Transparent and Accurate COVID-19 Diagnosis:Integrating Explainable AI with Advanced Deep Learning in CT Imaging

In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly significant.Despite its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is imperative.To bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning models.This integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional Neural Network(CNN)models through transfer learning and data augmentation.Our approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model generalization.The pivotal element of our methodology is the use of LIME,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s reasoning.This unique combination of an optimized Deep Neural Network(DNN)with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic process.Our method,validated on the SARS-COV-2 CT-Scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare systems.This innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19.

Low-Level Antibiotic Resistance among Staphylococcus aureus and Gram-Negative Pathogens from Infected Skin and Soft Tissues in Rural Kenya

Introduction: Bacterial skin and soft tissue infections (SSTIs) are a cause of frequent inpatient and outpatient care visits whose causative agents are associated with a high antimicrobial resistance burden. For insights on antimicrobial susceptibilities in a rural setting, we examined specimens from suspected SSTIs from two public health facilities in Kenya. We additionally assessed antibiotic use, appropriateness of empiric therapy and risk factors for SSTI. Methodology: Between 2021 and 2023, 265 patients at Kisii and Nyamira County Referral hospitals were enrolled. Wound swabs/aspirates were collected and processed following standard microbiological procedures. Identification and antimicrobial susceptibility were performed using the VITEK 2 Compact platform. Demographic, clinical, and microbiological data were analyzed with R Statistical software. Results: S. aureus was isolated in 16.2% (43/265) of patients with a methicillin resistance (MRSA) proportion of 14% (6/43). While 13/15 drugs elicited susceptibilities ranging from 84% - 100%, penicillin (16%) and trimethoprim-sulfamethoxazole [TMP-SXT] (23%) yielded the lowest susceptibilities. Escherichia coli (n = 33), Klebsiella pneumoniae (n = 8), Pseudomonas aeruginosa (n = 8), and Citrobacter species (n = 4) were the most commonly isolated gram-negative species. Gram-negative strains showed high susceptibilities to most of the tested drugs (71% - 100%) with the exception of ampicillin (18%), TMP-SXT (33%), and first and second generation cephalosporins. Conclusions: The low MRSA prevalence and generally high antibiotic susceptibilities for S. aureus and gram-negative bacteria present opportunities for antibiotic stewardship in the study setting. Diminished susceptibilities against penicillin/ampicillin and TMP-SXT accord with prevailing local data and add a layer of evidence for their cautious empiric use.

Beyond Defense: Proactive Approaches to Disaster Recovery and Threat Intelligence in Modern Enterprises

As cyber threats keep changing and business environments adapt, a comprehensive approach to disaster recovery involves more than just defensive measures. This research delves deep into the strategies required to respond to threats and anticipate and mitigate them proactively. Beginning with understanding the critical need for a layered defense and the intricacies of the attacker’s journey, the research offers insights into specialized defense techniques, emphasizing the importance of timely and strategic responses during incidents. Risk management is brought to the forefront, underscoring businesses’ need to adopt mature risk assessment practices and understand the potential risk impact areas. Additionally, the value of threat intelligence is explored, shedding light on the importance of active engagement within sharing communities and the vigilant observation of adversary motivations. “Beyond Defense: Proactive Approaches to Disaster Recovery and Threat Intelligence in Modern Enterprises” is a comprehensive guide for organizations aiming to fortify their cybersecurity posture, marrying best practices in proactive and reactive measures in the ever-challenging digital realm.

Heat and Mass Transfer for a Nanofluid Flow in Fluidized Bed Dryer in Presence of Induced Magnetic Field

This research entails the study of heat and mass transfer of nanofluid flow in a fluidized bed dryer used in tea drying processes in presence of induced magnetic field. A mathematical model describing the fluid flow in a Fluidized bed dryer was developed using the nonlinear partial differential equations. Due to their non-linearity, the equations were solved numerically by use of the finite difference method. The effects of physical flow parameters on velocity, temperature, concentration and magnetic induction profiles were studied and results were presented graphically. From the mathematical analysis, it was deduced that addition of silver nanoparticles into the fluid flow enhanced velocity and temperature profiles. This led to improved heat transfer in the fluidized bed dryer, hence amplifying the tea drying process. Furthermore, it was noted that induced magnetic field tends to decrease the fluid velocity, which results in uniform distribution of heat leading to efficient heat transfer between the tea particles and the fluid, thus improving the drying process. The research findings provide information to industries on ways to optimize thermal performance of fluidized bed dryers.

