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Abstract The significance of democratic commitment is highlighted on a global scale by the importance placed on offering a multitude of chances to establish enduring ecosystems and foster economic progress. Social finance, capital formation, and green finance have recently received a lot of attention from this perspective. Past studies mainly examined social finance and green finance in isolation. When investigating the individual impact of these important variables on economic growth in the occurrence of important events like the COVID-19 pandemic and other environmental pollution issues, most studies have mainly focused on developed countries, ultimately ignoring the different dynamics in emerging economies. The purpose of the research is to investigate the joint interaction between social finance (Social Impact Bonds), capital formation, and green finance (green loans, green investments, and green security) on economic development in BRICS economies. This research employed a longitudinal dataset from 2013 to 2023. The generalized method of moments (GMM) and fixed effect model (FEM) were employed in this research as statistical techniques for estimating parameters. The findings of this study revealed a positive joint relationship between green finance, capital formation, and social financing on economic development of BRICS economies. Therefore, measures that jointly promote social and green finance amidst environmental challenges or economic risks could present opportunities to mitigate the vulnerability of a country's economic prosperity. This research will surely guide policymakers in developing economies on how to give considerable attention to sustainable aspects while formulating policies and strategies aimed at economic development.

The microbial biosynthesis of proteins, primary metabolites, and chemicals is gaining extraordinary momentum and is presently viewed as an advancing approach in the industrial research sector. Increased threats to the environment and the possibility of declining petroleum assets have switched the spotlight to microbial cell factories (MCFs). Aside from possessing various advantages over chemical synthesis, such as less toxicity, cheaper methodologies, and an environmentally benign nature, microbes can be cultivated in fermenters, resulting in an effective bioprocessing approach in terms of industrial relevance. As the overwhelming majority of biodiversity is microbial, this review first highlights the microbial biodiversity of industrially vital microorganisms. Then, the paper delineates the production pathways for generating valuable bioproducts via microbial workhorses. Many host cells synthesize bio-compounds as a part of their natural mechanism; however, several techniques have also been developed to attain the desired end product from non-native microbes with selected properties. The microbial biosynthetic pathways can be categorized as native-existing pathways, heterologous pathways, and artificial de novo pathways. Systems metabolic engineering, which integrates metabolic engineering with evolutionary engineering, synthetic biology, and systems biology, has further revolutionized the field of engineering robust phenotypes. The employment of these strategies improves the performance of the strain, eventually achieving high titer and productivity rates of bio-chemicals. Modern trends and tools for exploiting native pathways and designing non-native-created pathways are also briefly discussed in this paper. Finally, the review discusses the use of microbial workhorses for producing a myriad of materials and chemicals, including carboxylic acids, amino acids, plant natural products (PNPs), carotenoids, flavors, and fragrances, unveiling the efficacy of utilizing microbial species to generate sustainable bio-based products.

The persistent expansion in world energy and synthetic compounds requires the improvement of renewable alternatives in contrast to non-sustainable energy wellsprings. Lignocellulose is an encouraging feedstock to be utilized in biorefineries for its conversion into value-added products, including biomaterials, biofuels and several bio-based synthetic compounds. Aside from all categories, biofuel, particularly bioethanol is the most substantial fuel derived from lignocellulosic biomass and can be obtained through microbial fermentation. Generally, extreme settings are required for lignocellulosic pretreatment which results in the formation of inhibitors during biomassdegradation. Occasionally, lignin polymers also act as inhibitors and are left untreated during the pretreatment, engendering inefficient hydrolysis. The valorization of lignocellulosic biomass by laccases can be viewed as a fundamental trend for improving bioethanol production. However, one of the main obstacles for developing commercially viable biofuel industries is the cost of enzymes, which can be resolved by utilizing laccases derived from microbial sources. Microbial laccases have been considered an exceptionally integral asset for delignification and detoxification of pretreated LCB, which amplify the resultant fermentation and saccharification processes. This review provides a summary of microbial laccases and their role in valorizing LCB to bioethanol, compelling enthralling applications in bio-refining industries all across the globe.

Abstract Bioinspired synaptic devices have shown great potential in artificial intelligence and neuromorphic electronics. Low energy consumption, multi‐modal sensing and recording, and multifunctional integration are critical aspects limiting their applications. Recently, a new synaptic device architecture, the ion‐gating vertical transistor (IGVT), has been successfully realized and timely applied to perform brain‐like perception, such as artificial vision, touch, taste, and hearing. In this short time, IGVTs have already achieved faster data processing speeds and more promising memory capabilities than many conventional neuromorphic devices, even while operating at lower voltages and consuming less power. This work focuses on the cutting‐edge progress of IGVT technology, from outstanding fabrication strategies to the design and realization of low‐voltage multi‐sensing IGVTs for artificial‐synapse applications. The fundamental concepts of artificial synaptic IGVTs, such as signal processing, transduction, plasticity, and multi‐stimulus perception are discussed comprehensively. The contribution draws special attention to the development and optimization of multi‐modal flexible sensor technologies and presents a roadmap for future high‐end theoretical and experimental advancements in neuromorphic research that are mostly achievable by the synaptic IGVTs.

In the context of biomedical data, an anomaly could refer to a rare or new type of disease, an aberration from normal behavior, or an unexpected observation requiring immediate attention. The detection of anomalies in biomedical data has a direct impact on the health and safety of individuals. However, anomalous events are rare, diverse, and infrequent. Often, the collection of anomalous data may involve significant loss of human life and healthcare costs. Therefore, traditional supervised machine and deep learning algorithms may not be directly applicable to such problems. Biomedical data are often collected in the form of images, electronic health records, and time series. Typically, an autoencoder (AE) or its corresponding variant is trained on normal data, and an anomaly is identified as a significant deviation from these data based on reconstruction error or other metrics. An Ensemble of AEs (EoAEs) can serve as a robust approach for anomaly detection in biomedical data by combining diverse and accurate views of normal data. An EoAE can provide superior detection to a single encoder; however, its performance can depend on various factors, including the diversity of the created data, the accuracy of the individual AEs, and the combination of their outcomes. Herein, we perform a comprehensive narrative literature review on the use of EoAEs when using different types of biomedical data. Such an ensemble provides a promising approach for anomaly detection in biomedical data, offering the potential for performance improvement by leveraging the strengths of diverse AEs. However, several challenges remain, such as the need for data specification and determination of the optimal number of AEs in the ensemble. By addressing these challenges, researchers can enhance the effectiveness of EoAEs for anomaly detection in various types of biomedical data. Furthermore, through this review, we highlight the significance of evaluating and comparing the performance of an EoAE with that of single AEs by establishing agreed-upon evaluation metrics and investigating normalization techniques for anomaly scores. We conclude the review by presenting challenges and open questions in the field with for future research.

