Turning trash into treasure: Torrefaction of mixed waste for improved fuel properties. A case study of metropolitan city.

Solid waste management is one of the biggest challenges of the current era. The combustible fractions in the waste stream turn out to be a good energy source if converted into refuse-derived fuel. Researchers worldwide are successfully converting it into fuel. However, certain challenges are associated with its application in gasifiers, boilers, etc. to co-fire it with coal. These include high moisture content, low calorific value, and difficulty to transport and store. The present study proposed torrefaction as a pretreatment of the waste by heating it in the range of 200 °C-300 °C in the absence of oxygen at atmospheric pressure. The combustible fraction from the waste stream consisting of wood, textile, paper, carton, and plastics termed as mixed waste was collected and torrefied at 225 °C, 250 °C, 275 °C, and 300 °C for 15 and 30 min each. It was observed that the mass yield and energy yield decreased to 45% and 62.96% respectively, but the energy yield tended to increase by the ratio of 1.39. Proximate analysis showed that the moisture content and volatile matter decreased for torrefied samples, whereas the ash content and fixed carbon content increased. Similarly, the elemental analysis revealed that the carbon content increased around 23% compared to raw samples with torrefaction contrary to hydrogen and oxygen, which decreased. Moreover, the higher heating value (HHV) of the torrefied samples increased around 1.3 times as compared to the raw sample. This pretreatment can serve as an effective solution to the current challenges and enhance refuse-derived fuel's fuel properties.

Hydrochemical, hydrogeological, and cancer health risk analysis of groundwater due to anthropogenic activities, Hasilpur, Pakistan.

Drinking water quality deteriorates rapidly due to anthropogenic activities and rapid population growth. These activities, in developing countries, will lead to water scarcity. In Pakistan, 70% of the population has no access to safe water, and people use canal water to drink. This study performed hydrochemical, hydrogeological, and cancer risk analyses on Tahsil Hasilpur, Bahawalpur, Pakistan. Thirteen tube wells were selected for groundwater and borehole log study. Twenty-two drinking water quality parameters were analyzed using standard methods and quality checks. The borehole data (2D and 3D) shows the abundance of sand (fine and coarse) with some uniformities, which changes the groundwater quality. The results of water quality parameters show that the concentration of TDS (2064-11,159 mg/L), Cl-1 (213-4917 mg/L), As+3 (0.048-0.158 mg/L), Pb+2 (1.294-1.673 mg/L), and Cd+2 (0.008-0.053 mg/L) were beyond guideline values. The statistical analysis showed that the parameters have a moderate to strong correlation (Pearson correlation), which may be due to the same origin (ANOVA). The principal component analysis and cluster analysis confirm the multiple sources of pollutants in the groundwater of the study area. The Piper, Durov, Stiff, and Scholler diagrams confirm that the groundwater system has an abundance of Ca+2 and Mg+2 with Cl-1. The Gibbs diagram showed that the groundwater is not saturated and tends to dissolve more minerals. The hazard quotient values are above 1.0, which indicates noncancer risk severity. The HQ trend was As+3 > Pb+2 > Cd+2 > Ni+2 > Cu+2 > Cr+2 > Zn+2 > Fe+2. The cancer risk values showed that 3-5 people/100 population were exposed to cancer risk. The trend of CR was As+3 > Cd+2 > Cr+2 > Pb+2 > Ni+2. The GIP mapping of pollutants showed that the concentration of pollutants near the canals was high compared to the locations away from the canal. The overall groundwater quality is alarming and needs immediate government attention.

Spatial soil loss prediction impacted by long-term land use/land cover change: a case study of Swat District.

