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Abstract Background A thorough psychosocial assessment is time-consuming, often requiring multiple sessions to uncover the psychological factors contributing to mental illness, such as depression. The duration varies depending on the severity of the patient’s condition and how effectively the psychotherapist can establish rapport. However, prolonged assessment periods pose a significant risk of patient deterioration. Methods The comprehensive psychosocial intervention, led by the Multi-Criteria Decision-Making (MCDM) approach utilizing the Multi-Objective Optimization by Ratio Analysis (MOORA) method, played a pivotal role in identifying the key psychological factors contributing to the depression of the client among the 21 factors specified by BDI-II analysis. Results The integration of the MOORA strategy compared to traditional psychotherapy on 254 samples demonstrates a Jaccard similarity coefficient of 0.8, with a minimum error margin of 7% (vulnerability index = 0.57), indicating a significant agreement between the two approaches, both converging towards a similar solution. For patients with extreme depression, the number of sessions reduced from 18 ± 2 to 11 ± 2, showing a 33–35% reduction (χ 2 = 6.94, p = 0.008). Severe depression patients experienced a reduction from 14 ± 2 to 8 ± 1 sessions i.e., 34–39% reduction (χ 2 = 8.32, p = 0.004). Moderate depression patients saw sessions drop from 9 ± 1 to 5 ± 1, i.e., 37–43% reduction (χ 2 = 0.29, p = 0.001). The accuracy for detecting dominant psychological factors improved to 82.88% for extreme, 86.74% for severe, and 90.34% for moderate depression, respectively. Conclusion The implementation of MOORA facilitated the identification and prioritization of key psychosocial intervention strategies, making the process significantly faster compared to traditional methods. This acceleration greatly enhanced the precision and efficacy of the work. Additionally, critical vulnerable factors were identified through ordered statistics and correlation analysis [Pearson (r) = 0.8929 and Spearman’s rank (ρ) = 0.7551] on the Beck Depression Inventory-II model. These findings were supported by other MCDM schemes such as EDAS and TOPSIS, demonstrating high stability and robustness in dynamic decision-making environments, maintaining consistency across scenarios adapted by different psychotherapists. Overall, the combined application of MCDM (MOORA) and targeted psychological interventions yielded substantial positive outcomes in enhancing the well-being of individuals with psychological illnesses, such as depression, cognitive, affective, and somatic syndromes.

The surge in Distributed Denial of Service (DDoS) attacks within SDN environments demands more potent defense strategies. While Moving Target Defense (MTD) holds promise, current MTD approaches against DDoS suffer from security gaps due to overwhelming malicious traffic and static detection areas. In order to tackle these difficulties, we have implemented BSDN-HMTD, a combination of deep learning and blockchain technologies within SDN environments, as a framework. Our strategy starts by employing blockchain technology to authenticate users. We use the NTRU-based Nyberg Rueppel Digital Signature Algorithm for this purpose. This ensures that only authenticated user flows are allowed for validation and forwarding. Within the forwarding layer, Quantum Convolutional Neural Networks (QCNN) evaluate authentic flows by analyzing many characteristics, effectively differentiating between regular, malicious, and dubious flows. Utilizing an Enhanced Spotted Hyena Optimization (EHSO) method to activate switches in real-time modifies the vulnerable points of attack, so impeding attackers and simultaneously decreasing energy usage. The Forwarding Layer Organizer (FLO) oversees the detection of possible attacker surveillance activities and transmits the collected information to local controllers in the control layer. The controllers, functioning in a structured controller network, carry out proactive Moving Target Defense (MTD) techniques, such as host virtual IP hopping, which make attacker plans more complex and raise their operational expenses. Reactive MTD actions are implemented based on the results of flow validation. These actions utilize techniques such as secure honeypots and host virtual IP hopping to effectively prevent attacks. The blockchain securely logs all processed data related to packet validation, authentication, and honeypot activities to ensure the protection of data privacy. Our studies, conducted using Network Simulator-3.26 (NS-3.26), show that our proposed framework outperforms existing techniques in terms of several validation criteria.

