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Anomaly detection in power systems is crucial for operational reliability and safety, often addressed through binary classification in existing research. However, a research gap exists in multiclass classification for enhanced reliability. To bridge this gap, this study employs four machine learning (ML) classifiers: Random Forest (RF), Decision Tree, Naive Bayes (NB), and Support Vector Machine (SVM) using comprehensive testing on a dataset comprising sixteen indices and their pair combinations (totaling 136 pairs). These classifiers, trained on a dataset derived from simulating a test system with hybrid DGs, exhibit superior anomaly detection, especially with the $\frac {dv}{dq}\& \frac {dv}{dp}$ pair. Among them, RF and DT classifier achieves precision, recall, and F score of unity and outperforming NB and SVM. The performance of the proposed RF and DT classifiers with $\frac {dv}{dq}\& \frac {dv}{dp}$ pair is compared with existing research papers in terms of accuracy and data division. The comparison shows that the proposed RF and DT classifiers with $\frac {dv}{dq}\& \frac {dv}{dp}$ pair achieve 100% accuracy even with 50% data division, whereas other techniques fail to achieve it even at 20% for testing and 80% for training. The study underscores the critical role of pair selection and classifier combinations in effective anomaly detection, facilitating the implementation of robust mitigating strategies for power system stability.

Background: Medical students are confronted with substantial academic and psychological stressors during their education. Studies report that examination stress can have a noteworthy effect on the leukocyte count of students. This study aimed to evaluate the physiological effects of examination stress on the total and differential leucocyte cell count in medical students at Peoples University of Medical and Health Sciences for Women, Nawabshah. Methods: A cross-sectional comparative study was conducted from April to October 2019, with 110 MBBS students, divided into two groups of 55 students using a non-probability randomized sampling technique with first-year and second-year female students, whereas hypertensive, diabetic, and with any psychiatric disease were excluded. Blood samples were taken before and during the examination. Data were analyzed by SPSS version 23. All the continuous variables were analyzed by the t-test for Mean±S.D while all the numerical variables were analyzed by Student’s t-test. Results: Pre-exam complete blood count (CBC) test reports of first-year students showed Neutrophils, Lymphocytes, Eosinophil, and Monocytes cell counts of 69.2182, 28.5455, 1.61, 1.2545 cell /microliter whereas complete blood count during exams showed 69.909, 27.9636, 1.41and 1.2000 cells/ microliter. In the second-year student, complete blood count before and during the exam showed Neutrophils 66.5273 to 64.0182, Lymphocytes 31.236 to 30.036, Eosinophil 1.92 to 1.76, and Monocytes 1.473 to 1.364 cells/microliter, the decreased values showing that there is an influence of exam stress on white blood cells. Conclusion: Exams in medical schools can be stressful enough to induce a substantial decrease in blood cell parameters among medical students.

Abstract Date palm is an important domestic cash crop in most countries. Sudden Decline Syndrome (SDS) causes a huge loss to the crop both in quality and quantity. The literature reports the significance of early detection of disease towards preventive measures to improve the quality of the crop. The number of prevailing detection methods limits to consideration of a certain aspect of disease identification. This study proposes a new hybrid fuzzy fast multi-Otsu K-Means (FFMKO) algorithm integrating the date palm image enhancement, robust thresholding, and optimal clustering for significant disease identification. The algorithm adopts a multi-operator image resizing cost function based on image energy and the dominant color descriptor, the adaptive Fuzzy noise filter, and Otsu image thresholding combined with K-Means clustering enhancements. Besides, we validate the process with histogram equalization and threshold transformation towards enhanced color feature extraction of date palm images. The algorithm authenticates findings on a local dataset of 3293 date palm images and, on a benchmarked data set as well. It achieves an accuracy of 94.175% for successful detection of SDS that outperforms the existing similar algorithms. The impactful findings of this study assure the fast and authentic detection of the disease at an earlier stage to uplift the quality and quantity of the date palm and boost the agriculture-based economy.

This paper introduces a novel hybrid controller designed for a wind turbine power generation system (WTPGS) that utilizes a permanent magnet synchronous generator (PMSG). This hybrid controller combines the adaptability of an adaptive neuro-fuzzy inference system (ANFIS) with the simplicity of a proportional-integral (PI) controller. The PI controllers are traditionally used for stability and noise handling. ANFIS adds adaptability, making it more suitable to cope with the variable nature of wind energy. The primary objective of this hybrid strategy is to augment the overall control performance and reliability of PMSG-based WTPGS when encountered with continuous variable wind conditions. However, implementing the PI controller alone with the WTPGS often suffers from high overshoot and sluggish response in nonlinear systems like WTPGS. In contrast, ANFIS controllers offer superior performance to PI and other artificial intelligence controllers but are still susceptible to noise issues. In this paper, the proposed WTPGS system is designed in MATLAB/Simulink software where a hybrid controller (ANFIS-PI) is implemented in the machine-side converter (MSC) and grid-side converter (GSC) of a variable speed PMSG-based wind turbine to enhance its performance subjected to wind variations. The hybrid controller is implemented in such a way that the ANFIS controller is implemented in the outer layers while the PI controller is applied in the inner layers of both MSC and GSC. The simulation results for this hybrid controller in the MSC outperform those of the conventional PI controller. They demonstrate minimal overshooting and settling time, maintaining consistent stability even when subjected to various test signals at different intervals. Similarly, the GSC also surpasses conventional PI controllers, achieving a significant 6.4% reduction in maximum overshoot and a decrease of 4.36 seconds in settling time. This highlights its strong suitability for wind turbine applications.

