Author Archives: Evelina Rosser

WHAT ARE SOME POTENTIAL CHALLENGES THAT STUDENTS MAY FACE WHEN DESIGNING A SELF BALANCING UNICYCLE

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Balance and Control: Achieving balance and control is one of the most significant challenges for designing a self-balancing unicycle. The unicycle only has one wheel, so achieving dynamic balance is far more difficult compared to a two-wheeled or three-wheeled vehicle. Precise and responsive control systems will need to be designed using sensors like gyroscopes and accelerometers to measure the vehicle’s angle and adjust the motor torque rapidly to prevent falls. Control algorithms will need to be sophisticated to handle all types of disruptions to balance like bumps, slopes, cornering, braking, and acceleration. Extensive testing and tuning of control parameters like gains and sensor fusion will likely be required.

Motor Power and Torque: Providing enough motor power and torque to move the unicycle and constantly correct its balance in all conditions is challenging. A high-torque motor needs to rapidly respond to control inputs to stabilize the vehicle, while also smoothly propelling it forward, backward, and through turns. The motor must be powerful enough to move the unicycle and rider up slopes and over varied terrains. At the same time, it needs to be lightweight to avoid making balance more difficult. Achieving this balance requires careful motor selection and mechanical design to efficiently transmit torque to the wheel.

Battery Life and Range: Powering the motor control system components like sensors, motor controller, and wheel motor with a battery introduces constraints on runtime and range. Batteries add significant weight, making balancing harder. Battery technology limitations mean energy-dense, long-lasting batteries are challenging to design within a small unicycle form factor while allowing adequate runtime for practical transportation usage. Innovations in battery materials, cell designs, and energy management systems would help maximize runtime and extend the operating range.

Rider Interface: An intuitive and easy-to-use interface is needed for the rider to provide inputs to lean, turn, brake, and propel the unicycle forward and backward. Controls need to be conveniently accessible but not interfere with balance, like handlebars on a bicycle. User inputs also require translations into signals the control system understands to generate appropriate motor torques. Natural user interfaces like gesture or voice control could simplify operation but introduce new technical challenges. Rider safety is paramount, so controls and interface design require extensive human factors testing.

Mechanical Design: Packaging the motor, battery, sensors, controller and other components within the small frame of a unicycle while maintaining a low center of gravity presents mechanical design challenges. Components need rigid mounting and strategic weight distribution to avoid compromising dynamic balance. Manufacturability of the frame and other parts with tight tolerances is also important. Durable and lightweight materials selection is critical to improve performance and reduce stresses on the control system. Wheels and pneumatic or solid tires also factor into mechanical design considerations for riding over varied surfaces.

Software and Control Algorithms: Advanced control software is required to process input signals, fuse sensor data, and apply control algorithms to calculate precisely timed torque outputs for balance correction. Sensor calibration, noise filtering, state estimation, robust control design, and observer techniques help software handle uncertain dynamics and disturbances. Modeling unicycle dynamics accounting for a rider adds complexity. Control algorithms must run predictively to be responsive enough for balance while avoiding instability from feedback delays. Extensive testing of software and algorithms on simulated and physical prototypes is necessary for refinement.

System Integration and Testing: Integrating all electrical, mechanical and software components into a cohesive and robust design presents its own set of challenges. Parts need standardized interfaces and rigorous assembly procedures. Testing each subsystem individually is important, but evaluating the fully integrated unicycle is most critical. Comprehensive testing protocols and extensive trials in various settings help validate safety, performance and reliability requirements are met before public usage. Unanticipated integration issues could emerge and require iterative design improvements. Harmonizing all aspects into a user-friendly product requires diligence.

As can be seen, self-balancing a wheeled vehicle as unconventional as a unicycle presents many engineering complexities spanning mechanics, electronics, software, controls, energy storage and human factors. Addressing each of the above challenges requires an interdisciplinary design approach, extensive modeling and testing, along with innovative solutions. While an ambitious goal, with perseverance and a calculated, research-driven methodology, a practical self-balancing unicycle could potentially become a reality. Close supervision would be needed until the maturity of such a system is proven for wider adoption.

HOW CAN SOCIETY ENSURE THAT GENETIC ENGINEERING IS USED RESPONSIBLY AND ETHICALLY

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Genetic engineering promises revolutionary medical advances but also raises serious ethical concerns if not adequately regulated. Ensuring its responsible and ethical development and application will require a multifaceted approach with oversight and participation from government, scientific institutions, and the general public.

Government regulation provides the foundation. Laws and regulatory agencies help define ethical boundaries, require safety testing, and provide oversight. Regulation should be based on input from independent expert committees representing fields like science, ethics, law, and public policy. Committees can help identify issues, provide guidance to lawmakers, and review proposed applications. Regulations must balance potential benefits with risks of physical or psychological harms, effects on human dignity and identity, and implications for societal equality and justice. Periodic review is needed as technologies advance.

