Circuit diagram of the solar garden light is shown in Fig. 1. It is built around a solar lamp controller IC CL0116 (IC1), a miniature solar cell, a bright white LED (LED1) and a few other components. Fig. 1: Circuit diagram of solar garden light. This circuit requires only a single Ni-Cd rechargeable battery to light up the white LED for more than five hours depending upon the ampere-hour (Ah
i need to find out whether my figs have sugar coated on them?
The label ingredients list would have to include sugar. Most likely if it says natural, there's no sugar coating. They are naturally a little gooey
What can you do with dried figs?
Fabulous Fig Bread Categories: Breads, Low-cal Yield: 22 slices 1 c Dried Figs, ground 1 c -Boiling water 3/4 c Pistachios 1 1/2 c All-purpose flour 1 ts Baking soda 1/4 ts Salt 4 tb Margarine or butter 1 c Sugar 3 tb Egg substitute; -OR- 1 -Egg 1 ts Vanilla In a medium-sized heat-proof bowl, combine the figs and pistachios, add boiling water and let cool to room temperature, about 1 hour. Position a rack in the center of the oven and preheat to 350 F. Grease an 8-1/2" by 4-1/2" loaf pan. In medium sized bowl, combine the flour, baking soda and salt. In a large bowl, beat the butter until fluffy. Gradually beat in the sugar. Add the egg and vanilla and beat for 1 to 2 minutes, or until lightened. Add the dry ingredients and beat until just blended. With a spoon, blend in the fig mixture. Turn into the prepared pan and bake 1 to 1 1/4 hours, or until the bread just begins to pull away from the sides of the pan, and the top springs back when lightly touched. Cool in pan on a rack for 15 minutes, and then turn out and cool completely. Wrap in plastic and store overnight before serving. Cut into thin slices. Yield: One 8-1/2" x 4-1/2" loaf - 22 slices Each serving contains approximately: Calories 124, Fat 3.39 g, Dietary Fiber 2.22 g, Carbohydrates 22.4 g, Protein 2.38 g, Sodium 94.8 mg, Cholesterol .026 mg Calories from protein: 7% Calories from carbohydrates: 69% Calories from fats: 24%
wat can i do with figs?
I do not give a fig about figs! No, really I've only eaten them in Fig Newtons and they were okay, have not tried a fig in its natural form
when is the best time to prune a fig tree?
Is it indoors or out? Just finished a class in pruning. Dead branches are pruned at any time. Actual remedial pruning is done when the plant is dormant. Outdoors, this is in January and February. Indoors, when the plant appears to be dormant...no further growth or leaf pruduction
What does kate Chopin's "Ripe Figs" mean?
This Site Might Help You. RE: What does kate Chopin's "Ripe Figs" mean? I do not have to write an essay about the story, I need to write a short sequel, moving the story from fall to spring. In order to to that, I want to know what Kate Chopin was trying to get across when she wrote it. I have a feeling it's has something to do with age and patience, but why does...
if i ate dried figs all day?
