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Managing aircraft landings in the Terminal Area (TMA) is a key challenge in Air Traffic Management (ATM) and has been gaining attention due to the consistent growth of air traffic congestion and delays. The Aircraft Landing Problem (ALP) deals with this issue by optimally scheduling the aircraft landings in the TMA so that the overall delay is minimized. Most modern models and solution methods addressing ALP allow aircraft re-positioning. However, majority of the airports still employ the First Come First Serve (FCFS) policy to manage landings. This study seeks to assess the optimality of the FCFS policy. Mixed Integer Programming (MIP) models are proposed to replicate FCFS, and the solutions are compared with the results from a state-of-the-art (SOA) model. Results suggest that FCFS results in 1.76 times more deviation from the target landing time than the SOA outcome on a single runway. While, in case of multiple runways, FCFS results are comparable with the SOA model outcomes. Findings also suggest that different holding times may result in different runway combinations. Implications of this study can help ATM decision-makers estimate the overall delay/deviation arising from FCFS and efficiently plan the available resource, workforce, and infrastructure augmentation decisions under FCFS policy.

Image sensors with energy-harvesting capabilities aid in the development of self-powered Internet of Things (IoT) nodes. By achieving simultaneous energy harvesting and imaging, these image sensors make it possible to eliminate batteries and directly achieve self-powered operation. The article proposes a 1-V wide swing image sensor with simultaneous energy harvesting and imaging modes. The proposed pixel utilizes a vertical n+/p-well/DNW/P-sub structures as the photodiode based on a standard 180-nm CMOS mixed-signal process. The n+/p-well is used for imaging, while the p-well/DNW and DNW/P-sub are used for energy harvesting with p-well and P-sub shorted together. Moreover, a traditional 4T pixel has been improved by using CMOS pairs as the switches and zero-threshold NMOS as the source follower. The rail-to-rail pixel output swing can be achieved. An image sensor with simultaneous energy harvesting and imaging modes, which consists of a $32\times 32$ pixel array, a controller, and a dual-channel PWM quantizer, has been designed and fabricated. Measurement results show that the average power consumption of the image sensor is approximately 899.6 nW with 25 fps at 3 klx. The proposed image sensor has a DR of 47.3 dB under a 1-V supply. The measured power generated by energy harvesting while maintaining imaging functionality is 194 pW/lx/mm2.

A growing number of additive manufacturing (AM) applications use induction heating because of its precision, affordability, safety, and cleanliness. It is widely used in many industrial processes, such as melting, welding, brazing, and preheating. Wire is a considerably more efficient material to use than powder when used as feedstock. Unfortunately, there is still much to learn about the application of induction heating as a heat source in extrusion-based metal additive manufacturing, particularly when wire feedstock is used. This gap was filled by investigating, inhouse developed metal AM system which consists of the combination of induction heating as a heat source and metal wire as a feedstock in additive manufacturing. For this kind of application, induction heating is especially useful since it produces heat inside the workpiece by creating eddy currents. Finite element analysis was initially used to analyze the suitability of extruder material for printing aluminum material. After this investigation, the ability to print aluminum alloy in an extrusion-based metal wire additive manufacturing process with a cast iron extruder has been evaluated through experimentation. Simulation and experimentation results confirm the suitability of cast iron as an extruder material for printing aluminium alloys in a semi-solid state. The tensile test results of wire samples printed through induction heated metal additive manufacturing have been comparable to those of the original wire due to printing the same in a semi-solid state. Though they did not reach the levels attained by wire arc additive manufacturing and casting processes, it was found that the new extrusion-based wire samples showed better elongation and yield strength than the original wire.

A stable and reliable memristor is a key novel device in analog artificial intelligence circuits. This article investigates the effect of the dielectric layer thickness on redox-based memristive devices, filling a gap in our current understanding of these devices. We focus the commonly used dielectric material, silicon dioxide (SiO2), which is typical of the back-end-of-line (BEOL) processes in silicon integrated circuits. By varying only the thickness of the SiO2 layer, we explore the relationship between different switching properties and dielectric thickness. These properties include switching behavior, stability, device noise, reliability, and memristor transient time. Our results indicate that, except for the SET and RESET voltage, the other properties are highly dependent on the dielectric thickness. Interestingly, the SET and RESET voltage remain unaffected by changes in dielectric layer thickness in our study.

This paper presents a quasi-stochastic mixed integer programming model to determine the optimal flight departure schedule and sequence at airports. The proposed formulation addresses two key gaps in the existing departure metering strategies: (i) the variability and effects of the standard instrument departure (SID) procedure and (ii) the prospect of en route conflicts because of departure delays. The variability and effects of the SID procedure are captured through a non-deterministic parameter defined as the SID track time. The probability of en route conflicts because of departure delay is estimated based on a new delay–conflict relationship. Data used for implementation and validation of the model was collected from operations on Runway 7 of Chennai International Airport, India. Overall results indicate that the proposed departure metering model can act as a pre-emptive departure delay strategy framework that takes care of the SID procedure and en route conflict possibility with no harm to the runway throughput. The implications of this work hint toward better management of the airport operations and the performance of the overall air traffic management system.