Soil erosion is a destructive consequence of land degradation caused by deforestation, improper farming practices, overgrazing, and urbanization. This irreversible effect negatively impacts the limited renewable soil resource, causing soil truncation, reduced fertility, and unstable slopes. To address the anticipation of erosion modulus resulting from long-term land use and land cover (LULC) changes, a study was conducted in the Swat District of Khyber Pakhtunkhwa (Kpk), Pakistan. The study aimed to predict and evaluate soil erosion concerning these changes using remote sensing (RS), geographic information systems (GIS), and the Revised Universal Soil Loss Equation (RUSLE) model. We also evaluated the impact of the Billion Tree Tsunami Project (BTTP) on soil erosion in the region. Model inputs, such as rainfall erosivity factor, topography factor, land cover and management factor, and erodibility factor, were used to calculate soil erosion. The results revealed that significant soil loss occurred under 2001, 2011, and 2021 LULC conditions, accounting for 67.26%, 61.78%, and 65.32%, falling within the category of low erosion potential. The vulnerable topographical features of the area indicated higher erosion modulus. The maximum soil loss rates observed in 2001, 2011, and 2021 were 80 t/ha-1/year-1, 120 t/ha-1/year-1, and 96 t/ha-1/year-1, respectively. However, the observed reduction in soil loss in 2021 as compared to 2001 and 2011 suggests a positive influence of the BTTP on soil conservation efforts. This study underscores the potential of afforestation initiatives like the BTTP in mitigating soil erosion and highlights the significance of environmental conservation programs in regions with vulnerable topography.

Predictive modelling framework on the basis of artificial neural network: A case of nano-powder mixed electric discharge machining.

In this modern era where Industry 4.0, plays a crucial role in enhancing productivity, quality, and resource utilization by digitalizing and providing smart operation to industrial systems. Therefore, there is a need to establish a framework that enhances productivity and quality of work to achieve the net-zero from industry. In this study, a comprehensive and generic analytical framework has been established to mitigate or lessen the research and technological gap in the manufacturing sector. In addition to that, the key stages involved in artificial intelligence (AI) based modelling and optimization analysis for manufacturing systems have also been incorporated. To assess the proposed AI framework, electric discharge machining (EDM) as a case study has been selected. The focus enlightens the emergence of optimizing the material removal rate (MRR) and surface roughness (SR) for Inconel 617 (IN617) material. A full factorial design of the experiment was carried out for experimentation. After that, an artificial neural network (ANN) as a modelling framework is selected, and fine-tuning of hyperparameters during training has been carried out. To validate the predictive performance of the trained models, an external validation (Valext) test has been conducted. Through sensitivity analysis (SA) on the developed AI framework, the most influential factors affecting MRR and SR in EDM have been identified. Specifically, powder concentration (Cp) contributes the most to the percentage significance, accounting for 79.00 % towards MRR, followed by treatment (16.35 %) and 4.67 % surfactant concentration (Sc). However, the highest % significance in SR is given by Sc (36.86 %), followed by Cp (33.23 %), and then treatment (29.90 %), respectively. Furthermore, a parametric optimization has been performed using the framework and found that MRR and SR are 93.75 % and 58.90 % better than experimental data. This successful performance optimization proposed by the framework has the potential for application to other manufacturing systems.

Development and characterization of letrozole-encapsulated polymeric beads for sustained release.

The study aimed to prepare and characterize biodegradable sustained-release beads of letrozole (LTZ) for treating cancerous disease. The ionotropic gelation method was used for the preparation and calcium chloride (CaCl2) was used as a gelating agent, while chitosan (CTS) and sodium alginate (NaAlg) as biodegradable polymeric matrices in the blend hydrogel beads. The beads were characterized for their size, surface morphology, drug entrapment efficiency, drug-polymer interaction and crystallinity using different analytic techniques, including optical microscopy, Scanning Electron Microscopy (SEM), UV-spectroscopy, Fourier-transform Infrared Spectroscopy (FTIR), Thermo gravimetric Analysis (TGA), Differential Scanning Calorimetry (DSC) and X-ray Diffraction Analysis (XRD) respectively. In vitro swelling studies were also applied to observe the response of these polymeric networks against different pH (at 1.2, 6.8 and 7.4 pH). The results from TGA and DSC exhibited that the components in the formulation possess better thermal stability. The XRD of polymeric networks displays a minor crystalline and significant amorphous nature. The SEM micrographs revealed that polymeric networks have uneven surfaces and grooves. Better swelling and in vitro outcomes were achieved at a high pH (6.8,7.4), which endorsed the pH-responsive characteristics of the prepared beads. Hence, beads based on chitosan and sodium alginate were successfully synthesized and can be used for the controlled release of letrozole.

DC-AAE: Dual channel adversarial autoencoder with multitask learning for KL-grade classification in knee radiographs.

