Deep neural networks (DNNs) have made a huge impact in the field of machine learning by providing unbeatable humanlike performance to solve real-world problems such as image processing and natural language processing (NLP). Convolutional neural network (CNN) and recurrent neural network (RNN) are two typical architectures that are widely used to solve such problems. Time sequence-dependent problems are generally very challenging, and RNN architectures have made an enormous improvement in a wide range of machine learning problems with sequential input involved. In this paper, different types of RNN architectures are compared. Special focus is put on two well-known gated-RNN’s Long Term Short Memory (LSTM) and Gated Recurrent Unit (GRU). We evaluated these models on the task of force estimation system in pouring. In this study, four different models including multi-layers LSTM, multi-layers GRU, single-layer LSTM and single-layer GRU) were created and trained. The result suggests that multi-layer GRU outperformed other three models.

Polymethyl methacrylate (PMMA) is a transparent thermoplastic with excellent optical properties, transparent surface, low moisture absorption, tensile and electrical resistance. In this study, the alloy was prepared through PMMA and cycloolefin copolymer (COC) due to some similar properties. The mechanical test showed that properties such as impact resistance, elongation, tensile, and flexural strength decreased by adding COC by up to 20% due to less incompatibility and miscibility, but mentioned properties improved by adding COC 40% due to sub-phase generation. The DSC and DMTA tests showed improvement in the thermal properties of alloys by adding 40% COC. SEM micrographs exhibited a softer surface and more phase elongation of the alloy. Finally, the sample was selected as the optimal sample in terms of mechanical properties irradiated by electron beam, and amplification results showed that a dose of 50 KGY increased the mechanical and thermal properties relatively.

Due to low working temperature, high energy density and low pollution, proton exchange fuel cells have been investigated under different operating conditions in different applications. Using platinum catalysts in methanol fuel cells leads to increasing the cost of this kind of fuel cell which is considered as a barrier to the commercialism of this technology. For this reason, a lot of efforts have been made to reduce the loading of the catalyst required on different supports. In this study, carbon black (CB) and carbon nanotubes (CNT) have been used as catalyst supports of the fuel cell as well as using the double-metal combination of platinum-ruthenium (PtRu) as anode electrode catalyst and platinum (Pt) as cathode electrode catalyst. The performance of these two types of electro-catalyst in the oxidation reaction of methanol has been compared based on electrochemical tests. Results showed that the carbon nanotubes increase the performance of the micro-fuel cell by 37% at maximum power density, compared to the carbon black. Based on thee-electrode tests of chronoamperometry and voltammetry, it was found that the oxidation onset potential of methanol for CNT has been around 20% less than CB, leading to the kinetic improvement of the oxidation reaction. The current density of methanol oxidation reaction increased up to 62% in CNT sample compared to CB supported one, therefore the active electrochemical surface area of the catalyst has been increased up to 90% by using CNT compared to CB which shows the significant rise of the electrocatalytic activity in CNT supported catalyst. Moreover, the resistance of the CNT supported sample to poisonous intermediate species has been found 3% more than CB supported one. According to the chronoamperometry test results, it was concluded that the performance and sustainability of the CNT electro-catalyst show remarkable improvement compared to CB electro-catalyst in the long term.

In this investigation, polymethyl methacrylate (PMMA) was mixed with cyclic olefin copolymer (COC) because of its hardness, strength, and transparency properties. The results of thermal analysis through TGA and DTG showed that the thermal properties of the alloy are improved using 40% cyclic olefin copolymer. Kinetics of thermal degradation (pyrolysis) of polymers have been studied and analyzed and thermal pyrolysis of polymethyl methacrylate and cyclic olefin copolymer thermoplastic polymer was conducted. The computation of kinetic analysis is measured along with the different methods used to study the kinetics. The activation energy (E) of degradation of studied materials was estimated using Ozawa Flynn and Wall (OFW), Starink and Kissinger’s methods, and evaluation of three kinetic parameters taken appropriate kinetic model in terms of percent change for both types of polymers have been proposed, and finally, simulated curves were compared with the experimental curves. Both mechanisms of degradation for COC and PMMA under nitrogen atmosphere will reflect intramolecular transfer and random scission of the main chain.

The simultaneous evaluation of heat/mass transference procures momentous significance in the polymer industry and engineering activities. Extensive utilization of such aspects includes propylene flares, heat exchangers, energy transfiguration in chilling towers, and amputation of post fortuitous heat in nuclear reactors. Owing to such prospective demands, we interpreted the Jeffrey liquid reactive flow under non-linear convection. We scrutinized the transference of heat/mass under generalized Fourier-Fick relations. Thermal conductivity depends on temperature while mass diffusivity is dependent on concentration. Besides, heat source along with first-order chemical reaction aspects are accounted for. Relevant transformations are exerted to achieve non-linear differential systems that are solved through the homotopy scheme. Influences of divers factors are exhibited via graphical benchmark.

A recent publication defined lockdown “revisionism” as “the spread of misinformation on lockdowns and other public health measures.” We used this publication to analyze the claim that questioning lockdowns or other public health interventions and mandates amounts to lockdown “revisionism” and “misinformation”. We suggest that the term ‘revisionism’, like the term ‘misinformation’ contained in its definition, were merely labels used to denigrate evidence-based contrary conclusions so as to avoid having to critically appraise the best evidence itself. We aim to describe how, by glossing over topics without fully engaging with the best evidence available, the assertions made do not withstand critical scrutiny. We suggest that, to ensure lessons are learned for the future, we must be willing to engage in rigorous and open debate – calling reasonable critical scrutiny of evidence ‘misinformation’, ‘disinformation’, or ‘revisionism’ is not supportive of this goal. Finally, we suggest that a main lesson from the COVID-19 pandemic is not to have an increased focus on so-called ‘misinformation’ and ‘revisionism’, but rather to re-discover the emergency management process of making decisions that ensure multidisciplinary representation, transparency, cost-benefit analyses of courses open using the best evidence available, and that protects against censorship and groupthink.

Appropriate estimation of soil settlement is of significant importance since it directly influences the performance of building and infrastructures that are built on soil. In particular, the settlement of fine-grained soils is critical because of low permeability and continuous settlement with time. Coefficient of consolidation (C_c) is a key parameter to estimate settlement of fine-grained soil layers. However, estimation of this parameter is time consuming, needs skilled technicians, and specific equipment. In this study, C_c was estimated using several soil parameters such as liquid limit (LL), plastic limit (PL), and initial void ratio (e₀). Estimating such parameters in laboratory is straight forward and needs substantially less time and cost compared to conventional tests to estimate C_c such as oedometer test. This study presents a novel prediction model for C_c of fine-grained soils using gene-expression programming (GEP). GEP is a biologically inspired technique capable of offering closed-form solution for the optimal solution. A database consisted of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate C_c based on LL, PL, and e₀. The performance of developed GEP-based model was evaluated through coefficient of determination (R²), root mean squared error (RMSE), and mean average error (MAE). High R² and low error values indicated the descent performance of the model. Furthermore, the model was evaluated using the additional performance measures and met all the suggested criteria. Furthermore, the model had a better performance in terms of R², RMSE, and MAE compared to most of existing models. It is expected that the developed model will decrease the time and cost associate with determining C_c of fine-grained soils.Keywords: evolutionary model, gene-expression programming (GEP), prediction, soil compression index, estimation, soil engineering, soil informatics, civil engineering, machine learning, data science, big data, soft computing, deep learning, forecasting, subject classification codes, construction informatics, computational intelligence (CI), artificial intelligence (AI), estimation

Appropriate estimation of soil settlement is of significant importance since it directly influences the performance of building and infrastructures that are built on soil. In particular, the settlement of fine-grained soils is critical because of low permeability and continuous settlement with time. Coefficient of consolidation (Cc) is a key parameter to estimate settlement of fine-grained soil layers. However, estimation of this parameter is time consuming, needs skilled technicians, and specific equipment. In this study, Cc was estimated using several soil parameters such as liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Estimating such parameters in laboratory is straight forward and needs substantially less time and cost compared to conventional tests to estimate Cc such as Oedometer test. This study presents a novel prediction model for Cc of fine-grained soils using gene-expression programming (GEP). GEP is a biologically inspired technique capable of offering closed-form solution for the optimal solution. A database consisted of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of developed GEP-based model was evaluated through coefficient of determination (R2), root mean squared error (RMSE), and mean average error (MAE). High R2 and low error values indicated the descent performance of the model. Furthermore, the model was evaluated using the additional performance measures and met all the suggested criteria. Furthermore, the model had a better performance in terms of R2, RMSE, and MAE compared to most of existing models. It is expected that the developed model will decrease the time and cost associate with determining Cc of fine-grained soils.

Obesity is characterized by surplus buildup of body lipids primarily in adipose tissue. Prevalence and incidence of obesity are ascending persistently at an alarming rate. Unusual eating behaviors like compulsion to eat (food addiction) and excessive consumption are the premier contributors to obesity. Both are under influence of a variety of stimuli, mainly being stress, emotions, dietary restrictions, sweetness, hyperpalatability, neural pathways, hormonal imbalance and genetics. This review summarizes the potential driving factors behind overeating and food addiction for understanding and exploring obesity linked novel agents and processes as an endeavor to advance treatment approaches. Obesity has been studied extensively throughout the world due to high incidence and association with several metabolic disorders including cardiovascular disorders. The food addition has considered one of potential key factor of obesity and excessive weight gain. Sedentary life style and availability of food also induce obesity.

In this study, our primary objective was to analyze the tradeoff between accuracy and complexity in machine learning models, with a specific focus on the impact of reducing complexity and entropy on the production of landslide susceptibility maps. We aimed to investigate how simplifying the model and reducing entropy can affect the capture of complex patterns in the susceptibility maps. To achieve this, we conducted a comprehensive evaluation of various machine-learning algorithms for classification tasks. We compared the performance of these algorithms in terms of accuracy and complexity, considering both "before" and "after" scenarios of dimensionality reduction using Principal Component Analysis (PCA). Our findings revealed that reducing complexity and lowering entropy can lead to an increase in model accuracy. However, we also observed that this reduction in complexity comes at the cost of losing important complex patterns in the produced landslide susceptibility maps. By simplifying the model and reducing entropy, certain intricate relationships and uncertain patterns may be overlooked, resulting in a loss of information and potentially compromising the accuracy of the susceptibility maps. The analysis encompassed a diverse range of machine learning algorithms, including Random Forest (RF), Extra Trees (EXT), XGboost, LightGBM, Catboost, Naive Bayes (NB), K-Nearest Neighbors (KNN), Gradient Boosting Machine (GBM), and Decision Trees (DT). Each algorithm was evaluated for its strengths and limitations, considering the tradeoff between accuracy and complexity. Before dimensionality reduction, the algorithms demonstrated promising results, with RF exhibiting excellent AUC/ROC scores and average accuracy. However, computational costs were noted as a potential drawback for RF, especially when dealing with large datasets. EXT showcased robust performance and good accuracy, while XGboost demonstrated its ability to handle complex relationships within large datasets, albeit requiring careful hyperparameter tuning. The efficiency and scalability of LightGBM made it a suitable choice for large datasets, although it displayed sensitivity to class imbalance. Catboost excelled in handling categorical features, but longer training times were observed for larger datasets. NB showcased simplicity and computational efficiency but assumed independence among features. KNN, known for its capability to capture local patterns and spatial relationships, was found to be sensitive to the choice of distance metric. GBM, while capturing complex relationships effectively, was prone to overfitting without proper regularization. DT, with its interpretability and ease of understanding, faced limitations in terms of overfitting and limited generalization. After dimensionality reduction, certain algorithms exhibited improvements in their AUC/ROC scores and average accuracy, including RF, EXT, XGboost, and LightGBM. However, for a few algorithms, such as NB and DT, a decrease in performance was observed. This study provides valuable insights into the performance characteristics, strengths, and limitations of various machine learning algorithms in classification tasks. Researchers and practitioners can utilize these findings to make informed decisions when selecting algorithms for their specific datasets and requirements. We also aim to identify the potential factors contributing to the high accuracy rates obtained from these ensembled algorithms and explore possible shortcomings of non-ensembled algorithms that may result in lower accuracy rates. By conducting a comprehensive analysis of these algorithms, we seek to provide valuable insights into the benefits and limitations of ensembled approaches for landslide susceptibility mapping. Our study sheds light on the challenges faced when balancing accuracy and complexity in machine learning models for landslide susceptibility mapping. It emphasizes the importance of carefully considering the level of complexity and entropy reduction in relation to the specific patterns and uncertainties present in the data. By providing insights into this tradeoff, our research aims to assist researchers and practitioners in making informed decisions regarding model complexity and entropy reduction, ultimately improving the quality and interpretability of landslide susceptibility maps.

The study is based on the notion that big data predictive analytics is important for developing strategic alliances performance of companies. this study investigates the relationship between big data predictive analytics, big data culture, and competitive strategies 'techniques were adopted, such as descriptive statistics, correlation,regression, etc. using SPSS and SmartPLS statistical software. Hypotheses were tested with bootstrapped analysis using SEM (through SmartPLS). The study developed a structural equation model by using the SEM analysis. The results of the SEM analysis suggested the hypothesized model of the study was valid. The results supported all the hypotheses of the study.Through empirical analysis, demonstrated the conclusion is that the big data predictive analytics has a positive and significant relationship with strategic alliance performance

Automation is at the core of modern industry. It aims to increase production rates, decrease production costs, and reduce human intervention in order to avoid human mistakes and time delays during manufacturing. On the other hand, human assistance is usually required to customize products and reconfigure control systems through a special process interface called Human Machine Interface (HMI). Machine Learning (ML) algorithms can effectively be used to resolve this tradeoff between full automation and human assistance. This paper provides an example of the industrial application of ML algorithms to help human operators save their mental effort and avoid time delays and unintended mistakes for the sake of high production rates. Based on real-time sensor measurements, several ML algorithms have been tried to classify paper rolls according to paper grammage in a white paper mill. The performance evaluation shows that the AdaBoost algorithm is the best ML algorithm for this application with classification accuracy (CA), precision, and recall of 97.1%. The generalization of the proposed approach for achieving a cost-effective mill construction by reducing the total number of the required physical sensors will be the subject of our future research.