This study investigates the influence of Withania somnifera root powder (WSRP) on different aspects of common carp (Cyprinus carpio), including growth, hematobiochemical parameters, antioxidant status, nonspecific immune response, and resilience to bacterial infections. Over a 60-day period, 180 common carp fingerlings (11.73 ± 0.52 g) were subjected to diets supplemented with increasing WSRP levels (0 (control), 1% (WSRP1), 2.5% (WSRP2.5), and 4% (WSRP4)). Nonspecific immune parameters were evaluated using serum samples collected at intervals of 0, 20, 40, and 60 days. After the feeding trial, the fish underwent experimental challenge with Aeromonas hydrophila, and relative percentage survival (RPS) was monitored for 14 days. The findings revealed a considerable (p 0.05) in serum glucose and cortisol levels compared to other groups. In conclusion, WSRP proves valuable for enhancing growth, blood parameters, antioxidant balance, immune response, and infection resistance in common carp, especially at 2.5%–4% dietary supplementation levels. In the future, it will be crucial to study the long-term effects of WSRP supplementation on fish health, as well as its potential for large-scale aquaculture and economic viability in the aquafeed industry.

Madam, Safety, cost-effectiveness, efficiency and equity are all components of quality healthcare. Key factors determining quality are’ and their families’ satisfactions and better disease outcomes.1 Doctors, paramedics, pharmacists, laboratory technicians, administrative staff, pharmaceutical companies, government agencies and policymakers are the caregivers who have an essential direct or indirect role in providing, sustaining and improving health care.1 A population's health is crucial to a state's stability and growth. Quality in healthcare has been a subject of discussion across the globe for decades, with developed nations contributing the majority of its measures and recommendations6. In contrast it is still battling to fully establish and function in emerging nations like Pakistan. The health system in Pakistan comprises primary, secondary and tertiary health centres. The latter are found in a few urban cities and are run by both the public and private sectors. Private bodies include profit as well as welfare-based organizations.2,3 According to the WHO, on average, there are 6.3 hospital beds per 10,000 population in Pakistan, which is below its recommendation and much less compared to other developing nations.2 Majority of the population reside in rural areas and thus their option in various illnesses is primary or secondary health unit.3 The major drawback in such centres is lack of attendance by doctors and paramedical staff, lack of competent clinician or general practitioner along with absence of equipment, state-of-the-art laboratory and latest treatment options, all contributing to delays in diagnosis and management.4,5 There is also a lack of patient registry, long-term follow-ups, appropriate and timely referrals to higher health care centre and disease discussion between clinicians.3 Due to such weakened basic units, many patients directly visit an urbanized tertiary health center, which is a major cause of overcrowding of our hospitals. Some break-through policies which could address this dilemma is giving incentives to the assigned staff, providing commute in case of long travels, improving the infrastructure and facilities within the centers, installation of biometrics and having regular audit of attendance, number of patients treated and medications provided. Conducting education seminars regarding work ethics would also contribute towards a professional working environment leading to better patient outcomes.3,4 Creating awareness regarding the responsibility and crucial role of general practitioners in primary and secondary health units along with recognition of their importance, would also help in diverting medical community towards opting for such roles.6 ---Continue

Background: Hereditary spherocytosis (HS) is characterized by spherocytes on the peripheral smear and heterogeneous clinical presentation (mild, moderate, moderate/severe, and severe) depending upon the severity of hemolytic anemia, jaundice, and splenomegaly. The underlying cause is defects in various red blood cell membrane proteins due to pathogenic mutations. Genotype-phenotype correlation is known. Clinical Description: We report a consanguineous family with a healthy daughter, multiple pregnancy losses due to fetal hydrops, and the current daughter. She was diagnosed with fetal anemia at 22 weeks gestation, developed fetal hydrops, was born premature and developed respiratory distress, severe anemia, severe jaundice, and postnatal hemolysis. Management: The case was managed since intrauterine life till date by multispecialty coordination. Intrauterine transfusions were given during pregnancy. In the neonatal period, she received surfactant for respiratory distress syndrome, double volume exchange transfusion and phototherapy for the jaundice, and repeated blood transfusions for the hemolytic anemia, that is still continuing. Rational and sequential investigations were planned and a systematic approach used. She was diagnosed with severe HS due to a homozygous likely pathogenic variant in the SPTA1 gene (affecting formation of spectrin), and heterozygous variant of uncertain significance in the ANK1 gene (that encodes ankyrin). At 3 years she has transfusion dependent hemolytic anemia, moderate splenomegaly, and age appropriate growth and development. Conclusions: HS is a rare but manageable (albeit challenging) cause of nonimmune hydrops fetalis. Genetic diagnosis allows precise management and family counselling.

Objectives: The study aimed to evaluate the impact of antenatal exposure of magnesium sulfate (MgSO4) on short- and long-term outcomes in preterm neonates born less than 32 weeks gestation. Methods: Single-center retrospective cohort study of 229 neonates born between 24 and 32 weeks gestation was conducted from January 2018 through December 2018 in a level III neonatal care unit in Kuwait. Antenatal MgSO4 exposure was collected from the medical records, and the indication was for neuroprotection effect. Brain MRI was done on 212 neonates (median gestational age 36 weeks), and brain injury was assessed using the Miller’s score. Neurodevelopmental outcome was assessed by Bayley-III scales of infant development at 36 months corrected age (N = 146). The association of exposure to MgSO4 with brain injury and neurodevelopmental outcomes was examined using multivariable regression analysis adjusting for gestational age at MRI and variables with p value

Career selection is one of the most important decisions in everyone's life. Being a student it's quite difficult to find the right career as the world is moving so fast and the competition level is too high so it is tough to choose the right job for anyone. According to the Council of Scientific and Industrial Research (CSIR) survey report, 40% of students are a bit confused about their career options. The productivity of human resources may reduce if someone chooses the wrong career. Therefore, we need an intelligent system that chooses your career based on a person's personality type. This study creates a personality profile of the person and suggests their best careers list according to their personality type after analysing the text written by the user such as a blog post, essay, or tweet. The main purpose of this project is to work on the text classification method, pre- processing of that dataset and convert it into main features and then train the model on a best-performed classification model. After applying various feature vector combinations and machine learning models that are detailed in this work, accuracies up to 93% were achieved.

