bluuebunny/crossref_metadata_2025_split huggingface dataset:
High-performance cost efficient simultaneous wireless information and power transfers deploying jointly modulated amplifying programmable metasurface
Authors: Wang Xin , Han Jia Qi , Li Guan Xuan , Xia De Xiao , Chang Ming Yang , Ma Xiang Jin , Xue Hao , Xu Peng , Li Rui Jie , Zhang Kun Yi , Liu Hai Xia , Li Long , Cui Tie Jun 
Published: September 2023

AbstractProgrammable metasurfaces present significant capabilities in manipulating electromagnetic waves, making them a promising candidate for simultaneous wireless information and power transfer (SWIPT), which has the potential to enable sustainable wireless communication in complex electromagnetic environments. However, challenges remain in terms of maximum power transmission distance and stable phase manipulation with high-power scattered waves. Additionally, waveform limitations restrict average scattered power and rectifier conversion efficiency, affecting data transmission rates and energy transmission distance. Here we show an amplifying programmable metasurface (APM) and a joint modulation method to address these challenges. The APM mitigates the peak-to-average power ratio and improves maximum power, phase response stability, average output power, and rectifier conversion efficiency. Through experimental validation, we demonstrate the feasibility of the SWIPT system, showcasing simultaneous LED array powering and movie video transmission. This innovative SWIPT system holds promise for diverse applications, including 6 G wireless communications, IoT, implanted devices, and cognitive radio networks.
Observation of non-superconducting phase changes in nitrogen doped lutetium hydrides
Authors: Xing Xiangzhuo , Wang Chao , Yu Linchao , Xu Jie , Zhang Chutong , Zhang Mengge , Huang Song , Zhang Xiaoran , Liu Yunxian , Yang Bingchao , Chen Xin , Zhang Yongsheng , Guo Jiangang , Shi Zhixiang , Ma Yanming , Chen Changfeng , Liu Xiaobing 
Published: September 2023

AbstractThe recent report of near-ambient superconductivity and associated color changes in pressurized nitrogen doped lutetium hydride has triggered worldwide interest and raised major questions about the nature and underlying physics of these latest claims. Here we report synthesis and characterization of high-purity nitrogen doped lutetium hydride LuH2±xNy. We find that pressure conditions have notable effects on Lu-N and Lu-NH chemical bonding and the color changes likely stem from pressure-induced electron redistribution of nitrogen/vacancies and interaction with the LuH2framework. No superconducting transition is found in all the phases at temperatures 1.8-300 K and pressures 0-38 GPa. Instead, we identify a notable temperature-induced resistance anomaly of electronic origin in LuH2±xNy, which is most pronounced in the pink phase and may have been erroneously interpreted as a sign of superconducting transition. This work establishes key benchmarks for nitrogen doped lutetium hydrides, allowing an in-depth understanding of its novel pressure-induced phase changes.
Mending cracks atom-by-atom in rutile TiO2 with electron beam radiolysis
Authors: Guo Silu , Yun Hwanhui , Nair Sreejith , Jalan Bharat , Mkhoyan K. Andre 
Published: September 2023

AbstractRich electron-matter interactions fundamentally enable electron probe studies of materials such as scanning transmission electron microscopy (STEM). Inelastic interactions often result in structural modifications of the material, ultimately limiting the quality of electron probe measurements. However, atomistic mechanisms of inelastic-scattering-driven transformations are difficult to characterize. Here, we report direct visualization of radiolysis-driven restructuring of rutile TiO2 under electron beam irradiation. Using annular dark field imaging and electron energy-loss spectroscopy signals, STEM probes revealed the progressive filling of atomically sharp nanometer-wide cracks with striking atomic resolution detail. STEM probes of varying beam energy and precisely controlled electron dose were found to constructively restructure rutile TiO2 according to a quantified radiolytic mechanism. Based on direct experimental observation, a “two-step rolling” model of mobile octahedral building blocks enabling radiolysis-driven atomic migration is introduced. Such controlled electron beam-induced radiolytic restructuring can be used to engineer novel nanostructures atom-by-atom.
Water-energy nexus in a desalination-based water sector: the impact of electricity load shedding programs
Authors: Salomons Elad , Housh Mashor , Katz David , Sela Lina 
Published: September 2023