Enhancing Pneumonia Detection in Pediatric Chest X-Rays Using CGAN-Augmented Datasets and Lightweight Deep Transfer Learning Models

Pneumonia ranks as a leading cause of mortality, particularly in children aged five and under. Detecting this disease typically requires radiologists to examine chest X-rays and report their findings to physicians, a task susceptible to human error. The application of Deep Transfer Learning (DTL) for the identification of pneumonia through chest X-rays is hindered by a shortage of available images, which has led to less than optimal DTL performance and issues with overfitting. Overfitting is characterized by a model’s learning that is too closely fitted to the training data, reducing its effectiveness on unseen data. The problem of overfitting is especially prevalent in medical image processing due to the high costs and extensive time required for image annotation, as well as the challenge of collecting substantial datasets that also respect patient privacy concerning infectious diseases such as pneumonia. To mitigate these challenges, this paper introduces the use of conditional generative adversarial networks (CGAN) to enrich the pneumonia dataset with 2690 synthesized X-ray images of the minority class, aiming to even out the dataset distribution for improved diagnostic performance. Subsequently, we applied four modified lightweight deep transfer learning models such as Xception, MobileNetV2, MobileNet, and EfficientNetB0. These models have been fine-tuned and evaluated, demonstrating remarkable detection accuracies of 99.26%, 98.23%, 97.06%, and 94.55%, respectively, across fifty epochs. The experimental results validate that the models we have proposed achieve high detection accuracy rates, with the best model reaching up to 99.26% effectiveness, outperforming other models in the diagnosis of pneumonia from X-ray images.

Spatial Heterogeneity Modeling Using Machine Learning Based on a Hybrid of Random Forest and Convolutional Neural Network (CNN)

Spatial heterogeneity refers to the variation or differences in characteristics or features across different locations or areas in space. Spatial data refers to information that explicitly or indirectly belongs to a particular geographic region or location, also known as geo-spatial data or geographic information. Focusing on spatial heterogeneity, we present a hybrid machine learning model combining two competitive algorithms: the Random Forest Regressor and CNN. The model is fine-tuned using cross validation for hyper-parameter adjustment and performance evaluation, ensuring robustness and generalization. Our approach integrates Global Moran’s I for examining global autocorrelation, and local Moran’s I for assessing local spatial autocorrelation in the residuals. To validate our approach, we implemented the hybrid model on a real-world dataset and compared its performance with that of the traditional machine learning models. Results indicate superior performance with an R-squared of 0.90, outperforming RF 0.84 and CNN 0.74. This study contributed to a detailed understanding of spatial variations in data considering the geographical information (Longitude & Latitude) present in the dataset. Our results, also assessed using the Root Mean Squared Error (RMSE), indicated that the hybrid yielded lower errors, showing a deviation of 53.65% from the RF model and 63.24% from the CNN model. Additionally, the global Moran’s I index was observed to be 0.10. This study underscores that the hybrid was able to predict correctly the house prices both in clusters and in dispersed areas.

The health care economic-industrial complex: Concepts and general characteristics

The strategic role of health care within the Brazilian development agenda has been increasingly recognized and institutionalized. Aside from its importance as a structural element of the Welfare State, health care plays a leading role in generating innovation. Despite this, the productive base of Brazil’s health care system is extremely fragile, jeopardizing both the universal provision of health care services and the country’s competitive insertion in the globalized environment. This suggests the need for a more systematic analysis of the complex relationships that exist between the technological and the social interests involved in the productive base of health care provision in Brazil. Therefore, the purpose of this article is to enhance our understanding of the productive base of health care, especially given its potential to contribute to a socially inclusive development model, which is the ultimate goal in Brazil.

