Green total-factor energy efficiency and its convergence of industrial sub-sectors in China

Existing literature ignores to consider multiple types of pollutants when analyzing energy efficiency and its convergence. Under this background, using SuperSBM-GML model, the improved entropy method, and convergence model, this paper calculates the green total-factor energy efficiency and its evolution trend of 35 China's industrial sub-sectors considering multiple pollutants, and analyzes its convergence. The results indicate that the average score of industrial green total-factor energy efficiency is low, and there are significant differences among sub-sectors: "Utilization of waste resources" is the highest, while "Manufacture of paper and paper products" is the lowest. The green total-factor energy efficiency shows an overall upward trend from 2006 to 2021, the main driving force comes from technological progress, but numerous sub-sectors have not sufficiently caught up with existing cutting-edge technologies. Specifically, the growth rate of green total-factor energy efficiency in high-energy-consumption sub-sectors is higher than low-to-medium-energy-consumption sub-sectors. There are both σ-convergence and β-convergence in low-to-medium-energy-consumption group, indicating that development of sub-sectors is stable and outstanding. Nevertheless, the high-energy-consumption group only exhibits conditional β-convergence, revealing an imbalance in energy efficiency development. Consequently, formulating the benchmark level of energy efficiency and developing energy efficiency "leader" system are suggested for low-to-medium and high-energy-consumption sub-sectors, respectively.

Earth Systems to Anthropocene Systems: An Evolutionary, System-of-Systems, Convergence Paradigm for Interdependent Societal Challenges

Humans have made profound changes to the Earth. The resulting societal challenges of the Anthropocene (e.g., climate change and impacts, renewable energy, adaptive infrastructure, disasters, pandemics, food insecurity, and biodiversity loss) are complex and systemic, with causes, interactions, and consequences that cascade across a globally connected system of systems. In this Critical Review, we turn to our "origin story" for insight, briefly tracing the formation of the Universe and the Earth, the emergence of life, the evolution of multicellular organisms, mammals, primates, and humans, as well as the more recent societal transitions involving agriculture, urbanization, industrialization, and computerization. Focusing on the evolution of the Earth, genetic evolution, the evolution of the brain, and cultural evolution, which includes technological evolution, we identify a nested evolutionary sequence of geophysical, biophysical, sociocultural, and sociotechnical systems, emphasizing the causal mechanisms that first formed, and then transformed, Earth systems into Anthropocene systems. Describing how the Anthropocene systems coevolved, and briefly illustrating how the ensuing societal challenges became tightly integrated across multiple spatial, temporal, and organizational scales, we conclude by proposing an evolutionary, system-of-systems, convergence paradigm for the entire family of interdependent societal challenges of the Anthropocene.

Pattern formation features might explain homoplasy: fertile surfaces in higher fungi as an example

Fungi show a high degree of morphological convergence. Regarded for a long time as an obstacle for phylogenetic studies, homoplasy has also been proposed as a source of information about underlying morphogenetic patterning mechanisms. The "local-activation and long-range inhibition principle" (LALIP), underlying the famous reaction-diffusion model proposed by Alan Turing in 1952, appears to be one of the universal phenomena that can explain the ontogenetic origin of seriate patterns in living organisms. Reproductive structures of fungi in the class Agaricomycetes show a highly periodic structure resulting in, for example, poroid, odontoid, lamellate or labyrinthic hymenophores. In this paper, we claim that self-organized patterns might underlie the basic ontogenetic processes of these structures. Simulations based on LALIP-driven models and covering a wide range of parameters show an absolute mutual correspondence with the morphospace explored by extant agaricomycetes. This could not only explain geometric particularities but could also account for the limited possibilities displayed by hymenial configurations, thus making homoplasy a direct consequence of the limited morphospace resulting from the proposed patterning dynamics.

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.

Educational boundaries explain strength and variation in global fertility convergence

This paper shows that the level and timing of fertility are converging strongly over different measures of educational attainment using 65 years of data from 146 countries. Global convergence patterns are primarily driven by high-income societies, while sub-Saharan Africa is the world region that is converging most slowly, if not converging at all. Most importantly, levels of education matter heavily for explaining strength and variation in global fertility convergence, with two intersecting educational gradients suggesting: (i) stronger convergence over tertiary education followed, in turn, by secondary and primary; (ii) stronger convergence over education completed relative to education attended. Our findings provide important insights for addressing key challenges in global development and demography, and for informing policymakers as they evaluate the suitability of specific educational policies aimed at further narrowing inequalities between societies-such as supporting higher education as well as the successful completion of targeted educational cycles.

