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This paper introduces and describes a dataset representing United States carbon dioxide (CO2) emissions from electricity consumption (scope 2 CO2 emissions) for the 2019–2021 time period separately for the residential, commercial, and industrial sectors. The spatial resolution is the U.S. census block group at annual time resolution. We also provide state-scale aggregate data. Given the increased interest in greenhouse gas mitigation at the urban landscape scale, this data offers the opportunity to better understand the emitting landscape and craft more targeted, efficient policy instruments. The approach starts with well-established estimates of scope 2 CO2 emissions at larger scales and performs a series of downscaling steps to allocate scope 2 emissions into block groups. This open-access scope 2 CO2 emissions dataset and methodology is valuable for a wide range of multidisciplinary studies, including climate science, environmental policy, urban planning, and socioeconomic research. [ABSTRACT FROM AUTHOR]

Positioning, navigation and time are the cornerstones of satellite navigation. These aspects are frequently affected by ionospheric variations caused by solar flares (SF). In this study, we have attempted to predict the range error (RE) caused by ionospheric delay in Global Positioning System (GPS) signals during six different X-class SF that occurred in the 25th solar cycle using two different approaches, namely, a recurrent neural network (RNN) and the ordinary Kriging-based surrogate model (OKSM). The total electron content (TEC) collected from Hyderabad station along with other input parameter includes the Planetary A and K index (Ap and Kp), solar sunspot number (SSN), disturbance storm time index (Dst), and radio flux measured at 10.7 cm (F10.7) were used for prediction. The OKSM uses the previous six days of datasets to predict the RE on the seventh day, whereas the RNN model uses the previous 45 days of datasets to predict the RE on the 46th day. The performance of both models is evaluated using statistical parameters such as root mean square error (RMSE), normalized root mean square error (NRMSE), Pearson's correlation coefficient (CC), and symmetric mean absolute percentage error (sMAPE). The results indicate that the OKSM performs well in adverse space weather conditions when compared to RNN. [ABSTRACT FROM AUTHOR]

A fundamental parameter to evaluate the beam delivery precision and stability on a clinical linear accelerator (linac) is the focal spot position (FSP) measured relative to the collimator axis of the radiation head. The aims of this work were to evaluate comprehensive data on FSP acquired on linacs in clinical use and to establish the ability of alternative phantoms to detect effects on patient plan delivery related to FSP. FSP measurements were conducted using a rigid phantom holding two ball-bearings at two different distances from the radiation source. Images of these ball-bearings were acquired using the electronic portal imaging device (EPID) integrated with each linac. Machine QA was assessed using a radiation head-mounted PTW STARCHECK phantom. Patient plan QA was investigated using the SNC ArcCHECK phantom positioned on the treatment couch, irradiated with VMAT plans across a complete 360° gantry rotation and three X-ray energies. This study covered eight Elekta linacs, including those with 6 MV, 18 MV, and 6 MV flattening-filter-free (FFF) beams. The largest range in the FSP was found for 6 MV FFF. The FSP of one linac, retrofitted with 6 MV FFF, displayed substantial differences in FSP compared to 6 MV FFF beams on other linacs, which all had FSP ranges less than 0.50 mm and 0.25 mm in the lateral and longitudinal directions, respectively. The PTW STARCHECK phantom proved effective in characterising the FSP, while the SNC ArcCHECK measurements could not discern FSP-related features. Minor variations in FSP may be attributed to adjustments in linac parameters, component replacements necessary for beam delivery, and the wear and tear of various linac components, including the magnetron and gun filament. Consideration should be given to the ability of any particular phantom to detect a subsequent impact on the accuracy of patient plan delivery. [ABSTRACT FROM AUTHOR]

Mosquitoes transmit quite a lot of deadly diseases, including chikungunya, brain fever, dengue fever, Japanese encephalitis, hemorrhagic fever, malaria, filariasis, yellow fever, and Zika fever. Every year, mosquito-borne diseases affect millions of people. For that reason, mosquito control is vital for public health, especially in tropical countries. To manage mosquitoes, various synthetic chemicals are used, each with its advantages and disadvantages. Chemical management has a satisfactory effect on reducing mosquito populations, even though mosquito resistance and the presence of pesticide residues in the environment and the development of resistance in mosquito control tactics are crucial concerns. It is difficult to control whether an insecticide-resistant mosquito population causes to the reappearance of vector-borne diseases. In this emergency circumstance, we need to look for alternatives. Alternatively, plant-based phytochemicals are still more widely used for mosquito control programs, in terms of cost since they are efficient and economical, eco-friendly, safe for humans and the environment, and may be used as an effective alternative to chemical larvicides. The current state of data on phytochemical sources has variation in larvicidal activity according to mosquito species, instar specificity, the polarity of solvents used during extraction, the nature of the active ingredient, and promising advances made in biological control of mosquitoes by plant-derived reviewed in this article. [ABSTRACT FROM AUTHOR]

Sandstone is a key material in many global heritage sites, including Jaisalmer Fort (Rajasthan), Petra (Jordan), and Angkor Wat (Cambodia). Its versatility makes it a popular building material, but environmental factors such as climatic conditions and fire incidents can cause significant damage. These factors can induce formation of microscopic changes, fissures, fractures, and intergranular porosity, leading to early failure. This vulnerability is of especial concern for heritage structures, where preserving the cultural and historical assets is critical task. In recent years, the frequency of fire incidents affecting heritage structures has increased such as the National Museum of Brazil fire in 2018, and the Notre Dame fire in 2019. Accurate assessment is crucial for developing effective remediation protocols for fire-damaged structures and ensuring the longevity of sandstone heritage sites globally. This study investigates the physical and mechanical properties of thermally treated Jodhpur sandstone, focusing on the role of cooling. The results demonstrate that various cooling methods, such as furnace cooling, air cooling, and water cooling, significantly influence the properties of sandstone. Specifically, the water-cooled specimens experienced the highest level of damage, characterized by the greatest decreases in density, uniaxial compressive strength (UCS), elastic modulus, and wave velocities. These findings underscore the importance of controlled cooling in heritage conservation, providing a scientific basis for improving preservation strategies for sandstone monuments exposed to thermal stress. [ABSTRACT FROM AUTHOR]

Chronic myeloid leukemia (CML) is a myeloproliferative neoplasm (MPN characterized by reciprocal translocation in the ABL1 and BCR region of chromosomes 9 and 22 respectively. Progression to the blast phase in chronic myeloid leukemia results in a poorer prognosis. It can be of either myeloid, lymphoid or a mixed lineage. Progression to the promyelocytic blast phase is very rare, and there are no evidence-based guidelines for its management. Thrombosis in CML is not well defined. Thrombosis can be seen in patients with acute promyelocytic leukemia (APL) with venous thrombosis (VTE) being more common than arterial thrombosis. Ischemic stroke as the presenting feature of blast phase progression in CML is extremely rare. We report a case of CML who presented to us with acute ischemic stroke and subsequently was diagnosed as CML transformed to the promyelocytic blast phase. She was successfully treated with dasatinib along with all-trans retinoic acid (ATRA) and arsenic trioxide (ATO). [ABSTRACT FROM AUTHOR]

