Search

Background and Aim: Coccidiosis, caused by protozoan parasites of the genus Eimeria, is a significant concern in poultry farming, leading to substantial economic losses worldwide. In Pakistan, poultry is a major component of the agricultural sector, with both broiler and egg-laying chickens playing crucial roles in meeting the country’s protein needs. Despite the importance of the poultry industry, there is limited data on prevalence and species distribution of Eimeria in different types of chickens in District Swabi, Khyber Pakhtunkhwa, Pakistan. This study aims to estimate the prevalence and determine the distribution of Eimeria species in broiler and egg-laying chickens in this region. Materials and Methods: Nine hundred fecal samples were collected from broiler (380) and egg-laying domestic chickens (520) in District Swabi, Pakistan. Microscopic analysis was used to identify Eimeria parasites in all samples. After microscopic examination for positive identification, Eimeria species were determined using polymerase chain reaction (PCR) assays. Results: Microscopic examination identified Eimeria oocysts in 44.4% (400/900) of the samples. Eimeria parasite infection significantly varied based on chicken type, age, and gender (p < 0.05). The study found that broiler chickens (52.63%, 235/450), young chickens (4–6 weeks) (55.5%, 285/500), and females (52.2%, 200/380) were more infected with Eimeria spp. than egg-laying domestic chickens (38.5%, 200/520), adults (above 6 weeks) (28.8%), and males (36.7%, 165/450). PCR indicated a distribution rate of 42.5% (170/400) Eimeria tenella, 26.25% (105/400) Eimeria acervulina, 20% (80/400) Eimeria maxima, and 11.25% (45/400) Eimeria mitis. None of Eimeria necatrix, Eimeria brunetti, or Eimeria praecox was found in the study. Conclusion: This study underlines the essential requirement for targeted interventions due to the prevalence and predominance of E. tenella among identified Eimeria species. Future research should focus on refined sampling strategies and investigate the clinical significance of these parasites for effective disease management in the local poultry industry.

Background and Aim: Trypanosomiasis, a parasitic infection caused by various Trypanosoma species, poses a significant threat to global livestock, affecting both human health and economic sectors. This study aimed to estimate the prevalence of Trypanosoma evansi in Southern Punjab, Pakistan, focusing on key ruminant species, including camels, cattle, buffaloes, goats, and sheep. Materials and Methods: A total of 240 blood samples, comprising 48 samples from each animal species (camel, cattle, buffaloes, goat, and sheep) were collected from three districts in Southern Punjab. The collected samples were subjected to thin smear microscopy, DNA extraction, and polymerase chain reaction (PCR) amplification. The molecular characterization was conducted using the TBR primer set, which targeted repeated satellite DNA regions and the cytochrome oxidase II gene of T. evansi. Results: About 22.08% (53/240) of overall samples were positive for trypanosomiasis, with prevalence rates being 23.75% (19/80), 21.25% (17/80), and 21.75% (17/80) for districts Muzaffargarh, Lodhran, and Bahawalpur, respectively. 5.83% (14/240) of samples tested for T. evansi using PCR were positive in the districts of Muzaffargarh 7.50% (6/80), Lodhran 5.00% (4/80), and Bahawalpur 5.00% (4/80). Among the animals tested, camels had the highest positivity rate. The microscopic examination confirmed infection rates of 45.83% (22/48) for camels, 18.75% (9/48) for cattle, 8.33% (4/48) for buffaloes, 18.75% (9/48) for goats, and 18.75% (9/48) for sheep (p < 0.001). PCR results did not reveal substantial differences (p < 0.05) in prevalence: camels 12.50% (6/48), cattle 6.25% (3/48), buffaloes 0% (0/48), goats 8.33% (4/48), sheep 2.08% (1/48); while distinct disparities were detected district-wise: Muzaffargarh 23.75% (19/80), Lodhran 21.25% (17/80), and Bahawalpur 21.25% (17/80). The PCR results for these districts were insignificantly different: 7.50% (6/80), 5% (4/80), and 5% (4/80). The microscopic infection rate in camels from Bahawalpur was 56.30% (9/16). The microscopic analysis in Buffaloes reported a 6.30% (1/16) infection rate, but PCR results indicated no infections (0%) in any district. A significant difference (p < 0.001) in identifying Trypanosoma species was found between positively and negatively tested animals in both microscopic and PCR methods. Conclusion: This study emphasizes the necessity of regularly using PCR-based screening for its superior sensitivity and specificity over traditional microscopy. The varying occurrence of trypanosomiasis among districts reflects the intricate nature of this diseases epidemiology in the region. Reducing economic losses from trypanosomiasis in Southern Punjab, Pakistan, requires targeted interventions, such as vector control measures and farmer education.

Composite construction materials are extensively employed and have proven to be more effective than their individual components, such as steel and concrete. This is because composites combine the strengths of different materials, resulting in enhanced properties such as increased strength-to-weight ratios, better durability, and improved thermal efficiency. However, despite the strong structural properties and energy-saving benefits of reinforced concrete sandwich panels, their use is restricted in many rural regions of developing nations like Pakistan, largely because of the economic limitations faced by the communities. Therefore, this study introduces cost-effective structural mud insulated panels (SMIPs) that utilize mud wythes and evaluates their mechanical performance. These panels consist of a lightweight Expanded Polystyrene (EPS) foam core, sandwiched between two galvanized steel-reinforced mud layers. The study includes flexural, compression, diagonal tension, and water absorption tests. Flexural tests revealed that the panels have substantial load-bearing capacity under bending forces. Whereas, Compression tests showed that the panels possess adequate strength and stiffness to support moderate structural loads. Additionally, Diagonal tension tests confirmed the panels’ ability to resist significant in-plane loads. Water absorption tests also indicated that the panels demonstrated adequate moisture resistance, staying within the acceptable limit of 15%. These findings highlight the mechanical and hydric properties of SMIPs, underscoring their potential in sustainable construction, especially in economically constrained regions.

