Limit Point Research Articles

Application of eDNA metabarcoding in the assessment of fish biodiversity in Philippine mangroves: Challenges and opportunities

Biodiversity assessments are important in designing mangrove conservation and restoration programs. In the Philippines, conventional biodiversity assessment methods (e.g., trap nets, fish visual census) can be time-consuming, labor-intensive, and expensive. In recent years, environmental DNA (eDNA) metabarcoding has been an emerging tool for rapid biodiversity monitoring as it is fast, non-intrusive, and can provide broader detection of fauna. But, it is still subject to various field sampling and laboratory analysis constraints. Here, we applied the eDNA metabarcoding method to document and assess fish biodiversity in mangroves from two biogeographic regions in the Philippines: Oriental Mindoro in the West Philippine Sea and Sorsogon in the Northern Philippine Sea. Using 12S genetic markers from eDNA water samples, we detected 89 fish species from 44 families. Only twelve species were commonly detected in both sites. Several species were found in the Philippines’ list of economically important aquatic organisms while one species (Epinephelus fuscoguttatus) was classified as Vulnerable in IUCN’s Red List of Threatened Species. Seventy-six percent more species were detected in Sorsogon probably because the sampling sites were within a seascape of mangroves, seagrasses, and coral reefs. In contrast, the lesser species detected in Oriental Mindoro could probably be because of the more limited sampling points to coastal fringes. Our results serve as baseline data and the first obtained using this method in the country. However, we observed some limitations that should be addressed to improve the method: (1) lack or absence of a comprehensive reference database specific to Philippine aquatic organisms; (2) low eDNA reads which could be attributed to insufficient on-site filtration due to turbid seawaters common in Philippine mangroves; and (3) possible cross-contaminations that can affect comparative analyses. Despite these limitations, we were able to demonstrate the usefulness of this technique in doing rapid assessments which could address knowledge gaps in Philippine mangrove biodiversity studies and contribute to its conservation programs.

Five Mysteries

In this essay, I outline 5 aspects that indicate a great mystery that pervades the universe. These mysteries are as such because no further knowledge can be gained which would explain them once and for all, in the way that accruing knowledge might dispel a false notion. They are insoluble and sit at the base of what we can know, a limit point to what can be grasped, but awareness of them, is at least the solid vessel within which knowledge can be poured metaphorically speaking, though in themselves not solid, as one can only but be aware of them and not grasp them as one would a cup. Nevertheless, recognition of our limits is the first step to gaining knowledge with the feelings of awe and wonder. This leads to humility, which is necessary to stave off the arrogance of human achievement and know-how.

Room temperature acetone gas sensing performance of ZnS-NiO nanocomposite sensor enhanced by UV activation for diabetes detection

Metal oxide semiconductors are popular in gas sensor applications.Current metal oxide-based acetone sensors struggle to deliver ppb-level acetone sensing at low temperatures for biological use. This study synthesizes 4mol% ZnS combined with NiO for acetone sensing. Nanoparticles were studied using FE-SEM, XPS, XRD, and UV-Vis spectroscopy. Mixed ZnS and NiO resulted in rapid gas channel movement and n-p heterojunction development, which increased acetone sensitivity. ZnS-NiO showed a gas sensing response of 163% for 20 ppm acetone at room temperature under UV irradiation, which is 2.3 times higher than pristine NiO (70% at 180 ∘C). ZnS improves sensor selectivity. Heterojunction sensor reaction and recovery times were 20 and 14 seconds, respectively. The composite sensor detection limit was 0.1 ppm. The increased sensitivity validates the n-p heterojunction. The sensor’s higher response values and lower detection limit point to breath analyzer applications such as diabetes detection.

A framework of data assimilation for wind flow fields by physics-informed neural networks

