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Data temperature is a response to the ever-growing amount of data. These data have to be stored, but they have been observed that only a small portion of the data are accessed more frequently at any one time. This leads to the concept of hot and cold data. Cold data can be migrated away from high-performance nodes to free up performance for higher priority data. Existing studies classify hot and cold data primarily on the basis of data age and usage frequency. We present this as a limitation in the current implementation of data temperature. This is due to the fact that age automatically assumes that all new data have priority and that usage is purely reactive. We propose new variables and conditions that influence smarter decision-making on what are hot or cold data and allow greater user control over data location and their movement. We identify new metadata variables and user-defined variables to extend the current data temperature value. We further establish rules and conditions for limiting unnecessary movement of the data, which helps to prevent wasted input output (I/O) costs. We also propose a hybrid algorithm that combines existing variables and new variables and conditions into a single data temperature. The proposed system provides higher accuracy, increases performance, and gives greater user control for optimal positioning of data within multi-tiered storage solutions.

Abstract Virtual and augmented reality digital twins are becoming increasingly prevalent in a number of industries, though the production of digital-twin systems applications is still prohibitively expensive for many smaller organisations. A key step towards reducing the cost of digital twins lies in automating the production of 3D assets, however efforts are complicated by the lack of suitable automated methods for determining the visual quality of these assets. While visual quality assessment has been an active area of research for a number of years, few publications consider this process in the context of asset creation in digital twins. In this work, we introduce an automated decimation procedure using machine learning to assess the visual impact of decimation, a process commonly used in the production of 3D assets which has thus far been underrepresented in the visual assessment literature. Our model combines 108 geometric and perceptual metrics to determine if a 3D object has been unacceptably distorted during decimation. Our model is trained on almost 4, 000 distorted meshes, giving a significantly wider range of applicability than many models in the literature. Our results show a precision of over 97% against a set of test models, and performance tests show our model is capable of performing assessments within 2 minutes on models of up to 25, 000 polygons. Based on these results we believe our model presents both a significant advance in the field of visual quality assessment and an important step towards reducing the cost of virtual and augmented reality-based digital-twins.

Objectives: Advances in perioperative management for thoracic surgery have accelerated the postoperative recovery of patients by decreasing postoperative pain and the incidence of complications. We aimed to study whether it's safe to remove chest drains on table in selected cases. Methods: This was a 5-year retrospective analysis of protocolized chest-drain removal on the operating table. The chest drain was removed in patients undergoing sublobar/wedge lung resection and other minor thoracic procedure (pleural biopsy, mediastinal mass biopsy/resection) via a thoracoscopic approach (video-assisted thoracoscopic surgery). Chest drains were removed at the end of the operation if air leak as documented by the digital drain was less than 20 mL/min. Outcome data on postdrain removal pneumothorax, effusion, and need for further intervention were obtained by reviewing the postoperative chest films, all reported by a radiologist. Results: Between 2016 and 2021, 107 patients underwent drain removal in theater. Mean age (standard deviation) was 58 (17) years and 54 (50.5%) were male. Postdrain removal pneumothorax occurred in 22 patients (21%), pleural effusion in 6 (5.6%), and 21 of 22 postoperative pneumothoraces were managed conservatively without reinsertion of chest drain. As it is our standard policy to leave no pneumothorax in patients undergoing surgical management of primary spontaneous pneumothorax, only 1 such patient (0.9%) had a drain reinserted as a result. The median (interquartile) length of hospital stay was 1 day (1-2), and 14 patients (13%) were discharged on surgery day. Conclusions: Our results demonstrate that on table chest-drain removal in selected cases is safe and repeatable using a digital drain, challenging the practice of routine drain insertion after thoracic surgery.

Quasi-Delay Insensitive (QDI) asynchronous circuits, such as NULL Convention Logic (NCL), are being utilized more and more in industry to mitigate timing issues associated with process, voltage, and temperature (PVT) variations, which are making timing closure for synchronous circuits more problematic as transistor feature size continues to shrink. This paper combines aspects of relaxation optimization with utilizing the NCL_X architecture for select portions of the circuit, resulting in an integrated optimization method for NCL circuits that achieves an average speedup of 6.8% to 12.5%, using on average 24.6% fewer to 3.7% more transistors, compared to existing NCL synthesis and optimization methods.

