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Evaluation of Macro Mineral Contents of Forages: Influence of Pasture and Seasonal Variation
Khan, Zafar Iqbal,Ashraf, M.,Hussian, Altaf Asian Australasian Association of Animal Productio 2007 Animal Bioscience Vol.20 No.6
Concentrations of major elements in forages were determined in relation to ruminant requirements at a livestock experimental station in Leiah district, Punjab-Pakistan using mapping techniques. The study investigated the influence of sampling periods and pasture types on the concentrations of calcium, magnesium, potassium, and sodium in forages. The implications of these forages for nutrition of ruminants were assessed for the livestock population at that particular experimental station, which are supported by the farm pastures. Within the farm, variations in the element status of the forages were related to soil pasture types. A tentative assessment of the mineral status of available forages at this farm for different pastures using guidelines developed for domestic animals indicated deficiencies of only Na, but forages contained adequate Ca, Mg, and K levels required for grazing ruminants. The concentrations of Na in the forage reserves indicated that the potential supply of this element to plants was limited from the soil to plants and from plants to the animals grazing them. Soil minerals were not measured in this study. The potential use of fertilizers, as pasture amendment as well as supplementation of ruminants, with a specifically tailored mineral mixture is important to livestock producers and environmentalists as well, because their use may improve forage nutritive value and in turn meet requirements of animals.
Haidar, Ahmed M. A,Mohamed, Azah,Hussian, Aini The Korean Institute of Electrical Engineers 2008 Journal of Electrical Engineering & Technology Vol.3 No.2
Vulnerability assessment of power systems is important so as to determine their ability to continue to provide service in case of any unforeseen catastrophic contingency such as power system component failures, communication system failures, human operator error, and natural calamity. An approach towards the development of on-line power system vulnerability assessment is by means of using an artificial neural network(ANN), which is being used successfully in many areas of power systems because of its ability to handle the fusion of multiple sources of data and information. An important consideration when applying ANN in power system vulnerability assessment is the proper selection and dimension reduction of training features. This paper aims to investigate the effect of using various feature extraction methods on the performance of ANN as well as to evaluate and compare the efficiency of the proposed feature extraction method named as neural network weight extraction. For assessing vulnerability of power systems, a vulnerability index based on power system loss is used and considered as the ANN output. To illustrate the effectiveness of ANN considering various feature extraction methods for vulnerability assessment on a large sized power system, it is verified on the IEEE 300-bus test system.
Ahmed M. A Haidar,Azah Mohamed,Aini Hussian 대한전기학회 2008 Journal of Electrical Engineering & Technology Vol.3 No.2
Vulnerability assessment of power systems is important so as to determine their ability to continue to provide service in case of any unforeseen catastrophic contingency such as power system component failures, communication system failures, human operator error, and natural calamity. An approach towards the development of on-line power system vulnerability assessment is by means of using an artificial neural network (ANN), which is being used successfully in many areas of power systems because of its ability to handle the fusion of multiple sources of data and information. An important consideration when applying ANN in power system vulnerability assessment is the proper selection and dimension reduction of training features. This paper aims to investigate the effect of using various feature extraction methods on the performance of ANN as well as to evaluate and compare the efficiency of the proposed feature extraction method named as neural network weight extraction. For assessing vulnerability of power systems, a vulnerability index based on power system loss is used and considered as the ANN output. To illustrate the effectiveness of ANN considering various feature extraction methods for vulnerability assessment on a large sized power system, it is verified on the IEEE 300-bus test system.
The Effect of Age on the Myosin Thermal Stability and Gel Quality of Beijing Duck Breast
Xiangru Wei,Teng Pan,Huan Liu,Laetithia Aude Ingrid Boga,Zubair Hussian,Raheel Suleman,Dequan Zhang,Zhenyu Wang 한국축산식품학회 2020 한국축산식품학회지 Vol.40 No.4
The effect of age (22, 30, 38, and 46 days) on Beijing duck breast myosin gels was investigated. The results showed that the water holding capacity (WHC) and gel strength were markedly improved at the age of 30 days. Differential scanning calorimetry suggested that the myosin thermal ability increased at the age of 30 and 38 days (p<0.05). A compact myosin gel network with thin cross-linked strands and small regular cavities formed at the age of 30 days, which was resulted from the higher content of hydrophobic interactions and disulfide bonds. Moreover, the surface hydrophobicity of myosin extracted from a 30-day-old duck breast decreased significantly under temperature higher than 80℃ (p<0.05). This study illustrated that myosin extracted from a 30-day-old duck’s breast enhanced and stabilized the WHC, thermal stability and molecular forces within the gel system. It concluded that age is an essential influencing factor on the myosin thermal stability and gel quality of Beijing duck due to the transformation of fibrils with different myosin character.
Network Forensics and Intrusion Detection in MQTT-Based Smart Homes
Lama AlNabulsi,Sireen AlGhamdi,Ghala AlMuhawis,Ghada AlSaif,Fouz AlKhaldi,Maryam AlDossary,Hussian AlAttas,Abdullah AlMuhaideb International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.4
The emergence of Internet of Things (IoT) into our daily lives has grown rapidly. It's been integrated to our homes, cars, and cities, increasing the intelligence of devices involved in communications. Enormous amount of data is exchanged over smart devices through the internet, which raises security concerns in regards of privacy evasion. This paper is focused on the forensics and intrusion detection on one of the most common protocols in IoT environments, especially smart home environments, which is the Message Queuing Telemetry Transport (MQTT) protocol. The paper covers general IoT infrastructure, MQTT protocol and attacks conducted on it, and multiple network forensics frameworks in smart homes. Furthermore, a machine learning model is developed and tested to detect several types of attacks in an IoT network. A forensics tool (MQTTracker) is proposed to contribute to the investigation of MQTT protocol in order to provide a safer technological future in the warmth of people's homes. The MQTT-IOT-IDS2020 dataset is used to train the machine learning model. In addition, different attack detection algorithms are compared to ensure the suitable algorithm is chosen to perform accurate classification of attacks within MQTT traffic.