Background: Food crop consumption is one of the primary routes through which the environment is polluted with radionuclides, metals, and metalloids. In recent years, the negative impacts of metals, metalloids, and radionuclides on crop quality via the food-human system have jeopardized both human health and food security. Because some metals, metalloids, and radionuclides are persistent and non-biodegradable, they build up in the human body's essential organs, causing a variety of serious health issues, including cancer. It is therefore absolutely essential to evaluate the potential cancer risks of radionuclides as well as metals and metalloids in the food crops consumed in the study area to avoid negative health consequences. Objective: The aim of this study was to assess the potential cancer risks of metals and metalloids (As, Cd, Cr, and Pb) and Radionuclides (U-238, Th-232, and K-40) via consumption of food crops cultivated around the Riruwai mining area, northeastern Nigeria. Methodology: Thirty-three (33) food crops (15 vegetables and 18 cereal crops) were collected directly from the farmlands in the study area. The concentrations of radionuclides, metals, and metalloid in each food crop were assessed using a microwave-plasma atomic emission spectrometry (MP-AES) and a high-purity germanium (HPGe) gamma-ray spectrometer, respectively. The measured concentrations of radionuclides, metals, and metalloid were used to calculate their lifetime cancer risks (LTCRs). Results: The findings of the study revealed that the concentrations of As, Cd, Cr, and Pb in all the investigated food crops were above the threshold limits, while lower activity concentrations in all the investigated food crops below the world average standard were observed in U-238, Th-232, and K-40. LTCRs of all the individual metal and metalloid as well as radionuclide in each food crop were determined to be below the global threshold limits, indicating no likelihood of cancer risk from consuming the food crops. However, it was discovered that the total cumulative LTCRs of all the metals and metalloid in all food crops combined were 2.51E-04, which was over twofold the acceptable limit of 1.00E-04 Conclusion: The individual metal, metalloid, and radionuclide LTCRs in each food crop were found to be below global threshold limits, indicating no cancer risk from consumption. However, the cumulative LTCRs of all the metals and metalloid in all food crops were found to exceed the acceptable level, which requires proper monitoring and government intervention. Cancer risk is influenced by factors beyond food consumption, such as alcohol consumption, heredity, and air pollution, so multidimensional studies are needed before making a final decision on cancer risks in the study area.

Abstract The growing concern of pediatric mortality demands heightened preparedness in clinical settings, especially within intensive care units (ICUs). As respiratory-related admissions account for a substantial portion of pediatric illnesses, there is a pressing need to predict ICU mortality in these cases. This study based on data from 1188 patients, addresses this imperative using machine learning techniques and investigating different class balancing methods for pediatric ICU mortality prediction. This study employs the publicly accessible “Paediatric Intensive Care database” to train, validate, and test a machine learning model for predicting pediatric patient mortality. Features were ranked using three machine learning feature selection techniques, namely Random Forest, Extra Trees, and XGBoost, resulting in the selection of 16 critical features from a total of 105 features. Ten machine learning models and ensemble techniques are used to make accurate mortality predictions. To tackle the inherent class imbalance in the dataset, we applied a unique data partitioning technique to enhance the model's alignment with the data distribution. The CatBoost machine learning model achieved an area under the curve (AUC) of 72.22%, while the stacking ensemble model yielded an AUC of 60.59% for mortality prediction. The proposed subdivision technique, on the other hand, provides a significant improvement in performance metrics, with an AUC of 85.2% and an accuracy of 89.32%. These findings emphasize the potential of machine learning in enhancing pediatric mortality prediction and inform strategies for improved ICU readiness.

The bumper beam assembly absorbs the kinetic energy and encounters deformation during low and high-velocity impact crash collisions and accidents. An optimal bumper energy-absorbing system should fulfill pedestrian safety requirements and be crashworthy in both high- and low-speed collisions. Bumper beams made of traditional metallic materials, especially from high-strength steel, are heavyweight under low production capacity. The lightweight structure of the assembly can be achieved by using composite materials to replace the metals addressing the weight issues. In this review article, literature related to bumper beam materials is studied along with applications and the best possible and optimum option to be considered as a replacement for metals. Different parameters which affect the design of the bumper beam assembly are also reviewed. The design of bumper beams has been studied based on the conceptual design and their importance in the early stage of manufacturing. The paper also discussed the comparison of different manufacturing processes used to fabricate bumper beam assembly. Moreover, literature related to experimental investigations is also studied and reviewed with respect to the numerical models of bumper beams based on different parameters. Based on the comparison, it is concluded that numerical models can be effectively used in the design of a high-performance bumper beam system.

In the original publication [...]

Fusion power plants can meet the energy demands of the world. The high thermal performance of a power generation system is still a challenge and one of the developing trends with a fusion reactor due to the high outlet temperature. A combined cycle system has been optimized to work at high outlet temperature and pressure of fusion power reactor with thermal power of 2500 MWth and its input parameter has been optimized along with impact of partial load to achieve high thermal performance very first time. The combined cycle based on the concept of two stages of expansion in the closed-Brayton-cycle and two reheaters in the Rankine cycle named Schematic-IV with the higher thermal performance of 58.19% as compared to Schematic-I, II and III has been proposed for a fusion power reactor. The increase in more than one expansion stage and reheat stage is not economical because it increased the thermal performance by about half of performance as compared to one. The effect of isentropic efficiency of compressor and heat rate have been investigated to validate the thermodynamic model and calculations. The optimized thermal performance of the combined cycle is 58.19% at a feed water mass flow rate of 592 kgs−1 and steam outlet temperature of 277 °C in the combined cycle. The heat rate decreased and thermal performance increased with the mass flow rate verifying that the thermodynamic model is correct. The thermodynamic analysis of the combined cycle-based nuclear reactor provides insights for better system thermal performance and reveals the effect of key parameters on the system performance.