that is a alot of fiber lol
Edible unclonable functions
Figure 1 shows that the photoluminescent properties of fluorescent silk proteins are used to realize multiple challenge-response pairs in an edible PUF platform with heightened security for on-dose authentication and anti-counterfeiting of medicines. Importantly, challenge-response pairs differentiate our protein-based PUFs from other common unique objects and tags29,63. In reaction to optical challenges, defined by a unique set of excitation and emission bands of different fluorescent proteins, the edible PUF made of silk protein (i.e., fibroin) and fluorescent proteins generates distinct output responses, which are used to extract digitized keys (Fig. 1a). The source of entropy is randomly distributed fluorescent silk microparticles seamlessly embedded in a covert thin transparent silk film. First, we take advantage of four different fluorescent proteins (i.e., eCFP, eGFP, eYFP, and mKate2) that have specific excitation and emission peaks in the visible wavelength range (Supplementary Table 1). Specifically, we utilize fluorescent protein-expressed silk produced by transgenic silkworms as recombinant proteins via the piggyBac transposase method (Supplementary Methods and Supplementary Fig. 1a). Silk proteins are an excellent biopolymer to be genetically hybridized with fluorescent protein genes46,55,57,59. Second, to fabricate fluorescent silk microparticles (Supplementary Fig. 2), fluorescent silk fibroin is regenerated into an aqueous solution with a low-temperature process, is freeze-dried, and is gently ground into zeolite-shaped microparticles with sizes of 99.3 7.9 m (mean standard deviation) (Fig. 2a, b and Supplementary Fig. 3). Third, an admixture of the fluorescent silk microparticles is broadcast on a large flat surface and a white silk fibroin solution is poured on top. After an ambient drying process in the dark, this thin transparent silk film with a thickness of 150 m is punched into 7 7 mm2 squares, resulting in all protein-based edible PUF devices (Methods and Supplementary Figs. 2 and 4). eCFP, eGFP, eYFP, and mKate2 silk cocoons possess bluish, greenish, yellowish, and reddish colors under white light illumination (Supplementary Fig. 1b). However, after the regeneration of the fluorescent silk, each type of fluorescent silk microparticles are not distinguishable in the naked eye, while maintaining their fluorescent properties (Fig. 2 and Supplementary Fig. 5). This fabrication process is scalable for mass production without using any sophisticated equipment and is safe for oral consumption without any organic solvents or synthetic polymers (e.g., methanol, ethanol, isopropanol, or polyvinyl alcohol) (Methods and Supplementary Fig. 6a). Analyses of mass spectroscopy, energy-dispersive X-ray spectroscopy, and in vitro cytotoxicity (cell viability test) assays support the overall nontoxicity of the edible PUF devices (Supplementary Methods and Supplementary Figs. 7-9). In Fig. 3, the flow diagram illustrates how a cryptographic key is extracted from an output response when challenged by a set of excitation and emission bands, including the raw output measurement, the bitstream extraction, and the final digitized security key. We mainly use four representative challenge-response pairs (n = 4) based on the excitation and emission peak wavelengths of the individual fluorescent proteins in silk (Supplementary Table 1 and Supplementary Fig. 1c). An input challenge (C ) is selected as a combination of the excitation and emission bands at specific wavelengths such as = 415 nm and = 460 nm; = 470 and = 510 nm; = 470 and = 560 nm; = 530 and = 630 nm, corresponding to eCFP, eGFP, eYFP, and mKate2 in silk, respectively. Upon optical excitation, a raw fluorescent image is recorded by a charge-coupled device (CCD) camera equipped with a conventional zoom lens via a tunable color filter (Methods and Supplementary Fig. 10a). In other words, the corresponding fluorescent image acts as an output response (R ). A resultant digitized key (K ) is obtained by an extractor that converts the fluorescent image of silk microparticles to a binary bitmap (Fig. 4). First, to improve the quality of binarization, we normalize the raw fluorescent image (300 pixels 300 pixels) by the maximum intensity (Fig. 4a). The noise is removed by applying a threshold of 20%. Fluorescent areas smaller than a specific pixel size of 20 are also considered as noise. Then, the image is resized to be 150 pixels 150 pixels with a binning process. Second, to ensure a low bit error rate (high reproducibility), we find the spatial peak position of each fluorescent silk microparticle where the highest intensity peaks of the microparticles are located, subsequently reducing the image size to 50 pixels 50 pixels (Fig. 