The cationic peptide octaarginine (R8) is a prominent cell-penetrating peptide and has been extensively researched as a carrier of diverse cell-destined cargo. In this work, we describe the coassembly of R8 with small molecule thiazolyl benzenesulfonamide (TBS) derivatives. Physical complexation of R8 with three TBS derivatives across a range of weight ratios results in the formation of a distinctive set of nano- and microstructures. A detailed structural characterization of the R8:TBS-derivative coassemblies has been performed by a combination of FTIR, XRD, SEM, and DSC. The major functional groups that facilitate coassembly include sulfonamide SO2and NH groups of the TBS derivatives, and the guanidinium of R8, via a combination of cation-π and hydrogen-bonding interactions. The R8:4F-TBS coassembly displays singular topological features compared to R8:4Br-TBS and R8:4CH3-TBS complexes. These differences are attributed to the changes in the preferred orientation of the guanidino groups of R8 with respect to the π-surface of TBS derivatives. The modulation of forces of interaction across the R8:TBS-derivative coassemblies aligns with their respective thermal stabilities. The single-crystal structure of bare 4F-TBS has been subjected to Hirshfeld and 2D fingerprinting analysis and indicates notable variations from the crystal packing of the R8:4F-TBS coassembly. The structural differences among the R8:TBS-derivative coassemblies correlate with distinctive profiles of antibacterial activity in each case. The coassembled structures exert a variable extent of bacterial membrane disruption and damage based on the unique disposition of R8 and the potency of small molecule in each case. The aqueous suspension of R8:4F-TBS displays significant outer membrane disruption and bacterial killing compared with the other complexes. This work successfully demonstrates the hitherto unreported potential for coassembly of cell-penetrating peptides with other entities. The coassembly of R8 with small molecules highlights an attractive strategy for tuning the functional properties of each component.

This paper presents a general instability model of mode-split MEMS gyroscopes. The proposed model can accurately predict the bias instability of a given device based on the applied angular rate and system parameters. The model consists of two noise models: bias instability and scale factor instability. Four flicker noise sources are considered that are the most significant contributors. These include phase flicker noise of the drive capacitance to voltage converter, sense analog-to-digital (ADC) scale factor instability, proof mass voltage flicker noise, and additive flicker noise. All the noise contributors are thoroughly analyzed and experimentally characterized on four triaxial research devices. Based on the results of the experimental characterization, the proposed scale factor and bias instability models are verified against the measurement data. We find a good match between the presented model and measurements. As anticipated by the proposed model, a reduction of the phase flicker noise of the drive capacitance to voltage converter has led to up to 50% improvement in bias instability.[2024-0018]

The high performance Neural Network accelerator architectures rely on either large external memory bandwidth and/or sparse computation paradigms which scale down unfavorably. Current state-of-the-art architectures also include on-chip SRAMs often more than 150 kB, establishing a lower bound on silicon area based on memory alone. This article presents an architecture which exploits programmable dataflow in combination with sparsity to make more efficient use of small on-chip memories. Its control logic supports an enlarged map space through uneven mappings, searched by a cost-model driven compiler to find points which allow prioritizing energy consumption and memory access over throughput. The problem of supporting sparse processing for flexible dataflows is circumvented by introducing an encoding scheme which provides sparsity metadata while still allowing random read and write access. Altogether, the system showcases enhanced performance in terms of external memory access while requiring only 51 kB of on-chip SRAM and small silicon area (0.5 mm2). The design achieves average energy efficiency of 4.4 TOPS/W and 9.7 inferences/s for a sparse AlexNet workload.

A hybrid drone called a biplane quadrotor operates in both low (during the horizontal flight) and high (transition maneuver) Angle of Attack (AoA). So, this paper focuses on enhancing aerodynamic force during the transition maneuver. Synthetic Jet actuators (SJAs) can modify airfoil shapes virtually, so either the flow reattaches or flow separation will be delayed. This delay can enhance the aerodynamic force. In this paper, CFD analysis is performed using Ansys Fluent to study the impact of SJA on NACA 0015 airfoil at high (30°) AoA. This study aims to find the best location of SJA for high AoA to get maximum lift enhancement. The outcome of this study reveals that at 85 %, we can get maximum enhancement in the lift.

The field of wireless communication systems has experienced significant advancements in recent years, leading to the emergence of two promising technologies: non-orthogonal multiple access (NOMA) and deep learning (DL)-based autoencoders (AE). Through power allocation, NOMA enables multiple users to share a single frequency band, while AE can compress and decompress data with high precision. Integrating NOMA and AE enables end-to-end (E2E) transmission with a superior signal-to-noise (SNR) ratio. To further enhance the wireless network's block error rate (BLER) performance, the multiple-input, multiple-output (MIMO) technique is also incorporated into the newly proposed system. With the incorporation of the MIMO signal, the system is abbreviated as the MIMO-NOMA-AE system. The suggested technique for detecting MIMO-NOMA-AE signals has demonstrated a remarkable performance gain in SNR surpassing the traditional successive interference cancellation (SIC)-based NOMA system. The proposed system also performs better than previously utilized deep neural network (DNN)-based SISO-NOMA-AE communication systems. While this method shows potential for future wireless communication systems, further research and testing are necessary to evaluate its practical feasibility and effectiveness.

In the midst of escalating seismic activity on a global scale, the imperative of devising ingenious structural solutions looms large, driven by the necessity to safeguard the security and soundness of constructed environments. This study delves into an inventive avenue for bolstering seismic resilience, by ingeniously merging hydraulic dampers (HD) into mass–spring–damper (MSD) systems through a configuration referred to as the integrated damper arrangement. This integration unfolds through the strategic placement of an HD at the centre of the spring of MSD system, optimizing the system's parameters via the dynamic optimizer Particle Swarm Optimization (PSO) method, emulating the PSO-derived output using MATLAB simulations and for best-of-best results assisted by Artificial neural network (ANN). In structure, building integrated systems work to protect the moments of tuned mass damper (TMD) system in their position and control the structural vibration to absorb the decapitated energy for proving stability. The central thrust of this research paper orbits around the ratio of both the systems that are combined and intricately integrated with their relevant parameters. This convergence of efforts culminates by ANN in peak displacement values of 0.7930 m and peak velocity reaching 1.7615 m per second. This study also establishes a seamlessly integrated system and a meticulously crafted analytical formula to govern this combined system, merging the worlds of structure and mechanics with precision in their application.

