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The hybrid parallel fuzzy convolutional neural network (HP-FCNN) is a ground-breaking method for medical image analysis that combines the interpretive capacity of fuzzy logic with the capabilities of a convolutional neural network (CNN). This novel combination tackles problems related to brain image processing, reducing problems such as noise and hazy borders that are common in magnetic resonance imaging (MRI). Unlike other CNN models, the HP-FCNN combines fine-grained fuzzy representations with crisp CNN features, improving interpretability by displaying hidden layers. This insight into activation patterns facilitates comprehension of the decision-making processes necessary for the diagnosis of brain diseases. The HP-FCNN outperforms other pretrained models (ResNet, DenseNet, visual geometry group (VGG), and EfficientNet) on measures such as the confusion matrix and area under the receiver operating characteristic curve (AUC-ROC), according to comparative assessments. Furthermore, the addition of adaptive class activation mapping (AD-CAM) enhances the HP-FCNN by identifying salient features during backpropagation and bolstering the network's capacity to enhance brain illness diagnosis and treatment planning. Our methodology, incorporating AD-CAM, yielded compelling results with a 96.86 F1-Score, 96.41 AUC, and 96.81 accuracy, showcasing the effectiveness of our approach in achieving high-performance metrics in brain MRI analysis. With a 15% increase in accuracy, a 10% increase in sensitivity, and a 12% decrease in false positives, the HP-FCNN outperforms its predecessors. These impressive advancements represent a quantifiable breakthrough in the capabilities of medical image processing technology; they are more than just anecdotal evidence.

Mobile learning has expanded the reach of electronic and distance education, which has been made possible by modern technology and globalization. There is a need to identify the elements that are essential in determining behavioral intention towards mobile learning because there is a dearth of scientific justification for their importance. The goal of the study is to ascertain how behavioral intention towards mobile learning are affected by perceived entertainment, perceived informativeness, perceived irritation, perceived trust, and perceived value. Moreover, the study examines the mediating role of attitude among the aforementioned variables and behavioral intention towards mobile learning. Finally, the moderating impact of social influence on the relationship between attitude and behavioral intention towards mobile learning is investigated. The multistage cluster sampling technique is used to gather data from 586 students. Multiple regression analysis is utilized to test hypotheses, whereas AMOS-23 is used for confirmatory factor analysis. Hayes & Preacher’s macro is applied to assess mediation and moderation. The results show that perceived entertainment, informativeness, trust, and value significantly and directly influence attitude towards mobile learning, but perceived irritation has a negative effect. Additionally, perceived entertainment, trust, and value have a direct and significant impact on behavioral intention towards mobile learning, whereas perceived irritation has a negative and informativeness has an insignificant impact. Finally, the attitude is supported as a mediator, while social influence confirms its role as a moderator. The study offers significant theoretical and practical implications.

Low-frequency oscillations (LFO) are created in the power system due to various disturbances. The LFO if not controlled, grows and causes the system separation. There is a huge financial loss due to the interruption of the power supply caused by disturbances. With the increasing complexity of the modern power system, there is a need for the design of a more accurate and detailed modelling. An Advanced Heffron Phillips Model (AHPM) is developed with a higher order Synchronous Generator Model 1.1, based on ten K-Constants for stability improvement. This AHPM employs the combination of snake optimisation algorithm (SOA) and linear quadratic regulator (LQR) from optimal control theory. The highest damping ratio (99.98%) is obtained by AHPM in coordination with PSS, and TCSC based on SOA and LQR. For various parameters, the settling time ranges from 1.5 to 2.0 seconds. This AHPM is robust and capable of meeting the challenges of grid integration with renewables.

Ultrasound shear wave elastography (SWE) is a novel technique that may provide non-invasive measurements of renal compliance. We aimed to investigate the relationship between intravenous (IV) fluid administration and change in SWE measurements. We hypothesised that following IV fluid administration in healthy volunteers, global kidney stiffness would increase and that this increase in stiffness could be quantified using SWE. Our second hypothesis was that graduated doses of IV fluids would result in a dose-dependent increase in global kidney stiffness measured by SWE.

