Search Results (649)

This study used a direct dispersion and filtration technique to produce hybrid buckypaper (BP) composites of copper oxide nanoparticles (NPs) and entangled multiwalled carbon nanotubes (CNTs). The photocurrent generation of the BP sheets under two different (direct and gradient) illumination conditions was investigated by varying copper oxide loadings (10–50 wt%). The structure and morphology of the composites examined through X-ray diffraction and scanning electron microscopy (SEM) confirmed the presence of monoclinic cupric oxide nanoparticles in the CNT network. The difference in electrical resistivity between bulk-filled and surface-filled CuO-BP composites was assessed using the four-probe Hall measurement. The studies disclosed that the surface-loaded CuO on the CNT network demonstrated a superior ON and OFF response under the gradient illumination conditions with peak values of 17.69 μA and 350.04 μV for photocurrent and photovoltage, respectively. The significant photocurrent observed at zero applied voltage revealed the existence of a photovoltaic effect in the BP composites. An intense photoresponse was detected in the surface-filled sample CuO-BP composite in both illumination conditions. Additionally, at an illumination level of 150 W/m², wavelength-dependent photovoltaic effects on pure BP were observed using red, green, and blue filters. Full article

The phenomenon conceptualized in this paper as political pentecostalism is described as a post-confessional Christian network of religious leaders and megachurches that has transformed the political scene on a global scale in the last decades. This new development represents a point of inflection in many respects within the trajectory of modern societies, particularly in the Global South, and adds new challenges to the already vexed relation between religion and civil society. Methodologically, this paper sets out to systematize the results of extensive ethnological studies about this phenomenon with the aim of reconstructing its genealogy, capturing its distinctive phenomenological traits, and distilling its central theological tenets. Defining the latter along the lines of spiritual warfare, dominion theology, and over-realized eschatology constitutes the condition for establishing as well as engaging its underlying political theology, which will be appraised in the last section. The criticism leveled there with the aid of the concepts of katechon, sovereignty, and common good is indicative of the necessity of a broader theological debate, given the multiple inconsistencies contained in its political theology, while being aware of the challenges that they pose to Christian theology as a whole. Full article

A number of computer experiments have investigated the effectiveness in terms of accuracy of the method for simultaneously determining the distributions of electrical conductivity and magnetic permeability in the subsurface zone of planar conductive objects when modeling the process of eddy-current measurement testing by surface probes. The method is based on the use of surrogate optimization, which involves the use of a high-performance neural network proxy-model of probe by means of a deep learning as part of the target quadratic function. The surrogate model acts as a carrier and storage of a priori information about the object and takes into account the influence of all the main factors essential in the formation of the probe output signal. The problems of the surrogate model’s cumbersomeness and mitigation of the “curse of dimensionality” effect are solved by applying techniques for reducing the dimensionality of the design space based on the PCA algorithm. We investigated options for compromise solutions regarding the dimensionality of the PCA-space and the accuracy of obtaining the desired material properties profiles by the optimization method. The results of modeling the inverse measurement problem indicate a fairly high accuracy of profile reconstruction. Full article

Federated learning enables multiple intelligent devices to collaboratively perform machine learning tasks while preserving local data privacy. However, traditional FL architectures face challenges such as centralization and lack of effective defense mechanisms against malicious nodes, particularly in large-scale edge computing scenarios, which can lead to system instability. To address these challenges, this paper proposes a cross-zone secure federated learning method with blockchain and credibility mechanism, named CS-FL. By constructing a dual-layer blockchain network and introducing a blockchain ledger between zone servers, CS-FL establishes a decentralized trust mechanism for index detection and model aggregation. In node selection, CS-FL considers multiple dimensions, including node quality, communication resources, and historical credibility, and employs a three-stage mechanism that introduces lightweight probe tasks to assess node status before formal FL training, ensuring high-quality nodes participate. Additionally, the credibility incentive mechanism penalizes nodes that bypass probe mechanism and engage in malicious behaviors, effectively mitigating the impact of deceptive attacks. Experimental results show that CS-FL significantly improves the defense performance of FL, reducing attack success rates from 75–85% to below 5–20% when facing different types of threats, and effectively maintaining the training accuracy of the FL model. This demonstrates the potential of CS-FL to enhance the security and stability of FL systems in complex edge computing scenarios. Full article

