Birla Institute of Technology and Science, Pilani - Dubai Campus

Two-dimensional materials have attracted great scientific attention due to their unusual and fascinating properties for use in electronics, spintronics, photovoltaics, medicine, composites, etc. Graphene, transition metal dichalcogenides such as MoS2, phosphorene, etc., which belong to the family of two-dimensional materials, have shown great promise for gas sensing applications due to their high surface-to-volume ratio, low noise and sensitivity of electronic properties to the changes in the surroundings. Two-dimensional nanostructured semiconducting metal oxide based gas sensors have also been recognized as successful gas detection devices. This review aims to provide the latest advancements in the field of gas sensors based on various two-dimensional materials with the main focus on sensor performance metrics such as sensitivity, specificity, detection limit, response time, and reversibility. Both experimental and theoretical studies on the gas sensing properties of graphene and other two-dimensional materials beyond graphene are also discussed. The article concludes with the current challenges and future prospects for two-dimensional materials in gas sensor applications.

Response Surface Methodology (RSM) is a statistical method to design experiments and optimize the effect of process variables. RSM is based on the principles of design of experiments or DOE. Design of experiments or DOE is a field of applied statistics that plans, conducts, analyses, and interprets controlled tests to assess factors that affect parameter values. Response surface methodology or RSM uses a statistical method for designing experiments and optimization. Despite the potential of response surface methodology to predict and optimize engine performance and emissions characteristics, a comprehensive review on RSM for biofuels, particularly for internal combustion engines (ICEs), is difficult to find. The review of response surface methodology is sometimes included together with other machine learning approaches such as ANN. Therefore, a review article that is exclusively written to address the specific of RSM for biofuel and ICE is required. This review article offers a fresh perspective on the application of RSM for biofuel in ICE. This article aims to critically review the RSM to optimize engine performance and emissions using biofuel. The study concludes with several possible research gaps for future works of RSM biofuel application. Although response surface methodology or RSM has drawbacks such as extrapolation inaccuracy outside the investigational ranges and discrete variables error, RSM has numerous advantages to design, model, estimate, and optimize biofuel for ICE with satisfactory accuracy. With its prediction and optimization capability, response surface methodology has the potential to assist the development of ICE optimization powered by biofuel for sustainability energy transition.

Diabetes has affected over 246 million people worldwide with a majority of them being women. According to the WHO report, by 2025 this number is expected to rise to over 380 million. The disease has been named the fifth deadliest disease in the United States with no imminent cure in sight. With the rise of information technology and its continued advent into the medical and healthcare sector, the cases of diabetes as well as their symptoms are well documented. This paper aims at finding solutions to diagnose the disease by analyzing the patterns found in the data through classification analysis by employing Decision Tree and Nave Bayes algorithms. The research hopes to propose a quicker and more efficient technique of diagnosing the disease, leading to timely treatment of the patients.

Ocular diseases have a significant effect on vision and quality of life. Drug delivery to ocular tissues is a challenge to formulation scientists. The major barriers to delivering drugs to the anterior and posterior segments include physiological barriers (nasolacrimal drainage, blinking), anatomical barriers (static and dynamic), efflux pumps and metabolic barriers. The static barriers comprise the different layers of the cornea, sclera, and blood-aqueous barriers whereas dynamic barriers involve conjunctival blood flow, lymphatic clearance and tear drainage. The tight junctions of the blood-retinal barrier (BRB) restrict systemically administered drugs from entering the retina. Nanocarriers have been found to be effective at overcoming the issues associated with conventional ophthalmic dosage forms. Various nanocarriers, including nanodispersion systems, nanomicelles, lipidic nanocarriers, polymeric nanoparticles, liposomes, niosomes, and dendrimers, have been investigated for improved permeation and effective targeted drug delivery to various ophthalmic sites. In this review, various nanomedicines and their application for ophthalmic delivery of therapeutics are discussed. Additionally, scale-up and clinical status are also addressed to understand the current scenario for ophthalmic drug delivery.