Speech enhancement is crucial both for human and machine listening applications. Over the last decade, the use of deep learning for speech enhancement has resulted in tremendous improvement over the classical signal processing and machine learning methods. However, training a deep neural network is not only time-consuming; it also requires extensive computational resources and a large training dataset. Transfer learning, i.e. using a pretrained network for a new task, comes to the rescue by reducing the amount of training time, computational resources, and the required dataset, but the network still needs to be fine-tuned for the new task. This paper presents a novel method of speech denoising and dereverberation (SD&D) on an end-to-end frozen binaural anechoic speech separation network. The frozen network requires neither any architectural change nor any fine-tuning for the new task, as is usually required for transfer learning. The interaural cues of a source placed inside noisy and echoic surroundings are given as input to this pretrained network to extract the target speech from noise and reverberation. Although the pretrained model used in this paper has never seen noisy reverberant conditions during its training, it performs satisfactorily for zero-shot testing (ZST) under these conditions. It is because the pretrained model used here has been trained on the direct-path interaural cues of an active source and so it can recognize them even in the presence of echoes and noise. ZST on the same dataset on which the pretrained network was trained (homo-corpus) for the unseen class of interference, has shown considerable improvement over the weighted prediction error (WPE) algorithm in terms of four objective speech quality and intelligibility metrics. Also, the proposed model offers similar performance provided by a deep learning SD&D algorithm for this dataset under varying conditions of noise and reverberations. Similarly, ZST on a different dataset has provided an improvement in intelligibility and almost equivalent quality as provided by the WPE algorithm.

We consider a continuous-time model describing the interaction between phytoplankton and zooplankton using a Holling type-II response. We then transform this continuous-time model into a discrete-time counterpart using a fractional-order discretization method. The paper explores the local stability of this obtained system concerning all equilibrium points and establishes the global asymptotic stability of its positive fixed point. The study also demonstrates that, under specific mathematical conditions, the system undergoes a Neimark–Sacker bifurcation around its positive equilibrium point. To effectively manage this bifurcation, two modified hybrid control techniques are introduced. The paper concludes by presenting illustrative numerical examples that validate the theoretical findings and assess the effectiveness and feasibility of the newly proposed control strategies. In addition, a comparative analysis is conducted between the modified hybrid techniques and an existing hybrid approach.

This paper presents a novel sound event detection (SED) system for rare events occurring in an open environment. Wavelet multiresolution analysis (MRA) is used to decompose the input audio clip of 30 seconds into five levels. Wavelet denoising is then applied on the third and fifth levels of MRA to filter out the background. Significant transitions, which may represent the onset of a rare event, are then estimated in these two levels by combining the peak-finding algorithm with the K-medoids clustering algorithm. The small portions of one-second duration, called 'chunks' are cropped from the input audio signal corresponding to the estimated locations of the significant transitions. Features from these chunks are extracted by the wavelet scattering network (WSN) and are given as input to a support vector machine (SVM) classifier, which classifies them. The proposed SED framework produces an error rate comparable to the SED systems based on convolutional neural network (CNN) architecture. Also, the proposed algorithm is computationally efficient and lightweight as compared to deep learning models, as it has no learnable parameter. It requires only a single epoch of training, which is 5, 10, 200, and 600 times lesser than the models based on CNNs and deep neural networks (DNNs), CNN with long short-term memory (LSTM) network, convolutional recurrent neural network (CRNN), and CNN respectively. The proposed model neither requires concatenation with previous frames for anomaly detection nor any additional training data creation needed for other comparative deep learning models. It needs to check almost 360 times fewer chunks for the presence of rare events than the other baseline systems used for comparison in this paper. All these characteristics make the proposed system suitable for real-time applications on resource-limited devices.

The chitin and chitosan biopolymers are extremely valuable because of their numerous industrial and pharmacological uses. Chitin and chitosan were extracted from the exoskeleton of Periplaneta americana (cockroaches) and termites using various acid and alkali techniques. The extraction process involves an initial demineralization step, during which integument dry powder was subjected to 500 mL (2.07 mol/L) of concentrated HCl at 100 degrees Celsius for 30 min, followed by meticulous rinsing with distilled water to restore the pH to its baseline. Deproteinization was conducted at 80 degrees Celsius using 500 mL (1 mol/L) of NaOH solution, which was repeated for 24 h. A total of 250 mL (0.06 mol/L) of NaOH was added at 100 degrees Celsius for 4 h to obtain chitosan, followed by extensive washing and subsequent drying. FTIR analysis was used to identify the functional groups in Periplaneta americana and termites. The crystallinity of these biopolymers, which have a face-centered cubic structure, was determined by X-ray diffraction analysis. This study assessed the analgesic properties of chitin and chitosan via an acetic-acid-induced writhing test in mice, revealing a significant reduction in writhing behavior following the chitin and chitosan extract. Notably, chitin exhibits the highest degree of analgesic activity compared to chitosan. Both chitin and chitosan show anti-inflammatory effects, with chitosan absorbing proton ions at sites of inflammation, while chitin effectively inhibits ear edema and elicits an analgesic response in mice. Furthermore, the present study revealed antipyretic activity, with termite chitin demonstrating the most significant effect at a concentration of 500 µL/mL, followed by chitosan and chitin at 100 µL/mL. These findings indicate the potential of using chitin and chitosan derived from termites and Periplaneta americana as natural anti-inflammatory compounds, implying prospective uses in anti-inflammatory, antipyretic, and analgesic capabilities.

Abstract The most promising candidates for solar cells and optoelectronic devices are the transition metals dichalcogenides. Here, we employed the well‐known density functional theory to examine the structural, optoelectronic, and elastic characteristics of novel binary chalcogenides. A direct band gap for ZrX2 and an indirect band gap for PtX2 materials were both confirmed by the band structure features. The valence and conduction band regions are formed by the interaction of the A‐d and X‐p bands. Both the formation energy and the cohesive energies are calculated. The phonon dispersion plots confirmed the stability of the structures. Furthermore, the significant optical constants are computed and explained for possible employment in the optoelectronic application. Our computed band gaps and refractive index were found to be inversely associated. The vital elastic properties are also calculated to discuss the mechanical stability of these materials. The greater bulk modulus and Young's modulus for ZrS2 as compared to the studied chalcogenides suggest this material to be harder and more compressible. The B/G values, confirm all the studied dichalcogenides to be ductile. The most ductile of these materials was ZrSe2 with a predicted B/G value of 7.65. The present work could primarily aid in the creation of diverse and potentially useful semiconducting devices and their applications.