Recovery of metals and other beneficial products from coal fly ash： a sustainable approach for fly ash management

Increasing production and disposal of coal fly ash （CFA） is a matter of serious environment concern. However, CFA contains various beneficial metals and mineral matters whose demand is increasing in the industrialized world, while natural supplies are diminishing. Therefore, recovery of these potential resources from CFA can be an alternative way to save mineral resources, as well as to reduce the environmental burden of CFA disposal. There are numerous methods developed for the recovery of beneficial products from CFA. Based on the US patents and journal literatures, the present review describes the recovery status and technologies of major elements such as Al, Si, Fe and Ti, and trace elements such as V, Ga, Ge, Se, Li, Mo, U, Au, Ag, Pt groups and rare earth elements （REEs） and other beneficial products such as magnetic materials, cenospheres, and unburned carbon from CFA. It also highlights the recovery efficiency and drawbacks for their extraction, and suggests future research to develop satisfactory results in terms of selective recovery and purification.

Effect of nodule count and austempering heat treatment on segregation behavior of alloying elements in ductile cast iron

The equilibrium partition ratio, k, has been measured for Mn, Mo, Si, Ni and Cu in a ductile iron with composition(wt.%): 3.45 C, 0.25 Mn, 0.25 Mo, 2.45 Si, 0.5Ni and 0.5Cu with different nodule counts obtained from different section sizes of13, 25, 75 mm in the as cast, austenitized(at 870 °C for times 1, 4 and 6 hours) and austempered(at 375 °C for times 1 to 1,440 min) samples. Results show that Mn and Mo segregate positively at cell boundaries, but Si, Ni and Cu concentrate in an inverse manner in the vicinity ofgraphite nodules and there is a depletion ofthese elements at cell boundaries. Segregation curves for Ni and Cu are more smooth than for Si. Carbide formation has been observed at cell boundaries. Based on the results, the partition ratios for all elements decrease with increasing the nodule count. More carbide with coarser morphology has been observed in the microstructure with a lower nodule count. Austenitization for a longer time can decrease partition ratio, but cannot eliminate it entirely. Increasing the austenitization temperature has the same effect. Austenitizing parameters have no significant effect on carbides volume fraction. The kinetics ofaustempering is faster in higher nodule counts and subsequently better mechanical properties including higher ductility, strength and toughness have been observed for all austempering conditions studied.

Origin of Traditional Chinese Medicine in Cuba in the 19th Century from Its Main Exponents and Some Notable Medical Descendants in the 20th Century

The Cuban people are made up of three major migratory currents, the Chinese are one of them. They brought their culture, the methods, and procedures of traditional Chinese medicine(TCM) in the 19th century. Few were able to return and so they created families in Cuba;some of their descendants dedicated themselves to medicine. In order to investigate the practices that were predecessors of TCM in Cuba in the 19th century, a qualitative phenomenological research was carried out, reviewing what was published by various sources, applying documentary analysis, logical historical analysis, abstraction, synthesis, and systematization of the results on the regularities of the work and human behavior of Chinese doctors in the Cuban 19th and 20th centuries. This made it possible to identify six Chinese doctors in the 19th century in Cuba who gave rise to the beginning of some practices of TCM in Cuba, and five from the 20th century, descendants of coolies who dedicated themselves to other specialties of medicine. It was found that despite their geographical and time disperse, they were all notorious for their outstanding professional and human behavior, with a trail of accumulated successes in achieving “almost the impossible” with the patient. They have left their mark on Cuban culture.

