Common Mistakes When Creating Legal Writing Samples

Common Mistakes When Creating Legal Writing SamplesCommon Mistakes When Creating Legal Writing SamplesTop-notch writing skills are crucial in the legal industry, especially for lawyers and paralegals. If you are applying for a legal position, employers will frequently request a writing sample. Furnishing a poor writing sample can destroy your chance of landing the job. On the other hand, an excellent writing sample can help you get a leg up on the competition. It is a good idea to build a portfolio of writing samples during school and your early years of practice. This portfolio should include your best work in a variety of genres. Below are five common mistakes applicants make in submitting writing samples. Poor Writing Sample A poorly written sample that contains fundamental errors in grammar, word choice, sentence construction or other quality problems is a red flag for employers. Below are a few issues to watch Sentence clarityWord choiceRedundancies and/or inaccuracies in th e textConsistencyTone/voiceContent organizationFlow/transitionsSentence structureGaps in contentPresentation Have a mentor, professor, co-worker or other trusted professional to review your writing samples. If your writing skills need work, take a few writing classes or hire a tutor to help you improve your writing. Typographical Errors While applicants give much attention to creating an error-free resume and cover letter, their writing samples often receive a less meticulous review. We have seen many writing samples with typographical errors many of them published online, in law review journals and legal publications or filed with the court. A single typo is enough to instill doubt in the reviewer and eliminate you from consideration. Off-Topic Samples Make sure your writing samples match the employers request and the needs of the position. If, for example, you are applying for an associate position, dont submit your senior term paper on psychosocial behavior. Instead, sub mit a sample that demonstrates that you can perform the job for which you are applying. For example, if you are applying for a position as an associate in the firms litigation department, submit a brief, motion or memorandum of law. If you are applying for a position as a corporate paralegal, submit a resolution, escrow trust agreement or related transactional document. Failure to Follow Instructions Always follow the job ad or potential employers instructions for submitting writing samples, particularly with respect to Type of samplesMake sure that the type of sample you submit (i.e., brief, correspondence, motion) matches the employers request in terms of format, style, and content. If you dont have a relevant sample in your portfolio, draft a new writing sample to fit.The number of writing samplesDont submit too many or too few samples. If a number is not specified, a good rule of thumb is two samples (one at a minimum and three samples max). Busy employers rarely have time t o read more than three writing samples. Length of writing samplesFollow the employers instructions regarding the length of your samples. In the legal field, writing samples tend to be longer (5-10 pages) to enable employers to evaluate your ability to make a persuasive legal argument and analyze points of law.?The manner of submissionSome employers may want samples submitted as e-mail attachments while others prefer that they appear in the body of the e-mail or are mailed to their address. Disclosing Confidential Information Writing samples in the legal profession require special care due to attorney/client privilege, sensitive information, and confidentiality concerns. When submitting writing samples from a past or present case or transaction, even if that case is closed or terminated, it is important to remove the names of all parties, names of clients, and any other sensitive or confidential information. To preserve the flow of your content, you can substitute fictitious name s, facts, and information.

