On convergence properties of the brain-state-in-a-convex-domain

Convergence in the presence of multiple equilibrium points is one of the most fundamental dynamical properties of a neural network (NN). Goal of the paper is to investigate convergence for the classic Brain-State-in-a-Box (BSB) NN model and some of its relevant generalizations named Brain-State-in-a-Convex-Body (BSCB). In particular, BSCB is a class of discrete-time NNs obtained by projecting a linear system onto a convex body of Rn. The main result in the paper is that the BSCB is convergent when the matrix of the linear system is symmetric and positive semidefinite or, otherwise, it is symmetric and the step size does not exceed a given bound depending only on the minimum eigenvalue of the matrix. This result generalizes previous results in the literature for BSB and BSCB and it gives a solid foundation for the use of BSCB as a content addressable memory (CAM). The result is proved via Lyapunov method and LaSalle's Invariance Principle for discrete-time systems and by using some fundamental inequalities enjoyed by the projection operator onto convex sets as Bourbaki-Cheney-Goldstein inequality.

Innovation Efficiency, Productive Performance and Undesirable Outputs across European Regions: Are there any missing links?

We introduce an analytical methodological framework that links knowledge generation efficiency with economic efficiency and the corresponding environmental impact for 199 European Regions during 2000-2018, using a benchmarking approach and especially a chain network DEA technique. A clear trade-off between knowledge generation efficiency and productive performance emerges. European regions which exhibit high innovation efficiency enjoy higher overall performance compared to their counterparts. In a second stage, we investigate the convergence patterns of the examined regions with respect to all the three facets of the estimated efficiency where the coexistence of multi-type convergence clubs is revealed.

Solving general convex quadratic multi-objective optimization problems via a projection neurodynamic model

A neural network model is constructed to solve convex quadratic multi-objective programming problem (CQMPP). The CQMPP is first converted into an equivalent single-objective convex quadratic programming problem by the mean of the weighted sum method, where the Pareto optimal solution (POS) are given by diversifying values of weights. Then, for given various values weights, multiple projection neural networks are employded to search for Pareto optimal solutions. Based on employing Lyapunov theory, the proposed neural network approach is established to be stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the single-objective problem. The simulation results also show that the presented model is feasible and efficient.

DCDLN: A densely connected convolutional dynamic learning network for malaria disease diagnosis

Malaria is a significant health concern worldwide, particularly in Africa where its prevalence is still alarmingly high. Using artificial intelligence algorithms to diagnose cells with malaria provides great convenience for clinicians. In this paper, a densely connected convolutional dynamic learning network (DCDLN) is proposed for the diagnosis of malaria disease. Specifically, after data processing and partitioning of the dataset, the densely connected block is trained as a feature extractor. To classify the features extracted by the feature extractor, a classifier based on a dynamic learning network is proposed in this paper. Based on experimental results, the proposed DCDLN method demonstrates a diagnostic accuracy rate of 97.23%, surpassing the diagnostic performance than existing advanced methods on an open malaria cell dataset. This accurate diagnostic effect provides convincing evidence for clinicians to make a correct diagnosis. In addition, to validate the superiority and generalization capability of the DCDLN algorithm, we also applied the algorithm to the skin cancer and garbage classification datasets. DCDLN achieved good results on these datasets as well, demonstrating that the DCDLN algorithm possesses superiority and strong generalization performance.

Capital deepening and land average grain yield convergence: Evidence from China

The shift of agricultural labor force to non-agricultural sectors has paralleled China's economic development, leading to a substantial rise in labor costs relative to capital. Consequently, the agricultural production has witnessed a shift towards capital-intensive practices. The capital deepening coincides with the significant increase in China's grain output while the main cause of capital deepening in China's grain production is poorly understood. This study examines the effect of increasing in various capital investments on the grain yield growth and growth convergence in China's main production areas, based on the data collected from the data set of the Compilation of Cost-Benefit Data of Agricultural Products (CCBDAP). Results show that the increases of chemical fertilizer, pesticide and machinery input have played key roles in the increase of grain yield. For early indica rice, japonica rice, wheat and maize, the average land output bears a β convergence. These findings suggest that more capital investments are supposed to accelerate the growth of grain yield per unit of land, take the opportunity of practicing the cross-provincial balance system of occupation and compensation of cultivated land. Moreover, strategic adjustments to the spatial distribution of grain cultivation are recommended to maximize the utilization of limited arable land resources while upholding national food security objectives.