Growth rates of Eucalyptus and other Australian native tree species derived from seven decades of growth monitoring

There is widespread interest in estimating and forecasting individual tree and forest growth rates for restoration and carbon sequestration objectives. Outside intensively managed forests, past attempts have been limited by the lack of accurate long-term monitoring in multi-age mixed native forests to provide estimates of both expected mean diameter increments and the statistical variation in those estimates. A dataset from Eucalyptus-dominated native forests in subtropical Queensland, Australia offers an opportunity to provide accurate estimates of tree and forest growth rates. Over 86,400 trees from 155 native species were identified and remeasured between 1936 and 2011 in 641 permanent sample plots across a 500-2000 mm mean annual rainfall gradient. Individual tree diameter at breast height （DBH） increments observed for all species ranged mainly from 0.01 to 0.5 cm yr-1 （94 % of values）, with consistentdifferences between rainfall zones （mean of 500- 2000 mm yr-1）, and varying differences between species （155） and stem diameter class （10-100 cm）. For some spe- cies, diameter increment increased progressively with rain- fall （e.g. Eucalyptus siderophloia, Eucalyptus propinqua, and Lophostemon confertus）, but in others （e.g. Corymbia citriodora subsp, variegata, Corymbia intermedia, and Eucalyptus biturbinata） the greatest diameter increments were recorded between 1200 and 1600 mm yr-1. Where there were sufficient data, most species exhibited a quadratic relationship between DBH increment and DBH class, but two species （ Callitris glaucophylla and Eucalyptus crebra） native to the 500-800 mm annual rainfall zone showed lin- ear increases in DBH increment with increasing DBH. Continued monitoring of these plots would add to their already great value.

Analysis of the radiation Resistance and Gain of Full-Wave Dipole Antenna for Different Feeding Design

This paper demonstrates the analysis of antenna pattern and gain for different designs of full-wave dipole antenna feeding techniques. Seven such techniques were studied and analyzed;symmetrical dual feeding in phase, symmetrical dual feeding out of phase, asymmetrical dual feeding in phase, asymmetrical dual feeding out of phase, symmetrical triple feeding in phase and symmetrical triple feeding out of phase. Symmetrical dual feeding in phase produced high gain as compared to the single and center-fed antennas. An improvement of about 3 to 3.5 dB was achieved comparing to center tap fed and off center fed. It was found that an asymmetrical dual feeding in-phase provides good performance, considering the directivity, pattern, and input impedance. A 2.46 dB gain has been attained. It was found that a symmetrical triple feeding provides an overall best performance with respect to gain, radiation pattern, beam width and input impedance.

ICT-based agricultural advisory services and nitrogen management practices:A case study of wheat production in China

Excessive use of nitrogen fertilizer in China and its adverse effects on agricultural production have been a national and global concern.In addition to massive public initiatives to promote sustainable farm practices,grass-rooted innovations are emerging in the niche,many of which take the forms of information and communication technologies(ICT)and digital services.This study examines the effects of ICT-based extension services provided by an entrepreneurial startup on adopting sustainable farming practices.We found no significant reduction in N-fertilizer use for wheat production.But the ICT-based services promoted farmers to adapt N-fertilizer use towards site-specific management.The business model of the entrepreneurial venture faces great challenges in becoming participatory and financially sustainable.

Annona muricata:Is the natural therapy to most disease conditions including cancer growing in our backyard?A systematic review of its research history and future prospects

Annona muricata(A.muricata)is a tropical plant species belonging to family Annonaceae and known for its many medicinal uses.This review focuses on the research history of its traditional uses,phytochemicals,pharmacological activities,toxicological aspects of the extracts and isolated compounds,as well as the in vitro propagation studies with the objective of stimulating further studies on this plant for human consumption and treatment.A.muricata extracts have been identified in tropical regions to traditionally treat diverse conditions ranging from fever to diabetes and cancer.More than 200 chemical compounds have been identified and isolated from this plant,the most important being alkaloids,phenols and acetogenins.Using in vitro studies,its extracts and phytochemicals have been characterized as antioxidant,anti-microbial,anti-inflammatory,insecticidal,larvicidal,and cytotoxic to cancer cells.In vivo studies have revealed anxiolytic,antistress,anti-inflammatory,immunomodulatory,antimalarial,antidepressant,gastro protective,wound healing,hepato-protective,hypoglycemic,anticancer and anti-tumoral activities.In silico studies have also been reported.In addition,clinical studies support the hypoglycemic as well as some anticancer activities.Mechanisms of action of some pharmacological activities have been elucidated.However,some phytochemical compounds isolated from A.muricata have shown a neurotoxic effect in vitro and in vivo,and therefore,these crude extracts and isolated compounds need to be further investigated to define the magnitude of the effects,optimal dosage,and mechanisms of action,long-term safety,and potential side effects.Additionally,more clinical studies are necessary to support the therapeutic potential of this plant.Some studies were also found to have successfully regenerated the plant in vitro,but with limited success.The reported toxicity notwithstanding,A.muricata extracts seem to be some of the safest and promising therapeutic agents of the 21st century and beyond that need to be studied further for better medicinal formulations and diseases management.

