Search Results (65)

Background/Objectives: Treatment of knee osteoarthritis (OA) with autologous stem cells from microfragmented adipose tissue (MFAT) has shown promising but varying results. Multiple stem cell types, including CD34⁺, CD146⁺, and CD271⁺ stem cells, have been identified within MFAT. Patient-specific heterogeneity in stem cell populations and the content of highly potent cells may be determining factors for a successful treatment outcome. The current study aimed to identify the most promising stem cell type in MFAT to treat OA, focusing on their chondrogenic and osteogenic differentiation performance. Methods: CD34⁺, CD146⁺, and CD271⁺ stem cells from the MFAT of eight patients with knee OA were separated using magnetic-activated cell sorting (MACS) and analyzed as subtypes. Unsorted cells were used as a control. Chondrogenic and osteogenic in vitro differentiation were assessed through Safranin-O and H&E staining, pellet size, and qPCR for chondrogenesis, as well as Alizarin Red S staining and qPCR for osteogenesis. Results: CD34⁺, CD146⁺, and CD271⁺ stem cells were doubled using MACS. All subtypes were able to undergo osteogenic differentiation with Alizarin Red S staining, revealing a significant increase in calcium deposits of induced cells compared to non-induced controls. CD146⁺ stem cells showed higher calcium deposition compared to CD34⁺, CD271⁺, and unsorted stem cells. All cell types could form chondrogenic pellets. CD271⁺ stem cells produced more proteoglycans, as shown by Safranin-O staining, than CD34⁺ and CD146⁺ stem cells, but not more than the unsorted stem cells. After differentiation induction, all cell types showed an upregulation of most chondrogenic and osteogenic biomarkers. Conclusions: CD146⁺ stem cells showed the highest osteogenic differentiation performance for calcium deposition, while CD271⁺ stem cells showed the greatest chondrogenic differentiation performance for proteoglycan formation. The prevalence of these stem cell types may play a critical role in the clinical effectiveness when treating OA. Full article

Cadmium (Cd), as the most prevalent heavy metal contaminant poses serious risks to plants, humans, and the environment. The ubiquity of this toxic metal is continuously increasing due to the rapid discharge of industrial and mining effluents and the excessive use of chemical fertilizers. Nanoparticles (NPs) have emerged as a novel strategy to alleviate Cd toxicity. Zinc oxide nanoparticles (ZnO-NPs) have become the most important NPs used to mitigate the toxicity of abiotic stresses and improve crop productivity. The plants quickly absorb Cd, which subsequently disrupts plant physiological and biochemical processes and increases the production of reactive oxygen species (ROS), which causes the oxidation of cellular structures and significant growth losses. Besides this, Cd toxicity also disrupts leaf osmotic pressure, nutrient uptake, membrane stability, chlorophyll synthesis, and enzyme activities, leading to a serious reduction in growth and biomass productivity. Though plants possess an excellent defense mechanism to counteract Cd toxicity, this is not enough to counter higher concentrations of Cd toxicity. Applying Zn-NPs has proven to have significant potential in mitigating the toxic effects of Cd. ZnO-NPs improve chlorophyll synthesis, photosynthetic efficiency, membrane stability, nutrient uptake, and gene expression, which can help to counter toxic effects of Cd stress. Additionally, ZnO-NPs also help to reduce Cd absorption and accumulation in plants, and the complex relationship between ZnO-NPs, osmolytes, hormones, and secondary metabolites plays an important role in Cd tolerance. Thus, this review concentrates on exploring the diverse mechanisms by which ZnO nanoparticles can alleviate Cd toxicity in plants. In the end, this review has identified various research gaps that need addressing to ensure the promising future of ZnO-NPs in mitigating Cd toxicity. The findings of this review contribute to gaining a deeper understanding of the role of ZnO-NPs in combating Cd toxicity to promote safer and sustainable crop production by remediating Cd-polluted soils. This also allows for the development of eco-friendly approaches to remediate Cd-polluted soils to improve soil fertility and environmental quality. Full article