Scientific institutions like universities also have an important responsibility. Institutional review boards can evaluate proposed genetic engineering research for ethical and safety issues before approval. Journals should require researchers to disclose funding sources and potential conflicts of interest. Institutions must foster a culture of responsible conduct where concerns can be raised without fear of reprisal. Peer review helps ensure methods and findings are valid, problems are identified, and results are communicated clearly and accurately.

Transparency from researchers is equally vital. Early and meaningful public engagement allows input that can strengthen oversight frameworks and build trust. Researchers should clearly explain purposes, methods, funding, uncertainties, and oversight in language the non-expert public can understand. Public availability of findings through open-access publishing or other means supports informed debate. Engagement helps address concerns and find ethical solutions. If applications remain controversial, delaying or modifying rather than dismissing concerns shows respect.

Some argue results should only be applied if a societal consensus emerges through such engagement. This risks paralysis or domination by a minority view. Still, research approvals could require engagement plans and delay controversial applications if outstanding public concerns exist. Engagement allows applications most in need of discussion more time and avenues for input before proceeding. The goal is using public perspectives, not votes, to strengthen regulation and address public values.

Self-governance within the scientific community also complements external oversight. Professional codes of ethics outline boundaries for techniques like human embryo research, genetic enhancement, or editing heritable DNA. Societies like genetics associations establish voluntary guidelines members agree to follow regarding use of new techniques, clinical applications, safety testing, and oversight. Such codes have legitimacy when developed through open processes including multiple perspectives. Ethics training for researchers helps ensure understanding and compliance. Voluntary self-regulation gains credibility through transparency and meaningful consequences like loss of certification for non-compliance.

While oversight focuses properly on research, broader societal issues around equitable access must also be addressed. Prohibitions on genetic discrimination ensure no one faces disadvantage in areas like employment, insurance or education due to genetic traits. Universal healthcare helps ensure therapies are available based on need rather than ability to pay. These safeguards uphold principles of justice, human rights and social solidarity. Addressing unjust inequalities in areas like race, gender and disability supports ethical progress overall.

Societal discussion also rightly focuses on defining human identity, enhancement and our shared humanity. Reasonable views diverge and no consensus exists. Acknowledging these profound issues and inquiring respectfully across differences supports envisioning progress all can find ethical. Focusing first on agreed medical applications while continuing open yet constructive discussions models the democratic and compassionate spirit needed. Ultimately the shared goal should be using genetic knowledge responsibly and equitably for the benefit of all.

A multifaceted approach with expertise and participation from diverse perspectives offers the best framework for ensuring genetic engineering progresses ethically. No system will prevent all problems but this model balances oversight, transparency, inclusion, justice and ongoing learning—helping to build understanding and trust so society can begin to realize genetic advances’ promise while carefully addressing uncertainties and implications these new technologies inevitably raise. With open and informed democratic processes, guidelines that prioritize well-being and human dignity, and oversight that safeguards yet does not hinder, progress can proceed in a responsible manner respecting all.

CAN YOU PROVIDE MORE EXAMPLES OF CAPSTONE PROJECTS IN THE FIELD OF DATA SCIENCE AND ANALYTICS?

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Customer churn prediction model.

One common capstone project is building a predictive model to identify customers who are likely to churn, or stop doing business with a company. For this project, you would work with a large dataset of customer transactions, demographics, service records, surveys, etc. from a company. Your goal would be to analyze this data to develop a machine learning model that can accurately predict which existing customers are most at risk of churning in the next 6-12 months.

Some key steps would include: exploring and cleaning the data, performing EDA to understand customer profiles and behaviors of churners vs non-churners, engineering relevant features, selecting and training various classification algorithms (logistic regression, decision trees, random forest, neural networks etc.), performing model validation and hyperparameter tuning, selecting the best model based on metrics like AUC, accuracy etc. You would then discuss optimizations like targeting customers identified as high risk with customized retention offers. Additional analysis could involve determining common reasons for churn by examining comments in surveys. A polished report would document the full end to end process, conclusions and business recommendations.

Customer segmentation analysis.

In this capstone, you would analyze customer data for a retail company to develop meaningful customer segments that can help optimize marketing strategies. The dataset may contain thousands of customer profiles with demographics, purchase history, channel usage, response to past campaigns etc. Initial work would involve data cleaning, feature engineering and EDA to understand natural clustering of customers. Unsupervised learning techniques like K-means clustering, hierarchical clustering and latent semantic analysis could be applied and evaluated.

The optimal number of clusters would be selected using metrics like silhouette coefficient. You would then profile each cluster based on attributes, labeling them meaningfully based on behaviors. Associations between cluster membership and other variables would also be examined. The final deliverable would be a report detailing 3-5 distinct and actionable customer personas along with recommendations on how to better target/personalize offerings and messaging for each group. Additional analysis of churn patterns within clusters could provide further revenue optimization strategies.