Short-range indoor localization is one of the key necessities in automation industries and healthcare setups. With its increasing demand, the need for more precise positioning systems is rapidly increasing. Millimeter-wave (mm-wave) technology is emerging to enable highly precise localization performance. However, due to the limited availability of low-cost mm-wave sensors, it is challenging to accelerate research on real data. Furthermore, noise due to the hardware components of a sensor incurs perturbation in the received signal, which corrupts the estimation of range and the angle of arrival (AoA). Due to the huge success of data-driven algorithms in solving regression problems, we propose a data-driven approach, which employs two deep learning (DL)-based regression models, i.e., dense neural network and convolutional neural network, and compare their performance with two machine learning-based regression models, linear regression and support vector regression, to reduce errors in the estimate of AoA and range obtained via an mm-wave sensor. Our main goal is to optimize the localization measurements acquired from a low-cost mm-wave sensor for short-range applications. This will accelerate the development of proof of concept and foster research on cost-effective mm-wave-based indoor positioning systems. All experiments were conducted using over-the-air data collected with an mm-wave sensor, and the validity of the experiments was verified in unseen environments. The results obtained from our experimental evaluations, both for in-sample and out-of-sample testing, indicate improvements in the estimation of AoA and range with our proposed DL models. The improvements achieved were greater than 15% for AoA estimation and over 85% for range estimation compared to the baseline methods.

In this letter, we propose a novel Fine-Tuned attribute Weighted Naïve Bayes (FT-WNB) classifier to identify the Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) for UltraWide Bandwidth (UWB) signals in an Indoor Positioning System (IPS). The FT-WNB classifier assigns each signal feature a specific weight and fine-tunes its probabilities to address the mismatch between the predicted and actual class. The performance of the FT-WNB classifier is compared with the state-of-the-art Machine Learning (ML) classifiers such as minimum Redundancy Maximum Relevance (mRMR)- $k$ -Nearest Neighbour (KNN), Support Vector Machine (SVM), Decision Tree (DT), Naïve Bayes (NB), and Neural Network (NN). It is demonstrated that the proposed classifier outperforms other algorithms by achieving a high NLoS classification accuracy of 99.7% with imbalanced data and 99.8% with balanced data. The experimental results indicate that our proposed FT-WNB classifier significantly outperforms the existing state-of-the-art ML methods for LoS and NLoS signals in IPS in the considered scenario.

ABSTRACTInfertility is rarely life threatening, however, it poses a serious global health issue posing far-reaching socio-economic impacts affecting 12–15% of couples worldwide where male factor accounts for 70%. Functional spermatogenesis which is the result of several concerted coordinated events to produce sperms is at the core of male fertility, Alternative splicing and microRNA (miRNA) mediated RNA silencing (RNAi) constitute two conserved post-transcriptional gene (re)programming machinery across species. The former by diversifying transcriptome signature and the latter by repressing target mRNA activity orchestrate a spectrum of testicular events, and their dysfunctions has several implications in male infertility. This review recapitulates the knowledge of these mechanistic events in regulation of spermatogenesis and testicular homoeostasis. In addition, miRNA payload in sperm, vulnerable to paternal inputs, including unhealthy diet, infection and trauma, creates epigenetic memory to initiate intergenerational phenotype. Naive zygote injection of sperm miRNAs from stressed father recapitulates phenotypes of offspring of stressed father. The epigenetic inheritance of paternal pathologies through miRNA could be a tantalizing avenue to better appreciate ‘Paternal Origins of Health and Disease’ and the power of tiny sperm.

Our study recognized the crucial role of early diagnosis of pulmonary radiological abnormalities such as pneumothorax, effusion, pneumonia, cardiomegaly, and COVID-19. We proposed FedXNet, which is a collaborative deep learning model based on federated learning (FL) exploiting edge computing resources efficiently to accurately deal with them and ensure privacy. Our developed model is notable for its integration of Multi-Headed Self-Attention, a complex technique that allows the model to focus on several parts of the input data at once. This improves the model’s capacity to uncover complex patterns and correlations within the medical images. This multi-class CNN system uses a thorough four-pronged approach: (1) facilitating cross-institutional, federated training without sacrificing the integrity of individual data, (2) image preprocessing to achieve robust model accuracy, (3) efficient Feature extraction using pre-trained models and our dedicated FedXNet architecture, as well as (4) a variety of classifiers tailored to each disease, resulting in impressive diagnostic performance for a range of thoracic diseases, including COVID-19. This model paves the way for a future where timely diagnosis and better patient outcomes become a reality, empowered by the collaborative spirit of FL exploiting edge computing resources of IoT for implementing robust deep learning models.

