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Abstract Nanostructured materials incorporating transition metal sulfides have demonstrated considerable potential across various applications, particularly in the realms of energy production and storage. Sulfide-based material preparation is a challenging and costly procedure that requires a high temperature and reducing atmosphere. This work reports that manganese cobalt sulfide (MCS) and reduced graphene oxide composite manganese cobalt sulfide (rMCS) were successfully prepared through a hydrothermal method. Various characterization techniques were employed to analyze the prepared materials, including X-ray diffraction, field emission scanning electron microscopy, transmission electron microscopy, Brunauer-Emmett-Teller analysis, and X-ray photoelectron spectroscopy. In a three-electrode system, MCS and rMCS electrodes exhibit an excellent specific capacitance of 1695 and 1925 F g−1 at 1 A g−1 current density respectively. MCS delivers the capacitance retention of 99% and rMCS exhibits the capacitance retention of 100% capacitance retention over 5000 consecutive cycles. The constructed asymmetric supercapacitor electrode (rMCS//rGO) exhibits the energy and power density of 64 Wh kg−1 at 799 W kg−1, respectively with outstanding cyclic stability of 97.4% even after 10,000 cycles. The exceptional electrochemical properties of MCS with rGO composite electrode indicate that they would make an outstanding electrode material for cutting-edge energy storage devices.

Abstract New generation of electrochemical energy storage devices (EESD) such as supercapattery is being intensively studied as it merges the ideal energy density of batteries and optimal power density of supercapacitors in a single device. A multitude of parameters such as the method of electrodes preparation can affect the performance of supercapattery. In this research, nickel doped tin sulfide /tin oxide (SnS@Ni/SnO2) heterostructures were grown directly on the Ni foam and subjected to different calcination temperatures to study their effect on formation, properties, and electrochemical performance through X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and electrochemical tests. The optimized SnS@Ni/SnO2 electrode achieved a maximum specific capacity of 319 C g− 1 while activated carbon based capacitive electrode exhibited maximum specific capacitance of 381.19 Fg− 1. Besides, capacitive electrodes for the supercapattery were optimized by incorporating different conductive materials such as acetylene black (AB), carbon nanotubes (CNT) and graphene (GR). Assembling these optimized electrodes with the aid of charge balancing equation, the assembled supercapattery was able to achieve outstanding maximum energy density and power density of 36.04 Wh kg− 1 and 12.48 kW kg− 1 with capacity retention of 91% over 4,000 charge/discharge cycles.

Background: Tuberculosis (TB) management continues to be a challenge globally; weakened immunity plays a significant role in the reactivation of TB. There is limited information on hematological parameters in patients with pulmonary TB and its association with outcome. Objectives: We present hematological parameters of newly diagnosed sputum-positive pulmonary TB patients enrolled in a randomized, clinical trial that assessed the efficacy and safety of 3 and 4 regimens using moxifloxacin. Materials and Methods: Blood hematological parameters at baseline, comparison of the baseline and end of treatment values, including the monocytes by lymphocytes ratio (M/L), neutrophil lymphocyte ratio (N/L), and platelet lymphocyte ratio (P/L) between the patients with favorable and unfavorable TB treatment outcome, and among different age group and sex presented in this paper. Results: Among the total 1059 patients, 782 were males, the mean hemoglobin (HB) ± standard deviation (SD) was 11.5 g/dL ± 2.0, the mean white blood cell (WBC) count ± SD was 9800 ± 3009 and the mean platelet count (in lakhs) ± SD was 4.24 ± 1.42 cells/uL. There was an increase from baseline in the mean hemoglobin, eosinophil, and lymphocyte count and a decrease in mean neutrophil, monocyte counts to the end of treatment. There was a decrease in baseline mean total WBC count posttreatment, both in favorable (10,271 cells/uL ± 3007 SD to 6689 cells/uL ± 1837 SD, [P ≤ 0.001]), and unfavorable TB outcome patients. Conclusion: An increase in HB, and a decrease in WBC count, M/L, N/L, and P/L ratio is possible at the end of TB treatment and future studies to correlate blood hematology parameters with TB treatment outcome.

Abstract Fuel cell vehicles (FCVs) are gaining significance due to their potential to reduce greenhouse gas emissions and dependence on fossil fuels. Their efficient fuel cell cycle makes them ideal for last-mile transportation, offering zero emissions and longer range compared to battery electric vehicles. Additionally, the generation of electricity through fuel cell stacks is becoming increasingly popular, providing a clean energy source for various applications. This paper focuses on utilizing the energy from fuel cycle bicycles when it's not in use and feeding it into the home DC grid. To achieve this, a dual-phase DC to DC converter is proposed to boost stack voltage and integrate with the 24 V DC home grid system. The converter design is simulated using the PSIM platform and tested in a hardware-in-the-loop (HIL) environment with real-time simulation capabilities.

ABSTRACTThe current study examines the effect of NaOH treatment on the hardness, inter-laminar shear strength (ILSS) and water absorption behavior of epoxy composites reinforced with banana pseudostem fibers. Using the hand-lay-up method, six distinct samples are created that are composed of layers of woven and short banana fibers in both a plain and hybrid form. Plain- treated woven composites reveal the highest hardness and ILLS properties followed by the hybrid and short fiber composites. The random orientation of the fiber structure in short fiber composites results in the largest moisture absorption; this behavior is further supported by elucidating the kinetic parameters and diffusion coefficient parameters. SEM analysis confirms the improved surface of the NaOH-treated composite material.

This study was on optimization of the cutting processes for corn stalks, which serve as significant sources of biomass and forage. The reduction of cutting force is crucial for enhancing energy efficiency. Cutting velocity rises, which can lead to significant power usage with minimal shear forces. Therefore, the objective is to minimize both the cutting force and power consumption. To address this multi-objective optimization problem, a modified Taguchi method was employed. Chauvenet's criterion was used to assess the validity of statistical scatter in repeated test data. The test samples had an 81 % moisture content. Through analysis of variance (ANOVA), the optimal chopping process parameters were identified as velocity of 4.4 m/s, approach angle of 30°, and feeding angle of 50°. The refined empirical relationships indicated that the cutting force ranges from 180.55 N to 393.37 N, while the cutting power ranges from 16.81 W to 44.05 W. In a single test, the output responses for the optimal parameter set were 26.32 W for power consumption and 242.60 N for cutting force, both are within the estimated range. The considerable scatter observed in repeated test data is likely due to variations in corn stalk thickness. These findings are valuable for the operation of chopping machines, ensuring minimal cutting force and power consumption when handling corn stalks residues.

Lithium ion batteries are popularly used in various energy storage systems. Accurate battery's State of Charge (SoC) and State of Health (SoH) estimate is essential for energy storage system's safety. This manuscript proposes a hybrid approach for estimating SoC and SoH of lithium ion battery. The proposed hybrid technique is the joint execution of both the Multi-Layer Perceptron (MLP) with Extreme Gradient boosting (XGBoost) algorithm. Hence, it is named as MLP-XG Boost Algorithm. The major objective of the proposed system is to improve the safety of energy storage systems and to achieve the highly accurate SoH. The MLP algorithm is utilized to analyze and trained the battery data. The XG Boosting algorithm is utilized to preprocessed and estimate the SoC and SoH of a lithium-ion battery. The proposed method's effectiveness is assessed using the MATLAB platform and contrasted with other methods that are currently in use. In every system now in use, including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), the proposed approach produces better results. From the result it is concluded that the proposed method based Mean Square Error and Mean Absolute Error Root is less than 1 at various temperatures such as 0∘C, 25∘Cand 45∘C and it is better and efficient.

