Abstract: A method for treating expansive soils includes excavating a hole in a superficial expansive soil layer from an excavation site to form the hole and form an expansive soil material. Further, the expansive soil material is mixed with an inert material, at least two chemical additives, and a binder to form an additive soil material. The additive soil material is further poured into the hole in the excavation site to form an additive soil layer. The inert material is a sand, the chemical additives are a sulfur and a silica fume, and the binder is a dicyclopentadiene. The sand and the sulfur are present in the additive soil material in an amount from 30 wt. % to 40 wt. % and 12 wt. % to 25 wt. %, respectively, based on a total weight of the additive soil material.

Abstract: A method for treating expansive soils includes excavating a hole in a superficial expansive soil layer from an excavation site to form the hole and form an expansive soil material. Further, the expansive soil material is mixed with an inert material, at least two chemical additives, and a binder to form an additive soil material. The additive soil material is further poured into the hole in the excavation site to form an additive soil layer. The inert material is a sand, the chemical additives are a sulfur and a silica fume, and the binder is a dicyclopentadiene. The sand and the sulfur are present in the additive soil material in an amount from 30 wt. % to 40 wt. % and 12 wt. % to 25 wt. %, respectively, based on a total weight of the additive soil material.

Abstract: A device, method, and non-transitory computer readable medium are described. The method includes receiving a dataset including hand written Arabic words and hand written Arabic alphabets from one or more users. The method further includes removing whitespace around alphabets in the hand written Arabic words and the hand written Arabic alphabets in the dataset. The method further includes splitting the dataset into a training set, a validation set, and a test set. The method further includes classifying one or more user datasets from the training set, the validation set, and the test set. The method further includes identifying the target user from the one or more user datasets. The identification of the target user includes a verification accuracy of the hand written Arabic words being larger than a verification accuracy threshold value.

Abstract: A method for treating expansive soils includes excavating a hole in a superficial expansive soil layer from an excavation site to form the hole and form an expansive soil material. Further, the expansive soil material is mixed with an inert material, at least two chemical additives, and a binder to form an additive soil material. The additive soil material is further poured into the hole in the excavation site to form an additive soil layer. The inert material is a sand, the chemical additives are a sulfur and a silica fume, and the binder is a dicyclopentadiene. The sand and the sulfur are present in the additive soil material in an amount from 30 wt. % to 40 wt. % and 12 wt. % to 25 wt. %, respectively, based on a total weight of the additive soil material.

Abstract: A waste management system includes a waste management device, a monitoring unit, and a communication unit. The waste management device includes a collection unit, a classification unit, a segregation unit, a plurality of sensors, a power unit, and a plurality of waste bins. The collection unit collects one or more waste articles. The classification unit utilizes a machine-learning model and identifies a waste category of the one or more waste articles. The segregation unit transports the one or more waste articles to a particular waste bin corresponding to the identified waste category. The power unit supplies power to the waste management device. The monitoring unit monitors the waste management device and controls a resultant action. The communication unit communicates one or more data between a control station and the waste management device using a plurality of IoT devices.

Abstract: A waste management system includes a waste management device, a monitoring unit, and a communication unit. The waste management device includes a collection unit, a classification unit, a segregation unit, a plurality of sensors, a power unit, and a plurality of waste bins. The collection unit collects one or more waste articles. The classification unit utilizes a machine-learning model and identifies a waste category of the one or more waste articles. The segregation unit transports the one or more waste articles to a particular waste bin corresponding to the identified waste category. The power unit supplies power to the waste management device. The monitoring unit monitors the waste management device and controls a resultant action. The communication unit communicates one or more data between a control station and the waste management device using a plurality of IoT devices.

Abstract: A device, method, and non-transitory computer readable medium are described. The method includes receiving a dataset including hand written Arabic words and hand written Arabic alphabets from one or more users. The method further includes removing whitespace around alphabets in the hand written Arabic words and the hand written Arabic alphabets in the dataset. The method further includes splitting the dataset into a training set, a validation set, and a test set. The method further includes classifying one or more user datasets from the training set, the validation set, and the test set. The method further includes identifying the target user from the one or more user datasets. The identification of the target user includes a verification accuracy of the hand written Arabic words being larger than a verification accuracy threshold value.

