Prairie Dog Optimization Algorithm with deep learning assisted based Aerial Image Classification on UAV imagery.

This study presents a Prairie Dog Optimization Algorithm with a Deep learning-assisted Aerial Image Classification Approach (PDODL-AICA) on UAV images. The PDODL-AICA technique exploits the optimal DL model for classifying aerial images into numerous classes. In the presented PDODL-AICA technique, the feature extraction procedure is executed using the EfficientNetB7 model. Besides, the hyperparameter tuning of the EfficientNetB7 technique uses the PDO model. The PDODL-AICA technique uses a convolutional variational autoencoder (CVAE) model to detect and classify aerial images. The performance study of the PDODL-AICA model is implemented on a benchmark UAV image dataset. The experimental values inferred the authority of the PDODL-AICA approach over recent models in terms of dissimilar measures.

Leveraging electrocardiography signals for deep learning-driven cardiovascular disease classification model.

Electrocardiography (ECG) is the most non-invasive diagnostic tool for cardiovascular diseases (CVDs). Automatic analysis of ECG signals assists in accurately and rapidly detecting life-threatening arrhythmias like atrioventricular blockage, atrial fibrillation, ventricular tachycardia, etc. The ECG recognition models need to utilize algorithms to detect various kinds of waveforms in the ECG and identify complicated relationships over time. However, the high variability of wave morphology among patients and noise are challenging issues. Physicians frequently utilize automated ECG abnormality recognition models to classify long-term ECG signals. Recently, deep learning (DL) models can be used to achieve enhanced ECG recognition accuracy in the healthcare decision making system. In this aspect, this study introduces an automated DL enabled ECG signal recognition (ADL-ECGSR) technique for CVD detection and classification. The ADL-ECGSR technique employs three most important subprocesses: pre-processed, feature extraction, parameter tuning, and classification. Besides, the ADL-ECGSR technique involves the design of a bidirectional long short-term memory (BiLSTM) based feature extractor, and the Adamax optimizer is utilized to optimize the trained method of the BiLSTM model. Finally, the dragonfly algorithm (DFA) with a stacked sparse autoencoder (SSAE) module is applied to recognize and classify EEG signals. An extensive range of simulations occur on benchmark PTB-XL datasets to validate the enhanced ECG recognition efficiency. The comparative analysis of the ADL-ECGSR methodology showed a remarkable performance of 91.24 % on the existing methods.

A Rare Presentation of a Periorbital Herpes Simplex Virus Pseudotumor in an Immunocompromised Patient.

Herpes simplex virus (HSV) frequently affects the ocular and genital regions, especially in immunocompromised individuals. On rare occasions, HSV infections can present as pseudotumors. These pseudotumors may mimic cancerous growths, condylomas, or hypertrophic lesions rather than the characteristic small ulcerations. The development of pseudotumors due to HSV is particularly uncommon, especially in the facial region. This atypical presentation poses significant diagnostic challenges and may potentially lead to erroneous identification as a cancerous growth. This case report details a 53-year-old African American man with human immunodeficiency virus (HIV) (noncompliant with antiretroviral therapy) presenting with a purulent ocular pseudotumor secondary to HSV infection, along with a review of the literature surrounding HSV pseudotumors.

Selection of appropriate location based on signed distance based ranking approach with interval type-2 trapezoidal fuzzy preference relations.

In real life situation, it is often difficult to judge the relative importance of different parameters being considered for evaluating some alternatives. In the context of fuzzy sets, it is a situation where it is difficult to define precise membership grades for attribute values. Here we require more generalized type of fuzzy sets which have a greater representational power than ordinary fuzzy sets. For this purpose we use "interval type-2 trapezoidal fuzzy preference relations (IT2TrFPRs)" in this article as a generalization of fuzzy preference relations and consider the environment discussed above, where there is no information on priority weights. A collective decision matrix will be constructed on the basis of hybrid averages using weighted averaging and signed distance based OWA operation. Then a least deviation model will be employed in order to determine the priority weight vectors. Finally, the alternatives will be ranked on the basis of weighted normalized signed distance of each alternative from the ideal solution. Moreover, a real life example of location selection is illustrated to elaborate the effectiveness of the proposed scheme.

Deep learning solutions for inverse problems in advanced biomedical image analysis on disease detection.

