Exploring the Structural Stability of 1T-PdO2 and the Interface Properties of the 1T-PdO2/Graphene Heterojunction.

Motivated by a recent study on the air stability of PdSe2, which also reports the metastability of the PdO2 monolayer [Hoffman A. N.. npj 2D Mater. Appl.2019, 3( (1), ), 50.], in this work, we use density functional theory (DFT) to further explore the thermal, dynamic, and mechanical stability of monolayer PdO2 and study its structural and electronic properties. We further studied its vertical heterojunction composed of 1T-PdO2 and graphene monolayers. We show that both the monolayer and the heterojunction are energetically and dynamically stable with no negative frequencies in the phonon spectrum and belong to the vdW-type. 1T-PdO2 is an indirect-band-gap semiconductor with band-gap values of 0.5 eV (GGA) and 1.54 eV (HSE06). The interface properties of the heterojunction show that the n-type Schottky barrier height (SBH) becomes negative at the vertical interface in the PdO2/graphene contact, forming an Ohmic contact and mainly suggesting the potential of graphene for efficient electrical contact with the PdO2 monolayer. However, at the same time, a negative band bending occurs at the lateral interface based on the current-in-plane model. Moreover, the optical absorption of the PdO2/graphene heterojunction under visible-light irradiation is significantly enhanced compared to the situation in their free-standing monolayers.

Synthesis and Characterization of Multicolor Luminescent and Thermally Stable Thioureas and Polythioamides.

Two new polythioamides were prepared through the polycondensation reaction between thiourea monomers and terephthaloyl dichloride, while the thiourea monomers were synthesized by the interaction of aromatic (4,4'-diaminophenylsulfone) or alicyclic (1,2-cyclohexanediamine) diamine with ammonium thiocyanate. The elemental composition of polythioamides was confirmed through CHN microanalysis. The structure and properties of thiourea monomers and polythioamides were determined through proton NMR, UV-Vis, FT-IR spectroscopy, fluorescence, TGA/DTA and SEM. The polythioamides indicated high thermal stabilities which were assessed from their Tmax (temperature indicating highest rate of weight loss) values (670 °C and 346 °C) observed in their DTG graphs. The thioureas and polythioamides were fluorescent and showed multicolor (violet, green, yellow, orange and red) emissions at different excitation wavelengths. All the synthesized compounds were also tested for their antifungal and antibacterial functions and showed antibacterial activity against Salmonella typhi, Bacillus subtilis and Staphylococcus aureus, and antifungal activity against Candida albicans.

Enhancing Cross-Subject Motor Imagery Classification in EEG-Based Brain-Computer Interfaces by Using Multi-Branch CNN.

A brain-computer interface (BCI) is a computer-based system that allows for communication between the brain and the outer world, enabling users to interact with computers using neural activity. This brain signal is obtained from electroencephalogram (EEG) signals. A significant obstacle to the development of BCIs based on EEG is the classification of subject-independent motor imagery data since EEG data are very individualized. Deep learning techniques such as the convolutional neural network (CNN) have illustrated their influence on feature extraction to increase classification accuracy. In this paper, we present a multi-branch (five branches) 2D convolutional neural network that employs several hyperparameters for every branch. The proposed model achieved promising results for cross-subject classification and outperformed EEGNet, ShallowConvNet, DeepConvNet, MMCNN, and EEGNet_Fusion on three public datasets. Our proposed model, EEGNet Fusion V2, achieves 89.6% and 87.8% accuracy for the actual and imagined motor activity of the eegmmidb dataset and scores of 74.3% and 84.1% for the BCI IV-2a and IV-2b datasets, respectively. However, the proposed model has a bit higher computational cost, i.e., it takes around 3.5 times more computational time per sample than EEGNet_Fusion.

Factors affecting youth empowerment and entrepreneurial initiatives: Social implications and way forward.

