Mdm | Date: 2014-04-17
A mouthpiece includes first and second bodies over teeth of a user. The bodies comprise frames with a bite pad between upper and lower teeth. The bite pad is molded in the frames while in situ in the mouth. The frames, prior to having the bite pads in place are hollow spaces with spanning elements spaced from each other projecting into the hollow space between a top and a bottom of each of the frames. An upper wall of the frame includes an inwardly directed lip directed towards a space inside the frames. A lower wall of the frame is extended in height relatively longer than the upper wall. A shim or a clip is inserted between the incisor edges for facilitating alignment of the incisor edges. The connector includes a slot for accommodating an extension of the shim when the shim is located with the connector.
Mdm | Date: 2014-09-18
A mouthpiece includes first and second bodies over teeth of a user and connector between the bodies. The bodies comprise frames with a bite pad between upper and lower teeth. The bite pad is molded in the frames while in situ in the mouth. The frames, prior to having the bite pads in place are hollow spaces with spanning elements spaced from each other projecting into the hollow space between a top and a bottom of each of the frames. The material of the bodies and connector employ a durometer selectively between Shore A 40-45, and selectively the amount of material is between 4-6 grams for each of a first base putty and for a second catalyst putty for the making of the guard, in combination with the frame. At least one putty is of a first color and selectively the second putty is a same or different color to the first color, and at least one frame is selectively clear or colored, and at least one of the putties includes a flavoring or is flavorless.
The global market for smart meters (also known as advanced metering infrastructure) isn’t just growing, it’s getting more complicated -- and while utilities don’t always keep up-to-date with the investments to manage this technology complexity, they can’t put them off forever. That means big spending ahead on for the data management and analytics software needed to make the most of the modern advanced metering infrastructure (AMI) network, according to GTM Research’s latest report, Utility AMI Analytics at the Grid Edge: Strategies, Markets and Forecasts. The report projects that utilities around the world will spend $10.1 billion on AMI analytics solutions and integration services through 2021, a significant increase over spending so far this decade. This boom in spending will be driven by two key changes in the market since the first big wave of AMI deployments about a decade ago. First of all, the next wave of regulator-mandated smart meter rollouts will need to do a lot more than serve as the digital replacements for meter readers. Second, the rise of distributed energy resources (DERs) on the edge of the grid is starting to open up revenue opportunities for utilities that can access the data to take advantage of them. These two trends are pushing utilities to confront the current limits of their AMI data collection and analytics, and start to enable the advanced features they’re looking to provide. Here's a breakdown of why this is happening today, and how utilities are reacting with their investment dollars. - Nobody’s justifying AMI deployments on meter reading alone. “Tapping into low-hanging fruit such as increased operational efficiency through improved the meter-to-cash processes no longer provides the overwhelming benefits that drove early AMI projects at U.S. electric utilities,” the report notes. That was OK for the earliest round of deployments, or those backed by American Recovery and Reinvestment Act stimulus funds. But nowadays, AMI networks need new value streams for positive business cases, including conservation through voltage reduction, improved customer engagement, demand management, and improved revenue assurance practices. We’ve seen some shining examples of these next-generation requirements from this year’s mega-deals in the smart meter space, including Con Ed’s $1.3 billion AMI rollout plan, or National Grid’s proposed 1.3 million meter deployment in Massachusetts. - AMI is just the first IT platform that requires integration. The scope of investment GTM Research is predicting over the next five years won’t be limited to just the utility AMI system, or the meter data management (MDM) system, or any other single system -- but to all of the above. “Many of the highest-value closed-loop AMI applications require data streams from the integration of traditional utility IT systems as well as operational systems,” the report notes, adding another layer of complexity to the data management challenge. “Inconsistency of input data formats -- including structured, unstructured, time series or transactional -- requires standardization and the application of an extract, transform and load (ETL) engine prior to performing analytics.” - The evolution of IT architectures has opened more nimble, interconnected AMI analytics. We’ve come a long way from the traditional utility IT infrastructure, in