bluuebunny/crossref_metadata_2025 huggingface dataset:
A Low-Power ECG Processor ASIC Based on an Artificial Neural Network for Arrhythmia Detection
Authors: Zhang Chen , Chang Junfeng , Guan Yujiang , Li Qiuping , Wang Xin’an , Zhang Xing
Published: August 2023

The early detection of arrhythmia can effectively reduce the risk of serious heart diseases and save time for treatment. Many healthcare devices have been widely used for electrocardiogram (ECG) monitoring. However, most of them can only complete simple two-classes detection and have unacceptable hardware overhead and energy consumption. For achieving accurate and low-power arrhythmia detection, a novel ECG processor application specific integrated circuit (ASIC) is proposed in this paper, which can perform the prediction of five types of cardiac arrhythmias and heart rate monitoring. To realize hardware-efficient R-peak detection, an ECG pre-processing engine based on a first derivative and moving average comparison method is proposed. Efficient arrhythmia detection is realized by the proposed low-power classification engine, which is based on a carefully designed lightweight artificial neural network (ANN) with good prediction accuracy. The hardware reuse strategy is used to implement the hardware logic of ANN, where computations are executed by only one processing unit (PU), which is controlled by a flexible finite state machine (FSM). Also, the weights of ANN are configurable to facilitate model updates. We validate the functionality of the design using real-world ECG data. The proposed ECG processor is implemented using 55 nm CMOS technology, occupying an area of 0.33 mm2. This design consumes 12.88 μW at a 100 kHz clock frequency, achieving a classification accuracy of 96.69%. The comparison results with previous work indicate that our design has advantages in detection performance and power consumption, providing a good solution for low-power and low-cost ECG monitoring.
Soil Quality in Rehabilitated Coal Mining Areas
Authors: Bosch-Serra Angela D. , Cruz Javier , Poch Rosa M.
Published: August 2023

In arid and semiarid environments, the sustainability of rehabilitation actions in degraded areas is a matter of concern. It has not been extensively researched. In a Spanish coal mining area, new soils to support vegetation were created (Technosols) using mine spoils and different organic wastes. Eight years after the establishment of rehabilitation measures, the quality of the works was assessed. Soil properties (organic matter, microbial biomass, structural stability and porosity) were evaluated as quality indicators. Thermogravimetric analysis differentiated between organic-C and other mineral-C sources. The degree of aggregation and the presence of interconnected pores and organic matter with different degrees of decomposition were identified by micromorphological techniques. Microbial biomass and water-stable aggregates satisfactorily traced the early pedogenesis of mine spoils, resulting in good indicators of the quality of new Technosols. Substrates prepared with sludges promoted soil development better than those created using pig slurries, primarily by favoring a higher porosity and organic matter content. Despite that, both treatments demonstrated, after 8 years, their ability to support the ecosystem services of biomass production, carbon sequestration, and organic matter recycling they were planned for, therefore ensuring their sustainability.
An Adaptive Cybersecurity Training Framework for the Education of Social Media Users at Work
Authors: Ben Salamah Fai , Palomino Marco A. , Craven Matthew J. , Papadaki Maria , Furnell Steven
Published: August 2023

Formalizing the approach towards risk management on social media is critical for organizations. Regrettably, a review of the state-of-the-art on cybersecurity training highlighted that the existing frameworks are either too generic or too cumbersome to be adapted to different organizations and needs. Thus, we developed the Adaptive Cybersecurity Training Framework for Social Media Risks (ACSTF-SMR), a framework that incorporates social media cybersecurity policies and best practices. The ACSTF-SMR enables organizations, trainers, and policymakers to address the challenges posed by social media in a way that satisfies employees’ training needs and adjusts to their preferences. We tested the ACSTF-SMR with 38 case studies. Employees’ behaviors, learning, and responses after training were assessed, and feedback was gathered to improve the framework. Interviews with policymakers were held to gain insight into the enforcement of social media policies. We conclude that the ACSTF-SMR is a reliable option to mitigate social media threats within organizations.
Investigating the Synergy between CO2 and PM2.5 Emissions Reduction: A Case Study of China’s 329 Cities
Authors: Wang Shangjiu , Zhang Shaohua , Cheng Liang
Published: August 2023

The synergetic reduction of CO2 and PM2.5 emissions has received much attention in China in recent years. A comprehensive evaluation of the synergy between CO2 emission reduction (CER) and PM2.5 emission reduction (PER) would provide valuable information for developing synergetic control policies. Thus, we constructed a comprehensive CO2-PM2.5-emission-reduction index system and evaluated the synergy between CER and PER, using the coupling coordination degree (CCD) and relative development degree (RDD) model in China’s 329 cities from 2003 to 2017. The spatiotemporal characteristics of the CCD were analyzed on the national, regional, and urban scales. Furthermore, we used the spatial autocorrelation analysis, kernel density estimation, and Dagum Gini coefficient to investigate the spatial autocorrelation, evolutionary characteristics, and regional differences of the CCD. The results indicate that (1) the synergy between CO2 and PM2.5 emissions’ reductions showed an upward trend, and the lowest CCD values occurred in NW and Shanghai on the regional and urban scales, respectively; (2) the CCD showed obvious spatial clustering characteristics, with 75% of the cities located in the “High–High” or “Low–Low” clustering zones in the Moran scatter plots in 2017; (3) the polarization of CCD in SC, MYR, and SW showed intensified trends; (4) and the hypervariable density was the largest contributor to the overall difference in the CCD. Our findings suggest that more attention should be paid to the top-level design of the policies, technological innovation, and cross-regional or intercity cooperation.
Energetic and Spectroscopic Properties of the Low-Lying Isomers of C5H: A High-Level Ab Initio Study
Authors: Satpati Sayon , Roy Tarun , Anoop Anakuthil , Thimmakondu Venkatesan S. , Ghosal Subhas
Published: August 2023