Enhancement of mechanical properties by using TiN/TiCN/TiC multilayer thin films deposited on commercially pure cast Titanium (CP-Ti), Ti6Al4V and silicon (Si) substrates via magnetron sputtering technique was investigated in this study. The structural, chemical and mechanical properties of the coatings were characterized by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), nanoindentation and scratch test. Results of the XRD analysis showed reflections corresponded to FCC (1 1 1) cubic and polycrystalline structure for TiN/TiCN/TiC films. XPS analysis revealed formation of titanium nitride, titanium carbonitride and titanium carbide in the coatings. According to SEM images, the coatings demonstrated dense cross-sectional morphology and columnar structure as well as good adhesion to the substrate with a thickness of 1.77 μm deposited on silicon (1 0 0). Scratch and nanoindentation test results showed the best mechanical behavior for the coated Ti6Al4V substrate material with the 19.96 GPa hardness and 25 N critical load values, because of its higher hardness and toughness of substrate in compared to Cp-Ti substrate.

Enhancement of mechanical properties by using TiN/TiCN/TiC multilayer thin films deposited on commercially pure cast Titanium (CP-Ti), Ti6Al4V and silicon (Si) substrates via magnetron sputtering technique was investigated in this study. The structural, chemical and mechanical properties of the coatings were characterized by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), nanoindentation and scratch test. Results of the XRD analysis showed reflections corresponded to FCC (1 1 1) cubic and polycrystalline structure for TiN/TiCN/TiC films. XPS analysis revealed formation of titanium nitride, titanium carbonitride and titanium carbide in the coatings. According to SEM images, the coatings demonstrated dense cross-sectional morphology and columnar structure as well as good adhesion to the substrate with a thickness of 1.77 μm deposited on silicon (1 0 0). Scratch and nanoindentation test results showed the best mechanical behavior for the coated Ti6Al4V substrate material with the 19.96 GPa hardness and 25 N critical load values, because of its higher hardness and toughness of substrate in compared to Cp-Ti substrate.

The indole nucleus is an important element of many natural and synthetic molecules with significant biological activity. Nonetheless, the co-presence of transitional metals in organic scaffold may represent an important factor in the development of effective medicinal agents. This review covers some of the relevant and recent achievements in the biological, chemical and pharmacological activity of important indole-based metal complexes in the area of drug discovery.

Winter frost injury is a major limiting factor for olive cultivation in temperate regions. The response of olive shoots to freezing stress can be used for selecting resistant genotypes to freezing. The electrolyte leakage (EL) and tetrazolium tests (TZ) are commonly used to evaluate dead tissues in cold stress studies. The temperature-response curve of dead tissues to lethal temperature (LT) is measured with models to calculate LT50 and LT90. In this study, we evaluated the accuracy and efficiency of eighteen non-linear regression models (NLRs) in calculating LT50 and LT90 of freezing stress in different olive cultivars at various stages of dormancy. After evaluating the prediction performance of NLR models, it was found that only eight models were suitable for the purpose of this research out of the 18 models examined. The 2p-logistic and Gompertz models were selected for modeling EL and TZ, respectively. Our research findings indicate that the Roughani, Kawi, and Zard varieties of olive trees exhibit the best performance in cold weather conditions. Our findings provide valuable insights into selecting frost-resistant cultivars and designing effective strategies for cold acclimation in olive cultivation.

Recycled tire fibers (RTF) are currently one of the most abundant waste not being recovered due to several processing hurdles and the presence of high amounts of residual ground rubber particles (GR). Therefore, this study proposes a simple approach to separate most of rubber particles from fibers and to determine their rubber content using thermogravimetric analysis (TGA)/calcination. Afterwards, clean fiber (CF) and GR are used as fillers for recycled post-consumer low density polyethylene (rLDPE), and their effects on the physical properties are investigated. Accordingly, a series of composites with CF and GR is prepared at different filler concentrations (0-30%) via extrusion compounding before using compression molding and injection molding for comparison. In all cases, injection molding leads to higher strength and modulus, but lower elongation at break. The results show that incorporating 30 wt.% of CF into rLDPE yields a remarkable improvement in tensile strength (15%), tensile modulus (192%) and flexural modulus (142%). On the other hand, the incorporation of up to 30 wt.% of GR results in a reduction of both tensile strength and flexural modulus by 15%, confirming the critical role of the cleaning process for RTF in achieving the best results.

This paper investigates and explores the minor crops production in Pakistan and its association with the agricultural gross domestic product. The agriculture sector of Pakistan has a rich contribution to the economic growth and development. Like major crops; minor crops also have a vital role to boost up the agriculture sector. Time span data was used in this study and it was collected from the Economy Survey of Pakistan annual reports. Augmented Dickey-Fuller (ADF) unit root test and Ordinary Least Square (OLS) method was used to analyze the data and results were interpreted by employing the Johansen co-integration test. Study results reveal that bajra, barely and jowar has a significant impact on the agricultural gross domestic product, while the total cropped area has a negative impact on AGDP. On the basis of the study results, we recommend the policy implications.

Utilization of medicine derived from plants has been documented in various traditional systems of medicines throughout the world for different diseases. Therefore, the current research work was carried out to evaluate the phytochemical and biological potential of Ficus carica L ethanolic leaf extract. For this purpose, physicochemical and phytochemical analysis, total phenolic and flavonoids contents was performed. Antioxidant potential was determined by FRAP, DPPH and H2O2 assay. Proteinase inhibition, heat induced hemolysis and BSA denaturation assay was carried out for inflammation. Antiglycation potential was assessed by fructosamine assay, Congo-red assay and estimation of free carbonyl groups. In-vivo antidiabetic, anti-obesity, effect on liver and kidney were investigated by high fat high sugar diet model. FTIR, HPLC and LCMS/MS analysis was performed to find out the compounds. Tablet based formulation was developed using wet granulation method and tested for physicochemical parameters. Results shows that total phenolic and flavonoids content was (333 mg GAE/g) and (123 mg RE/g) respectively, H2O2 assay shows 35.6% free radical inhibition potential, DPPH assay shows (IC50) inhibitory concentration at 0.58 mg/mL, and 88.769 µg/g Fe2SO4 solution FRAP values. Proteinase inhibition assay, Heat induced hemolysis and BSA-denaturation assay shows percentage inhibition of 28 ± 0.01, 55 ± 0.03 and 51.2 ± 0.05 respectively. In Antiglycation activity β-amyloid formation assay shows absorption of 0.017, Fructosamine assay shows inhibition of 19.4±0.06 and free carbonyl group estimation shows inhibition of 17.0±0.03. In in-vivo study FLE at (500 mg/kg) significantly decreased total bilirubin (p ≤ 0.001), decreased (p ≤ 0.05) alanine transferase, aspartate aminotransferase, alkaline phosphatase, decreased blood glucose, triglycerides and total cholesterol (p ≤ 0.01) while HDL (p ≤ 0.01) was significantly increased in comparison to obesity and diabetic-obesity groups. Different functional groups presence was confirmed by FTIR spectrum. HPLC analysis revealed the existence of ferulic acid, rutin, chlorogenic acid, coumarin and thymoquinone and these results are corelated by LC-MS/MS. Pre-compression characterization showed angle of repose (33.04°), bulk density (0.54 g/cm3), tapped density (0.73 g/cm3), compressibility index (35.179 %) and Hausner’s ratio (1.35), while the post-compression characterization showed weight variation 826±4.17 mg, thickness 10.50±0.25 kg/cm2, friability 0.57 %, and disintegration time 8.19 min. From the above results it is concluded that F. carica L. leaves extract has strong antioxidant, antidiabetic and hypolipidemic potential and can be used for an obesity and diabetes treatment.

Abstract: In recent years, the construction engineering sector has undergone a transformative shift towards the integration of machine learning (ML) techniques, particularly in predicting the properties of steel fiber-reinforced concrete (SFRC). Despite the theoretical sophistication of existing models, a persistent challenge remains – their opacity, lacking transparency and real-world relevance for practi-tioners. To address this gap and advance our current understanding, this study employs the extra gradient (XG) Boosting algorithm, crafting a comprehensive ap-proach. Grounded in a meticulously curated database drawn from 43 seminal pub-lications, encompassing 420 distinct records, this research focuses predominantly on three primary fiber types: crimped, hooked, and mil-cut. Complemented by hands-on experimentation involving 20 diverse SFRC mixtures, this empirical campaign is further illuminated through the strategic use of Partial Dependence Plots (PDPs), revealing intricate relationships between input parameters and con-sequent compressive strength. A pivotal revelation of this research lies in the identification of optimal SFRC formulations, offering tangible insights for real-world applications. The developed ML model stands out not only for its sophistication but also its tangible accuracy, evidenced by exemplary performance against inde-pendent datasets, boasting a commendable mean target-prediction ratio of 99%. To bridge the theory-practice gap, we introduce a user-friendly digital interface, thri-ughly designed to guide professionals in optimizing and accurately predicting the compressive strength of SFRC. This research thus contributes to the construction and civil engineering sectors by enhancing predictive capabilities and refining mix designs, fostering innovation, and addressing the evolving needs of the industry.

Non-viral gene carriers because of their limited side effects, biocompatibility, simplicity and taking the advantages of electrostatic interactions have shown noticeable potential in gene delivery. The low transfection rate of non-viral vectors under physiological conditions is a significant issue. Here, the aim of this study was to investigate the efficacy of hydrophilic and hydrophobic groups on gene carriers such as two synthesized amphiphilic polymer of dextran-stearic acid-spermine (DSASP) with verified lipid and amine conjugations that associated with Fe3O4 superparamagnetic nanoparticles to promote the target delivery and decrease the transfection time using static magnetic field. Our findings illustrate that magnetic nanoparticles are spherical with positive surface charges and superparamagnetic behaviors. The DSASP–pDNA/MNPs offered a strong pDNA condensation, protection against DNase degradation, significant cell viability in HEK 293T cells and. Although conjugations of spermine play a critical role in transfection efficiency, amphiphilic polymer with more derivatives of stearic acid showed better transfection yields. Therefore, DSASP amphiphilic magnetic carriers offer new insights for gene delivery due to the amine contents and ameliorate the uptake of complexes via cell membrane based on its hydrophilic surface.

At present there is a renewed interest in theories of ”modified” gravity. Here, under a more drastic modification enforced by Galilei group, we obtain a completely new gravitational structure, and which exists in addition to the already available general relativity of today. Correlated with this, we show that in addition, there is a new ”modified” quantum mechanics, in as much as it exists as an independent and new ”pure” non-relativistic quantum me- chanics, and which has no relativistic counterpart. This is in addition to the present quantum mechanics, where the relativistic and non-relativistic structures are counterparts of each other. The above holds, firstly due to the correlation between Galilei group and quantum mechanics. These math- ematical conclusions are consolidated by the fact that there exists a physical Majorana interaction between each neutron- proton pairs in nuclei. Galilei invariance of Majorana exchange in Majorana interaction, shows that the mass here is of pure gravitational nature, and which is immune to the other three forces. This makes an amazing connection between the gravitational force and the quantum mechanics. This pure gravitational mass would man- ifest itself as dark matter of the universe. It is our new modified gravity that generates the dark matter.

General phytochemical screening of the aerial parts of Consolida orientalis revealed the presence of steroids, terpenes, phenolic compounds, saponins, fatty acids, alkaloids. This study was conducted to investigate the bioactivities of extracts, isolation and identification the compounds from aerial parts of C.orientalis. The main goal of the present study is identifying and characterizing the antioxidant activity of the Consolida orientalis and biological isolation of active terpenoid. Aerial parts of the plant were dried at room temperature and reduced to small pieces, followed by using extraction with ethyl acetate percolation. Tree complementary analysis system was used, DPPH free radical scavenging test, total phenolic metabolites and FRAP. The total phenolic content was 38.83±2.09 mg gallic acid corresponding to g-1 extract with regarding to standard curve (y=0.0054x+0.0488, r2=0.995). IC50 value for DPPH radical – scavenging was 987.11±28.66 mgml-1. The extract was exhibited a medium reducing power compared with Vit C. The isolation and purification was afforded white crystalline powder which was subjected to physical, chemical and spectral identification by IR, ¹H- and ¹³C- NMR and GC-MS. Isolated compound was identified as β-sitosterol. That is a terpenoid with melting point 133.4-134.5 ͦ c and with molecular formula C₂₉H₅₀O.

In this paper, we proposed a new model of non-linear electrodynamics with parameter. Firstly, we study the weak limit approximation and by using the Gauss Bonnet theorem, we obtain the deflection angle of photon from magnetized black hole and effect of bnon-linear electrodynamics. In doing so, we find the corresponding optical metric after that we calculate the Gaussian curvature which is used in Gauss Bonnet theorem. Then we show the deflection angle in the leading order terms. We also analyzed that our results reduces into Maxwell's electrodynamics and RN solution with the reduction of parameters. Moreover, we also investigate the graphical behavior of deflection angle w.r.t correction parameter, black hole charge and impact parameter.

Cloud computing has revolutionized how industries store, process, and access data. However, the increasing adoption of cloud technology has also raised concerns regarding data security. Machine learning (ML) is a promising technique to enhance cloud computing security. This paper focuses on utilizing ML techniques (Support Vector Machine, XGBoost, and Artificial Neural Networks) to progress cloud computing security in the industry. The selection of 11 important features for the ML study satisfies the study's objectives. This study focused on identifying gaps in utilizing ML techniques in cloud cyber security. Moreover. this study aims at developing a practical strategy for predicting the employment of machine learning in an Industrial Cloud environment regarding trust and privacy issues. The efficiency of the employed models is assessed by applying validation matrices of Precision, Accuracy, Recall values, F1 score, R.O.C. curves, and Confusion matrix. The results demonstrated that the X.G.B. model outperformed in terms of all the matrices with an Accuracy of 97.50 %, 97.60 % Precision, 97.60 % Recall values, and 97.50 % F1 score. This research highlights the potential of ML algorithms in enhancing cloud computing security for industries. It emphasizes the need for continued research and development to create more advanced and efficient security solutions for cloud computing.