Gastrointestinal disorders are a class of prevalent disorders in the world. Capsule endoscopy is considered an effective diagnostic modality for diagnosing such gastrointestinal disorders, especially in small intestinal regions. The aim of this work is to leverage the potential of deep convolutional neural networks for automated classification of gastrointestinal abnormalities from capsule endoscopy images. This method developed a deep learning architecture, GastroNetV1, an automated classifier, to detect abnormalities in capsule endoscopy images. The gastrointestinal abnormalities considered are ulcerative colitis, polyps, and esophagitis. The curated dataset consists of 6000 images with “ground truth” labeling. The input image is automatically classified as ulcerative colitis, a polyp, esophagitis, or a normal condition by a web-based application designed with the trained algorithm. The classifier produced 99.2% validation accuracy, 99.3% specificity, 99.3% sensitivity, and 0.991 AUC. These results exceed that of the state-of-the-art systems. Hence, the GastroNetV1 could be used to identify the different gastrointestinal abnormalities in the capsule endoscopy images, which will, in turn, improve healthcare quality.

Breast cancer is a medical condition characterized by the uncontrolled proliferation of cells in the breast tissue. Breast cancer can originate from various parts of the breast, and methods such as Breast ultrasound, Diagnostic mammogram, Breast magnetic resonance imaging, and Biopsy are currently used for its diagnosis. However, these methods have certain limitations, and their size can be a hindrance. To overcome this, low-power, flexible antennas can be designed for bio-communication between medical equipment and external instrumentation. Flexible and wearable antennas have advantages such as affordability, ease of fabrication, and high gain. In this article, a microstrip patch antenna operating at 2.45 GHz and made of polyamide material is designed using High Frequency Structure Simulator software. The simulation results show the patch antenna has a gain of 1, −14.81 dB return loss at 2.45 GHz based on |S11| ≤ −10 dB. The directive radiation pattern with axial ratio of 63.39 dB and voltage standing wave ratio ≤3. Furthermore, the hardware development of proposed antenna with polyamide substrate provides the resonance frequency nearing to simulation results as 2.318 GHz with return loss of −28.19 dB. Based on mathematical analysis, simulation and hardware results, the proposed antenna is a superior option for breast cancer detection.

In the present study, the polyvinylidene fluoride (PVDF) was incorporated with polyvinylpyrrolidone (PVP) and titanium dioxide (TiO2) nanoparticles to prepare PVDF-TiO2-PVP composite membrane for Bovine Serum Albumin (BSA) rejection from protein contaminated wastewater. The preparation of PVDF-TiO2-PVP composite membrane was conducted by a non-solvent phase inversion (NIPS) process with varying PVDF weight percentages ranging from 16 to 19 wt%. The polymer concentration plays a critical role in the formation of composite membranes that is commonly overlooked. The primary goal of this study is to examine how the concentration of polymers affects the process of membrane creation, the ensuing structure, and the properties of these membranes. Presently, there is a restricted comprehension concerning the impact of PVDF polymer concentration in the fabrication of membranes using the NIPS approach, particularly for PVDF-TiO2-PVP composite membranes. FE-SEM analysis revealed the lowest skin layer thickness and the number of macro-void formations in a PVDF composite membrane were at 16 wt% PVDF, compared to composite membranes over higher PVDF concentrations from 17 to 19 wt%. There were only slight variations in the percentages of individual elements across all PVDF-TiO2-PVP membranes, yet the values were identical for C, O, F, and Ti. The viscosity was lowest at 16 wt percent PVDF (576.5 MPa s) and greatest at 19 wt percent PVDF (2054.3 MPa s). It showed that 19%wt PVDF viscosity increased fourfold higher than 16%wt. As the concentration of PVDF was increased, a related decrease in the surface porosity of the membrane was observed. The highest hydrophobicity with the contact angle of 79.62° of the composite membranes were detected at 19 wt% of PVDF loading. The maximum water flux (265.43 L/m2h) and BSA flux (250.31 L/m2h) were obtained at 19 wt% of PVDF loading. The lowest PVDF concentration of 16 wt% rejected BSA with 91.01 % efficiency. At 19 wt% PVDF, the rejection rate dropped nearly sixfold to 16.34 %.