Fourteen highly reactive isomers of C5H and their ionic counterparts have been theoretically investigated using density functional theory (DFT) and coupled-cluster methods. The linear C5H (l-C5H) radical, pent-1,3-diyn-5-yliden-1-yl (1), along with its cationic form and the cyclic C5H (c-C5H), 1-ethynylcycloprop-1-en-2-yl-3-ylidene (2), have recently been detected in the Taurus Molecular Cloud-1. By using the UCCSD(T)/cc-pCVTZ level of theory, the calculated rotational constants and other spectroscopic parameters are found to be in good agreement with the available experimental data for isomers 1 and 2. Therefore, the current theoretical study may assist synthetic chemists and molecular spectroscopists in detecting other isomers in the laboratory or in the interstellar medium (ISM). Thermodynamically favorable rearrangement schemes for forming low-lying isomers 1, 2, and 3 have also been studied theoretically, and (2λ3-cycloprop-2-en-1-ylidene)ethenylidene (3) with a large dipole moment (μ = 4.73 Debye) is proposed to be a plausible candidate for detection in the ISM.
The Effect of Choline Salt Addition to Trehalose Solution for Long-Term Storage of Dried and Viable Nuclei from Fully Grown Oocytes
Authors: Orozco Cabral Joseph A. , Lee Pei-Chih , Wang Shangping , Wang Yizhou , Zhang Yong , Comizzoli Pierre , Elliott Gloria D.
Published: August 2023

Although drying techniques are exciting alternatives to cryopreservation, it remains challenging to maintain tightly controlled temperatures and humidity levels during storage of dried products. The objective of this study was to determine if the addition of choline acetate to trehalose solution could enable a wider range of storage conditions for preservation of nuclei from fully grown oocytes, by allowing temporary humidity excursions (>44% relative humidity) that may lead to crystallization of trehalose and loss of DNA integrity. Using domestic cat germinal vesicle oocytes as a model, we characterized the recovery as well as the integrity of samples after microwave-assisted dehydration. Exposure to choline acetate alone did not impair the germinal vesicle’s DNA integrity and only had a negative impact on the chromatin configuration. Choline acetate addition enabled us to reach lower moisture contents after 25 min of microwave-assisted drying. Sample recovery after rehydration was also better in the presence of choline acetate. The integrity of the germinal vesicle’s DNA was not affected, while the chromatin configuration was impaired by the presence of choline acetate during dehydration. Importantly, choline acetate addition helped to maintain an amorphous state (absence of detrimental crystallization) during excursion from ideal humidity conditions.
Golf Optimization Algorithm: A New Game-Based Metaheuristic Algorithm and Its Application to Energy Commitment Problem Considering Resilience
Authors: Montazeri Zeinab , Niknam Taher , Aghaei Jamshid , Malik Om Parkash , Dehghani Mohammad , Dhiman Gaurav
Published: August 2023

In this research article, we uphold the principles of the No Free Lunch theorem and employ it as a driving force to introduce an innovative game-based metaheuristic technique named Golf Optimization Algorithm (GOA). The GOA is meticulously structured with two distinctive phases, namely, exploration and exploitation, drawing inspiration from the strategic dynamics and player conduct observed in the sport of golf. Through comprehensive assessments encompassing fifty-two objective functions and four real-world engineering applications, the efficacy of the GOA is rigorously examined. The results of the optimization process reveal GOA’s exceptional proficiency in both exploration and exploitation strategies, effectively striking a harmonious equilibrium between the two. Comparative analyses against ten competing algorithms demonstrate a clear and statistically significant superiority of the GOA across a spectrum of performance metrics. Furthermore, the successful application of the GOA to the intricate energy commitment problem, considering network resilience, underscores its prowess in addressing complex engineering challenges. For the convenience of the research community, we provide the MATLAB implementation codes for the proposed GOA methodology, ensuring accessibility and facilitating further exploration.
Assessment of the Antioxidant Capacity of Commercial Coffee Using Conventional Optical and Chromatographic Methods and an Innovative Electrochemical DNA-Based Biosensor
Authors: Morais Stephanie L. , Rede Diana , Ramalhosa Maria João , Correia Manuela , Santos Marlene , Delerue-Matos Cristina , Moreira Manuela M. , Soares Cristina , Barroso Maria Fátima
Published: August 2023