AbstractReliance on water production by desalination as a solution to water scarcity is growing worldwide. High energy demands of seawater desalination raise new challenges for both water and energy management and highlight the importance of understanding the operational dependencies of the water sector on energy supplies. This study provides an in-depth analysis of the impact of the water-energy nexus in a desalination-based water sector, using Israel as a case study. Being large energy consumers, desalination plants are part of the Electricity Load Shedding Program (ELSP), which government energy regulators invoke in times of energy shortage. We focus on the interdependency between the two sectors as manifested at the time of ELSP utilization during an extreme heat wave. We show that energy shedding compensation is 6 to 14 times greater than the economic loss to the desalination plant from no water production, creating an obvious economic incentive to participate in ELSPs. However, this imbalance has a substantial negative impact on the water sector, which may compromise the level of service. Our evaluation concludes that the government authorities regulating water and energy need an official mechanism and policy for joint management strategies that can ensure economic efficiency and reduce the risk of power and water shortages during extreme events.
Aquaculture 4.0: hybrid neural network multivariate water quality parameters forecasting model
Authors: Eze Elias , Kirby Sam , Attridge John , Ajmal Tahmina 
Published: September 2023

AbstractThis study examined the efficiency of hybrid deep neural network and multivariate water quality forecasting model in aquaculture ecosystem. Accurate forecasting of critical water quality parameters can allow for timely identification of possible problem areas and enable decision-makers to take pre-emptive remedial actions that can significantly improve water quality management in aquaculture industry. A novel hybrid deep learning neural network multivariate water quality parameters forecasting model is developed with the aid of ensemble empirical mode decomposition (EEMD) method, deep learning long-short term memory (LSTM) neural network (NN), and multivariate linear regression (MLR) method. The presented water quality forecasting model (shortened as EEMD–MLR–LSTM NN model) is developed using multivariate time-series water quality sensor data collected from Loch Duart company, a Salmon offshore aquaculture farm based around Scourie, northwest Scotland. The performance of the novel hybrid water quality forecasting model is validated by comparing the forecast result with measured water quality parameters data and the real Phytoplankton data count from the aquaculture farm. The forecast accuracy of the results suggests that the novel hybrid water quality forecasting model can be used as a valuable support tool for water quality management in aquaculture industries.
Characterization and stability evaluation of Egyptian propolis extract nano-capsules and their application
Authors: Amin Azza A. , Mahmoud Khaled F. , Salama Manal F. , Longo Vincenzo , Pozzo Luisa , Seliem Effat I. , Ibrahim Mona A. 
Published: September 2023

AbstractThe increasing demand for natural products and biotechnological activities from bees facilitate their widespread use in food preservation and beneficial effects on humans. This study aimed to prepare and characterize the nano-capsules of Qaluiobia (PQG) governorates propolis extracted with water, ethanol and supercritical fluid-carbon dioxide at 50 °C with co-solvent. Propolis bioavailability was analyzed and introduced to prepare crackers to extend their shelf life. Nano-encapsulation was examined using transmission electron microscopy (TEM), differential scanning calorimetry (DSC) and antioxidant activity. Ethanol and supercritical fluid-carbon dioxide (SCF-CO2) at 50 °C with ethanol as co-solvent recorded higher yield, antioxidant activities, total phenolics and total flavonoids. SCF-CO2 extracts had a higher flavonoid concentration. It was revealed that propolis nano-capsules had high-temperature stability and cytotoxic effects against the three tested human cancer cell lines (i.e. PC3, MCF7 and HePG2). The higher overall acceptability of crackers fortified with PQG was achieved with SCF-CO2 at 50 °C and ethanol extract nano-capsules, i.e. 86.57% and 86.29% respectively. The higher ability to retain antioxidant activity reduces the increase of peroxide value (PV), preventing rancidity and increasing the shelf life of crackers during the storage period. Practical application: This study can provide a suitable method for extracting bioactive compounds from propolis, and improve the biological properties and activities by nano-encapsulation, also reveals the extent of its use as a natural antioxidant and anticancer and its application in bakery products as a functional food.
Identification and verification of microtubule associated genes in lung adenocarcinoma
Authors: Wei YuHui , Yang CaiZhen , Wei JinMei , Li WenTao , Qin YuanWen , Liu GuangNan 
Published: September 2023