Carbon Nanotube Super Springs

Carbon Nanotube Super Springs Carbon Nanotube Super Springs For certain applications, mechanical springs are superior to electrochemical batteries. A springs stored energy can be released quickly, with high power density. Springs can be recharged (redeformed) many times without degradation (as long as they are not deformed beyond their limits) and can hold stored energy without leakage. Springs also store energy robustly in the face of wide temperature swings.The energy density of springs made of conventional materials, however, is much lower than the energy density of batteries. Current lithium-ion batteries offer energy densities of about 500 kJ/kgthree orders of magnitude greater than the energy densities of high-performance steel springs in bending. But batteries have their own limitations, being typically optimized for either high power density or high energy density it is difficult to optimize simultaneously for both.What if you want the robustness and power capabilities of spr ings, but also need the energy density of batteries? One option is to look for new materials for springs.The ideal high-performance spring material will have both high material stiffness (like high-carbon steel) and high deformability (like rubber). Advanced nanoscale materials offer new options.Grown on a substrate using vapor deposition, each of these pillars contains a million aligned nanotubes.One promising candidate is the carbon nanotube (CNT). A CNT is essentially a graphene sheeta carbon lattice one atom thickrolled up to form a tube and capped off at the ends. Such a single-wall nanotube can be as small as 1 nanometer in diameter and under a micrometer in length. Larger multi-wall tubes (made of multiple coaxial layers of carbon atoms) may be a few hundred nanometers across and more than a centimeter long.Like graphene, the effective material stiffness of CNTs is quite highabout five times that of high-carbon steel. Moreover, the low-defect structure of the carbon lattice e nables CNTs to undergo large deformations before suffering failureup to six times the maximum yield strains of high-carbon steel under tensile loading (stretching).Although a single carbon nanotube may have exceptional mechanical properties, it is far too small to store a macroscopically useful amount of energy. Large numbers of CNTs must be deformed in a uniform way. For high energy density per unit volume, the CNTs must be tightly packed. And the complete system must be braced in a lightweight structure that can support the load of stored mechanical energy, plus have a means of extracting the energy for use.To examine the fhigkeit for elastic energy storage in CNTs, in 2006, Timothy Havel and I formed a team at the Massachusetts Institute of Technology. Continuum modeling revealed that for maximum energy storage, the ideal system would consist of many long, small-diameter, single-wall CNTs in well-ordered groupings and loaded in tension. Moreover, the system needs a support struct ure made of a high-performance material such as diamond or silicon carbide.When A. John Hart at the University of Michigan joined ur group, we could test our mathematical models in experiments on real CNT springs. Hart pioneered a thermal chemical vapor deposition technique that grows well-aligned forests of multi-wall CNTs, as well as pillars (patterned sections of the forest). Although they do not have the single-wall structure that our modeling identified as ideal, a small piece of a CNT forest has 1 to 10 million parallel nanotubes several millimeters long, a significant fraction of them extending the full length of the test structure. The chunk of forest may be tested as is, or may be densified first using capillary forces or mechanical rolling to bring the CNTs closer together.So far, we have looked at when and how the multi-wall CNT springs fail when they are stretched in tension we have also tested their performance under cyclic tensile loading, mimicking the storage and ext raction of energy from CNT springs. To date, the maximum recoverable energy density measured to date is about 5 kJ/kg, corresponding to total energy stored of about 1.5 microjoules. Although this energy density is much lower than the maximum theoretical energy density of springs composed of ideally ordered single-wall CNTs, it already exceeds the maximum energy density per unit weight of steel in bending by a factor of ten.What could be done with an energy storage medium having the energy density of batteries and the temperature insensitivity and burst power capability of a spring? For high-power applications, springs could be released in groups for low-power applications, energy could be released gradually through a ratcheting mechanism similar to the spring-driven escapement of a mechanical clock.One potential application might be portable home tools that currently run on gasoline engines. For example, after winding up your spring-powered leaf blower, you could clear leaves more q uietly and cleanly than with a gas-powered blower. Spring-based energy storage may also have advantages for leak-free long-term backup power supplies.Given the promise of CNT springs, its easy to imagine ways they could help power a green and efficient future.Adapted from Carbon Super Springs by Carol Livermore, for Mechanical Engineering, March 2010.The ideal high-performance spring material will have both high material stiffness (like high-carbon steel) and high deformability (like rubber).