Alzheimer's is a progressive and debilitating neurological disorder characterized by cognitive decline, memory loss, and impaired daily functioning. It is an irreversible brain disease that destroys memory, thinking, and the ability to carry out daily activities. It poses significant challenges for patients and healthcare providers. Modern societies are trying to enhance the quality of people's lives, including Alzheimer's patients. In this study, we explored the potential of social robots to provide emotional support, improve cognitive function, and facilitate communication among Alzheimer's patients. This was achieved by initiating conversations on various topics such as family, relationships, and daily activities. This paper contributes to the literature by introducing a novel and well-organized framework for building an Alzheimer's care robot. Further, this study enriches the literature by introducing the Alzheimer Care Companion Robot (ACCR), designed to identify Alzheimer's patients. The ACCR initiates conversations in the native Arab-Kuwaiti dialect, displaying relevant memories through images and videos on its screen to assist in memory recall based on the individuals' life experiences. The proposed ACCR consists of 271 conversations belonging to three main categories: active, proactive, and graphical user interface (GUI) dialogs comprising 112 dialogs, 109 dialogs, and 50 dialogs for active, proactive, and GUI, respectively. The experimental result illustrated the success of the proposed solution.

All the operations of the power system should be smooth, continuous, reliable, and efficient. An electric fault interrupts the normal flow of power, damages the electrical equipment, creates abnormal voltages and currents, and deteriorates the system's power quality. In this manuscript, a novel device, the SPLIT TCSC, is implemented with the traditional proportional-integral (PI) controller and with controllers based on machine learning algorithms such as artificial neural network, and random forest for fault optimisation on IEEE 14-bus system. These controllers showed better results than the PI controller with respect to voltage during fault time and THD reduction. The voltage drop is around 30 kV, and the THD is around 1.95% with the system based on random forest. The results showed that power is utilised more effectively with the application of these algorithms in the system, along with improvements in the power quality, reliability, and efficiency of the system.

Effective clinical education is essential for managing tracheostomy patients safely and efficiently. Simulation-based training has shown greater efficacy than traditional methods in various clinical settings. Our internal training programme, called the Tracheostomy Refresher Program (TRP) was used to enhance nurses' skills in tracheostomy care.

I live in Pakistan. (Yes, Pakistan!) I hope some of your readers do not suddenly go for their sidearms after the mention of my beloved country. I used to get [...]

Techniques for scaling-up the direct-current (dc) triboelectricity generation in MoS2multilayer-based Schottky nanocontacts are vital for exploiting the nanoscale phenomenon for real-world applications of energy harvesting and sensing. Here, we show that scaling-up the dc output can be realized by using various MoS2multilayer-based heterojunctions including metal/semiconductor (MS), metal/insulator (tens of nanometers)/semiconductor (MIS), and semiconductor/insulator (a few nanometers)/semiconductor (SIS) moving structures. It is shown that the tribo-excited energetic charge carriers can overcome the interfacial potential barrier by different mechanisms, such as thermionic emission, defect conduction, and quantum tunneling in the case of MS, MIS, and SIS moving structures. By tailoring the interface structure, it is possible to trigger electrical conduction resulting in optimized power output. We also show that the band bending in the surface-charged region of MoS2determines the direction of the dc power output. Our experimental results show that engineering the interface structure opens up new avenues for developing next-generation semiconductor-based mechanical energy conversion with high performance.

Tribo-induced potential at a metal-insulator-semiconductor sliding junction results in very high interfacial electric field E(∼107–108V/m). Such fields are used to separate photo-generated electron-hole pairs that transport through the junction via quantum tunneling. We show that this phenomenon can generate direct current with high current density J. The strong synergetic effect between the photo-excitation and the interfacial electronic excitation at the MIS sliding contact boosts the photocurrent output in Si-based Schottky solar cell by ∼50 times and the triboelectric power output by ∼28 times. Charge relaxation dynamics calculations agree well with conductive atomic force microscopy results and show the coupling effect between the photo- and the tribo-induced electric fields. Experimental observation of the tribo-photovoltaic effect provides new fundamental understanding of electromechanical coupling, and thus offers a new direction for future solar-mechanical energy co-harvesting.

