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A multi-hypothesis marginal multi-target Bayes filter for heavy-tailed observation noise is proposed to track multiple targets in the presence of clutter, missed detection, and target appearing and disappearing. The proposed filter propagates the existence probabilities and probability density functions (PDFs) of targets in the filter recursion. It uses the Student's t distribution to model the heavy-tailed non-Gaussian observation noise, and employs the variational Bayes technique to acquire the approximate distributions of individual targets. K-best hypotheses, obtained by minimizing the negative log-generalized-likelihood ratio, are used to establish the existence probabilities and PDFs of targets in the filter recursion. Experimental results indicate that the proposed filter achieves better tracking performance than other filters. [ABSTRACT FROM AUTHOR]

The random finite set (RFS) approach for multi-target tracking is widely researched because it has a rigorous theoretical basis. However, many prior parameters such as the clutter density, survival probability and detection probability of the target, pruning threshold, merging threshold, initial state of the birth object and its error covariance matrix are required in the standard RFS-based filters. In real application scenes, it is difficult to obtain these prior parameters. To address this problem, an adaptive marginal multi-target Bayes filter without the need for clutter density is proposed. This filter obviates the need for prior clutter density and survival probability. Instead of using the prior initial states of newborn targets and their error covariance matrices, it uses two scans of observations to generate the initial states of potential birth targets and their error covariance matrices according to the least squares technique. Simulation results reveal that the proposed adaptive filter has smaller OSPA and OSPA(2) errors as well as less cardinality error than the adaptive RFS-based filters. The OSPA and OSPA(2) errors have been reduced by more than 20% compared to those of the adaptive RFS-based filters. [ABSTRACT FROM AUTHOR]

The selection of suitable subcontractors for large construction companies is crucially important for the overall success of their projects. As the construction industry advances, a growing number of criteria need to be considered in the subcontractor selection process than simply considering the biding prices. This paper proposed a hybrid multi-criteria structure entropy weight (SEW)—TOPSIS group decision-making model that considers 10 criteria. The proposed model was able to handle large amount of subcontractors' performance data that were collected in different types. Additionally, the model can integrate experts' judgments while accounting for their varying level of expertise and correcting for their biases. This paper also provided a case study to demonstrate the proposed model's effectiveness and efficiency, as well as its applicability of large construction companies. While this study was applied to construction subcontractors' selection, the proposed methodology can also be easily extended to various decision-making scenarios with similar requirements. [ABSTRACT FROM AUTHOR]

Background: Insulin efsitora alfa (efsitora) is a new basal insulin designed for once-weekly administration. Data on safety and efficacy have been limited to small, phase 1 or phase 2 trials., Methods: We conducted a 52-week, phase 3, parallel-design, open-label, treat-to-target trial involving adults with type 2 diabetes who had not previously received insulin. Participants were randomly assigned in a 1:1 ratio to receive efsitora or degludec. The primary end point was the change in the glycated hemoglobin level from baseline to week 52; we hypothesized that efsitora would be noninferior to degludec (noninferiority margin, 0.4 percentage points). Secondary and safety end points included the change in the glycated hemoglobin level in subgroups of participants using and not using glucagon-like peptide-1 (GLP-1) receptor agonists, the percentage of time that the glucose level was in the target range of 70 to 180 mg per deciliter in weeks 48 through 52, and hypoglycemic episodes., Results: A total of 928 participants underwent randomization (466 to the efsitora group and 462 to the degludec group). The mean glycated hemoglobin level decreased from 8.21% at baseline to 6.97% at week 52 with efsitora (least-squares mean change, -1.26 percentage points) and from 8.24% to 7.05% with degludec (least-squares mean change, -1.17 percentage points) (estimated treatment difference, -0.09 percentage points; 95% confidence interval [CI], -0.22 to 0.04), findings that showed noninferiority. Efsitora was noninferior to degludec with respect to the change in the glycated hemoglobin level in participants using and not using GLP-1 receptor agonists. The percentage of time that the glucose level was within the target range was 64.3% with efsitora and 61.2% with degludec (estimated treatment difference, 3.1 percentage points; 95% CI, 0.1 to 6.1). The rate of combined clinically significant or severe hypoglycemia was 0.58 events per participant-year of exposure with efsitora and 0.45 events per participant-year of exposure with degludec (estimated rate ratio, 1.30; 95% CI, 0.94 to 1.78). No severe hypoglycemia was reported with efsitora; six episodes were reported with degludec. The incidence of adverse events was similar in the two groups., Conclusions: In adults with type 2 diabetes who had not previously received insulin, once-weekly efsitora was noninferior to once-daily degludec in reducing glycated hemoglobin levels. (Funded by Eli Lilly; QWINT-2 ClinicalTrials.gov number, NCT05362058.)., (Copyright © 2024 Massachusetts Medical Society.)