Identity Dynamics and Conflict in Collaborative Processes: The Case of Participatory Management of Protected Areas in Benin

The research reported in this paper investigated the role of identity construction in the emergence and escalation of conflict in the participatory management of protected areas in Benin. The study shows that social identity salience was dynamic and played an important role in the emergence and escalation of conflict in the studied cases. Conflicts emerged when identities became salient as a result of the stake-holders’ framing of contextual factors as a threat to their identity. The conflicts escalated when decisions and actions undertaken in the management process were framed as top-down and as posing a threat to the identities of the stakeholders. We conclude that, although the government in the management of the protected areas introduced participation, unilateral decisions taken about the way the conflicts should be managed caused disappointment and distrust, and thus led to a greater distance between the parties involved and to conflict escalation.

A Multiplicative Bias Correction for Nonparametric Approach and the Two Sample Problem in Sample Survey

Let two separate surveys collect related information on a single population U. Consider situation where we want to best combine data from the two surveys to yield a single set of estimates of a population quantity (population parameter) of interest. This Article presents a multiplicative bias reduction estimator for nonparametric regression to two sample problem in sample survey. The approach consists to apply a multiplicative bias correction to an estimator. The multiplicative bias correction method which was proposed, by Linton & Nielsen, 1994, assures a positive estimate and reduces the bias of the estimate with negligible increase in variance. Even as we apply this method to the two sample problem in sample survey, we found out through the study of it asymptotic properties that it was asymptotically unbiased, and statistically consistent. Furthermore an empirical study was carried out to compare the performance of the developed estimator with the existing ones.

Occurrence of Highly Resistant Microorganisms in Ruai Wastewater Treatment Plant and Dandora Dumpsite in Nairobi County, Kenya

Wastewater treatment plants and solid dump sites act as potential reservoirs of highly resistant bacterial strains. This study presents information on the patterns of antimicrobial resistance among pathogenic bacteria in the sewage treatment plants and solid dump site in Nairobi County, Kenya. We employed standard microbiological methods to isolate and identify bacteria from these selected sites. Members of Escherichia, Klebsiella, Serratia, Shigella, Pseudomonas, Citrobacter spp, Enterobacter, Salmonella, Staphylococcus, Bacillus, Yersinia, Vibro cholera, Providencia, Morganella and Proteus spp were isolated. Escherichia and Klebsiella recorded the highest occurrence at 13.9% and 10.3% in wastewater. Shigella, Serratia Citrobacterfreundii, Pseudomonas, Staphylococcus, Escherichia and Klebsiella recorded the highest level of occurrence at 15.1%, 14.1%, 12%, 11%, 10.4%, 10.3%, and 8.3% in dumpsite respectively. Drug sensitivity using the Disc diffusion method showed that Ampicillin, Ceftazidime, Cefotaxime, Amoxicillin, and Cefepime had the highest levels of inactivity at (68.5%, 89.5%), (64.2%, 87%), (54.5%, 80.2%), (52%, 74%) and (49.1%, 72.4%) respectively while Levofloxacin and Gentamicin showed the smallest level of antibiotic resistance at (4.8%, 4.7%) and (4.2%, 2.6%) in both sites. Members of Escherichia, Klebsiella, Proteus vulgaris, Salmonella, Pseudomonas, Shigella and Serratia showed the highest level of ESBLs production at 28.9%, 18.4%, 15.8%, 10.5%, 7.9%, 7.9%, 7.9% and 7.9% in the wastewater whereas Shigella, Escherichia, Serratia, Pseudomonas, Klebsiella, and Proteus vulgaris showed the highest level ESBLs production at 45.5%, 10.9%, 9.1%, 9.1%, 7.3% and 7.3% from the dump site. All the antibiotics tested except for Gentamicin, Chloramphenicol and Ciprofloxacin differed significantly in terms of resistance levels (p < 0.05). The study concluded that, wastewater treatment plant and dump sites bear heavy burdens of potentially virulent resistant bacterial strains which may constitute major public health hazards to scavengers and those living near these sites. There is need therefore to educate people on the use of appropriate protective materials and the settlement patterns of individuals and communities near these sites.