The biofloc technology system (BFT) is considered to be one of the sustainable aquaculture systems, which is based on the principle of nutrient recycling with the addition of a carbon source to give dominance to heterotrophic microorganisms. The objective of this study was to evaluate the effect of sugar cane molasses and tapioca flour as carbon sources on the water quality, growth, hematology, immune status, and non-specific antioxidant status of Oreochromis juveniles. Methodologically, the experiment was carried out for 10 weeks on 225 juvenile Nile tilapia with initial body weights of 47.0 ± 1.3 g that were randomly distributed in 09 tanks (1000 L) with a stocking density of 25 tilapias per tank; the treatments were: BFT + SM (S molasses), BFT + TF tapioca flour (TF), and a control with no carbon source added. The control group was fed 100% feed, while the BFT experimental groups were fed microbial flocs along with 75% feed. The results revealed that the water quality parameters were affected by the carbon sources, but were adequate for normal fish welfare, and the biofloc volume was higher (28.94) with the TF carbon source. The growth performance, such as weight gain (98.61), survival (99.01), and improved feed conversion ratio (FCR) (1.69), was recorded in BFT + TF. Significant improvements in WBCs, HCT, HB, lymphocytes, plasma proteins, albumin, and non-specific immune factors (lysozyme activity, immunoglobulins levels, and ACH50) were observed in biofloc-reared fish with tapioca flour as the carbon source compared to the control and sugarcane molasses groups. Moreover, significant increases in catalase (CAT) and superoxide dismutase (SOD) were found in the biofloc-reared fish with different carbon sources. In conclusion, the use of BFT + TF was found to affect improving the water quality, growth, hematology, immunity, and antioxidant status of juvenile Tilapia.

The selection of a suitable job applicant from the pool of thousands applications is often daunting job for an employer. The categorization of job applications submitted in form of Resumes against available vacancy(s) takes significant time and efforts of an employer. Thus, Resume Classification System (RCS) using the Natural Language Processing (NLP) and Machine Learning (ML) techniques could automate this tedious process. Moreover, the automation of this process can significantly expedite and transparent the applicants’ screening process with mere human involvement. This experimental study presents an automated NLP and ML-based RCS that classifies the Resumes according to job categories with performance guarantees. This study employs various ML algorithms and NLP techniques to measure the accuracy of RCS and proposes a solution with better accuracy and reliability in different settings. To demonstrate the significance of NLP and ML techniques for RCS, the extracted features were evaluated on nine ML classification models namely Support Vector Machine - SVM (Linear, SGD, SVC and NuSVC), Naïve Bayes (Bernoulli, Multinomial and Gaussian), K-Nearest Neighbor (KNN), and Logistic Regression (LR). The Term-Frequency-Inverse-Document-Frequency (TF-IDF) feature representation scheme was proved suitable for RCS. The developed models were evaluated using the Confusion Matrix, F-Score, Recall, Precision, and overall Accuracy. The experimental results indicate that using the One-Vs-Rest-Classification strategy for this multi-class Resume classification task, the SVM class of Machine Learning classifiers performed better on the study dataset of over nine hundred sixty plus parsed resumes with more than 96% accuracy. The promising results suggest that NLP and ML techniques employed in this study could be used for developing an efficient RCS.

Honey is a high-demand product in many countries because it is high in nutritional value and rich in antioxidants. Thus, the demand for honey is increased. However, the productivity of honey is naturally lower than its demand. Therefore, honey has often become a target for adulteration. Adulteration of honey is a critical issue because the nutritional value of pure honey is reduced by adding cheap and easily available sweeteners, affecting the consumers’ health. Thus, investigating honey authenticity is popular among regulatory bodies, the food industry, retail sellers, and consumers. Several works have been done to predict the authenticity of honey using various physicochemical features. Few other works have also classified honey on the basis of geographical or botanical origin. However, previous studies have three major limitations. First, the existing studies used the imbalanced datasets, and the performance of these studies further needs attention. Second, as far as we know, no researcher has attempted to use machine learning approaches in investigating the adulteration of Pakistani honey. Finally, the dataset for predicting the authenticity of Pakistani honey is lacking. Therefore, this study proposes a novel classification model to address the aforementioned weaknesses by classifying the authenticity of Pakistani honey using machine learning algorithms and several physicochemical features. This work also presents three classification models systematically to classify the Pakistani honey into three levels. The first level classifies whether the honey is original or branded. The second level classifies the geographical origin. The botanical origin of honey is classified in the third level. Our experimental results show that the proposed features coupled with machine learning algorithms can predict the authenticity of Pakistani honey with outstanding results. We believe that our proposed work will be proved beneficial in reducing the adulteration of Pakistani honey.

Background: Drought stress at different growth stages significantly alters growth, yield, and quality traits of wheat. However, great variability exists among genotypes regarding their response to drought stress. Therefore, determining the impacts of drought stress on yield and quality traits would help to select the superior genotypes. Methods: This study investigated the effects of drought stress on wheat grain yield, chlorophyll, and protein contents. Fourteen (14) recently developed elite bread wheat (Triticum aestivum L.) genotypes were used in this study for evaluation under irrigated (full irrigation) and drought conditions (half of normal irrigation). The data relating to growth, yield and protein contents were recorded. Results: Significant differences (P ≤ 0.01) were noted among genotypes for all recorded traits. Drought stress significantly reduced the days to 50 % heading, days to 50 % maturity, grain filling, plant height (cm), number of spikes per m2, chlorophyll index (SPAD), peduncle length (cm), number of grains spike-1, thousand grain weight (g) and grain yield (kg ha−1). However, protein contents were increased under drought stress. Correlation analysis showed significant positive association of grain yield with thousand grain weight, number of spikes per m2, spike length, chlorophyll index, grain filling period and number of grains spike-1 under both irrigated and drought stress conditions. The protein contents expressed positive and negative relationship with yield under drought stress and irrigated conditions, respectively. Bioplot analysis revealed that genotype ‘V-19618′ and ‘V-19600′ proved superior under drought conditions regarding grain yield and related traits, while genotype ‘V-19574′ proved better under both irrigated and drought conditions. Conclusions: These identified genotypes, i.e., ‘V-19618′ and ‘V-19600′ can be utilized in future wheat breeding programs to induce desirable characters for producing drought tolerant wheat genotypes.

