Prevalence of Acute Malnutrition in Pre-School Children in a Rural Area of Northern Sudan

Objectives: To determine the prevalence of acute malnutrition in pre-school children in Karma Albald village, Northern Sudan. Design: Prospective observational study. Setting: Four kindergartens in Karma Albald village, Northern Sudan. Subjects: Pre-school children attending kindergartens in Karma Albald village (n = 163). Results: Using the World Health Organization case definitions and weight-for-height growth chart, wasting was observed in 29 of 163 children (17.8%); nine children had severe wasting. Socio-economic data showed that 70 children (43%) were from large families (families with four or more children) and 40 were from ‘poor’ families; 21 fathers and 12 mothers had poor literacy. All of the risk factors associated with malnutrition that were studied (that is, economic status, family size, order of the child in the family, and other socio-economic indicators) did not reach statistical significance. Conclusions: The prevalence of malnutrition was high in this cohort. Effective interventions are needed to tackle this major child health problem

Semantic Detection of Targeted Attacks Using DOC2VEC Embedding

The targeted attack is one of the social engineering attacks. The detection of this type of attack is considered a challenge as it depends on semantic extraction of the intent of the attacker. However, previous research has primarily relies on the Natural Language Processing or Word Embedding techniques that lack the context of the attacker\u27s text message. Based on Sentence Embedding and machine learning approaches, this paper introduces a model for semantic detection of targeted attacks. This model has the advantage of encoding relevant information, which helps to improve the performance of the multi-class classification process. Messages will be categorized based on the type of security rule that the attacker has violated. The suggested model was tested using a dialogue dataset taken from phone calls, which was manually categorized into four categories. The text is pre-processed using natural language processing techniques, and the semantic features are extracted as Sentence Embedding vectors that are augmented with security policy sentences. Machine Learning algorithms are applied to classify text messages. The experimental results show that sentence embeddings with doc2vec achieved high prediction accuracy 96.8%. So, it outperformed the method applied to the same dialog dataset

Recent Trends in Plasmonic Nanowire Solar Cells

Light trapping is crucial for low-cost and highly efficient nanowire (NW) solar cells (SCs). In order to increase the light absorption through the NWSCs, plasmonic materials can be incorporated inside or above the NW design. In this regard, two novel designs of plasmonic NWSCs are reported and analyzed using 3D finite difference time domain method. The geometrical parameters of the reported designs are studied to improve their electrical and optical efficiencies. The ultimate and power conversion efficiencies (PCE) are used to quantify the conversion efficiency of the light into electricity. The first design relies on funnel shaped SiNWs with plasmonic core while the cylindrical NWs of the second design are decorated by Ag diamond shaped. The calculated ultimate efficiency and PCE of the plasmonic funnel design are equal to 44% and 18.9%, respectively with an enhancement of 43.3 % over its cylindrical NWs counterpart. This enhancement can be explained by the coupling between the three optical modes, supported by the upper cylinder, lower cone and plasmonic material. Moreover, the cylindrical SiNWs decorated by Ag diamond offer an ultimate efficiency and short-circuit current density of 25.7%, and 21.03 mA∕cm2, respectively with an improvement of 63% over the conventional cylindrical SiNWs

Organic Petrological and Geochemical Evaluation of Jurassic Source Rocks from North Iraq

Immature Jurassic oil shale is widely distributed and frequently outcropping in North Iraq. The organic-rich Jurassic sedimentary sequence, including prolific oil shale, was recorded in Banik area in Duhok Governorate of North Iraq. This sequence was systematically sampled from the geological formations; Sehkanyian, Sargelu and Naokelekan. The organic geochemical parameters were analyzed for 72 samples as well as one oil sample. A detailed study of petrologic properties was carried out for 12 samples. Based on TOC content, the Sargelu and Naokelekan formations can be considered as good to excellent source rocks, whereas Sehkanyian Formation has no potential since the TOC does not exceed 0.1 %. The samples of Sargelu and Naokelekan formations contain both kerogen types I and II indicating marine organic matter mainly derived from algae and phytoplankton organisms proposing typical oil prone source kerogen. This is further confirmed by the predominance of alginite and liptodetrinite macerals, where liptinite maceral group contribute more than 90% relative to other maceral contents. In general, Sargelu Formation samples have Production Index (PI), Tmax and fluorescence parameters (λmax and red/green quotient) suggesting immature to early mature stage of thermal maturity. The calculated ratios of Pr/Ph, Pr/nC17 and Ph/nC18for the extracted bitumen and the oil sample, suggest generation of bitumen from marine organic matter deposited under reducing conditions at an early thermal maturity stage