The recent surge in the use of Deep Neural Networks (DNNs) has also made its mark in the field of Audio Enhancement (AE), providing much better quality than the classical methods. Although, there are dedicated audio processing DNNs, yet, many recent models of AE have utilized U-Net: a DNN based on Convolutional Neural Network (CNN), fundamentally developed for image segmentation. It is found that the useful features hidden in the time domain are highlighted when the audio signal is converted to a spectrogram, which can be treated as an image. In this article, we will review the recent work, utilizing U-Nets for different AE applications. Different than other published reviews, this review focuses entirely on AE techniques based on image U-Nets. We will discuss the need for AE, U-Net comparison to other DNNs, the benefits of converting the audio to 2D, input representations that are useful for different AE applications, the architecture of vanilla U-Net and the pre-trained models, variations in vanilla architecture incorporated in different E models, and the state-of-the-art AE algorithms based on U-Net in various applications. Apart from speech and music, this article discusses a wide range of audio signals e.g. environmental, biomedical, bioacoustics, and industrial sounds, not covered collectively in a single article in previously published studies. The article ends with the discussion of colored spectrograms in future AE applications.

Abstract The intensified quest for efficient materials drives us to study the alkali (Na)-based niobate (NaNbO3) and tantalate (NaTaO3) perovskites while exploiting the first-principles approach based on density functional theory, coded within WIEN2K. While using the Birch Murnaghan fit, we find these materials to be stable structurally. Similarly, the ab-initio molecular dynamics simulations (AIMD) at room temperature reveals that the compounds exhibit no structural distortion and are stable at room temperature. By using the recommended modified Becke–Johnson potential, we determine the electronic characteristics of the present materials providing insight into their nature: they are revealed to be indirect semiconductors with the calculated bandgaps of 2.5 and 3.8 eV for NaNbO3 and NaTaO3, respectively. We also determine the total and partial density of states for both materials and the results obtained for the bandgap energies of these materials are consistent with those determined by the band structure. We find that both compounds exhibit transparency to the striking photon at low energy and demonstrate absorption and optical conduction in the UV region. The elastic study shows that these compounds are mechanically stable, whereas NaNbO3 exhibits stronger ability to withstand compressive as well as shear stresses and resists change in shape while NaTaO3 demonstrates weaker ability to resist change in volume. We also find that none of the compound is perfectly isotropic and NaNbO3 and NaTaO3 are ductile and brittle in nature, respectively. By studying the optical properties of these materials, we infer that they are promising candidates for applications in optoelectronic devices. We believe that this report will invoke the experimental studies for further investigation.

The continuous V-shaped structure with hypervapotron for divertor target can improve the cooling performance and detachment in the strike point area. The design and manufacture of this structure has been described in this work. The continuous V-shaped structure in a flat-type divertor is beneficial for divertor detachment because it has a deep slot that can trap neutral particles. The target cooled by hypervapotron is designed to sustain stationary heat flux up to ∼ 10 MW· m−2. The hypervapotron structure is directly connected at the root of the V configuration and expands the potential candidate area of the strike point for better adaptability to different plasma configurations. The simulation results demonstrate that temperature of the structure meets the allowance values of the materials because the strike point hits at the inner tip of V-shaped structure, i.e. the extreme point. The heat-sink composite panel used is made of CuCrZr and 316L stainless steel and is formed by explosive welding. The V-shaped structure is made by bending, machining, and brazing. Finally, three continuous V-shaped structure mockups were fabricated and tested to demonstrate the feasibility of engineering design.

Laser-induced breakdown spectroscopy (LIBS) was employed for the depth-resolved identification of impurities deposited on the small test tiles used in the experimental advanced superconducting tokamak (EAST). LIBS spectra show the impurity elements of molybdenum (Mo), tungsten (W), carbon (C), copper (Cu), lithium (Li), titanium (Ti), silicon (Si), iron (Fe), and chromium (Cr) in the impurity deposition on the test tiles used in the EAST. The analysis of impurities was performed at various spot positions on the surfaces of the tiles. The impurity deposition is the final destination of the migrating eroded materials in the EAST. The interaction of high-heat plasma with the plasma-facing components (PFCs) causes the erosion of W upper and lower divertors, Mo main wall, the doped graphite (C, Si & Ti) limiters, Li wall conditioning, and CuCrZr heat conductors. Surface morphology was performed by using the scanning electron microscope (SEM) and energy-dispersive X-ray spectroscopy (EDX). Depth profile analysis showed the thickness of impurity deposition is in fractions of micrometers because the impurity signals appeared just in a few laser shots. The spectral intensity of impurity was observed differently during the consecutive laser shots on various spot positions that showed the impurity deposition is uneven on the tiles’ surfaces.

The human liver exhibits variable characteristics and anatomical information, which is often ambiguous in radiological images. Machine learning can be of great assistance in automatically segmenting the liver in radiological images, which can be further processed for computer-aided diagnosis. Magnetic resonance imaging (MRI) is preferred by clinicians for liver pathology diagnosis over volumetric abdominal computerized tomography (CT) scans, due to their superior representation of soft tissues. The convenience of Hounsfield unit (HoU) based preprocessing in CT scans is not available in MRI, making automatic segmentation challenging for MR images. This study investigates multiple state-of-the-art segmentation networks for liver segmentation from volumetric MRI images. Here, T1-weighted (in-phase) scans are investigated using expert-labeled liver masks from a public dataset of 20 patients (647 MR slices) from the Combined Healthy Abdominal Organ Segmentation grant challenge (CHAOS). The reason for using T1-weighted images is that it demonstrates brighter fat content, thus providing enhanced images for the segmentation task. Twenty-four different state-of-the-art segmentation networks with varying depths of dense, residual, and inception encoder and decoder backbones were investigated for the task. A novel cascaded network is proposed to segment axial liver slices. The proposed framework outperforms existing approaches reported in the literature for the liver segmentation task (on the same test set) with a dice similarity coefficient (DSC) score and intersect over union (IoU) of 95.15% and 92.10%, respectively.