Machine learning with its high precision algorithms, Precision agriculture (PA) is a new emerging concept nowadays. Many researchers have worked on the quality and quantity of PA by using sensors, networking, machine learning (ML) techniques, and big data. However, there has been no attempt to work on trends of artificial intelligence (AI) techniques, dataset and crop type on precision agriculture using internet of things (IoT). This research aims to systematically analyze the domains of AI techniques and datasets that have been used in IoT based prediction in the area of PA. A systematic literature review is performed on AI based techniques and datasets for crop management, weather, irrigation, plant, soil and pest prediction. We took the papers on precision agriculture published in the last six years (2013-2019). We considered 42 primary studies related to the research objectives. After critical analysis of the studies, we found that crop management; soil and temperature areas of PA have been commonly used with the help of IoT devices and AI techniques. Moreover, different artificial intelligence techniques like ANN, CNN, SVM, Decision Tree, RF, etc. have been utilized in different fields of Precision agriculture. Image processing with supervised and unsupervised learning practice for prediction and monitoring the PA are also used. In addition, most of the studies are forfaiting sensory dataset to measure different properties of soil, weather, irrigation and crop. To this end, at the end, we provide future directions for researchers and guidelines for practitioners based on the findings of this review

This paper offers a new indulgence to the present literature by integrating the role of fiscal decentralization (FD) in affecting ecological footprint (EF). So, this study considered the effect of FD on EF in the existence of energy consumption (EC), technological innovation (TI), gross domestic product (GDP), and trade openness (TOP) from 1990 to 2018 in Pakistan. We employ econometric methods like Bayer & Hanck cointegration, fully modified ordinary least squares, dynamic ordinary least squares, and canonical cointegration regression for empirical analysis. Moreover, the frequency domain causality test is used to conclude the causal impact of FD, EC, TI, GDP, and TOP on EF. The regression results disclose that EC, GDP, and TOP boost EF in Pakistan; however, FD and TI promote the sustainability of the environment by reducing EF. Besides, the frequency causality outcomes indicate that FD, EC, TI, GDP, and TOP have insinuations for EF in the long term. As a policy recommendation, this research suggests that Pakistan could successfully integrate strategies to increase ecological quality by allowing the lower level of government to utilize eco-friendly technological innovations.

Background: Inferior vena cava (IVC) malformations are rare developmental anomalies and are usually asymptomatic. Agenesis of IVC is an uncommon form of IVC malformation, and symptomatic neonatal presentation is unusual. We describe an unusual case of symptomatic venous thrombosis in the neonatal period after central venous cannulation, which was later found to have agenesis of IVC. Clinical Description: An extreme preterm (28 weeks) baby developed deep vein thrombosis of the femoral and external iliac vein after femoral venous catheterization and required anticoagulation therapy. Later, during the hospital stay, he was noted to have developed dilated abdominal veins over the flanks and paraspinal region, with the direction of flow below upward 2 months after the resolution of the thrombus. Management and Outcome: Ultrasonography (USG) color Doppler ruled out portal hypertension or persistence of IVC obstruction. Computed tomography (CT) venography resolved it to be the case of agenesis of the infrarenal portion of IVC. No consensus guidelines exist for neonatal management and it is usually conservative. Conclusion: IVC developmental anomalies including agenesis of IVC are very rare and are usually an asymptomatic condition, but can cause symptoms due to venous congestion in the lower limb or pressure from enlarged collaterals and rarely associated with deep venous thrombosis usually seen in young adults. Neonatal presentation is extremely rare. Diagnosis is by USG color Doppler and CT venography. Neonatal management guidelines are lacking and the risk of anticoagulation must be cautiously weighed against the risk of hemorrhage.

The internet of things is an emerging technology used in cloud computing and provides many services of the cloud. The cloud services users mostly suffer from service delays and disruptions due to service cloud resource management based on vertical and horizontal scalable systems. Adding more resources to a single cloud server is called vertical scaling, and an increasing number of servers is known as horizontal scaling. The service-bursts significantly impact the vertical scaled environment where the scale-up degrades the service quality and users’ trust after reaching the server’s maximum capacity. Besides, the horizontally scaled environment, though being resilient, is cost-inefficient. It is also hard to detect and manage bursts online to sustain application efficiency for complex workloads. Burst detection in real-time workloads is a complicated issue because even in the presence of auto-scaling methods, it can dramatically degrade the application’s efficiency. This research study presents a new bursts-aware auto-scaling approach that detects bursts in dynamic workloads using resource estimation, decision-making scaling, and workload forecasting while reducing response time. This study proposes a hybrid auto-scaled service cloud model that ensures the best approximation of vertical and horizontal scalable systems to ensure Quality of Service (QoS) for smart campus-based applications. This study carries out the workload prediction and auto-scaling employing an ensemble algorithm. The model pre-scales the scalable vertical system by leveraging the service-load predictive modeling using an ensemble classification of defined workload estimation. The prediction of the upcoming workload helped scale-up the system, and auto-scaling dynamically scaled the assigned resources to many users’ service requests. The proposed model efficiently managed service-bursts by addressing load balancing challenges through horizontal auto-scaling to ensure application consistency and service availability. The study simulated the smart campus environment model to monitor the time-stamped diverse service-requests appearing with different workloads.

