Transfer Issues Research Articles

Multi-view domain adaption based multi-scale convolutional conditional invertible discriminator for cross-subject electroencephalogram emotion recognition.

Cross subject Electroencephalogram (EEG) emotion recognition refers to the process of utilizing electroencephalogram signals to recognize and classify emotions across different individuals. It tracks neural electrical patterns, and by analyzing these signals, it's possible to infer a person's emotional state. The objective of cross-subject recognition is to create models or algorithms that can reliably detect emotions in both the same person and several other people. Accurately predicting emotions poses challenges due to dynamic traits. Models struggle with feature extraction, convergence, and negative transfer issues, hindering cross subject emotion recognition. The proposed model employs thorough signal preprocessing, Short-Time Geodesic Flow Kernel Fourier Transform (STGFKFT) for feature extraction, enhancing classifiers' accuracy. Multi-view sheaf attention improves feature discrimination, while the Multi-Scale Convolutional Conditional Invertible Puma Discriminator Neural Network (MSCCIPDNN) framework ensures generalization. Efficient computational techniques and the puma optimization algorithm enhance model robustness and convergence. The suggested framework demonstrates extraordinary success with high accuracy, of 99.5%, 99% and 99.50% for SEED, SEED-IV, and DEAP dataset sequentially. By incorporating these techniques, the proposed method aims to precisely recognition emotions, and accurately captures the features, thereby overcoming the limitations of existing methodologies.

More Than One Agent? Authority Expansion and Delegation Dynamics in the EU

ABSTRACTRecent studies focus on the issue of authority transfer to supranational institutions. While examining the opportunities and obstacles for expanding the Union's competencies, this literature often overlooks the effects of adopting ambitious policies on their implementation modes. This paper argues that the costs associated with the expansion of EU authority and opportunities for blame‐shifting drive delegation choices and define the relative discretion granted to agents. Proposals for expanding EU authority increase the likelihood of the exclusively supranational implementation path being selected by the principals while undermining the appeal of the national path. In contrast, aiming to preserve opportunities for blame‐shifting while maintaining some degree of control over implementation, the EU principals increasingly turn to joint delegation, where the Commission and national administrations cooperate. Yet, even within the partner‐like relationship of joint implementation, national agents enjoy broader discretionary leeway.

Angle-based transfer support matrix machine for roller bearing fault diagnosis under limited labeled data

In real-world fault diagnosis, the process of acquiring fault vibration signals frequently faces various challenges, which lead to certain fault types being represented by a scarcity of vibration signal data. Although support matrix machine (SMM) exhibits excellent classification ability in matrix-based data, its ideal model is difficult to establish with limited labeled samples. To address this limitation, a novel angle-based transfer support matrix machine (ATSMM) is proposed, which integrates matrix-based classifier and transfer learning theory. ATSMM employs an angle-based transfer learning method, which aims to reduce the angle between the normals of the target domain hyperplane and the source domain hyperplane, enabling the transfer of structural information across different domains. Meantime, ATSMM effectively addresses the issue of negative transfer that frequently occurs in transfer learning, which adds the weights calculated by the maximum mean discrepancy method to further improve the transfer effectiveness of the target domain model. The superiority of ATSMM is verified by two different roller bearing datasets, and the outcomes of experiments exhibit that the proposed ATSMM has excellent classification performance compared to alternative methods, especially in scenarios where the labeled sample size is limited.

"Technology Transfer Challenges in MSMEs: Evaluating IP Commercialization Pathways in the Pharma Sector"