In many electronic applications, the dielectric and structural properties of reinforced composites are vital. In this research work, the influence of fiber proportion on the properties of a silica fiber/epoxy (SFE) composite was investigated. The structure, morphology, dielectric constant and loss factor, mechanical properties, and thermal stability were determined. The increase of wt.% of silica fiber (SiO_{2 (f)}) x = 30 to 90, reduced the dielectric constant (εr) and dielectric loss (δ) of the SFE composite from their original values to 18.9% and 48.5%, lowering local charge displacement towards the applied electric field. The SFE composite showed higher mechanical properties with the increase in SiO_{2 (f)}, x = 30 to 80, the tensile strength (UTS) was raised from 91.6 MPa to 155.7 MPa, the compression strength (UCS) was increased from 261.1 MPa to 409.6 MPa and the flexural strength was enhanced from 192.3 MPa to 311.9 MPa. Upon further addition of SiO_{2 (f)} to the composite, i.e., x = 90, the mechanical properties were reduced a little, but the dielectric properties were not changed. Increasing SiO_{2 (f)} improved the thermal stability as weight loss was found to be 69% (x = 30) and 24% (x = 90), and average moisture absorption was found to be 1.1 to 1.8%. A silica fiber/epoxy composite, for microelectronics, can be made from a low-cost fiber, and its dielectric properties as well as its mechanical and thermal stability can be tuned or improved by varying fiber fractions. Full article

Natural resource rent (NRR) can be a blessing for the economic growth of resource-rich economies but may cause environmental problems. The present research explores the effects of NRR, economic growth, trade openness (TO), and foreign direct investment (FDI) on the carbon intensities of different sources of carbon emissions in Saudi Arabia from 1968 to 2021. The environmental Kuznets curve (EKC) is substantiated in the relationship between economic growth and the carbon intensities of gas emissions and cement emissions in the long run. The EKC is also validated in models of the carbon intensities of oil emissions, gas flaring emissions, and aggregated CO₂ emissions in the short run. TO reduces the carbon intensities of oil emissions, gas emissions, and cement emissions in the long run. FDI mitigates the carbon intensity of gas flaring emissions but increases the carbon intensity of cement emissions. NRR increases the carbon intensities of all investigated sources of emissions in a linear analysis. In a nonlinear analysis, increasing NRR increases and decreasing NRR reduces the carbon intensities of all sources of emissions except aggregated CO₂ emissions. In the short-run results, TO decreases the carbon intensity of gas flaring emissions and increases the carbon intensities of gas emissions and cement emissions. FDI decreases the carbon intensities of all sources of emissions. In a linear analysis, NRR reduces the carbon intensities of oil emissions and cement emissions and increases the carbon intensities of gas emissions and gas flaring emissions. In a nonlinear analysis, increasing NRR reduces the carbon intensity of cement emissions and increases the carbon intensities of gas emissions and gas flaring emissions. Moreover, decreasing NRR reduces the carbon intensities of gas emissions, gas flaring emissions, and aggregated CO₂ emissions and increases the carbon intensities of oil emissions and cement emissions. The effect of NRR is asymmetrical in models of the carbon intensities of aggregated CO₂ emissions, oil emissions, and gas flaring emissions and symmetrical in models of the carbon intensities of gas emissions and cement emissions. Full article

Artificial intelligence has many applications in various industries, including agriculture. It can help overcome challenges by providing efficient solutions, especially in the early stages of development. When working with tree leaves to identify the type of disease, diseases often show up through changes in leaf color. Therefore, it is crucial to improve the color brightness before using them in intelligent agricultural systems. Color improvement should achieve a balance where no new colors appear, as this could interfere with accurate identification and diagnosis of the disease. This is considered one of the challenges in this field. This work proposes an effective model for olive disease diagnosis, consisting of five modules: image enhancement, feature extraction, clustering, and deep neural network. In image enhancement, noise reduction, balanced colors, and CLAHE are applied to LAB color space channels to improve image quality and visual stimulus. In feature extraction, raw images of olive leaves are processed through triple convolutional layers, max pooling operations, and flattening in the CNN convolutional phase. The classification process starts by dividing the data into clusters based on density, followed by the use of a deep neural network. The proposed model was tested on over 3200 olive leaf images and compared with two deep learning algorithms (VGG16 and Alexnet). The results of accuracy and loss rate show that the proposed model achieves (98%, 0.193), while VGG16 and Alexnet reach (96%, 0.432) and (95%, 1.74), respectively. The proposed model demonstrates a robust and effective approach for olive disease diagnosis that combines image enhancement techniques and deep learning-based classification to achieve accurate and reliable results. Full article

