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Over the past two decades, the CICECO-hub scientists have devoted substantial efforts to advancing bioactive inorganic materials based on calcium phosphates and alkali-free bioactive glasses. A key focus has been the deliberate incorporation of therapeutic ions like Mg, Sr, Zn, Mn, or Ga to enhance osteointegration and vascularization, confer antioxidant properties, and impart antimicrobial effects, marking significant contributions to the field of biomaterials and bone tissue engineering. Such an approach is expected to circumvent the uncertainties posed by methods relying on growth factors, such as bone morphogenetic proteins, parathyroid hormone, and platelet-rich plasma, along with their associated high costs and potential adverse side effects. This comprehensive overview of CICECO-hub's significant contributions to the forefront inorganic biomaterials across all research aspects and dimensionalities (powders, granules, thin films, bulk materials, and porous structures), follows a unified approach rooted in a cohesive conceptual framework, including synthesis, characterization, and testing protocols. Tangible outcomes [injectable cements, durable implant coatings, and bone graft substitutes (scaffolds) featuring customized porous architectures for implant fixation, osteointegration, accelerated bone regeneration in critical-sized bone defects] were achieved. The manuscript showcases specific biofunctional examples of successful biomedical applications and effective translations to the market of bone grafts for advanced therapies.

Safety concerns about the battery are primary challenges for the maker of the electric vehicle (EV) system. At continuous work, maintaining the optimal temperature is a crucial scientific issue. It has restricted the widespread use of batteries. Hence, maintaining the optimal temperature for E-vehicles has been analyzed in this study. To achieve more energy density in charging, the discharging cycle and compact structure have been essential. In this study, a direct air-cooling system has been addressed to reduce the temperature of the battery pack, resulting in a significant improvement in system efficiency. The 1.44 kgwatt (kW) battery back has been constructed for the E-vehicle by Simulink diagram to analyze the system. In this study, two various scenarios have been made, such as with air cooling and without cooling, to validate the outcomes. Torque and speed characteristics have been analyzed for this study. Air-cooling-based batteries for e-vehicle simulation have delivered better outcomes, such as high power charging and discharging and a discharge depth of 92%. The differential battery pack temperature has been reduced by increasing the cooling air flow rate to 2 ms−1, 4 ms−1, and 6 ms−1. To attain an even delivery of coolant air flow, a design criterion has been suggested. The outcome of the study has been evaluated through numerical analysis.

This paper describes the operation of a Permanent Magnet Brushless Direct Current (PMBLDC) motor without a position sensor. In this case, the sensorless operation is enhanced by an effective hybrid technique that detects the back electromotive force (Back EMF) of the zero crossing point (ZCP) from the terminal voltages. The Adaptive Neuro Fuzzy Interference System (ANFIS) controller, which is based on Particle Swarm Optimization (PSO) and uses PSO to train its operation, is combined in the proposed hybrid analysis. The PMBLDC motor's ANFIS controller receives the line voltages as input, and it uses this information to estimate the sample signals that are then sent to the ZCP detection circuit. Appropriate commutation control of the inverter is generated by the ZCP detecting circuit. By varying the ANFIS consequent parameters, the PSO algorithm iterates until the error between the sample output and the real training data reaches a low value. The MATLAB/Simulink platform is utilized to implement the suggested sensorless controller action. To verify the controller's performance, a comparison with the other soft computing methods is also carried out.

Electric vehicle (E-vehicle) will be an alternate transportation vehicle in this world due to unavailability of fossil fuel. The CO2 emissions from fossil fuel based vehicle have been considerably reduced. In this study, E-vehicle has been modelled for both open loop and closed loop using MATLAB/Simulink. The rechargeable lithium-ion battery has been adopted for proposed system, and it has become prominent energy storage device especially for E-vehicle application. Speed sensor has been incorporated in closed loop system. Speedometer also added in both open and closed loop system. Performance of the E-vehicle has been analysed and compared by ratio of output to input motor power. Error deviation has been estimated. The simulation results of closed loop system have been revealed that better stability in dynamic state of input. The proposed closed loop system has been attained the better efficiency than open loop and, moreover fluctuation and harmonic level were within the tolerance level. Through closed loop system, E-vehicle has been attaining the smooth working state at minimum duration than open loop. The E-vehicle parameters such as vehicle speed, travelling distance, torque, efficiency and output power have been estimated for both open and closed loop system. With help of sensor, performance of the closed loop system has been improved.

