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The need for efficient nanotechnology has led to unexpected developments. Conserving continuous thermal propagation is essential in many industrial and thermal systems because it improves the efficiency of thermal engineering engines and machinery. Therefore, a promising platform to increase thermal power energy is the hybridization of magnetic nanoparticles in a heat-supporting, non-Newtonian fluid. In light of the above applications, a mathematical model is established to analyze the variable fluid features of the thermally radiative and chemically reactive flow of a micropolar Williamson ternary hybrid nanofluid with electromagnetohydrodynamic and electroosmosis forces on a porous stretching surface. Stratification boundary conditions and variable fluid properties were used to analyze the thermal and solutal behavior of the fluid flow. Furthermore, to measure the disorder of the flow system, entropy generation was considered by the impact of Joule heating and viscous dissipation. To develop the numerical scheme BVP4C in MATLAB, we first converted the mathematical flow model into two ordinary differential equations using a suitable transformation. The graphical and numerical results were determined against several parameters of a ternary hybrid nanofluid (MWCNT,Al2O3,SiC)({\rm{MWCNT}},\hspace{0.25em}{\rm{A}}{{\rm{l}}}_{2}{{\rm{O}}}_{3},\hspace{0.25em}{\rm{SiC}}) and unary nanofluid (Al2O3)({\rm{A}}{{\rm{l}}}_{2}{{\rm{O}}}_{3}). The results indicate that the heat transfer rate is more prominent in the ternary hybrid nanofluid than in the unary nanofluid because the addition of nanofluids to the base fluid is used to improve the heat transport rate. It can be seen from the figures that a greater estimation of the magnetic and electric field parameters improves the fluid velocity because, for low values of M≤1M\le 1, the aiding force is dominant compared to the retarding force, which results in an improvement in the velocity profile. Furthermore, the entropy generation rate increases for higher values of the Brinkman number and temperature ratio parameter because more heat is produced due to the greater values of Br{\rm{Br}}.

Two-dimensional (2D) materials play a crucial role as fundamental electrical components in modern electronics and optoelectronics next-generation artificial intelligent devices. This study presents a methodology for creating a laterally uniform p–n junction by using a partial oxygen plasma-mediated strategy to introduce p-type doping in single channel MoTe2 device. The MoTe2 field effect transistors (FETs) show high electron mobility of about ∼23.54 cm2 V−1 s−1 and a current ON/OFF ratio of ∼106 while p-type FETs show hole mobility of about ∼9.25 cm2 V−1 s−1 and current ON/OFF ratio ∼105 along with artificially created lateral MoTe2 p–n junction, exhibited a rectification ratio of ∼102 and ideality factor of ∼1.7 which is proximity to ideal-like diode. Thus, our study showed a diversity in the development of low-power nanoelectronics of next-generation integrated circuits.

The impact of Marangoni convection has noteworthy applications in nanotechnology, atomic reactor, silicon wafers, semiconductor processing, soap films, materials sciences, thin-film stretching, crystal growth, and melting and welding processes. On the other hand, thermophoretic particle deposition (TPD) has a significant application in building ventilation systems, crushed coal burners, thermal exchangers, and air cleaners. Inspired by these applications, the present work mainly concentrates on the Marangoni convection flow of Al2O3/water-based nanofluid over a stretching sheet in a porous medium with TPD in the presence of Newtonian heating. Additionally, heat absorption/generation in energy expression is considered. A suitable similarity variable is applied to simplify the partial differential equations into a set of ordinary differential equations (ODEs). Furthermore, Runge Kutta Fehlberg fourth fifth order method along with the shooting technique is implemented to solve the reduced ODEs. Furthermore, mathematical computational software helps to acquire a numerical solution. The velocity of nanofluid increases for enhancement of Marangoni number and diminishes for porosity parameter. The heat absorption/generation parameter improves thermal dispersion in both common wall temperature and Newtonian heating cases. For the upgradation in the thermophoretic parameter, the concentration decreases and the rate of mass transfer increases. The rate of heat transfer increases as the heat source parameter grows and decreases as the heat sink parameter decreases. In all of the profiles, nanofluid outperforms viscous fluid.

