Range Of Fields Research Articles

Utilizing the multifrequency resonances of the electron spins in diamond nitrogen-vacancy centers under strong radio frequency driving for quantum sensing

Multifrequency resonances in the pulsed-optically detected magnetic resonance (ODMR) spectra of electron spins in ensemble nitrogen-vacancy (NV) centers in diamonds are investigated under strong radio frequency (RF) driving at a MHz frequency range and weak microwave driving at a GHz frequency range in a bias static magnetic field for quantum sensing applications. First, we demonstrate that the coherent destruction of tunneling, which leads to the disappearance of the main resonance peaks, can be utilized for precise calibration of the RF amplitude. Next, we clarify the condition for enhancing the sensitivity of a DC magnetic field under strong RF driving at the RF frequency that matches the split frequency due to the hyperfine interaction between 15N nuclear spins and the NV electron spins. Our findings indicate that strong RF driving increases the sensitivity of the DC magnetic field by enhancing the ODMR contrast and reducing the linewidth. The above results contribute to certifying the quantitative accuracy of RF imaging and enhancing the sensitivity of the DC magnetic field imaging using ensemble NV centers in diamonds.

Conductive/Insulating Bioinks with Multitechnology Compatibility and Adjustable Performance.

Conducting/insulating inks have received significant attention for the fabrication of a wide range of additive manufacturing technology. However, current inks often demonstrate poor biocompatibility and face trade-offs between conductivity and mechanical stiffness under physiological conditions. Here, conductive/insulating bioinks based on two-dimensional materials are proposed. The conductive bioink, graphene (GR)-poly(lactic-co-glycolic acid) (PLGA), is prepared by introducing conductive GR into a degradable polymer matrix, PLGA, while the insulating bioink, boron nitride (BN)-PLGA, is synthesized by adding insulating BN. By optimizing the material ratios, this work achieves precise control of the electromechanical properties of the bioinks, thereby enabling the flexible construction of conductive networks according to specific requirements. Furthermore, these bioinks are compatible with a variety of manufacturing technologies such as 3D printing, electrospinning, spin coating, and injection molding, expanding their application range in the biomedical field. Overall, the results suggest that these conducting/insulating bioinks offer improved mechanical, electronic, and biological properties for various emerging biomedical applications.

Seeking to be seen as legitimate members of the scientific community? An analysis of British American Tobacco and Philip Morris International’s involvement in scientific events

IntroductionFor decades, tobacco companies manipulated and misused science. They funded and disseminated favourable research and suppressed research that showed the harms of their products, deliberately generating misinformation. While previous work...

PLUME: Record, Replay, Analyze and Share User Behavior in 6DoF XR Experiences.

From education to medicine to entertainment, a wide range of industrial and academic fields now utilize eXtended Reality (XR) technologies. This diversity and growing use are boosting research and leading to an increasing number of XR experiments involving human subjects. The main aim of these studies is to understand the user experience in the broadest sense, such as the user cognitive and emotional states. Behavioral data collected during XR experiments, such as user movements, gaze, actions, and physiological signals constitute precious assets for analyzing and understanding the user experience. While they contribute to overcome the intrinsic flaws of explicit data such as post-experiment questionnaires, the required acquisition and analysis tools are costly and challenging to develop, especially for 6DoF (Degrees of Freedom) XR experiments. Moreover, there is no common format for XR behavioral data, which restrains data-sharing, and thus hinders wide usages across the community, replicability of studies, and the constitution of large datasets or meta-analysis. In this context, we present PLUME, an open-source software toolbox (PLUME Recorder, PLUME Viewer, PLUME Python) that allows for the exhaustive record of XR behavioral data (including synchronous physiological signals), their offline interactive replay and analysis (with a standalone application), and their easy sharing due to our compact and interoperable data format. We believe that PLUME can greatly benefit the scientific community by making the use of behavioral and physiological data available for the greatest, contributing to the reproducibility and replicability of XR user studies, enabling the creation of large datasets, and contributing to a deeper understanding of user experience.