Inverse problems in biomedical image analysis represent a significant frontier in disease detection, leveraging computational methodologies and mathematical modelling to unravel complex data embedded within medical images. These problems include deducing the unknown properties of biological structures or tissues from the observed imaging data, presenting a unique challenge in decoding intricate biological phenomena. Regarding disease detection, this technique has played a critical role in optimizing diagnostic efficiency by extracting meaningful insights from different imaging modalities like molecular imaging, MRI, and CT scans. Inverse problems contribute to uncovering subtle abnormalities by employing iterative optimization techniques and sophisticated algorithms, enabling precise and early disease detection. Deep learning (DL) solutions have emerged as robust mechanisms for addressing inverse problems in biomedical image analysis, especially in disease recognition. Inverse problems involve reconstructing unknown structures or parameters from observed data, and the DL model excels in learning complex representations and mappings. This study develops a DL Solution for Inverse Problems in the Advanced Biomedical Image Analysis on Disease Detection (DLSIP-ABIADD) technique. The DLSIP-ABIADD technique exploits the DL approach to solve inverse problems and detect the presence of diseases on biomedical images. To solve the inverse problem, the DLSIP-ABIADD technique uses a direct mapping approach. Bilateral filtering (BF) is used for image preprocessing. Besides, the MobileNetv2 model derives feature vectors from the input images. Moreover, the Henry gas solubility optimization (HGSO) method is applied for optimal hyperparameter selection of the MobileNetv2 model. Furthermore, a bidirectional long short-term memory (BiLSTM) model is deployed to identify diseases in medical images. Extensive simulations have been involved to illustrate the better performance of the DLSIP-ABIADD technique. The experimentation outcomes stated that the DLSIP-ABIADD technique performs better than other models.

Amelioration of fat clay treated with CNS and cohesionless soils.

The present study evaluates the amelioration of fat clay by blending it with cohesive non-swelling soil (CNS) and cohesionless silty sandy soil (Kassu). The fat clay sample with a liquid limit (LL) of 50 and a plasticity index (PI) of 26 was collected from Narowal, while CNS and Kassu samples were procured from Lahore's outskirts. Geotechnical tests on the virgin soil indicated its unsuitability for construction. Laboratory tests, including modified Proctor compaction, unconfined compression, California bearing ratio (CBR), and one-dimensional consolidation, were performed on samples blended with 0-35% CNS or Kassu in 5% intervals. The LL and PI of fat clay decreased significantly with the addition of 35% CNS (LL: 50-32%, PI: 24 to 13) and Kassu (LL: 50-29%, PI: 24-12). The CBR value increased from 4 to 7%, making the blended soil suitable for subgrade use. Unconfined compression tests showed a strength increase from 102 to 185 kPa with 35% CNS and up to 140 kPa with 25% Kassu. Compaction tests revealed improved maximum dry unit weight and reduced optimum moisture content. Swell potential decreased from 4 to 1.2 and 0.26% with CNS and Kassu additions. Regression models predict swell pressure and ultimate swell potential. The study concludes that blending fat clay with CNS and Kassu significantly improves its geotechnical properties, with CNS being more effective in controlling swell characteristics.

Impacts of poultry by-product meal substituting fishmeal on growth efficiency, body composition, liver, and intestine morphology of European sea bass, Dicentrarchus labrax.

A twelve week feeding experiment was conducted to evaluate the replacement of fishmeal (FM) with poultry by-product meal (PBM) in practical diets for European sea bass, Dicentrarchus labrax with an average initial weight of 0.89 g. Five isocaloric (5.1 kcal lipid g-1) and isonitrogenous (451 g protein kg-1) diets were formulated with PBM replacing FM at levels of 0% (control), 25%, 50%, 75%, and 100%. The experiment was carried out in 30-in. nylon mesh net cages (hapas). At the termination of the trial, growth performance including final body weight, weight gain, specific growth rate, and protein growth rate of diets containing up to 75% PBM were comparable to those of the control group, whereas the diet with 100% PBM resulted in a significantly lower values (p < 0.05). Feed utilization exhibited variation among the treatments (p < 0.05). Whole body composition also showed significant differences across the dietary treatments. Essential amino acid (EAA) contents specifically arginine (Arg), histidine (His), methionine (Met), and threonine (Thr) in the whole body of fish fed diets with up to 50% PBM replacement were not significantly different from those in the control group. Furthermore, the intestinal microvilli length, width and absorption area increased significantly (p < 0.05) with PBM replacement levels up to 50%. Histological analysis of the liver revealed mild vacuolation of hepatocytes in fish fed up to 50% PBM,while pre-pancreatic fatty degeneration of hepatocytes was observed in fish fed diets with 75% and 100% PBM. Therefore, this study demonstrates that PBM can replace up to 50% of FM in the diets of European sea bass without adverse effects on growth performance, body composition, or liver and intestine morphology.