This research examines and analyzes the measured electromagnetic characteristics of vegetal soil for Wireless Underground Sensor Networks applied to precision agriculture. For this, we used Wireless Underground Sensor Network (WUSN) technology, which consists of sensors that communicate through the soil to collect data on irrigation, such as temperature and humidity, for good plant growth. However, underground communication channels and signal transmission are required to travel through a dense and heterogeneous soil mixture. For the measurement results of the vegetal soil dielectric parameters, a precision domain sensing probe operating at 433 Mhz was used. Moreover, the different choices of capacitance, inductance, and varactor were included, with a reasonable estimation of the dielectric permittivity, ranging from 2 to 15, and an unlimited range of conductivities. Despite promising results in predicting the dielectric permittivities, several improvements were made to the mode for low permittivity values, and it was designed to accommodate a wide range of dielectric permittivities. Full article

The tumor microenvironment (TME) is a critical factor in cancer progression, driving tumor growth, immune evasion, therapeutic resistance, and metastasis. Understanding the dynamic interactions within the TME is essential for advancing cancer management. Molecular imaging provides a non-invasive, real-time, and longitudinal approach to studying the TME, with techniques such as positron emission tomography (PET), magnetic resonance imaging (MRI), and fluorescence imaging offering complementary strengths, including high sensitivity, spatial resolution, and intraoperative precision. Recent advances in imaging probe development have enhanced the ability to target and monitor specific components of the TME, facilitating early cancer diagnosis, therapeutic monitoring, and deeper insights into tumor biology. By integrating these innovations, molecular imaging offers transformative potential for precision oncology, improving diagnostic accuracy and treatment outcomes through a comprehensive assessment of TME dynamics. Full article

Ghost imaging is a technique for indirectly reconstructing images by utilizing the second-order or higher-order correlation properties of the light field, which exhibits a robust ability to resist interference. On the premise of ensuring the quality of the image, effectively broadening the imaging range can improve the practicality of the technology. In this paper, a dual-path computational ghost imaging method based on convolutional neural networks is proposed. By using the dual-path detection structure, a wider range of target image information can be obtained, and the imaging range can be expanded. In this paper, for the first time, we try to use the two-channel probe as the input of the convolutional neural network and successfully reconstruct the target image. In addition, the network model incorporates a self-attention mechanism, which can dynamically adjust the network focus and further improve the reconstruction efficiency. Simulation results show that the method is effective. The method in this paper can effectively broaden the imaging range and provide a new idea for the practical application of ghost imaging technology. Full article

Objectives: Periodontitis (PD) patients frequently suffer from comorbidities, necessitating increased attention to disease management and monitoring. The aim of this study is to describe the prevalence and patterns of comorbidities among patients with PD in a private periodontal referral practice. Methods: This study involved 3171 adults with PD. Data on demographics, lifestyle, number of teeth, pockets of size ≥ 6 mm, bleeding on probing, periodontal inflammatory surface area, and comorbidities were extracted from electronic patient records. Descriptive and statistical analyses, including t-tests, chi-square tests, cluster analysis, binomial logistic regression analysis, and hypergraph network analysis, were performed. Results: Among this PD population, 47% had a comorbidity, and 20% had multimorbidity (≥2 diseases). Based on the disease patterns, two distinct clusters emerged: Cluster 1 was dominated by respiratory tract conditions (asthma, lung disease, and allergic rhinitis), allergies, and hypothyroidism, while Cluster 2 primarily included cardiometabolic diseases (angina pectoris, hypertension, diabetes mellitus (DM), and hyperthyroidism). The hypergraph network analysis for those with multimorbidity identified two main groups: (i) pulmonary conditions (lung disease, asthma, allergic rhinitis, and allergies) and (ii) cardiometabolic disorders (hypertension, myocardial infarction, cerebrovascular disease, and DM). Hypertension, allergies, and allergic rhinitis showed high centrality, serving as central nodes frequently co-occurring with other diseases. Conclusions: Nearly half of the PD patients in a private periodontal referral practice were found to have comorbidities, primarily clustering into cardiometabolic and respiratory tract diseases. These findings, based on real-world data, should encourage dental professionals to integrate systemic conditions into their care strategies. They could also guide policymakers and practitioners in developing evidence-based approaches to mitigate the reciprocal negative effects of PD and comorbidities. Full article