As one of the most popular beverages in the world, coffee is a rich source of non-enzymatic bioactive compounds with antioxidant capacity. In this study, twelve commercial coffee beverages found in local Portuguese markets were assessed to determine their total phenolic and flavonoid contents, as well as their antioxidant capacity, by conventional optical procedures, namely, ferric reducing antioxidant power and DPPH-radical scavenging assay, and non-conventional procedures such as a homemade DNA-based biosensor against two reactive radicals: HO• and H2O2. The innovative DNA-based biosensor comprised an adenine-rich oligonucleotide adsorbed onto a carbon paste electrode. This method detects the different peak intensities generated by square-wave voltammetry based on the partial damage to the adenine layer adsorbed on the electrode surface by the free radicals in the presence/absence of antioxidants. The DNA-based biosensor against H2O2 presented a higher DNA layer protection compared with HO• in the presence of the reference gallic acid. Additionally, the phenolic profiles of the twelve coffee samples were assessed by HPLC-DAD, and the main contributors to the exhibited antioxidant capacity properties were caffeine, and chlorogenic, protocatechuic, neochlorogenic and gallic acids. The DNA-based sensor used provides reliable and fast measurements of antioxidant capacity, and is also cheap and easy to construct.
Task and Resting-State Functional Connectivity Predict Driving Violations
Authors: Ju Uijong
Published: August 2023

Aberrant driving behaviors cause accidents; however, there is a lack of understanding of the neural mechanisms underlying these behaviors. To address this issue, a task and resting-state functional connectivity was used to predict aberrant driving behavior and associated personality traits. The study included 29 right-handed participants with driving licenses issued for more than 1 year. During the functional magnetic resonance imaging experiment, participants first recorded their resting state and then watched a driving video while continuously rating the risk and speed on each block. Functional connectome-based predictive modeling was employed for whole brain tasks and resting-state functional connectivity to predict driving behavior (violation, error, and lapses), sensation-seeking, and impulsivity. Resting state and task-based functional connectivity were found to significantly predict driving violations, with resting state significantly predicting lapses and task-based functional connectivity showing a tendency to predict errors. Conversely, neither impulsivity nor sensation-seeking was associated with functional connectivity. The results suggest a significant association between aberrant driving behavior, but a nonsignificant association between impulsivity and sensation-seeking, and task-based or resting state functional connectivity. This could provide a deeper understanding of the neural processing underlying reckless driving that may ultimately be used to prevent accidents.
Development and Validation of the Body Cognition Assessment System
Authors: Ikejiri Ikumi , Murakami Takashi , Yamauchi Ryosuke , Yamaguchi Hideaki , Kodama Takayuki
Published: August 2023

Body awareness, which comprises the sense of body possession and action ownership, is essential for the adaptive movement of humans in response to external environments. However, existing body cognition assessments include many overt elements of cognitive functional activity, but no assessment captures the latent body cognition necessary for exercise and daily life activities. Therefore, this study aimed to devise a body cognition assessment system (BCAS) to examine the functional basis of body cognition in healthy participants and investigate its usefulness. The BCAS was used to assess body cognition on three occasions, and BCAS values were calculated from the results of the assessment. The intraclass correlation coefficient (ICC) was used to determine reproducibility. Neural activity in the brain during somatocognition assessment while conducting the BCAS was measured by electroencephalogram. Moreover, the functional basis for somatocognition with the BCAS was also investigated. The results demonstrated that the BCAS values varied across the three administrations (ICC (1.3) = 0.372), and changes in the state of neural activity in the brain were observed. The results suggest that assessment using the BCAS may be a new indicator of ever-changing body cognition.
Teacher Support, Academic Self-Efficacy, Student Engagement, and Academic Achievement in Emergency Online Learning
Authors: Huang Liang , Wang Dongsheng
Published: August 2023

With a sample of 651 university students experiencing emergency online learning during COVID-19, this study constructed a structural equation modelling to examine the effects of teacher support on students’ academic achievement, with a particular focus on the mediating roles of academic self-efficacy and student engagement. The results show that teacher support had significant total influences on university students’ academic achievement. Furthermore, academic self-efficacy and student engagement, respectively, mediated the effects of teacher support on students’ academic achievement. In addition, academic self-efficacy and student engagement sequentially mediated the effects of teacher support on students’ academic achievement. Research implications are also discussed.
Increase Government Support for Build-to-Rent: Should We Expect Affordable Housing?
Authors: Swanzy-Impraim Samuel , Ge Xin Janet , Mangioni Vince
Published: August 2023