ESSENTIAL OIL COMPOSITION OF ARTEMISIA VULGARIS GROWN IN EGYPT

Objective: The objective of this research was to evaluate the significance of the plant's origin and to assess the essential oil composition of Artemisia vulgaris grown in Egypt simultaneously evaluating the effect of environmental conditions on essential oil composition.Methods: Seeds were planted and the essential oils extracted, using hydrodistillation, from the plants that grew. The resulting essential oils were examined, using gas chromatography linked to mass spectrometry (GC-MS). Thus also evaluating the essential oil chemotype fingerprint†in A. vulgarisResults:  The study identified: the most abundant compounds being camphor, 3, 5-dimethylcyclohexane, germacrene D, cubebene, yomogi alcohol, artemisia alcohol, caryophyllene, while is lower concentrations thujopsene, muurolene, borneol, terpinen-4-ol, valencene, elemene and humulene. Despite the origins of the seeds, the chemical profile was very similar to those of plants grown in Egypt, thus suggesting essential oil composition was significantly influenced by the environmental conditions.Conclusion: Based on the present study, It is suggested that seed origin may play a less significant part if the seed is planted in an environment different to that of its origin, this study proved that and favors the plant-environment interaction to influence the secondary metabolite composition. This supports that plant metabolite profiles are greatly affected by the environment they are grown in.Â

Cyclic Self-Organizing Map for Object Recognition

Object recognition is an important machine learning (ML) application. To have a robust ML application, we need three major steps: (1) preprocessing (i.e. preparing the data for the ML algorithms); (2) using appropriate segmentation and feature extraction algorithms to abstract the core features data and (3) applying feature classification or feature recognition algorithms. The quality of the ML algorithm depends on a good representation of the data. Data representation requires the extraction of features with an appropriate learning rate. Learning rate influences how the algorithm will learn about the data or how the data will be processed and treated. Generally, this parameter is found on a trial-and-error basis and scholars sometimes set it to be constant. This paper presents a new optimization technique for object recognition problems called Cyclic-SOM by accelerating the learning process of the self-organizing map (SOM) using a non-constant learning rate. SOM uses the Euclidean distance to measure the similarity between the inputs and the features maps. Our algorithm considers image correlation using mean absolute difference instead of traditional Euclidean distance. It uses cyclical learning rates to get high performance with a better recognition rate. Cyclic-SOM possesses the following merits: (1) it accelerates the learning process and eliminates the need to experimentally find the best values and schedule for the learning rates; (2) it offers one form of improvement in both results and training; (3) it requires no manual tuning of the learning rate and appears robust to noisy gradient information, different model architecture choices, various data modalities and selection of hyper-parameters and (4) it shows promising results compared to other methods on different datasets. Three wide benchmark databases illustrate the efficiency of the proposed technique: AHD Base for Arabic digits, MNIST for English digits, and CMU-PIE for faces

Isolation and Identification of Bacterial Species from the Human Gallbladders Bile of Sudanese Patients

Background: Gallbladder infections are one of the most important problems that affect Sudanese patients.Objectives: To isolate bacterial species from infected human gallbladder's bile in Sudanese patients admitted for cholecystectomy due to calcoulus or acalcoulus cholecystitis.Materials and Methods: A total of 100 bile specimens from 100 patients (88 females and 12 males), were examined in this study. Bile specimens were collected from three different operating theatres including IbnSena Hospital, Sudan Private Clinic and Omdurman Teaching Hospital.Results: Six bacterial species were recognized in bile specimens, four of them are gramnegative and two are gram- positive species. In the present study, bacteria were isolated from 40 specimens out of the 100 bile specimens cultured with an overall incidence of 40%. It was noted that all positive bacterial bile cultures correlated with the presence of gallstones except three Salmonellae which were isolated from bile of acalculus gallbladders. The most prevalent bacteria isolated were E.coli which was isolated from 24 specimens out of the 100 bile specimens. On the other hand, Staphylococcus aureus and Pseudomonas spp. were less frequently isolated from bile specimens showing frequencies of 4 (4%) for each.Conclusion: The finding of this study indicated that Escherichia coli were the most prevalent bacteria which isolated from human bile. As well as, the study revealed that certain bacterial species such as Salmonellae possess characters which allow them to cause cholecystitis without need to gallstones formation.Key words: Gallbladder Bile, Bacterial isolates, Bile specimens, Cholecystectomy; Bacterial cholecystitis, Acalculus gallbladders

User friendly system for the visually impaired in learning Al-Quran

This study presents a method to enable the visually impaired Muslim to learn and read the Al-Quran using Braille Display with software help. The system reads the database which contains all verses of Al-Quran and user will need to select the verse and ayah to read. Besides that, this system can be used in a class to teach visually impaired students to learn Al-Quran. Every word or character typed by the instructor in the main Braille Panel will be transmitted to the sub Braille Panel that is connected to the main Braille Panel. The selected verse of Al-Quran and ayah will also generate an index before being transmitted to the Braille Panel. The index will be transmitted to the Braille Display for people to touch and read the display. A user friendly Graphical User Interface (GUI) will be used to fulfill the ergonomics for the visually impaired user's physical capabilities. Several approaches are used to design and implement the interface for the visually impaired like speech or sound output and Braille display. The Braille codes can be displayed using the Braille panel. The design interface and structure of the system for the visually impaired users in learning Al-Quran is presented