Development of Data-driven Machine Learning Models and their Potential Role in Predicting Dengue outbreak.

ABSTRACT: Dengue fever is one of the most widespread vector-borne viral infections in the world, resulting in increased socio-economic burdens. The WHO has reported that 2.5 billion people are infected with dengue fever across the world, resulting in high mortalities in tropical and subtropical regions. The current article endeavors to present an overview of predicting dengue outbreaks through data-based machine-learning models. This artificial intelligence model uses real-world data such as dengue surveillance, climatic variables, and epidemiological data and combines big data with machine learning algorithms to forecast dengue. Monitoring and predicting dengue incidences have been significantly enhanced through innovative approaches. This involves gathering data on various climatic factors, including temperature, rainfall, relative humidity, and wind speed, along with monthly records of dengue cases. The study functions as an efficient warning system, enabling the anticipation of dengue outbreaks. This early warning system not only alerts communities but also aids relevant authorities in implementing crucial preventive measures.

Response of the Pardosa astrigera bacterial community to Cry1B protein.

While genetically modified (GM) crops bring economic benefits to human beings, their impact on non-target organisms has become an important part of environmental safety assessments. Symbiotic bacteria play an important role in eukaryotic biological functions and can adjust host communities to adapt to new environments. Therefore, this study examined the effects of Cry1B protein on the growth and development of non-target natural enemies of Pardosa astrigera (L. Koch) from the perspective of symbiotic bacteria. Cry1B protein had no significant effect on the health indicators of P. astrigera (adults and 2nd instar spiderlings). 16S rRNA sequencing results revealed that Cry1B protein did not change the symbiotic bacteria species composition of P. astrigera, but did reduce the number of OTU and species diversity. In 2nd instar spiderlings, neither the dominant phylum (Proteobacteria) nor the dominant genus (Acinetobacter) changed, but the relative abundance of Corynebacterium-1 decreased significantly; in adult spiders, the dominant bacteria genera of females and males were different. The dominant bacterial genera were Brevibacterium in females and Corynebacterium-1 in males, but Corynebacterium-1 was the dominant bacteria in both females and males feeding on Cry1B. The relative abundance of Wolbachia also increased significantly. In addition, bacteria in other genera varied significantly by sex. KEGG results showed that Cry1B protein only altered the significant enrichment of metabolic pathways in female spiders. In conclusion, the effects of Cry1B protein on symbiotic bacteria vary by growth and development stage and sex.

Risk modelling of ESG (environmental, social, and governance), healthcare, and financial sectors.

Climate change poses enormous ecological, socio-economic, health, and financial challenges. A novel extreme value theory is employed in this study to model the risk to environmental, social, and governance (ESG), healthcare, and financial sectors and assess their downside risk, extreme systemic risk, and extreme spillover risk. We use a rich set of global daily data of exchange-traded funds (ETFs) from 1 July 1999 to 30 June 2022 in the case of healthcare and financial sectors and from 1 July 2007 to 30 June 2022 in the case of ESG sector. We find that the financial sector is the riskiest when we consider the tail index, tail quantile, and tail expected shortfall. However, the ESG sector exhibits the highest tail risk in the extreme environment when we consider a shock in the form of an ETF drop of 25% or 50%. The ESG sector poses the highest extreme systemic risk when a shock comes from China. Finally, we find that ESG and healthcare sectors have lower extreme spillover risk (contagion risk) compared to the financial sector. Our study seeks to provide valuable insights for developing sustainable economic, business, and financial strategies. To achieve this, we conduct a comprehensive risk assessment of the ESG, healthcare, and financial sectors, employing an innovative approach to risk modelling in response to ecological challenges.

Fabrication of polydopamine decorated carbon cloth as support material to anchor CeO2 nanoparticles for electrochemical detection of ethanol.