which different IT systems were more or less siloed off (GIS, CIS, OMS, EMS, MDM, etc.) from one another. The first big shift in this paradigm came with the rise of service-oriented architectures, which translate and route data between enterprise systems using a common data bus, reducing integration complexity and improving upgrade and interface management. The second has been the rise of cloud infrastructure, open-source data management software and virtualization-enabled scalable data storage and processing, which have opened opportunities for utilities and their vendors to put AMI data to use in ways that would have otherwise been prohibitively expensive or complex. - Utilities’ data analytics needs cover the range of IT architectures available. The problem for utilities in reaching this vision lies in their recent past, the report notes. Many have built themselves into a mix of standalone, point-to-point integrations: “System-wide solutions that support near-real-time aggregation and analysis of the volume and granularity of data provided by smart meters have yet to be successfully implemented in the utility industry. This has led most utilities to rely on existing database structures, delaying development of high-performance use cases.” That’s why GTM Research predicts continued demand for “turnkey solutions that can be rapidly integrated with existing back-end IT systems with limited customization,” along with analytics to improve the performance of already-deployed AMI networks, the report notes -- utilities want to make better use of what they already have. - DERs make smarter meters and analytics necessary. The growth in distributed energy resources (DERs) has become an increasingly important part of grid operations, customer relations and long-term economic planning, and the need for data analytics to manage this interplay “increases the need for utilities to receive timely, consistent net-load data from customer sites to improve situational awareness, enable utilities to offer customers specialized rates, and ensure accurate and efficient billing,” the report notes. These requirements are driving limited deployments in countries and states where AMI has not been mandated, such as Germany, as well as driving investment in states undergoing major regulatory reforms, like New York under its Reforming the Energy Vision (REV) initiative, or California with its Integration of Distributed Energy Resources (IDER) proceeding. The distributed energy data management challenge also presents utilities with a new set of actors -- DER-owning customers, or third-party operators and aggregators -- that need to be involved. - Stimulus and mandates are huge drivers for region-by-region investment. “The 2009 American Recovery and Reinvestment Act and California’s smart-meter mandate catalyzed the U.S. market, leading to the deployment of nearly 30 million smart meters in less than a decade,” the report notes. Meanwhile, the European Union, China, and Japan have all enacted legislation mandating some level of adoption of smart meters as part of broader clean energy and smart city initiatives. Each offers the potential for tens of millions -- or in China’s case, hundreds of millions -- of new smart meters per country. Of course, what governments give, they can take away, as we’ve seen from slowed and reduced AMI rollouts in Europe.
Light waveforms whose spectra extend over more than two optical octaves in the visible and adjacent spectral range (about 1.1–4.6 eV) (Extended Data Fig. 1a) are generated by the nonlinear broadening of laser pulses (approximately 22 fs, 1 mJ, 790 nm) through a hollow-core fibre (HCF) filled with Ne gas (about 2.3 bar). The energy of the generated supercontinuum at the exit of the HCF is about 550 μJ. The spectra of these pulses are divided by dichroic beam-splitters into four almost equally wide spectral bands centred in the near infrared (NIR; about 1.1–1.75 eV), visible (vis; about 1.75–2.5 eV), visible-ultraviolet (vis-UV; about 2.5–3.5 eV) and deep ultraviolet (DUV; about 3.5–4.6 eV). The pulses in these bands are individually compressed by dispersive mirrors to durations of a few femtoseconds before they are spatially and temporally superimposed to yield a single beam/pulse at the exit of the apparatus. The pulses are temporally characterized by a transient-grating frequency-resolved optical gating (TG-FROG) apparatus. The durations T of the pulses in different channels of the synthesizer were measured to be T ≈ 8.5 fs, T ≈ 7 fs, T ≈ 6.5 fs and Τ ≈ 6.5 fs (see inset to Fig. 2a). The synthesizer apparatus transmits about 82% of the energy of the incoming supercontinuum. As a result, the pulse energy at the exit of the apparatus is about 320 μJ, and is distributed among the four channels as Ch ≈ 255 μJ, Ch ≈ 45 μJ, Ch ≈ 15 μJ and Ch ≈ 4 μJ, where Ch denotes the energy of channel i. For the synthesis of optical attosecond pulses, both precise control of the relative delay between the constituent pulses in the synthesizer as well as an intensity control over the spectral channels is required. To this end, we followed a new approach, which effectively enables the passive spectral intensity