Mathematical Modelling and Experimental Investigation of a Low Temperature Proton Exchange Membrane Fuel Cell

This paper presents dynamic modeling of 1000 W EC6C Proton Exchange Membrane fuel cell (PEMFC) manufactured by Edibon. Experiments were carried out to investigate the performance of the system and a dynamic electrical model was implemented in Matlab/Simulink. The simulation model was able to predict efficiency, power and fuel cell potential. The model was also tested with load variations to find out the real time responses. The results were validated by experimental findings. The comparison showed that the model was effective and could be used in optimization of the fuel cell system operated at low temperatures under 80 degrees.

Effect of Iron Powder (Fe₂O₃) on Strength, Workability, and Porosity of the Binary Blended Concrete

In this study, the effect of iron powder (Fe2O3) on the compressive strength, tensile strength, workability, and porosity of the binary blended concrete were experimentally investigated. For this purpose, Portland cement was partially replaced by 1.5%, 2.5%, 3.5%, and 5% by weighing of iron powder. The amount of water-binder-ratio was considered constant. The workability of the fresh composite concrete was determined using cone Abrams method;mechanical properties were determined included compressive and tensile strengths at 7, 14, and 28 days and durability evaluated by water absorption and permeable porosity. It was observed that the compressive and tensile strengths change with the replacement of iron powder by up to 5%. However, the maximum improvement was gained at 2.5 wt% for compressive strength and 1.5 wt% for tensile strength. The workability of the fresh mixtures decreased when iron powder amount increased. It was observed that the porosity decreased respectively by 21.88% and 26.77% at 1.5 wt% and 2.5 wt% replacement. Moreover, this present study shows the importance and benefits to improve concrete properties by using micro-particles materials.

Compressive and Flexural Strengths of Cement Stabilized Earth Bricks Reinforced with Treated and Untreated Pineapple Leaves Fibres

This study compares the effect of treated pineapple leaves fibres (T-PALF) with sodium hydroxide solution and untreated fibres (N-PALF) on the compressive and flexural strength of earth bricks stabilized with 3% and 5% cement. The fibre content ranged from 0% to 5% in steps of 1% by weight. The compressive strength tests were made at 7, 14, 21 and 28 days of curing;the flexural strength test were conducted at 28th day only. The results show that the T-PALF had a higher compressive strength when comparing to the N-PALF. The highest compressive strength of the bricks was obtained at 28 days of curing. The compressive strength at 28 days of stabilized brick at 3% and 5% of cement reinforced with T-PALF were 4.01 and 4.81 MPa, respectively, while the one reinforced with N-PALF was 3.19 and 4.63 MPa, respectively. The results further show that the highest flexural strength of both stabilized bricks at 3% and 5% of cement reinforced with T-PALF and N-PALF was obtained with the bricks stabilized with 5% of cement reinforced with T-PALF. This results show that bricks stabilized with 5% cement and reinforced with 3% of treated fibres content are good for construction of load bearing walls. It was observed;a significant improvement of the reinforced blocks under flexure than under compression.