Microplastic pollution has become a global concern, with potential negative impacts on various ecosystems and wildlife species. Among these species, ducks (Anas platyrhynchos) are particularly vulnerable due to their feeding habits and proximity to aquatic environments contaminated with microplastics. The current study was designed to monitor microplastic (MP) pollutants in the freshwater ecosystem of the Panjkora River, Lower Dir, Pakistan. A total of twenty (20) duck samples were brought up for four months and 13 days on the banks of the river, with no food intake outside the river. When they reached an average weight of 2.41 ± 0.53 kg, all samples were sacrificed, dissected, and transported in an ice box to the laboratory for further analysis. After sample preparation, such as digestion with 10% potassium hydroxide (KOH), density separation, filtration, and identification, the MP content was counted. A total of 2033 MP particles were recovered from 20 ducks with a mean value of 44.6 ± 15.8 MPs/crop and 57.05 ± 18.7 MPs/gizzard. MPs detected in surface water were 31.2 ± 15.5 MPs/L. The major shape types of MPs recovered were fragments in crop (67%) and gizzard (58%) samples and fibers in surface water (56%). Other types of particles recovered were fibers, sheets, and foams. The majority of these detected MP particles were in the size range of 300–500 µm (63%) in crops, and 50–150 µm (55%) in gizzards, while in water samples the most detected particles were in the range of 150–300 µm (61%). Chemical characterization by FTIR found six types of polymers. Low-density polyethylene (LDPE) had the greatest polymer detection rate (39.2%), followed by polyvinyl chloride (PVC) (28.3%), high-density polyethylene (HDPE) (22.7%), polystyrene (6.6%), co-polymerized polypropylene (2.5%), and polypropylene homopolymer (0.7%). This study investigated the presence of microplastics in the crops and gizzards of ducks, as well as in river surface water. The results revealed the significant and pervasive occurrence of microplastics in both the avian digestive systems and the surrounding water environment. These findings highlight the potential threat of microplastic pollution to wildlife and ecosystems, emphasizing the need for further research and effective mitigation strategies to address this pressing environmental concern.

The tribological enhancement of nano-lubricant that consist of formulated Eichhornia Crassipes carbon nanotubes (EC-CNTs) dispersed in base rapeseed oil using sodium dodecyl sulfate (SDS) and oleic acid (OA) as surfactants was conducted in this study. The experiment was done using high frequency reciprocating rig, applying 0.5 mass%, 1 mass% and 1.5 mass% EC-CNTs alongside 2% and 5% of the various surfactants and sonicated for 25 minutes and 50 minutes respectively. The use of surfactants and sonication was to reduce agglomeration. The outcome showed that SDS yielded better results than OA, further revealed that the use of 2% SDS under sonication time of 50 minutes reduced the solution agglomeration compared to 5% SDS and sonication time of 25 minutes. The tribological test conducted on EC-CNTs concentration effects was based on friction and wear reduction, load carrying ability, lubricant film stability, wear scar micrograph and mechanism of particles. The results indicated that inclusion of nanoparticles substantially enhanced the tribological characteristics. However, pronounced enhancement was recorded with 1 mass% EC-CNT compared to counterpart lubricants. Friction and wear reduction by 1 mass% EC-CNT were 65.4% and 63.6% respectively against base oil. The nanoparticles exhibited excellent mechanism for which the tribological enhancement was achieved.

The current manuscript studies a discrete-time phytoplankton-zooplankton model with Holling type-II response. The original model is modified by considering the condition that the phytoplankton population is getting infected with an external toxic substance. To obtain the discrete counterpart from a continuous-time system, Euler's forward method is applied. Moreover, a consistent discrete-time phytoplankton-zooplankton model is obtained by using a nonstandard difference scheme. The boundedness character for every positive solution is discussed, and the local stability of obtained system about each of its fixed points is discussed. The existence of period-doubling bifurcation at a positive equilibrium point is discussed for the discrete system obtained by Euler's forward method. In addition, the comparison of the consistent discrete-time version with its inconsistent counterpart is provided. It is proved that the discrete-time system obtained by using a nonstandard scheme is dynamically consistent as there is no chance for the existence of period-doubling bifurcation in that system. In order to control the period-doubling bifurcation and Neimark-Sacker bifurcation, an improved hybrid control strategy is applied. Finally, we have provided some interesting numerical examples to explain our theoretical results.

Lead-based reactors have high core outlet temperature and have the potential to achieve high performance of Power Generation System (PGS). The increase in the thermal performance of the PGS is really a key challenge for the lead-based reactor. In this paper, a reference scheme with two reheater has been chosen for lead-based reactor with 400 MWth to improve the thermal performance of the system based on the optimization of inlet parameters to overcome and meet the energy demands. The influence of turbine pressure, feed water temperature and condenser back pressure have been analyzed to improve the performance of lead-based power reactor. The analysis results show that as turbine pressure is increased from 18 MPa to 22 MPa at steam inlet temperature of 465 °C, the thermal efficiency of system is increased from 44.30% to 46.35%. As PGS is operated at 22 MPa and the temperature of the feed water is increased from 335 °C to 356 °C, the thermal efficiency of system can be increased up to 49.45% and it decreased with the increase of condenser back pressure from 0.004 MPa to 0.015 MPa. The maximum thermal efficiency of the system was 45.28% at 0.004 MPa condenser back pressure. This work can be used for the analysis and optimization of a nuclear power plant based on the heavy metal cooled reactor concept.

Abstract Cogeneration system based on Small Modular Lead‐cooled Fast Reactor (SMLFR) becomes attractive due to its good characteristics of flexible location, safety, thermal efficiency, and economy. The conventional cogeneration systems using coal, gas, or renewable energy as the thermal resource have lower thermal performance as compared to LFR based cogeneration systems due to lack of sustainable energy resources. A modified concept design of cogeneration system based on a 35‐MWth SMLFR has been proposed to improve the thermal performance. A new concept of District Heating (DH) structure layout along with optimization based on the exhaust steam/water drainage position. The thermodynamic model based on energy and exergy methods has been used to design and calculate the energy losses of the cogeneration system components. The energy utilization rate can be increased significantly by optimizing the DH. The thermal efficiency of the proposed system reaches up to 73.64% with an increase of 3.51% and the exergy efficiency reaches up to 59.31% with an increase of 5.01%. The increase of thermal performance under the different heating demands will lead to better energy conservation and environmental safety. This study can be further used as the reference for the design and optimization of SMLFR cogeneration system based on thermodynamic and exergy analysis.