Patents support technological innovations in any economy and would also support a clean environment. We investigate the effects of economic growth, patents, industrialization, and urbanization on CO₂ emissions in 17 Middle East and North Africa (MENA) economies by applying spatial econometrics. We substantiate the Environment Kuznets Curve (EKC) in the domestic economies and the whole MENA region as per direct and total estimates. Moreover, urbanization increases CO₂ emissions in local economies and reduces neighboring nations’ emissions. The total effect of urbanization is found to be insignificant. Industrial value added increases CO₂ emissions in domestic and neighboring countries, as well as in the whole MENA region. Patents increase CO₂ emissions in domestic economies. However, patents reduce CO₂ emissions in neighboring countries and the MENA region. Thus, patents have a pleasant effect on the environment in the whole MENA region. It is suggested that the MENA economies focus more on patents to reduce CO₂ emissions. Moreover, urbanization and the industrial sector should be checked to protect the environment. Full article

Heavy metal pollution in marine ecosystems is an escalating environmental concern, largely driven by anthropogenic activities, and poses potential threats to ecological health and human well-being. This study embarked on a comprehensive investigation into the concentrations of heavy metals in sediment samples and evaluated their potential ecological and health risks with a focus on Eastern St. Martin’s Island (SMI), Bangladesh. Sediment samples were meticulously collected from 12 distinct sites around the island, and the concentrations of heavy metals, including Mn, Fe, Ni, Zn, Cr, Pb, and Cu, were quantified utilizing atomic absorption spectrometry (AAS). The results revealed that the average concentrations of the metals, in descending order, were Mn (269.5 ± 33.0 mg/kg), Fe (143.8 ± 21.7 mg/kg), Ni (29.6 ± 44.0 mg/kg), Zn (27.2 ± 4.34 mg/kg), Cr (8.09 ± 1.67 mg/kg), Pb (5.88 ± 0.45 mg/kg), and Cu (3.76 ± 0.60 mg/kg). Intriguingly, the concentrations of all the measured metals were found to be within permissible limits and comparatively lower than those documented in various national and international contexts. The ecological risk assessment, based on multiple sediment quality indices such as the geoaccumulation index, contamination factor, and pollution load index, indicated a moderate risk to the aquatic ecosystem but no significant adverse impact on sediment quality. Additionally, the human health risk assessment, encompassing non-carcinogenic hazard indices for different age groups, was considerably below the threshold, signifying no immediate health risk. The total carcinogenic risk was also found to be below acceptable levels. These findings underscore the current state of heavy metal pollution in Eastern St. Martin’s Island, providing valuable insights for environmental monitoring and management. While the immediate risks were not alarming, the study highlights the imperative need for sustained monitoring and the implementation of rigorous regulations to curb heavy metal pollution in order to safeguard both ecological and human health. This warrants the development of policies that are both adaptive and preemptive to ensure the sustainable utilization and conservation of marine resources. Full article

China is the largest total pollution emitter country on the globe and a vast literature has investigated the environmental Kuznets curve (EKC) hypothesis in China. Thus, we aim to review empirical studies on the testing of the EKC hypothesis using different pollution proxies and area samples in China. The EKC hypothesis can be validated by establishing an inverted U-shaped or an N-shaped relationship between pollution and economic growth. In this review of the Chinese literature, the validity of the EKC hypothesis is found more often than its absence. In comparison, a higher proportion of the studies validated the EKC hypothesis using global pollution proxies compared with local pollution proxies. Moreover, a greater percentage of the studies substantiated the EKC hypothesis using Chinese provincial and city-level data compared with aggregate national data. To validate these findings, we applied logistic regression, and the chance of the validity of the EKC hypothesis was found to be 5.08 times higher than the absence of the EKC if a study used a global pollution proxy. Moreover, the chance of the existence of the EKC hypothesis was found to be 4.46 times higher than the nonexistence of the EKC if a study used Chinese provincial, city, sectoral, or industrial data. Full article