The primary objective of this research is to examine the effects of green knowledge management (GKM) on green technological innovation (GTI) and sustainable performance in construction firms. The study also investigates the role of artificial intelligence (AI) as a moderator of the relationship between GKM and green human capital (GHC). A survey questionnaire was used to obtain data from 309 construction firms in Pakistan, and the AMOS-24 and SPSS PROCESS macro software packages were used to test the hypotheses. The findings revealed that GKM had significant positive impacts on GTI and long-term performance. Aspects of GIC (e.g., green structural capital, green relational capital, and green human capital) were found to be significant mediators of GKM and GTI interactions and correlations between GKM and sustainable performance. Furthermore, the study showed that AI significantly influenced the relationship between GKM and GHC. The study's findings have important theoretical and practical implications for organizations and governments. The study theoretically contributes to the knowledge-based view of the firm by providing empirical evidence of the role of various GIC characteristics as mediators in the interactions between GKM, GTI, and sustainable performance. In practice, the findings suggest that firms can improve GTI and sustainable performance by investing in GKM and GIC.

AbstractIn order to improve the comparability between the geometric information content of vector area objects, this article proposes a method for measuring the geometric information content of area objects based on discrepancy degree of shape points. First, the method selects circles with unique geometric feature as the reference shape for extracting geometric features, and the geometric in-formation carried by each shape point of area objects is represented by the discrepancy degree between the area object and the reference circle at the point position. Second, the proposed method measures the geometric information content of area objects from both local and global perspectives. To avoid the subjectivity of assigning feature weights based on empirical experience, the article uses the relationships between the radii of three reference circles (MIC: Maximum Inscribed Circle, EAC: Equal-area circle, and MCC: Minimum Circumscribed Circle) as adaptive weight parameters for local and global structural geometric information. The amount of geometric information at each shape point is obtained by weighted summation, and the total geometric information content of an area object is the sum of the amount of geometric information of all shape points. To verify the effectiveness and rationality of the proposed method, this article designs a noise simulation dataset for simply building area objects and an empirical ranking dataset for evaluating the measurement performance of the proposed method. The experimental results show that the proposed method achieves a Kendall rank correlation coefficient of 0.88 on the empirical ranking dataset, which is higher than that of the nine existing representative methods. The proposed method is more consistent with human cognition and is highly correlated with the amount and intensity of noise information. Moreover, the proposed method achieves the comparability of geometric information content of area objects and the adaptive determination of geometric feature weights. The proposed method is an effective method for measuring the geometric information quantity of area objects.

Mixed matrix membranes are thought to have the ability to separate gases. The current research investigates the isolation of CO2 from methane (CH4) and nitrogen (N2) using a mixed matrix membrane. Bio-MOF-11 was combined with polyether sulfone to establish a membrane (PES). Experiments were carried out to determine the efficiency of the established membrane. Results showed that the Lewis basic sites present in Bio-MOF-11, which have a higher affinity for CO2, increase the permeability and selectivity of pristine polyether sulfone. At 30% filler loading, CO2 permeability improved from 2.20 to 3.90 Barrer, while CO2/CH4 and CO2/N2 selectivity improved from 9.57 to 11.14 with 30% filler loading. In addition, at 30% filler loading, CO2 solubility drops from 1.57 to 1.20.

The excessive use of fossil has resulted in the drastic exhaustion of natural energy sources, leading to environmental challenges and energy crises. Owing to rising energy demand there is a dire need to shift towards renewable energies from lignocellulosic biomass. The present study assessed the co-production of biohydrogen (H2) and biomethane (CH4) by utilizing a less explored halophyte Atriplexcrassifolia. Various reaction parameters were evaluated for their effect on biohydrogen and biomethane production in batch experiments. One parameter at a time experimental strategy was chosen for production optimization. Hydrogen and methane yields along with their production rates were assessed at different incubation times, temperatures, pH, substrate concentrations, and inoculum sizes in acidogenesis and methanogenesis stages, respectively. In the first stage, maximum cumulative hydrogen production of 66 ± 0.02 mL, with hydrogen yield of 13.2 ± 0.03 mL/g, and hydrogen production rate (HPR) of 1.37 ± 0.05 mL/h was attained when the reaction mixture (5 g Atriplexcrassifolia and 10 mL pretreated sewage sludge) was processed at 37°C and pH 5.5 after 48 h of incubation. While in the second stage, maximum cumulative methane production, i.e., 343 ± 0.12 mL, methane yield (MY) of 8.5 ± 0.07 mL/mL, and methane production rate (MPR) of 0.8 ± 0.05 mL/h was achieved after 18 days of incubation of reaction mixture (40 mL of hydrogenic slurry with 80 mL inoculum) at 45°C and pH 8. Furthermore, a 51% and 24% rise in biohydrogen and biomethane production respectively were recorded when the gases were produced at these optimized reaction conditions. The results ensure halophyte Atriplexcrassifolia as an imperative renewable energy resource and proposed that effective optimization of the process further increased the coproduction of biohydrogen and biomethane.

The scalability of integrated photonics hinges on low-loss chip-scale components, which are important for classical applications and crucial in the quantum domain. An important component is the power splitter, which is an essential building block for interferometric devices. Here, we use inverse design by topology optimization to devise a generic design framework for developing power splitters in any material platform, although we focus the present work on silicon photonics. We report on the design, fabrication, and characterization of silicon power splitters and explore varying domain sizes and wavelength spans around a center wavelength of 1550 nm. This results in a set of power splitters tailored for ridge, suspended, and embedded silicon waveguides with an emphasis on compact size and wide bandwidths. The resulting designs have a footprint of $2\,\mu\textrm{m}\times3\,\mu\textrm{m}$ and exhibit remarkable 0.5 dB bandwidths exceeding 300 nm for the ridge and suspended power splitters and 600 nm for the embedded power splitter. We fabricate the power splitters in suspended silicon circuits and characterize the resulting devices using a cutback method. The experiments confirm the low excess loss, and we measure a 0.5 dB bandwidth of at least 245 nm—limited by the wavelength range of our lasers.

Optoelectronic devices designed with double perovskites are potentially emerging nowadays because of their interesting properties, such as simple and stable crystal structure. Our study theoretically explored the optoelectronic, mechanical, and thermoelectric characteristics of Rb2ScTlX6 (X = Cl, Br, I) based on the density functional theory of the Wein2k code. The thermodynamic and structural stability is determined based on the enthalpy of formation and tolerance factor. The character of the compositions has been checked that either they are ductile or brittle by the elastic constants which are used to measure Passion and Pugh's ratio. For the elaboration of band gaps, Tran-Blaha mBJ potential is used, and the measured values of energy bandgaps are 3.5, 3.0, and 2.4 eV for Rb2ScTlCl6, Rb2ScTlBr6, and Rb2ScTlI6, respectively, using TB-mBJ plus spin-orbital coupling (SOC). These compositions are potentially used in solar cell devices like heterojunction solar cells because their electronic characteristics are controllable. In the energy range 0–12 eV, the compositions under consideration exhibit a single-peaked response, while the replacement of anions from Cl to I caused a shift in optical structures towards lower energies. The significant value of the figure of merits 0.77–0.95 makes them suitable for thermoelectric device applications.