In this article, I shall discuss, after a brief biography, Bell's profound contributions to science. This will include those that have had a lasting impact, such as his works on CPT theorem, Bell's theorem, anomalies. In addition, I shall briefly mention his other works on bringing together cosmology and quantum mechanics as well as on observing the Unruh effect in accelerator beams. [ABSTRACT FROM AUTHOR]

Medical image processing generally contains high components of noise produced by interference, compression and use of imperfect instrument during acquisition or transmission. An effective imaging devicei.e. Magnetic Resonance Imagingmay diagnose the disease by acute analysis of dissectional anatomical soft tisses of human. In general, MR images are of poor contrast in lieu of blurriness, out of focused and lack of brightness iside the machine. In this paper, hybridization approaches i.e. Median-Wiener filter (MW), Wiener-Median filter (WM) and other combinations like WMWM & MWMW are proposed for MR image enhancement. The results are further compared with various filtering algorithms i.e. Average filter, Median Filter, Wiener Filter &Gaussian filter and in terms of MSE, PSNR, RMSE, MAE. The proposed hybridization filtering technique gives better outcomes comparatively. [ABSTRACT FROM AUTHOR]

Due to resource shortages and device diversity, energy efficiency and scalability issues are critical in the Internet of Things (IoT) space. Managing edge resources consistently to encourage resource sharing among devices is complex, given IoT's device heterogeneity and dynamic environmental conditions. In response to these challenges, our research presents a suite of intelligent techniques tailored for optimizing resources in IoT devices. Our solution's core component is a thorough full-stack system architecture made to flexibly handle a diverse range of IoT devices, each of which operates under resource limitations. This paradigm centers on the deployment of multiple edge servers, strategically positioned to cater to the unique requirements of IoT devices, which exhibit compatibility with heterogeneity, high performance, and adaptive intelligence. To realize this vision, we create a clustered environment within the realm of heterogeneous IoT devices. We employ an African vulture's optimization algorithm (AVOA), approach to establish connections between Cluster Head (CH) nodes. Following this crucial step, we meticulously select edge nodes situated in close proximity to the data source for transmission, reducing energy consumption and latency. Our proposed Multi-Edge-IoT system sets a new standard for efficiency within the IoT ecosystem, outperforming existing approaches in key metrics such as energy consumption, latency, communication overhead, and packet loss rate. It represents a significant stride towards the harmonious and resource-efficient operation of IoT devices in an increasingly interconnected world. [ABSTRACT FROM AUTHOR]

Efficacy of two Indian isolates of entomopathogenic nematodes (EPNs) viz., Heterorhabditis bacteriophora MK256358 and Steinernema feltiae MK256355 were tested in laboratory against the larvae of cabbage butterfly, Pieris brassicae. Larval mortality was found directly proportional to initial inoculum level of infective juveniles (IJs). Susceptibility of larvae varied with respect to their variable size. H. bacteriophora MK256358 @ 25 IJs/larva caused 100% mortality to 3rd instar larvae at 72 h but @ 75 IJs/larva, the same mortality was achieved in 48 h. S. feltiae MK256355 @ 100 IJs/larva caused 100% mortality to 3rd instar larvae of P. brassicae at 48 h. H. bacteriophora MK256358 @ 25 and 100 IJs/larva resulted in 100% mortality to 4th and 5th instar larvae, respectively at 72 h, however S. feltiae MK256355 was unable to cause 100% mortality to either 4th or 5th instar larvae at any inoculum level or time period used in the study. LD50 and LT50 values of H. bacteriophora MK256358 were lower than S. feltiae MK256355 indicating that less nematode dose and time is required to kill 50% pest population. Reproduction capacity of nematode within the host was directly proportional to individual larval size and nematode inoculum level and for H. bacteriophora MK256358, it was higher and statistically significant (P ≤ 0.05) from S. feltiae MK256355. Our experimental findings open new avenues for utilization of EPNs against P. brassicae and set the basis for safe insect pest management programme. [ABSTRACT FROM AUTHOR]

Purpose: Research indicates that exposure to conflict, natural disasters, and internal displacement can increase mental health conditions. Since the end of the civil conflict within Sri Lanka, the country has worked to increase access to mental health services to meet the needs of conflict-affected populations, however, gaps remain. To address this, integration of mental health services into primary care can reduce the strain on growing specialized care. As part of a larger study primary care practitioners (doctors), public health professionals (nurses, midwives), and community representatives (teachers, social workers) were trained to deliver mental health services in primary care across the heavily impacted Northern Province. The aim was to reduce mental health stigma among enrolled healthcare workers and community representatives by 50%. Methods: Stigma was measured across all participant groups at six time points: pre- and post- initial training at baseline, pre- and post- refresher training 3-months after initial training, and pre- and post- refresher training 6-months after initial training. Results: Results indicate a small improvement in average stigma scores at the 6-month refresher point for primary care practitioners, and no meaningful difference in average scores across time points for public health professionals or community representatives. Conclusion: World Health Organization mhGAP training appears to reduce stigma among primary care practitioners and could be an effective strategy to counteract mental health stigma in low resource settings. Future research should investigate underlying mechanisms of stigma reduction to improve delivery of mental health services in primary care and community settings. [ABSTRACT FROM AUTHOR]

The current study is focused on evaluating the effectiveness of composite armour made from ceramic, fabric and metal, employing various configurations. Additionally, it incorporates Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis to compare the performance of different configurations of composite armour plates having 20 mm thickness against high-velocity hard steel core armour-piercing projectiles, particularly for vehicle armour applications. The ceramic component of the armour comprises hardened alumina (99.6%), Kevlar® fabric-based fibre-matrix composites, and an epoxy blend reinforced with carbon nanotubes (CNTs). The design of the armour prioritized maintaining a ceramic tile as the frontal impact surface, complemented by an aluminium (Al 2024-T3) plate and a CNT-reinforced Kevlar® composite on the rear. The armour was engineered as a functionally graded composite material with varying levels of hardness and toughness. To ensure equitable comparison across armour plates, the total thickness of the composite was consistently set at 20 mm, and its performance was assessed under two distinct impact velocities: 725 and 550 m/s. A multi-criterion based decision-making (MCDM) approach was employed to comprehensively analyse the armour's performance across multiple parameters. The study shows that the stacking sequence of the materials significantly alters the ballistic resistance of composite panel despite possessing similar arial density. Also, Certain configurations outperforms others despite having lesser arial density. [ABSTRACT FROM AUTHOR]