Despite their substantial contribution to pharmaceutical innovation, MSMEs have several barriers to technology transfer and IP commercialization and are likely to be greatly affected worldwide by the COVID-19 crisis. This paper looks at these challenges, assesses the current frameworks for commercialization and puts forward guidelines for how to develop effective frameworks to build on the role of MSMEs in pharmaceutical development. Utilizing a multimethod approach of topic modeling, correspondence analysis, and a trend analysis, the study shows that the process by which technology transfer and IP management takes place in pharmaceutical MSMEs is complex. The following are the major issues observed when scaling up: Strategic partnerships are crucial for commercialization, internal capacity is also a critical component and a strong focus on the market need. The study also highlights the social impacts of efficient technology transfer; of which, include improved availability and cost-effectiveness of new healthcare technologies. With regards to managerial implications, this paper establishes that there is a call for creating symbiotic relationships, staff development, and handling of market issues. Papers to be explored for future research are:- examination of the effects of the digitization and advanced technologies on technology transfer activities- the relation between technology transfer, Intellectual Property, sustainability and ethical issues in drug development. In this aspect, it is possible to identify the following challenges and opportunities that can be addressed by development stakeholders in order to ensure that innovation and development are achieved by pharmaceutical MSMEs and benefit the public health of societies, and thus the well-being of individuals and communities. DOI: https://doi.org/10.52783/pst.1104

Regulating Charge Transfer in a Heterojunction Photoanode for Efficient Photo‐Charging of Zn‐Air/Sulfion Hybrid Batteries

AbstractIntegrating sulfion‐rich wastewater purification with photo‐charging Zn‐Air batteries enables dual benefits for solar energy storage and environmental protection. However, photo‐charging efficiency is hindered by charge transfer issues. Here, the study synthesizes a CdS@CdIn2S4 core–shell photoelectrode and uses Kelvin Probe Force Microscopy (KPFM) to probe surface potential changes and photo‐charge transfer under illumination. The outer shell (CdIn2S4) exhibits upward band bending and a Conduction Band (CB) more negative than bulk CdS, leading to the accumulation of photoelectrons on the surface, which is detrimental to sulfion oxidation. Vacancy engineering aligns the Fermi energy of two materials, creating an optimal type‐II configuration that accumulates holes on the surface, thereby boosting the photo‐charging current density in Zn‐Air/Sulfion hybrid batteries by 18‐fold. A benchmark photo‐charging current density of 1.26 mA cm−2 is therefore achieved for Zn‐Air/Sulfion hybrid batteries. This work demonstrates the effectiveness of regulating charge transfer pathways in photo‐charging Zn‐Air/Sulfion hybrid batteries, showing potential applications in various photo‐assisted batteries.

Non-submerged attached growth process for low-concentration methane emission treatment using downflow hanging sponge

The gas-liquid mass transfer issue has always been a challenge in the biological oxidation of methane waste gas treatment. Therefore, this study aims to apply the Downflow Hanging Sponge (DHS) that widely used in wastewater treatment, to mitigate the gas-liquid mass transfer obstacles on methane waste gas treatment. The comprehensive experimental results indicate that among the three sponge carriers, the Melamine Foam (MF) exhibits the strongest wettability, characterized by smaller pore size and a uniformly arranged pore structure. And it demonstrates excellent performance in methane degradation. (maintaining methane degradation efficiency above 5 mol·h−1m−3 in the third phase.) Furthermore, the combined analysis of microbial QPCR and KEGG reveals that the MF sponge possesses a more diverse microbial population and superior methane oxidation capability compared to the other two sponges. This study confirms the superiority of the MF sponge as a biological carrier, capable of enhancing the efficiency of DHS treatment of methane waste gas and providing valuable suggestions for carrier selection and modification.

Impact of speed on injury severity in single-vehicle run-off-road crashes: Insights from partially temporal constrained modeling approach

Single-vehicle run-off-road crashes accounts for approximately 35% of all the traffic fatalities in the U.S during the period of 2019–2021. This paper explores the association between driving speed and injury severity outcomes of single-vehicle run-off-road crashes. The single-vehicle run-off-road crash data from 2019 to 2021 on Interstate freeways in Florida are utilized, and categorized into periods of pre-, during-, and post-COVID-19 pandemic. The partially constrained temporal and temporal unconstrained random parameters logit models are developed considering three injury severity outcomes: no injury, minor injury and serious injury/fatality. Multiple variables in terms of driver, vehicle, roadway, environmental, crash, and temporal attributes are observed to significantly affect the injury severity. Moreover, temporal instability and transferability issues are validated through likelihood ratio test and out-of-sample prediction. In the partially constrained models, numerous variables such as indicators of new vehicle, male driver, and restraint-protected driving consistently yield identical parameter values across all periods, whereas various variables clearly illustrate the distinct differences across the three periods and three speed intervals. The marginal effects in the unconstrained models also display the obvious differences across three periods and three speed intervals. Moreover, the findings corroborate the increased risk outcomes linked to larger speed differences and the COVID-19 pandemic period. These results provide better understanding of the risk mechanisms underlying run-off-road crashes and furnish valuable direction for the formulation of effective safety interventions.