This paper concerns Quality of Service (QoS) provisioning in Optical Packet Switching (OPS) networks. We address the topic by proposing Dynamic Wavelength Grouping (DWG) for OPS, in this technique, available wavelengths are dynamically partitioned and allocated at each network link to particular classes of service. DWG adapts to traffic fluctuations by tracking the load status of every class of traffic and then schedules optical packets over group of wavelengths assigned for each class of service. Performance is examined using an own developed simulator. This simulator models various aspects of OPS in far detail. Two network topologies are studied under various parameter settings and traffic scenarios. DWG clearly outperforms its earlier static counterpart (SWG). It appears as flexible approach, efficiently functional even when the share of the high-priority load is temporarily low. Consequently, it minimizes the total blocking probability while preserving the desired service differentiation. We report also significant improvements in the network throughput.

Internet of Things (IoT) enables smart campuses more convenient for cloud services. The availability of cloud resources to its users appears as a fundamental challenge. The existing research presents several auto-scaling techniques to scale the resources with the increase in users’ demands. However, still, the cloud users of auto-scaled servers experience service disruption, delayed responses, and the occurrence of service bursts. The prevailing burst management framework exhibits limitations in the context of burdening the existing auto-scaled machines for cost estimation and resource allocation. This research presents a 3-axis auto-scaling framework for load balancing and resource allocation by incorporating a dedicated cost estimator and allocator (on the z-axis). The cost estimation server develops a log of existing load estimates of vertical and horizontal servers and scales the new users’ requests in case the vertical threshold is breached with new requests. The cost estimator, in its data structure, keeps track of the current resources available at both vertical and horizontal servers. The historical information of available resources and the new resources’ requests is decided by the cost estimator as per demand and supply scenario. The general characteristics of servers are resources pooling, requests queue development, burst identification, automatic scaling, and load balancing. The cost estimator also prioritizes vertical servers for resource allocations, and switches to the horizontal server when the vertical server reaches its 75% quota of resources. The study simulates 1000 users’ requests of smart campus, adopts state-of-the-art ensemble with bagging strategy and handles an effective class imbalance situation.

BackgroundIntravascular imaging has been used to assess the morphology of lesions causing an acute coronary syndrome (ACS) in native vessels (NV) and identify differences between plaques that ruptured (PR) and caused an event and those that ruptured without clinical manifestations. However, there is no data about the morphological and physiological characteristics of neoatherosclerotic plaques that ruptured (PR-NA) which constitute a common cause of stent failure.MethodsWe retrospectively analyzed data from patients admitted with an acute myocardial infarction that had optical coherence tomography (OCT) imaging of the culprit vessel before balloon pre-dilation. OCT pullbacks showing PR were segmented at every 0.4 mm. The extent of the formed cavity, lipid and calcific tissue, thrombus, and macrophages were measured, and the fibrous cap thickness (FCT) and the incidence of micro-channels and cholesterol crystals were reported. These data were used to reconstruct a representative model of the native and neoatherosclerotic lesion geometry that was processed with computational fluid dynamics (CFD) techniques to estimate the distribution of the endothelial shear stress and plaque structural stress.ResultEighty patients were included in the present analysis: 56 had PR in NV (PR-NV group) and 24 in NA segments (PR-NA group). The PR-NV group had a larger minimum lumen area (2.93 ± 2.03 vs. 2.00 ± 1.26 mm2, p = 0.015) but similar lesion length and area stenosis compared to PR-NA group. The mean FCT (186 ± 65 vs. 232 ± 80 μm, p = 0.009) and the lipid index was smaller (16.7 ± 13.8 vs. 25.9 ± 14.1, p = 0.008) while the of calcific index (8.3 ± 9.5 vs. 2.2 ± 1.6%, p = 0.002) and the incidence of micro-channels (41.4 vs. 12.5%, p = 0.013) was higher in the PR-NV group. Conversely, there was no difference in the incidence of cholesterol crystals, thrombus burden or the location of the rupture site between groups. CFD analysis revealed higher maximum endothelial shear stress (19.1 vs. 11.0 Pa) and lower maximum plaque structural stress (38.8 vs. 95.1 kPa) in the PR-NA compared to the PR-NV model.ConclusionWe reported significant morphological and physiological differences between culprit ruptured plaques in native and stented segments. Further research is needed to better understand the causes of these differences and the mechanisms regulating neoatherosclerotic lesion destabilization.

This paper adds to the existing body of knowledge by incorporating the role of fiscal decentralization (FD) in influencing CO2 emissions. Therefore, this study looked at the effect of FD on CO2 emissions in the presence of nonrenewable energy consumption (NRE), renewable energy consumption (REN), gross domestic product (GDP), and trade openness (TOP) for the period 1994–2018 in Japan. Thus, the current work intends to fill this knowledge gap by employing econometric techniques such as Bayer and Hanck cointegration, dynamic ordinary least squares (DOLS), fully modified ordinary least squares (FMOLS), and canonical cointegration regression (CCR). Additionally, the frequency domain causality analysis is used in the investigation to determine the causal impact of FD, NRE, REN, GDP, and TOP on CO2 emissions. The novelty of the frequency-domain approach is that it can differentiate between nonlinearity and causality levels and show causality among parameters with different frequencies. The DOLS, FMOLS, and CCR results reveal that NRE, GDP, and TOP augment CO2 emissions in Japan, whereas FD and REN increase the quality of the atmosphere. Furthermore, the frequency causality test results show that FD, REN, GDP, and TOP have implications for CO2 emissions in the long run, while NRE raises CO2 emissions in the medium run. As a policy direction, the current study suggests expanding renewable energy consumption in Japan by emphasizing more on Sustainable Development Goals (7, 8, and 13).