control and facilitates the attosecond streaking characterization of the generated optical waveforms in the same set-up. The EUV attosecond probe is generated first (Extended Data Fig. 1b) by focusing the light transients from the synthesizer into a quasi-static Ne gas cell. The EUV radiation, which emerges collinearly to the driver waveform, is transmitted through a thin, round Zr foil, while the optical pulse, which is transmitted around the geometrical margins of this foil, forms an annular beam. A double-mirror module consisting of a concave, multilayer, coated inner mirror and a metal–dielectric–metal (MDM)-coated concave annular sector (outer mirror) (Extended Data Fig. 2) of the same focal length (f = 12.5 cm), focuses the light transients and the EUV attosecond probe into a second Ne gas nozzle placed near the entrance of a time-of-flight (TOF) spectrometer (Extended Data Fig. 1b). One of the essential characteristics of the MDM is that the imposed spectral control results in negligible phase distortions over the whole spectral range of the supercontinuum pulse. This was experimentally verified by FROG measurements of the pulses in the constituent channels upon reflection off the MDM unit. At the same time, because the EUV probe is generated before the spectral intensity control of the optical transient, it overcomes a fundamental limitation of a half-cycle field: the efficient generation of intense EUV-probe pulses. This is because a half-cycle field does not involve at least two intense field crests, which are required for ionization and subsequent acceleration of electrons27 leading to high harmonic generation. Single-cycle optical pulses are generated by physically suppressing a part (>3 eV) of the high-frequency spectrum of the optical attosecond pulse. We focus optical attosecond pulses into a quasi-static cell, which replaces the Ne gas jet used for performing attosecond-streaking characterization of their fields, filled with Kr atoms at a moderate pressure (about 80 mbar) (Extended Data Fig. 1b). We probe the nonlinear polarization of the system by recording VUV spectra that emerge collinearly with the driving optical field using a spectrometer placed downstream from the cell. The spectra are sampled at energies higher than about 5 eV, that is, beyond the constituent spectrum of the optical attosecond pulses, and extend no higher than the ionization threshold of neutral Kr (I ≈ 14 eV). The concept of the carrier-envelope phase ϕ (CEP) is most readily understood in the time domain28. The carrier-envelope phase is the interval Δt between the maximum of the envelope and the maximum of the instantaneous field, translated in phase at the centroid frequency ω of the spectrum: ϕ = ω × Δt . Any electric field can be decomposed, according to Hilbert’s transform as in which A(t) is the envelope (the modulus of the analytical field) and ϕ is the global (or absolute) phase. The maximum of the field is determined by solving On the basis of this equation and the estimation , we distinguish two different regimes: (1) For pulses with durations longer than one cycle, and the solution of equation (2) yields ϕ = ω × Δt = ϕ , which confirms the equivalence between the global (ϕ ) and carrier-envelope (ϕ ) phases. (2) For sub-cycle pulses, Δω/ω > 1 and so the solution of equation (2) is not trivial because, in a fraction of a cycle, the envelope varies substantially. Consequently, ω × Δt < ϕ and the global and carrier-envelope phases are no longer equivalent. Extended Data Fig. 3a presents the case of a half-cycle pulse—an optical attosecond pulse. The sinusoidal waveform (red) reaches its maximum at 0.2 periods instead of 0.25, which is expected for a wave with a phase of π/2 rad. Extended Data Fig. 3b shows the CEP determined using the method of the field maximum depending on the pulse duration, for three settings of the global phase. This comparison reveals a considerable difference between ϕ and ϕ in the short pulse regime. In view of the above justification, it is clear that the CEP is an accurate description of the global phase only for pulses longer than one cycle. To theoretically study the nonlinear dipole dynamics in Kr atoms exposed to intense optical attosecond pulses, we used two models. In the first, we solve the three-dimensional time-dependent Schrödinger equation (TDSE) within the single-active-electron approximation. To this end, we used a central potential for Kr, which was calculated using optimized effective-potential methods29. In the second, and in order to describe instantaneous response, we assumed an adiabatic model based on a two-level system12, 30, in which the dipole moment, in the quasi-static approximation, can be expressed as: where d is chosen to match the nonlinear polarizability of Kr (refs 31, 32), ω is the excitation energy and E(t) denotes the electric field of the optical attosecond pulse. To access the nonlinear component of the induced electronic dipole moment at a given intensity of the driving field in both models, we perform a second calculation at a much lower (about six orders of magnitude) intensity. As a next step, we subtract the virtually linear dipole