Scientific Domains Research Articles

Testing the consistency of performance scores reported for binary classification problems

Binary classification is a fundamental task in machine learning, with applications across various scientific domains. Whether conducting fundamental research or refining practical applications, scientists typically assess and rank classification techniques based on performance metrics such as accuracy, sensitivity, and specificity. However, reported performance scores may not always provide a reliable basis for research ranking. The unreliability can be attributed to undisclosed or unconventional practices related to cross-validation, typographical errors, and other factors.In a given experimental setup with a specific number of positive and negative test items, performance scores can only assume specific, interrelated values. Based on this observation, in this paper, we introduce numerical techniques to assess the consistency of reported performance scores with the assumed experimental setup. Importantly, the proposed approaches do not rely on statistical inference. Instead, they use numerical methods (interval computing and integer linear programming) to identify inconsistencies with certainty.Through three applications in different fields of medicine, we demonstrate how the proposed tests can detect inconsistencies, thereby safeguarding the integrity of research fields. The power analyses of the tests in these applications show at least 71% of power when the performance scores are reported to four decimal places. In the investigated areas, the tests have so far identified inconsistencies in more than 100 scientific papers. To benefit the scientific community, we have made the consistency tests available in the open-source Python package mlscorecheck.

UV hybrid photon detector based on GaN photocathodes and Si low gain avalanche diode

Photon detectors featuring single-photon sensitivity play a crucial role in various scientific domains, including high-energy physics, astronomy, and quantum optics. Fast response time, high quantum efficiency, and minimal dark counts are the characteristics that render them ideal candidates for detecting individual photons with exceptional signal-to-noise ratios, at frequencies in the range of hundreds of MHz. Here, we report on our first design and operational results on a Hybrid Photon Detector (HPD) that combines the high quantum efficiency of a Gallium Nitride (GaN) photocathode and the low noise characteristics of a Si-based Low-Gain Avalanche Diode (LGAD). This hybrid detection scheme has the potential to reach single-photon detection sensitivity with high quantum efficiency, low noise levels and capable of operating at hundreds of MHz repetition rates.

Recent Development and Advancement in Quantum Dots in Pharmaceutical and Biomedical Fields for the Delivery of Drugs

Abstract: Nanoscale semiconductors known as quantum dots (QDs) are essential for drug testing because they bridge the gap between nanotechnology and the testing of drugs. QDs are a valuable tool in theranostics and treatment because of their unique physicochemical features. Due to their photoluminescence and electronic properties, including broad and continuous absorption spectra, narrow emission spectra from visible to near-infrared wavelengths, and long-lasting and high brightness, they are suitable probe materials for use in (bio)sensing (immunological) platforms. Several studies use QDs due to their optical, magnetic, electrical, photochemical, and biological features that allow them to be employed in various scientific domains. When utilized in drug delivery systems, fluorescent markers, such as QDs, can track the metabolism of drugs in the human body. Many medicinal applications, such as disease diagnosis and medication research, can benefit from these fluorescent tests. In this review article, the application of QD in drug delivery and immunoassay sensing has been described in detail.

Bifurcations of phase portraits and chaotic behaviors of the (2+1)-dimensional double‐chain DNA system with beta derivative: A qualitative approach

Qualitative analysis in mathematical modeling has become an important research area within the broad domain of nonlinear sciences. In the realm of qualitative analysis, the bifurcation method is one of the significant approaches for studying the structure of orbits in nonlinear dynamical systems. To apply the bifurcation method to the (2 + 1)-dimensional double-chain Deoxyribonucleic Acid system with beta derivative, the bifurcations of phase portraits and chaotic behaviors, combined with sensitivity and multi-stability analysis of this system, are examined. Initially, the bifurcations of phase portraits are visually identified at the obtained equilibrium points of a planar dynamical system via both Hamiltonian and Jacobian algorithms. The obtained results indicate Jacobian algorithm is more efficient in identifying the stability of bifurcations than the Hamiltonian algorithm for this system. Subsequently, by introducing an external perturbation term into the planar dynamical system, the chaotic behavior is effectively identified by using a variety of tools, such as two- and three-dimensional phase portraits, time series, Lyapunov exponents, and Poincaré maps. The findings suggest that the perturbed dynamical system deviates from regular patterns and exhibits behavior ranging from periodic to quasi-periodic and from quasi-periodic to chaotic. Finally, the sensitivity and multi-stability of the system are examined using the Runge-Kutta method to assess the model's response to minor variations in initial conditions through numerical solutions, revealing that the model is sensitive and multi-stable. The outcomes of this study will enhance a relationship between applied mathematicians and experimental biologists, helping to explore hidden features of Deoxyribonucleic Acid through the studied model.