Symptomic Mimicry Between SARS-CoV-2 and the Common Cold Complex

The recent changes in COVID-19 symptoms suggest convergent evolution of respiratory diseases. This process is analogous to the emergence of animal mimetic complexes and complements previously identified types of mimicry. A novel pathogen might go unnoticed or insufficiently counteracted if it resembles a disease that the host already faced on multiple occasions, which creates a selective pressure towards a typical symptomic phonotype. In short, the reason why so many unrelated pathogens cause similar symptoms may correspond to the reasons that drove the evolution of the 'warning' wasp-like colouration in various insect species.

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.

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.

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.

Application of the 2-archive multi-objective cuckoo search algorithm for structure optimization

The study suggests a better multi-objective optimization method called 2-Archive Multi-Objective Cuckoo Search (MOCS2arc). It is then used to improve eight classical truss structures and six ZDT test functions. The optimization aims to minimize both mass and compliance simultaneously. MOCS2arc is an advanced version of the traditional Multi-Objective Cuckoo Search (MOCS) algorithm, enhanced through a dual archive strategy that significantly improves solution diversity and optimization performance. To evaluate the effectiveness of MOCS2arc, we conducted extensive comparisons with several established multi-objective optimization algorithms: MOSCA, MODA, MOWHO, MOMFO, MOMPA, NSGA-II, DEMO, and MOCS. Such a comparison has been made with various performance metrics to compare and benchmark the efficacy of the proposed algorithm. These metrics comprehensively assess the algorithms' abilities to generate diverse and optimal solutions. The statistical results demonstrate the superior performance of MOCS2arc, evidenced by enhanced diversity and optimal solutions. Additionally, Friedman's test & Wilcoxon's test corroborate the finding that MOCS2arc consistently delivers superior optimization results compared to others. The results show that MOCS2arc is a highly effective improved algorithm for multi-objective truss structure optimization, offering significant and promising improvements over existing methods.

Midkine-a interacts with Ptprz1b to regulate neural plate convergence and midline formation in the developing zebrafish hindbrain

A midline in the developing central nervous system allows symmetric distribution of neural progenitors that later establish functional, bilaterally symmetric neural circuits. In the zebrafish hindbrain, a midline forms early during neurulation as a result of coordinated cell convergence and midline-crossing cell divisions (C-divisions). These processes are controlled by the Wnt/planar cell polarity (PCP) pathway that positions progenitors close to a presumptive midline to perform C-divisions. Other upstream cues that control the extent of neural plate convergence, however, remain unclear. Midkine (Mdk) and pleiotrophin (Ptn) are structurally related heparin-binding growth factors that are dynamically expressed in the developing hindbrain. We show that two zebrafish Mdks, Mdka and Mdkb, as well as Ptn interact with distinct affinities in vivo with the protein tyrosine phosphatase receptor Ptprz1b. Zebrafish mdka and ptprz1b mutants exhibit impaired neural plate convergence along with misplaced C-divisions, defective cell polarity and transiently duplicated midlines. These defects are absent in mdka; mdkb double mutants suggesting antagonistic roles of Mdka and Mdkb to coordinate convergence and C-divisions. Overexpression of Drosophila Prickle, a key component of the Wnt/PCP pathway, rescued the midline duplications in mdka and ptprz1b mutants that exhibited significantly reduced levels of prickle pk1b, pk2a, and pk2b expression. Ptprz1b overexpression, on the other hand, up-regulated pk2a transcription. Our findings therefore suggest roles for Mdka, Mdkb and Ptprz1b in coordinating neural plate convergence, neural progenitor positioning and midline formation by controlling the levels of prickle expression.

Convergence Weakness with Intact Medial Rectus Function in Proptosis due to Orbital Tumors

Background

The pathway underlying ocular convergence is less clear. Convergence weakness is usually a clinical sign of dorsal midbrain pathology. An orbital tumor causing such phenomenon is unknown. We describe its occurrence in patients with proptosis due to orbital tumors.

Materials and Methods

Series of six patients with axial proptosis secondary to orbital tumors that demonstrated varying forms of convergence weakness (deficiency to complete failure).

Results

Three had weakness of convergence with drifting away of proptosed eyeball on attempted near vision, while the other three had failure. One of these had convergence weakness in nonproptosed eye. MRI excluded a central etiology in all.

Conclusions

The article highlights an interesting phenomenon of deficient convergence in the presence of intact medial rectus function in patients with proptosis. The symmetry of orbital contents possibly plays a significant role in the process of convergence.