Efficient optical property screening of :Ce (RE = , and Y): Based on activator level position in host band gap

Property screening is essential to identify optical materials with desirable luminescent and thermal properties for specific applications under various conditions. As promising candidates for optical applications across diverse fields, Ce-doped ▪ (RE = ▪, and Y) are studied by utilizing first-principles calculations based on density-functional theory. By employing a simple yet effective approach to analyze the electronic structures, we gain insights into the absorption edge characteristics of ▪, and evaluate the luminescent and thermal properties from the 4f and 5d positions of Ce in the host band gaps. Consequently, it is evident that the absence of luminescence in Ce-doped ▪ is due to the 5d excited states of ▪ being situated in the conduction band of ▪, regardless of its space group. While the 4f and 5d states of ▪ reside within the band gap of other ▪ hosts, potentially leading to luminescence. Excluding certain lanthanides due to potential charge transfer issues with ▪, our focus is directed towards the luminescent properties of Ce-doped ▪, ▪, ▪, and ▪. Based on schematic configuration coordinate diagrams, we conclude more significant thermal quenching resulting from 5d ionization in ▪:Ce and ▪:Ce compared to ▪:Ce and ▪:Ce. Furthermore, we provide valuable insights into the experimental phenomenon that the energy barrier for thermal quenching is smaller than that for thermal ionization in ▪:Ce. Additionally, we assess the photoelectric and thermoelectric performance based on the thermal and optical charge transition levels of Ce in ▪. Based on our study of the luminescent properties and thermal behaviors among Ce-doped ▪, we offer suggestions for screening Ce-activated optical materials, which provide valuable guidance for the design and optimization of specific materials.

SPHERICAL GRAPHENE FILM GROWN ON CARBIDE FOR SENSOR MATRIX

Graphene has amazing applications for sensors due to its excellent performances like high strength and good conductivity, but the transfer issue is in the way of its application perspective. Direct growth of spherical graphene films (SGFs) on cemented carbide may offer a good avenue for various applications in sensor technology, especially for electrochemical sensors. Four common methods for graphene preparation are chemical stripping, chemical vapor deposition (CVD), metal catalysis, and laser fabrication; and subject to transfer issues during usage. In order to overcome this limitation, the fabrication of in-situ growth of SGFs on carbide is proposed as a solution for constructing sensor matrices. This review explores various in-situ SGFs and their potential applications in sensors. The findings presented here shed light on transfer-free graphene with controllable structures that can serve as excellent candidates for sensor matrices.

Enhancing Open-Set Domain Adaptation through Optimal Transport and Adversarial Learning

Open-Set Domain Adaptation (OSDA) is designed to facilitate the transfer of knowledge from a source domain to a target domain, where the class space of the source is a subset of the target. The primary challenge in OSDA is the identification of shared samples in the target domain to achieve domain alignment while effectively segregating private samples in the target domain. In attempts to address this challenge, numerous existing methods leverage weighted classifiers to mitigate the negative transfer issue induced by private classes in the target domain and recognize all these samples as a whole unknown class. However, this strategy may result in inadequate acquisition of discriminative information within the target domain and an unclear decision boundaries. To overcome these limitations, we propose a novel framework termed Optimal Transport and Adversarial Learning (OTAL). Our approach innovatively introduces Optimal Transport (OT) with a similarity matrix for feature-to-prototype mapping in clustering, enabling the model to learn discriminative information and capturing the intrinsic structure of the target domain. Furthermore, we introduce a three-way domain discriminator to aid in the construction of decision boundary between known and unknown classes, while simultaneously aligning the distribution of known samples. Experimental results on three image classification datasets (Office-31, Office-Home and VisDA-2017) demonstrate the superior performance of OTAL when compared to existing state-of-the-art methods.