This study aims to identify the factors that impact the empowerment of Pakistani youth and their entrepreneurial initiatives in Pakistan. A sample of 350 youngsters from renowned Pakistani universities across the country was selected for this purpose. The questionnaire was administered in person and electronically. Statistical Package of Social Sciences (SPSS) was used for analyzing the data. The findings indicated that all the hypothesized factors (government policies, lack of political participation, employment opportunities, and social engagement) had an impact on youth empowerment and their ability to initiate entrepreneurial activities. This study identified the set of factors that impact empowerment in youngsters, and this model can be extended to other contexts and additional factors can be included. The analytical findings from this study serve to help the government formulate appropriate policies for underserved youth and include them in the policy-making process so that the factors that hinder their empowerment and entrepreneurial initiatives could be addressed.

An ML-Enabled Internet of Things Framework for Early Detection of Heart Disease.

Healthcare occupies a central role in sustainable societies and has an undeniable impact on the well-being of individuals. However, over the years, various diseases have adversely affected the growth and sustainability of these societies. Among them, heart disease is escalating rapidly in both economically settled and undeveloped nations and leads to fatalities around the globe. To reduce the death ratio caused by this disease, there is a need for a framework to continuously monitor a patient's heart status, essentially doing early detection and prediction of heart disease. This paper proposes a scalable Machine Learning (ML) and Internet of Things-(IoT-) based three-layer architecture to store and process a large amount of clinical data continuously, which is needed for the early detection and monitoring of heart disease. Layer 1 of the proposed framework is used to collect data from IoT wearable/implanted smart sensor nodes, which includes various physiological measures that have significant impact on the deterioration of heart status. Layer 2 stores and processes the patient data on a local web server using various ML classification algorithms. Finally, Layer 3 is used to store the critical data of patients on the cloud. The doctor and other caregivers can access the patient health conditions via an android application, provide services to the patient, and inhibit him/her from further damage. Various performance evaluation measures such as accuracy, sensitivity, specificity, F1-measure, MCC-score, and ROC curve are used to check the efficiency of our proposed IoT-based heart disease prediction framework. It is anticipated that this system will assist the healthcare sector and the doctors in diagnosing heart patients in the initial phases.

Deep Eutectic Solvents for Extraction and Preconcentration of Organic and Inorganic Species in Water and Food Samples: A Review.

Deep eutectic solvents (DESs) have been developed as green solvents and these are capable as alternatives to conventional solvents used for the extraction of organic and inorganic species from food and water samples. The continuous generation of contaminated waste and increasing concern for the human health and environment have compelled the scientific community to investigate more ecological schemes. In this concern, the use of DESs have developed in one of the chief approach in the field of chemistry. These solvents have appeared as a capable substitute to conventional hazardous solvents and ionic liquids. The DESs has distinctive properties, easy preparation and components availability. It is not only used in scienctific fields but also used in quotidian life. There are many advantages of DESs in analytical chemistry, they are largely used for extraction and determination of inorganic and organic compounds from different samples. In previous a few years, several advanced researches have been focused on the separation and preconcentration of low level of pollutants using DESs as the extractants. This review summarizes the use of DESs in the separation and preconcentration of organic and inorganic species from water and food samples using various microextraction processes.

Modeling of cadmium(II) removal in a fixed bed column utilizing hydrochar-derived activated carbon obtained from discarded mango peels.

Cadmium is found in many underdeveloped countries' aquatic bodies. Therefore, contaminated water should be treated before consumption; henceforth, efficient and customized point-of-use filtration is foreseeable. Traditionally, carbon-based sorbents have been utilized for such treatments, but alternative sources are also being investigated. Hydrochars made from mango peels using a thermal activation process were employed as an adsorbent instead of activated carbon in this investigation. The prepared material was porous with active surface functionalities, and the interaction of cadmium with the surface was possibly ion-exchange in nature. The performance of a material for a candle water filtering system with a 2.5 cm internal diameter and a 30.48 cm column height was determined using the parameters acquired by the Thomas model. The material was found to be highly efficient at 453.5 L/min/Filter water, whereas 31670.6 L/min/Filter can be treated if the break point and exhaustion point are considered, respectively, as the candle replacement time. These findings indicate that activated hydrochar might be a suitable sorbent for removing cadmium ions from contaminated water.

Past, Present, and Future of EEG-Based BCI Applications.