Build-to-rent (BTR) is an important pathway for rental housing supply across the cities of Australia. The discourse has become more prevalent as stakeholders seek government support to expand the sector to provide, amongst other reform measures, affordable rental accommodation. This paper examines the effectiveness of BTR as a pathway to affordable housing supply in Australia and identifies the additional reforms needed to increase that supply further. Interviews with key stakeholders were used to assess the success of the asset class to date and how it is likely to perform in increasing affordable rental housing supply, as well as what additional reforms may be required to expedite supply. The paper found that while BTR aims to scale up rental housing supply, its limitations in its present form demonstrate the need for additional reforms. At present, unfavorable taxes, inner-city locational attributes, and ultramodern services make the asset class a high-end housing model. Experts posit that BTR rental prices are justified given the quality amenities and the 24/7 onsite professional management. While it is a premium product, several BTR projects in Australia have included portions of those projects for affordable housing. The study concluded that BTR can generate affordable housing outcomes in Australia. As more developments take place across the private sector, several regulatory reforms in land release and taxes are needed to produce a sizable portfolio of affordable BTR. Without government support and regulation through additional supply-side incentives, development and recurrent holding costs will remain high, making BTR unaffordable to many urban residents.
Required Changes to Unlock Value Generation through Implementing BIM and IoT for Universities FM Services
Authors: Fialho Beatriz Campos , Fabricio Márcio Minto , Codinhoto Ricardo
Published: August 2023

The digital transformation of operation and maintenance (O&M) activities has been guiding the facilities management sector towards a strategic role focused on the performance of businesses and sustainable use of natural resources over assets’ lifecycles. Despite the application of information and communication technologies (ICTs), such as building information modelling (BIM) and the Internet of Things (IoT), demonstrating potential for improving such practices, evidence related to its implementation process from an FM professional’s perspective is still much needed to unlock billions of USD trapped in inefficient and fragmented processes. This research seeks such evidence by characterising current and potential applications of BIM and IoT-based solutions for FM, specifically on maintenance services. This exploratory research is based on a multiple case study where three universities’ FM sectors were investigated. Data collection and analysis included semi-structured interviews, focus groups, and inductive content analysis. By considering technology as a scaffold to enhance human activities, the investigation provided a deep understanding of technological, procedural, and policy issues in implementing BIM and IoT-based solutions in FM activities and their related benefits and limitations. Clarifying the necessary resources for such implementation could support FM sectors and organizations in justifying human and financial investments and planning the gradual digital transformation of services.
Brain Metabolomics in Fragile X-Associated Tremor/Ataxia Syndrome (FXTAS)
Authors: Salcedo-Arellano Maria Jimena , Johnson Michael D. , McLennan Yingratana A. , Hwang Ye Hyun , Juarez Pablo , McBride Erin Lucille , Pantoja Adriana P. , Durbin-Johnson Blythe , Tassone Flora , Hagerman Randi J. , Martínez-Cerdeño Verónica
Published: August 2023

The course of pathophysiological mechanisms involved in fragile X-associated tremor/ataxia syndrome (FXTAS) remains largely unknown. Previous proteomics and metabolomics studies conducted in blood samples collected from FMR1 premutation carriers with FXTAS reported abnormalities in energy metabolism, and precursors of gluconeogenesis showed significant changes in plasma expression levels in FMR1 premutation carriers who developed FXTAS. We conducted an analysis of postmortem human brain tissues from 44 donors, 25 brains with FXTAS, and 19 matched controls. We quantified the metabolite relative abundance in the inferior temporal gyrus and the cerebellum using untargeted mass spectrometry (MS)-based metabolomics. We investigated how the metabolite type and abundance relate to the number of cytosine-guanine-guanine (CGG) repeats, to markers of neurodegeneration, and to the symptoms of FXTAS. A metabolomic analysis identified 191 primary metabolites, the data were log-transformed and normalized prior to the analysis, and the relative abundance was compared between the groups. The changes in the relative abundance of a set of metabolites were region-specific with some overlapping results; 22 metabolites showed alterations in the inferior temporal gyrus, while 21 showed differences in the cerebellum. The relative abundance of cytidine was decreased in the inferior temporal gyrus, and a lower abundance was found in the cases with larger CGG expansions; oleamide was significantly decreased in the cerebellum. The abundance of 11 metabolites was influenced by changes in the CGG repeat number. A histological evaluation found an association between the presence of microhemorrhages in the inferior temporal gyrus and a lower abundance of 2,5-dihydroxypyrazine. Our study identified alterations in the metabolites involved in the oxidative-stress response and bioenergetics in the brains of individuals with FXTAS. Significant changes in the abundance of cytidine and oleamide suggest their potential as biomarkers and therapeutic targets for FXTAS.
Increased Osteoclastogenesis in Absence of TG2 Is Reversed by Transglutaminase Inhibition—Evidence for the Role for TG1 in Osteoclast Formation
Authors: Ebrahimi Samani Sahar , Kaartinen Mari T.
Published: August 2023