Objective: The study was conducted to investigate the gut content and record morphometric and hematological parameters in the common hoopoe (Upupa epops). Materials and Methods: Twenty samples of healthy birds (10 from each sex) were collected from different locations in Okara District, Punjab, Pakistan, from September 2020 to March 2021. Birds were captured live for blood samples and morphometric and gut analyses. Results: It was revealed that the concentrations of different hematological parameters were as follows: hemoglobin, 20.03g/dl; red blood cells, 3.28 × 106/μl; white blood cells, 326.67 × 103/ μl; hematocrit, 56.47%; MCV, 173.33 FL; MCH, 57.4 pg; MCHC, 57.4 pg; PLT, 8.33/μl; and RDW, 8.33/μl. The percentages of neutrophils, lymphocytes, monocytes, and eosinophils were 84.67%, 11.67%, 2.00%, and 1.67%, respectively. The gut content of the common hoopoe mostly consisted of Coleoptera and Acrididae larvae. However, Lepidoptera, Gryllotalpidae, and sand were also recorded, along with seeds of Salvadora persica. Conclusions: There were no significant differences between male and female U. epops in feeding content, total weight of the gut, or weight of the empty gut. Regarding the morphometric param¬eters, there was a significant difference in both sexes' wingspan, body length, and body weight. Males were significantly heavier than females. [J Adv Vet Anim Res 2022; 9(2.000): 290-294]

Objectives: To study the result of primary repair with ileostomy in cases of typhoid ileal perforation. Study Design: Quasi-experimental study. Place and Duration of Study: Department of Surgery, Jinnah Postgraduate Medical Center, Karachi Pakistan, from May 2019 to Jun 2020 Methodology: Participants who were operated on for typhoid ileal perforation were included, while patients who presented with a hollow viscus perforation other than ileal perforation were excluded from the study. Peritoneal lavage and exploration were performed. All per-operative findings were documented. In addition, the socio-demographic and clinical parameters, including the postoperative complications and morbidity/mortality, were documented. Results: Out of the 73 individuals with ileal perforation included in this research, 52 underwent primary repair, whereas 21 underwent ileostomy. Patients who had undergone ileostomy had a higher morbidity (61.90%) and mortality (19.00%) rate than patients in the Primary Repair Group. In addition, patients in the ileostomy Group had significantly higher rates of wound dehiscence and infection, electrolyte imbalance, and weight loss than patients in the Primary Repair Group. Conclusion: The current study indicates that primary repair is an ideal procedure for typhoid ileal perforation compared to an ileostomy.

Takotsubo Cardiomyopathy is a cardiac syndrome of rare type involving apical akinesis of the left ventricle. Symptoms of acute myocardial infarction, i.e. chest pain, S-T changes, and positive cardiac enzymes, are observed in patients with Takotsubo Cardiomyopathy. Cardiac angiography of Takotsubo Cardiomyopathy patients manifest left ventricular apical ballooning with no remarkable coronary artery stenosis. Mostly the cases are managed on the lines of treatment for Acute Coronary Syndrome. We present a case of Takotsubo Cardiomyopathy in a teenage girl from Karachi, Pakistan. The prevalence of Takotsubo Cardiomyopathy in Pakistan is yet to be mapped due to its rarity. Key Words: Takotsubo Cardiomyopathy, Rare Cardiac Syndrome, Teenage Girl, Karachi, Pakistan.

Bioethanol is believed to be an influential revolutionary gift of biotechnology, owing to its elevating global demand and massive production. Pakistan is home to a rich diversity of halophytic flora, convertible into bounteous volumes of bioethanol. On the other hand, the accessibility to the cellulosic part of biomass is a major bottleneck in the successful application of biorefinery processes. The most common pre-treatment procedures existent include physicochemical and chemical approaches, which are not environmentally benign. To overcome these problems, biological pre-treatment has gained importance but the drawback is the low yield of the extracted monosaccharides. The current research was aimed at exploring the best pre-treatment method for the bioconversion of halophyte Atriplex crassifolia into saccharides using three thermostable cellulases. Atriplex crassifolia was subjected to acid, alkali and microwave pre-treatments, followed by compositional analysis of the pre-treated substrates. Maximum delignification i.e. 56.6% was observed in the substrate pre-treated using 3% HCl. Enzymatic saccharification using thermostable cellulases also validated the results where the highest saccharification yield i.e. 39.5% was observed for the sample pre-treated using same. Maximum enzymatic hydrolysis of 52.7% was obtained for 0.40 g of the pre-treated halophyte Atriplex crassifolia where Endo-1,4-β-glucanase (300U), Exo-1,4-β-glucanase (400U) and β-1,4-glucosidase (1000U) were simultaneously added and incubated for 6 h at 75°C. The reducing sugar slurry obtained after optimization of saccharification was utilized as glucose in submerged fermentation for bioethanol production. The fermentation medium was inoculated with Saccharomyces cerevisiae, incubated at 30°C and 180 rpm for 96 h. Ethanol production was estimated using potassium dichromate method. Maximum production of bioethanol i.e. 16.33% was noted at 72 h. It can be concluded from the study that Atriplex crassifolia owing to its high cellulosic content after pre-treatment using dilute acid method, yields substantial amount of reducing sugars and high saccharification rates when subjected to enzymatic hydrolysis using thermostable cellulases, under optimized reaction conditions. Hence, the halophyte Atriplex crassifolia is a beneficial substrate that can be utilized to extract fermentable saccharides for bioethanol production.

Heterogeneous networks are an integral part of the 5G cellular networks as they are one of the important enabling technologies for increased coverage and capacity. However, interferences in multi-tiered architecture bottleneck its performance. Although multiple schemes have been proposed for efficient radio resource management to handle the interferences in heterogeneous networks but provision of quality of service to macrocell and small cell user equipment simultaneously, is still an open research problem. Intelligent schemes for radio resource management in heterogeneous networks have proved their effectiveness due to their self-optimization capabilities. In this research article, a cooperative Q-Learning, algorithm is proposed for efficient joint radio resource management in ultra-dense heterogeneous networks to handle interferences by adaptive power allocation to small cell base stations while considering the minimum quality of service requirements. In this proposed cooperative Q-Learning algorithm, small cell base stations interacts with the neighboring small cell base stations to exchange information and performs self-optimization based on a joint reward function. The proposed solution not only provided significant improvement in the capacity of macrocell and small cell user equipment as compared to other state of art Q-Learning based radio resource management schemes but also ensure the provision of quality of service to all macrocell and small cell user equipment simultaneously in the cluster of 16 small cells. The proposed solution provided a minimum capacity of 2 b/s/Hz to macrocell and small cell user equipment which is 100% higher than the minimum quality of service requirements defined in literature where none of recently proposed solution could meet minimum quality of service requirements. The results analysis shows that cooperation among the small cells yields a significant improvement of 48% in capacity of small cell user equipment at the cost of a slight increase in computational time as compared to independent learning.

Functional Magnetic Resonance Imaging (FMRI) is a noninvasive test to analyze several medical ailments by using magnetic resonance imaging (MRI) to detect the abnormalities in the active part of the brain and evaluate the minute changes in the blood flow, which cannot otherwise be accomplished with other imaging techniques. With its vast applications in healthcare, it has become one of the most explored studies by the researcher’s community, therefore, the current paper aims to address a comprehensive systematic literature review (SLR) of the application of FMRI in healthcare. The SLR scrutinized and assessed the currently available literature using inclusion and exclusion criteria. The chief motive of conducting SLR on the current research was to eradicate the biases and make it more systematic as compared to the informal literature review. The outcomes of the review state that due to accessibility of the public datasets and the data augmentation practices, the application of FMRI in Healthcare has remarkably raised from the last five years and its application is practically available for every disease diagnosis. The performance of the diagnosis of the disease is more effectual and proficient as equal to the human experts performing it manually.