In this article, we present dielectric modulation-based label free detection of biomolecules at a cavity under gate metal located near the tunneling junction of charge plasma based dual metal-double gate tunnel FET (CP-DM DGTFET). The biomolecules that settle down in the cavity act as dielectric above the tunneling junction and cause drain current to increase/ decrease. To eliminate the short channel impact and to enhance ION/IOFF ratio without compromising any other device features, dual metal gate architecture with laterally split dielectric is used. The biosensor is capable of recognizing neutral, positive, and negative charges with the highest drain current sensitivity of $2.9\times 10 ^{9}$ Ccm-2 and highest ION/IOFF ratio of $9.02\times 10 ^{11}$ for biomolecule gelatin with dielectric constant k =12. This work explores the effectiveness of charge-plasma based DM DGTFET for detection of biomolecules such as gelatin (k =12), keratin (k =8), bacteriophage T7 (k =6.3), and APTES (k =3.57) with varied charge distribution at various biasing. The drain current sensitivity, subthreshold slope, drain current, ON/OFF current ratio are computed for various biomolecules and their variations with respect to cavity length, biomolecule charge, etc. are used to detect unknown biomolecules. Finally, RF analysis such as gain bandwidth product, cut-off frequency, transit time, and linearity/selectivity of biosensors are incorporated. The device shows better linearity and less distortion for high –k biomolecules. The results of our studies are reproducible for repeated computed analysis.

The device dimension down scaling beyond 14 nm technology node utilization of device architecture and new materials is a need of the semiconductors industry. In this paper, three materials, In $_{\mathrm {1-x}}$ GaxAs/ In $_{\mathrm {1-x}}$ GaxP, In $_{1-}$ xGax GAA JL FET developed and analyzed for their analog performance at high temperature. In $_{\mathrm {1-x}}$ GaxAs has a composition of 53% InAs and 47% GaAs with a band gap of 0.75 eV, whereas In $_{\mathrm {1-x}}$ GaxP and In $_{\mathrm {1-x}}$ GaxN exhibit a wider band gap of 1.90 eV and 3.2 eV, respectively, and exhibit different analog performance. An analytical model that is temperature-dependent for the drain current of In $_{\mathrm {1-x}}$ GaxAs, In $_{\mathrm {1-x}}$ GaxP, In $_{\mathrm {1-x}}$ GaxN GAA JL FET with mobility variations has also been developed. The ratio of transconductance-to-normalized drain current (gm/I $_{\mathrm {DS}}$ ) is utilized to examine analog properties. This ratio is a crucial measure of analog performance since it reveals the sensitivity and linearity of the device. All device parameters such as transconductance (g $_{\mathrm {m}}$ ), gm/IDS, cut-off frequency, and intrinsic gain were investigated for higher temperatures ranging from T =300 K to T =500 K. The newly developed device parameters have very low-temperature sensitivity, making these three material devices potential candidates for high temperature applications.