Osteoclasts are multinucleated, bone-resorbing giant cells derived from monocyte–macrophage cell lines. Increased bone resorption results in loss of bone mass and osteoporosis. Osteoclast and bone marrow macrophages have been shown to express three TG enzymes (TG2, Factor XIII-A, and TG1) and TG activity to regulate osteoclast differentiation from bone marrow macrophages in vitro. In vivo and in vitro studies have demonstrated that the deletion of TG2 causes increased osteoclastogenesis and a significant loss of bone mass in mice (Tgm2−/− mice). Here, we confirm that TG2 deficiency results in increased osteoclastogenesis in vitro and show that this increase can be reversed by a TG inhibitor, NC9, suggesting that other TGs are responsible for driving osteoclastogenesis in the absence of TG2. An assessment of total TG activity with 5-(biotinamido)-pentylamine, as well as TG1 and FXIII-A activities using TG-specific Hitomi peptides (bK5 and bF11) in Tgm2−/− bone marrow flushes, bone marrow macrophages, and osteoclasts, showed a significant increase in total TG activity and TG1 activity. Factor XIII-A activity was unchanged. Aspartate proteases, such as cathepsins, are involved in the degradation of organic bone matrix and can be produced by osteoclasts. Moreover, Cathepsin D was shown in previous work to be increased in TG2-null cells and is known to activate TG1. We show that Pepstatin A, an aspartate protease inhibitor, blocks osteoclastogenesis in wild-type and Tgm2−/− cells and decreases TG1 activity in Tgm2−/− osteoclasts. Cathepsin D protein levels were unaltered in Tgm2−/−cells and its activity moderately but significantly increased. Tgm2−/− and Tgm2+/+ bone marrow macrophages and osteoclasts also expressed Cathepsin E, and Renin of the aspartate protease family, suggesting their potential involvement in this process. Our study brings further support to the observation that TGs are significant regulators of osteoclastogenesis and that the absence of TG2 can cause increased activity of other TGs, such as TG1.
Optimization of Enzymatic Synthesis of D-Glucose-Based Surfactants Using Supported Aspergillus niger Lipase as Biocatalyst
Authors: Spalletta Alexis , Joly Nicolas , Martin Patrick
Published: August 2023

Surfactants are amphiphilic molecules with the ability to modify the surface tension between two surfaces. They can be obtained by various methods, the main one being synthetic, from petroleum-based substrates. Their universal use in a wide range of fields has created a global market and, consequently, ecological, and economic expectations for their production. Biocatalyzed processes, involving enzymes, can address this objective with processes complying with the principles of green chemistry: energy saving, product selectivity, monodispersity, and reduction in the use of solvents, with energy eco-efficiency. For example, fatty-acid carbohydrate esters are biobased surfactants that can be synthesized by lipases. In this work, we were interested in the synthesis of D-glucose lauric ester, which presents interesting properties described in the literature, with Aspergillus niger lipase, rarely described with sugar substrates. We optimized the synthesis for different parameters and reaction media. This lipase appeared to be highly selective for 6-O-lauroyl-D-glucopyranose. However, the addition of DMSO (dimethyl sulfoxide) as a co-solvent displays a duality, increasing yields but leading to a loss of selectivity. In addition, DMSO generates more complex and energy-intensive purification and processing steps. Consequently, a bio-sourced alternative as co-solvent with 2MeTHF3one (2-methyltetrahydrofuran-3-one) is proposed to replace DMSO widely described in the literature.
Insight into the Adsorption Behavior of Carbon Nanoparticles Derived from Coffee Skin Waste for Remediating Water Contaminated with Pharmaceutical Ingredients
Authors: Salih Mutaz , Abdulkhair Babiker Y. , Alotaibi Mansour
Published: August 2023

Coffee skins, a cheap, agricultural waste, were carbonized in a tubular furnace under a nitrogen stream and then ball milled to fabricate coffee skin-carbon-nanoparticles (CCNPs). SEM showed 35.6–41.6 nm particle size. The 26.64 and 43.16 peaks in the XRD indicated a cubic graphite lattice. The FT-IR broadband revealed a 2500–3500 cm−1 peak, suggesting an acidic O-H group. CCNPs possessed a type-H3-loop in the N2-adsorption-desorption analysis, with a surface of 105.638 m2 g−1. Thereafter, CCNPs were tested for ciprofloxacin (CPXN) adsorption, which reached equilibrium in 90 min. CCNPs captured 142.6 mg g−1 from 100 mg L−1 CPXN, and the 5:12 sorbent mass-to-solution volume ratio was suitable for treating up to 75 mg L−1 contamination. The qt dropped from 142.6 to 114.3 and 79.2 mg g−1 as the temperature rose from 20 °C to 35 °C and 50 °C, respectively, indicating exothermic adsorption. CPXN removal efficiency decreased below pH 5.0 and above pH 8.0. Kinetically, CPXN adsorption fits the second-order model and is controlled by the liquid-film mechanism, indicating its preference for the CCNPs’ surface. The adsorption agreement with the liquid-film and Freundlich models implied the ease of CPXN penetration into the CCNP inner shells and the multilayered accumulation of CPXN on the CCNPs’ surface. The negative ∆H° and ∆G° revealed the exothermic nature and spontaneity of CPXN adsorption onto the CCNP. The CCNPs showed an efficiency of 95.8% during four consecutive regeneration-reuse cycles with a relative standard deviation (RSD) of 3.1%, and the lowest efficiency in the fourth cycle was 92.8%.
Targeting FAK/PYK2 with SJP1602 for Anti-Tumor Activity in Triple-Negative Breast Cancer
Authors: Jeon Myeongjin , Hong Sungpyo , Cho Hyoungmin , Park Hanbyeol , Lee Soo-Min , Ahn Soonkil
Published: August 2023