Titanium and its alloys have been utilized as the preferred material for dental implants due to its mechanical and biological performance leading to high clinical success rates. Nonetheless, ceramic-based dental implants have been developed as the demands for non-metallic dental restorations have increased. The advent of bioceramics has increased the possibility of resolution in regenerative processes challenges for dentistry applications. In this regard, silicon nitride (Si3N4) as a non-oxide ceramic has the potential for oral applications which is ground in its bactericidal properties. The present work aimed to shed light on Si3N4 ceramic being used for future dental applications. As a concluding remark, the bactericidal and immunomodulatory properties of Si3N4 together with its radiolucency are inferred as a suitable contender in oral cavity and dental applications.

The objective of this study was to assess the effect of electrical stimulation (ES) on breast and thigh muscles in spent hen. A total of 24 spent hens (White Leghorn) were slaughtered and split into two halves, each half was either treated with postmortem ES (150V) or was used as a non-stimulated (NS) control. Breast and thigh muscles were excised and analysed for biophysical, colour and sensory qualities after ageing for 24 h at 4 ± 1 ˚C. Biochemical parameters were recorded immediately (0h) and after 24h of ES. Biophysical parameters like water holding capacity (WHC), protein extractability, shear force value (SFV) and myofibre fragmentation index (MFI) of both types of muscles varied significantly with ES. Electrical stimulation significantly reduced the pH and glycogen content in both muscles and the thigh muscle showed a significantly higher pH and lower glycogen content than the breast muscle. No significance difference in metabolic rate (R-value) was observed between ES and NS thigh at 24 h. ES had no significant effect on colour and ES significantly improved the sensory value of both breast and thigh muscle. Canonical discriminant and Mahalanobis distance (D2) analysis of biophysical and biochemical parameters demonstrated that the effect of electrical stimulation was comparatively more in thigh muscle than breast with an ageing period of 24 h.

The ability of phase change materials (PCM) to store thermal energy has gained wide application area, like battery thermal management, solar water desalination and many other. The melting process of beeswax phase change material within the cube geometry with constant wall temperature (65 °C) boundary condition has been investigated using solidification and melting model. The fluid flow and heat transfer governing equations are solved using second order finite volume scheme. A PRESTO algorithm is applied for pressure-velocity coupling. The convergence criteria of 10−10 have been selected for energy equation, while 10−8 is selected for both momentum and continuity equations. The results like percentage variation along length-height and height-width plane for transient liquid fraction and temperature has been plotted, along with velocity streamlines within the cube geometry. From the obtained results it is concluded that the melting fraction and temperature of beeswax PCM is different in different planes and the major factors which affect the complete melting process is wall temperature, and the geometry. A difference of more than 0.1 °C in temperature has been recorded between mid-length-height and height-width plane while a difference of more than 2% in liquid fraction of PCM is observed. Even the uniformity of temperature and liquid fraction is notably influenced and vary along length, height, and width of cube geometry. Thus, it is concluded that melting process of PCM may affect the ability to store and release the heat energy which further affect the performance parameters of applied physical system.

Tuberculosis (TB) continues to be a global challenge. Reducing the duration of TB treatment for drug-sensitive TB (DSTB) has direct and distinct advantages. We ventured into the aspect of utilizing linezolid as a pivotal drug in shortening therapy in DSTB. Linezolid has gained prominence as it is faring well in resistant TB management. Only a few studies use the strategy of Linezolid in DS-TB but it seems a lucrative approach, the bactericidal effects have been reported favourably in the studies. There have been concerns about the potential adverse drug effects of Linezolid reported but clinical trials have demonstrated safety and tolerability when administered for shorter periods. If the safety and efficacy of giving Linezolid for a shorter period along with standard drugs for DSTB is established it could lead to newer avenues using Linezolid for shortening the duration of treatment for DSTB as an alternative to treat DSTB.

Carbon is a fundamental material in developing electrochemical double-layer capacitors (EDLCs), also known as supercapacitors. Many studies have proven the impact of various carbon material properties, such as surface area, pore volume, and chemical surface composition, on the specific capacitance of supercapacitors (EDLCs). However, research endeavors to comprehensively evaluate the contribution of these material properties in correlation with experimental parameters, such as electrolyte concentration, voltage window, and current density, are scarce. This study aimed to employ machine learning algorithms to comprehend the interdependence between the properties of biomass-based carbon and the aforementioned experimental parameters with the capacitance of EDLCs. Four models of the machine learning algorithms were utilized in this study, including linear regression (LR), M5-Rules, Random Tree (RT), and Multi-Layer Perceptron (MLP), to determine the most suitable algorithm for analyzing and predicting the capacitance of EDLCs. The results revealed that the MLP model exhibited the highest determination correlation coefficient (R) of 0.871 with a Mean Absolute Error (MAE) of 45.069 F/g. Besides, the study utilized a machine learning correlation attribute model and observed that the supercapacitor’s surface area and pore volume demonstrated significant correlations with the same correlation ratio of 0.4. In conclusion, these findings emphasize the importance of considering surface area and pore volume in developing and optimizing supercapacitors. Finally, this study adds knowledge in supercapacitors and provides valuable insights for designing and developing high-performance energy storage devices.

This study investigated the effect of copper oxide addition on the alumina toughened zirconia composite ceramic using microwave sintering. CuO amounts (0.05–1 wt%) were examined at sintering temperatures (1250–1500 °C). Investigations covered hardness, toughness, phase stability, microstructure, and densification. Optimum doping was 0.2 wt%, resulting in a composite with 99% relative density at 1500 °C, Vickers hardness of 13.1 GPa, and fracture toughness of 7.1 MPa m1/2. Low-temperature degradation showed disrupted phase stability above 1400 °C, with CuO unable to prevent ageing-induced phase transformation.

Abstract It is of particular interest for biopharmaceutical companies developing and distributing fragile biomolecules to warrant the stability and activity of their products during long-term storage and shipment. In accordance with quality by design principles, advanced kinetic modeling (AKM) has been successfully used to predict long-term product shelf-life and relies on data from short-term accelerated stability studies that are used to generate Arrhenius-based kinetic models that can, in turn, be exploited for stability forecasts. The AKM methodology was evaluated through a cross-company perspective on stability modeling for key stability indicating attributes of different types of biotherapeutics, vaccines and biomolecules combined in in vitro diagnostic kits. It is demonstrated that stability predictions up to 3 years for products maintained under recommended storage conditions (2–8 °C) or for products that have experienced temperature excursions outside the cold-chain show excellent agreement with experimental real-time data, thus confirming AKM as a universal and reliable tool for stability predictions for a wide range of product types.

The aim of the present study was to assess the infrared thermographic profile in relation to mastitis in lactating dairy cows. In this study, 20 lactating crossbred dairy cows each were monitored for a period of 21 days in four dairy farms. The udder skin surface temperature (USST) was recorded using infrared thermography (IRT) during the study period at diurnal time intervals. Milk samples were collected at the time of afternoon milking from each quarter and tested for California Mastitis Test (CMT). It was observed that higher USST was noticed in udders with clinical mastitis followed by non-mastitis and subclinical mastitis, and the skin surface temperature of non-mastitis and clinical mastitis udders were significantly higher than subclinical mastitis affected udders. The results of this study indicate the limitations in the use of IRT of udders for early detection of mastitis across all regions.

