Miracle-dz/UniFAR-FTUs huggingface dataset:
Highly Sensitive TiO2/Au/Graphene Layer-Based Surface Plasmon Resonance Biosensor for Cancer Detection

In this article, a hybrid TiO2/Au/graphene layer-based surface plasmon resonance (SPR) sensor with improved sensitivity and capability for cancer detection is presented. The finite element method (FEM) was used for numerical analysis. The proposed SPR biosensor was structured based on the angular analysis of the attenuated total reflection (ATR) method for the detection of various types of cancer using the refractive index component. The resonance angle shifted owing to the increment of normal and cancerous cells’ refractive index, which varied between 1.36 and 1.401 for six different types of normal and cancerous cells. According to numerical results, the obtained sensitivities for skin (basal), cervical (HeLa), adrenal gland (PC12), blood (Jurkat), and breast (MCF-7 and MDA-MB-231) cancer cells were 210 deg/RIU, 245.83 deg/RIU, 264.285 deg/RIU, 285.71 deg/RIU, 292.86 deg/RIU, and 278.57 deg/RIU, respectively. Furthermore, the detection accuracy (DA), figure of merits (FOM), and signal-to-noise ratio (SNR) were also obtained, with values of 0.263 deg−1, 48.02 RIU−1, and 3.84, respectively. Additionally, the distribution of the electric field and the propagation of the magnetic field for resonant and non-resonant conditions of the proposed structure were illustrated. It was found that an enhanced field was exhibited on the surface of the plasmonic material for resonant conditions. We also measured the penetration depth of 180 nm using decayed electric field intensity. Furthermore, the impact of using a TiO2/Au/graphene layer was demonstrated. We further conducted analyses of the effects of the thickness of the gold layer and the effects of additional graphene layers on overall sensitivities for six different types of cancer. The proposed TiO2/Au/graphene layered structure exhibited the highest overall sensitivity in terms of detecting cancerous cells from healthy cells. Moreover, the proposed sensor was numerically analyzed for a wide range of biological solutions (refractive index 1.33–1.41), and the sensor linearity was calculated with a linear regression coefficient (R2) of 0.9858. Finally, numerical results obtained in this manuscript exhibited high sensitivity in comparison with previously reported studies.
A Data-Driven Bandwidth Allocation Framework With QoS Considerations for EONs

This paper proposes a data-driven bandwidth allocation (BA) framework for periodically and dynamically reconfiguring an elastic optical network according to predictive BA (PBA) models. The proposed framework is scalable to the number of network connections and also adaptive to the increasing traffic of each network connection separately and to the overall network load as well. This is achieved by formulating the BA problem as a partially observable Markov decision process (POMDP), which constitutes a reinforcement learning (RL) algorithm. Specifically, RL is performed continuously and independently (locally) for each network connection according to the most recent data that describe the traffic demand behavior of each network connection. A central controller monitors the network performance that is jointly achieved from all the PBA models and is capable of dynamically modifying the reward function of the POMDP, ensuring that the quality-of-service (QoS) requirements are met. A reward function <inline-formula><tex-math notation="LaTeX">$R(C)$</tex-math></inline-formula> is examined with a clear impact on the network performance when <inline-formula><tex-math notation="LaTeX">$C$</tex-math></inline-formula> is modified. For evaluating the network performance, for each <inline-formula><tex-math notation="LaTeX">$R(C)$</tex-math></inline-formula>, the routing and spectrum allocation (RSA) problem is solved according to an integer linear programming (ILP) algorithm and an RSA heuristic alternative, with both the ILP and the heuristic RSA taking as inputs the outputs of the inferred PBA models. Results indicate that, with the appropriate settings of <inline-formula><tex-math notation="LaTeX">$C$</tex-math></inline-formula>, bandwidth is efficiently allocated, while ensuring that the QoS requirements are met.
Characteristics and Sonophotocatalytic Activity of Natural Sphalerite under Ultrasonic (1.7 MHz) and UVA LED (365 nm) Irradiation