Author Gender Identification (AGI) is an extensively studied subject owing to its significance in several domains, such as security and marketing. Recognizing an author’s gender may assist marketers in segmenting consumers more effectively and crafting tailored content that aligns with a gender’s preferences. Also, in cybersecurity, identifying an author’s gender might aid in detecting phishing attempts where hackers could imitate individuals of a specific gender. Although studies in Arabic have mostly concentrated on written dialects, such as tweets, there is a paucity of studies addressing Modern Standard Arabic (MSA) in journalistic genres. To address the AGI issue, this work combines the beneficial properties of natural language processing with cutting-edge deep learning methods. Firstly, we propose a large 8k MSA article dataset composed of various columns sourced from news platforms, labeled with each author’s gender. Moreover, we extract and analyze textual features that may be beneficial in identifying gender-related cues through their writings, focusing on semantics and syntax linguistics. Furthermore, we probe several innovative deep learning models, namely, Convolutional Neural Networks (CNNs), LSTM, Bidirectional LSTM (BiLSTM), and Bidirectional Encoder Representations from Transformers (BERT). Beyond that, a novel enhanced BERT model is proposed by incorporating gender-specific textual features. Through various experiments, the results underscore the potential of both BERT and the textual features, resulting in a 91% accuracy for the enhanced BERT model and a range of accuracy from 80% to 90% accuracy for deep learning models. We also employ these features for AGI in informal, dialectal text, with the enhanced BERT model reaching 68.7% accuracy. This demonstrates that these gender-specific textual features are conducive to AGI across MSA and dialectal texts. Full article

Efficiently reporting soil-specific information is of key importance for plant growth but can be quite demanding as well. Indeed, it may require expensive digitizers, subscriptions to services for communication links between each sensor and the cloud, and the incorporation of power-hungry elements. Added to this, soil sensors may vary drastically, e.g., in terms of power characteristics, response times, or interfacing options. The need for improved energy autonomy increases reporting complexity, as it presupposes that the participating components will enter a low-power (sleep) state when not in action. Furthermore, the IoT nodes hosting the sensing instruments should be able to work unattended for long periods under varying environmental conditions. In response to the aforementioned physical and technical challenges, this work highlights the details behind the cooperation of a cost-effective microprocessor equipped with a radio transceiver and some simple and widely available electronic components to form nodes that can host a diverse set of soil sensors and deliver reliable data in satisfactory ranges. The sensitivity and power efficiency of the LoRa protocol make it ideal for rural agri-field use; in the meantime, optimized action/sleep management, along with tiny solar panels, guarantee sustainable operation. The proposed system was tested utilizing various typical soil instruments, and its range coverage, consumption, and measurement quality were thoroughly evaluated under different installation settings, thus providing guidance for similar implementations and indicating its suitability for a wide set of monitoring applications. Full article

Multiple input multiple output (MIMO) antennas have recently received attention for improving wireless communication data rates in rich scattering environments. Despite this, the challenge of isolation persists prominently in compact MIMO-based electronics. Various techniques have recently emerged to address the isolation issues, among which the defected ground structure (DGS) stands out as a cost-effective solution. Additionally, selecting the appropriate feed mechanism is crucial for enhancing the key performance indicators of MIMO antennas. However, there has been minimal focus on how different feed methods impact the operation of MIMO antennas integrated with DGS. This paper begins with a comprehensive review of diverse antenna design, feeding strategies, and DGS architectures. Subsequently, the causal relationships between various feed networks and DGSs has been established through modeling, simulation, fabrication, and measurement of MIMO antennas operating within the sub-6 GHz spectrum. Particularly, dual elements of MIMO antennas grounded by a slotted complementary split ring resonator (SCSRR)-based DGS were excited using four standard feed methods: coaxial probe, microstrip line, proximity coupled, and aperture coupled feed. The influence of each feed network on the performance of MIMO antennas integrated with SCSRR-based DGSs has been thoroughly investigated and compared, leading to guidelines for feed network selection. The coaxial probe feed network provided improved isolation performance, ranging from 16.5 dB to 46 dB in experiments.The aperture and proximity-coupled feed network provided improvements in bandwidth of 38.7% and 15.6%, respectively. Furthermore, reasonable values for envelope correlation coefficient (ECC), diversity gain (DG), channel capacity loss (CCL), and mean effective gain (MEG) have been ascertained. Full article