calculated at the lower intensity from the original one, after multiplying it by the corresponding ratio between the two intensities. The calculated global-phase spectrograms (spectral emission as a function of the global phase) using the adiabatic and TDSE models are shown in Extended Data Fig. 4a, b. In accordance with the discussion in the main text, the adiabatic model (Extended Data Fig. 4a) predicts uniform modulations of the spectral amplitude of the emitted spectral components as a function of the global phase ϕ . In contrast, the spectrogram calculated using the TDSE model embodies the signatures of the delayed electronic response (Extended Data Fig. 4b) in the form of asynchronous amplitude modulations between different frequencies/energies of the emitted dipole. These features are present in the entire emitted spectrum, not only close to the resonant area (10–14 eV). We show that these effects can be used to extract the dynamics of the nonlinear response by reconstructing the global-phase (ϕ ) spectrograms recorded in our experiments. Extended Data Fig. 4c shows a global-phase spectrogram simulated for a single-cycle pulse using the TDSE Kr model. In this regime of single-cycle pulses, the global-phase spectrogram does not show discernible variation over ϕ , which is experimentally verified (see Fig. 3e–g). An essential innovation introduced by using optical attosecond pulses is their unique capability to drive nonlinear dynamics in quantum systems without inducing a substantial degree of ionization or excitation, that is, without greatly altering the original system. In experiments where the polarization of the system is to be probed, both excessive ionization and excitations markedly modify the system, resulting in a considerable degree of ‘contamination’ in the emitted signal from the new atomic entities; such contamination is challenging to resolve both experimentally and theoretically. This capability is unique to isolated sub-cycle pulse structures and is not observed for trains of sub-cycle field modulation33 because the nanosecond-long exposure of atoms to such fields yields a substantial degree of ionization, which is actually the means to trace the waveform in these experiments34. The ionization and excitation probability of Kr atoms calculated by the solution of the TDSE and the waveform used in our experiments for a range of intensities (summarized in Extended Data Table 1) verify this conjecture. Experimentally, we also verified this fact by using the previously established and highly sensitive technique of attosecond transient absorption spectroscopy18. The interaction of optical attosecond pulses with matter, even at excessive intensities such as those used in our experiments and simulations, can be mostly considered as a scattering process. The system and the pulse virtually do not exchange energy; rather, the pulse probes the system via nonlinear scattering and the emission of coherent radiation. Identifying the dominant nonlinearities in the interaction between the optical attosecond pulses and the Kr atoms, and understanding the dependence of these nonlinearities on the variation of the global phase (ϕ ) of the optical attosecond pulse—the key control point of the interaction in experiments with such waveforms—is essential for the development of intuitive models that can describe the nonlinear dynamics. As we show below, it allows the development of a robust methodology that permits the reconstruction of the dynamics of the response from global-phase spectrograms. To this end, we theoretically (using TDSE simulations in Kr) studied the intensity dependence of the yield of the spectral emission (averaged over the range 0–8 eV) under optical attosecond pulses for the range of intensity settings used in our experiments. The corresponding nonlinearities can be evaluated from the slope of the linear fit of the data in the log–log diagram of Extended Data Fig. 5. A linear fitting over the entire range of intensities studied (2 × 1013 –10 × 1013 W cm−2) reveals a slope of about 4, and suggests the dominance and coexistence of the two most essential nonlinearities in centrosymmetric systems: the third- (E3) and the fifth- (E5) order nonlinearities—broadly know as bound-electronic nonlinearities. The study presented in the previous paragraph highlights the dominance of bound-electron nonlinearities in the response and the low or negligible sensitivity of the nonlinearity to the global phase of our pulses. To account for the non-instantaneous response revealed in our TDSE simulations, and inspired by previous approaches in ultrafast spectroscopy35, in which the nonlinear response is decomposed into instantaneous and delayed components, we describe the nonlinear dipole moment as a sum over instantaneous third- and fifth-order nonlinearities, as well as the fifth-order delayed nonlinearity according to equation (1): Here, a, b and c are coefficients, dt represents a delay of the fifth-order response and E(t, ϕ ) is the electric field of the optical attosecond pulse for a global phase ϕ . One would generally expect delayed terms to be considered for all nonlinearities involved (including