Accurate binary classification of electroencephalography (EEG) signals is a challenging task for the development of motor imagery (MI) brain–computer interface (BCI) systems. In this study, two sliding window techniques are proposed to enhance the binary classification of MI. The first one calculates the longest consecutive repetition (LCR) of the sequence of prediction of all the sliding windows and is named SW-LCR. The second calculates the mode of the sequence of prediction of all the sliding windows and is named SW-Mode. Common spatial pattern (CSP) is used for extracting features with linear discriminant analysis (LDA) used for classification of each time window. Both the SW-LCR and SW-Mode are applied on publicly available BCI Competition IV-2a data set of healthy individuals and on a stroke patients’ data set. Compared with the existing state of the art, the SW-LCR performed better in the case of healthy individuals and SW-Mode performed better on stroke patients’ data set for left- versus right-hand MI with lower standard deviation. For both the data sets, the classification accuracy (CA) was approximately 80% and kappa ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\kappa $ </tex-math></inline-formula> ) was 0.6. The results show that the sliding window-based prediction of MI using SW-LCR and SW-Mode is robust against intertrial and intersession inconsistencies in the time of activation within a trial and thus can lead to a reliable performance in a neurorehabilitative BCI setting.

Highway construction projects carry substantial risks. This paper highlights the risks associated with highway construction projects in the United Arab Emirates (UAE). Thirty-three risks are identified through detailed literature review. A questionnaire is then developed to solicit the opinion of construction professionals as to the probability and impact of those risks in addition to their proper allocation. Fifty-one surveys were completed and used in the analysis. The priority of each risk is calculated by multiplying the probability with the impact for each risk. The priority helps identify the most significant risks. The relative importance index (RII) for the risk priority is calculated based on all responses for each risk. The most significant risks include inefficient planning, unexpected ground utilities, quality and integrity of design, delays in approvals, and delays in expropriations. Internal project risks are found to be more significant than external risks. Contractors and consultants are in agreement on the risk rankings. Recommended allocation of risks is also presented. This research assists project managers to better estimate risks prior to the commencement of a project and allows them to develop proper mitigation measures at an early stage of a project.

Two-dimensional molybdenum disulfide (MoS2) based nanosheets functionalized or loaded with an antimicrobial agent have recently attracted attention as highly efficient antibacterial agent. MoS2 sheets act as the photothermal transducers in inducing bacterial cell death on impingement of NIR radiation or enabled cell inactivation by wrapping around the cells. However, the intrinsic ability of MoS2 to act as an effective antibacterial agent without the use of any external stimuli or antimicrobial agent is still not well explored. This study provides a detailed mechanism of antibacterial action of chitosan exfoliated MoS2 nanosheets (CS-MoS2) by deciphering the key events happening both at the membrane surface and inside the bacteria as a result of interaction of bacterial cells with the nanosheets. A simple, green, one-step process was employed for synthesizing stable and positively charged MoS2 nanosheets. The prepared nanosheets showed excellent bactericidal activity against both Gram-positive (MIC = 90 μg/mL, MBC = 120 μg/mL) and Gram-negative bacteria (MIC = 30 μg/mL, MBC = 60 μg/mL). Investigations into deciphering the mechanism of action revealed that the CS-MoS2 nanosheets interacted strongly with the bacterial cells through electrostatic interactions and caused rapid depolarization of the membranes through dent formations. On account of strong van der Waals and electrostatic forces occurring between the CS-MoS2 nanosheets and membrane phospholipid molecules, deepening of dents occurred, which resulted in complete membrane disruption and leakage of cytoplasmic contents. This led to inactivation of the bacterial respiratory pathway through inhibition of dehydrogenase enzymes and induced metabolic arrest in the cells. Simultaneously, disruption of the antioxidant defense system of the cells by increased levels of intracellular ROS subjected the cells to oxidative damage and added to the overall bactericidal action. The nanosheets also displayed antibiofilm properties and were found to be compatible with mammalian cells even at high concentrations.

Protests are an integral part of democracy and an important source for citizens to convey their demands and/or dissatisfaction to the government. As citizens become more aware of their rights, there has been an increasing number of protests all over the world for various reasons. With the advancement of technology, there has also been an exponential rise in the use of social media to exchange information and ideas. In this research, we gathered data from the microblogging website Twitter concerning farmers’ protest to understand the sentiments that the public shared on an international level. We used models to categorize and analyze the sentiments based on a collection of around 20,000 tweets on the protest. We conducted our analysis using Bag of Words and TF-IDF and discovered that Bag of Words performed better than TF-IDF. In addition, we also used Naive Bayes, Decision Trees, Random Forests, and Support Vector Machines and also discovered that Random Forest had the highest classification accuracy.

