Artificial intelligence–built analysis framework for the manufacturing sector: performance optimization of wire electric discharge machining system

In the era of industry 4.0, digitalization and smart operation of industrial systems contribute to higher productivity, improved quality, and efficient resource utilization for industrial operations and processes. However, artificial intelligence (AI)–based modelling and optimization analysis following a generic analysis framework is lacking in literature for the manufacturing sector thereby impeding the inclusion of AI for its potential application's domain. Herein, a comprehensive and generic analysis framework is presented depicting the key stages involved for carrying out the AI-based modelling and optimization analysis for the manufacturing system. The suggested AI framework is put into practice on wire electric discharge machining (WEDM) system, and the cutting speed of WEDM is adjusted for the stainless cladding steel material. Artificial neural network (ANN), support vector machine (SVM), and extreme learning machine (ELM) are three AI modelling techniques that are trained with meticulous hyperparameter tuning. A better-performing model is chosen once the trained AI models have undergone the external validation test to investigate their prediction performance. The sensitivity analysis on the developed AI model is performed and it is found that pulse on time (Pon) is the noteworthy factor affecting the cutting speed of WEDM having the percentage significance value of 26.6 followed by the Dw and LTSS, with the percentage significance value of 17.3 and 16.7 respectively. The parametric optimization incorporating the AI model is conducted and the results pertain to the cutting speed are 27.3% higher than the maximum value of cutting speed achieved for WEDM. The cutting speed performance optimization is realized following the proposed AI-based analysis framework that can be applied, in general, to other manufacturing systems therefore unlocking the potential of AI to contribute to industry 4.0 for the smart operation of manufacturing systems

Sustainable EDM of Inconel 600 in Cu-mixed biodegradable dielectrics: Modelling and optimizing the process by artificial neural network for supporting net-zero from industry

The properties of Nickel-based superalloy(s) like stability at extreme conditions, greater strength, etc., complicate its cutting through conventional operations. Therefore, electric discharge machining (EDM) is preferred for its accurate cutting. However, the conventional dielectric i.e., kerosene used in EDM is hydrocarbon based which generates toxic fumes and contribute to the CO2 emissions during the discharging process in EDM. This affects the operator’s health and the environment. Therefore, the potentiality of five biodegradable dielectrics has been deeply examined herein to address the said issues. Nano copper powder is also employed for uplifting the cutting proficiency of these dielectrics. A set of 15 experiments was performed via full factorial design. An artificial neural network (ANN) is constructed to model and optimize the material removal rate (MRR), surface roughness (SR), and specific energy consumption (SEC). The highest MRR (5.527 mm3 /min) was achieved in coconut oil whereas for obtaining the lowest SR, the sunflower oil at powder concentration (Cp) of 1.0 g/100 ml is the best choice. Sunflower oil also gave a 17.05% better surface finish compared to other dielectrics. Amongst the biodegradable dielectrics, olive oil consumes lowest specific energy (SEC) i.e., 264.16 J/mm3 which is 28.8% less than the SEC of other oils. Furthermore, the maximum CO2 reduction of 72.8 ± 1.4% is achieved with Olive oil in comparison to that found with kerosene in EDM. The multi-objective optimization is conducted and sunflower oil with Cp of 0.667 g/100 ml is termed out to be optimal solution. The biodegradable dielectrics have demonstrated excellent performance for EDM to support net-zero goals from the industrial sector

Use of OER in public schools of Pakistan

The presentation provides a brief overview of Open Educational Resources in Pakistan. 8300 public school students have been included in the survey as well as 3326 in Universities. The survey indicates the need for training in how to combine and share resources, as well as adding open licensing information to the teacher resources

Profile of dementia patients from a tertiary care center in Karachi, Pakistan

BACKGROUND: According to an estimate currently over 46 million people live with dementia worldwide and 58% reside in developing countries. However like some other developing countries, not much is known about the demographics, characteristics, and associated conditions of those suffering from dementia in Pakistan. OBJECTIVE: To study profile of dementia patients from a tertiary care hospital in Karachi, Pakistan