Induction heating as a heat source in extrusion-based metal additive manufacturing (EMAM) is gaining popularity due to its inherent benefits as a safe, clean, and precise heating source. Metal wire as a feedstock material is economically viable and highly efficient in terms of material utilization compared to powder feedstock materials. The combination of induction heating with metal wire as a feedstock through an EMAM system has been investigated in this article. The article aims to identify the standoff distance for printing aluminum 5356 in EMAM using induction heating ( Figure 1). In induction heating-based AM, it is crucial to identify the standoff distance between the extruder and substrate to maintain a constant extruder tip temperature and ensure a consistent liquid/solid fraction ratio for uniform material deposition. The approach adopted in this article is to perform electromagnetic and thermal multifield coupling model simulations of extruder with the substrate through a two-dimensional axe-symmetrical model using COMSOL multiphysics. The simulation model analyzed the extruder tip temperature at variable standoff distance cases during extruder heating and material deposition processes. A practical approach for thermal cartography has been used to identify extruder tip temperature during experimentation using an infrared imager. The extruder tip temperature results obtained through simulation have been compared with experimental results and found to be in close agreement with each other. A favorable temperature range of 575–625 °C is obtained through a standoff distance of 2.0 mm compared to other standoff distances taken for the study.

The exigency for efficient drilling fluids (mud) in the oil and gas industry has prompted the development of novel additives to address challenges encountered in high-temperature wells. Copper oxide nanoparticles (CuO NPs) possess distinctive characteristics, including low toxicity and exceptional thermal stability. This study explored the synthesis of CuO NPs using a natural extract from Colocasia esculentaleaves. The extract is abundant in phytochemicals and serves as both reducing and capping agents. The potential of the biogenic CuO NPs to enhance the lubricity in mud has been explored for the first time. Their ability to form a continuous and thin lubricating film at the mud-drill string interface is expected to reduce the frictional resistance between them significantly. Besides this, the filtration and rheological performance of the developed mud have also been investigated. The formulation exhibited significant enhancements in lubricity, with a 27% increase, and filtering performance, with a 48% increase. The rheological profile, which exhibited shear-thinning behavior, demonstrated strong agreement with the Herschel–Bulkley model. Furthermore, the NPs showed the capability of reducing the negative effects of heat-induced deterioration on the characteristics of mud. This highlights their potential as advantageous substances for drilling operations conducted at high temperatures. The results emphasize the advancement of drilling fluid technologies that are both environmentally benign and economically feasible by utilizing the advantages of biogenic CuO NPs. This study aims to enhance green chemistry methodologies in the oil and gas sector by tackling issues related to both performance and sustainability.

This research is aimed to understand the intentions of small grocery retailers in adopting social media for marketing purposes. The research used an integrated TAM-TOE framework combined with an SOR framework for understanding factors influencing the intention of small grocery retailers to adopt social media. Mixed methods were used for the study. Initially, in-depth interviews were conducted with 55 retailers to develop a grounded model, which was verified through a survey of 874 retailers. Findings suggest that perceived usefulness and perceived ease of use were positively related to relative advantage, interaction, organizational competency and complexity, bandwagon effect, and competitive pressure. Retailer age and retail outlet also had moderation effects. Small grocery retailers who understand the relative advantage of social media are more likely to adopt it, influenced by the bandwagon effect. Intense competition is also pushing retailers to adopt social media to avoid losing customers and missing out on potential opportunities.

Purpose To describe the findings of Multicolour® imaging (MCI) in the presence of residual emulsified silicone oil (SiO) droplets.Case report A 62-year-old man had previously undergone surgery for a traumatic retinal detachment in the left eye, followed by SiO removal a few years ago. His presenting visual acuity was 6/60 in the left eye and intraocular pressure was 12 mm Hg. Clinical examination revealed the presence of emulsified residual SiO droplets in the vitreous cavity, ‘inverse hyperoleon’ at the posterior pole and pathological myopia. Retinal findings were documented using optical coherence tomography (OCT), fundus autofluorescence (FAF), and MCI on the Spectralis (Heidelberg, Germany) machine.Results On OCT, the emulsified SiO droplets in the preretinal and intraretinal layers appeared as multiple hyperreflective dots with no underlying shadowing trapped beneath the residual posterior hyaloid or preretinal membrane in the region of the posterior staphyloma. Residual emulsified silicon oil on the retinal surface appeared to have greenish hue on the composite multicolour image and was hyperreflective on individual blue and green reflectance images and dark and hyporeflective on the infrared reflectance image. The SiO droplets appeared dark and were difficult to identify on the short wavelength FAF image.Conclusion To the best of our knowledge, this case report describes the MCI findings in emulsified SiO droplets for the first time.

