Riffat Mumtaz Malik, Muhammad Asif Naveed, Ghulam Murtaza Rafique
In the present technology–driven healthcare system, information literacy (IL) is a crucial skill that deserves attention and focus. As integral members of the medical profession, nurses must adopt IL skills to deliver high- quality patient-centered care by taking informed and evidence-based clinical decisions-making. To explore the effect of IL skills on clinical decision making (CDM) abilities of professional nurses; assess the relationship between these variables (IL skills and CDM) and to investigate the perceived IL skills and CDM abilities of nurses employed in private sector hospitals in Punjab, Pakistan. A quantitative nature survey was conducted using a questionnaire. Out of 500 conveniently selected participants, 306 responded. Data was analyzed using both descriptive and inferential statistics. Findings revealed that a positive correlation exists between information literacy skills and Clinical Decision Making of nurses. Regression analysis proved that there is a positive effect of IL skill on the CDM of nurses. Findings indicate that nurses possess strong IL skills and are competent in clinical decision making (CDM) abilities. The empirical evidence yielded by this investigation substantiates the hypothesis that a statistically significant and positive correlation was found between nurses’ IL skills and CDM capacities of nursing professionals. Nurses professionals having full command of IL skills can make sound and well- informed decisions in CDM process, which guarantees the delivery of safe healthcare services in the workplace.
Information theory, Management information systems
In battlefield environments, drones depend on high-resolution imagery for critical tasks such as target identification and situational awareness. However, acquiring clear images of distant targets presents a significant challenge. To address this, we propose a supervised learning approach for image super-resolution. Our network architecture builds upon the U-Net framework, incorporating enhancements to the encoder and decoder through techniques such as Discrete Wavelet Transform, Channel Attention Residual Modules, Selective Kernel Feature Fusion, Weight Normalization, and Dropout. We evaluate our model on a super-resolution dataset and compare its performance against other networks, highlighting the importance of minimizing trainable parameters for real-time deployment on resource-constrained drone platforms. The effWicacy of our proposed network is further validated through image recognition tasks and real-world scenario testing. By enhancing image clarity at extended ranges, our approach enables drones to detect adversaries earlier, facilitating proactive countermeasures and improving mission success rates
Optical Burst Switching (OBS) is considered a promising optical switching technology for the future. However, a key issue of the OBS network is reducing dropped bursts due to contentions because there is no optical buffer at intermediate nodes. Several methods have been proposed to address burst contention, such as wavelength conversion, Fiber Delay Line (FDL) usage, deflection routing, or burst retransmission. Among these methods, deflection routing and burst retransmission are two approaches that do not modify the network infrastructure and can take advantage of idle resources on alternative connections. However, uncontrolled burst retransmissions and misrouting can lead to increased collisions, and potentially endless collision handling loops. This paper proposes a hybrid model of limited burst retransmission and deflection routing. Simulation results show that the proposed model has significantly improved resource utilization efficiency, burst-dropping probability, and end-to-end transmission delay.
The amount of ultrasound (US) breast exams continues to grow because of the wider endorsement of breast cancer screening programs. When a solid lesion is found during the US the primary task is to decide if it requires a biopsy. Therefore, our goal was to develop a noninvasive US grayscale image analysis for benign and malignant solid breast lesion differentiation. We used a dataset consisting of 105 ultrasound images with 50 benign and 55 malignant non-cystic lesions. Features were extracted from the source image, the image of the gradient module after applying the Sobel filter, and the image after the Laplace filter. Subsequently, eight gray-level co-occurrence matrices (GLCM) were constructed for each lesion, and 13 Haralick textural features were calculated for each GLCM. Additionally, we computed the differences in feature values at different spatial shifts and the differences in feature values between the inner and outer areas of the lesion. The LASSO method was employed to determine the most significant features for classification. Finally, the lesion classification was carried out by various methods. The use of LASSO regression for feature selection enabled us to identify the most significant features for classification. Out of the 13 features selected by the LASSO method, four described the perilesional tissue, two represented the inner area of the lesion and five described the image of the gradient module. The final model achieved a sensitivity of 98%, specificity of 96%, and accuracy of 97%. Considering the perilesional area, Haralick feature differences, and the image of the gradient module can provide crucial parameters for accurate classification of US images. Features with a low AUC index (less than 0.6 in our case) can also be important for improving the quality of classification.
Luis Alfredo Moctezuma, Yoko Suzuki, Junya Furuki
et al.