Naturally occurring sono- and photoactive minerals, which are abundant on Earth, represent an attractive alternative to the synthesized sonophotocatalysts as cost-effective materials for water and wastewater treatment. This study focuses on characterizing and evaluating the sonophotocatalytic activity of natural sphalerite (NatS) from Dovatka deposit (Siberia) under high-frequency ultrasonic (US, 1.7 MHz) and ultraviolet light-emitting diodes (UVA LED, 365 nm) irradiation towards degradation of 4-chlorophenol as a model organic pollutant. Since raw natural sphalerite did not exhibit a measurable photocatalytic activity, it was calcined at 500, 900 and 1200 °C. The natural sphalerite after calcination at 900 °C (NatS*) was found to be the most effective for sonophotocatalytic degradation of 4-chlorophenol, attaining the highest efficiency (55%, 1 h exposure) in the following row: UV < US ≈ UV/US ≈ US/NatS* < UV/NatS* < UV/US/NatS*. Addition of 1 mM H2O2 increased the removal to 74% by UV/US/NatS*/H2O2 process. An additive effect between UV/NatS* and US/NatS* processes was observed in the sonophotocatalytic system as well as in the H2O2-assisted system. We assume that the sonophotocatalytic hybrid process, which is based on the simultaneous use of high-frequency ultrasound, UVA light, calcined natural sphalerite and H2O2, could provide a basis of an environmentally safe and cost-effective method of elimination of organic pollutants from aqueous media.
Improved Markov stable state simulation for maintenance planning

Purpose The purpose of this paper is to propose a software (Equipment State Simulator) used for predicting equipment performance parameters required for maintenance planning. Design/methodology/approach This maintenance software was developed from the derived stable state probability models using algebraic substitution and computation of total operational period, number of breakdowns, total downtime, mean time between failures and mean time to repair of equipment/component(s) at preventive maintenance and corrective maintenance states. The models were derived using mechanistic modeling technique such that all the relevant variables were accounted for. Findings Analysis of this software revealed that its predictions reckon with the actual performance of the test specimens by about 99 percent. Originality/value The research proposes a maintenance model and software for predicting state probabilities of manufacturing systems degradation. This program also predicts maintenance action(s) required by the equipment based on the predetermined alert levels.
Reliable Backhauling in Aerial Communication Networks Against UAV Failures: A Deep Reinforcement Learning Approach

Unmanned Aerial Vehicles (UAVs) can be utilized as aerial base stations to establish wireless communication networks in various challenging scenarios, such as emergency disaster areas and rural areas. Under large regions, the aerial communication networks would require UAVs to form wireless (backhaul) links among each other to provide end-to-end wireless services between two or more ground users (via one or more UAVs). Such UAV backhauling in aerial communication networks may be severely compromised if one or more UAVs are knocked off during the time of operation – it may be due to UAV hardware/software faults, limited battery, malicious attacks, etc. Deep reinforcement learning (DRL) has emerged as a powerful tool for learning tasks with large state and continuous action spaces. In this paper, we leverage emerging DRL to achieve reliable backhauling in an aerial communication network that remains functional and supports end-to-end wireless services even under various random and/or targeted UAV node failures. The proposed method (i) maximizes the reliability of UAV backhauling with joint consideration for communication coverage, (ii) learns the complex environment and its dynamics, and (iii) makes 3D positioning decisions for each UAV under the guidance of two deep neural networks. Our performance evaluation reveals that the proposed DRL approach outperforms the baseline method in terms of wireless coverage and network reliability against UAV failures.
Avaliação físico-química da farinha de pupunha (Bactris gasipaes Kunth) para uso alimentício