Rat is one of the most commonly used model animals for biological research. The organs and systems of human being are somewhat similar to that of rat in structure as well as functions, making it a valuable choice for research experimentation in biological sciences. A number of studies have been conducted to evaluate the potential risks and toxicity of different elements on the physiology and histology of rats. There is need to address certain environmental factors affecting rats condition during experimentation. Reproduction along with its pathologies is under investigation on larger scale throughout the world, being central for the existence of a species. These studies focus on the major factors that influence reproductive function. Review of literature clearly indicated the unwanted consequences of over nutrition, malnutrition, high or low temperature, non-enriched housing, improper handling, intense or poor light exposure and environmental pollution on histology and hormonal profile of reproductive system of rats.

In this study, the prevalent methodology for design of the industrial policy in developing countries was critically assessed, and it was shown that the mechanism and content of classical method is fundamentally contradictory to the goals and components of the endogenous growth theories. This study, by proposing a new approach, along settling Schumpeter's economic growth theory as a policy framework, designed the process of entering, analyzing and processing data as the mechanism of the industrial policy in order to provide "theoretical consistency" and "technical and Statistical requirements" for targeting the growth stimulant factor effectively.

This study aims to formulate binary cementitious systems containing silica fume (SF) and micro quartz (MQ) to improve cementitious concrete's durability and mechanical properties. In this investigation, we examined the effects of different essential variables, such as the level of SF and MQ replacement and the ratio of the water-binder (w/b). In this study, the w/b ratios were 0.25, 0.3, 0.35, and 0.4. Replacement levels for SF were 8, 10, and 12%, while replacement levels for MQ were 5, 8, 10, 15, 25, and 35%. The porosity and permeability decreased with an increase in replacement levels, regardless of the type of fine material used or the amount of replacement. Meanwhile, the strength increased markedly with SF replacement, reaching its maximum MQ of 25% at the SF level. The strength of the SF mixing was the highest in all mixes, and it remained the highest until 15% of MQ was replaced. The ultrafine size of MQ particles contributes to the improvement of compressive strength, porosity, and permeability in a similar way to SF particles, despite their high crystalline structure. Furthermore, in this study, analysis of variance (ANOVA) was employed to verify the influence of each variable on the studied response.

Background: The biofilm-forming bacteria Porphyromonas gingivalis (P. gingivalis) is primarily responsible for periodontal disorders, which continue to be a global health issue. Despite the widespread use of antibiotics like metronidazole, other potent therapeutic a crucial quorum sensing molecule implicated in the formation of P. gingivalis biofilm. Approaches are now being explored due to the growing challenge of antibiotic resistance. Objective: In particular, chemicals from Symphytum Officinale (S) and Panax Ginseng (G) plants will be examined in this study to assess if they have the ability to inhibit biofilm and to prevent the development of acylated homoserine lactones (AHLs), a quorum sensing molecule. Materials and Methods: With Metronidazole serving as a control, the antibacterial effects of these substances (Symphytum Officinale and Panax Ginseng) were examined against a standard strain and a clinical isolate of P. gingivalis. Dilutions of these plant extracts were used either alone (G+S) or in conjunction with metronidazole (G+S+F), to assess their antibacterial activity using antibacterial susceptibility test (inhibition zone), biofilm inhibition and disruption assay (optical density) and quorum sensing inhibition assay (AHL). Results: The combinations of Symphytum officinale, Panax Ginseng and metronidazole (S+G+F) showed the maximum effectiveness with highest zone of inhibition (25.000±0.001 mm) and biofilm inhibition (98.46%), and were comparable to G+S (inhibition zone of 24.341±0.593 mm and biofilm inhibition of 97.76%), and significantly different in the degree of biofilm inhibition with different treatment scenarios. Additionally, the plant's extracts and combinations are specific concentrations had a significant effect on the suppression of the generation of AHL (p < 0.05). Conclusion: According to preliminary research, these plant-derived substances, especially when paired with metronidazole, significantly inhibited the growth of P. gingivalis biofilm providing valuable information for the development of innovative therapeutic approaches for periodontitis and other biofilm-associated illnesses.

Background: To implement the new marker in clinical practice, reliability assessment, validation, and standardization of utilization must be applied. This study evaluated the reliability of TILs and TSR assessment through conventional microscopy by comparing observers’ estimations. Methods: Intratumoral and tumor-front stromal TILs, and TSR were assessed by three pathologists using 86 HE slides CRC. TSR and TILs were categorized using one and four different proposed cut-off systems, respectively and agreement was assessed using the intraclass coefficient (ICC) and Cohen’s kappa statistics. Pairwise evaluation of agreement was performed using the Fliess Kappa statistic and the concordance rate and it was visualized by Bland-Altman plots. Results: For the evaluation of intratumoral stromal TILs, ICC of 0.505 (95% CI:0.35-0.64) was obtained, Kappa values were in the range of 0.21 to 0.38, and concordance rates in the range of 0.61 to 0.72. For the evaluation of tumor-front TILs, ICC was 0.52 (95% CI:0.32 - 0.67), the overall kappa value ranged from 0.24 to 0.30, and the concordance rate ranged from 0.66 to 0.72. For estimating the TSR, the ICC was 0.48 (95% CI:0.35 - 0.60), the kappa value was 0.49 and the concordance rate was 0.76. Conclusion: The agreement between pathologists in estimating these markers corresponds to poor to the moderate agreement, however, implementing immune scores in daily practice requires more concentration in inter-observer agreements.

In this article, we analyze the thermodynamics, thermal fluctuations and quasi-normal modes of a torus-like charge AdS black hole. The parameters of a torus-like charged AdS black hole are its mass, electric charge, and cosmological constant. We used a torus-like charge AdS black hole to evaluate thermodynamics through thermal fluctuations and found that it significantly affected the corrected thermodynamic potentials. Black holes of larger radii are more stable and significant. The effect of thermal fluctuation has been investigated on the other thermodynamic potentials such as the Hemohltz free energy, Gibbs free energy, enthalpy, and specific heat, using the concept of corrected entropy. Finally, we use null geodesics to deal with quasi-normal modes. The null geodesics provide the photon sphere angular velocity and Lyapunov exponent, which are the real and imaginary components of the quasi-normal modes in the eikonal limit. With inceasing charge and photon radius, angular velocity reaches its maximum, similar to Lyapunov's exponent.

Kynurenine or tryptophan catabolite (TRYCAT) pathway contributes to the pathophysiology of major depression disorder (MDD) and major depressive episodes (MDE) in bipolar disorder and suicidal behaviors. The consequences of the overactivation of this pathway large reduced tryptophan (TRP) levels in peripheral blood and the CNS and increased levels of neurotoxic TRYCATs including kynurenine (KYN), 3-hydroxy kynurenine (3HK), quinolinic acid (QA), xanthurenic acid (XA), and picolinic acid (PA). However, other TRYCATs are protective, such as kynurenic acid (KA) and anthranilic acid (AA). Inflammation and cell-mediated immune activation along with oxidative and nitrosative stress (O&NS) may stimulate the first and rate-limiting enzyme of this pathway, namely indoleamine-2,3-dioxygenase (IDO). Therefore, during depression, balancing neuroprotective versus neurotoxic TRYCATs and balancing activation of the immune response system (IRS) versus the compensatory immune response system is crucial for achieving better treatment outcomes. Furthermore, targeting the causes of TRYCAT pathway activation (immune activation and O&NS) is probably the most effective strategy to treat depression. In the present review, we aim to provide a comprehensive explanation of the impact of TRYCATs in terms of pathophysiology and treatment of MDD and MDE.

From the hospital wastewater, a novel bacteriophage was isolated and characterized. According to characterization properties, this bacteriophage belongs to the Siphoviridae family, the maximum bacteriophage titer was recorded at 37°C and a pH of 7.2, had a 44,789 bp linear double-strand DNA genome, and within the genome sequence, there are 61 genes, all of which are encoded into proteins. Although this bacteriophage does not have any virulence factors or antimicrobial resistance genes and had specific lytic activity against some antimicrobial resistance S. aureus clinical isolates.

Since the beginning, humans advanced their civilization by making better tools to improve their lives. Tools and products were designed for better living considering manufacturing issues, cost and time as predominant criteria. It has become clear that not considering environment and society, both at local/global levels, has now become a major impediment affecting living conditions on a large portion of the Earth and in many societies. Design methodologies should lead to creative solutions with consideration to engineering and economics for practicality but also to environmental and social constraints for sustainability. We propose a comprehensive design methodology based on multidisciplinary design to include the knowledge of humanities, environmentalists, science and engineering, and allowing for experts’ inputs from these areas to provide a holistic approach to engineering design . For example, experts in humanities are expected to interact with stakeholders to evaluate their value systems to provide guidance for the design. The methodology that we synthesize is new and combines (i) Societal level impacts at all scales, (ii) Environmental impacts and (iii) Engineering design with economic impacts, including uncertainty considerations. The proposed design methodology is called Social-Environmental-Economical-Engineering Framework (SEEEF). It can utilize concepts and tools such as Circular Design, Doughnut Economics, design based on environmental life cycle analysis, among others. SEEEF is quantity based and provides steps for evaluating any project or product in an objective manner and will help train engineers in design for sustainability. It also provides non-engineers with a significant role in design to increase their understanding of the hard constraints of engineering. Ultimately, SEEEF allows society to take an informed decision considering short/long term and local/global impacts of the design and the pertinent uncertainties.

Schizophrenia comprises various symptom domains, including positive and negative symptoms. Machine learning showed that a) negative symptoms are significantly interrelated with PHEM (psychosis, hostility, excitation, and mannerism) symptoms, formal thought disorders (FTD) and psychomotor retardation (PMR); and b) stable phase schizophrenia comprises two distinct classes, namely Major Neuro-Cognitive Psychosis (MNP, largely overlapping with deficit schizophrenia) and Simple NP (SNP). In this study, we recruited 120 MNP patients and 54 healthy subjects and measured the above-mentioned symptom domains. In MNP, there were significant associations between negative and PHEM symptoms, FTD and PMR. A single latent trait, which is essentially unidimensional, underlies these key domains of schizophrenia and MNP and additionally shows excellent internal consistency reliability, convergent validity, and predictive relevance. Confirmatory Tedrad Analysis indicates that this latent vector fits a reflective model. The lack of discriminant validity shows that PHEM and negative symptoms greatly overlap and probably measure the same construct. Soft Independent Modeling of Class Analogy (SIMCA) shows that MNP (diagnosis based on negative symptoms) is better modeled using PHEM symptoms, FTD, and PMR than negative symptoms. In conclusion, in stable phase MNP, a restricted sample of the schizophrenia population, negative and PHEM symptoms, FTD and PMR belong to one underlying latent vector reflecting overall severity of schizophrenia (OSOS). The bi-dimensional concept of “positive” and “negative” symptoms cannot be validated and, therefore, future research in stable phase schizophrenia should consider that the latent phenomenon OSOS as well as its 8 reflective manifestations are the key factors of schizophrenia phenomenology.

In this study, we present different methods of estimating fuzzy reliability of a two-parameter Rayleigh distribution via the maximum likelihood estimator, median first-order statistics estimator, quartile estimator, L-moment estimator, and mixed Thompson-type estimator. The mean-square error MSE as a measurement for comparing the considered methods using simulation through different values for the parameters and unalike sample sizes is used. The results of simulation show that the fuzziness values are better than the real values for all sample sizes, as well as the fuzzy reliability at the estimation of the Maximum

A microwave technique prepared a superabsorbent polymer (SAP) by grafting two hydrophilic monomers onto a polysaccharide substrate. The monomers used are acrylic acid (AA) or acrylamide (AM) to graft onto a pullulan substrate (PUL) to form PUL-g- AA (SAP1) and PUL-g- AM (SAP2), respectively. The monomers (AM/AA) graft together onto a PUL substrate to form PUL-g-(AM/AA) (SAP3). Grafting parameters such as grafting efficiency with percentage, conversion of monomer to polymer, gel content, water retention, water adsorption capacity, and swelling kinetics were determined. Additionally, the effect of environmental pH (2, 4, 7, 9, and 12) and sodium dodecylbenzene sulfonate (SDBS) surfactant was evaluated, where SDBS was added by 1,2,3,4 and 5 mM to form (SAP4 to SAP8). FTIR results show that AM grafted on PUL through an aliphatic C-N bond, while AA grafting occurred through a single C-C bond. The grafting efficiency with AM is higher than with AA, as well as their gel contents. Water absorbance capacity increased with grafting of AA or AM separately, and water retention was enhanced. The highest absorbent capacity, water retention, gel content, and grafting parameters values were obtained with 3 mM SDBS content and pH7. The swelling kinetics showed that the increases in the theoretical and experimental swelling equilibriums were 72 % and 82%, respectively, at SAP6 compared to the values of these parameters in SAP3. The water absorption capacity of the hydrogel increases with increasing pH to 7 and then gradually decreases. XRD improves the crystallinity and crystalline size of the hydrogel after grafting polymerization for AM/AA into PUL, in addition to enhancing thermal stability. On the contrary, FE-SEM demonstrated that SDBS improves the porosity and pore size of the hydrogel surface in SAP6.

Enteropathogenic microorganisms like Escherichia coli cause severe intestinal problems by disrupting the gut homeostasis. The live microorganisms, when given in adequate quantities provide several beneficial effects to the host are known as probiotics. One of the pronounced benefits conferred by probiotic is to antagonize the growth of enteropathogens competing for adherence to the intestinal epithelium. Bifidobacterium is the major genera of human especially infant are intestinal microbiota. In current study, Bifidobacterium bifidum was isolated from the infant stools and probiotic potential was assessed using prescribed tolerance tests against low pH, gastric juices and bile salts. Anti-infectious activity of probiotic Bifidobacterium bifidum against enteropathogenic E. coli was checked both in vitro and in vivo using agar well diffusion assay and mice model respectively. Mice feces were evaluated for both Bifidobacterium bifidum and E. coli counts in all groups and analyzed statistically. In vitro results showed Bifidobacterium bifidum possess marked antibacterial activity against E. coli. There was significant decrease in enteropathogenic E. coli burden in the mice group fed with Bifidobacterium bifidum before and after challenge. In conclusion, the endogenous Bifidobacterium bifidum have excellent probiotic potential and can be used prophylactic and treatment option against enteropathogens.

