13 research outputs found
Energy conservation potentials of an office buildings in Northern Nigeria: a case study of Katsina secretariat complex
No full textThe importance of energy conservation in our contemporary world cannot be overemphasized, efficient utilization of energy has significant impact in improving economy at all levels of human endeavour. No doubt, adequate and appropriate utilization of energy especially electrical energy boosts up any organizational developmental activities. Recently, research interest has emphasis towards efficient energy utilization and energy conservation as the effective means of reducing energy consumption in buildings thereby reducing its maintenance cost. This paper investigated and analysed the energy consumption characteristics of Katsina state secretariat complex for the period of 3 years (i.e. from 2014 to 2016) based on site surveys and analysis of the energy end users present, using the records of electricity utility bills and Automotive Gas Oil (AGO), being the two energy carriers of the complex. Records have shown that, the secretariat complex average electricity and AGO annual consumptions were found as 1045661.95 kWh and 116650.33 litres of AGO (which is equivalent to 1250491.54 kWh) respectively. The investigation revealed a distinct consumption pattern, indicating peak energy consumption during the hot months of April to August due to significant air conditioning requirements. The result of the investigation of the energy conservation potentials in the secretariat complex have shown that, energy savings of up to 6.5% of the total energy can be achieved by switching-off all security lights during the day. While turning off the air conditioners in the early morning hours of between 8am to 10am would provide a saving of up to 19% of the total energy. Furthermore, a saving of 16.5% of the total energy can be achieved when the incandescent lamps are replaced with the energy efficient ones. The energy conserving measures (ECMs) followed in this research has shown significant savings in terms of both energy and cost, and if well implemented can give way for a sustainable energy management of similar office buildings in future
Mechanical properties of coconut shell powder reinforced PVC composites in automotive applications / Muhammad Hanafi Md Sah … [et al.]
No full textCoconut shell powder (CSP) (which is used in reinforced Polyvinyl Chloride (PVC)) is one of the possible candidates of materials suitable as automotive components; however, appropriate tests need to be done to evaluate whether it meets all requirements. CSP-reinforced composites are made with PVC matrix within the range of 0 - 20 phr and the effect of the reinforcement of the natural fibres on the mechanical behaviour of PVC has been analysed. Both Universal Tensile Machines and Impact Testing Machines are used to determine the mechanical properties of CSP/PVC composites (such as the tensile, flexural and impact strength as well as its modulus of elasticity). The experimental results indicated that tensile strength, impact strength and flexural strength improved by 42%, 25% and 23%, respectively, when compared to the pure system
AI-Enhanced Criminal Investigations and Ḥudūd Offenses: A Sharīʿah Compliance Framework
No full textThe article examines the interface of Islamic law and emerging AI technology in criminal investigation. It takes the legal normative doctrinal approach to examine predictive policing and facial recognition against Qurʾānic evidentiary and confessionary requirements. The analysis identifies a fundamental doctrinal tension: machine-generated evidence cannot substitute for the traditional proof Sharīʿah requires. A hybrid model is proposed on the grounds of maqāṣid al-Sharīʿah, granting artificial intelligence secondary, not primary, evidence status. Safeguards include judicial oversight, open algorithms, accountability, enforcement of data protection law, and an outright prohibition on AI input where uncertainty persists. The model operates within Islamic legal doctrine, conforming to technological advancement while upholding the Qurʾān's overarching commitment to justice. The study illustrates that the ethical frameworks underlying the development and deployment of AI can be consonant with Islamic teaching, specifically the values of justice and common good in the Qurʾān and related jurisprudence.Makalah ini meneliti persimpangan hukum Islam dan teknologi AI yang muncul dalam investigasi kriminal. memanfaatkan metode doktrinal normatif hukum untuk menyelidiki teknologi kepolisian prediktif dan teknologi pengenalan wajah dalam bukti Al-Qur'an dan standar pengakuan dosa. Temuan-temuan tersebut menyoroti ketegangan doktrinal yang kritis: bukti yang dihasilkan mesin tidak dapat menggantikan bukti konvensional yang diwajibkan oleh Syariah. Berdasarkan maqāṣid al-Sharī'ah, artikel ini mengusulkan model hibrida yang menempatkan kecerdasan buatan sebagai bukti sekunder daripada bukti primer. Langkah-langkah keamanan dasar memerlukan pengawasan yudisial, algoritma terbuka, akuntabilitas, dan penegakan hukum perlindungan data, dengan larangan langsung tambahan terhadap input AI jika ada ketidakpastian. Model ini sesuai dengan prinsip-prinsip hukum Islam karena mengikuti teknologi terbaru untuk diterapkan, yang berfungsi di bawah prinsip-prinsip keadilan Al-Qur'an secara keseluruhan sebagaimana ditafsirkan dalam kitab suci. dengan demikian memastikan bahwa penggunaan AI selaras dengan tujuan keadilan dan kesejahteraan publik dalam hukum Islam