Most of the Thermal (Infrared) cameras nowadays are equipped with a motorized lens for focusing a scene manually. The subjective nature of manual focusing makes it an inefficient and cumbersome process. In contrast, Autofocusing (AF) obtains the best focused image based on a quantitative measure with the benefits of convenience and intelligence. Various AF systems for visual cameras have been developed, but relatively less amount of work has been done for thermal imaging systems. This paper presents a Vision and Control based Autofocusing System (VCAFS) comprising: (1) an uncooled thermal camera with motorized lens, (2) a passive contrast-based focus measure, (3) a smoothing operator to avoid local extrema, and (4) two different lens motion controllers. Experimental results show the efficacy of the proposed system on live videos even when the scene and its depth are continuously changing.

The goal of the current work was to create structural analogues of a beta lactam antibiotic that might be possibly effective against bacterial resistant strains. FTIR, 1H NMR, 13C NMR, and CHNS analyses were used to perform the spectroscopic study on the compounds M1-8. The effects of the aforementioned substances on gram-positive and gram-negative bacterial strains were investigated. Most of the eight compounds had antibacterial activity that was lower than or equivalent to that of the original medication, but two molecules, M2 and M3, surprisingly, had stronger antibacterial activity. The findings of synthesized analogues against alpha-glucosidase and DPPH inhibition were found to be modest, whereas M2, M3, and M7 strongly inhibited the urease. To comprehend the potential mode of action, a molecular docking research was conducted against urease and -amylase. The research may help in the quest for novel chemical compounds that would be effective against bacteria that are resistant to antibiotics.

In identifying mutations occurring in distinct cow breeds, genetic elements must be taken into consideration. More recently, these hereditary features have gained attention throughout the world. As in many underdeveloped nations, to bridge the deficit in molecular genetics, multiple solutions are required. The inner membrane anion carrier superfamily contains the uncoupling proteins (UCPs), vital to energy regulation. Research on heredity has shown that variations in the UCP2 and UCP3 genes are connected to obesity and metabolic syndrome. This research aimed to investigate if any mutation in the UCP 2 and UCP 3 genes are related to many characteristics in Pakistan’s three indigenous cattle breeds using artificial neural network (ANN). For better analysis, the output of the ANN model is loaded into the Primer Premier 3 software. Using polymerase chain reaction-single strand conformation polymorphism (PCR-SSCP) and sequencing, the results of this study indicated 07 variations in the exon 4 region of the UCP2 gene and 03 variants in the exon 3 area of the UCP3 gene among 215 indigenous cow breeds. The association study revealed that the g.C35G mutation in the UCP3 gene is strongly related to meat quantity characteristics such as carcass weight and drip percentage (P0.05) but not with body height or hip width (P>0.05). Sequence analysis showed five distinct diplotypes: AA, BC, AC, CC, and CD. Cattle with the novel heterozygous diplotype BC perform better in carcass trait and drip percentage than animals with other genotypes. The study’s findings suggest that the UCP3 gene may be utilized for marker-assisted selection (MAS) and breed mixing in Pakistan cattle breeds to aid in the country’s economic growth.

The current study was designed to synthesize, characterize, and screen the molecular and biological activities of different metformin derivatives that possess potent antidiabetic potential with minimal side-effects. Metformin-based derivatives containing the metal complexes Cu II (MCu1–MCu9) and Zn II (MZn1–MZn9) were generated using aromatic aldehydes and ketones in a template process. The novel metal complexes were characterized through elemental analysis, physical state, melting point, physical appearance, Fourier-transform infrared (FTIR) spectroscopy, UV/visible (UV/Vis) spectroscopy, 1H nuclear magnetic resonance (NMR) spectroscopy, and 13C-NMR spectroscopy. Screening for inhibitory activity against the enzymes α-amylase and α-glucosidase, and molecular simulations performed in Schrödinger were used to assess the synthesized derivatives’ biological potential. Met1, Met2, Met3, and Met8 all displayed activities that were on par with the reference in an enzymatic inhibition assay (amylase and glucosidase). The enzyme inhibition assay was corroborated by molecular simulation studies, which also revealed a competitive docking score compared to the gold standard. The Swiss ADME online web server was utilized to compute ADME properties of metformin analogues. Lipinski’s rule of five held true across all derivatives, making it possible to determine the percentage of absorption. Metformin derivatives showed significant antidiabetic activities against both targeted enzymes, and the results of this work suggest that these compounds could serve as lead molecules for future study and development.

Abstract Background and Aims Coronary calcification remains a significant challenge for the contemporary interventional cardiologist. We aim to describe the use of intravascular lithotripsy (IVL) in a range of real‐world settings. Methods A retrospective two‐center analysis of patients treated with IVL between June 2018 and November 2019. Technical and procedural success, as well as procedural complications and 30‐day outcomes (death, myocardial infarction, or repeat target vessel revascularization), was recorded. Results Sixty‐five patients underwent IVL: 80% were male and the mean age was 70.1 ± 12.0 years. 54% of patients presented with acute coronary syndrome (ACS) and 68% of patients had intracoronary imaging. Twelve patients required IVL within pre‐existing stents, and 12 underwent IVL in the left main stem. All balloons were successfully delivered with 98.5% procedural success. There was a significant gain in MLA post PCI of 261.9 ± 100% following IVL. There were two procedural complications. At 30‐day follow‐up, there was one death, and one patient required a repeat procedure due to stent underexpansion. Conclusions In this largest real‐world series of imaging‐guided IVL for calcified lesions to date, we demonstrate that IVL is deliverable, safe, and effective at calcium modification especially when intracoronary imaging is used.