Molecular phylogeny of Japanese marine Tanytarsini chironomids (Chironomidae: Chironominae)

Tanytarsini is a large tribe of Chironomidae with at least 11 recorded marine species grouped in three genera. In this study, we performed a phylogenic analysis using molecular data from 13 Tanytarsini genera, including all Japanese marine species in a large tribe of Chironominae, to estimate their phylogenetic positions and evolutionary history. The phylogenetic reconstruction grouped the marine species in two clades. One clade was composed of five marine Tanytarsus and two marine Pontomyia species within a larger clade of Tanytarsus. Pontomyia is considered to have morphologically specialized and adapted to marine environments by rapid evolution, although it formed a clade with Tanytarsus. The only one species of Tanytarsus, T. pelagicus, clustered as a member of the second clade, which was mainly composed by species of the genus Paratanytarsus. Thus, we redescribe T. pelagicus as Paratanytarsus pelagicus.

Dying together: A convergence analysis of fatalities during COVID-19

Governments implemented countermeasures to mitigate the spread of the COVID-19 virus. This had a severe effect on the economy. We examine convergence patterns in the evolution of COVID-19 deaths across countries. We aim to investigate whether countries that implemented different measures managed to limit the number of COVID-19 deaths. We extend the most recent macro-growth convergence methodology to examine convergence of COVID-19 deaths. We combine a long memory stationarity framework with the maximal clique algorithm. This provides a rich and flexible club formation strategy that goes beyond the stationary/non stationary approach adopted in the previous literature. Our results suggest that strict measures (even belated) or an aggressive vaccination scheme can confine the spread of the disease while maintaining the strictness of the measures steady can lead to a burst of the virus. Finally, we observe that fiscal measures did not have an effect on the containment of the virus.

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.

Energy poverty and the convergence hypothesis across EU member states

Energy poverty is an emerging issue towards global affairs. Currently, the development of energy-related policies is becoming essential, with regard to new societies, social inclusion and social rights. In this paper, we examine the dynamic patterns of energy poverty among 27 EU member states between 2005 and 2020. We use the log-t regression test to investigate the convergence hypothesis, and the P&S data-driven algorithm to detect potential convergence clubs. The empirical results of energy poverty indicators are mixed, and the convergence hypothesis of the states is rejected. Instead, convergence clubs are exhibited, implying that groups of countries converge to different steady states in the long run. In view of the convergence clubs, we suggest that the affordability of heating services is potentially explained by structural conditions of housing, climate conditions and energy costs. Besides, the adverse financial and social conditions for the European households have significantly triggered the arrears on utility bills. Moreover, a significant proportion of households do not have basic sanitation services.

The convergence of 2,6-dichloro-1,4-benzoquinone in the whole process of lignin phenol precursor chlorination

The formation and decomposition of 2,6-dichloro-1,4-benzoquinone, an emerging disinfection byproduct (DBP), was studied in the chlorination of lignin phenol precursors. The results show that DCBQ and the related hydroxyl DCBQ (DCBQ-OH) acts as the intermediate products of the chlorination process of the three typical lignin phenol precursors (p-hydroxybenzoic acid, protocatechuic acid, and gallic acid). The contributions of lignin phenol precursors to the overall formation of the targeted DBPs were determined based on the observed abundances of individual lignin phenols and their DBP yields. DCBQ and DCBQ-OH were generated within 2-6 h, the relative abundance of the yields of mol carbon atoms in DCBQ corresponding to the mol carbon atoms in the three model precursors (DCBQ-C) was about 0.01%-14.37% under different pH conditions. With the chlorination reaction time increased (after two or four h), the concentrations of DCBQ and DCBQ-OH entirely decreased, and the decomposition of DCBQ do not follow a pseudo-first-order kinetics during chlorination. Conversely, the decomposition of DCBQ generated from p-hydroxybenzoic acid followed a pseudo-second-order kinetics. Moreover, the formation of trichloromethane (TCM), dichloroacetic acid (DCAA), and trichloroacetic acid (TCAA) was also detected during the chlorination. The contribution of the decomposed DCBQ was mainly to TCAA and the unknown DBPs within 2-12 h, and DCBQ decomposition pathway was affected by pH. Moreover, except for DCBQ/DCBQ-OH and TCM/HAAs, there were still 73.6%-92.41% unknown products (including non-halogenated aromatic DBPs and chlorine-substituted DBPs) needing to identify during the chlorination process for lignin phenols. Overall, revealing the formation and decomposition of DCBQ during the chlorination of lignin phenol precursors would contribute to the effective development of drinking water treatment processes for the removal of highly toxic intermediates generated during disinfection.

