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This research focuses on improving the photoelectrochemical performance of binary heterostructure Ag2S/ZnO NRs/ITO by manipulating synthesis conditions, particularly the concentrations of sliver nitrate AgNO3 and thiourea CS(NH2)2. The photoelectrochemical performance of Ag2S/ZnO nanorods on indium tin oxide (ITO) nanocomposite was compared to pristine ZnO NRs/ITO photoanode. The hydrothermal technique, an eco-friendly, low-cost method, was used to successfully produce Ag2S/ZnO NRs at different concentrations of AgNO3 and CS(NH2)2. The obtained thin films were characterized using field emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), ultraviolet-visible spectroscopy (UV-vis), and photoelectrochemical studies (PECs). We observed that there was an enhancement in absorbance in the visible region and effective photoelectron transfer between the Ag2S/ZnO NRs/ITO photoelectrode and the electrolyte Red-Ox when illuminated with 100 mW cm−2. Increasing the concentration of AgNO3 caused a remarkable decrease in the optical bandgap energy (Eg) values. However, we noticed that there was an unstable trend in Eg when the concentration of CS(NH2)2 was adjusted. The photoelectrochemical studies revealed that at a bias of 1.0 V, and 0.005 M of AgNO3 and 0.03 M of CS(NH2)2, the maximum photocurrent of the Ag2S/ZnO NRs/ITO photoanode was 3.97 mA/cm2, which is almost 11 times that of plain ZnO nanorods. Based on the outcomes of this investigating, the Ag2S/ZnO NRs/ITO photoanode is proposed as a viable alternative photoanode in photoelectrochemical applications.

This paper proposes a new multi-objective technique to solve the problem of optimal location and sizing of Distributed Generation Photovoltaic (DGPV) in the power system transmission network. The technique: Multi-objective Chaotic Mutation Immune Evolutionary Programming (MOCMIEP) was developed based on Pareto optimality to solve the DGPV location and sizing problem. The proposed technique determines the optimal location and sizing of DGPV, therefore will minimize the multiple objective functions, namely the active power losses and Fast Voltage Stability Index (FVSI) simultaneously. The method was tested on IEEE 118-Bus Reliability Test System (RTS). The results revealed that the proposed technique had the ability to acquire a set of Pareto solutions for the decision maker to choose depending on the system priorities.

This research investigated tribological properties of Jatropha oil (vegetable oil)to find clean, new, and renewable lubricant source of industrial applications. The study was performed utilizing a fourball tribotester, CCD camera, scanning electron microscope (SEM)and viscometer. The experiment was conducted using different normal loads (300, 400, and 500 N) and temperatures (75, 95 and 105°C). The test was followed ASTM D4172 standard. The evaluation was focused on the viscosity, flash temperature parameter, coefficient of friction, wear scar diameter and worn surface observation. All results of Jatropha oil were compared with mineral hydraulic oil to evaluate the lubricity performance of Jatropha oil. The results indicated that the Jatropha had better anti-friction and anti-wear ability than hydraulic mineral oil under various temperature and loads. In conclusion, Jatropha oil has bright possibility to be produced as commercial industrial lubricant.

Abstract Mitigating the effects of environmental deterioration requires a focus on not just CO2 emissions from energy consumption, but also environmental pollution from industry sectors. To reach this goal, recent studies have extended ecological footprint (EF) analysis to identify the ecological drivers of various key industry sectors. The role of the phosphorus (P) industry on the EF within the environmental Kuznets curve (EKC) framework for China is the emphasis of this study. Autoregressive distributive lag (ARDL) as well as the impulse response function and robustness analysis were used to consider a time from 1985 to 2018. The study verifies the EKC hypothesis for China in both the long and the short run, and indispensable determinants are proposed to be included to assure the model’s fitness and robustness when conducting EF analysis of industry sectors. Energy consumption–based carbon emissions have been verified as the dominant contributor to EF, but P use and urbanization have a significant lagged positive influence on EF in the short run. P exports, in particular, have been highlighted as a critical driver of the EF of China’s P industry. The conducted frequency domain causality test reinforced the above findings and demonstrated bidirectional causality at different frequencies. This work suggests that formulating plausible P export policies to alleviate the conflict between the output of China’s P industry and the environmental sustainability of this industry are necessary. In this context, “multidisciplinary, multidimensional, and practical solutions” are most desirable for sustainable P management.