This study explores the environmental and economic implications of Internal Combustion Engine (ICE) vehicles, Hybrid Electric Vehicles (HEVs), Plug-in Hybrid Electric Vehicles (PHEVs), and Electric Vehicles (EVs). A case study in Indonesia is selected, focusing to achieve net zero emissions by 2050 and improve air quality. The research compares key factors such as emission costs, fueling time costs, maintenance costs, and vehicle selection for a comprehensive understanding of each vehicle type's viability in the automotive landscape. The analysis reveals that EVs exhibit the lowest CO and CO2 emissions of about 20 %, while HEVs and PHEVs demonstrate significant reductions compared to ICE vehicles. However, EVs produce higher NOx and N2O emissions of more than 70 %, indicating a dependence on fossil fuels for electricity generation. Air quality-related emissions, including SOx and PM10, are 90 % and 85 % higher in EVs, emphasizing the need for enhanced emission control technologies and the adoption of renewable energy sources. Despite their higher selling price and emission costs, EVs possess the lowest maintenance costs among the evaluated vehicles at only 0.00419 USD/km. Ultimately, HEVs present the most balanced combination of selling price, emission cost, and maintenance cost, making them an appealing option for the market. This study provides valuable insights for policymakers, automotive manufacturers, and consumers in transitioning towards more sustainable transportation solutions.

Agriculture plays a significant role in meeting food needs and providing food security for the increasingly growing global population, which has increased by 0.88% since 2022. Plant diseases can reduce food production and affect food security. Worldwide crop loss due to plant disease is estimated to be around 14.1%. The lack of proper identification of plant disease at the early stages of infection can result in inappropriate disease control measures. Therefore, the automatic identification and diagnosis of plant diseases are highly recommended. Lack of availability of large amounts of data that are not processed to a large extent is one of the main challenges in plant disease diagnosis. In the current manuscript, we developed datasets for food grains specifically for rice, wheat, and maize to address the identified challenges. The developed datasets consider the common diseases (two bacterial diseases and two fungal diseases of rice, four fungal diseases of maize, and four fungal diseases of wheat) that affect crop yields and cause damage to the whole plant. The datasets developed were applied to eight fine-tuned deep learning models with the same training hyperparameters. The experimental results based on eight fine-tuned deep learning models show that, while recognizing maize leaf diseases, the models Xception and MobileNet performed best with a testing accuracy of 0.9580 and 0.9464 respectively. Similarly, while recognizing the wheat leaf diseases, the models MobileNetV2 and MobileNet performed best with a testing accuracy of 0.9632 and 0.9628 respectively. The Xception and Inception V3 models performed best, with a testing accuracy of 0.9728 and 0.9620, respectively, for recognizing rice leaf diseases. The research also proposes a new convolutional neural network (CNN) model trained from scratch on all three food grain datasets developed. The proposed model performs well and shows a testing accuracy of 0.9704, 0.9706, and 0.9808 respectively on the maize, rice, and wheat datasets.

We have analyzed 127 publications for this review paper, which discuss applications of Reinforcement Learning (RL) in marketing, robotics, gaming, automated cars, natural language processing (NLP), internet of things security, recommendation systems, finance, and energy management. The optimization of energy use is critical in today’s environment. We mainly focus on the RL application for energy management. Traditional rule-based systems have a set of predefined rules. As a result, they may become rigid and unable to adjust to changing situations or unforeseen events. RL can overcome these drawbacks. RL learns by exploring the environment randomly and based on experience, it continues to expand its knowledge. Many researchers are working on RL-based energy management systems (EMS). RL is utilized in energy applications such as optimizing energy use in smart buildings, hybrid automobiles, smart grids, and managing renewable energy resources. RL-based energy management in renewable energy contributes to achieving net zero carbon emissions and a sustainable environment. In the context of energy management technology, RL can be utilized to optimize the regulation of energy systems, such as building heating, ventilation, and air conditioning (HVAC) systems, to reduce energy consumption while maintaining a comfortable atmosphere. EMS can be accomplished by teaching an RL agent to make judgments based on sensor data, such as temperature and occupancy, to modify the HVAC system settings. RL has proven beneficial in lowering energy usage in buildings and is an active research area in smart buildings. RL can be used to optimize energy management in hybrid electric vehicles (HEVs) by learning an optimal control policy to maximize battery life and fuel efficiency. RL has acquired a remarkable position in robotics, automated cars, and gaming applications. The majority of security-related applications operate in a simulated environment. The RL-based recommender systems provide good suggestions accuracy and diversity. This article assists the novice in comprehending the foundations of reinforcement learning and its applications.

Behavior recognition using motion sensors is getting prominence over other systems such as e-healthcare and life-log analysis systems especially in the healthcare domain for improving life expectancy and healthcare access. Accelerometers have been used in smart environments to recognize behavior since the last decade but heavy computation involved in recognizer model made them less acceptable. This paper proposed a computationally less expensive model with better recognition results for improved human behavior understanding system. Hierarchical features are used to ensure robustness as a performance attribute in the proposed system. These hierarchical features involve statistical features like signal magnitude, abrupt changes, and temporal variation among coordinates. Moreover, the extracted features are examined through the process of learning, training, and symbolization with the help of linear support vector machine. The examination of our recognition results based on feature extraction strategy show that our model excels others in terms of accuracy and computation time. The proposed system should be considered as a recommendation for systems involving human behavior recognition i.e. kindergarten, elderly at old-age houses and patients with Parkinson diseases.