We introduce a centrifugal microfluidic disc that accepts a small volume in (∼5 μL), performs sample cleanup on human serum samples, and delivers a small volume out, for subsequent metabolite analysis by surface assisted laser desorption/ionization (SALDI) mass spectrometry (MS) or hydrophilic interaction liquid chromatography (HILIC)-MS. The centrifugal microfluidic disc improves the MS results by removing proteins and lipids from serum. In the case of SALDI-MS, sample background electrolytes are segregated from analytes during the spotting process by the action of the SALDI-chip during drying, for further cleanup, while HILIC separates the salts in HILIC-MS. The resulting mass spectra of disc-prepared samples show a clean background and high signal-to-noise ratio for metabolite peaks. Several representative ionic metabolites from human serum samples were successfully quantified. The performances of the sample preparation disc for SALDI-MS and HILIC-MS were assessed and were comparable. Reproducibility, sample bias, and detection limits for SALDI-MS compared well to ultrafiltration sample preparation.

A low cost and simple method for fabrication of a gas sensor device was developed using reduced graphene oxide (rGO)-doped poly (3, 4-ethylenedioxythiophene)-poly (styrenesulfonate) (PEDOT-PSS) organic thin film for the detection of ammonia gas at room temperature. Scanning electron microscopy, atomic force microscopy, Fourier transform infrared spectroscopy, X-ray photospectrometry and thermogravimetric analysis were used for the analysis of morphological, structural and thermal behavior of the prepared thin films. The doping of reduced graphene oxide in PEDOT-PSS was observed to increase the conductivity of pristine PEDOT-PSS thin films by threefold. The gas sensing mechanism of pure and doped PEDOT-PSS thin films were studied at room temperature by fabricating the sensor device on indium tin oxide-coated glass substrate. The gases such as ammonia, carbon monoxide, nitrogen dioxide and nitrogen were used to test the sensing performance in the prepared thin films. The reduced graphene oxide-doped PEDOT-PSS thin films show improved sensitivity towards all test gases, mainly toward ammonia gas with fast response and recovery times. The gas sensitivity of 10 wt% rGO-doped PEDOT-PSS thin film was observed to be about 87% for ammonia gas. The sensor stability test shows that, the prepared sensor is highly stable even after a period of 1 month. Due to improved sensitivity, stability and improved response and recovery times, these rGO-doped PEDOT-PSS organic thin films could be used to detect ammonia gas at low concentrations at room temperature.

This paper examines whether visual speech information can be effective within audio-masking-based speaker separation to improve the quality and intelligibility of the target speech. Two visual-only methods of generating an audio mask for speaker separation are first developed. These use a deep neural network to map the visual speech features to an audio feature space from which both visually derived binary masks and visually derived ratio masks are estimated, before application to the speech mixture. Second, an audio ratio masking method forms a baseline approach for speaker separation which is extended to exploit visual speech information to form audio-visual ratio masks. Speech quality and intelligibility tests are carried out on the visual-only, audio-only, and audio-visual masking methods of speaker separation at mixing levels from -10 to +10 dB. These reveal substantial improvements in the target speech when applying the visual-only and audio-only masks, but with highest performance occurring when combining audio and visual information to create the audio-visual masks.

With the massive increase in wireless data traffic in recent years, multi-tier wireless networks have been deployed to provide much higher capacities and coverage. However, heterogeneity of wireless networks bring new challenges for interference analysis and coordination due to spatial randomly distributed transmitters. In this paper, we present a distance-dependent interference alignment (IA) approach for a generic two-tier heterogeneous wireless network, where transmitters in the first and second tiers are distributed as poisson point process (PPP) and poisson cluster process, respectively. The feasibility condition of the IA approach is used to find upper bound of the number of interference streams that can be aligned. The proposed IA scheme maximizes the second-tier throughput by using the tradeoff between signal-to-interference ratio and multiplexing gain. It is shown that acquiring accurate knowledge of the distance between the receiver in the second-tier and the nearest cross-tier transmitter only brings insignificant throughput gain compared with statistical knowledge of distance. Furthermore, the remaining cross-tier and intercluster interferences are modeled and analyzed using stochastic geometry technique. Numerical results validate the derived expressions of success probabilities and throughput, and show that the distance-dependent IA scheme significantly outperforms the traditional IA scheme in the presence of path-loss effect.