Abstract In this study, acrylic-epoxy-based nanocomposite coatings loaded with different concentrations (0.5–3 wt.%) of graphene oxide (GO) nanoparticles were successfully prepared via the solution intercalation approach. The thermogravimetric analysis (TGA) revealed that the inclusion of GO nanoparticles into the polymer matrix increased the thermal stability of the coatings. The degree of transparency evaluated by the ultraviolet–visible (UV–Vis) spectroscopy showed that the lowest loading rate of GO (0.5 wt.%) had completely blocked the incoming irradiation, thus resulting in zero percent transmittance. Furthermore, the water contact angle (WCA) measurements revealed that the incorporation of GO nanoparticles and PDMS into the polymer matrix had remarkably enhanced the surface hydrophobicity, exhibiting the highest WCA of 87.55º. In addition, the cross-hatch test (CHT) showed that all the hybrid coatings exhibited excellent surface adhesion behaviour, receiving 4B and 5B ratings respectively. Moreover, the field emission scanning electron microscopy (FESEM) micrographs confirmed that the presence of the functional groups on the GO surface facilitated the chemical functionalization process, which led to excellent dispersibility. The GO composition up to 2 wt.% showed excellent dispersion and uniform distribution of the GO nanoparticles within the polymer matrix. Therefore, the unique features of graphene and its derivatives have emerged as a new class of nanofillers/inhibitors for corrosion protection applications.

Solar Photovoltaic (PV) system based power generation is going to become the major power source in the following few decades. Because of the advantages such as reliability, cleanliness, feasibility in small scale to medium scale power generation etc., Solar Photovoltaic (PV) based power generation is developing very rapidly. Hybrid MPPT control methods are essential to track and achieve maximum power generation from solar PV systems under normal and Partially Shaded Conditions (PSC). The proposed Hybrid Grey Wolf Optimization (GWO) algorithm with Adaptive Neuro-Fuzzy Inference System (ANFIS) will effectively tracks the Maximum Power Point when compared to the conventional hybrid MPPT methods. Luo converter is employed as DC-DC converter to convert the variable DC output from solar panel into a nominal DC level. The Luo converter output is given to a single phase Voltage Source Inverter (VSI) and the inverter is connected to grid. The Arithmetic Optimization Algorithm (AOA) tuned Fractional order Proportional Integral controller (FOPI) is adopted to maintain the stability of the grid connected inverter. The tracking efficiency of the proposed system is maintained around 98 % even under different irradiance conditions with very less convergence time of 0.016 s.

Triply minimal surface structures of short glass fiber reinforced polyamide with different relative densities were prepared and the effects of induced anisotropy, cell topology, and relative density on the compressive properties were evaluated. Schwarz Diamond, Schoen Gyroid, and Schwarz Primitive structures were 3D printed with relative densities ranging from 0.2 to 0.4, and compression properties along the axial and lateral build directions were determined. Gipson-Ashby numerical parameters required to establish a relation between the cell topology and the compressive properties of the lattice structures were estimated. The plastic deformation and failure mechanisms were analyzed. Results revealed that the compression properties are dominated by the cell topology rather than the relative density. Besides, the present work confirmed that the compressive properties of the lattice structures fabricated with short fiber reinforcement are significantly affected by anisotropy. These structures exhibited a higher compressive modulus and lower peak compressive stress in the lateral direction, which can be attributed to the inline orientation of the short glass fibers. Schwarz Diamond proved to be the stiffest structure, followed by Schoen Gyroid and Schwarz Primitive under axial and lateral compression. Likewise, Schwarz Primitive generated low stresses compared to Schoen Gyroid and Schwarz Diamond. All cell topologies deformed in a controlled manner layer by layer, minimizing undulations in the load-bearing capacity of the structures. D and G structures compressed in the axial direction exhibited significant strain hardening and identical structures compressed in the lateral direction exhibited stable post-yield behavior with negligible undulations which is highly preferred for crashworthiness applications.

This study presents a review of the effect of nano-additives in improving the mechanical properties of composites. Nano-additives added to composites, also termed nanocomposites, have promising applications in aerospace, medical, biomedical, automotive, and military. The nanoparticles alter either the surface, bulk, or both, depending upon the process, and dramatically change the thermal conductivity, tensile strength, flexural strength, fatigue strength, impact resistance, vibration resistance, buckling, post-buckling, nanoparticles surface modification, and application of machine learning as well as optimization methods in nanocomposite materials. Such transformations in composite materials are extensively studied by researchers and positive implications are successfully deployed in various applications. Interestingly, the recent findings revealed that the weak chemical bonding between the fiber and matrix phase is the main reason for delamination, however, by the addition of nanoparticles, the chances of delamination are reduced even under excessive loading. Graphene and multi-walled carbon nanotubes (MWCNTs) are the most excessively reported nanomaterials for enhancing the vibration behavior and energy absorption capacity, as well as decreasing the adverse effects due to porosity within the composite structure. Also, machine learning techniques showed to be a promising way to further improve the mechanical properties while reducing the total cost of the fabrication process by predicting and providing optimum fabrication characteristics with acceptable accuracy compared to realistic conditions.

Abstract This paper presents a novel method that uses the cold metal transfer based wire arc additive manufacturing process to fabricate functionally graded Inconel 825–SS316L walls. The optical micrograph of Inconel 825 exhibits continuous and discontinuous dendritic structures. The SS316L region comprises 5% of δ-ferrite in primary austenitic (γ) dendrites which was confirmed by the Creq/Nieq ratio of 1.305. The functionally graded interface reveals a partially mixed zone with a transition from the elongated dendrites to fine equiaxed dendrites. The tensile properties of the fabricated wall were determined at room temperature using specimens extracted from Inconel 825, SS316L, and the interface regions. The morphology of the tensile tested specimens revealed significant plastic deformation, indicating ductile failure. The fracture toughness of the wall was experimentally investigated by employing the crack tip opening displacement (CTOD) test. The fracture morphology exhibited a ductile mode of fracture with striations perpendicular to the direction of crack development. Elemental mapping revealed that there was no evidence of elemental segregation on the fractured surfaces, and the elements were uniformly dispersed. The CTOD measures 0.853 mm, 0.873 mm on the Inconel 825 side and the SS316L side respectively. The test results confirm that both the Inconel 825 and SS316L sides have good fracture toughness.

Masala Company is the FMCG in the current scenario it is booming and undergoing a rapid growth. They are coming out with the innovative strategies, and they are completely ready to face this competitive market. This leads to know the Retailer preference of the masala products. The present study is undertaken to find out the Retailer Preference towards masala products in Virudhunagar District. The objectives of the study are to know about what the factors are influence the retailers’ to prefer the masala products in their retail outlet. Also used to study about preference of the retailers’ on purchase of masala products. The survey was collected from 190 Retail shop respondents, using a Structured Questionnaire through Survey. The data collected is analysed using the statistical tools such as Correlation and Chi square test. From the analysis it is found that the Retailers’ respondents who are in Sivakasi, Sattur, Satchapuram and Thiruthangal purchase masala products. The factors which influence them to make purchase is quality, customer and the reasonable price of the masala products. Also, they prefer masala products which are fast-moving, consumer preferring masala products and purchase which makes profit for them, they are highly agreed with it. And maximum number of retailers’ used to purchase Turmeric powder and Chilli powder in their retail outlet.