O objetivo geral deste trabalho foi avaliar as características físico-químicas da farinha de pupunha adquirida em uma feira da cidade de Manaus/AM. Os cachos de pupunha foram adquiridos na Feira da Banana, na cidade de Manaus/AM, e levados para o Laboratório de Alimentos e Nutrição (LAN), do Instituto Nacional de Pesquisas da Amazônia (INPA). Os frutos foram selecionados quanto a injúrias e maturação. Os frutos selecionados seguiram para a despolpa, desidratação, secagem, trituração e acondicionamento. A farinha de pupunha foi submetida a análises físico-químicas de umidade, lipídios, fibra total, cinzas, proteínas, carboidratos, valor energético, pH, sólidos solúveis totais, acidez, açúcares, amido, além de compostos fenólicos e flavonoides. A farinha de pupunha possui baixo teor de umidade, com 2,83 %, teor de lipídeos de 12,72% , proteínas de 4,89 %, carboidratos de 71,78 %, além de excelentes resultados para compostos fenólicos, com 88,74 mg/100g e flavonoides com 14,52 mg/100g. A farinha de pupunha apresentou excelente qualidade físico-química evidenciando que pode ser uma excelente alternativa para se tornar um produto com alta viabilidade para o mercado.
Association of urinary polycyclic aromatic hydrocarbon metabolites with symptoms among autistic children: A case–control study in Tianjin, China

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous pollutants that are associated with altered neurodevelopment and various neurodevelopmental disorders. However, studies evaluating internal biomarkers of PAH exposure in reference to the severity of autism spectrum disorder (ASD) symptomology and autistic behaviors are scarce. Hence, we conducted a case–control study evaluating 12 urinary hydroxylated PAH metabolites (i.e., hydroxy‐PAHs) in 101 children with autism and 101 neurotypical children, matching according to sex and age in a 1:1 ratio. In children with ASD, the severity of symptomology and autistic behaviors were assessed using the child autism rating scale (CARS) and the autism behavior checklist (ABC). We found that urinary levels of nine of the hydroxy‐PAHs were statistically significantly higher in the ASD group, with the exception of 2‐hydroxynaphthalene (2‐OHNap) and 4‐hydroxyphenanthrene (4‐OHPhe). Moreover, urinary hydroxy‐PAH levels were associated with ASD risk, with odds ratios ranging from 1.86 to 17.19. Exposures to 1‐hydroxynaphthalene (1‐OHNap, β = 3.32), hydroxyphenanthrenes (1/2/3 + 9‐OHPhes, β = 3.41–5.12), 1‐hydroxypyrene (1‐OH‐Pyr; β = 3.91), 2‐hydroxybenzofuran (2‐OHDBF; β = 3.93), and ∑OH‐PAHs (β = 4.67) were positively associated with CARS scores after adjusting for covariates (all p < 0.05). When applying the ABC scale, 1‐OHPyr levels were positively associated with ABC total scores (β = 18.54), with the strongest associations evidenced in regard to the social relatedness (β = 6.51) and language domains (β = 6.51) (all p < 0.05). Bayesian kernel machine regression (BKMR) showed consistent positive exposure responses for 1‐OHNap, 1‐OHPhe, and 3 + 9‐OHPhe levels in regard to CARS scores, and for 1‐OHPyr levels in regard to ABC total scores. Our findings suggest that children with ASD may have higher urinary levels of hydroxy‐PAHs, and that these biomarker levels are associated with an increased odds of ASD, an increased severity of autism symptomology, and increased autistic behaviors in children with autism.
FIP Effect and FIP-Dependent Bias in the Solar Corona

Using EUV spectra of an active region observed off the solar disk by the SOHO/SUMER spectrograph on the SOHO spacecraft, we investigate the dependence of the FIP effect on the height above the photosphere, and its relation to plasma magnetic structures present in the field of view. We also investigate the possibility of the FIP bias in the low-FIP elements to be FIP-dependent, so that different abundance anomalies must be found even within the low-FIP class of elements, which can provide important constraints on the FIP effect models.
Measuring the local complementarity of population, amenities and digital activities to identify and understand urban areas of interest