A flexible CeO2 nanostructured polydopamine-modified carbon cloth (CeO2/PDA/CC) interface was fabricated via electrodeposition for ethanol detection. The fabrication method involved two consecutive electrochemical steps in which dopamine was firstly electrodeposited on carbon fibers, followed by the electrochemical growth of CeO2 nanoparticles. The CeO2/PDA-based electroactive interface exerts an impressive electrochemical performance on the flexible sensor due to strong synergistic effect of the PDA functionalization with more active sites. Moreover, catalytic activity of CeO2 nanostructures anchored on highly conductive CC incorporate superior electrocatalytic performance of the fabricated interface. The designed electrochemical sensor showed a wide response to ethanol in the linear range 1 to 25 mM with a detection limit of 0.22 mM. The CeO2/PDA/CC flexible sensor showed good anti-interference ability and excellent repeatability and reproducibility (RSD = 1.67%). The fabricated interface performed well in saliva samples with satisfactory recoveries, corroborating the viability of CeO2/PDA/CC integrated interface for practical implementation.

Graphene-Based Strain Sensing of Cementitious Composites with Natural and Recycled Sands.

Structural health monitoring is crucial for ensuring the safety and reliability of civil infrastructures. Traditional monitoring methods involve installing sensors across large regions, which can be costly and ineffective due to the sensors damage and poor compliance with structural members. This study involves systematically varying the graphene nanoplatelets (GNPs) concentration and analyzing the strength performance and piezoresistive behavior of the resulting composites. Two different composites having natural and recycled sands with varying percentages of GNPs as 2%, 4%, 6%, and 8% were prepared. Dispersion of GNPs was performed in superplasticizer and then ultrasonication was employed by using an ultrasonicator. The four-probe method was utilized to establish the piezoresistive behavior. The results revealed that the compressive strength of mortar cubes with natural sand was increased up to a GNP content of 6%, beyond which it started to decline. In contrast, specimens with recycled sand showed a continuous decrease in the compressive strength. Furthermore, the electrical resistance stability was observed at 4% for both natural and recycled sands specimens, exhibiting linearity between the frictional change in the resistivity and compressive strain values. It can be concluded from this study that the use of self-sensing sustainable cementitious composites could pave their way in civil infrastructures.

Soil erosion vulnerability and soil loss estimation for Siran River watershed, Pakistan: an integrated GIS and remote sensing approach.

Soil erosion is a problematic issue with detrimental effects on agriculture and water resources, particularly in countries like Pakistan that heavily rely on farming. The condition of major reservoirs, such as Tarbela, Mangla, and Warsak, is crucial for ensuring an adequate water supply for agriculture in Pakistan. The Kunhar and Siran rivers flow practically parallel, and the environment surrounding both rivers' basins is nearly identical. The Kunhar River is one of KP's dirtiest rivers that carries 0.1 million tons of suspended sediment to the Mangla reservoir. In contrast, the Siran River basin is largely unexplored. Therefore, this study focuses on the Siran River basin in the district of Manshera, Pakistan, aiming to assess annual soil loss and identify erosion-prone regions. Siran River average annual total soil loss million tons/year is 0.154. To achieve this, the researchers integrate Geographical Information System (GIS) and remote sensing (RS) data with the Revised Universal Soil Loss Equation (RUSLE) model. Five key variables, rainfall, land use land cover (LULC), slope, soil types, and crop management, were examined to estimate the soil loss. The findings indicate diverse soil loss causes, and the basin's northern parts experience significant soil erosion. The study estimated that annual soil loss from the Siran River basin is 0.154 million tons with an average rate of 0.871 tons per hectare per year. RUSLE model combined with GIS/RS is an efficient technique for calculating soil loss and identifying erosion-prone areas. Stakeholders such as policymakers, farmers, and conservationists can utilize this information to target efforts and reduce soil loss in specific areas. Overall, the study's results have the potential to advance initiatives aimed at safeguarding the Siran River watershed and its vital resources. Protecting soil resources and ensuring adequate water supplies are crucial for sustainable agriculture and economic development in Pakistan.