Condition monitoring is a primary principle for predictive maintenance. In this regard, several techniques have been employed, each of which has its strengths and weaknesses. In this work, the relationships between the electrical (ε', ε", tan δ) and chemical (Fe, Pb, Cu, Cr, Al, Si, Zn) properties of engine lubricants were investigated using soft computing models. The models' performance was evaluated considering some criteria, including RMSE and MAPE. The RBF model was determined as the best one to predict chemical properties (Fe, Pb, Cu, Cr, Al, Si, Zn) through electrical indexes (ε', ε", tan δ). The RBF’s parameters (e.g., hidden size and training algorithm) were then optimized for optimum performance. For instance, in the RBF model implemented to predict Al, the best hidden size and training algorithmic were 15 and 'trainlm', respectively. Finally, by sensitivity analysis it was observed that eliminating any of the inputs does not improve the performance of the RBF model performance. In general, the results of the current research showed that the electrical properties (e.g., ε', ε", tan δ) have the capability of estimating the contaminants in the lubricants which can improve the existing monitoring methods.

Lamb wave-based structural health monitoring is widely acknowledged as a reliable 11 method for damage identification, classification, localization and quantification. However, due to 12 the complexity of Lamb wave signals, especially after interacting with structural components and 13 defects, interpreting these waves and extracting useful information about the structure's health is 14 still a major challenge. However, deep learning-based strategy offers a great opportunity to address 15 such challenges as the algorithm can operate directly on raw discrete time-domain signals. Unlike 16 traditional methods, which often require careful feature engineering and preprocessing, deep learn-17 ing can automatically extract relevant features from the raw data. This paper proposes an autoen-18 coder based on a bidirectional long short-term memory network with maximal overlap discrete 19 wavelet transform layer to detect the signal anomaly and determine the location of the damage in 20 the composite structure. This approach has the potential to greatly enhance our ability to detect and 21 locate structural damage in composite structures, thereby increasing safety and efficiency.

The most frequent, noticeable, and frequent natural calamity in the karakoram region is landslides. Extreme landslides have occurred frequently along Karakoram highway, particularly during the monsoon, causing a major loss of life and property. Therefore, it was necessary to look for a solution to increase growth and vigilance in order to lessen losses related to landslides caused by natural disasters. By utilizing contemporary technologies, an early warning system might be developed. Artificial neural networks (ANNs) are widely used nowadays across many industries. This paper's major goal is to provide new integrative models for assessing landslide susceptibility in a prone area of north of Pakistan. To do this, the training of an artificial neural network (ANN) is supervised using metaheuristic and Bayesian techniques: particle swarm optimization algorithm (PSO), Genetic algorithm (GA), Bayesian optimization Gaussian process (BO_GP), and Bayesian optimization Gaussian process (BO_TPE). 304 previous landslides and the eight most prevalent conditioning elements combine to form a geographical database. The models are hyper-parameter optimized, and the best ones are employed to generate the susceptibility maps. The area under the receiving operating characteristic curve (AUROC) accuracy index found demonstrated that the maps produced by both Bayesian and metaheuristic algorithms are highly accurate. The effectiveness and efficiency of applying artificial neural networks (ANNs) for landslide mapping, susceptibility analysis, and forecasting are studied in this research it’s observed from experimentation that the performance differences for GA, BO_GP, and PSO compared to BO_TPE are relatively small, ranging from 0.3166% to 1.8399%. This suggests that these techniques achieved comparable performance to BO_TPE in terms of AUC. However, it's important to note that the significance of these differences can vary depending on the specific context and requirements of the ML task. Additionally in this study, we explore eight feature selection algorithms to determine the geospatial variable importance for landslide susceptibility mapping along the KKH. The algorithms considered include Information Gain, Gain Ratio, OneR Classifier, Subset Evaluators, Principal Components, Relief Attribute Evaluator, Correlation, and Symmetrical Uncertainty. These algorithms enable us to evaluate the relevance and significance of different geospatial variables in predicting landslide susceptibility. By applying these feature selection algorithms, we aim to identify the most influential geospatial variables that contribute to landslide occurrences along the KKH. The algorithms encompass a diverse range of techniques, such as measuring entropy reduction, accounting for attribute bias, generating single rules, evaluating feature subsets, reducing dimensionality, and assessing correlation and information sharing. The findings of this study will provide valuable insights into the critical geospatial variables associated with landslide susceptibility along the KKH. These insights can aid in the development of effective landslide mitigation strategies, infrastructure planning, and targeted hazard management efforts. Additionally, the study contributes to the field of geospatial analysis by showcasing the applicability and effectiveness of various feature selection algorithms in the context of landslide susceptibility mapping.

Electro-spun ultra-fine fibers exhibit two significant properties: a high surface-to-volume ratio and a relatively defect-free molecular structure. Due to the high surface-to-volume ratio, electro-spun materials are well suited for activities requiring increased physical contact, such as providing a site for a chemical reaction or filtration of small-sized physical materials. However, electrospinning has many shortcomings, including difficulties in producing inorganic nanofibers and a limited number or variety of polymers used in the process. The fabrication of nanofiber bundles via electrospinning is explored in this analytical study, as well as the relationship between extrinsic electrospinning parameters and the relative abundance of various fiber morphologies. Numerous variables could impact the fabrication of nanofibers, resulting in a variety of morphologies; therefore, adequate ambient conditions and selecting the appropriate solvent for achieving a homogenous polymer solution and uniform electro-spun materials are examined. Finally, common polymers suitable for electrospinning and the promising applications of ultra-fine fibers achieved via electrospinning are studied in this paper.

The modeling and prediction of crop seed yield can be a vital improvement in the precision agriculture industry as it provides reliable assessments of the effectiveness of agro-traits. Here, multiple machine learning (ML) techniques are established for predicting sesame (Sesamum indicum L.) seed yield (SSY) and incorporating agro-morphological features. Models utilized for coupled PCA-ML (Principal component analysis-Machine Learning) methods were compared with original ML models to evaluate predicted efficiency. The Gaussian process regression (GPR) and Radial basis function neural network (RBF-NN) models exhibited the most accurate SSY predictions with determination coefficients or R2 values of 0.99 and 0.91, respectively. The root-mean-square error (RMSE) for the ML models fluctuated between 0 to 0.30 t/ha (metric tons/hectare) for the varied modeling process phases. Estimation of sesame seed yield with coupled PCA-ML models improved performance accuracy. The K-fold process suggested the utilization of datasets with the lowest error rates to ensure the continued accuracy of GPR and RBF models. Sensitivity analysis revealed the capsule number per plant (CPP), seed number per capsule (SPC), and 1000-seed weight (TSW) were the most significant seed yield determinants.

CONTEXT– Energy is widely involved in human activity and corresponding emissions of SOX, NOX and CO2 from energy generation processes affect global climate change. Clean fuels are desired by society because of their reduced greenhouse gas emissions. Hydrogen is once such candidate fuel. Much hydrogen is produced from fossil fuel, with biomass being an alternative process. OBJECTIVE– The project compared the environmental impact of hydrogen production by natural gas steam reforming vs. biomass gasification. METHOD–Environmental impact was calculated from the input and output data from life cycle inventory analysis. The impact assessment was focused on greenhouse gas emission, acidification, and eutrophication. Models of the two processes were developed and analysed in OpenLCA. The agribalyse database was used to connect inventory flow data to environmental impacts. FINDINGS– For all three metrics, biomass gasification had lower impacts than natural gas steam reforming, sometimes by large margins. For biomass gasification the silica sand production contributes most to all three impact categories, whereas for natural gas steam reforming it is the LPG extraction.

Based on the resource-based theory, the current study examines the relationship between competitive strategies and strategic alliance performance. Furthermore, big data predictive analytics is treated as a boundary condition between competitive strategies and strategic alliance performance. Big data of predictive analytics in operations and industrial management has been a focal point in the current era. There has been little attention has about big data predictive analytics influences on competitive strategies and strategic alliance performance, especially in developing countries like Pakistan. A survey instrument was used to record the responses from 331 employees of the telecom sectors companies working in Pakistan. Study findings show that big competitive strategies have a positive and significant relationship with strategic alliances performance. It was also found that big data predictive analytics plays the role of moderator between competitive strategies and strategic alliance performance. The study add a new perspective and contribution to the literature on big data predictive analytics, strategic alliance performance, and competitive strategies in Pakistan's telecom sector companies. Further, the study results explain that big data analytics is just like the companies' lifeblood in the current era. The efficient and effective use of big data analytics, companies can boost their standards in a competitive environment.

In this work, we study the weak gravitational lensing in the background of Kerr-Newman black hole with quintessential dark energy. Initially, we compute the deflection angle of light by charged black hole with quintessential dark energy by utilizing the Gauss-Bonnet theorem. Firstly, we suppose the light rays on the equatorial plane in the axisymmetric spacetime. In doing so, we first find the corresponding optical metrics and then calculate the Gaussian optical curvature to utilize in Gauss-Bonnet theorem. Consequently, we calculate the deflection angle of light for rotating charged black hole with quintessence. Additionally, we also find the deflection angle of light for Kerr-Newman black hole with quintessential dark energy. In order to verify our results, we derive deflection angle by using null geodesic equations which reduces to the deflection angle of Kerr solution with the reduction of specific parameters. Furthermore, we analyze the graphical behavior of deflection angle $\Theta$ w.r.t to quintessence parameter $\alpha$, impact parameter $b$, BH charge $Q$ and rotation parameter $a$. Our graphical analysis retrieve various results regarding to the deflection angle by the Kerr-Newman black hole with quintessential dark energy.

In this paper, we are interested in a model of exact asymptotically flat charged hairy black holes in the background of dilaton potential. We study the weak gravitational lensing in the spacetime of hairy black hole in Einstein-Maxwell theory with a non-minimally coupled dilaton and its non-trivial potential. In doing so, we use the optical geometry of the flat charged hairy black hole for some range of parameter $\gamma$. For this purpose, by using Gauss-Bonnet theorem, we obtain the deflection angle of photon in a spherically symmetric and asymptotically flat spacetime. Moreover, we also investigate the impact of plasma medium on weak gravitational lensing by asymptotically flat charged hairy black hole with a dilaton potential. Our analytically analyses show the effect of the hair on the deflection angle in weak field limits.

The high figure of merit and earth abundance of Cu₁₂Sb₄S₁₃ thermoelectric ma- terials have recently attracted many attentions toward these type of complex compounds. Intrinsic low thermal conductivity, as well as tunable electronic transport properties, make them suitable for thermoelectric power generation. In this study, we perform a comparative theoretical study on the substituted compounds, primarily at the Cu site including known tetrahedrite Cu₁₂Sb₄S₁₃, by means of first-principles calculations. The density functional theory of electric structure is applied to investigate the result of substitution.

In the present research, we will develop some integral inequalities of Hermite Hadamard type for differentiable η-convex function. Moreover, our results include several new and known results as special cases.

Over-capacity flight scheduling by commercial airlines due to the surging demand in recent years creates congestion and significant delays at major airports. This maximizing throughput attitude calls for tactical flight rescheduling to comply with airports’ capacity limitations and distribute the peak hour demand over the course of a day. Such displacements of flights may cause significant problems and costs for airlines and some cancellations or missed connections for the passengers. This paper presents an optimization model for flight rescheduling at a schedule-coordinated airport to minimize congestion and flight delays. The optimization model is used to make better scheduling intervention decisions considering the airport resource constraints and safety of operation. We have also developed a simulation method to replicate arrival and departure processes in such an airport. The simulation adheres to a first come first served (FCFS) discipline and enforces runway capacity constraints and minimum turnaround times. We compare the delays caused by an ad hoc FCFS operation with those obtained by the optimization model. Computational results from a case study demonstrate that a reduction of 40.7% and 61.6% in total delay times for the arrival and departure flights can be achieved by the optimization model. It also facilitates implementing a collaborative decision-making system for better coordination with commercial airlines.

Background. The aim of this study is to examine whether biomarkers of the immune-inflammatory response (IRS) and endogenous opioid (EOS) systems are associated with affective symptoms in schizophrenia. Methods. We recruited 115 schizophrenia patients and 43 healthy controls and assessed the Hamilton Depression (HDRS) and Anxiety (HAM-A) rating Scale scores as well as serum levels of interleukin (IL)-6, IL-10, eotaxin (CCL11), high mobility group box 1 (HMGB1), Dickkopf-related protein 1 (DKK1), and mu (MOR) and kappa (KOR) opioid receptors.Results. The HDRS and HAM-A scores are significantly and positively correlated with a) psychosis, hostility, excitation, mannerism, negative symptoms, psychomotor retardation, and formal thought disorders; and b) lowered scores on semantic and episodic memory, executive functions, and attention tests as measured with the Brief Assessment of Cognition in Psychiatry. Both HDRS and HAM-A are significantly increased in non-responders to treatment as compared with partial responders. Both affective scores are strongly associated with latent vectors extracted from all symptoms, reflecting overall severity of psychosis (OSOS), and neurocognitive test scores, reflecting a generalized cognitive decline (G-CoDe). The HDRS score was strongly and positively associated with IL-6, HMGB1, KOR, and MOR levels, and the HAM-A score with IL-6, IL-10, CCL11, HMGB1, KOR, and MOR levels. A single latent trait may be extracted from OSOS, G-CoDe, and the HDRS and HAMA scores, and this latent vector score is strongly predicted by HMGB1, MOR, and DKK1.Conclusion. Immune-inflammatory and EOS pathways contribute to the phenome of schizophrenia, which comprises OSOS, affective, and physiosomatic symptoms, and G-CoDe.