Sensors Allow Robots to Feel Sensation

Sensors Allow Robots to Feel Sensation Sensors Allow Robots to Feel Sensation Sensors Allow Robots to Feel SensationThe line between human and machine is getting thinner every day. We have robots that can reason, predict, and even work in partnership with humans and other robots. But in their interactions with the physical world, these machines have always been limited.That is changing.A group of engineers from Stanford Universitys Zhenan Bao Research Group, in partnership with Seoul National Universitys College of Engineering, has developed an artificial nerve that, when used with a robotic brain, allows robots to feel and react to external stimulus just like we do. Soon, this could become a key part of a multisensory artificial nervous system that empowers the next generation of thinking, feeling robots. The technology could also be used in prosthetic limbs to allow patients to feel and interact with their replacement body parts just as they would natural limbs.Its a complex techno logy, but the concept is simple. In ur skin, we have sensors that can detect even the lightest stich, neurons that transmit that touch to other parts of the body, and synapses that take that information and translate it into the feelings that we recognize and respond to.A touch on the knee first causes the muscles in that area to stretch, sending impulses up the associated neurons to the synapses, which recognize the response and sends signals to the knee muscles to contract reflexively and to the brain to recognize the sensation. We call it an involuntary reaction, but its anything but automatic.For You Making the Emotional RobotThe artificial mechanosensory nerves are composed of three essential components mechanoreceptors (resistive pressure sensors), neurons (organic ring oscillators), and synapses (organic electrochemical transistors), says Tae-Woo Lee, an associate professor in the Department of Materials Science and Engineering, Hybrid Materials at Seoul National University w ho worked on the project. The pressure information from artificial mechanoreceptors can be converted to action potentials through artificial neurons. Multiple action potentials can be integrated into an artificial synapse to actuate biological muscles and recognize braille characters.Sandwiched inside layers of plastic, these sensors react to pressure. Image Bao Research GroupThe Bao Labs artificial system mimics human functionality by linking dozens of different pressure sensors together, creating a voltage boost between their electrodes whenever a touch is detected. This change is recognized by a ring oscillator, which converts the voltage change into a series of electrical pulses that are picked up by a third component, the synaptic transistor. The transistor translates those transmission pulses into patterns that match the patterns that organic neurons transmit in the brain.The artificial synaptic transistor is the real development in Baos work. It allows the artificial system t o interact with natural, human systems as well as robotic brains.Wide-Ranging ApplicationsThe research, led by Zhenan Bao, a professor of chemical engineering at Stanford, was first reported in Science, and featured a video that demonstrated the systems capabilities. In the video, the Bao Lab used the technology to sense the motion of a small rod over pressure sensors. It also shows how the technology could be used to identify Braille characters by touch. Most impressively, the researchers inserted an electrode from their artificial neuron to a neuron in the body of a cockroach, using the signal to cause the insects leg to contract.This proved that the artificial nerve circuit could be embedded as part of a biological system, enabling prosthetic devices that offer better neuro integration than is currently available.Previous prosthetics usually use a pneumatic actuation of artificial muscle, which are bulky and bedrngnis so dexterous, Prof. Lee says. Our artificial nerve can be embe dded in the prosthetics aesthetically without bulky pneumatic components. We believe that our artificial nerve can operate the artificial muscle in the prosthetics more delicately and aesthetically. Most prosthetics do not have a sensing function for touch, and the conventional prosthetics require a complicated software algorithm to make the artificial muscle move. But our mechanosensory nerve can detect touch and then the output signal can be directly transmitted to actuate the muscle.There is also potential for this technology in the robotics space, explains Dr. Yeongin Kim, formerly a graduate student in the Bao Lab who worked on the artificial nerves project. In particular, it could lead to the creation of so-called soft robotics, in which robots are constructed from materials that look and feel more similar to organics. The process of mimicking the synapses and neurons of the biological nervous system in the realm of robotics could go a long way toward the development of machin e learning and robots that can teach themselves new skills.The advantage of machine learning is you dont need to teach a robot every detail, Kim says. You can just make it learn a difficult job, and the robot can train on that difficult task by itself. In these cases, hardware like the artificial nervous system can be useful because of the role that synapses play in learning and sensation.In our bodies, the network of neurons and synapses can process information from the environment and control the activators that impact what we feel and how we respond, Kim says. That kind of signal processing can be useful in training what we want a robot to do and not do. This thinking is becoming popular in neuromorphic computing as well as robotic engineering, and we expect our system to provide the hardware architecture for machine learning that can be used in future neurorobots.Whatever the application, this technology is still in the early stages of development and it remains to be seen what commercial potential it holds. One goal of the work, though, is to further the development of bio-inspired materials with soft mechanical properties that can be used in sophisticated neurorobots or neuroprosthesis, performing in ways that are comparable to or even better than biological systems.Bioinspired soft robots and prosthetics can be used for people with neurological disorders and more, he says. There are many interesting commercial applications of our technology.Tim Sprinkle is an independent writer. Read MoreLow-Tech Solutions Fight HungerInsect-Sized Robot Takes FlightRobots Make Self-Repairing Cities Possible For Further DiscussionThe advantage of machine learning is you dont need to teach a robot every detail. You can just make it learn a difficult job, and the robot can train on that difficult task by itself. Dr. Yeongin Kim, Stanford University