Objective: Two phase 3 galcanezumab trials were conducted in Europe and North America to analyze the reduction of weekly cluster headache (CH) attack frequency in populations with episodic and chronic CH. The current study aims to illustrate prospectively recorded baseline clinical data from these trials and to identify possible predictors of response., Methods: Patients (aged 18-65 years) met The International Classification of Headache Disorders 3rd edition-beta criteria for CH. Attacks were evaluated using an electronic headache diary for 7-day (episodic) or 14-day (chronic) eligibility assessments before patients were randomized 1:1 to monthly subcutaneous galcanezumab 300 mg or placebo., Results: Data were collected from 106 patients with episodic and 237 with chronic CH. Overall, the mean age [standard deviation] was 45.4 [11.0] years; patients were predominantly White (84.5%), male (75.8%), and European (77.6%). Patients with episodic CH reported 17.5 [10.0] attacks/week; patients with chronic CH reported 18.8 [10.2] attacks/week. The average pain severity score (range 0-4) was 2.5 [0.7] for episodic CH and 2.7 [0.7] for chronic CH. Higher attack frequency was a possible predictor of response to galcanezumab; potential negative predictors of response were greater attack severity and duration., Conclusion: This large dataset of patients with CH provides reliable systematically and prospectively collected information on disease characteristics. The analysis in episodic CH underscores potential predictors of response worth considering for future CH trial design., Clinical Trial Registration: ClinicalTrials.gov, identifiers: NCT02397473 and NCT02438826., Competing Interests: RJ reports speaker's fees from Novartis, ATI, Lundbeck, TEVA, and Allergan and institutional payments for clinical trials with Eli Lilly and Company, Lundbeck, and ATI. CT reports fees for advisory boards or speaker's fees for Allergan/AbbVie, Eli Lilly and Company, Dompé, Lundbeck, Novartis, and TEVA and institutional payments for clinical trials with Allergan/AbbVie, Eli Lilly and Company, Lundbeck, Novartis, and TEVA. TM, JB, CZ, YD, and JM are full-time employees of Eli Lilly and Company and/or one of its subsidiaries and are minority holders of company stock. SA reports no commercial or financial relationships that could be construed as a potential conflict of interest. At the time the study was conducted, SA was a full-time employee of Eli Lilly and Company, Indianapolis, IN, USA, and was a minority holder of company stock., (Copyright © 2023 Jensen, Tassorelli, Myers Oakes, Bardos, Zhou, Dong, Aurora and Martinez.)

The electrochemical carbon dioxide reduction reaction (CO 2 RR) is a promising method to realize carbon recycling and sustainable development because of its mild reaction conditions and capability to utilize the electric power generated by renewable energy such as solar, wind, or tidal energy to produce high-value-added liquid fuels and chemicals. However, it is still a great challenge to deeply understand the reaction mechanism of CO 2 RRs involving multiple chemical processes and multiple products due to the complexity of the traditional catalyst's surface. Organic ligand-protected metal nanoclusters (NCs) with accurate compositions and definite atom packing structures show advantages for revealing the reaction mechanism of CO 2 RRs. This Review focuses on the recent progress in CO 2 RRs catalyzed by atomically precise metal NCs, including gold, copper, and silver NCs. Particularly, the influences of charge, ligand, surface structure, doping of Au NCs, and binders on the CO 2 RR are discussed in detail. Meanwhile the reaction mechanisms of CO 2 RRs including the active sites and the key reaction intermediates are also discussed. It is expected that progress in this research area could promote the development of metal NCs and CO 2 RRs.