Holt Oram Syndrome (HOS) falls in rare prevalence category with probability of 0.7 in 100,000 live births. It is a rare autosomal dominant multiple malformation syndrome characterized by abnormalities affecting hands, wrists, arms,congenital heart defects and/or conduction problems. Genetic mutations observed in TBX5 gene is attributed as the main cause of HOS. Our patient in his late 40s was diagnosed with Holt Oram Syndrome. He presented with typical conditions of congenital heart abnormalities (ASD) and upper limb malformations.

This study is focused on the production of biodiesel from waste cooking oils to turn the waste into energy by biological means. Physicochemical analysis of the oil samples was performed by measuring their density, acid value and saponification value for evaluating their efficacy towards biodiesel synthesis. Metal catalyzed glycerolysis and acid catalyzed esterification was carried out to reduce the free fatty acid number of oils. Transesterification of treated oils was done by using different alkalis and lipase (free and immobilized) as catalysts. Furthermore, the impact of all the reaction parameters including time, temperature, catalysts and methanol concentration was observed. Biodiesel was purified by wet and dry washing. Maximum product formation was achieved with 1% potassium hydroxide (KOH) followed by 4% free lipase and 6% immobilized lipase. Chemical composition analysis by gas chromatography and mass spectrometry (GCMS) showed the presences of about 97 and 93% fatty acid methyl esters for KOH and lipase based biodiesel, respectively. Fuel properties including distillation temperature, kinematic viscosity, flash, fire, cloud and pour point of the final product were found in accordance with the standard American Society Testing and Materials biodiesel specifications.

In the present study, CoAl-NO3 Layered Double Hydroxides (CoAl-NO3-LDH) was synthesized and an enolate anion of 4-Aminoacetophenone (AAP) was intercalated into LDH following the reconstruction approach. The CoAl-NO3-LDH and CoAl-AAP-LDH were characterized by Fourier-Transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD), scanning electron microscope (SEM) and energy dispersive X-ray (EDX) analyses. CoAl-AAP-LDH worked well for adsorption of Cd(II) ions from aqueous samples at optimum pH 7, adsorbent dosage 25 mg, concentration of Cd(II) ions 25 mg L-1 and shaking time 20 min at 25 °C. Different isotherms such as Langmuir, Freundlich and Dubinin-Radushkevich (D-R) isotherms fitted well to adsorption data with correlation coefficient (R2) of 0.998, 0.982 and 0.992, respectively. Monolayered (Qm) and multi-layered (KF) capacities of CoAl-AAP-LDH for adsorption of Cd(II) ions were calculated and found to be 34.40 and 19.44 mg g-1, respectively. Sorption energy was calculated and found to be 9.13 kJ mol-1, indicating chemisorption or ion exchange sorption mechanism. The method worked well for the adsorption of Cd(II) ions from wastewater samples.

While past research has focused on the benefits of social media during pandemics, this study emphasizes the possible negative effects of social media use among healthcare professionals. It has been stated that healthcare professionals are exposed to COVID-19 and its impacts on the mental health of these workers. Even though recognizing the importance of healthcare professionals during the pandemic, the impacts of COVID-19 on the mental health of healthcare professionals have been rarely considered for investigation by researchers. By applying differential susceptibility to the media effects model (DSMM), the current article investigated the effect of COVID-19 information overload (CIO) on psychological and mental well-being and underline mechanisms. Time-wave technique was applied to collect the data. This study tested moderated mediation model by collecting data from 314 healthcare professionals. The findings stated that COVID-19 information overload impacted COVID-19 fatalism and COVID-19 exhaustion directly. Likewise, COVID-19 fatalism mediated the association between CIO and COVID-19 exhaustion. Moreover, the COVID-19 stressor moderated this mediating relationship. This study proposes several practical recommendations for healthcare professionals, social media platform providers, health authorities, organizations, and institutions on how to use social media effectively and sustainably during the global COVID-19 epidemic.

Magnetic resonance imaging is the most generally utilized imaging methodology that permits radiologists to look inside the cerebrum using radio waves and magnets for tumor identification. However, it is tedious and complex to identify the tumorous and nontumorous regions due to the complexity in the tumorous region. Therefore, reliable and automatic segmentation and prediction are necessary for the segmentation of brain tumors. This paper proposes a reliable and efficient neural network variant, i.e., an attention-based convolutional neural network for brain tumor segmentation. Specifically, an encoder part of the UNET is a pre-trained VGG19 network followed by the adjacent decoder parts with an attention gate for segmentation noise induction and a denoising mechanism for avoiding overfitting. The dataset we are using for segmentation is BRATS’20, which comprises four different MRI modalities and one target mask file. The abovementioned algorithm resulted in a dice similarity coefficient of 0.83, 0.86, and 0.90 for enhancing, core, and whole tumors, respectively.

The sustainability and economic viability of the bioethanol production process from lignocellulosic biomass depend on efficient and effective pretreatment of biomass. Traditional pretreatment strategies implicating the use of mineral acids, alkalis, and organic solvents release toxic effluents and the formation of inhibitory compounds posing detrimental effects on the environment and interfering with the enzymatic saccharification process, respectively. Ionic liquids (ILs) as green solvents were used to overcome this issue, but the deep eutectic solvent as an emerging class of ionic liquids performed better in terms of making the process environmentally and economically viable. The green solvent-based pretreatment strategy applied in the current research was levulinic, acid-based natural deep eutectic solvent (NADES). Three different hydrogen bond acceptors (HBAs)—acetamide, betaine, and choline chloride—in combination with levulinic acid as hydrogen bond donor (HBD) in (HBD: HBA) molar ratio 2:1, were screened for biomass pretreatment. The best deep eutectic solvent was levulinic acid: choline chloride in an optimized molar ratio of 1:0.5, resulting in 91% delignification. The physicochemical parametric optimization of saccharification exhibited maximum enzymatic hydrolysis of 25.87% with 125 mg of pretreated sawdust via simultaneous addition of three thermostable cellulases [i.e., endo-1,4-β-D-glucanase (240 U), exo-1,4-β-D-glucanase (180 U), and β-glucosidase (320 U)] for 5 h of incubation at 75°C. The reducing sugar slurry obtained from the saccharified biomass was then added to a fermentation medium for bioethanol production, and a maximum of 11.82% of production was obtained at 30°C, 72 h, and 180 rpm using a 2.5% 24 h old Saccharomyces cerevisiae seed culture. The current study revealed that the levulinic-based deep eutectic solvent exhibited remarkable delignification, which led to the efficient enzymatic hydrolysis of sawdust and hence bioethanol production. Furthermore, it will prospect new avenues in bioethanol production using a deep eutectic solvent. Deep eutectic solvent overcame the issues posed by ionic liquids: toxicity, expensive and complex preparation, and non-biodegradability.