Giant adrenal gland pseudo-cyst: a case report with literature review.

Adrenal Gland Cysts are rare among all the pathologic cysts that occur in human beings; the pseudo-cyst variety even rarer. Adrenal pseudo-cysts are asymptomatic, non-functional, small, and incidentally discovered disease entities. Their clinical presentation is usually the result of their mass effects. Thanks to the advanced diagnostic technology, more such cases are being discovered timely and managed surgically, before life-threatening complications occur. Open surgical treatment remains the treatment of choice for giant cysts.

Stimulants and donors promote megaplasmid pND6-2 horizontal gene transfer in activated sludge.

The activated sludge process is characterized by high microbial density and diversity, both of which facilitate antibiotic resistance gene transfer. Many studies have suggested that antibiotic and non-antibiotic drugs at sub-inhibitory concentrations are major inducers of conjugative gene transfer. The self-transmissible plasmid pND6-2 is one of the endogenous plasmids harbored in Pseudomonas putida ND6, which can trigger the transfer of another co-occurring naphthalene-degrading plasmid pND6-1. Therefore, to illustrate the potential influence of stimulants on conjugative transfer of pND6-2, we evaluated the effects of four antibiotics (ampicillin, gentamycin, kanamycin, and tetracycline) and naphthalene, on the conjugal transfer efficiency of pND6-2 by filter-mating experiment. Our findings demonstrated that all stimulants within an optimal dose promoted conjugative transfer of pND6-2 from Pseudomonas putida GKND6 to P. putida KT2440, with tetracycline being the most effective (100 µg/L and 10 µg/L), as it enhanced pND6-2-mediated intra-genera transfer by approximately one hundred-fold. Subsequently, seven AS reactors were constructed with the addition of donors and different stimulants to further elucidate the conjugative behavior of pND6-2 in natural environment. The stimulants positively affected the conjugal process of pND6-2, while donors reshaped the host abundance in the sludge. This was likely because stimulant addition enhanced the expression levels of conjugation transfer-related genes. Furthermore, Blastocatella and Chitinimonas were identified as the potential receptors of plasmid pND6-2, which was not affected by donor types. These findings demonstrate the positive role of sub-inhibitory stimulant treatment on pND6-2 conjugal transfer and the function of donors in re-shaping the host spectrum of pND6-2.

Spillovers and connectedness in foreign exchange markets: The role of trade policy uncertainty.

This paper analyses the directional spillover effects and connectedness for both return and volatility of nine US dollar exchange rates of globally most traded currencies under the influence of trade policy uncertainty. We find two interesting results over the study period ranging from December 1993 to July 2019. First, there exists asymmetric spillovers and connectedness among the considered exchange rates when trade policy uncertainty is present. Second, the volatility spillover is stronger than the return connectedness between exchange rate and trade policy uncertainty. These findings are robust to the presence of economic policy uncertainty effects. Concomitantly, the trade policy uncertainty patterns are also found to be useful for predicting currency market dynamics. Our findings contribute to the debate on the impact of trade policy uncertainty on the global economy and financial sector.

Economic burden of tobacco use in Pakistan.

BACKGROUND: The lack of reliable tobacco healthcare and economic cost estimates leaves the tobacco industry undertaxed and thriving in Pakistan and makes the country as one of the top tobacco-consuming nations. To facilitate effective tobacco tax policymaking, this study estimates the economic cost of smoking-attributable diseases and deaths in Pakistan. METHODS: A nationally representative sample survey of 13 000 households was administered to gather the data required to estimate different cost components of smoking-attributable diseases through the prevalence-based approach. FINDINGS: The total smoking-attributable economic cost of all diseases and deaths in Pakistan in the year 2018-2019 for persons aged 35 years or older is 615.07 billion ($3.85 billion). Similarly, three major diseases, namely cancer, cardiovascular disease and respiratory disease, along with associated deaths, cost the nation PKR437.8 billion ($2.7 billion) of which 77% is the indirect cost. The three major diseases make 71% of the total estimated cost, nearly two-thirds of which is borne by rural residents, nine-tenth by males and more than four-fifths by the citizens in the 35-64 years age group. CONCLUSION: The total annual economic costs of all smoking-attributable diseases and deaths and those of the three major diseases equal 1.6% and 1.15% of Pakistan's gross domestic product, respectively. The tax contribution of tobacco sector is merely 20% of the total estimated cost. The finding of huge economic and health costs of smoking makes a convincing case for policymakers to realise the true value of the industry's contribution and raise tobacco taxes to the level of full cost recovery.