Unleashing the Power of Reliance for Post-Approval Changes: A Journey with 48 National Regulatory Authorities

Post-approval changes (PACs) to marketed products are routinely introduced to continuously enhance the product lifecycle management. However, bringing a chemistry, manufacturing and control (CMC) change through the global health authorities can be a complex and lengthy process taking up to several years, therefore negatively impacting supply continuity. In order to accelerate the review and approval of regulatory submissions and ensure continuous supply to patients, the World Health Organization (WHO) is strongly supporting the implementation of reliance among National Regulatory Authorities (NRAs). While some promising developments have been made with the use of reliance pathways for initial marketing authorizations, reliance is still not widely used for PACs. With the support of the European Medicines Agency (EMA) and WHO, Roche launched a reliance pilot based on EMA approval to file a supply critical variation for a monoclonal antibody. The variation constitutes major changes to the approved manufacturing process. Sameness of the product is ensured by submitting to all participants the same variation package as in the EU. The objectives of the pilot are to ensure continuous supply of this critical medicine by targeting global approval in 6.5 months, to promote regulatory convergence by waiving country specific requirements, and enhance greater transparency by sharing EMA Committee for Medicinal Products for Human Use (CHMP) final assessment report and Q&As to participating NRAs. Globally 48 NRAs have agreed to join the pilot. This article outlines the process of establishing the pilot project, including a planning phase and an engagement phase with the EMA, WHO and the participating NRAs.

Evolutionary transition of hypervirulent Klebsiella pneumoniae to multidrug-resistant hypervirulent Klebsiella pneumoniae: Indian experience

An emerging pathotype of Klebsiella pneumoniae, initially identified in Southeast Asian countries, has now spread to multiple countries, including India. These convergent strains, carrying both resistance and virulence determinants, are classified as multidrug-resistant Hypervirulent Klebsiella pneumoniae (MDR-HvKp). Since the initial reports, there has been a concerning surge in infections caused by this pathotype globally. In this context, we aim to shed light on the evolutionary changes that have taken place in this relatively novel pathotype. Understanding these changes is crucial for devising diagnosis and targeted intervention strategies to mitigate the spread of MDR-HvKp infections.

Many-objective ant lion optimizer (MaOALO): A new many-objective optimizer with its engineering applications

Many-objective optimization (MaO) is an important aspect of engineering scenarios. In many-objective optimization algorithms (MaOAs), a key challenge is to strike a balance between diversity and convergence. MaOAs employs various tactics to either enhance selection pressure for better convergence and/or implements additional measures for sustaining diversity. With increase in number of objectives, the process becomes more complex, mainly due to challenges in achieving convergence during population selection. This paper introduces a novel Many-Objective Ant Lion Optimizer (MaOALO), featuring the widely-popular ant lion optimizer algorithm. This method utilizes reference point, niche preserve and information feedback mechanism (IFM), to enhance the convergence and diversity of the population. Extensive experimental tests on five real-world (RWMaOP1- RWMaOP5) optimization problems and standard problem classes, including MaF1-MaF15 (for 5, 9 and 15 objectives), DTLZ1-DTLZ7 (for 8 objectives) has been carried out. It is shown that MaOALO is superior compared to ARMOEA, NSGA-III, MaOTLBO, RVEA, MaOABC-TA, DSAE, RL-RVEA and MaOEA-IH algorithms in terms of GD, IGD, SP, SD, HV and RT metrics. The MaOALO source code is available at: https://github.com/kanak02/MaOALO.

A novel skin cancer detection model using modified finch deep CNN classifier model

Skin cancer is one of the most life-threatening diseases caused by the abnormal growth of the skin cells, when exposed to ultraviolet radiation. Early detection seems to be more crucial for reducing aberrant cell proliferation because the mortality rate is rapidly rising. Although multiple researches are available based on the skin cancer detection, there still exists challenges in improving the accuracy, reducing the computational time and so on. In this research, a novel skin cancer detection is performed using a modified falcon finch deep Convolutional neural network classifier (Modified Falcon finch deep CNN) that efficiently detects the disease with higher efficiency. The usage of modified falcon finch deep CNN classifier effectively analyzed the information relevant to the skin cancer and the errors are also minimized. The inclusion of the falcon finch optimization in the deep CNN classifier is necessary for efficient parameter tuning. This tuning enhanced the robustness and boosted the convergence of the classifier that detects the skin cancer in less stipulated time. The modified falcon finch deep CNN classifier achieved accuracy, sensitivity, and specificity values of 93.59%, 92.14%, and 95.22% regarding k-fold and 96.52%, 96.69%, and 96.54% regarding training percentage, proving more effective than literary works.

