 25 dB and 12 dB channel gain compared with the conventional STAR-RIS and BS handover, respectively, and 95% improvement in the overall system’s efficiency compared to STAR-RIS and 13% compared to BS-BS handover.

Corneal Collagen Cross-linking for Keratoconus in Pediatric and Developmentally Delayed Patients.

Corneal cross-linking (CXL) is the standard of care in patients with keratoconus but presents unique challenges in children and developmentally delayed patients. We present our clinical decision-making algorithm, CXL surgical technique, and outcomes in these groups. A retrospective chart review was undertaken at a tertiary referral center of all patients who underwent CXL for keratoconus at University of Pittsburgh Medical Center (UPMC) Children's Hospital of Pittsburgh between October 1, 2017, and April 1, 2021. Demographic information along with preoperative, intraoperative, and postoperative ophthalmic examination findings were collected. The main outcome measures were indications of CXL, postoperative complications, and visual acuity (VA). Forty-eight eyes of 34 patients [21 patients (30 eyes) with developmental delay (DD) and 13 patients (18 eyes) with no DD (NDD)] underwent epithelium-off, standard CXL. General anesthesia was used for CXL in all patients except for 3 with NDD. A temporary central tarsorrhaphy was performed in all patients with DD and 7 patients with NDD. The remaining got a bandage contact lens. There were no immediate postoperative complications. A trend toward improvement in VA was noted postoperatively. The mean logMAR VA (with habitual correction) was 0.67 preoperatively and 0.57 postoperatively (P = 0.3) in DD and 0.52 and 0.36, respectively (P = 0.13), in NDD. This retrospective review presents a technique for assessment and treatment of keratoconus in children and those with DD. Our technique ensures timely diagnosis and provides a safe method for CXL in these groups. Temporary central tarsorrhaphy is a well-tolerated option to reduce postoperative pain.

Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System.

This paper presents an innovative approach towards space-ground integrated communication systems by combining terrestrial cellular networks, UAV networks, and satellite networks, leveraging advanced slicing technology. The proposed architecture addresses the challenges posed by future user surges and aims to reduce network overhead effectively. Central to our approach is the introduction of a marginal mobile station (MS)-assisted network resource allocation decision architecture. Building upon this foundation, we introduce the DP-DQN model, an enhanced decision-making algorithm tailored for MSs in dynamic network environments. Furthermore, this study introduces a feedback mechanism to ensure the accuracy and adaptability of the marginalization model over time. Through extensive simulations and experimental validations, our DP-DQN-based edge decision method demonstrates substantial potential in alleviating core network overhead while improving success access ratios compared to conventional methods.

Intelligent Decision-Making Algorithm for UAV Swarm Confrontation Jamming: An M2AC-Based Approach

Unmanned aerial vehicle (UAV) swarm confrontation jamming offers a cost-effective and long-range countermeasure against hostile swarms. Intelligent decision-making is a key factor in ensuring its effectiveness. In response to the low-timeliness problem caused by linear programming in current algorithms, this paper proposes an intelligent decision-making algorithm for UAV swarm confrontation jamming based on the multi-agent actor–critic (M2AC) model. First, based on Markov games, an intelligent mathematical decision-making model is constructed to transform the confrontation jamming scenario into a symbolized mathematical problem. Second, the indicator function under this learning paradigm is designed by combining the actor–critic algorithm with Markov games. Finally, by employing a reinforcement learning algorithm with multithreaded parallel training–contrastive execution for solving the model, a Markov perfect equilibrium solution is obtained. The experimental results indicate that the algorithm based on M2AC can achieve faster training and decision-making speeds, while effectively obtaining a Markov perfect equilibrium solution. The training time is reduced to less than 50% compared to the baseline algorithm, with decision times maintained below 0.05 s across all simulation conditions. This helps alleviate the low-timeliness problem of UAV swarm confrontation jamming intelligent decision-making algorithms under highly dynamic real-time conditions, leading to more effective and efficient UAV swarm operations in various jamming and electronic warfare scenarios.

An Improved NSGA-II Algorithm for MASS Autonomous Collision Avoidance under COLREGs

Autonomous collision avoidance decision making for maritime autonomous surface ships (MASS), as one of the key technologies for MASS autonomous navigation, is a research hotspot focused on by relevant scholars in the field of navigation. In order to guarantee the rationality, efficacy, and credibility of the MASS autonomous collision avoidance scheme, it is essential to design the MASS autonomous collision avoidance algorithm under the stipulations of the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs). In order to enhance the autonomous collision avoidance decision-making capability of MASS in accordance with the relevant provisions of COLREGs, an improved NSGA-II autonomous collision avoidance decision-making algorithm based on the good point set method (GPS-NSGA-II) is proposed, which incorporates the collision hazard and the path cost of collision avoidance actions. The experimental results in the four simulation scenarios of head-on situation, overtaking situation, crossing situation, and multi-ship encounter situation demonstrate that the MASS autonomous collision avoidance decision making based on the GPS-NSGA-II algorithm under the constraints of COLREGs is capable of providing an effective collision avoidance scheme that meets the requirements of COLREGs in common encounter situations and multi-ship avoidance scenarios promptly, with a promising future application.