Natural fiber’s hydrophilic property leads to reduced bond strength for its incompatibility with the matrix. This problem can be sorted out by treating the surface of the fiber. In this connection, the chemical, physical, thermal, and surface morphological properties of alkali-treated Arial root Ficus Amplissima fiber (ARFAF) have been investigated. By using the TGA, the alkali treatment of ARFAF was optimized, and 5% (w/v) NaOH and 45 min of the soaking period was found to be the best. Optimally treated ARFAF improved the cellulose content from (52.64 wt. %) to (61.67 wt. %) and reduced the hemicellulose from (10.64 wt. %) to (6.22 wt. %), crystallinity index improved from (39%) to (43.33%), tensile strength improved from (250.7 ± 11.26MPa) to (278.4 ± 13.20 MPa), thermal stability improved from (200°C) to (230°C), and it improved the surface topography compared with raw ARFAF.

The chemical, physical, thermal, and surface morphological properties of aerial root Ficus amplissima fiber (ARFAF) were studied. Chemical composition analysis of ARFAF quantified the cellulose (52.64 wt.%) and hemicellulose (10.64 wt.%) contents. The results of X-ray diffraction analysis revealed that the crystallinity index of ARFAF was 39% and the crystallite size was 8.15 nm. Thermal stability and cellulose degradation temperature of ARFAF were estimated to be 200 and 335°C, respectively. The surface topography was studied by scanning electron microscopy. All the above outcomes confirmed that ARFAF is a suitable material to use as reinforcement in the polymer composite.

In this research work, the cold metal transfer (CMT)-based wire-arc additive manufacturing (WAAM) process was used to fabricate a multi-layered wall using SS316L wire electrode. The wall was built with optimised CMT-WAAM parameters derived from a multi-variable regression analysis. The mechanical and microstructural characteristics of the multilayered wall were studied at the bottom, middle, and top regions. The macrostructure in all three regions of the wall indicates that the layers have completely fused together. The optical micrograph displays fine equi-axed dendrites in the bottom region, columnar grains with residual δ-ferrite in the middle region, and columnar dendritic structure in the top region. The tensile and impact test values of the bottom, middle, and top regions of the wall show that the values are comparable to their wrought counterparts. It was observed that the hardness value decreased with an increase in the build height. Scanning electron microscopy (SEM) analysis of the fractured surfaces of the tensile and impact test specimens showed dimples, indicating high ductility of the as-built wall and successful fabrication.

Chromium (Cr)-doped cobalt ferrite nanoparticles were synthesized using a sol–gel autocombustion method, with the chemical formula CoCrxFe2xO4. The value of x ranged from 0.00 to 0.5 in 0.1 increments. X-ray diffraction analysis confirmed the development of highly crystalline cubic spinel structures for all samples, with an average crystallite size of approximately 40 to 45 nm determined using the Scherrer equation. Pellets were prepared using a traditional ceramic method. The magnetic and magnetostrictive properties of the samples were tested using strain gauge and VSM (vibrating sample magnetometer) techniques. The results of the magnetic and magnetostrictive tests showed that the chromium-substituted cobalt ferrites exhibited higher strain derivative magnitudes than pure cobalt ferrite. These findings indicated that the introduction of chromium into the cobalt ferrite structure led to changes in the material’s magnetic properties. These changes were attributed to anisotropic contributions, resulting from an increased presence of Co2+ ions at B-sites due to the chromium substitutions. In summary, this study concluded that introducing chromium into the cobalt ferrite structure caused alterations in the material’s magnetic properties, which were explained by changes in the cationic arrangement within the crystal lattice. This study successfully explained these alterations using magnetization and coercivity data and the probable cationic dispersion.

This work aims to fabricate the functionally graded Inconel 825 - SS316L wall using the Cold Metal Transfer (CMT) based Wire Arc Additive Manufacturing (WAAM) process. The mechanical behavior of the fabricated wall was appraised with the help of room temperature tensile and microhardness tests on the specimens cut from Inconel 825, SS316L, and the interface regions. Fracture morphology revealed the failure of all the specimens after sufficient plastic deformation, indicating ductile fracture. A change in microhardness was observed at the interface region. The optical micrograph revealed a continuous cellular–dendritic and discontinuous cellular–dendritic region in the Inconel 825 region, whereas the SS316L region was comprised of δ ferrite in the primary austenitic (γ) dendrites. The Energy Dispersive Spectroscopy (EDS) line scans of the FGM wall revealed a gradual change in the Ni and Fe content throughout the interface, signifying a successful fabrication of the FGM.

Objective: To estimate the incidence of major adverse cardiovascular events (MACE) with genotype test-guided antiplatelet therapy in patients undergoing percutaneous coronary intervention (PCI) for acute coronary syndrome. Methods: Patients who had undergone PCI for acute coronary syndrome as well as stable coronary artery disease were recruited. Salivary samples were obtained from these patients and genotyped for CYP2C19∗2, CYP2C19∗3 variations by sequencing method (GAAP x method). Patients were categorized as normal (GG, GG) (29%), intermediate (AG) (52%) or poor metabolizes (homozygous variant AA) (19%). Dual antiplatelets were given based on the genotyping data. Poor metabolizes received newer agent (ticagrelor), intermediate metabolizes received double-dose of clopidogrel and normal metabolizes received therapeutic doses of clopidogrel. All subjects were followed-up for six months. Results: Based on the genotyping data of CYP2C19∗2 and CYP2C19∗3 variations, it was found that most patients were categorized as ‘intermediate’ (78, 51.65%), followed by ‘normal’ (43, 28.48%) and ‘poor’ metabolizes (30, 19.87%). Only 3 (1.5%) of 151 patients reported MACE at follow-up. Conclusions: Genotyping for CYP2C19 variations to assess clopidogrel resistance in patients undergoing PCI and subsequent drug selection helps reduce MACE after coronary intervention.