Generating sufficient current density for powering electronic devices remains as one of the critical challenges of mechanical energy harvesting techniques based on piezo and triboelectricity, mainly due to the high impedance of the insulating material systems. Here we report on producing sustainable tunneling current using an unbiased, triboelectrically charged metal-insulator-semiconductor (MIS) point contact system, consisting of p-type silicon, silicon oxide and a metal tip. The native thin oxide (~ 1.6 nm) on the silicon surface provides a natural pathway for quantum mechanical tunneling of the triboelectrically generated electrons into the silicon substrate. Lateral back and forth sliding motion of the tip, irrespective of the direction of motion, generates a constant direct current (d.c.) with very high current density. The measured current shows an exponential decay with the thickness of oxide layer deposited with atomic layer deposition (ALD), confirming the quantum mechanical tunneling mechanism. It is proposed that the contact potential difference enhanced by triboelectric charging provides potential difference between metal point contact and the substrate. With single metallic micro probe sliding on a moderately doped p-type silicon, an open circuit voltage (Voc) of 300–400 mV and a short-circuit direct current (Isc) of 3–5 μA (a corresponding high current density, J, in the order of 1–10 A/m2) have been observed. It is predicted from conductive-atomic force microscopy (C-AFM) experiment that the theoretical Jcan be as high as 104A/m2. This new concept has the potential as a green energy harvesting technique where a broad range of material candidates and device configurations could be used.

Studies evaluating titration of antihypertensive medication using self-monitoring give contradictory findings and the precise place of telemonitoring over self-monitoring alone is unclear. The TASMINH4 trial aimed to assess the efficacy of self-monitored blood pressure, with or without telemonitoring, for antihypertensive titration in primary care, compared with usual care.

The direct conversion of mechanical energy into electricity by nanomaterial-based devices offers potential for green energy harvesting1–3 . A conventional triboelectric nanogenerator converts frictional energy into electricity by producing alternating current (a.c.) triboelectricity. However, this approach is limited by low current density and the need for rectification2 . Here, we show that continuous direct-current (d.c.) with a maximum density of 106A m−2can be directly generated by a sliding Schottky nanocontact without the application of an external voltage. We demonstrate this by sliding a conductive-atomic force microscope tip on a thin film of molybdenum disulfide (MoS2). Finite element simulation reveals that the anomalously high current density can be attributed to the non-equilibrium carrier transport phenomenon enhanced by the strong local electrical field (105−106V m−2) at the conductive nanoscale tip4 . We hypothesize that the charge transport may be induced by electronic excitation under friction, and the nanoscale current−voltage spectra analysis indicates that the rectifying Schottky barrier at the tip–sample interface plays a critical role in efficient d.c. energy harvesting. This concept is scalable when combined with microfabricated or contact surface modified electrodes, which makes it promising for efficient d.c. triboelectricity generation. A large triboelectric direct current can be generated via the nanoscale sliding friction of a conductive-AFM tip on a MoS2thin film.

In this paper, we analyze the performance of an uplink large-scale multiple-input-multiple-output system with a single base station (BS) serving spatially distributed multiantenna user devices (UDs) within a fixed coverage area. Stochastic geometry is used to characterize the spatially distributed users while large dimensional random matrix theory is used to achieve deterministic approximations of the sum rate of the system. In particular, the users in the vicinity of the BS are considered to follow a Poisson point process within the fixed coverage area. The sum rate of this system is analyzed with respect to different number of antennas at the BS as well as the intensity of the users within the coverage area of the cell. Closed-form approximations for the deterministic rate at low and high signal-to-noise ratio regimes are derived that have very low computational complexity. The deterministic rate for a general $k$th ordered user is also derived. It is shown that the deterministic approximations offer a reliable estimate of the ergodic sum rate obtained by Monte Carlo simulations. We also briefly touch on the growing issue of power consumption in wireless systems by analyzing the energy efficiency of the system using a power consumption model, taking into consideration the circuit power consumption, which is a function of the number of antennas of the BS and UDs.

This paper investigates the performance of cooperative spectrum sensing in cognitive radio networks using the stochastic geometry tools. In order to cope with the diversity of received signal-to-noise ratios at secondary users, a practical and efficient cooperative spectrum sensing model is proposed and investigated based on the generalized likelihood ratio test detector. In order to investigate the cooperative spectrum sensing system, the theoretical expressions of the probabilities of false alarm and detection of the local decision are derived. The optimal number of cooperating secondary users is then investigated to achieve the minimum total error rate of the final decision by assuming that the secondary users follow a homogeneous Poisson point process. Moreover, the theoretical expressions for the achievable ergodic capacity and throughput of the secondary network are derived. Furthermore, the technique of determining an appropriate number of cooperating secondary users is proposed in order to maximize the achievable ergodic capacity and throughput of the secondary network based on a target total error rate requirement. The analytical and simulation results validate the chosen optimal number of collaborating secondary users in terms of spectrum sensing, achievable ergodic capacity, and throughput of the secondary network.