Early plant disease diagnosis is necessary for several purposes, including reducing yield losses, monitoring and predicting infections, detecting host resistance, and studying basic host–pathogen biological processes. However, early detection has been limited by a trained workforce and the ability to identify the problem early in the growing season. Artificial intelligence/machine learning algorithms can help fill this gap. Automatic leaf detection using machine learning is proposed in this study. The presented approach consists of three stages: pre‐processing, feature extraction, and classification. Initially, the input image is transformed into the red, green, and blue formatand the noise in the green band is removed using a median filter. Then important features of the green band are extracted. After feature extraction, the extracted features are fed to the optimized artificial neural network classifier to classify an image as normal or diseased. To improve artificial neural network (ANN) performance, the ANN parameters are chosen optimally using the adaptive sunflower optimization (ASFO) algorithm. Then, the infected region is separated using a level set segmentation algorithm. The efficiency of our work is analyzed based on accuracy, sensitivity, and specificity; the proposed method reached the maximum accuracy of 97.94% for plant disease prediction. An objective of the study was to improve yield prediction.Plant disease diagnosis is necessary for several purposes and can reduce yield loss.An objective of the study was early detection of plant leaf disease.

To survive, adapt, and develop, cells respond to external and internal stimuli by tightly regulating transcription. Transcriptional regulation involves the combinatorial binding of a repertoire of transcription factors to DNA, which often results in switch-like binary outputs akin to Boolean logic gates. Recent experimental studies have demonstrated that in eukaryotes, transcription factor binding to DNA often involves energy expenditure, thereby driving the system out of equilibrium. The governing principles of transcriptional logic operations out of equilibrium remain unexplored. Here, we employ a simple two-input, single-locus model of transcription that can accommodate both equilibrium and nonequilibrium mechanisms. Using this model, we find that nonequilibrium regimes can give rise to all the logic operations accessible in equilibrium. Strikingly, energy expenditure alters the regulatory function of the two transcription factors in a mutually exclusive manner. This allows for the emergence of new logic operations that are inaccessible in equilibrium. Overall, our results show that energy expenditure can expand the range of cellular decision-making without the need for more complex promoter architectures.

Diagnostics of plant diseases are essential to predicting yield loss, detecting infections, studying host–pathogen interactions, and detecting host resistance. Early detection of plant leaf disease can increase yields. This is possible if there are automated systems to help farmers diagnose rice diseases from the pictures of plant leaves. Therefore, automatic leaf detection using machine learning is proposed in this study. The proposed approach has three stages, namely, pre-processing, feature extraction, and classification. Initially, the input image is converted into red, green and blue and the noise in the green band is removed using a median filter. Initially, the input image is converted into red, green and blue and the noise in the green band is removed using a median filter. Then, important features of the green band are extracted. An Optimized Support Vector Machine (OSVM) classifier uses the extracted features to classify an image as normal or pathological. To improve SVM performance, the SVM parameters are chosen optimally using the Adaptive Sunflower Optimization (ASFO) algorithm. Then, the infected region is separated using a level-set segmentation algorithm. The efficiency of our work is analyzed based on accuracy, sensitivity, and specificity, the proposed method reached the maximum accuracy of 97.54% for plant disease prediction.

Introduction Previous publications have assessed the diversity among medical students, residents, faculty, and department leaders in surgery and medicine overall. We aim to evaluate the diversity among medical school deans in the United States. We quantify and compare the representation of women and underrepresented minority surgeon and non-surgeons.Methods 151 allopathic medical schools were included. Data regarding demographics, education, training, and previous leadership position were collected from institutional websites, online resources, and July 2021 Association of American Medical Colleges Council of Deans. Demographics for surgeon and non-surgeon were compared using chi square and logistic regression with 5% significance interval.Results 21.9% (n = 33) of all medical school deans were surgeons. 21.2% (n = 7) were women, which was not significantly different from non-surgeons (22%, P= .92). All the women surgeons were non-Hispanic white, similar to all deans (P= .83). 78.8% (n = 26) of all surgeon deans were non-Hispanic White compared to 84.7% (n = 100) overall (P= .28). There were 13 Black deans, four of whom were surgeons, and only one Hispanic dean, who was not a surgeon. Surgeons were more likely to be fellows of their professional society (P= .012).Conclusion The demographic diversity of surgeon and non-surgeon US medical school deans is not significantly different. The deficiencies in leadership diversity in medicine persists among medical school deans. There remains substantial room to improve the representation of women and underrepresented minorities as deans.

Liver damage is one of the leading diseases, resulting in high morbidity. It is more relevant in the context of bad food habits, environmental pollution, and biohazards. The present study aimed to investigate the role of semi-purified water extract of Pueraria tuberosaon carbon tetrachloride (CCl4)-induced liver injury and also its mechanism of action regarding transcriptomic status in liver tissue about inflammation, hypoxia, and apoptosis. Liver injury was induced in Charles foster rats via intraperitoneal injection (IP) of CCl4, 0.1 mg/100gm body weight, diluted with olive oil (30%) twice a week for 20 days. PTWE was given via oral route daily simultaneously with CCl4 at the dose of 50 mg/100 g and 100 mg/100 g body weight. On 21st day all rats were sacrificed. Biochemical tests and histological studies were done. mRNA expression of bcl-2, caspase-3, bax, and GAPDH and protein expression of iNOS, BCL-2, HIF-1α, VEGF, β-Tubulin was done. Simultaneous treatment of PTWE with CCl4 decreased the level of NO, PC, SGOT, SGPT, ALP, LPO, and iNOS, HIF-1α, VEGF, bax, and caspase-3 expression. In addition, PTWE increased the SOD, Catalase, GSH level, and bcl-2 expression as well as normalized the architecture of hepatic tissue. Immunohistochemical staining showed the decreased accumulation of CD45, VEGF, α-SMA, collagen, and desmin after PTWE treatment. This study suggests that PTWE inhibits fibrosis by reducing the accumulation of α-SMA, collagen, and desmin in CCl4-induced toxicity. The mechanism of protective action is through its anti-inflammatory (iNOS, NO, CD45), anti-apoptotic (bcl-2, bax, caspase-3), anti-hypoxic (HIF-1α, VEGF), and anti-fibrotic (α-SMA, collagen, desmin) potentials.