Various types of measurement techniques, such as Light Detection and Ranging (LiDAR) devices, anemometers, and wind vanes, are extensively utilized in wind energy to characterize the inflow. However, these methods typically gather data at limited points within local wind fields, capturing only a fraction of the wind field’s characteristics at wind turbine sites, thus hindering detailed wind field analysis. This study introduces a framework using Physics-informed Neural Networks (PINNs) to assimilate diverse sensor data types. This includes line-of-sight (LoS) wind speed, velocity magnitude and direction, velocity components, and pressure. Moreover, the parameterized Navier–Stokes (N–S) equations are integrated as physical constraints, ensuring that the neural networks accurately represent atmospheric flow dynamics. The framework accounts for the turbulent nature of atmospheric boundary layer flow by including artificial eddy viscosity in the network outputs, enhancing the model’s ability to learn and accurately depict large-scale flow structures. The reconstructed flow field and the effective wind speed are in good agreement with the actual data. Furthermore, a transfer learning strategy is employed for the online deployment of pre-trained PINN, which requires less time than that of the actual physical flow. This capability allows the framework to reconstruct wind flow fields in real time based on live data. In the demo cases, the maximum error between the effective wind speed reconstructed online and the actual value at the wind turbine site is only 3.7%. The proposed data assimilation framework provides a universal tool for reconstructing spatiotemporal wind flow fields using various measurement data. Additionally, it presents a viable approach for the online assimilation of real-time measurements. To facilitate the utilization of wind energy, our framework’s source code is openly accessible.

Hahn series and Mahler equations: Algorithmic aspects

AbstractMany articles have recently been devoted to Mahler equations, partly because of their links with other branches of mathematics such as automata theory. Hahn series (a generalization of the Puiseux series allowing arbitrary exponents of the indeterminate as long as the set that supports them is well ordered) play a central role in the theory of Mahler equations. In this paper, we address the following fundamental question: is there an algorithm to calculate the Hahn series solutions of a given linear Mahler equation? What makes this question interesting is the fact that the Hahn series appearing in this context can have complicated supports with infinitely many accumulation points. Our (positive) answer to the above question involves among other things the construction of a computable well‐ordered receptacle for the supports of the potential Hahn series solutions.

A uniqueness theorem for meromorphic functions

In this paper, we prove the uniqueness theorem for a special class of meromorphic functions on the complex plane $\mathbb{C}$. In particular, we study the class of meromorphic functions $f$ in the domain $\mathbb{C}\setminus K'$, where $K'$ is the finite set of limit points of simple poles of the function $f$. In this class, we describe non-trivial subclasses in which every function $f$ can be uniquely determined by the residues of the function $f$ at its poles. The result covered in this paper is a part of a problem in a spectral operator theory.

RECURRENCE SETS FOR PARTIAL INVERSE

Two types of recurrence sets are introduced for inverse semigroup partial actions in topological spaces. Some applications to the basic dynamical properties of the action are indicated, covering topics as topological transitivity, limit points and periodic points. We then explore the connections of these recurrence sets with similar notions for related types of imperfect symmetries (prefix inverse semigroup expansions, partial group and groupoid actions).

Isogeometric method for buckling prediction and post-buckling analysis of variable stiffness composite underwater pressure shell

Composite cylindrical pressure hulls are thin-walled structures widely used for autonomous underwater vehicles. Buckling failure is one of the most important failure modes for these shells under external pressure. In existing buckling studies of cylindrical pressure hulls, FEM is the most popular analysis method but not efficient enough when dealing with structures with complex material distributions such as the variable stiffness composite shells. Motivated by this, an isogeometric method for buckling prediction and post-buckling analysis of variable stiffness composite underwater pressure shell is proposed in this article. In this method, based on Reissner–Mindlin shell formulas undergoing large deformation, a buckling analysis framework in IGA forms is established. Then a modified arc-length method is proposed based on kinematics used in the shell formulas, and is used to overcome the inaccuracy caused by the 2 degrees of freedom node rotation in the prediction step of arc-length method. In addition, the influence of bifurcation is considered, which may seriously affect the precision of buckling behavior simulation when the limit point is close to a bifurcation point. The computational accuracy of this framework has been validated in a series of constant stiffness composite shell cases involving buckling load prediction and post-buckling behavior simulation. For variable stiffness composite hull cases, this framework achieves the same precision as FEM with fewer elements. Therefore, the proposed framework provides an efficient way for the buckling design of underwater variable stiffness composite pressure hulls.

Marine Debris Collection Model with Intermediate Transition Station (ITS) Systems Planning in Jakarta

Marine debris in Jakarta is a complex problem because the amount is too large every day. Addressing this critical gap, this study proposes a model for efficient waste transportation through the development of an Intermediate Transition Station (ITS) system. This system not only facilitates the effective movement of waste but also incorporates a recovery process to reduce the volume of debris before it reaches the landfill. The ITS serves as a pivotal node in the waste management network, ensuring the continuity of waste flow from accumulation points to processing facilities. With the ITS, the total marine debris managed amounts to 39.96 tons/day, of which 9.33 tons/day is recovered, leaving a significantly reduced residue of 25.63 tons/day or 1.134 m3/day for landfill disposal. Implementing the Haul Container System (HCS) within this framework, the study demonstrates that waste transportation from the ITS to Jakarta's Bantar Gebang Integrated Waste Processing Site can be streamlined to just 3 trips/day. By providing a structured and strategic approach to marine debris transportation, the ITS model aims to prevent the re-pollution of Jakarta's water bodies and significantly reduce the carbon footprint by 67.2% from current conditions, thereby offering a sustainable solution to this pressing urban environmental challenge.

