Intrinsic Nature Research Articles

Attributed Graph Force Learning.

In numerous network analysis tasks, feature representation plays an imperative role. Due to the intrinsic nature of networks being discrete, enormous challenges are imposed on their effective usage. There has been a significant amount of attention on network feature learning in recent times that has the potential of mapping discrete features into a continuous feature space. The methods, however, lack preserving the structural information owing to the utilization of random negative sampling during the training phase. The ability to effectively join attribute information to embedding feature space is also compromised. To address the shortcomings identified, a novel attribute force-based graph (AGForce) learning model is proposed that keeps the structural information intact along with adaptively joining attribute information to the node's features. To demonstrate the effectiveness of the proposed framework, comprehensive experiments on benchmark datasets are performed. AGForce based on the spring-electrical model extends opportunities to simulate node interaction for graph learning.

The Concept of Alienation and Alienation in Organizations

Although the concept of alienation has been studied in the literature for years, it is a topic that has not lost its currency. This article delves into the enduring significance of the concept of alienation, examining its historical development, causes, and consequences within the context of organizational settings. Alienation, characterized by an individual's loss of faith in their environment and self, manifests as a disorder leading to profound loneliness and hopelessness. The study particularly focuses on workplace alienation, highlighting the inherent conflict between job demands and an employee's intrinsic nature. Organizational alienation is explored as a nuanced reflection of an individual's emotions arising from dissatisfaction within the employment context.
 The research conducts a thorough analysis of individual, social, and sociological consequences, unraveling the far-reaching impact of alienation on both personal and organizational levels. In response to the challenges posed by alienation in organizations, strategic interventions are proposed to mitigate its pervasive effects and cultivate a positive and cohesive organizational environment. This comprehensive exploration amalgamates insights from existing literature, providing a professional and detailed examination of the complex phenomenon of alienation. in this article, the influence of culture (Cultural Norms and Values, Communication Style, Inclusiveness and Diversity, Leadership Style, Organizational Structure, Workplace Rituals and Traditions, and Employee Recognition and Pay Systems) on organizational alienation and the importance of culture in this context are emphasized. The article contributes valuable information to deepen the understanding of alienation's implications for individuals and organizations, offering insights for developing strategies to address and prevent alienation in organizational contexts.

Interfacial polarization-driven relaxation in CuO epitaxial thin films

In this manuscript, we examine the electrical behavior of pulse laser deposition grown epitaxial (111) oriented CuO thin films using impedance spectroscopy to understand the microscopic origin of their relaxor-like characteristics. Temperature (T) dependent variation of the real part of dielectric permittivity (ε′) shows a relaxor ferroelectric-like anomaly with Vogel–Fulcher relation fitting well with the observed dielectric behavior, and thus, pointing toward a relaxor ferroelectric nature of the CuO thin film. However, the loss tangent and frequency-dependent dielectric spectroscopy measurements suggest the need to further explore the different mechanisms to understand the origin of observed relaxor behavior. Deconvolution of the impedance spectra reveals that interfacial contributions dominate in the dielectric response. Moreover, deconvoluted capacitances are temperature-independent within the specified temperature range, thereby excluding the possibility of a ferroelectric transition suggested by ε′ vs T data. The DC bias measurement of dielectric permittivity and I–V measurements reveal the MW (Maxwell–Wagner) nature of the observed dielectric anomaly. The measurements also suggest interface-limited Schottky conduction as the predominant conduction mechanism in the CuO thin films. This work demonstrates that the apparent relaxor behavior observed in the CuO thin film is related to extrinsic, i.e., interfacial polarization effect, instead of the intrinsic ferroelectric nature of the material.

Landslide spatial prediction using cluster analysis

Temporal clustering is an intrinsic nature of landslide occurrences, therefore it should be considered in data-driven landslide spatial prediction (i.e., susceptibility assessment). However, it remains problematic regarding how to determine landslide temporal clusters and how to integrate susceptibility maps derived from different landslide temporal clusters. In this paper, a general framework of landslide spatial prediction model considering the temporal clustering of landslides is proposed. This novel framework first assesses landslide susceptibility separately based on each landslide temporal cluster identified by spatiotemporal clustering analysis and then integrates separate assessments by weighted averaging. In a case study, this general framework is implemented using the stacking network landslide susceptibility assessment method and used in the landslide spatial prediction of the Sanming City and Wenchuan seismic areas. The results show that the proposed framework outperformed traditional susceptibility models that do not consider landslide temporal clustering, and the integration of susceptibility models based on all landslide temporal clusters will promote the performance of landslide spatial prediction because levels of knowledge in long-term spatiotemporal landslide activities are considered. This novel general framework highlights the benefit of considering landslide temporal clustering in landslide spatial prediction and can provide better support for landslide risk management.