Exploring the ecological meanings of temperature sensitivity of ecosystem respiration from different methods

Temperature sensitivity (Q10) of ecosystem respiration (Re) is a critical parameter for predicting global terrestrial carbon dynamics and its response to climate warming. However, the determination of Q10 has been controversial. In this study, we scrutinized the underpinnings of three mainstream methods to reveal their relationships in estimating Q10 for Re in the Heihe River Basin, northwest China. Specifically, these methods are Q10 estimated from the long-term method (Q10_long), short-term method (Q10_short), and the low-frequency (Q10_lf) and high-frequency (Q10_hf) signals decomposed by the singular spectrum analysis (SSA) method. We found that: 1) Q10_lf and Q10_long are affected by the confounding effects caused by non-temperature factors, and are 1.8 ± 0.3 and 1.7 ± 0.3, respectively. 2) The high-frequency signals of the SSA method and short-term method have consistent roles in removing the confounding effects. Both Q10_short and Q10_hf reflect the actual response of respiration to temperature. 3) Overall, Q10_long has a larger variability (1.7 ± 0.3) across different biomes, whereas Q10_short and Q10_hf show convergence (1.4 ± 0.2 and 1.3 ± 0.1, respectively). These results highlight the fact that Q10 can be overestimated by the long-term method, whereas the short-term method and high-frequency signals decomposed by the SSA method can obtain closer and convergent values after removing the confounding effects driven by non-temperature factors. Therefore, it is recommended to use the Q10 value estimated by the short-term method or high-frequency signals decomposed by the SSA method to predict carbon dynamics and its response to global warming in Earth system models.

Investigations into the anatomical location, physiological function, clinical implications, and significance of the nucleus of Perlia

BACKGROUND

The article discusses the investigations into the nucleus of Perlia (NP), a spindle-shaped nucleus located in the dorsal aspect of the oculomotor complex. However, there is still debate over its exact location and function, with conflicting findings in nonhuman primates. Therefore, the current study aimed the describe the location, function, clinical and surgical implications of NP.

METHODS

A systematic review was conducted to identify studies related to the following MeSH terms: "perlia nucleus" OR "nucleus of "perlia" OR "convergence nucleus" OR "nucleus of convergence" OR "Perlia's nucleus". The search was conducted until September 2022.

RESULTS

The location of the NP has been consistently reported in various studies, with most describing it as situated ventral to the Edinger-Westphal nucleus (EW) and dorsomedial to the oculomotor complex. The incidence of the NP in humans has been reported to range from 9 to 40%. In primates, it was observed to be absent in 77% of midbrains, while well developed in 9%. It is also noted that the NP is not a single nucleus, but rather a group of nuclei that are interconnected and involved in the coordination of eye movements that contain parasympathetic neurons.

CONCLUSIONS

The study of the NP holds clinical implications for understanding the neural mechanisms underlying the irregularities in the pupillary light reflex, such as anisocoria or abnormal responses to light, diagnosis, and treatment of neurological disorders like Horner's syndrome, and management of eye movement disorders including one-and-a-half syndrome, vertical gaze palsy, skew deviation and ptosis. The current study also highlighted the limitations of previous studies, including variations in the reported prevalence of the NP, limitations of the histological techniques, and inconsistent findings across human and animal studies.

On random conformity bias in cultural transmission of polychotomous traits

Mathematical models of conformity and anti-conformity have commonly included a set of simplifying assumptions. For example, (1) there are m=2 cultural variants in the population, (2) naive individuals observe the cultural variants of n=3 adult "role models," and (3) individuals' levels of conformity or anti-conformity do not change over time. Three recent theoretical papers have shown that departures from each of these assumptions can produce new population dynamics. Here, we explore cases in which multiple, or all, of these assumptions are violated simultaneously: namely, in a population with m variants of a trait where conformity (or anti-conformity) occurs with respect to n role models, we study a model in which the conformity rates at each generation are random variables that are independent of the variant frequencies at that generation. For this model a class of symmetric constant equilibria exist, and it is possible that all of these equilibria are simultaneously stochastically locally stable. In such cases, the effect of initial conditions on subsequent evolutionary trajectories becomes very complicated.