T‌h‌e e‌v‌a‌l‌u‌a‌t‌i‌o‌n o‌f p‌a‌v‌e‌m‌e‌n‌t‌s b‌e‌i‌n‌g u‌t‌i‌l‌i‌z‌e‌d u‌s‌i‌n‌g f‌a‌l‌l‌i‌n‌g w‌e‌i‌g‌h‌t d‌e‌f‌l‌e‌c‌t‌o‌m‌e‌t‌e‌r (F‌W‌D) i‌s o‌n‌e o‌f t‌h‌e m‌o‌s‌t i‌m‌p‌o‌r‌t‌a‌n‌t c‌o‌m‌p‌o‌n‌e‌n‌t‌s o‌f t‌h‌e p‌a‌v‌e‌m‌e‌n‌t m‌a‌n‌a‌g‌e‌m‌e‌n‌t s‌y‌s‌t‌e‌m i‌n m‌a‌n‌y c‌o‌u‌n‌t‌r‌i‌e‌s. T‌h‌e c‌o‌m‌p‌u‌t‌a‌t‌i‌o‌n o‌f p‌a‌v‌e‌m‌e‌n‌t l‌a‌y‌e‌r p‌r‌o‌p‌e‌r‌t‌i‌e‌s t‌o e‌s‌t‌i‌m‌a‌t‌e t‌h‌e r‌e‌m‌a‌i‌n‌i‌n‌g l‌i‌f‌e o‌f t‌h‌e p‌a‌v‌e‌m‌e‌n‌t a‌n‌d a‌l‌s‌o p‌a‌v‌e‌m‌e‌n‌t m‌a‌i‌n‌t‌e‌n‌a‌n‌c‌e h‌a‌s a‌l‌w‌a‌y‌s b‌e‌e‌n o‌f i‌n‌t‌e‌r‌e‌s‌t t‌o r‌o‌a‌d p‌a‌v‌e‌m‌e‌n‌t r‌e‌s‌e‌a‌r‌c‌h‌e‌r‌s a‌n‌d e‌n‌g‌i‌n‌e‌e‌r‌s. O‌n t‌h‌e o‌t‌h‌e‌r h‌a‌n‌d, t‌h‌e b‌a‌c‌k-c‌a‌l‌c‌u‌l‌a‌t‌i‌o‌n i‌s w‌i‌d‌e‌l‌y u‌s‌e‌d f‌o‌r t‌h‌e e‌s‌t‌i‌m‌a‌t‌i‌o‌n o‌f t‌h‌e p‌a‌v‌e‌m‌e‌n‌t l‌a‌y‌e‌r p‌r‌o‌p‌e‌r‌t‌i‌e‌s. I‌n m‌o‌s‌t b‌a‌c‌k-c‌a‌l‌c‌u‌l‌a‌t‌i‌o‌n m‌e‌t‌h‌o‌d‌s, t‌h‌e c‌o‌m‌m‌e‌r‌c‌i‌a‌l s‌o‌f‌t‌w‌a‌r‌e s‌u‌c‌h a‌s A‌B‌A‌Q‌U‌S, A‌N‌S‌Y‌S, e‌t‌c., a‌r‌e u‌s‌e‌d a‌s n‌u‌m‌e‌r‌i‌c‌a‌l s‌i‌m‌u‌l‌a‌t‌i‌o‌n e‌n‌g‌i‌n‌e. B‌u‌t i‌t i‌s d‌i‌f‌f‌i‌c‌u‌l‌t t‌o i‌n‌t‌e‌g‌r‌a‌t‌e t‌h‌e‌m w‌i‌t‌h t‌h‌e o‌p‌t‌i‌m‌i‌z‌a‌t‌i‌o‌n e‌n‌g‌i‌n‌e a‌n‌d c‌o‌n‌s‌e‌q‌u‌e‌n‌t‌l‌y, t‌h‌e‌y r‌e‌q‌u‌i‌r‌e t‌h‌e p‌r‌e-g‌e‌n‌e‌r‌a‌t‌e‌d a‌r‌t‌i‌f‌i‌c‌i‌a‌l a‌n‌a‌l‌y‌t‌i‌c‌a‌l d‌a‌t‌a f‌o‌r s‌e‌a‌r‌c‌h s‌p‌a‌c‌e. ‌u‌b‌s‌e‌q‌u‌e‌n‌t‌l‌y, m‌u‌c‌h c‌o‌m‌p‌u‌t‌a‌t‌i‌o‌n‌a‌l t‌i‌m‌e d‌u‌e t‌o t‌h‌e l‌a‌r‌g‌e n‌u‌m‌b‌e‌r o‌f i‌t‌e‌r‌a‌t‌i‌o‌n‌s i‌s r‌e‌q‌u‌i‌r‌e‌d. I‌n t‌h‌i‌s p‌a‌p‌e‌r, t‌h‌e d‌i‌f‌f‌e‌r‌e‌n‌t‌i‌a‌l q‌u‌a‌d‌r‌a‌t‌u‌r‌e m‌e‌t‌h‌o‌d (D‌Q‌M) i‌s e‌m‌p‌l‌o‌y‌e‌d t‌o a‌n‌a‌l‌y‌z‌e t‌h‌e p‌a‌v‌e‌m‌e‌n‌t. B‌y c‌o‌m‌b‌i‌n‌i‌n‌g t‌h‌i‌s m‌e‌t‌h‌o‌d w‌i‌t‌h h‌a‌r‌m‌o‌n‌y s‌e‌a‌r‌c‌h (H‌S) o‌p‌t‌i‌m‌i‌z‌a‌t‌i‌o‌n a‌l‌g‌o‌r‌i‌t‌h‌m, a c‌o‌m‌p‌u‌t‌a‌t‌i‌o‌n‌a‌l‌l‌y e‌f‌f‌i‌c‌i‌e‌n‌t m‌o‌d‌e‌l i‌s d‌e‌v‌e‌l‌o‌p‌e‌d f‌o‌r c‌a‌l‌c‌u‌l‌a‌t‌i‌o‌n o‌f s‌u‌r‌f‌a‌c‌e d‌e‌f‌l‌e‌c‌t‌i‌o‌n‌s s‌o t‌h‌a‌t i‌t s‌i‌g‌n‌i‌f‌i‌c‌a‌n‌t‌l‌y r‌e‌d‌u‌c‌e‌s t‌h‌e o‌v‌e‌r‌a‌l‌l c‌o‌m‌p‌u‌t‌a‌t‌i‌o‌n‌a‌l t‌i‌m‌e f‌o‌r b‌a‌c‌k-c‌a‌l‌c‌u‌l‌a‌t‌i‌o‌n. A‌s a‌n a‌p‌p‌l‌i‌c‌a‌t‌i‌o‌n o‌f t‌h‌e p‌r‌o‌p‌o‌s‌e‌d h‌y‌b‌r‌i‌d d‌i‌f‌f‌e‌r‌e‌n‌t‌i‌a‌l q‌u‌a‌d‌r‌a‌t‌u‌r‌e m‌e‌t‌h‌o‌d a‌n‌d h‌a‌r‌m‌o‌n‌y s‌e‌a‌r‌c‌h (D‌Q-H‌S) o‌p‌t‌i‌m‌i‌z‌a‌t‌i‌o‌n a‌l‌g‌o‌r‌i‌t‌h‌m, a n‌u‌m‌e‌r‌i‌c‌a‌l e‌x‌a‌m‌p‌l‌e f‌o‌r b‌a‌c‌k-c‌a‌l‌c‌u‌l‌a‌t‌i‌o‌n o‌f e‌l‌a‌s‌t‌i‌c m‌o‌d‌u‌l‌u‌s o‌f a t‌h‌r‌e‌e-l‌a‌y‌e‌r p‌a‌v‌e‌m‌e‌n‌t s‌t‌r‌u‌c‌t‌u‌r‌e‌s i‌s p‌r‌e‌s‌e‌n‌t‌e‌d. T‌h‌e r‌e‌s‌u‌l‌t‌s o‌f t‌h‌i‌s s‌t‌u‌d‌y w‌i‌t‌h d‌i‌f‌f‌e‌r‌e‌n‌t p‌o‌p‌u‌l‌a‌t‌i‌o‌n‌s s‌h‌o‌w t‌h‌a‌t t‌h‌i‌s m‌e‌t‌h‌o‌d c‌a‌n b‌e u‌s‌e‌d t‌o c‌a‌l‌c‌u‌l‌a‌t‌e t‌h‌e e‌l‌a‌s‌t‌i‌c m‌o‌d‌u‌l‌u‌s o‌f t‌h‌e l‌a‌y‌e‌r‌s i‌n l‌e‌s‌s t‌h‌a‌n 20 i‌t‌e‌r‌a‌t‌i‌o‌n‌s. T‌o d‌e‌m‌o‌n‌s‌t‌r‌a‌t‌e t‌h‌e e‌f‌f‌i‌c‌i‌e‌n‌c‌y o‌f t‌h‌e o‌p‌t‌i‌m‌i‌z‌a‌t‌i‌o‌n a‌l‌g‌o‌r‌i‌t‌h‌m f‌o‌r h‌a‌r‌m‌o‌n‌i‌c s‌e‌a‌r‌c‌h a‌n‌d t‌h‌e c‌o‌n‌v‌e‌r‌g‌e‌n‌c‌e i‌n‌d‌e‌p‌e‌n‌d‌e‌n‌c‌e o‌f o‌p‌t‌i‌m‌a‌l s‌o‌l‌u‌t‌i‌o‌n‌s, t‌h‌e p‌r‌o‌b‌l‌e‌m w‌i‌t‌h 10 p‌o‌p‌u‌l‌a‌t‌i‌o‌n‌s a‌n‌d r‌e‌p‌e‌t‌i‌t‌i‌o‌n o‌f 20 i‌s p‌e‌r‌f‌o‌r‌m‌e‌d 10 t‌i‌m‌e‌s w‌i‌t‌h a s‌e‌r‌i‌e‌s o‌f r‌a‌n‌d‌o‌m n‌u‌m‌b‌e‌r‌s. T‌h‌e r‌e‌s‌u‌l‌t‌s i‌n‌d‌i‌c‌a‌t‌e t‌h‌a‌t t‌h‌e i‌n‌d‌e‌p‌e‌n‌d‌e‌n‌c‌e o‌f t‌h‌e m‌e‌t‌h‌o‌d o‌f o‌p‌t‌i‌m‌i‌z‌a‌t‌i‌o‌n f‌r‌o‌m r‌a‌n‌d‌o‌m v‌a‌l‌u‌e‌s. F‌a‌s‌t c‌o‌n‌v‌e‌r‌g‌e‌n‌c‌e, h‌i‌g‌h p‌r‌e‌c‌i‌s‌i‌o‌n a‌n‌d l‌o‌w c‌o‌m‌p‌u‌t‌a‌t‌i‌o‌n‌a‌l c‌o‌s‌t a‌r‌e t‌h‌e a‌d‌v‌a‌n‌t‌a‌g‌e‌s o‌f t‌h‌i‌s p‌r‌o‌p‌o‌s‌e‌d m‌e‌t‌h‌o‌d t‌o e‌s‌t‌i‌m‌a‌t‌e u‌n‌k‌n‌o‌w‌n p‌a‌r‌a‌m‌e‌t‌e‌r‌s o‌f a m‌u‌l‌t‌i‌l‌a‌y‌e‌r s‌t‌r‌u‌c‌t‌u‌r‌e, i‌n‌c‌l‌u‌d‌i‌n‌g p‌a‌v‌e‌m‌e‌n‌t s‌t‌r‌u‌c‌t‌u‌r‌e.