The fractional model has been developed for the thermal flow of hybrid nanofluid due to the inclined surface. The thermal investigation of the hybrid nanomaterial is predicted by utilizing the molybdenum disulphide nanoparticles and graphene oxide nanomaterials. The flow computations for mixed convection flow of nanoparticles and base fluids are performed due to vertical oscillating plate. The simulations for the formulated model have been done ρ-Laplace transform technique for Caputo fractional simulations. Definitions of Mittage–Leffler function and ρ-Laplace transform are also presented for the governing model. The application of updated definitions of ρ-Laplace transform for the Caputo fractional model is quite interesting unlike traditional Laplace transforms. The comparative investigation for both types of nanoparticles is performed for heat and mass transfer rates. It is observed that the heat enhancement rate due to water-based nanoparticles is relatively impressive compared to the kerosene oil-based nanomaterials.

In energy scarcity, particularly in Agri-based developing economies, bio-coal briquetting is the most suitable means of meeting sustainable energy needs utilizing agricultural waste. In this study, briquettes were made from an indigenously designed briquetting machine for investigating coal–biomass proportion blend using coal from Dara Adam Khel, Khyber Pakhtunkhwa in Pakistan, and pomegranate/olive waste to analyze their resulting calorific value, strength, and geochemical properties. A central composite design (CCD) and response surface methodology (RSM) were employed to design the experiments and analyze the data. Regression models were developed for each biomass type, demonstrating their adequacy and reliability for further analysis and prediction. Energy Dispersive X-ray Spectroscopy (EDX) analysis provided insights into the elemental composition of the coal briquettes. Mechanical analysis was performed to assess the strength of the briquettes, with varying compositions showing different levels of strength. Optimization using a composite desirability function yielded an optimal calorific value of 6728 kJ/kg. In summary, this study underscores the viability of bio-coal briquetting as a transformative solution to alleviate energy scarcity. Repurposing agricultural waste curtails resource depletion while mitigating waste disposal challenges. The optimized calorific value aligns with eco-friendly energy initiatives, advancing a greener path toward energy security.

Nickel is a known hepatotoxic, haemotoxic, pulmonary toxic, nephrotoxic, reproductive toxic, carcinogenic, phytotoxic and neurotoxic agent. The adverse ecological impacts from unnecessary heavy metals include contamination of water and soil which pose serious threats to human health. This study was conducted to screen plants for the phytoremediation of nickel from sixty-one sites of the Lower Dir. Nickel-metal was analyzed in the soil, roots and shoots of plants. The total concentration of nickel in soil, roots and shoots was found to be in the range of 1.03-18.98, 12.63-540.73 and 12.00-295.86 mg kg–1 dry weight basis (DW) respectively. The highest nickel contents were present in the roots of Xanthium strumarium (540.73) and shoots of Bryophyllum daigremontianum (295.86). None of the plant species were identified as hyper accumulators for nickel but based on BCF, TF and BAC values most of the species showed feasibility for its phytoextraction and phytostabilization. Xanthium strumarium, Filago hurdwarica, Ranunculus arvensis, Medicago lupulina, Cannabis sativa, Geranium rotundifolium and Cerastium glomeratum are suggested for the phytostabilization of nickel whereas Bryophyllum daigremontianum, Rosularia adenotricha, Iris germanica, Asplenium dalhousiae and Isatis tinctoria for the phytoextraction of soil contaminated with nickel.

Artificial intelligence (AI)-based multispectral remote sensing has been the best supporting tool using limited resources to enhance the lithological mapping abilities with accuracy, supported by ground truthing through traditional mapping techniques. The availability of the dataset, choice of algorithm, cost, accuracy, computational time, data labeling, and terrain features are some crucial considerations that researchers continue to explore. In this research, support vector machine (SVM) and artificial neural network (ANN) were applied to the Sentinel-2 MSI dataset for classifying lithologies having subtle compositional differences in the Kohat Basin’s remote, inaccessible regions within Pakistan. First, we used principal component analysis (PCA), minimum noise fraction (MNF), and available maps for reliable data annotation for training SVM and (ANN) models for mapping ten classes (nine lithological units + water). The ANN and SVM results were compared with the previously conducted studies in the area and ground truth survey to evaluate their accuracy. SVM mapped ten classes with an overall accuracy (OA) of 95.78% and kappa coefficient of 0.95, compared to 95.73% and 0.95 by ANN classification. The SVM algorithm was more efficient concerning computational efficiency, accuracy, and ease due to available features within Google Earth Engine (GEE). Contrarily, ANN required time-consuming data transformation from GEE to Google Cloud before application in Google Colab.