Multi-CNN based logical reasoning system for facial expression recognition on small-sample datasets

Facial expression recognition has long been an area of great interest across a wide range of fields. Deep learning is commonly employed in facial expression recognition and demonstrates excellent performance in large-sample classification tasks. However, deep learning models often encounter challenges when confronted with small-sample expression classification problems, as they struggle to extract sufficient relevant features from limited data, resulting in subpar performance. This paper presents a novel approach called the Multi-CNN Logical Reasoning System, which is based on local area recognition and logical reasoning. It initiates the process by partitioning facial expression images into two distinct components: eye action and mouth action. Subsequently, it utilizes logical reasoning based on the inherent relationship between local actions and global expressions to facilitate facial expression recognition. Throughout the reasoning process, it not only incorporates manually curated knowledge but also acquires hidden knowledge from the raw data. Experimental results conducted on two small-sample datasets derived from the KDEF and RaFD datasets demonstrate that the proposed approach exhibits faster convergence and higher prediction accuracy when compared to classical deep learning-based algorithms.

Research on lightweight pavement disease detection model based on YOLOv7

Rapid urbanization has made road construction and maintenance imperative, but detecting road diseases has been time-consuming with limited accuracy. To overcome these challenges, we propose an efficient YOLOv7 road disease detection model. Our approach involves integrating MobilieNetV3 as the backbone feature extraction network to reduce the network’s parameters and computational requirements. Additionally, we introduce the BRA attention module into the spatial pyramid pooling module to eliminate redundant information and enhance the network’s feature representation capability. Moreover, we utilize the F-ReLU activation function in the backbone network, expanding the convolutional layers’ receptive field range. To optimize the model’s boundary loss, we employ the Wise-IoU loss function, which places more emphasis on the quality of ordinary samples and enhances the overall performance and generalization ability of the network. Experimental results demonstrate that our improved detection algorithm achieves a higher recall rate and mean average precision (mAP) on the public dataset (RDD) and the NJdata dataset in Nanjing’s urban area. Specifically, compared to YOLOv7, our model increases the recall rate and mAP on RDD by 3.3% and 2.6%, respectively. On the NJdata dataset, our model improves the recall rate and mAP by 1.9% and 1.3%, respectively. Furthermore, our model reduces parameter and computational requirements by 30% and 22.5%, respectively, striking a balance between detection accuracy and speed. In conclusion, our road disease detection model presents an effective solution to address the challenges associated with road disease detection in urban areas. It offers improved accuracy, efficiency, and generalization capabilities compared to existing models.

Facial emotion recognition using enhanced multi-verse optimizer method

In recent years, facial emotion recognition has gained significant improvement and attention. This technology utilizes advanced algorithms to analyze facial expressions, enabling computers to detect and interpret human emotions accurately. Its applications span over a wide range of fields, from improving customer service through sentiment analysis, to enhancing mental health support by monitoring emotional states. However, there are several challenges in facial emotion recognition, including variability in individual expressions, cultural differences in emotion display, and privacy concerns related to data collection and usage. Lighting conditions, occlusions, and the need for diverse datasets also impacts accuracy. To solve these issues, an enhanced multi-verse optimizer (EMVO) technique is proposed to improve the efficiency of recognizing emotions. Moreover, EMVO is used to improve the convergence speed, exploration-exploitation balance, solution quality, and the applicability in different types of optimization problems. Two datasets were used to collect the data, namely YouTube and surrey audio-visual expressed emotion (SAVEE) datasets. Then, the classification is done using the convolutional neural networks (CNN) to improve the performance of emotion recognition. When compared to the existing methods shuffled frog leaping algorithm-incremental wrapper-based subset selection (SFLA-IWSS), hierarchical deep neural network (H-DNN) and unique preference learning (UPL), the proposed method achieved better accuracies, measured at 98.65% and 98.76% on the YouTube and SAVEE datasets, respectively.

Leveraging Artificial Intelligence for Expediting Implementation Efforts.