Abstract The interaction among phytoplankton and zooplankton is one of the most important processes in ecology. Discrete-time mathematical models are commonly used for describing the dynamical properties of phytoplankton and zooplankton interaction with nonoverlapping generations. In such type of generations a new age group swaps the older group after regular intervals of time. Keeping in observation the dynamical reliability for continuous-time mathematical models, we convert a continuous-time phytoplankton–zooplankton model into its discrete-time counterpart by applying a dynamically consistent nonstandard difference scheme. Moreover, we discuss boundedness conditions for every solution and prove the existence of a unique positive equilibrium point. We discuss the local stability of obtained system about all its equilibrium points and show the existence of Neimark–Sacker bifurcation about unique positive equilibrium under some mathematical conditions. To control the Neimark–Sacker bifurcation, we apply a generalized hybrid control technique. For explanation of our theoretical results and to compare the dynamics of obtained discrete-time model with its continuous counterpart, we provide some motivating numerical examples. Moreover, from numerical study we can see that the obtained system and its continuous-time counterpart are stable for the same values of parameters, and they are unstable for the same parametric values. Hence the dynamical consistency of our obtained system can be seen from numerical study. Finally, we compare the modified hybrid method with old hybrid method at the end of the paper.

The globally soaring energy prices and electricity shortfall are major hurdles in the economic development of Pakistan. To cope with periodic power outages, small and medium enterprise (SME) business owners have to fall back on alternate power sources such as backup generators and uninterruptible power supplies (UPS), which further increase the per kWh cost of electricity, power quality issues, and greenhouse gas (GHG) emissions. On the contrary, grid-tied solar photovoltaic (PV) systems are not only economical and sustainable but support the national power grid to mitigate environmental emissions. This study aims to investigate and compare the techno-economic viability of grid-connected solar photovoltaic power plants for the manufacturing SME sector in four different districts of Punjab, Pakistan. Based on the technical, financial, and environmental indicators, a detailed techno-economic, sensitivity, and GHG emission analysis is conducted using RETScreen Expert software. The research findings clearly show that the proposed solar PV projects for all four locations are technically, financially, and environmentally viable, however, Sargodha as compared to other sites is the most feasible location with the highest capacity factor of 17.8 %, highest internal rate of return 14.9 %, lowest payback period 7.7 years, and least levelized cost of electricity 8.5 ¢/kWh. For validation, the simulation results are compared with performance metrics from PV plants erected in various parts of the world. Applying the same research approach to the whole industrial sector of Punjab recommends adding 13,469 MW of PV capacity to satisfy the industry’s 20446.21 GWh annual energy consumption and to cut emissions by 90,17,581 t CO2 per year. This research work presents guidelines for researchers to evaluate the feasibility of suitable PV technologies for the SME sector thereby helping investors to have a holistic view of potential investment zones.

Heart failure is a devastating disease that has high mortality rates and a negative impact on quality of life. Heart failure patients often experience emergency readmission after an initial episode, often due to inadequate management. A timely diagnosis and treatment of underlying issues can significantly reduce the risk of emergency readmissions. The purpose of this project was to predict emergency readmissions of discharged heart failure patients using classical machine learning (ML) models based on Electronic Health Record (EHR) data. The dataset used for this study consisted of 166 clinical biomarkers from 2008 patient records. Three feature selection techniques were studied along with 13 classical ML models using five-fold cross-validation. A stacking ML model was trained using the predictions of the three best-performing models for final classification. The stacking ML model provided an accuracy, precision, recall, specificity, F1-score, and area under the curve (AUC) of 89.41%, 90.10%, 89.41%, 87.83%, 89.28%, and 0.881, respectively. This indicates the effectiveness of the proposed model in predicting emergency readmissions. The healthcare providers can intervene pro-actively to reduce emergency hospital readmission risk and improve patient outcomes and decrease healthcare costs using the proposed model.

Abstract Purpose This study used machine learning classification of texture features from MRI of breast tumor and peri-tumor at multiple treatment time points in conjunction with molecular subtypes to predict eventual pathological complete response (PCR) to neoadjuvant chemotherapy. Materials and method This study employed a subset of patients (N = 166) with PCR data from the I-SPY-1 TRIAL (2002–2006). This cohort consisted of patients with stage 2 or 3 breast cancer that underwent anthracycline–cyclophosphamide and taxane treatment. Magnetic resonance imaging (MRI) was acquired pre-neoadjuvant chemotherapy, early, and mid-treatment. Texture features were extracted from post-contrast-enhanced MRI, pre- and post-contrast subtraction images, and with morphological dilation to include peri-tumoral tissue. Molecular subtypes and Ki67 were also included in the prediction model. Performance of classification models used the receiver operating characteristics curve analysis including area under the curve (AUC). Statistical analysis was done using unpaired two-tailed t-tests. Results Molecular subtypes alone yielded moderate prediction performance of PCR (AUC = 0.82, p = 0.07). Pre-, early, and mid-treatment data alone yielded moderate performance (AUC = 0.88, 0.72, and 0.78, p = 0.03, 0.13, 0.44, respectively). The combined pre- and early treatment data markedly improved performance (AUC = 0.96, p = 0.0003). Addition of molecular subtypes improved performance slightly for individual time points but substantially for the combined pre- and early treatment (AUC = 0.98, p = 0.0003). The optimal morphological dilation was 3–5 pixels. Subtraction of post- and pre-contrast MRI further improved performance (AUC = 0.98, p = 0.00003). Finally, among the machine-learning algorithms evaluated, the RUSBoosted Tree machine-learning method yielded the highest performance. Conclusion AI-classification of texture features from MRI of breast tumor at multiple treatment time points accurately predicts eventual PCR. Longitudinal changes in texture features and peri-tumoral features further improve PCR prediction performance. Accurate assessment of treatment efficacy early on could minimize unnecessary toxic chemotherapy and enable mid-treatment modification for patients to achieve better clinical outcomes.