Laser metal deposition (LMD) is generative manufacturing that allows fabrication on existing metal parts with five axes, utilizing different metal powders independent of the substrate metal. LMD provides convenience to embed sensor closely underneath functional surfaces. The optical fiber is flexible and multifunctional, well suited to be used as a sensor and embedded beneath metal for measuring parameters such as temperature and strain. However, in order to maintain the functionality of optical fiber after welding, it is crucial to choose right deposition parameters for LMD. The parameters of LMD may vary for different metals and finding appropriate parameters require iterations and optimizations. Also, necessary shielding techniques are required to protect the fiber from heat. This paper identifies optimized parameters for LMD taking into account the surface quality of functional surface and the heat impact while upholding the fibers. Further, optimized parameters are applied to embed the optical fiber. Further, the heat impact of welding is inspected visually and through microcomputed tomography (Micro-CT) scans. Achieved results are compared with the previous experiment. Results show that the identified optimized parameters of LMD are relevant for welding optical fiber inside metal. No heat impact on core fiber is evident thus preserving its functionality. Improvement of surface quality and effects of LMD on optical characteristics of the fibers is proposed in future work.

Transient simulations are traditionally used to predict characteristics of MEMS gyroscopes. However, this time-consuming and complex method yields little information about relationships between component parameters and system parameters as well as dominant sources of noise. This article reports analytical model for phase noise impact on bias instability (BIS) of open-loop mode-split MEMS gyroscopes. This phase space-based model is derived and analyzed. Its results are verified through transient simulation. Known noise sources have been considered and investigated for the influence on BIS of MEMS gyroscopes. The model predicts phase-locked loop (PLL) input noise to be a dominant contributor to the rate signal BIS. The results of this work can be used to investigate the phase noise of any signal in the system. We further highlight the importance of the demodulation phase for the noise performance of the rate signal, using the model. This saves significant time when compared using the complex transient gyroscope models. Additionally, previously ignored complex transfer functions of components can now be explored. These are further validated with exemplary gyroscope parameters. The model is finally used to derive phase noise specification that fulfills defined requirements of a gyroscope.

This work presents a comprehensive study on the dielectric properties of shellac-based composites with varying filler concentrations of (SiC) and iron (Fe) particles, complemented by scanning electron microscopy (SEM) analysis. Shellac, a natural biopolymer known for its excellent film-forming abilities, biodegradability, and insulating properties, was chosen as the matrix material. The dielectric properties, including permittivity and dielectric loss, are measured by LCR Meter across a frequency range from 100 Hz to 8 MHz to evaluate the effects of filler concentration. This study reveals that the incorporation of SiC and Fe particles significantly enhances the dielectric constant and exhibits complex frequency-dependent behavior in dielectric loss. SEM analysis provided insights into the microstructural changes induced by the fillers, correlating with the observed dielectric properties. The results indicate that the dielectric performance of shellac composites can be effectively tailored through the precise control of SiC and Fe particle concentrations, attributed to interfacial polarization and Maxwell-Wagner-sillars effects. This work underscores the potential of shellac composites as sustainable, high performance dielectric materials for advanced electronic applications, contributing to the development of eco-friendly electronic devices.

Fluorescence optical fiber temperature sensors have found widespread use in harsh environments with electromagnetic interference, high voltages, flammability, and combustibility due to their excellent insulating properties. However, conventional fluorescence detection systems are constructed from discrete photodiodes and other front-end circuit chips, which cause bulky size, inferior detection efficiency, and degraded signal-to-noise performance. To address these issues, an integrated optical front-end circuit for fluorescence detection was proposed. The front-end circuit monolithically integrated a ring-gate-isolated photodiode (RGI-PD) device, a transimpedance amplifier (TIA), and a bandgap reference (BGR) circuit. The RGI-PD enabled the detection of the fluorescence afterglow signals, while the TIA circuit converted the photocurrent generated by the RGI-PD into a measurable photovoltage output. The RGI-PD utilized a P-well guard ring structure to prevent edge breakdown by isolating the avalanche region from the shallow trench. The dummy ring-gate created an inverted pyramid hole profile, focusing the electric field on the planar junction to enhance photodetection, which reduced trapping effects and dark counts. The proposed front-end circuit was fabricated using a standard $0.18~\mu \text{m}$ bipolar CMOS DMOS (BCD) process. The measurement results demonstrated that the avalanche breakdown voltage (BV) of the RGI-PD was 13.05 V. The front-end circuit achieved a responsivity of 17.29 A/W at 500 nm wavelength, under a 13.88 V reverse bias voltage applied on the RGI-PD. Moreover, the front-end’s dark voltage measured 0.908 V, with an optimal temperature coefficient (TC) of 41.01 ppm/°C within the temperature range of −40 °C to 85 °C. The proposed front-end circuit was suitable for fluorescence fiber temperature sensing applications.