Quantum random number generator based on LED

Quantum random number generators (QRNGs) produce random numbers based on the intrinsic probabilistic nature of quantum mechanics, making them true random number generators. In this paper, we design and fabricate an embedded QRNG that produces random numbers based on fluctuations of spontaneous emission and absorption in a light-emitting diode. To achieve a robust and reliable QRNG, we compare some usual post-processing methods and select the finite impulse response method for a real-time device. This device could pass the National Institute of Standards and Technology tests, the generation rate is 1 Mbit/s, and the randomness of the output data is invariant in time.

Utilization of waste cooking oil as a substitute for plasticizers in the production of carrageenan/cornstarch bioplastic

In the current investigation, to develop a carrageenan/cornstarch bioplastic production technique suitable for commercial-scale production, an alternative method to the commonly used solvent-casting technique was implemented through a hot-melt extrusion process. Moreover, to enhance the water resistance of the bioplastic and reduce production costs, waste cooking oil (WCO) was used as a substitute for glycerol plasticizer. Various weight ratios of WCO to glycerol (w/w) were used in formulations. All the material ingredients were compounded using a double-screw extruder at 115°C. Subsequently, the resulting extrudate was then cut into resin pellets, followed by hot compression to produce bioplastic film samples. The film samples underwent testing for mechanical properties, water resistance, biodegradability, chemical structure and morphology. The overall results showed that the addition of WCO improved the tensile properties and water resistance and accelerated biodegradation. These findings were attributed to the presence of ester groups in the polymer structure, the intrinsic hydrophobic nature of WCO and the formation of voids within the film structure, as revealed by Fourier transform infrared spectroscopy and scanning electron microscopy analyses. Based on the findings, the utilization of WCO in carrageenan/cornstarch-based bioplastic production offers potential advantages for commercial applications.

Development and performance evaluation of re-crosslinkable preformed particle gel under high temperature and high salt conditions

Self-healable hydrogels have attracted significant interests in the fields of environmental protection, oil plugging and electronic sensors, etc. Benefiting from the synergistic contribution of hydrogen bonding and ligand chelation, in this work, we prepared a series of self-healable hydrogels via a simple and inexpensive approach, which overcame the limitations of the intrinsic nature of dynamic covalent bonds and non-covalent interactions under harsh conditions. Systematic study of the effect of temperature, salt concentration and particle size on the physicochemical performance of the re-crosslinking progress revealed that re-crosslinked gels using particles of 0.20–0.28 mm (in a solution with salt concentration of 10%) at 130 °C presented an elastic modulus of 62 Pa at a frequency of 10 Hz and remained elastic after 45 days of aging (elastic modulus: 18,170 Pa). Of particular interest, gel particles with a particle size of 0.125–0.28 mm can be reassembled at temperatures up to 150 °C or salt concentrations up to 20%, which is superior to a plethora of previous reports (self-healing temperature usually less than 130 °C, salt concentration <10%). Employment of the green inexpensive sodium D-gluconate instead of partial acrylamide makes the gel product a promising candidate material for oil plugging, targeting fractured reservoirs for consistent control.

Promoting the carrier mobility of Nb2SiTe4 through cation coordination engineering

Ternary two-dimensional (2D) monoclinic Nb2SiTe4 has garnered significant attention for its potential applications in anisotropic photoelectronics. Yet, its intrinsic indirect bandgap nature and low hole mobility, attributed to the short Nb–Nb dimer configurations, hinder the efficient photogenerated carrier separation and transport. In this Letter, using density functional theory calculations, we demonstrate the interlayer intercalation of Si results in the formation of a metastable orthorhombic Nb2SiTe4 structure devoid of detrimental short Nb–Nb dimers. Notably, this Si intercalation leads to a remarkable reduction of hole effective masses of orthorhombic Nb2SiX4 (X = S, Se, and Te), a crucial factor for achieving high carrier mobility. Taking the orthorhombic Nb2SiTe4 monolayer as an example, the calculated hole mobility (&amp;gt;100 cm2 V−1 s−1) is comparable in magnitude to the respectable hole mobility observed in multiple layers of the monoclinic Nb2SiTe4. To simultaneously enhance electron and hole mobility, we establish a van der Waals junction between the monoclinic and orthorhombic Nb2SiTe4 structures, achieving high and comparable carrier mobilities. The Nb2SiTe4 junction exhibits a nearly direct bandgap of 0.35 eV, rendering it suitable for infrared light harvesting. Furthermore, carriers within the Nb2SiTe4 junction become spatially separated across different layers, resulting in an intrinsic built-in electric field, which is superior for efficient photo-generated charge separation and decreases the potential nonradiative carrier recombination. Our findings highlight the impact of cation coordination engineering on the electronic and optical properties of 2D Nb2SiTe4 and provide a feasible solution to achieve better carrier transport in low-dimensional photovoltaic functionalities.