Plants are a major source of food for the world population. Plant diseases contribute to production loss, which can be tackled with continuous monitoring. Manual plant disease monitoring is both laborious and error-prone. Early detection of plant diseases using computer vision and artificial intelligence (AI) can help to reduce the adverse effects of diseases and also overcome the shortcomings of continuous human monitoring. In this work, we propose the use of a deep learning architecture based on a recent convolutional neural network called EfficientNet on 18,161 plain and segmented tomato leaf images to classify tomato diseases. The performance of two segmentation models i.e., U-net and Modified U-net, for the segmentation of leaves is reported. The comparative performance of the models for binary classification (healthy and unhealthy leaves), six-class classification (healthy and various groups of diseased leaves), and ten-class classification (healthy and various types of unhealthy leaves) are also reported. The modified U-net segmentation model showed accuracy, IoU, and Dice score of 98.66%, 98.5%, and 98.73%, respectively, for the segmentation of leaf images. EfficientNet-B7 showed superior performance for the binary classification and six-class classification using segmented images with an accuracy of 99.95% and 99.12%, respectively. Finally, EfficientNet-B4 achieved an accuracy of 99.89% for ten-class classification using segmented images. It can be concluded that all the architectures performed better in classifying the diseases when trained with deeper networks on segmented images. The performance of each of the experimental studies reported in this work outperforms the existing literature.

Magnetic resonance imaging (MRI) is commonly used in medical diagnosis and minimally invasive image-guided operations. During an MRI scan, the patient’s electrocardiogram (ECG) may be required for either gating or patient monitoring. However, the challenging environment of an MRI scanner, with its several types of magnetic fields, creates significant distortions of the collected ECG data due to the Magnetohydrodynamic (MHD) effect. These changes can be seen as irregular heartbeats. These distortions and abnormalities hamper the detection of QRS complexes, and a more in-depth diagnosis based on the ECG. This study aims to reliably detect R-peaks in the ECG waveforms in 3 Tesla (T) and 7T magnetic fields. A novel model, Self-Attention MHDNet, is proposed to detect R peaks from the MHD corrupted ECG signal through 1D-segmentation. The proposed model achieves a recall and precision of 99.83% and 99.68%, respectively, for the ECG data acquired in a 3T setting, while 99.87% and 99.78%, respectively, in a 7T setting. This model can thus be used in accurately gating the trigger pulse for the cardiovascular functional MRI.

This paper considers the problem of anomaly detection in an outdoor environment where surveillance cameras are usually installed to monitor activities of general public. A novel solution is proposed which combines audio and visual data to automatically detect abnormal activities. The proposed anomaly detection algorithm makes use of both visual and audio features to automatically detect anomalous activities in scenes. Visual features such as optical flow technique combined with particle swam optimization and social force model are used, whereas, acoustic features such as, energy, zero crossing rate, volume, spectral-centroid, spectral spread, spectral roll-off, spectral flux, cross correlation and the mel-frequency cepstral coefficients (MFCCs) are used. An anomaly inference is developed which is based on both visual and audio features. The performance of the proposed algorithm is evaluated by testing it on the publicly available UMN datasets combined with the audio recordings. The proposed algorithm is compared with state-of-the-art techniques and is shown to achieve improved performance in terms of accuracy.

Big data analytics has shown tremendous success in several fields such as businesses, agriculture, health, and meteorology, and education is no exception. Concerning its role in education, it is used to boost students’ learning process by predicting their performance in advance and adapting the relevant instructional design strategies. This study primarily intends to develop a system that can predict students’ performance and help teachers to timely introduce corrective interventions to uplift the performance of low-performing students. As a secondary part of this research, it also explores the potential of collaborative learning as an intervention to act in combination with the prediction system to improve the performance of students. To support such changes, a visualization system is also developed to track and monitor the performance of students, groups, and overall class to help teachers in the regrouping of students concerning their performance. Several well-known machine learning models are applied to predict students performance. Results suggest that experimental groups performed better after treatment than before treatment. The students who took part in each class activity, prepared and submitted their tasks perform much better than other students. Overall, the study found that collaborative learning methods play a significant role to enhance the learning capability of the students.

In this paper we consider a system of second order rational difference equations. We mainly discuss the boundedness and persistence, existence of fixed point, and uniqueness of positive fixed point, local and global behavior of positive fixed point and rate of convergence of every positive solution of the system under discussion. It will be shown that the system under discussion exhibits some special dynamics such as same mathematical condition for existence of fixed point and its global stability. Finally, some numerical examples are provided for verification of theoretical results.

Coronavirus disease (COVID-19) is a pandemic disease, which has already caused thousands of causalities and infected several millions of people worldwide. Any technological tool enabling rapid screening of the COVID-19 infection with high accuracy can be crucially helpful to the healthcare professionals. The main clinical tool currently in use for the diagnosis of COVID-19 is the Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires specialized medical personnel. X-ray imaging is an easily accessible tool that can be an excellent alternative in the COVID-19 diagnosis. This research was taken to investigate the utility of artificial intelligence (AI) in the rapid and accurate detection of COVID-19 from chest X-ray images. The aim of this paper is to propose a robust technique for automatic detection of COVID-19 pneumonia from digital chest X-ray images applying pre-trained deep-learning algorithms while maximizing the detection accuracy. A public database was created by the authors combining several public databases and also by collecting images from recently published articles. The database contains a mixture of 423 COVID-19, 1485 viral pneumonia, and 1579 normal chest X-ray images. Transfer learning technique was used with the help of image augmentation to train and validate several pre-trained deep Convolutional Neural Networks (CNNs). The networks were trained to classify two different schemes: i) normal and COVID-19 pneumonia; ii) normal, viral and COVID-19 pneumonia with and without image augmentation. The classification accuracy, precision, sensitivity, and specificity for both the schemes were 99.7%, 99.7%, 99.7% and 99.55% and 97.9%, 97.95%, 97.9%, and 98.8%, respectively. The high accuracy of this computer-aided diagnostic tool can significantly improve the speed and accuracy of COVID-19 diagnosis. This would be extremely useful in this pandemic where disease burden and need for preventive measures are at odds with available resources.