Abstract We present a new approach to classifying the sleep stage that incorporates a computationally inexpensive method based on permutations for channel selection and takes advantage of deep learning power, specifically the gated recurrent unit (GRU) model, along with other deep learning methods. By systematically permuting the electroencephalographic (EEG) channels, different combinations of EEG channels are evaluated to identify the most informative subset for the classification of the 5-class sleep stage. For analysis, we used an EEG dataset that was collected at the International Institute for Integrative Sleep Medicine (WPI-IIIS) at the University of Tsukuba in Japan. The results of these explorations provide many new insights such as the (1) drastic decrease in performance when channels are fewer than 3, (2) 3-random channels selected by permutation provide the same or better prediction than the 3 channels recommended by the American Academy of Sleep Medicine (AASM), (3) N1 class suffers the most in prediction accuracy as the channels drop from 128 to 3 random or 3 AASM, and (4) no single channel provides acceptable levels of accuracy in the prediction of 5 classes. The results obtained show the GRU’s ability to retain essential temporal information from EEG data, which allows capturing the underlying patterns associated with each sleep stage effectively. Using permutation-based channel selection, we enhance or at least maintain as high model efficiency as when using high-density EEG, incorporating only the most informative EEG channels.
Emmanuel Effah, Ousmane Thiare, Alexander M. Wyglinski
This paper presents an in-depth contextualized tutorial on Agricultural IoT (Agri-IoT), covering the fundamental concepts, assessment of routing architectures and protocols, and performance optimization techniques via a systematic survey and synthesis of the related literature. The negative impacts of climate change and the increasing global population on food security and unemployment threats have motivated the adoption of the wireless sensor network (WSN)-based Agri-IoT as an indispensable underlying technology in precision agriculture and greenhouses to improve food production capacities and quality. However, most related Agri-IoT testbed solutions have failed to achieve their performance expectations due to the lack of an in-depth and contextualized reference tutorial that provides a holistic overview of communication technologies, routing architectures, and performance optimization modalities based on users’ expectations. Thus, although IoT applications are founded on a common idea, each use case (e.g., Agri-IoT) varies based on the specific performance and user expectations as well as technological, architectural, and deployment requirements. Likewise, the agricultural setting is a unique and hostile area where conventional IoT technologies do not apply, hence the need for this tutorial. Consequently, this tutorial addresses these via the following contributions: (1) a systematic overview of the fundamental concepts, technologies, and architectural standards of WSN-based Agri-IoT, (2) an evaluation of the technical design requirements of a robust, location-independent, and affordable Agri-IoT, (3) a comprehensive survey of the benchmarking fault-tolerance techniques, communication standards, routing and medium access control (MAC) protocols, and WSN-based Agri-IoT testbed solutions, and (4) an in-depth case study on how to design a self-healing, energy-efficient, affordable, adaptive, stable, autonomous, and cluster-based WSN-specific Agri-IoT from a proposed taxonomy of multi-objective optimization (MOO) metrics that can guarantee an optimized network performance. Furthermore, this tutorial established new taxonomies of faults, architectural layers, and MOO metrics for cluster-based Agri-IoT (CA-IoT) networks and a three-tier objective framework with remedial measures for designing an efficient associated supervisory protocol for cluster-based Agri-IoT networks.
In the world of image analysis, effectively handling large image datasets is a complex challenge that requires using deep neural networks. Siamese neural networks, known for their twin-like structure, offer an effective solution to image comparison tasks, especially when data volume is limited. This research explores the possibility of enhancing these models by adding supplementary outputs that improve classification and help find specific data features. The article shows the results of two experiments using the Fashion MNIST and PlantVillage datasets, incorporating additional classification, regression, and combined output strategies with various weight loss configurations. The results from the experiments show that for simpler datasets, the introduction of supplementary outputs leads to a decrease in model accuracy. Conversely, for more complex datasets, optimal accuracy was achieved through the simultaneous integration of regression and classification supplementary outputs. It should be noted that the observed increase in accuracy is relatively marginal and does not guarantee a substantial impact on the overall accuracy of the model.
Stefano Follesa, Sabrina Cesaretti, Francesco Armato
In a scene radically varied by the effects of the pandemic, a reflection opens on which guidelines and methods should turn today educational research, an area no less spared, which also manifests the fragility of a system made of static habits. The knowledge of how design originates from the ability to adapt to the changes of a society in continuous evolution, in which modernity has however unquestionably marked the loss of forms built over the centuries, implementing a radical break with the past. Investigating the variations of teaching through the comparison between historical models and new tools and processes of the digital age, the paper questions the concept of form, proper to the design project but also immaterial tool of culture, a means of coexistence and a place of mutual exchange, to define the changeability we are witnessing in the transition from classrooms to home desks. In fact, it is increasingly necessary to re-establish relations between the parties involved, to restore a communicative capacity that knows how to overcome difficulties and fears in the awareness that, as in the most famous physical law, nothing is created or destroyed, but it only changes in its form.
In the real world, decision-making process is related to alternative evaluation with respect to multiple conflicting criteria. In this paper, a hybrid group approach to solve the site selection problem is developed by integrating two methods which are the Best Worst Method (BWM) and Evaluation based on Distance from Average (EDAS). The multi-criteria group decision-making methods aggregate expert preferences and present the best agreement. BWM method is used to do pairwise comparisons in a structured way to determine the criteria weight. EDAS is a method used for alternative ranking which is useful for the decision issues that contain conflicting criteria. In order to show the procedures and the application of the developed method, a case study for site selection of plastic manufacturing company is developed. A comparison made between the proposed method and the AHP method is developed to ensure that the proposed method offers reliable results. Also, sensitivity analysis is conveyed for robustness validation.