Maize (Zea mays L.), a multi-utility cereal crop, has a high requirement for nitrogen (N). However, conventional N application methods fail to achieve potential yield and contribute to environmental pollution. Thus, a 2-year (2022 and 2023) field investigation was conducted to understand the effect of sub-surface drip-fertigation (SSDF) and legume residue incorporation (3 t ha−1) on improving maize yield, uptake, remobilization, and use-efficiency of N. Four N levels, viz., 0, 50, 75, and 100% recommended dose of N (RDN) applied in 3-splits (3S) and 4-splits (4S), were compared with conventional maize cultivation, fertilized with 150 kg N ha−1 (CMC-N150). Variable N fertigation with SSDF significantly affected the dry matter (DM) and N remobilization from leaves towards kernel; at lower doses of N maize tended to mobilize higher DM and N towards kernel. Better growth, higher nutrient uptake, and better remobilization efficiency of DM/N led to higher grain yield under 75% RDN (19–23% increase) and 100% RDN (20–25% increase) sub-surface drip-fertigated, compared to CMC-N150. Using 50% RDN through SSDF yielded grain on par with CMC-N150, thus saving 50% N. Moreover, applying N in 4-splits (4S) gave 3–5% yield advantage over 3-splits. Partial factor productivity of N (PFPN) and agronomic N-use efficiency (AEN) of different SSDF treatments increased by 18–100% and 40–99%, respectively over CMC-N150. Greengram residue incorporation improved maize yield by 5–8%, PFPN by 4–5% over no-residue incorporation. Overall, there was a 25–50% N economy with concomitant yield improvement under SSDF over conventional N management. The SSDF coupled with legume residue incorporation could be a promising technology for enhancing N-use efficiency globally; however, it needs policy support for adoption in the producer's field. [ABSTRACT FROM AUTHOR]

The following study investigates the effects of Rabinowitsch fluid on journal bearing lubrication under the influence of velocity slip and variable viscosity. The finite difference method is used to produce a numerical solution to the 2D Modified Reynolds equation. The findings verify the previous results, which indicate that the dilatant (shear-thickening) fluid pressure is higher than the Newtonian and pseudo-plastic (shear-thinning) fluids. The results show that in the presence of slip, the pressure distribution rises with the piezo- viscosity parameter (variable viscosity) by up to 75%. Compared to the Newtonian, and pseudo-plastic (shear thinning) fluid, the dilatant (shear-thickening) fluid produces the highest pressure. A higher eccentricity ratio, slip-velocity, piezo-viscosity parameter, and dilatant fluid enhances the load-carrying capacity. The reduction in the attitude angle plays an integral part in the load-carrying capacity of the journal bearing. The attitude angle decreases for all values of the bearing ratio; the dilatant fluid has the most significant reduction in attitude angle compared to the Newtonian and pseudo-plastic fluids. Slip effects reduce the attitude angle, and a further reduction occurs when the piezo-viscosity and slip factors are combined. The slip-velocity, lower values of the bearing's length to diameter ratio, greater piezo-viscosity, higher values of the eccentricity ratio, and the dilatant fluid minimize friction. The Rabinowitsch fluid properties and the addition of both slip and variable viscosity increase bearing performance by increasing the pressure distribution, load-carrying capacity of the journal bearing and reduction of the frictional parameter, and should be considered when optimizing pressure, load, and friction for the creation of the ideal journal bearing. [ABSTRACT FROM AUTHOR]

To evaluate the utility of CD43 and CD200 in differentiating chronic lymphocytic leukemia (CLL) from other mature B-cell neoplasms. This was a cross-sectional study on patients diagnosed with B-cell neoplasms on flowcytometry. The median fluorescence intensity (MFI) of CD43, CD200 expressing neoplastic B-cells were compared between the CLL and non-CLL B-cell neoplasms followed by receiver operating characreristic curve (ROC) analysis. In addition, the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of CD43 and CD200 in diagnosing CLL were analysed. A total of 137 patients were included. The CLL group consisted 87 patients and non-CLL group consisted 50 patients. The Mann–Whitney U test showed significant CD43 expression (U = 997.5, Z= − 5.265, p < 0.001) and CD200 expression (U = 932.0, Z = − 5.5, p < 0.01) in CLL patients compared to non-CLL patients. The area under the curve were 0.771 and 0.786 for MFI of CD43 and CD200 in differentiating CLL from non-CLL group respectively. The optimal cut-off of MFI for CD43 and CD200 were 1323 and 1775 respectively. The sensitivity, specificity, PPV and NPV of CD43 in diagnosing CLL cases were 97.7%, 66%, 83.3% and 94.2% respectively. The sensitivity, specificity, PPV and NPV of CD200 in diagnosing CLL cases were 100%, 32%, 71.9% and 100% respectively. CD43 and CD200 are useful markers in differentiating CLL from other mature B-cell neoplasms with higher MFI expression of both markers found in CLL. [ABSTRACT FROM AUTHOR]

Ultrasonography is widely used to screen thyroid tumors because it is safe, easy to use, and low-cost. However, it is simultaneously affected by speckle noise and other artifacts, so early detection of thyroid abnormalities becomes difficult for the radiologist. Therefore, various researchers continuously address the limitations of sonography and improve the diagnosis potential of US images for thyroid tissue from the last three decays. Accordingly, the present study extensively reviewed various CAD systems used to classify thyroid tumor US (TTUS) images related to datasets, despeckling algorithms, segmentation algorithms, feature extraction and selection, assessment parameters, and classification algorithms. After the exhaustive review, the achievements and challenges have been reported, and build a road map for the new researchers. [ABSTRACT FROM AUTHOR]

On considering modern society, the wireless communication system plays a most significant role. This system has kept evolving and deployed into a wireless system of Fifth Generation (5G). One of the significant factors of the 5G system has utilized Machine Learning (ML) as well as Artificial Intelligence (AI) for the wireless network. Each of the building block and components of the wireless system, which is familiar and involves one or other ML/AI techniques are required. Here, the 5G generation has been used as the digital technology as well as run over higher radio frequencies. Further, the development of new techniques as well as the advanced features over the 5G network has raised some issues for the networking operators. ML is regarded as the AI that is accepted to unlock the capability of difficult large-scale issues over traditional Multiple-Input Multiple-Output (MIMO) systems. Today's wireless system has incomplete the MIMO system that has become common in recent years due to the increased potential in both energy efficiency and spectrum efficiency at a significant rate. AI-dependent ML has resolved the issues and then offers more energy efficiency and throughput in the 5G system. Hence, an effective artificial intelligence-based solution is introduced for resolving the aforementioned challenges to execute an efficient MIMO system in this proposal. In this work, the operations that are carried out for developing an efficient MIMO communication system are channel estimation, spectrum sensing of channels, fault/anomaly detection, resource allocation, and edge computation offloading. For performing all these functions, a deep learning method called Adaptive Autoencoder-Based Dilated DNN with Attention Mechanism (AADD-AM) is implemented. The parameters of the designed model are optimized with the help of Modified Update of Ant Lion and Horse herd Optimization (MU-ALHO) to achieve an accurate and effective outcome. The performance of the suggested efficient MIMO model is validated by comparing it with various performance metrics. Throughout the result analysis, the accuracy and sensitivity rate of the designed model is 94.86% and 95.18%. Therefore, it is revealed that the recommended model achieves high-speed wireless communication in a wide range of applications. [ABSTRACT FROM AUTHOR]