The increasing demand for high-speed, energy-efficient, and miniaturized electronics has led to significant challenges and compromises in the domain of conventional clock-based digital designs, most notably reduced circuit reliability, particularly in mission-critical hardware. At scaled technology nodes, devices are vulnerable to transient or soft errors, such as Single Event Upset (SEU) and Single Event Latch-up (SEL). External radiation, internal electromagnetic interference (EMI), or noise are the primary sources of these errors, which can compromise the circuit functionality. In response to these challenges, the Quasi-Delay-Insensitive (QDI) Null Convention Logic (NCL) asynchronous design paradigm has emerged as a promising alternative, offering advantages such as ultra-low power performance, reduced noise and EMI, and resilience to process, voltage, and temperature variations. Moreover, its unique architecture and insensitivity to timing variations offers a degree of resistance against transient errors; however, it is not entirely resilient. Several resiliency schemes are available to detect and mitigate soft errors in QDI circuits, with approaches based on redundancy proving to be the most effective in ensuring complete resilience across all major QDI implementation paradigms, including NCL, Pre-charge/Weak-charge Half Buffers (PCHB/WCHB), and Sleep Convention Logic (SCL). This research focuses on one such redundancy-based resiliency scheme for QDI NCL circuits, known as the dual-modular redundancy-based NCL (DMR-NCL) architecture, and addresses the absence of formal methods for the verification and analysis of such circuits. A novel methodology has been proposed for formally verifying the correctness of DMR-NCL circuits synthesized from their synchronous counterparts, covering both safety (functional correctness) and liveness (the absence of deadlock). In addition, this research introduces a formal framework for the vulnerability analysis of DMR-NCL circuits against SEU/SEL. To demonstrate the framework’s efficacy and scalability, a prototype computer-aided support tool has been developed, which verifies and analyzes multiple DMR-NCL benchmark circuits of varying sizes and complexities.

Abstract As the number of devices connected to the Internet of Things (IoT) increases significantly, it leads to an exponential growth in the number of services that need to be processed and stored in the large-scale Cloud-based service repositories. An efficient service indexing model is critical for service retrieval and management of large-scale Cloud-based service repositories. The multilevel index model is the state-of-art service indexing model in recent years to improve service discovery and combination. This paper aims to optimize the model to consider the impact of unequal appearing probability of service retrieval request parameters and service input parameters on service retrieval and service addition operations. The least-used key selection method has been proposed to narrow the search scope of service retrieval and reduce its time. The experimental results show that the proposed least-used key selection method improves the service retrieval efficiency significantly compared with the designated key selection method in the case of the unequal appearing probability of parameters in service retrieval requests under three indexing models.

The synchronous design paradigm dominates today’s semiconductor industry. However, this clocked approach is facing major challenges with today’s high-speed, low-power design expectations, using processes with ever-increasing physical level variability. Several clock related issues surface in designs operating at higher frequencies, which make clock management increasingly difficult. Quasi-delay insensitive (QDI) asynchronous (clockless) designs have proved to be effective in circumventing the major limiting factors associated with the clocked designs. NULL Convention Logic (NCL) is one such QDI asynchronous design paradigm, which presents itself as a promising alternative to conventional synchronous circuits and has already found numerous commercial applications due to its low power, robust architecture, and ease of design reuse. This paper presents the evolution of NCL based asynchronous paradigm over the past two decades, primarily focusing on existing fundamental research in NCL design automation, spanning over NCL synthesis, optimization, testing, and verification. The methods are systematically analyzed to determine their limitations and future research directions.

Drawing upon signaling theory we investigate the impact of holding firm risk spread on foreign subsidiary's TMT gender diversity. Signaling theory conceptualizes that TMT gender diversity is a legitimacy-seeking strategy. The sample consists of Chinese 123 foreign subsidiaries from 19 countries from 2010 to 2019. Using System GMM estimation technique to investigate the impact of holding firm’s risk spread on foreign subsidiaries TMT gender diversity. The study results suggest that the holding firm’s risk spread has a positive and significant impact on foreign subsidiary TMT gender diversity. Thus, against higher holding firms’ risk, the foreign subsidiaries use the TMT gender diversity strategy to transmit signals to get legitimacy and buffer the spread effect. Based upon our study results the international business policymakers are suggested to use corporate governance strategies against holding firms’ risk spread to get legitimacy in the market.