The supplementary cementitious materials (SCM) can be used as a replacement of cement in the construction industry to minimize the drawbacks of normal concrete such as the emission of carbon dioxide so as to be eco-friendly. This paper presents the effect of ultra-fine slag (i.e. 25% of alccofine) as a replacement of cement for different water to binder ratios (i.e. 0.38, 0.4 and 0.45). The effect of alccofine on the concrete strength properties were studied at 7 and 28 days wherein considerable strength enhancement was observed compared to normal concrete. Thermogravimetric analysis was also carried out in which mass loss and decomposition of hydration products from concrete was determined. X-ray diffraction showed the crystalline forms of ettringite and calcium silicate hydrate. Therefore, it was concluded from this study that alccofine can be used as a viable substitute to cement in normal concrete considering its positive effects on property enhancement and ecofriendless.

Malware development has seen diversity in terms of architecture and features. This advancement in the competencies of malware poses a severe threat and opens new research dimensions in malware detection. This study is focused on metamorphic malware, which is the most advanced member of the malware family. It is quite impossible for anti-virus applications using traditional signature-based methods to detect metamorphic malware, which makes it difficult to classify this type of malware accordingly. Recent research literature about malware detection and classification discusses this issue related to malware behavior. The main goal of this paper is to develop a classification method according to malware types by taking into consideration the behavior of malware. We started this research by developing a new dataset containing API calls made on the windows operating system, which represents the behavior of malicious software. The types of malicious malware included in the dataset are Adware, Backdoor, Downloader, Dropper, spyware, Trojan, Virus, and Worm. The classification method used in this study is LSTM (Long Short-Term Memory), which is a widely used classification method in sequential data. The results obtained by the classifier demonstrate accuracy up to 95% with 0.83 $F_1$-score, which is quite satisfactory. We also run our experiments with binary and multi-class malware datasets to show the classification performance of the LSTM model. Another significant contribution of this research paper is the development of a new dataset for Windows operating systems based on API calls. To the best of our knowledge, there is no such dataset available before our research. The availability of our dataset on GitHub facilitates the research community in the domain of malware detection to benefit and make a further contribution to this domain.

Background: The purpose of this study is to compare the clinical and radiological outcome of medial versus posterior triceps splitting approach in open reduction internal fixation of displaced supracondylar fracture of humerus in children. Methods: A retrospective review of total 70 children with medial approach (n=30) and posterior triceps splitting approach (n=40) children was made. Time to radiological union, postoperative Bauman’s angle and Lateral humerocapitellar angle was compared for radiological outcome. Cosmetic and functional outcome was assessed and compared using Flynn’s criteria. Results: Radiological parameters like time to union, postoperative Bauman’s angle and Lateral humerocapitellar angle were similar and non-significant between medial and posterior triceps splitting approach. Flynn’s cosmetic outcome was similar between these approach (p=0.198). Loss of ROM was significant in posterior triceps splitting approach (p=0.00). Flynn’s functional outcome was better with medial approach as compared to posterior triceps splitting approach with statistical significance (p=0.00). Conclusions: Medial approach has better functional outcome compared to posterior triceps splitting approach in open reduction internal fixation of displaced supracondylar fracture of humerus in children. Keywords: Children; Humerus; Open reduction; Supracondylar

This paper means to inspect the regression between the Price to Book ratio as a proxy for earning management and payout proportion as a proxy to dividend policy. This paper utilizes multivariate analysis to examine the relationship between the price to book ratio and dividend policy. Using 11 years’ annual data from 2006-2016, this paper delivers new confirmation demonstrating that timely payment of dividend has a positive impact on the reputation of the company in the market. Dividend payment boosts up investors’ confidence to invest in the company. This study helps the corporate superintendents and stock financial experts to focus on the relationship of the dividend. No past review has handled the issue of the contingent relationship between the price to book ratio and payout proportion in Pakistani Industry and specifically on cement, food and oil and gas sectors. Abstrak dalam Bahasa Indonesia : Tulisan ini bermaksud untuk meneliti regresi antara Price to Book Ratio sebagai proksi untuk manajemen penghasilan dan proporsi pembayaran sebagai proksi untuk kebijakan dividen.Penelitian ini menggunakan analisis multivariat untuk menguji hubungan antara Price to Book Ratio dan kebijakan dividen. Dengan menggunakan data tahunan selama 11 tahun dari 2006-2016, artikel ini memunculkan suatu konfirmasi yang menunjukkan bahwa pembayaran dividen tepat waktu memiliki dampak positif pada reputasi perusahaan di pasar. Pembayaran dividen meningkatkan kepercayaan investor untuk berinvestasi di perusahaan. Studi ini membantu pengawas perusahaan dan ahli keuangan saham untuk fokus pada hubungan dividen. Belum ada penelitian sebelumnya yang menangani masalah hubungan kontingen antara Price to Book Ratio dan proporsi pembayaran dalam Industri di Pakistan dan khususnya pada sektor semen, pangan dan minyak dan gas.

This study examine the relationship between banking sector development and economic growth in South Asian Association for Regional Cooperation (SAARC) over the period of 1980-2013 by entailing ordinary least square (OLS), two stages least square (TSLS), and panel TSLS fixed-effect model. Key banking sector development variables include money and quasi money (RQM) and domestic credit to private sector by banks (PC) have a positive and statistically robust effect on economic growth in all models. The study concludes that no economy can develop without a substantial growth in the banking sector and it is important to have a sound and rigorous banking system for building a sustained economic growth. Therefore, the SAARC economies are suggested to focus on the development of the banking sector for their long run growth.

Antimicrobial resistant organisms are associated with significant morbidity and mortality and have considerable burden on healthcare costs. Antimicrobial discovery was a major breakthrough in the field of infectious diseases and drug discovery until the unexpected rise of antimicrobial resistance which is now a public health threat. Imprudent use of antimicrobials have led to the emergence of microbial resistance strains and favoured microorganisms to successfully exploit every possible resistance mechanisms which includes, but not limited to, genetic mutations, gene pickup, horizontal gene transfer and heterologous expression. They have extended themselves to community settings highlighting the importance of reservoirs of antibiotic resistant microbes in environment. The antimicrobial therapeutic repertoire is also shrinking on a steady pace for present or impossible to treat multidrug resistant infections. This short review briefly discusses the different molecular mechanisms of antibiotic resistance which allow microbes to exhibit multidrug resistance trait. Prompt actions to limit the emergence and dissemination of multi-drug resistant superbugs through novel therapeutic approaches should be designed.