Background and objectives: Nirmatrelvir/ritonavir was administered orally to manage mild to moderate symptoms of COVID-19 in adult patients. The objectives of this study were to (i) evaluate the cost-effectiveness of prescribing nirmatrelvir/ritonavir within 5 days of a COVID-19 illness in order to avert hospitalization within a 30-day period in the Malaysia setting; (ii) determine how variations in pricing and hospitalization rates will affect the cost-effectiveness of nirmatrelvir/ritonavir. Methods: The 30-day hospitalization related to COVID-19 was determined using 1 to 1 propensity score-matched real-world data in Malaysia from 14 July 2022 to 14 November 2022. To determine the total per-person costs related to COVID-19, we added the cost of drug (nirmatrelvir/ritonavir or control), clinic visits and inpatient care. Incremental cost-effectiveness ratio (ICER) per hospitalization averted was calculated. Results: Our cohort included 31,487 patients. The rate of hospitalization within 30 days was found to be 0.35% for the group treated with nirmatrelvir/ritonavir, and 0.52% for the control group. The nirmatrelvir/ritonavir group cost an additional MYR 1,625.72 (USD 358.88) per patient. This treatment also resulted in a reduction of 0.17% risk for hospitalization, which corresponded to an ICER of MYR 946,801.26 (USD 209,006.90) per hospitalization averted. Conclusion: In Malaysia, where vaccination rates were high, nirmatrelvir/ritonavir has been shown to be beneficial in the outpatient treatment of adults with COVID-19 who have risk factors; however, it was only marginally cost effective against hospitalization for healthy adults during the Omicron period. [ABSTRACT FROM AUTHOR]

Extreme learning machines (ELMs) are shown to be efficient and effective learning algorithms for regression and classification tasks. ELMs, however, are typically utilized to solve supervised learning problems. Only a handful of research on ELMs focuses on exploring unlabeled data. One representative work is the unsupervised extreme learning machine (US-ELM), where the standard ELM is expanded for unsupervised learning based on Laplacian regularization. However, Laplacian regularization has poor extrapolation power since it tends to bias the solution towards a constant function. In this paper, we propose a new framework termed Hessian unsupervised ELM (HUS-ELM) to enhance the unsupervised learning of ELM. In particular, Hessian regularization can properly exploit the intrinsic local geometry of the data manifold compared to Laplacian regularization. This leverages the performance of HUS-ELM in unsupervised learning problems since the Hessian regularization can correctly reflect the positional relationship between the unlabeled samples. Six publicly available datasets are used to evaluate the proposed algorithm. The experimental results indicate that the proposed method performs better than other unsupervised learning methods in terms of clustering accuracy. [ABSTRACT FROM AUTHOR]

Computer-Aided Diagnosis (CAD) system is preferred for automatic thyroid tumor ultrasound image characterization instead of manual assessment by the experts. Segmentation and control despeckling are the important pre-processing stages required to develop an effective CAD system. This work primarily aims to design an efficient CAD system for thyroid tumor characterization using ultrasound (US) images. Here, Edge Preserving Smoothing despeckling filter and encoder decoder-based ResNet50 segmentation model are used as pre-processing stages of the proposed CAD system to enhance its performance for thyroid tumor characterization. Extracting the image features using pre-trained models effectively captures the underlying textural and morphological characteristics exhibited by thyroid tumors in ultrasound images. The pre-trained- models learn by automatic feature extraction representing the underlying characteristics using multiple stages by convolution with various filters. The pre-train-based neural network classifies tumors more accurately due to learning the extract multiple sets of features. Accordingly, fifteen (15) deep learning-based pre-trained models have been utilized in the present work to extract information from the thyroid tumor US images and train the PCA-SVM classifier. These pre-train models have been taken from different categories of deep learning algorithms, including Series / DAG / Lightweight architectures, namely AlexNet, VGG16, VGG19, Darknet19, Darknet53, GoogleNet, DenseNet201, ResNet18, ResNet50, ResNet101, EfficientNetb0, NasNetMobile, MobileNet, SqueezeNet, and ShuffleNet for characterization of thyroid tissues. An exhaustive set of experiments have been conducted, and the best-performing pre-trained models have been selected as optimal feature extractors based on classification accuracy. Thus, the features extracted from the best-performing pre-trained network, i.e., ResNet50, are fed to the PCA-SVM classifier to yield an efficient CAD system for classifying TTUS images. The optimal CAD design proposed in the present work yields 99.5% classification accuracy to distinguish between benign and malignant thyroid tumors. [ABSTRACT FROM AUTHOR]

Advancements in space weather forecasting have become crucial for understanding and mitigating the impacts of solar activity on Earth's ionosphere. This research focuses on the prediction of Total Electron Content (TEC) during M-class solar flare events in 2023. TEC is a vital parameter for satellite communications and navigation, making accurate forecasting imperative. Two prediction models, Long Short-Term Memory (LSTM) neural networks and Surrogate Models based on Ordinary Kriging (OKSM), are employed. LSTM, known for capturing temporal dependencies, is contrasted with OKSM, a geostatistical interpolation technique capturing spatial autocorrelation. The study utilizes TEC measurements from the Hyderabad (HYDE) GPS station for model training and evaluation along with solar and geomagnetic parameters. The performance metrics for both models across various solar flare dates are measured using Root Mean Square Error (RMSE), Normalized RMSE, Correlation Coefficient (CC), and Symmetric Mean Absolute Percentage Error(sMAPE). The research interprets the results, highlighting the strengths and limitations of each model. Notable findings include LSTM's proficiency in capturing temporal variations and OKSM's unique spatial perspective. Different solar flare intensities are analyzed separately, demonstrating the model's adaptability to varying space weather conditions. The average performance metrics during M 4.65 SF events for the OKSM model, in terms of Root Mean Square Error is 5.61, Normalized RMSE is 0.14, Correlation Coefficient is 0.9813, and Symmetric Mean Absolute Percentage Error is 14.90. Similarly, for LSTM, the corresponding averages are 10.03, 0.24, 0.9313, and 28.64. The research contributes valuable insights into the predictive capabilities of LSTM and OKSM for TEC during solar flare events. The outcomes aid in understanding the applicability of machine learning and geostatistical techniques in space weather prediction. As society's reliance on technology susceptible to space weather effects grows, this research is pivotal for enhancing space weather forecasts and ensuring the robustness of critical technological infrastructure on Earth. [ABSTRACT FROM AUTHOR]

As crowdfunding success relies heavily on designing campaigns that attempt to influence potential backers within a fixed time frame, this study leverages signaling theory to explore the time-varying direct and interactive effects of rhetorical (i.e., emotional tone, cognitive tone, communal language style, and linguistic style match) and substantive (i.e., backer support) signals employed in project descriptions on funding formation. Using linguistic inquiry and word count (LIWC) and functional data analysis (FDA), we analyze data on 1245 projects from a rewards-based crowdfunding platform (Kickstarter) and demonstrate that (a) the four rhetorical signals are positively related to funding formation, (b) emotional and cognitive tone exert stronger effects in early phases of projects, whereas linguistic style match and communal language style exert stronger effects in later phases, and (c) the dynamic effects of rhetorical signals on funding formation are greater when the number of backers increases. We conclude with implications for crowdfunding research and insights for improving campaign success. [ABSTRACT FROM AUTHOR]