AbstractAssociated with high morbidity and mortality, lung adenocarcinoma (LUAD) is lacking in effective prognostic prediction and treatment. As chemotherapy drugs commonly used in clinics, microtubule-targeting agents (MTAs) are limited by high toxicity and drug resistance. This research aimed to analyze the expression profile of microtubule-associated genes (MAGs) in LUAD and explore their therapy efficiency and impact on prognosis. Key MAGs were identified as novel molecular targets for targeting microtubules. The LUAD project in The Cancer Genome Atlas (TCGA) database was used to identify differently expressed MAGs. On the one hand, a microtubule-related prognostic signature was constructed and validated, and its links with clinical characteristics and the immune microenvironment were analyzed. On the other hand, hub MAGs were obtained by a protein–protein interaction (PPI) network. Following the expression of hub MAGs, patients with LUAD were classified into two molecular subtypes. A comparison was made of the differences in half-maximal drug inhibitory concentration (IC50) and tumor mutational burden (TMB) between groups. In addition, the influence of MAGs on the anticancer efficacy of different therapies was explored. MAGs, which were included in both the prognosis signature and hub genes, were considered to have great value in prognosis and targeted therapy. They were identified by quantitative real-time polymerase chain reaction (qRT-PCR). A total of 154 differently expressed MAGs were discovered. For one thing, a microtubule-related prognostic signature based on 14 MAGs was created and identified in an external validation cohort. The prognostic signature was used as an independent prognostic factor. For another, 45 hub MAGs were obtained. In accordance with the expression profile of 45 MAGs, patients with LUAD were divided into two subtypes. Distinct differences were observed in TMB and IC50 values of popular chemotherapy and targeted drugs between subtypes. Finally, five genes were included in both the prognosis signature and hub genes, and identified by qRT-PCR. A microtubule-related prognosis signature that can serve as an independent prognostic factor was constructed. Microtubule subtype influenced the efficacy of different treatments and could be used to guide therapy selection. In this research, five key MAGs, including MYB proto-oncogene like 2 (MYBL2), nucleolar and spindle-associated protein 1 (NUSAP1), kinesin family member 4A (KIF4A), KIF15 and KIF20A, were verified and identified. They are promising biomarkers and therapeutic targets in LUAD.
Benchmarking data-driven filtering for denoising of TCRpMHC single-cell data
Authors: Montemurro Alessandro , Povlsen Helle Rus , Jessen Leon Eyrich , Nielsen Morten 
Published: September 2023

AbstractPairing of the T cell receptor (TCR) with its cognate peptide-MHC (pMHC) is a cornerstone in T cell-mediated immunity. Recently, single-cell sequencing coupled with DNA-barcoded MHC multimer staining has enabled high-throughput studies of T cell specificities. However, the immense variability of TCR-pMHC interactions combined with the relatively low signal-to-noise ratio in the data generated using current technologies are complicating these studies. Several approaches have been proposed for denoising single-cell TCR-pMHC specificity data. Here, we present a benchmark evaluating two such denoising methods, ICON and ITRAP. We applied and evaluated the methods on publicly available immune profiling data provided by 10x Genomics. We find that both methods identified approximately 75% of the raw data as noise. We analyzed both internal metrics developed for the purpose and performance on independent data using machine learning methods trained on the raw and denoised 10x data. We find an increased signal-to-noise ratio comparing the denoised to the raw data for both methods, and demonstrate an overall superior performance of the ITRAP method in terms of both data consistency and performance. In conclusion, this study demonstrates that Improving the data quality from high throughput studies of TCRpMHC-specificity by denoising is paramount in increasing our understanding of T cell-mediated immunity.
The diversity of isofrequency surface topologies in a hypercrystal composed of ferrite- and semiconductor-based metamaterials
Authors: Fedorin Illia 
Published: September 2023