Fluorescence monitoring of endogenous microRNA (miRNA or miR) activity related to neuronal development using nano-sized materials provides crucial information on miRNA expression patterns in a noninvasive manner. In this study, we report a new method to monitor intracellular miRNA124a using quantum dot-based molecular beacon (R9-QD-miR124a beacon). The R9-QD-miR124a beacon was constructed using QDs and two probes, miR124a-targeting oligomer and arginine rich cell-penetrating peptide (R9 peptide). The miR124a-targeting oligomer contains a miR124a binging sequence and a black hole quencher 1 (BHQ1). In the absence of target miR124a, the R9-QD-miR124a beacon forms a partial duplex beacon and remained in quenched state because the BHQ1 quenches the fluorescence signal of the R9-QD-miR124a beacon. The binding of miR124a to the miR124a binding sequence of the miR124a-targeting oligomer triggered the separation of the BHQ1 quencher and subsequent signal-on of a red fluorescence signal. Moreover, enhanced cellular uptake was achieved by conjugation with the R9 peptide, which resulted in increased fluorescent signal of the R9-QD-miR124a beacons in P19 cells during neurogenesis due to the endogenous expression of miR124a.

Introduction: Definite paucity of data pertaining to spectrum of renal and urinary tract diseases in our state and in various parts of India forms the basis of this study. Available data has emphasized more on specific clinical syndromes and chronic renal diseases rather than over all spectrums of renal and urinary tract diseases, that too in adult population. Aim: The present study a retrospective analysis, forms one of the basic data of paediatric nephrology and urology related disorders in our state. Materials and Methods: Retrospective analysis of the case records of all the hospitalized patients with renal and urinary tract diseases between 2012 and 2013 were performed. Case records were analysed and categorized into various groups like; Urinary Tract Infections (UTI), Acute Kidney Injury (AKI), Acute Glomerulonephritis (AGN), Nephrotic Syndrome (NS), haematuria, Polycystic Kidney Disease (PCKD), Posterior Urethral Valve (PUV), Vesicoureteric Reflux (VUR), Chronic Kidney Disease (CKD), Congenital Anomalies of Kidney and Urinary Iract (CAKUT) and others. These groups were divided into subgroups to get more insight about the pattern of these diseases. Results: Out of 28114 patients hospitalized between 2012 and 2013 years, 447 (232 males and 215 females) patients were diagnosed of renal and urinary tract diseases which forms 1.58% the total admitted patients. Among these patients 32.9% (147/447) were diagnosed Acute Kidney Injury (AKI); 24.1% (108/447): Urinary Tract Infection (UTI); 9.6% (43/447): Acute Glomerulonephritis (AGN); 5.6% (25/447): bilateral hydronephrosis with UTI; 4.47% (20/447): nephrotic syndrome (NS); 3.5% (16/447): haematuria; and 4% (18/447) were having CAKUT (Congenital Anomalies Of Kidney And Urinary Tract). In addition to this there were 17 cases of Renal Tubular Acidosis (RTA), 3 cases of Barter syndrome and one case of Liddle syndrome. Conclusion: A substantial number of children are hospitalized with renal and urinary tract diseases with delayed ages of presentation, which at times have suffered irreversible renal damage that could have been prevented or treated if diagnosed earlier. Our study indicates that majority of these renal and urinary tract diseases are preventable and treatable. Henceforth, there is a need to develop a comprehensive service for the children with renal and urinary tract diseases in Jammu & Kashmir (J&K) India.

The fundamental functionality of WSN (Wireless Sensor Networks) is to transport data from sensor nodes to the sink. To increase the fault tolerance, inherent sensor node redundancy in WSN can be exploited but the reliability guarantees are not ensured. The data transport process in WSN is devised as a set of operations on raw data generated in response to user requirements. The different operations filter the raw data to rationalize the reliable transport. Accordingly, we provide reliability models for various data transport semantics. In this paper we argue for the effectiveness of the proposed reliability models by comparing analytically and via simulations in TOSSIM.

Joubert syndrome (JS) is a rare developmental disorder of the central nervous system, characterised by brainstem and cerebellar malformations, hypotonia, episodic hyperapnea and apnea and mental retardation. It may be associated other systemic abnormalities like ocular (e.g., retinal dysplasia, etc.), oculomotor, musculoskeletal and renal (e.g., cystic dysplasia, nephronophthisis), with renal failure. We describe a case of JS with nephronophthisis in neurofibromatosis Type 1 leading to end-stage renal disease, a association that has never been described earlier in the medical literature to the best of our knowledge.

The pomfret fish, particularly the black pomfret (Parastromateus niger), serves as a significant economic resource in South Asia, particularly in the coastal regions of Balochistan, Pakistan. This study investigates various biological parameters of Parastromateus niger collected from the Gaddani coastal area of Pakistan, focusing on length-weight relationships, gut fullness, and population demographics. A total of 117 fish specimens were collected, with females dominating the samples. Length and weight data were analyzed separately for males and females, with statistical calculations revealing strong correlations (r2 values ranging from 0.918 to 0.985) between length and weight for both sexes. Gut fullness was categorized into five levels, with the highest percentage observed during July, suggesting potential breeding activity during this period. The zooplanktons were identified as primary dietary components of Parastromateus niger. The study also highlights the importance of regulatory measures to sustainably manage fish populations, especially during breeding seasons, and calls for further research into the intensive culturing and feeding regimes of black pomfret to enhance its production. Overall, these findings contribute to a better understanding of the biological characteristics and potential for aquaculture development of Parastromateus niger in the region. [ABSTRACT FROM AUTHOR]