Molecular set representation learning

Computational representation of molecules can take many forms, including graphs, string encodings of graphs, binary vectors or learned embeddings in the form of real-valued vectors. These representations are then used in downstream classification and regression tasks using a wide range of machine learning models. However, existing models come with limitations, such as the requirement for clearly defined chemical bonds, which often do not represent the true underlying nature of a molecule. Here we propose a framework for molecular machine learning tasks based on set representation learning. We show that learning on sets of atom invariants alone reaches the performance of state-of-the-art graph-based models on the most-used chemical benchmark datasets and that introducing a set representation layer into graph neural networks can surpass the performance of established methods in the domains of chemistry, biology and material science. We introduce specialized set representation-based neural network architectures for reaction-yield and protein–ligand binding-affinity prediction. Overall, we show that the technique we denote molecular set representation learning is both an alternative and an extension to graph neural network architectures for machine learning tasks on molecules, molecule complexes and chemical reactions.

The gated cascade diffusion model: An integrated theory of decision making, motor preparation, and motor execution.

This article introduces an integrated and biologically inspired theory of decision making, motor preparation, and motor execution. The theory is formalized as an extension of the diffusion model, in which diffusive accumulated evidence from the decision-making process is continuously conveyed to motor areas of the brain that prepare the response, where it is smoothed by a mechanism that approximates a Kalman-Bucy filter. The resulting motor preparation variable is gated prior to reaching agonist muscles until it exceeds a particular level of activation. We tested this gated cascade diffusion model by continuously probing the electrical activity of the response agonists through electromyography in four choice tasks that span a variety of domains in cognitive sciences, namely motion perception, numerical cognition, recognition memory, and lexical knowledge. The model provided a good quantitative account of behavioral and electromyographic data and systematically outperformed previous models. This work represents an advance in the integration of processes involved in simple decisions and sheds new light on the interplay between decision and motor systems. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

On diagonally structured scheme for nonlinear least squares and data-fitting problems

Recently, structured nonlinear least-squares (NLS) based algorithms gained considerable emphasis from researchers; this attention may result from increasingly applicable areas of these algorithms in different science and engineering domains. In this article, we coined a new efficient structured-based NLS algorithm. We developed a diagonal Hessian-based formulation for solving NLS problems. We derived the quasi-Newton update based on a diagonal matrix scheme subject to a modified structured secant condition. Also, we show that the algorithm’s search direction satisfies a sufficient descent condition under some standard assumptions. Subsequently, we also prove the global convergence of the algorithm and then eventually show its linear convergence rate for strongly convex functions. Furthermore, to show case the proposed algorithm’s performance, we experimented numerically by comparing it with other approaches on some benchmark test functions available in the literature. Finally, the introduced scheme is applied to solve some data-fitting problems

SYSTEM REVISITED – AGAIN

AbstractThis paper reviews the revised foundational definitions of system and systems engineering in the recently published (2023) fifth edition of the INCOSE Systems Engineering Handbook. The new INCOSE definitions derive from an earlier INCOSE Fellows' Initiative on System and Systems Engineering Definitions that began in 2016 and finally reported in 2019. After introducing the concept of system and reviewing the new definition, the paper concludes that the concept, not rooted in a single science or exemplar domain, is so pervasive as be a meta‐concept that does not have a dominant scientific definition. It proposes further work towards a more scientific definition of an engineered or artificial system – the primary interest of INCOSE – at a lower level of abstraction.While the authors define systems engineering functionally as a process or approach, we see the essence of systems engineering as abstraction. Using the more accepted metaphysical distinction between the real, virtual, and abstract, we define the output of systems engineering as an abstract (simplified symbolic) representation or model that is the basis for defining the real; firstly, the virtual representation of something not (yet) existing physically but made to appear so and, finally, the physical. Thus, we position systems engineering as the abstract phase within a three‐phase abstract‐virtual‐physical engineering design and realization process.