Knee osteoarthritis (OA) is a frequent musculoskeletal disorder that leads to physical disability in older adults. Manual OA assessment is performed via visual inspection, which is highly subjective as it suffers from moderate to high inter-observer variability. Many deep learning-based techniques have been proposed to address this issue. However, owing to the limited amount of labelled data, all existing solutions have limitations in terms of performance or the number of classes. This paper proposes a novel fully automatic Kellgren and Lawrence (KL) grade classification scheme in knee radiographs. We developed a semi-supervised multi-task learning-based approach that enables the exploitation of additional unlabelled data in an unsupervised as well as supervised manner. Specifically, we propose a dual-channel adversarial autoencoder, which is first trained in an unsupervised manner for reconstruction tasks only. To exploit the additional data in a supervised way, we propose a multi-task learning framework by introducing an auxiliary task. In particular, we use leg side identification as an auxiliary task, which allows the use of more datasets, e.g., CHECK dataset. The work demonstrates that the utilization of additional data can improve the primary task of KL-grade classification for which only limited labelled data is available. This semi-supervised learning essentially helps to improve the feature learning ability of our framework, which leads to improved performance for KL-grade classification. We rigorously evaluated our proposed model on the two largest publicly available datasets for various aspects, i.e., overall performance, the effect of additional unlabelled samples and auxiliary tasks, robustness analysis, and ablation study. The proposed model achieved the accuracy, precision, recall, and F1 score of 75.53%, 74.1%, 78.51%, and 75.34%, respectively. Furthermore, the experimental results show that the suggested model not only achieves state-of-the-art performance on two publicly available datasets but also exhibits remarkable robustness.

Enhanced structural integrity of Laser Powder Bed Fusion based AlSi10Mg parts by attaining defect free melt pool formations.

This research aims to fabricate an AlSi10Mg parts using Laser Powder Bed Fusion technique with enhanced structural integrity. The prime novelty of this research work is eliminating the balling and sparring effects, keyhole and cavity formation by attaining effective melt pool formation. Modelling of the Laser Powder Bed Fusion process parameters such as Laser power, scanning speed, layer thickness and hatch spacing is carried out through Complex Proportional Assessment technique to optimize the parts' surface attributes and to overcome the defects based on the output responses such as surface roughness on scanning and building side, hardness and porosity. The laser power of 350 W, layer thickness of 30 µm, scan speed of 1133 mm/s, and hatch spacing of 0.1 mm produces significantly desirable results to achieve maximum hardness and minimum surface roughness and holding the porosity of < 1%. The obtained optimal setting from this research improves the structural integrity of the printed AlSi10Mg parts.

Mask-Transformer-Based Networks for Teeth Segmentation in Panoramic Radiographs.

Teeth segmentation plays a pivotal role in dentistry by facilitating accurate diagnoses and aiding the development of effective treatment plans. While traditional methods have primarily focused on teeth segmentation, they often fail to consider the broader oral tissue context. This paper proposes a panoptic-segmentation-based method that combines the results of instance segmentation with semantic segmentation of the background. Particularly, we introduce a novel architecture for instance teeth segmentation that leverages a dual-path transformer-based network, integrated with a panoptic quality (PQ) loss function. The model directly predicts masks and their corresponding classes, with the PQ loss function streamlining the training process. Our proposed architecture features a dual-path transformer block that facilitates bi-directional communication between the pixel path CNN and the memory path. It also contains a stacked decoder block that aggregates multi-scale features across different decoding resolutions. The transformer block integrates pixel-to-memory feedback attention, pixel-to-pixel self-attention, and memory-to-pixel and memory-to-memory self-attention mechanisms. The output heads process features to predict mask classes, while the final mask is obtained by multiplying memory path and pixel path features. When applied to the UFBA-UESC Dental Image dataset, our model exhibits a substantial improvement in segmentation performance, surpassing existing state-of-the-art techniques in terms of performance and robustness. Our research signifies an essential step forward in teeth segmentation and contributes to a deeper understanding of oral structures.

Electric Discharge Machining of Ti6Al4V ELI in Biomedical Industry: Parametric Analysis of Surface Functionalization and Tribological Characterization.