The FT-IR and FT-Raman spectra was recorded within the range of 4000 -500 cm−1 and compared with the theoretically obtained spectra. The 13C and 1H NMR chemical shifts have been calculated using the GIAO technique, and the output has been compared with experimental data. The optimized molecular structure and stability analysis of the aztreonam compound have been calculated by the DFT/B3LYP/6–311++G(d,p) level of theory. The molecular electrostatic potential, chemical descriptors, and HOMO-LUMO energies were also calculated. The in silico pharmacological evaluation shows that the title molecule exhibit drug-likeness, and ADMET properties. The ADMET results also indicate that the molecule can be employed in anti-tuberculosis treatments to discover new drugs. Furthermore, molecular docking analyses have been performed to understand the most active binding sites of the compound with the target protein. The molecules are docked with the PknB protein, with free binding energy values of Aztreonam is -7.25 and Mitoxantrone is -6.79 kcal/mol.

Recently, some Indian states reported an outbreak of a ‘mysterious illness’ involving more than a hundred children younger than nine years, with fever and skin lesions. These lesions were more prominent and manifested in the palms, soles, oral cavity, and other body parts. This was initially considered to be a new illness and was described as ‘tomato flu’. The disease was contagious but found to be mild in severity. Later in time, it has been identified to be a hand, foot and mouth disease (HFMD), caused by Coxsackievirus-16 (CA-16). So, the term ‘tomato flu’ should not be used since it is a misnomer for this disease. The mild and uncomplicated HFMD cases usually did not require any antiviral therapy. CA-16 may be associated with a severe, life-threatening HFMD, especially in children with immunocompromised, malnourished, chemotherapy, or underlying medical conditions. However, there are some antiviral agents, intravenous immunoglobulin (IVIG), and vaccines available for the treatment and prevention of HFMD. In this review, we have observed that acyclovir and oseltamivir were effective for patients with serious forms of HFMD. In some cases, the administration of antiviral agents along with IVIG was proven to be more effective. Out of the antiviral agents studied, we observed that oseltamivir and acyclovir were effective with less adverse effects. Three inactivated whole-virus EV-71 vaccines have been licensed, while a recombinant VP1 vaccine against CA-16 is under development. Many types of research revealed that there are many potential and prospective antiviral candidates that can be employed in the near future. The antiviral drug umifenovir (arbidol) was found to be very effective against HFMD under in vivo laboratory conditions. There were some prospective antiviral agents, required to pass through phases of clinical trials.

Abstract Oral squamous cell carcinoma (OSCC), the most common type of head and neck cancers, is associated with high recurrence, metastasis, low long-term survival rates and poor treatment outcome. As deregulated miRNA expression plays a crucial role in malignant transformation and cancer progression, the present study is aimed at profiling the miRNA expression pattern in OSCC and developing a new miRNA prognostic signature for oral cancer. MiRNA expression profiling was performed using MiRNA microarray in 30 tumor and 18 normal samples. MiRNA signature obtained was validated with quantitative real time PCR (qRT-PCR) in 144 tumor and 36 normal samples. The potential targets, clinical implications and prognostic value of the miRNA signature were elucidated by various bioinformatics and statistical analyses. Microarray profiling identified a set of 105 miRNAs to be differentially expressed in OSCC, out of which a subset of 19 most dysregulated miRNAs were validated by qRT-PCR. In silico analysis revealed the signature miRNAs to be involved in various cancer associated pathways. Up-regulation of miR-196a, miR-21, miR-1237 and downregulation of miR-204, miR-144 was associated with poor prognosis of OSCC patients. The mir-196a/miR-204 expression ratio emerged as best predictor for disease recurrence and patient survival. Altogether, our study identified a miRNA signature for OSCC with prognostic significance.