The population growth of major cities in Iran and the lack of attention to scientific and managerial principles in urban planning in recent years have created numerous issues for citizens and urban planners, pushing cities towards instability. Tehran, as the capital of Iran, is among the cities facing numerous urban challenges. It appears that these factors will present serious challenges to the city's future sustainability. Many articles have assessed and analyzed indicators of sustainability in cities, but less attention has been paid to indicators of instability and real issues that pose challenges to urban planning sustainability. In this regard, the present study aims to identify the challenges of sustainability in urban planning by re-evaluating indicators related to urban sustainability and assessing indicators of instability in urban planning from a realistic perspective. The results indicate that the major indicators of urban planning instability have been summarized into three economic, social, and environmental indices. In the economic index, the unstable components in urban planning include uncertainty in economic policies, housing prices, non-standard housing, income, employment, unemployment, cost of living, and female-headed households. In the environmental index, the unstable components in urban planning consist of climate change, heat islands, floods, transportation management, energy security, noise and air pollution, urban traffic, water quality and quantity, waste disposal quality, urban green infrastructure, environment as an aesthetic issue, and changing the approach to the environment with changes in individuals. In the social index, the unstable components in urban planning encompass social interaction, access to facilities and services, access to social harms services, sense of identity and belonging, neighborly relations, social segregation, public participation in city affairs, and security and welfare.

No studies have examined the immune fingerprint of major neuro-cognitive psychosis (MNP) or deficit schizophrenia using M1 macrophage cytokines in combination with chemokines such as CCL-2 and CCL-11. The present study delineated the neuro-immune fingerprint of MNP/deficit schizophrenia by analyzing plasma levels of IL-1β, sIL-1RA, TNF-α, sTNFR1, sTNFR2, CCL-2 and CCL-11 in MNP (n=120) versus healthy controls (n=54) in association with neurocognitive deficits (as assessed with the Brief Assessment of Cognition in Schizophrenia) and PHEMN (psychotic, hostility, excitation, mannerism and negative) symptoms. All immune biomarkers were significantly higher in MNP than in normal controls. MNP was best predicted by a combination of CCL-11, TNF-α, IL-1β and sIL-1RA which yielded a bootstrapped (n=2000) area under the Receiver Operating Curve of 0.985. Composite scores reflecting M1 macrophage activity and neurotoxic potential including combined effects of CCL-11 plus CCL-2 were significantly increased in MNP. Nevertheless, the effects of increased IL-1β and TNF-α in MNP were attenuated (statistically) by increased sIL-1RA and sTNFR2, two negative immune-regulatory markers. A large part of the variance in PHEM (38.4%-52.6%) and negative (65.8-7439%) symptoms was explained by combinations of immune markers whereby CCL-11 was consistently the most important. The immune markers also explained a large part of the variance in the Mini Mental State examination, list learning, digit sequencing task, category instances, controlled word association, symbol coding and Tower of London. Soft Independent Modeling of Class Analogy performed on the biomarkers showed that the inter-class distance between the models constructed around MNP and controls was 19.3 indicating a good separation. Partial Least Squares analysis showed that 72.7% of the variance in overall phenomenology was explained by the regression on IL-1β, sIL-1RA, CCL-11, TNF-α (all positively) and education (inversely). It is concluded that the combination of the above-mentioned markers defines MNP as a distinct neuro-immune disorder and that those markers in combination explain a large part of the variance in memory and executivive impairments and PHEMN symptoms.

This research paper aims to examine the relationship between CO2, temperature, area, fertilizers and rice production in Pakistan. This study used Augmented Dickey Fuller (ADF) and Phillips Perron (PP) unit root tests to check the order of integration of each variable. The cointegration analysis with ARDL bounds testing approach is used to examine the impact of climate change on rice production in Pakistan over time series data from the period 1968 to 2014. The parameter stability test of the model is also checked at the end. The results of estimation show that the important variables of the study are cointegrated demonstrating the presence of long-run association among them. Furthermore, climate change factors, e.g. CO2 and temperature have a long-run and short-run positive effect on the production of rice in Pakistan. This present work is original and it is first time empirically tested the impact of climate change on rice production in Pakistan. The annual time series data of 47 years enhances the validity of the empirical findings. The most fruitful finding of this research is that rice production in Pakistan is positively influenced by emission of carbon dioxide (CO2) at 5 percent significance level in both long-run and short-run.

We employ the solution concept of ex post Nash equilibrium to predict the interaction of a finite number of agents competing in a finite number of basic games simultaneously. The competition is called a multi-game. For each agent, a specific weight, considered as private information, is allocated to each basic game representing its investment in that game and the utility of each agent for any strategy profile is the weighted sum, i.e., convex combination, of its utilities in the basic games. Multi-games can model decision making in multi-environments in a variety of circumstances, including decision making in multi-markets and decision making when there are both material and social utilities for agents as, we propose, in the Prisoner's Dilemma and the Trust Game. Given a set of pure Nash equilibria, one for each basic game in a multi-game, we construct a pure Bayesian Nash equilibrium for the multi-game. We then focus on the class of so-called uniform multi-games in which each agent is constrained to play in all games the same strategy from an action set consisting of a best response per game. Uniform multi-games are equivalent to multi-dimensional Bayesian games where the type of each agent is a finite dimensional vector with non-negative components. A notion of pure type-regularity for uniform multi-games is developed and it is shown that a multi-game that is pure type-regular on the boundary of its type space has a pure ex post Nash equilibrium which is computed in constant time with respect to the number of the types and is independent of prior probability distributions. We then develop an algorithm, linear in the number of types of the agents, which tests if a multi-game is pure type-regular on the boundary of its type space in which case it returns a pure ex post Nash equilibrium for the multi-game.

In this paper, existence and stability results of non-local integro-differential equation with Hilfer derivative of order ω∈(1,2) have been investigated. In this derivative is applied in RLC circuit equation of various fractional order. To proposed problem the existence solutions are derived using Schaefer’s fixed point theorem and the Banach contraction principle is using for uniqueness results. To demonstrate the effectiveness and applicability of our theoretical conclusions and two-step Lagrange polynomial interpolation were used to solve four numerical results.

In this article, we present the use of the coupled land surface model and groundwater flow model SWAT-MODFLOW with the decision support tool WEAP (Water Evaluation and Planning software) to predict future surface water abstraction scenarios in a complex river basin, under conditions of climate change. The modeling framework is applied to the Dee River catchment in Wales, United Kingdom. Regarding hydrology, the coupled model improves overall water balance and low streamflow conditions, compared to a stand-alone SWAT model. The calibrated SWAT-MODFLOW is employed with high resolution climate model data from the UKCP18 project with future scenario of RCP85 from 2020 to 2040. Then, water supply results from SWAT-MODFLOW are fed into WEAP as input for the river reach in the downstream region of the river basin. This system is utilized to create various future scenarios of surface water abstraction of public water supply in the downstream region: maximum licensed withdraw, 50 % authorized abstractions, monthly time series with 1% increase of water use, and maximum water withdraw year based on historical records repeated every year with 1% increase of water use, to estimate the unmet demands and streamflow requirement. This modeling approach can be used in other river basins to manage scenarios of supply and demand.

High pressure crystallization technique has been successfully used to prepare an elusive form II of a non-steroidal anti-inflammatory drug, Mefenamic acid. Single crystal of form II was grown at 0.3 GPa from an 4:1 methanol/ ethanol mixture as a solvent using Diamond Anvil Cell. Comparison of crystal structures show that the efficient packing of MA molecules in Form II results from the structural flexibility of MA acid molecules. Compression studies performed on the single crystal of form I resulted in 14 % decrease of unit cell volume up to 2.5 GPa. No phase transition was observed up to this pressure. A reconstructive phase transition is required to induce conformational changes in the structure, which is confirmed by crystallization at high pressure results.

In recent years, there has been a growing interest in the preoperative modeling of fluid-structure interaction in the treatment of cerebral aneurysms. In this study, we investigate two cases involving laminar incompressible fluid flow interacting with hyperelastic materials (a vertical flap and aneurysm walls) under the effect of fluid flow. We present a finite element method (FEM) for these prototypical two-dimensional ($2D$) configurations, taking into account the complex flows and deformation of the variant's structure models. We use an Arbitrary Lagrangian-Eulerian (ALE) formulation in a continuum, fully monolithic coupled way, and discretize the fluid and solid domains using the quadratic LBB stable $P_2P_1$ finite element pair to approximate the displacement, velocity, and pressure spaces independently. The resulting discretized form of the nonlinear algebraic system is linearized using a variant of Newton's procedure, and the Jacobian matrices are approximated via the divided difference method. The resulting linear systems are solved using a direct solver MUMPS. We evaluate hydrodynamic forces such as drag and lift coefficients for nonlinear elastic material models, including Saint-Venant Kirchoff, Neo-Hookean, and Mooney-Rivlin (2 parameters) separately. To gain more physical insight into the problem, we verify the computed results by comparing the velocity, viscosity, and pressure fields. Our aim is to qualitatively analyze the changes in the mechanics of the wall behavior of the elasticity of the flap vs. the complexity of deformation, interface, details for prototypical flow situations, corresponding displacement, and deformation. We also provide a physical interpretation of blood flow magnitude and basic hemodynamic phenomena of wall shear stresses (WSS) in aneurysma and the range of deformation. Overall, this study presents a comprehensive approach to modeling fluid-structure interaction in cerebral aneurysms, which could help in the development of more effective treatment strategies.

Blending lignin as the second most abundant polymer in nature with nanostructured compounds such as dendritic polymers will not only add value to lignin, but also increase its application in various fields. In this study, softwood Kraft lignin/polyamidoamine dendritic polymer (PAMAM) blends were fabricated by solution electrospinning method to produce bead-free nanofiber mats. The mats were characterized by scanning electron microscopy (SEM), Fourier transform infrared (FTIR), zeta potential, and thermogravimetry analysis (TGA). The chemical intermolecular interactions between lignin functional groups and abundant amino groups in PAMAM were investigated by FTIR and viscosity measurement. These interactions enhanced the mechanical and thermal characteristics of lignin/PAMAM mats, providing further potential applications at industry level.

Machine learning (ML) has a large capacity to learn and analyze a large volume of data. This study aimed to train different algorithms to discriminate between healthy and pathologic corneal images by evaluating digitally processed spectral-domain optical coherence tomography (SD-OCT) corneal images. A set of 22 SD-OCT images belonging to a random set of corneal pathologies was compared to 71 healthy corneas (control group). A binary classification method was applied; three approaches of ML were used. Once all images were analyzed, representative areas from every digital image were also processed and analyzed for a statistical feature comparison between healthy and pathologic corneas. The best performance was obtained from transfer learning - support vector machine (TL-SVM) (AUROC = 0.94, SPE 88%, SEN 100%) and transfer learning – random forest (TL- RF) method (AUROC = 0.92, SPE 84%, SEN 100%), followed by convolutional neural network (CNN) (AUROC = 0.84, SPE 77%, SEN 91%) and random forest (AUROC = 0.77, SPE 60%, SEN 95%). The highest diagnostic accuracy in classifying corneal images was achieved with the TL-SVM and the TL-RF models. In image classification, CNN was a strong predictor. This pilot experimental study developed a systematic mechanized system to discern pathologic from healthy corneas.

A subset of patients with schizophrenia experience physio-somatic symptoms reminiscent of chronic fatigue and fibromyalgia. In schizophrenia, these symptoms contribute to impaired quality of life, and are strongly related to neuro-cognitive deficits, and increased IgA responses to tryptophan catabolites. Negative and PHEM (psychosis, hostility, excitation, mannerism) symptoms, psychomotor retardation (PMR) and formal thought disorders, appear to be manifestations of a single trait reflecting overall severity of schizophrenia (OSOS). In this study, 120 patients with deficit schizophrenia (DEFSCZ) and 54 healthy subjects were assessed with the FibroFatigue (FF) rating scale, and the above-mentioned symptom domains as well as neuro-cognitive tests and biomarkers were measured. In DEFSCZ, there were robust associations between the FF score and all above-mentioned symptom domains, and impairments in semantic and episodic memory and executive functions. Furthermore, the FF score loaded highly on an OSOS latent vector (LV), which showed adequate convergent validity, internal consistency reliability and predictive relevance and fitted a reflective model. Soft Independent Modelling of Class Analogy (SIMCA) showed that the FF items discriminated DEFSCZ from controls with an overall accuracy of 100%. Interleukin IL-1β, IL-1 receptor antagonist (sIL-1RA), tumour necrosis factor (TNF)-α and CCL-11 (eotaxin) explained 66.8% of the variance in the FF score and 59.4% of the variance in OSOS. In conclusion, these data show that physio-somatic symptoms are a core component of the phenomenology of DEFSCZ and are largely mediated by neurotoxic effects of activated immune pathways, including aberrations in CCL-11, IL-1β and TNF-α signalling.

Peste des Petits Ruminants (PPR) is one of the major transboundary animal diseases that severely impact livestock production and trade worldwide. An investigation was carried into the seroprevalence and risk factors of PPR in small ruminants, camels, and wild ruminants in Bahrain. A total of 1240 serum samples were collected from 119 sheep flocks and 1224 serum samples from 125 goat herds. In addition, 100 sera from camels and 38 sera from wild ruminants, were collected. Serum samples were tested for the presence of PPR specific antibodies using a competitive ELISA. Flock management and health information of the flocks and herds were collected using a semi-structured questionnaire. The individual prevalence of PPR in sheep and goats was 26% and 25.5%, respectively. The flock or herd level prevalence of PPR was 22.7% and 29.6% in sheep and goats, respectively. None of the tested camels or wild ruminants were positive for PPR-specific antibodies. Sheep and goats from the Northern governorate were more likely to be seropositive to PPR than in other governorates. On the individual animal level, the univariate analysis suggested; age (<12 months), sex (male), and health status (weak animals) as risk factors for PPR seropositivity. On the flock/herd level, the multivariable logistic regression analysis identified large flock/herd size and semi-intensive farming as risk factors for PPR seropositivity.