The built environment of hospital buildings are generally not accepted to be pleasant. In the design of healthcare facility, it is quite important that its design, spatial arrangement and areal distribution must respond to curative needs of people so as the outcome emerge in the form of healing environment in the physical spaces.This kind of healing environment is quite adequately available in the developed countries of the global north. However; in developing countries of the global south like Pakistan, the healing environment in healthcare facilities is neither documented nor evident in any available published literature. Whereas, it needs to be well documented and analyzed. Thus, this study aims to identify the healing environment in two selected hospital buildings in Pakistan that is ICP (Peshawar Institute of Cardiology) and FCP (FC Hospital Peshawar) by determining the percentage of wasted spaces within the building. The selected buildings are analyzed from the following aspects; circulation pattern in the hospital, accessibility, connectivity and barrier free movement within buildings, along with the walkability status of over stressed staff inside the hospital building while navigating, functionality of the schematic designs, the problem of the users concerning repetitiveness in their circulation pattern and the way to increase the efficiency of spaces, their spread and flow in the hospital building. In general, three key factors were investigated in this study, therefore, design determinents, areal distribution and adequate spatial organization. In this regard, total eight design determinants were investigated, i.e. entry, parking, waiting area, connectivity, visibility, walkability, accessibility, and way finding. Study approached the spatial simulation method therefore 3M analysis which is a Japanese model referred as Muda (waste), Muri (over burden), Mura (unevenness) based on the Kaizen theory for eliminating wasted spaces from Hospital buildings. Based on the findings and through the approached tools, the waste spaces within the hospital buildings were removed up to 40%.

Coal dust explosion constitutes a significant hazard in underground coal mines, coal power plants and other industries utilising coal as fuel. Knowledge of the explosion mechanism and the factors causing coal explosions is essential to investigate for the identification of the controlling factors for preventing coal dust explosions and improving safety conditions. However, the underlying mechanism involved in coal dust explosions is rarely studied under Artificial Intelligence (AI) based modelling. Coal from three different regions of Khyber Pakhtunkhwa, Pakistan, was tested for explosibility in 1.2 L Hartmann apparatus under various particle sizes and dust concentrations. First, a random forest algorithm was used to model the relationship between inputs (coal dust particle size, coal concentration and gross calorific value (GCV)), outputs (maximum pressure (Pmax) and the deflagration index (Kst)). The model reported an R2 value of 0.75 and 0.89 for Pmax and Kst. To further understand the impact of each feature causing explosibility, the random forest AI model was further analysed for sensitivity analysis by SHAP (Shapley Additive exPlanations). The study revealed that the most critical parameter affecting the explosibility of coal dust were particle size > GCV > concentration for Pmax and GCV > Particle size > Concentration for Kst. Mutual interaction SHAP plots of two variables at a time revealed that with 400 gm/L), explosibility is relatively lower irrespective of GCV and particle sizes.

Biomass materials such as CS (Cotton Stalks) and RH (Rice Husk) are a renewable source of energy. As biomass resources have potential to offer a uninterpted supply of biofuels through thermal decomposition processes. Nevertheless, an appropriate understanding of reaction kinetics and thermal properties of biomasses play a vital role in designing of the commercial plants using biomass as a raw material for energy generation. The kinetic characteristics of the CS and RH samples under pure nitrogen 99.9% and pure oxygen 99.9% was performed. The temperature was raised from ambient to 900oC maintaining the heating rate of 10oC/min. CS and RH decomposition was noticed in three dissimilar regions. The kinetic characteristics such as (pre-exponential factor, the order of reaction and activation energy) were calculated for both selected materials. The activation energies calculated under nitrogen environment for CS and RH was 68.77 and 72.31 kJ/mole, whereas the regression coefficient (R2) was 0.9877 and 0.9731 respectively. The activation energies under oxygen environment were higher, it was 106 and 118 kJ/mole. The regression coefficient (R2) under oxygen environment was 0.9987 and 0.99883 for above sample sequence.

In last few decades the demand of lipase has been dramatically increase due to its useful use in numbers of biochemical industries. Varieties of natural and synthetic carriers and methodologies have been used to improve lipase activities by the process of immobilization in order to enhance its activities in term of its resistance to high temperature, pH and to increase its reusibity and storage capacity. Due to the expensive nature the recycling of the lipase has been the target of the researchers to decrease its cost in the industrial process. Magnetic iron oxide organic/inorganic hybrid nanoparticles as a support have been mostly used in the lipase immobilization due its large surface area, less toxicity, bio compatibility, easily functionalization, separation avoiding long time consuming process like filtration and centrifuging.