Pnictogen bond-driven control of the molecular interaction between organophosphorus and acetylcholinesterase enzyme.

This study addresses a comprehensive assessment of the interaction between chemical warfare agents (CWA) and acetylcholinesterase (AChE) systems, focus on the intriguing pnictogen-bond interaction (PnB). Utilizing the crystallographic data from the Protein Data Bank pertaining to the AChE-CWA complex involving Sarin (GB), Cyclosarin (GF), 2-[fluoro(methyl)phosphoryl]oxy-1,1-dimethylcyclopentane (GP) and venomous agent X (VX) agents, the CWA is systematically displaced by increments of 0.1 Å along the PO bond axis, extending its distance by 4 Å from the original position. The AIM analysis was carried out and consistently revealed the presence of a significant interaction along the PO bond. Investigating the intrinsic nature of the PnB, the NBO and the EDA analysis unearthed the contribution of orbital factors to the overall energy of the system. Strikingly, this observation challenges the conventional σ-hole explanation commonly associated with such interactions. This finding adds a layer of complexity to understanding of PnB, encouraging further exploration into the underlying mechanisms governing these intriguing chemical phenomena.

Monolithic Conjugated Microporous Polymer Aerogel for Triboelectric Nanogenerator

AbstractTriboelectric nanogenerator (TENG) has emerged as a novel nanotechnology for energy harvesting and self‐powered sensing via utilizing various low‐frequency mechanical energies throughout the surrounding environment. However, designing TENG with high physical robustness, flexibility, and output performance is rather challenging. This study develops monolithic conjugated microporous polymer aerogel (CMPA) with delocalized π‐conjugation to construct TENG with a robust porous structure. The as‐produced CMPA is composed of hollow intertwined nanotubes with a high degree of flexibility and high specific surface area (up to 528 m2 g−1). Due to its intrinsic nature of conjugated electron transfer and architectural characteristics, the CMPA‐based TENG (effective area: 4.9 cm2) shows a high sensitivity to mechanical force and excellent electric output (open‐circuit voltage: 72 V, short‐circuit peak current: 3.2 µA and transferred charge: 48 nC). Moreover, the CMPA‐based TENG can charge micro‐electronic devices and sense human motions. This work will inspire the design of aerogel‐based electronic devices and the management of renewable energy resources.

Agents preserving privacy on intelligent transportation systems according to EU law

AbstractIntelligent Transportation Systems are expected to automate how parking slots are booked by trucks. The intrinsic dynamic nature of this problem, the need of explanations and the inclusion of private data justify an agent-based solution. Agents solving this problem act with a Believe Desire Intentions reasoning, and are implemented with JASON. Privacy of trucks becomes protected sharing a list of parkings ordered by preference. Furthermore, the process of assigning parking slots takes into account legal requirements on breaks and driving time limits. Finally, the agent simulations use the distances, the number of trucks and parkings corresponding to the proportions of the current European Union data. The performance of the proposed solution is tested in these simulations with three different distances against an alternative with complete knowledge. The difference in efficiency, the number of illegal breaks and the traveled distances are measured in them. Comparing the results, we can conclude that the nonprivate alternative is slightly better in performance while both alternatives do not produce illegal breaks. In this way the simulations show that the proposed privacy protection does not impose a relevant handicap in efficiency.

Fluctuation Relation for the Dissipative Flux: The Role of Dynamics, Correlations and Heat Baths.