Enhancing durability and sustainability of industrial floors: A comparative analysis of dry-shake surface hardeners.

This study investigates the development of a cost-effective and sustainable dry-shake surface hardener for enhancing the durability of industrial concrete floors. Utilizing locally sourced materials, the research aimed at not only ensuring the hardener's strength and finish but also its economic viability and environmental friendliness. Fourteen unique mixtures were formulated by altering the sand ratios and incorporating superplasticizers to optimize the composition. These mixtures underwent rigorous testing over 7, 14, and 28 days, evaluating their compressive and flexural strengths, flowability, water absorption, and impact resistance. The findings revealed that the modified floor hardener, specifically the FH-12 mixture, exhibited superior performance across all tested parameters. It showed higher compressive and flexural strengths, enhanced impact resistance, and reduced water absorption compared to other variants and commercially available hardeners. Notably, the use of finer coarse sand and the adjustment of superplasticizer quantities significantly contributed to these outcomes. This breakthrough demonstrates the potential of employing locally available materials to create a durable, cost-effective, and environmentally friendly solution for industrial flooring. The study underscores the importance of material characterization and methodical formulation in developing construction materials that meet the dual criteria of performance and sustainability. This option is preferred for its lower environmental impact and compatibility with sustainable practices, contributing to Sustainable Development Goal 9 on industry, innovation, and infrastructure. It highlights the role of floor hardeners in global sustainability efforts.

Implementation and proficiency analysis of enhanced graph algorithm for DC microgrid applications.

Integrating renewable energy generation with the conventional grid supports reduces carbon emissions in the atmosphere. Despite technical advancements in protection strategies, critical issues concerning renewable integration in microgrid structures require standardized solutions. The essential aspects that need to be concentrated during securing the grids are rapid fault interruption, false tripping and blinding of protection. This study proposes an innovative approach to enhance fault isolation speed through the implementation of a grid monitoring system (GMS) coupled with a fault identification method based on Kosaraju's algorithm. This algorithm operates on the principles of overvoltage and overcurrent detection. The study assesses the efficacy of this approach by examining its integration with a Z-source circuit breaker and conducting tests on different fault types within a 13-bus system. Real-time simulations using Opal RT software are employed to experimentally validate the proposed methodology, ensuring its efficacy in fault interruption and isolation.

Real-time navigation of mecanum wheel-based mobile robot in a dynamic environment.

Path planning and control of a mobile robot, in a dynamic environment, has been an important research topic for many years. In this paper an algorithm for autonomous motion of a mobile robot is proposed, with mecanum wheels, to reach a goal while avoiding obstacles through the shortest path in a dynamic environment. The proposed method uses a hybrid A⁎ and a velocity obstacle algorithms for path planning and obstacle avoidance. The A⁎ algorithm is implemented to explore the shortest path from starting position to the goal while avoiding all the static obstacles. However, in real time applications the dynamic obstacles need to be avoided, therefore, for such a case velocity obstacle algorithm is unified with the A⁎ algorithm. Initially, the proposed algorithm is verified through simulations. Then it is implemented using experimental setup in real time environment using single and multiple static obstacles as well as on a dynamic obstacle. It can be observed that the robot reaches the goal, effectively by avoiding static and dynamic obstacles. Moreover, the performance of the proposed work is evaluated through qualitative comparison between proposed method and recently published work, showing that the proposed algorithm is gives better features than existing work. In the end, the possible application of mobile robot having mecanum wheels with proposed path planning method is also given in the paper.

Novel design and performance evaluation of an indirectly forced convection desiccant integrated solar dryer for drying tomatoes in Pakistan.