GTPase sperm-associated antigen 1 is studied in the context of mammalian oogenesis and female fertility. It is found to have a role in oocyte meiotic execution via its involvement in AMPK and MAPK signaling pathways.

Autophagy and apoptosis play significant roles in PRRSV infection and replication. However, the interaction between these 2 processes in PRRSV replication is still far from been completely understood. In our studies, the exposure of MARC-145 cells to PRRSV confirmed the activation of autophagy and subsequent induction of apoptosis. The inhibition of autophagy by 3-methyladenine (3-MA) caused a significant increase in PRRSV-induced apoptosis, showing a potential connection between both mechanisms. Moreover, we observed an increase in Bad expression (a pro-apoptotic protein) and Beclin1 (an autophagy regulator) in virus-infected cells up to 36h. Co-immunoprecipitation assays showed the formation of Bad and Beclin1 complex in PRRSV infected cells. Accordingly, Bad co-localized with Beclin1 in MARC-145 infected cells. Knockdown of Beclin1 significantly decreased PRRSV replication and PRRSV-induced autophagy, while Bad silencing resulted in increased autophagy and enhanced viral replication. Furthermore, PRRSV infection phosphorylated Bad (Ser112) to promote cellular survival. These results demonstrate that autophagy can favor PRRSV replication by postponing apoptosis through the formation of a Bad-Beclin1 complex.

The innate immune system provides protection against invading neurotropic viruses. It acts as the first line of defense against invading viruses and plays an elementary role in their pathogenesis. The list of viruses capable of infecting human central nervous system (CNS) is quite long, most important of them are Japanese Encephalitis virus (JEV), rabies virus, West Nile virus (WNV), herpes simplex virus (HSV), St. Louis encephalitis virus (SLEV), La Crosse virus, tick borne encephalitis virus (TEBE) and polio virus. Germ line pattern recognition receptors (PRRs) such as Toll like receptors (TLRs), nucleotide binding oligomerization domain (NOD) - like receptors (NLRs), retinoic acid-inducible gene I (RIG-I) -like helicases or RIG-I-like receptors (RLRs) and cytosolic DNA sensors recognize the pathogen associated molecular patterns (PAMPs) and initiate an immune response against invading pathogen. Although PRRs were originally characterized in peripheral immune cells but accumulating evidence also suggest their crucial roles in CNS to combat against neurotropic viruses. In this review, we will highlight the recent developments in our understating of the mechanisms by which PRRs in resident brain cells provide protection against invading neurotropic viruses.

The mineral oil has the disadvantage of its relatively low flashpoint. The transformers used inside buildings have a less-flammable liquid. Mineral oil is also a pollutant to the environment, and its insulating properties deteriorate quickly. As an alternative, the researchers examined the suitability of Coconut oil. In this research work, different laboratory tests were carried out to ascertain the suitability of refined coconut oil in replacing mineral transformer oil used for insulation and cooling purposes. For laboratory studies seven oil samples were prepared. Each sample containing 500 ml of refined coconut oil, 100 grams of paper insulated copper strips, varied quantity (in ppm) of passivator Di-tert-butyl-p-cresol (DBPC). Oil samples were processed through nitrogen bubbling for degasification of dissolved oxygen in oil. It is seen from the studies conducted that the refined coconut oil with additives provides better electrical properties. Dissipation factor and leakage current were found to have decreased. Results show that DBPC and Irgamet-39 play a crucial role in effectively preventing oil sludge formation and deterioration. It has also been found that 500 ppm of Irgamet-39 has the most optimum effect in prolonging the life of the liquid insulation.