An electroencephalography (EEG)-based brain-computer interface (BCI) is a system that provides a pathway between the brain and external devices by interpreting EEG. EEG-based BCI applications have initially been developed for medical purposes, with the aim of facilitating the return of patients to normal life. In addition to the initial aim, EEG-based BCI applications have also gained increasing significance in the non-medical domain, improving the life of healthy people, for instance, by making it more efficient, collaborative and helping develop themselves. The objective of this review is to give a systematic overview of the literature on EEG-based BCI applications from the period of 2009 until 2019. The systematic literature review has been prepared based on three databases PubMed, Web of Science and Scopus. This review was conducted following the PRISMA model. In this review, 202 publications were selected based on specific eligibility criteria. The distribution of the research between the medical and non-medical domain has been analyzed and further categorized into fields of research within the reviewed domains. In this review, the equipment used for gathering EEG data and signal processing methods have also been reviewed. Additionally, current challenges in the field and possibilities for the future have been analyzed.

GC Determination of Fluorouracil in Serum by Using Hexafluroroacetylacetone as Derivatizing Reagent.

A gas chromatographic (GC) procedure has been developed for the determination of fluorouracil (5-FU) after pre-column derivatization with hexafluoroacetylacetone. GC separation was from column DB-1 (30 m × 0.32 mm id) and the determination was by flame-ionization detection. The derivatization conditions were optimized at pH 4, heating at 90°C for 40 min and extraction of the derivative was in chloroform. Using the conditions nucleobases cytosine, uracil, thymine, adenine and guanine separated completely from fluorouracil. The linear calibration range and LOD for 5-fluorouracil were 0.5-40.0 and 0.2 µg/mL, respectively. The derivatization, elution and separation were repeatable in terms of retention time and peak height/peak area (n = 5) and relative standard deviations (RSD) were within 3.5%. The method was applied for the analysis of serum spiked with 5-FU with recovery of 95.5-97.5% with RSD 1.5-3.1%.

An Intelligent Multi-Floor Navigational System Based on Speech, Facial Recognition and Voice Broadcasting Using Internet of Things.

Modern technologies such as the Internet of Things (IoT) and physical systems used as navigation systems play an important role in locating a specific location in an unfamiliar environment. Due to recent technological developments, users can now incorporate these systems into mobile devices, which has a positive impact on the acceptance of navigational systems and the number of users who use them. The system that is used to find a specific location within a building is known as an indoor navigation system. In this study, we present a novel approach to adaptable and changeable multistory navigation systems that can be implemented in different environments such as libraries, grocery stores, shopping malls, and official buildings using facial and speech recognition with the help of voice broadcasting. We chose a library building for the experiment to help registered users find a specific book on different building floors. In the proposed system, to help the users, robots are placed on each floor of the building, communicating with each other, and with the person who needs navigational help. The proposed system uses an Android platform that consists of two separate applications: one for administration to add or remove settings and data, which in turn builds an environment map, while the second application is deployed on robots that interact with the users. The developed system was tested using two methods, namely system evaluation, and user evaluation. The evaluation of the system is based on the results of voice and face recognition by the user, and the model's performance relies on accuracy values obtained by testing out various values for the neural network parameters. The evaluation method adopted by the proposed system achieved an accuracy of 97.92% and 97.88% for both of the tasks. The user evaluation method using the developed Android applications was tested on multi-story libraries, and the results were obtained by gathering responses from users who interacted with the applications for navigation, such as to find a specific book. Almost all the users find it useful to have robots placed on each floor of the building for giving specific directions with automatic recognition and recall of what a person is searching for. The evaluation results show that the proposed system can be implemented in different environments, which shows its effectiveness.

Clinical Practice Guidelines For The Management Of Colorectal Cancer, A Consensus Statement By The Society Of Surgeons® And Surgical Oncology Society Of Pakistan®.

A joint effort by the Society of Surgeons Pakistan and Society of Surgical Oncology Pakistan, these guidelines provide a framework for the practicing surgeons involved in care and management of patients with colorectal cancer. The guidelines take into account the issues related to our local circumstances and provide a minimum standard of care that must be given to these patients. The Guideline Committee had members from all disciplines, including surgery, surgical oncology, medical oncology and radiation oncology. The guidelines have attempted to simplify things to understand and follow for the practicing surgeons. With these guidelines we wish to eliminate disparities in treatment among institutions and prevent any under treatment of patients.