Decision algorithms for reversibility of 1D cellular automata under reflective boundary conditions

Reversibility is one of the most significant properties of cellular automata (CA). In this paper, we focus on the reversibility of one-dimensional finite CA under reflective boundary conditions (RBC). We present two algorithms for deciding the reversibility of one-dimensional CA under RBC. Both algorithms work for not only linear rules but also non-linear rules. The first algorithm is to determine what we call the “strict reversibility” of CA. The second algorithm is to compute what we call the “reversibility function” of CA. Reversibility functions are proved to be periodic. Based on the algorithms, we list some experiment results of one-dimensional CA under RBC and analyse some features of this family of CA.

ProtecT-2-D trial protocol: cardiovascular protection in patients with type 2 diabetes and established heart and/or vascular disease at a cardio-metabolic clinic—a randomized controlled trial

BackgroundCardiovascular disease remains the primary cause of morbidity and mortality despite advancements in the treatment of patients with type 2 diabetes. Effective diabetes management extends beyond blood glucose control and includes cardiovascular prevention and treatment. However, the conventional healthcare model often emphasizes single-disease-specific management, leading to fragmented care. We aim to establish an affordable Cardio-Metabolic Clinic (CMC) that can provide comprehensive assessment and specialized care with a focus on cardiovascular protection.MethodsThe ProtecT-2-D study is a prospective, randomized control trial at the Cardiovascular Research Unit, Odense University Hospital Svendborg, Denmark. In this study, 1500 participants with type 2 diabetes and cardiovascular disease will be randomly assigned in a 2:1 ratio to receive either the intervention: treatment in the CMC, or the control: standard of care. The Cardio-Metabolic Clinic applies a decision-making algorithm coded with the latest guidelines to evaluate lifestyle factors and manage medical treatment. Health examinations are conducted at baseline and after three years, and clinical events will be assessed through registry and journal audits after five and ten years. The primary outcome is the time to the first occurrence of a composite of cardiovascular deaths, non-fatal acute myocardial infarctions, non-fatal stroke, or hospitalization due to heart failure at a time frame of five years.DiscussionThe Cardio-Metabolic Clinic represents a pioneering approach to diabetes management that aims to improve patient outcomes by reducing the cardiovascular disease burden. This study could transform diabetes care and offer a multidisciplinary, cost-effective, and specialized treatment. We need to establish the efficacy and feasibility of a CMC to integrate comparable clinics into broader healthcare systems, and potentially enhance cardiovascular health in patients with type 2 diabetes.Trial registration numberClinicalTrials.gov NCT06203860.Graphical

Психометрия латентных характеристик: инструменты, проблемы обработки ранговой информации, решения

<p>The paper examines the problems of correctness and adequacy of the use of psychometric tools in psychosocial research and applications. The main attention is paid to the problem of correct processing of rank information, which is non-numeric information that does not allow the use of classical mathematical operations, starting with the addition operation. The approach of forming the results of processing expert, ranked information in the scale of relations based on the Analytic Hierarchy Process (AHP), which includes a measurement apparatus and decision-making algorithms, is presented. An example is given that simulates the situation of an expert council and demonstrates unacceptable contradictions that arise with the "numerical" approach to processing rank information. The algorithms of the AHP normative approach are presented, which allow obtaining numerical, personalized assessments of psychosocial characteristics. The basic concepts of the Rasch Measurement (RM) for comparing the capabilities of AHP and RM in psychosocial applications are described. Some critical remarks about the methods of psychometry are considered. It is shown that the origins of the criticisms are associated with a superficial understanding of the basic concepts of psychometric measurements and the scope of their applications, as well as with elementary errors when using the apparatus of psychometric instruments.</p>

P-175 Morphokinetic growth variance score as a tool to identify euploid embryos with the highest live birth potential