The fluctuation relation stands as a fundamental result in nonequilibrium statistical physics. Its derivation, particularly in the stationary state, places stringent conditions on the physical systems of interest. On the other hand, numerical analyses usually do not directly reveal any specific connection with such physical properties. This study proposes an investigation of such a connection with the fundamental ingredients of the derivation of the fluctuation relation for the dissipation, which includes the decay of correlations, in the case of heat transport in one-dimensional systems. The role of the heat baths in connection with the system's inherent properties is then highlighted. A crucial discovery of our research is that different lattice models obeying the steady-state fluctuation relation may do so through fundamentally different mechanisms, characterizing their intrinsic nature. Systems with normal heat conduction, such as the lattice ϕ4 model, comply with the theorem after surpassing a certain observational time window, irrespective of lattice size. In contrast, systems characterized by anomalous heat conduction, such as Fermi-Pasta-Ulam-Tsingou-β and harmonic oscillator chains, require extended observation periods for theoretical alignment, particularly as the lattice size increases. In these systems, the heat bath's fluctuations significantly influence the entire lattice, linking the system's fluctuations with those of the bath. Here, the current autocorrelation function allows us to discern the varying conditions under which different systems satisfy with the fluctuation relation. Our findings significantly expand the understanding of the stationary fluctuation relation and its broader implications in the field of nonequilibrium phenomena.

Realization of large-area ultraflat chiral blue phosphorene

Blue phosphorene (BlueP), a theoretically proposed phosphorous allotrope with buckled honeycomb lattice, has attracted considerable interest due to its intriguing properties. Introducing chirality into BlueP can further enrich its physical and chemical properties, expanding its potential for applications. However, the synthesis of chiral BlueP remains elusive. Here, we demonstrate the growth of large-area BlueP films on Cu(111), with lateral size limited by the wafer dimensions. Importantly, we discovered that the BlueP is characterized by an ultraflat honeycomb lattice, rather than the prevailing buckled structure, and develops highly ordered spatial chirality plausibly resulting from the rotational stacking with the substrate and interface strain release, as further confirmed by the geometric phase analysis. Moreover, spectroscopic measurements reveal its intrinsic metallic nature and different characteristic quantum oscillations in the image-potential states, which can be exploited for a range of potential applications including polarization optics, spintronics, and chiral catalysis.

Analysis of Meshfree Galerkin Methods Based on Moving Least Squares and Local Maximum-Entropy Approximation Schemes

Over the last two decades, meshfree Galerkin methods have become increasingly popular in solid and fluid mechanics applications. A variety of these methods have been developed, each incorporating unique meshfree approximation schemes to enhance their performance. In this study, we examine the application of the Moving Least Squares and Local Maximum-Entropy (LME) approximations within the framework of Optimal Transportation Meshfree for solving Galerkin boundary-value problems. We focus on how the choice of basis order and the non-negativity, as well as the weak Kronecker-delta properties of shape functions, influence the performance of numerical solutions. Through comparative numerical experiments, we evaluate the efficiency, accuracy, and capabilities of these two approximation schemes. The decision to use one method over the other often hinges on factors like computational efficiency and resource management, underscoring the importance of carefully considering the specific attributes of the data and the intrinsic nature of the problem being addressed.

Joint Dataset Reconstruction and Power Control for Distributed Training in D2D Edge Network

The intrinsic nature of non-independent and identically distributed datasets on heterogeneous devices slows down the distributed model training process and reduces the training accuracy. To settle this problem, we propose a dataset reconstruction scheme to transform the data distribution of training device’s dataset into independent and identically distributed dataset via data exchange among trusted devices. For energy efficiency, we further consider power control for the devices. We then formulate an optimization problem, which is a mixed integer non-linear programming problem, to minimize the total energy consumption for each round of distributed training. Due to the NP-hardness and coupling property of the optimization problem, we decompose it into two subproblems for dataset reconstruction and power control, respectively. An approximation algorithm is designed to obtain a near-optimal auxiliary devices set for dataset reconstruction with minimum energy consumption, while meeting the variance constraint of the optimization problem. We prove that approximation algorithm has a worst-case approximation ratio of 1+ln|Ωi(t)|, where |Ωi(t)| is the required data samples for dataset reconstruction of each training device. For power control, we design a dynamic programming algorithm to further reduce the energy consumption. For comparison, we propose three benchmark schemes that adopt either one of the algorithms or neither. We also customize three baseline algorithms based on the state-of-the-arts to compare with our proposed algorithm. Numerical results show that, our proposed algorithm outperforms three benchmarks on the average energy consumption for one round for different cases. When varying the labels that each device owns, our proposed algorithm outperforms the other three baseline algorithms on training accuracy. Besides, when setting a target accuracy, our proposed algorithm always has the lowest energy consumption.

Controlling human causal inference through in silico task design

Learning causal relationships is crucial for survival. The human brain's functional flexibility allows for effective causal inference, underlying various learning processes. While past studies focused on environmental factors influencing causal inference, a fundamental question remains: can these factors be manipulated for strategic causal inference control? This paper presents a task control framework for orchestrating causal learning task design. It utilizes a two-player game setting where a neural network learns to manipulate task variables by interacting with a human causal inference model. Training the task controller to generate experimental designs, we confirm its ability to accommodate complexities of environmental causal structure. Experiments involving 126 human subjects successfully validate the impact of task control on performance and learning efficiency. Additionally, we find that task control policy reflects the intrinsic nature of human causal inference: one-shot learning. This framework holds promising potential for applications paving the way for targeted behavioral outcomes in humans.