Fraud detection in insurance claims.

Insurance fraud costs companies billions annually. Here the goal would be to develop a model that can accurately detect fraudulent insurance claims from a historical claims dataset. Features like claimant demographics, details of incident, repair costs, eyewitness accounts, past claim history etc. would be included after appropriate cleaning and normalization. Sampling techniques may be used to address class imbalance inherent to fraud datasets.

Various supervised algorithms like logistic regression, random forest, gradient boosting and deep neural networks would be trained and evaluated on metrics like recall, precision and AUC. Techniques like SMOTE for improving model performance on minority classes may also be explored. A GUI dashboard visualizing model performance metrics and top fraud indicators could be developed to simplify model interpretation. Deploying the optimal model as a fraud risk scoring API was also suggested to aid frontline processing of new claims. The final report would discuss model evaluation process as well as limitations and compliance considerations around model use in a sensitive domain like insurance fraud detection.

Drug discovery and molecular modeling.

With advances in biotech, data science is playing a key role in accelerating drug discovery processes. For this capstone, publicly available gene expression datasets as well as molecular structure datasets could be analyzed to aid target discovery and virtual screening of potential drug candidates. Unsupervised methods like principal component analysis and hierarchical clustering may help identify novel targets and biomarkers.

Techniques in natural language processing could be applied to biomedical literature to extract relationships between genes/proteins and diseases. Cheminformatics approaches involving property prediction, molecular fingerprinting and substructure searching could aid in virtual screening of candidate molecules from database collections. Molecular docking simulations may further refine candidates by predicting binding affinity to protein targets of interest. Lead optimization may involve generating structural analogs of prioritized molecules and predicting properties like ADMET (absorption, distribution, metabolism, excretion, toxicity) profiles.

The final report would summarize key findings and ranked drug candidates along with discussion on limitations of computational methods and need for further experimental validation. Visualizations of molecular structures and interactions may help communicate insights. The project aims to demonstrate how multi-omic datasets and modern machine learning/AI are revolutionizing various stages of drug development process.

HOW CAN INTERNSHIPS HELP STUDENTS IN TERMS OF THEIR FUTURE CAREER PROSPECTS

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Internships provide students with invaluable hands-on work experience in their chosen field of study or career interest. Being able to gain real-world experience within a professional workplace setting is hugely beneficial for students as they approach graduation and look towards their long term career goals.

One of the primary ways internships help students is by allowing them to apply the knowledge and skills they are learning in the classroom to practical work tasks and projects. This helps students test out whether their academic interests and strengths are a good match for the types of roles and responsibilities within a certain profession. It gives students a taste of what having a particular job would truly be like on a day to day basis.

Many students pursue internships to help determine whether their initial career ideas after graduation are still the right path, or to explore new options they may not have previously considered. Having career-relevant experience to include on a resume when job searching makes recent graduates much more attractive candidates compared to those without any practical work experience. Employers want to see that candidates can transition smoothly from education to employment.

The connections students are able to make during internships are extremely valuable for future career networking and opportunities. Interns get to know professionals within their organizations and fields of interest on a personal level. These contacts can turn into references, advice resources, or even potential leads on open roles. Some internships even turn into post-graduation job offers. The relationships built during internships are a long term investment in one’s career capital.

Through exposure to real work projects and responsibilities, internships also allow students to gain hard and soft skills not easily taught inside a classroom. Things like problem solving, communication, teamwork, understanding workplace culture, prioritization, meeting deadlines, and more can start to be developed. Students learn how to be professional, ask good questions, take initiative, and adapt to a work environment.

Some other career benefits of internships include exposing students to different organizational structures, business functions, technologies, processes, and industries they may want to consider pursuing long term. They help students identify what workplace settings or professional roles might be the best personal fit before fully committing to one path post-graduation. Internships are lower risk ways to explore career options.

For many students, internships provide that all important confidence boost knowing they can successfully apply their learning and handle real responsibilities before entering the full time workforce. They reduce the shock of going straight from academia to full time employment without any previous professional experience. internships ease new graduates into their careers.

Networking is an invaluable soft skill students can start developing through internships. The connections made with professionals can turn into references, advice sources, or even leads on jobs after graduation. Some internships result in job offers directly from the employer. All of these help increase graduates’ career prospects dramatically compared to relying solely on broader job searches.

There is also evidence that having relevant internship experience on one’s resume can increase graduates’ starting salaries. Employers know the value of candidates who arrive with skills honed by tackling authentic work tasks versus only academic experience. This ‘return on investment’ of seeking hands-on experience while in college continues paying dividends for years to come in career success and earnings potential.