AbstractRecent studies have centered on the potential for effectively controlling the topology state of iso-frequency surfaces in artificial photonic structures using external fields. This paper delves into the topological transitions and singularity states of the isofrequency surface of a highly anisotropic superlattice. This superlattice is composed of alternating layers of ferrite-dielectric and semiconductor-dielectric metamaterials. The superlattice is placed in an external magnetic field in the Voigt geometry that is parallel to the boundaries of the structure layers and perpendicular to the periodicity axis. Material properties of both constituent metamaterials are described in terms of effective components of permittivity and permeability in the long-wave approximation. An external magnetic field influences the properties of transverse electric (TE) waves in the ferrite-dielectric metamaterial, and the properties of transverse magnetic (TM) waves in the semiconductor-dielectric metamaterial. This results in the iso-frequency surface transition from a closed ellipsoid to an open hyperboloid for both TE and TM waves in various configurations. Furthermore, the superlattice can be identified as a hypercrystal under certain conditions, specifically when the constituent metamaterials possess a hyperbolic isofrequency surface state. This research demonstrates that the isofrequency surface properties of the studied hypercrystal can be effectively controlled by altering the external magnetic field, the fill factors of metamaterials, and frequency. Special attention is devoted to investigating the topological singularities that take place when iso-frequency surfaces of TE and TM polarized waves intersect. This intersection leads to the degeneracy of the hypercrystal’s isofrequency surface and the potential observation of unique phenomena such as conical refraction or the existence of surface states.
A lightweight weak semantic framework for cinematographic shot classification
Authors: Li Yuzhi , Lu Tianfeng , Tian Feng 
Published: September 2023

AbstractShotis one of the fundamental unit in the content structure of a film, which can provide insights into the film-director’s ideas. By analyzing the properties and types of shots, we can gain a better understanding of a film’s visual language. In this paper, we delve deeply into the task of shot type classification, proposing that utilizing multimodal video inputs can effectively improve the accuracy of the task, and that shot type classification is closely related to low-level spatiotemporal semantic features. To this end, we propose a Lightweight Weak Semantic Relevance Framework (LWSRNet) for classifying cinematographic shot types. Our framework comprises two modules: a Linear Modalities Fusion module (LMF Module) capable of fusing an arbitrary number of video modalities, and a Weak Semantic 3D-CNN based Feature Extraction Backbone (WSFE Module) for classifying shot movement and scale, respectively. Moreover, to support practical cinematographic analysis, we collect FullShots, a large film shot dataset containing 27K shots from 19 movies with professionally annotations for movement and scale information. Following experimental results validate the correctness of our proposed hypotheses, while our framework also outperforms previous methods in terms of accuracy with fewer parameters and computations, on both FullShots and MovieShots datasets. Our code is available at (https://github.com/litchiar/ShotClassification).
Qualitative, energy and environmental aspects of microwave drying of pre-treated apple slices
Authors: Taghinezhad Ebrahim , Kaveh Mohammad , Szumny Antoni , Figiel Adam , Blasco José 
Published: September 2023