This paper comprehensively examines passive and active energy retrofit strategies as a highly effective approach for reducing building energy consumption and mitigating CO2 emissions while enhancing comfort and sustainability. The paper further examines energy simulation software and assesses the integration of renewable energy systems in building to improve energy efficiency. The review used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, ensuring a rigorous and comprehensive analysis. In addition, the study utilized bibliometric analysis with VOSviewer to provide valuable insights into the research trends and influential publications in building energy retrofits. Bibliometric analysis reveals strong collaboration among 17 authors, emphasizing their significant contributions. Keywords like energy retrofitting and efficiency are prominent, indicating their importance in academic literature. Findings show passive strategies are more effective in reducing energy consumption, though a combined approach with active strategies can yield optimal results. Retrofitting presents challenges, such as substantial initial costs and regulatory barriers. User acceptance is crucial, considering potential disruptions. The review highlights the importance of energy simulation software, with tools like EnergyPlus, eQUEST, and IES VE identified for evaluating and identifying cost-effective retrofit measures in building performance. By providing comprehensive insights into the various strategies and tools available for retrofitting buildings to achieve energy efficiency and sustainability goals, this review serves as an authoritative resource for building owners, managers, and professionals in the building industry. It offers invaluable guidance for informed decision-making and facilitates implementing effective, energy-efficient, and sustainable building retrofitting practices. [ABSTRACT FROM AUTHOR]

Cu2ZnSn(S,Se)4 is a non-toxic, earth-abundant photovoltaic absorber. However, its efficiency is limited by a large open circuit voltage (VOC) deficit occurring due to its antisite defects and improper band alignment with toxic CdS buffer. Therefore, finding an absorber and non-toxic buffers that reduce VOC deficit is crucial. Herein, for the first time, Ag2BaTiSe4 is proposed as an alternative absorber using SCAPS-1D wherein a new class of alkaline earth metal chalcogenide such as MgS, CaS, SrS, and BaS is applied as buffers, and their characteristics are compared with CdS to identify their potential and suitability. The buffer and absorber properties are elucidated by tuning their thickness, carrier concentration, and defect density. Interestingly, optimization of the buffer's carrier concentration suppressed the barrier height and accumulation of charge carriers at the absorber/buffer interface, leading to efficiencies of 18.81%, 17.17%, 20.6%, 20.85%, 20.08% in MgS, CaS, SrS, BaS, and CdS-based solar cells respectively. Upon optimizing Ag2BaTiSe4, MoSe2, and interface defects maximum efficiency of > 28% is achieved with less VOC loss (~ 0.3 V) in all solar cells at absorber's thickness, carrier concentration, and defect density of 1 µm, 1018 cm−3, 1015 cm−3 respectively, underscoring the promising nature of Ag2BaTiSe4 absorber and new alkaline earth metal chalcogenide buffers in photovoltaics. [ABSTRACT FROM AUTHOR]

Earthquake indicators provide a huge advantage in the preparation for the calamity and its aftermaths. Total electron content (TEC) is an ionospheric measurement that varies before the possible happenstance of an earthquake. In this research paper, ordinary kriging based surrogate model (OKSM) which is used for forecasting ionospheric TEC variation is adapted as an earthquake indicators in low-latitude and mid-latitude regions. Seven International GNSS Service (IGS) stations from the mid-latitude and low-latitude region are chosen for observation. The stations are from different countries such as Indonesia (BAKO), Ecuador (RIOP), Greece (ORID), Cyprus (NICO), Hawaii (HNLC) and Italy (MATE & MAT1). The OKSM prediction program is performed with previous 2 months of TEC data from the observed date and its accompanying solar parameters such as Planetary K and A index (Kp & Ap), Radio Flux at 10.7 cm (F10.7) and disturbance storm time (DST) index acquired from OMNIWEB data servers. The performance of the model is evaluated using statistical metrics such as root mean square error (RMSE), normalized – RMSE (NRMSE), mean absolute error (MAE), Pearson's correlation coefficient (PCC) and chi-squared goodness of fit test. The evaluation of the prediction model on the same date shows that the performance deviation of OKSM is in the range of 2–5 TECU and also establishes the fact that the prediction capability of the OKSM is accurate. Application of the OKSM constructed with previous days data collected from high earthquake prone areas by omitting effects of solar storms and solar activities will act as a highly economical and simple early warning indicator of an earthquake. [ABSTRACT FROM AUTHOR]

Active infrared thermography (IRT) helps address the constraints of conventional bone density diagnostic tactics, such as various bones requiring different testing methods, unilateral diagnosis, radiation exposure, testing duration, etc. Although active IRT provides promising diagnostic outcomes, it has limited depth penetration into the body, resulting in limited resolution. Despite the fact that numerous post-processing techniques are utilized to boost the intensity penetration of modulated thermal excitation, it is by no means restricted. Consequently, we tend to employ a nanoparticle coating approach in this study. We have coated iron oxide and titanium oxide nanoparticles with two promising excitations, linear frequency modulated (LFM) and Gaussian-weighted linear frequency- modulated (GWLFM). This research aimed to determine the effect of nanoparticle coating in conjunction with GWLFM as it offers increased compression qualities, sensitivity, and resolution. This is accomplished by constructing bone LFM models with varying bone densities and forms. As in the current study, nanoparticles and GWLFM are utilized to attain high depth resolution. Using signal-to-noise ratio, we evaluated diverse coating outcomes with LFM and GWLFM (SNR). In conjunction with GWLFM, titanium- and iron-based nanoparticle coatings are capable of enhancing the depth penetration for bone density measurement, according to our analysis. [ABSTRACT FROM AUTHOR]

Parameter estimation and associated uncertainty quantification is an important problem in dynamical systems characterised by ordinary differential equation (ODE) models that are often nonlinear. Typically, such models have analytically intractable trajectories which result in likelihoods and posterior distributions that are similarly intractable. Bayesian inference for ODE systems via simulation methods require numerical approximations to produce inference with high accuracy at a cost of heavy computational power and slow convergence. At the same time, Artificial Neural Networks (ANN) offer tractability that can be utilized to construct an approximate but tractable likelihood and posterior distribution. In this paper we propose a hybrid approach, where Laplace-based Bayesian inference is combined with an ANN architecture for obtaining approximations to the ODE trajectories as a function of the unknown initial values and system parameters. Suitable choices of customized loss functions are proposed to fine tune the approximated ODE trajectories and the subsequent Laplace approximation procedure. The effectiveness of our proposed methods is demonstrated using an epidemiological system with non-analytical solutions—the Susceptible-Infectious-Removed (SIR) model for infectious diseases—based on simulated and real-life influenza datasets. The novelty and attractiveness of our proposed approach include (i) a new development of Bayesian inference using ANN architectures for ODE based dynamical systems, and (ii) a computationally fast posterior inference by avoiding convergence issues of benchmark Markov Chain Monte Carlo methods. These two features establish the developed approach as an accurate alternative to traditional Bayesian computational methods, with improved computational cost. [ABSTRACT FROM AUTHOR]