Analytical Evaluation of the WebSocket and WebTransport Protocols for the Class of SCADA Systems Operating Within the Intranet

The purpose of research. In recent years, web technologies have been actively used in many process control systems, including SCADA. Such systems allow continuous monitoring of the test object in real time. In this regard, there is a need for frequent data transmission for their further display. To solve this problem, network protocols are used, which continue to develop rapidly to this day.Materials and methods. SCADA systems are used in many industries: aviation, ground transportation, rocket and space technology, systems for receiving and processing telemetry information. Among them, a class of SCADA systems operating within the intranet stands out. This can be an internal network of the enterprise, a hangar or a small test stand for tracking the indexes of measuring equipment. Therefore, today, more than ever, open and publicly available materials are important for understanding modern network solutions that reduce the time spent on data transmission and their further visualization. Such information will be of particular value to students studying network and web technologies.Results. As a result of the research, taking into account the limitations of the SCADA system and network being developed, the time characteristics for the WebSocket and WebTransport protocols have been determined, and the most optimal solution for a typical load is proposed. In the presented paper, the issues of data transfer between the client and server parts for a class of SCADA systems operating within an intranet are considered. In this regard, it is proposed to consider the two most suitable solutions for this task. These are application layer network protocols of the OSI model: WebSocket and WebTransport. In addition, transport layer protocols (TCP and QUIC) are taken into account, since they can also affect time characteristics. Data transmission issues are considered in the context of the SCADA data display and monitoring system. The server part is developed using C#, and the client is developed using JavaScript. A number of experiments are carried out for boundary cases and typical loads, on the basis of which the dependence of data transmission time on their volume is determined.Conclusion. The approach given in the paper on collecting network traffic and analyzing time characteristics will also be useful as part of the educational process for teaching students in the courses “Computer Networks” and “Internet Technologies”.

Solubility prediction and interaction mechanism of FOX-7 in ten solvents by molecular and thermodynamic modeling

Solubility is a fundamental physicochemical property of materials. Current solubility prediction models are often highly parameterized by existing data, leading to a serious issue of transferability; thus, it is still challenging to enhancing their accuracy and generalization-ability. In this study, we employ molecular dynamics (MD) simulations and some thermodynamic modeling methods (SMD, COSMO-RS, and COSMO-SAC) to predict the relative solubility of 1,1-diamino-2,2-dinitroethylene (FOX-7) in ten solvents. A meticulous comparison with experimental data reveals that the alchemical free energy approach based on MD simulations outperforms SMD, COSMO-RS and COSMO-SAC models in solubility prediction. Furthermore, we identify that the more negative solvation free energy corresponds to the higher solubility, and the stronger solute–solvent hydrogen bonding corresponds to the higher solubility for low polar solvents too. Finally, it is confirmed that the electrostatic attraction dominates the binding energy between solute and solvent molecules. This work transcends traditional solubility prediction by offering a robust theoretical framework and computational strategy, proving both economical and environmentally benign. It paves the way for the rational selection of solvents for FOX-7 crystallization and provides a versatile tool for predicting the solubility of organic compounds.

Understanding the Implications of Non-Accused DNA at Crime Scenes for Criminal Defence Lawyers

While DNA evidence has revolutionized criminal investigations, its presence in legal cases becomes problematic when the DNA of non-accused individuals is detected at crime scenes. This essay explores the issue of unintentional DNA transfer and its implications for defence attorneys. This study questions the prevailing assumption that DNA presence equates to guilt by analyzing a burglary case study, demonstrating how DNA can be unintentionally deposited by those not involved in the crime. The findings reveal the complexities surrounding forensic evidence, showing that individuals who haven't physically interacted with crime scene objects can still leave their DNA behind. This complexity underscores the need for lawyers to adopt a more nuanced perspective, emphasizing the importance of expert testimony, rigorous investigative methods, and contextual evaluations in accurately assessing the implications of DNA evidence within the judicial system. Understanding these intricacies is vital for police officers and legal professionals including judiciary to navigate carefully and sincerely cases where DNA evidence plays a critical role in proving or disproving the guilt of the accused person.