Quercitrin is an anti-lung cancer agent. It is a naturally occurring flavonoid and its derivatives are mainly present in nuts and beverages. It is mainly available as a glycoside, and the quercitrin glycosides are found to prevent the metastasis of cancer. Quercitrin is optimized with 6–311++G(d,p) basis set using the B3LYP method to attain its minimum energy structure. The vibrational studies of the Quercitrin compound were elucidated with reference to Potential Energy Distribution (PED). The geometrical parameters were obtained and correlated with experimental values. To examine the nature of the charge transfer mechanism of Quercitrin, the HOMO-LUMO energy gap is computed. The anti-cancer activity of Quercitrin has been explored using molecular docking study that are used to estimate how the ligand interacts with protein, specifically to identify the best-fit orientation of the ligand, its binding mode, and intermolecular interactions of amino acid residues in the binding region of B-RAF kinase protein. The binding affinity of the compound Quercitrin (-7.14 kcal/mol) was found using AutoDock and validated with a Glide XP score in Schrodinger tool (-8.01 kcal/mol). MD simulations of protein-ligand complexes were monitored for 100 ns, from which the RMSD, RMSF, Rg, H-bonds, and interaction energy calculations were executed. From these investigations, it is identified that the compound quercitrin has maintained good structural stability, compactness, higher Hydrogen bonds, and interaction energies than the Imidazopyridinyl benzamide inhibitor.

Renewable energy generation is increasingly employed nowadays for multitudes of reasons such as global warming, depletion of conventional sources of energy and emission constraints. Even though the wind generators constitute a potential source of energy, the uncertainties associated with them make the operation complex. As a consequence, the successful operation and planning of the present distributed generation dominated power systems requires exact estimate of wind power. Numerous wind power estimation techniques based on Machine Learning were available. This work attempts to compare the wind power estimation efficiency of a few machine learning approaches. At first, the performance of a Feed Forward Neural Network with different activation functions is considered. Next, Support Vector Regression Machine with different kernels is utilized for estimating the wind power. Then, deep Learning networks such as Long Short-Term Memory network, Convolutional Neural Network and Recurrent Neural Network are employed for assessing the future wind power and their ability is analyzed. Finally, a comparative chart is prepared to evaluate the efficacy and usefulness of the different machine learning techniques employed for estimating wind power.

OBJECTIVE: Legionellae are free-living bacteria present in many types of water bodies. Legionella pneumophila can cause severe fatal pneumonia called Legionnaire’s disease. These bacteria are commonly transmitted through air coolers. To examine the distribution and characterization of pathogenic Legionellae isolates from different water sources. MATERIALS AND METHODS: A sum of 500 water specimens (100 samples from each) was obtained from various water bodies in and around Jimma town, Ethiopia. Water specimens were treated with mild acidic solution, subsequently concentrated and cultured on Legionella selective BCYE agar medium. Criteria such as capacity of Legionellae to tolerate and produce growth in different pH optima, bactericidal effect of various levels of chlorine on Legionella isolates and lethal effects of divergent temperature conditions were framed. Legionella species distinctions were carried out using phenotypic (biotyping) tests. Legionella pathogenicity experiments on animal model and antibiotic susceptibility tests were performed on these bacterial isolates. RESULTS: The prevalence and distribution of Legionellae in various types of water bodies were observed to be, wells (35%), lakes (30%), ponds (15%), water tanks (10%), and canals (10%). It was observed that many of the pathogenic Legionellae isolates can tolerate and multiply in a wide range of pH 5-9. Legionella can also withstand the usual concentration of chlorine in water bodies. Our experiments have also revealed that these bacteria can multiply at a variable range of temperature from 25 to 45˚C. The current study expressed that a total of 6 species of Legionella have been identified. Furthermore, it also suggested that L. pneumophila was identified more frequently than other species. Many isolated strains of Legionellae were also showing resistance to many commonly employed antibiotics. Instilling isolates of Legionella through intranasal route in guinea pigs proved that most of L. pneumophila strains were possessing high virulence followed by other species. Lethal and severe infections due to Legionellae can arise from the infection of these bacteria present in water bodies. These bacteria can tolerate and multiply in wide ranges of physicochemical conditions of water. CONCLUSIONS: Among the isolated species, L. pneumophila was the dominant and highly pathogenic species. Moreover, many isolates of L. pneumophila expressed multiple resistances to antibiotics. In spite of rapid development of medical field, health officials face a huge task ahead to curb the morbidity and mortality due to Legionellae especially in developing countries.