The process of drying agricultural products for food preservation is a difficult task that requires a significant amount of energy. The increasing cost and depletion of fossil fuels have led to the development of a food dryer that utilizes renewable energy sources. This research paper proposes the design and performance evaluation of an indirectly forced convection desiccant integrated solar dryer (IFCDISD) at the Solar Energy Research Lab at USPCAS-E, NUST Pakistan. Tomatoes were chosen as the test product due to their importance and widespread consumption. The drying process involves slicing the tomatoes and placing them on the IFCDISD rack, where a desiccant called calcium chloride (CaCl2) is integrated into the dryer. The experiments were conducted during both sunshine (SS) hours and Off-sunshine (OSS) hours. The IFCDISD operates using sunlight during SS hours and utilizes the absorbed heat of CaCl2 in OSS hours via a forced DC brushless fan powered by battery charged thro solar panel. The tomatoes were weighed before and after each drying mode, and the moisture removal was calculated. The results show that the dryer efficiency was 50.14 % on day 1, 66 % on day 2, and an overall efficiency of 58.07 %. The moisture content removal was 42.858 % on day 1, 22.9979 % on day 2, and an overall moisture content removal of 58.07 %. Moreover, the payback period is 5.1396 and the carbon mitigation was recorded as 2.0335, and the earned carbon credit was recorded as 11559.6.

Interactive Pathology Tutorial in Neoplastic Hematology Disorders for Medical Hematology-Oncology Fellows.

Hematology-oncology (HO) fellows receive limited instruction in the process of establishing a diagnosis for hematologic neoplasms, and learning neoplastic hematology often occurs in limited encounters. In the current study, we developed a web-based interactive pathology tutorial in neoplastic hematologic disorders for HO fellows to work up simulated cases and establish the diagnosis. An online system ("Pathology Playground") was utilized to load case materials including microscopic images and ancillary studies. Twelve high-yield simulated cases of common leukemias and lymphoma were included. At the beginning of each case, trainees review the clinical history and slide images, and then, they are given the option to request additional pathology work-up. Based on the results, they can enter their diagnostic impression. If the diagnosis is correct, the user is shown a short educational presentation. If the diagnosis is not correct, the user gets notified by the message "Incorrect." The tutorial was integrated in the educational curriculum of our HO fellowship program, and bimonthly teaching sessions were held to review two cases each time. During the sessions, trainees request ancillary studies to complete the diagnostic work-up using the software and interpret the findings. As the case is being worked up by the trainee, the hematopathologists and HO fellowship program director discuss the findings, the appropriate work-up tools, and the implications on management. All of our six HO fellows attended the sessions, and a survey from the trainees showed high ease of use of the system and they viewed it as a very useful educational tool. A pre-test and post-test were administered for one of the sessions, and the result showed improvement in the average from 62 to 73%. Expanding the use of this online interactive tutorial and incorporating additional cases would enhance its value as a learning resource.

Multigrid simulations of non-Newtonian fluid flow and heat transfer in a ventilated square cavity with mixed convection and baffles.

The impact of baffles on a convective heat transfer of a non-Newtonian fluid is experimentally studied within a square cavity. The non-Newtonian fluid is pumped into the cavity through the inlet and subsequently departs from the cavity via the outlet. Given the inherent non-linearity of the model, a numerical technique has been selected as the method for obtaining the outcomes. Primarily, the governing equations within the two-dimensional domain have been discretized using the finite element method. For approximating velocity and pressure, we have employed the reliable P 2 - P 1 finite element pair, while for temperature, we have opted for the quadratic basis. To enhance convergence speed and accuracy, we employ the powerful multigrid approach. This study investigates how key parameters like Richardson number (Ri), Reynolds number (Re), and baffle gap b g influence heat transfer within a cavity comprising a non-Newtonian fluid. The baffle gap ( b g ) has been systematically altered within the range of 0.2-0.6, and for this research, three distinct power law indices have been selected namely: 0.5, 1.0, and 1.5. The primary outcomes of the investigation are illustrated through velocity profiles, streamlines, and isotherm visualizations. Furthermore, the study includes the computation of the Nu avg (average Nusselt number) across a range of parameter values. As the Richardson number (Ri) increases, Nu avg also rises, indicating that an increase in Ri results in augmented average heat transfer. Making the space between the baffles wider makes heat flow more intense. This, in turn, heats up more fluid within the cavity.