Background: Breast cancer is rare in men. This population-based study aimed to determine outcomes of male breast cancer in relation to residence and other variables. Methods: In this retrospective cohort study, men diagnosed with breast cancer in Saskatchewan during 2000–2019 were evaluated. Cox proportional multivariable regression analyses were performed to determine the correlation between survival and clinicopathological and contextual factors. Results: One hundred-eight eligible patients with a median age of 69 years were identified. Of them, 16% had WHO performance status ≥ 2 and 61% were rural residents. The stage at diagnosis was as follows: stage 0, 7%; I, 31%; II, 42%; III, 11%; IV, 8%. Ninety-eight percent had hormone receptor-positive breast cancer. The median disease-free survival of urban patients was 97 (95% CI: 50–143) vs. 64 (46–82) months of rural patients (p = 0.29). The median OS of urban patients was 127 (94–159) vs. 93 (32–153) months for rural patients (p = 0.27). On multivariable analysis, performance status ≥ 2, hazard ratio (HR) 2.82 (1.14–6.94), lack of adjuvant systemic therapy, HR 2.47 (1.03–5.92), and node-positive disease, HR 2.32 (1.22–4.40) were significantly correlated with inferior disease-free survival in early-stage invasive breast cancer. Whereas stage IV disease, HR 7.8 (3.1–19.5), performance status ≥ 2, HR 3.25 (1.57–6.71), and age ≥ 65 years, HR 2.37 (1.13–5.0) were correlated with inferior overall survival in all stages. Conclusions: Although residence was not significantly correlated with outcomes, rural men had numerically inferior survival. Poor performance status, node-positive disease, and lack of adjuvant systemic therapy were correlated with inferior disease-free survival. Full article

For curing of fiber-reinforced epoxy composites, an alternative to thermal heating is the use of microwave energy, which cures quickly and consumes less energy. Employing thermal curing (TC) and microwave (MC) curing methods, we present a comparative study on the functional characteristics of fiber-reinforced composite for microelectronics. The composite prepregs, prepared from commercial silica fiber fabric/epoxy resin, were separately cured via thermal and microwave energy under curing conditions (temperature/time). The dielectric, structural, morphological, thermal, and mechanical properties of composite materials were investigated. Microwave cured composite showed a 1% lower dielectric constant, 21.5% lower dielectric loss factor, and 2.6% lower weight loss, than thermally cured one. Furthermore, the dynamic mechanical analysis (DMA) revealed a 20% increase in the storage and loss modulus along with a 15.5% increase in the glass transition temperature (Tg) of microwave-cured compared to thermally cured composite. The fourier transformation infrared spectroscopy (FTIR) showed similar spectra of both the composites; however, the microwave-cured composite exhibited higher tensile (15.4%), and compression strength (4.3%) than the thermally cured composite. These results illustrate that microwave-cured silica-fiber-reinforced composite exhibit superior electrical performance, thermal stability, and mechanical properties compared to thermally cured silica fiber/epoxy composite in a shorter time and the expense of less energy. Full article

For environment-friendly, safe and nonpersistent chemical control of a significant polyphagous insect pest, Helicoverpa armigera, discovery of growth-regulating xenobiotics can offer a sustainable alternative to conventional insecticides. For this purpose, chemically synthesized compounds to inhibit sterol carrier protein (SCP-2) function using in silico and in vivo assays were evaluated to estimate their impact on the survivals and lifetable indices of H. armigera. From nine chemically synthesized compounds, OA-02, OA-06 and OA-09 were selected for this study based on binding poses mimicking cholesterol, a natural substrate of sterol carrier protein and molecular dynamics simulations. In vivo bioassays revealed that all compounds significantly reduced the larval and pupal weight accumulations and stadia lengths. Subsequently, the pupal periods were prolonged upon treatment with higher doses of the selected compounds. Moreover, OA-09 significantly reduced pupation and adult emergence rates as well as the fertility of female moths; however, fecundity remained unaffected, in general. The life table parameters of H. armigera were significantly reduced when treated with OA-09 at higher doses. The population treated with 450 μM of OA-09 had the least net reproductive rates (Ro) and gross reproductive rate (GRR) compared to the control population. The same compound resulted in a declining survival during the early stages of development coupled with reduced larval and pupal durations, and fertility. These results have a significant implication for developing an effective and sustainable chemical treatment against H. armigera infestation. Full article