Lubricants are used during the metal cutting process to remove the heat generated during the process and to reduce the Coefficient of friction (CoF). In this paper an attempt was made to synthesize an environment-friendly nano-lubricant using virgin coconut oil as base fluid by separately blending Copper (Cu) and Silver (Ag) nanoparticles in concentrations of 0.1%, 0.25%, 0.5% and 1% on the mass basis. The nanoparticles were blended in coconut oil by the means of ultrasonic shaking to obtain a uniform blend. The dependence of the Coefficient of friction of nanofluids and its performance on the concentration of the nanoparticles is well reported by researchers; therefore the optimal concentration of the nanoparticles was identified by Pin-on-Disk tribological testing. The following tests were conducted on the synthesised nano-lubricant such as Coefficient of friction (Pin-on-disk test), viscosity, flash & fire point and real time turning operation to determine the cutting force, and operating temperature. From the wear test results, it was inferred that copper added coconut nanofluids exhibited lower Coefficient of friction as compared to silver added coconut nanofluids. Further, it was identified that the lowest Coefficient of friction in the range was exhibited by copper added coconut nanofluids of 0.25% concentration. When these nanolubricants were tested in a real time turning operation using lathe tool dynamometer, it was found that 0.25% concentration of copper added coconut nanolubricant exhibited minimum cutting force and operating temperature indicating minimum coefficient of friction. Keywords: Nanolubricants, Cutting fluid, Cutting force, Cutting temperature, Coefficient of friction

The result of a deprivation of oxygen and glucose to the brain, hypoxic–ischemic encephalopathy (HIE), remains the most common cause of death and disability in human neonates globally and is mediated by glutamate toxicity and inflammation. We have previously shown that the enzyme glutamate carboxypeptidase (GCPII) is overexpressed in activated microglia in the presence of inflammation in fetal/newborn rabbit brain.We assessed the therapeutic utility of a GCPII enzyme inhibitor called 2-(3-Mercaptopropyl) pentanedioic acid (2MPPA) attached to a dendrimer (D-2MPPA), in order to target activated microglia in an experimental neonatal hypoxia-ischemia (HI) model using superoxide dismutase transgenic (SOD) mice that are often more injured after hypoxia-ischemia than wildtype animals.SOD overexpressing and wild type (WT) mice underwent permanent ligation of the left common carotid artery followed by 50 min of asphyxiation (10% O2) to induce HI injury on postnatal day 9 (P9). Cy5-labeled dendrimers were administered to the mice at 6 h, 24 h or 72 h after HI and brains were evaluated by immunofluorescence analysis 24 h after the injection to visualize microglial localization and uptake over time. Expression of GCPII enzyme was analyzed in microglia 24 h after the HI injury. The expression of pro- and anti-inflammatory cytokines were analyzed 24 h and 72 h post-HI. Brain damage was analyzed histologically 7 days post-HI in the three randomly assigned groups: control (C); hypoxic-ischemic (HI); and HI mice who received a single dose of D-2MPPA 6 h post-HI (HI+D-2MPPA).First, we found that GCPII was overexpressed in activated microglia 24 h after HI in the SOD overexpressing mice. Also, there was an increase in microglial activation 24 h after HI in the ipsilateral hippocampus which was most visible in the SOD+HI group. Dendrimers were mostly taken up by microglia by 24 h post-HI; uptake was more prominent in the SOD+HI mice than in the WT+HI. The inflammatory profile showed significant increase in expression of KC/GRO following injury in SOD mice compared to WT at 24 and 72 h. A greater and significant decrease in KC/GRO was seen in the SOD mice following treatment with D-2MPPA. Seven days after HI, D-2MPPA treatment decreased brain injury in the SOD+HI group, but not in WT+HI. This reduced damage was mainly seen in hippocampus and cortex.Our data indicate that the best time point to administer D-2MPPA is 6 h post-HI in order to suppress the expression of GCPII by 24 h after the damage since dendrimer localization in microglia is seen as early as 6 h with the peak of GCPII upregulation in activated microglia seen at 24 h post-HI. Ultimately, treatment with D-2MPPA at 6 h post-HI leads to a decrease in inflammatory profiles by 24 h and reduction in brain injury in the SOD overexpressing mice.

The effect of reinforcement phases on indentation resistance and damage behavior of glass/epoxy laminates was investigated in this research work. Woven glass fiber mat and nonwoven chopped glass fiber mat were used as fiber reinforcement phases for fabricating the laminates. Low-velocity impact and quasi-static indentation tests were performed on both laminates to investigate the contact behavior and energy-absorbing capability. Moreover, the acoustic emission (AE) technique was employed to monitor the indentation damage resistance. AE parameters including normalized cumulative counts (NCC), normalized cumulative energy (NCE), rise angle (RA), and felicity ratio (FR) were analyzed. The bidirectional laminates showed premature load drops and drastic changes in the normalized cumulative counts/energy profile in the beginning of loading cycles, indicating the development of macrodamage such as debonding/delamination. AE sentry function results of bidirectional laminates show longer PII function at the earlier stages, associated with minor PIII function and greater PIV function, indicating the continuous degradation and progression of damage. In contrast, the chopped laminates exhibited superior postimpact performance than the bidirectional laminates. The presence of randomly oriented fibres prevents the delamination crack propagation during compression loading, which was attributed with the increased residual compressive strength.