A Comprehensive Radiologic Review of Shoulder Girdle Trauma.

Radiologic knowledge of different fracture patterns involving the shoulder girdle is an important tool to generate clinically relevant reports, identify concomitant injuries, guide management decisions, and predict and minimize complications, such as nonunion, osteoarthritis, osteonecrosis, and hardware failure. Complex unstable injuries like scapulothoracic dissociation can also occur because of shoulder girdle trauma. Management options may vary from conservative to surgical, depending on the fracture type and patient factors. Injuries around the shoulder girdle can involve the glenohumeral articulation, scapula, superior shoulder suspensory complex, acromioclavicular joints, and scapulothoracic articulation.

Impact of a Comfortable in our Skin interactive workshop on social media awareness and self-confidence in adolescents.

Social media use contributes to body dissatisfaction and reduced quality of life among adolescents. This study examines the impact of social media use and skin conditions on body image and suggests that a Comfortable in Our Skin (CIOS) pilot community-based workshop may promote healthier body image and social media usage among urban adolescents.

Association between efficiency and quality of care of public healthcare facilities: Evidence from Pakistan.

BACKGROUND: Maternal and child health is an important component of the Sustainable Development Goals. Pakistan has one of the worst maternal and neonatal health outcomes in the world. This is despite significant health system investments across the country. AIMS: The objectives of this study are twofold. First, the study estimates the technical efficiency of the public healthcare facilities in Pakistan, defined as the number of obstetric deliveries compared to the number of medical specialists, nurses, and other health and non-health staff members. Second, the study evaluates the relationship between efficiency and quality of care; the latter is measured in terms of maternal and neonatal mortality. MATERIALS & METHODS: The data were taken from the Pakistan Health Facility Assessment Survey. Efficiency score was calculated for 843 public healthcare facilities, using Stochastic Frontier Analysis. We then used two-stage residual inclusion approach with bootstrapping to evaluate the relationship between efficiency and quality. RESULTS AND DISCUSSION: The average efficiency score was 0.48 (range: 0-1) and none of the public healthcare facilities were on the frontier, implying that efficiency gains can be made across the board. The relationship between efficiency and quality is found to be positive and statistically significant, that is, more efficient healthcare facilities also had lower rates of maternal and neonatal mortality. CONCLUSION: We conclude that more efficient public healthcare facilities also had lower mortality rates, probably due to better infrastructure and health system financing.

IoMT-Based Osteosarcoma Cancer Detection in Histopathology Images Using Transfer Learning Empowered with Blockchain, Fog Computing, and Edge Computing.

Bone tumors, such as osteosarcomas, can occur anywhere in the bones, though they usually occur in the extremities of long bones near metaphyseal growth plates. Osteosarcoma is a malignant lesion caused by a malignant osteoid growing from primitive mesenchymal cells. In most cases, osteosarcoma develops as a solitary lesion within the most rapidly growing areas of the long bones in children. The distal femur, proximal tibia, and proximal humerus are the most frequently affected bones, but virtually any bone can be affected. Early detection can reduce mortality rates. Osteosarcoma's manual detection requires expertise, and it can be tedious. With the assistance of modern technology, medical images can now be analyzed and classified automatically, which enables faster and more efficient data processing. A deep learning-based automatic detection system based on whole slide images (WSIs) is presented in this paper to detect osteosarcoma automatically. Experiments conducted on a large dataset of WSIs yielded up to 99.3% accuracy. This model ensures the privacy and integrity of patient information with the implementation of blockchain technology. Utilizing edge computing and fog computing technologies, the model reduces the load on centralized servers and improves efficiency.

