Piper chaba Stem Extract Facilitated the Synthesis of Iron Oxide Nanoparticles as an Adsorbent to Remove Congo Red Dye.

In this study, a straightforward, eco-friendly, and facile method for synthesizing iron oxide nanoparticles (IONPs) utilizing Piper chaba steam extract as a reducing and stabilizing agent has been demonstrated. The formation of stable IONPs coated with organic moieties was confirmed from UV-vis, FTIR, and EDX spectroscopy and DLS analysis. The produced IONPs are sufficiently crystalline to be superparamagnetic having a saturation magnetization value of 58 emu/g, and their spherical form and size of 9 nm were verified by XRD, VSM, SEM, and TEM investigations. In addition, the synthesized IONPs exhibited notable effectiveness in the removal of Congo Red (CR) dye with a maximum adsorption capacity of 88 mg/g. The adsorption kinetics followed pseudo-second-order kinetics, meaning the adsorption of CR on IONPs is mostly controlled by chemisorption. The adsorption isotherms of CR on the surface of IONPs follow the Langmuir isotherm model, indicating the monolayer adsorption on the homogeneous surface of IONPs through adsorbate-adsorbent interaction. The IONPs have revealed good potential for their reusability, with the adsorption efficiency remaining at about 85% after five adsorption-desorption cycles. The large-scale, safe, and cost-effective manufacturing of IONPs is made possible by this environmentally friendly process.

Uncovering local aggregated air quality index with smartphone captured images leveraging efficient deep convolutional neural network.

The prevalence and mobility of smartphones make these a widely used tool for environmental health research. However, their potential for determining aggregated air quality index (AQI) based on PM2.5 concentration in specific locations remains largely unexplored in the existing literature. In this paper, we thoroughly examine the challenges associated with predicting location-specific PM2.5 concentration using images taken with smartphone cameras. The focus of our study is on Dhaka, the capital of Bangladesh, due to its significant air pollution levels and the large population exposed to it. Our research involves the development of a Deep Convolutional Neural Network (DCNN), which we train using over a thousand outdoor images taken and annotated. These photos are captured at various locations in Dhaka, and their labels are based on PM2.5 concentration data obtained from the local US consulate, calculated using the NowCast algorithm. Through supervised learning, our model establishes a correlation index during training, enhancing its ability to function as a Picture-based Predictor of PM2.5 Concentration (PPPC). This enables the algorithm to calculate an equivalent daily averaged AQI index from a smartphone image. Unlike, popular overly parameterized models, our model shows resource efficiency since it uses fewer parameters. Furthermore, test results indicate that our model outperforms popular models like ViT and INN, as well as popular CNN-based models such as VGG19, ResNet50, and MobileNetV2, in predicting location-specific PM2.5 concentration. Our dataset is the first publicly available collection that includes atmospheric images and corresponding PM2.5 measurements from Dhaka. Our codes and dataset are available at  https://github.com/lepotatoguy/aqi .

Green Coffee Bean Extract Assisted Facile Synthesis of Reduced Graphene Oxide and Its Dye Removal Activity.

To discharge the colored effluents from industries there needs to be effective and affordable treatment options. Adsorption using reduced graphene oxide (rGO) as an adsorbent is a prominent one. In this study, green coffee bean extract (GCBE) is utilized as a safe reducing agent for the reduction of graphene oxide (GO) to synthesize rGO. The formation of rGO is confirmed by a new peak in the UV-vis spectra at 275 nm and a diffraction peak in the XRD patterns at 22°. The effective formation of rGO is further substantiated by a change in the GO peak's properties in the FTIR, EDX, and Raman spectra and a weight loss change in TGA. The SEM and TEM analyses demonstrate the effective production of the nano-sheets of rGO having exfoliated and segregated in a few layers. Furthermore, the obtained rGO exhibited outstanding efficacy in wastewater cleanup, effectively adsorbing MB as a prototype organic dye. The kinetics and isotherm study suggested that the adsorption leads by the chemisorption and monolayer formation on the homogeneous surface of rGO. The maximum adsorption capacity is found to be 89.3 mg g-1. This process offers a fresh opportunity for the economical and safe production of rGO for wastewater treatment.

eDakterBari: A human-centered solution enabling online medical consultation and information dissemination for resource-constrained communities in Bangladesh.