In Ensemble classifiers, the Combination of multiple prediction models of classifiers is important for making progress in a variety of difficult prediction problems. Ensemble of classifiers proved potential in getting higher accuracy compared to single classifier. Even though by the usage ensemble classifiers, still there is in-need to improve its performance. There are many possible ways available to increase the performance of ensemble classifiers. One of the ways is sampling, which plays a major role for improving the quality of ensemble classifier. Since, it helps in reducing the bias in input data set of ensemble. Sampling is the process of extracting the subset of samples from the original dataset. In this research work, analysis is done on sampling techniques for ensemble classifiers. In ensemble classifier, specifically one of the probability based sampling techniques is being always used. Samples are gathered in a process which gives all the individuals in the population of equal chances, such that, sampling bias is removed. In this paper, analyse the performance of ensemble classifiers by using various sampling techniques and list out their drawbacks.

Background: Clinical trials are mandatory for evidence-based practice. Hardly, any data are available regarding the number of clinical trials and their methodological quality that are conducted in allied fields of medicine. Objective: The present study was envisaged to assess methodological quality of trials in allied medical fields. Materials and Methods: Registered clinical trials in World Health Organization International Clinical Trials Registry Platform (http://apps.who.int/trialsearch/AdvSearch.aspx) in the following fields were extracted: Acupuncture; Ayurveda; biofeedback; complementary and alternate medicine; herbal; homeopathy; massage; naturopathy; Reiki; Siddha; Unani; and yoga. The eligible studies were assessed for the following key details: Type of sponsors; health condition in which the trial has been conducted; recruitment status; study design; if randomization was present, method of randomization and allocation concealment; single or multi-centric; retrospective or prospective registration; and publication status in case of completed studies. Results: A total of 276 clinical trials were registered majority of which have been proposed to be conducted in the field of oncology and psychiatry. Most of the clinical trials were done in single centers (87.75%), and almost all the clinical trials were investigator-initiated with pharmaceutical company sponsored studies contributing to a maximum extent of 24.5%. A large majority of the study designs were interventional where almost 85% of the studies were randomized controlled trials. However, an appropriate method of randomization was mentioned only in 27.4%, and the rate of allocation concealment was found to be just 5.5%. Only 1–2% of the completed studies were published, and the average rate of retrospective registration was found to be 23.6% in various fields. Conclusion: The number of clinical trials done in allied fields of medicine other than the allopathic system has lowered down, and furthermore focus is required regarding the methodological quality of these trials and more support from various organizations.

This study aims to provide an accurate and realistic prediction model for electricity demand using population, imports, exports, per capita Gross Domestic Product (GDP) and per capita Gross National Income (GNI) data for India. Four different models were used for different combinations of the above five input variables and the effect of input variables on the estimation of electricity demand has been demonstrated. In order to train the network 29 years data and to test the network 9 years data have been used. The future electricity demand for a period of 8 years from 2013 to 2020 has been predicted. The performance of the ANFIS technique is proved to be better than Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN).

In general, the identification and verification are done by passwords, pin number, etc., which is easily cracked by others. Biometrics is a powerful and unique tool based on the anatomical and behavioral characteristics of the human beings in order to prove their authentication. This paper proposes a novel recognition methodology of biometrics named as Finger Knuckle print (FKP). Hence this paper has focused on the extraction of features of Finger knuckle print using Scale Invariant Feature Transform (SIFT), and the key points are derived from FKP are clustered using K-Means Algorithm. The centroid of K-Means is stored in the database which is compared with the query FKP K-Means centroid value to prove the recognition and authentication. The comparison is based on the XOR operation. Hence this paper provides a novel recognition method to provide authentication. Results are performed on the PolyU FKP database to check the proposed FKP recognition method.