BERTs of a feather: Studying inter- and intra-group communication via information theory and language models

When communicating, individuals alter their language to fulfill a myriad of social functions. In particular, linguistic convergence and divergence are fundamental in establishing and maintaining group identity. Quantitatively characterizing linguistic convergence is important when testing hypotheses surrounding language, including interpersonal and group communication. We provide a quantitative interpretation of linguistic convergence grounded in information theory. We then construct a computational model, built on top of a neural network model of language, that can be deployed to measure and test hypotheses about linguistic convergence in "big data." We demonstrate the utility of our convergence measurement in two case studies: (1) showing that our measurement is indeed sensitive to linguistic convergence across turns in dyadic conversation, and (2) showing that our convergence measurement is sensitive to social factors that mediate convergence in Internet-based communities (specifically, r/MensRights and r/MensLib). Our measurement also captures differences in which social factors influence web-based communities. We conclude by discussing methodological and theoretical implications of this semantic convergence analysis.

Abnormal occipital and frontal activity during voluntary convergence in intermittent exotropia: A task-fMRI study

Intermittent exotropia (IXT) is characterized by intermittently outward deviation of the eye and involved with vergence dysfunction. This study aimed to investigate the brain areas related to voluntary convergence and cortical activation changes between IXT patients and normal subjects. A total of 21 subjects, including 11 IXT patients and 10 age- and sex-matched normal subjects, were recruited for this study. A voluntary convergence task was employed, with changes in brain function measured by functional magnetic resonance imaging (fMRI). Correlations between cortical activation and clinical measurements were conducted by Pearson's correlation analysis. fMRI results showed that during voluntary convergence, the medial frontal gyrus (MFG) and bilateral occipital cortex were activated in the normal group, whereas only activation of the occipital cortex in IXT patients. Compared with the normal, IXT patients showed hypo-activation of both the MFG and cuneus during the task. The activation of MFG was negatively correlated to the duration of IXT. This study demonstrates that both MFG and occipital cortex may participate in voluntary convergence in normal subjects, while IXT patients have an aberrant cortical function of the MFG and cuneus, and the duration of IXT likely influences the severity of MFG. These findings may provide valuable insights for understanding the relationship between convergence and IXT.

Carbapenem-resistant hypervirulent ST23 Klebsiella pneumoniae with a highly transmissible dual-carbapenemase plasmid in Chile

BACKGROUND

The convergence of hypervirulence and carbapenem resistance in the bacterial pathogen Klebsiella pneumoniae represents a critical global health concern. Hypervirulent K. pneumoniae (hvKp) strains, frequently from sequence type 23 (ST23) and having a K1 capsule, have been associated with severe community-acquired invasive infections. Although hvKp were initially restricted to Southeast Asia and primarily antibiotic-sensitive, carbapenem-resistant hvKp infections are reported worldwide. Here, within the carbapenemase production Enterobacterales surveillance system headed by the Chilean Public Health Institute, we describe the isolation in Chile of a high-risk ST23 dual-carbapenemase-producing hvKp strain, which carbapenemase genes are encoded in a single conjugative plasmid.

RESULTS

Phenotypic and molecular tests of this strain revealed an extensive resistance to at least 15 antibiotic classes and the production of KPC-2 and VIM-1 carbapenemases. Unexpectedly, this isolate lacked hypermucoviscosity, challenging this commonly used hvKp identification criteria. Complete genome sequencing and analysis confirmed the K1 capsular type, the KpVP-1 virulence plasmid, and the GIE492 and ICEKp10 genomic islands carrying virulence factors strongly associated with hvKp. Although this isolate belonged to the globally disseminated hvKp clonal group CG23-I, it is unique, as it formed a clade apart from a previously reported Chilean ST23 hvKp isolate and acquired an IncN KPC-2 plasmid highly disseminated in South America (absent in other hvKp genomes), but now including a class-I integron carrying blaVIM-1 and other resistance genes. Notably, this isolate was able to conjugate the double carbapenemase plasmid to an E. coli recipient, conferring resistance to 1st -5th generation cephalosporins (including combinations with beta-lactamase inhibitors), penicillins, monobactams, and carbapenems.