The superior engineering properties and excellent biocompatibility of titanium alloy (Ti6Al4V) stimulate applications in biomedical industries. Electric discharge machining, a widely used process in advanced applications, is an attractive option that simultaneously offers machining and surface modification. In this study, a comprehensive list of roughening levels of process variables such as pulse current, pulse ON time, pulse OFF time, and polarity, along with four tool electrodes of graphite, copper, brass, and aluminum are evaluated (against two experimentation phases) using a SiC powder-mixed dielectric. The process is modeled using the adaptive neural fuzzy inference system (ANFIS) to produce surfaces with relatively low roughness. A thorough parametric, microscopical, and tribological analysis campaign is established to explore the physical science of the process. For the case of the surface generated through aluminum, a minimum friction force of ~25 N is observed compared with the other surfaces. The analysis of variance shows that the electrode material (32.65%) is found to be significant for the material removal rate, and the pulse ON time (32.15%) is found to be significant for arithmetic roughness. The increase in pulse current to 14 A shows that the roughness increased to ~4.6 µm with a 33% rise using the aluminum electrode. The increase in pulse ON time from 50 µs to 125 µs using the graphite tool resulted in a rise in roughness from ~4.5 µm to ~5.3 µm, showing a 17% rise.

Achieving effective interlayer bonding of PLA parts during the material extrusion process with enhanced mechanical properties.

The additive manufacturing technique of material extrusion has challenge of excessive process defects and not achieving the desired mechanical properties. The industry is trying to develop certification to better control variations in mechanical attributes. The current study is a progress towards understanding the evolution of processing defects and the correlation of mechanical behavior with the process parameters. Modeling of the 3D printing process parameters such as layer thickness, printing speed, and printing temperature is carried out through L27 orthogonal array using Taguchi approach. In addition, CRITIC embedded WASPAS is adopted to optimize the parts' mechanical attributes and overcome the defects. Flexural and tensile poly-lactic acid specimens are printed according to ASTM standards D790 and D638, respectively, and thoroughly analyzed based on the surface morphological analysis to characterize defects. The parametric significance analysis is carried out to explore process science where the layer thickness, print speed, and temperature significantly control the quality and strength of the parts. Mathematical optimization results based on composite desirability show that layer thickness of 0.1 mm, printing speed of 60 mm/s, and printing temperature of 200 °C produce significantly desirable results. The validation experiments yielded the maximum flexural strength of 78.52 MPa, the maximum ultimate tensile strength of 45.52 MPa, and maximum impact strength of 6.21 kJ/m2. It is established that multiple fused layers restricted the propagation of cracks with minimum thickness due to enhanced diffusion between the layers.

Co-occurrence of familial Mediterranean fever with systemic lupus erythematosus in South Asian population.

BACKGROUND AND OBJECTIVE: Familial Mediterranean fever (FMF) and systemic lupus erythematosus (SLE) are autosomal recessive auto-inflammatory diseases, triggered by FMF-associated gene mutations and auto-antigens. The literature on the co-occurrence of these two disorders is limited to case reports and their correlation is considered rare. We investigated the proportion of FMF among SLE patients when compared with a healthy adult cohort in South Asia. METHODS: For this observational study, data from our institutional database were collected for the patients diagnosed with SLE. The control group was randomly selected from the database and were age- matched for SLE. The overall proportion of FMF among patients with and without SLE was considered. Student's t-test, Chi-square, and ANOVA were used for univariate analysis. RESULTS: The study population included 3623 SLE patients and 14,492 controls. In the SLE group, there was a significantly higher proportion of FMF patients compared with the non-SLE group (1.29% vs. 0.79% respectively; p=0.015). SLE was prevalent in Pashtun's (50%) in the middle socioeconomic group while FMF was dominant in Punjabi's and Sindhi's (53%) in the low socioeconomic class. CONCLUSION: This investigation demonstrates that FMF is more prevalent in a South-Asian population cohort of SLE patients.