This paper describes the application of an evolutionary algorithm, Restart Covariance Matrix Adaptation Evolution Strategy (RCMA-ES) to the Generation Expansion Planning (GEP) problem. RCMA-ES is a class of continuous Evolutionary Algorithm (EA) derived from the concept of self-adaptation in evolution strategies, which adapts the covariance matrix of a multivariate normal search distribution. The original GEP problem is modified by incorporating Virtual Mapping Procedure (VMP). The GEP problem of a synthetic test systems for 6-year, 14-year and 24-year planning horizons having five types of candidate units is considered. Two different constraint-handling methods are incorporated and impact of each method has been compared. In addition, comparison and validation has also made with dynamic programming method.

Power System planning starts with Electric load (demand) forecasting. Accurate electricity load forecasting is one of the most important challenges in managing supply and demand of the electricity, since the electricity demand is volatile in nature; it cannot be stored and has to be consumed instantly. The aim of this study deals with electricity consumption in India, to forecast future projection of demand for a period of 19 years from 2012 to 2030. The eleven input variables used are Amount of CO2 emission, Population, Per capita GDP, Per capita gross national income, Gross Domestic savings, Industry, Consumer price index, Wholesale price index, Imports, Exports and Per capita power consumption. A new methodology based on Artificial Neural Networks (ANNs) using principal components is also used. Data of 29 years used for training and data of 10 years used for testing the ANNs. Comparison made with multiple linear regression (based on original data and the principal components) and ANNs with original data as input variables. The results show that the use of ANNs with principal components (PC) is more effective.

In general, the identification and verification are done by passwords, pin number, etc., which is easily cracked by others. In order to overcome this issue biometrics is a unique tool for authenticate an individual person. Nevertheless, unimodal biometric is suffered due to noise, intra class variations, spoof attacks, non-universality and some other attacks. In order to avoid these attacks, the multimodal biometrics i.e. combining of more modalities is adapted. In a biometric authentication system, the acceptance or rejection of an entity is dependent on the similarity score falling above or below the threshold. Hence this paper has focused on the security of the biometric system, because compromised biometric templates cannot be revoked or reissued and also this paper has proposed a multimodal system based on an evolutionary algorithm, Particle Swarm Optimization that adapts for varying security environments. With these two concerns, this paper had developed a design incorporating adaptability, authenticity and security.

A sample of dropout learners enrolled in the B.Sc. programme of School of Sciences, Indira Gandhi National Open University, India was surveyed to study the factors which influenced them to withdraw from the programme. The research indicates that the major reasons for dropout are the distance of study centre from the residence of learner, insufficient support from study centre and more time consuming courses of the programme. The results of this study are significant from the points of view of improving the present instructional and delivery strategies of B.Sc. Programme. This will also help in reducing the students' dropout rate for this programme by implementing timely interventions at the different stages of study. Keywords: Dropouts, Withdrawal, Open Distance learning, interventions, retention, support system

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Atmospheric CO2 concentration is expected to reach 460-560 ppm by the year 2050 with an increase of 3.2-4.0°C in temperature. Elevated CO2 and temperature affect fruit crops to a greater extent by affecting flowering, yield and quality of fruits. In the current study, the effect of eCO2 on mango cv. Alphonso was examined under open top chambers (OTC), with ambient CO2 (380 ppm) and elevated CO2 (550 ppm) levels, which were compared with the plants grown outside OTC under ambient conditions. The results revealed that the maximum number of vegetative shoot emergences was observed in OTC under both eCO2 and aCO2 conditions. The photosynthetic rate declined by 25% inside OTC due to increased air and leaf temperature compared to ambient plants placed outside the chambers. Significantly higher reproductive shoots emerged under aCO2 conditions, whereas, no reproductive shoots were observed in aCO2 under OTC, however, few reproductive shoots were observed under eCO2 in OTC. The stomatal number was increased inside OTC chambers under aCO2, but the same was not observed under eCO2 conditions. The other physiological parameters, such as specific leaf weight, chlorophyll content, relative water content, stem girth and total wax content were appeared to be better in eCO2 conditions compared to aCO2 inside OTC and ambient conditions outside OTC. The increase in stomatal number and complete repression of flowering inside OTC at aCO2 was mainly due to higher temperatures compared to outside and this effect of temperature was reduced by eCO2. The results of the study indicated that eCO2 may improve growth rates, flowering and reduce water loss in mango plants. [ABSTRACT FROM AUTHOR]