Abstract: A device, method, and non-transitory computer readable medium are described. The method includes receiving a dataset including hand written Arabic words and hand written Arabic alphabets from one or more users. The method further includes removing whitespace around alphabets in the hand written Arabic words and the hand written Arabic alphabets in the dataset. The method further includes splitting the dataset into a training set, a validation set, and a test set. The method further includes classifying one or more user datasets from the training set, the validation set, and the test set. The method further includes identifying the target user from the one or more user datasets. The identification of the target user includes a verification accuracy of the hand written Arabic words being larger than a verification accuracy threshold value.

Abstract: A device, method, and non-transitory computer readable medium are described. The method includes receiving a dataset including hand written Arabic words and hand written Arabic alphabets from one or more users. The method further includes removing whitespace around alphabets in the hand written Arabic words and the hand written Arabic alphabets in the dataset. The method further includes splitting the dataset into a training set, a validation set, and a test set. The method further includes classifying one or more user datasets from the training set, the validation set, and the test set. The method further includes identifying the target user from the one or more user datasets. The identification of the target user includes a verification accuracy of the hand written Arabic words being larger than a verification accuracy threshold value.

Abstract: A device, method, and non-transitory computer readable medium are described. The method includes receiving a dataset including hand written Arabic words and hand written Arabic alphabets from one or more users. The method further includes removing whitespace around alphabets in the hand written Arabic words and the hand written Arabic alphabets in the dataset. The method further includes splitting the dataset into a training set, a validation set, and a test set. The method further includes classifying one or more user datasets from the training set, the validation set, and the test set. The method further includes identifying the target user from the one or more user datasets. The identification of the target user includes a verification accuracy of the hand written Arabic words being larger than a verification accuracy threshold value.

Abstract: Methods for determining the capacity of a battery cell, including a lithium-ion battery cell, and a plurality of battery cells include measuring surface temperature and voltage of the battery cell(s). Then, a constant-current discharge cycle is conducted. Subsequently, final surface temperature and voltage are measured. These measurements enable calculating the battery temperature rise value and the maximum differential temperature value. An empirical method determines the differential temperature gain value. From these values, a capacity parameter is calculated. Then, a differential voltage value is derived from the initial and final battery cell voltages. Further, first, second, and third capacity coefficients are empirically determined, allowing the calculation of the capacity of the battery cell(s).

Abstract: A vertical farming spraying system including a robotic mobile sprayer with a base and driving members. The robotic mobile sprayer includes a first cylindrical element connected to a first pump and a second cylindrical element connected to a second pump. A first mechanical support is mounted on the base, supporting a humidity sensor and a humidifier sprayer connected to the first pump; and a second mechanical support is mounted on the base, supporting a disinfection sensor and a disinfection sprayer connected to the second pump. A control unit controls operation of the driving members and mechanical supports, and of the pumps through the respective sensors via a flow control sensor. The vertical farming spraying system further includes a vertically oriented elongated cylindrical support encircled by a spiral path, designed to guide the robotic mobile sprayer from the base to the top for humidification and disinfection operations.

Abstract: A method for treating expansive soils includes excavating a hole in a superficial expansive soil layer from an excavation site to form the hole and provide an expansive soil material. The expansive soil material is mixed with an inert material, at least two chemical additives, and a binder to form an additive soil material. The additive soil material is further poured into the hole in the excavation site to form an additive soil layer. The inert material is a sand, the chemical additives are a sulfur and a silica fume, and the binder is a dicyclopentadiene. The sand and the sulfur are present in the additive soil material in an amount from 30 wt. % to 40 wt. % and 12 wt. % to 25 wt. %, respectively, based on a total weight of the additive soil material.