Co-occurrence of familial Mediterranean fever with systemic lupus erythematosus in South Asian population / Coocurrencia de fiebre mediterránea familiar con lupus eritematoso sistémico en la población del sudeste asiático

Background and objective: Familial Mediterranean fever (FMF) and systemic lupus erythematosus (SLE) are autosomal recessive auto-inflammatory diseases, triggered by FMF-associated gene mutations and auto-antigens. The literature on the co-occurrence of these two disorders is limited to case reports and their correlation is considered rare. We investigated the proportion of FMF among SLE patients when compared with a healthy adult cohort in South Asia. Methods: For this observational study, data from our institutional database were collected for the patients diagnosed with SLE. The control group was randomly selected from the database and were age- matched for SLE. The overall proportion of FMF among patients with and without SLE was considered. Student's t-test, Chi-square, and ANOVA were used for univariate analysis. Results: The study population included 3623 SLE patients and 14,492 controls. In the SLE group, there was a significantly higher proportion of FMF patients compared with the non-SLE group (1.29% vs. 0.79% respectively; p=0.015). SLE was prevalent in Pashtun's (50%) in the middle socioeconomic group while FMF was dominant in Punjabi's and Sindhi's (53%) in the low socioeconomic class. Conclusion: This investigation demonstrates that FMF is more prevalent in a South-Asian population cohort of SLE patients.(AU)

Antecedentes y objetivo: La fiebre mediterránea familiar (FMF) y el lupus eritematoso sistémico (LES) son enfermedades autoinflamatorias autosómicas recesivas, desencadenadas por mutaciones genéticas asociadas a la FMF y autoantígenos. La literatura sobre la coexistencia de estos dos trastornos se limita a informes de casos y su correlación se considera rara. Investigamos la proporción de FMF entre pacientes con LES en comparación con una cohorte de adultos sanos en el sudeste asiático. Métodos: Para este estudio observacional se recolectaron datos de nuestra base de datos institucional para los pacientes diagnosticados con LES. El grupo control se seleccionó al azar de la base de datos y se emparejaron por edad y sexo para LES. Se consideró la proporción global de FMF entre pacientes con y sin LES. Se utilizaron la prueba t de Student, χ2 y ANOVA para el análisis univariado. Resultados: La población de estudio incluyó a 3.623 pacientes con LES y 14.492 controles. En el grupo de LES, hubo una proporción significativamente mayor de pacientes con FMF en comparación con el grupo sin LES (1,29 vs. 0,79%, respectivamente; p=0,015). LES prevaleció en la región pastún (50%) en el grupo socioeconómico medio, mientras que FMF fue dominante en Punyab y Sind (53%) en la clase socioeconómica baja. Conclusión: Esta investigación demuestra que la FMF es más frecuente en una cohorte de pacientes con LES del sudeste asiático.(AU)

Anti-Hu-Associated Encephalomyelitis as a Presentation of Primary Extrapulmonary Small Cell Carcinoma of the Small Bowel: A Case Report.

Small cell carcinoma (SCC) is a neuroendocrine tumor (NET) commonly found in the lung, known for rapid proliferation and early metastasis. Extrapulmonary small cell carcinomas (ESCC) are rare, with GI tract carcinomas exceedingly so. Due to the lack of clinical data on the treatment of ESCC, the standard regimen is the same as the SCC of the lung. Documented accounts of paraneoplastic encephalomyelitis associated with NETs are also uncommon. We present a patient who suffered from neurologic deficits before being diagnosed with paraneoplastic encephalomyelitis from a duodenal ESCC. The patient presented with ear pain and hematemesis. New symptoms arose after the resolution of initial symptoms, including shortness of breath and numbness. Autoimmune workup was positive for anti-Hu antibodies. A position emission tomography (PET) scan showed increased uptake in the duodenal region. Biopsy results from a duodenal ulcer revealed poorly differentiated neuroendocrine carcinoma with positive synaptophysin and strong positivity of Ki-67, consistent with ESCC. Numerous treatments, including platinum-based chemotherapy, yielded no neurologic improvement for the patient. This case details an atypical presentation of ESCC, which should be considered in patients suspected of paraneoplastic encephalomyelitis.

Residual attention based uncertainty-guided mean teacher model for semi-supervised breast masses segmentation in 2D ultrasonography.