Stable Comparisons, Punctuation, and Usage

This study offers an analysis of the methods of punctuation marking for stable comparisons in the punctuation practice of the 20th and 21st centuries. The research material comprises a micro-corpus, which includes more than 200 contexts of usage for stable comparisons, semantically similar free comparative constructions, as well as occasional transformations of stable comparisons. Punctuation actions involving stable comparisons are conceptualized as a distinct punctuation experiment, wherein diverse punctuation responses are formulated contingent upon the intrinsic nature of the stable comparisons themselves and the conditions governing their use. It is demonstrated that the punctuation practice of the 20th and 21st centuries reflects a trend towards punctuation differentiation not only between stable comparisons and free comparative constructions but also within stable comparisons themselves. The analysis of punctuation facts leads to the conclusion that the punctuation marking of stable comparisons is determined by derivational, pragmatic, and context-syntactic factors, which are taken into account by writers in their punctuation actions. It is shown that while derivational and context-syntactic factors prompt punctuation marking of stable comparisons, pragmatic factors merely render the use of delimiting commas more preferable. It is emphasized that adequate punctuation codification in the realm of stable comparisons cannot be achieved without reference to the evidence of punctuation practice.

Dualism, the Causal Closure of the Physical, and Philip Goff’s Case for Panpsychism

Abstract The article discusses Philip Goff’s latest projects of developing panpsychist research program as one that is capable of revealing the place of consciousness in the physical world and accounting for the intrinsic nature of physical reality, while avoiding the problem of the causal closure of the physical that is supposed to be pernicious for psychophysical dualism. The case is made that on the one hand, dualism has pretty good resources to meet the inductive no-gap objection appealing to the causal closure, while on the other hand, insofar as this objection has some force, panpsychism in the forms discussed in Goff’s book Galileo’s Error (Goff, P. 2019. Galileo’s Error: Foundations for a New Science of Consciousness. New York: Pantheon Books) faces nearly the same difficulties and its capability of overcoming these difficulties is problematic. While the hybrid cosmopsychism, as developed in Goff’s later manuscript, circumvents the causal closure problem, it does so at the price of relying on assumptions that are hardly intelligible.

Ontology of Plagiarism: the Non Academic Perspectives

Existence of plagiarism is an occupation of education in two dimensions: for learning and a mental process construct of lack of acknowledgement of innovations in learning. Unfortunately, the common concepts of plagiarism has not been clarified in learning process. This paper argues that plagiarism is an inherent natural process of learning. With the objectives; to examine the intrinsic nature of plagiarism and to explore the trans-disciplinary existence of plagiarism in human knowledge as categorised by Dewey Classification scheme 000-999. The study applied positivist paradigm and investigated the existence in relationship between learning processes and plagiarism. The study quantitatively measured opinions of 28 participants in these processes using the Likert scale. Dewey Decimal Classification Systems was used to examine epistemic harnessing of plagiarism in the advancement of different disciplines. The findings were that plagiarism is a natural, intrinsic process of learning through which research, innovations and evolution builds on. The paper concludes by putting a case that acknowledgement and development of referencing and citation technologies are evidence of ontological realities of plagiarism and evidence of the learning process

Effective bandgap narrowing and enhanced optoelectronic performance of Cs2PtBr6 double perovskites by pressure engineering.

Tuning the structure-property relations of perovskites by pressure engineering holds great promise for discovering materials with favorable properties. The newly synthesized Cs2PtBr6 double perovskite exhibits excellent water resistance and chemical stability. Yet its photoelectric conversion efficiency is limited by its intrinsic wide-bandgap nature. In this work, based on density functional theory calculations, we demonstrate the bandgap narrowing of Cs2PtBr6 via pressure engineering and maintain its structural stability. Strikingly, upon applying pressure up to 12 GPa, the bandgap value decreases to 1.34 eV, which exactly reaches the optimal bandgap required by the Shockley-Queisser efficiency limit. Moreover, optical calculation analysis shows that the optical absorption of Cs2PtBr6 exhibits a significant improvement within the visible range. Therefore, the potential of Cs2PtBr6 as a photovoltaic material by pressure engineering is improved. This work is useful for designing and synthesizing new perovskite materials with enhanced performance.