A High Gain Embedded Helix and Dielectric Rod Antenna with Low Side Lobe Levels for IoT Applications.

In this paper, a novel embedded helix dielectric rod antenna is presented for high gain radiation with circular polarization (CP) and low side lobe levels for IoT Applications. Different from the conventional dielectric rod antennas, this proposed antenna is an integrated structure that combines the advantages of the helix and dielectric rod antennas. The presented antenna mainly consists of three parts: a tapered helix as primary feeding for CP, a dielectric rod with printed loops embedded for higher directivity, and a dielectric rod end for improving the gain further. After studying and analyzing the working principles of each part, an optimum design operating at 8-9.7 GHz is carried out as an example. A prototype is also fabricated and tested. The measured results show that the prototype can provide 18.41 dB maximum gain within the length of 7.7 λ. The side lobe level is below -20 dB, and the axial ratio is better than 1.14 dB in the whole frequency band. Compared with the traditional helix antenna and dielectric rod antenna with the same electric length, the presented antenna has a higher gain with a lower side lobe level and with good polarization purity.

Network Meddling Detection Using Machine Learning Empowered with Blockchain Technology.

The study presents a framework to analyze and detect meddling in real-time network data and identify numerous meddling patterns that may be harmful to various communication means, academic institutes, and other industries. The major challenge was to develop a non-faulty framework to detect meddling (to overcome the traditional ways). With the development of machine learning technology, detecting and stopping the meddling process in the early stages is much easier. In this study, the proposed framework uses numerous data collection and processing techniques and machine learning techniques to train the meddling data and detect anomalies. The proposed framework uses support vector machine (SVM) and K-nearest neighbor (KNN) machine learning algorithms to detect the meddling in a network entangled with blockchain technology to ensure the privacy and protection of models as well as communication data. SVM achieves the highest training detection accuracy (DA) and misclassification rate (MCR) of 99.59% and 0.41%, respectively, and SVM achieves the highest-testing DA and MCR of 99.05% and 0.95%, respectively. The presented framework portrays the best meddling detection results, which are very helpful for various communication and transaction processes.

Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning.

Oral cancer is a dangerous and extensive cancer with a high death ratio. Oral cancer is the most usual cancer in the world, with more than 300,335 deaths every year. The cancerous tumor appears in the neck, oral glands, face, and mouth. To overcome this dangerous cancer, there are many ways to detect like a biopsy, in which small chunks of tissues are taken from the mouth and tested under a secure and hygienic microscope. However, microscope results of tissues to detect oral cancer are not up to the mark, a microscope cannot easily identify the cancerous cells and normal cells. Detection of cancerous cells using microscopic biopsy images helps in allaying and predicting the issues and gives better results if biologically approaches apply accurately for the prediction of cancerous cells, but during the physical examinations microscopic biopsy images for cancer detection there are major chances for human error and mistake. So, with the development of technology deep learning algorithms plays a major role in medical image diagnosing. Deep learning algorithms are efficiently developed to predict breast cancer, oral cancer, lung cancer, or any other type of medical image. In this study, the proposed model of transfer learning model using AlexNet in the convolutional neural network to extract rank features from oral squamous cell carcinoma (OSCC) biopsy images to train the model. Simulation results have shown that the proposed model achieved higher classification accuracy 97.66% and 90.06% of training and testing, respectively.

Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning.