Identifying and understanding areas of interest are essential for urban planning. These areas are normally defined from static features of the resident population and urban amenities. Research has emphasised the importance of human mobility activity to capture the changing nature of these areas throughout the day, and the use of digital applications to reflect the increasing integration between material and online activities. Drawing on mobile phone data, this paper develops a novel approach to identify areas of interest based on the degree of complementarity of digital activities, available amenities and population levels. As a case study, we focus on the largest urban agglomeration of Chile, Santiago, where we identify three distinctive groups of areas: those concentrating (1) high availability of amenities; (2) high diversity of amenities and digital activities; and (3) areas lacking amenities, yet, presenting high usage of digital leisure and mobility applications. These findings identify areas where digital activities and local amenities play a complementary role in association with local population levels, and provide data-driven insights into the structure of material and digital activities in urban spaces that may characterise large Latin American cities.
Effects of Flash Evaporation Conditions on the Quality of UHT Milk by Changing the Dissolved Oxygen Content in Milk

This study assessed the impact of reducing dissolved oxygen (DO) content on the quality of UHT milk using a flash deoxygenation treatment. Flash deoxygenation was designed based on preheated milk reaching boiling early under low-pressure conditions to remove DO from the milk. Two parameters were designed for flash deoxygenation: preheating temperature 65 °C, −0.08 Mpa, and 70 °C, −0.06 Mpa. The flash conditions were applied to two UHT sterilization conditions (135 °C for 10 s and 145 °C for 5 s). After deoxygenation, the total oxidation (TOTOX) value of UHT milk was reduced by 1.4~1.71, and the protein carbonyl (PC) value was reduced by 1.15~1.52 nmol/mg of protein. The maximum inhibition rates of furusine and 5-HMF were 33.23 ± 1.72% and 25.43 ± 3.14%, respectively. The particle size was reduced by 0.141~0.178 μm. The ketones and stale aldehydes causing oxidized taste in the UHT milk were significantly reduced. This study showed that the oxidation and Maillard reactions of UHT milk were significantly inhibited, stability was improved, and the content of undesirable volatile flavor substances was reduced after flash deoxygenation. Therefore, reducing DO content was beneficial to improving the quality of UHT milk.
Atlas of Nervous System Vascular Malformations: A Systematic Review

Vascular malformations are frequent in the head and neck region, affecting the nervous system. The wide range of therapeutic approaches demand the correct anatomical, morphological, and functional characterization of these lesions supported by imaging. Using a systematic search protocol in PubMed, Google Scholar, Ebsco, Redalyc, and SciELO, the authors extracted clinical studies, review articles, book chapters, and case reports that provided information about vascular cerebral malformations, in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 385,614 articles were grouped; using the inclusion and exclusion criteria, three of the authors independently selected 51 articles about five vascular cerebral malformations: venous malformation, brain capillary telangiectasia, brain cavernous angiomas, arteriovenous malformation, and leptomeningeal angiomatosis as part of Sturge–Weber syndrome. We described the next topics—“definition”, “etiology”, “pathophysiology”, and “treatment”—with a focus on the relationship with the imaging approach. We concluded that the correct anatomical, morphological, and functional characterization of cerebral vascular malformations by means of various imaging studies is highly relevant in determining the therapeutic approach, and that new lines of therapeutic approaches continue to depend on the imaging evaluation of these lesions.
Adjustment Method of “FAST” Active Reflector Based on Optimal Fitting Strategy

The adjustment method of the fast active reflector of “Chinese heavenly eye” is studied. Firstly, this paper studies the offset characteristics of fast main cable node position, combined with the position of the celestial body to be measured, the follow-up coordinate system is established, and the optimal fitting model of active reflector adjustment based on discrete main cable node is established by using the idea of optimal fitting of the ideal surface. Secondly, the genetic algorithm is used to solve the minimum root mean square error (RMSE). The offset of the triangular reflector is driven by the offset of the main cable node, so that the working area is closer to the ideal paraboloid, and the maximum electromagnetic wave signal receiving ratio is achieved. Finally, rotate the coordinate data of each main cable node according to the known celestial body angle and use the Lagrange operator method to obtain the shortest distance from the actual offset point to the ideal paraboloid as the objective function, with the radial expansion range of the actuator and the variation range of the distance between adjacent points as constraints, and the size of the electromagnetic wave contact area is the basis for judging the amount of information, and a reflector adjustment model for the optimal displacement strategy of discrete main cable nodes is established. Through the test, concluded that the optimal fitting strategy is ideal, the model can accurately obtain the fast active reflector adjustment method under the constraint conditions. Through the test, the conclusion that the optimal fitting strategy is ideal is obtained, and the fast active reflector adjustment method that the model can accurately obtain under the constraints is verified.
Full-length 16S rRNA amplicon sequencing reveals the variation of epibiotic microbiota associated with two shrimp species of Alvinocarididae: possibly co-determined by environmental heterogeneity and specific recognition of hosts