Abstract Study question Is the longitudinal growth pattern of euploid embryos associated with live birth rate? Summary answer Embryos with lower growth variance score (GVS), i.e., pointing towards a steadier growth during time-lapse monitoring (TLM), have higher live birth rates. What is known already Dynamic TLM imaging has been increasingly adopted by fertility clinics as an attempt to improve the ability of selecting embryos with the highest potential for implantation. Many authors have identified and incorporated markers of embryonic morphokinetics into decision algorithms for embryo (de)selection. However, longitudinal changes during this temporal process, and the impact of such changes on embryonic competence remains unknown. Aiming to model the reference ranges of euploid embryonic growth as a single growth trajectory might provide an additive value to blastocyst morphological grade to identify highly competent euploid embryos. Study design, size, duration This observational cohort study was performed in a tertiary referral IVF centre between October 2017 and June 2021, including a total of 340 single euploid autologous frozen embryo transfers. Pre-implantation genetic diagnosis for aneuploidy (PGT-A) performed by next generation sequency (NGS). Pregnancy outcomes were assessed as live birth, pregnancy loss or not pregnant. Participants/materials, setting, methods Standard morphokinetic parameters (tPB2 to tEB) of all euploid embryos cultured in TLM system were used to develop the reference ranges for the growth variance score (GVS) model. Nuclear errors at two- and four-cell stages, and blastocyst grade before trophectoderm (TE)-biopsy were also annotated. GVS of euploid embryos was evaluated for its association with pregnancy outcomes, and to its predictive ability to live birth potential over TE and inner cell mass (ICM) grade morphology. Main results and the role of chance A total of 189 (55.6%) euploid transfers resulted in a live birth, 48 (14.1%) in pregnancy loss and 103 (30. 3%) did not result in pregnancy. The median time for euploid embryo blastulation was 109.9 hours (95% CI:98.8-121.0 hours). Lower quality ICM (OR: 0.13, 95% CI: 0.05-0.33, P < 0.001) and TE (OR: 0.46, 95% CI:0.22-0.94, P = 0.034), multinucleation at 4-cell stage (OR: 0.042, 95% CI:0.21-0.84, P = 0.016) and higher GVS (OR: 0.59, 95% CI: 0.45-0.77, P < 0.001), significantly decreased the odds of live birth. In the multivariable analysis, only GVS (OR: 0.62, 95% CI: 0.46-0.84, P = 0.002) and lower ICM quality (OR: 0.21, 95% CI: 0.06-0.65, P = 0.009) had a significant effect on live birth. There was a significant trend towards higher live birth rates with lower GVS among embryos of similar ICM grades: 85% vs 65% live births in low vs. high GVS for embryos with ICM type A, 76% vs. 48% for ICM type B, 67% vs. 21% for ICM type C, respectively (P for trend <0.001). Addition of the GVS to embryo morphology model (ICM, TE, biopsy day) significantly improved the model’s ability to predict live birth (AUC: 0.67 vs. 0.62, P = 0.015). Limitations, reasons for caution The exclusion of IVF cases limits the utility of the model to ICSI-derived embryos. The utility of GVS should be replicated further in external cohorts to confirm its reproducibility in other clinical and laboratory settings. Wider implications of the findings By capturing the dynamic growth pattern of embryos, GVS may serve as a valuable tool in prioritizing PGT-A tested embryos for transfer. Its impact was even more prominent for selecting suboptimal blastocysts, which is a more challenging subset of embryos to choose due to their likelihood of reduced viability. Trial registration number Not Applicable

A comprehensive guide to removable partial dentures. Part 1: patient selection, design principles and decision algorithms for component selection

This two-part series provides a comprehensive guide to fabricating definitive metal partial dentures, addressing the challenges dental practitioners face in mastering prosthesis design. Part 1 explores diagnostic procedures, indications for metal partial dentures, design principles, and surveying techniques. It emphasizes the understanding of biomechanical forces and discusses design principles such as support, retention and stability, along with the components that provide these functions in a metal partial denture. Decision algorithms for selecting various components are presented to guide clinical practitioners in efficient designing. The series aims to equip dental professionals with a thorough understanding of the theoretical foundations and practical methods for effective metal partial denture fabrication. CPD/Clinical Relevance: This series provides essential knowledge and practical techniques for designing and fabricating effective metal partial dentures.

A Comprehensive Study on Decision-Making Algorithms in Retail and Project Management using Double Framed Hypersoft Sets

In today's dynamic business environment, effective decision-making plays a crucial role in the success of retail businesses and project management endeavors. This paper presents a comprehensive study of decision-making algorithms, focusing on the utilization of Double Framed Hypersoft Sets (DFHS) in retail and project management contexts. The study explores the theoretical foundations of DFHS and its practical applications in customer segmentation, personalized marketing, project selection, and resource allocation. Various algorithms and methodologies incorporating DFHS are discussed and analyzed, highlighting their advantages, limitations, and real-world implications. Through a series of numerical examples, case studies, and comparative analyses, this study provides valuable insights into the role of DFHS in optimizing decision processes, improving business outcomes, and fostering innovation in retail and project management domains.

Evaluating Explanations From AI Algorithms for Clinical Decision-Making: A Social Science-Based Approach.

Explainable Artificial Intelligence (XAI) techniques generate explanations for predictions from AI models. These explanations can be evaluated for (i) faithfulness to the prediction, i.e., its correctness about the reasons for prediction, and (ii) usefulness to the user. While there are metrics to evaluate faithfulness, to our knowledge, there are no automated metrics to evaluate the usefulness of explanations in the clinical context. Our objective is to develop a new metric to evaluate usefulness of AI explanations to clinicians. Usefulness evaluation needs to consider both (a) how humans generally process explanations and (b) clinicians' specific requirements from explanations presented by clinical decision support systems (CDSS). Our new scoring method can evaluate the usefulness of explanations generated by any XAI method that provides importance values for the input features of the prediction model. Our method draws on theories from social science to gauge usefulness, and uses literature-derived biomedical knowledge graphs to quantify support for the explanations from clinical literature. We evaluate our method in a case study on predicting onset of sepsis in intensive care units. Our analysis shows that the scores obtained using our method corroborate with independent evidence from clinical literature and have the required qualities expected from such a metric. Thus, our method can be used to evaluate and select useful explanations from a diverse set of XAI techniques in clinical contexts, making it a fundamental tool for future research in the design of AI-driven CDSS.