A well-accessible healthcare system is an important measure of the progress of a country, as access to adequate healthcare is one of everyone's very basic human rights. When a community lives below the poverty line, unfortunately, it gets deprived of the basic human rights like healthcare, which is a reality to many resource-constrained communities around the world. The number of such resource-constrained communities in developing countries is large. Orphans present a prominent example in this regard in the context of Bangladesh. Orphans suffer greatly from many diseases due to their resource-constrained environment of livings and they are unable to take a minimum care of their own health. Their lack of resources, inadequate literacy skills, and limited (or no) access to technology leave them in such a position that they are ignorant of healthcare services available for them directly or through technological means. Considering all these unavoidable real aspects and the fact that such resource-constrained communities are very little focused in the literature for aiding them in getting bare minimum healthcare services, in this study, we leverage technology and relevant appropriate intermediaries to bridge the gap between the orphans in the orphanages and healthcare services offered by medical doctors. To accomplish so, we conduct a series of field studies over the intended communities. The orphanage teachers and administrators, being in proximity, are the most effective ones to operate as intermediaries for the orphan children, as revealed through our field studies. Therefore, we use these intermediaries to help the orphans to get basic healthcare services via an Android healthcare app called 'Shastho-sheba'. We also use our findings from the field study to specifically tailor and modify the application for intermediaries to use on behalf of the orphans so that health professionals can provide direct healthcare services to them over the Internet. Finally, we look into our proposed techno-social solution in the context of HCI to ensure that the service is used more effectively.

A transformer-based generative adversarial learning to detect sarcasm from Bengali text with correct classification of confusing text.

Sarcasm detection research in Bengali is still limited due to a lack of relevant resources. In this context, getting high-quality annotated data is costly and time-consuming. Therefore, in this paper, we present a transformer-based generative adversarial learning for sarcasm detection from Bengali text based on available limited labeled data. Here, we use the Bengali sarcasm dataset 'Ben-Sarc'. Besides, we construct another dataset containing Bengali sarcastic and non-sarcastic comments from YouTube and newspapers to observe the model's performance on the new dataset. On top of that, we utilize another Bengali sarcasm dataset 'BanglaSarc' to further prove our models' robustness. Among all models, the Bangla BERT-based Generative Adversarial Model has achieved the highest accuracy with 77.1% for the 'Ben-Sarc' dataset. Besides, this model has achieved the highest accuracy of 68.2% for the dataset constructed from YouTube and newspaper, and 97.2% for the 'BanglaSarc' dataset.

Green Synthesized Silver Nanoparticles: A Potential Antibacterial Agent, Antioxidant, and Colorimetric Nanoprobe for the Detection of Hg2+ Ions.

Silver nanoparticles (AgNPs) prepared by green synthesis have a lot of potentials in various fields. Among them, as an antioxidant, antibacterial agent, and nanoprobe for the colorimetric detection of mercury (Hg2+) ions is thought to be the most important. The antibacterial, antioxidant, and colorimetric sensing potential of the greenly produced AgNPs utilizing Piper chaba stem extract are all predicted in this investigation. By using the disc diffusion method, the antibacterial activity of greenly produced AgNPs are assessed, and the findings are measured from the zone of inhibition (ZOI). It is revealed that the Staphylococcus aureus, Micrococcus spp., Escherichia coli, and Pseudomonas aeruginosa bacterial strains are significantly resisted by the greenly produced AgNPs. The antioxidant activity test of AgNPs reveals a considerable impact on free radical scavenging having the inhibitory concentration (IC 50) is 1.13 mL (equivalent to 0.45 mg mL-1). Also, with a low limit of detection of 28 ppm, the resulting AgNPs are used as highly selective and economical colorimetric sensors for Hg2+ detection. The study's findings support the hypothesis that Piper chaba stems can serve as a source for the production of AgNPs with high antibacterial and antioxidant activity and usefulness for simple colorimetric readings of Hg2+.

An Artificial Intelligence-Based Smartphone App for Assessing the Risk of Opioid Misuse in Working Populations Using Synthetic Data: Pilot Development Study.