the third-order); the energy diagram of Extended Data Fig. 6 offers an intuitive explanation of our choice to limit the delayed terms to only fifth-order nonlinearities. Indeed, delayed response in the range 0–9 eV will primarily involve virtual transitions, which can coherently couple at least two electronic states of the system (ground and excited states or combinations of excited states). The diagram demonstrates this assuming virtual transition compatible with the energy spectrum of our optical attosecond pulse (1.1–4.6 eV). Such transitions can only occur within the fifth-order response (higher-order Kerr effects). The diagram also highlights—through the multiphoton picture—that at this extreme limit of pulse duration and corresponding bandwidth, non-resonant and resonant response are virtually inseparable. As a result, the coherent dynamics induced between two or more states of the system will be manifested at each nonlinearly emitted spectral component in the process. Extended Data Fig. 7a–c shows representative, synthetic, global-phase spectrograms for three values of the parameter dt (dt = 0 (instantaneous response), dt = 20 as and dt = 30 as), generated by equation (1) in the spectral range of our experiments (see Fig. 4). The synthetic spectrograms highlight the capability of the model to capture key features of the experimental spectrograms (for example, Figs 1 or 4), such as the profoundly asynchronous modulation of the emission in the range 7–8 eV and the weakening of the amplitude in the range 6–6.5 eV—both are the result of dynamic nonlinear interference between delayed and instantaneous terms in equation (1). To further verify the validity of our model, we used the TDSE simulations in Kr as a basis to explore how a dipole described by the above equations could represent the (below resonances) nonlinear response of the system. Extended Data Fig. 7d shows the TDSE simulated dipoles (black line) and their fitting with equation (1). We investigated both the capability of the model to fit only a fraction (spectrally filtered from 5.5–8 eV; Extended Data Fig. 7d) of the dipole as well the entire dipole (0–8 eV) below resonances (Extended Data Fig. 7e). A single set of the parameters a, b, c and dt in equation (1), and a given peak intensity of the pulse, can precisely reproduce the nonlinear dipole (black lines) for all settings of the global phase. Representative examples for two (extreme) settings of the global phase ϕ ≈ 0 (left panels of Extended Data Fig. 7d and e) and ϕ ≈ π/2 rad (right panels of Extended Data Fig. 7d and e) are shown. Furthermore, the parameters a, b, c and dt extracted from fitting part of the nonlinear spectrum (5.5–8 eV) (Extended Data Fig. 7d) are identical to those required to fit the entire range (0–8 eV) (Extended Data Fig. 7e). As a conclusion, even a limited fraction of the nonlinear spectrum contains information about the interfering terms in equation (1) The model works well for a wide range of peak intensities (not shown); therefore, we conclude that it is adequate for reconstructing the response. The most critical test of the capability of our model to reconstruct experimental data and to trace the time-domain nonlinear dipole dynamics, is to perform a numerical experiment. To do so, we create a theoretical spectrogram using the TDSE model (where the nonlinear dipole is known a priori) and attempt to reconstruct this nonlinear dipole using (i) equation (1), (ii) the spectrogram constructed by the TDSE simulation, and (iii) the a priori measured driver field waveform. For both experimental and synthetic data, we used a quickly converging nonlinear algorithm to perform the reconstructions. It is based on a commercial, highly optimized numerical routine that uses the ‘trust-region’ method36, which is usually used for constrained problems. The root-mean-square deviation (r.m.s.d.) was determined, and defined as the main parameter to optimize our reconstruction via its minimization. The r.m.s.d. is calculated as: Where and X are the computed and original (measured) spectra, respectively, at certain global-phase settings, ω = 5.5 eV, ω = 8 eV, is the number of global-phase settings of the measured VUV spectrogram, and n is the number of frequencies involved (from ω to ω ). The results of this study are summarized in (Extended Data Fig. 8a, b), in which the original and the reconstructed spectrograms are shown. The reconstruction parameters in equation (1) are a = 0.180, b = 0.096, c = −0.177 and dt = 67 as. Extended Data Figure 8c, d compares the reconstructed (red line) and the a priori known nonlinear dipole (black line) for two different settings of the global phase. Blue lines show the instantaneous response for the same waveforms for comparison. We study the delayed nonlinear bound-electronic response over a wide range of intensities ((2–8) × 1013 W cm−2). The evaluated delays between the instantaneous and the TDSE-simulated dipoles as function of the driver field intensity are shown in Extended Data Fig. 9a; the delays between the instantaneous dipoles and those reconstructed from our measured spectrograms (see Fig. 4) are shown in Extended Data Fig. 9b.