Association of longitudinal pet ownership with wheezing in 3-year-old children using the distributed lag model: the Japan Environment and Children’s Study

BackgroundTime-varying exposures like pet ownership pose challenges for identifying critical windows due to multicollinearity when modeled simultaneously. The Distributed Lag Model (DLM) estimates critical windows for time-varying exposures, which are mainly continuous variables. However, applying complex functions such as high-order splines and nonlinear functions within DLMs may not be suitable for situations with limited time points or binary exposure, such as in questionnaire surveys.Objectives(1) We examined the estimation performance of a simple DLM with fractional polynomial function for time-varying binary exposures through simulation experiments. (2) We evaluated the impact of pet ownership on childhood wheezing onset and estimate critical windows.Methods(1) We compared logistic regression including time-varying exposure in separate models, in one model simultaneously, and using DLM. For evaluation, we employed bias, empirical standard error (EmpSE), and mean squared error (MSE). (2) The Japan Environment and Children's Study (JECS) is a prospective birth cohort study of approximately 100,000 parent-child pairs, registered across Japan from 2011 to 2014. We applied DLM to the JECS data up to age 3. The estimated odds ratios (OR) were considered to be within critical windows when they were significant at the 5% level.Results(1) DLM and the separate model exhibited lower bias compared to the simultaneously model. Additionally, both DLM and the simultaneously model demonstrated lower EmpSEs than the separate model. In all scenarios, DLM had lower MSEs than the other methods. Specifically, where critical windows is clearly present and exposure correlation is high, DLM showed MSEs about 1/2 to 1/200 of those of other models. (2) Application of DLM to the JECS data showed that, unlike other models, a significant exposure effect was observed only between the ages of 0 and 6 months. During that periods, the highest ORs were 1.07 (95% confidence interval, 1.01 to 1.14) , observed between the ages of 2 and 5 months.Conclusions(1) A simple DLM improves the accuracy of exposure effect and critical windows estimation. (2) 0–6 months may be the critical windows for the effect of pet ownership on the wheezing onset at 3 years.

On the limit points of an infinitely repeated rational expectations equilibrium

AbstractWe study the rational expectations equilibrium (REE) in the framework of a repeated economy. In each repetition agents observe the sequence of asymmetric REE’s occurred in the past to update their private information. We show that, in the limit, agents reach a symmetric information REE which exists universally (and not generically) and it is Pareto efficient and obviously incentive compatible. We also prove the converse result, i.e., given a symmetric information REE, we can construct a sequence of approximate asymmetric REE allocations that converges to the symmetric information REE. In view of the above results, the symmetric information REE provides a rationalization for the asymmetric one.

Some results on $I_2$-deferred statistically convergent double sequences in fuzzy normed spaces

The primary objective of this study is to introduce the concepts of $I_2$-deferred Cesàro summability and $I_2-$ deferred statistical convergence for double sequences in fuzzy normed spaces (FNS). Furthermore, the aim is to explore the connections between these concepts and subsequently establish several theorems pertaining to the notion of $I_2$-deferred statistical convergence in FNS for double sequences. We further define $I_2$-deferred statistical limit points and $I_2$-deferred statistical cluster points of a sequence within FNS and explore the relationships among these concepts.