The integration of compatibilisers with thermoplastics has revolutionised the field of polymer composites, enhancing their mechanical, thermal, and rheological properties. This study investigates the synergistic effects of incorporating SEBS-g-MAH on the mechanical, thermal, and rheological properties of polycarbonate/acrylonitrile-butadiene-styrene/graphene oxide (PC/ABS/GO) (PAGO) and the properties of polycarbonate/acrylonitrile-butadiene-styrene/graphene oxide (PC/ABS/rGO) (PArGO) composites through the melt blending method. The synergistic effects on thermal stability and processability were analysed by using thermogravimetry (TGA), melt flow index (MFI), and Fourier-transform infrared spectroscopy (FTIR). The addition of SEBS-g-MAH improved the elongation at break (EB) of PAGO and PArGO up to 33% and 73%, respectively, compared to the uncompatibilised composites. The impact strength of PAGO was synergistically enhanced by 75% with the incorporation of 5 phr SEBS-g-MAH. A thermal analysis revealed that SEBS-g-MAH improved the thermal stability of the composites, with an increase in the degradation temperature (T80%) of up to 17% for PAGO at 1 phr SEBS-g-MAH loading. The compatibilising effect of SEBS-g-MAH was confirmed by FTIR analysis, which indicated interactions between the maleic anhydride groups and the PC/ABS matrix and GO/rGO fillers. The rheological measurements showed that the incorporation of SEBS-g-MAH enhanced the melt flowability (MFI) of the composites, with a maximum increase of 38% observed for PC/ABS. These results demonstrate the potential of SEBS-g-MAH as a compatibiliser for improving the unnotched impact strength (mechanical), thermal, and rheological properties of PC/ABS/GO and PC/ABS/rGO composites, achieving a synergistic effect.

Dewatered sludge (DS) is a sewage sludge with a unique property due to extracellular polymeric substances (EPSs) and polymer flocculants. These components form a stable 3D polymer network to increase dewatering efficiency, leaving behind valuable materials such as lipids. This article explored the influences of DS particle size on lipid yield and the effects of extraction on the chemical, morphological, and thermal properties of the residual dewatered sludge (RDS). Lipid yields with unimodal distribution were observed across the particle size ranges (

The global catastrophe known as COVID-19 has shattered the world’s socioeconomic structure. Effective and affordable diagnosis techniques are crucial for better COVID-19 therapy and the eradication of bogus cases. Due to the daily upsurge in cases, hospitals only have a small supply of COVID-19 test kits. The study describes a deep Convolutional Neural Network (CNN) design for categorizing chest x-ray images in the diagnosis of COVID-19. The lack of a substantial, high-quality chest x-ray picture collection made efficient and exact CNN categorization problematic. The dataset has been pre-processed using an image enhancement strategy to provide an effective training dataset for the proposed CNN model to achieve performance. The proposed model achieves 99.73% of accuracy, 98.95% of specificity, 99.47% of precision, 99.62% of sensitivity, and 98.71% of F1 score. A comparative study between the proposed model and numerous CNN-based COVID-19 detection algorithms is carried out to demonstrate that it outperforms other models. When evaluated on a separate dataset, the suggested model excelled over all other models, generally and explicitly.

Software reliability growth models (SRGMs) are universally admitted and employed for reliability assessment. The process of software reliability analysis is separated into two components. The first component is model construction, and the second is parameter estimation. This study concentrates on the second segment parameter estimation. The past few decades of literature observance say that the parameter estimation was typically done by either maximum likelihood estimation (MLE) or least squares estimation (LSE). Increasing attention has been noted in stochastic optimization methods in the previous couple of decades. There are various limitations in the traditional optimization criteria; to overcome these obstacles metaheuristic optimization algorithms are used. Therefore, it requires a method of search space and local optima avoidance. To analyze the applicability of various developed meta-heuristic algorithms in SRGMs parameter estimation. The proposed approach compares the meta-heuristic methods for parameter estimation by various criteria. For parameter estimation, this study uses four meta-heuristics algorithms: Grey-Wolf Optimizer (GWO), Regenerative Genetic Algorithm (RGA), Sine-Cosine Algorithm (SCA), and Gravitational Search Algorithm (GSA). Four popular SRGMs did the comparative analysis of the parameter estimation power of these four algorithms on three actual-failure datasets. The estimated value of parameters through meta-heuristic algorithms are approximately near the LSE method values. The results show that RGA and GWO are better on a variety of real-world failure data, and they have excellent parameter estimation potential. Based on the convergence and R2 distribution criteria, this study suggests that RGA and GWO are more appropriate for the parameter estimation of SRGMs. RGA could locate the optimal solution more correctly and faster than GWO and other optimization techniques.

