My Recipe

Stir-fried Shredded Chicken with Spicy Garlic Sauce

Ingredients:

12 oz boneless skinless chicken breast or thigh
1/2 oz dried shredded black fungus (woodear)
8 oz green bell pepper
1½ Tbsp garlic (minced)
1 Tbsp ginger (minced)
1½ Tbsp green onion (chopped)
1½ Tbsp chili soybean (辣豆瓣)

Chicken Marinade:

2 tsp light soy sauce
dash Szechuan ground pepper
2 Tbsp water
1 tsp cornstarch
2 tsp oil

Sauce:

1 Tbsp light soy sauce
1½ tsp sugar
1/4 tsp chicken broth mix
2 tsp cooking wine
1 tsp sesame oil
2 tsp white vinegar
1¼ tsp cornstarch
1 Tbsp water

Method:

  1. Cut chicken into thin shreds. Add marinade and set aside for about 10 minutes.
  2. Soak dried black fungus in hot water for about 30 minutes. Rinse and drain.
  3. Cut bell pepper into thin strips.
  4. Mix sauce ingredients and set aside.
  5. Heat wok and add 1 Tbsp oil. Stir-fry black fungus for about 1 minute. Add bell pepper and stir-fry for 30 seconds. Remove and set aside.
  6. Reheat wok and add 1½ Tbsp oil. Sauté half of the garlic and ginger until fragrant. Add half of the chicken and stir-fry until no longer pink. Remove. Add another 1½ Tbsp oil to wok. Sauté the remaining garlic and ginger until fragrant. Stir-fry the remaining chicken until no longer pink. Return previously cooked chicken to wok. Add chili soybean. Toss to combine. Return black fungus and bell pepper to wok. Toss for 30 seconds. Add sauce ingredients and green onion. Toss until sauce thickens. Serve at once.

Nutrition value for 1/6 portion of recipe:

Calorie 200, Fat 12.9 g, Carbohydrate 8 g, Fibre 1 g, Sugar 2 g, Cholesterol 36 mg, Sodium 530 mg, Protein 14 g.


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What’s for Lunch?

Chinese Noodle Set Lunch

The Menu

  • Dan Dan Noodle (坦々麺)
  • Cooked Rice
  • Dessert – Coffee Jelly

Eating the Rice with the Soup

Coffee Jelly

Wireless Network Detects Falls by the Elderly

University of Utah electrical engineers have developed a network of wireless sensors that can detect a person falling. This monitoring technology could be linked to a service that would call emergency help for the elderly without requiring them to wear monitoring devices.

For people age 65 and older, falling is a leading cause of injury and death. Most fall-detection devices monitor a person’s posture or require a person to push a button to call for help. However, these devices must be worn at all times. A 2008 study showed 80 percent of elderly adults who owned call buttons didn’t use the device when they had a serious fall, largely because they hadn’t worn it at the time of the fall.

Now, University of Utah electrical engineers Brad Mager and Neal Patwari have constructed a fall-detection system using a two-level array of radio-frequency sensors placed around the perimeter of a room at two heights that correspond to someone standing or lying down. These sensors are similar to those used in home wireless networks. As each sensor in the array transmits to another, anyone standing – or falling – inside the network alters the path of signals sent between each pair of sensors.

Mager is presenting the new fall-detection system Tuesday, Sept. 10 in London at the 24th Annual Institute of Electrical and Electronics Engineers International Symposium on Personal, Indoor and Mobile Radio Communications.

The team plans to develop this proof-of-concept technology into a commercial product through Patwari’s Utah-based startup company, Xandem Technology. The study was funded by the National Science Foundation.

“The idea of ‘aging-in-place,’ in which someone can avoid moving to a nursing home and live in their own home, is growing,” says Patwari, senior author of the study and associate professor of electrical and computer engineering at the University of Utah. “Ideally, the environment itself would be able to detect a fall and send an alert to a caregiver. What’s remarkable about our system is that a person doesn’t need to remember to wear a device.”

By measuring the signal strength between each link in the network – similar to the number of “bars” on your cell phone – an image is generated to show the approximate location of a person in the room with a resolution of about six inches. This imaging technique, called radio tomography, uses the one-dimensional link measurements from the sensor network to build up a three-dimensional image.

“With this detection system, a person’s location in a room or building can be pinpointed with high accuracy, eliminating the need to wear a device,” says Mager, a graduate student in electrical and computer engineering and first author of this study. “This technology can also indicate whether a person is standing up or lying down.”

What’s more, the system is programmed to detect whether a fall was indeed a dangerous one, rather than someone simply lying down on the floor. By conducting a series of experiments measuring the amount of time that elapsed when a person fell, sat down, or laid down on the ground, the researchers determined a time threshold for accurately detecting a fall. This information was fed back into algorithms used to determine whether a given event was a fall or one of the other benign activities.

Source: The University of Utah

Turkey Salad

Ingredients

1/4 cup red wine vinegar
1/4 cup lemon juice
2 tbsp olive oil
1/4 cup Dijon mustard
1/4 cup honey
1/4 cup finely chopped red onion
zest of 1 small orange, grated
1 cup dried apricot, slivered
4 cups cooked turkey, cubed
1½ cups celery, chopped
1½ cups red cabbage, chopped
3/4 cup raisins

Method

  1. Whisk together vinegar, lemon juice, oil, mustard and honey. Add onion, orange zest and apricot. Let dressing sit at room temperature for about 1 hour.
  2. In a large bowl, mix turkey, celery, cabbage and raisins. Pour dressing over salad and toss well to coat.
  3. Chill for several hours or overnight. Serve on a large plate lined with lettuce leaves.

Makes 6 servings.

Source: Lean on Turkey

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