Triple-negative breast cancer (TNBC) presents significant challenges due to its aggressive nature and limited treatment options. Focal adhesion kinase (FAK) has emerged as a critical factor promoting tumor growth and metastasis in TNBC. Despite encouraging results from preclinical and early clinical trials with various FAK inhibitors, none have yet achieved clinical success in TNBC treatment. This study investigates the therapeutic potential of a novel dual inhibitor of FAK and PYK2, named SJP1602, for TNBC. In vitro experiments demonstrate that SJP1602 effectively inhibits FAK and PYK2 activities, showing potent effects on both kinases. SJP1602 shows concentration-dependent inhibition of cell growth, migration, invasion, and 3D spheroid formation in TNBC cell lines, surpassing the efficacy of other FAK inhibitors. Pharmacokinetic studies in rats indicate favorable bioavailability and sustained plasma concentrations of SJP1602, supporting its potential as a therapeutic agent. Furthermore, in TNBC xenograft models, SJP1602 exhibits significant dose-dependent inhibition of tumor growth. These promising results emphasize the potential of SJP1602 as a potent dual inhibitor of FAK and PYK2, deserving further investigation in clinical trials for TNBC treatment.
Influence of Deposition Conditions and Thermal Treatments on Morphological and Chemical Characteristics of Li6.75La3Zr1.75Ta0.25O12 Thin Films Deposited by Nanosecond PLD
Authors: Curcio Mariangela , Brutti Sergio , Celeste Arcangelo , Galasso Agostino , De Bonis Angela , Teghil Roberto
Published: August 2023

The production of thin films has been extensively studied due to their unique properties that make them highly useful in a wide range of scientific and technological applications. Obtaining thin films with well-defined stoichiometry and crystallinity is a challenging task, especially when dealing with materials of complex stoichiometry. Among diverse methodologies for the manufacture of thin films, pulsed laser deposition (PLD) stands out as a versatile technique for producing crystalline films with complex chemical compositions. In this study, nanosecond PLD was employed to manufacture thin films of Ta-doped Li7La3Zr2O12 (LLZTO), a garnet-like oxide that has been proposed as solid electrolyte for Li-ion solid state batteries. Two distinct deposition atmospheres were investigated: vacuum conditions at 10−3 Pa and an oxygen-enriched environment with 10 Pa of O2 gas buffer. To mitigate lithium losses during deposition, a minor addition of lithium oxide was incorporated into the target. The effects of deposition atmosphere and the impact of post-deposition annealing on the structural, compositional, and morphological properties of LLZTO thin films were analysed through a multi-technique approach. The results suggest deposition under oxygen pressure led to the growth of compact, crystalline films characterized by homogenous elemental distribution across the surface and throughout the film’s depth. These films closely resemble the composition of the target LLZTO material, offering valuable insights for the fabrication of high-quality complex oxide thin films.
The Use of Radiomic Tools in Renal Mass Characterization
Authors: Gutiérrez Hidalgo Beatriz , Gómez Rivas Juan , de la Parra Irene , Marugán María Jesús , Serrano Álvaro , Hermida Gutiérrez Juan Fco , Barrera Jerónimo , Moreno-Sierra Jesús
Published: August 2023

The incidence of renal mass detection has increased during recent decades, with an increased diagnosis of small renal masses, and a final benign diagnosis in some cases. To avoid unnecessary surgeries, there is an increasing interest in using radiomics tools to predict histological results, using radiological features. We performed a narrative review to evaluate the use of radiomics in renal mass characterization. Conventional images, such as computed tomography (CT) and magnetic resonance (MR), are the most common diagnostic tools in renal mass characterization. Distinguishing between benign and malignant tumors in small renal masses can be challenging using conventional methods. To improve subjective evaluation, the interest in using radiomics to obtain quantitative parameters from medical images has increased. Several studies have assessed this novel tool for renal mass characterization, comparing its ability to distinguish benign to malign tumors, the results in differentiating renal cell carcinoma subtypes, or the correlation with prognostic features, with other methods. In several studies, radiomic tools have shown a good accuracy in characterizing renal mass lesions. However, due to the heterogeneity in the radiomic model building, prospective and external validated studies are needed.
Proteomics in Childhood Acute Lymphoblastic Leukemia: Challenges and Opportunities
Authors: Kourti Maria , Aivaliotis Michalis , Hatzipantelis Emmanouel
Published: August 2023

Acute lymphoblastic leukemia (ALL) is the most common cancer in children and one of the success stories in cancer therapeutics. Risk-directed therapy based on clinical, biologic and genetic features has played a significant role in this accomplishment. Despite the observed improvement in survival rates, leukemia remains one of the leading causes of cancer-related deaths. Implementation of next-generation genomic and transcriptomic sequencing tools has illustrated the genomic landscape of ALL. However, the underlying dynamic changes at protein level still remain a challenge. Proteomics is a cutting-edge technology aimed at deciphering the mechanisms, pathways, and the degree to which the proteome impacts leukemia subtypes. Advances in mass spectrometry enable high-throughput collection of global proteomic profiles, representing an opportunity to unveil new biological markers and druggable targets. The purpose of this narrative review article is to provide a comprehensive overview of studies that have utilized applications of proteomics in an attempt to gain insight into the pathogenesis and identification of biomarkers in childhood ALL.
An Evaluation of Sun-Glint Correction Methods for UAV-Derived Secchi Depth Estimations in Inland Water Bodies
Authors: Tiškus Edvinas , Bučas Martynas , Vaičiūtė Diana , Gintauskas Jonas , Babrauskienė Irma
Published: August 2023