Prevalence of Antibiotic Resistant Diarrheagenic Escherichia coli Isolated from Stool Samples of Diarrheic Children Under 5 Years in Sokoto, Nigeria
No full textDiarrheal diseases continue to pose substantial public health challenges, especially in children under the age of 5. Diarrheagenic Escherichia coli (DEC) is the second most common cause of diarrhea in children after Rotavirus. This study aimed to assess the prevalence of antibiotic-resistant DEC recovered from diarrheic children aged 0-5 years in Sokoto. Stool samples were obtained from 300 diarrheic children attending two hospitals in Sokoto. Bacterial isolates that showed colonial morphology suggestive of E. coli were subjected to antibiotic susceptibility testing. PCR was carried out to confirm the presence of DEC and resistant genes among the multiple antibiotic-resistant isolates. Structured questionnaires were administered to determine the risk factors that predispose the children to diarrhea. The results revealed a 21% prevalence of E. coli isolates, out of which 75% displayed resistance to Ampicillin, 75% to Nalidixic acid, 30% to Gentamycin, 23% to Ofloxacin, 74% to Cefotaxime, 23% to Ceftriaxone, 18% to Nitrofurantoin, 10% to Imipenem, and 73% to Cefuroxime. Out of the 30 E. coli isolates with a MAR index of ≥ 0.2, 12 were found to be multidrug-resistant (MDR). All four MDR E. coli selected were confirmed to be DEC using the UidA gene. Out of all the four MDR DEC confirmed, only one class 1 integron was detected, raising concern about the misuse of commonly used antibiotics. This study highlights the need for implementing antibiotic stewardship programs and infection control measures to combat the growing threat of antibiotic-resistant DEC within Sokoto
ICT utilization and its barriers in Jigawa State primary health care centers
No full textIncreasingly, Information and Communication Technology (ICT) has become a vital tool used in all sectors to carry out tasks effectively and efficiently. The use of ICT in health care sectors has the potential to improve the quality of service, diagnosis and retrieval of information. However, majority of the studies were concentrated on investigating ICT prospective in Tertiary Health Care (THC) and Secondary Health Care (SHC) with Primary Health Care (PHC) been left out. This paper therefore investigates the utilization and barriers to ICT usage in 10 Primary Health Care (PHC) centers located in Jigawa State. The paper takes a twofold approach to combine the views of two parties; the Health Care Personals and the Citizens using the PHC services. A questionnaire survey involving 80 Health Care Personal and 40 Citizens is used in the research. The results revealed that ICT utilization is very poor among Health Care Personnel, ICT improves patient managements and the main barriers to ICT utilization include unavailability of facilities, lack of staff training, Lack of awareness and insufficient knowledge. These findings can be used to improve the quality of healthcare delivery in PHCs.Keywords: Citizens, Barriers, Survey, Services, Primary, Health, Car
Adaptive Optimization of Deep Learning Models on AES based Large Side Channel Attack Data
No full textDeep learning-based side-channel analysis is an efficient and suitable technique for profiling side-channel attacks. In order to obtain the better performance, it is highly necessary to analyze an in-depth training stage in which the optimization of relevant hyperparameters should be a vital process. During the training phase, hyperparameters that are connected to the architecture of the neural network are often selected; however, hyperparameters that impact the training process are to be effectively analyzed. This was represented by an optimized hyperparammeter that consists of considerable impact on attacking behaviour, which is the primary focus of our research. Our research has shown that even while the popular optimizers Adam and RMSprop are capable of delivering satisfactory outcomes, they are also tend to being overfit. Hence, it is necessary to use condensed training periods, simple profiling models, and explicit regularization in order to avoid this problem. On the other hand, the performance of optimizers of the SGD type is only satisfactory when momentum is used which results in slower convergence and less overfit. In conclusion, the research results provide a better use of Adagrad in the cases of longer training datasets or big profiling models
Design of Tools for Visualizing Thermodynamic Concepts in Steam Power Plant Trainer Processes with Web-Based Exploratory Data Analysis (EDA)