Expedited implementation of evidence into practice and policymaking is critical to ensure the delivery of effective care and improve health-care outcomes. Implementation science deals with the designing of methods and strategies for increasing and facilitating the uptake of evidence into practice and policymaking. Nevertheless, the process of designing and selecting methods and strategies for implementing evidence is complicated because of the complexity of health-care settings where implementation is desired. Artificial intelligence (AI) has revolutionized a range of fields, including genomics, education, drug trials, research, and health care. This commentary discusses how AI can be leveraged to expedite implementation science efforts for transforming health-care practice. Four key aspects of AI use in implementation science are highlighted: (a) AI for implementation planning (e.g., needs assessment, predictive analytics, and data management), (b) AI for developing implementation tools and guidelines, (c) AI for designing and applying implementation strategies, and (d) AI for monitoring and evaluating implementation outcomes. Use of AI along the implementation continuum from planning to delivery and evaluation can enable more precise and accurate implementation of evidence into practice.

Transcendental philosophy and logic diagrams

AbstractLogic diagrams have seen a resurgence in their application in a range of fields, including logic, biology, media science, computer science and philosophy. Consequently, understanding the history and philosophy of these diagrams has become crucial. As many current diagrammatic systems in logic are based on ideas that originated in the 18th and 19th centuries, it is important to consider what motivated the use of logic diagrams in the past and whether these reasons are still valid today. This paper proposes that transcendental philosophy was a key inspiration for the development of logic diagrams and that such diagrams can be employed in transcendental arguments, even after the linguistic turn.

Enhanced Documentation and Evaluation of Grouting Process, through the Fusion of Non-Destructive Testing and Evaluation Information—The Case Study of the Katholikon of the Monastery of Panagia Varnakova

The restoration of historic buildings and structures involves a wide range of scientific and technical fields. The grouting process is among an array of rehabilitation and preservation interventions and aims to homogenize the structure after the implementation of strengthening measures. The process can provide important information regarding the state of preservation of the examined structure and correlate the progress of the process with the pathology of the monument. To achieve this, the analysis of typical raw grouting data is progressively fused with additional information from the diagnostic studies, non-destructive testing, geospatial information, and from the calculation and analysis of grouting indices. The restoration project of the Katholikon of the Monastery of Panagia Varnakova in Fokida, Greece was selected as the case study, due to its large scale and the severe earthquake damage it has sustained, which has necessitated comprehensive strengthening interventions and extensive grouting. The implementation of an integrated methodological approach validated the enhanced level of co-analysis, revealing information that is not readily deduced from a typical approach. Selected sub-areas of the Katholikon are presented, demonstrating how the observed pathology can be correlated with the results of the grouting process, while incorporating 3D data, and findings from structural and non-destructive analyses.

Advancing Sustainable Development: Broad Applications of Passive Radiative Cooling

With the increasing demand for energy worldwide, researchers from different fields have been striving to improve the sustainability and proper utilization of energy resources. Passive radiative cooling, as a natural energy transport method, can achieve cooling without additional external energy input. This review provides a comprehensive examination of passive radiative cooling, including its fundamental theories and latest development. A particular emphasis is placed on the diverse range of fields where passive radiative cooling has been applied, notably including but not limited to construction and architecture. The current state of applications, potential challenges that may arise with wider adaption and promising research directions for each field are thoroughly discussed. This review emphasizes the extensive potential and practical viability of passive radiative cooling in diverse applications and identifies pressing challenges and future research directions aimed at scaling up real-world implementation.

Ion-acoustic solitary waves in Mars’ lower ionosphere

This study suggests that ionic species dynamics generate electrostatic ion-acoustic solitary waves (IASWs) in Mars’ lower ionosphere. It has been determined through observations that the lower ionosphere of Mars, specifically between 200 and 350 km above the surface, is mostly made up of ions like O2+ , O +, and CO2+ , along with superthermal electrons. This study investigates the occurrence, propagation, and properties of IASWs. The derivation of the evolution equation is conducted for waves with a small-but-finite-amplitude. In order to analyze IASWs with large amplitude, the Sagdeev pseudo-potential is obtained. The model predicts the propagation of electrostatic IASWs within an electric field range of 1 to 6 mV/m and a pulse duration period of 0.5 to 4 ms. Fast Fourier analysis of the electric field pulse reveals pulses from 0.8 to 4 kHz.