Abstract The current study is proposing a design envelope for porous Ti-6Al-4V alloy femoral stems to survive under fatigue loads. Numerical computational analysis of these stems with a body-centered-cube (BCC) structure is conducted in ABAQUS. Femoral stems without shell and with various outer dense shell thicknesses (0.5, 1.0, 1.5, and 2 mm) and inner cores (porosities of 90, 77, 63, 47, 30, and 18%) are analyzed. A design space (envelope) is derived by using stem stiffnesses close to that of the femur bone, maximum fatigue stresses of 0.3σys in the porous part, and endurance limits of the dense part of the stems. The Soderberg approach is successfully employed to compute the factor of safety N f > 1.1. Fully porous stems without dense shells are concluded to fail under fatigue load. It is thus safe to use the porous stems with a shell thickness of 1.5 and 2 mm for all porosities (18–90%), 1 mm shell with 18 and 30% porosities, and 0.5 mm shell with 18% porosity. The reduction in stress shielding was achieved by 28%. Porous stems incorporated BCC structures with dense shells and beads were successfully printed.

In the agriculture field, one of the recent research topics is recognition and classification of diseases from the leaf images of a plant. The recognition of agricultural plant diseases by utilizing the image processing techniques will minimize the reliance on the farmers to protect the agricultural products. In this paper, Recognition and Classification of Paddy Leaf Diseases using Optimized Deep Neural Network with Jaya Algorithm is proposed. For the image acquisition the images of rice plant leaves are directly captured from the farm field for normal, bacterial blight, brown spot, sheath rot and blast diseases. In pre-processing, for the background removal the RGB images are converted into HSV images and based on the hue and saturation parts binary images are extracted to split the diseased and non-diseased part. For the segmentation of diseased portion, normal portion and background a clustering method is used. Classification of diseases is carried out by using Optimized Deep Neural Network with Jaya Optimization Algorithm (DNN_JOA). In order to precise the stability of this approach a feedback loop is generated in the post processing step. The experimental results are evaluated and compared with ANN, DAE and DNN. The proposed method achieved high accuracy of 98.9% for the blast affected, 95.78% for the bacterial blight, 92% for the sheath rot, 94% for the brown spot and 90.57% for the normal leaf image.

In the present work, the effect of varying the carbonate to phosphate (CO32−/PO43−) molar ratios from 0.5 to 4 on the sintering behaviour of carbonated hydroxyapatite (CHA) synthesized by a wet chemical method were investigated. The sintering was performed under carbon dioxide atmosphere at 900 °C to maintain the B-type CHA structure. The derived powders as well as the sintered samples were characterized to determine the phase present, crystallographic parameters, the functional group resulting from the substitution of CO32− for PO43−, microstructural evolution under different molar ratios, bulk density, Vickers hardness and fracture toughness. It was found that as the CO32−/PO43− ratio increase, this was accompanied by an increased in the c/a lattice ratio. The sintering studies indicated that the all the CHA was thermally stable and retained the apatite structure after sintering. The relative density of the sintered CHA was found to decrease along with the Vickers hardness and fracture toughness as the CO32−/PO43− ratio increased from 0.5 to 4. The improvement in the mechanical properties was associated with improvement in the relative density and the larger grain size of the sintered samples. Resumen: En el presente trabajo, se investigó el efecto de variar las relaciones molares de carbonato a fosfato (CO32-/PO43-) de 0,5 a 4 sobre el comportamiento de sinterización de la hidroxiapatita carbonatada (CHA) sintetizada por un método químico húmedo. La sinterización se realizó bajo una atmósfera de dióxido de carbono a 900° C para mantener la estructura de CHA de tipo B. Los polvos derivados y las muestras sinterizadas se caracterizaron para determinar la fase presente, los parámetros cristalográficos, el grupo funcional resultante de la sustitución de CO32 por PO43, la evolución microestructural en diferentes relaciones molares, la densidad aparente, la dureza Vickers y la resistencia a la fractura. Se encontró que a medida que aumentaba la relación CO32−/PO43−, esto iba acompañado de un aumento en la relación de celosía c/a. Los estudios de sinterización indicaron que todo el CHA era térmicamente estable y retuvo la estructura de apatita después de la sinterización. Se encontró que la densidad relativa del CHA sinterizado disminuía junto con la dureza Vickers y la resistencia a la fractura, ya que la relación CO32−/PO43− aumentó de 0.5 a 4. La mejora en las propiedades mecánicas se asoció con una mejora en la densidad relativa y la mayor Tamaño de grano de las muestras sinterizadas. Keywords: Carbonated hydroxyapatite, Sintering, Mechanical properties, Microstructure, Bioceramics, Palabras clave: Hidroxiapatita carbonatada, Sinterización, Propiedades mecánicas, Microestructura, Biocerámica

Past studies have shown that phosphate recovery in the form of struvite is relatively a simple process, which can be achieved by adding a magnesium source in stored urine. However, struvite recovery process at a decentralised level becomes uneconomical due to high input cost of magnesium salts and operational cost. While use of cheaper alternative magnesium sources such as bittern, low-grade MgO and wood ash could lead to partial cost reduction, it is also important to reduce the overall operational costs to make struvite recovery process economically viable and sustainable. In this study, a continuous flow reactor was developed for low-cost struvite recovery from stored urine at decentralised community scale operations. Our study revealed that over 81.2% of phosphate present in urine can be recovered in the form of struvite. Comparison of results from stirred and unstirred experiments shows that higher recovery efficiency is obtained due to minimal loss of fines. Operation and financial assessment of the process shows that struvite recovery can be profitable due to continuous operation of the reactor requiring minimal process control and manpower requirement.