Deep eutectic solvents (DESs) have upgraded the practices of valorizing lignocellulosic feedstock by lessening biomass recalcitrance through delignification in precise and economical manner. In this study, the influence of a series of deep eutectic mixtures was evaluated on the halophyte Atriplex crassifolia for achieving elevated biogas production. Initially, the biomass was pretreated via several DESs with varying hydrogen bond donors (HBDs) including carboxylic acids, amine/amide, and polyols/glycols. DES composed of choline chloride (ChCl) and lactic acid (LA) evidenced as the most effective solvent in achieving high lignin removal rates and was further optimized by evaluating the parameters of molar ratio of DES components, solid-to-liquid ratio, and solvent addition. A maximum delignification value of 89.5% was achieved by 15% diluted ChCl: LA (1:2) DES at a biomass loading of 1:15. The solubilization rate of diluted ChCl: LA was also raised up to 38%. FT-IR analysis revealed significant lignin elimination from ChCl: LA pretreated substrates. Moreover, ≥88% of ChCl: LA DES was recovered after up to three pretreatment cycles, retaining ≥85% delignification efficiency. Fresh DES-pretreated Atriplex crassifolia recorded 32.2 mL/g of biogas production yield due to increased cellulosic content. The findings validated Atriplex crassifolia as an efficient feedstock for biogas production and confirmed the affectivity of ChCl: LA pretreatment in eliminating the lignin barrier, ultimately making cellulosic sugars readily biodegradable and highly accessible for anaerobic microorganisms.

The detection of Generalized Tonic Clonic Seizures (GTCS) and Falls is of utmost importance due to the increase in prevalence of epilepsy and Sudden Death in Epileptic Patients during CoVID-19 pandemic, and prevention of serious injuries in Fall risk groups such as elderly requiring continuous monitoring for disease management and assisted living etc. Monitoring of Activities of Daily Living (ADLs) can assist in the detection of symptoms and onset of neurological disorders such as Alzheimer’s, stroke, and epileptic seizures. With a host of embedded sensors, improved memory, enhanced processing capabilities and availability to masses, smartphones can be used for Human Activity Recognition (HAR) through continuous monitoring of ADLs. This paper presents a tri-axial accelerometer-based approach to detect and classify activities performed by individuals by applying machine learning algorithms including RF, J48, NB, LMT and SVM to movement data. Movement data is collected in real-time from the embedded accelerometer of a smartphone worn by individual on upper-left arm in unconstraint environment. It is pre-processed using time and frequency domain analysis and spatial domain features are computed. Supervised machine learning techniques are applied to classify ADLs into five classes based on the intensity of movements: Stationary, Light Ambulatory, Intense Ambulatory, GTCS and Falls. We also used training data from MyNeuroHealth dataset collected from 23 individuals including epilepsy patients. Based on gathered results, Random Forest outperforms other classifiers with classification accuracy of 99.6% for stationary, 81.5% for light ambulatory, 99.8% for intense ambulatory and GTCS, and 97.2% for Falls corresponding to training data of 14000 samples. To date, activity classification in our system has been implemented on cloud instead of mobile phone application as subjects are using smartphones with dissimilar software and hardware specifications for assisted living applications.

The characterization of petrified wood provides valuable information about paleoclimatology and geological history and helps to reconstruct the past forest flora of different parts of the earth. The present study was undertaken to evaluate the anatomical characteristics of fossil wood of the Miocene age collected from the Manchar Formation exposed at Thano Bula Khan, Sindh, Pakistan. In order to carry out a detailed anatomical investigation, three-dimensional sections were prepared using a petrotome. The microscopic analysis allowed us to study vessel size and arrangement, wood parenchyma, fibers, and xylem rays. Based on the comparison between recent and fossil wood, we concluded that the investigated characters are comparable with those of the genus Atalantia Corrêa of the Rutaceae family. Therefore, it was named as Atalantioxylon thanobolensis sp. nov. with reference to the location of Thano Bula Khan from which the fossil wood was collected.

Household food security is a major issue in developing countries like Pakistan. Despite significant breakthroughs in grain production within the country, the problem of food availability and utilization persists. Diet is one of the most potent determinants of nutritional condition. The dietary intake method has been utilized to determine the food security status of households, which depends on various factors. There are no automatic and user-friendly methods available to decide food security status, which is generally determined by manually calculating calorie intakes. Due to its high performance and precision, machine learning holds major significance. In this paper, the status of food security has been examined by applying machine learning algorithms, namely, support vector machine, naïve Bayes, k-nearest neighbors, random forest, logistic regression, and neural network, on survey data of households for best predicting the status. A food analysis (FA) app has been developed to automatically predict the FAO status of a household’s food security by implementing the random forest model that found higher precision among algorithms. Additionally, the proposed mobile app will also be helpful for collecting the households’ data. Furthermore, the objective of the study was to enhance food security awareness among individuals.

Objective: To share our experience of percutaneous trans-catheter aortic valve implantation in patients with severe symptomatic aortic stenosis. Study Design: A retrospective cross sectional study. Place and Duration of Study: The study was conducted at Armed Forces Institute of Cardiology/National Institute of Heart Diseases (AFIC/NIHD) Rawalpindi, from Mar 2015 to Feb 2020. Methodology: Retrospective analysis of all consecutive patients who underwent percutaneous trans-catheter aortic valve implantation was done to assess its immediate, short and long term outcome and safety. Twenty patients have undergone trans-catheter aortic valve implantation since 2015 in the institute. Base line blood chemistry including creatinine clearance, ultra-sonography abdomen, carotid Doppler, chest X-ray, High-Resolution Computed Tomography chest was done in all cases as part of the protocol. Mean age of the patients was 73 ± 7.91. There were sixteen males (80.0%) and four females (20.0%). All patients under went procedure through transfemoral route. Valve structure and peripheral vasculature for suitability of the procedure was assessed by computerized coronary tomographic angiography with TAVI protocol. In eleven patients aortic valve was trileaflet (55.0%) and in remaining nine it was bicuspid (45.0%). Mean gradient across the valve pre-procedure was 56.37 ± 9.14. Thirteen patients (65.0%) presented with angina/dysnoea NYHA III, 6 patients with syncope (30.0%) and one (5.0%) had heart failure. Two patients had undergone previous coronary artery bypass surgery. Procedure was carried out under general anesthesia in all patients except one. Balloon expandable Edwards Sapienvalve was implanted in two patients and self-expandable Core Valve/Evolut R in eighteen patients. Results: Seventeen patients underwent the procedure successfully with reduction of the mean gradients immediately after valve implantation to less than 15 mmHg recorded in the cath labangiographically subsequently complemented by transthoracic echocardiography. There were 3 deaths during the index hospitalization. Two occurred in the catheterization laboratory, one death was due to development of severe acute aortic regurgitation and second was due to acute coronary obstruction. Third death occurred due to acute kidney injury after seven days. Five patients died in next three months during follow up. One patient required permanent pacemaker because of development of left bundle branch block and second degree atrio-ventricular block post procedure. Conclusion: Transcatheter aortic valve implantation in patients with severe symptomatic aortic stenosis is a very effective and procedurally safe option and reasonable alternative to surgical valve replacement in high operative risk individuals.