Contemporary Systems Engineering for the UN SDGs and NAE Grand Challenges

Abstract: Systems engineering programs at US universities have been focusing more on sustainability, but systems approaches to sustainability are found in programs outside of a systems engineering context. Transdisciplinary collaboration has been emphasized to make progress toward the United Nations' Sustainable Development Goals (SDGs), requiring new approaches to collaborative understanding on the student and faculty levels in academic environments. This paper provides a qualitative network analysis of systems approaches to sustainability across disciplines using a US university as a case study. The analysis mapped systems approaches at Worchester Polytechnic Institute (WPI) within and outside of WPI's Systems Engineering Program. We specifically focused on thematic areas regarding systems in social science domains pertaining to the SDGs, which need to be brought into a systems engineering context. This paper aims to identify potential areas of collaboration to accelerate progress toward the SDGs using systems approaches.

An Analysis of K–12 Climate Change Science Standards in the United States

Abstract To produce a climate-literate society willing to take action, students must be educated on the causes, changes, impacts, and solutions of climate change. One way to ensure students are educated on climate change is to have robust science standards. However, little is known about the collective climate change standards in the United States. Therefore, the purpose of this study is to conduct an analysis of the U.S. K–12 science standards to uncover where the climate change standards are located in different grade levels and the extent to which the collective U.S. curriculum covers topics of climate change. This study was a qualitative content analysis of the U.S. K–12 climate change standards. The results show that most U.S. climate change standards are found within the high school grade levels and Earth and space science domains. All U.S. states address topics of climate change within their standards; however, general mentions of climate change were cited most often. Finally, the majority of states address both natural and anthropogenic causes of climate change. Implications for policymakers and educators are included.

In Silico Human Mobility Data Science: Leveraging Massive Simulated Mobility Data (Vision Paper)

Human mobility data science using trajectories or check-ins of individuals has many applications. Recently, we have seen a plethora of research efforts that tackle these applications. However, research progress in this field is limited by a lack of large and representative datasets. The largest and most commonly used dataset of individual human trajectories captures fewer than 200 individuals, while datasets of individual human check-ins capture fewer than 100 check-ins per city per day. Thus, it is not clear if findings from the human mobility data science community would generalize to large populations. Since obtaining massive, representative, and individual-level human mobility data is hard to come by due to privacy considerations, the vision of this work is to embrace the use of data generated by large-scale socially realistic microsimulations. Informed by both real data and leveraging social and behavioral theories, massive spatially explicit microsimulations may allow us to simulate entire megacities at the person level. The simulated worlds, which do not capture any identifiable personal information, allow us to perform “in silico” experiments using the simulated world as a sandbox in which we have perfect information and perfect control without jeopardizing the privacy of any actual individual. In silico experiments have become commonplace in other scientific domains such as chemistry and biology, permitting experiments that foster the understanding of concepts without any harm to individuals. This work describes challenges and opportunities for leveraging massive and realistic simulated alternate worlds for in silico human mobility data science.

Mobility Data Science: Perspectives and Challenges

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of Global Positioning System (GPS)–equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated a significant impact in various domains, including traffic management, urban planning, and health sciences. In this article, we present the domain of mobility data science. Towards a unified approach to mobility data science, we present a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state-of-the-art, and describe open challenges for the research community in the coming years.

A Comparative Analysis of Two Semi Analytic Approaches in Solving Systems of First-Order Differential Equations