BACKGROUND: Opioid use disorder (OUD) is an addiction crisis in the United States. As recent as 2019, more than 10 million people have misused or abused prescription opioids, making OUD one of the leading causes of accidental death in the United States. Workforces that are physically demanding and laborious in the transportation, construction and extraction, and health care industries are prime targets for OUD due to high-risk occupational activities. Because of this high prevalence of OUD among working populations in the United States, elevated workers' compensation and health insurance costs, absenteeism, and declined productivity in workplaces have been reported. OBJECTIVE: With the emergence of new smartphone technologies, health interventions can be widely used outside clinical settings via mobile health tools. The major objective of our pilot study was to develop a smartphone app that can track work-related risk factors leading to OUD with a specific focus on high-risk occupational groups. We used synthetic data analyzed by applying a machine learning algorithm to accomplish our objective. METHODS: To make the OUD assessment process more convenient and to motivate potential patients with OUD, we developed a smartphone-based app through a step-by-step process. First, an extensive literature survey was conducted to list a set of critical risk assessment questions that can capture high-risk behaviors leading to OUD. Next, a review panel short-listed 15 questions after careful evaluation with specific emphasis on physically demanding workforces-9 questions had two, 5 questions had five, and 1 question had three response options. Instead of human participant data, synthetic data were used as user responses. Finally, an artificial intelligence algorithm, naive Bayes, was used to predict the OUD risk, trained with the synthetic data collected. RESULTS: The smartphone app we have developed is functional as tested with synthetic data. Using the naive Bayes algorithm on collected synthetic data, we successfully predicted the risk of OUD. This would eventually create a platform to test the functionality of the app further using human participant data. CONCLUSIONS: The use of mobile health techniques, such as our mobile app, is highly promising in predicting and offering mitigation plans for disease detection and prevention. Using a naive Bayes algorithm model along with a representational state transfer (REST) application programming interface and cloud-based data encryption storage, respondents can guarantee their privacy and accuracy in estimating their risk. Our app offers a tailored mitigation strategy for specific workforces (eg, transportation and health care workers) that are most impacted by OUD. Despite the limitations of the study, we have developed a robust methodology and believe that our app has the potential to help reduce the opioid crisis.

Towards associating negative experiences and recommendations reported by Hajj pilgrims in a mass-scale survey.

The annual Hajj presents diversified negative experiences to millions of pilgrims worldwide. The negative experiences and recommendations to overcome them as per pilgrims' feedback are yet to be analyzed from an aggregated perspective in the literature, which we do in this paper. To do so, first, we perform a large-scale survey (n=988) using our comprehensive questionnaire. Then, we perform both quantitative (e.g., clustering) and qualitative (e.g., thematic) analyses on the survey data. Our quantitative analysis reveals up to seven clusters of negative experiences. Further, going beyond the quantitative analysis, our qualitative analysis reveals 21 types of negative experiences, 20 types of recommendations, and nine themes connecting the negative experiences and recommendations. Accordingly, we reveal associations among the negative experiences and recommendations based on the themes in thematic analysis and present the associations through a tripartite graph. However, we have some limitations in this study, such as fewer female and young participants. In future, we plan to collect more responses from female and young participants and extend our work by analyzing linkages in the tripartite graph by augmenting the edges within the graph with appropriate weights. Overall, the findings of this study are expected to facilitate the prioritization of tasks for the management personnel in charge of the Hajj pilgrimage.

A ubiquitous method for predicting underground petroleum deposits based on satellite data.

The method of finding new petroleum deposits beneath the earth's surface is always challenging for having low accuracy while simultaneously being highly expensive. As a remedy, this paper presents a novel way to predict the locations of petroleum deposits. Here, we focus on a region of the Middle East, Iraq to be specific, and conduct a detailed study on predicting locations of petroleum deposits there based on our proposed method. To do so, we develop a new method of predicting the location of a new petroleum deposit based on publicly available data sensed by an open satellite named Gravity Recovery and Climate Experiment (GRACE). Using GRACE data, we calculate the gravity gradient tensor of the earth over the region of Iraq and its surroundings. We use this calculated data to predict the locations of prospective petroleum deposits over the region of Iraq. In the process of our study for making the predictions, we leverage machine learning, graph-based analysis, and our newly-proposed OR-nAND method altogether. Our incremental improvement in the proposed methodologies enables us to predict 25 out of 26 existing petroleum deposits within the area under our study. Additionally, our method shows some prospective petroleum deposits that need to be explored physically in the future. It is worth mentioning that, as our study presents a generalized approach (demonstrated through investigating multiple datasets), we can apply it anywhere in the world beyond the area focused on in this study as an experimental case.