The increase in the industrial revolution, urbanization, and deficit in crude oil resources urged scientists to look for sustainable and renewable energy resources. Microbial fuel cells (MFCs) are bio-electrochemical devices that convert the chemical energy of bio mass into electrical energy utilizing microorganisms as biocatalysts. MFC is an enthralling technology, and it has many important applications. There are different designs of MFC currently in use, each having their pros and cons; however, the stacked MFC is so far generating the highest power output. Different factors, discussed in this review, contribute toward the efficiency of the process. An ample amount of research has been done to convert the theoretical framework of MFC into a practical application; however, the later still remains a big challenge. A plethora of technical problems is in the way for MFC to be driven toward up-scaling and real-world applications. This review discusses electron transfer mechanisms in MFCs and important factors affecting the performance of MFC. Reactor design is a main limiting factor in MFC technology, and this review also focuses on recent improvements in reactor designs and modifications. Moreover, some main applications of MFC technology, limitations, recent advancements, and future prospects are discussed.

Abstract Background Butyric acid is a platform chemical material, the production of which has been greatly stimulated by the diverse range of downstream applications in many industries. In particular, higher quality butyric acid used in food and medicine, is more dependent on microbiological production methods. Hence, the bio-oxidation of butanol to butyric acid has been identified as a promising method with good potential economic and environmental benefits. However, both butanol and butyric acid are usually intensively toxic to most microorganisms as well as the bio-oxidation pathway. To develop a green, efficient and competitive microbiological method is the primary work to overcome the bottleneck of butyric acid industry. Result A combined bioprocess was designed with alternative whole-cell catalysis for butyric acid bio-conversion from butanol by Gluconobacter oxydans in a sealed-oxygen supply bioreactor (SOS). In the operation system, the escape of volatile substrates and toxic chemicals to cells can be avoided by the use of a sealed bioreactor, combined with the rejuvenation of cells by supplying energy co-factors. Finally, during a one-batch whole-cell catalysis, the utilization rate of substrate increased from 56.6 to 96.0% by the simple skill. Additionally, the techno-practical bioprocess can realize the purpose of cell-recycling technology through the rejuvenation effect of co-factor. Finally, we obtained 135.3 g/L butyric acid and 216.7 g/L sorbose during a 60-h whole-cell catalysis. This techno-practical technology provides a promising approach to promote the industrial production of butyric acid with more competitiveness. Conclusion The techno-practical biotechnology has powerfully promoted the process of butyric acid production by microorganisms, especially makes up for the lack of aerobic fermentation in the industry, and surmounts the shortcomings of traditional anaerobic fermentation. At the same time, this technically practical system provides a promising approach for the promotion of the industrial production of butyric acid in a more competitive manner.

Naringenin (NaR) is a flavonoid, mostly found in citrus fruit. Its IUPAC name is 5,7-Dihydroxy-2-(4-hydroxyphenyl)chroman-4-one. Naringenin is lipophilic in nature and possessing antioxidant property. In present study,7-Dihydroxy-2-(4-hydroxyphenyl)chroman-4-one was incorporated onto CuO nanoparticles (CuO-NPs) for enhancing its antioxidant activity. The CuO-NPs and its functionalized form (CuO-NaR) were characterized by Fourier-transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD), Scanning electron microscope (SEM) and Energy dispersive X-ray (EDX) analysis. The CuO-NPs, CuO-NaR and NaR were examined for their comparative antioxidant activities followed by DPPH (2,2-Diphenyl-1-picrylhydrazyl) method. The results revealed that the antioxidant activity of CuO-NaR was enhanced significantly.

For vertical organic field-effect transistors (VOFETs) to reach their potential for display and sensor applications, further improvements to fabrication methods are required. Here, the authors report microfabricated VOFETs featuring rolled-up metallic nanomembrane electrodes and displaying multi-sensing capability.

Natural coal was collected from Thar coal field, block IX in Province Sindh, Pakistan. The Thar coal was modified to Activated Thar Coal Adsorbent (ATCA) by reacting with concentrated H2SO4, which enhanced its porosity by dehydration as concentrated H2SO4 is strongly dehydration agent. The surface oxidation of coal was carried out by refluxing in concentrated HNO3. The ATCA was subjected to FT-IR spectrometer for characterization, which revealed the presence of CO and COOH moieties onto its surface. The ATCA was packed in column for adsorption of Pb(II) ions and recovered ≥95.6% with relative error ≤4.4% and RSD ≤3.7% at pH 7.0, ATCA dosage 400 mg, sample volume 400 mL (flow rate of 2.0 mL min−1) and eluent solution 5.0 mL of 0.5 mol L−1 HCl (flow rate of 1.0 mL min−1). LOD (0.03 μg L−1) and LOQ (0.11 μg L−1) of method were calculated followed by blank recording (n = 15) with preconcentration factor (PF) of 80. A linear response was observed with regression equation of y = 0.0934x + 0.0411 and R2 = 0.99 for concentration range of 0.5–5.0 mg L−1 (before preconcentration). Similarly, a linear response was also observed with regression equation of y = 7.4358x + 0.0429 and R2 = 0.99 for concentration range of 0.005–0.050 mg L−1 (after preconcentration). Breakthrough curve was plotted for obtaining total saturation capacity (152.5 mg g−1) of ATCA for adsorption of Pb(II) ions. The method worked well on real water samples with recovery ≥92 ± 4.5% of Pb(II) ions.

Pakistan is currently facing huge hurdles to maintain round the clock supply of electric power in the major areas. This makes more annoying when a short fall increase by the failure of the power transmissions and even this increases more in summer season when due to overload and high-level environmental effects of heat power transformers failure rate increases numerously. The transformers being damaged due to the over heat and high load across one or two of its three phases. Mismanagement of the power distribution causes most of the problems when a heavy power load observed on single phase whereas other phases were not equally loaded. The system design will provide a systematic solution to protect transformer and fault detection using PLC, phase monitoring and temperature sensing with power management of particular distribution and give notification through GSM and information sharing with control room/Grid station through Internet of Things (IoT). In case of overloading and heating the system will automatically cutoff the breakers of load, which will help to save the life of transformer. The operator in control room is only required to monitor updates from system regularly, to achieve real time data acquisition through GSM technology, which is adopted to update in time. The prime aspect of this solution is efficiently handling of the system load while maintaining the equipment in good condition. The protection schemes so far designed can successfully protect the transformer and mitigate the risk of enormous destruction caused by transformer explosion, protecting major and expensive power system equipment and human life.