Comparative analysis of enriched flyash based cement-sand compressed bricks under various curing regimes.

This study investigates the substitution of traditional burnt clay bricks (BCB), used since 7000 BCE, with environmentally friendly Fly Ash-Cement and Sand Composite Bricks (FCBs), utilizing industrial waste like Coal Fly Ash (CFA) from thermal power plants. The research encompasses two phases: the first involves experimental production of FCBs, while the second focuses on optimizing FCBs by varying CFA (50%, 60%, 70%), Ordinary Portland Cement (OPC) content (9%-21%), and incorporating stone dust (SD) and fine sand. Comprehensive tests under normal and steam curing conditions, adhering to ASTM C 67-05 standards, include X-Ray Diffraction (XRD), Energy Dispersive X-Ray (EDX), and Scanning Electron Microscopy (SEM) analyses. Results indicate that steam curing enhances early strength, with an optimized mix (MD: 5S) achieving a compressive strength of 15.57 MPa, flexural strength of 0.67 MPa, water absorption rate of 20.08%, and initial rate of water absorption of 4.64 g/min per 30 in2, devoid of efflorescence. Notably, a 9% OPC and 50% CFA mix (MD: 1S) shows improved early strength of 4.95 MPa at 28 days. However, excessive CFA replacement (70%) with lesser cement content negatively impacts physio-mechanical properties. This research underscores the potential of FCBs as a sustainable and economically viable alternative to BCBs in the construction industry.

Chaotic image encryption algorithm with improved bonobo optimizer and DNA coding for enhanced security.

Image encryption involves applying cryptographic approaches to convert the content of an image into an illegible or encrypted format, reassuring that illegal users cannot simply interpret or access the actual visual details. Commonly employed models comprise symmetric key algorithms for the encryption of the image data, necessitating a secret key for decryption. This study introduces a new Chaotic Image Encryption Algorithm with an Improved Bonobo Optimizer and DNA Coding (CIEAIBO-DNAC) for enhanced security. The presented CIEAIBO-DNAC technique involves different processes such as initial value generation, substitution, diffusion, and decryption. Primarily, the key is related to the input image pixel values by the MD5 hash function, and the hash value produced by the input image can be utilized as a primary value of the chaotic model to boost key sensitivity. Besides, the CIEAIBO-DNAC technique uses the Improved Bonobo Optimizer (IBO) algorithm for scrambling the pixel position in the block and the scrambling process among the blocks takes place. Moreover, in the diffusion stage, DNA encoding, obfuscation, and decoding process were carried out to attain encrypted images. Extensive experimental evaluations and security analyses are conducted to assess the outcome of the CIEAIBO-DNAC technique. The simulation outcome demonstrates excellent security properties, including resistance against several attacks, ensuring it can be applied to real-time image encryption scenarios.

Hepatic Benign Cystic Mesothelioma in Adults: A Case Report of a Rare Hepatic Cyst.

Benign cystic mesothelioma (BCM), also known as peritoneal inclusion cyst, is a benign mesothelial lined cystic lesion, nearly always described in the pelvis of adult females. The hepatic location of BCM is rarely reported in the literature. We report a case of hepatic benign cysts in a 65-year-old woman that was incidentally discovered by imaging studies 12 years ago as a small cyst. Recently, the patient started having abdominal discomfort, distension, and anxiety. A CT scan revealed two low-density fluid-filled cystic lesions, the largest in the caudate lobe measuring up to 10.7 cm and causing a mass effect on hepatic veins and inferior vena cava. Laparoscopic marsupialization of the large liver cyst was done without complications. On gross examination, the collapsed cyst wall was a thin partly translucent pale tan to pink membranous structure with fine vascularity. No discrete nodularity or solid lesion was identified. Microscopic examination showed a thin fibro-connective wall lined by a single layer of flat cuboidal cells with no cellular atypia. The cyst lining showed characteristic calretinin-positive immunohistochemical reactivity for mesothelium, supporting the diagnosis of BCM. Hepatic BCM is among a broad differential spectrum of cystic liver lesions ranging from developmental, reactive, inflammatory, and infectious lesions, benign to premalignant or frankly malignant neoplasms with different treatment strategies. Although BCM is the rarest among the long list of differential diagnoses of hepatic cysts, its identification in this rarely reported location is essential to avoid aggressive surgical treatment.