Escalating post-disaster rescue missions through ad-hoc victim localization exploiting Wi-Fi networks.

The number of disasters, accidents, and casualties in disasters is increasing, however, technological advancement has yet to ripe benefits to emergency rescue operations. This contrast is even more prominent in the Global South. The consequences are a huge loss of wealth and resources, but more importantly, the loss of lives. Locating victims of disasters as quickly as possible while speeding up rescue operations can lessen these losses. Traditional approaches for effective victim localization and rescue often requires the establishment of additional infrastructure during the construction period. Which in the context of countries of the global south such as - Bangladesh, is not followed for most of the industrial and household constructions. In this paper, we conduct a study to better understand the challenges of victim localization in emergency rescue operations and to overcome them using "whatever" resources available at hand without needing prior infrastructure facilities and pre-calibration. We design and develop a solution for this purpose and deployed it in several emulated disaster-like scenarios. We analyze and discuss the results obtained from our experiments. Finally, we point out the design implications of an infrastructure-independent and extensive emergency rescue system.

Green approaches in synthesising nanomaterials for environmental nanobioremediation: Technological advancements, applications, benefits and challenges.

Green synthesis approaches of nanomaterials (NMs) have received considerable attention in recent years as it addresses the sustainability issues posed by conventional synthesis methods. However, recent works of literature do not present the complete picture of biogenic NMs. This paper addresses the previous gaps by providing insights into the stability and toxicity of NMs, critically reviewing the various biological agents and solvents required for synthesis, sheds light on the factors that affect biosynthesis, and outlines the applications of NMs across various sectors. Despite the advantages of green synthesis, current methods face challenges with safe and appropriate solvent selection, process parameters that affect the synthesis process, nanomaterial cytotoxicity, bulk production and NM morphology control, tedious maintenance, and knowledge deficiencies. Consequently, the green synthesis of NMs is largely trapped in the laboratory phase. Nevertheless, the environmental friendliness, biocompatibility, and sensitivities of the resulting NMs have wider applications in biomedical science, environmental remediation, and consumer industries. To the scale-up application of biogenic NMs, future research should be focused on understanding the mechanisms of the synthesis processes, identifying more biological and chemical agents that can be used in synthesis, and developing the practicality of green synthesis at the industrial scale, and optimizing the factors affecting the synthesis process.

Dimension- and position-controlled growth of GaN microstructure arrays on graphene films for flexible device applications.

This paper describes the fabrication process and characteristics of dimension- and position-controlled gallium nitride (GaN) microstructure arrays grown on graphene films and their quantum structures for use in flexible light-emitting device applications. The characteristics of dimension- and position-controlled growth, which is crucial to fabricate high-performance electronic and optoelectronic devices, were investigated using scanning and transmission electron microscopes and power-dependent photoluminescence spectroscopy measurements. Among the GaN microstructures, GaN microrods exhibited excellent photoluminescence characteristics including room-temperature stimulated emission, which is especially useful for optoelectronic device applications. As one of the device applications of the position-controlled GaN microrod arrays, we fabricated light-emitting diodes (LEDs) by heteroepitaxially growing InxGa1-xN/GaN multiple quantum wells (MQWs) and a p-type GaN layer on the surfaces of GaN microrods and by depositing Ti/Au and Ni/Au metal layers to prepare n-type and p-type ohmic contacts, respectively. Furthermore, the GaN microrod LED arrays were transferred onto Cu foil by using the chemical lift-off method. Even after being transferred onto the flexible Cu foil substrate, the microrod LEDs exhibited strong emission of visible blue light. The proposed method to enable the dimension- and position-controlled growth of GaN microstructures on graphene films can likely be used to fabricate other high-quality flexible inorganic semiconductor devices such as micro-LED displays with an ultrahigh resolution.

Biogenic Synthesis and Catalytic Efficacy of Silver Nanoparticles Based on Peel Extracts of Citrus macroptera Fruit.