People across the world have been fascinated by solar eclipses for thousands of years. Solar eclipses are not only fascinating to observe but also provide opportunities for scientific research. During a solar eclipse, the quantity of solar energy reaching the Earth's surface is reduced as the Moon passes in front of the Sun. This reduction in solar energy can have an effect on the Total Electron Content of the Earth's ionosphere. In this paper, prediction and analysis of TEC variations in the Ionosphere during the solar eclipses happened on 26.12.2019 between 04:51 to 7:34 hours (UTC) and 09.03.2016 between 12:18 to 1:02 hours (UTC) over the Indonesia region were done by using two models: Ordinary Kriging based Surrogate Model (OKSM) and Feed-Forward Neural Network (FFNN). During the eclipse period, the TEC values were predicted by the OKSM and FFNN models and it is validated using literature. For this study, the GPS data belonging to the BAKO station situated in Indonesia were collected from IONOLAB servers and the input parameters were collected from the OMNIWEB servers. Forty days prior TEC data and input parameters were used to predict the TEC values. The credibility of the predicted results is assessed using statistical factors such as RMSE, CC, MAE, MAPE, sMAPE and R-Square. The statistical results show OKSM has performed well when compared to the FFNN model over the annular and total solar eclipse period. The study suggests that combining multiple modelling methods, such as OKSM and FFNN can improve our understanding of ionospheric variability during solar eclipses and provide more accurate predictions of TEC variations. This has important implications for satellite communications and navigation systems that rely on accurate TEC measurements for positioning and timing. [ABSTRACT FROM AUTHOR]

In this paper, we propose a zero knowledge undeniable signature scheme over non-abelian group. Security of the proposed scheme relies on the intractability of Discrete Logarithm Problem with Conjugacy Search Problem (DLCSP) (Goel et al in Appl Algebra Eng Commun Comput 27(6):523–535, 2016). The scheme is proved to be secure with respect to unforgeability, unimpersonation and invisibility in random oracle model. [ABSTRACT FROM AUTHOR]

This study developed a new tool, differential staining fluorescence microscopy (DSFM), to measure the biovolume and track the location of enteric pathogens in mixed-species biofilms which can pose a risk to food safety in beef processing facilities. DSFM was employed to examine the impact of pathogenic bacteria, Escherichia coli O157:H7 and three different Salmonella enterica strains on mixed-species biofilms of beef processing facilities. Fourteen floor drain biofilm samples from three beef processing plants were incubated with overnight BacLight stained enteric pathogens at 7 °C for 5 days on stainless steel surface then counter-stained with FM-1-43 biofilm stain and analyzed using fluorescence microscopy. Notable variations in biovolume of biofilms were observed across the fourteen samples. The introduction of E. coli O157:H7 and S. enterica strains resulted in diverse alterations of biofilm biovolume, suggesting distinct impacts on mixed-species biofilms by different enteric pathogens which were revealed to be located in the upper layer of the mixed-species biofilms. Pathogen strain growth curve comparisons and verification of BacLight Red Stain staining effectiveness were validated. The findings of this study show that the DSFM method is a promising approach to studying the location of enteric pathogens within mixed-species biofilms recovered from processing facilities. Understanding how foodborne pathogens interact with biofilms will allow for improved targeted antimicrobial interventions. [ABSTRACT FROM AUTHOR]

Balanced nutrition is key to achieving sustainable output with efficient utilization of natural resources. A two-year field experiment was conducted to determine the combined effect of silicon (Si) and phosphorus (P) on system crop productivity (SP), system water productivity (SWP), system irrigation water use efficiency (SIWUE), and system profitability of aerobic-rice-wheat cropping system (ARWCS) in the Indo-Gangetic Plains of India. The four doses of Si viz. 0, 40, 80, and 120 kg Si ha– 1(S0, S1, S2 and S3) and four doses of P viz. 0, 30, 60, and 90 kg P2O5 ha−1(P0, P1, P2, and P3) were applied during both years. The results revealed that the application of P3 and S3 enhanced the rice equivalent yield (REY) and SP by 26–38.4% and 27.6–39.6%, respectively over control treatment. Similarly, SWP and SIWUE were also increased by 31.2–38.3% and 26.6–39.5% under the same treatment. Further, the application of P3 and S3 reduced the lodging by 69.4% and 85.9% in rice and wheat, respectively, over control. Further, the same treatment reduced the incidence of yellow stem borer and pink stem borer by 40.3–63.3% and 39.8–51.7%in rice and wheat, respectively. The soil available P and Si were also improved significantly with the application of S3. Therefore, the application of Si and P in aerobic rice-wheat cropping systems could be recommended for improving system crop productivity, SWP, SIWUE, besides improving soil health and crop resilience. [ABSTRACT FROM AUTHOR]

Due to the severe underwater environment, research into Underwater Wireless Sensor Networks is at its most undeveloped stage. Due to the restricted sensor batteries and significant variations in water parameters, like temperature, turbulence, flooding, pH, salinity, and others, this research is not as simple as it would seem. To increase node battery life and provide load balancing, several routing protocols are implemented. In this article, we propose the Effective Load Balancing with Sensor Injection routing method, which uses hop distance calculations to enable sensors to connect with sinks through other intermediary sensors. In order to communicate among sensors, localization-based routing protocol is employed. With the use of weight factor calculations, each node determines the minimal distance needed for connection to another node and calculates distance from the lowest hop node. This algorithm balances the load using the sensor injection approach. When weight factor of any node becomes high, it sends request to Network Management System (NMS) for load balancing. NMS directs Autonomous Underwater Vehicle for new sensor injection as neighbour of high factor node. Thus, by using this sensor injection method network lifetime gets increased. Simulation is carried out on NS 2.30 and improved results are obtained in comparison to existing schemes w.r.t., number of dead nodes and alive nodes at every round. [ABSTRACT FROM AUTHOR]

Thalassemia is among the most common hereditary disorders in the world. Approximately 5% of the world's population are carriers of hemoglobinopathies, and 2.9% are carriers of beta thalassemia. Haemoglobin A2 (HbA2) constitutes less than 3% of the total hemoglobin (Hb) in adults, and the determination of Hb A2 levels is important to diagnose the beta thalassemia trait (BTT). In some cases, the level of HbA2 is not typically elevated, and some difficulties may arise in making the diagnosis. Cation exchange high-performance liquid chromatography (HPLC) and HbCZE (haemoglobin capillary zone electrophoresis) are considered acceptable methods to diagnose BTT, but these vary in their accuracy and cut-offs. In this study, we attempted to compare HbA2 values using two methods, HPLC and HbCZE, in 536 whole blood samples sent by physician-ordered hemoglobinopathy screening over two years. This included antenatal women, patients with anemia not responding to iron, and cases of familial screening where either a child or a sibling had been diagnosed with hemoglobinopathy or thalassemia. The performance characteristics of both machines were compared for the detection of the 5 most common hemoglobin variants: Hb A, HbF, HbS, Hb C, and HbE. On comparing the HbA2 values, the HPLC showed higher values for HbA2 as compared to HbCZE, while the HbF and HbS measurement agreement was good between both methods. Normal ranges and mean normal values of HbA2 differ between different methods and different manufacturers; hence, each institute using these machines should validate its cutoffs. [ABSTRACT FROM AUTHOR]