The fate of intracellular S1P regulates lipid droplet turnover and lipotoxicity in pancreatic beta-cells

Lipotoxicity has been considered the main cause of pancreatic beta-cell failure during type 2 diabetes development. Lipid droplets (LD) are believed to regulate the beta-cell sensitivity to free fatty acids (FFA), but the underlying molecular mechanisms are largely unclear. Accumulating evidence points, however, to an important role of intracellular sphingosine-1-phosphate (S1P) metabolism in lipotoxicity-mediated disturbances of beta-cell function. In the present study, we compared the effects of an increased irreversible S1P degradation (S1P-lyase, SPL overexpression) with those associated with an enhanced S1P recycling (overexpression of S1P phosphatase 1, SGPP1) on LD formation and lipotoxicity in rat INS1E beta-cells. Interestingly, although both approaches led to a reduced S1P concentration, they had opposite effects on the susceptibility to FFA. Overexpression of SGPP1 prevented FFA-mediated caspase-3 activation by a mechanism involving an enhanced lipid storage capacity and prevention of oxidative stress. In contrast, SPL overexpression limited LD biogenesis, content, and size, while accelerating lipophagy. This was associated with FFA-induced hydrogen peroxide formation, mitochondrial fragmentation, and dysfunction, as well as ER stress. These changes coincided with the upregulation of proapoptotic ceramides but were independent of lipid peroxidation rate. Also in human EndoC-βH1 beta-cells, suppression of SPL with simultaneous overexpression of SGPP1 led to a similar and even more pronounced LD phenotype as that in INS1E-SGPP1 cells. Thus, intracellular S1P turnover significantly regulates LD content and size and influences beta-cell sensitivity to FFA.

Three-point support analysis and optimization design under the change of gravity center of diamond lathe

The position of three-point support has an important impact on the stiffness and accuracy of the diamond lathe. To determine the position of the support, this paper commences by conducting static and dynamic analysis on the large-size platform machine tool whose base is 2340×1540 mm under the two support schemes. It determines the basic layout scheme of the support points of the machine tool under the change of gravity center. Subsequently, by combining the least squares fitting and particle swarm optimization algorithm with the optimization goal of minimum deformation of the machine tool base working surface, the position of the support points is optimized under the influence of the Change of Gravity center. The results indicate that the optimal solution of the design variable within the design interval can be obtained through limited sampling points and optimization iterations. The average and maximum deformation of the base working surface are 0.867 μm and 1.45 μm. They decreased by 11.17% and 26.02% compared with before optimization.

DRL-Based URLLC-Constraint and Energy-Efficient Task Offloading for Internet of Health Things.

Internet of Health Things (IoHT) is a promising e-Health paradigm that involves offloading numerous computational-intensive and delay-sensitive tasks from locally limited IoHT points to edge servers (ESs) with abundant computational resources in close proximity. However, existing computation offloading techniques struggle to meet the burgeoning health demands in ultra-reliable and low-latency communication (URLLC), one of the 5G application scenarios. This article proposes a Multi-Agent Soft-Actor-Critic-discrete based URLLC-constrained task offloading and resource allocation (MASACDUA) scheme to maximize throughput while minimizing power consumption on the remote side, considering the long-term URLLC constraints. The URLLC constraint conditions are formulated using extreme value theory, and Lyapunov optimization is employed to divide the problem into task offloading and computation resource allocation. MASAC-discrete and a queue backlog-aware algorithm are utilized to approach task offloading and computation resource allocation, respectively. Extensive simulation results demonstrate that MASACDUA outperforms traditional DRL algorithms under different IoHT points and data arrival rate intervals and achieves superior performance in delay, bound violation probability, and other characteristics related to URLLC.

Journey over destination: dynamic sensor placement enhances generalization

Reconstructing complex, high-dimensional global fields from limited data points is a challenge across various scientific and industrial domains. This is particularly important for recovering spatio-temporal fields using sensor data from, for example, laboratory-based scientific experiments, weather forecasting, or drone surveys. Given the prohibitive costs of specialized sensors and the inaccessibility of certain regions of the domain, achieving full field coverage is typically not feasible. Therefore, the development of machine learning algorithms trained to reconstruct fields given a limited dataset is of critical importance. In this study, we introduce a general approach that employs moving sensors to enhance data exploitation during the training of an attention based neural network, thereby improving field reconstruction. The training of sensor locations is accomplished using an end-to-end workflow, ensuring differentiability in the interpolation of field values associated to the sensors, and is simple to implement using differentiable programming. Additionally, we have incorporated a correction mechanism to prevent sensors from entering invalid regions within the domain. We evaluated our method using two distinct datasets; the results show that our approach enhances learning, as evidenced by improved test scores.

Uniform rational polytopes of foliated threefolds and the global ACC

AbstractIn this paper, we show the existence of uniform rational lc polytopes for foliations with functional boundaries in dimension . As an application, we prove the global ACC for foliated threefolds with arbitrary DCC coefficients. We also provide applications on the accumulation points of lc thresholds of foliations in dimension .