ObjectivesInformation technology (IT) has an important role in the emergency department (ED) functioning, but staff attitudes can influence the way IT is used. Qualitative research into the perceptions of the ED staff has identified a variety of individual, environmental and system factors that may influence attitudes towards using IT. The authors aimed to determine which factors predict attitudes towards using IT and which factors are the most influential.MethodsFindings from a previous qualitative study were used to develop a self-administered questionnaire measuring individual, environmental and system factors, along with staff attitudes towards using IT. The questionnaire was sent to 535 staff working in three English EDs. Simple linear regression was used to examine the relationship between each potential predictor and user attitude, and multiple regression was used to identify the most important predictors.ResultsCompleted questionnaires were returned by 362/535 participants (68%). The factors with the strongest positive association with staff attitudes towards using IT were the perceived individual impact of technology (r2=39%, p<0.001), perceived usefulness (r2=7%, p<0.001), perceived ease of use (r2=2%, p=0.006), perceived subjective norms (r2=1%, p=0.013) and computer experience (r2=1%, p=0.034).ConclusionThe perceived individual impact of technology is the most important factor in determining ED staff attitude towards using IT. The ED staff are more likely to view using IT systems positively if they can see direct individual benefits arising from their use.

Micro/nanochannel resonators have been used to measure cells, suspended nanoparticles, or liquids, primarily at or near room temperature while their high temperature operation can offer promising applications such as calorimetric measurements and thermogravimetric analysis. To date, global electrothermal or local photothermal heating mechanisms have been attempted for channel resonators, but both approaches are intrinsically limited by a narrow temperature modulation range, slow heating/cooling, less quantitative heating, or time-consuming optical alignment. Here, we introduce heater-integrated fluidic resonators (HFRs) that enable fast, quantitative, alignment-free, and wide-range temperature modulation and simultaneously offer resistive thermometry and resonant densitometry. HFRs with or without a dispensing nozzle are fabricated, thoroughly characterized, and used for high throughput thermophysical properties measurements, microchannel boiling studies, and atomized spray dispensing. The HFR, without a doubt, opens a new avenue for nanoscale thermal analysis and processing and further encourages the integration of additional functions into channel resonators.

Device-to-device (D2D) or Sidelink aided communication is regarded as one of the most promising technologies to improve the spectral efficiency of the 5G and beyond communication system. However, two main challenges exist: 1) the selection of the optimal number of devices for improving the spectral efficiency, and 2) improving the physical layer security of such a communication system. The optimal device improves the secrecy capacity, and the selection of optimal devices enhances the physical layer security. Therefore, in this paper, we propose a double threshold-based optimal device selection (ODS) scheme for a cooperative wireless network with amplify and forward (AF) protocol in the presence and absence of an eavesdropper to enhance the physical layer security for the D2D network. The proposed scheme is analyzed with different distance cases, device scenarios, and modulation schemes. The bit error rate (BER) performance analysis concludes that a performance gain of more than 4 dB is achieved at the BER of 0.004 for the proposed double threshold-based ODS scheme compared to the existing ODS scheme at a high signal to noise ratio (SNR). Furthermore, the proposed scheme enhances the physical layer security.

This paper reports doubly clamped microchannel embedded resonators with two independent and parallel channels integrated for effective sample density tuning for the first time. With the aid of such a unique design, each fluidic channel can be independently accessed thus different liquid samples can be loaded simultaneously. The proposed fluidic resonators are batch fabricated by depositing silicon nitride, polysilicon, and silicon nitride sequentially on top of a set of 4-inch silicon wafers and sacrificing the middle polysilicon layer with potassium hydroxide (KOH). The sacrificial process defines two parallel channels and releases doubly clamped beam resonators simultaneously. In addition, an off-chip vacuum clamp with optical and fluidic access is custom-made to operate each resonator with enhanced quality factor. The microfluidic resonators mounted on the custom vacuum clamp are thoroughly characterized with a laser Doppler vibrometer and used to measure the effective sample density ranging from 395 to 998 kg/m3.

An amendment to this paper has been published and can be accessed via the original article.

Receptor-free, in-situ sensing of chemical and biological analytes with high selectivity and sensitivity is a highly sought-after goal. While a resonating microfluidic, hollow channel microcantilever is an ideal platform for sensing pico liters (pL) of liquid analytes based on changes in the specific gravity it does not offer any chemical selectivity. Fabricating these hollow channel cantilevers as bi-material beams allows them to be extremely sensitive to small changes in temperature as well. When a liquid confined in such a cantilever is illuminated with tunable IR radiation, it undergoes bending whenever the liquid analyte absorbs the light at a particular wavelength. Monitoring the cantilever bending as a function of illuminating wavelength provides IR spectrum of the analyte confined in the channel. This method combines the selectivity of IR spectroscopy and the sensitivity of a cantilever for molecular recognition of pL volume of liquid samples. This nanomechanical calorimetric infrared spectroscopy is an ideal technique for physical and chemical characterization of pico liter volumes of liquid analytes.