The human Fc-gamma receptors (FcγRs) link adaptive and innate immunity by binding immunoglobulin G (IgG). All human low-affinity FcγRs are encoded by the FCGR2/3 locus containing functional single nucleotide polymorphisms (SNPs) and gene copy number variants. This locus is notoriously difficult to genotype and high-throughput methods commonly used focus on only a few SNPs. We performed multiplex ligation-dependent probe amplification for all relevant genetic variations at the FCGR2/3 locus in >4,000 individuals to define linkage disequilibrium (LD) and allele frequencies in different populations. Strong LD and extensive ethnic variation in allele frequencies was found across the locus. LD was strongest for the FCGR2C-ORF haplotype (rs759550223+rs76277413), which leads to expression of FcγRIIc. In Europeans, the FCGR2C-ORF haplotype showed strong LD with, among others, rs201218628 (FCGR2A-Q27W, r 2 = 0.63). LD between these two variants was weaker (r 2 = 0.17) in Africans, whereas the FCGR2C-ORF haplotype was nearly absent in Asians (minor allele frequency

Polyaniline (PANI) is a conductive polymer easily converted into a conducting state. However, its limited mechanical properties have generated interest in fabricating PANI composites with other polymeric materials. In this study, a PANI–prevulcanized latex composite film was synthesized and fabricated in two phases following chronological steps. The first phase determined the following optimum parameters for synthesizing nanosized PANI, which were as follows: an initial molar ratio of 1, a stirring speed of 600 rpm, a synthesis temperature of 25 °C, purification via filtration, and washing using dopant acid, acetone, and distilled water. The use of a nonionic surfactant, Triton X-100, at 0.1% concentration favored PANI formation in a smaller particle size of approximately 600 nm and good dispersibility over seven days of observation compared to the use of anionic sodium dodecyl sulfate. Ultraviolet–visible spectroscopy (UV-Vis) showed that the PANI synthesized using a surfactant was in the emeraldine base form, as the washing process tends to decrease the doping level in the PANI backbone. Our scanning electron microscopy analysis showed that the optimized synthesis parameters produced colloidal PANI with an average particle size of 695 nm. This higher aspect ratio explained the higher conductivity of nanosized PANI compared to micron-sized PANI. Following the chronological steps to determine the optimal parameters produced a nanosized PANI powder. The nanosized PANI had higher conductivity than the micron-sized PANI because of its higher aspect ratio. When PANI is synthesized in smaller particle sizes, it has higher conductivity. Atomic force microscopy analysis showed that the current flow is higher across a 5 µm2 scanned area of nanosized PANI because it has a larger surface area. Thus, more sites for the current to flow through were present on the nanosized PANI particles.

Spray dryer had long been used to dry liquid materials and produce dry crystalline products. However, the drying of the bittern to produce quality salt crystals has not been widely published. Therefore, the purpose of this study was to examine the effect of drying conditions of the bittern using a spray dryer to produce salt with a high natrium chloride (NaCl) content. Drying was carried out in the hot air temperature (105–125 °C), drying air flow rate (25–45 ml/min), feed flow rate (20–30 ml/min), and concentration of maltodextrin (10–30%). The parameters were observed water content, NaCl content, yield, and mean particle diameter size (MPDS). The results showed that the inlet air temperature of 125 °C can significantly reduce the water content faster and produce higher NaCl levels than the inlet air temperature of 105 °C. The salt crystals produced at higher maltodextrin concentrations have lower water content and high NaCl content. The best-operating conditions are at a hot air temperature of 125 °C, a drying airflow rate of 45 m/s, and a maltodextrin concentration of 25% because it produces salt crystals with high NaCl content. Overall, these results indicate that the bittern can be dried using a spray dryer with potential NaCl content as a raw material for the pharmaceutical industry.