CONCLUSIONS

We reported the isolation in Chile of high-risk carbapenem-resistant hvKp carrying a highly transmissible conjugative plasmid encoding KPC-2 and VIM-1 carbapenemases, conferring resistance to most beta-lactams. Furthermore, the lack of hypermucoviscosity argues against this trait as a reliable hvKp marker. These findings highlight the rapid evolution towards multi-drug resistance of hvKp in Chile and globally, as well as the importance of conjugative plasmids and other mobile genetic elements in this convergence. In this regard, genomic approaches provide valuable support to monitor and obtain essential information on these priority pathogens and mobile elements.

Switching and non-switching dead-beat sliding mode control with monotonic convergence

Performance requirements necessitate control designs that assure not only transient response specifications but also steady-state accuracy. Monotonic convergence of the tracking error is crucial for an efficient control design to prevent the performance degradation caused by overshooting. This needs a balanced consideration of both reaching conditions and the monotonic convergence, in the context of sliding mode control. In this paper, the dynamic behaviour of the dead-beat sliding mode control is characterized and the signum function is replaced by employing a non-switching one, in order to reduce chattering. The paper conducts a thorough analysis of monotonic convergence of both the switching and the non-switching error dynamics. By deriving the conditions for monotonic convergence, the control parameters can be strategically chosen to ensure monotonic convergence of the tracking error. Numerical and experimental results are presented to validate effectiveness of the proposed control scheme, which evaluate the tracking performance achieved by both the switching and the non-switching control methods.

Asia Core Dossier: Standardizing CMC Requirement to Facilitate Best Case Submissions in Asia

When the regulatory requirements are converged or harmonized, the country-specific variance of countries is often reduced or omitted, and this facilitates the possibility of preparing a core dossier that caters to multiple countries. When such options of a core dossier are acceptable to multiple countries, the resource required to prepare the dossier and the time taken to prepare it is also reduced, thus eliminating resource constraints in supporting dossier planning and preparation and indirectly facilitating earlier submission in countries. In this paper, the authors have illustrated a process applied to standardize the dossier requirements amongst selected countries in Asia, producing an output of a core dossier that applies to four submission types amongst these countries. The core dossier adopts the International Council for Harmonization-Common Technical Dossier format as a reference. Main focus is the standardization of format and requirements within the Module 3 or Chemistry Manufacturing Controls sections of the dossier, which from the authors' organizational experience usually notes a higher variances and country-specific elements. Development of the dossier standardization process is due to an internal hurdle within the authors' organization, where global resource constraints and prioritizations of dossier preparation and compliance review process needed to be improved to facilitate earlier or near-simultaneously submissions in the majority of the Asia countries. The paper demonstrates an assessment of the dossier components and standardization to assemble a fit-for-purpose core dossier termed 'Asia Core Dossier' (ACD). ACD has been successfully implemented within the authors' organization to reduce country-specific requirements and facilitate earlier (fit for strategy) submissions in the selected Asia countries. The paper also discusses the tangible benefits of the authors' experiences from utilizing the ACD. Regulatory professionals in different organizations could reference the ACD as a template for preparing a simplified and efficient dossier and as a relevant component of Good Submission Practice (GSubP).

A multi-detector comparison to determine convergence of measured relative output factors for small field dosimetry

The TRS-483 Code of Practice (CoP) provides generic relative output correction factors, [Formula: see text], for a range of detectors and beam energies as used in small field dosimetry. In this work, the convergence of the relative output factors (ROFs) for 6 MV X-ray beams with and without flattening filters was investigated under different combinations of beam collimation and published detector correction factors. The SFD, PFD and CC04 (IBA) were used to measure ROFs of a TrueBeam STx linear accelerator with small fields collimated by the high-definition MLC, which has 2.5 and 5.0 mm projected leaves. Two configurations were used for the collimators: (1) fixed jaws at 10 × 10 cm2 and (2) with a 2 mm offset from the MLC edge, in line with the recommended geometry from IROC-H as part of their auditing program and published dataset. The [Formula: see text] factors for the three detectors were taken from the TRS483 CoP and other published works. The average differences of ROFs measured by detectors under MLC fields with fixed jaws and with 2 mm jaws offset for the 6 MV-WFF beam are 1.4% and 1.9%, respectively. Similarly, they are 2.3% and 2.4% for the 6MV-FFF beam. The relative differences between the detector-average ROFs and the corresponding IROC-H dataset are 2.0% and 3.1% for the 6 MV-WFF beam, while they are 2.4% and 3.2% for the 6MV-FFF beam at the smallest available field size of 2 × 2 cm2. For smaller field sizes, the average ROFs of the three detectors and corresponding results from Akino and Dufreneix showed the largest difference to be 6.6% and 6.2% under the 6 MV-WFF beam, while they are 3.4% and 3.6% under the 6 MV-WFF beam at the smallest field size of 0.5 × 0.5 cm2. Some well-published specific output correction factors for different small field detector types give better convergence in the calculation of the relative output factor in comparison with the generic data provided by the TRS-483 CoP. Relative output factor measurements should be performed as close as possible to the clinical settings including a combination of collimation systems, beam types and using at least three different types of small field detector for more accurate computation of the treatment planning system. The IROC-H dataset is not available for field size smaller than 2 × 2 cm2 for double checks and so that user should carefully check with other publications with the same setting.