In this research, we examined whether appreciation and depreciation in oil price, interest rate, exchange rate, industrial production, and inflation have the same effects on the stock market returns by using nonlinear autoregressive distributed lag (nonlinear ARDL). All nine economic sectors and aggregate stock market are considered in this study. Moreover, monthly data is utilized from January 1990 to November 2016 and from May 2000 to November 2016 for the aggregate market and the nine sectors, respectively. The bound test results showed strong evidence that all sectors (excluding plantation sector) including the aggregate market are cointegrated. Furthermore, the findings of this study implied that oil price shocks have an adverse impact on the stock market returns in most cases regardless whether oil price shocks are in an appreciation or depreciation direction. This shows that the Malaysian market is inefficient and very sensitive to the oil price fluctuations. In addition, the findings showed there is a long run asymmetric link between oil price shocks, interest rate, exchange rate, industrial production, inflation and stock market returns at both aggregate and sectors level in most cases. Keywords: Stock market returns, Oil price shocks, Nonlinear ARDL

Abstract This paper presents a parametric study conducted on electronically controlled solenoid direct fuel injector running on compressed natural gas. The purpose of the study is to identify the influential injector parameters on the output fuel mass flow rate. These injector parameters are to be optimize in the next stage of the study. The parametric study is conducted using zero-dimensional, first principle injector model which consist of electro-magnetic, mechanical and flow sub-models. In current study, seven (six input and one output) selected parameters have been analysed which are the injection pressure, injection duration, nozzle diameter, armature mass, the input voltage, spring constant and the output mass flow rate. Each input parameters are varied in the prescribed range based on the literature. Based on the study, the most influential parameters (in rank) are the nozzle diameter, the armature mass and the injection duration. The input voltage, the injection pressure and the spring constant were found to have no impact on the injector mass flow rate based on the values of parameter’s sensitivities. Based on the results, the potential parameters to be optimized are identified.

The aim of this study was to pyrolyze individual (oil palm shell, empty fruit bunch and sawdust) as well as blend biomass in a thermogravimetric mass spectrometry (TG-MS) from room temperature to 800 °C at constant heating rate of 15 °C/min. The results showed that the onset TG temperature for blend biomass shifted slightly to lower values. Activation energy values were also found to decrease slightly after blending the biomass. Interestingly, the MS spectra of selected gases (H2O CH4, H2O, C2H2, C2H4 or CO, CH2O, CH3OH, HCl, C3H6, CO2, HCOOH, and C6H12) evolved from blend biomass showed decreased in the intensity as compared to their individual biomass. Overall, the blend biomass showed synergy which provides ways to expand the possibility of utilizing multiple feedstocks in one thermo-chemical system.