The implementation of inorganic adsorbents for the removal of heavy metals from industrial effluents generates secondary waste. Therefore, scientists and environmentalists are looking for environmentally friendly adsorbents isolated from biobased materials for the efficient removal of heavy metals from industrial effluents. This study aimed to fabricate and characterize an environmentally friendly composite bio-sorbent as an initiative toward greener environmental remediation technology. The properties of cellulose, chitosan, magnetite, and alginate were exploited to fabricate a composite hydrogel bead. The cross linking and encapsulation of cellulose, chitosan, alginate, and magnetite in hydrogel beads were successfully conducted through a facile method without any chemicals used during the synthesis. Energy-dispersive X-ray analysis verified the presence of element signals of N, Ca, and Fe on the surface of the composite bio-sorbents. The appearance and peak’s shifting at 3330–3060 cm−1 in the Fourier transform infrared spectroscopy analysis of the composite cellulose–magnetite–alginate, chitosan–magnetite–alginate, and cellulose–chitosan–magnetite–alginate suggested that there are overlaps of O-H and N-H and weak interaction of hydrogen bonding with the Fe3O4 particles. Material degradation, % mass loss, and thermal stability of the material and synthesized composite hydrogel beads were determined through thermogravimetric analysis. The onset temperature of the composite cellulose–magnetite–alginate, chitosan–magnetite–alginate, and cellulose–chitosan–magnetite–alginate hydrogel beads were observed to be lower compared to raw-material cellulose and chitosan, which could be due to the formation of weak hydrogen bonding resulting from the addition of magnetite Fe3O4. The higher mass residual of cellulose–magnetite–alginate (33.46%), chitosan–magnetite–alginate (37.09%), and cellulose–chitosan–magnetite–alginate (34.40%) compared to cellulose (10.94%) and chitosan (30.82%) after degradation at a temperature of 700 °C shows that the synthesized composite hydrogel beads possess better thermal stability, owing to the addition of magnetite and the encapsulation in the alginate hydrogel beads.

Magnetic chitosan/cellulose nanofiber-Fe(III) [M-Ch/CNF-Fe(III)] composites were isolated for the elimination of Cr(VI), Cu(II), and Pb(II) from aqueous solution. Various analytical methods, such as field emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FT-IR), X-ray diffraction analysis (XRD), and thermogravimetric analysis (TGA) were employed to determine the morphological, physicochemical, and thermal properties of the isolated M-Ch/CNF-Fe(III) composites. It was found that the M-Ch/CNF-Fe(III) composites were porous materials, and they have the potential to be implemented as an adsorbent for heavy metals removal. The adsorption efficiency of M-Ch/CNF-Fe(III) composites was determined for Cr(VI), Cu(II), and Pb(II) elimination with changing pH (pH 1.0–8.0), adsorbent doses (0.05–1.0 g), time (15–90 min), and temperature (28–80 °C). In addition, isothermal and kinetics studies were conducted to assess the adsorption behavior and mass transfer phenomena of M-Ch/CNF-Fe(III) composites as an adsorbent for Cr(VI), Cu(II) and Pb(II) elimination from aqueous solution. The outcomes of the present study reveal that the M-Ch/CNF-Fe(III) composites could be utilized as an adsorbent for the Cr(VI), Cu(II), and Pb(II) elimination from industrial effluents.