Despite low spatial resolutions, thermal infrared bands (TIRs) are generally more suitable for mineral mapping due to fundamental tones and high penetration in vegetated areas compared to shortwave infrared (SWIR) bands. However, the weak overtone combinations of SWIR bands for minerals can be compensated by fusing SWIR-bearing data (Sentinel-2 and Landsat-8) with other multispectral data containing fundamental tones from TIR bands. In this paper, marble in a granitic complex in Mardan District (Khyber Pakhtunkhwa) in Pakistan is discriminated by fusing feature-oriented principal component selection (FPCS) obtained from the ASTER, Landsat-8 Operational Land Imager (OLI), Thermal Infrared Sensor (TIRS) and Sentinel-2 MSI data. Cloud computing from Google Earth Engine (GEE) was used to apply FPCS before and after the decorrelation stretching of Landsat-8, ASTER, and Sentinel-2 MSI data containing five (5) bands in the Landsat-8 OLI and TIRS and six (6) bands each in the ASTER and Sentinel-2 MSI datasets, resulting in 34 components (i.e., 2 × 17 components). A weighted linear combination of selected three components was used to map granite and marble. The samples collected during field visits and petrographic analysis confirmed the remote sensing results by revealing the region’s precise contact and extent of marble and granite rock types. The experimental results reflected the theoretical advantages of the proposed approach compared with the conventional stacking of band data for PCA-based fusion. The proposed methodology was also applied to delineate granite deposits in Karoonjhar Mountains, Nagarparker (Sindh province) and the Kotah Dome, Malakand (Khyber Pakhtunkhwa Province) in Pakistan. The paper presents a cost-effective methodology by the fusion of FPCS components for granite/marble mapping during mineral resource estimation. The importance of SWIR-bearing components in fusion represents minor minerals present in granite that could be used to model the engineering properties of the rock mass.

With the emerging technologies of augmented reality (AR) and virtual reality (VR), the learning process in today’s classroom is much more effective and motivational. Overlaying virtual content into the real world makes learning methods attractive and entertaining for students while performing activities. AR techniques make the learning process easy, and fun as compared to traditional methods. These methods lack focused learning and interactivity between the educational content. To make learning effective, we propose to use handheld marker-based AR technology for primary school students. We developed a set of four applications based on students’ academic course of primary school level for learning purposes of the English alphabet, decimal numbers, animals and birds, and an AR Globe for knowing about different countries around the world. These applications can be played wherever and whenever a user wants without Internet connectivity, subject to the availability of a tablet or mobile device and the required target images. These applications have performance evaluation quizzes (PEQs) for testing students’ learning progress. Our study investigates the effectiveness of AR-based learning materials in terms of learning performance, motivation, attitude, and behavior towards different methods of learning. Our activity results favor AR-based learning techniques where students’ learning motivation and performance are enhanced compared to the non-AR learning methods.

In this study an effect of acidic pre-treatment on the CS (Canola Straw) and BD (Buffalo Dung) by anaerobic co-digestion was investigated. H2O2 (Hydrogen Peroxide) is a mainly accustomed reagent, used as a bleaching agent in the different industries such as paper and wood. In the present study, it was used as a pre-treatment chemical at varying concentrations in batch reactors. The co-digestion of CS and BD was carried out in SAMPTS (Semi-Automatic Methane Potential Test System) at mesophilic (37±1oC) conditions. The CS was pretreated in glass bottles with different concentrations of the H2O2 for seven days. The inoculum used in the present study was an effluent of the CSTR (Continuous Stirred Tank Reactor), which was treating BD at mesophilic conditions. The specific methane production from the codigestion of canola straw and BD, by the pre-treatment of H2O2 at concentrations of 0.5, 1.0, and 1.5% were 530.8, 544.5, and 510.3 NmL CH4 g/VS, respectively. The significant reduction in the volatile solids of CS was observed at the optimum pre-treatment of 1.0% H2O2.