Shrimps of the family Alvinocarididae, endemic species to deep sea chemosynthetic ecosystems, harbor epibiotic microbes on gills which probably play important roles in the survival of the shrimps. Among them, Alvinocaris longirostris and Shinkaicaris leurokolos occupy different ecological niches within the same hydrothermal vent in Okinawa Trough, and A. longirostris also exists in a methane seep of the South China Sea. In this study, full-length 16S rRNA sequences of the gill associated bacteria of two alvinocaridid species from different chemosynthetically ecological niches were first captured by single-molecule real-time sequencing. Totally, 120,792 optimized circular consensus sequences with ∼1,450 bp in length were obtained and clustered into 578 operational taxonomic units. Alpha diversity analysis showed seep A. longirostris had the highest species richness and evenness (average Chao1 = 213.68, Shannon = 3.39). Beta diversity analysis revealed that all samples were clearly divided into three groups, and microbial community of A. longirostris from seep and vent were more related than the other comparisons. By permutational multivariate analysis of variance, the most significant community compositional variance was detected between seep A. longirostris and vent S. leurokolos (R2 = 0.731, P = 0.001). The taxon tags were further classified into 21 phyla, 40 classes, 89 orders, 124 families and 135 genera. Overall, the microbial communities were dominated by Campylobacteria and Gammaproteobacteria. Alphaproteobacteria, Bacteroidia, Verrucomicrobiae, Bacilli and other minor groups were also detected at lower abundance. Taxonomic groups recovered from the vent S. leurokolos samples were only dominated by Sulfurovaceae (94.06%). In comparison, gill-associated microbiota of vent A. longirostris consisted of more diverse sulfur-oxidizing bacteria, including Sulfurovaceae (69.21%), Thiotrichaceae (6.77%) and a putative novel Gammaproteobacteria group (14.37%), while in seep A. longirostris, Gammaproteobacteria un-group (44.01%) constituted the major component, following the methane-oxidizing bacteria Methylomonadaceae (19.38%), and Sulfurovaceae (18.66%). Therefore, the gill associated bacteria composition and abundance of alvinocaridid shrimps are closely related to the habitat heterogeneity and the selection of microbiota by the host. However, the interaction between these alvinocaridid shrimps and the epibiotic communities requires further study based on metagenome sequencing and fluorescence in situ hybridization.
Hardware Accelerators for Real-Time Face Recognition: A Survey

Real-time face recognition has been of great interest in the last decade due to its wide and varied critical applications which include biometrics, security in public places, and identification in login systems. This has encouraged researchers to design fast and accurate embedded and portable systems that are capable of detecting and recognizing a large number of faces at almost a video frame rate. Due to the increasing volume of reference faces, traditional general-purpose computing engines such as the ones based on Intel’s Pentium processors have shown not to be adequate and various dedicated hardware accelerators based on either Graphical Processing Units (GPU), Field Programmable Gate Arrays (FPGA), Application Specific Integrated Circuits (ASIC), or even multi-core Central Processing Units (CPU) have emerged. Earlier published review papers on face detection/recognition have discussed face detection and face recognition algorithms enhancement that improve the detection accuracy. Nevertheless, none of them have reviewed the hardware accelerators used for this application. Accordingly, this paper aims to provide a comprehensive review of the most recent face recognition algorithms and associated embedded hardware systems targeting real-time performance. A detailed comparison between neural network and non-neural network-based algorithms in terms of accuracy and processing time is provided. Discussions on their suitability to be implemented into parallel hardware architectures such as Single Instruction Multiple Thread (SIMT) or Single Instruction Multiple Data (SIMD) is also discussed.