This study investigates the ballistic performance of a ceramic composite target plate consisting of Alumina with the Dyneema fiber. The objective is to achieve a lightweight structure. Different thickness combinations of alumina/Dyneema targets were studied based on the areal density of the targets. Energy absorption criteria were used to determine the ballistic performance of the target. The ANSYS/LS-DYNA® code was used to conduct the finite-element numerical simulations. 12.7 mm projectile core and 7.62 mm projectile core was used to normally impact the target plate. The projectile impact velocities were in the range–500–887 m/s. First, the model is verified by comparing it with existing experimental studies. Subsequently, various thickness combinations of alumina with different numbers of layers of Dyneema were studied numerically to determine the effective thickness combination of the entire target. The initial kinetic energy of the target dissipated into the ceramic fracture, and the remaining energy in the deformation of the composite plate. Projectile erosion and the target's energy absorption are the determining factors of the study. [ABSTRACT FROM AUTHOR]

Microbial communities present in body fluids can assist in distinguishing between types of body fluids. Metagenomic studies have reported bacterial genera which are core to specific body fluids and are greatly influenced by geographical location and ethnicity. Bacteria in body fluids could also be due to bacterial infection; hence, it would be worthwhile taking into consideration bacterial species associated with diseases. The present review reports bacterial species characteristic of diseased and healthy body fluids across geographical locations, and bacteria described in forensic studies, with the aim of collating a set of bacteria to serve as the core species-specific markers for forensic body fluid identification. The most widely reported saliva-specific bacterial species are Streptococcus salivarius, Prevotella melaninogenica, Neisseria flavescens, with Fusobacterium nucleatum associated with increased diseased state. Lactobacillus crispatus and Lactobacillus iners are frequently dominant in the vaginal microbiome of healthy women. Atopobium vaginae, Prevotella bivia, and Gardnerella vaginalis are more prevalent in women with bacterial vaginosis. Semen and urine-specific bacteria at species level have not been reported, and menstrual blood bacteria are indistinguishable from vaginal fluid. Targeting more than one bacterial species is recommended for accurate body fluid identification. Although metagenomic sequencing provides information of a broad microbial profile, the specific bacterial species could be used to design biosensors for rapid body fluid identification. Validation of microbial typing methods and its application in identifying body fluids in a mixed sample would allow regular use of microbial profiling in a forensic workflow. [ABSTRACT FROM AUTHOR]

In the finishing phase of the conventional manufacturing process of foams, the outer skin of the foam is usually removed. In the present investigation, the effect of outer skin is considered to understand its effect on the impact-resistance and energy absorption behavior of the foam. An assessment of the impact-resistance of various foams is carried out using numerically simulated drop-weight impact hammer test. Here, six different types of foam materials are considered for investigation of their energy absorption characteristics, assessed for understanding the behavior of the foams with-skin and without-skin. The effect of density and variation in drop-weight height is also investigated. The criteria chosen for the evaluation of the comparative study include reaction force-time history, displacement-time history, the variation in the reaction force with displacement, and specific energy absorption (SEA). Additionally, to compare the different foam materials, the obtained peak responses of the above-considered criteria are normalized based on the respective specific mass of the foam. Based on this study, it is concluded that the presence of skin significantly improved the impact-resistance for most of the foams considered herein. Further, an effective way to choose a foam material for the target application is also discussed. Moreover, polymeric syntactic foam, with a density of 220 kg/m3, showed relatively superior behavior than other foams considered in the present investigation. [ABSTRACT FROM AUTHOR]

Ultrasound features related to thyroid lesions structure, shape, volume, and margins are considered to determine cancer risk. Automatic segmentation of the thyroid lesion would allow the sonographic features to be estimated. On the basis of clinical ultrasonography B-mode scans, a multi-output CNN-based semantic segmentation is used to separate thyroid nodules' cystic & solid components. Semantic segmentation is an automatic technique that labels the ultrasound (US) pixels with an appropriate class or pixel category, i.e., belongs to a lesion or background. In the present study, encoder-decoder-based semantic segmentation models i.e. SegNet using VGG16, UNet, and Hybrid-UNet implemented for segmentation of thyroid US images. For this work, 820 thyroid US images are collected from the DDTI and ultrasoundcases.info (USC) datasets. These segmentation models were trained using a transfer learning approach with original and despeckled thyroid US images. The performance of segmentation models is evaluated by analyzing the overlap region between the true contour lesion marked by the radiologist and the lesion retrieved by the segmentation model. The mean intersection of union (mIoU), mean dice coefficient (mDC) metrics, TPR, TNR, FPR, and FNR metrics are used to measure performance. Based on the exhaustive experiments and performance evaluation parameters it is observed that the proposed Hybrid-UNet segmentation model segments thyroid nodules and cystic components effectively. [ABSTRACT FROM AUTHOR]

Encoder-decoder-based semantic segmentation models classify image pixels into the corresponding class, such as the ROI (region of interest) or background. In the present study, simple / dilated convolution / series / directed acyclic graph (DAG)-based encoder-decoder semantic segmentation models have been implemented, i.e., SegNet (VGG16), SegNet (VGG19), U-Net, mobileNetv2, ResNet18, ResNet50, Xception and Inception networks for the segment TTUS(Thyroid Tumor Ultrasound) images. Transfer learning has been used to train these segmentation networks using original and despeckled TTUS images. The performance of the networks has been calculated using mIoU and mDC metrics. Based on the exhaustive experiments, it has been observed that ResNet50-based segmentation model obtained the best results objectively with values 0.87 for mIoU, 0.94 for mDC, and also according to radiologist opinion on shape, margin, and echogenicity characteristics of segmented lesions. It is noted that the segmentation model, namely ResNet50, provides better segmentation based on objective and subjective assessment. It may be used in the healthcare system to identify thyroid nodules accurately in real time. [ABSTRACT FROM AUTHOR]

Three-dimensional numerical simulations were carried out using ANSYS-LSDYNA® to know the ballistic performance of the ceramic/metal composite armour structure when subjected to normal impact of 7.62 and 12.7 mm API projectiles. Two distinct ceramic, boron carbide (B4C) and alumina (Al2O3) having backing plates of 6061-T6 aluminum were considered as target materials. Keeping total equivalent thickness constant, 15 mm, the thickness of ceramic and the backing plate was varied in combination of 3–12, 6–9, 7.5–7.5, 9–6, and 12–3 mm. The velocity of both the projectiles was varied in between 600–800 m/s. The obtained numerical results were first validated with the available experimental results and subsequently the ballistic resistance of ceramic/metal targets were compared with the ballistic resistance of Armox-500T steel against identical projectiles. The ballistic performance of the above targets was explored with the residual kinetic energy of the projectile, projectile erosion and kinetic energy absorbed by the target. The kinetic energy absorbed by the target was shown to dissipate into the fragments that leave the ceramic and in plastic deformation. Two different methodologies, smoothed particle hydrodynamics (SPH) and finite element analysis (FEA) were considered to carry out the numerical simulation to know their ability to predict the ballistic phenomenon. It was found that SPH simulation can properly predict the impact between target and projectile and showed good prediction accuracy. The highest resistance was obtained for boron carbide having 7.5 mm thickness closely followed by 9 mm boron carbide thickness. Target configuration with alumina showed inferior performance as compared to boron carbide target. The results showed that the thickness of front ceramic plate had a significant effect on the projectile erosion. Maximum projectile erosion of 30.6% and 10% were observed for 7.62 and 12.7 mm API, respectively, at velocity of 800 m/s impact velocity. [ABSTRACT FROM AUTHOR]