Preparation of Purpurin-Fe2+ Complex Natural Dye and Its Printing Performance on Silk Fabrics.

In order to shorten the process of textile printing with natural dyes, develop new methods, and improve the color fastness and quality of printed products, this study presents a novel approach by synthesizing a natural complex dye through the interaction between purpurin and Fe2+ ions, resulting in a compound named purpurin-Fe2+ (P-Fe). This synthesized complex dye was meticulously characterized using state-of-the-art analytical techniques, including Fourier transform infrared spectroscopy (FT-IR), ultraviolet-visible (UV-Vis) spectrophotometry, and scanning electron microscopy energy-dispersive spectroscopy (EDS). The characterization confirmed the successful complexation of purpurin with Fe2+ ions. The prepared complex dye P-Fe was used for the printing of silk fabric. The optimized printing process involves steaming at a temperature of 100 °C for a duration of 20 min. In comparison to fabrics printed using direct dyes, the K/S values of the fabric printed with the P-Fe complex showed a significant enhancement, with all color fastness ratings achieving grade four. Furthermore, the proportion of metal elements on the white background of the printed fabric was found to be less than 0.180%, and the level of whiteness was above 50. The application of the P-Fe dye in silk fabric printing not only streamlines the printing process but also enhances the depth and speed of the printed color, effectively addressing the issue of color transfer onto a white background, which is commonly associated with natural dyes.

Localized High-Concentration Electrolyte in Li-Mediated Nitrogen Reduction for Ammonia Synthesis.

The lithium-mediated nitrogen reduction reaction (Li-NRR) is a promising green alternative to the Haber-Bosch process for ammonia synthesis. The solid electrolyte interphase (SEI) is crucial for high efficiency and stability, as it regulates reactant diffusion and suppresses side reactions. The SEI properties are greatly influenced by the Li+ ion solvation structure, which is controllable through electrolyte engineering. Although anion-derived SEI enhances selectivity and stability, it has typically been engineered using high-concentration electrolytes (HCEs), which face mass transfer, viscosity, and cost issues. In this study, a localized high-concentration electrolyte (LHCE) in the Li-NRR is first introduced, enabling the formation of anion-derived SEI in a low-concentration electrolyte (LCE) by enhancing the Li-anion coordination using an antisolvent. Among various antisolvents, 1,1,2,2-tetrafluoroethyl-2,2,3,3-tetrafluoropropyl ether (TTE) achieves the highest ammonia Faradaic efficiency (73.6 ± 2.5%), more than double that of the LCE (34.3 ± 2.8%) and exceeding the HCE (56.0 ± 2.8%). Systematic calculations and experimental analyses show that the LHCE exhibits anion-rich solvation structures and formsthin, inorganic SEI. Moreover, the LHCE has advantages of low viscosity and high N2 solubility, which facilitate mass transport. This study suggests the application of LHCE as an effective electrolyte engineering strategy to enhance the Li-NRR efficiency.

Entropy generation analysis and thermal performance of absorber tube in parabolic trough solar collector inserted with eccentric structure insert

To address the issue of uneven heat transfer in parabolic trough solar collectors, this study introduces an innovative insert (composed of vortex generators) layout. The vortex generators are strategically placed in areas with high heat flux density, while their number is reduced in regions with low heat flux density. By adjusting the position of the central rod, the inserts are defined as three different layout strategies (central structure, low eccentric structure, and upper eccentric structure), and their thermal performance is analyzed. The differences in fluid flow and heat transfer induced by different structures are also compared. The results show that the upper eccentric structure can induce unevenly distributed high-intensity mixing vortices, and through the ejection and sweeping movements of these vortices, the high-temperature fluid in areas with high heat flux density is transported to positions with low geothermal flow. Among all the studied configurations, the upper eccentric structure achieves a Nusselt number increase ranging from 1.16 to 2.34 times and a friction number value increase between 1.47 and 5.38 times. In terms of heat transfer performance, the highest value is 1.43 when the number of vortex generators is 6 and Reynold number is 13,750. Additionally, the thermal performance of the inserted tubes is analyzed using the field synergy principle and entropy generation method.