Cardiovascular diseases are one of the most severe causes of mortality, annually taking a heavy toll on lives worldwide. Continuous monitoring of blood pressure seems to be the most viable option, but this demands an invasive process, introducing several layers of complexities and reliability concerns due to non-invasive techniques not being accurate. This motivates us to develop a method to estimate the continuous arterial blood pressure (ABP) waveform through a non-invasive approach using Photoplethysmogram (PPG) signals. We explore the advantage of deep learning, as it would free us from sticking to ideally shaped PPG signals only by making handcrafted feature computation irrelevant, which is a shortcoming of the existing approaches. Thus, we present PPG2ABP, a two-stage cascaded deep learning-based method that manages to estimate the continuous ABP waveform from the input PPG signal with a mean absolute error of 4.604 mmHg, preserving the shape, magnitude, and phase in unison. However, the more astounding success of PPG2ABP turns out to be that the computed values of Diastolic Blood Pressure (DBP), Mean Arterial Pressure (MAP), and Systolic Blood Pressure (SBP) from the estimated ABP waveform outperform the existing works under several metrics (mean absolute error of 3.449 ± 6.147 mmHg, 2.310 ± 4.437 mmHg, and 5.727 ± 9.162 mmHg, respectively), despite that PPG2ABP is not explicitly trained to do so. Notably, both for DBP and MAP, we achieve Grade A in the BHS (British Hypertension Society) Standard and satisfy the AAMI (Association for the Advancement of Medical Instrumentation) standard.

Haemoglobin (Hb) electrophoresis is a method of blood testing used to detect thalassaemia. However, the interpretation of the result of the electrophoresis test itself is a complex task. Expert haematologists, specifically in developing countries, are relatively few in number and are usually overburdened. To assist them with their workload, in this paper we present a novel method for the automated assessment of thalassaemia using Hb electrophoresis images. Moreover, in this study we compile a large Hb electrophoresis image dataset, consisting of 103 strips containing 524 electrophoresis images with a clear consensus on the quality of electrophoresis obtained from 824 subjects. The proposed methodology is split into two parts: (1) single-patient electrophoresis image segmentation by means of the lane extraction technique, and (2) binary classification (normal or abnormal) of the electrophoresis images using state-of-the-art deep convolutional neural networks (CNNs) and using the concept of transfer learning. Image processing techniques including filtering and morphological operations are applied for object detection and lane extraction to automatically separate the lanes and classify them using CNN models. Seven different CNN models (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, SqueezeNet and MobileNetV2) were investigated in this study. InceptionV3 outperformed the other CNNs in detecting thalassaemia using Hb electrophoresis images. The accuracy, precision, recall, f1-score, and specificity in the detection of thalassaemia obtained with the InceptionV3 model were 95.8%, 95.84%, 95.8%, 95.8% and 95.8%, respectively. MobileNetV2 demonstrated an accuracy, precision, recall, f1-score, and specificity of 95.72%, 95.73%, 95.72%, 95.7% and 95.72% respectively. Its performance was comparable with the best performing model, InceptionV3. Since it is a very shallow network, MobileNetV2 also provides the least latency in processing a single-patient image and it can be suitably used for mobile applications. The proposed approach, which has shown very high classification accuracy, will assist in the rapid and robust detection of thalassaemia using Hb electrophoresis images.

Sound event detection (SED) plays an important role in understanding the sounds in different environments. Recent studies on standardized datasets have shown the growing interest of the scientific community in the SED problem, however, these did not pay sufficient attention to the detection of artificial and natural sound. In order to tackle this issue, the present article uses different features in combination for detection of machine-generated and natural sounds. In this article, we trained and compared a Stacked Convolutional Recurrent Neural Network (S-CRNN), a Convolutional Recurrent Neural Network (CRNN), and an Artificial Neural Network Classifier (ANN) using the DCASE 2017 Task-3 dataset. Relative spectral–perceptual linear prediction (RASTA-PLP) and Mel-frequency cepstrum (MFCC) features are used as input to the proposed multi-model. The performance of monaural and binaural approaches provided to the classifier as an input is compared. In our proposed S-CRNN model, we classified the sound events in the dataset into two sub-classes. When compared with the baseline model, our obtained results show that the PLP-based ANN classifier improves the individual error rate (ER) for each sound event, e.g., the error rate (ER) is improved to 0.23 for heavy vehicle events and 0.32 for people walking, and minor gains are shown in other events as compared to the baseline. Our proposed CRNN performs well when compare to the baseline and to our proposed ANN model. Moreover, in cross-validation trials, the results in the evaluation stage demonstrate a significant improvement compared to the best performance of DCASE 2017 Task-3, reducing the ER to 0.11 and increasing the F1-score by 10% in the evaluation dataset. Erosion and dilation were used during post-processing.

This paper deals with the qualitative study of a discrete-time prey-predator model with Holling type-II response. Particularly, we obtained a dynamically consistent prey-predator discrete-time model by applying a nonstandard difference scheme. We explore the novel explicate parametric conditions for the local stability of positive equilibrium point. Moreover, it is shown that there exists Nimark-sacker bifurcation for the unique positive steady-state of given system. In order to control the bifurcation, we introduced a new control strategy. Moreover, some interesting numerical simulations are provided in order to verify the theoretical discussion and to explore the effectiveness and feasibility of new design control strategy.

This manuscript deals with the qualitative study of certain properties of an immunogenic tumors model. Mainly, we obtain a dynamically consistent discrete-time immunogenic tumors model using a nonstandard difference scheme. The existence of fixed points and their stability are discussed. It is shown that a continuous system experiences Hopf bifurcation at one and only one positive fixed point, whereas its discrete-time counterpart experiences Neimark–Sacker bifurcation at one and only one positive fixed point. It is shown that there is no chance of period-doubling bifurcation in our discrete-time system. Additionally, numerical simulations are carried out in support of our theoretical discussion.