Editorial for Human Resources Management and Services (Volume 6, Issue 1)

Volume 6, Issue 1 covers a wide range of fields, mainly discussing issues related to human resource management in enterprises, hotels, schools and organizations; spanning Indonesia, the United States, China, Malaysia and several other countries. By reading these articles, readers can gain a broader understanding of the status and development trends of human resource management and services in various fields and countries in today’s society.

1H Spin-Lattice Relaxation Processes in Solutions of H2N-Fe3O4 Nanoparticles: Insights from NMR Relaxometry.

1H spin-lattice relaxation experiments have been performed for water and glycerol/water solutions of H2N-Fe3O4 superparamagnetic nanoparticles (NPs) of about 7 nm diameter. The experiments encompass a broad frequency range covering 3 orders of magnitude, from 10 kHz to 10 MHz (referring to 1H resonance frequency), and have been performed in the temperature range from 298 to 313 K, varying the concentration of the superparamagnetic species. This extensive dataset has been used for twofold purposes. The first one is to serve as a challenge for thorough tests of theoretical models describing nuclear relaxation in solutions of superparamagnetic NPs, depending on their magnetic properties and dynamics of the solvent molecules. The challenge is posed by the wish to reproduce the data in a broad range of magnetic fields (not only at high fields) and by the need to explain the differences in the relaxation scenarios for water and glycerol/water solutions by varying only the solvent parameters. The second purpose is to get insights into the magnetic properties (electronic relaxation properties) of the nanoparticles due to their high applicational potential.

Scattering spectra models for physics.

Physicists routinely need probabilistic models for a number of tasks such as parameter inference or the generation of new realizations of a field. Establishing such models for highly non-Gaussian fields is a challenge, especially when the number of samples is limited. In this paper, we introduce scattering spectra models for stationary fields and we show that they provide accurate and robust statistical descriptions of a wide range of fields encountered in physics. These models are based on covariances of scattering coefficients, i.e. wavelet decomposition of a field coupled with a pointwise modulus. After introducing useful dimension reductions taking advantage of the regularity of a field under rotation and scaling, we validate these models on various multiscale physical fields and demonstrate that they reproduce standard statistics, including spatial moments up to fourth order. The scattering spectra provide us with a low-dimensional structured representation that captures key properties encountered in a wide range of physical fields. These generic models can be used for data exploration, classification, parameter inference, symmetry detection, and component separation.

Metal Organic Frameworks Synthesis: The Versatility of Triethylamine.

Metal Organic Frameworks (MOFs) are organic-inorganic hybrid materials with exceptionally customizable composition and properties. MOFs intrinsically possess open metal sites, tunable pore size/shape and an ultra-large specific surface area, and have obtained significant attention over the past 30 years. Furthermore, through the integration of functional moieties such as, molecules, functional groups, noble metal clusters and nanocrystals or nanoparticles into MOFs, the resulting composites have greatly enriched the physical and chemical properties of pure MOFs, enabling their application in a wider range of fields. Triethylamine (TEA) as an organic base has consistently played a fundamental role in the development of MOFs. In this Concept, the versatility of triethylamine when involved in the synthesis of MOFs is discussed. Four sections are used to elaborate on the role of TEA including: (1) Single crystal synthesis; (2) Size and morphology control; (3) Counterion of MOFs; (4) MOFs composites synthesis. In the last part, we highlight the potential of TEA for further developments.