Avec l'augmentation de la demande d'énergie et les sources d'énergie limitées, le système fonctionne à sa capacité maximale. Par conséquent, la capacité de déterminer la stabilité de la tension avant l'effondrement de la tension a fait l'objet d'une grande attention en raison de la complexité du système d'alimentation. Dans cet article, une prédiction de l'indice de stabilité de la tension (VSI) basée sur le réseau neuronal à fonction de base radiale (RBFNN) pour le réseau Super Grid irakien, 400KV. Des données d'apprentissage ont été obtenues pour divers paramètres de variables de charge à l'aide du flux de charge et de la méthode FVSI conventionnelle. L'entrée les données ont été effectuées en utilisant un test de 135 échantillons avec différentes tensions de bus (V b ), puissances active et réactive de bus (P b , Q b ), angle de charge de bus (δ b ) et FVSI ij. Le modèle RBFNN a quatre entrées représentant le (V b , P b , Q b et δ b ), seize nœuds à la couche cachée et un nœud de sortie représentant FVSI ij ont été utilisés pour évaluer la sécurité en ligne. La méthode proposée a été testée dans l'IEEE 30 et un système pratique. Les résultats de simulation montrent que le le procédé proposé est plus approprié pour l'évaluation de la stabilité de la tension en ligne en termes de détection automatique de la ligne de transmission critique lorsque des charges réelles ou réactives supplémentaires sont ajoutées. Con el aumento de la demanda de energía y las fuentes de alimentación limitadas, el sistema funciona a su máxima capacidad. Por lo tanto, la capacidad de determinar la estabilidad de la tensión antes del colapso de la tensión ha recibido una gran atención debido a la complejidad del sistema de alimentación. En este documento, se ha realizado una predicción del índice de estabilidad de la tensión (VSI) basada en la red neuronal de función de base radial (RBFNN) para la red iraquí Super Grid, 400KV. Se han obtenido datos de aprendizaje para diversas configuraciones de variables de carga utilizando el flujo de carga y el método FVSI convencional. La entrada los datos se realizaron mediante el uso de una prueba de 135 muestras con diferentes voltajes de bus (V b ), potencia activa y reactiva del bus (P b , Q b ), ángulo de carga del bus (δ b ) y FVSI ij. El modelo RBFNN tiene cuatro entradas que representan (V b , P b , Q b y δ b ), dieciséis nodos en la capa oculta y un nodo de salida que representa FVSI ij se han utilizado para evaluar la seguridad en línea. El método propuesto se ha probado en el IEEE 30 y un sistema práctico. En los resultados de la simulación muestran que el método propuesto es más adecuado para la evaluación de la estabilidad de la tensión en línea en términos de detección automática de la línea de transmisión crítica cuando se añaden cargas reales o reactivas adicionales. With the increase in power demand and limited power sources has caused the system to operate at its maximum capacity.Therefore, the ability of determine voltage stability before voltage collapse has received a great attention due to the complexity of power system.In this paper a prediction of voltage stability index (VSI) based on radial basis function neural network (RBFNN) for the Iraqi Super Grid network, 400KV.Learning data has been obtained for various settings of load variables using load flow and conventional FVSI method.The input data was performed by using a 135 samples test with different bus voltage (V b ), Bus active and reactive power (P b , Q b ), bus load angle (δ b ) and FVSI ij .The RBFNN model has four input representing the (V b , P b , Q b and δ b ), sixteen nodes at hidden layer and one output node representing FVSI ij have been used to assess the security on line.The proposed method has been tested in the IEEE 30 and a practical system.In Simulation results show that the proposed method is more suitable for on-line voltage stability assessment in term of automatically detection of critical transmission line when additional real or reactive loads are added. مع زيادة الطلب على الطاقة ومحدودية مصادر الطاقة، تسبب النظام في العمل بأقصى طاقته. لذلك، حظيت قدرة تحديد استقرار الجهد قبل انهيار الجهد باهتمام كبير بسبب تعقيد نظام الطاقة. في هذه الورقة، تم التنبؤ بمؤشر استقرار الجهد (VSI) بناءً على الشبكة العصبية الوظيفية الشعاعية (RBFNN) للشبكة الفائقة العراقية، 400 كيلو فولت. تم الحصول على بيانات التعلم لإعدادات مختلفة من متغيرات الحمل باستخدام تدفق الحمل وطريقة FVSI التقليدية. تم إجراء البيانات باستخدام اختبار 135 عينة بجهد ناقل مختلف (V b )، وناقل نشط وقوة تفاعلية (P b ، Q b )، وزاوية تحميل الناقل (δ b ) و FVSI ij. يحتوي نموذج RBFNN على أربعة مدخلات تمثل (V b ، P b ، Q b و δ b )، وستة عشر عقدة في طبقة مخفية وعقدة خرج واحدة تمثل FVSI ij تم استخدامها لتقييم الأمان على الخط. تم اختبار الطريقة المقترحة في IEEE 30 ونظام عملي. في نتائج المحاكاة تظهر أن الطريقة المقترحة أكثر ملاءمة لتقييم استقرار الجهد عبر الإنترنت من حيث الكشف التلقائي عن خط النقل الحرج عند إضافة أحمال حقيقية أو تفاعلية إضافية.