Abstract: A device, method, and non-transitory computer readable medium are described. The method includes receiving a dataset including hand written Arabic words and hand written Arabic alphabets from one or more users. The method further includes removing whitespace around alphabets in the hand written Arabic words and the hand written Arabic alphabets in the dataset. The method further includes splitting the dataset into a training set, a validation set, and a test set. The method further includes classifying one or more user datasets from the training set, the validation set, and the test set. The method further includes identifying the target user from the one or more user datasets. The identification of the target user includes a verification accuracy of the hand written Arabic words being larger than a verification accuracy threshold value.

Abstract: A device, method, and non-transitory computer readable medium are described. The method includes receiving a dataset including hand written Arabic words and hand written Arabic alphabets from one or more users. The method further includes removing whitespace around alphabets in the hand written Arabic words and the hand written Arabic alphabets in the dataset. The method further includes splitting the dataset into a training set, a validation set, and a test set. The method further includes classifying one or more user datasets from the training set, the validation set, and the test set. The method further includes identifying the target user from the one or more user datasets. The identification of the target user includes a verification accuracy of the hand written Arabic words being larger than a verification accuracy threshold value.

Abstract: A device, method, and non-transitory computer readable medium are described. The method includes receiving a dataset including hand written Arabic words and hand written Arabic alphabets from one or more users. The method further includes removing whitespace around alphabets in the hand written Arabic words and the hand written Arabic alphabets in the dataset. The method further includes splitting the dataset into a training set, a validation set, and a test set. The method further includes classifying one or more user datasets from the training set, the validation set, and the test set. The method further includes identifying the target user from the one or more user datasets. The identification of the target user includes a verification accuracy of the hand written Arabic words being larger than a verification accuracy threshold value.

Abstract: A device, method, and non-transitory computer readable medium are described. The method includes receiving a dataset including hand written Arabic words and hand written Arabic alphabets from one or more users. The method further includes removing whitespace around alphabets in the hand written Arabic words and the hand written Arabic alphabets in the dataset. The method further includes splitting the dataset into a training set, a validation set, and a test set. The method further includes classifying one or more user datasets from the training set, the validation set, and the test set. The method further includes identifying the target user from the one or more user datasets. The identification of the target user includes a verification accuracy of the hand written Arabic words being larger than a verification accuracy threshold value.

Abstract: A device, method, and non-transitory computer readable medium are described. The method includes receiving a dataset including hand written Arabic words and hand written Arabic alphabets from one or more users. The method further includes removing whitespace around alphabets in the hand written Arabic words and the hand written Arabic alphabets in the dataset. The method further includes splitting the dataset into a training set, a validation set, and a test set. The method further includes classifying one or more user datasets from the training set, the validation set, and the test set. The method further includes identifying the target user from the one or more user datasets. The identification of the target user includes a verification accuracy of the hand written Arabic words being larger than a verification accuracy threshold value.

Abstract: A device, method, and non-transitory computer readable medium are described. The method includes receiving a dataset including hand written Arabic words and hand written Arabic alphabets from one or more users. The method further includes removing whitespace around alphabets in the hand written Arabic words and the hand written Arabic alphabets in the dataset. The method further includes splitting the dataset into a training set, a validation set, and a test set. The method further includes classifying one or more user datasets from the training set, the validation set, and the test set. The method further includes identifying the target user from the one or more user datasets. The identification of the target user includes a verification accuracy of the hand written Arabic words being larger than a verification accuracy threshold value.

Abstract: A circuit for charging a rechargeable battery is described. The circuit includes a first DC voltage source, a bus capacitor Cbus, an H-bridge circuit, a rechargeable battery, a second DC voltage source, an impedance meter and a microcontroller. The impedance meter measures an impedance of the rechargeable battery. The microcontroller is connected to the impedance meter and to each gate contact. The microcontroller generates a set of drive signals that are configured to generate a sinusoidal with ripple current at an output terminal of the H-bridge circuit. The microcontroller adjusts a frequency of the sinusoidal with ripple current based on the impedance of the rechargeable battery and identifies a frequency of the sinusoidal with ripple current at which the rechargeable battery has a minimum internal impedance. The microcontroller adjusts the set of drive signals to generate the sinusoidal with ripple current at the identified frequency and recharge the rechargeable battery.