Micro-electric discharge machining (Micro-EDM) is deployed for machining hard-to-machine materials, such as various grades of titanium alloys, heat-treated alloy steels, composites, tungsten carbides, and so forth. Mild steel is known for its easy machinability. However, conventional machining of mild steel can often lead to the built-up edge formation on the tool. There is a minimal focus on machining ductile materials using nonconventional machining processes. This is due to the rapid work hardening in cold forming conditions. In the present study, the aluminium alloy 6061 and mild steel AISI 304 were taken as a work piece. Input pulse on factors considered as three levels and orthogonal array utilized to optimize the EDM parameters. Numerical results confirm the influence of input parameters in the response. The highest MRR is obtained at Ton = 40 μs and Toff = 4 μs, and the least MRR is acquired at Ton = 20 μs and Toff = 3 μs. The fruit fly algorithm and the cockroach swarm algorithm were used to predict the optimal minimized MRR value. The experimental results show that the cockroach swarm algorithm was performing better than the fruit fly algorithm in the MRR minimization process.

The functioning of a solar hybrid power system is investigated in this research using a unique fuzzy control method. Turbines, solar photovoltaics, diesel engines, fuel cells, aqua-electrolyzes, and other autonomous generation products are used in the hybrid renewable energy system. Further energy storage components of the system include the batteries, turbine, and ultracapacitor. This research incorporates a supercapacitor hybrid energy storage system (HESS) into a solar hybrid power generating system, allowing the consumption and energy storage space and power output to be significantly increased. This study’s approach incorporates a decentralized power generation system with a HESS while increasing electrical output in phases utilizing a dynamic reactive power compensation scheme and a conductance-fuzzy dual-mode control strategy. Due to a nonlinear behavior of photovoltaic (PV) devices’ power output, maximum power point tracking (MPPT) methods must be used to create the greatest power. Infrequently developing atmospheric circumstances, traditional MPPT algorithms do not work adequately. Modeling is used to determine the microgrid’s power output to the photovoltaic hybrid power generating organization, as well as the optimization method for each device in the network. The dynamic power factor correction scheme and also the conductance-fuzzy dual-mode control approach are primarily used in this study to optimize the solar hybrid renewable energy system.

Nowadays, energy storage system is utilized in many countries for energy planning in the future. The changes in solar radiation lead to the overproduction of electricity in a solar photovoltaic generator. A hybrid energy storage system would play an important role in enhancing the reliability of power generation using the solar system. The microgrid is the indispensable infrastructure of the smart grid in photovoltaic systems. In this paper, the energy storage system within the microgrid of the PV system is analysed. The storage system configuration and topologies of the microgrid are analysed with power electronic interference, control scheme, and optimization of the renewable source and energy storage system. A general sizing technique for HESS in a PV system based on pinch analysis and design space. The size of HESS scales that connect generator ratings to storage capacity is created by utilizing the pinch analysis on load and resource data. The design space is a feasible combination of a short-, long-, and medium-sized energy storage system in a PV generator.

The current-voltage association of the single-diode photovoltaic (PV) cell comparable circuit system was characterized by its nonlinear implicit logical equation that would be hard to be resolved numerically. Because of the difficulty, various strategies for explaining this equation by using numerical approaches have been developed. The double-diode model is used to depict the PV cell in this research. This design is more accurate at the low irradiance levels, allowing for an extra accurate estimate performance of the PV system. The number of input variables is decreased to four to save time, and the values of Rp and Rs are calculated using an effective iterative technique. This research analyzes and compares three commonly used strategies for explaining the current-voltage equation parameter of a single-diode solar PV model. The chaotic optimization approach (COA) is used to evaluate the single-diode and double-diode solar cell characteristics. The suggested method relies on experimentally established current-voltage (I-V) characteristics. The suggested approach uses the curves of I-V characteristics obtained in the research laboratory for several standards of the solar temperature and the radiation and demonstrates its applicability in terms of efficacy, accuracy, and the simplicity of execution in an extensive range of real-world situations. As a conclusion, COA-based restriction approximation is beneficial to photovoltaic power generator manufacturers that want a timely and efficient PV cell/module model. It demonstrates that no single approach performs the best among all parameters and the method selection is always a trade-off depending on the user’s focus.

The analysis of different components of a grid-linked hybrid energy system (HES) comprising a photovoltaic (PV) system is presented in this work. Due to the increase of the population and industries, power consumption is increasing every day. Due to environmental issues, traditional power plants alone are insufficient to supply customer demand. In this case, the most important thing is to discover another approach to meet customer demands. Most wealthy countries are now concentrating their efforts on developing sustainable materials and investing considerable amounts of money in product development. Wind, solar, fuel cells, and hydro/water resources are among the most environmentally benign renewable sources. To control the variability of PV generation, this sort of application necessitates the usage of energy storage systems (ESSs). Lithium-ion (Li-ion) batteries are the most often used ESSs; however, they have a short lifespan due to the applied stress. Hybrid energy storage systems (HESSs) started to evolve as a way to decrease the pressure on Li-ion batteries and increase their lifetime. This study represents a great power management technique for a PV system with Li-ion batteries and supercapacitor (SC) HESS based on an artificial neural network. The effectiveness of the suggested power management technique is demonstrated and validated using a conventional PV system. Computational models with short-term and long-term durations were used to illustrate their effectiveness. The findings reveal that Li-ion battery dynamical stress and peak value are reduced, resulting in longer battery life.

Wire cut EDM is a quite regularly used machining process in mechanical and electronic industries. This research has attempted to machine aluminum alloy for which experimental design was prepared using Box-Behnken design. Different combinational options of pulse-on time (P1), pulse-off time (P2), servo wire feed (WF), and current (I) were investigated and surface roughness after machining was observed. Collected 27 datasets were further used in Adaptive Neuro Fuzzy Inference System (ANFIS) to produce about 500 datasets. These 500 datasets are approximated data derived from experimental datasets, known as synthetic data. Data model was further developed and used in Gorilla Troops Optimizer (GTO) to locate the optimum machining parameters. With the excellent three search operators: move towards other gorillas, migrate towards unknown places, and migrate towards known places, GTO has produced the lowest surface roughness value of 0.500953 μm when the machining parameters of pulse-on time, pulse-off time, wire feed, and current values were set as 121 μs, 52 μs, 3 m/min, and 166A, respectively. To ensure the accuracy of the synthetic data-based model and optimality, verification and validation were conducted. Wilcoxon signed rank test was conducted for the pairwise comparison of GTO with each of its competing algorithms at the significance level of σ = 0.05. Friedman test was conducted to calculate the average ranking of each algorithm and to detect the global differences between all compared algorithms. Outperforming performance by GTO algorithm in machining of the selected material is found.

Currently, electric cars are receiving a lot of attention because of their low fuel consumption and pollution-free nature. The efficiency of electric vehicles is constantly being improved via a variety of studies and applications. The battery is a significant portion of an electric vehicle’s overall component count. Because the electric vehicle’s battery acts like the vehicle’s heart, battery performance and efficiency in electric vehicles (EVs) are examined in this research.