The advent of high throughput sequencing has enabled researchers to systematically evaluate the genetic variations in cancer, resulting in identifying many cancer-associated genes. Although cancers in the same tissue are widely categorized in the same group, they demonstrate many differences concerning their mutational profiles. Hence there is no “silver bullet” for the treatment of a cancer type. This reveals the importance of developing a pipeline to identify cancer-associated genes accurately and re-classify patients with similar mutational profiles. Classification of cancer patients with similar mutational profiles may help discover subtypes of cancer patients who might benefit from specific treatment types. In this study, we propose a new machine learning pipeline to identify protein-coding genes mutated in a significant portion of samples to identify cancer subtypes. We applied our pipeline to 12270 samples collected from the International Cancer Genome Consortium (ICGC), covering 19 cancer types. Here we identified 17 different cancer subtypes. Comprehensive phenotypic and genotypic analysis indicates distinguishable properties, including unique cancer-related signaling pathways, in which, for most of them, targeted treatment options are currently available. This new subtyping approach offers a novel opportunity for cancer drug development based on the mutational profile of patients. We also comprehensive study the causes of mutations among samples in each subtype by mining the mutational signatures, which provides important insight into their active molecular mechanisms. Some of the pathways we identified in most subtypes, including the cell cycle and the Axon guidance pathways, are frequently observed in cancer disease. Interestingly, we also identified several mutated genes and different rates of mutation in multiple cancer subtypes. In addition, our study on “gene-motif” suggests the importance of considering both the context of the mutations and mutational processes in identifying cancer-associated genes. The source codes for our proposed clustering pipeline and analysis are publicly available at: https://github.com/bcb-sut/Pan-Cancer.

Background: Methadone is a centrally-acting synthetic opioid analgesic widely used in the methadone maintenance therapy (MMT) programs throughout the world. Considering its neurotoxic effects particularly on the cerebellum, this study aims to address the behavioral and histological alterations in the cerebellar cortex associated with methadone administration. Materials and Methods: Twenty-four adult male albino rats were randomized into two groups of control and methadone treatment. Methadone was subcutaneously administered (2.5–10 mg/kg) once a day for two consecutive weeks. The functional and structural changes in the cerebellum were compared to the control group. Results: Our data revealed that treating rats with methadone not only induced cerebellar atrophy, but also prompted the actuation of microgliosis, astrogliosis, and apoptotic biomarkers. We further demonstrated that treating rats with methadone increased complexity of astrocyte processes and decreased complexity of microglia processes. Our result showed that methadone impaired motor coordination and locomotor performance and neuromuscular activity. Additionally, relative gene expression of TNF-α, caspase-3 and RIPK3 increased significantly due to methadone. Conclusions: Our findings suggest that methadone administration has a neurodegenerative effect on the cerebellar cortex via dysregulation of microgliosis, astrogliosis, apoptosis, and neuro-inflammation.

Family Phyllanthaceae is one of the largest segregates of the eudicot order Malpighiales and its species are herb, shrub, and tree, which are mostly distributed in tropical regions. Certain taxonomic discrepancies exist at genus and family level. Here, we report chloroplast genomes of three Phyllanthaceae species—Phyllanthus emblica, Flueggea virosa, and Leptopus cordifolius— and compare them with six others previously reported Phyllanthaceae chloroplast genomes. The species of Phyllanthaceae displayed quadripartite structure, comprising inverted repeat regions (IRa and IRb) that separate large single copy (LSC) and small single copy (SSC) regions. The length of complete chloroplast genome ranged from 154,707 bp to 161,093 bp; LSC from 83,627 bp to 89,932 bp; IRs from 23,921 bp to 27,128 bp; and SSC from 17,424 bp to 19,441 bp. Chloroplast genomes contained 111 to 112 unique genes, including 77 to 78 protein-coding, 30 transfer RNA (tRNA), and 4 ribosomal RNA (rRNA) that showed similarities in arrangement. The number of protein-coding genes varied due to deletion/pseudogenization of rps16 genes in Baccaurea ramiflora and Leptopus cordifolius. High variability was seen in number of oligonucleotide repeats while analysis of guanine-cytosine (GC) content, codon usage, amino acid frequency, simple sequence repeats analysis, synonymous and non-synonymous substitutions, and transition and transversion substitutions showed similarities in all Phyllanthaceae species. We detected a higher number of transition substitutions in the coding sequences than non-coding sequences. Moreover, the high number of transition substitutions was determined among the distantly related species in comparison to closely related species. Phylogenetic analysis shows the polyphyletic nature of the genus Phyllanthus which requires further verification. We also determined suitable polymorphic coding genes, including rpl22, ycf1, matK, ndhF, and rps15 which may be helpful for the reconstruction of the high-resolution phylogenetic tree of the family Phyllanthaceae using a large number of species in the future. Overall, the current study provides insight into chloroplast genome evolution in Phyllanthaceae.

Membranes are a selective barrier that allows certain species (molecules and ions) to pass through while blocking others. Some rely on size exclusion, where larger molecules get stuck while smaller ones permeate through. Others use differences in charge or polarity to attract and repel specific species. Membranes can be used to purify air and water by allowing only air and water molecules to pass through but preventing contaminants such as microorganisms and particles or to separate target gas or vapor, such as H2, CO₂, from other gases. The higher the flux and selectivity, the better a material is for membranes. The desirable performance can be tuned through material type (polymers, ceramics, and biobased materials), microstructure (porosity and tortuosity), and surface chemistry. Most membranes are made from plastic from petroleum-based resources, which contribute to global climate change and plastic pollution. Cellulose can be an alternative sustainable resource to make renewable membranes. Cellulose exists in plant cell walls as natural fibers, which can be broken down into smaller components such as cellulose fibrils, nanofibrils, nanocrystals, and cellulose macromolecules through mechanical and chemical processing. Membranes made from reassembling these particles and molecules have variable pore architecture, porosity, and separation properties and therefore have a wide range of applications in nano-, micro-, and ultrafiltration or forward osmosis. Despite their advantages, cellulose membranes face some challenges. Improving the selectivity of membranes for specific molecules often comes at the expense of permeability. The stability of cellulose membranes in harsh environments or under continuous operation needs further improvement. Research is ongoing to address these challenges and develop advanced cellulose membranes with enhanced performance. This article reviews the microstructure, fabrication methods, and potential applications of cellulose membranes, providing some critical insights into processing-structure-property relationships for current state-of-the-art cellulosic membranes that could be used to rationally improve their performance.

Digital Image Correlation (DIC) is an optical technique used to measure surface displacements and strains in materials and structures. This technique has demonstrated significant utility in structural examination and monitoring. This manuscript offers a comprehensive review of contemporary research and applications that have leveraged the DIC technique in laboratory-based structural tests. The reviewed works encompass a broad spectrum of structural components, such as concrete beams, columns, pillars, masonry walls, infills, composite materials, structural joints, steel beams, slabs, and other structural elements. These investigations have underscored the efficacy of DIC as a metrological instrument for precise quantification of surface deformation and strain in these structural components. Moreover, the constraints of the DIC technique have been highlighted, especially in scenarios involving extensive or complex test configurations. Notwithstanding these constraints, the DIC methodology has validated its effectiveness as a strain measurement instrument, offering numerous benefits such as non-invasive operation, full-field measurement capability, high precision, real-time surveillance, and compatibility with integration into other measurement instruments and methodologies.

The Chiltan Formation is a potential hydrocarbon-producing reservoir in the Indus Basin, Pakistan. However, its diagenetic alterations and heterogeneous behavior lead to significant challenges in accurately characterizing the reservoir and production performance. This manuscript aims to utilize Chiltan Limestone core samples to conduct a detailed analysis of the diagenetic impacts on reservoir quality. The formation evaluation was carried out through thin section analysis, SEM-EDS and FTIR investigation, as well as plug porosity and permeability measurements under different stress conditions. In results, Petrography revealed three microfacies with distinct diagenetic features and micro-nano fossil assemblages, including intraclasts, pelloids, bioclasts, and stylolites. Diagenesis has a significant impact on petrophysical properties, leading to increased reservoir heterogeneity. The specified depositional environment exposed the alteration of the Chiltan formation during distinct diagenetic phases in marine, meteoric, and burial settings. Pore morphology and mineralogy reveals complex micro-pore structure with various carbonate mineral phases and cement types in the formation samples. The average porosity and permeability of the core samples were found to be 3.4% and 0.449 mD respectively, indicating poor reservoir quality. Stress sensitivity was determined using Klinkenberg corrected permeability, which indicated a reduction in measured and absolute permeability due to increasing confining stress, further reducing the pore throat structure of the formation. In conclusion, the Chiltan formation possesses intricate reservoir heterogeneity and varied micro-pore structure caused by diagenesis and depositional settings. The formation is classified as a low-quality reservoir due to its non-uniform pore geometry, which exhibits low petrophysical properties resulting from overburden stress. The results of this study provide solid foundation in reservoir characterization and quality assessment which has implications for exploring and exploitation of indigenous resources.

Background: Aberrations in endothelial cells, immune and oxidative pathways are associated with atherosclerosis (ATS) and unstable angina (UA). The role of trace elements, minerals, and the endogenous opioid system (EOS) in UA are less well established. Methods: We measured lipid, insulin resistance (IR), and immune, trace element (copper and zinc), mineral (magnesium, calcium), EOS (β-endorphin and mu-opioid receptor (MOR)) and antioxidant (vitamin D3) biomarkers in patients with ATS (n=60) and UA (n=60) and healthy controls (n=58). Results: ATS patients showed increased atherogenic and IR indices, IL-6, IL-10, β-endorphin, copper and magnesium, and lower zinc than healthy controls. Logistic regression showed that UA was significantly discriminated from ATS without UA with an accuracy of 85.5% using calcium, IL-10, β-endorphin, MOR, triglycerides, IR (all positively), and copper and vitamin D3 (inversely). Neural networks showed that UA was discriminated from ATS without UA with an area under the ROC curve of 0.942 using MOR, β-endorphin, calcium, insulin resistance, vitamin D3 and copper as input variables. We found that 50.0% of the variance in IR was explained by the regression on copper, IL-10, IL-6 (all positively), and zinc (inversely), while 32.9% of the variance in the atherogenic index of plasma was explained by copper, IL-10 (both positively), and magnesium (inversely). Conclusion: UA is not only mediated by insulin resistance, atherogenicity, and immune disorders, but also by aberrations in the endogenous opioid system and trace elements as well as lowered antioxidant levels. Copper appears to play a key role in IR and atherogenicity.

This study aims to assess food security status of rural, peri-urban and urban households and role of socio-economic factors in ensuring food security. A survey was conducted to collect primary (quantitative) data from 630 respondents using proportionate sampling technique from Punjab, Pakistan. Cronbach’s Alpha reveals stability (>0.75) of data scale items. Using Dietary Intake Assessment (DIA), findings reveal urban households as highly food-secure whereas rural household as most vulnerable to food security at 2450kcal/day per person. Study indicates food price increase as the leading factor in preventing food security among households. Contrary, study identifies risk factors associated to food quality under the abundant availability and access as the new dimension to household food insecurity. Further, results of binary logit model show that socio-economic factors such as monthly income, total number of earners, education of household head and access to market are positively related with food security status whereas, social isolation, volatility in food prices and risk factors in food quality are negatively related with household food security. Therefore, study proposes educational orientation and entrepreneurial development as sustainable solution to ensure food security among masses. Besides, further country level researches are advised to deep delve the mounting menace of food security.

The main goal of this paper is to study the weak gravitational lensing by Horndeski black hole in weak field approximation. In order to do so, we exploit the Gibbons-Werner method to the optical geometry of Horndeski black hole and implement the Gauss-Bonnet theorem to accomplish the deflection angle of light in weak field region. Furthermore, we have endeavored to extend the scale of our work by comprising the impact of plasma medium on the deflection angle as properly. Later, the graphical influence of the deflection angle of photon on Horndeski black hole in plasma and non-plasma medium is examined.

The principal objective of this project is to investigate the gravitational lensing by asymptotically flat black holes in the framework of Horndeski theory in weak field limits. To achieve this objective, we utilize the Gauss-Bonnet theorem to the optical geometry of asymptotically flat black holes and applying the Gibbons-Werner technique to achieve the deflection angle of photons in weak field limits. Subsequently, we manifest the influence of plasma medium on deflection of photons by asymptotically flat black holes in the context of Horndeski theory. We also examine the graphical impact of deflection angle on asymptotically flat black holes in the background of Horndeski theory in plasma as well as non-plasma medium.

Necrotizing pneumonia due to Methicillin-Resistant Staphylococcus Aureus (MRSA) is devastating and difficult to treat in preterm infants. We report a case of severe MRSA necrotizing pneumonia in a preterm infant. As an add-on rescue therapy to vancomycin, linezolid rapidly cured this case after the failure of vancomycin plus rifampicin. This rapid cure suggests that adjunctive rather than rescue linezolid may be considered in such cases.

This study tries to focusing on applying information communication technology for economic importance of Mid Oil Company (MDOC), which is among contemporary oil companies in Iraq. Since ICT forms a significant aspect of achieving organizational economic objectives, most of public and private bodies seek to utilize ICT tool and techniques to improve work efficiencies. The emerging growth in ICT has made it process and service automation possible in managing organizational activities. To determine this effect of ICT on organizations, this study formulated statistical research model at MDOC through using fifteen questionnaires, which were distributed on three axes. The outcome of the questionnaire analysis revealed that there is inadequate comprehensive infrastructure to suitably support ICT within the selected organization management. In addition, it revealed that there are no ideal standards to maintain and sustain internet networks. Based on these revelations, the study presents recommendation for the adoption of ICT with the aim of minimizing cost, enhancing communication and managing infrastructure. Also recommends the need to support IT department within an organization, with essential equipment, in terms of man and machine, for better efficiency to employ the key of ICT Impact in Iraqi governmental oil companies.

This paper investigates the photocatalytic characteristics of Ag Nanowire (AgNW)/TiO2 and AgNW/TiO2/Graphene oxide (GO) nanocomposites. Samples were synthesized by the direct coating of TiO2 particles on the surface of silver nanowires. As-prepared AgNW/TiO2 and AgNW/TiO2/GO nanocomposites were characterized by electron microscopy, X-ray diffraction, UV/visible absorption spectroscopy, and infrared spectroscopy. Transmission electron microscope (TEM) images confirmed the successful deposition of TiO2 nanoparticles on the surface of AgNWs. The photocatalytic activity of synthesized nanocomposites was evaluated using Rhodamine B (RhB) in an aqueous solution as the model organic dye. Results showed that synthesized AgNW/TiO2/GO nanocomposite has superior photocatalytic activities when it comes to the decomposition of RhB.

Keywords: Wall-like RC columns; Strengthening; FRP; NSM rebars; Steel plates; Testing; FE modeling; Preloading.