Dental plaques are notorious and lead to dental caries responsible for dental decay. Streptococci are the leading microorganisms associated with dental plaques. These are Gram-positive, normal microbial flora, nonmotile, non-spore forming, and facultative anaerobes. These include Alpha, Beta, and Gamma hemolytic species. Streptococcus sp. produces a high amount of lactic acid through the fermentation of sugars, causes lowering of the pH leading to the plaque formation around teeth, and serves as a biofilm. Microbial biofilm provides certain attachment sites for growth and colonization of other bacteria, and also causes resistance to the antimicrobial agents. These Streptococci can be transmitted to the infants through parents or caretakers' kiss. This mode of transmission is the key role for the contribution of S. mutans in dental caries. In Pakistan, a national survey was conducted in 1992 on tooth decay, which showed that two teeth per person were decayed, missing, or filled, thus becomes an alarming situation. Minimal knowledge related to tooth decay is observed in the clinical settings; however, no similar studies have been carried out in Pakistan.

Aflatoxins are the secondary metabolites produced by Aspergillus flavus and Aspergillus parasiticus and have severe pathological effects on the health of human and animals. The present study was designed to investigate the toxicopathological changes induced by aflatoxins and mitigative potential of Lactobacillus plantarum in broiler birds. One hundred and eighty broiler chicks at one day of age was procured from the local market, and chicks were equally divided into six groups with thirty birds in each group. These birds were treated with aflatoxins (300 and 600 µg/kg) and Lactobacillus plantarum (1 × 108 cfu/kg of feed) in different combinations. The first group was kept as the control, and only a basal diet was provided to birds (BD). In the second group (AF1), the first level of aflatoxins (300 µg/kg) was fed to the birds. In the third group (AF2), the second level of aflatoxins (600 µg/kg) was fed to birds. In the fourth group (AF1LP), Lactobacillus plantarum was given with first level of aflatoxins. In the fifth group (AF2LP), Lactobacillus plantarum was given with the second level of aflatoxins, and in the 6th group (BDLP), Lactobacillus plantarum alone was fed to the chicks. This experimental study was continued for 42 days. Birds were slaughtered after 42 days, and different parameters were assessed. Parameters studied were gain in body weight, organ weight along with some histopathological, hematological, biochemical parameters and residues of aflatoxins in liver and kidney. Lactobacillus plantarum improved the body weight gain and restored the relative organ weight. Hepatic and renal biomarkers returned to normal concentrations, serum proteins were restored in combination group AF1LP, and partial amelioration was observed in the AF2LP group. Red blood cells, white blood cells, hemoglobin centration and packed cell volume became normalized in the AF1LP group, while partial amelioration was observed in the AF2LP group. LP also reduced the concentration of aflatoxin residues in liver kidney and improved the TAC concentrations. The results of this study elucidated the mitigative potential of Lactobacillus plantarum against serum biochemical, histopathological, hematological and toxicopathological changes induced by aflatoxins in the chicks.

Automatic and intelligent railway locomotive inspection and maintenance are fundamental issues in high-speed rail applications and intelligent transportation system (ITS). Traditional locomotive equipment inspection is carried out manually on-site by workers, and the task is exhausting, cumbersome, and unsafe. Based on computer vision and machine learning, this paper presents an approach to the automatic detection of the locomotive speed sensor equipment, an important device in locomotives. Challenges to the detection of speed sensor mainly concerns complex background, motion blur, muddy noise, and variable shapes. In this paper, a cascade learning framework is proposed, which includes two learning stages: target localization and speed sensor detection, to reduce the complexity of the research object and solve the imbalance of samples. In the first stage, histogram of oriented gradient feature and support vector machine (HOG-SVM) model is used for multi-scale detection. Then, an improved LeNet-5 model is adopted in the second stage. To solve the problem of the imbalance of positive and negative samples of speed sensor, a combination strategy which draws on four individual classifiers is designed to construct an ensemble of classifier for recognition, and the results of three different algorithms are compared. The experimental results demonstrate that our approach is effective and robust with respect to changes in speed sensor patterns for robust equipment identification.