This study investigates the application of unoccupied aerial vehicles (UAVs) equipped with a Micasense RedEdge-MX multispectral camera for the estimation of Secchi depth (SD) in inland water bodies. The research analyzed and compared five sun-glint correction methodologies—Hedley, Goodman, Lyzenga, Joyce, and threshold-removed glint—to model the SD values derived from UAV multispectral imagery, highlighting the role of reflectance accuracy and algorithmic precision in SD modeling. While Goodman’s method showed a higher correlation (0.92) with in situ SD measurements, Hedley’s method exhibited the smallest average deviation (0.65 m), suggesting its potential in water resource management, environmental monitoring, and ecological modeling. The study also underscored the quasi-analytical algorithm (QAA) potential in estimating SD due to its flexibility to process data from various sensors without requiring in situ measurements, offering scalability for large-scale water quality surveys. The accuracy of SD measures calculated using QAA was related to variability in water constituents of colored dissolved organic matter and the solar zenith angle. A practical workflow for SD acquisition using UAVs and multispectral data is proposed for monitoring inland water bodies.
Rubber Tree Recognition Based on UAV RGB Multi-Angle Imagery and Deep Learning
Authors: Liang Yuying , Sun Yongke , Kou Weili , Xu Weiheng , Wang Juan , Wang Qiuhua , Wang Huan , Lu Ning
Published: August 2023

The rubber tree (Hevea brasiliensis) is an important tree species for the production of natural latex, which is an essential raw material for varieties of industrial and non-industrial products. Rapid and accurate identification of the number of rubber trees not only plays an important role in predicting biomass and yield but also is beneficial to estimating carbon sinks and promoting the sustainable development of rubber plantations. However, the existing recognition methods based on canopy characteristic segmentation are not suitable for detecting individual rubber trees due to their high canopy coverage and similar crown structure. Fortunately, rubber trees have a defoliation period of about 40 days, which makes their trunks clearly visible in high-resolution RGB images. Therefore, this study employed an unmanned aerial vehicle (UAV) equipped with an RGB camera to acquire high-resolution images of rubber plantations from three observation angles (−90°, −60°, 45°) and two flight directions (SN: perpendicular to the rubber planting row, and WE: parallel to rubber planting rows) during the deciduous period. Four convolutional neural networks (multi-scale attention network, MAnet; Unet++; Unet; pyramid scene parsing network, PSPnet) were utilized to explore observation angles and directions beneficial for rubber tree trunk identification and counting. The results indicate that Unet++ achieved the best recognition accuracy (precision = 0.979, recall = 0.919, F-measure = 94.7%) with an observation angle of −60° and flight mode of SN among the four deep learning algorithms. This research provides a new idea for tree trunk identification by multi-angle observation of forests in specific phenological periods.
Validation of the Flight Dynamics Engine of the X-Plane Simulator in Comparison with the Real Flight Data of the Quadrotor UAV Using CIFER
Authors: Do Minh-Hoang , Lin Chin-E , Lai Ying-Chih
Published: August 2023

The vertical take-off and landing (VTOL) of unmanned aerial vehicles (UAVs) is extensively employed in various sectors. To ensure adherence to design specifications and mission requirements, it is vital to verify flight control and system performance using an accurate dynamic model specific to UAV configuration. Traditionally, engineers follow a sequential approach in UAV design, which involves multiple design iterations comprising CAD drawings, material collection, fabrication, flight tests, system identification, modifications, dynamic model extraction, checking if the results meet requirements, and then repeating the process. However, as UAVs become larger, heavier, and more enduring to meet complex system demands, the costs and time associated with each design iteration of creating a new UAV escalate exponentially. The bare-airframe dynamics of the UAV are crucial for engineers to design a controller and validate handling quality and performance. This paper proposes a novel method to accurately predict the dynamic model of the bare airframe for quadrotor UAVs without physically constructing them in the real world. The core concept revolves around converting the quadrotor UAV design from CAD software into a UAV model within an X-Plane simulator. Leveraging the CIFER software’s two key features—frequency domain system identification and parametric model fitting—the unstable bare-airframe dynamics are extracted for both the UAV model in X-Plane and a real-world DJI 450 UAV with the same physical configuration. This paper provides essential parameters and guidance for constructing a 92% high-fidelity dynamic model of the given UAV configuration in X-Plane. The flight test results demonstrate excellent alignment with the simulation outcomes, instilling confidence in the effectiveness of the proposed method for designing and validating new UAVs. Moreover, this approach significantly reduces the time and cost associated with the traditional design process, which requires an actual build of the UAV and many flight tests to verify the performance.
An Enhanced Neural Network Algorithm with Quasi-Oppositional-Based and Chaotic Sine-Cosine Learning Strategies
Authors: Xiong Xuan , Li Shaobo , Wu Fengbin
Published: August 2023