Opencast limestone mines or limestone quarries are considered challenging ecosystems for soil fungi as they are highly degraded land with specific conditions, including high temperature, prolonged sunlight exposure, and a lack of organic matter, moisture, and nutrients in soil. In such ecosystems, certain fungi can survive and have a crucial function in maintaining soil ecosystem functions. Unfortunately, we know very little about taxonomic diversity, potential functions, and the ecology of such fungi, especially for a limestone quarry in a tropical region. Here, we characterized and compared the living soil fungal communities in an opencast limestone mine, including mining site and its associated rehabilitation site (9 months post-rehabilitation), with the soil fungal community in a reference forest, using the amplicon sequencing of enrichment culture. Our results showed that living fungal richness in the quarry areas was significantly lower than that in the reference forest, and their community compositions were also significantly different. Living fungi in the mining sites mostly comprised of Ascomycota (Eurotiomycetes and Sordariomycetes) with strongly declined abundance or absence of Basidiomycota and Mucoromycota. After nine months of rehabilitation, certain taxa were introduced, such as Hypoxylon spp. and Phellinus noxius, though this change did not significantly differentiate fungal community composition between the mining and rehabilitation plots. The majority of fungi in these plots are classified as saprotrophs, which potentially produce all fifteen soil enzymes used as soil health indicators. Network analysis, which was analyzed to show insight into complex structures of living fungal community in the limestone quarry, showed a clear modular structure that was significantly impacted by different soil properties. Furthermore, this study suggests potential taxa that could be useful for future rehabilitation.

Little is known about microbial communities of aquatic plants despite their crucial ecosystem function in aquatic ecosystems. Here, we analyzed the microbiota of an aquatic rheophyte, Hanseniella heterophylla, growing at three areas differing in their degree of anthropogenic disturbance in Thailand employing a metabarcoding approach. Our results show that diverse taxonomic and functional groups of microbes colonize H. heterophylla. Proteobacteria, Actinobacteria, Dothideomycetes, and Sordariomycetes form the backbone of the microbiota. Surprisingly, the beneficial microbes reported from plant microbiomes in terrestrial habitats, such as N-fixing bacteria and ectomycorrhizal fungi, were also frequently detected. We showed that biofilms for attachment of H. heterophylla plants to rocks may associate with diverse cyanobacteria (distributed in eight families, including Chroococcidiopsaceae, Coleofasciculaceae, Leptolyngbyaceae, Microcystaceae, Nostocaceae, Phormidiaceae, Synechococcaceae, and Xenococcaceae) and other rock biofilm-forming bacteria (mainly Acinetobacter, Pseudomonas, and Flavobacterium). We found distinct community compositions of both bacteria and fungi at high and low anthropogenic disturbance levels regardless of the study areas. In the highly disturbed area, we found strong enrichment of Gammaproteobacteria and Tremellomycetes coupled with significant decline of total bacterial OTU richness. Bacteria involved with sulfamethoxazole (antibiotic) degradation and human pathogenic fungi (Candida, Cryptococcus, Trichosporon, and Rhodotorula) were exclusively detected as indicator microorganisms in H. heterophylla microbiota growing in a highly disturbed area, which can pose a major threat to human health. We conclude that aquatic plant microbiota are sensitive to anthropogenic disturbance. Our results also unravel the potential use of this plant as biological indicators in remediation or treatment of such disturbed ecosystems.

Abstract Background Insulin resistance is core cause of metabolic syndrome. Determining insulin resistance is one of the foremost requirements imperative to understanding the pathophysiology of disease. The gold standard “Euglycaemic clamp test” is cumbersome, long and non-feasible in routine clinical setups to diagnose metabolic syndrome. Various continuous and steady state insulin resistance indices are now available in literature. We plan to evaluate commonly utilized steady state insulin resistance indices directly and Homeostasis Model Assessment for Insulin Resistance (HOMAIR) with added triglyceride (HOMA-TG index). Methods The cross-sectional study was carried from Jan-2016 to Dec-2018 at PNS HAFEEZ and department of chemical pathology, AFIP with following objectives: (1) To evaluate steady state insulin resistance markers for diagnosing metabolic syndrome as per IDF defined criteria by ROC curve analysis, (2) to measure Kendal Concordance between various insulin resistance indices and (3) to correlate steady state insulin resistance markers with anthropometric and lipid indices. After several exclusions we selected 224 subjects based upon “non-probability convenience sampling” for inclusion in study. Clinical history, anthropometric measures were calculated and sampling was done for insulin, glucose and other biochemical parameters. Metabolic syndrome was diagnosed as per IDF criteria, while HbA1c was utilized to diagnose diabetes mellitus. Pearson correlation was used to correlate various steady state insulin resistance indices including HOMAIR, HOMA2 index, QUICKI, G/I ratio, HOMA-TG index and serum insulin. AUC was calculated by ROC analysis for all surrogate insulin measures in diagnosis of metabolic syndrome. Results “HOMA-TG index” has shown the highest AUC for diagnosing metabolic syndrome along with higher correlation with lipid markers and anthropometric indices in comparison to other steady-state insulin resistance markers. Furthermore, QUICKI and G/I ratio showed the lowest AUC for detection of metabolic syndrome. Conclusion “HOMA-TG index” has shown highest AUC for metabolic syndrome diagnosis. However, QUICKI and G/I ration showed the lowest AUC for detection of metabolic syndrome. It is hoped that the potential “HOMA-TG index” may provide better diagnostic efficiency for diagnosing metabolic syndrome.

Depleting supplies of fossil fuel, regular price hikes of gasoline and environmental deterioration have necessitated the search for economic and eco-benign alternatives of gasoline like lignocellulosic biomass. However, pre-treatment of such biomass results in development of some phenolic compounds which later hinder the depolymerisation of biomass by cellulases and seriously affect the cost effectiveness of the process. Dephenolification of biomass hydrolysate is well cited in literature. However, elimination of phenolic compounds from pretreated solid biomass is not well studied. The present study was aimed to optimize dephenoliphication of wheat straw using various alkalis i.e., Ca(OH)2 and NH3; acids i.e., H2O2, H2SO4, and H3PO4; combinations of NH3+ H3PO4 and H3PO4+ H2O2 at pilot scale to increase enzymatic saccharification yield. Among all the pretreatment strategies used, maximum reduction in phenolic content was observed as 66 mg Gallic Acid Equivalent/gram Dry Weight (GAE/g DW), compared to control having 210 mg GAE/g DW using 5% (v/v) combination of NH3+H3PO4. Upon subsequent saccharification of dephenoliphied substrate, the hydrolysis yield was recorded as 46.88%. Optimized conditions such as using 1%+5% concentration of NH3+ H3PO4, for 30 min at 110°C temperature reduced total phenolic content (TPC) to 48 mg GAE/g DW. This reduction in phenolic content helped cellulases to act more proficiently on the substrate and saccharification yield of 55.06% was obtained. The findings will result in less utilization of cellulases to get increased yield of saccharides by hydrolyzing wheat straw, thus, making the process economical. Furthermore, pilot scale investigations of current study will help in upgrading the novel process to industrial scale.

Stomata are the main medium of plants for the trade of water, regulate the gas exchange, and are responsible for the process of photosynthesis and transpiration. The stomata are surrounded by guard cells, which help to control the rate of transpiration by opening and closing the stomata. The stomata states (open and close) play a significant role in describing the plant’s health. Moreover, stomata counting is important for scientists to investigate the numbers of stomata that are open and those that are closed to measure their density and distribution on the surface of leaves through different sampling techniques. Although a few techniques for stomata counting have been proposed, these approaches do not identify and classify the stomata based on their states in leaves. In this research, we have developed an automatic system for stomata state identification and counting in quinoa leaf images through the transformed learning (neural network model Single Shot Detector) approach. In leaf imprint, the state of stomata has been determined by measuring the correlation between the area of stomata and the aperture of each detected stoma in the image. The stomata states have been classified through the Support Vector Machine (SVM) algorithm. The overall identification and classification accuracy of the proposed system are 98.6% and 97%, respectively, helping researchers to obtain accurate stomatal state information for leaves in an efficient and simple way.