Self-driving vehicles demand efficient and reliable depth sensing technologies. Lidar, with its capacity for long-distance, high-precision measurement, is a crucial component in this pursuit. However, conventional mechanical scanning implementations suffer from reliability, cost, and frame rate limitations. Solid-state lidar solutions have emerged as a promising alternative, but the vast amount of photon data processed and stored using conventional direct time-of-flight (dToF) prevents long-distance sensing unless power-intensive partial histogram approaches are used. In this paper, we introduce a groundbreaking ‘guided’ dToF approach, harnessing external guidance from other onboard sensors to narrow down the depth search space for a power and data-efficient solution. This approach centers around a dToF sensor in-which the exposed time widow of independent pixels can be dynamically adjusted. We utilize a 64-by-32 macropixel dToF sensor and a pair of vision cameras to provide the guiding depth estimates. Our demonstrator captures a dynamic outdoor scene at 3 fps with distances up 75 m. Compared to a conventional full histogram approach, on-chip data is reduced by over 25 times, while the total laser cycles in each frame are reduced by at least 6 times compared to any partial histogram approach. The capability of guided dToF to mitigate multipath reflections is also demonstrated. For self-driving vehicles where a wealth of sensor data is already available, guided dToF opens new possibilities for efficient solid-state lidar.

In this paper, we discuss the weak gravitational lensing in the context of stringy black holes. Initially, we examine the deflection angle of photon by charged stringy black hole. For this desire, we compute the Gaussian optical curvature and implement the Gauss-Bonnet theorem to investigate the deflection angle for spherically balanced spacetime of stringy black hole. We also analyze the influence of plasma medium in the weak gravitational lensing for stringy black hole. Moreover, the graphical impact of coupling constant $\alpha$, impact parameter $b$ , black hole charge $Q$ on deflection angle by charged stringy black hole has been studied in plasma as well as non-plasma medium.

Load variations in any power system result in losses escalation and voltage drops. With the sensible and optimal allocation of distributed generators (DGs), these problems could be considerably mitigated. It has been seen in the priorly existing methods that ideally allocation of DGs has been carried out during fixed loads and constant power requirements. However, in real scenarios the loads are always variable and allocation of DGs must be done in accordance to the variations of the connected load. Therefore, the current paper addressed the aforementioned problem by distinctive optimal allocation of DGs for each variability of 24hour load horizon. However, the single exclusive solution is considered among all allocations of 24 hours. The min-max regret concept has been utilized in order to deal with such methodology. Altogether, 24 scenarios are analyzed wherein each scenario corresponds to a specific hour of the respective day. The optimal allocation of DGs in terms of their optimal sizing and placement has been carried out by using three algorithms including battle royal optimization (BRO), accelerated particle swarm optimization (APSO) and genetic algorithm (GA). The multi-objective optimization problem is evaluated on the basis of minimum value criterion of the multi-objective index (MO). MO comprises of active and reactive power losses and voltage deviation. Hence, in order to find robustness of proposed technique, Conseil international des grands réseaux electriques (CIGRE’s) MV benchmark model incorporating 14 buses has been considerably used as a test network. In the end the results of three proposed algorithms have been compared.

The growing importance of hydrogen as an energy carrier in a future decarbonized energy system has led to a surge in its production plans. However, the development of infrastructure for hydrogen delivery, particularly in the hard-to-abate sectors, remains a significant challenge. While the construction of new pipelines entails substantial investment, repurposing existing pipelines offers a cost-effective approach to jump-starting hydrogen networks. Many European countries are exploring the possibility of utilizing their current pipeline infrastructure for hydrogen transport. Yet, a critical aspect requiring extensive research is the expansion planning of these networks to accommodate increased capacity in the future. This study investigates the utilization of existing natural gas pipeline infrastructure for hydrogen transportation as a cost-saving measure. It explores two expansion strategies, namely pipeline looping and compression, for repurposing a segment of a 342 km x 36inch existing pipeline, from the Escravos – Lagos gas pipeline system (ELPS) in Nigeria, for hydrogen transport. Employing the Promax® process simulator tool, the study assesses compliance with the API RP 14E and ASME B31.12 standards for hydrogen and hydrogen-methane blends. Both expansion strategies demonstrate acceptable velocity and pressure drop characteristics for hydrogen blends of up to 40%. Additionally, the increase in hydrogen content leads to heightened compression power requirements until approximately 80% hydrogen in the blends for compression, and a corresponding extension in looping length until around 80% hydrogen in the blend for looping. Moreover, the compression option proves to be more economically viable for all investigated proportions of hydrogen blends for the PS1-PS5 segment of the Escravos – Lagos gas pipeline case study. The percentage price differentials between the two expansion strategies reach as high as 495% for a 20% hydrogen proportion in the blend. This study offers valuable insights into the technical and economic implications of repurposing existing natural gas infrastructure for hydrogen transportation.

The current study aimed to evaluate the influence of different sintering temperatures on the properties of copper aluminum oxide (CuAlO₂) pellets synthesized from copper oxide (CuO) and aluminum hydroxide (Al(OH)₃) for application in smart infrastructure systems. The pellets were sintered at 400 K, 1000 K, and 1300 K, in the presence of nitrogen gas flow to reduce the amount of oxygen availability. The CuAlO₂ sintered nanoparticles were chemically analyzed by X-ray diffractometry, and the nanostructure of the materials was studied by scanning electron microscopy. The transmittance of the sintered materials was examined by ultraviolet/visible (UV/Vis) spectrophotometry, and 88% transparency was observed for the pellets sintered at 1300 K. Electrical conductivity was measured at 0.905 mS/cm, indicating a semiconducting behavior.

A new class of tetrasubstituted imidazole based compounds was synthesized using a multicomponent one-pot synthesis scheme through a cyclo condensation reaction of benzil, aromatic primary amines, aldehydes and ammonium acetate in glacial acetic acid. The synthesized compounds have been analyzed and characterized by melting point, color, conductivity method, CHN analysis, FT-IR, and UV-Visible. The reaction proceeding was examined by TLC after regular intervals of period. To test biological activity, the synthesized compounds have been examined against various bacterial strains. From the analysis of the antibacterial activity of these synthesized compounds demonstrated that all three imidazole compounds have considerable to significant activity against the strains, and compound K2 was found potent comparatively.

Objective: A previous study showed that schizophrenia is accompanied by lowered levels of trace/metal elements including cesium. There are no data whether changes in cesium, rubidium and rhenium are associated with activated immune-inflammatory pathways, cognitive impairments, and the symptomatology of schizophrenia. Methods: This study measured cesium, rubidium, and rhenium, cognitive impairments (using the Brief Assessment of Cognition in Schizophrenia) and the cytokines/chemokines interleukin (IL)-1β, tumor necrosis factor (TNF)-α, and CCL11 (eotaxin) in 120 schizophrenia patients and 54 healthy controls. Severity of illness was assessed using the Brief Psychiatric Rating Scale (BPRS), the Scale for the Assessment of Negative Symptoms (SANS), the Fibromyalgia and Chronic Fatigue Syndrome Rating (FF) Scale and the Hamilton Depression Rating Scale (HAM-D). Results: Serum cesium was significantly lower in schizophrenia patients as compared with controls. Serum cesium was significantly and inversely associated with CCL11 and TNF-α, but not IL-1β. Moreover, there were significant inverse associations between serum cesium levels and the BPRS, FF, HAM-D and SANS scores and positive correlations between cesium and neurocognitive probe results including the Tower of London, Symbol Coding, Controlled Word Association, Category Instances, Digit Sequencing Task, and List Learning tests. Conclusion: The results suggest that lowered serum cesium levels may play a role in the pathophysiology of SCZ, specific symptom domains including negative, depressive and fatigue symptoms, neurocognitive impairments (spatial working, episodic and semantic memory and executive functions) and neuro-immune pathways as well.

Salmonella enterica includes enteric pathogens of zoonotic potential, possessing one of the largest pools of temperate phages in their genomes. One such class of phages previously called Podoviridae (Now genera placed directly under class Caudoviricetes temporarily) is the largest group of temperate phages that lysogenize clinically and economically important Salmonella enterica serovars. These phages are capable of generalized transduction and are well known for carrying foreign DNA (mobile genetic elements and resistance genes) to new bacterial species that play a major role in the evolution, pathogenicity, and host adaptability of Salmonella serovars. Here we report a novel species of podo viruses; Salmonella phage BIS08P22 (BIS08) that infects Salmonella enterica serovar Typhimurium strain SE-BS17 (Acc. NO: MZ503545). Phage BIS08 was viable only at biological pH and temperature (pH7 and 37 °C) and features 41,574 -base pair (bp) linear ds DNA genome with 47% GC content. It encodes 73 putative Open Reading Frames (ORFs), has a mosaic arrangement, and shares only 16 core genes with its closest homologs. BIS08 genome possesses only 52 % homology (genome-wide Nucleotide homology by VIRIDIC analysis) with members of the genus Lederbergvirus which is not sufficient to place it in the same genus. This phage has a unique terminase enzyme (DNA packaging motor) with no nucleotide and protein homology with any known member of the Lederbergvirus genus. We carried out a detailed phylogenetic and genome analysis of this novel phage and proposed its placement in a new genus. Our study also suggests a revision in the classification of the genus Lederbergvirus. We also performed in silico structural analysis of the BIS08 unique terminase enzyme comparing it to its close homologs.

Yemen is a water-scarce country with inadequate fresh water and considerable groundwater depletion, as well as a lack of adequate surface water. The study region is considered an arid region, and there is insufficient water to meet the needs of the region's yearly population growth rate of 4 %. This study aims to assess the water resources in the Sana'a region and to identify the current water situation and forecast for the future. Rainfall changes spatial and temporal in very few quantities and an annual average of 267 mm. Water harvesting facilities are entirely filled by 75% of the total water facilities in the rainy seasons. The groundwater level in Sana’a Basin decreases about 6-8 meters annually due to the increase in the number of wells, the abundance of abstraction, and the lack of recharge. The amount of abstraction exceeds 400% of the recharge in the Sana'a basin. The water per capita is 70 - 85 m³ annually. It is an abstraction from the aquifers by private wells. The crop cultivated area decreased from 184217 hectares in 2007 to 122583 hectares in 2018 due to lack of water. The sewage treatment plant treats 18.25 Mm³ annually, with less than 70 % efficiency. The water deficit is about 500 to 723 Mm³ annually; it is an abstraction from the aquifers by private wells. The current water situation in the Sana'a region is catastrophic, and the planning and management must ensure a water balance in the future.

Conventional mechanical Fourier Transform Spectrometers (FTS) are able to simultaneously measure absorption and dispersion spectra of gas-phase samples. However, they usually need very long measurement times to achieve time-resolved spectra with a good spectral and temporal resolution. Here, we present a mid-infrared dual-comb-based FTS in an asymmetric configuration, providing broadband absorption and dispersion spectra with a spectral resolution of 5 GHz, a temporal resolution of 20 μs, and a total measurement time of a few minutes. We used the dual-comb spectrometer to monitor the reaction dynamics of methane and ethane in an electrical plasma discharge. We observed ethane/methane formation as a recombination reaction of hydrocarbon radicals in the discharge in various static and dynamic conditions. The results demonstrate a new analytical approach for measuring fast molecular absorption and dispersion changes and monitoring fast dynamics of chemical reactions, which can be interesting for chemical kinetic research and particularly for the combustion and plasma analysis community.

Blood perfusion quantification is important vital parameters in different diagnostic procedure, using infrared thermography imaging; it is reliable to use this technique as non-contact, non-invasive blood flow measurement method. Therefore, we developed a measurement protocol for blood flow over the arm's anterior surface. By using the superficial brachial and radial veins to be monitored under the impact of cold-excitation of (2 °C to 5 °C), the blood perfusion signal was detected using thermal imager of long-wave infrared spectral range (LWIR, 7μm - 14 μm). The simulation of Penne's bioheat transfer equation was performed to be compared with results obtained from the infrared thermography. Furthermore, the proposed blood flow monitoring using external adjusting of the excitation temperature, by using (cold-compress, or cold air-stream) applied to the region under testing. The signal detected resembles to the hemodynamic pulse of the superficial veins, in the definition of systolic and diastolic phases of the cardiac cycle. Moreover, statistical analysis applied to the BFIRT signals from 24 subjects to estimate the skin's mean temperature after recovery from the thermal excitation.

The monitoring of sick newborns is a challenging task that health care providers in Neonatal Intensive Care Units (NICU) must contend with each day. Conventionally, newborns are monitored via probes that are affixed to their skin and attached to processing monitors (Fig.1). However, an alternative exists in contactless imaging to record such physiological signals (Physio-Markers), surface changes and internal structures which can be used independently of, or in conjunction with conventional monitors. Advantages of contactless monitoring methods include: i) quick data generation; ii) lack of contact with skin, which reduces skin breakdown and decreases risk of infection; and iii) minimizing the number of probes and monitors affixed to the skin, which allows greater body surface-area for other care. This paper is an attempt to build a foundation for and to provide a vision of the potential neonatal clinical applications of technologies that use non-contact modalities such as Visible Light Imaging (VLI), Near InfraRed Spectrum (NIRS), and Thermal Imaging (TI) using InfraRed Spectrum (IRS).