For competitive or selective industries like technology, consulting, finance, media and more – internships have almost become a prerequisite for many full time roles post-graduation. Completing quality internships at prestigious employers demonstrates to future hiring managers a student’s commitment, potential, and “real world” aptitude in their field. Employers prefer candidates who bring this experiential learning to the table.

When seeking competitive student or graduate programs like MBAs, law degrees, medical residencies and fellowships – many highly ranked schools put an emphasis on applicants who have held substantive career-related internships or research experiences alongside their academic pedigree. These experiences demonstrate to selection committees a candidate’s motivated initiative and commitment to successfully exploring their intended career path from an early stage.

Internships provide students with hands-on experience applying classroom learning in a workplace, help determine the right career fit through low-risk exploration, build invaluable industry and professional connections, develop key hard and soft skills for long term career success, and significantly increase graduates’ competitiveness for prestigious jobs and further education opportunities. They offer rewards that far exceed the commitment during a student’s studies and set them up superbly for maximizing future career prospects and potential.

HOW DID YOU ENSURE THE SECURITY AND PRIVACY OF CUSTOMER PAYMENTS WITHIN THE APP

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We understand that security and privacy are top priorities for any application that handles sensitive customer financial data. From the beginning stages of designing the app architecture, we had security experts review and advise on our approach. Some of the key things we implemented include:

Using encrypted connections. All network traffic within the app and between the app and our backend servers is sent over encrypted HTTPS connections only. This protects customer payment details and other sensitive data from being compromised during transmission. We implemented TLS 1.2 with strong cipher suites to ensure connection encryption.

Storage encryption. Customer payment card numbers and other financial details are never stored in plain text on our servers or in the app’s local storage. All such data is encrypted using AES-256 before being written to disk or database. The encryption keys are themselves securely encrypted and stored separately with access restrictions.

Limited data retention. We do not retain customer payment details for any longer than necessary. Card numbers are one-way hashed using SHA-256 immediately after payment authorization and the plaintext is deleted from our servers. Transaction history is stored but payment card details are truncated and not kept beyond a few days to limit exposure in case of a data breach.

Authentication and authorization. Multi-factor authentication is enforced for all admin access to backend servers and databases. Application programming interfaces for payment processing are protected with OAuth2 access tokens which expire quickly. Roles based access control restricts what each user can access and perform based on their assigned role.

Input validation. All inputs from the app are sanitized and validated on the backend before processing to prevent SQL injection, cross site scripting and other attacks. We employ whitelisting and escape special characters to avoid code injection risks.

Vulnerability scanning. Infrastructure and application code are scanned regularly using tools like OWASP ZAP, Burp Suite and Qualys to detect vulnerabilities before they can be exploited. We address all critical and high severity issues promptly based on a risk based prioritization.

Secure configuration. Our servers are hardened by disabling unnecessary services, applying updates/patches regularly, configuring logging and monitoring. We ensure principles of least privilege and defense in depth are followed. Regular security audits monitor for any configuration drift over time.

Penetration testing. We engage independent security experts to conduct penetration tests of our apps and infrastructure periodically. These tests help identify any vulnerabilities that may have been missed otherwise along with improvement areas. All high risk issues are resolved as top priority based on their feedback.

Incident response planning. Though we make all efforts to prevent security breaches, we recognize no system is completely foolproof. We have formal incident response procedures defined to handle potential security incidents quickly and minimize impact. This includes plans for appropriate notifications, investigations, remediation steps and reviews post-incident.

Monitoring and logging. Extensive logging of backend activities and user actions within the app enables us to detect anomalies and suspicious behavior. Customized alerts have been configured to notify designated security teams of any events that could indicate a potential threat. Logs are sent to a centralized SIEM for analysis and correlation.

Customer education. We clearly communicate to customers how their payment details are handled securely within our system through our privacy policy. We also provide educational materials to create awareness on secure online financial practices and how customers can help maintain security through vigilance against malware and phishing.

Third party security assessments. Payment processors and gateways we integrate with conduct their own security assessments of our apps and processes. This adds an extra layer of verification that we meet industry best practices and regulatory requirements like PCI-DSS. Dependencies are also evaluated to monitor for any risks introduced through third parties.

Keeping abreast with evolving threats. The cyber threat landscape continuously evolves with new attack vectors emerging. Our security team closely tracks developments to enhance our defenses against emerging risks in a timely manner. This includes adopting new authentication standards, encryption algorithms and other security controls as needed based on advisory updates from cybersecurity researchers and organizations.

The above measures formed a comprehensive security program aligned with industry frameworks like OWASP, NIST and PCI-DSS guidelines. We put security at the core of our app development right from the architecture design phase to ensure strong controls and protections for handling sensitive customer financial data in a responsible manner respecting their privacy. Regular monitoring and testing help us continuously strengthen our processes considering an attacker perspective. Data protection and customer trust remain top priorities.