Biogenically synthesized silver nanoparticles (AgNP) increase the fascination over chemical ones due to their facile and green synthetic process. This study reports the development of an eco-friendly and cost-effective synthesis of AgNPs using an aqueous extract of Citrus macroptera fruit peel, an agricultural waste, as a sole agent with both reducing and capping abilities. The formation of AgNPs was verified by the surface plasmon resonance peak at 426 nm in the UV-vis spectrum, X-ray diffraction pattern, and transmission electron micrography images. The AgNPs obtained under the optimized conditions consist of face-centered cubic crystals and spherical morphology with an average size of 11 nm. The AgNPs are coated with phytochemicals in the C. macroptera fruit peel extract and are stably dispersible due to their negatively charged nature. The AgNPs effectively catalyzed the reduction of 4-nitrophenol to 4-aminophenol and the degradation of methyl orange and methylene blue in the presence of sodium borohydride. This method employing a fruit peel extract is facile, efficient, eco-friendly, and cost-effective and has potential for industrial green fabrication of AgNPs.

Source, distribution and emerging threat of micro- and nanoplastics to marine organism and human health: Socio-economic impact and management strategies.

The nature of micro- and nanoplastics and their harmful consequences has drawn significant attention in recent years in the context of environmental protection. Therefore, this paper aims to provide an overview of the existing literature related to this evolving subject, focusing on the documented human health and marine environment impacts of micro- and nanoplastics and including a discussion of the economic challenges and strategies to mitigate this waste problem. The study highlights the micro- and nanoplastics distribution across various trophic levels of the food web, and in different organs in infected animals which is possible due to their reduced size and their lightweight, multi-coloured and abundant features. Consequently, micro- and nanoplastics pose significant risks to marine organisms and human health in the form of cytotoxicity, acute reactions, and undesirable immune responses. They affect several sectors including aquaculture, agriculture, fisheries, transportation, industrial sectors, power generation, tourism, and local authorities causing considerable economic losses. This can be minimised by identifying key sources of environmental plastic contamination and educating the public, thus reducing the transfer of micro- and nanoplastics into the environment. Furthermore, the exploitation of the potential of microorganisms, particularly those from marine origins that can degrade plastics, could offer an enhanced and environmentally sound approach to mitigate micro- and nanoplastics pollution.

Impact of COVID-19 on the social, economic, environmental and energy domains: Lessons learnt from a global pandemic.

COVID-19 has heightened human suffering, undermined the economy, turned the lives of billions of people around the globe upside down, and significantly affected the health, economic, environmental and social domains. This study aims to provide a comprehensive analysis of the impact of the COVID-19 outbreak on the ecological domain, the energy sector, society and the economy and investigate the global preventive measures taken to reduce the transmission of COVID-19. This analysis unpacks the key responses to COVID-19, the efficacy of current initiatives, and summarises the lessons learnt as an update on the information available to authorities, business and industry. This review found that a 72-hour delay in the collection and disposal of waste from infected households and quarantine facilities is crucial to controlling the spread of the virus. Broad sector by sector plans for socio-economic growth as well as a robust entrepreneurship-friendly economy is needed for the business to be sustainable at the peak of the pandemic. The socio-economic crisis has reshaped investment in energy and affected the energy sector significantly with most investment activity facing disruption due to mobility restrictions. Delays in energy projects are expected to create uncertainty in the years ahead. This report will benefit governments, leaders, energy firms and customers in addressing a pandemic-like situation in the future.

Analysis of One-Dimensional Ivshin-Pence Shape Memory Alloy Constitutive Model for Sensitivity and Uncertainty.

Shape memory alloys (SMAs) are classified as smart materials due to their capacity to display shape memory effect and pseudoelasticity with changing temperature and loading conditions. The thermomechanical behavior of SMAs has been simulated by several constitutive models that adopted microscopic thermodynamic or macroscopic phenomenological approaches. The Ivshin-Pence model is one of the most popular SMA macroscopic phenomenological constitutive models. The construction of the model requires involvement of parameters that possess inherent uncertainty. Under varying operating temperatures and loading conditions, the uncertainty in these parameters propagates and, therefore, affects the predictive power of the model. The propagation of uncertainty while using this model in real-life applications can result in performance discrepancies or failure at extreme conditions. In this study, we employed a probabilistic approach to perform the sensitivity and uncertainty analysis of the Ivshin-Pence model. Sobol and extended Fourier Amplitude Sensitivity Testing (eFAST) methods were used to perform the sensitivity analysis for simulated isothermal loading/unloading at various operating temperatures. It is evident that the model's prediction of the SMA stress-strain curves varies due to the change in operating temperature and loading condition. The average and stress-dependent sensitivity indices present the most influential parameters at several temperatures.

Sensitivity and Uncertainty Analysis of One-Dimensional Tanaka and Liang-Rogers Shape Memory Alloy Constitutive Models.