Soybean (Glycine max L.) is a vital leguminous crop known for its rich nutritional profile, includ-ing high protein and oil content, making it a valuable resource in global agriculture and human nutrition. In Pakistan due to regulatory issues non-GMO soybean is grown therefore, the universal herbicide control method is not applicable. Similarly, for other crops, specific herbicides are avail-able but for soybean no crop specific herbicide is present. A comprehensive study was conducted over two growing seasons, in 2022 and 2023, at Agronomic Research Farm University of Agri-culture Faisalabad. The research involved 16 different treatments, including both six pre-emergence and eight post-emergence herbicides as well as a control group and weedy check. Findings of this study showed that critical importance of weed management in soybean cultiva-tion, as unchecked weed infestation can lead to substantial yield losses. Among the pre-emergence herbicide S-Metolachlor + Pendimethalin performed best and showed higher plant height (54.33 cm & 53.67 cm), 100 grain weight (11.57 g & 12 g), seed yield plant-1 (13.07 g and 12.87 g), lower Susceptibility Index (10%), higher weed control percentage (91.21% & 95.65%) higher weed con-trol efficiency (92.02% & 93.43%), herbicide efficiency index (27.54% & 32.48%) and lower weed index (18.16% & 21.70%) during 2022 and 2023 respectively. However, in post emergence herbi-cide the Fluizefop-p-butyl is most effective herbicide due to less crop injury 10% Susceptibility In-dex, higher plant height (42.67 cm & 44 cm), whole plant weight (93.33 g & 95 g), 100-grain weight (11.40 g & 11.77 g), seed yield plant-1 (12.87 g & 13.13 g), higher weed control percentage (91.12% & 91.03%) higher weed control efficiency (89.73% & 92.36%, herbicide efficiency index (21.18% & 30.41%) and lower weed index (19.42% & 20.08%) in both 2022 and 2023 respectively. Notably, herbicides such as S-Metolachlor + Atrazine + Mesotrione, Topramezone, Mesosulfu-ron-methyl + Iodosulfuron-methyl sodium and Mesotrione + Atrazine led to complete plant mor-tality, resulting lethal to soybean. The findings of this study support the hypothesis that screened the best suitable herbicides with less plant injury, and higher efficiency index. So, it is recom-mended that the application of S-Metolachlor + Pendimethalin as PRE and Fluizefop-p-butyl as POST showed the least injury and best weed control results with higher grain yield.

This study aims to evaluate the patient’s dose exposure from Computed Tomography Pulmonary Angiography (CTPA) examination and to estimates the cancer risk induced from the examinations based on the patient’s size. One hundred patients were recruited, and data information was collected retrospectively. A multi-detector (MDCT) (Philips Brilliance 128, USA) scanner were utilized for the CTPA examination, and dose data were obtained from the system. The effective diameter of each subjects’ image was measured for the Size-specific dose estimates (SSDEs). All subjects divided into Group 1 (19 – 25 cm), Group 2 (25-28 cm) and Group 3 (28-38 cm), where the association between gender were analysed. Effective dose (E), SSDE, organ dose and cancer risk of each group were evaluated and compared statistically using independent t-test and one-way ANOVA. The range of mean CTDI_vol, DLP and E values were (6.44 – 17.42 mGy), (239 – 631 mGy), (5.19 – 13.90 mSv), respectively. In respective with the patient’s effective diameter, the mean SSDE value for Group 1, Group 2 and Group 3 were 9.93 ± 3.89 mGy, 13.70 ± 9.04 mGy and 22.29 ± 7.35 mGy. The organ dose and cancer risk attained for breast, lung and liver were 17.05 ± 10.40 mGy (194 per one million procedure), 17.55 ± 10.86 mGy (192 per one million procedure) and 15.04 ± 9.75 mGy (53 per one million procedure), respectively. Lung and breast with more massive patient’s effective diameter received the highest dose exposure which increases the probability of the cancer risk. CTDI_vol was found to be underestimated, and SSDE provides more accurate in describing the radiation dose and cancer risk. Body effective diameter found to be significant on the estimation except for gender. Therefore, it is essential to apply optimised protocols in order to reduce patient’s exposure during CTPA examination.

In Schizophrenia, pathway-genotypes may be constructed by combining interrelated immune biomarkers with changes in specific neurocognitive functions that represent aberrations in brain neuronal circuits. These constructs provide insight on the phenome of schizophrenia and show how pathway-phenotypes mediate the effects of genome X environmentome interactions on the symptomatology/phenomenology of schizophrenia. Nevertheless, there is a lack of knowledge how to construct pathway-phenotypes using Partial Least Squares (PLS) path modeling and Soft Independent Modeling of Class Analogy (SIMCA). This paper aims to provide a step-by-step utilization guide for the construction of pathway-phenotypes that reflect aberrations in the neuroimmune - brain circuit axis (NIBCA) in deficit schizophrenia. This NIBCA index is constructed using immune biomarkers (CCL-2, CCL-11, IL-1β, sIL-1RA, TNF-α, sTNFR1, sTNFR2) and neurocognitive tests (Brief Assessment of Cognition in Schizophrenia) predicting overall severity of schizophrenia (OSOS) in 120 deficit SCZ and 54 healthy participants. Using SmartPLS path analysis, a latent vector is extracted from those biomarkers and cognitive tests, which shows a good construct reliability (Cronbach alpha and composite reliability) and replicability and which is reflectively measured through its NIBCA manifestations. This NIBCA pathway-phenotype explains 75.0% of the variance in PHEMN (psychotic, hostility, excitation, mannerism and negative) symptoms. Using SIMCA, we constructed a NIBCA pathway-class that defines deficit schizophrenia as a qualitatively distinct nosological entity and which allows patients with deficit schizophrenia to be authenticated as belonging to the deficit schizophrenia class. In conclusion, our nomothetic approach to develop a nomological network combining neuro-immune and neurocognitive phenome markers to predict OSOS and cross-validate a diagnostic class generated replicable models reflecting the key phenome of the illness, which may mediate the effects of genome X environmentome interactions on the final outcome phenome features, namely symptomatology and phenomenology.

A crucial role is played by personal biomedical data when it comes to maintaining proficient access to health records by patients as well as health professionals. However, it is difficult to get a unified view pertaining to health data that have been scattered across various health center/hospital sections. To be specific, health records are distributed across many places and cannot be found integrated easily. In recent years, blockchain is regarded as a promising explanation that helps to achieve individual biomedical information sharing in a secured way along with privacy preservation, because of its benefit of immutability. This research work put forwards a blockchain-based managing scheme that helps to establish interpretation improvements pertaining to electronic biomedical systems. In this scheme, two blockchain were employed to construct the base of it, where the second blockchain algorithm is used to generate a secure sequence for the hash key that generated in first blockchain algorithm. The adaptively feature enable the algorithm to use multiple data types and combine between various biomedical images and text records as well. All the data, including keywords, digital records as well as the identity of patients are private key encrypted along with keyword searching capability so as to maintain data privacy preservation, access control and protected search. The obtained results which show the low latency (less than 750 ms) at 400 requests / second indicate the ability to use it within several health care units such as hospitals and clinics.

Face milling is a well known commercial process highly used in heavy industries that consumes high amount of power. Besides power issue, modern manufacturing industries are aiming for per part cost reduction keeping the product quality unimpaired. Unexpectedly if the part is rejected in any stage of manufacturing, the cost of manufacturing dramatically increases. Major cause of part rejection is excessive tool wear that imparts poor surface profile or catastrophic tool failure that causes adherence of broken tool debris onto machined surface. Furthermore, the tool wear is associated with sliding distance (frictional distance) and the tool life quantifies the cost of tools. As such, from the perspective of manufacturing industries it is imperative to optimize the surface quality parameter, cost of part, power consumption, and material removal – this is exactly what is accomplished here. By this work, it is possible to conserve power consumption, produce parts with lower cost, manufacture with uncompromising surface quality and enhanced material removal rate. Moreover, as intermediate factors of interest, the influences of sliding distance, tool life and tool flank wear on the overall machining performance are evaluated. The multi-objective optimization by Grey Relational Analysis (GRA) revealed that for improved product performance and fast manufacturing (case 1) optimum results are: feed per tooth fz = 0.25 mm/tooth, cutting speed vc = 392.6 m/min and cutting length l = 0.5 mm; for resource conservation (case 2) the optimum results are: feed per tooth fz = 0.125 mm/tooth, cutting speed vc = 392.6 m/min, cutting length l = 0.5 mm.

The world is looking towards organizations for social responsibility to contribute to a sustainable environment. Employees’ organizational citizenship behavior for the environment (OCBE) is a voluntary environmental-oriented behavior that is important for organizations’ environmental performance. Based on social learning theory, the study examined the effects of responsible leadership in connection with OCBE by using a sample of 520 employees of manufacturing and service sector including engine manufacturing, petroleum plants banking and insurance sector organizations of China. Further, the role of psychological ownership and employee environmental commitment were used as mediators and moderators simultaneously. The direct, mediation, and moderation model results exposed a positive relationship between responsible leadership and OCBE via employee psychological ownership and employee environmental commitment. The study also revealed that the indirect effect is stronger when employees hold higher employee environmental commitment. The theoretical and practical implications for environmental sustainability in respect of organizations as well as future research directions are discussed.

Background: This study was conducted to investigate medical students' lifestyle habits, including sleep quality, eating and drinking pattern, physical activity, and social status. Method: This research project is part two of a multi-institutional cross-sectional observational study conducted among medical students from six medical colleges in Saudi Arabia between September and December 2019. Results: 675 medical students were enrolled electively into the lifestyle study. About half of this number were male students, and the majority aged 18-24 years. Most students (87.6%) slept between 4-8 hours a day, and over 44% were dissatisfied with their sleep. Only 28.1% had three meals a day; about 40% of them usually or always skipped breakfast. While 44% usually or always eat fast food, 44.7% drink 2 liters of water per day. Moreover, male students were significantly consuming fast foods than females, p<0.001. The majority (63.3%) revealed they usually or always drink black coffee daily. Females were significantly more inclined to regular coffee consumption than males, p<0.001. Only 4.3% exercising for 30 minutes or more daily. The majority (65%) of the students were introverted; they had few close friends. Yet, 81% were somewhat satisfied or satisfied with their social life. Male students were predicted to be significantly more satisfied with their social life than females, p=0.001. Only 4.6% smoked cigarettes daily. Whereas 7.1% smoke e-cigarette daily. In contrast, only 0.3% use shisha (hookah) daily. Male medical students were substantially more predicted to be inclined to e-cigarette use than females (p <0.001. The top five leisure activities of a medical student are surfing social media (75.9%), watching movies (61.3%), hanging out with friends (58.1%), spent time with their family (55.4%), and browsing the internet (53.6%). Female medical students were significantly more inclined to surfing social media than male medical students, p=0.022; also, watching movies was preferred for females compared to males, p=0.006. Conclusion: This study revealed that the majority of medical students in Saudi Arabia exhibited healthy lifestyles to some extent, and these health-promoting behaviors differed based on gender, especially concerning physical activity and eating patterns. The findings of this study provide relevant information for future actions that will be geared towards effectively decreasing the occurrence of chronic illnesses and improving future doctor’s well-being.

A hemostatic sponge prototype was successfully synthesized from fish gelatin as an alternative to mammalian gelatin, which was mixed with alginate in certain combinations and double cross-linking of calcium ions and gamma irradiation at a dose of 20 kGy to improve the characteristics and effectiveness of its function as a local hemostatic agent. There was an improvement in the physicochemical and mechanical properties, porosity index, absorption capacity, biodegradation properties, biocompatibility, and hemocompatibility of the FGAS prototypes compared with the pure fish gelatin sponge. Hemostatic activity tests showed that the means of clotting time, prothrombin time, and activated partial thromboplastin time were shorter in the FGAS prototype than in the negative control, and there was no significant difference compared with the commercial gelatin sponge. The hemostatic mechanism of the FGAS prototype combined a passive mechanism as a concentrator factor and an active mechanism through the release of calcium ions as a coagulation factor in the coagulation cascade process.

Jellyfish search (JS) algorithm impersonates the foraging behavior of jellyfish in the ocean. It is a new developed meta-heuristic algorithm that solves complex and real world optimization problems. The global explore capability and robustness of JS are strong, but JS still has great development space in solving complex optimization problems with high dimensions and multiple local optima. Therefore, an enhanced jellyfish search (EJS) algorithm is developed in this study, and three improvements are made: (i) By adding sine and cosine learning factors, the jellyfish can learn from both random individual and best individual during Type B motion in swarm to enhance the optimization capability and convergence speed; (ii) Adding local escape operator can skip local optimal trap and boost the exploitation ability of JS; (iii) Opposition-based learning operator and quasi-opposition learning operator can increase and strengthen the population distribution more diversified, and better individuals are selected from present and new opposition-solution to participates in the next iteration, which can boost the solution’s quality, meanwhile convergence speed is fasted and its precision is increased. In addition, the performance contrast of the developed EJS and some previous outstanding and advanced methods are evaluated on CEC2017, CEC2019 test suite and six real engineering example of case. It is demonstrated that EJS algorithm escaped the trap of local optimum, enhanced the solution’s quality and the calculation speed. What’s more, the practical engineering applications of EJS algorithm also verify its superiority and effectiveness in solving both constrained and unconstrained optimization problems, and it stretched one’s mind for solving such optimization problems.

Introduction: Periodontitis is a severe oral infection that can contribute to systemic inflammation. A large body of evidence suggests a role for systemic inflammation in the initiation of neurodegenerative disease. This systematic review synthesized data from observational studies to investigate the association between periodontitis and neuroinflammation in adults. Methods and materials: A systematic literature search of PubMed, Web of Science, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) was performed for studies published from the date of inception up to September 2021. Search terms for the exposure “oral disease” and outcome “dementia”, “neuroinflammation” and “cognitive decline” were used. Study selection and data extraction were independently undertaken by two reviewers. The final eligible articles were included only if the exposure is periodontitis and the outcome is cognitive impairment or dementia or a topic related to this condition, and if the study was conducted in an adult population. The quality and risk of bias were assessed by Newcastle Ottawa Scale (NOS). Qualitative synthesis was used to narratively synthesize the results. Six cohort studies, three cross-sectional studies, and two case-control studies met the inclusion criteria. These eleven studies were only narratively synthesized. Meta-analysis was not performed due to the methodological heterogeneity of the studies. Results: All the included studies show evidence of an association between periodontitis and cognitive im-pairment or dementia and Alzheimer’s disease pathology. Conclusion: Nonetheless, the mechanisms responsible for the association between periodontitis and dementia are still unclear.

The term "code smell" refers to an indication of a problem with the quality of source code. Numerous studies have been conducted to identify problematic features in source code. Initially, the focus was on utilizing metric-based and heuristic-based approaches. In recent years, however, there has been a shift towards using machine learning and deep learning (DL) techniques for smell detection. Nevertheless, the current algorithms are still considered to be in the early stages of development. Recognizing the challenges associated with identifying smells using DL methods, both academics and software developers have made efforts to address these obstacles. This work involves constructing and evaluating new DL models for code smell detection. Two models are built upon stacked autoencoders, employing a hybrid architecture that combines bidirectional long short-term memory and convolutional neural network components.