Development and characterization of non-proprietary ultra high performance concrete.

This paper presents the developmental process of ultra-high performance concrete (UHPC), the most advanced form of concrete. The entire process exclusively utilized locally available materials. The mixes were prepared without using any specialized mixer or treatments, such as elevated pressure, etc. The primary objective of the research was to develop low-cost non-proprietary version of UHPC by optimizing both cementitious and non-cementitious materials to attain the highest levels of workability, compressive strength, flexural strength and durability. The research utilizes a trial-and-error approach, subjecting specimens to curing in both regular and heated water. The findings validate the viability of producing self-compacting UHPC with compressive strength ranging from 120 to 160 MPa, employing local materials and manufacturing methods. Raw materials and mixing sequence had a significant influence on the fresh and hardened properties of UHPC. The inclusion of steel fibers and the application of heat treatment remarkably enhanced the compressive strength. Furthermore, cost analysis revealed that this particular UHPC is only slightly over four times more expensive than conventional concrete, in contrast to commercially available UHPC, which is approximately 10 times expensive than traditional concrete.

Up-scaling of fly ash-based geopolymer concrete to investigate the binary effect of locally available metakaolin with fly ash.

Owing to the increasing threat to environment due to the emission of greenhouse gases from cement industry globally, various promising solutions has been introduced in the past decades. The development of geoplymer concrete (GPC) is one of the contribution by the researches towards ecofriendly and sustainable construction. In this research, geopolymer concrete (GPC) is optimized by adding fixed amount of fly Ash (FA) and alkali activator to fine aggregate ratio as 0.5 with varying Molarity from 12 M to 16 M and Na2SiO3/NaOH ratio from 1.5 to 2.5. Physical and mechanical properties along with effect of heat and ambient curing conditions were investigated at various ages. The optimized mixture of fly ash based geopolymer concrete was then up scaled by blending with locally available Metakaolin (MK) with different dosages (i.e., 10%, 20%, 30%, 40%). The aim of the study is to identify the binary effect of FA and MK on overall performance of geopolymer concrete. Results showed that 30% FA-MK based GPC depicted 21%, 19% and 26% more compressive strength, split tensile strength and flexural strength respectively than Fly Ash based GPC alone at heat cured condition. This can be explained mainly due to two facts namely binary action of metakaolin that enhances compaction of GPC and pozzolanic activity of MK that expedite geopolymeric strength causing phases. The results were further verified by Modified Chapelle test and FTIR. Morphology of the developed GPC is also examined from SEM images. The work is an effort to utilize the fly ash produced by coal power plants to effectively address UN sustainable development goal related to sustainable cities and communities.

Skeletal age evaluation using hand X-rays to determine growth problems.

A common clinical method for identifying anomalies in bone growth in infants and newborns is skeletal age estimation with X-ray images. Children's bone abnormalities can result from several conditions including wounds, infections, or tumors. One of the most frequent reasons for bone issues is that most youngsters are affected by the slow displacement of bones caused by pressure applied to the growth plates as youngsters develop. The growth plate can be harmed by a lack of blood supply, separation from other parts of the bone, or slight misalignment. Problems with the growth plate prevent bones from developing, cause joint distortion, and may cause permanent joint injury. A significant discrepancy between the chronological and assessed ages may indicate a growth problem because determining bone age represents the real level of growth. Therefore, skeletal age estimation is performed to look for endocrine disorders, genetic problems, and growth anomalies. To address the bone age assessment challenge, this study uses the Radiological Society of North America's Pediatric Bone Age Challenge dataset which contains 12,600 radiological images of the left hand of a patient that includes the gender and bone age information. A bone age evaluation system based on the hand skeleton guidelines is proposed in this study for the detection of hand bone maturation. The proposed approach is based on a customized convolutional neural network. For the calculation of the skeletal age, different data augmentation techniques are used; these techniques not only increase the dataset size but also impact the training of the model. The performance of the model is assessed against the Visual Geometry Group (VGG) model. Results demonstrate that the customized convolutional neural network (CNN) model outperforms the VGG model with 97% accuracy.