4b). Then, the peak positions are only assigned to 1 bits and other pixels are 0 bits. Third, to remove the bias of 0-bits, we apply an enhanced version of the von Neumann bias compression algorithm with two-pass tuple-output debiasing (Fig. 4c)64. Because the fluorescent peaks are relatively rare events in the entire image due to the density of the fluorescent microparticles, global bias is present such that 0-bits are generated consistently more often than 1-bits. Finally, after debiasing, we use first 64 bits as a digitized key in each response, because a typical minimum number of peaks in the fluorescent images is 32. Combining four challenge-response pairs (n = 4) together, the final digitized key size results in 256 bits (=4 64). We assess the quality of randomness of the edible PUF-generated binary sequences, using the NIST statistical test suite that was originally designed to evaluate random and pseudorandom number generators65. One of the minimal requirements of PUFs is randomness with high entropy, as PUFs rely on an entropy source to create an unclonable output response66,67. When PUF responses are used for cryptographic key generation, it is also critical to evaluate the randomness to ensure the unpredictability of the keys generated by PUFs. The NIST statistical test suite includes 15 different tests to quantify the randomness of bitstreams. Each test focuses on a specific aspect of randomness (Supplementary Table 2). Some of the tests rely on the minimum sequence length of 1 106 and the minimum number of substrings (blocks) of 55, requiring a total stream of 5.5 107 bits. On the other hand, the key size of the edible PUFs is significantly shorter than those of random number generators. To use seven statistical tests that require a reasonable stream length, we explore the randomness of binary sequences summed from 30 different PUFs (Fig. 5). Specifically, we collect a total of 7680 bits from 30 different PUFs (256 bits for each PUF) and divide the bitstream into 60 sequences to perform each statistical test 60 times on individual 128-bit long sequences (Supplementary Dataset 1). Each statistical test returns two results; a p-value of a chi-squared (2) test and a pass rate (i.e., proportion), as shown in Table 1. As summarized in Table 1, the binary sequence from the 30 different PUFs passes all of seven NIST randomness tests without any post-processing. The parameter values used in each test and the characteristics of the NIST randomness tests are summarized in Supplementary Table 2. In other words, the bitstream (7680 bits) extracted from the 30 PUFs is statistically random, supporting the idea that the output responses of all protein-based PUFs can be unpredictable and unclonable. This result also supports the idea that our simple broadcasting process of particulate fluorescent silk offers a random spatial distribution as a straightforward yet effective entropy source60,61. To evaluate the basic PUF performance, we examine the digitized keys of the edible PUFs. We first estimate the bit uniformity by checking the equal probability of observing 1-bit or 0-bit states: where K is the lth binary bit of the key and s is the key size. Basically, the bit uniformity is the Hamming Weight (i.e., number of 1 bits in a binary sequence) of the s-bit key. For 30 different PUFs, the distribution of bit uniformity converges to the ideal value of 0.5 (Fig. 6a). Then, to evaluate the device uniqueness of each PUF, we calculate an inter-device Hamming Distance (HD) by counting a number of different bits between two PUFs under the same challenge. The device uniqueness measures the degree of correlation between digitized keys measured from two different PUFs. Ideally, the digitized keys from any two selected PUF devices should be uncorrelated, indicating that the state of a PUF is unknown even when the states of other PUFs are known. The inter-device HD between any two PUF devices can be defined: where K and K are s-bit keys of the ith PUF device and the jth PUF device among q different PUFs, respectively. The 30 different PUF devices generate a total of C (=30 29/2 = 435) comparisons. In Fig. 6b, the histogram of the normalized inter-device HDs is well fitted into a Gaussian distribution with a center at 0.5032 with a standard deviation (SD) of 0.0458, which is close to its ideal value of 0.5, exhibiting the excellent device uniqueness of all of the edible PUFs. In addition, we investigate the degree of correlation among the digitized keys of four responses in each PUF by calculating an average HD (Fig. 6c). The 30 different PUFs result in a mean HD value of 0.499 with a SD of 0.0041, indicating that the individual digitized keys in each PUF are also unique. When the number of challenge-response pairs is extended to seven, seven resultant digitized keys