Synergistic effects of β-NaFeO2 Ferrite Nanoparticles for Photocatalytic Degradation, Antibacterial, and Antioxidant Applications

Here, synthesis and thorough characterization of β-NaFeO2 nanoparticles utilizing a co-precipitation technique is presented. XRD analysis confirmed a hexagonal-phase structure of β-NaFeO2. SEM revealed well-dispersed spherical nanoparticles with an average diameter of 45 nm. The FTIR spectrum analysis revealed weak adsorption bands at 1054 cm-1 suggested metal-metal bond stretching (Fe-Na). UV-Visible spectroscopy indicates a 4.4 eV optical band gap. Colloidal stability of β-NaFeO2 was evidenced via Zeta potential (-28.5 mV) and Dynamic Light Scattering (DLS) measurements. BET analysis reveals a substantial 343.27 m2 g-1 surface area with mesoporous characteristics. Antioxidant analysis indicates efficacy comparable to standard antioxidants, while concentration-dependent antibacterial effects suggest enhanced efficacy against Gram-positive bacteria, particularly Streptococcus. The Photocatalytic activity of β-NaFeO2 showed significant pollutant degradation (\u3e90% efficiency), with increased degradation rates at higher nanoparticle concentrations, indicating potential for environmental remediation applications

Enhancing EDM Machining Precision through Deep Cryogenically Treated Electrodes and ANN Modelling Approach

The critical applications of difficult-to-machine Inconel 617 (IN617) compel the process to be accurate enough that the requirement of tight tolerances can be met. Electric discharge machining (EDM) is commonly engaged in its machining. However, the intrinsic issue of over/undercut in EDM complicates the achievement of accurately machined profiles. Therefore, the proficiency of deep cryogenically treated (DCT) copper (Cu) and brass electrodes under modified dielectrics has been thoroughly investigated to address the issue. A complete factorial design was implemented to machine a 300 μm deep impression on IN617. The machining ability of DCT electrodes averagely gave better dimensional accuracy as compared to non-DCT electrodes by 13.5% in various modified dielectric mediums. The performance of DCT brass is 29.7% better overall compared to the average value of overcut (OC) given by DCT electrodes. Among the non-treated (NT) electrodes, the performance of Cu stands out when employing a Kerosene-Span-20 modified dielectric. In comparison to Kerosene-Tween-80, the value of OC is 33.3% less if Kerosene-Span-20 is used as a dielectric against the aforementioned NT electrode. Finally, OC’s nonlinear and complex phenomena are effectively modeled by an artificial neural network (ANN) with good prediction accuracy, thereby eliminating the need for experiments

Towards artificial intelligence empowered performance enhancement of EDM process using nano-graphene mixed bio-dielectric supporting the carbon neutrality and sustainable development

The growing population with every passing day sets an alarming situation with respect to the conservation climate protocols. The increasing needs of society also demand a significant enhancement in the manufacturing capacity to augment the situation. However, it’s a stringent requirement of the hour to propose sustainable and clean manufacturing processes to realize the goal of carbon neutrality to support a healthy life on the earth. Specifically, the processes that are energy intensive like electric discharge machining (EDM) are of serious concern regarding sustainability viewpoint. The role of the said process cannot be essentially eliminated as advent of new materials of superior characteristics demand the application of EDM for accurate cutting of intricate profiles. Nevertheless, the commonly used oil-based dielectric (kerosene) in EDM releases aerosol, deposit particles, oxides of carbon (CO2 & CO), thus contributing to the environmental contamination. It is pertinent to mention that industries are compelled to tune their processes to achieve the goals of Net-Zero. Therefore, this study thoroughly investigates the potential of nano-graphene mixed rice bran oil to make the EDM process cleaner and sustainable which has never been investigated so far. Moreover, the process has been successfully modeled using artificial neural network (ANN) and optimized by non-dominated sorting genetic algorithm-II (NSGA-II) which is another novel aspect of this study as it eradicates the need of extensive experimentation. Experimentation has been performed via Taguchi’s experimental strategy followed by a detailed explanation of the findings based on process physics. In comparison to the traditional dielectric an improvement of 98.8% in material removal rate (MRR) and 93.9% reduction in specific energy consumption (SEC) are realized if the said novel combination is applied without compromising the quality. CO2 emissions determined for both rice bran oil and kerosene oil have revealed that rice bran oil provides 99.96% lesser CO2 emission in comparison to its counterpart