In modern conditions financial business is the driving mechanism for the commercial activity of such means of speculating with existing instruments of the financial market, but also with the emission of new ones. The purpose of writing a scientific article is to identify the main and most effective means of investment, to identify their advantages and disadvantages.
Modern biometric systems based on face recognition demonstrate high recognition quality, but they are vulnerable to face presentation attacks, such as photo or replay attack. Existing face anti-spoofing methods are mostly based on texture analysis and due to lack of training data either use hand-crafted features or fine-tuned pretrained deep models. In this paper we present a novel CNN-based approach for face anti-spoofing, based on joint analysis of the presence of a spoofing medium and eye blinking. For training our classifiers we propose the procedure of synthetic data generation which allows us to train powerful deep models from scratch. Experimental analysis on the challenging datasets (CASIA-FASD, NUUA Imposter) shows that our method can obtain state-of-the-art results.
A shear flow device contained in the microscope incubator has newly been designed to study the effect of the shear stress field on the biological cell in vitro. The culture medium was sandwiched with a constant gap between a lower stationary culture plate and an upper rotating parallel plate to make a Couette type of shear field with the perpendicular shear slope. The wall shear stress (Τ) on the lower culture disk was controlled by the rotating speed of the upper disk. The shear stress Τ increases in proportion to the distance from the axis of rotation. After cultivation for 24 hours for adhesion of cells on the lower plate without flow, Τ < 2 Pa was applied on cells for 24 hours subsequently. HUVEC (human umbilical vein endothelial cell) tends to be elongated and aligned under < 2 Pa of the shear stress. C2C12 (mouse myoblast cell line), on the other hand, maintains elongated shape and tends to migrate to the lower shear stress direction (< 2 Pa). The experimental system is useful to study the quantitative relationships between the shear stress and the cell behaviors: deformation, orientation, and migration.
This paper studies the problem of neutrosophic portfolios of financial assets as part of the modern portfolio theory. Neutrosophic portfolios comprise those categories of portfolios made up of financial assets for which the neutrosophic return, risk and covariance can be determined and which provide concomitant information regarding the probability of achieving the neutrosophic return, both at each financial asset and portfolio level and also information on the probability of manifestation of the neutrosophic risk. Neutrosophic portfolios are characterized by two fundamental performance indicators, namely: the neutrosophic portfolio return and the neutrosophic portfolio risk. Neutrosophic portfolio return is dependent on the weight of the financial assets in the total value of the portfolio but also on the specific neutrosophic return of each financial asset category that enters into the portfolio structure. The neutrosophic portfolio risk is dependent on the weight of the financial assets that enter the portfolio structure but also on the individual risk of each financial asset. Within this scientific paper was studied the minimum neutrosophic risk at the portfolio level, respectively, to establish what should be the weight that the financial assets must hold in the total value of the portfolio so that the risk is minimum. These financial assets weights, after calculations, were found to be dependent on the individual risk of each financial asset but also on the covariance between two financial assets that enter into the portfolio structure. The problem of the minimum risk that characterizes the neutrosophic portfolios is of interest for the financial market investors. Thus, the neutrosophic portfolios provide complete information about the probabilities of achieving the neutrosophic portfolio return but also of risk manifestation probability. In this context, the innovative character of the paper is determined by the use of the neutrosophic triangular fuzzy numbers and by the specific concepts of financial assets, in order to substantiating the decisions on the financial markets.
Dmitry V. Nesterenko, Roman A. Pavelkin , Shinji Hayashi
In this work, we consider the use of planar sensing structures, which support excitation of surface plasmon polarition (SPP) modes, for detecting changes in solvents, i.e. water, ethanol, isopropanol. In the structures under study, SPP modes propagate along the interfaces between metals and general solvents. The analysis of characteristics of the resonance response is based on Fano’s approximation within the coupled-mode theory in the visible and infrared regions. The maximum sensitivity and field enhancement are revealed in the near- and mid-infrared regions in the case of ethanol and isopropanol, which enables sensing applications beyond the regions of water absorption.
Petr Konstantinovich Skorobogatov, Konstantin Alekseevvich Epifantsev, Nikolai Sergeyevich Djatlov
et al.
The results of the single pulsing electrical overstress (EOS) series with energy below the threshold of failure for modern submicron IC’s design are presented. The study was conducted on two types of modern sub-micron VLSI. The obtained results confirm the possibility of accumulation of the effects of damage from repeated exposure EOS in modern IC’s and allow you to get the dependence describing the additive nature of damage the IC’s during exposure to subthreshold EOS. The obtained dependence agrees well with the Arrhenius equation, which indicates the thermal nature of the damage when exposed to a series of subthreshold EOS.The method of the IC’s testing is proposed to determine the level of the IC’s EOS hardness to the effects of multiple different pulsing voltages.