The limit of human intelligence

In 1998, Fields medallist Stephen Smale [Smale (1998) [1]] proposed his famous eighteen problems to the mathematicians of this century. The statement of his eighteenth problem is simple but very important. The statement of his problem is, “What are the limits of intelligence, both artificial and human?”. To answer the limit of human intelligence, in this paper, we introduce cognitive-consequence space and cognitive-consequence topology, and mainly prove that deductive and non-deductive parts of a human mind will never be empty. It proves a human being will continue to think and solve problems using both deductive and non-deductive inferences as long as they are alive. Hence, we conclude that human intelligence is limitless. We also introduce cognitive closure, cognitive similarity distance, cognitive limit point, cognitive-continuous function, consequence ideal, consequence filter, Gödel's incompleteness black hole, and study related properties. We also provide suitable justifications to show that cognitive consequence topological space is not similar to that of any existing topological space because it connects cognitive space and consequence operator in one frame to find the limit of human intelligence. Moreover, we also provide justifications to state that artificial intelligence has limitations. Thus, we conclude that human intelligence will always remain superior to artificial intelligence.

Cluster Mapping of Waste Exposure Using DBSCAN Approach: Study of Spatial Patterns and Potential Distribution in Bantul Regency

High level of plastic waste production is a common issue in various places, including the Special Region of Yogyakarta province. It is proven that the Piyungan Integrated Waste Disposal Site (TPST) or Final Disposal Site (TPA) in Bantul Regency has been closed several times due to capacity exceeding the quota and some blockages from residents around the TPA. Issues related to microplastic contamination resulting from discarded plastic waste are fascinating to study, considering the long-term impact of microplastic contamination on human health. This research aims to map the distribution of locations with the potential for waste accumulation to reduce the negative consequences of microplastic contamination. The population used included TPS and markets in Bantul District, with the sample being the distribution of TPS and market points in Bantul District in 2023. The results of checking the point distribution pattern using the quadrant and nearest neighbor method showed that the distribution of waste accumulation points had a clustered design; thus, it could be continued with cluster analysis using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method. Meanwhile, the number of clusters was determined iteratively from a combination of DBSCAN parameters (MinPts and Epsilon). The best method was evaluated based on the Silhouette Coefficient (SC) value, which in this case was 0.78 (MinPts 7 and Eps 1500) and included in the strong category. Subsequently, exploration was carried out by reducing the MinPts value and the lowest limit value of strong SC.

On inherent hyperelastic crease

We present analyses of the inherent hyperelastic crease that entails an unstable degenerative singular perturbation field over a uniformly compressed hyperelastic half-space. The analyses reveal asymptotic far-field characteristics of the singular field that bring out admissible incremental elastic deformation mechanisms of the inherent creasing instability typically observed at a compressive strain of ∼0.354 in neo-Hookean solids. The admissible singular perturbation field has an asymptotic leading-order incremental-stress singularity decaying 1/r2 as the distance from the crease tip r→∞. In general, asymptotic incremental-deformation fields of 1/r2 stress singularity can be introduced by surface flaws. We found that the singular field creates a far-field eigenmode of three energy-release angular sectors separated by two energy-elevating sectors of incremental deformation. The far-field eigenmode braces the near-tip energy-release field of the surface flaw against the transition to an unstable self-similar expansion field of creasing. Two asymptotic far-field parameters can characterize the braced-incremental-deformation (bid) and crease fields. One is the Burgers vector of a projected subsurface dislocation of the asymptotically singular far field. The other is a dimensionless shape factor representing the ratio of the subsurface depth of the dislocation to the Burgers vector. Our analyses show that the shape factor gauges the transition from the stable bid field to the unstable inherent crease field at the crease limit point as the compression increases. Two tangential-manifold conservation integrals are revealed to identify the two parameters. At the crease-limit point, matched asymptotes lead to the ratio between the two separate scaling parameters of the asymptotic solutions of the crease-tip folding field and the leading-order far field. To this end, we introduced a novel finite element method (FEM) for simulating the crease field nearly in a hyperelastic half-space with a finite-size simulation domain. Furthermore, we uncovered with the Gent model (1996) that strain-stiffening in hyperelasticity alters the dependence of the shape factor on the compressive strain, raising crease resistance. The new findings in hyperelastic crease mechanisms will help study ruga mechanics of self-organization and design soft-material structures and skin conditions for high crease resistance.