No full textThermodynamics is considered one of the most complex and challenging subjects for many students. This is primarily due to comprehending abstract concepts such as entropy, enthalpy, and energy flow, which involve complex mathematical equations and are rarely accompanied by tangible visualizations. This research aims to design, develop, and test a data-based visualization tool for thermodynamics testing results. This study collected and processed data from thermodynamics testing and simulations, such as the mini-steam power plant trainer used as a teaching aid in thermodynamics education, as the foundation for designing a data-based visualization tool for thermodynamics concepts. The visualization tool was created using the Python programming language integrated with the web-based Streamlit framework. The designed visualization tool encompasses various features, including automated data reporting, visualization of variable correlations using correlation heatmaps, Sankey diagrams for visualizing energy flow, and the capability to predict electrical output using machine learning integrated with three different machine learning algorithms. The visualization tool was evaluated by thermodynamics experts using a Likert scale. Based on the results obtained, the experts gave an average score of 4 in the information accuracy aspect in the good category. This shows that the information displayed in this visualization tool is by thermodynamics learning at Padang State University. In the visualization aspect, experts gave an average score of 4.25, which is in the Good and Very Good range. In alignment with the education aspect, experts gave an average score of 3.75, which is close to the good category. This shows that this aspect is considered suitable for studying thermodynamics, although shortcomings still need to be corrected. Experts gave a relatively high assessment of the Ease-of-Use aspect, with an average score of 4.5, with a range of Good and Very Good. This enables students to better understand complex patterns, cause-and-effect relationships, and parameter changes within thermodynamics concepts
Optimizing stainless steel tensile strength analysis: through data exploration and machine learning design with Streamlit
No full textThe use of Exploratory Data Analysis (EDA) and machine learning in material science has rapidly advanced in recent years. EDA enables researchers to thoroughly explore and analyze material datasets, while machine learning allows for the development of predictive models capable of understanding complex patterns within the data. This study aims to develop an optimization tool to enhance the analysis of tensile strength in stainless steel by leveraging integrated data exploration and machine learning approaches within the Streamlit framework. The developed tool consists of four main features: data visualization, correlation analysis, 3D visualization, and machine learning. The developed machine learning model has 14 input variables, including chemical elements and heat treatment temperatures. In this research, the machine learning features comprise three models: Decision Tree, Random Forest, and Artificial Neural Network. The research findings indicate that the optimization tool can automatically display stainless steel tensile strength data using available pandas profiling in the visualization feature. The correlation feature can illustrate the relationship between chemical elements and heat treatment temperatures concerning stainless steel tensile strength. The 3D visualization feature can be utilized to identify optimal values of chemical elements and heat treatment temperatures according to desired tensile strength. Meanwhile, the machine learning feature can accurately predict stainless steel tensile strength based on chemical composition and heat treatment temperatures. This is evident from the performance evaluation metrics of the Random Forest model, which achieved MAE of 10.36, RMSE of 14.44, and R-squared of 0.9
Thermal conductivity and specific heat capacity of different compositions of Yttria stabilized zirconia-nickel mixtures
No full textCeramic-metal composites also known as functionally gradient materials (FGM) are composite materials which are fabricated in order to have a gradual variation of constituent materials’ thermal and mechanical properties so as to have a smooth variation of the material properties in order to improve the overall performance and reduce the thermal expansion mismatch between ceramic and metal. The objective of the study is to determine the thermal properties of various percentage composition of Yttria stabilized zirconia-Nickel mixtures for application as thermal barrier coating materials in automotive turbocharger turbine volute casing. Specific heat capacity of different percentage composition of ceramic-metal powder composite were determined using DSC822 differential scanning calorimeter (Mettle Tolodo, Switzerland) at temperature ranges between 303K to 873K. While the thermal conductivity of the different percentage composition of ceramic-metal composite structures were determined using P5687 Cussons thermal conductivity apparatus (Manchester, UK) which uses one-dimensional steady-state heat conduction principle. The results have indicated that the specific heat capacity of the FGM increases sharply with an increase in temperature while the thermal conductivity of the FGM decreases with an increase in temperature. These results strongly agree with the theoretical and experimental values as well as the rule of mixtures obtainable in literature, which indicated the suitability of these FGM materials for thermal barrier coating applications