Intracytoplasmic sperm injection outcome using ejaculated sperm and retrieved sperm in azoospermic men.

Introduction:We aimed to determine pregnancy and miscarriage rates following intracytoplasmic sperm injection (ICSI) cycles using retrieved epididymal and testicular sperm in azoospermic men and ejaculated sperm in oligospermic and normospermic men. Materials AndMethods: This retrospective study was carried out on 517 couples who underwent ICSI. They included 96 couples with azoospermia and 421 with oligospermia or normal sperm count in the male partner. Of the men with azoospermia, 69 underwent percutaneous epididymal aspiration (PESA) and 47 underwent testicular sperm extraction (TESE). In the 421 men with oligospermia or normal sperm count, ejaculated sperm was used for ICSI. The differences in the outcomes of ICSI using PESA or TESE and ejaculated sperm were evaluated. The main outcome measures were pregnancy and miscarriage rates.Results: No significant differences were seen in pregnancy and miscarriage rates with surgically retrieved and ejaculated sperm. The pregnancy rates (including frozen embryo transfer) were 43.5%, 36.2%, and 41.4% in couples with PESA, TESE, and ejaculated sperm, respectively (P = .93). The miscarriage rates were 16.7%, 23.5%, and 12.1%, respectively (P = .37).Conclusion: Intracytoplasmic sperm injection in combination with PESA and TESE is an effective method and can successfully be performed to treat men with azoospermia. The outcomes with these procedures are comparable to ICSI using ejaculated sperm

Self-medication amongst university students of Karachi: prevalence, knowledge and attitudes

OBJECTIVE: To determine the prevalence, attitude and knowledge of self-medication amongst university students of Karachi, Pakistan. METHODS: This cross-sectional, study was conducted from Jan-Feb 2007. A convenience sample was taken from 2 medical and 2 non-medical universities of the city of Karachi, Pakistan. Data was analyzed using SPSS v 14 and associations were tested using the Chi square test. RESULTS: Of the 572 participants (mean age=21 +/- 1.8 years, Male: Female ratio=1:1.5), 295 were medical and 277 were non-medical students. The prevalence of self-medication was 76%. Forty three percent students stated that they alter the regimen of prescribed medicines while 61.9% stated that they stop taking a prescribed medicine without consulting a doctor. The most common reason for self-medication was previous experience (50.1%) and the most common symptoms were headache (72.4%), flu (65.5%), and fever (55.2%). Commonly used medicines were analgesics (88.3%), antipyretics (65.1%) and antibiotics (35.2%). Eighty seven percent of students thought self-medication could be harmful and 82.5% students thought that it was necessary to consult a doctor before taking a new medicine. There was no significant difference between the self medication practices of medical and non medical students (p=0.8) CONCLUSION: Prevalence of self-medication is high in the educated youth, despite majority being aware of its harmful effects. There is a need to educate the youth to ensure safe practices. Strict policies need to be implemented on the advertising and selling of medications to prevent this problem from escalating

Disseminated Tuberculosis Presenting as Baker’s Cyst Infection

In the absence of coexisting immunocompromised state and lack of specific symptoms a reactivation of treated mycobacterial tuberculosis (MTB) infection is generally not considered in the differential diagnosis of leg pain. We present a unique case of disseminated tuberculosis presenting as an infected Baker’s cyst in a 73-year-old immunocompetent male