Nonvolatile electrical control of 2D Cr2Ge2Te6 and intrinsic half metallicity in multiferroic hetero-structures.

The electrical control of two-dimensional (2D) van der Waals ferromagnets is a step forward for the realization of spintronic devices. However, using this approach for practical applications remains challenging due to its volatile memory. Herein, we adopt an alternative strategy, where the bistable ferroelectric switches (P↑ and P↓) of Sc2CO2 (SCO) assist the ferromagnetic states of Cr2Ge2Te6 (CGT) in order to achieve non-volatile memories. Moreover, MXene SCO, being an aided layer in multiferroic CGT/SCO hetero-structures, also modifies the electronic properties of CGT to half metal by its polarized P↓ state. In contrast, the P↑ state does not change the semiconducting nature of CGT. Hence, non-volatile, electrical-controlled switching of ferromagnetic CGT can be engineered by the two opposite ferroelectric states of single layer SCO. Importantly, the magnetic easy axis of CGT switches from in-plane to out-of-plane when the direction of electric polarization of SCO is altered from P↓ to P↑.

Silver Nanoparticles for a Colorimetric Determination of Putrescine and Cadaverine in Biological Samples.

A convenient and uncomplicated scheme has been projected for the quantitative determination of essential diamines putrescine (PUT) and cadaverine (CAD) via sodium dodecyl sulfate protected silver nanoparticles (SDS-AgNPs). This scheme is based on the chemical interaction of a SDS-AgNPs probe with PUT and CAD, leading to a color change from yellow to red or reddish brown. The interaction was investigated through different techniques such as using a UV-visible spectrophotometer, Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), dynamic light scattering spectroscopy (DLS) and the zeta potential. Both amines possess a close resemblance in structure (except for the addition of one more methylene group in CAD), and no any distinguishable color change was noted. However, the maximum absorption band at 580 and 600 nm was demonstrated for PUT and CAD correspondingly. The methodical response was observed at absorption ratios of 580/410 and 600/410 nm, with the linear regression within 4 - 12 and 6 - 14 µg/mL for PUT and CAD. The detection limits calculated for both the diamines PUT and CAD were 0.333 and 1.638 µg/mL. The scheme was successfully applied for determinations in biological samples, including spiked blood plasma and urine. Putrescine exhibited % recovery within 95.717 - 105.200%, while cadaverine was within 95.940 - 105.109%, respectively. The scheme was reproducible and precise with inter-day RSD (n = 5) within 1.126, 0.018% and the intraday RSD (n = 5) was within 0.005, 0.002% for PUT and CAD, respectively.

Early and accurate detection and diagnosis of heart disease using intelligent computational model.

Heart disease is a fatal human disease, rapidly increases globally in both developed and undeveloped countries and consequently, causes death. Normally, in this disease, the heart fails to supply a sufficient amount of blood to other parts of the body in order to accomplish their normal functionalities. Early and on-time diagnosing of this problem is very essential for preventing patients from more damage and saving their lives. Among the conventional invasive-based techniques, angiography is considered to be the most well-known technique for diagnosing heart problems but it has some limitations. On the other hand, the non-invasive based methods, like intelligent learning-based computational techniques are found more upright and effectual for the heart disease diagnosis. Here, an intelligent computational predictive system is introduced for the identification and diagnosis of cardiac disease. In this study, various machine learning classification algorithms are investigated. In order to remove irrelevant and noisy data from extracted feature space, four distinct feature selection algorithms are applied and the results of each feature selection algorithm along with classifiers are analyzed. Several performance metrics namely: accuracy, sensitivity, specificity, AUC, F1-score, MCC, and ROC curve are used to observe the effectiveness and strength of the developed model. The classification rates of the developed system are examined on both full and optimal feature spaces, consequently, the performance of the developed model is boosted in case of high variated optimal feature space. In addition, P-value and Chi-square are also computed for the ET classifier along with each feature selection technique. It is anticipated that the proposed system will be useful and helpful for the physician to diagnose heart disease accurately and effectively.