The resolution of systems of first-order ordinary differential equations (ODEs) stands as a pivotal pursuit with extensive implications across scientific and engineering domains. In tackling this fundamental task, this study undertakes a rigorous comparative assessment of two semi-analytic methodologies, the Variational Iterative Method (VIM) and the New Iterative Method (NIM). Motivated by the need to address a critical research gap, our investigation delves into these approaches' relative merits and demerits. Firstly, it conducts a comprehensive examination of VIM, a well-established method, juxtaposed with NIM, a relatively unexplored approach, to uncover their comparative strengths and limitations. Secondly, the study contributes to the existing knowledge in numerical methods for ODEs by shedding light on essential performance characteristics, including convergence properties, computational efficiency, and accuracy, across a diverse array of ODE systems. Through meticulous numerical experimentation, we not only reveal practical insights into the efficacy of VIM and NIM but also bridge a significant knowledge gap in the field of numerical ODE solvers. Our findings highlight VIM as the more effective method, thus advancing our understanding of semi-analytic approaches for solving ODE systems and furnishing valuable guidance for practitioners and researchers in selecting the most suitable method for their specific applications

Resilience in inter-organizational networks of red buses: dealing with their daily disruptions in critical infrastructures

This research aims to determine how the inter-organizational (supply) system that serves and oversees the critical infrastructures might lessen the negative effects of routine interruptions. Furthermore, this research aims to gain insight into the types of disruptions that Red Buses experience and their strategies to manage them. The subject matter of this discourse pertains to the domains of science, numbers, and statistics. This research employs a quantitative research methodology to examine the effects of routine disruptions on inter-organizational systems and to evaluate the corresponding hypotheses. The individuals involved in this investigation work and operate the Red Bus Service in Karachi. By random sampling, a sample of 152 workers and drivers was selected from the Red Bus Service in Karachi. Data was collected from both electronic and non-electronic sources. Data was collected using Google Forms distributed via various internet channels such as WhatsApp and email addresses. Data was analyzed by Structural Equation Modeling (SEM); we used SmartPLS 4 software for that. The finding shows that disturbance co-occurrence has a negative relationship with CI resilience to focal disruption, a positive relationship with non-routineness, and a negative relationship with economic performance.

Solid-State Fluorescent Organic Polymers for Visual Detection and Elimination of Heavy Metals in Water.

Selective sensing and removal of toxic heavy metals from water are highly essential since their presence poses significant health and environmental hazards. Herein, we designed and synthesized a novel fluorescent nonconjugated organic polymer by strategically incorporating two key functional groups, namely, a dansyl fluorophore and dithiocarbamate (DTC). Different characterization techniques, including 1H nuclear magnetic resonance (NMR), scanning electron microscopy (SEM), X-ray diffraction (XRD), energy-dispersive X-ray analysis (EDAX), Fourier transform infrared (FTIR), and fluorescence spectroscopy, were performed to understand its structure and material properties. The quantum yield of 4.72% and its solid-state fluorescence indicate that it has potential for various applications in several technological and scientific domains. In this study, we investigated a specific application involving the detection and elimination of heavy metals from water. Interestingly, the presence of dansyl and DTC moieties demonstrated remarkable selectivity toward Cu2+, Co2+, Ni2+, Fe3+, and Fe2+ sensing, displaying distinct color changes specific to each metal. Cu2+ resulted in a yellow color, Co2+ showed a green color, Ni2+ displayed a pale yellowish-green color, and Fe2+/Fe3+ exhibited a brown color. The LOD (limit of detection) for each metal was obtained in the nanomolar range by using a fluorescence spectrometer and the micromolar range from UV-visible spectra: 13.27 nM and 0.518 μM for Cu2+, 8.27 nM and 0.581 μM for Co2+, 14.36 nM and 0.140 μM for Ni2+, 14.95 nM and 0.174 μM for Fe2+, and 15.54 nM and 0.33 μM for Fe3+. Moreover, the DTC functionality on its backbone facilitates effective interaction with the aforementioned heavy metals, subsequently removing them from water (except Fe2+ and Fe3+), validating its dual functionality as both an indicator and a purifier for heavy metals in water. The polymer exhibited a maximum adsorption capacity of 192.30 mg/g for Cu2+, 159.74 mg/g for Co2+, and 181.81 mg/g for Ni2+. Furthermore, this approach exhibits versatility in crafting fluorescent polymers with adjustable attributes that are suitable for a wide range of applications.