Emergency response systems require precise and accurate information about an incident to respond accordingly. An eyewitness report is one of the sources of such information. The research community has proposed diverse techniques to identify eyewitness messages from social media platforms. In our previous work, we created grammar rules by exploiting the language structure, linguistics, and word relations to automatically extract feature words to classify eyewitness messages for different disaster types. Our previous work adopted a manual classification technique and secured the maximum F-Score of 0.81, far less than the static dictionary-based approach with an F-Score of 0.92. In this work, we enhanced our work by adding more features and fine-tuning the Linguistic Rules to identify feature words related to Twitter Eyewitness messages for Disaster events, named as LR-TED approach. We used linguistic characteristics and labeled datasets to train several machine learning and deep learning classifiers for classifying eyewitness messages and secured a maximum F-score of 0.93. The proposed LR-TED can process millions of tweets in real-time and is scalable to diverse events and unseen content. In contrast, the static dictionary-based approaches require domain experts to create dictionaries of related words for all the identified features and disaster types. Additionally, LR-TED can be evaluated on different social media platforms to identify eyewitness reports for various disaster types in the future. Full article

Smart grid plays a vital role in energy management systems. It helps to mitigate the demand side management of electricity by managing the microgrid. In the modern era, the concept of hybrid microgrids emerged which helps the smart grid management of electricity. Additionally, the Internet of Things (IoT) technology is used to integrate the hybrid microgrid. Thus, various policies and topologies are employed to perform the task meticulously. Pakistan being an energy deficient country has recently introduced some new policies such as Energy Wheeling Policy (EWP), Energy Import Policy (EIP), and Net Metering/Distributed Generation Policy (NMP) to manage the electricity demand effectively. In addition, the Energy Efficiency and Conservation Act (EECA) has also been introduced. In this paper, we present the overview and impact of these policies in the context of the local energy market and modern information and communication mechanisms proposed for smart grids. These new policies primarily focus on energy demand–supply for various types of consumers such as the demand for bulk energy for industrial ventures and the distributed production by consumers. The EWP deals with obtaining power from remote areas within the country to ease the energy situation in populated load centers and the EIP highlights energy import guidelines from foreign countries. The NMP deals with the integration of renewable energy resources and EECA is more focused on the measures and standardization for energy efficiency and conservation. The benefits and challenges related to EWP, NMP, and EIP have also been discussed concerning the present energy crisis in Pakistan. The generalized lessons learned and comparison of a few aspects of these policies with some other countries are also presented. Full article

Sensor networks’ network connectivity must be restored as part of any solution. This strategy’s goal was to come up with a concept. Many approaches to restoring connections after a network outage can be implemented by relying on these factors: low mobility, minimal field coverage drop and a reduction in the overall number of messages sent. All of the following objectives can be met with this solution. Based on detailed simulations and a comparison with the PACR and SNR methods, it can be concluded that the proposed methodology is effective. The sensor nodes’ batteries slowly depleted over time due to power restrictions. Network nodes fail as a result; data transmission stops, and the network’s lifespan is shortened because of it. As a result, one of the most difficult challenges in wireless sensor networks is to minimize energy consumption while also maximizing the network’s lifespan. In this study, the network lifetime of a wireless sensor network is extended through the use of special jumping nodes. Instead of using wheels or other means of transportation, these nodes leap into the network, and they are used to recharge other nodes in the network that are dying upon request. Results show that the proposed technique works more efficiently with figures of 83.76%, 84.84% and 87.3% for SNR, PACR and the proposed technique, respectively, with 250 nodes. This significantly increases the network’s lifetime. The simulation results suggest that the proposed technique outperforms other strategies that have been used in the literature. Full article