Is there any convergence in the CO2 emission efficiency of airlines?

This paper examines the CO₂ emission efficiency of airlines in the years 2011 and 2018 by using the Atmosfair Airline Index. This index gives reliable results since it encompasses data from more than 100 airlines and considers important variables in the calculation of CO₂ emissions. Firstly, we investigate the regional differences and the effect of the share of government ownership in the CO₂ emission efficiency of airlines. These factors have not been taken into account in other studies by using such a comprehensive index. Secondly, by utilizing the Barro and Sala-i Martin model that is commonly used to examine the regional income convergence model in economics, we also check whether there is a convergence in the CO₂ emission efficiency of airlines or not. As a result, in terms of efficiency growth, we find that airlines in Europe are more successful compared to airlines from other regions. Furthermore, increases in the share of government ownership in airlines negatively affect the CO₂ emission efficiency in Asia, whereas it is insignificant in Europe and America. Moreover, there is no convergence in the CO₂ emission efficiency of airlines from all regions. This shows that low-efficient airlines are not catching up with high-efficient airlines. Lastly, we find that charter airlines are more efficient in terms of CO₂ emissions.

A direct discretization recurrent neurodynamics method for time-variant nonlinear optimization with redundant robot manipulators

Discrete time-variant nonlinear optimization (DTVNO) problems are commonly encountered in various scientific researches and engineering application fields. Nowadays, many discrete-time recurrent neurodynamics (DTRN) methods have been proposed for solving the DTVNO problems. However, these traditional DTRN methods currently employ an indirect technical route in which the discrete-time derivation process requires to interconvert with continuous-time derivation process. In order to break through this traditional research method, we develop a novel DTRN method based on the inspiring direct discrete technique for solving the DTVNO problem more concisely and efficiently. To be specific, firstly, considering that the DTVNO problem emerging in the discrete-time tracing control of robot manipulator, we further abstract and summarize the mathematical definition of DTVNO problem, and then we define the corresponding error function. Secondly, based on the second-order Taylor expansion, we can directly obtain the DTRN method for solving the DTVNO problem, which no longer requires the derivation process in the continuous-time environment. Whereafter, such a DTRN method is theoretically analyzed and its convergence is demonstrated. Furthermore, numerical experiments confirm the effectiveness and superiority of the DTRN method. In addition, the application experiments of the robot manipulators are presented to further demonstrate the superior performance of the DTRN method.

A fractional gradient descent algorithm robust to the initial weights of multilayer perceptron

For multilayer perceptron (MLP), the initial weights will significantly influence its performance. Based on the enhanced fractional derivative extend from convex optimization, this paper proposes a fractional gradient descent (RFGD) algorithm robust to the initial weights of MLP. We analyze the effectiveness of the RFGD algorithm. The convergence of the RFGD algorithm is also analyzed. The computational complexity of the RFGD algorithm is generally larger than that of the gradient descent (GD) algorithm but smaller than that of the Adam, Padam, AdaBelief, and AdaDiff algorithms. Numerical experiments show that the RFGD algorithm has strong robustness to the order of fractional calculus which is the only added parameter compared to the GD algorithm. More importantly, compared to the GD, Adam, Padam, AdaBelief, and AdaDiff algorithms, the experimental results show that the RFGD algorithm has the best robust performance for the initial weights of MLP. Meanwhile, the correctness of the theoretical analysis is verified.

Development of Explicit Schemes for Diffusive SEAIR COVID-19 Epidemic Spreading Model: An Application to Computational Biology

In this contribution, a first-order time scheme is proposed for finding solutions to partial differential equations (PDEs). A mathematical model of the COVID-19 epidemic is modified where the recovery rate of exposed individuals is also considered. The linear stability of the equilibrium states for the modified COVID-19 model is given by finding its Jacobian and applying Routh-Hurwitz criteria on characteristic polynomial. The proposed scheme provides the first-order accuracy in time and second-order accuracy in space. The stability of the proposed scheme is given using the von Neumann stability criterion for standard parabolic PDEs. The consistency for the proposed scheme is also given by expanding the involved terms in it using the Taylor series. The scheme can be used to obtain the condition of getting a positive solution. The stability region of the scheme can be enlarged by choosing suitable values of the contained parameter. Finally, a comparison of the proposed scheme is made with the existing non-standard finite difference method. The results indicate that the non-standard classical technique is incapable of preserving the unique characteristics of the model's epidemiologically significant solutions, whereas the proposed approaches are capable of doing so. A computational code for the proposed discrete model scheme may be made available to readers upon request for convenience.

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.

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.

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.

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.