The COVID-19 pandemic affected all industries and presented manufacturing firms with enormous challenges, with considerable changes in consumer demand for goods and services. Supply chain management disruption caused by the COVID-19 outbreak resulted in several socio-economic roadblocks. The slow propagation of disruption risk results in a ripple effect along the entire chain. The lack of resilience and risk management capability is the prime cause, attributed to the unavailability of digital resources, skills, and knowledge. The main objective of this article is to develop supply chain capability for disruption risk management and supply chain resilience for competitive gain in terms of controlling the ripple effect. The resource-based view approach was used to develop the theoretical structure in this article. Supply chain digitalization and viability provide necessary resources to develop the capability for managing risk and resilience to tackle the impact of disruptions due to pandemics, war, recession, and other such massive challenges on the supply chain. Seven hypotheses were proposed and evaluated for relevance using structural equation modeling (SEM). In total, 199 valid responses to a survey on SEM were gathered and examined using the AMOS V-21 software. Our research findings supported all the proposed hypotheses, thereby generating positive theoretical evidence for practitioners to digitalize their supply chain for enhanced supply chain capabilities and effective control of the ripple effect.

Metastasis occurs frequently after resection of pancreatic cancer (PaC). In this study, we hypothesized that multi-parametric analysis of pre-metastatic liver biopsies would classify patients according to their metastatic risk, timing and organ site. Liver biopsies obtained during pancreatectomy from 49 patients with localized PaC and 19 control patients with non-cancerous pancreatic lesions were analyzed, combining metabolomic, tissue and single-cell transcriptomics and multiplex imaging approaches. Patients were followed prospectively (median 3 years) and classified into four recurrence groups; early (<6 months after resection) or late (>6 months after resection) liver metastasis (LiM); extrahepatic metastasis (EHM); and disease-free survivors (no evidence of disease (NED)). Overall, PaC livers exhibited signs of augmented inflammation compared to controls. Enrichment of neutrophil extracellular traps (NETs), Ki-67 upregulation and decreased liver creatine significantly distinguished those with future metastasis from NED. Patients with future LiM were characterized by scant T cell lobular infiltration, less steatosis and higher levels of citrullinated H3 compared to patients who developed EHM, who had overexpression of interferon target genes (MX1and NR1D1) and an increase of CD11B+natural killer (NK) cells. Upregulation of sortilin-1 and prominent NETs, together with the lack of T cells and a reduction in CD11B+NK cells, differentiated patients with early-onset LiM from those with late-onset LiM. Liver profiles of NED closely resembled those of controls. Using the above parameters, a machine-learning-based model was developed that successfully predicted the metastatic outcome at the time of surgery with 78% accuracy. Therefore, multi-parametric profiling of liver biopsies at the time of PaC diagnosis may determine metastatic risk and organotropism and guide clinical stratification for optimal treatment selection.

AbstractMultidisciplinary rounds (MDR) constitute a patient-centered care model wherein professionals from diverse disciplines collaborate in real time to provide specialized expertise. The MDR team, encompassing care partners, hospitalists, nurses, pharmacists, and more, employs a collaborative approach that optimizes patient care through shared goals, electronic record access, regular reviews, and patient involvement. MDRs have evolved to reduce patient mortality, complications, length of stay, and readmissions, and they enhance patient satisfaction and utilization of ancillary services. Family engagement in MDRs further transforms relationships from adversarial to collaborative, leading to improved comprehension of treatment strategies and smoother navigation of challenging conversations. Despite challenges such as time constraints, limited patient coverage, and hierarchical barriers, MDRs are being increasingly conducted across healthcare settings, with positive outcomes.

Plant species are confronted with several abiotic and biotic stress conditions during their life cycle, and they often try to surmount the internal stressed environment with the help of different chemical agents. Apart from their involvement in normal growth and development, the phytohormones also react to various environmental stimuli experienced by the plants. Although most of the pro-metabolic processes are involved in the process of adaptation to stress, the key role played by phytohormones goes unnoticed. Depending on the characteristics of the stressor, these plant growth regulators can interact in either an antagonistic or cooperative way to overcome the trouble faced by the plant species. The main intention of this review work is to discuss the potential role of various phytohormones in maintaining a balance against various abiotic and biotic stresses at both molecular and physiological levels. Possible research discontinuity and a few questions have been addressed in such a way that they can provide direction for future researchers in this field.

Synaptic stochasticity is an important feature of biological neural networks that is not widely explored in analog memristor networks. Synaptic Sampling Machine (SSM) is one of the recent models of the neural network that explores the importance of the synaptic stochasticity. In this paper, we present a memristive Echo State Network (ESN) with Extended-SSM (ESSM). The circuit-level design of the single synaptic sampling cell that can introduce stochasticity to the neural network is presented. The architecture of synaptic sampling cells is proposed that have the ability to adaptively reprogram the arrays and respond to stimuli of various strengths. The effect of stochasticity is achieved by randomly blocking the input with the probability that follows Bernoulli distribution, and can lead to the reduction of the memory capacity requirements. The blocking signals are randomly generated using Circular Shift Registers (CSRs). The network processing is handled in analog domain and the training is performed offline. The performance of the neural network is analyzed with a view to benchmark for hardware performance without compromising the system performance. The neural system was tested on ECG, MNIST, Fashion MNIST and CIFAR10 dataset for classification problem. The advantage of memristive CSR in comparison with conventional CMOS based CSR is presented. The ESSM-ESN performance is evaluated with the effect of device variations like resistance variations, noise and quantization. The advantage of ESSM-ESN is demonstrated in terms of performance and power requirements in comparison with other neural architectures.