Monomer Transport via Collision in Emulsion Polymerization

AbstractOne of the questions still remaining regarding emulsion polymerization is the issue of mass transfer versus reaction limitation since, by its nature, emulsion polymerization requires monomer transport across an aqueous phase. This question is brought into focus lately by the growing use of miniemulsion polymerization in which the monomer transport step is much less important. This paper will explore the possible rate of monomer transport via collision and ratio this to the maximum rate of polymerization using the Damkohler analysis formality. Results indicate that systems relying on monomer transport by collision will be almost universally monomer‐transport‐limited, thus exhibiting lower‐than‐expected rates of polymerizations.

Chemically Transferable Electronic Coarse Graining for Polythiophenes.

Recent advances in machine-learning-based electronic coarse graining (ECG) methods have demonstrated the potential to enable electronic predictions in soft materials at mesoscopic length scales. However, previous ECG models have yet to confront the issue of chemical transferability. In this study, we develop chemically transferable ECG models for polythiophenes using graph neural networks. Our models are trained on a data set that samples over the conformational space of random polythiophene sequences generated with 15 different monomer chemistries and three different degrees of polymerization. We systematically explore the impact of coarse-grained representation on ECG accuracy, highlighting the significance of preserving the C-β coordinates in thiophene. We also find that integrating unique polymer sequences into training enhances the model performance more efficiently than augmenting conformational sampling for sequences already in the training data set. Moreover, our ECG models, developed initially for one property and one level of quantum chemical theory, can be efficiently transferred to related properties and higher levels of theory with minimal additional data. The chemically transferable ECG model introduced in this work will serve as a foundation model for new classes of chemically transferable ECG predictions across chemical space.

On the classical approach to describing the diffusion of cosmic rays in a turbulent medium

The inhomogeneous structure of the interstellar medium (ISM) is characterized by largescale fluctuations that significantly affect the cosmic ray propagation process. Accounting for this influence can not only lead to adjustments in the diffusion process parameters but even to pass from differential operators to integral ones. The most crucial characteristics of a turbulent medium is its power spectrum. Including appropriate approximations of this spectrum allows us to consider this problem in the framework of the traditional diffusion approach [1, 2]. This article explores the analytical representations of this spectrum applied in the cosmic ray transfer theory, including the four-parameter Uchaikin—Zolotarev approximation, derived from the generalized Ornstein—Zernike equation. Testing of the latter revealed that, with carefully chosen parameters, it accurately replicates numerical modeling results both in the inertial interval and beyond. Therefore, it can be effectively employed in addressing cosmic ray transfer issues within a turbulent interstellar medium.

Dual-defect AgBiO3/DCN S-scheme heterojunction for booted photocatalytic removal of Norfloxacin: Interfacial charge transfer, mechanism and toxicity assessment insights

Antibiotics pollution may pose a great risk to ecosystem and human health. In this work, an innovative dual-defect AgBiO3/DCN S-scheme heterojunction was constructed to overcome the sluggish charge separation and transfer issue and showed exceptional photodegradation performance towards norfloxacin (NFL) pollution under the visible light. The degradation efficiency of NFL reached 95.30 % within 120 min respectively, the rate constant was 3.64 times higher than that of defective g-C3N4 (DCN). The systematically characterizations proved that the enhanced photocatalytic activity was benefited from optimized band structures, improved carrier separation efficiency, due to dual defects and S-scheme heterojunction in AgBiO3/DCN. the intermediate degradation products and degradation pathway of NFL were speculated by UPLC-HRMS, and the environmental toxicity degree of degradation products was analyzed according to the T.E.S.T software. The charge transfer pathway was confirmed as an S-scheme mechanism through a combination of band structure analysis, alongside electron paramagnetic resonance (EPR) experiments.The quenching experiments and ESR analyses indicated that h+ and O2•− radicals played predominant roles. The efficient and stable photocatalytic NFL degradation efficiency and broad environmental adaptability proved potential practical application. This work may shed light on designing new dual-defect heterojunction with photocatalytic antibiotic removal capability in the innovative water treatment process.