Polymeric membranes offer straightforward modification methods that make industry scaling affordable and easy; however, these materials are hydrophobic, prone to fouling, and vulnerable to extreme operating conditions. Various attempts were made in this study to fix the challenges in using polymeric membranes and create mixed-matrix membrane (MMMs) with improved properties and hydrophilicity by adding titanium dioxide (TiO2) and pore-forming agents to hydrophobic polyvinylidene fluoride (PVDF). The PVDF mixed-matrix ultrafiltration membranes in this study were made using the non-solvent phase inversion approach which is a simple and effective method for increasing the hydrophilic nature of membranes. Polyvinylpyrrolidone (PVP) and polyethylene glycol (PEG) as pore-forming chemicals were created. Pure water flux, BSA flux, and BSA rejection were calculated to evaluate the mixed-matrix membrane’s efficiency. Bovine serum albumin (BSA) solution was employed in this study to examine the protein rejection ability. Increases in hydrophilicity, viscosity, and flux in pure water and BSA solution were achieved using PVP and PEG additives. The PVDF membrane’s hydrophilicity was raised with the addition of TiO2, showing an increased contact angle to 71°. The results show that the PVDF–PVP–TiO2 membrane achieved its optimum water flux of 97 L/(m2h) while the PVDF–PEG–TiO2 membrane rejected BSA at a rate greater than 97%. The findings demonstrate that use of a support or additive improved filtration performance compared to a pristine polymeric membrane by increasing its hydrophilicity.

Medical education is one of the most sought-after disciplines for its prestigious and noble status. Institutions endeavor to identify admissions criteria to register bright students who can handle the complexity of medical training and become competent clinicians. This study aims to apply statistical and educational data mining approaches to study the relationship between pre-admission criteria and student performance in medical programs at a public university in Saudi Arabia. The present study is a retrospective cohort study conducted at the College of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia between February and November 2022. The current pre-admission criterion is the admission score taken as the weighted average of high school percentage (HSP), general aptitude test (GAT) and standard achievement admission test (SAAT), with respective weights of 0.3, 0.3 and 0.4. Regression and optimization techniques have been applied to identify weightages that better fit the data. Five classification techniques—Decision Tree, Neural Network, Random Forest, Naïve Bayes and K-Nearest Neighbors—are employed to develop models to predict student performance. The regression and optimization analyses show that optimized weights of HSP, GAT and SAAT are 0.3, 0.2 and 0.5, respectively. The results depict that the performance of the models improves with admission scores based on optimized weightages. Further, the Neural Network and Naïve Bayes techniques outperform other techniques. Firstly, this study proposes to revise the weights of HSP, GAT and SAAT to 0.3, 0.2 and 0.5, respectively. Secondly, as the evaluation metrics of models remain less than 0.75, this study proposes to identify additional student features for calculating admission scores to select ideal candidates for medical programs.

Demand for determining renewable lipids feedstock for the production of biodiesel is increasing with the rapid depletion of petroleum diesel. The present study was conducted to assess the feasibility of utilizing municipal sewage sludge (MSS) as a potential lipids feedstock for biodiesel production. The lipids’ extraction and separation from MSS were conducted using supercritical CO2 (scCO2) with varying treatment time (15–120 min), temperature (30–80°C), pressure (10–50 MPa), and addition of cosolvents (1–10 wt.%). The modified Gompertz equation and Arrhenius equation were employed to evaluate lipids’ extraction and kinetics behavior from municipal sewage sludge using scCO2. About 27% of lipids were extracted from MSS with scCO2 at a temperature of 60°C, pressure of 30 MPa, treatment time of 60 min, and 5 wt.% of ethanol (EtOH) as cosolvent. The modified Gompertz equation was adequately fitted with experimental data of the lipids’ extraction from MSS using scCO2. The kinetics properties analyses revealed that the scCO2 extraction technology was highly dependent on pressure than the temperature for the extraction of the lipids from MSS. The physicochemical characteristics and fatty acid contents of the scCO2 extracted lipids from MSS and sewage sludge biodiesel were determined using a variety of analytical techniques. The physicochemical properties of the sewage sludge biodiesel were compared with the international standard specifications of biodiesel, such as the American Society for Testing and Materials specifications for diesel fuel (ASTM D6751) and European Standard (EN 14214) specifications.