Kidney cancer is a very dangerous and lethal cancerous disease caused by kidney tumors or by genetic renal disease, and very few patients survive because there is no method for early prediction of kidney cancer. Early prediction of kidney cancer helps doctors start proper therapy and treatment for the patients, preventing kidney tumors and renal transplantation. With the adaptation of artificial intelligence, automated tools empowered with different deep learning and machine learning algorithms can predict cancers. In this study, the proposed model used the Internet of Medical Things (IoMT)-based transfer learning technique with different deep learning algorithms to predict kidney cancer in its early stages, and for the patient's data security, the proposed model incorporates blockchain technology-based private clouds and transfer-learning trained models. To predict kidney cancer, the proposed model used biopsies of cancerous kidneys consisting of three classes. The proposed model achieved the highest training accuracy and prediction accuracy of 99.8% and 99.20%, respectively, empowered with data augmentation and without augmentation, and the proposed model achieved 93.75% prediction accuracy during validation. Transfer learning provides a promising framework with the combination of IoMT technologies and blockchain technology layers to enhance the diagnosing capabilities of kidney cancer.

A comparative study of the effect of health insurance on women's use of health facility delivery: Evidence from demographic health survey in Benin Republic.

Studies in the Benin Republic have identified contextual factors that determine health facility delivery among women of reproductive age. However, it is not certain if the same set of factors predicts facility delivery for women who enrolled in health insurance and those who did not. The study seeks to compare the determinants of health facility delivery for mothers under health insurance and those that are not in the Benin Republic. The study used data for 33,078 women of reproductive age, drawn from the most recent Benin demographic and health survey (2017-18). The characteristics of the women were described using simple proportions and frequency. Binary Logistic regression was used to examine determinants of health facility delivery for both groups of women. The result showed that only 0.7% of the women were under health insurance coverage. The prevalence of health facility delivery was high in the enrolled group but not in the unenrolled group (98.3% vs. 87.8%). The uniform determinants of health facility delivery across the two groups were household wealth, education, employment, land/house ownership, media exposure, a minimum of four antenatal contacts, and place of residence. To improve the coverage of health facility delivery, a multi-pronged approach should be used to improve household socioeconomic status, encourage media use among women, expand education opportunities for women, and specifically target rural women in Benin.

Groundwater quality assessment and its vulnerability to pollution: a study of district Nowshera, Khyber Pakhtunkhwa, Pakistan.

Groundwater is the drinking water source for the majority of rural settlements of district Nowshera, Khyber Pakhtunkhwa, Pakistan. The study aimed to analyze the groundwater quality and its vulnerability to pollution and to develop its spatial distribution mapping. For this purpose, forty-eight groundwater samples were collected from dug wells, tube wells, and hand pumps of sixteen villages and analyzed for physicochemical parameters. The XY coordinates of the sample's sources were marked by Magellan Triton 1500 handheld global positioning system (GPS). The results were compared with WHO and Pak-EPA guidelines. The results of the majority of selected parameters were found within the WHO and Pak-EPA guidelines; however, in certain areas the concentration of total dissolved solids (TDS), chlorides (Cl-), and alkalinity were higher than the guideline limits. Based on cumulative water quality the excellent water quality prevails over an area of 376 km2 (21% of district area), good water quality 726 km2 (42%), poor 424 km2 (24%), very poor 116 km2 (6%), and unfit for drinking 84 km2 (4%). The water of the Nizampur and Rashaki areas were categorized unfit for drinking. The groundwater quality of nearly one-half of the district varies from poor to very poor, and the soil type and vadose zone sediment/material was found the key reason for groundwater contamination. Based on the infiltration capacity of vadose zone material, the study area was divided into four water pollution vulnerable zones. The low vulnerable zone covers an area of 104 km2, moderate 862 km2, high 667 km2, and very high 93 km2. The most important factor which determines the vulnerability of the groundwater to contamination is the vadose zone material/sediment which in turn determines the soil infiltration capacity. The generated groundwater susceptibility and water quality maps provide critical information for identifying optimal locations for supply wells.