Brief Report: Convergence and Discrepancy Between Self- and Informant-Reported Depressive Symptoms in Young Autistic Adults

PURPOSE

Autistic individuals exhibit elevated rates of depression; however, assessment is complicated by clinical presentations and limited validation in this population. Recent work has demonstrated the utility of the Beck Depression Inventory (BDI-II) in screening for depression in ASD. The current study extends this work by examining the convergence and divergence of self- and informant-reported depression in autistic (n = 258) and non-autistic (n = 255) young adults.

METHODS

Participants completed the BDI-II as a self-report measure of depression; informants completed the Achenbach Adult Behavior Checklist. Analyses probed for between-group differences in rates of depression symptoms, convergence between self- and informant-reported depression, and discrepancy between self- and informant-reported depression.

RESULTS

Results indicated significantly higher rates of depressive symptoms in the autistic group. Convergence was significant in both groups, with significantly greater agreement in the autistic group. There was differential divergence, with the autistic group reporting significantly lower scores relative to informants, and the non-autistic group reporting significantly higher scores relative to informants.

CONCLUSIONS

Consistent with prior reports, results suggest that depression rates are elevated in autism. Additionally, while the BDI-II may be adequate for screening depressive symptoms in speaking autistic young adults, eliciting information from a close adult informant provides valuable diagnostic information, due to clinically critical concerns about underreporting in this population. Although controlled in analyses, between-group differences in gender, age, race, and informant identity, and a predominantly White and non-Latinx sample, limit the generalizability of these results.

Dynamic distribution, regional differences and convergence of health workforce allocation in township health centers in China

This study evaluated the dynamic distribution, regional differences, and convergence of health workforce allocation in Township Health Centers in China during 2011-2020 using data obtained from the China Health Statistics Yearbook (2012-2021). The Gini coefficient and kernel density estimation were chosen to examine the dynamic distribution and regional differences in health workforce allocation in Township Health Centers in China and their components. σ convergence and β convergence were used to investigate the change trend of health workforce allocation in Township Health Centers. The results show that between 2011 and 2020, the number of licensed doctors and registered nurses per thousand population in Township Health Centers both increased largely and regional disparities still exist. In 2020, the largest differences in the density of licensed doctors and registered nurses were found in the eastern and central regions, respectively, and the intensity of trans-variation contributed the most to the overall disparities. The allocation of licensed doctors and registered nurses both exhibited σ convergence, absolute and conditional β convergence, indicating that the regional differences in health workforce in THCs among provinces will decrease. The growth of healthcare workforce was positively impacted by the urbanization rate, growth rate of government health expenditures and growth domestic product per capita, but negatively impacted by population density in rural areas and fiscal self-sufficiency.

Relationship between floating marine debris accumulation and coastal fronts in the Northeast coast of the USA

Floating marine debris (FMD) is one of the world's most concerning issues due to its potential impact on biodiversity, communities, and ecosystem services. FMD transport and concentrations are driven by fronts, generated by oceanographic processes, and the accumulation of FMD has been reported in gyres, eddies, tidal fronts, salinity fronts, and coastal fronts. This study explores the relationship between fronts and FMD accumulation in the Gulf of Maine (GoM) and the surrounding coastal areas (USA). Frontal edge detection algorithms were applied to sea surface temperature (SST) imagery from the Moderate-resolution Imaging Spectroradiometer (MODIS) between 2002 and 2012. Frontal location is spatially correlated with FMD concentrations collected by the Sea Education Association. Higher concentrations of FMD are associated with frontal frequencies (FF) of 5-10 %. FMD is trapped between fronts and the coastline in accumulation zones. These results highlight the need to consider coastal FMD hotspots, given these are areas of high biodiversity value.