Biodiesel use in homogeneous charge compression ignition (HCCI) engines has the potential to improve engine performance and combustion characteristics, while at the same time reducing harmful emissions. Diesel engines have the dilemma of PM and NOx trade-off. HCCI engine emerges as a new combustion mode to reduce such trade-off, while at the same time maintaining the superiority of diesel over gasoline engine with better fuel economy and engine performance. HCCI engine is a promising technology employing the premixed combustion to ensure the homogeneous and lean mixture under autoignition event. However, controlling its combustion phasing remains a major challenge. Also, HCCI engine suffers from high HC and CO emissions. To control its combustion timing and reduce high emissions from HCCI engine, fuel is one of the important parameters. The use of biodiesel fuel can achieve that goal. This study aims to review the effect of biodiesel addition in HCCI engine. The scope includes the progress and development of biodiesel in HCCI engines, encompassing results from both experimental and numerical studies. Insights into the prospect of biodiesel for HCCI engine were provided. Also, the challenges of fuel and engine aspect were discussed with potential solutions for future improvement. By playing around with several parametric variables such as inlet air temperature, exhaust gas ratio and injection pressure, HCCI engine fuelled with biodiesel fuel could improve the engine performance and combustion characteristics, while at the same time reducing harmful pollutants such as CO, HC, NOx and smoke emissions.

Carbon emissions play the central role in global warming. Manufacturing firms are significant contributors to carbon emissions. In many countries, regulatory authorities are taking actions to reduce emissions. Carbon taxation and cap-and-trade schemes are two mechanisms implemented in many countries. In the present paper, the author analyzes a production-inventory model under a carbon tax system. The production rate is assumed to be a decision variable and can be set at any level within machine limits. A proportion of items produced are defective, and this proportion depends on the production rate. Demand depends on the selling price. Unit price is a decreasing function of the production rate. Emissions can be reduced to some extent by capital investment on green technology, and this capital investment amount is a decision variable. Customers are categorized as retail customers and wholesale customers. A discount is offered to the wholesale customers on the regular selling price. The results are illustrated by a numerical example and a sensitivity analysis is performed.

. Interventions such as nutritional supplementation during pregnancy and the postnatal period could help prevent growth faltering, but programmatic action has been insufficient to eliminate the high burden of stunting and wasting in low- and middle-income countries. Identification of age windows and population subgroups on which to focus will benefit future preventive efforts. Here we use a population intervention effects analysis of 33 longitudinal cohorts (83,671 children, 662,763 measurements) and 30 separate exposures to show that improving maternal anthropometry and child condition at birth accounted for population increases in length-for-age z-scores of up to 0.40 and weight-for-length z-scores of up to 0.15 by 24 months of age. Boys had consistently higher risk of all forms of growth faltering than girls. Early postnatal growth faltering predisposed children to subsequent and persistent growth faltering. Children with multiple growth deficits exhibited higher mortality rates from birth to 2 years of age than children without growth deficits (hazard ratios 1.9 to 8.7). The importance of prenatal causes and severe consequences for children who experienced early growth faltering support a focus on pre-conception and pregnancy as a key opportunity for new preventive interventions.

. Stunting, a form of linear growth faltering, increases the risk of illness, impaired cognitive development and mortality. Global stunting estimates rely on cross-sectional surveys, which cannot provide direct information about the timing of onset or persistence of growth faltering-a key consideration for defining critical windows to deliver preventive interventions. Here we completed a pooled analysis of longitudinal studies in low- and middle-income countries (n = 32 cohorts, 52,640 children, ages 0-24 months), allowing us to identify the typical age of onset of linear growth faltering and to investigate recurrent faltering in early life. The highest incidence of stunting onset occurred from birth to the age of 3 months, with substantially higher stunting at birth in South Asia. From 0 to 15 months, stunting reversal was rare; children who reversed their stunting status frequently relapsed, and relapse rates were substantially higher among children born stunted. Early onset and low reversal rates suggest that improving children's linear growth will require life course interventions for women of childbearing age and a greater emphasis on interventions for children under 6 months of age.