Breast tumor is the second deadliest disease among women around the world. Earlier tumor diagnosis is extremely important for improving the survival rate. Recent deep-learning techniques proved helpful in the timely diagnosis of various tumors. However, in the case of breast tumors, the characteristics of the tumors, i.e., low visual contrast, unclear boundary, and diversity in shape and size of breast lesions, make it more challenging to design a highly efficient detection system. Additionally, the scarcity of publicly available labeled data is also a major hurdle in the development of highly accurate and robust deep-learning models for breast tumor detection. To overcome these issues, we propose residual-attention-based uncertainty-guided mean teacher framework which incorporates the residual and attention blocks. The residual for optimizing the deep network by enabling the flow of high-level features and attention modules improves the focus of the model by optimizing its weights during the learning process. We further explore the potential of utilizing unlabeled data during the training process by employing the semi-supervised learning (SSL) method. Particularly, the uncertainty-guided mean-teacher student architecture is exploited to demonstrate the potential of incorporating the unlabeled samples during the training of residual attention U-Net model. The proposed SSL framework has been rigorously evaluated on two publicly available labeled datasets, i.e., BUSI and UDIAT datasets. The quantitative as well as qualitative results demonstrate that the proposed framework achieved competitive performance with respect to the previous state-of-the-art techniques and outperform the existing breast ultrasound masses segmentation techniques. Most importantly, the study demonstrates the potential of incorporating the additional unlabeled data for improving the performance of breast tumor segmentation.

Helical Milling of CFRP/Ti6Al4V Stacks Using Nano Fluid Based Minimum Quantity Lubrication (NF-MQL): Investigations on Process Performance and Hole Integrity.

The structural components in the aeronautical industry require CFRP/Ti6Al4V stacks to be processed together, which results in poor hole integrity due to the thermal properties of the materials and challenges related to processability. These challenges include quality variation of the machined holes because of the limitations in process properties. Therefore, a novel solution through helical milling is investigated in the study using nano fluid based minimum quantity lubrication (NF-MQL). The analysis of variance shows, for Ti6Al4V, eccentricity (PCR = 28.56%), spindle speed (Ti) (PCR = 42.84%), and tangential feed (PCR = 8.61%), and for CFRP, tangential feed (PCR = 40.16%), spindle speed (PCR = 28.75%), and eccentricity (PCR = 8.41%) are the most significant parameters for diametric error. Further on, the rise in the circularity error is observed because of prolonged tool engagement at a higher value of tangential feed. Moreover, the surface roughness of Ti was reduced with an increasing percentage of MoS2 in the lubricant. The spindle speed (37.37%) and lubricant (45.76%) have a potential influence on the processing temperature, as evident in the analysis of variance. Similarly, spindle speed Ti (61.16%), tangential feed (23.37%), and lubrication (11.32%) controlled flank wear, which is critical to tool life. Moreover, the concentration of MoS2 decreased edge wear from ~105 µm (0.5% concentration) to ~70 µm (1% concentration). Thorough analyses on process performance in terms of hole accuracy, surface roughness, processing temperature, and tool wear are carried out based on the physical science of the process for cleaner production. The NF-MQL has significantly improved process performance and hole integrity.

Studying green financing with economic development in BRI countries perspective: does public-private investment matter?

The study aims to test and inquire about the nexus between green financing and economic development in the belt-and-road initiative (BRI) member countries with the role of public-private investments. The study used the generalized method of moments (GMM) assessment approach from 1990Q1 to 2018Q4 to evaluate research topicality. The study's findings have uncovered some hidden insights and presented new insights. In line with this, the study's findings highlighted that an accurate role of green financing is significant in economic development in BRI economies throughout the research period. The results demonstrate that the GMM method of investigation has distinct validity in establishing the overall significance of results about the role of public-private investments in BRI nations. However, the study results about public-private investments showed a varying impact on nations with high GDP per capita. Moreover, the findings from this research indicate that the development of renewable energy sources is also widely aided by applying environmental regulations. At the same time, factors including population size, technological innovation, industrial structure, and foreign direct investment (FDI) significantly control the relationship between green financing, public-private investments, and economic development. On these findings, the study also recommended practical directions for stakeholders.

A Novel Flushing Mechanism to Minimize Roughness and Dimensional Errors during Wire Electric Discharge Machining of Complex Profiles on Inconel 718.