In this article, fifth order finite volume multi-resolution weighted essentially non-oscillatory (MR-WENO) scheme is developed for solving one-dimensional non linear viscous quantum hydrodynamical model for semiconductor devices with constant temperature. This non linear model consists of three equations, two equations for the current density and electron density, and third equation is the Poisson equation. The first two equations include the viscous and quantum correction terms. Further, numerical technique is used to obtain the solution due to high non linearity of considered model. The multi-resolution technique is used to reduce the computational cost of high resolution numerical scheme. In the procedure of derivation of the MR-WENO scheme unequal central spatial stencils are used and linear weights can be chosen any positive numbers with only restriction that their total sum is one. Various numerical test problems are considered to check the validity and accuracy of the derived numerical scheme. Further, the results obtained from considered numerical scheme are compared with those of kinetic flux vector splitting numerical scheme.

Recent developments in cloud computing and the Internet of Things have enabled smart environments, in terms of both monitoring and actuation. Unfortunately, this often results in unsustainable cloud-based solutions, whereby, in the interest of simplicity, a wealth of raw (unprocessed) data are pushed from sensor nodes to the cloud. Herein, we advocate the use of machine learning at sensor nodes to perform essential data-cleaning operations, to avoid the transmission of corrupted (often unusable) data to the cloud. Starting from a public pollution dataset, we investigate how two machine learning techniques (kNN and missForest) may be embedded on Raspberry Pi to perform data imputation, without impacting the data collection process. Our experimental results demonstrate the accuracy and computational efficiency of edge-learning methods for filling in missing data values in corrupted data series. We find that kNN and missForest correctly impute up to 40% of randomly distributed missing values, with a density distribution of values that is indistinguishable from the benchmark. We also show a trade-off analysis for the case of bursty missing values, with recoverable blocks of up to 100 samples. Computation times are shorter than sampling periods, allowing for data imputation at the edge in a timely manner.

In this article, the space-time conservation element and solution element scheme is extended to simulate the unsteady compressible two-phase flow in pipes. The model is non-conservative and the governing equations consist of three equations, namely, two mass conservation equations for each phase and one mixture-momentum equation. In the third equation, the non-conservative source term appears, which describes the sum of gravitational and frictional forces. The presence of source term and two mass conservation equations in considered model offers difficulties in developing the accurate and robust numerical techniques. The suggested space-time conservation element and solution element numerical scheme resolves the volume-contact discontinuities efficiently. Furthermore, the modified central upwind scheme is also extended to solve the same two-phase flow model. The number of test problems is considered, and the results obtained by space-time conservation element and solution element scheme are compared with the solutions of modified central upwind scheme. The numerical results show better performance of the space-time conservation element and solution element method as compare to the modified central upwind scheme.

Abstract With the rapid increase of users over social media, cyberbullying, and hate speech problems have arisen over the past years. Automatic hate speech detection (HSD) from text is an emerging research problem in natural language processing (NLP). Researchers developed various approaches to solve the automatic hate speech detection problem using different corpora in various languages, however, research on the Urdu language is rather scarce. This study aims to address the HSD task on Twitter using Roman Urdu text. The contribution of this research is the development of a hybrid model for Roman Urdu HSD, which has not been previously explored. The novel hybrid model integrates deep learning (DL) and transformer models for automatic feature extraction, combined with machine learning algorithms (MLAs) for classification. To further enhance model performance, we employ several hyperparameter optimization (HPO) techniques, including Grid Search (GS), Randomized Search (RS), and Bayesian Optimization with Gaussian Processes (BOGP). Evaluation is carried out on two publicly available benchmarks Roman Urdu corpora comprising HS-RU-20 corpus and RUHSOLD hate speech corpus. Results demonstrate that the Multilingual BERT (MBERT) feature learner, paired with a Support Vector Machine (SVM) classifier and optimized using RS, achieves state-of-the-art performance. On the HS-RU-20 corpus, this model attained an accuracy of 0.93 and an F1 score of 0.95 for the Neutral-Hostile classification task, and an accuracy of 0.89 with an F1 score of 0.88 for the Hate Speech-Offensive task. On the RUHSOLD corpus, the same model achieved an accuracy of 0.95 and an F1 score of 0.94 for the Coarse-grained task, alongside an accuracy of 0.87 and an F1 score of 0.84 for the Fine-grained task. These results demonstrate the effectiveness of our hybrid approach for Roman Urdu hate speech detection.