The concept of smart city evolved with the integration of information and communication technology (ICT) in various sub-systems and processes in urban environment. The development of the smart cities is the best possible solution to major urban issues. It contributes towards economic and social development of the residents. It aims to provide the cordial environment in the domains of healthcare, education, transportation, power generation and dissipation, security, living, industry, etc., to the inhabitants to make their lives comfortable. Sustainability of these services is another major objective in a smart city framework. Along with the true realization of the idea of a smart city, advanced computational and communication technologies are contributing hugely towards its sustainable development. Communication technologies act as backbone to ensure connectivity at the various levels in a smart city framework. Novel smart city solutions for different application domains are designed and deployed by the industry using advanced computational technologies like IoT, Artificial Intelligence, Blockchain, Big Data and Cloud Computing. In this work, authors discuss the concept of smart city, its architecture and sustainability. Different operational domains in a smart city ecosystem are elaborated. The cyber physical aspect of the smart cities is discussed in brief. The role of various computational and communication technologies in the sustainable development of smart cities is presented. Limiting factors in the deployment of various advanced technologies in different smart city domains are highlighted. Security issues associated with the technological sustainable development of different smart city services along with existing solutions are discussed. The article is concluded by highlighting the future research directions.

Edge computing (EC), is a technological game changer that has the ability to connect millions of sensors and provide services at the device end. The broad vision of EC integrates storage, processing, monitoring, and control of operations in the Edge of the network. Though EC provides end-to-end connectivity, speeds up operation, and reduces latency of data transfer, security is a major concern. The tremendous growth in the number of Edge Devices and the amount of sensitive information generated at the device and the cloud creates a broad surface of attack and therefore, the need to secure the static and mobile data is imperative. This article is a comprehensive survey that describes the security and privacy issues in various layers of the EC architecture that result from the networking of heterogeneous devices. Second, it discusses the wide range of machine learning and deep learning algorithms that are applied in EC use cases. Following this, this article broadly details the different types of attacks that the Edge network confronts, and the intrusion detection systems and the corresponding machine learning algorithms that overcome these security and privacy concerns. The details of machine learning and deep learning techniques for EC security are tabulated. Finally, the open issues in securing Edge networks and future research directions are provided.

. Prevailing methods to measure wasting rely on cross-sectional surveys that cannot measure onset, recovery and persistence-key features that inform preventive interventions and estimates of disease burden. Here we analyse 21 longitudinal cohorts and show that wasting is a highly dynamic process of onset and recovery, with incidence peaking between birth and 3 months. Many more children experience an episode of wasting at some point during their first 24 months than prevalent cases at a single point in time suggest. For example, at the age of 24 months, 5.6% of children were wasted, but by the same age (24 months), 29.2% of children had experienced at least one wasting episode and 10.0% had experienced two or more episodes. Children who were wasted before the age of 6 months had a faster recovery and shorter episodes than did children who were wasted at older ages; however, early wasting increased the risk of later growth faltering, including concurrent wasting and stunting (low length-for-age z-score), and thus increased the risk of mortality. In diverse populations with high seasonal rainfall, the population average weight-for-length z-score varied substantially (more than 0.5 z in some cohorts), with the lowest mean z-scores occurring during the rainiest months; this indicates that seasonally targeted interventions could be considered. Our results show the importance of establishing interventions to prevent wasting from birth to the age of 6 months, probably through improved maternal nutrition, to complement current programmes that focus on children aged 6-59 months.

Variability in solar irradiance has an impact on the stability of solar systems and the grid’s safety. With the decreasing cost of solar panels and recent advancements in energy conversion technology, precise solar energy forecasting is critical for energy system integration. Despite extensive research, there is still potential for advancement of solar irradiance prediction accuracy, especially global horizontal irradiance. Global Horizontal Irradiance (GHI) (unit: KWh/m2) and the Plane Of Array (POA) irradiance (unit: W/m2) were used as the forecasting objectives in this research, and a hybrid short-term solar irradiance prediction model called modified multi-step Convolutional Neural Network (CNN)-stacked Long-Short-Term-Memory network (LSTM) with drop-out was proposed. The real solar data from Sweihan Photovoltaic Independent Power Project in Abu Dhabi, UAE is preprocessed, and features were extracted using modified CNN layers. The output result from CNN is used to predict the targets using a stacked LSTM network and the efficiency is proved by comparing statistical performance measures in terms of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MAE), and R2 scores, with other contemporary machine learning and deep-learning-based models. The proposed model offered the best RMSE and R2 values of 0.36 and 0.98 for solar irradiance prediction and 61.24 with R2 0.96 for POA prediction, which also showed better performance as compared to the published works in the literature.