A field experiment was conducted to study the forage yield and qualitative traits of different varieties of millet with different days of harvest during the summer, 2014 at Agronomic Research Area, University of Agriculture Faisalabad. Experiment was laid out in randomized complete block design (RCBD) under factorial arrangement having three replications. Pearl millet seeds of three varieties viz. BS-2011, Ghana White and MB-87 were grown in 30 cm apart rows. Net plot size was 3.6 m × 8.0 m. Three different harvesting times were adopted i.e. 55, 65 and 75 days after sowing (DAS). Maximum plant height of pearl millet was recorded for cultivar BS-2011 at harvest time of 75 DAS. Maximum leaf area per plant was observed for the cultivar BS-2011 when it was harvested 75 DAS. Maximum dry matter percentage was also attained in cultivar BS-2011 where plots were harvested at 75 DAS. The highest forage yield was obtained where variety BS-2011 was grown and harvested at 75 DAS. Similarly, maximum dry matter production of BS-2011 was recorded in plots harvested at 75 DAS followed by Ghana White and MB-87 harvested at 55 DAS. Higher crude protein content was recorded where plots were harvested at 55 DAS and cultivar BS-2011 was sown. Higher crude fiber and total ash percentage was also seen in BS-2011. Finally, cultivar BS-2011 proved best for cultivation with harvest time of 75 DAS under Faisalabad conditions to obtain higher forage yield and better quality. Keywords: Millet, Phenology, Harvest time, Forage yield, Forage Quality

The prevalent use of the Internet of Things (IoT) devices over the Sea, such as, on oil and gas platforms, cargo, and cruise ships, requires high-speed connectivity of these devices. Although satellite based backhaul links provide vast coverage, but they are inherently constrained by low data rates and expensive bandwidth. If a signal propagated over the sea is trapped between the sea surface and the Evaporation Duct (ED) layer, it can propagate beyond the horizon, achieving long-range backhaul connectivity with minimal attenuation. This paper presents experimental measurements and simulations conducted in the Industrial, Scientific, and Medical (ISM) Band Wi-Fi frequencies, such as 5.8 GHz to provide hassle-free offshore wireless backhaul connectivity for IoT devices over the South China Sea in the Malaysian region. Real-time experimental measurements are recorded for 10 km to 80 km path lengths to determine average path loss values. The fade margin calculation for ED must accommodate additional slow fading on top of average path loss with respect to time and climate-induced ED height variations to ensure reliable communication links for IoT devices. Experimental results confirm that 99% link availability of is achievable with minimum 50 Mbps data rate and up to 60 km distance over the Sea to connect offshore IoT devices.

The aerostatic thrust bearing’s performance under vibration brings certain changes in stiffness and stability, especially in the range of 100 to 10,000 Hz, and it is accompanied by significant increase in fluctuations due to the changes in frequency, and the size of the gas film damping. In this research work, an analysis is carried out to evaluate the impact of throttling characteristics of small size orifice on stiffness and stability optimization of aerostatic thrust bearings. There are two types of thrust bearing orifices such as: partial porous multiple orifice and porous thrust bearings and their effects on variations in damping and dynamic stiffness are evaluated. A simulation based analysis is carried out with the help of the perturbation analysis model of an aerostatic thrust bearing simulation by using FLUENT software (CFD). Therefore, two models of aerostatic thrust bearings—one with the porous and other with partial porous orifice are developed—are simulated to evaluate the effects of perturbation frequencies on the damping and dynamic stiffness. The results reveal a decrease in the amplitude of dynamics capacity with an increase in its frequency, as well as a decrease in the damping of partial porous aerostatic thrust bearings with an increase in the number of orifices. It also reveals an increase in the radius of an orifice with an increment of damping of bearing at the same perturbation frequency and, with an increase in orifice height, a corresponding decrease in the damping characteristics of bearings and in the dynamic stiffness and coefficient of damping of bearing film in the frequency range less than 100 Hz.

This research is performed for dealing with some of the important working capital management policies and efficiency regarding to manufacturing sector of Pakistan. For this purpose data from 37 firms have been taken for the period 2009-2014. Data envelopment analysis (DEA) and Tobit regression analysis has been performed to achieve the objective of this study. DEA results indicate that only fifteen companies require increase in inputs to attain better output whereas six companies require decrease in the input. However sixteen companies have to consistent with their existing proportionate of inputs to sustain the output maximization. Tobit regression analysis concludes that average collection period has significant negative impact on efficiency and current ratio, gross working capital turnover ratio and financial leverage ratio have positive significant impact on efficiency.

Social networking sites (SNS) have transformed the communication systems; along with its positive effects, maladaptive usage of SNS brings some adverse outcomes too. The current study investigates the adverse impact of SNS usage. It focuses on social overload, information overload, and SNS exhaustion resultants of maladaptive usage causing dissatisfaction and regrets influencing the customer continuation intentions. Stressor-Strain-Outcome (SSO) framework is adopted in this study to investigate the antecedents of user intentions to discontinue the SNS usage. In the proposed research model, stressors, strains, and outcome are empirically examined with data collected from 505 SNS users. Findings based on statistical analysis show that psychological and behavioral alterations caused by maladaptive usage force users to discontinue the SNS usage due to dissatisfaction and regret caused by SNS usage. The excessive usage cause social overload, information overload, and SNS exhaustion source to dissatisfaction and consequent regret that push the user to decide to discontinue SNS. This research work develops theoretical implication for future SNS-based work and put forward a practical suggestion for organizations using SNS, SNS user, and SNS service providers.

The booming demand for energy across the world, especially for petroleum-based fuels, has led to the search for a long-term solution as a perfect source of sustainable energy. Lignocellulosic biomass resolves this obstacle as it is a readily available, inexpensive, and renewable fuel source that fulfills the criteria of sustainability. Valorization of lignocellulosic biomass and its components into value-added products maximizes the energy output and promotes the approach of lignocellulosic biorefinery. However, disruption of the recalcitrant structure of lignocellulosic biomass (LCB) via pretreatment technologies is costly and power-/heat-consuming. Therefore, devising an effective pretreatment method is a challenge. Likewise, the thermochemical and biological lignocellulosic conversion poses problems of efficiency, operational costs, and energy consumption. The advent of integrated technologies would probably resolve this problem. However, it is yet to be explored how to make it applicable at a commercial scale. This article will concisely review basic concepts of lignocellulosic composition and the routes opted by them to produce bioenergy. Moreover, it will also discuss the pros and cons of the pretreatment and conversion methods of lignocellulosic biomass. This critical analysis will bring to light the solutions for efficient and cost-effective conversion of lignocellulosic biomass that would pave the way for the development of sustainable energy systems.