Gas Chromatographic and Spectrophotometric Determination of Diclofenac Sodium, Ibuprofen, and Mefenamic Acid in Urine and Blood Samples.

OBJECTIVES: Non-steroidal anti-inflammatory drugs (NSAIDs) are widely used for the treatment of acute to chronic pain. A simple, fast, and reliable gas chromatographic (GC) method with flame ionization detection has been developed for the determination of NSAIDs such as diclofenac sodium, ibuprofen, and mefenamic acid after derivatization with ethyl chloroformate. MATERIALS AND METHODS: The GC conditions were optimized as elution from a DB-1 column (30 mx0.32 mm id) at column temperature 150 °C for 3 min, followed by a heating rate of 20 °C/min up to 280 °C and a hold time of 5 min. The nitrogen flow rate was 2.5 mL/min. For spectrophotometric studies, the absorbance was measured against methanol at a wavelength of 200-500 nm. RESULTS: The calibration curves were linear within 2-10 µg/mL with limits of detection of 0.4-0.6 µg/mL of each drug. The derivatization elution, separation, and quantitation were repeatable (n=3) with relative standard deviation (RSD) within 3.9%. The method was applied for the analysis of the drugs from pharmaceutical formulations and the results of the analysis agreed with labeled values with RSDs within 0.5-3.9%. The results were also confirmed by standard addition method. The percent recovery was calculated with spiked deproteinized human blood serum and urine samples and % recovery of the drugs was obtained within 96-98% with RSDs within 3.1%. CONCLUSION: The validated method proved its ability for the assay of NSAIDs in bulk and dosage form in a short analysis time. The method was also useful for the analysis of biological samples.

Controlling Electronic Devices with Brain Rhythms/Electrical Activity Using Artificial Neural Network (ANN).

The purpose of this research study was to explore the possibility to develop a brain-computer interface (BCI). The main objective was that the BCI should be able to recognize brain activity. BCI is an emerging technology which focuses on communication between software and hardware and permitting the use of brain activity to control electronic devices, such as wheelchairs, computers and robots. The interface was developed, and consists of EEG Bitronics, Arduino and a computer; moreover, two versions of the BCIANNET software were developed to be used with this hardware. This BCI used artificial neural network (ANN) as a main processing method, with the Butterworth filter used as the data pre-processing algorithm for ANN. Twelve subjects were measured to collect the datasets. Tasks were given to subjects to stimulate brain activity. The purpose of the experiments was to test and confirm the performance of the developed software. The aim of the software was to separate important rhythms such as alpha, beta, gamma and delta from other EEG signals. As a result, this study showed that the Levenberg-Marquardt algorithm is the best compared with the backpropagation, resilient backpropagation, and error correction algorithms. The final developed version of the software is an effective tool for research in the field of BCI. The study showed that using the Levenberg-Marquardt learning algorithm gave an accuracy of prediction around 60% on the testing dataset.

Silver Nanoparticles with Sodium Dodecyl Sulfate as a Colorimetric Probe for the Detection of Dithiocarbamate Pesticides in Environmental Samples.

A rapid and simple colorimetric approach has been developed for sensing dithiocarbamate pesticides (ziram, zineb and maneb) in environmental samples using sodium dodecyl sulfate capped silver nanoparticles (SDS-AgNPs). They were characterized by a UV-vis spectrophotometer, FT-IR, SEM and EDX. Dithiocarbamates on interactions with SDS-AgNPs induce the aggregation of NPs, leading to a color change from yellow to greyish or dark brownish, depending on the concentration of pesticides added. A shift in the wavelength was observed from 400 to 570 nm with the broad band. The absorption ratio, 570/400 nm, was found to be linearly related to the concentration of pesticides in the range of 195.7 - 733.9 ng/mL for ziram, 17.6 - 66.2 ng/mL for zineb and 16.9 - 63.6 ng/mL for maneb with the detection limits of 149.3, 4.0 and 9.1 ng/mL for ziram, zineb and maneb, respectively. The method was successfully applied for the determination of DTCs in environmental samples (tap water, tomato, mango beverage) with percentage recoveries of 94.8 - 108.4% for ziram, 93.7 - 105.4% for zineb and 93.2 - 107.6% for maneb. The procedure was repeatable with inter-day RSD (n = 5) within 3.4, 4.8, 7.6% and intra-day RSD (n = 5) within 1.2, 1.7 and 1.8% for the ziram, zineb and maneb respectively.