Exploring the Golden Ratio in Nature by Using a STEAM Approach: A Diagnostic and Quasi-Experimental Study at a Senior University

This research addresses the social exclusion of elderly citizens in terms of lifelong education via an interdisciplinary STEAM (science, technology, engineering, arts, and mathematics) approach. Technological literacy among older people is a critical factor in social exclusion. This study seeks to provide senior citizens with competencies in scientific, artistic, mathematical, and technological domains by enhancing scientific and technological literacy. The research developed a series of non-formal education sessions on the golden ratio using a STEAM educational approach. A quantitative methodology approach was carried out by using a diagnostic survey of the participants’ conceptions and a subsequent quasi-experimental study to evaluate the impact of the intervention. This study, conducted with 37 senior citizens (n = 37), found positive results aligning with the existing literature on the potential of the STEAM approach. The STEAM approach proved to be engaging for seniors, offering a holistic and interdisciplinary educational experience. Despite the limited availability of science educational programs for seniors and the scarcity of studies on lifelong learning using the STEAM approach, this research highlights the need for such initiatives, especially given the growing senior population. Applying STEAM education shows promise in enhancing scientific literacy and motivation among adult learners. By integrating mathematical concepts, such as the golden ratio, with practical applications in arts and natural sciences, STEAM education can provide a rich, motivating, and accessible learning experience, promoting active and healthy ageing through lifelong learning. Further research and development in this area could maximise educational benefits for the senior population.

Metagenomics: Advances in Environmental Microbiology, Human Health, and Agriculture

Metagenomics, as a revolutionary technology, plays an important role in the study of environmental microbial ecosystems. Metagenomics has revolutionized our understanding of microbial communities by enabling the comprehensive analysis of genetic material directly from environmental samples. This paper reviews recent advances in metagenomics and its applications in environmental microbiology, human health, and agriculture. We highlight key methodologies, significant findings, and the potential of metagenomics to drive innovations across various scientific and practical domains.

Correlation aversion and bivariate stochastic dominance with respect to reference functions

This paper introduces an extension of stochastic dominance, moving from univariate to bivariate analysis by incorporating a reference function. Our approach offers flexibility in reference function selection, improving upon previous studies cohesively. Bivariate orderings are invaluable tools in actuarial sciences, facilitating the assessment and management of dependencies between risks and lifelengths within multiple insurance contracts. These advancements hold promising practical implications, particularly within the actuarial sciences domain.

Уголовно-правовые и криминологические инструменты противодействия наркотизму (на примере диссертаций, защищенных под руководством профессора Прохоровой Марины Леонидовны)

The article is an overview of criminal legal and criminological tools used in the fight against drug addiction, with an emphasis on research conducted under the guidance of Professor Marina Leonidovna Prokhorova. The authors analyze the final conclusions of dissertations devoted to these issues, including conceptual approaches to combating drug addiction, determining the role of the criminal subculture and the deviant environment of drug addicts in influencing a healthy society, as well as mechanisms for the rehabilitation of people suffering from drug addiction. It is underscored that these dissertations have collectively engendered an entire scientific domain, consolidating developed criminal law and criminological means for counteracting drug addiction. Diverse methods and strategies utilized to address the challenges associated with overcoming drug addiction and rehabilitation of drug addicts are characterized herein. The integration of these doctrinal tenets is advocated for the formulation of a robust and balanced criminal law policy aimed at preempting drug addiction.

Deep Learning Enabled Transmission of Full‐Stokes Polarization Images Through Complex Media

AbstractPolarization images offer crucial functionalities across multiple scientific domains, providing access to physical information beyond conventional measures such as intensity, phase, and spectrum of light. However, the challenge of transmitting polarization images through complex media has restricted their application in optical communication and imaging. Here, a novel approach utilizing deep learning for the transmission of full‐Stokes polarization images through scattering media is presented. It is demonstrated that any input polarization image can be reconstructed in a single shot by employing only an intensity sensor. By supervised training of a deep neural network, high‐accuracy full‐Stokes reconstruction is achieved from the speckle pattern detected by an intensity camera. Leveraging the deep learning based polarization decoder, a polarization‐colored encoding scheme is devised to enable increased‐capacity data transmission through disordered channels. Fast, wavelength‐independent, on‐chip, polarization imaging in complex media enables the utilization of polarization‐structured light in multimode fibres and opaque materials, unlocking new possibilities in optical communication, cryptography, and quantum technology.