Abstract Lotus (Nelumbo nucifera, G.) is a perennial aquatic plant, which is utilized for a variety of therapeutic and food applications. Muffins are a kind of baking product that is typically consumed for breakfast or as a snack. Currently, many baked goods in the market use fat replacers to lower the overall fat content, while conserving consumer approval. The study's objectives were to produce low-fat muffins and to characterize the types of quality changes that occurred in the muffins. Lotus rhizome was sliced, dried and converted into powder, which used as fat substitute in muffins, at 0, 5, 10, 15 and 20% replacement levels, with names as M0, M1, M2, M3 and M4, respectively. Composite flours having these proportions of lotus Rhizome Powder (LRP) were analyzed for proximate and rheological parameters. Increasing levels of LRP caused increased water absorption and dough development time of the composite flours. Proximate analyses of muffins showed highest value of moisture (25.05%), ash (2.43%), fiber (0.99%), protein (11.89%) and NFE (58.45%) in M4. Whereas, fat content and energy value were significantly reduced in M4, as highest fat was found to be in M0 (36.65%) and highest calories in M0 (576.5 kcal), which were significantly reduced to 26.24% and 514.5 kcal, respectively in M4. High yeast and mold count was of M0 at first day as 98 CFU/g, which was increased to 129 CFU/g at day 7, whereas M4 presented this count to be 44 CFU/g at day one, which was increased to 70.33 CFU/g at day 7. Texture analysis showed softest texture of M4 as firmness value was 1887 g, which was significantly high in M0 as 2285 g. The research revealed that M3 best performed out of all treatments as 15% replacement of fat with LRP significantly improved the rheological properties of the flour, ash and fiber content were high, whereas fat contents were significantly low in these muffins. Textural and sensory analysis also supported the M3 due to softer texture and significantly high color, appearance, flavor, and overall acceptability scores. From the results of this study, it could be concluded that a 15% replacement of shortenings with LRP could be considered as optimum for developing good quality baked muffins. Graphical Abstract

The use of information and communication technologies (ICTs) has considerably increased among students, including students with vision impairment (SVI). There has been limited work on the information and digital needs of people with disabilities, particularly SVI who were studying for their masters or MPhil and were involved in research activities in Pakistan. This study is an effort to explore various obstacles, expected services and support for SVIs from their universities, libraries, and Higher Education Commission (HEC) Pakistan. This study is qualitative in nature, and an interview guide was used to collect data from the participants in Lahore, Pakistan. The results of this study showed that students with vision impairment use a variety of digital information resources. However, very few use the higher education commission of Pakistan subscribed databases due to restricted access and complex interfaces. The hindrances include lack of university support, limited training opportunities, non-availability of digital library services, and others. The participants highlighted a variety of needed library services and support from their departmental as well as central libraries. It is assumed that the results of this study will help the relevant authorities to make friendly policies and improve library services to facilitate the SVI.

In early December 2019, the coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) first emerged in Wuhan, China. As of May 10th, 2020, a total of over 4 million COVID-19 cases and 280,000 deaths have been reported globally, reflecting the raised infectivity and severity of this virus. Amongst hospitalised COVID-19 patients, there is a high prevalence of established cardiovascular disease (CVD). There is evidence showing that COVID-19 may exacerbate cardiovascular risk factors and preexisting CVD or may lead to cardiovascular complications. With intensive care units operating at maximum capacity and such staggering mortality rates reported, it is imperative during this time-sensitive COVID-19 outbreak to identify patients with an increased risk of adverse outcomes and/or myocardial injury. Preliminary findings from COVID-19 studies have shown the association of biomarkers of acute cardiac injury and coagulation with worse prognosis. While these biomarkers are recognised for CVD, there is emerging prospect that they may aid prognosis in COVID-19, especially in patients with cardiovascular comorbidities or risk factors that predispose to worse outcomes. Consequently, the aim of this review is to identify cardiovascular prognostic factors associated with morbidity and mortality in COVID-19 and to highlight considerations for incorporating laboratory testing of biomarkers of cardiovascular performance in COVID-19 to optimise outcomes.