Background: Controversy occurs in the relationship between serum ferritin levels, insulin resistance and risk of developing anemia in adolescents with family history of diabetes. Aims & Objectives: This study was designed to find out the association between serum ferritin levels with risk of developing anemia in non-diabetic adolescents with family history of type 2 diabetes mellitus (T2DM). Materials & Methods: A descriptive cross-sectional was conducted in a local medical institute of Lahore. Study included 50 non-diabetic, non-obese male / female adolescent with family history of type 2 diabetes mellitus and 50 healthy male/female non-obese adolescents without family history of T2DM considered as controls. Level of serum ferritin and serum insulin was estimated by Enzyme Linked Immunosorbent Assay. Blood glucose was estimated by auto-analyzer. Insulin resistance was calculated by HOMA-IR index and beta cell function was assessed by HOMA- beta index. Results: Levels of fasting blood sugar, insulin resistance were found to be increased with decreased level of serum ferritin and decrease beta cell function in both male as well as female cases as compared to controls. Negative correlation was found between serum ferritin and insulin resistance while a positive correlation was found between serum ferritin and beta cell function. Conclusions: Low level of serum ferritin is associated with reduced beta cell function and increased insulin resistance. This may increase the risk of developing iron deficiency anemia that can affect both immune system as well as increase susceptibility to infections.

The biologically treated palm oil mill effluent (POME) urges further treatment to minimize the residual pollutant concentration for safe discharge in the nearest watercourse. In the present study, the post-treatment of biologically treated POME was conducted using ferrous sulfate monohydrate (FeSO4·H2O) as a coagulant. The influence of the FeSO4·H2O coagulation of POME was determined on the elimination of biochemical oxygen demand (BOD), suspended solids (SS), and chemical oxygen demand (COD) with varying flocculation time (min), slow mixing speed (rpm), coagulant doses (g/L) and pH. The FeSO4·H2O coagulation–flocculation experimental conditions were designed following the central composite design (CCD) of experiments and optimized by employing response surface methodology (RSM) based on the optimal SS, COD, and BOD elimination from POME. The maximum BOD, SS, and COD elimination achieved were about 96%, 97%, and 98%, respectively, at the optimized experimental condition. The surface morphology and elemental composition analyses of raw FeSO4·H2O and sludge generated after coagulation revealed that the FeSO4·H2O effectively removed the colloidal and suspended particles from POME. The well-fitted kinetic model equation was the pseudo-second-order kinetic equation to describe the FeSO4·H2O coagulation–flocculation behavior. The thermodynamics properties analyses revealed that the FeSO4·H2O coagulation of POME was non-spontaneous and endothermic. The residual SS, COD, and BOD in treated POME were determined to be 28.27 ± 5 mg/L, 147 ± 3 mg/L, and 6.36 ± 0.5 mg/L, respectively, lower the recommended discharged limits as reported by the Department of Environment (DoE), Malaysia.

BackgroundGlobal gene expression profiling can provide insight into the underlying pathophysiology of disease processes. Kawasaki disease (KD) is an acute, self-limited vasculitis whose etiology remains unknown. Although the clinical illness shares certain features with other pediatric infectious diseases, the occurrence of coronary artery aneurysms in 25% of untreated patients is unique to KD.MethodsTo gain further insight into the molecular mechanisms underlying KD, we investigated the acute and convalescent whole blood transcriptional profiles of 146 KD subjects and compared them with the transcriptional profiles of pediatric patients with confirmed bacterial or viral infection, and with healthy control children. We also investigated the transcript abundance in patients with different intravenous immunoglobulin treatment responses and different coronary artery outcomes.ResultsThe overwhelming signature for acute KD involved signaling pathways of the innate immune system. Comparison with other acute pediatric infections highlighted the importance of pathways involved in cell motility including paxillin, relaxin, actin, integrins, and matrix metalloproteinases. Most importantly, the IL1β pathway was identified as a potential therapeutic target.ConclusionOur study revealed the importance of the IL-1 signaling pathway and a prominent signature of innate immunity and cell migration in the acute phase of the illness.