A shape memory alloy (SMA) can remember its original shape and recover from strain due to loading once it is exposed to heat (shape memory effect). SMAs also exhibit elastic response to applied stress above the characteristic temperature at which transformation to austenite is completed (pseudoelasticity or superelasticity). Shape memory effect and pseudoelasticity of SMAs have been addressed by several microscopic thermodynamic and macroscopic phenomenological models using different modeling approaches. The Tanaka and Liang-Rogers models are two of the most widely used macroscopic phenomenological constitutive models for describing SMA behavior. In this paper, we performed sensitivity and uncertainty analysis using Sobol and extended Fourier Amplitude Sensitivity Testing (eFAST) methods for the Tanaka and Liang-Rogers models at different operating temperatures and loading conditions. The stress-dependent and average sensitivity indices have been analyzed and are presented for determining the most influential parameters for these models. The results show that variability is primarily caused by a change in operating temperature and loading conditions. Both models appear to be influenced by the uncertainty in elastic modulus of the material significantly. The analyses presented in this paper aim to provide a better insight for designing applications using SMAs by increasing the understanding of these models' sensitivity to the input parameters and the cause of output variability due to uncertainty in the same input parameters.

Microbiological Quality Assessment of Popular Fresh Date Samples Available in Local Outlets of Dhaka City, Bangladesh.

It is evident that date fruits provide a wide variety of essential nutrients which impart potential human health benefits. In Bangladesh, the popularity of date and its consumption surge few-fold during Ramadan among Muslims owing to the profound emotion related to religious belief that breaking of dawn-to-dusk fasting with dates is fulfilling a Sunnah. The present study aimed to explore the microbiological quality of the five most popular fresh date samples (Nagal, Dhapas, Boroi, Morium, and Tunisia) purchased from different locations of Dhaka City, Bangladesh. Among 25 samples tested, 23 were observed to carry 3.30-5.65 Log CFU/gm aerobic bacteria and 3.30-5.36 Log CFU/gm yeasts and molds population. Coliform bacteria and Escherichia coli were not found in any of the samples analyzed in this study. However, except Nagal and Boroi from Mohammadpur and Mirpur, respectively, none of the samples were found safe to consume according to the microbiological grade recommended by Woolworths Quality Assurance Standard (WQAS), 2009, as fresh dates with microbial load can cause food poisoning or even foodborne intoxication. Date samples with less or no processing is responsible for this contamination which can be subsided and eliminated by appropriate handling and hygiene practices during postharvest processing.

Dietary Patterns and Dietary Adequacy of Street Children in Dhaka, Bangladesh.

The negative impact of poverty on the biological well-being of children is well established. The purpose of the present study was to evaluate the impact of the stress of full-time street life on the dietary patterns and dietary adequacy of street children living in Dhaka, Bangladesh. This was accomplished by comparing the nutritional patterns of full-time street children with those of other poor children in Dhaka who also spend their days on the streets but who return to their families at night. There were few differences between groups, possibly due to street children being a select group. However, there were significant sex differences in both groups of children, with boys tending to have more diverse and more adequate diets than girls. The cause of this latter pattern could not be determined with the available data but may involve cultural values favoring males over females.

Near Surface Mounted Composites for Flexural Strengthening of Reinforced Concrete Beams.

Existing structural components require strengthening after a certain period of time due to increases in service loads, errors in design, mechanical damage, and the need to extend the service period. Externally-bonded reinforcement (EBR) and near-surface mounted (NSM) reinforcement are two preferred strengthening approach. This paper presents a NSM technique incorporating NSM composites, namely steel and carbon fiber-reinforced polymer (CFRP) bars, as reinforcement. Experimental and analytical studies carried out to explore the performance of reinforced concrete (RC) members strengthened with the NSM composites. Analytical models were developed in predicting the maximum crack spacing and width, concrete cover separation failure loads, and deflection. A four-point bending test was applied on beams strengthened with different types and ratios of NSM reinforcement. The failure characteristics, yield, and ultimate capacities, deflection, strain, and cracking behavior of the beams were evaluated based on the experimental output. The test results indicate an increase in the cracking load of 69% and an increase in the ultimate load of 92% compared with the control beam. The predicted result from the analytical model shows good agreement with the experimental result, which ensures the competent implementation of the present NSM-steel and CFRP technique.