Over the past three decades, luminescence thermometry has gained significant attention among researchers and practitioners. The method has progressed in terms of utilizing temperature-sensitive luminescent materials, obtaining temperature read-outs from luminescence, developing applications, and improving performance. This paper reviews and critically analyzes routes for improving luminescence thermometry performance, in particular the sensitivity, accuracy, and precision of the method. These include the use of highly temperature-sensitive probes, temperature read-outs from luminescence with improved sensitivity, multiparameter temperature-reading methods, the applications of principal component analysis and artificial neural networks, and sensor fusion. Full article

Background/Objectives: Telomeres consist of repetitive nucleotide sequences and associated proteins that safeguard chromosome ends from degradation and fusion with neighboring chromosomes. As cells divide, telomeres shorten due to the end-replication problem and oxidative stress, ultimately contributing to cellular senescence. Telomeres therefore play a role in cellular health and aging. Measuring telomere length has emerged as a significant biomarker in various fields of research, including aging, cancer, and chronic diseases. Accurate measurement of telomere length is critical for interpreting research findings and clinical applications. Variability in measurement techniques can lead to inconsistent results, underscoring the need for standardized protocols. Methods and Results: The Telomere Research Network (TRN), an initiative from the National Institute of Aging and National Institute of Environmental Health Sciences, has established recommended guidelines to standardize the measurement of telomere length using qPCR to ensure accuracy and reproducibility in population-based studies. The monochrome multiplex quantitative PCR (MMqPCR) assay has emerged as a robust method endorsed by the TRN for its accuracy and reproducibility in quantifying telomere length in epidemiology ad population based studies. The absolute telomere length (aTL) qPCR assay is currently being evaluated by the TRN for its capability to utilize an oligomer standard, enabling the generation of absolute telomere lengths. The oligomer feature facilitates a more direct comparison of results across experiments and laboratories. Conclusions: This paper outlines a novel dual-labeled multiplex aTL method by incorporating dual-labeled multiplex probes to measure average absolute telomere length, providing a clear advantage over the relative telomere length assay, which quantifies the ratio of telomeric repeats to single-copy gene numbers. Full article

The development of network measurement technologies has greatly increased the speed of network scans, but it also poses risks for the stability of the scanned networks. How to reduce probing traffic and enhance the effectiveness of probing has become a new research issue. In this paper, we utilize network measurement and machine learning techniques, leveraging public interfaces from network mapping platforms to construct a dataset with 44 feature dimensions. By combining the categorical boosting (CatBoost) model with the particle swarm optimization (PSO) algorithm for heuristic optimization, we propose a host port openness prediction model that integrates the PSO algorithm and the CatBoost model. Through comparisons with various machine learning models, the effectiveness of our proposed model was validated. Using this model in network scanning can save approximately 65% of bandwidth on average, effectively reducing the impact on the probed network. Full article

For this article, a low-cost, compact, and flexible inkjet-printed electromagnetic sensor was investigated for its chemical and biomedical applications. The investigated sensor design was used to estimate variations in the concentration of chemicals (ethanol and methanol) and biochemicals (hydrocortisone—a chemical derivative of cortisol, a biomarker of stress and cardiovascular effects). The proposed design’s sensitivity was further improved by carefully choosing the frequency range (0.5–4 GHz), so that the analyzed samples showed approximately linear variations in their dielectric properties. The dielectric properties were measured using a vector network analyzer (VNA) and an Agilent 85070E Dielectric Probe Kit. The sensor design had a resonant frequency at 2.2 GHz when investigated without samples, and a consistent shift in resonant frequency was observed, with variation in the concentrations of the investigated chemicals. The sensitivity of the designed sensor is decent and is comparable to its non-flexible counterparts. Furthermore, the simulation and measured results were in agreement and were comparable to similar investigated sensor prototypes based on non-flexible Rogers substrates (Rogers RO4003C) and Rogers Droid/RT 5880), demonstrating true potential for chemical, biomedical applications, and healthcare. Full article