New Fluorescent, Thermally Stable and Film Forming Polyimines Containing Naphthyl Rings.

Three new aliphatic-aromatic polymers having naphthyl rings were prepared by the polycondenstion of dialdehydes or diketone monomers with 1,5-naphthalenediamine or 1,4-phenylenediamine. The monomers were prepared by the reaction of aromatic aldehyde or ketone with 1,6-dibromohexane. The molecular mass of the monomers was confirmed through E.I mass spectroscopy. The structures of monomers and polymers were characterized by 1HNMR, FT-IR, UV-Vis Spectroscopy, SEM and TG/DTA. Fluorescence emissions of monomers and polymers were recorded and their quantum yields were calculated, all the compounds showed fluorescence property and indicated violet, blue-green, orange and red light emissions. The quantum yields of the polymers were obtained within the range of 0.04 to 24.3%. The semicrystalline and amorphous nature of the polymers was analyzed through powdered X-ray diffraction. Antimicrobial activities of the polymers were examined against different bacterial and fungal species. Thin film forming ability of the synthesized polymers was evaluated by making their blends with PVC (poly vinyl chloride) in different w/w% ratios.

Water quality assessment of Ramser site, Indus Delta, Sindh, Pakistan.

Seven representative sampling stations were selected from Indus Delta comprising of (1) Keti Bander, (2) Darya Peer, (3) Kharo Chanr, (4) Jati, (5) Shah Bander, (6) Nariri Dhand and (7) Left Bank Outfall Drain (LBOD) near Rupa Mari. The sampling scheme was repeated seven times during 2014-2015 for 1 year. The samples were analysed for temperature, conductivity, pH, hardness, chloride, alkalinity, dissolved oxygen, biological oxygen demand, chemical oxygen demand, sulphate, orthophosphate-P, nitrite-N, nitrate-N, Na, K, Fe, Ni, Cr, Co, Cd, Zn, Cu, Pb and As. The analysis was carried out using standard analytical procedures. A number of parameters crossed the WHO permissible limits and water quality guidelines for aquatic life and indicated pollution within coastal region. Coefficient of correlation (r) among physicochemical parameters and metal ions were examined and a number of parameters did not correlate positively and did not indicate natural origin and may indicate their presence due to human activity. The samples were examined for cluster analysis, and principal component analysis. The samples also indicated the presence of Presumptive Coliform, Faecal coliform and E. coli.

A highly selective and sensitive ultrasonic assisted dispersive liquid phase microextraction based on deep eutectic solvent for determination of cadmium in food and water samples prior to electrothermal atomic absorption spectrometry.

A simple, fast, green, sensitive and selective ultrasonic assisted deep eutectic solvent liquid-phase microextraction technique was used for preconcentration and extraction of cadmium (Cd) in water and food samples by electrothermal atomic absorption spectrometry (ETAAS). In this technique, a synthesized reagent (Z)-N-(3,5-diphenyl-1H-pyrrol-2-yl)-3,5-diphenyl-2H-pyrrol-2-imine (Azo) was used as a complexing agent for Cd. The main factors effecting the pre-concentration and extraction of Cd such as effect of pH, type and composition of deep eutectic solvent (DES), volume of DES, volume of complexing agent, volume of tetrahydrofuran (THF) and ultrasonication time have been examined in detail. At optimum conditions the value of pH and molar ratio of DES were found to be 6.0 and 1:4 (ChCl:Ph), respectively. The detection limit (LOD), limit of quantification (LOQ), relative standard deviation (RSD) and preconcentration factor (PF) were observed as 0.023 ng L-1, 0.161 ng L-1, 3.1% and 100, correspondingly. Validation of the developed technique was observed by extraction of Cd in certified reference materials (CRMs) and observed results were successfully compared with certified values. The developed procedure was practiced to various food, beverage and water samples.