The Effect of Cycloplegia on Ocular Alignment and AC/A Ratio

Purpose

The present study sets out to investigate the effect of cyclopentolate-induced cycloplegia on distance and near deviation and the accommodative convergence/accommodation (AC/A) ratio.

Methods

This prospective study was performed on 30 subjects. The inclusion criteria included a lack of any active ocular pathology and systemic diseases, no history of ocular surgery, and nonuse of various medications. Refraction, near and distance deviation were measured for all subjects, and the same examinations were repeated after the administration of two drops of cyclopentolate 1% to both eyes.

Results

The obtained data from 30 subjects, including 19 males, with a mean age of 22.53 ± 1.74 years were analyzed. The mean ± SD of near deviation in dry and cycloplegic conditions were -6.9 ± 8.1 and +6.4 ± 9.1 prism diopters, respectively, which were statistically significant (P < 0.001). Distance deviation in cycloplegic conditions demonstrated an average difference of 0.8 prism diopters, compared to dry conditions (P < 0.001). AC/A ratios were 4.7 ± 2.5 and 9.7 ± 3.9 (Δ/D) in non-cycloplegic and cycloplegic conditions, respectively, which was a statistically significant difference (P < 0.001). The multiple regression indicated that among all under study variables, refraction (B coefficient: -2.4; P < 0.001) and near pre-cycloplegic deviation (B coefficient: 0.56; P < 0.001) were significantly associated with post-cycloplegic near deviation.

Conclusion

The results of this study indicated that cycloplegia causes a considerable esophoric shift in near deviation and a negligible esophoric shift in distance deviation. As a result, the AC/A ratio demonstrated a significant increase due to unequal changes in near and distance deviation.

The green productivity of broiler production in China: Considering the resource utilization of manure

Resource constraints and environmental challenges have emerged as serious impediments to the sustainable development of China's broiler industry, with potentially adverse consequences. The pursuit of sustainable development in China's broiler industry is predicated on significant reductions in manure and pollutant emissions from broiler farming. This study utilizes the slacks-based model and the global Malmquist-Luenberger index to calculate the green total factor productivity of broiler breeding across various provinces and scales from 2005 to 2020 within a joint production framework of considering undesirable outputs and desirable outputs. Fluctuations in economic distribution of broiler breeding are characterized using the kernel density estimation, and a convergence analysis is performed via absolute and conditional β convergence methods. The results revealed an overall upward trend in China's broiler farming green total factor productivity from 2005 to 2020, corresponding to green total factor productivity in small-, medium-, and large-scale broiler breeding were 1.015, 1.017, and 1.009, respectively. The kernel density curve implies a narrowing trend in the discrepancy of green total factor productivity levels among provinces in broiler breeding of varying scales. For all scales, broiler breeding's green total factor productivity demonstrates considerable conditional and absolute β convergence. Therefore, improving the efficiency of broiler breeding while addressing externalities requires the cultivation of broilers at different scales across diverse regions, coupled with an increased focus on improving the utility efficiency of broiler waste fertilization.

A survey of methods for handling initial state shifts in iterative learning control

This paper introduces three types of controllers: a PID-type iterative learning controller, an adaptive iterative learning controller, and an optimal iterative learning controller, and reviews the history and research status of initial shifts rectifying algorithms. Initial state shifts have attracted research attention because they affect both the tracking performance and system stability. This study focuses on the current common initial shifts rectifying methods and analyzes the underlying mechanism in detail. To verify the effectiveness of the presented initial shifts rectifying algorithms, we simulated those using ideal first- and second-order systems. Finally, directions for the future development of iterative learning control (ILC) and some challenging topics related to initial shifts rectifying for ILC are presented. This article aims to introduce recent developments and advances in initial shifts rectifying algorithms and discuss the directions for their further exploration.

Does reflective equilibrium help us converge?

I address the worry that reflective equilibrium is too weak as an account of justification because it fails to let differing views converge. I take up informal aspects of convergence and operationalise them in a formal model of reflective equilibrium. This allows for exploration by the means of computer simulation. Findings show that the formal model does not yield unique outputs, but still boosts agreement. I conclude from this that reflective equilibrium is best seen as a pluralist account of justification that cannot be accused of resulting in an "anything goes" relativism.