are still uncorrelated with a mean HD value of 0.5089 with a SD of 0.0766 (Supplementary Figs. 11 and 12). In addition, we calculate an encoding capacity of the edible PUF-generated binary sequences. The encoding capacity simply means a number of codes that can be generated and is defined as cs where c is the bit-level (c = 2 for binary bits of 0 and 1) and s is the key size38,68. To accurately estimate the encoding capacity, it is important to use an appropriate key size. When an imaging scheme is used, one may think that the total number of pixels (variables) is the digitized key size. In this case, the actual encoding capacity can be less than this nominal encoding capacity, because each individual pixel (variable) cannot be completely independent. One way for estimating the number of independent pixels (variables) is to analyze the degrees of freedom; s = p(1 p)/2, where p is the mean probability and is the standard deviation31,34. In Fig. 6b, the resultant width of the inter-device HD distribution shows that significant subsets of the key are mutually independent, corresponding to the degree of freedom (or number of independent variables) of 120 (0.5032 (1 0.5032)/0.04582). As a result, the edible PUF has c = 2 and s = 120, resulting in a relatively large encoding capacity of 2120 (1.3292 1036). Importantly, the debiasing process is useful not to comprise the actual coding capacity. If a security key is biased with too many 0 s or 1 s, the actual coding capability is often diminished. A large encoding capacity could be utilized to provide information on manufacturer-determined data, including dose information (e.g., dosage strength, dose frequency, and expiration date), manufacturing details (e.g., location, date, batch, and lot number), and distribution path (e.g., country, distributor, wholesaler, and chain). If a higher encoding capacity is required for a specific application, the key size of our edible PUF can simply be scaled by further optimizing the density of fluorescent silk microparticles, which allows for a larger number of peaks in each image. The number of challenge-response pairs can also be increased by incorporating additional combinations of the excitation and emission bands (Supplementary Fig. 11). To examine the feasibility for reliable PUFs, we test the readout reproducibility and stability of the security keys from the identical PUF device. The reproducibility of a PUF represents the ability of generating the identical keys following the same repeated challenges. We calculate an intra-device HD, which is quantitatively described by a bit error rate (i.e., percentage of error bits out of response bits with an ideal value of 0) from 10 challenge-response cycles (nine pairwise comparisons) for each PUF device. For the ith PUF device, an average intra-device HD captures the readout reproducibility: where K and K are the original s-bit reference key and a s-bit key extracted from the same PUF device at a different time-point t and m is the number of repeated measurements. Figure 6b shows a relatively low mean value of 0.0632 with a SD of 0.0164 estimated from the intra-device HD histogram for the 30 different PUFs at the same 10 challenge cycles. We further examine the long-term reliability under the same challenges after 60 days in the laboratory environment (i.e., stored at 22 2 C and 40-50% relative humidity in the dark) (Supplementary Fig. 13). When the fluorescent intensity of the raw fluorescent images taken 60 days apart is compared, the correlation coefficients (r) of the four responses (R , R , R , and R ) range from 0.833 to 0.983. For the pixel positions of the first 32 peaks in the binarized images, the r values are even higher than 0.895 for all of the responses. These results support the potential reliability of the protein-based edible PUFs, although the reproducibility assessments do not reflect extremely harsh conditions, given the medical applications. We further estimate a false positive rate and a false negative rate from the inter-device and intra-device variabilities. When PUFs are used for authentication, the false positive rate is the probability that PUF A is authenticated as PUF B. The false negative rate is the probability that a correct PUF fails to be authenticated. The resulting false positive and false negative rates are 9.6394 1013 and 3.0982 1012, respectively, assuming that the inter-device and intra-device variabilities follow Gaussian distributions (Supplementary Fig. 14). The pairwise comparison map of cross-HD analyses further shows that all of the 30 different PUFs are highly uncorrelated (Fig. 6d) where the diagonal line indicates the intra-HD values for the identical PUF device itself, while the off-diagonal points represent the inter-HD values compared with the other PUF devices.