Virtual reality (VR) has been widely used as a tool to assist people by letting them learn and simulate situations that are too dangerous and risky to practice in real life, and one of these is road safety training for children. Traditional video- and presentation-based road safety training has average output results as it lacks physical practice and the involvement of children during training, without any practical testing examination to check the learned abilities of a child before their exposure to real-world environments. Therefore, in this paper, we propose a 3D realistic open-ended VR and Kinect sensor-based training setup using the Unity game engine, wherein children are educated and involved in road safety exercises. The proposed system applies the concepts of VR in a game-like setting to let the children learn about traffic rules and practice them in their homes without any risk of being exposed to the outside environment. Thus, with our interactive and immersive training environment, we aim to minimize road accidents involving children and contribute to the generic domain of healthcare. Furthermore, the proposed framework evaluates the overall performance of the students in a virtual environment (VE) to develop their road-awareness skills. To ensure safety, the proposed system has an extra examination layer for children’s abilities evaluation, whereby a child is considered fit for real-world practice in cases where they fulfil certain criteria by achieving set scores. To show the robustness and stability of the proposed system, we conduct four types of subjective activities by involving a group of ten students with average grades in their classes. The experimental results show the positive effect of the proposed system in improving the road crossing behavior of the children.

The reliability, scalability, and stability of routing schemes are open challenges in highly evolving vehicular ad hoc networks (VANETs). Cluster-based routing is an efficient solution to cope with the dynamic and inconsistent structure of VANETs. In this paper, we propose a cluster-based routing scheme (hereinafter referred to as connectivity-based clustering), where link connectivity is used as a metric for cluster formation and cluster head (CH) selection. Link connectivity is a function of vehicle density and transmission range in the proposed connectivity-based clustering scheme. Moreover, we used a heuristic approach of spectral clustering for the optimal number of cluster formation. Lastly, an appropriate vehicle is selected as a CH based on the maximum Eigen-centrality score. The simulation results show that the suggested connectivity-based clustering scheme performs well in the optimal number of cluster selections, strongly connected (STC) route selection, and route request messages (RRMs) in the discovery of a particular path to the destination. Thus, we conclude that link connectivity and the heuristic approach of spectral clustering are valuable additions to existing routing schemes for high evolving networks.

During underground construction, the behavior of the ground is influenced by characteristics of the rock mass with situ stresses and ground water, cross section of the excavation area, excavation method, and the rate of excavation. These fundamental features are considered to ensure the support and stability of underground excavations and achieve long-term successful operation. However, the ground composition of the Himalayas hinders tunnel excavation, especially in case of mechanized tunneling; this causes time and cost overruns. This study has reviewed the recently completed Neelum–Jhelum Hydroelectric Project; the project complexities, geological environments involving significant overburden and tectonic stresses, and effects of the excavation method on tunnel stability were analyzed. The major challenges that were encountered during construction are discussed herein along with their countermeasures. An analysis of project-related data reveals that latest techniques and approaches considering rock mechanics were used to complete the project; the existing approaches and methods were accordingly verified and extended. Apart from ground composition, the excavation methods used play an important role in the occurrence of severe rock bursts. Thus, the findings of this study are expected to be helpful for future tunneling projects in the Himalayas.

In several regions of the Pakistan, crop cultivation is leading to the production crop residues and its disposal problems. It has been suggested that the co-digestion of the crop residues with the buffalo dung might be a disposal way for the wasted portion of the crops' residue. The objective of present study was to optimize the anaerobic co-digestion of canola straw and the buffalo dung through batch experiments in order to obtain maximum methane production. The optimization was carried out in three stages. In first stage, the best canola straw to buffalo dung ratio was evaluated. In second stage, the best concentration of sodium hydrogen carbonate was assessedas the alkaline pretreatment chemical, whereas in the third stage most suitable particle size of the canola strawwas evaluated. The assessment criteria for the optimization of a co-digestion were cumulative methane production and ABD (Anaerobic Biodegradability). The results yield that anaerobic co-digestibility of the canola straw and the buffalo dung is obviously influenced by all the three factors of optimization. The maximum methane production was obtained as 911 NmL from the canola straw to buffalo dung ratio of 40:60, the alkaline doze of 0.6 gNaHCO3 / gVS and canola straw particle size of 2mm. However, because of the higher shredding cost to produce 2mm sized canola straw, particle size 4mm could be the best canola straw particle size.