One of the sustainability goals in the aeronautical industry includes developing cost-effective, high-performance engine components possessing complex curved geometries with excellent dimensional precision and surface quality. In this regard, several developments in wire electric discharge machining have been reported, but the influence of flushing attributes is not thoroughly investigated and is thus studied herein. The influence of four process variables, namely servo voltage, flushing pressure, nozzle diameter, and nozzle-workpiece distance, were analyzed on Inconel 718 in relation to geometrical errors (angular and radial deviations), spark gap formation, and arithmetic roughness. In this regard, thorough statistical and microscopical analyses are employed with mono- and multi-objective process optimization. The grey relational analysis affirms the reduction in the process's limitations, validated through confirmatory experimentation results as 0.109 mm spark gap, 0.956% angular deviation, 3.49% radial deviation, and 2.2 µm surface roughness. The novel flushing mechanism improved the spark gap by 1.92%, reducing angular and radial deviations by 8.24% and 29.11%, respectively.

Enhancement of Magnetic and Dielectric Properties of Ni0.25Cu0.25Zn0.50Fe2O4 Magnetic Nanoparticles through Non-Thermal Microwave Plasma Treatment for High-Frequency and Energy Storage Applications.

Spinel ferrites are widely investigated for their widespread applications in high-frequency and energy storage devices. This work focuses on enhancing the magnetic and dielectric properties of Ni0.25Cu0.25Zn0.50 ferrite series through non-thermal microwave plasma exposure under low-pressure conditions. A series of Ni0.25Cu0.25Zn0.50 ferrites was produced using a facile sol-gel auto-ignition approach. The post-synthesis plasma treatment was given in a low-pressure chamber by sustaining oxygen plasma with a microwave source. The structural formation of control and plasma-modified ferrites was investigated through X-ray diffraction analysis, which confirmed the formation of the fcc cubical structure of all samples. The plasma treatment did not affect crystallize size but significantly altered the surface porosity. The surface porosity increased after plasma treatment and average crystallite size was measured as about ~49.13 nm. Morphological studies confirmed changes in surface morphology and reduction in particle size on plasma exposure. The saturation magnetization of plasma-exposed ferrites was roughly 65% higher than the control. The saturation magnetization, remnant magnetization, and coercivity of plasma-exposed ferrites were calculated as 74.46 emu/g, 26.35 emu/g, and 1040 Oe, respectively. Dielectric characteristics revealed a better response of plasma-exposed ferrites to electromagnetic waves than control. These findings suggest that the plasma-exposed ferrites are good candidates for constructing high-frequency devices.

How External Environment and Altruistic Traits Drive Eco-Centric Entrepreneurial Intention Among Youth in the Post-COVID-19 Era?

Green entrepreneurship is a recent business phenomenon that is related to ecological issues. In the time of COVID-19, every business entity is looking for a unique way to be more resilient and noticeable. In this regard, green entrepreneurs hold the potential to manage scarce resources, fulfill social responsibility, and put forward the solution to environmental degradation in the new normal of the post-COVID-19 era. The current venture investigated the opportunity recognition and readiness behavior to execute green entrepreneurship intentions during the post-COVID-19 situation (specifically by underlining the role of market orientation). The present model examined the institutional theory. It put forward the opportunity recognition behavior in the post-COVID-19 era, which can improve youth readiness to opt for green entrepreneurship. The study collected data from 196 university graduate students via online sources by using purposive sampling. The study found that the proposed hypothesis has been proved significant while defining green entrepreneurial intentions. Particularly, the study showed that warm glow was the strongest factor to predict green entrepreneur intention. Moreover, this study can contribute to employing the institutional theory as a novel aspect in the academic sphere.

The helix of CO2, household income, and oil pricing under the assumption of Keynesian consumption function: A policy-mix scenario of oil-importing South Asia for SDGs-2030.

The purpose of this study is to explore energy prices and their impact on household consumption under the condition of Keynesian consumption theory in South Asian countries over the 1995-2020 periods. By employing the panel ordinary least square model estimation technique, the study attempted to find the relationship between household income and consumption under the theory of Keynesian consumption function. Furthermore, we investigated the relationship between household consumption and environmental sustainability, policy mix variables, and energy pricing. First of all, this study confirms the existence of Keynesian consumption theory in these economies of South Asia. Furthermore, energy pricing, environmental sustainability, and inflation rate are the factors that inducing toward high household consumption in South Asia. Considering the policy mix factors, inflation rate contribution positively while tax rate inducing this consumer for low household consumption. Based on the empirical analysis, this study suggested some parameters to these Asian economies particularly and other similar economies generally.