Global optimization problems have been a research topic of great interest in various engineering applications among which neural network algorithm (NNA) is one of the most widely used methods. However, it is inevitable for neural network algorithms to plunge into poor local optima and convergence when tackling complex optimization problems. To overcome these problems, an improved neural network algorithm with quasi-oppositional-based and chaotic sine-cosine learning strategies is proposed, that speeds up convergence and avoids trapping in a local optimum. Firstly, quasi-oppositional-based learning facilitated the exploration and exploitation of the search space by the improved algorithm. Meanwhile, a new logistic chaotic sine-cosine learning strategy by integrating the logistic chaotic mapping and sine-cosine strategy enhances the ability that jumps out of the local optimum. Moreover, a dynamic tuning factor of piecewise linear chaotic mapping is utilized for the adjustment of the exploration space to improve the convergence performance. Finally, the validity and applicability of the proposed improved algorithm are evaluated by the challenging CEC 2017 function and three engineering optimization problems. The experimental comparative results of average, standard deviation, and Wilcoxon rank-sum tests reveal that the presented algorithm has excellent global optimality and convergence speed for most functions and engineering problems.
Evaluation and Benefit of Imprecise Value Prediction for Certain Types of Instructions
Authors: Radenković Uroš , Mićović Marko , Radivojević Zaharije
Published: August 2023

Based on branch prediction, value prediction has emerged as a solution for problems caused by true data dependencies in pipelined processors. While branch predictors have binary outcomes (taken/not taken), value predictors face a challenging task as their outcomes can take any value. Because of that, coverage is reduced to enhance high accuracy and minimise costly recovery from misprediction. This paper evaluates value prediction, focusing on instruction execution with imprecisely predicted operands whose result can still be correct. Two analytical models are introduced to represent instruction execution with value prediction. One model focuses on correctly predicted operands, while the other allows for imprecisely predicted operands as long as the instruction results remain correct. A trace-driven simulator was developed for simulation purposes, implementing well-known predictors and some of the predictors presented at the latest Championship Value Prediction. The gem5 simulator was upgraded to generate program traces of SPEC and EEMBC benchmarks that were used in simulations. Based on the simulation result, proposed analytical models were compared to reveal the conditions under which a model with imprecisely predicted operands, but still correct results, achieved better execution time than a model with correctly predicted operands. Analysis revealed that the accuracy of the correct instruction result based on the predicted operand, even when the predicted operand is imprecise, is higher than the accuracy of the correctly predicted operand. The accuracy improvement ranges from 0.8% to 44%, depending on the specific predictor used.
Design of a Crossed Dielectric Resonator-Loaded, Dual-Band Dual-Polarized Differential Patch Antenna with Improved Port Isolation and Gain
Authors: Wang Dongdong , Liu Yudong , Liang Jia
Published: August 2023

To meet the urgent requirement for more channel capacity in modern wireless communication systems, antennas with more operation bands are demanded. However, large amounts of antennas suffer from low radiation gains and low port isolation levels. In view of this, a differentially fed, dual-wideband, dual-polarized patch antenna is proposed in this paper. Compared with conventional crossed-feeding structures, the proposed crossed dielectric resonator (CDR) can provide extra resonances with improved isolation levels and radiation gain. Further, four shorting pins are introduced to the radiating patch to help improve the impedance-matching performance. In addition, the proposed antenna also has a very compact size of 0.46λ × 0.46λ × 0.12λ. Finally, a prototype of the proposed antenna is fabricated to validate the design concept. The measured results show that the proposed antenna generates dual wide bands of 1.86–2.52 GHz and 3.26–3.72 GHz for |S11| < −10 dB. High radiation gains of 8.9 ± 0.9 dBi and 10.8 ± 1.2 dBi are also obtained, as well as high port isolation levels of better than 38.4 dB and 36.2 dB at the two bands. The excellent performance of the proposed antenna makes it a promising candidate for 4G/5G wireless communication systems.
Research on Log Anomaly Detection Based on Sentence-BERT
Authors: Hu Caiping , Sun Xuekui , Dai Hua , Zhang Hangchuan , Liu Haiqiang
Published: August 2023

Log anomaly detection is crucial for computer systems. By analyzing and processing the logs generated by a system, abnormal events or potential problems in the system can be identified, which is helpful for its stability and reliability. At present, due to the expansion of the scale and complexity of software systems, the amount of log data grows enormously, and traditional detection methods have been unable to detect system anomalies in time. Therefore, it is important to design log anomaly detection methods with high accuracy and strong generalization. In this paper, we propose the log anomaly detection method LogADSBERT, which is based on Sentence-BERT. This method adopts the Sentence-BERT model to extract the semantic behavior characteristics of log events and implements anomaly detection through the bidirectional recurrent neural network, Bi-LSTM. Experiments on the open log data set show that the accuracy of LogADSBERT is better than that of the existing log anomaly detection methods. Moreover, LogADSBERT is robust even under the scenario of new log event injections.