Social media sites are a popular medium for interaction in the modern, expanding globe, where everyone has a connection with social media in some way. The people are accustomed to reading reviews before making decisions, for instance reading comments for movies, eateries, online stores, and a variety of other products. Taking reviews entails being aware of what other people think. It can be described in one way as sentiment analysis or even as opinion mining. For correctly predicting sentiments from social corpus data, the turbulent flow optimized deep fused ensemble model is a novel and sophisticated approach for sentiment analysis. The preprocessed data were used to extract a variety of features, including bag of words, term frequency-inverse term frequency, word to vector, and Glove. Then, the contemporary turbulent flow of water-based optimization mechanism was used to select the features that would be most useful for training the classifier. In addition, the cutting-edge deep fused ensemble voting classifier is employed to construct a precise decision function in accordance with the average probability of the number of classifiers. This work uses the well-known benchmarking social corpus datasets from IMDB, Twitter, Airlines, Amazon, Crowded Flower, and Apple for system analysis and study.

AbstractIntroduction:Pulmonary arterial compliance, the dynamic component of pulmonary vasculature, remains inadequately studied in patients with left to right shunts. We sought to study the pulmonary arterial compliance in patients with left to right shunt lesions and its utility in clinical decision-making.Materials and methods:In this single-centre retrospective study, we reviewed cardiac catheterisation data of consecutive patients of left to right shunt lesions catheterised over one year. In addition to the various other parameters, pulmonary arterial compliance was calculated, as indexed pulmonary flow (Qpi) / (Heart rate × pulse pressure in the pulmonary artery). RC time was also calculated, as the product of pulmonary arterial compliance and pulmonary vascular resistance index. Patients were divided into “operable,” “borderline,” and “inoperable” based on the decision of the treating team, and the pulmonary arterial compliance values were evaluated in these groups to study if it can be utilised to refine the operability decision.Results:298 patients (Median age 16 years, 56% <18 years) with various acyanotic shunt lesions were included. Overall, the pulmonary arterial compliance varied with Qpi, pulmonary artery mean pressure, and pulmonary vascular resistance index, but did not vary with age, type of lesion, or transpulmonary gradients. The median pulmonary arterial compliance in patients with normal pulmonary artery pressure (Mean pulmonary artery pressure less than 20 mmHg) was 4.1 ml/mmHg/m2(IQR 3.2). The median pulmonary arterial compliance for operable patients was 2.67 ml/mmHg/m2(IQR 2.2). Median pulmonary arterial compliance was significantly lower in both inoperable (0.52 ml/mmHg/m2, IQR 0.34) and borderline (0.80 ml/mmHg/m2, IQR 0.36) groups when compared to operable patients (p < 0.001). A pulmonary arterial compliance value lower than 1.18 ml/mmHg/m2identified inoperable patients with high sensitivity and specificity (95%, AUC 0.99). However, in borderline cases, assessment by this value did not agree with empirical clinical assessment.The median RC time for the entire study population was 0.47 S (IQR 0.30). RC time in operable patients was significantly lower than that in the inoperable patients (Median 0.40 IQR 0.23 in operable, 0.73 0.25 in inoperable patients (p < 0.001).Conclusions:Addition of pulmonary arterial compliance to the routine haemodynamic assessment of patients with shunt lesions may improve our understanding of the pulmonary circulation and may have clinical utility.

AbstractsOcimum americanumL. is a highly valued aromatic plant whose leaves are used not only in traditional cuisine but also in the food, cosmetic, and pharmaceutical industries. This study aimed to identify the chemical profile of essential oil extracted from the leaves of Ocimum americanumby GC-FID and GC/MS grown in acidic soil conditions in the NE region of India. Furthermore, the biological activities like antioxidant, anti-diabetic, anti-inflammatory, and anti-tyrosinase activity were analyzed, as per standard methods. GC and GC/MS analysis identified 16 compounds in which citral-a (48.25%) was found as a major compound, followed by carveol (41.64%). The antioxidant activity was performed using DPPH and ABTS methods; and IC50value obtained was 8.85 μL/mL and 28.82 μL/mL, respectively. The leaf essential oil of O. americanumexhibited strong anti-diabetic and anti-inflammatory activities, whereas moderate anti-tyrosinase activities with IC50values of 40.84, 12.81, and 182.74 μL/mL, respectively. Furthermore, the ant-microbial activity was evaluated for four bacterial and four fungal strains, revealing that O. americanumessential oil expresses antifungal activity against S. cereviaceaeonly with a MIC value of 5±0.47 μg/mL. These evidences clearly indicates that essential oil obtained from the leaf of O. americanumshowed potential anti-inflammatory and anti-diabetic activity that can be used as an alternative in pharmaceuticals and food industries as a natural source.

Heme oxygenases (Hmoxs) are enzymes that catalyze the first and rate-limiting step in the degradation of heme to carbon monoxide, iron, and biliverdin. The two main isozymes, namely Hmox1 and Hmox2, are encoded by two different genes. Mutation of the Hmox1 gene in mice is known to cause extensive prenatal lethality, and limited information is available about the expression of Hmox proteins in developing mouse embryos. In this study, immunohistochemistry was used to perform a detailed investigation comparing Hmox proteins in Hmox1 wild-type and knockout (KO) mouse embryos collected from wild-type and heterozygous timed-matings. Western analysis for Hmoxs was also done in the organs of late-gestation embryos. The results demonstrated cytoplasmic and nuclear localization of Hmoxs in all the organs examined in wild-type embryos. Interestingly, Hmox2 immunoreactive protein signals were significantly low in most of the organs of mid- and late-gestation Hmox1-KO embryos. Furthermore, relative levels of Hmox2 were revealed to be significantly lower in the lung and kidney of late-gestation Hmox1-KO embryos by western analysis, which complemented the immunohistochemistry findings in these two organs. The current study provides detailed immunoexpression patterns of Hmox proteins in wild-type and Hmox1-KO mouse embryos in mid- and late-gestation:

This paper presents the mathematical modeling and optimal trajectory tracking control of a 3-degree-of-freedom parallel manipulator, commonly known as Maryland manipulator. Three unlike sequential trajectories are considered, and the trajectory-tracking control of a manipulator is performed by the LQR-PID controller. The tuning of LQR-PID is done by using the Grey Wolf Optimizer (GWO), a meta-heuristic method. The two other traditional benchmarking algorithms are Particle Swarm Optimizer (PSO) and Genetic Algorithm (GA). The main goal of this work is to calculate the optimum values of LQR gain parameters i.e. Qand Rmatrices and PID controller’s gains i.e. Kp, Kiand Kdusing three different optimization algorithms, and their performance comparisons are highlighted in simulation results and discussion section. According to the obtained simulation results, the proposed GWO methodology is more efficient and more accurate in trajectory tracking control and generates optimal torques to the input links of the manipulator when compared with other evolutionary optimization algorithms such as PSO and GA.

Modern communication systems require an antenna with miniaturized size and large bandwidth with high data rate. In this article, a circular monopole antenna is presented with hexagonal patch on both sides of microstrip feedline and an I- shaped rectangular slot etched at the center of feedline to provide impedance matching. The modified loaded ground reduces the coupling between ground plane and radiation patch. This antenna has compact dimension of 30 mm × 25 mm with electrical size of 0.25λ× 0.20λ.This antenna has been designed and simulated in HFSS commercial software and fabricated on FR4 substrate of thickness 1.6 mm with relative permittivity 4.4, loss tangent of 0.02. The experimental results are compared with simulated one, which are in close agreement. It attains a frequency band ranging from 2.4 to 56.6 GHz with reflection coefficient <−10, percentage bandwidth of 183.72%, Bandwidth Ratio (BR) 23.58:1, and Bandwidth Dimension Ratio (BDR) of 3674.40. The peak gain and radiation efficiency of this antenna is 12.77 dBi and 98.82% at 45.84 and 5.6 GHz respectively. Time domain analysis has been done, which proclaims that the proposed antenna has SWB characteristics. It may be useful for high data rate transmission, medical imaging, and other SWB applications.

Background Neonatal sepsis is reported to be the 8th most common cause of under 5 mortality all over the world. Any delay in diagnosis is critical in contributing to neonatal mortality; thus, early diagnosis is necessary.Objectives To determine the risk factors, clinical features, hematological parameters, and bacteriological profile associated with neonatal sepsis and to assess the antibiotic sensitivity in SNCU.Methods This prospective study was conducted in SNCU of the tertiary care center of central India, over the duration of 1 year enrolling 150 participants. All the neonates suspected to be suffering from neonatal sepsis were included. Data was compiled using MS Excel and analyzed using IBM SPSS.Results Median age of neonates was 2 days (IQR: 1-5) with a male predominance. Median birth weight of neonates was 2 kg (IQR: 1.5-2.8). Blood cultures and antibiotic sensitivity revealed that most common organisms involved were staphylococcus aureus (34%; sensitive to penicillin, resistant to ampicillin) and klebsiella (20%; sensitive to colistin, resistant to clindamycin). 17.3% neonatal mortality was observed, amongst predictors of mortality, low birth weight, neonates delivered by LSCS, advanced maternal age, and leucopenia were significant predictors in univariate and multivariate analysis.Conclusion Antibiotic resistance is one of the most important problems of public health importance which is rampant in cases of early, as well as late onset sepsis.

A wideband multiple-input multiple-output (MIMO) antenna for C-band applications is presented in this paper. It consists of 2 ×${\times}$ 1 hexagonal motifs which serves as radiating elements. The side of each hexagonal element is 5.73 mm and the overall dimension of the proposed MIMO antenna is 30 ×${\times}$ 20 ×${\times}$ 1.6 mm3. The hexagonal motifs are embedded on inexpensive FR-4 lossy substrate. Thereafter, a circle with radius 1 mm is etched at the center of each hexagonal motif. Inverted L-shaped stubs are employed to attain efficient isolation between the nearby antenna elements. Four inverted L-shaped stubs are included to further enhance the isolation and eliminate the antenna size increment simultaneously, by suppressing the surface waves. Before using the decoupling approach, the MIMO antenna had an impedance bandwidth of 6.7 GHz and an isolation of −20 dB. The suggested antenna’s impedance bandwidth and isolation are 5.83 GHz and −52 dB, respectively, after using a decoupling approach (inverted L-shaped stub). The proposed MIMO antenna had a peak gain and radiation efficiency of 3–7 dBi and better than 90% (with decoupling), 2.75 to 6 dBi and greater than 60% (without decoupling). To verify the proposed design, a prototype was developed to compare the simulated parameters with the measured one. The envelope correlation coefficient (ECC) was less than 0.05, and channel capacity loss (CCL) less than 0.3 was achieved with DGS. Besides, scattering matrix such as return loss S11and transmission coefficient S21between two elements, radiation pattern, and current distribution was also favorable. Thus, the proposed MIMO antenna is advantageous to be used for C-band applications such as developing new generation air-borne and space-borne radars, as well as X-band applications.