Primary splenic lymphomas are rare with the majority of lymphomas in spleen being secondary to an extra-splenic lymphoma. We aimed to analyze the epidemiological profile of the splenic lymphoma and review the literature. This was a retrospective study including all splenectomies and splenic biopsies from 2015 to September 2021. All the cases were retrieved from Department of Pathology. Detailed histopathological, clinical and demographic evaluation was done. All the lymphomas were classified according to WHO 2016 classification. A total of 714 splenectomies were performed for a variety of benign causes, as part of tumor resections and for the diagnosis of lymphoma. Few core biopsies were also included. A total of 33 lymphomas diagnosed in the spleen, primary splenic lymphomas constituted 84.84% (n = 28) of the cohort with 5 (15.15%) having the primary site elsewhere. The primary splenic lymphomas constituted 0.28% of all the lymphomas arising at various sites. Adult population (19–65 years) formed the bulk (78.78%) with a slight male preponderance. Splenic marginal zone lymphomas (n = 15, 45.45%) comprised of major proportion of cases followed by primary splenic diffuse large B-cell lymphoma (n = 4, 12.12%). Splenectomy was the main course of treatment for SMZL with a good overall outcome, with chemotherapy ± radiotherapy forming the mainstay in other lymphomas. Lymphomas in spleen can be infiltrative or a primary, hence proper clinic-radiological and pathological evaluation is required. Appropriate management is guided by the precise and detailed evaluation by the pathologist, requiring understanding of the same. [ABSTRACT FROM AUTHOR]

Sandwich panels have wide applicability in blast- and impact-resistant design of structures due to their phenomenal energy absorption capacity, albeit being considerably light in weight. A sandwich panel consists of core(s) and facesheets, wherein the core contributes majorly to the energy absorption capacity and the facesheets contribute the stiffness of the sandwich panel. The dynamic response of sandwich panel subjected to blast and/or impact loads can be obtained with the help of analytical solutions, experiments/tests, and finite element analyses. In this article, the influence of the variations in the geometry and material of the different components of a sandwich panel, on the overall dynamic response of the panel is reported extensively based on the studies conducted so far. Moreover, a comprehensive review is conducted on the behavior of sandwich panels with respect to the various shapes of cores and facesheets, different types of core grading, and the use of stiffeners. This state-of-the-art review also gives a clear insight on the various available and widely used experiment/test setups and finite element tools for conducting blast and impact test/analysis on sandwich panels. [ABSTRACT FROM AUTHOR]

Recent events have highlighted large-scale systemic racial disparities in U.S. criminal justice based on race and other demographic characteristics. Although criminological datasets are used to study and document the extent of such disparities, they often lack key information, including arrestees' racial identification. As AI technologies are increasingly used by criminal justice agencies to make predictions about outcomes in bail, policing, and other decision-making, a growing literature suggests that the current implementation of these systems may perpetuate racial inequalities. In this paper, we argue that AI technologies should be investigated to understand how they recognize racial categories and whether they can be harnessed to fill missing race data. By bridging this gap, we can work towards better understanding racial inequalities in a wide range of contexts, most notably criminal justice. Using a multidisciplinary perspective, we rethink the design and methodology used in facial processing technology (FPT) based on supervised deep learning model (DLM) image classification. By modifying standard FPT pipelines to tackle multiple sources of DLM bias, we propose an experimental methodology based on ethical AI principles to generate binary (Black and White) racial categories using mugshots. We go beyond simply reporting DLM accuracies and address fundamental issues such as generalizability and interpretability by using a "self-auditing" approach. First, we evaluate the inference performances of 42 fine-tuned DLMs using unseen test images from the same dataset but subject to varying data augmentations. Next, to interpret and validate our methodological approach, we apply gradient-based saliency maps to assess the consistency of facial region relevance and attribution. Finally, drawing upon insights from three areas (computer science, sociology, and law), we investigate the efficacy of our DLM-based method as a tool for detecting racial inequalities in criminal justice. [ABSTRACT FROM AUTHOR]

Gene drive-modified mosquitoes (GDMMs) are being developed as possible new tools to prevent transmission of malaria and other mosquito-borne diseases. To date no GDMMs have yet undergone field testing. This early stage is an opportune time for developers, supporters, and possible users to begin to consider the potential regulatory requirements for eventual implementation of these technologies in national or regional public health programs, especially as some of the practical implications of these requirements may take considerable planning, time and coordination to address. Several currently unresolved regulatory questions pertinent to the implementation of GDMMs are examined, including: how the product will be defined; what the registration/approval process will be for placing new GDMM products on the market; how the potential for transboundary movement of GDMMs can be addressed; and what role might be played by existing multinational bodies and agreements in authorization decisions. Regulation and policies applied for registration of other genetically modified organisms or other living mosquito products are assessed for relevance to the use case of GDMMs to prevent malaria in Africa. Multiple national authorities are likely to be involved in decision-making, according to existing laws in place within each country for certain product classes. Requirements under the Cartagena Protocol on Biodiversity will be considered relevant in most countries, as may existing regulatory frameworks for conventional pesticide, medical, and biocontrol products. Experience suggests that standard regulatory processes, evidence requirements, and liability laws differ from country to country. Regional mechanisms will be useful to address some of the important challenges. [ABSTRACT FROM AUTHOR]

The size of the esophageal hiatus is clinically important for preserving the integrity of the lower esophageal sphincter mechanism. The purpose of this study was to systematically establish the mean hiatal surface area (HSA) for normal North American adults under physiologic conditions and assess the relationship between sex and age on HSA. Multi-Detector Computer Tomogram (MDCT) images of the esophageal hiatus in 119 healthy adult subjects (61 males and 58 females with an age range of 24–88 years) were retrospectively analyzed using the multi-planar reconstruction (MPR) technique to directly measure their hiatal length (long axis), width (short axis) and surface area at end inspiration. The mean HSA for males was 2.88 cm2, with a standard deviation of 0.74 cm2. The mean HSA for females was 2.51 cm2, with a standard deviation of 0.68 cm2. There was a statistically significant difference in HSA between males and females (p = 0.0053); however, there was no statistically significant difference between the HSA among different age groups (p = 0.8439). Similarly, significant differences between males and females were demonstrated in both the length (p = 0.0263) and width (p = 0.0311) measurements, but there was no evidence of an association of these parameters with age. For the first time, the normal size of the hiatus at end inspiration has been established noninvasively for a population of healthy adults from MDCT images. [ABSTRACT FROM AUTHOR]