A Managed Approach to On-Street Sex Working: Messages for Social Work Practice

Abstract Presently, there is limited research examining sex work and social work in the UK; however, the needs of sex workers often intersect with various areas of practice. This article sets out to enhance social work practice by drawing upon empirical findings from an Independent Review of the Managed Approach to On-Street Sex Working (2020) commissioned by the Safer Leeds Partnership. The Review evaluated the implementation of an approach to on-street sex work introduced in 2014, which established the parameters of on-street sex working in the city, including geographical boundaries and hours. Furthermore, it implemented a multi-agency response to managing problems or needs associated with its presence in the community. The Review of the Managed Approach employed a mixed-method approach that included sex workers, residents, businesses and professionals. However, this article will focus on thematically analysed qualitative data from semi-structured interviews conducted with sex workers and specialist agencies and focus groups with professionals working across a range of related fields. It will explore examples of good practice relevant to social work, whilst highlighting broader structural impediments that increase marginalisation for sex workers. In order to do so, it will draw upon the work of Butler (2006, 2009, 2020) to examine the data.

Exploring slip effects of ferrofluid film flow over a slanted rough surface

We examined the influences of slip parameters on the velocity and thermal characteristics of a ferrofluid film of fixed thickness. The flow is generated on a rough and inclined whirling surface that is positioned in an external magnetic (dipole) field. The similarity transformation reduces the model equations (continuity, momentum, energy and concentration), and the solution of the normalized coupled ordinary differential equations is carried out through the finite element process. The influences of slip effects, Brownian motion, thermophoresis and a heat source on the velocity (radial, tangential and axial), gravity (drainage, induced), temperature profile and concentration profile are determined. The tangential flow and temperature are both decreased by an increase in the velocity slip parameter, whereas drainage, induced, radial and axial flows are increased. Enlarging the thermal slip parameter decreases the temperature. Improving slip parameters (velocity and thermal) also improves the concentration profile. Both Nusselt and Sherwood numbers are found to improve on improving the velocity slip parameter, while they decrease on decreasing the thermal slip parameter. The results and insights from this work could be applied to a wide range of medicinal fields, such as targeted medication therapy and delivery, tissue engineering, etc. as well as different industrial processes including coating, lubrication, heat transfer, etc.

Modern Lines in Geochemistry: Anniversary Conference

Abstract —On 21–25 November, 2022, Vinogradov Institute of Geochemistry, Siberian Branch of the Russian Academy of Sciences (Irkutsk), organized and held an All-Russian conference celebrating the 65th anniversary since the foundation of the Institute and the 105th anniversary since the birth of its first director, Academician Lev Vladimirovich Tauson, who headed the Institute from 1961 to 1989. The results reported at the conference encompass a wide range of research fields in modern geochemistry, including isotope geochemistry of igneous, metamorphic, and sedimentary rocks in various geodynamic settings; chemistry of ore-magmatic systems and modern methods of mineral exploration; environmental geochemistry, geoecology, and paleoclimate; laboratory modeling and thermodynamic calculations of natural and production-related processes and materials; advanced analytical methods and information technologies for geosciences. The conference presentations pay tribute to Lev Tauson whose academic carrier, as well as all creative activity, had been closely related with the development of the Institute of Geochemistry. The preface paper provides a review of topics discussed at the conference concerning various geodynamic and geochemical problems, including sources of material, petrogenesis, and metallogeny.

Harnessing Gravity‐Induced Instability of Soft Materials: Mechanics and Application

AbstractThis work offers a comprehensive overview of how gravity affects soft materials, with a particular emphasis on gravity‐induced instability. Soft materials, including biological tissues, elastomers, and gels, are characterized by low elastic moduli and the ability to undergo significant deformations. These large deformations can lead to instabilities and the emergence of distinctive surface patterns when even small perturbations are introduced. An in‐depth understanding of these gravity‐induced instabilities in soft materials is of paramount importance for both fundamental scientific research and practical applications across diverse domains. The underlying mechanisms governing these instabilities are delved in and elucidate the techniques employed to study and manipulate them. Further, the gravity‐induced wrinkling and the Rayleigh‐Taylor (RT) instability in soft materials are zoomed in, highlighting how altered gravity environments impact natural and synthetic systems. Lastly, current and potential applications are underscored where gravity‐induced instabilities are already making an impact or may hold promise in the near future. In sum, the exploration of gravity‐induced instabilities in soft materials paves the way for innovative applications and advancements in a wide range of fields.