The electron beam welding process is widely used in the connection among titanium alloy material parts of aero-engines. Its mechanical properties need to meet the requirements of long life and high reliability. In this paper, the static strength and the fatigue failure behavior of the electron beam weldments of TC17 titanium alloy were investigated experimentally under low amplitude high frequency (20 kHz), and the mechanical response and failure mechanism under different external loading conditions were analyzed. In summary, the samples were found to have anisotropic microstructure. The tensile strength of the PWHT of TC17 EBW joint was ~4.5% lower than that of the base metal. Meanwhile, compared with the base metal, the fatigue strength was reduced by 45.5% at 109 cycles of fatigue life. The fracture analysis showed that the fatigue failure of the welded joint of TC17 alloy was caused by the welded pores and the fatigue cracks initiated from the welded pores. A fine granular area (FGA) was observed around the crack initiation region. The existence of pores caused the stress intensity factor of the fine granular area (KFGA) to be inversely proportional to the fatigue life. The KFGA calculation formula was modified and the fatigue crack propagation threshold of the welded joint of TC17 alloy was calculated (3.62 MPa·m1/2). Moreover, the influences of the effective size and the relative depth of the pores on the very long fatigue life of the electron beam welded joint of TC17 titanium alloy were discussed.

ABD (Anaerobic Biodegradability) and BMP (Biochemical Methane Potential) of banana plant waste, canola straw, cotton stalks, rice straw, sugarcane trash and wheat straw co-digested with buffalo dung was evaluated through AMPTS (Automatic Methane Potential Test System). The substrates were analyzed for moisture, TS (Total Solids) and VS (Volatile Solids), ultimate analysis (CHONS), pH and TA (Total Alkalinity). The BMPobserved during incubation of 30 days at the temperature of 37±0.2°C was 322 Nml CH4/g VSadd for wheat straw followed by 260, 170, 149, 142 and 138 Nml CH4/gVSadd for canola straw, rice straw, cotton stalks, banana plant waste and sugarcane trash respectively, whereas the maximum theoretical BMP was 481 Nml CH4/gVSadd for cotton stalks, followed by 473, 473, 446, 432 and 385 Nml CH4/gVSadd for wheat straw, banana plant waste, canola straw, rice straw and sugarcane trash respectively. The percentage ABD values were in the range of 68-30%. In addition to this, the effect of lignin content in the crop residue was evaluated on the ABD. The results of this study indicate that, the co-digestion of the crop residues with buffalo dung is feasible for production of renewable methane

Kinetic studies of AD (Anaerobic Digestion) process are useful to predict the performance of digesters and design appropriate digesters and also helpful in understanding inhibitory mechanisms of biodegradation. The aim of this study was to assess the anaerobic kinetics of crop residues digestion with buffalo dung. Seven crop residues namely, bagasse, banana plant waste, canola straw, cotton stalks, rice straw, sugarcane trash and wheat straw were selected from the field and were analyzed on MC (Moisture Contents), TS (Total Solids) and VS (Volatile Solids) with standard methods. In present study, three first order exponential models namely exponential model, exponential lag phase model and exponential curve factor model were used to assess the kinetics of the AD process of crop residues and the effect of lag phase and curve factor was analyzed based on statistical hypothesis testing and on information theory. Assessment of kinetics of the AD of crop residues and buffalo dung follows the first order kinetics. Out of the three models, the simple exponential model was the poorest model, while the first order exponential curve factor model is the best fit model. In addition to statistical hypothesis testing, the exponential curve factor model has least value of AIC (Akaike's Information Criterion) and can generate methane production data more accurately. Furthermore, there is an inverse linear relationship between the lag phase period and the curve factor.

Governments these days are demanding better Smart City technologies in order to connect with citizens and understand their demands. For such governments, much needed information exists on social media where members belonging to diverse groups share different interests, post statuses, review and comment on various topics. Aspect extraction from this data can provide a thorough understanding of citizens’ behaviors and choices. Also, categorization of these aspects can better summarize societal concerns regarding political, economic, religious and social issues. Aspect category detection (ACD) from people reviews is one of the major tasks of aspect-based sentiment analysis (ABSA). The success of ABSA is mainly defined by the inexpensive and accurate machine-processable representation of the raw input sentences. Previous approaches rely on cumbersome feature extraction procedures from sentences, which adds its own complexity and inaccuracy in performing ACD tasks. In this paper, we propose an inexpensive and simple method to obtain the most suitable representation of a sentence-vector through different algebraic combinations of a sentence’s word vectors, which will act as an input to any machine learning classifier. We have tested our technique on the restaurant review data provided in SemEval-2015 and SemEval-2016. SemEval is a series of global challenges to evaluate the effectiveness of disambiguation of word sense. Our results showed the highest F1-scores of 76.40% in SemEval-2016 Task 5, and 94.99% in SemEval-2015 Task 12.