News Article | January 27, 2016
How are you reading this story—on your work phone or computer, or on a personal device that you use for work? If it’s the latter, you’re in the majority. Sixty percent of respondents in a 2014 survey said their companies already had a bring your own device (BYOD) policy in place, and another 14% said their companies were developing one. The year before, Garter researchers predicted that half of employers would actually require their staff to use their own devices for work by this year. There’s no comprehensive data to tell whether that’s happened, but it at least sounds plausible, and the upsides for companies are easy to see—like not having to buy and service hardware for every single person on staff. Nevertheless, corporate data and cybersecurity concerns are growing along with employees’ penchants for working from their own devices. Employers are much more interested in destroying information than rifling through it. That’s led to the worry that, armed with BYOD policies, companies may be able to snoop on employees’ texts, photos, and personal emails or enforce code of conduct violations for not safe for work (NSFW) social media posts. But it turns out they’re much more interested in destroying information than rifling through it. By and large, it's employees who've unwittingly encouraged BYOD policies in the first place. In drafting them, companies are mainly scrambling to codify a behavior many of us have already adopted. "Most IT managers have a pretty good handle on the company laptops, desktops, and mobiles," Robert Siciliano, a security expert at BestCompany.com, explains, "but they are quickly losing control when employees bring [in] their new . . . mobile device and connect it to the corporate network." Probably no one sat you down the day you were hired and told you to start checking work emails on your own smartphone, but you’ve been doing it ever since—and putting your employer at risk in the process. "Now the IT guy has to worry if that last app you downloaded will infect other computers on the network," Siciliano says. What’s more, "Almost all businesses operate under some form of regulation where fines or penalties are imposed in the event of a data breach: the leak of personally identifiable information like names, addresses, account numbers, and health records." But so far, the protections employers are writing into BYOD policies, says Sonya Rosenberg, a labor and employment partner at the Chicago law firm Neal Gerber Eisenberg, "are kind of all over the map"—which for employees, can lead to confusion or worse. If your company doesn’t have a BYOD agreement "and you just happen to use your own device for work," Rosenberg explains, "then you certainly, as an employee, would have broader privacy rights." Asked what those typically consist of, though, Rosenberg laughs. "Honestly, it depends." "It's unlikely an employer would ever want access to your personal info in the normal course," Mitzi Hill, an Atlanta-based technology attorney with Taylor English Duma LLP, tells Fast Company, and it "might not have a right to page through your photos if you simply make work calls from the road." Plus, as Rosenberg points out, many "states have laws or are in the process of passing laws that prevent employers from accessing password-protected social media accounts." "But if you text, email, send web links, send photos, etc., for work from your personal device," Hill cautions, "you may be inviting the employer into those more private repositories of information." It’s precisely those mixed-used situations that BYOD policies, and the technology that supports them, are meant to address. The most common way companies do that is by installing mobile device management (MDM) software on employees’ devices. And according to a 2014 white paper by the IBM-owned company Fiberlink (which sells an MDM product called MaaS360), any MDM solution worth its salt "should be able to parse what information it can access and what it cannot." Still, the situations that might impel your company to scroll through your photos or peek at your emails—let alone punish you for them—are pretty rare. Hill mentions two: when your employer "is subject to a lawsuit in which you could be a witness, or if you and [your] employer get into a dispute." But the law is only beginning to grapple with these questions, and in the meantime, usage agreements for company devices, Rosenberg says, usually aren’t written broadly enough to govern conduct on personal gadgets, too. That’s all the more reason, in her view, why BYOD policies are so important: "Otherwise you’re arguing about what an employer can and can’t do. If you have a policy that defines it, everybody knows what’s up." So if your employer isn’t using a BYOD policy to look over your selfie-taking shoulder, what might it be doing instead that you may not know about? Well, for one thing, reserving the right to wipe data from that iPhone you bought last year, used for work this week, and left in an Uber last night. If that’s something many employees find troubling, it’s admittedly something of a nuclear option. The Fiberlink white paper reminds companies that "it’s all about context . . . If time wasters like Angry Birds rub against corporate policies but are not offenses, an immediate wipe is heavy-handed." Most MDM tools let employers zero in on the data and assets that matter to them. By Fiberlink’s estimate, "some 86% of device wipes are selective; only corporate data is wiped." Another thing your employer may want to do under a BYOD agreement is keep tabs on when you log in and out of company accounts. The Fair Labor Standards Act requires employers to accurately track hourly workers’ time on the clock. As Rosenberg explains, "If an employee is still logging in at 12:30 a.m. to answer his boss’s emails, that raises some questions" about overtime pay, for instance, that an MDM solution could identify. I started by asking how you're reading this story, but it was only later that it occurred to me how I wrote it: mostly on my personal laptop. That realization didn't hit me, though, until an email from a source I'd contacted for this story landed in my work inbox. When it did, I was out grabbing lunch, so I read it on my iPhone. Then I went back to scrolling Instagram.