The integration of renewable energy sources with microgrids using IoT and energy management technologies has become a promising solution for achieving sustainable and efficient energy systems. In this paper, propose a methodology for integrating renewable energy sources with microgrids using IoT and energy management technologies, and apply an Artificial Neural Network (ANN) algorithm for energy demand prediction. The proposed methodology aims to optimize the energy consumption of the micro grid by utilizing renewable energy sources and energy storage devices. Validate the proposed methodology using a real-world dataset, and compare the performance with traditional forecasting methods. The results show that the proposed methodology outperforms traditional methods in terms of accuracy and efficiency. The proposed methodology can be utilized in various micro grid applications for load forecasting and energy consumption optimization.

Identifying and quantifying the biological concentrations of certain biomolecules such as dopamine, glucose, tyrosine, and cholesterol, etc. has become the basis for medical diagnosis in the treatment of a number of related diseases. In most cases, the concentrations of these biomolecules in biofluids like blood acts as a biomarker and becomes crucial in the treatment of diseases. On the other hand, advanced ceramics refers to oxides (alumina, zirconia), non-oxides: (carbides, borides, nitrides, silicides), Composites (particulate reinforced combinations of oxides and non-oxides), etc. This review article discusses recent developments in the field of electrochemical sensors developed using metal and metal oxide based advanced ceramics with an emphasis on developments in the field over the past five years. The article presents the key results, important findings, and interesting chemistry of biosensing advanced ceramic based electrochemical biosensors for some important biomolecules such as acetaminophen, glucose, and dopamine, etc.

Effect of multi-directional forging (MDF) on wear properties of Mg–Zn alloys (with 2, 4, and 6 wt% Zn) is investigated. Dry sliding wear test was performed using pin on disk machine on MDF processed and homogenized samples. Wear behavior of samples was analyzed at loads of 10 N and 20 N, with sliding distances of 2000 m and 4000 m, at a sliding velocity of 3 m/s. Microstructures of worn samples were observed under scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and x-ray diffraction (XRD) and the results were analyzed. Mechanical properties were evaluated using microhardness test. After 5 passes of MDF, the average grain size was found to be 30 ± 4 µm, 22 ± 3 µm, and 18 ± 3 µm, in Mg–2%Zn, Mg–4%Zn, and Mg–6%Zn alloys, respectively, with significant improvement in hardness in all cases. Wear resistance was improved after MDF processing, as well as, with increment in Zn content in Mg alloy. However, it decreased when the load and the sliding distance increased. Worn surface exhibited ploughing, delamination, plastic deformation, and wear debris along sliding direction, and abrasive wear was found to be the main mechanism. Keywords: Multi-directional forging, Mg–Zn alloy, Microhardness, Wear, Coefficient of friction

This paper presents a statistical process control (SPC) chart for variables in a new feature for the paper machine in the paper industry. The traditional control charts like X-bar and R charts may be inadequate sometimes when the process exhibits abnormal situations which could lead to false decisions. When we are interested in detecting small shifts, Exponentially Weighted Moving average (EWMA) control chart provides the correct picture to make right decisions without affecting the process unnecessarily. This study is aimed at designing EWMA control chart in paper manufacturing process.

The development in internet technologies provides various innovative business opportunities and creates competitiveness among the electronic marketers in online marketing. The satisfaction of customers in e-purchase depends on various problems and facilities which are prevailing in the online market. The study was conducted with the aim of analyzing and ranking the problems of customers in online purchase. The study also examined the relationship between the demographic variables and problems faced by online customers. The data was collected from 512 online customers who are living in Bengaluru city, Karnataka through questionnaires during the month of August 2018. Garret ranking was used to find out the ranking distribution pattern of customers in online purchase. The problems ‘product variation’ and ‘faulty products’ have been quoted as the major problems faced by the online customers irrespective of the demographic their profile. The ranking pattern of the third major problem ‘delay in delivery’ differs with respect to education and customers’ income. The ranking pattern for the problem ‘fake website’ was significantly different with respect to gender, age and education of customers.

Chilli (Capsicum annuum L.) produces fruits with varying orientation and number at each node. They are considered as fruiting habit traits in chilli. Farmer, producer and consumer preference for fruiting habit traits varies from region to region. For increased acceptability by farmers and consumers, high yielding cultivars should be bred with preferred fruiting habit traits. An investigation was carried out to study the influence of fruiting habit traits on fruit yield and its components in chilli at Bengaluru, India. The F2 individuals derived from crosses involving parents differing for single or both fruiting habit traits were grouped into fruiting habit classes. The means of fruiting habit classes in F2 populations were compared and statistically tested. The non-significant mean differences of fruiting habit classes for fruit yield and its components indicated lack of influence of fruiting habit traits on fruit yield and its components.

This paper presents an exploratory review on various attempts made in the literature for improving the performance of diesel engine, since last decade. The review explains the evolution of various performance improvement methods followed by explaining the modeling techniques, state-of-the-art metrics that define the performance of the diesel engines. Subsequently, the review is confined to artificial intelligence methodologies for improving the performance improvement. This review addresses an important challenge for enhancing the performance of the diesel engine by exploiting the optimization algorithms. The significant challenges are the noisy experimental scenario, robustness, imprecise and temporal variations of approximations of fitness models. Since these challenges still exist in the optimization algorithms and diesel engine modeling, there is an extensive scope for researchers who are working on a diesel engine. This contemplates the reporting of various advancements in the optimization models for further enhancement of engine performance. Keywords: Engine, Modeling, Artificial Intelligence, Control, Diesel

In this work, experiments on mechanical properties such as tensile, flexural, effects, and stiffness testing are performed on natural fiber granulated composites (NFGC) manufactured using a hybrid additive manufacturing technique. The natural fiber granulated composites are prepared using the powdered form of sugarcane, jute, ramie, banana, pineapple fiber, and seashell powder with a volume fraction of 0.8. In the hybrid additive manufacturing technique, the fused deposited modeling (FDM) machine is modified by combining with the shape deposition modeling (SDM) to print the specimens layer by layer, and the influence of the number of layers on the mechanical properties is analyzed. The results concluded that increasing the number of layers from 6 to 12 improved the mechanical properties such as tensile strength, flexural strength, impact strength, and hardness values by 40.84, 50.04, 21.55, and 20.55%, respectively. Further, a novel technique can be utilized for developing the composites in replacement with conventional methods.

Solar energy conversion efficiency has improved by the advancement technology of photovoltaic (PV) and the involvement of administrations worldwide. However, environmental conditions influence PV power output, resulting in randomness and intermittency. These characteristics may be harmful to the power scheme. As a conclusion, precise and timely power forecast information is essential for the power networks to engage solar energy. To lessen the negative impact of PV electricity usage, the offered short-term solar photovoltaic (PV) power estimate design is based on an online sequential extreme learning machine with a forgetting mechanism (FOS-ELM) under this study. This approach can replace existing knowledge with new information on a continuous basis. The variance of model uncertainty is computed in the first stage by using a learning algorithm to provide predictable PV power estimations. Stage two entails creating a one-of-a-kind PI based on cost function to enhance the ELM limitations and quantify noise uncertainty in respect of variance. As per findings, this approach does have the benefits of short training duration and better reliability. This technique can assist the energy dispatching unit list producing strategies while also providing temporal and spatial compensation and integrated power regulation, which are crucial for the stability and security of energy systems and also their continuous optimization.