AbstractIn the present research, response parameters such as specific energy consumption (SEC), thermal efficiency (TE), energy efficiency (EF), drying time (DT), greenhouse gas (GHG) emission (such as CO2 and NOx), and quality features (color variation and shrinkage) were modeled by response surface methodology (RSM) for apple slices dried in a microwave dryer under ultrasonication (30 ℃—10 min) and blanching (80 °C—2 min) pretreatments. Also, RSM was applied to optimize two independent parameters including microwave power and sample thickness in the levels 100, 200, and 300 W and 2, 4, and 6 mm, respectively. The results indicated the significant influence (P < 0.01) of the independent parameters on the response parameters. The vales of SEC, DT, GHG emission, shrinkage, and color difference were linearly decreased with the declining sample thickness and increasing microwave power, while the energy and thermal efficiencies were increased by a quadratic equation. The use of ultrasonication and blanching pretreatments decreased the SEC, GHG emissions, and DT; while improving the quality of the samples as compared to the non-treated slices. The optimization results showed the optimal drying times (31.55, 82.19, and 50.55 min), SEC (3.42, 10.07, and 4.37 MJ/kg), CO2 with natural gas (1539.75, 1518.75, and 4585 g), CO2 with gas oil (3662.53, 2099.25, 2721.25 g), NOx with natural gas (10.094, 9.956, and 12.906 g), and NOx with gas oil (12.934, 12.758, and 16.538 g) at a microwave power of 300 W and sample thickness of 2 mm with desirability of 0.921, 0.935, and 0.916 for control samples, ultrasonicated, and blanched, respectively.
Lightweight image steganalysis with block-wise pruning
Authors: Hong Eungi , Lim KyungTae , Oh Tae-Woo , Jang Haneol 
Published: September 2023

AbstractImage steganalysis is the task of detecting a secret message hidden in an image. Deep steganalysis using end-to-end deep learning has been successful in recent years, but previous studies focused on improving detection performance rather than designing a lightweight model for practical applications. This caused a deep steganalysis model to be heavy and computationally costly, making the model infeasible to deploy in real-world applications. To address this issue, we study an effective model design strategy for lightweight image steganalysis. Considering the domain-specific characteristics of steganalysis, we propose a simple yet effective block removal strategy that progressively removes a sequence of blocks from deep classification networks. This method involves the gradual removal of convolutional neural network blocks, starting from deeper ones. By doing so, the number of parameters and FLOPs are decreased without compromising the detection performance. Experimental results show that our removal strategy makes the EfficientNet-B0 variants 9.58 $$\times$$
×
smaller and has 2.16 $$\times$$
×
fewer FLOPs than the baseline while retaining detection accuracy of 90.73% and 82.40% that are on par with the baseline on BOSSBase and ALASKA#2 datasets, respectively. Backed by our in-depth analyses, the results indicate that only a few early layers are sufficient for effective image steganalysis.
Impact of compact TiO2 interface modification on the crystallinity of perovskite solar cells
Authors: Takahashi Saemi , Uchida Satoshi , Jayaweera Piyankarage V. V. , Kaneko Shoji , Segawa Hiroshi 
Published: September 2023

AbstractThe effect of TiO2 interfacial morphology on perovskite crystallinity was investigated by modifying the micro and nanoscale surface roughness of compact TiO2. While surface treatments of the compact TiO2 layer are recognized as effective strategies to enhance the photovoltaic performance of perovskite solar cells, the discussion regarding the crystallinity of perovskite atop TiO2 has been limited. In this study, we explored the impact of micro and nano scale interface morphology on perovskite crystal formation and its subsequent effects on device performance. Surprisingly, despite the absence of noticeable voids at the interface between the compact TiO2 and perovskite layers, the perovskite crystal morphology exhibited significant improvement following either micro or nanoscale interfacial modification. This enhancement ultimately led to improved photoconversion efficiency and reduced I–V hysteresis. These results emphasize the importance of underlayer surface morphology in the perovskite crystallization and suggest that the presence of grain boundaries within the perovskite layer may also contribute to I–V hysteresis in perovskite solar cells.
Quantum formalism for the dynamics of cognitive psychology
Authors: Brody Dorje C. 
Published: September 2023

AbstractThe cognitive state of mind concerning a range of choices to be made can be modelled efficiently by use of an element of a high-dimensional Hilbert space. The dynamics of the state of mind resulting from information acquisition can be characterised by the von Neumann–Lüders projection postulate of quantum theory. This is shown to give rise to an uncertainty-minimising dynamical behaviour equivalent to Bayesian updating, hence providing an alternative approach to representing the dynamics of a cognitive state, consistent with the free energy principle in brain science. The quantum formalism, however, goes beyond the range of applicability of classical reasoning in explaining cognitive behaviour, thus opening up new and intriguing possibilities.